[House Hearing, 115 Congress]
[From the U.S. Government Publishing Office]
PROS AND CONS OF RESTRICTING SNAP PURCHASES
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HEARING
BEFORE THE
COMMITTEE ON AGRICULTURE
HOUSE OF REPRESENTATIVES
ONE HUNDRED FIFTEENTH CONGRESS
FIRST SESSION
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FEBRUARY 16, 2017
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Serial No. 115-2
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Printed for the use of the Committee on Agriculture
agriculture.house.gov
PROS AND CONS OF RESTRICTING SNAP PURCHASES
=======================================================================
HEARING
BEFORE THE
COMMITTEE ON AGRICULTURE
HOUSE OF REPRESENTATIVES
ONE HUNDRED FIFTEENTH CONGRESS
FIRST SESSION
__________
FEBRUARY 16, 2017
__________
Serial No. 115-2
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Printed for the use of the Committee on Agriculture
agriculture.house.gov
______
U.S. GOVERNMENT PUBLISHING OFFICE
24-325 PDF WASHINGTON : 2017
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COMMITTEE ON AGRICULTURE
K. MICHAEL CONAWAY, Texas, Chairman
GLENN THOMPSON, Pennsylvania COLLIN C. PETERSON, Minnesota,
Vice Chairman Ranking Minority Member
BOB GOODLATTE, Virginia, DAVID SCOTT, Georgia
FRANK D. LUCAS, Oklahoma JIM COSTA, California
STEVE KING, Iowa TIMOTHY J. WALZ, Minnesota
MIKE ROGERS, Alabama MARCIA L. FUDGE, Ohio
BOB GIBBS, Ohio JAMES P. McGOVERN, Massachusetts
AUSTIN SCOTT, Georgia FILEMON VELA, Texas, Vice Ranking
ERIC A. ``RICK'' CRAWFORD, Arkansas Minority Member
SCOTT DesJARLAIS, Tennessee MICHELLE LUJAN GRISHAM, New Mexico
VICKY HARTZLER, Missouri ANN M. KUSTER, New Hampshire
JEFF DENHAM, California RICHARD M. NOLAN, Minnesota
DOUG LaMALFA, California CHERI BUSTOS, Illinois
RODNEY DAVIS, Illinois SEAN PATRICK MALONEY, New York
TED S. YOHO, Florida STACEY E. PLASKETT, Virgin Islands
RICK W. ALLEN, Georgia ALMA S. ADAMS, North Carolina
MIKE BOST, Illinois DWIGHT EVANS, Pennsylvania
DAVID ROUZER, North Carolina AL LAWSON, Jr., Florida
RALPH LEE ABRAHAM, Louisiana TOM O'HALLERAN, Arizona
TRENT KELLY, Mississippi JIMMY PANETTA, California
JAMES COMER, Kentucky DARREN SOTO, Florida
ROGER W. MARSHALL, Kansas LISA BLUNT ROCHESTER, Delaware
DON BACON, Nebraska
JOHN J. FASO, New York
NEAL P. DUNN, Florida
JODEY C. ARRINGTON, Texas
______
Matthew S. Schertz, Staff Director
Anne Simmons, Minority Staff Director
(ii)
C O N T E N T S
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Page
Conaway, Hon. K. Michael, a Representative in Congress from
Texas, opening statement....................................... 1
Prepared statement........................................... 2
Submitted report............................................. 153
Peterson, Hon. Collin C., a Representative in Congress from
Minnesota, opening statement................................... 3
Witnesses
Rachidi, Ph.D., Angela K., Research Fellow in Poverty Studies,
American Enterprise Institute, Washington, D.C................. 4
Prepared statement........................................... 5
Schanzenbach, Ph.D., Diane Whitmore, Director and Senior Fellow,
Economic Studies, Brookings Institution; Professor of Social
Policy and of Economics, The Hamilton Project, Northwestern
University, Washington, D.C.................................... 11
Prepared statement........................................... 12
Submitted question........................................... 395
Sarasin, Leslie G., President and Chief Executive Officer, Food
Marketing Institute, Arlington, VA............................. 17
Prepared statement........................................... 19
Weidman, John, Deputy Executive Director, The Food Trust,
Philadelphia, PA............................................... 29
Prepared statement........................................... 31
Wansink, Ph.D., Brian, John S. Dyson Professor of Marketing and
Director, Cornell University Food and Brand Lab, Ithaca, NY.... 33
Prepared statement........................................... 34
Submitted Material
Feeding Texas, submitted policy brief............................ 282
Secretaries' Innovation Group, submitted statement............... 283
Allison, Ph.D., David B., Distinguished Quetelet Endowed
Professor; Associate Dean for Research & Science; Director,
Office of Energetics; Director, Nutrition & Obesity Research
Center, Department of Nutrition Sciences, School of Health
Professions, University of Alabama at Birmingham, submitted
letter......................................................... 284
PROS AND CONS OF RESTRICTING SNAP PURCHASES
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THURSDAY, FEBRUARY 16, 2017
House of Representatives,
Committee on Agriculture,
Washington, D.C.
The Committee met, pursuant to other business, at 10:24
a.m., in Room 1300 of the Longworth House Office Building, Hon.
K. Michael Conaway [Chairman of the Committee] presiding.
Members present: Representatives Conaway, Thompson,
Goodlatte, King, Rogers, Gibbs, Austin Scott of Georgia,
Crawford, Hartzler, Denham, LaMalfa, Davis, Yoho, Allen, Bost,
Rouzer, Kelly, Comer, Marshall, Bacon, Faso, Dunn, Arrington,
Peterson, David Scott of Georgia, Costa, Walz, Fudge, McGovern,
Lujan Grisham, Kuster, Nolan, Bustos, Maloney, Plaskett, Adams,
Evans, Lawson, O'Halleran, Panetta, Soto, and Blunt Rochester.
Staff present: Bart Fischer, Caleb Crosswhite, Callie
McAdams, Haley Graves, Jackie Barber, Jadi Chapman, Jennifer
Tiller, Mary Rose Conroy, Stephanie Addison, Keith Jones,
Kellie Adesina, Lisa Shelton, Troy Phillips, John Konya, Nicole
Scott, and Carly Reedholm.
OPENING STATEMENT OF HON. K. MICHAEL CONAWAY, A REPRESENTATIVE
IN CONGRESS FROM TEXAS
The Chairman. This hearing of the Committee on Agriculture
entitled, Pros and Cons of Restricting SNAP Purchases, will
come to order. Thank you.
I want to welcome our witnesses to today's hearing, and
thank them for taking the time to share their views on a very
timely and somewhat sensitive topic, the idea of restricting
SNAP purchases. This hearing is a continuation of the
conversation had at a Member roundtable last October. There are
good arguments to be made on both sides of this issue, and this
discussion will be yet another addition to the Committee's
commitment to strengthening the Supplemental Nutrition
Assistance Program.
On November 18 of last year, USDA released a report
entitled, Foods Typically Purchased by Supplemental Nutrition
Assistance Program Households. This study analyzed food
purchase data collected at the point of sale to assess
differences in the purchasing patterns of SNAP and non-SNAP
households. Ultimately, the report found that about 40 of
every dollar of every purchase dollar was spent on basic items
like meat, fruits, vegetables, milk, eggs, and bread. Another
20 was spent on sweetened drinks, desserts, salty snacks,
candy, and sugar. The remaining 40 was spent on a variety of
items such as cereal, prepared foods, other dairy products,
rice, beans, and other cooking ingredients. To be clear, when
comparing spending on broad food categories, the data show that
both SNAP and non-SNAP households make similar food choices.
However, the report also confirms that there are differences in
spending in individual food categories. One can also reasonably
infer from the report that billions in taxpayer dollars are
being spent on items like sweetened beverages and prepared
desserts.
This report, while not the sole basis of this hearing, begs
the question of whether certain food or beverage items should
be restricted as eligible food items in SNAP. While it is
important to have this discussion, we can all agree that no one
in America ought to go hungry, and SNAP is essential to
providing nutrition to the most vulnerable citizens during
tough times.
Our goal is to provide much-needed nutrition and to
encourage Americans to eat healthier. To that end, this
Committee has historically advocated for nutrition education
and healthy eating incentive programs. Today, we will consider
whether additional restrictions should be added to that mix.
Thank you again to the witnesses for being here today. We
look forward to your testimony.
[The prepared statement of Mr. Conaway follows:]
Prepared Statement of Hon. K. Michael Conaway, a Representative in
Congress from Texas
I want to welcome our witnesses to today's hearing and thank them
for taking the time to share their views on a very timely and somewhat
sensitive topic--the idea of restricting SNAP purchases. This hearing
is a continuation of the conversation had in a Member roundtable last
October. There are good arguments to be made on both sides of this
issue, and this discussion will be yet another addition to the
Committee's commitment to strengthening the Supplemental Nutrition
Assistance Program.
On November 18th of last year, USDA released a report entitled,
Foods Typically Purchased by Supplemental Nutrition Assistance Program
Households. This study analyzed food purchase data collected at the
point of sale to assess differences in the purchasing patterns of SNAP
and non-SNAP households.
Ultimately, the report found that about 40 of every food purchase
dollar was spent on basic items like meat, fruits, vegetables, milk,
eggs, and bread.
Another 20 was spent on sweetened drinks, desserts, salty snacks,
candy, and sugar. The remaining 40 was spent on a variety of items
such as cereal, prepared foods, other dairy products, rice, beans, and
other cooking ingredients.
To be clear, when comparing spending on broad food categories, the
data show that both SNAP and non-SNAP households made similar food
choices. However, the report also confirms that there are differences
in spending on individual food categories. One can also reasonably
infer from the report that billions in taxpayer dollars are being spent
on items like sweetened beverages and prepared desserts.
The report, while not the sole basis of this hearing, begs the
question of whether certain food or beverage items should be restricted
as eligible food items in SNAP. While it's important to have this
discussion, we can all agree that no one in America ought to go hungry,
and SNAP is essential in providing nutrition to the most vulnerable
citizens during tough times.
Our goal is to provide much needed nutrition and to encourage
Americans to eat healthier. To that end, this Committee has
historically advocated for nutrition education and healthy eating
incentive programs. Today, we will consider whether additional
restrictions should be added to that mix. Thank you again to the
witnesses for being here today. We look forward to your testimony.
With that, I now turn to the Ranking Member for any comments he
would like to make.
The Chairman. I now turn to the Ranking Member for any
comments that he would like to make.
STATEMENT OF HON. COLLIN C. PETERSON, A REPRESENTATIVE IN
CONGRESS FROM THE STATE OF MINNESOTA
Mr. Peterson. Thank you, Mr. Chairman.
We have had 16 SNAP hearings, we are now taking a look at
how SNAP recipients are purchasing food, what kind of food they
are purchasing with their SNAP dollars.
Before we get too far, though, I think it is important to
again note that the overwhelming theme of the testimony we have
heard in the last Congress is that while there are some areas
for improvement, SNAP works. We heard testimony opposing
efforts to block grant SNAP and on the importance of keeping
SNAP within the farm bill.
Those of us who have been around a while know that this is
a complicated program, and I would urge Members to keep that in
mind as we work on the farm bill this next year. I don't think
there is one single issue that is the problem, and I don't
think there is one single solution that will magically somehow
improve SNAP efficiency.
Looking specifically at SNAP food choice, it would seem
pretty straightforward that we not allow SNAP dollars to be
spent on junk food. But the problem is, how do you define that?
This is something that I took a look at when I was Chairman.
In Minnesota, they tried this. Somehow or another they
requested a waiver from FNS to disallow candy, I don't know how
they did this, but when they were defining candy, if the candy
didn't contain wheat it was banned, but if it did contain
wheat, it wasn't. So a Kit-Kat bar was okay under what they
were doing, and a Hershey bar was not. So I don't know. When
you go down this route, you are opening a real can of worms,
and from what I can tell talking to my folks back home, that
grocery stores have really no interest in being the food
police. USDA has been resistant to this effort as well. And
from what I know, when you look at how, and the kind of food,
SNAP recipients buy, it is really not different from the food
of people that are not on SNAP. The underlying issue is all of
us in the United States do a bad job of deciding what to eat,
and we can all use some guidance probably. But I am not sure
the government is the way to provide that.
So I am hopeful that we can be open-minded. The discussion
on these issues can continue and our efforts can continue, so
that we learn more about how SNAP actually works, and I look
forward to hearing today's witnesses and yield back.
The Chairman. I thank the gentleman. The chair would remind
or request that other Members submit their opening statements
for the record so witnesses may begin their testimony to ensure
that there is ample time for questioning.
I want to thank our panel for being here. It is, by all
arguments, some of the best informed folks, and it is a
balanced panel. We have folks on both sides of the issue, and
we have folks who have to administer the program, whatever it
is we come up with. So we have a terrific panel and I am
excited to hear from them after reading their testimony last
night.
Today, we have with us Dr. Angela Rachidi. She is a
Research Fellow, Poverty Studies at American Enterprise
Institute here in Washington, D.C. We have Diane Whitmore
Schanzenbach, Director of The Hamilton Project, Senior Fellow,
Economic Studies, the Brookings Institute here in D.C. We have
Leslie Sarasin, CEO of the Food Marketing Institute in
Arlington, Virginia. We have Mr. John Weidman, who is the
Deputy Executive Director, The Food Trust, Philadelphia,
Pennsylvania. And we have Brian Wansink, the Director of
Cornell University Food and Brand Lab at Ithaca, New York. And
given everyone's last names, I came sort of close to getting
some of those right. So Dr. Rachidi, if you will, please, 5
minutes.
STATEMENT OF ANGELA K. RACHIDI, Ph.D., RESEARCH
FELLOW IN POVERTY STUDIES, AMERICAN ENTERPRISE
INSTITUTE, WASHINGTON, D.C.
Dr. Rachidi. Thank you. Chairman Conaway, Ranking Member
Peterson, and other Members of the Committee, thank you for the
opportunity to testify this morning on restrictions on
purchases in the Supplemental Nutrition Assistance Program, or
SNAP. My name is Angela Rachidi, and I am a Research Fellow in
Poverty Studies at the American Enterprise Institute, or AEI.
Prior to joining AEI, I was the Deputy Commissioner for Policy
and Evaluation at the New York City Department of Human
Resources, or HRA. HRA administers SNAP, and during my time
there, we provided benefits to almost two million New Yorkers
each month.
Most relevant for my testimony today is my experience
drafting a proposal for a demonstration project in New York
City to restrict the use of SNAP benefits to purchase sweetened
beverages. Regrettably, it was denied by the U.S. Department of
Agriculture in 2011.
I will make four main points today. First, obesity and the
related health problems remain one of the most challenging
public health issues of our time, with sweetened beverages
identified as one of the main contributors. Second, the
integrity of SNAP as a publicly funded program rests on how
well its implementation matches the stated goals of the
program. Third, this problem is not unique to low-income
households, but SNAP offers one opportunity for government to
play a positive role. And fourth, a demonstration project to
test a restriction on sweetened beverages in SNAP is consistent
with bipartisan efforts to support evidence-based policy
making.
For my oral testimony, I won't go through all of the
research on obesity, the related health problems, and its
connection to sweetened beverages. But I do want to say,
however, that obesity is a major public health crisis that
affects all Americans, no matter their income status, and for
this reason, it requires a multi-faceted public health
approach.
High sweetened beverage consumption is not unique to SNAP
households, but supporting such purchases, especially at the
levels suggested in the data, directly contradicts the stated
goals of the program. The Food Stamp Act of 1977 states that
the goal is to provide for improved levels of nutrition among
low-income households through a cooperative Federal-state
program of food assistance. This purpose holds today.
For a program with a stated goal of improving nutrition,
accepting such a large percentage of spending on beverages with
no nutritional value seems counterintuitive and likely
undermines public support for the program. Estimates suggest
SNAP households spend almost ten percent of their food budgets
on these products. Allowing the purchase of sweetened beverages
also directly competes with nutritional education programming,
and it competes against costs associated with obesity, which
sweetened beverages are a large contributor to; estimates
suggest that obesity costs $147 billion per year.
Placing restrictions on SNAP should be part of a broader
approach to address this problem. Some believe that educating
SNAP recipients on healthy eating is a better approach. I would
argue that it should not be one or the other, and the USDA's
own research supports this. The USDA's Healthy Incentives
Program, which gave financial incentives to SNAP households to
purchase fruits and vegetables had no effect on sweetened
beverage consumption, even though these households did eat more
fruits and vegetables. The Summer EBT for Children Program
found that a WIC-based model which provided restrictions was
more effective than a SNAP-based model, which did not allow
restrictions. And another study not conducted through the USDA
found that restrictions plus incentives was most effective in
reducing sweetened beverage intake.
As part of a broader approach toward evidence-based policy
making, a demonstration project is needed. I believe that with
cooperation from the USDA and funding from Congress, a
demonstration project is feasible. A random assignment
experiment similar to the Healthy Incentives Pilot could be
conducted. With the technology that exists today, this would
not be overly burdensome on retailers. In fact, when we
developed the proposal in New York City, we spoke to retailers
and they told us that it would not be that difficult to
implement such a restriction, since they program their EBT
systems anyway.
In conclusion, with a new Congress and Administration, I am
hopeful that a demonstration project in a few states will be
allowed in order to test whether a restriction could be
effective. At a time when leaders of both parties are promoting
evidence-based policy making, testing such an idea and
rigorously evaluating the results should receive broad support.
Thank you, and I can respond to any questions that you may
have.
[The prepared statement of Dr. Rachidi follows:]
Prepared Statement of Angela K. Rachidi, Ph.D., Research Fellow in
Poverty Studies, American Enterprise Institute, Washington, D.C.
The Supplemental Nutrition Assistance Program (SNAP): Time to Test a
Sweetened Beverage Restriction
Chairman Conaway, Ranking Member Peterson, and other Members of the
Committee, thank you for the opportunity to testify this morning on
restrictions on purchases in the Supplemental Nutrition Assistance
Program or SNAP.
My name is Angela Rachidi, and I am a Research Fellow in Poverty
Studies at the American Enterprise Institute (AEI). Prior to joining
AEI, I spent almost a decade at the New York City Human Resources
Administration (HRA) as the Deputy Commissioner for Policy and
Evaluation. HRA is New York City's main social service agency and
administers SNAP. During my time at HRA, the city provided SNAP
benefits to almost two million New Yorkers each month.
In my role, I studied all aspects of the program. Most relevant for
today is my experience--under the direction of then-Mayor Michael
Bloomberg, Commissioners for Health Thomas Friedan and Thomas Farley,
and HRA Commissioner Robert Doar--drafting a proposal for a
demonstration project in New York City to restrict the use of SNAP
benefits to purchase sweetened beverages. We proposed a restriction as
a way to support the overarching goal of the program, which is to
improve nutrition. Regrettably, it was denied by the U.S. Department of
Agriculture (USDA) in 2011.
In the years since I left HRA, the public health problems caused by
sweetened beverages have not solved themselves. I am here today to urge
the Committee to support demonstration projects that test whether a
sweetened beverage restriction in SNAP can improve the health and well-
being of SNAP recipients.
I will make four main points to support this recommendation:
1. Obesity and related health problems remain one of the most
challenging public health issues of our time, affecting
millions of poor and non-poor Americans, with sweetened
beverages identified as one the main contributors.
2. The integrity of SNAP as a publicly-funded program rests on how
well its implementation matches the stated goals of the
program. Congress has stated that the purpose of SNAP is to
support nutrition among low-income households, which is
directly contradicted by allowing sweetened beverages to be
purchased.
3. This public health problem is complex and requires a
comprehensive approach that includes multiple strategies,
including changes to SNAP.
4. A demonstration project to test a sweetened beverage restriction
in SNAP is consistent with bipartisan efforts to support
evidence-based policymaking. Through rigorous evaluation, a
demonstration project could assess whether government
efforts can achieve potential gains, such as better health,
without adversely affecting other measures of well-being.
Before I get to these main points, I want to state clearly that
SNAP is one of the more effective Federal safety net programs in the
U.S. A large body of research shows that it reduces poverty, improves
food security among low-income households, and has positive effects on
infant health and long-term benefits for children who receive it.\1\ In
the average month in 2016, 44.2 million Americans received SNAP for a
total cost of $70.9 billion.\2\ Among American households, 12.7 percent
were food-insecure in 2015 and 5.0 percent had very low food
insecurity; percentages which likely would be much higher without
SNAP.\3\ In 2015, SNAP lifted almost 4.6 million people out of poverty,
according to the Supplemental Poverty Measure.\4\
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\1\ See Judith Bartfield, et al., eds, SNAP Matters: How Food
Stamps Affect Health and Well-Being (Stanford, CA: Stanford University
Press, 2015); Douglas Almond, Hilary W. Hoynes, and Diane Whitmore
Schanzenbach, ``Inside the War on Poverty: The Impact of Food Stamps on
Birth Outcomes,'' Review of Economics and Statistics 93, no. 2 (May
2011): 387-403; and Hilary Hoynes, Diane Whitmore Schanzenbach, and
Douglas Almond, ``Long-Run Impacts of Childhood Access to the Safety
Net,'' American Economic Review 106, no. 4 (April 2016): 903-34.
\2\ U.S. Department of Agriculture, Food and Nutrition Service
``Supplemental Nutrition Assistance Program Participation and Costs,''
February 3, 2017, https://www.fns.usda.gov/sites/default/files/pd/
SNAPsummary.pdf.
\3\ Alisha Coleman-Jensen, et al., ``Household Food Security in the
United States in 2015,'' U.S. Department of Agriculture, Economic
Research Services, September 2016, https://www.ers.usda.gov/webdocs/
publications/err215/err-215.pdf?v=42636.
\4\ Trudi Renwick and Liana Fox, ``The Supplemental Poverty
Measure: 2015,'' U.S. Census Bureau, September 2016, http://
www.census.gov/content/dam/Census/library/publications/2016/demo/p60-
258.pdf.
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Beyond these national statistics, I saw first-hand the positive
impacts that SNAP had on individuals and families in New York City. It
serves a wide variety of households, including the elderly, the
disabled, and working families. However, as with any government
program, it can always be improved. And as a nutrition assistance
program, SNAP could do more to support healthy eating among recipient
households, especially children.
Obesity, Health Problems, and the Connection to Sweetened Beverages
The National Institutes of Health has termed obesity ``a
devastating public-health crisis for the United States,'' \5\ and for
good reason. Among all Americans, 37.9 percent of adults (age 20 or
older) were obese in 2013-2014 and over 70 percent were overweight or
obese.\6\ Among children, 20.6 percent of 12-19 year olds and 17.4
percent of 6-11 year olds were obese in those same years.\7\ According
to the Centers for Disease Control and Prevention (CDC), people who are
obese are a greater risk for a variety of health issues, including type
2 diabetes, heart disease, stroke, some cancers, low quality of life,
and certain mental illnesses.\8\
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\5\ National Institutes of Health, ``About We Can! Background,''
February 13, 2013, https://www.nhlbi.nih.gov/health/educational/wecan/
about-wecan/background.htm.
\6\ Centers for Disease Control and Prevention, National Center for
Health Statistics, ``Obesity and Overweight,'' June 13, 2016, https://
www.cdc.gov/nchs/fastats/obesity-overweight.htm.
\7\ Ibid.
\8\ Centers for Disease Control and Prevention, ``The Health
Effects of Overweight and Obesity,'' June 5, 2015, https://www.cdc.gov/
healthyweight/effects/.
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Excessive sugar consumption is considered one of the primary causes
of obesity, with sugar-sweetened beverages specifically linked to
excessive weight gain and obesity, and the related health problems that
result.\9\ Because of these known associations and because sweetened
beverages have no nutritional value, the White House Task Force on
Childhood Obesity issued a report in 2010 that included recommendations
calling for the nation's food assistance programs to be part of the
solution by encouraging access to nutritious foods and offering
incentives and eliminating disincentives to healthy eating habits.\10\
In addition, according to the 2015-2020 Dietary Guidelines for
Americans:
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\9\ Brian K. Kit, et al., ``Trends in Sugar-Sweetened Beverage
Consumption Among Youth and Adults in the United States: 1999-2010,''
American Journal of Clinical Nutrition 98, no. 1 (May 2013): 180-88.
\10\ White House Task Force on Childhood Obesity, ``Solving the
Problem of Childhood Obesity Within a Generation,'' May 2010, https://
letsmove.obamawhitehouse.archives.gov/sites/letsmove.gov/files/
TaskForce_on_Childhood_Obesity_May2010_FullReport.pdf.
The two main sources of added sugars in U.S. diets are sugar-
sweetened beverages and snacks and sweets. Many foods high in
calories from added sugars provide few or no essential
nutrients or dietary fiber and, therefore, may contribute to
excess calorie intake without contributing to diet quality;
intake of these foods should be limited to help achieve healthy
eating patterns within calorie limits. There is room for
Americans to include limited amounts of added sugars in their
eating patterns, including to improve the palatability of some
nutrient-dense foods, such as fruits and vegetables that are
naturally tart (e.g., cranberries and rhubarb). Healthy eating
patterns can accommodate other nutrient-dense foods with small
amounts of added sugars, such as whole-grain breakfast cereals
or fat-free yogurt, as long as calories from added sugars do
not exceed ten percent per day, total carbohydrate intake
remains within the AMDR [Acceptable Macronutrient Distribution
Range], and total calorie intake remains within limits.\11\
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\11\ U.S. Department of Agriculture, Dietary Guidelines for
Americans 2015-2010, December 2015, 31, https://health.gov/
dietaryguidelines/2015/resources/2015-2020_Dietary_
Guidelines.pdf.
The USDA's Dietary Guidelines go on to note that the ``the major
source of added sugars in typical U.S. diets is beverages, which
include soft drinks, fruit drinks, sweetened coffee and tea, energy
drinks, alcoholic beverages, and flavored waters.'' \12\ In fact,
almost \1/2\ of added sugars consumed by the U.S. population come from
sweetened beverages.\13\
---------------------------------------------------------------------------
\12\ Ibid.
\13\ Ibid.
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This is why it is so alarming that such a notable percentage of
food/beverage purchases in American households are for sweetened
beverages, according to a recent USDA study.\14\ Among SNAP households,
9.25 percent of food purchases were for sweetened beverages and 7.10
percent of non-SNAP households were for the same. SNAP households spent
more on sweetened beverages than fruits and milk combined. According to
the National Health and Nutrition Examination Survey (NHANES), low-
income children are more likely to consume sweetened beverages and
intake more calories from sweetened beverages than higher-income
children.\15\ Children participating in SNAP in particular were more
likely than nonparticipants to consume sweetened beverages,\16\ and 63
percent of adults receiving SNAP consumed a sweetened beverage on the
day of the NHANES.\17\ Also according to the NHANES, more than \1/2\ of
adult SNAP recipients drank regular soda and 24 percent drank another
sweetened beverage on the day of the survey.\18\ Sweetened beverage
consumption is high among all American households, with low-income
households and SNAP recipients no exception.
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\14\ U.S. Department of Agriculture, Food and Nutrition Service,
``Foods Typically Purchased by Supplemental Nutrition Assistance
Program (SNAP) Households,'' November 2016, https://www.fns.usda.gov/
sites/default/files/ops/SNAPFoodsTypicallyPurchased.pdf.
\15\ Euna Han and Lisa M. Powell, ``Consumption Patterns of Sugar-
Sweetened Beverages in the United States,'' Journal of the Academy of
Nutrition and Dietetics 113, no. 1 (January 2013): 43-53.
\16\ Cindy Leung, et al., ``Associations of Food Stamp
Participation with Diet Quality and Obesity in Children,'' Pediatrics
131, no. 3 (March 2013): 463-72.
\17\ Sara N. Bleich, Seanna Vine, and Julia A. Wolfson, ``American
Adults Eligible for SNAP Consume More Sugary Beverages Than Ineligible
Adults,'' Preventative Medicine 57, no. 6 (December 2013), https://
www.ncbi.nlm.nih.gov/pmc/articles/PMC3842507/.
\18\ U.S. Department of Agriculture, Food and Nutrition Service,
``Diet Quality Among SNAP Recipients by SNAP Participation Status: Data
from the National Health and Nutrition Examination Survey, 2007-2010,''
May 2015, https://www.fns.usda.gov/sites/default/files/ops/NHANES-
SNAP07-10.pdf.
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Program Integrity
High sweetened beverage consumption is not unique to SNAP
households. But supporting such purchases, especially at levels
suggested in the data, directly contradicts the stated goals of the
program. The Food Stamp Act of 1977, which outlines the purpose of the
program, states that the goal is ``to provide for improved levels of
nutrition among low-income households through a cooperative Federal-
state program of food assistance.'' \19\
---------------------------------------------------------------------------
\19\ Food Stamp Program Act of 1977, https://www.fns.usda.gov/
sites/default/files/PL_106-580.pdf.
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Public health experts have clearly determined that sweetened
beverages have no nutritional value and are a major contributor to
obesity and related health problems. Few can argue the reverse. Yet,
almost ten percent of food and beverage spending among SNAP households
is on these products.
To be fair, it is unclear whether SNAP households would make these
purchases with their own money if they were restricted from SNAP or
even in the absence of SNAP. However, for a program with a stated goal
of improving nutrition, accepting such a large percentage of spending
on beverages with no nutritional value seems counterintuitive and
likely undermines public support for the program.
Beyond these concerns, allowing the purchase of sweetened beverages
directly competes with the USDA's nutrition education programming at
the Federal and state level. Approximately $350 million is spent per
year on SNAP Nutrition Education activities, with more spent by the
states.\20\ The Farm Bill of 2008 authorized an additional $20 million
to test demonstration projects designed to increase healthy eating.
Federal dollars dedicated to improving nutrition are in direct
competition with benefit dollars being spent to purchase sweetened
beverages.
---------------------------------------------------------------------------
\20\ U.S. Department of Agriculture, Economic Research Service,
``Nutrition Education,'' October 12, 2016, https://www.ers.usda.gov/
topics/food-nutrition-assistance/supplemental-nutrition-assistance-
program-snap/nutrition-education/.
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Separately from SNAP, not confronting the problems created by
obesity has substantial impacts on Federal medical expenditures.
Medical costs associated with obesity (which largely fall on Medicare
and Medicaid) are estimated to be at least $147 billion per year.\21\
Not only is SNAP contributing to sweetened beverage consumption, but it
may be adding to other Federal expenditures related to medical costs
associated with obesity.
---------------------------------------------------------------------------
\21\ See Eric A. Finkelstein, et al., ``Annual Medical Spending
Attributable to Obesity: Payer and Service-Specific Estimates,'' Health
Affairs 28, no. 5 (2009): w822-31, http://content.healthaffairs.org/
content/28/5/w822.full.pdf.
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Problem Is Complex and Requires a Comprehensive Approach
As I already mentioned, the public health challenges posed by
sweetened beverages are not unique to low-income households. But
restrictions could be part of a broader approach to address the
problem. Already, the USDA pilot tested a Healthy Incentive program,
which gave financial incentives to SNAP households to purchase fruits
and vegetables. The results of the evaluation found that the financial
incentives increased consumption of certain fruits and vegetables by a
small, but statistically significant amount.\22\ It also found that
retailers had little trouble implementing the pilot. But the incentives
had no effect on added sugars, which included no change to sweetened
beverage consumption.
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\22\ See U.S. Department of Agriculture, Food and Nutrition
Service, Healthy Incentives Pilot Final Evaluation Report, September
2014, https://www.fns.usda.gov/snap/healthy-incentives-pilot-final-
evaluation-report.
---------------------------------------------------------------------------
In another study, researchers randomly assigned low-income
households not receiving SNAP into four different groups to test
incentives, restrictions, and both. They found that the incentive plus
restriction group (the restriction was on sweetened beverages and other
sweets) had positive effects on fruit consumption and reduced sweetened
beverage and other sweets intake.\23\ The incentive-alone and
restriction-alone group showed no difference compared with the control
group. Although this was not conducted with SNAP households (given that
the USDA has not allowed testing restrictions), it suggests that
restrictions could be used to reduce sweetened beverage consumption.
---------------------------------------------------------------------------
\23\ Lisa Harnack, et al., ``Effects of Subsidies and Prohibitions
on Nutrition in a Food Benefit Program: A Randomized Clinical Trial,''
JAMA Internal Medicine 176, no. 11 (November 2016): 1610-19.
---------------------------------------------------------------------------
Similarly, although not the main purpose, a study of the Summer
Electronic Benefit Transfer for Children Program published in 2016
found that only a Women, Infant, and Children (WIC)-based model, which
restricted what could be purchased with benefits, including sweetened
beverages, led to a reduction in sweetened beverage consumption among
families who participated.\24\ The SNAP-based model, which had no
restrictions, did not reduce sweetened beverage consumption.
---------------------------------------------------------------------------
\24\ U.S. Department of Agriculture, Food and Nutrition Service,
``Summer Electronic Benefit Transfer for Children (SEBTC)
Demonstration: Summary Report,'' May 2016, https://www.fns.usda.gov/
sites/default/files/ops/sebtcfinalreport.pdf.
---------------------------------------------------------------------------
Another recent study surveyed SNAP and non-SNAP participants on
their perceptions of the program and areas for improvement around
nutrition. Just over \1/2\ of SNAP participants supported removing
sweetened beverages from products allowed under SNAP, and almost 80
percent of non-SNAP participants supported the same.\25\ In 2011, we
surveyed New York City SNAP participants on their consumption patterns
and attitudes around restrictions. We found that almost 70 percent of
surveyed SNAP participants supported restricting sweetened beverages
from SNAP (49 percent) or didn't care one way or the other (16
percent).
---------------------------------------------------------------------------
\25\ Cindy W. Leung, et al., ``Improving the Nutritional Impact of
SNAP: Perspectives from the Participants,'' American Journal of
Preventive Medicine 52, no. 2 (February 2017): 252.
---------------------------------------------------------------------------
This research suggests that a restriction may be beneficial, but
likely as part of other efforts to achieve the same. It also suggests
that combining a restriction with incentives, broader nutrition
education programs, and public messaging may reduce sweetened beverage
consumption among those exposed.
SNAP Demonstration Project to Test Restrictions
For these reasons, and as part of a broader approach toward
evidence-based policymaking, a demonstration project to test a
sweetened beverage restriction in SNAP is needed. It could involve a
few states or localities to assess whether the potential gains, such as
better health, can be achieved without adverse effects on other
measures of well-being. In a bipartisan effort in 2010, under the
direction of Governor David Patterson and Mayor Michael Bloomberg, and
in partnership with the New York City Department of Health, we
submitted a proposal to the USDA to administer a demonstration project
in New York City that would restrict sweetened beverages from SNAP.
Our main objective was to test whether a restriction would lead to
changes in consumption of sweetened beverages and other food groups
among SNAP recipients, as well as whether a restriction could be
implemented. We designed a rigorous evaluation to compare like counties
within New York City (one would experience the restriction while the
other would not), as well as to assess whether retailers could
appropriately implement the restriction and whether participants could
follow the changes. We proposed using survey data and retailer data to
assess changes in consumption patterns over time, as well as
qualitative work to assess the retailer and participant experience.
Regrettably, our proposal, which was to be funded completely by the
city and the state, was denied by the USDA in 2011.
Since our proposal in 2010, we now know more about the Healthy
Incentive[s] Pilot and the Summer EBT pilot. Both studies suggest that
more can be done to improve nutrition and reduce sweetened beverage
consumption among SNAP households. The logical next step is to conduct
a study of SNAP restrictions. Given what was learned from those
studies, a demonstration project is not only possible, but has been
made more feasible. With cooperation from the USDA and funding from
Congress, a demonstration project involving a few states could greatly
expand our knowledge of what works in combating sweetened beverage
consumption and the obesity crisis.
To give you a sense of how this might work, the Healthy
Incentive[s] Pilot operated in 2010-2012 reprogrammed EBT data systems
at the retailer source to identify and calculate incentives as part of
the program. A similar approach could be taken, but with restrictions.
Participants assigned to the restriction group would receive special
EBT cards and retailer EBT systems would be programmed to not allow
sweetened beverage purchases among those SNAP households. With the
technology systems in place today, implementing this type of
demonstration project would not be overly burdensome on retailers. In
fact, as part of the Healthy Incentive[s] Pilot, few retailers
identified problems, and few said the pilot affected store operations.
This type of design is not only possible, but it would provide a strong
treatment and control study that would tell us whether any changes in
sweetened beverage consumption were due the restrictions or not.
When we developed the New York City proposal, retailers were
consulted about the ease or difficulty of implementing such a
restriction. Retailers with EBT systems indicated that it could be done
fairly easily since restrictions are already in place for other
purchases, such as alcohol or nonfood items. One concern was retailers
who do not use EBT systems, instead using manual systems. But these
retailers make up a small share of overall SNAP sales and, as part of a
demonstration project, could be counseled to ensure that they
understand who is restricted from purchasing sweetened beverages and
who is not. As part of the data collection effort, the evaluators would
know whether households assigned to the restriction group were allowed
to purchase sweetened beverages or not.
With a new Congress and Administration, I am hopeful that a
demonstration project in a few states be allowed in order to test
whether a restriction could be effective. Given the problems of obesity
and the toll it takes on our poor communities, this is an issue that
receives bipartisan support. For example, the bipartisan National
Commission on Hunger recommended in its 2015 report that Congress pass
legislation to restrict sweetened beverages from SNAP. As a first step,
Congress could authorize funding for demonstration projects.
Conclusion
Some may ask why restrict sweetened beverages and no other foods
with added sugar. Even though precedent exists in other government
programs to determine what is nutritious and what is not, there are two
reasons for starting with sweetened beverages. First, the research is
clear that sweetened beverages are a much larger contributor to added
sugars in the diets of Americans today (almost 50 percent of added
sugars comes from these products) than other products. Second, the
amount of spending on sweetened beverages far surpasses what is spent
on other candies and sweets. And added sugars are often combined with
other nutritious foods, such as whole grain cereals, yogurts, or nuts.
The case against sweetened beverages in a nutrition assistance program
seems clear.
Some also argue that restrictions would be overly burdensome on
retailers. While I respect the views of industry professionals,
retailers already place restrictions on what can be purchased with SNAP
benefits through their EBT systems, and the definition of sweetened
beverage could be defined in a way that is very straightforward.
In terms of how a restriction might affect low-income households, I
am sympathetic to not wanting the government to stigmatize or unfairly
targeted poor households. But SNAP is a government-funded program with
a clearly stated goal: to improve the nutrition of low-income
households. Not only is allowing sweetened beverages inconsistent with
that goal, it actually may work against it by contributing to poor
health. I also question how detrimental a restriction could be, given
that certain restrictions already apply, other food assistance programs
implement restrictions, and the majority of SNAP households either
support the restriction or do not care when asked on surveys. It is
also possible that SNAP benefits are fungible, and many SNAP households
use their own money for food purchases, suggesting that a restriction
may not have much effect on consumption. However, it is unclear how
SNAP households would respond to a restriction until it is tested and
rigorously evaluated.
In conclusion, a restriction on sweetened beverages should be
tested as part of a demonstration project for the purpose of improving
public health. At a time when leaders of both parties are promoting
evidence-based policymaking, testing such an idea and rigorously
evaluating the results should receive broad support. I urge Congress to
support pilot projects and urge the USDA to approve any requests from
states.
Thank you, and I can respond to any questions that you may have.
The Chairman. Thank you, Dr. Rachidi.
Dr. Schanzenbach?
STATEMENT OF DIANE WHITMORE SCHANZENBACH, Ph.D.,
DIRECTOR AND SENIOR FELLOW, ECONOMIC STUDIES, THE HAMILTON
PROJECT, BROOKINGS INSTITUTION;
PROFESSOR OF SOCIAL POLICY AND OF ECONOMICS, NORTHWESTERN
UNIVERSITY, WASHINGTON, D.C.
Dr. Schanzenbach. Thank you. Chairman Conaway, Ranking
Member Peterson, and Members of the Committee, thanks for the
opportunity to appear before you today. My name is Diane
Schanzenbach. I am the Director of The Hamilton Project, which
is an economic policy initiative at Brookings Institution. I am
also a Professor of Social Policy of Economics at Northwestern
University in Illinois.
SNAP is a highly efficient and effective program. It lifted
nearly five million children out of poverty in 2014. SNAP is
targeted efficiently to families who need benefits the most. It
reduces the likelihood that families have trouble affording
food, and serves as an automatic fiscal stabilizer in times of
economic downturn. It also has extremely low rates of both
error and fraud.
A key reason for SNAP's success is that it relies on the
private-sector to provide efficient access to food from grocery
stores and other retail outlets. The reliance on the program on
the free market system has been a feature of SNAP since the
beginning. With a few restrictions, recipients have been able
to optimize which items to purchase, and from which retail
stores, subject to prevailing prices, and also to their own
taste preferences and nutritional needs.
SNAP also has long-term benefits to children. My own recent
research study, which is the only long-term causal study on
SNAP access, found that those who had access to SNAP benefits
during childhood were more likely to graduate from high school,
they grew up to be healthier, and for women in particular, they
grew up to be more economically self sufficient as adults, all
due to childhood access to SNAP benefits, because this is an
investment in children.
There has been much media discussion of the November 2016
USDA report on the typical food purchase patterns by SNAP
participants and non-participants. The top line finding of that
report is that SNAP and non-SNAP families have extremely
similar spending patterns. The study did not address the more
fundamental question, namely, how does SNAP change the types of
groceries that participants buy? By increasing a family's
resources available to purchase groceries, SNAP is expected to
increase not only the quantity, but also the quality of foods
purchased. SNAP families are able to buy more nutritious foods
that they otherwise could not afford.
Additional restrictions on SNAP purchases will undermine
the effectiveness and the efficiency of the program. In
particular, SNAP restrictions will be difficult to structure
and practice. In the case of a proposed ban on the purchase of
soft drinks or sweetened beverages, it will be unlikely to
change consumption patterns.
So recall that SNAP benefits are modest. They are
approximately $4.50 per person per day, and as a result, almost
everyone who participates in the program has to supplement
their SNAP purchases with groceries purchased out of their own
cash income. So what will happen if a soft drink purchase is
banned using SNAP benefits? Well, we would expect there to be
no consumption change. A family could continue to purchase the
same basket of goods. They will just have to make certain at
the checkout line to pay for the soft drinks out of their cash
instead of their SNAP benefits. In other words, a ban will
likely increase the administrative cost of the program, both to
the USDA and to retailers, and increase the stigma faced by
recipients when they use SNAP, but not have the benefit of
actually inducing any behavioral changes. It will be all costs
and no benefits.
I think there are better policy options that are more
likely to improve the diets of SNAP recipients. Market-based
policies that reduce the relative price of healthy foods can
increase that consumption. For example, as you know, the
Healthy Incentives Pilot in Massachusetts increased consumption
of targeted healthy foods by 25 percent. Exploring ways to
replicate or scale this type of program nationally would
provide an effective and a market-based path forward toward
achieving the goal of increasing healthy food consumption of
SNAP recipients.
Strengthening SNAP is a smart public investment that will
improve both public health and economic growth, but banning
certain foods will raise the administrative burdens and costs
of the program, making it less efficient, but is unlikely to
change consumption.
By contrast, policy changes that strengthen the purchasing
power of SNAP benefits and allow markets to function without
undue interference are more likely to improve dietary choices
of recipients and reduce food insecurity.
Thank you, and I am looking forward to questions.
[The prepared statement of Dr. Schanzenbach follows:]
Prepared Statement of Diane Whitmore Schanzenbach, Ph.D., Director and
Senior Fellow, Economic Studies, The Hamilton Project, Brookings
Institution; Professor of Social Policy and of Economics, Northwestern
University, Washington, D.C.
Chairman Conaway, Ranking Member Peterson, and Members of the
Committee:
Thank you for the opportunity to appear before you today at this
hearing on the Pros and Cons of Restricting Purchases in the
Supplemental Nutrition Assistance Program (SNAP).
My name is Diane Schanzenbach, I am Director of the Hamilton
Project, an economic policy initiative at the Brookings Institution,
where I am also a Senior Fellow in Economic Studies.
I am also a Professor of Social Policy and Economics at
Northwestern University. For the past 2 decades, I have conducted and
published numerous peer-reviewed research studies and book chapters on
the U.S. safety net, including SNAP and the Food Stamp Program. I also
study childhood obesity, food consumption, and food insecurity. I
recently served as a member of the Institute of Medicine's Committee on
Examination of the Adequacy of Food Resources and SNAP Allotments.
My testimony today draws primarily from research that I have
conducted or reviewed that considers the role of SNAP and other
influences on food consumption and food insecurity.
SNAP is a highly efficient and effective program. It lifted nearly
five million people out of poverty in 2014 (the most recent data
available).\1\ SNAP is targeted efficiently to families who need
benefits the most, reduces the likelihood that families have trouble
affording food, and serves as an automatic fiscal stabilizer in times
of economic downturns.2-3 It has extremely low rates of both
error and fraud.4-5 SNAP also has long-term benefits to
children. My own recent research study found that those who had access
to SNAP benefits during childhood were more likely to graduate from
high school, grew up to be healthier, and women in particular were more
likely to become economically self-sufficient due to childhood access
to SNAP benefits, as shown in Figure 1.
---------------------------------------------------------------------------
\1\ Sherman, Arloc. 2015, September 16. ``Safety Net Programs Lift
Millions From Poverty, New Census Data Show.'' Center on Budget and
Policy Priorities, Washington, D.C. Available at: http://www.cbpp.org/
blog/safety-net-programs-lift-millions-from-poverty-new-census-data-
show.
\2\ Institute for Research on Poverty. 2015, November. ``SNAP, Food
Security, and Health.'' Policy Brief No. 8, Institute for Research on
Poverty, University of Wisconsin-Madison, Madison, WI. Available at:
http://www.irp.wisc.edu/publications/policybriefs/pdfs/PB8-SNAPFoodSecu
rityHealth.pdf.
\3\ Schanzenbach, Diane Whitmore, Lauren Bauer, and Greg Nantz.
2016, April 21. ``Twelve Facts about Food Insecurity and SNAP.''
Economic Facts, The Hamilton Project, Washington, D.C. Available at:
http://www.hamiltonproject.org/papers/twelve_facts_about_food_
insecurity_and_snap.
\4\ Rosenbaum, Dottie. 2014, July 2. ``SNAP Error Rates at All-Time
Lows.'' Report, Center on Budget and Policy Priorities, Washington,
D.C. http://www.cbpp.org/research/snap-error-rates-at-all-time-lows.
\5\ U.S. Department of Agriculture (USDA). 2013, August 15. ``USDA
Releases New Report on Trafficking and Announces Additional Measures to
Improve Integrity in the Supplemental Nutrition Assistance Program.''
Food and Nutrition Service, U.S. Department of Agriculture, Washington,
D.C. Available at: https://www.fns.usda.gov/pressrelease/2013/fns-
001213.
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Figure 1. Impact of Access to Food Stamps During Early Life on Adult
Health and Economic Outcomes
Access to food stamps in early life improves health outcomes
in men and women and economic self-sufficiency in women in
later life.
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Sources: Hoyes, Schanzenbach, and Almond 2016.
Note: Hollowed bars are not statistically significant.
Generally, economists advise policymakers not to interfere in the
private market unless there is a compelling reason to do so--such as a
market failure or another inefficiency that would be improved through
government intervention. In the case of SNAP, the fundamental problem
the program is meant to address is not a market failure, but is instead
a lack of resources available to purchase food. Government assistance
is needed because some families, generally temporarily, do not have
adequate resources to purchase enough food to sustain an active,
healthy lifestyle. When they receive SNAP, participating families have
more resources they can use to purchase groceries. Once the fundamental
problem of resource adequacy is addressed, recipients can interact with
the private market to obtain the food they need.
A key reason for SNAP's success is that it relies on the private-
sector to provide efficient access to food, through grocery stores and
other retail outlets. The reliance of the program on the free market
system has been a feature of SNAP since the beginning. With few
restrictions, recipients have been able to optimize which items to
purchase and from what retail stores, subject to prevailing prices and
their own tastes, preferences, and nutritional needs.
In my opinion, additional restrictions on SNAP purchases will
undermine the effectiveness and the efficiency of the program. In
particular, based on my research on SNAP and food consumption I believe
that SNAP restrictions: will be difficult to structure in practice,
will be inefficiently targeted, and in many cases--such as a proposed
ban of the purchase of soft drinks or sweetened beverages--will be
unlikely to change consumption patterns. There are better policy
options for promoting healthy eating patterns, both for SNAP recipients
and for all Americans.
SNAP Restrictions will be Difficult to Structure in Practice
There are a few broad types of restrictions that have gained policy
traction. One set involves narrowly targeting the commodities that can
be purchased with SNAP, another involves restricting the purchase of
unhealthy foods broadly, or sodas or sugar sweetened beverages in
particular, and another proposes banning purchases of certain luxury
foods. Each of these will be difficult to implement in practice because
of the complexities involved in determining which items would fall
under the ban. In addition, the restrictions would increase the
administrative burden on private businesses, and particularly on small
establishments.
The complexities arise in part because of the sheer number of
products that would need to be classified. Consumers have vast
differences in their tastes and preferences, and the market responds by
providing variety. There are more than 650,000 food and beverage
products on the market today, and 20,000 more are introduced
annually.\6\ The complexity is multiplied because there is no clear
standard for defining foods as ``healthy'' or ``unhealthy,'' or as
luxury goods. Creating such standards would be difficult at best, and
would entail substantial administrative costs to categorize and track
the nutritional profile of each good to produce a SNAP-eligible foods
list. The list would have to be maintained continuously and
communicated to retailers and consumers in real time. My prediction is
that the additional bureaucracy needed to support such an undertaking
is not likely to save taxpayer money.
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\6\ USDA. 2016, October 12. ``New Products.'' Economic Research
Service, U.S. Department of Agriculture, Washington, D.C. Available at:
https://www.ers.usda.gov/topics/food-markets-prices/processing-
marketing/new-products/.
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Furthermore, items should not be classified in a manner that
suggests a particular food is always ``good'' or ``bad.'' The Academy
of Nutrition and Dietetics, the largest organization of food and
nutrition professionals, has adopted a position statement that the
``total diet'' or overall pattern of food eaten should be the most
important focus of healthy eating.\7\ All foods can fit into a healthy
diet if consumed in moderation and with appropriate portion size, and
as a result no particular food should be always banned.
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\7\ Freeland-Graves, Jeanne H., and Susan Nitzke. 2013. ``Position
of the Academy of Nutrition and Dietetics: Total Diet Approach to
Healthy Eating.'' Journal of the Academy of Nutrition and Dietetics 113
(2): 307-17. Available at: http://www.andjrnl.org/article/S2212-
2672(12)01993-4/abstract.
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SNAP Improves Diets
By focusing on the descriptive question of what SNAP participants
buy, the USDA study did not address the more fundamental question--
namely how does SNAP change the types of groceries that participants
buy? Economists have strong predictions about the impact of SNAP: by
increasing a family's resources available to purchase groceries, SNAP
is expected to increase both the quantity and the quality of foods
purchased, and it has. When SNAP increases low-income families' grocery
purchasing power, they are able to buy more nutritious foods they
otherwise could not afford. While this is a surprisingly hard question
to study empirically, a recent study found that a $30 increase in
monthly SNAP benefits would increase participants' consumption of
nutritious foods such as vegetables and healthy proteins, while
reducing food insecurity and consumption of fast food, as shown in
Figure 2 below.\8\
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\8\ Anderson, Patricia M., and Kristin F. Butcher. 2016, June 14.
``The Relationships Among SNAP Benefits, Grocery Spending, Diet
Quality, and the Adequacy of Low-Income Families' Resources.'' Report,
Policy Futures, Center on Budget and Policy Priorities, Washington,
D.C. Available at: http://www.cbpp.org/research/food-assistance/the-
relationships-among-snap-benefits-grocery-spending-diet-quality-and-
the.
---------------------------------------------------------------------------
Figure 2. Estimated Impact of a $30 Increase in Monthly Per Capita SNAP
Benefits
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Source: Anderson and Butcher 2016.
Note: Percentages for the dark green bars represent change in
consumption. Food insecurity is defined as having difficulty at
some time during the year providing enough food for all
household members due to lack of resources. The hollowed bars
are not statistically significant.
Similar impacts were found in a randomized controlled trial of a
Summer EBT program that gave families $60 per month in benefits per
eligible child during the summer months, to offset the loss of school
meals. The study found that children assigned to receive additional
benefits improved their diets, consuming more fruits, vegetables, whole
grains, and dairy products, and fewer sugar-sweetened beverages.\9\
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\9\ Briefel, Ronette, Ann Collins and Anne Wolf. 2013, November 8.
``Impact of the Summer Electronic Benefits Transfer for Children
(SEBTC) Demonstration on Children's Nutritional Status.'' Panel Paper,
Mathematica Policy Research and Abt Associates, Washington, D.C.
Available at: https://appam.confex.com/appam/2013/webprogram/
Paper7254.html.
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SNAP and Non-SNAP Households Have Similar Consumption
There has been much media discussion of the November 2016 USDA
report on typical food purchase patterns by SNAP participants and non-
participants.\10\ The top-line finding of that report is that SNAP and
non-SNAP households have extremely similar food spending patterns. Out
of every dollar spent by SNAP families:
---------------------------------------------------------------------------
\10\ USDA. 2016, November 18. ``Foods Typically Purchased by
Supplemental Nutrition Assistance Program (SNAP) Households.''
Nutrition Assistance Program Report, Office of Policy Support, Food and
Nutrition Service, U.S. Department of Agriculture, Washington, D.C.
Available at: https://www.fns.usda.gov/snap/foods-typically-purchased-
supplemental-nutrition-assistance-program-snap-households.
Around 40 went to what the study classifies as ``basic
---------------------------------------------------------------------------
items'' such as meat, fruits, vegetables, eggs, bread and milk.
Around 20 went to salty snacks, sugar, candy and sweetened
beverages, with 5 going to soft drinks.
The remaining 40 spent on other goods, including prepared
foods, cereal, rice, beans, and dairy products.
The USDA findings are consistent with my own published research
using the Consumer Expenditure Survey that also found similar spending
patterns across food categories for SNAP and non-SNAP households.\11\
---------------------------------------------------------------------------
\11\ Hoynes, Hilary W., Leslie McGranahan, and Diane W.
Schanzenbach. 2014. ``SNAP and Food Consumption.'' Discussion Paper
2014-03, Center for Poverty Research, University of Kentucky,
Lexington, KY. Available at: http://uknowledge.uky.edu/cgi/
viewcontent.cgi?article=
1008&context=ukcpr_papers.
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Public-health advocates rightly point out that sugar-sweetened
beverages are the largest source of excess calories in the average
American diet, and they provide no nutritional benefit.12-13
The obesity epidemic has hit Americans across all income levels, and
public-health advocates are right to call attention to our excessive
consumption of sugar-sweetened beverages as one probable cause.\14\ The
USDA study indicates that this is an issue across the income
distribution, and there is no need to single out SNAP recipients for
their consumption of soft drinks. Among the spending observed in the
USDA study, about 5 of each dollar went to the purchase of soft
drinks. This rate is similar to non-SNAP households, which spend an
average of four percent of their grocery dollars on soft drinks.
---------------------------------------------------------------------------
\12\ Welsh, J.A., A.J. Sharma, L. Grellinger, and M.B. Vos. 2011.
``Consumption of Added Sugars is Decreasing in the United States.''
American Journal of Clinical Nutrition 94 (3): 726-34. Available at:
https://www.ncbi.nlm.nih.gov/pubmed/21753067.
\13\ The Nutrition Source. ``Public Health Concerns: Sugary
Drinks.'' School of Public Health, Harvard University, Cambridge, MA.
Available at: https://www.hsph.harvard.edu/nutritionsource/healthy-
drinks/beverages-public-health-concerns/.
\14\ Center for Disease Control and Prevention. 2016, September 1.
``Adult Obesity Facts.'' Center for Disease Control and Prevention,
U.S. Department of Health & Human Services, Atlanta, GA. Available at:
https://www.cdc.gov/obesity/data/adult.html.
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A Soda Ban Will Not Reduce Soda Consumption
Another option that has been proposed is to disallow only the
purchase of soft drinks or sweetened beverages with SNAP benefits.
These proposals exaggerate the potential impacts on consumption such
bans would have, because the rationale for the bans is based on a false
understanding of how SNAP benefits work. SNAP benefits are modest--
approximately $4.50 per person per day--and as a result nearly all
families supplement their SNAP purchases with groceries purchased from
their cash income. This occurs by design, and is why the program is
called the Supplemental Nutrition Assistance Program; it is intended in
most cases to extend a family's food purchasing power, not to cover 100
percent of food purchases. Estimates suggest that 70 to 80 percent of
participants, perhaps even higher, supplement their SNAP spending with
cash.
What will happen if soft drink purchases are banned using SNAP
benefits? Take a typical family that spends the average amount--$12 per
month--on soft drinks, and supplements their SNAP spending with
spending out of cash resources. Our best prediction is that there will
be no consumption change as a result of the SNAP restriction; such a
family can continue to purchase the same basket of goods, but they
would have to make certain to pay for the soft drinks out of their own
cash instead of their SNAP benefits. In other words, a ban will likely
increase the administrative costs of the program to both the USDA and
retailers, and increase the stigma faced by recipients when they use
the benefits, but not have the benefit of inducing any behavioral
changes.
Recommendations
There are better policy options that are more likely to improve the
diets of SNAP recipients, particularly when you consider that, over the
past decade, fresh fruits and vegetables have become relatively more
expensive compared to foods that are considered less healthy, as shown
in Figure 3 below. In response, market-based policies can increase the
affordability of healthy foods and provide incentives for low-income
families to purchase them.
One approach that merits further consideration is the USDA's
randomized controlled trial of the Healthy Incentives Pilot in
Massachusetts. This pilot program gave SNAP recipients an immediate 30
rebate for every dollar they spent on a narrowly defined group of
fruits and vegetables.\15\ In response to this price rebate,
consumption of the targeted healthy foods increased by 25 percent.\16\
In recent years, many local areas and even a few states have taken a
similar approach by awarding bonus dollars for benefits used at
farmers' markets, allowing recipients to stretch their food budget
farther when they buy fresh produce. To date, these programs have been
successful. Exploring ways to replicate or scale these types of
programs nationally would provide a more constructive and effective
path forward toward achieving the goal of increasing healthy food
consumption by SNAP recipients.
---------------------------------------------------------------------------
\15\ USDA. 2015, September 2. ``Healthy Incentives Pilot.'' Report,
Food and Nutrition Service, U.S. Department of Agriculture, Washington,
D.C. Available at: https://www.fns.usda.gov/hip/healthy-incentives-
pilot.
\16\ Bartlett, Susan, Jacob Klerman, Parke Wilde, Lauren Olsho,
Michelle Blocklin, Christopher Logan, and Ayesha Enver. 2013. ``Healthy
Incentives Pilot (HIP) Program.'' Food and Nutrition Services, Office
of Policy Support, U.S. Department of Agriculture, Washington, D.C.
Available at: https://www.fns.usda.gov/sites/default/files/
HIP_Interim.pdf.
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Figure 3. Price Levels by Food Category, 1980-2016
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Source: Bureau of Labor Statistics 2016.
Note: Base year of the index (100) is the average for 1982-
84.
Strengthening SNAP and reducing food insecurity in the more than 22
million U.S. households that receive nutritional assistance on a
monthly basis is a smart public investment that will improve both
public health and economic growth. Banning certain foods will raise the
administrative burdens and cost of the program, but is unlikely to
change consumption. By contrast, policy changes that strengthen the
purchasing power of SNAP benefits and allow markets to function without
undue interference are more likely to improve dietary choices of
recipients and reduce food insecurity.
Thank you, and I look forward to answering any questions you might
have.
The Chairman. Thank you.
Ms. Sarasin, 5 minutes.
STATEMENT OF LESLIE G. SARASIN, PRESIDENT AND CHIEF EXECUTIVE
OFFICER, FOOD MARKETING INSTITUTE,
ARLINGTON, VA
Ms. Sarasin. Good morning. Thank you very much. I am Leslie
Sarasin. I serve as President and CEO of FMI. Our members'
grocery stores are located in every Congressional district in
the country.
Grocers play an important role in the efficient delivery of
safe, affordable food for both the SNAP and the WIC Programs.
We appreciate this Committee's work to better understand SNAP
this morning.
Becoming an authorized SNAP retailer is a complicated
process. Retailers must submit specified paperwork and
credentials, and adhere strictly to the SNAP operating rules
and ongoing training for their associates. Violation of SNAP
operating rules results in revocation of both the SNAP and the
WIC licenses.
SNAP authorized stores code all products within the
electronic checkout system as either SNAP eligible or
ineligible. When an eligible item is scanned, the system
deducts the product's price from the customer's SNAP EBT card.
When an ineligible item is scanned, the cashier is prompted to
ask the customer for another form of payment. Approximately 50
percent of SNAP transactions are multi-tendered, such that
another form of payment is also used to pay for non-food items,
ineligible items, or eligible food items that exceed the
balance available on the SNAP EBT card. If a customer tries to
purchase a tobacco or alcohol product with their SNAP EBT
benefits, the electronic system will freeze until the product
is actually removed. Within the electronic systems, WIC
eligible items are charged against that benefit first, followed
by those eligible for SNAP, and finally, the cashier must
collect another form of payment: cash, check, debit, or credit
for all remaining items.
Grocery transactions for SNAP customers vary significantly
throughout the month. Data indicate the first transaction of
the month is usually the largest and may contain larger
quantities of protein and perishables. By the last week of the
month, customers typically purchase maximum calories at minimum
cost.
We appreciate the Committee's recognition of the role
grocers play in the SNAP program. FMI has announced a new
industry SNAP task force to identify areas where the program
works well, and also to consider those that may require
improvement. Some suggest that limiting what customers can buy
with SNAP, making it more like WIC, may help achieve these
goals. Doing so would place a tremendous burden, both on USDA
and on food retailers, and likely would not achieve policy
goals. Please consider two recent examples from the WIC
Program.
When USDA began the Fresh Fruits and Vegetables Cash Value
Voucher Program, it subjectively decided all fresh fruits and
vegetables were eligible, except white potatoes. As many of you
will recall, this ban on white potatoes unleashed a great
debate throughout Congress and the industry. In the end, after
more than a year of debate and consideration of actual science,
USDA reversed the ban to allow white potatoes to be purchased
through WIC. This was one item out of the tens of thousands
found in each of our members' stores that would have to be
studied and debated before USDA can make a determination as to
whether a product is in or out.
Second, if our goal with SNAP is to provide short-term
lifelines to needy Americans so they can get and keep a job to
earn enough to support their families without government
benefits, such limitations seem unlikely to help accomplish
that goal at a reasonable cost. Doing so will require
additional USDA staff to make these decisions for all products
currently in market, as well as the estimated 20,000 new
products introduced every year. USDA would also need to
maintain a real time list downloadable to every electronic
payment system in the country.
I should note that in 2004, Congress directed USDA to
create an electronically downloadable real time UPC database
for all WIC eligible foods. Today, retailers are still waiting
for this list. The fact that nearly 13 years later we are still
waiting for the list shows the complexity of creating and
keeping one updated in real time, even for a list of products
as small as WIC's. A similar SNAP database would include more
than 100 times the number of products, along with more than the
20,000 that are introduced every year. Could it be done?
Probably so, but we expect it would be both challenging and
expensive.
Finally, FMI members are incredible contributors to their
communities. They are the largest contributors to our nation's
food banks, create good paying jobs, and help build our future
workforce. We look forward to working with the Committee on
SNAP and other related issues, and I am also happy to answer
any questions you may have.
[The prepared statement of Ms. Sarasin follows:]
Prepared Statement of Leslie G. Sarasin, President and Chief Executive
Officer, Food Marketing Institute, Arlington, VA
Chairman Conaway, Ranking Member Peterson, and Members of the
Committee,
My name is Leslie Sarasin, and I serve as President and Chief
Executive Officer of Food Marketing Institute,\1\ a trade association
that represents food retailers and wholesalers, as well as their
suppliers of products and services. FMI members are located in every
Congressional district across the country. FMI's maxim when referring
to its member companies is ``Feeding Families and Enriching Lives,'' a
responsibility we take very seriously.
---------------------------------------------------------------------------
\1\ Food Marketing Institute proudly advocates on behalf of the
food retail industry. FMI's U.S. members operate nearly 40,000 retail
food stores and 25,000 pharmacies, representing a combined annual sales
volume of almost $770 billion. Through programs in public affairs, food
safety, research, education and industry relations, FMI offers
resources and provides valuable benefits to more than 1,225 food retail
and wholesale member companies in the United States and around the
world. FMI membership covers the spectrum of diverse venues where food
is sold, including single owner grocery stores, large multi-store
supermarket chains and mixed retail stores. For more information, visit
www.fmi.org and for information regarding the FMI foundation, visit
www.fmifoundation.org.
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Food Retail Role
In the context of ``feeding families,'' our industry is pleased to
maintain an important role in facilitating the efficient delivery in
our stores of safe, affordable food products for both the Supplemental
Nutrition Assistance Program (SNAP) and the Special Supplemental
Nutrition Program (WIC). I appreciate the work this Committee is
undertaking to better understand the operations of SNAP and the
differences between a short-term hunger program as contemplated in SNAP
and a longer-term nutrition program as contemplated in WIC.
As you know, the WIC program serves mothers and their children up
to age 5. FMI members redeem very specific food prescriptions designed
to ensure moms and their babies have access to the early nutrition they
need for optimum physical and mental development. This important
nutrition program is overseen by the House Education and the Workforce
Committee and is currently up for reauthorization.
SNAP, the program under the full purview of this Committee, is one
in which FMI members serve as the delivery mechanism for benefits.
SNAP, a program created to address hunger among Americans, is designed
to supplement the food budgets for seniors and/or families experiencing
financial difficulty, or on a longer-term basis, individuals who are
disabled.
As designed, SNAP allows customers to purchase approved food
products from a SNAP-authorized retailer. Becoming an authorized SNAP/
WIC retailer is not a simple process, and that process requires
completion of specified paperwork and the providing of many
credentials, including a business license, a photo ID for each owner of
the business and proof of a social security number. This information
may be requested at reauthorization or at any time throughout the
process. Once approved, retaining SNAP/WIC authorization is not a
foregone conclusion. The food retailer must agree to adhere strictly to
the SNAP operating rules, violation of which results in having both the
SNAP and WIC licenses revoked. Additionally, authorized retailers must
agree to ongoing training programs for their associates to ensure they
understand and adhere to all SNAP rules and regulations, as delineated
in USDA's 25 page training guide.
SNAP has been enhanced in recent years by moving from a paper-based
program that issued ``food stamps'' to an electronic benefits transfer
program known as ``EBT,'' through which benefits are downloaded
electronically to a government-issued debit card which then may be
utilized at store level by SNAP benefit recipients. This movement to
EBT has increased the efficiency of the program and enhanced its
accountability by reducing the opportunity for fraud and human error.
The program also benefitted from the work of this Committee and then
Nutrition Subcommittee Chairman Bob Goodlatte, whose efforts focused on
ensuring interoperability and consistency of the program across state
lines. The EBT Interoperability and Portability Act (P.L. 106-171),
signed into law in 2000, ensures that EBT transactions operate
consistently from state to state. This law has significantly reduced
the incidence of error and has allowed shoppers living in border state
areas to seek the best prices through which to stretch their SNAP
benefits. It also has enabled those who must cross state lines for
emergency reasons, such as to care for a sick relative or to escape the
disastrous results of a natural event like Hurricane Sandy, to continue
receiving benefits in a seamless manner.
As the front line purveyors of SNAP, authorized retailers maintain
a unique and special vantage point from which to see SNAP transactions.
At the time of food purchase, SNAP recipients input their unique,
secret PIN after swiping their card. As is the case with commercial
debit cards, the PIN is an important added authentication to prevent a
stolen card from being used by an unauthorized person.
All products in SNAP-authorized stores are coded within the
electronic checkout system as being either eligible or ineligible for
purchase with SNAP benefits. This designation often can be seen on a
paper receipt with the initials ``FS.'' When a SNAP customer places
products on the checkout conveyor belt, the checkout system scans each
item as either eligible or ineligible for SNAP. If an item is eligible,
the system deducts the product's price from the customer's SNAP EBT
card. If ineligible, it prompts the cashier to ask the customer for
another form of payment. Examples of ineligible items include laundry
detergent and diapers, since they are not food items, and a hot
rotisserie chicken, since hot, ready-to-eat food items are not eligible
for purchase with SNAP benefits.
Data indicate that approximately 50% of supermarket customers using
SNAP benefits when purchasing groceries also use other forms of
payment, either to pay for non-food items, ineligible products or for
eligible food items that exceed the remaining balance on the SNAP EBT
card. It also is my understanding that if a customer attempts to
purchase a tobacco product or alcoholic beverage, the electronic system
will freeze and will not allow the transaction to continue until the
tobacco or alcohol product is removed.
Those not fully involved in the SNAP transactional process can find
it baffling and can often be confused about products that are eligible
and those that are ineligible and therefore paid for through other
means, and even in some cases by products that are eligible but not
paid for with SNAP benefits in a particular transaction. Under the
electronic systems in place today, the items eligible for WIC are
charged against that benefit first, followed by those eligible for SNAP
benefits, and finally, the cashier must collect another form of
payment--cash, check, debit or credit--for all remaining items not
eligible under either of the programs and/or for items that exceed the
dollar or prescription value of the benefits. As a result, while the
items the electronic system charges to the SNAP benefit are eligible to
be purchased with SNAP, they may not necessarily be designated by the
customer to be the specific items purchased with SNAP benefits. This
occurs, for example, when a SNAP customer places $100 worth of eligible
items, such as bananas, eggs and bread, and has only $80 in benefits on
the EBT card; the electronic system deducts $80 from the grand total of
SNAP-eligible items, but does not necessarily attribute the $80 to a
specific array of products on the checkout conveyor belt.
It is worth noting that grocery transactions for SNAP customers
vary significantly throughout the month. Data indicate the first
transaction of the month is likely the largest and may contain larger
quantities of protein, perishables, or even a splurge item. The
purchases of second and third weeks of the month are often more
balanced, and the purchases made in the last week of the month
typically find customers purchasing maximum calories at minimum cost.
This variation among purchases is particularly noteworthy in the
seven states that continue to issue benefits to all recipients on only
1 day of the month, rather than spreading issuance dates throughout the
month. There are four states that distribute benefits on only 2 or 3
days each month. Expanding the dates for issuing SNAP benefits allows
supermarkets to better address supply chain issues on fresh and
perishable items and allows labor needs to be spread throughout the
month into full-time positions rather than having them concentrated in
a segment of the month with multiple part-time positions to accommodate
the volume of SNAP shoppers trying to redeem benefits on one day. A
chart of state issuance time frames is attached to this testimony.
Need for Sound Public Policy
FMI member companies appreciate the Committee's recognition that
food retailers are engaged and informed partners in the SNAP and WIC
programs, as evidenced by the invitation for this testimony. As your
partners in this endeavor, we hope you will consider several issues of
concern to food retailers.
Against the backdrop of food retailers' commitment to enrich the
lives of individuals in the communities they serve, we suggest that as
the Committee examines SNAP, it keep in mind the larger goals and
purpose of this hunger program. A strategic policy-oriented discussion
could help make an already good program even better. If, however, the
consideration becomes bogged down in energy zapping tactical questions
of specific product(s) to be considered for elimination from SNAP, this
program enhancement will be made much more difficult, if not
impossible. FMI respectfully submits that changes to the program should
be part of a broad policy discussion with clearly articulated desired
results and delineation of the most effective and efficient means to
achieve those results.
We at FMI would be pleased to participate in that ``results''
discussion. To assist in that process, we have announced the
development of an industry SNAP Task Force to identify areas of the
program we find to be exceptional, to make sure those are not
eliminated, and to consider those we believe may require improvement in
order to achieve your policy goals.
As I understand them, among the Committee goals are the following:
To ensure no unfair penalty on individuals who find
themselves on the edge of the benefits cliff and who are trying
to move to a higher paying job;
To ensure SNAP is the most efficient program possible,
eliminating fraud and opportunities for fraud on both the
delivery and recipient side;
To make SNAP the least burdensome possible for individuals
whose participation in the program may actually reduce
government health care, social services, and education costs,
such as seniors with a fixed income, disabled individuals and
families supporting children under the age of 18; and
To identify and prepare individuals who receive SNAP
benefits for enhanced employment opportunities.
It has been suggested that achievement of these goals might be
facilitated by development of a prescription of limitations for SNAP
purchases, perhaps similar to those that exist in the WIC program.
While this may seem an attractive option, I respectfully suggest that
prior to doing so we first identify the result being sought in
undertaking such a change in the program.
To demonstrate how a tactical reaction may actually prove to be
inconsistent with a policy goal, it is worthwhile to consider an
anecdote from the most recent reauthorization of the WIC nutrition
program. At that time, similar debates occurred regarding products that
should or should not be authorized under the WIC program. There were a
number of factions, including farmers touting the unique benefits of
the crops they were growing. Ultimately, WIC was updated to allow for
the first time a fresh fruits and vegetables benefit and all fruits and
vegetables were allowed under this program, with one exception. The
exception made was for white potatoes, deemed at the time not to be
nutritionally significant. Yet, just 1 year later, the Institute of
Medicine issued a report indicating that Americans suffer from
relatively high incidences of a deficiency in potassium, for which
white potatoes serve as a good source under definitions established by
the Food and Drug Administration. Moreover, we are now in the process
in this country of redesigning the Nutrition Facts Panel that appears
on food products to add potassium as a required element so that
consumers can begin to address this deficiency. In the last Congress,
in 2015 a change was made to allow white potatoes as a vegetable in the
WIC program.
From experience previously in my career while serving as the
President and Chief Executive Officer of the American Frozen Food
Institute, I can relay anecdotes regarding the treatment of frozen
foods, specifically frozen fruits and vegetables, that are
nutritionally equivalent and in some cases nutritionally superior, to
their unfrozen counterparts in not being declared WIC eligible by some
states to the utter detriment of both the programs and the frozen fruit
and vegetable industries.
Of course, the discussions today will hardly illuminate specific
issues such as these, but it is critical as we consider changes to
Federal hunger programs such as SNAP that we identify the policy goals
to be achieved, rather than just focus on a potentially desirable sound
bite. I would respectfully suggest that if our goal with SNAP is to
provide needy Americans a short-term lifeline to allow them to get and
keep a job so they earn enough to support their families without
government benefits, the unilateral limitation of any specific product
is unlikely to help accomplish that goal. It is worth noting that doing
so will also increase the need for additional USDA staff to make and
encode these determinations for an estimated 20,000 new products
introduced into the marketplace annually and then download these
electronically on a real-time basis to every electronic payments system
in the country.
SNAP was designed and currently serves as a hunger program. It is a
supplementary program for the customers whose circumstances require
them to rely upon it for a season of their life, and for these
individuals it is a life-saver. Eighty-two percent of all SNAP benefits
in FY 2015 went to households that included a child, an elderly person
or a person with disabilities.
There have been a number of limitations suggested for this program
whether it be no meats, no desserts, no snacks, no soft drinks and even
no white bread. Not only do such limitations appear incongruous to the
policy positioning of a program designed to provide temporary
assistance addressing hunger considerations, but they also would prove
an administrative nightmare, increasing the cost of acceptance and
slowing down checkout lines in an industry that historically has
experienced only just more than a 1% profit margin and in which every
second of delay affects profitability and ultimately the number of
associates that can be hired and the prices in a store.
Language was included in the WIC reauthorization legislation in
2004 directing the Secretary to develop an electronically downloadable
list of WIC-eligible products on a state by state basis. This has still
not been completed because of its complexity. A similar type of
electronic list for SNAP would easily involve 100 times more products
making it a 100 times more complex. Could it be done? Probably so. But
if it hasn't been done in the WIC program in spite of a 15 year old
Congressional directive, it likely would not be easy or inexpensive.
And at the end of the day, we must ask ourselves what the policy goal
is that this level of expenditure of time and money would achieve.
We are truly blessed in this country with the safest, most abundant
and most affordable food supply in the world. We believe that with that
blessing comes the responsibility to lift up those individuals in our
communities who may need an extra hand, with the goal that they might
provide an extra hand for someone else at another time in the future.
FMI member companies are the largest contributors to our nation's
food banks. In 2016, food retailers donated more than 1.3 billion of
the four billion meals Feeding America provided to families in need.\2\
We are also constantly developing new ways to enhance this donation
level by decreasing food waste. In fact, we have spent much of the past
year working with our supplier partners at the Grocery Manufacturers
Association (GMA) on efforts to reduce customer confusion regarding
product date labels, frequently misunderstood to be expiration dates.
FMI and GMA have just announced an industry-driven voluntary program to
reduce dozens of terms currently in use on date labels and move (to the
extent possible) to two primary labels: ``BEST if used by'' to indicate
quality and ``USE by'' for perishable products that may have potential
degradation implications.
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\2\ Source: Feeding America, 2016 Annual Report, Available at
http://www.feedingamerica.org/about-us/about-feeding-america/annual-
report/2016-feeding-america-annual-report.pdf, pp.13.
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I am pleased to answer any questions you may have and to serve as a
resource to this Committee as you work to make SNAP even more
efficient. I also have to call out the exceptional FNS retailer
management division at USDA headed by Andrea Gold. Through hurricanes,
tornados and floods as well as new store openings or changes in
ownership, we could not have had a better resource than Andrea and her
team to help our members through their challenges.
State-by-State Monthly SNAP Benefit Issuance Schedule
(Current as of February 13, 2017; Food Marketing Institute Research)
------------------------------------------------------------------------
State Day(s) of SNAP Benefit Distribution
------------------------------------------------------------------------
Alabama In August 2013, the state expanded their
distribution dates, moving from the 4th
to the 18th of the month to the 4th
through the 23rd of the month. To
assist in the transition, recipients
received \1/2\ of their benefit on
their original date and \1/2\ on their
new date in the month.
Alaska ** The main SNAP issuance is all on the
first day of the month. Smaller
supplemental issuances for new
applicants and late recertifications
occur daily throughout the month.
Arizona SNAP benefits are distributed over the
first 13 days of the month by the first
letter of the recipients' last name.
For example: last names that begin with
A or B are distributed on the first day
of the month; 2nd day of the month: C
and D; etc.
Arkansas Arkansans receive their benefits on
these 8 days: 4th, 5th, 8th, 9th, 10th,
11th, 12th or 13th of each month, based
on the last number of their [S]ocial
[S]ecurity [N]umber.
California California is different in that each
county distributes SNAP to those who
qualify. The payments go out to all
those who qualify between the 1-10 of
the month. Others (i.e., new
applicants) get paid throughout the
month depending on when they were
accepted.
Colorado Food Stamp benefits are distributed on
the first 10 days of the month by the
recipient's last digit of their
[S]ocial [S]ecurity [N]umber.
Connecticut SNAP benefits and cash are distributed
on the first 3 days of the month, by
the first letter of the recipient's
last name. (A-F are available on the
first; G-N on the second and O-Z are
distributed on the third day of the
month.)
Delaware Benefits are made available over 23
days, beginning with the 2nd day of
every month, based on the first letter
of the client's last name.
District of Columbia Benefits are made available from the 1st
to the 10th of every month, based on
the first letter of the client's last
name.
Florida All SNAP recipients moved from a 15 day
distribution to a 28 day distribution
in April 2016. In March 2016, to assist
in the new transition, benefits were
``split.'' Recipients received the
first half of their benefits on their
``old'' date and received the second
half of their monthly benefits on what
was their ``new'' date going forward.
The ACCESS Florida system assigns
benefit availability dates based on the
case number recipients received when
they became eligible for the SNAP
program.
Georgia In September 2012, SNAP benefits in
Georgia expanded from the 5th to the
14th, and then finally to the current
5th to 23rd of each month, distributed
every other day.
Hawaii Benefits are made available on the 3rd
and the 5th of every month, based on
the first letter of the client's last
name.
Idaho Benefits were previously made available
on the first day of every month. (Prior
to August 2009, benefits were
distributed on 5 consecutive days at
the beginning of each month, but this
was later moved to 1 day.) In 2014,
H.B. 565 was enacted. The bill requires
the state Department of Health and
Welfare to issue SNAP benefits over the
course of 10 consecutive days within a
month. Bonus money received from USDA
paid for the cost of the change.
Currently, and since July 1, 2016,
benefits are distributed over the first
10 days of each month based on the last
number of the birth year of the
recipient; for example, a birthday of 8/
25/64 would receive benefits on the 4th
day of each month.
Illinois SNAP benefits are made available on
these 12 days of the month: 1st, 3rd,
4th, 7th, 8th, 10th, 11th, 14th, 17th,
19th, 21st, and 23rd of every month,
based on a combination of the type of
case and the case name.
Indiana On January 1, 2014, the state
implemented an expanded schedule for
the distribution of benefits during the
fifth through the twenty-third day of
each month, to be issued every-other-
day, based on the first letter of the
recipient's last name. For example: A
or B = benefits available on the 5th;
first Letter of the Last Name is: C or
D = benefits available on the 7th.
Previously, benefits were made
available on the first 10 calendar days
each month. (TANF is issued on the
first of the month.)
Iowa Benefits are made available over the
first 10 calendar days of every month,
based on the first letter of the
client's last name.
Kansas Benefits are made available over the
first 10 calendar days of every month,
based on the first letter of the
client's last name.
Kentucky Benefits are made available over the
first 19 calendar days of every month,
based on the last digit of the client's
case number. This was recently expanded
from the previous 10 day distribution.
Louisiana Benefits are made available between the
1st and the 14th of every month, based
on the last digit of the client's SSN.
(Elderly and disabled benefits are made
available between the 1st and the 4th
of every month.)
Maine Benefits are available the 10th to the
14th of every month based on the last
digit of the recipient's birthday.
Maryland In January 2016, the distribution
schedule was changed. Benefits are now
distributed from the 4th to the 23rd of
every month, based on the first three
letters of the client's last name.
Previously, benefits were distributed
from the 6th through the 15th of the
month. This was accomplished through a
5 month phase-in.
Massachusetts Distribution is based on the last digit
of each recipient's [S]ocial [S]ecurity
[N]umber and distributed over the first
14 days of the month.
Michigan In January 2011, SNAP moved from a 7 day
distribution to the current
distribution, which is from the 3rd to
the 21st, distributed every-other-day,
based on the last digit of the head of
household's recipient identification
number. For example, clients' numbers
ending with 0 will receive food
benefits on the 3rd of the month;
numbers ending with 1, food benefits
will be available on the 5th of the
month.
Minnesota Benefits are staggered over 10 calendar
days, beginning on the 4th through the
13th of every month, without regard to
weekends or holidays, based on the last
digit of the client's case number.
Mississippi Effective February 2017, benefits are
made available from the 4th to the 21st
of every month, based on the last two
digits of the client's case number.
Benefits were previously distributed
from the 5th to the 19th (15 days) of
every month.
Missouri Benefits are made available over the
first 22 days of every month, based on
the client's birth month and last name.
Montana Benefits are distributed over 5 days by
the last number of the recipient's case
number, from the 2nd to the 6th of
every month.
Nebraska Nebraska distributes benefits during the
first 5 calendar days of the month. The
day of distribution is based on the
last digit of the [S]ocial [S]ecurity
[N]umber.
Nevada ** In Nevada, food stamp benefits are
issued on the first day of each month.
New Hampshire ** New Hampshire benefits are available on
the 5th of every month.
New Jersey The monthly SNAP allotment is available
over the first 5 days of the month. The
day is based on the number in the 7th
position of their case number. Some of
the cases still receive their benefits
based on the assignment at the time the
county was converted to EBT. In Warren
County, all benefits are made available
on the 1st of the month.
New Mexico Benefits are made available over 20 days
every month, based on the last two
digits of the SSN.
New York The process is twofold as follows: in
New York City, recipients receive their
SNAP benefits within the first 13
business days of the month, according
to the last digit of their case number,
not including Sundays or holidays. The
actual dates change from 1 month to the
next, so NYC publishes a 6 month
schedule showing the exact availability
dates. For the remainder of New York
State, recipients receive their
benefits within the first 9 days of the
month, also according to the last digit
of their case number, including Sundays
and holidays.
North Carolina Effective July 2011, the state expanded
its 10 day distribution schedule.
Benefits are now distributed from the
3rd to the 21st of every month, based
on the last digit of the primary
cardholder's Social Security Number.
North Dakota ** Benefits are made available on the first
day of every month.
Ohio In April 2014, Ohio expanded its SNAP
distribution from the first 10 days of
the month to the first 20 days of the
month, staggered every 2 days. This
only affected SNAP recipients who moved
from one county to another; recipients
who experienced a 1 day or more break
in eligibility; and, all new
recipients. Recipients who were on SNAP
before April 2014 did not see a change.
Oklahoma Benefits are made available from the 1st
to the 10th of every month, based on
the last digit of the client's SNAP
case number.
Oregon SNAP is distributed on the first 9 days
of the month as such: [S]ocial
[S]ecurity [N]umbers ending with ``0''
or ``1'' distribute on the 1st day of
the month, numbers ending with a ``2''
are distributed on the 2nd day of the
month and so on.
Pennsylvania Benefits are made available over the
first 10 business days of every month
(excluding weekends and holidays) based
on the last digit of the client's case
number.
Rhode Island ** Benefits are made available on the first
day of every month.
South Carolina In 2012, South Carolina expanded from a
9 day to a 19 day issuance. Current
recipients stayed within the 9 day
distribution, but all new recipients
were given a date that expanded into
the 19 days.
South Dakota ** Benefits are made available on the 10th
day of every month.
Tennessee In October 2012, Tennessee expanded
distribution from 10 to 20 days.
Texas Benefits are made available over the
first 15 days of the month, based on
the last digit of the client's SNAP
case number.
Utah Benefits are made available on the 5th,
11th, or 15th of every month, based on
the first letter of the client's last
name: A-G available on the 5th; H-O
available on the 11th; P-Z available on
the 15th.
Vermont ** Vermont benefits are available on the
first of every month.
Virginia On September 1, 2012, benefit
distribution was moved from 1 day a
month to 5 days, and then eventually to
the current 1st to the 9th day of every
month, based on the last digits of the
client's case number.
Washington Benefits are staggered over the first 10
days of the month based on the last
digit of the households' assistance
unit number. Weekends and holidays do
not affect the schedule. However,
beginning February 1, 2017, an
expansion of distribution was fully
implemented. Going forward, it will be
the first 20 days of the month.
West Virginia Benefits are made available over the
first 9 days of every month, based on
the first letter of the client's last
name.
Wisconsin Benefits are made available over the
first 15 days of every month, based on
the eighth digit of the client's SSN.
Wyoming SNAP is distributed on the first 4 days
of the month.
------------------------------------------------------------------------
Notes:
** States with asterisks are those that only distribute benefits on 1
day a month. There are seven that still do so. Warren County, New
Jersey distributes only 1 day a month, although the rest of the state
distributes over 5 days. Also, there are four states that distribute
SNAP just 2 or 3 days a month.
Additional Distribution Information:
There is no limit on the number of days for stagger. The only condition
in regulation is that no single household's issuance should exceed 40
days between issuances.
Currently, benefit recipients may only be issued their benefits one time
a month, or within 40 days.
Supplemental Nutrition Assistance Program: One-Month Change in Total Participation
(Prepared by the Food Research and Action Center (FRAC))
(Data as of January 6, 2017)
----------------------------------------------------------------------------------------------------------------
Percent Change September
State September 2016 October 2016 2016 vs. October 2016
----------------------------------------------------------------------------------------------------------------
Kentucky 657,389 671,628 2.2
Arkansas 399,538 403,376 1.0
South Carolina 746,646 752,030 0.7
Texas 3,864,686 3,891,234 0.7
Wyoming 33,806 33,977 0.5
Kansas 246,179 247,281 0.4
Nevada 441,986 443,138 0.3
Montana 119,863 120,065 0.2
Vermont 78,034 78,092 0.1
Massachusetts 771,436 771,512 0.0
Washington 952,711 951,845 ^0.1
Colorado 467,426 466,789 ^0.1
Idaho 176,217 175,976 ^0.1
Pennsylvania 1,858,232 1,855,129 ^0.2
Arizona 964,979 963,303 ^0.2
Mississippi 555,278 554,225 ^0.2
Hawaii 173,669 173,289 ^0.2
Florida 3,287,446 3,279,009 ^0.3
West Virginia 351,391 350,474 ^0.3
Georgia 1,688,832 1,683,945 ^0.3
Indiana 710,738 708,476 ^0.3
Oregon 712,084 709,684 ^0.3
Iowa 378,478 377,126 ^0.4
Minnesota 465,211 463,461 ^0.4
New York 2,950,208 2,938,258 ^0.4
New Jersey 857,779 854,146 ^0.4
Missouri 770,944 767,403 ^0.5
Alabama 830,742 826,790 ^0.5
Wisconsin 712,582 709,134 ^0.5
Oklahoma 621,462 618,434 ^0.5
California 4,252,654 4,230,399 ^0.5
South Dakota 95,655 95,153 ^0.5
Connecticut 424,431 422,181 ^0.5
Maryland 720,566 716,620 ^0.5
Delaware 149,158 148,340 ^0.5
New Hampshire 95,393 94,823 ^0.6
Maine 183,299 182,095 ^0.7
Ohio 1,564,498 1,553,901 ^0.7
Virginia 811,949 806,332 ^0.7
Utah 214,505 212,903 ^0.7
Michigan 1,434,550 1,423,008 ^0.8
North Dakota 54,622 54,124 ^0.9
Tennessee 1,083,880 1,071,344 ^1.2
Illinois 1,931,575 1,907,969 ^1.2
North Carolina 1,470,079 1,450,485 ^1.3
New Mexico 480,493 473,398 ^1.5
Rhode Island 168,973 166,365 ^1.5
District of Columbia 132,308 126,322 ^4.5
Louisiana 1,042,876 943,685 ^9.5
Nebraska 177,912 153,419 ^13.8
Alaska 84,825 71,768 ^15.4
-------------------------------------------------------------------------------
Total......................... 43,493,149 43,215,557 ^0.6
----------------------------------------------------------------------------------------------------------------
The following areas receive Nutrition Assistance Grants which provide benefits analogous to the Supplemental
Nutrition Assistance Program: Puerto Rico, American Samoa, and the Northern Mariana[ Islands]. In addition,
2015 and 2016 data are preliminary and are subject to significant revision.
* State where October 2016 SNAP data include disaster assistance (D-SNAP).
Supplemental Nutrition Assistance Program: One-Year Change in Total Participation
(Prepared by the Food Research and Action Center (FRAC))
(Data as of January 6, 2017)
----------------------------------------------------------------------------------------------------------------
Percent Change October
State October 2015 October 2016 2015 vs. October 2016
----------------------------------------------------------------------------------------------------------------
Louisiana 879,541 943,685 7.3
Montana 113,462 120,065 5.8
Wyoming 32,729 33,977 3.8
Texas 3,777,317 3,891,234 3.0
New Mexico 460,048 473,398 2.9
Alaska 69,996 71,768 2.5
North Dakota 53,271 54,124 1.6
Nevada 439,498 443,138 0.8
Delaware 147,127 148,340 0.8
Oklahoma 613,397 618,434 0.8
Pennsylvania 1,873,447 1,855,129 ^1.0
South Dakota 96,692 95,153 ^1.6
Massachusetts 786,492 771,512 ^1.9
New York 2,996,649 2,938,258 ^1.9
Iowa 384,685 377,126 ^2.0
West Virginia 359,001 350,474 ^2.4
Arizona 991,567 963,303 ^2.9
Colorado 481,892 466,789 ^3.1
Connecticut 439,210 422,181 ^3.9
Rhode Island 173,148 166,365 ^3.9
Virginia 844,204 806,332 ^4.5
Minnesota 485,317 463,461 ^4.5
Utah 222,981 212,903 ^4.5
Ohio 1,629,349 1,553,901 ^4.6
California 4,436,189 4,230,399 ^4.6
Hawaii 182,226 173,289 ^4.9
Illinois 2,007,492 1,907,969 ^5.0
New Jersey 899,481 854,146 ^5.0
Georgia 1,774,540 1,683,945 ^5.1
Vermont 82,364 78,092 ^5.2
South Carolina 793,218 752,030 ^5.2
Maine 192,404 182,095 ^5.4
Kentucky 713,911 671,628 ^5.9
Michigan 1,513,129 1,423,008 ^6.0
Alabama 881,402 826,790 ^6.2
Wisconsin 756,434 709,134 ^6.3
Oregon 759,386 709,684 ^6.5
Kansas 265,478 247,281 ^6.9
New Hampshire 101,894 94,823 ^6.9
Idaho 189,385 175,976 ^7.1
Maryland 779,303 716,620 ^8.0
Tennessee 1,168,238 1,071,344 ^8.3
Washington 1,043,008 951,845 ^8.7
Missouri 843,876 767,403 ^9.1
District of Columbia 140,654 126,322 ^10.2
Indiana 799,663 708,476 ^11.4
Florida 3,708,499 3,279,009 ^11.6
Mississippi 628,354 554,225 ^11.8
Arkansas 457,380 403,376 ^11.8
North Carolina 1,647,808 1,450,485 ^12.0
Nebraska 176,363 153,419 ^13.0
-------------------------------------------------------------------------------
Total......................... 45,368,265 43,215,557 ^4.7
----------------------------------------------------------------------------------------------------------------
The following areas receive Nutrition Assistance Grants which provide benefits analogous to the Supplemental
Nutrition Assistance Program: Puerto Rico, American Samoa, and the Northern Mariana[ Islands]. In addition,
2015 and 2016 data are preliminary and are subject to significant revision.
* State where October 2016 SNAP data include disaster assistance (D-SNAP).
Supplemental Nutrition Assistance Program: Five-Year Change in Participation
(Prepared by the Food Research and Action Center (FRAC))
(Data as of January 6, 2017)
----------------------------------------------------------------------------------------------------------------
Percent Change October
State October 2011 October 2016 2011 vs. October 2016
----------------------------------------------------------------------------------------------------------------
Nevada 351,686 443,138 26.0
New Mexico 432,289 473,398 9.5
California 3,867,094 4,230,399 9.4
Connecticut 396,517 422,181 6.5
Illinois 1,831,037 1,907,969 4.2
Pennsylvania 1,785,240 1,855,129 3.9
Louisiana 916,060 943,685 3.0
Delaware 144,612 148,340 2.6
Hawaii 169,405 173,289 2.3
Wyoming 33,252 33,977 2.2
Florida 3,225,957 3,279,009 1.6
West Virginia 347,064 350,474 1.0
Maryland 709,681 716,620 1.0
Oklahoma 624,112 618,434 ^0.9
Rhode Island 168,694 166,365 ^1.4
Montana 121,992 120,065 ^1.6
Colorado 480,566 466,789 ^2.9
New York 3,060,107 2,938,258 ^4.0
Alaska 74,792 71,768 ^4.0
New Jersey 890,859 854,146 ^4.1
Iowa 398,574 377,126 ^5.4
Texas 4,174,348 3,891,234 ^6.8
South Dakota 103,282 95,153 ^7.9
Massachusetts 838,603 771,512 ^8.0
North Dakota 59,383 54,124 ^8.9
Alabama 910,034 826,790 ^9.1
District of Columbia 140,003 126,322 ^9.8
Georgia 1,870,781 1,683,945 ^10.0
Virginia 896,420 806,332 ^10.0
Oregon 798,772 709,684 ^11.2
Ohio 1,766,584 1,553,901 ^12.0
Nebraska 174,941 153,419 ^12.3
North Carolina 1,655,694 1,450,485 ^12.4
Minnesota 531,728 463,461 ^12.8
Washington 1,095,139 951,845 ^13.1
South Carolina 867,258 752,030 ^13.3
Mississippi 645,220 554,225 ^14.1
Wisconsin 828,362 709,134 ^14.4
Arizona 1,138,220 963,303 ^15.4
Tennessee 1,280,908 1,071,344 ^16.4
New Hampshire 114,744 94,823 ^17.4
Vermont 94,604 78,092 ^17.5
Arkansas 490,487 403,376 ^17.8
Kansas 302,633 247,281 ^18.3
Missouri 950,725 767,403 ^19.3
Kentucky 842,885 671,628 ^20.3
Indiana 901,967 708,476 ^21.5
Michigan 1,884,542 1,423,008 ^24.5
Idaho 233,194 175,976 ^24.5
Utah 285,695 212,903 ^25.5
Maine 251,189 182,095 ^27.5
-------------------------------------------------------------------------------
Total......................... 46,224,722 43,215,557 ^6.5
----------------------------------------------------------------------------------------------------------------
The following areas receive Nutrition Assistance Grants which provide benefits analogous to the Supplemental
Nutrition Assistance Program: Puerto Rico, American Samoa, and the Northern Mariana[ Islands]. In addition,
2015 and 2016 data are preliminary and are subject to significant revision.
* State where October 2016 SNAP data include disaster assistance (D-SNAP).
Share of Population Participating in SNAP
(Prepared by the Food Research and Action Center (FRAC))
(Data as of January 6, 2017)
----------------------------------------------------------------------------------------------------------------
Population Estimate SNAP Participants,
State (2015) October 2016 Share of Population
----------------------------------------------------------------------------------------------------------------
New Mexico 2,085,109 473,398 22.7
Louisiana 4,670,724 943,685 20.2
West Virginia 1,844,128 350,474 19.0
District of Columbia 672,228 126,322 18.8
Mississippi 2,992,333 554,225 18.5
Oregon 4,028,977 709,684 17.6
Alabama 4,858,979 826,790 17.0
Georgia 10,214,860 1,683,945 16.5
Tennessee 6,600,299 1,071,344 16.2
Florida 20,271,272 3,279,009 16.2
Oklahoma 3,911,338 618,434 15.8
Rhode Island 1,056,298 166,365 15.7
Delaware 945,934 148,340 15.7
South Carolina 4,896,146 752,030 15.4
Nevada 2,890,845 443,138 15.3
Kentucky 4,425,092 671,628 15.2
New York 19,795,791 2,938,258 14.8
Illinois 12,859,995 1,907,969 14.8
Pennsylvania 12,802,503 1,855,129 14.5
North Carolina 10,042,802 1,450,485 14.4
Michigan 9,922,576 1,423,008 14.3
Texas 27,469,114 3,891,234 14.2
Arizona 6,828,065 963,303 14.1
Maine 1,329,328 182,095 13.7
Arkansas 2,978,204 403,376 13.5
Ohio 11,613,423 1,553,901 13.4
Washington 7,170,351 951,845 13.3
Missouri 6,083,672 767,403 12.6
Vermont 626,042 78,092 12.5
Wisconsin 5,771,337 709,134 12.3
Hawaii 1,431,603 173,289 12.1
Iowa 3,123,899 377,126 12.1
Maryland 6,006,401 716,620 11.9
Connecticut 3,590,886 422,181 11.8
Montana 1,032,949 120,065 11.6
Massachusetts 6,794,422 771,512 11.4
South Dakota 858,469 95,153 11.1
California 39,144,818 4,230,399 10.8
Indiana 6,619,680 708,476 10.7
Idaho 1,654,930 175,976 10.6
Alaska 738,432 71,768 9.7
Virginia 8,382,993 806,332 9.6
New Jersey 8,958,013 854,146 9.5
Colorado 5,456,574 466,789 8.6
Kansas 2,911,641 247,281 8.5
Minnesota 5,489,594 463,461 8.4
Nebraska 1,896,190 153,419 8.1
North Dakota 756,927 54,124 7.2
New Hampshire 1,330,608 94,823 7.1
Utah 2,995,919 212,903 7.1
Wyoming 586,107 33,977 5.8
-------------------------------------------------------------------------------
Total......................... 321,418,820 43,215,557 13.4
----------------------------------------------------------------------------------------------------------------
The following areas receive Nutrition Assistance Grants which provide benefits analogous to the Supplemental
Nutrition Assistance Program: Puerto Rico, American Samoa, and the Northern Mariana[ Islands]. In addition,
2015 and 2016 data are preliminary and are subject to significant revision.
* State where October 2016 SNAP data include disaster assistance (D-SNAP).
The Chairman. Thank you.
Mr. Weidman, 5 minutes.
STATEMENT OF JOHN WEIDMAN, DEPUTY EXECUTIVE DIRECTOR, THE FOOD
TRUST, PHILADELPHIA, PA
Mr. Weidman. Thank you, Chairman Conaway and Ranking Member
Peterson, for inviting me to testify. My name is John Weidman.
I am Deputy Executive Director for The Food Trust, a
Pennsylvania-based nonprofit working nationally to improve
access to healthy food.
This year through a grant from the Robert Wood Johnson
Foundation, we have launched the Center for Healthy Food
Access, a national collaborative effort aimed at improving the
health of children. I am here today to talk about strategies
The Food Trust has been employing to improve health and
encourage healthy eating among SNAP participants.
We believe that to have the greatest impact, it takes a
comprehensive approach that includes access, education, and
incentives. In Pennsylvania, we have been improving access by
opening farmers' markets, working with corner stores to stock
healthier products, and incentivizing new supermarket
development. Our team of nutrition educators is providing
innovative and engaging programing through SNAP-Ed to teach
children and adults how to eat healthy, cook, and shop on a
budget. And we run a successful Food Bucks Program that
provides $2 worth of free produce for every $5 spent with SNAP
at farmers' markets and a local supermarket.
Based on research, this comprehensive approach is working.
A peer-reviewed study published in the journal Pediatrics found
that our SNAP-Ed program reduced childhood overweight by 50
percent. More recently, data collected on the BMI of
Philadelphia children is showing that after decades of rising
childhood obesity rates, we are finally seeing them drop. The
strategies that are being implemented, access to healthy food,
nutrition education, SNAP incentives, are happening all around
the country and they are not only changing eating habits and
preventing diet-related disease, but they are also creating
jobs and spurring economic development.
I want to share a brief story about Nicole Speller, a
participant in one of our free 6 week SNAP-Ed cooking workshops
that take place in over 500 community sites across southeastern
Pennsylvania. Nicole had decided to make a change and improve
her health. She also happened to be a fantastic cook, and each
week she would share the recipe she was learning with her
neighbors and her church. Upon completing the workshop series,
Nicole started her own healthy cooking class at her church.
This is just one example of how SNAP-Ed is helping to create a
culture of health, and it is happening in innovative ways in
every state in the nation.
Of course, understanding how to eat healthy is only part of
the problem. Accessing healthy food continues to be a challenge
for millions of Americans. Over the last decade, we have seen
incredible success through public-private partnerships to
incentivize grocery stores to meet the need for better access.
In Pennsylvania, through the leadership of now-Congressman
Dwight Evans, we have the Pennsylvania Fresh Financing
Initiative, which funded 88 grocery store projects in urban and
rural areas, and created 5,000 jobs. Based on this successful
model, there is now the Federal HFFI and programs in many other
states.
Most recently through Governor Kasich's Ohio Fresh Food
Program, Vinton County, a rural county in southeast Ohio, is
now slated for a new grocery store to open after the only store
in the entire county had previously closed. This store will now
serve seniors and working families who have been unable to
satisfy the very basic human need of going to the store to buy
food.
The same grocers who we work with on HFFI programs also
stress the need of the importance of nutrition education. It
makes sense if grocers open a store and stock it with fresh
produce, they need nutrition education to drive demand for
healthy food. This is why both access and education go hand-in-
hand, not only to drive better health outcomes, but also to
ensure that stores are profitable and serve as economic
anchors.
Last, I want to discuss incentives that help make healthy
choices more affordable. In Philadelphia, 73 percent of Philly
Food Bucks users report eating more fruits and vegetables, and
SNAP sales at our farmers' markets have increased 300 percent
since we launched the program. In Michigan, the Double Up Food
Bucks Program is available throughout the state at farmers'
markets and supermarkets, and around the country, hospitals are
now participating in Veggie programs, allowing physicians
to prescribe fruits and vegetables to low-income patients. The
USDA FINI Program has supported the expansion of these SNAP
incentive programs. Making healthier food more affordable makes
it easier for low-income families to make healthier choices.
Many parents might try putting a plate of fresh carrots in
front of a toddler. If he doesn't like it, they can just fix
him something else to eat. But imagine if you only have enough
money to afford one plate of food. The decision to try new
things becomes much more difficult.
In closing, there is no silver bullet to prevent diet-
related disease like obesity and diabetes, but the costs are
real. A recent study calculated the cost of diet-related
disease at $427 billion. A comprehensive approach that combines
access, nutrition education, incentives, and includes public-
private partnerships holds the most promise for stemming these
rising healthcare costs. Congress has moved forward to address
obesity and diabetes through innovative programs like SNAP-Ed,
FINI, and HFFI. SNAP is the foundation of this comprehensive
approach and keeps millions of families from going hungry, and
it is a critical economic pillar in low-income urban and rural
communities. Without SNAP, stores would close, jobs would be
lost, families would go hungry, and it would increase the need
for food stamps. Quite a vicious cycle, if there ever was one,
but by expanding access to healthy food, nutrition education,
and SNAP incentives in the next farm bill, we can improve
health, increase revenues for American farmers, create jobs in
urban and rural areas, and control rising healthcare costs.
Thank you for asking me to testify.
[The prepared statement of Mr. Weidman follows:]
Prepared Statement of John Weidman, Deputy Executive Director, The Food
Trust, Philadelphia, PA
Thank you, Chairman Conaway and Ranking Member Peterson, for
inviting me to testify. My name is John Weidman, and I am the Deputy
Executive Director of The Food Trust, a Pennsylvania based nonprofit
that works nationally to improve access to affordable nutritious food.
We were founded in 1992, and 2017 marks our 25th Anniversary. This
year, through a grant from the Robert Wood Johnson Foundation we have
launched the Center for Healthy Food Access: a national collaborative
effort aimed at improving the health of children in America. I am here
today to talk about the strategies that The Food Trust has been
employing to improve health and encourage healthy eating among SNAP
participants. We believe that to have the greatest impact it takes a
comprehensive approach that includes improving access, providing
nutrition education, and utilizing SNAP incentives. In Pennsylvania, we
have been improving access by opening and maintaining farmers[']
markets in low-income neighborhoods, working with small food stores to
stock healthier products, and incentivizing new supermarket development
through the Pennsylvania Fresh Food Financing Initiative, the national
model for Healthy Food Financing programs. Our team of dieticians and
nutrition educators is providing innovative and engaging programming
through the SNAP-Ed program to teach children and adults how to eat
healthy, how to cook, and how to shop on a budget. And we run a
successful Food Bucks program that provides $2 worth of free fruits and
vegetables for every $5 spent with SNAP at Philadelphia farmers[']
markets and a local supermarket chain.
Based on research that has been conducted in Philadelphia, this
comprehensive approach is working. A peer-reviewed study published in
the journal Pediatrics found that our SNAP-Ed funded school nutrition
education program reduced childhood overweight by 50%.\1\ More
recently, data collected on the Body Mass Index (BMI) of Philadelphia
children is showing that after decades of rising childhood obesity
rates, we are finally seeing them drop.\2\ The strategies that are
being implemented in Pennsylvania--access to healthy food, nutrition
education, and SNAP Incentives--are happening all around the country,
and they are not only changing eating habits and preventing diet-
related diseases like heart disease and diabetes, but they are also
creating jobs and spurring economic development in struggling urban and
rural communities.
---------------------------------------------------------------------------
\1\ Foster, G.D., Sherman, S., Borradaile, K.E., Grundy, K.M.,
Veur, S.S., Nachmani, J., Karpyn, A., Kumanyika, S., Shults, J. (2008).
A Policy-Based School Intervention to Prevent Overweight and Obesity.
Pediatrics, 121(4). doi:10.1542/peds.2007-1365.
\2\ Robbins, J.M., Mallya G., Wagner A., Buehler J.W. Prevalence,
Disparities, and Trends in Obesity and Severe Obesity Among Students in
the School District of Philadelphia, Pennsylvania, 2006-2013. Prev.
Chronic. Dis. 2015; 12; 150185. DOI; http://dx.doi.org/10.5888/
pcd12.150185.
---------------------------------------------------------------------------
I want to share a brief story about Nicole Speller, a participant
in one of our free 6 week SNAP-Ed cooking workshops that take place in
over 500 community sites: libraries, community centers, and churches
across southeastern Pennsylvania. Nicole had decided to make a change
and improve her health. She also happened to be a fantastic cook, and
each week she would share the recipes and nutrition tips she was
learning with her neighbors and her church group. Upon completing the
workshop series, Nicole started her own healthy cooking class at her
church. This is just one example of how SNAP-Ed is helping to create a
culture of health, and it is happening in innovative ways in every
state in the nation. In addition to our cooking workshops, we also use
Share Our Strength's excellent Cooking Matters program to teach how to
shop healthy in the supermarket and make healthy choices on a budget.
We also work directly with thousands of school children each year to
teach them about food, farming, and eating healthy.
Of course, understanding how to eat healthier is only part of the
problem for many SNAP participants. Accessing healthy food continues to
be a challenge for millions of Americans. Over the last decade, we have
seen incredible success through public-private partnerships to
incentivize grocery stores, farmers['] markets, and other healthy food
retail solutions to meet the need for better access. In Pennsylvania,
thanks in large part to now-Congressman Dwight Evans, our Fresh Food
Financing Initiative funded 88 grocery store projects in urban and
rural areas and created 5,000 jobs. Based on this successful model, we
now have the Federal Healthy Food Financing Initiative (HFFI) and
programs in New York, Illinois, Mississippi, Colorado, and other
states. Most recently, through Governor Kasich's Ohio Fresh Food
Program, Vinton County--a rural county in southeast Ohio--is now slated
for a new grocery store to open after the only store in the county had
previously closed. This store will now serve seniors and working
families who have been unable to satisfy the very basic human need of
going to the store to buy food.
While the HFFI model was developed working directly with grocers
who want to improve access in under-served areas, they also stress the
importance of nutrition education. It makes sense: if grocers open a
store and stock it with fresh produce, they need nutrition education
programs to drive demand for purchasing healthy food. For this reason,
some grocers are now hiring registered dieticians to guide consumers in
the store. Grocers understand the need to improve eating habits, but at
the end of the day they cannot stock food that does not sell. This is
why both access and education go hand-in-hand, not only to drive better
health outcomes, but also to ensure that stores are profitable and
serve as economic anchors for small towns and urban neighborhoods.
In addition to the vital role the Federal Government plays,
partnerships with the private sector are a critical component of the
solution. Consumer demand for healthy products is growing, and many
operators and manufacturers are shifting their product portfolios in a
healthier direction. At the same time, retailers are developing
innovative ways to sell these products. Grocers, bodega owners, and
farmers have been indispensable partners in all of the efforts I have
been discussing. We are partnering with food manufacturers such as
Campbell Soup Company, which is spearheading a 10 year initiative in
Camden, New Jersey, to improve health and reduce food insecurity. GSK
(GlaxoSmithKline), another corporate partner, is funding a city-wide
initiative called Get HYPE Philly! that is focused on youth leadership
development, healthy eating and exercise, and education and job skills.
We need more of these innovative partnerships in the years ahead.
Last, I want to discuss incentives that encourage SNAP participants
to try healthier foods and that make healthier choices more affordable.
As I mentioned, The Food Trust launched our Philly Food Bucks program
in 2011, and it has been a huge success. Seventy-three percent of
Philly Food Bucks users report eating more fruits and vegetables, and
SNAP sales at farmers['] markets have increased 300% since the start of
the program. Based in Michigan, the Fair Food Network has greatly
expanded their Double Up Food Bucks program in farmers' markets and
grocery stores across the country. Wholesome Wave, based in
Connecticut, is bringing SNAP incentives to health care, allowing
physicians to ``prescribe'' fruits and vegetables to low-income
patients for redemption at local farmers['] markets. In 2014, USDA
launched FINI, the Food Insecurity Nutrition Incentive program, which
has supported research, piloting, and expansion of SNAP incentive
programs. Making healthier food more affordable makes it easier for
low-income families to take risks when trying new foods. Many parents
might try putting a plate of fresh carrots and peas in front of a
toddler. If he sticks out his tongue and says yuck, they can just fix
him something else to eat. (This is based on personal experience. I
have a 3 year old). But imagine if you only have enough money to afford
one plate of food--the decision to try new things becomes much more
difficult.
In closing, there is no silver bullet to prevent diet-related
diseases like obesity and diabetes, but the costs are real. A recent
study by the Milken Institute calculated the direct medical costs for
diet-related disease in 2014 at $427.8 billion.\3\ Soda and sugary
drinks are a big driver of the problem and Congress has moved forward
to address obesity and diabetes through innovative programs like SNAP-
Ed, FINI and HFFI. A comprehensive approach that combines access,
nutrition education, and SNAP incentives holds the most promise for
stemming these rising healthcare costs and building new, healthier
habits. SNAP is the foundation of this comprehensive approach. It keeps
millions of families from going hungry and is a critical economic
pillar for lower income urban and rural communities. Without SNAP,
stores would close, jobs would be lost, more families would drop into
poverty, and more people would need food stamps. A vicious cycle, if
there ever was one. By expanding access to healthy food, nutrition
education, and incentives in the next farm bill we can improve health,
increase revenues for American farmers, create jobs in urban and rural
areas, and control rising healthcare costs.
---------------------------------------------------------------------------
\3\ Waters, H., & DeVol, R. (2016). Weighing Down America: The
Health and Economic Impact of Obesity. Retrieved from Milken Institute:
http://assets1c.milkeninstitute.org/assets/Publication/ResearchReport/
PDF/Weighing-Down-America-WEB.pdf.
---------------------------------------------------------------------------
Thank you for the opportunity to testify, I look forward to your
questions.
The Chairman. Thank you very much.
Dr. Wansink?
STATEMENT OF BRIAN WANSINK, Ph.D., JOHN S. DYSON
PROFESSOR OF MARKETING AND DIRECTOR, CORNELL
UNIVERSITY FOOD AND BRAND LAB, ITHACA, NY
Dr. Wansink. Thank you for giving me the opportunity to
present my perspective on the pros and cons of restricting SNAP
purchases. I will be addressing three questions today: first,
what happens when food purchases are restricted; second, who
has the most potential to shop healthier; and third, how can
this be best encouraged? Thank you.
First, as a behavioral scientist and Director of the
Cornell Food and Brand Lab, I focus on changing behaviors in a
practical way. But as former USDA Executive Director from the
Center for Nutrition Policy and Promotion, the Dietary
Guidelines, I focused on changing eating behaviors in a
scalable way. What I want to emphasize is our best and worst
eating habits start in the grocery store. If we can change what
people bring home, we change what they eat.
Now how do food restrictions influence people? Well, I have
two exhibits. First, how does shopping behavior change after
versus before people receive SNAP benefits? Well, there is a
new 6 year study of SNAP recipients in Rhode Island that shows
that spending on SNAP eligible products went up once they
received the benefits, but the general purchase of SNAP
ineligible benefits, the soft drinks and things like this, did
not go down. What they do is they trace some of this to people
buying more convenient products when they get SNAP benefits.
Exhibit 2 looks at incentives. When we specifically
financially incentivize shoppers to buy more fruits and
vegetables, what happens? In one 6 month study of 208 families
in Utica, New York, we gave shoppers ten percent more money
back in a debit card when they bought healthy foods like fruits
and vegetables. When low-income shoppers were given this, they
spent $33 more per week with $12 of that being on healthier
foods, but $21 being on less healthy foods such as snack foods.
The money they saved on healthy foods, they also spent on less
healthy foods.
Now these are both preliminary reports. They do show that
when people are incentivized to buy healthy foods, they do, but
they also buy less healthy foods.
What I want to look at is who has the biggest potential to
eat better? Now we make a mistake when we only look at all SNAP
recipients as a homogenous group of shoppers. Instead, people
are in a pyramid like this. It goes in a hierarchy of healthy
disposition. If you see something like this, there are people
at the top who are very vigilant shoppers. These are people who
know the number of calories in a Coke, the number of calories
in Fritos. They care about what they eat. No change is going to
influence what they buy. At the very bottom, you have health
disinterested shoppers. Again, these are people who are either
resigned or they are disinterested in shopping healthier, and
again, no change is going to have much impact on what they buy.
Who we can influence is this middle group, the health
predisposed shoppers, because these are the people who want to
eat better, but they just need the help and the nudge to do so.
Now if we look at what is going to work best for these
health predisposed shoppers, the question is how do we do this?
Will the restriction work? And second, will something else work
better?
Now I said earlier it is not clear whether the hassles of
related retailing shopper dignity would merit a change, but
there might be a solution to this. So for instance, one option
would be to give a SNAP recipient an option. They can use 100
percent of their SNAP benefits to purchase whatever they
wanted, or if they agreed themselves to restricting--let's just
say to produce. Maybe they get a bonus. They get 125 percent
more. Now we are not sure how this would work, and it does
merit testing as mentioned earlier, but a second option is far
easier to implement and can be scaled very quickly. It involves
providing simple guidelines to retailers, maybe even a
certification on how to make it easier for SNAP shoppers, all
shoppers, to buy healthier foods by making it more convenient,
attractive, and normal to do so.
There is a precedent for this healthier by design shopping
program that is beginning to work in food deserts. Last year,
the National Association of Convenience Stores developed and
launched a new tool kit of evidence-based tactics that could be
used to increase the sales of healthier foods. It is one reason
why when you buy gas, you often find a basket of bananas next
to the cash register. That is because of this program. These
are small, easy changes to make, and they are win-win benefits
for both retailers, SNAP recipients, and us. But systematically
giving other retailers the guidance on how to make these
healthy nudges and credit them for doing so would benefit SNAP
shoppers just as well as it is benefitting us.
Another way this retail program is underway is the Nordic
solution to sustainability and obesity, it is related to the
EAT Foundation and GreeNudge. And over there, supermarkets are
being guided to make small changes in signage, service, and
structure, and it has increased fruits and vegetables
consumption for that.
Now in summary, and this is a third alternative, but I will
give three things. SNAP recipients get benefits and restricted
benefits, but they do not necessarily buy only healthier foods.
They buy everything else. Second, there are three segments of
shoppers; and third, there are different ways to best encourage
this health predisposed segment.
Thanks for this opportunity to talk with you.
[The prepared statement of Dr. Wansink follows:]
Prepared Statement of Brian Wansink, Ph.D., John S. Dyson Professor of
Marketing and Director, Cornell University Food and Brand Lab, Ithaca,
NY
Good morning, Chairman Conway, Ranking Member Peterson, Members of
the Committee: Thank you for giving me the opportunity to present my
perspective on the pros and cons of restricting SNAP purchases. I will
be addressing three questions today: (1) What happens when food
purchases are restricted? (2) Who has the most potential to shop
healthier, and (3) How can this be best encouraged?
When Happens When Food Purchases are Restricted?
As a behavioral scientist and Director of the Cornell Food and
Brand Lab, I focus on changing eating behaviors in a practical way. As
the former USDA Executive Director for the Center for Nutrition Policy
and Promotion--the Dietary Guidelines--I focused on changing eating
behaviors in a scalable way.
When Food Stamps were first introduced, their purpose was to fill
bellies with calories. Seventy years later we have another important
opportunity. Fill bellies with the right calories. With increasing
health care costs threatening the future of the American economy, one
place we can begin turning this around--starting tonight--is with what
we eat in our homes. Of all the health concerns that face Americans,
diet-related disease and obesity are the ones that we can tackle most
immediately.
What is critical to remember, however, is this: Our best and worse
eating habits start in the grocery store. If we can change what people
bring home from the grocery store or market, we can change how they
eat.
Do people shop differently when they're given extra money--such as
a rebate or SNAP benefits? Two preliminary studies give us some insight
here.
Exhibit No. 1. How does shopping behavior change after versus
before people receive SNAP benefits? A new 6 year study of SNAP
recipients in Rhode Island showed that the spending on SNAP eligible
products went up once they received benefits, but the general purchase
of SNAP ineligible benefits did not go down (Hastings and Shaprio
2017). Further unpublished analyses (learned through conversation) also
suggest that purchase of convenient-to-eat foods goes up once a person
receives SNAP benefits. They trade their SNAP benefits for convenience.
Exhibit No. 2 looks at incentives. What if we specifically
financially incentivize shoppers to buy more fruits and vegetables? In
one 6 month study of 208 families in Utica, NY, we gave shoppers a 10%
bonus--10% more money back on their debit card--when they bought
healthy foods such as fruits and vegetables. When low-income shoppers
(poverty ratio less than 1.3) were given this extra money as a subsidy,
they spent $33 more per week on healthier foods--including fruits and
vegetables, but they also spent $21 more per week on less healthy
foods, such as snack foods (Cawley, et al., 2016). Some of the money
they saved on the healthy foods, they appeared to spend on less healthy
foods.
Although both of these are single, preliminary white papers in the
National Bureau of Economic Research, they point at the idea that extra
money--in the form of SNAP benefits or subsidies--changes the way
people shop. They do buy more of the healthy, incentivized foods, but
they also buy more of the less healthy foods. They just use their own
money instead.
A key question, however, is ``Who has the most potential to eat
better?''
The Hierarchy of Health Predisposition
When I was the Executive Director of the USDA's Center for
Nutrition Policy and Promotion, I saw people off-handedly dismiss
potentially useful ideas for new initiatives if they would not benefit
100% of the population under discussion.
In trying to solve difficult problems, it is very useful to not
view 100% of all people--such as all SNAP benefit recipients--as the
same. Some people already eat very healthy, some people do not want to
eat healthy, and some people want to, but they need help. When trying
to predict how a SNAP shopper would respond to a restriction, it is
useful to understand that there is a Hierarchy of Health
Predisposition.
Not all SNAP shoppers shop alike and we can view them--like all
shoppers--on how predisposed they are to wanting to make a healthier
shopping decision. We can view them as belonging to one of three fluid
groups within a Hierarchy of Health Predisposition. The top segment of
this hierarchy are Health Vigilant shoppers. They are highly informed,
conscious of calories, and they are influenced by nutrition
information. At the bottom extreme, Health Disinterested shoppers have
little interest in changing their eating choices because of either the
effort, sacrifice, or perceived futility of doing so. The segment in
the middle are the Health Predisposed shoppers. They would prefer to
make healthier food choices, but they have difficulty consistently
doing so unless it involves very little sacrifice on their part. This
Predisposed segment is the one that buys the 100 calorie packages of
snacks and the sugar-free yogurt. For all people, this segment is
larger on New Years Day than it was in December; it was larger this
past Monday morning than it was during the prior Friday night's
shopping trip.
The Hierarchy of Health Predisposition
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
One reason nutrition guidance systems (such traffic lights or
Guiding Stars) have had only modest influences on the sales of healthy
food may be because they mainly resonate with only the top of the
Hierarchy. Health Disinterested shoppers ignore these programs, and
Heath Predisposed shoppers inconsistently follow them. If the only
segment they reach are the Vigilant shoppers, interventions like this
will have hardly any impact on sales since this segment is already
shopping in a healthy way.
This is important because SNAP restrictions may not have the same
impact on healthy shopping behavior that we desire. The Health Vigilant
shoppers will already be shopping healthy, and they do not need them.
At the other extreme, Healthy Disintereseted shoppers might simply
rechannel their own money toward what they would have bought anyway.
What this importantly raises is the question as to whether there other
ways to guide SNAP shoppers to eat healthier--particularly those in
this middle section.
Non-Restrictive Options to Encourage Healthier SNAP Shopping Patterns
One extreme way to try and encourage SNAP shoppers to eat better is
to restrict what they can purchase. Some people might say this is not
practical for retailers. Other people might say this is not respectful
of the dignity or free choice of SNAP shoppers.
What is not asked when it comes to restricting SNAP purchases is,
``Will it even work?'' As just noted, for the Health Vigilant, it
wouldn't have any impact because they already eat healthy. At the other
end, for the Health Disinterested, it may not work because they will
simply spend their cash on what they would have otherwise bought
anyway. There are two open questions: (1) Will a restriction work with
the Health Predisposed--this middle segment, and (2) Would something
else work better?
First, as said earlier, it is not clear if the retail hassles and
the shopper dignity and free choice issues related to a restriction
would merit a change. There may be a solution to this, however. Suppose
a nutritionally predisposed shopper had one of two options. One option
would be to have 100% of their SNAP benefits to purchase whatever they
wanted (foods that are currently eligible). A second option would be
that they could agree to self-restrict themselves from buying certain
foods in exchange for, say, 125% of their SNAP benefits. In effect, if
they agreed to restrict their SNAP benefits to buy only predetermined
healthy foods--say fruits, vegetables, whole grains, lean meat and
dairy--they would get more 25% (or however much) more buying power.
Such a system would still give people an option--they could either
choose the 100% unrestricted plan or they could choose the 125%
restricted plan--and it would help those who wanted to eat better to
more easily do so. Of course, we have no evidence of how effective this
would be in practice, but it is an idea that merits pilot testing. It
lets people be free to choose while also providing them an incentive to
eat better. The SNAP recipient chooses what they want.
A second option is far easier to implement and can be scaled
quickly. It involves providing simple guidelines to retailers--perhaps
even a certification--on how to make it easier for SNAP shoppers (and
all shoppers) to buy healthier foods by making it more convenient,
attractive, and normal (the CAN framework) to do so. This notion of
``Healthy Shopping by Design'' is fashioned off of the Smarter
Lunchroom Movement which is a USDA-sponsored initiative that trains
food service directors on the dozens of ways they can guide students
toward making healthier selections in the school lunchroom (Hanks, et
al., 2013). The 66-point scorecard shows whether the way they set up,
serve, and promote foods make kids fit or fat. For instance, a score of
25 out of 66 indicates there is easy room for improvement, but also
points at the 41 other changes they could make (Appendix).
There is precedent for a Healthy Shopping by Design program that is
beginning to work in food deserts. In 2016, the National Association of
Convenience Stores, working with the Cornell Food and Brand Lab
developed and launched a new toolkit titled, ``Ideas That Work to Grow
Better-for-You Sales,'' and they include evidence-based tactics to
increase the sales of healthier foods. It is one reason you can often
buy a banana when you buy gas--they are sitting right next to the cash
register (Lenard and Schare 2016). These are small easy changes to
make, but they are win-win and benefit both retailers and (food desert)
shoppers.
Systematically giving other retailers the guidance of how to make
healthy nudges, and the credit for doing so could change healthy
shopping for SNAP shoppers just as the Smarter Lunchroom Movement is
changing lunchtime for school children (Wansink 2017; 2014). In Norway,
this is currently underway as a Nordic Solution to sustainability and
obesity (which is related to the EAT Foundation and GreeNudge). Over
there, supermarkets are being guided how to make small changes to the
signage, structure, and service, and the results have been increased
fruit and vegetable sales for all (Wansink, Karvold, and Tran 2017).
Summary
1. Giving SNAP recipients more benefits or restricted benefits may
not lead them to only buy healthier food (they will also
buy more convenient foods and less healthier foods).
2. There are three segments of shoppers: the Health Vigilant, the
Health Predisposed, and the Health Disinterested. The
easiest win will be to focus efforts programming on the
Health Predisposed segment.
3. There are at least two ways to try and influence the Health
Predisposed segment. One might be giving them 100% of their
unrestricted benefits, or 130% of restricted benefits. A
second would be to work with retailers to show them how
they can be even more profitable by making it convenient,
attractive, and normal for SNAP shoppers--indeed all
shoppers--to shop healthier. Just as this program is
responsible for putting bananas by the convenience store
checkouts, and more vegetables in Norwegian shopping carts,
it could be successful on a larger scale with supermarkets
and other stores accepting SNAP benefits.
Thank you for this opportunity to share my perspective with you.
References
Cawley, John, Andrew S. Hanks, David R. Just, and Brian Wansink
(2016), ``Incentivizing Nutrition Diets: A Field Experiment of Relative
Price Changes and How They Are Framed,'' National Bureau of Economics
Research, Working paper 21929.
Hanks, Andrew S., David R. Just, and Brian Wansink (2013),
``Smarter Lunchrooms Can Address New School Lunchroom Guidelines and
Childhood Obesity,'' Journal of Pediatrics, 162: 4 (April), 867-869.
Hastings, Justine and Jesse M. Shapiro (2017), ``How are SNAP
Benefits Spent? Evidence from a Retail Panel,'' National Bureau of
Economic Research, Working paper.
Lenard, Jeff and Carolyn Schnare (2016), ``Eight Low-Cost--and
Proven--Tactics for How C-Store Operators and Grow Their Healthy
Offer,'' NACS Magazine, August, 30-36.
NACS (2016), ``NACS Toolkit Helps C-Stores Grown Better-for-You
Sales,'' May 26 http://www.nacsonline.com/Media/Daily/Pages/
ND0526161.aspx#.WKPMM
neZNPs.
Wansink, Brian (2014), Slim by Design--Mindless Eating Solutions
for Everyday Life, New York, NY: William Morrow.
Wansink, Brian, Knut Karevold, and Huy Tran (2017), ``Supermarket
Interventions to Sell Sustainable Fruits and Vegetables: The Nordic
Solution to Healthier Shopping,'' Cornell Food and Brand Lab, working
paper.
Wansink, Brian (2017), ``Healthy Profits: An Interdisciplinary
Retail Framework that Increases the Sales of Healthy Foods, Journal of
Retailing, in press.
Appendix. Example of Scorecards that Encourage Healthier Choices
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Exhibit 1
How Are SNAP Benefits Spent? Evidence from a Retail Panel
Justine S. Hastings, Jesse M. Shapiro
Working Paper 23112
http://www.nber.org/papers/w23112
This work has been supported (in part) by awards from the Russell
Sage Foundation, the Robert Wood Johnson Foundation's Policies for
Action program, and the Laura and John Arnold Foundation. Any opinions
expressed are those of the author(s) alone and should not be construed
as representing the opinions of these Foundations. This project
benefited from the suggestions of Ken Chay, Raj Chetty, David Cutler,
Amy Finkelstein, Xavier Gabaix, Peter Ganong, Ed Glaeser, Nathan
Hendren, Hilary Hoynes, Larry Katz, David Laibson, Kevin Murphy, Mandy
Pallais, Devin Pope, Diane Whitmore Schanzenbach, and Andrei Shleifer,
from audience comments at Brown University, Clark University, Harvard
University, the Massachusetts Institute of Technology, and the
Quantitative Marketing and Economics Conference, and from comments by
discussant J.P. Dube. We thank our dedicated research assistants for
their contributions. The views expressed herein are those of the
authors and do not necessarily reflect the views of the National Bureau
of Economic Research.
At least one co-author has disclosed a financial relationship of
potential relevance for this research. Further information is available
online at http://www.nber.org/papers/w23112.ack.
NBER working papers are circulated for discussion and comment
purposes. They have not been peer-reviewed or been subject to the
review by the NBER Board of Directors that accompanies official NBER
publications.
2017 by Justine S. Hastings and Jesse M. Shapiro. All rights
reserved. Short sections of text, not to exceed two paragraphs, may be
quoted without explicit permission provided that full credit, including
notice, is given to the source.
Abstract
We use a novel retail panel with more than 6 years of detailed
transaction records to study the effect of participation in the
Supplemental Nutrition Assistance Program (SNAP) on household spending.
We frame our approach using novel administrative data from the state of
Rhode Island. The marginal propensity to consume SNAP-eligible food
(MPCF) out of SNAP benefits is 0.5 to 0.6. The MPCF out of cash is much
smaller. These patterns obtain even for households for whom SNAP
benefits are economically equivalent to cash in the sense that benefits
do not cover all food spending. We reject the hypothesis that
households respect the fungibility of money in a semiparametric setup.
A post-hoc model of mental accounting rationalizes these facts and
others.
Justine S. Hastings, Jesse M. Shapiro,
Brown University, Economics Department,
Department of Economics, Box B,
64 Waterman Street, Brown University,
Providence, RI 02912, Providence, RI 02912,
and NBER, and NBER,
[email protected]; [email protected].
A online appendix is available at http://www.nber.org/data-
appendix/w23112.
1 Introduction
This paper studies how receipt of benefits from the Supplemental
Nutrition Assistance Program (SNAP) affects household spending. SNAP is
of special interest to economists for at least two reasons. First, the
program is economically important: it is the second-largest means-
tested program in the United States after Medicaid (Congressional
Budget Office 2013), enrolling 19.6 percent of households in fiscal
2014.\1\
---------------------------------------------------------------------------
\1\ There were 22,743,911 participating households in fiscal 2014
(FNS 2016a) and 116,211,092 households in the U.S. on average from
2010-2014 (U.S. Census Bureau 2016).
---------------------------------------------------------------------------
Second, the program's stated objectives sit awkwardly with economic
theory. On signing the bill to implement the predecessor Food Stamp
Program, President Lyndon Johnson declared that the program would
``enable low-income families to increase their food expenditures''
(Johnson 1964). The Food and Nutrition Service of the USDA says that
SNAP is important for ``helping low-income families put food on the
table'' (FNS 2012). Yet although SNAP benefits can only be spent on
food, textbook demand theory (Mankiw 2000; Browning and Zupan 2004)
predicts that, for the large majority of SNAP recipients who spend more
on food than they receive in benefits,\2\ SNAP benefits are
economically equivalent to cash.\3\ As typical estimates of the
marginal propensity to consume food (MPCF) out of cash income are close
to 0.1,\4\ the textbook treatment says that SNAP benefits should mostly
subsidize non-food spending.
---------------------------------------------------------------------------
\2\ Hoynes, et al., (2015) find that spending on food at home is at
or above the SNAP benefit level for 84 percent of SNAP recipient
households. Trippe and Ewell (2007) report that 73 to 78 percent of
SNAP recipients spend at least ten percent more on food than they
receive in SNAP benefits.
\3\ Consider a household with monthly income y and SNAP benefits b.
If the household spends on SNAP-eligible food then she has
y^max (0,^b) available to buy other goods. Let U
(,n) denote the household's strictly monotone,
differentiable, and strictly quasiconcave utility function defined over
the dollar amount of SNAP-eligible food consumption and
other consumption n. Suppose that there is a solution
* = arg max U
(,y^max(0,^b)) such that *>b.
The first-order necessary condition for this program is a necessary and
sufficient condition for a solution to the program max fU
(;y+b^) in which the benefits are given in cash.
Therefore * = arg max
fU(,y+b^).
\4\ Castner and Mabli (2010) estimate an MPCF out of cash income of
0.07 for SNAP participants. Hoynes and Schanzenbach (2009) estimate an
MPCF out of cash income of 0.09-0.10 for populations with a high
likelihood of participating in the Food Stamp Program.
---------------------------------------------------------------------------
Estimating the effect of SNAP benefits on spending is challenging
because it requires good measurement of household spending and suitably
exogenous variation in program participation or benefits. Survey-based
measures of household spending are error-prone and sensitive to the
mode of elicitation (Ahmed, et al., 2006; Browning, et al., 2014;
Battistin and Padula 2016). Important components of SNAP eligibility
and benefit rules are set nationally, and major program changes have
often coincided with other policy changes or economic shocks
(Congressional Budget Office 2012), making it difficult to separate the
effect of SNAP from the effect of these contextual factors.
In this paper we analyze a novel panel consisting of detailed
transaction records from February 2006 to December 2012 for nearly \1/
2\ million regular customers of a large U.S. grocery retailer. The data
contain information on method of payment, including whether payment was
made using a government benefit card. We use the panel to study the
effect of transitions on and off of SNAP, and of legislated changes in
SNAP benefits, on household spending.
We adopt three approaches to isolating the causal effect of SNAP on
spending: a panel event-study design using trends prior to SNAP
adoption to diagnose confounds, an instrumental variables design
exploiting plausibly exogenous variation in the timing of program exit,
and a differences-in-differences design exploiting legislated changes
to benefit schedules.
We motivate each of these approaches with findings from novel Rhode
Island administrative data. The data show that household income and
size change in the months preceding a household's transition on to
SNAP, motivating our panel event-study design. The data also show that
SNAP spell lengths are typically divisible by 6 months because of the
recertification process, motivating our instrumental-variables design.
National administrative records show discrete jumps in SNAP benefits
associated with legislated program changes in 2008 and 2009, motivating
our differences-in-differences design.
By construction our retail panel includes purchases at a single
grocery chain. Rhode Island administrative data show that it is
possible to reliably infer transitions on to SNAP using data from a
single grocery chain, by focusing on consecutive periods of non-SNAP
use followed by consecutive periods of SNAP use. Additional data,
including a survey conducted by the retailer, show that SNAP
participation is only weakly related to a household's choice of
retailer.
Graphical analysis of our panel event-study design shows that after
adoption of SNAP, households in the retailer panel increase SNAP-
eligible spending by about $110 a month, equivalent to a bit more than
\1/2\ of their monthly SNAP benefit. There is no economically
meaningful trend in SNAP-eligible spending prior to adoption of SNAP.
Graphical analysis of our instrumental-variables and differences-in-
differences designs also implies an MPCF out of SNAP in the range of
0.5 to 0.6.
We exploit large swings in gasoline prices during our sample period
to estimate the MPCF out of cash for the retail panelists. We observe
gasoline spending at the retailer and confirm that increases in
gasoline prices lead to significant additional out-of-pocket expenses
for panelist households. We estimate that every $100 per month of
additional gasoline spending reduces food spending by less than $10, in
line with past estimates of the MPCF out of cash for the SNAP-recipient
population (e.g., Castner and Mabli 2010) but far below the estimated
MPCF out of SNAP.
Turning to SNAP-ineligible spending at the retailer, we estimate an
MPC of 0.02 out of SNAP benefits, and a (statistically
indistinguishable) MPC of 0.04 out of cash.
We develop an economic model of food spending by households for
whom SNAP benefits do not cover all food spending and are therefore
fungible with cash. We show how to test the hypothesis of fungibility,
allowing for the endogeneity of cash income and SNAP benefits, and for
the possibility that different households' consumption functions do not
share a common parameterization or parametric structure. Our tests
consistently reject the null hypothesis that households treat SNAP
benefits as fungible with other income.
We extend our economic model to include mental accounting following
the approach in Farhi and Gabaix (2015). The extension is post-hoc. By
design, it rationalizes the finding that the MPCF is greater out of
SNAP benefits than out of cash. It also predicts that, following SNAP
receipt, households will allocate relatively less effort to bargain-
hunting in the food domain than in the non-food domain. We find that
SNAP receipt reduces the store-brand share of expenditures and the
share of items on which coupons are redeemed, but only for SNAP-
eligible foods.
We also discuss the responses from qualitative interviews conducted
at a food pantry as part of a Rhode Island pilot proposal to modify
SNAP benefit timing. Respondents were not scientifically sampled, and
it is not appropriate to derive general conclusions from these
interviews. Nevertheless, we find that they provide useful context for
our analysis.
This paper contributes to a large literature on the effects of SNAP
and the predecessor Food Stamp Program on food spending, recently
reviewed by Bitler (2015) and Hoynes and Schanzenbach (2016). There are
four strands to this literature. The first strand studies the effect of
converting food stamp benefits to cash. Moffitt (1989) finds that a
cashout in Puerto Rico did not affect food spending. Wilde and Ranney
(1996) find that behavior in two randomized cashout interventions is
not consistent with fungibility; Schanzenbach (2002) finds that
behavior in these same interventions is consistent with fungibility.\5\
The second strand, reviewed in Fox, et al. (2004), either compares
participants to nonparticipants or relates food spending to the size of
a household's benefit, either across households or over time. Wilde
(2001) and Hoynes and Schanzenbach (2009), among others, criticize this
strand of the literature for using a source of variation in program
benefits that is likely related to non-program determinants of
spending.\6\ The third strand studies randomized evaluations of program
extensions or additions. Collins, et al. (2016) study a randomized
evaluation of the Summer Electronic Benefit Transfer for Children
program and use survey data to estimate an MPCF out of program benefits
of 0.58.
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\5\ Fox, et al. (2004) question the validity of the findings from
Puerto Rico and one of the randomized interventions, arguing that the
best evidence indicates that cashout reduces food spending.
\6\ Wilde, et al. (2009) address the endogeneity of program
benefits by exploiting variation in whether household food spending is
constrained by program rules. Li, et al. (2014) use panel data to study
the evolution of child food insecurity in the months before and after
family entry into the food stamp program.
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The fourth strand exploits policy variation in program availability
and generosity. Studying the initial rollout of the Food Stamp Program
using survey data, Hoynes and Schanzenbach (2009) estimate an MPCF out
of food stamps of 0.16 to 0.32, with confidence interval radius ranging
from 0.17 to 0.27. Hoynes and Schanzenbach (2009) estimate an MPCF out
of cash income of 0.09 to 0.10 and cannot reject the hypothesis that
the MPCF out of food stamps is equal to the MPCF out of cash income.
Studying the effect of a 2009 SNAP benefit expansion using survey data,
Beatty and Tuttle (2015) estimate an MPCF out of SNAP benefits of 0.53
to 0.64 (they do not report a confidence interval on these values) and
an MPCF out of cash income of 0.15.\7\ Closest to our study, Bruich
(2014) uses retail scanner data with method-of-payment information to
study the effect of a 2013 SNAP benefit reduction, estimating an MPCF
out of SNAP benefits of 0.3 with confidence interval radius of 0.15.\8\
Bruich (2014) does not report an MPCF out of cash income. We estimate
an MPCF out of SNAP benefits of 0.5 to 0.6 with confidence interval
radius as low as 0.015, and an MPCF out of cash income of no more than
0.1.
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\7\ Nord and Prell (2011) estimate the effect of the 2009 benefit
expansion on food security and food expenditures. Ratcliffe, et al.
(2011) and Yen, et al. (2008) estimate the effect of SNAP and food
stamps, respectively, on food insecurity, using state-level policy
variables as excluded instruments.
\8\ Andreyeva, et al. (2012) and Garasky, et al. (2016) use retail
scanner data to describe the food purchases of SNAP recipients, but not
to estimate the causal effect of SNAP on spending.
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This paper contributes new evidence of violations of fungibility in
a large-stakes real-world decision with significant policy relevance.
That households mentally or even physically separate different income
sources according to spending intentions is well-documented in
hypothetical-choice scenarios (e.g., Heath and Soll 1996; Thaler 1999)
and ethnographic studies (e.g., Rainwater, et al., 1959). Much of the
recent literature documenting this behavior in real-world markets
focuses on consumer choice settings with little direct policy relevance
(e.g., Milkman and Bashears 2009; Hastings and Shapiro 2013; Abeler and
Marklein forthcoming). Important exceptions include Kooreman's (2000)
study of a child tax credit in the Netherlands, Feldman's (2010) study
of a change in U.S. Federal income tax withholding, and Benhassine, et
al.'s (2015) study of a labeled cash transfer in Morocco.
Methodologically, this paper shows how to test for the fungibility
of money without assuming that the consumption function takes a
particular parametric form or that the consumption function is
identical for all households.\9\ Our approach nests Kooreman's (2000),
but avoids the concern that a rejection of fungibility is due to
misspecification of functional forms (Ketcham, et al., 2016).
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\9\ Whereas classical tests of consumer rationality (Varian 1983;
Blundell, et al., 2003) require observing price changes, we provide a
set of intuitive sufficient conditions on the model and the measurement
process that permit testing based on income variation alone.
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Finally, the paper presents new evidence from novel administrative
data on SNAP recipients in Rhode Island, including the first evidence
we are aware of from state administrative data on how household wage
income evolves before and after entry into SNAP.\10\ Although we
present these findings primarily as background, they are of interest in
their own right as evidence on the contextual factors associated with
SNAP adoption.
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\10\ Other recent studies analyzing linked unemployment insurance
and SNAP data include Anderson, et al., (2012) and Leung and O'Leary
(2015).
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2 Background and Evidence from Administrative and Survey Data
2.1 Rhode Island Administrative Data
We use Rhode Island state administrative records housed in a secure
facility at the Rhode Island Innovative Policy Laboratory at Brown
University. Personally identifiable information has been removed from
the data and replaced with secure identifiers that make it possible to
link different records associated with the same individual or
household. These records are not linked to our retail panel.
We obtain the state's SNAP records from October 2004 through June
2016. These data define the months of benefit receipt and the
collection of individuals associated with every household on SNAP in
every month. We assume that a household's composition is unchanged
prior to its first benefit receipt and that it does not change from its
most recent composition between the end of any given period of benefit
receipt and the start of the next period. We exclude from our analysis
any household whose membership we cannot uniquely identify in every
month,\11\ or whose adult composition changes during the sample period.
The final sample consists of 185,534 unique households.
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\11\ This can occur either because we lack a unique identifier for
a member individual or because a given individual is associated with
multiple households in the same month.
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From SNAP records we compute, for each household and month, the
total number of children in the household under 5 years old. From the
records of the state unemployment insurance system we compute, for each
household and quarter,\12\ the sum of total unemployment insurance
benefits received from and total earnings reported to the state
unemployment insurance system by all individuals who are in the
household as of the quarter's end.\13\ We refer to this total as
household income, but we note that it excludes income not reported to
the Rhode Island unemployment insurance system, such as social security
benefits and out-of-state earnings.
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\12\ Data on earnings are missing from our database for the fourth
quarter of 2004 and the second quarter of 2011.
\13\ We exclude from our analysis any household-quarter in which
the household's total quarterly earnings exceed the 99.9999th
percentile or in which unemployment insurance benefits in any month of
the quarter exceed three times the 4 week equivalent of the 2016
maximum weekly benefit of $707 (Rhode Island Department of Labor and
Training 2016).
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We also obtain records of all debits and credits to SNAP Electronic
Benefit Transfer (EBT) cards for the period September 2012 through
October 2015. From these we identify all household-months in which the
household received a SNAP benefit and all household-months in which the
household spent SNAP benefits at a large, anonymous retailer in Rhode
Island (``Rhode Island Retailer'') chosen to be similar to the retailer
that provided our retail panel. Although these data can be linked to
the SNAP records using a household identifier, we do not exploit that
link in the analysis that follows.
2.2 Changes in Household Circumstances Around SNAP Adoption
Household income and household size are major determinants of SNAP
eligibility (FNS 2016b). We therefore hypothesize that entry into SNAP
is associated with a decline in household income and a rise in
household size. Figure 1 shows that this hypothesis is confirmed in our
administrative data. The figure shows panel event-study plots of
household income and number of children as a function of time relative
to SNAP adoption, which we define to occur on the first quarter or
month, respectively, of a household's first SNAP spell. In the period
of SNAP adoption, household income declines and the number of children
rises, on average.
Past research shows that greater household size and lower household
income are associated, respectively, with greater and lower at-home
food expenditures among the SNAP-recipient population (Castner and
Mabli 2010).\14\ It is therefore unclear whether these contextual
factors should contribute a net rise or fall in food expenditures in
the period of SNAP adoption. Because Figure 1 shows that these factors
trend substantially in the periods preceding SNAP adoption, we can
assess their net effect by studying trends in spending prior to
adoption.
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\14\ Past research also finds that unemployment--a likely cause of
the decline in income associated with SNAP adoption--is associated with
a small decline in spending on food for home consumption. Using cross-
sectional variation in the Continuing Survey of Food Intake by
Individuals, Aguiar and Hurst (2005) estimate that unemployment is
associated with nine percent lower at-home food expenditure. Using
pseudo-panel variation in the Family Expenditure Survey, Banks, et al.
(1998) estimate that unemployment is associated with a 7.6 percent
decline in the sum of food consumed in the home and domestic energy.
Using panel variation in the Panel Study of Income Dynamics, Gough
(2013) estimates that unemployment is associated with a statistically
insignificant one to four percent decline in at-home food expenditure.
Using panel variation in checking account records, Ganong and Noel
(2016) estimate that the onset of unemployment is associated with a 3.1
percent decline in at-home food expenditure. Aggregate data seem to
confirm these findings: real average annual at-home food expenditure
fell by 1.6 percent from 2006 to 2009, during which time the
unemployment rate more than doubled (Kumcu and Kaufman 2011).
---------------------------------------------------------------------------
Figure 1 therefore motivates our panel event-study research design,
in which we use trends in spending prior to SNAP adoption to diagnose
the direction and plausible magnitude of confounds.
2.3 Length of SNAP Spells and the Certification Process
When a state agency determines that a household is eligible for
SNAP, the agency sets a certification period at the end of which
benefits will terminate if the household has not documented continued
eligibility.\15\ The certification period may not exceed 24 months for
households whose adult members are elderly or disabled, and may not
exceed 12 months otherwise (FNS 2014). In practice, households are
frequently certified for exactly these lengths of time, or for other
lengths divisible by 6 months (Mills, et al., 2014).
---------------------------------------------------------------------------
\15\ Federal rules state that ``the household's certification
period must not exceed the period of time during which the household's
circumstances (e.g., income, household composition, and residency) are
expected to remain stable'' (FNS 2014).
---------------------------------------------------------------------------
Figure 2 shows the distribution of SNAP spell lengths in Rhode
Island administrative data. The figure shows clear spikes in the
density at spell lengths divisible by 6 months.
Figure 2 motivates our instrumental variables research design,
which exploits the 6 month divisibility of certification periods as a
source of plausibly exogenous timing of program exit.
2.4 Legislated Changes in SNAP Benefit Schedules
Appendix Figure 1 shows the average monthly SNAP benefit per U.S.
household from February 2006 to December 2012, which coincides with the
time frame of our retail panel. The series exhibits two discrete jumps,
which correspond to two legislated changes in the benefit schedule: an
increase in October 2008 due to the 2008 Farm Bill and an increase in
April 2009 due to the American Recovery and Reinvestment Act.
Appendix Figure 1 motivates our differences-in-differences research
design, which exploits these legislated benefit increases.
2.5 Inferring SNAP Adoption from Single-Retailer Data
Households can spend SNAP at any authorized retailer. We will
conduct our analysis of food spending using data from a single retail
chain. Changes in a household's choice of retailer could be mistaken
for program entry and exit in single-retailer data. We use our EBT
panel to evaluate the importance of these mistakes and to determine how
best to infer program transitions in single-retailer data.
For each K . 1,-,12 and for each household in our EBT panel, we
identify all cases of K consecutive months without SNAP spending at the
Rhode Island Retailer followed by K consecutive months with SNAP
spending at the Rhode Island Retailer. We then compute the share of
these transition periods in which the household newly enrolled in SNAP
within 2 months of the start of SNAP spending at the retailer, where we
define new enrollment as receipt of at least $10 in SNAP benefits
following a period of at least 3 consecutive months with no benefit.
Figure 3 plots the share of households newly enrolling in SNAP as a
function of the radius K of the transition period. For low values of K,
many transitions reflect retailer-switching rather than new enrollments
in SNAP. The fraction of transitions that represent new enrollments
increases with K. For K = 6 and above, the fraction constituting new
enrollments is over 86 percent. When we focus on households who do the
majority of their SNAP spending at the retailer in question--arguably a
sample more comparable to the households in our retail panel--this
fraction rises to 96 percent.
Figure 3 motivates our definition of SNAP adoption in the retailer
data.
2.6 SNAP Participation and Choice of Retailer
Even if we isolate suitably exogenous changes in SNAP participation
and benefits, our analysis of single-retailer data could be misleading
if SNAP participation directly affects retail choice.
Ver Ploeg, et al. (2015) study the types of stores at which SNAP
recipients shop using nationally representative survey data collected
from April 2012 through January 2013. For 46 percent of SNAP
recipients, the primary grocery retailer is a supercenter, for 43
percent it is a supermarket, for three percent it is another kind of
store, and for eight percent it is unknown. The corresponding values
for all U.S. households are 45 percent, 44 percent, four percent, and
seven percent. As with primary stores, the distribution of alternate
store types is nearly identical between SNAP recipients and the
population as a whole. SNAP recipients' choice of store type is also
nearly identical to that of low-income non-recipients. While this
evidence does not speak directly to the causal effect of SNAP on choice
of store type, it seems to cast doubt on the hypothesis that SNAP
receipt per se is a major factor determining where households shop.
As further evidence, a companion note to this paper analyzes
Nielsen Homescan data and finds little relationship at the state-year
level between changes in the market shares of major retailers and
changes in the number of SNAP recipients in the state.
In the next section we present further evidence on retailer
substitution using survey data collected by the retailer that supplied
our panel.
3 Retailer Data and Definitions
3.1 Purchases and Demographics
We obtained anonymized transaction-level data from a large U.S.
grocery retailer with gasoline stations on site. The data comprise all
purchases in five states made using loyalty cards by households who
shop at one of the retailer's stores at least every other month. We
observe 6.02 billion purchases made on 608 million purchase occasions
by 486,570 households from February 2006 through December 2012. We
exclude from our analysis the 1,214 households who spend more than
$5,000 in a single month.
For each household, we observe demographic characteristics
including age, household composition, and ZIP [C]ode. We use these data
in robustness checks and to study heterogeneity in our estimates.
For each item purchased, we observe the quantity, the pre-tax
amount paid, a flag for the use of WIC, and the dollar amount of
coupons or other discounts applied to the purchase. For each purchase
occasion, we observe the date, a store identifier, and a classification
of the store into a retailer division, a grouping based on the store's
brand and distribution geography. We also observe the main payment
method used for the purchase, defined as the payment method (e.g.,
cash, check, government benefit) accounting for the greatest share of
expenditure. For purchase occasions in March 2009 and later, we
additionally observe the exact breakdown of spending by payment method.
We classify a purchase occasion as a SNAP purchase occasion if the
main payment method is a government benefit and WIC is not used. Using
the detailed payment data for purchase occasions in March 2009 and
later, we calculate that SNAP is used in only 0.23 percent of the
purchase occasions that we do not classify as SNAP purchase occasions.
The appendix table shows that our key results are not sensitive to
excluding WIC users from the sample.
We define a SNAP month as any household-month with positive total
spending across SNAP purchase occasions.\16\ Of the household-months in
our panel, 7.8 percent are SNAP months. Of the households in our panel,
43 percent experience at least 1 SNAP month.
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\16\ Using our detailed payment data for March 2009 and later, we
can alternatively define a SNAP month as any month in which a household
uses SNAP. This definition agrees with our principal definition in all
but 0.27 percent of household-months.
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3.2 Product Characteristics
The retailer provided us with data on the characteristics of each
product purchased, including an indicator for whether the product is
store-brand, a text description of the product, and the product's
location within a taxonomy.
We classify products as SNAP-eligible or SNAP-ineligible based on
the retailer's taxonomy and the guidelines for eligibility published on
the USDA website.\17\ Among all non-fuel purchases in our data, 71
percent of spending goes to SNAP-eligible products, 25 percent goes to
SNAP-ineligible products, and the remainder goes to products that we
cannot classify.
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\17\ Grocery and prepared food items intended for home consumption
are generally SNAP-eligible (FNS 2017). Alcohol, tobacco, pet food, and
prepared food intended for on-premise consumption are SNAP-ineligible
(FNS 2017).
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We use our detailed payment data for purchases made in SNAP months
in March 2009 or later to validate our product eligibility
classification. Among all purchases made at least partly with SNAP in
which we classify all products as eligible or ineligible, in 98.6
percent of cases the expenditure share of SNAP-eligible products is at
least as large as the expenditure share paid with SNAP. Among purchases
made entirely with SNAP, in 98.7 percent of cases we classify no items
as SNAP-ineligible. Among purchases in which all items are classified
as SNAP-ineligible, in more than 99.9 percent of cases SNAP is not used
as a payment method.
3.3 Shopping Effort
For each household and month we compute the store-brand share of
expenditures and the share of items for which coupons are redeemed for
both SNAP-eligible and SNAP-ineligible purchases. Prior evidence
suggests that both of these can serve as a proxy for households'
efforts to save money.\18\ We adjust these measures for the composition
of purchases as follows. For each item purchased, we compute the store-
brand share of expenditure among other households buying an item in the
same product category in the same retailer division and the same
calendar month and week. The expenditure-weighted average of this
measure across purchases by a given household in a given month is the
predicted store-brand share, i.e., the share of expenditures that would
be store-brand if the household acted like others in the panel who buy
the same types of goods. Likewise, we compute the share of other
households buying the same item in the same retailer division, month,
and week who redeem coupons, and compute the average of this measure
across purchases by a given household in a given month to form a
predicted coupon use. We subtract the predicted from the actual value
of each shopping effort measure to form measures of adjusted store-
brand share and adjusted coupon redemption share.
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\18\ Store-brand items tend to be less expensive than national-
brand alternatives, and correspondingly are more popular among lower-
income households (Bronnenberg, et al., 2015). Coupon use rose during
the Great Recession, reflecting households' greater willingness to
trade time for money (Nevo and Wong 2015).
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3.4 Monthly Spending and Benefits
For each household in our panel we calculate total monthly spending
on SNAP-eligible items, fuel, and SNAP-ineligible items excluding fuel.
We calculate each household's total monthly SNAP benefits as the
household's total spending across all SNAP purchase occasions within
the month.\19\
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\19\ Our concept of total SNAP benefits has a correlation of 0.98
with the exact amount of SNAP spending calculated using detailed
payment information in SNAP months March 2009 and later.
---------------------------------------------------------------------------
Our data corroborate prior evidence (e.g., Hoynes, et al., 2015)
that, for most households, SNAP benefits do not cover all SNAP-eligible
spending. For 93 percent of households who ever use SNAP, average SNAP-
eligible spending in non-SNAP months exceeds average SNAP benefits in
SNAP months. SNAP-eligible spending exceeds SNAP benefits by at least
$10 in 93 percent of SNAP months and by at least five percent in 92
percent of SNAP months. The appendix table reports estimates of key
parameters for the subset of households for whom, according to various
definitions, SNAP benefits are inframarginal to total food spending.
3.5 SNAP Adoption
Motivated by the analysis in section 2.5, we define a SNAP adoption
as a period of 6 or more consecutive non-SNAP months followed by a
period of 6 or more consecutive SNAP months. We refer to the first SNAP
month in an adoption as an adoption month. We define a SNAP adopter as
a household with at least one SNAP adoption. Our panel contains a total
of 24,456 SNAP adopters.
Panel A of Figure 4 shows the share of SNAP adopters with positive
SNAP spending in each of the 12 months before and after a household's
first SNAP adoption. Panel B of Figure 4 shows average SNAP benefits
before and after adoption. Following adoption, the average household
receives about $200 in monthly SNAP benefits. For comparison, the
average U.S. SNAP benefit per household in fiscal 2009, roughly at the
midpoint of our sample period, was $276 (FNS 2016a).
We conduct the bulk of our analysis using the sample of SNAP
adopters. The appendix tablepresents our key results for alternative
samples.
3.6 Retailer Share of Wallet
Spending patterns suggest that panelists buy a large fraction of
their groceries at the retailer. Mabli and Malsberger (2013) estimate
average 2010 spending on food at home by SNAP recipients of $380 per
month using data from the Consumer Expenditure Survey. Hoynes et al.
(2015) find that average per-household food expenditures are 20 to 25
percent lower in the Consumer Expenditure Survey than in the
corresponding aggregates from the National Income and Product Accounts.
In the 6 months following a SNAP adoption, average monthly SNAP-
eligible spending in our data is $469.
Panelists also seem to buy a large fraction of their gasoline at
the retailer: average monthly fuel spending at the retailer is $97 in
the 6 months following SNAP adoption, as compared to Mabli and
Malsberger's (2013) estimate of $115.
Survey data from the retailer suggest that SNAP use is associated
with a reduction in the retailer's share of overall category spending.
During the period June 2009 to December 2011, the retailer conducted an
online survey on a convenience sample of customers. The survey asked:
About what percentage of your total overall expenses for
groceries, household supplies, or personal care items do you,
yourself, spend in the following stores?
Respondents were presented with a list of retail chains including the
one from which we obtained our data. Excluding responses in which the
reported percentages do not sum to 100, we observe at least one
response from 961 of the households in our panel. Among survey
respondents that ever use SNAP, the average reported share of wallet
for the retailer is 0.61 for those surveyed during non-SNAP months (N =
311 survey responses) and 0.53 for those surveyed during SNAP months (N
= 80 survey responses).\20\ The same qualitative pattern obtains among
SNAP adopters, and in responses to a retrospective question about
shopping frequency.\21\
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\20\ The difference in means is statistically significant (t =
2.15, p = 0.032).
\21\ The question asks, ``In your opinion, do you think you,
yourself have been shopping more, less, or about the same amount at the
retailer over the past 3 months?'' Among households surveyed in a SNAP
month, 60 percent report that their frequency of shopping at the
retailer has stayed ``about the same.'' Among those saying that it has
not stayed the same, a majority (59 percent) say that it has decreased.
---------------------------------------------------------------------------
Taken at face value, these findings suggest that retailer
substitution will tend, if anything, to bias downward the estimated
effect of SNAP participation on food spending. In the appendix table we
verify that our results are robust to restricting attention to
households with relatively few supermarkets in their county, for whom
opportunities to substitute across retailers are presumably more
limited.
4 Descriptive Evidence
4.1 Marginal Propensity To Consume Out of SNAP Benefits
Figure 5 shows the evolution of monthly spending before and after
SNAP adoption for our sample of SNAP adopters. Each plot shows
coefficients from a regression of spending on a vector of indicators
for months relative to the household's first SNAP adoption. Panel A
shows that SNAP-eligible spending increases by approximately $110 in
the first few months following SNAP adoption. Recall from Figure 4 that
the average household receives monthly SNAP benefits of approximately
$200 following SNAP adoption. Taking the ratio of the increase in
spending to the benefit amount, we estimate an MPCF out of SNAP
benefits between 0.5 and 0.6.
Panel B shows that SNAP-ineligible spending increases by
approximately $5 following SNAP adoption, implying an MPC of a few
percentage points. The increase in SNAP-ineligible spending is smaller
in both absolute and proportional terms than the increase in SNAP-
eligible spending. The online appendix shows directly that the share of
spending devoted to SNAP-eligible items increases significantly
following SNAP adoption. This finding is not consistent with the
hypothesis that SNAP leads to a proportional increase in spending
across all categories due to substitution away from competing
retailers.
Following the analysis in section 2.2, trends in spending prior to
adoption should provide a sense of the influence of changes in
contextual factors on spending. Panel A shows very little trend in
SNAP-eligible spending prior to SNAP adoption. Panel B shows, if
anything, a slight decline in SNAP-ineligible spending prior to
adoption, perhaps due to economic hardship. Neither of these patterns
seems consistent with the hypothesis that the large increase in SNAP-
eligible spending that occurs at SNAP adoption is driven by changes in
contextual factors.
Figure 6 shows the evolution of monthly spending during a monthly
clock that begins at SNAP adoption and resets every 6 months. Panels A
and B show that SNAP participation and benefits fall especially quickly
in the first month of the clock, consistent with the finding in section
2.3 that SNAP spell lengths tend to be divisible by 6 months.
Participation and benefits also fall more quickly in the sixth month,
perhaps reflecting error in our classification of adoption dates.
Panel C of Figure 6 shows that the pattern of SNAP-eligible
spending closely follows that of SNAP benefits. Benefits decline by
about $12 more in the first month of the cycle than in the second.
Correspondingly, SNAP-eligible spending declines by $6 to $7 more in
the first month than in the second. Taking the ratio of these two
values implies an MPCF out of SNAP benefits between 0.5 and 0.6,
consistent with the evidence in Figure 5.
Appendix Figure 2 plots the evolution of SNAP-eligible spending
around the legislated benefit changes described in section 2.4. The
plot shows that likely SNAP recipients' SNAP-eligible spending
increases relative to that of likely non-recipients around the periods
of benefit increases. The online appendix reports the results of a
differences-in-differences analysis of these changes in the spirit of
Bruich (2014) and Beatty and Tuttle (2015). We estimate an MPCF out of
SNAP benefits of 0.53, and if anything a negative effect of benefit
expansions on SNAP-ineligible spending.
4.2 Marginal Propensity To Consume Out of Cash
Two pieces of indirect evidence suggest that an MPCF out of SNAP of
0.5 to 0.6 is too large to be consistent with households treating SNAP
benefits as fungible with other income.
The first is that, for the average SNAP recipient, food at home
represents only 22 percent of total expenditure (Castner and Mabli
2010). Engel's Law (Engel 1857; Houthakker 1957) holds that the budget
share of food declines with total resources, and hence that the budget
share exceeds the MPCF. Engel's Law is not consistent with a budget
share of 0.22 and an MPCF of 0.5 to 0.6.
The second is that prior estimates of the MPCF out of cash for low-
income populations are far below 0.5. Castner and Mabli (2010) estimate
an MPCF of 0.07 for SNAP recipients. Hoynes and Schanzenbach (2009)
estimate an MPCF of 0.09-0.10 for populations with a high likelihood of
entering the Food Stamp Program. Assessing the literature, Hoynes and
Schanzenbach (2009) note that across ``a wide range of data (cross
sectional, time series) and econometric methods'' past estimates of the
MPCF out of cash income are in a ``quite tight'' range from 0.03 to
0.17 for low-income populations.
For more direct evidence, we study the effect on spending of the
large changes in gasoline prices during our sample period. These
changes affect the disposable income available to households and
therefore give us a window into the MPCF out of cash income.
Panel A of Figure 7 shows the time-series relationship between
gasoline prices and fuel expenditure for SNAP adopters at different
quartiles of the distribution of average fuel expenditure. Those
households in the upper quartiles exhibit substantial changes in fuel
expenditure when the price of gasoline changes. For example, during the
run-up in fuel prices in 2007, part of an upward trend often attributed
to increasing demand for oil from Asian countries (e.g., Kilian 2010),
households in the top quartile of fuel spending increased their
spending on fuel by almost $100 per month. Households in lower
quartiles increased their fuel spending by much less.
Panel B of Figure 7 shows the time-series relationship between
gasoline prices and SNAP-eligible expenditure for the same groups of
households. The relationship between the two series does not appear
consistent with an MPCF out of cash income of 0.5 to 0.6. For example,
if the MPCF out of cash income were 0.5 we would expect households in
the top quartile of fuel spending to decrease SNAP-eligible spending
significantly during the run-up in fuel prices in 2007. In fact, we see
no evidence of such a pattern, either looking at the top quartile in
isolation, or comparing it to the lower quartiles.
The absence of a strong response of SNAP-eligible spending to fuel
prices is consistent with prior evidence of a low MPCF out of cash. It
is not consistent with the hypothesis that changes in income drive
large changes in the retailer's share of wallet, as such income effects
would lead to a relationship between gasoline prices and measured SNAP-
eligible spending.
4.3 Quantitative Summary
Table 1 presents two-stage least squares (2SLS) estimates of a
series of linear regression models. In each model the dependent
variable is the change in spending from the preceding month to the
current month. The endogenous regressors are the change in the SNAP
benefit and the change in the additive inverse of fuel spending. The
coefficients on these endogenous regressors can be interpreted as MPCs.
Each model includes calendar month fixed effects. (Household fixed
effects are implicit in the first-differencing of the variables in the
model.)
All models use the interaction of the change in the price of
regular gasoline and the household's average monthly number of gallons
of gasoline purchased as an excluded instrument. This instrument
permits estimating the MPC out of cash following the logic of Figure 7.
Models (1), (2), and (3) of Table 1 all use the change in SNAP-
eligible spending as the dependent variable. The models differ in the
choice of excluded instruments for SNAP benefits. In model (1), the
instrument is an indicator for whether the month is an adoption month.
In model (2), it is an indicator for whether the month is the first
month of the 6 month SNAP clock. These instruments permit estimating
the MPCF out of SNAP following the logic of Figures 5 and 6,
respectively. In model (3), both of these instruments are used.
Estimates of models (1), (2), and (3) indicate an MPCF out of SNAP
between 0.55 and 0.59 and an MPCF out of cash close to 0. In model (3),
confidence intervals exclude an MPCF out of SNAP below 0.57 and an MPCF
out of cash above 0.1. In all cases, we reject the null hypothesis that
the MPCF out of SNAP is equal to the MPCF out of cash.
Model (4) parallels model (3) but uses SNAP-ineligible spending as
the dependent variable. We estimate an MPC out of SNAP of 0.02 and an
MPC out of cash of 0.04. We cannot reject the hypothesis that these two
MPCs are equal.
The appendix table shows that the conclusion that the MPCF out of
SNAP exceeds the MPCF out of cash holds when we exclude households for
whom SNAP benefits may not be economically equivalent to cash, and
restrict to single-adult households to limit the role of intra-
household bargaining.
The online appendix reports that the implied MPCF out of SNAP is
slightly higher in the household's first SNAP adoption than in
subsequent SNAP adoptions. We cannot reject the hypothesis that the
MPCF is equal between first and subsequent adoptions. The online
appendix also reports estimates of the MPCF out of SNAP and cash for
various demographic groups.
5 Model and Tests for Fungibility
5.1 Model
In each month t . 1,-,T , household i receives SNAP benefits
bit % 0 and disposable cashincome yit > 0. The
household chooses food expenditure fit and nonfood expenditure nit to
solve
(1)
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
where jit is a preference shock and Ui () is a
utility function strictly increasing in and n. The variables
(bit,yit, xit) are random with support
Vi.
Assumption 1. For each household i, optimal food spending can be
written as
(2)
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
where fi () is a function with range [0,yit +
bit].
A sufficient condition for assumption 1 is that, for each household
i, at any point (b,y,j) . Vi the function Ui
( ,y+b^;j) is smooth and strictly concave in
and has a stationary point * > b. Then
optimal food spending exceeds the level of SNAP benefits even if
benefits are disbursed as cash, so the ``kinked'' budget constraint in
(1) does not affect the choice of fit.
For each household and month, an econometrician observes data
(it,bit,yit,zit)
where zit is a vector of instruments. A concern is that
xit is determined partly by contextual factors such as job
loss that directly affect yit and bit.
Assumption 2. Let nit =
(yit+bit)^E(yit+bit D
zit). For each household i, the instruments zit
satisfy
(3)
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Proposition 1. Under assumptions 1 and 2, for each household i
(4)
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
for some function qi ().
Proof. Let Pi denote the CDF of
(jit,vit). Then
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
where the first equality follows from assumption 1 and the second from
assumption 2. See Blundell and Powell (2003, p. 330).
Example. (Cobb-Douglas) Suppose that for each household i there is
bi . (0,1) such that:
(5)
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
with bi (y+b)+j > b and (1^bi) (y+b) > j at all
points in Vi. Then assumption 1 holds with
(6)
and, under assumption 2, proposition 1 applies with
(7)
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
for ai 6 E(jit).
Remark 1. In his study of a child tax credit in the Netherlands,
Kooreman (2000) assumes a version of (6), which he estimates via
ordinary least squares using cross-sectional data under various
restrictions on ai, bi, and jit.
5.2 Testing for Fungibility
Index a family of perturbations to the model by g. Let
git be food spending under perturbation g, with
(8)
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
for i () the function defined in assumption 1. We
may think of g as the excess sensitivity of food spending to SNAP
benefits. The null hypothesis that the model holds is equivalent under
(8) to g = 0.
Let Yit = E(yit +
bitDzit) and Bit =
E(bitDzit) and observe that
(9)
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
where E(eitDYit,Bit) = 0. The nuisance
terms qi () have been ``partialled out'' of (9) as in
Robinson (1988). The target g can be estimated via OLS regression of
(git ^ E
(gitDYit)) on (Bit ^E
(BitDYit)).
Remark 2. It is possible to allow for measurement error in
it that depends on
(yit+bit). Say that for known function m(),
unobserved measurement error hit independent of
zit, and unknown function lit () we have that
measured food spending fit follows
(10)
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Then under perturbations m((git) =
m(it) + gbit an analogue of (9) holds,
replacing git with m(git).
Examples include additive measurement error, where m() is the identity
function, and multiplicative measurement error, where m() is the
natural logarithm. The latter case has a simple interpretation as one
in which the econometrician observes spending at a single retailer
whose share of total household food spending is given by exp (lit
(yit+bit,hit)).
Remark 3. The reasoning above is unchanged if bit and
yit are each subject to an additive measurement error that
is mean-independent of zit. In this case, we can simply let
Yit and Bit represent the conditional
expectations of the corresponding mismeasured variables.
5.3 Implementation and Results
With (9) in mind, estimation proceeds in three steps:
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
We let git be SNAP-eligible spending, bit
be SNAP benefits, and yit be the additive inverse of fuel
spending. We let the instruments zit be given by the number
of SNAP adoptions experienced by household i as of calendar month t,
and the product of the average price of regular gasoline with the
household's average monthly number of gallons of gasoline purchased.
In step 1, we estimate (Yit,Bit) via first-
differenced regression of (yit+bit) and bit
on zit.
In step 2, we consider four specifications for estimating
(E(gitDYit) ,
E(BitDYit)). In the first, we estimate these via
first-differenced regression of git and Bit
on Yit, pooling across households. In the second, we
estimate these via first-differenced regression of git
and Bit on Yit, separately by household. In the
third, we estimate these via first-differenced regression of
git and Bit on indicators for the
quintiles of Yit, separately by household. In the fourth, we
estimate these via locally weighted polynomial regression of
git and Bit on Yit,
separately by household. Thus, the first specification implicitly
treats qi as linear and homogeneous across households, the
second treats qi as linear and heterogeneous across
households, and the third and fourth allow qi to be
nonlinear and heterogeneous across households.
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Table 2 presents the results. Across all three specifications, our
estimates of g are 0.5 or greater, and in all cases we can reject the
null hypothesis that g = 0 with a high level of confidence.
6 Interpretation
We speculate that households treat SNAP benefits as part of a
separate mental account, psychologically earmarked for spending on
food. In this section we discuss results of qualitative interviews
conducted at a food pantry in Rhode Island. We then present
quantitative evidence that we think suggests a mental accounting
explanation, and present a post-hoc model of mental accounting that
rationalizes this evidence.
6.1 Qualitative Interviews with SNAP-Recipient Households
As part of preparation related to a state proposal to pilot a
change to SNAP benefit distribution, Rhode Island Innovative Policy
Laboratory staff conducted a series of qualitative interviews at a
large food pantry in Rhode Island in May, July, and August 2016.
Interviewees were approached in the waiting room of the pantry and were
offered a $5 gift card to a grocery retailer in exchange for
participating. Interviews were conducted in English and Spanish.
Interviewees were selected from those waiting to be served at the
food pantry and were not sampled scientifically. Interviews were
conducted primarily to inform the implementation of the pilot program
and the responses should not be taken to imply any generalizable
conclusions. We report them here as context for our quantitative
evidence.
Of the 25 interviews conducted, 19 were with current SNAP
recipients. Of these, all but three reported spending non-SNAP funds on
groceries each month, with an average out-of-pocket spending of $100
for those reporting positive out-of-pocket spending.
Each interviewee was asked the following two questions, which we
refer to as SNAP and CASH:
(SNAP) Imagine that in addition your current benefit, you
received an extra $100 in SNAP benefits at the beginning of the
month. How would this change the way that you spend your money
during the month? [emphasis added]
(CASH) Imagine that you received an additional $100 in cash
at the beginning of the month. How would this change the way
that you spend your money during the month? [emphasis added]
Of the 16 SNAP-recipient interviewees who report nonzero out-of-pocket
spending on groceries, 14 chose to answer questions SNAP and CASH.
Interviewers recorded verbal responses to each question as
faithfully as possible. The most frequently occurring word in response
to the SNAP question is ``food,'' which occurs in eight of the 14
responses. Incorporating mentions of specific foods or food-related
terms like ``groceries,'' the fraction mentioning food rises to ten out
of 14 responses. The word ``food'' occurs in three of the 14 responses
to CASH; more general food related terms occur in five of the 14
responses to CASH.
Several responses seem to suggest a difference in how the household
would spend $100 depending on the form in which it arrives. For
example, in response to question SNAP one interviewee said ``[I would]
buy more food.'' In response to CASH the same interviewee said ``[I
would buy] more household necessities.'' Another interviewee said in
response to SNAP that ``[I would buy] more food, but the same type of
expenses. If I bought $10 of sugar, now [I would buy] $20.'' In
response to CASH, the same interviewee said that ``[I would spend it
on] toilet paper, soap, and other necessary home stuff, or medicine.''
A third interviewee said in response to SNAP that ``I would buy more
food and other types of food . . .'' and in response to CASH that ``I
could buy basic things that I can't buy with [SNAP].'' \22\
---------------------------------------------------------------------------
\22\ The bracketed term is a translation for the Spanish word
cupones. This word is literally translated as ``coupons'' but is often
used to refer to SNAP. (See, for example, Project Bread 2016.)
---------------------------------------------------------------------------
Some responses suggest behavior consistent with inframarginality.
For example one interviewee's answer to SNAP included the observation
that ``I would probably spend $100 less out of pocket,'' although this
interviewee also mentions increasing household expenditures on seafood
and produce. Another interviewee answered SNAP with ``[I] would spend
all in food, and also buy soap [and] things for [my] two kids.''
6.2 Quantitative Evidence on Shopping Effort
If SNAP recipients consider SNAP benefits to be earmarked for food,
they may view a dollar saved on food as less valuable than a dollar
saved on non-food purchases. To test this hypothesis, we study the
effect of SNAP on bargain-seeking behavior.
Figure 8 shows the evolution of the adjusted store-brand share
before and after SNAP receipt for our sample of SNAP adopters. Each
plot shows coefficients from a regression of the adjusted store-brand
share on a vector of indicators for months relative to SNAP adoption.
Among SNAP-eligible items, panel A shows a trend towards a greater
store-brand share prior to SNAP adoption, perhaps reflecting the
deterioration in households' economic well-being that normally triggers
entry into a means-tested program. Once households adopt SNAP, there is
a marked and highly statistically significant drop in the store-brand
share. Because we have adjusted store-brand share for the composition
of purchases, this decline is driven not by changes in the categories
of goods purchased, but by a change in households' choice of brand
within a category.
Panel B of Figure 8 shows an analogous plot for SNAP-ineligible
items. The adjusted storebrand share of SNAP-ineligible expenditure
rises before SNAP adoption and does not decline significantly following
adoption. Regression analysis presented in the online appendix shows
that we can confidently reject the hypothesis that the change in
adjusted store-brand share at SNAP adoption is equal between SNAP-
eligible and SNAP-ineligible products.
Figure 9 shows analogous evidence for coupon use. Following SNAP
adoption, the average adjusted coupon redemption share declines for
both SNAP-eligible and SNAP-ineligible products, but the decline is
more economically and statistically significant for SNAP-eligible
products than for SNAP-ineligible products. Because we have adjusted
the coupon redemption share for the basket of goods purchased, these
patterns are not driven by changes in the goods purchased, but rather
by households' propensity to redeem coupons for a given basket of
goods. Regression analysis presented in the online appendix shows that
we can reject the hypothesis that the change in the adjusted coupon
redemption share at SNAP adoption is equal between SNAP-eligible and
SNAP-ineligible products.
6.3 Post-Hoc Model of Mental Accounting
To fix ideas and rationalize the preceding evidence, we specify a
model of mental accounting based on Farhi and Gabaix (2015). Return to
the setup of section 5, considering for ease of notation a single
household and time period, and ignoring the preference shock j. Let
preferences over food consumption and non-food consumption n
be Cobb-Douglas, and suppose that the household can exert effort
sf % 0 and sn % 0, respectively, to reduce the
cost of a given unit of consumption in the food and non-food domains,
respectively. Finally, suppose that the household exhibits a distaste
for deviating from a psychological default level of food spending,
determined in part by the earmarking of SNAP benefits. Formally, write
the household's problem as
(11)
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Here, the function c (), which is smooth and strictly increasing in its
argument, describes the cost of shopping effort. The function d (),
which is smooth, strictly decreasing and strictly convex, describes the
return to shopping effort in terms of prices paid. The parameter k > 0
indexes the importance of sticking to the household's default plan to
spend amount b of SNAP benefits and amount by of cash income on food.
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
In this sense, the model in (11) can rationalize both the tendency
to consume food out of SNAP in greater proportion than out of cash
income, and the tendency to reduce bargain-hunting in the food domain
(relative to the non-food domain) after receipt of SNAP. The model is
post-hoc in that the specification of the target spending (by + b) is
arbitrary and does not derive from portable psychological primitives.
7 Conclusions
We use data from a novel retail panel to study the effect of the
receipt of SNAP benefits on household spending behavior. Novel
administrative data motivates three approaches to causal inference. We
find that the MPCF out of SNAP benefits is 0.5 to 0.6 and larger than
the MPCF out of cash. We argue that these findings are not consistent
with households treating SNAP funds as fungible with non-SNAP funds,
and we support this claim with formal tests of fungibility that allow
different households to have different consumption functions.
We speculate that households treat SNAP benefits as part of a
separate mental account. Responses to hypothetical choice scenarios in
qualitative interviews suggest that some households plan to spend SNAP
benefits differently from cash. Quantitative evidence shows that, after
SNAP receipt, households reduce shopping effort for SNAP-eligible
products more so than for SNAP-ineligible products. A post-hoc model of
mental accounting based on Farhi and Gabaix (2015) rationalizes these
facts.
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Table 1: Estimated Marginal Propensities To Consume
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Table 2: Tests of Fungibility
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Figure 1: Household income and size before and after SNAP adoption
Panel A: Household Income
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Panel B: Number of Children Under Five Years of Age
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Notes: Data are from Rhode Island administrative records from
October 2004 through June 2016. See section 2.1 for details on
sample definition and variable construction. Each panel plots
coefficients from a regression of the dependent variable on a
vector of lead and lagged indicators for periods relative to
SNAP adoption, defined as the first period in which the
household receives SNAP. The period immediately prior to
adoption (``^1'') is the omitted category. Each regression
includes time period fixed effects, household fixed effects,
and indicators for observations more than 1 year before or
after adoption. In panel A, a time period is a calendar quarter
and the unit of analysis is a household-quarter. In panel B, a
time period is a month and the unit of analysis is the
household-month. In both panels, the error bars are R2
coefficient standard errors and standard errors are clustered
by household. Dotted lines show the sample mean of the
dependent variable across observations within 1 year (4
quarters or 12 months) of SNAP adoption. Each coefficient
series is shifted by a constant so that the observation-count-
weighted mean of the regression coefficients is equal to the
sample mean of the corresponding dependent variable.
Figure 2: Distribution of Lengths of SNAP Spells
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Notes: Data are from Rhode Island administrative records from
October 2004 through June 2016. See section 2.1 for details on
sample definition and variable construction. The plot shows a
histogram of the distribution of SNAP spell lengths, where a
spell is defined as a set of consecutive months in which the
household is entitled to a SNAP benefit in each month according
to state program records. Spells longer than 36 months are
excluded from the sample.
Figure 3: Inferring SNAP Adoption from Single-Retailer Data
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Notes: Data are from Rhode Island EBT transaction records
from September 2012 through October 2015. See section 2.1 for
details on sample definition and variable construction. The
figure plots the fraction of transition periods of a given
radius in which the household newly enrolled in SNAP within 2
months of the start of SNAP spending at the Rhode Island
Retailer. We define new enrollment as the receipt of at least
$10 in SNAP benefits following a period of at least 3
consecutive months with no benefit. A transition period of
radius K is a period in which a household exhibits K
consecutive months without SNAP spending at the Rhode Island
Retailer followed by K consecutive months with SNAP spending at
the Rhode Island Retailer. Households who mainly spend SNAP at
the Rhode Island Retailer are those who spend at least \1/2\ of
their total EBT expenditures between September 2012 and October
2015 at the Rhode Island Retailer.
Figure 4: SNAP Use and Benefits Before and After SNAP Adoption
Panel A: SNAP Use
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Panel B: SNAP Benefits
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Notes: The sample is the set of SNAP adopters. Panel A plots
the share of households with positive SNAP spending in each of
the 12 months before and after the household's first SNAP
adoption. Panel B plots the average SNAP benefit in each of the
12 months before and after the first SNAP adoption.
Figure 5: Monthly Expenditure Before and After SNAP Adoption, By SNAP
Eligibility of Product
Panel A: SNAP-Eligible Spending
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Panel B: SNAP-Ineligible Spending
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Notes: Each figure plots coefficients from a regression of
SNAP-eligible or SNAP-ineligible spending on a vector of lead
and lagged indicators for month relative to the household's
first SNAP adoption, with the month prior to SNAP adoption
(``^1'') as the omitted category. The unit of observation for
each regression is the household-month. Error bars are R2
coefficient standard errors. Standard errors are clustered by
household. Each regression includes calendar month fixed
effects, household fixed effects, and two indicators for
observations before and after 12 months of SNAP adoption. The
dotted lines show the sample mean of household monthly
expenditure across observations within 12 months of SNAP
adoption. Each coefficient series is shifted by a constant so
that the observation-count-weighted mean of the regression
coefficients is equal to the sample mean of the corresponding
dependent variable.
Figure 6: Participation, Benefits, and Spending Over the 6 Month SNAP
Clock
Panel A: SNAP Use
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Panel B: SNAP Benefits
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Panel C: SNAP-Eligible Spending
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Notes: Each figure plots coefficients from a regression of
the dependent variable on a vector of indicators for the
position of the current month in a monthly clock that begins in
the most recent adoption month and resets every 6 months or at
the next SNAP adoption, whichever comes first. The unit of
observation for each regression is the household-month. The
sample is the set of SNAP adopters. Error bars are R2
coefficient standard errors. Standard errors are clustered by
household. Each regression includes calendar month fixed
effects. The omitted category consists of the first 6 months
(inclusive of the adoption month) after the household's most
recent SNAP adoption, all months after the first 24 months
(inclusive of the adoption month) following the household's
most recent adoption, and all months for which there is no
preceding adoption. In Panel A, the dependent variable is the
change in an indicator for whether the household-month is a
SNAP month. In Panel B, the dependent variable is the change in
monthly SNAP benefits. In Panel C, the dependent variable is
the change in monthly SNAP-eligible spending.
Figure 7: Monthly Expenditure and the Price of Gasoline
Panel A: Fuel Spending
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Panel B: SNAP-Eligible Spending
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Notes: Panel A plots average monthly fuel spending by
quartile of average monthly fuel spending. Panel B plots
average monthly SNAP-eligible spending by quartile of average
monthly fuel spending. The unit of observation is the
household-month and the sample is the set of SNAP adopters who
ever purchase fuel. The lower portion of both plots shows the
price of gasoline, computed as the quantity-weighted average
spending per gallon on regular grade gasoline among all
households before any discounts or coupons.
Figure 8: Store-Brand Share Before and After SNAP Adoption, By SNAP
Eligibility of product
Panel A: SNAP-Eligible Products
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Panel B: SNAP-Ineligible Products
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Notes: Each figure plots coefficients from a regression of
adjusted store-brand share of expenditures on a vector of lead
and lagged indicators for month relative to the household's
first SNAP adoption, with the month prior to SNAP adoption
(``^1'') as the omitted category. The unit of observation for
each regression is the household-month. Error bars are R2
coefficient standard errors. Standard errors are clustered by
household. Each regression includes calendar month fixed
effects, household fixed effects, and two indicators for
observations before and after 12 months of SNAP adoption. The
dotted line shows the sample mean of the store-brand share of
expenditure across observations within 12 months of SNAP
adoption. Each coefficient series is shifted by a constant so
that the observation-count-weighted mean of the regression
coefficients is equal to the sample mean of the store-brand
share of expenditure in the given SNAP eligibility group.
Figure 9: Coupon Use Before and After SNAP Adoption, By SNAP
Eligibility of Product
Panel A: SNAP-Eligible Products
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Panel B: SNAP-ineligible products
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Notes: Each figure plots coefficients from a regression of
the adjusted coupon redemption share on a vector of lead and
lagged indicators for month relative to the household's first
SNAP adoption, with the month prior to SNAP adoption (``^1'')
as the omitted category. The unit of observation for each
regression is the household-month. Error bars are R2
coefficient standard errors. Standard errors are clustered by
household. Each regression includes calendar month fixed
effects, household fixed effects, and two indicators for
observations before and after 12 months of SNAP adoption. The
dotted line shows the sample mean of the share of purchases
using a coupon across observations within 12 months of SNAP
adoption. Each coefficient series is shifted by a constant so
that the observation-count-weighted mean of the regression
coefficients is equal to the sample mean of the share of
purchases using a coupon in the given SNAP eligibility group.
Appendix Table: Results for Alternative Samples and Specifications
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Appendix Figure 1: Legislated Changes in SNAP Benefits
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Notes: This figure plots the average monthly SNAP benefit per
U.S. household between February 2006 and December 2012. The
series was obtained directly from the United States Department
of Agriculture Food and Nutrition Service via http://
www.fns.usda.gov/sites/default/files/pd/
SNAPZip69throughCurrent.zip. The vertical lines at October 2008
and April 2009 denote the implementation dates of changes in
SNAP benefits due to the farm bill and American Recovery and
Reinvestment Act (ARRA), respectively.
Appendix Figure 2: Monthly SNAP Benefits and SNAP-Eligible Spending
Around Benefit Changes
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Notes: The sample includes all households in the retailer
panel that have at least 2 consecutive SNAP months during the
panel. The figure plots coefficients from a regression of SNAP
benefits and SNAP-eligible spending on interactions between the
share of calendar months between February 2006 and December
2007 during which each household used SNAP and calendar month
indicators, with the January 2008 interaction normalized to
zero. The unit of observation is the household-month and only
months from January 2008 to December 2009 are included in the
regression. Error bars and shaded region represent R2
coefficient standard errors. Standard errors are clustered by
household. Each regression includes household and calendar
month fixed effects. Each coefficient series is seasonally
adjusted by subtracting from each coefficient the corresponding
coefficient from an auxiliary regression of the dependent
variable on interactions between the share of months between
February 2006 and December 2007 during which each household
used SNAP and year and seasonal month indicators. The auxiliary
regressions include household, year, and seasonal month fixed
effects and are estimated using only data from January 2010 to
December 2012. Each coefficient series is shifted by a constant
so that the observation-count-weighted mean of the regression
coefficients is equal to the sample mean of the corresponding
dependent variable among households who used SNAP in every
month between February 2006 and December 2007. Vertical lines
at October 2008 and April 2009 denote the implementation dates
of changes in SNAP benefits due to the farm bill and American
Recovery and Reinvestment Act (ARRA), respectively.
Exhibit 2
Incentivizing Nutritious Diets: A Field Experiment of Relative Price
Changes and How They Are Framed
John Cawley, Andrew S. Hanks, David R. Just, Brian Wansink
Working Paper 21929
http://www.nber.org/papers/w21929
We gratefully acknowledge financial support from the National
Institutes of Health (NIH) grant 1RC1HD063370-01. The NIH played no
other role in the conduct of the study. Cawley gratefully acknowledges
support from an Investigator Award in Health Policy Research from the
Robert Wood Johnson Foundation. The Cornell University Institutional
Review Board approved the design of this study (Protocol
ID#1110002491). For helpful comments and suggestions, we thank Heather
Royer and participants at the American Society of Health Economists
biennial conference, the NBER Summer Institute, the International
Health Economics Association conference, the TIGER conference in
Toulouse France, and seminar participants at the Indiana University,
McGill University, University of Oxford, the University of
Pennsylvania, and the University of Sydney. The views expressed herein
are those of the authors and do not necessarily reflect the views of
the National Bureau of Economic Research.
At least one co-author has disclosed a financial relationship of
potential relevance for this research. Further information is available
online at http://www.nber.org/papers/w21929.ack.
NBER working papers are circulated for discussion and comment
purposes. They have not been peer-reviewed or been subject to the
review by the NBER Board of Directors that accompanies official NBER
publications.
2016 by John Cawley, Andrew S. Hanks, David R. Just, and Brian
Wansink. All rights reserved. Short sections of text, not to exceed two
paragraphs, may be quoted without explicit permission provided that
full credit, including notice, is given to the source.
Abstract
This paper examines how consumers respond to price incentives for
nutritious relative to less-nutritious foods, and whether the framing
of the price incentive as a subsidy for nutritious food or a tax on
non-nutritious food influences consumers' responses. Analyzing
transaction data from an 8 month randomized controlled field experiment
involving 208 households, we find that a 10% relative price difference
between nutritious and less nutritious food does not significantly
affect overall purchases, although low-income households respond to the
subsidy frame by buying more of both nutritious and less-nutritious
food.
John Cawley, David R. Just,
2312 MVR Hall, Cornell University,
Department of Policy Analysis and 210C Warren Hall,
Management and Department of Ithaca, NY 14850,
Economics,
Cornell University, [email protected];
Ithaca, NY 14853,
and NBER,
[email protected];
Andrew S. Hanks Brian Wansink,
Ohio State University Cornell University,
130A Campbell Hall 475H Warren Hall,
1787 Neil Ave. Ithaca, NY 14850,
Columbus, OH 43210 [email protected].
[email protected];
Introduction
Diet-related chronic disease is a global problem. Worldwide, the
annual deaths due to high blood pressure total 7.5 million, high blood
glucose (diabetes) 3.4 million, overweight and obesity 2.8 million, and
high cholesterol 2.6 million (WHO, 2009). In the U.S., 37% of the adult
population has cardiovascular disease, 16% has high total blood
cholesterol, 34% has hypertension, 11% has diabetes, and it is
estimated that 41% will be diagnosed with some form of cancer during
their lifetime (USDA, 2010). Moreover, 35.1% of adults and 16.9% of
youths in the U.S. are obese (Ogden, et al., 2014). Even in low-income
countries, the top ten risk factors for preventable death include high
blood pressure, high blood glucose, and high cholesterol (WHO, 2009).
The problems with many modern diets, which contribute to these high
rates of chronic disease (McCullough, et al., 2002), are that they
contain too much saturated fats, cholesterol, added sugars, added
sodium, and refined grains, and too little whole grains and fresh
fruits and vegetables (USDA, 2010).
As a result of the high rates of chronic disease, there have been
calls for taxes on energy-dense less-nutritious foods from many medical
and public health organizations, such as the World Health Organization
(2015), U.S. Dietary Guidelines Advisory Committee (2015), British
Medical Association (2015), Institute of Medicine (2009), and the
International Obesity Task Force (2005), which urged all European Union
member countries to enact taxes on energy-dense foods. There have also
been numerous calls in medical journals for taxes to incentivize a
healthy diet (e.g., Brownell and Frieden, 2009, and Jacobson and
Brownell, 2000). Taxes on energy-dense foods are arguably the most
commonly-advocated anti-obesity policy.
Policymakers worldwide have responded to this call for action.
Numerous countries, such as Australia, Canada, Denmark, Fiji, Finland,
France, Hungary, Norway, and Mexico, have recently implemented taxes on
energy-dense, less-nutritious foods (see e.g., World Health
Organization, 2015, Sassi, et al., 2013, and Thow, et al., 2011). In
the U.S., 34 states tax soft drinks sold in grocery stores, at an
average rate of 4.02%, and 15 states tax snacks sold in grocery stores
at an average rate of 1.2% (Chriqui, et al., 2008). In early 2015,
Berkeley, California became the first U.S. city to impose an excise tax
on sugar-sweetened beverages (Cawley and Frisvold, 2015).
To some extent, an individual's diet and any resulting chronic
disease or premature mortality can be seen as a private, individual
decision. However, there are two economic rationales for government
intervention to incentivize healthier diets. First, there are external
costs of a poor diet that operate through private and public health
insurance (Cawley, 2015). Premiums that fund private health insurance,
and the taxes that fund public health insurance, are not a function of
diet, and as a result, the costs of treating diet-related chronic
disease are borne not only by those with the disease but also by others
in the same insurance pools and by taxpayers. The exact magnitude of
these external costs is not known, but they are undoubtedly large given
the enormous medical care costs. Indeed, it is estimated that the
annual direct medical care costs total $273 billion for cardiovascular
disease (CDC, 2015a), $315.8 billion for obesity (Cawley, Meyerhoefer,
et al., 2015), $116 billion for diabetes (CDC, 2015b), and $263.8
billion for cancer (this includes both direct and indirect costs; CDC
2015c). To pool these separate estimates would result in some degree of
double-counting, but the overall cost of these diseases is clearly very
high.
Behavioral economics offers a second rationale for government
intervention to incentivize healthier diets. Individuals may have time-
inconsistent preferences; they may want to eat a nutritious diet so as
to be healthy in the future, but in the short run may be tempted by
immediate gratification (Laibson, 2014). Some have argued that optimal
taxes should reflect not only externalities but also internalities
associated with time-inconsistent preferences, and that in such cases
sin taxes can make those who engage in such activities happier because
it helps them help themselves (Gruber and Mullainathan, 2005).
Whether or not food taxes and subsidies are effective is an
empirical question. However, it is challenging to estimate the effect
of existing food taxes on purchases and consumption. In the U.S.,
state-level taxes are so small that it is very difficult to measure
their effects (Fletcher, Frisvold, and Tefft, 2010; Chaloupka, et al.,
2011; Fletcher, et al., 2011). For national taxes, it is difficult to
disentangle the effect of the tax from time effects; i.e., it is hard
to identify a geographic control group. For both, policy endogeneity is
a problem.
As an alternative approach, researchers have used field experiments
to measure consumer responsiveness to price changes. For example, the
USDA's Healthy Incentives Pilot for recipients of the Supplemental
Nutrition Assistance Program (SNAP) offered a 30 rebate to the
Electronic Benefit Transfer card for each dollar spent on fruits and
vegetables. The program resulted in 0.22 cups/day more fruits and
vegetable consumed by participating adults (USDA, 2013). Other field
experiments paired their price changes with related interventions such
as signs or marketing, the effect of which is confounded with the price
change. For example, a set of experiments conducted by researchers at
the University of Minnesota manipulated prices in cafeterias and
vending machines (but also increased signage) and found that a 50%
subsidy for fruits and salads tripled sales, but sales fell to baseline
after the subsidy was removed (French, et al., 1997; Jeffrey, et al.,
1994). Elbel, et al. (2013) opened their own store in a hospital, and
imposed a 30% tax on unhealthy foods, which they juxtaposed next to
healthier alternatives. They estimate that the tax increased the
probability of consumers choosing healthier alternatives by 11
percentage points. The generalizability is unclear given that the store
was a researcher-created environment that involved deliberate
juxtapositioning of healthier and less healthy options.
This paper contributes to the literature that uses field
experiments to measure consumer responsiveness to changes in food
prices. A review of the literature by Epstein, et al. (2012) finds only
four studies that manipulated prices of foods in supermarkets; all
provided discounts for healthy foods, and three of the four examined
only purchases of a subset of available foods. Other experiments
manipulating food prices took place in laboratories, cafeterias and
restaurants, farmer[s'] markets, and vending machines (Epstein, et al.,
2012). In a recent study, nutritious foods were subsidized 12.5% or 25%
and less-nutritious items were taxed 12.5% or 25%, depending on the
treatment, in a simulated online market place with 6000 food items.
Calories purchased of taxed foods decreased and calories purchased of
subsidized foods increased, but overall calories did not change between
baseline and price change interventions, suggesting substitution of
calories towards foods neither taxed nor subsidized. Yet, there is
evidence of improved nutrient quality of foods purchased in the subsidy
condition (Epstein, et al., 2015).
Another relevant recent study is that of List, Samek, and Zhu
(2015). They conducted a field experiment at a grocery store in a high-
poverty area of Chicago. They enlisted 222 participants for a 6.5 month
study and examined the effect of two treatments: $1 incentive to
purchase at least 5 cups of fresh fruits and vegetables on their
shopping trip, and information on preparing fruits and vegetables. They
find little effect from the information, but find large effects of the
incentives (it doubles purchases of fresh fruits and vegetables) that
persist after the incentives end.
The contribution of this research is to estimate the responsiveness
of consumers to a price change--with no other interventions such as
additional signage or juxtapositioning of alternatives--in the
consumer's usual retail environment. In other words, we observe
consumers buying their usual items in the supermarket in which they
typically shop. We observe all food purchases made at the supermarket
(and provide incentives for subjects to do all of their food shopping
at the supermarket), and we rely on an objective system that classifies
food as nutritious and less-nutritious and which is already in place in
the supermarket.
We conduct a randomized controlled field experiment in order to
measure the impact of a 10% relative price difference between
nutritious and less-nutritious food in order to answer three research
questions: (1) Are consumers' food purchases responsive to less-
nutritious food being made 10% more expensive than nutritious food? (2)
Does that responsiveness depend on whether the price change is framed
as a tax on less-nutritious food, a subsidy for nutritious food, or
both? (3) Do the answers differ by the education or income of the
consumer?
We hypothesize that the relative price change will decrease
purchases of less-nutritious foods and increase purchases of nutritious
foods. We also hypothesize that those told that the 10% price
difference is a tax will respond more, relative to those who are told
that the 10% price difference is a subsidy; this is motivated by
prospect theory, which posits that people interpret gains and losses
relative to a reference point (Kahneman and Tversky, 1979). In
particular, people may respond more when the tradeoff is framed as a
loss rather than a foregone reward (Gachter, et al., 2009; Homonoff,
2015), which suggests that people may be more responsive to the frame
of a tax on less-nutritious food than that of a subsidy for nutritious
food.
Additionally, we hypothesize that responses to the relative price
change may differ by socioeconomic status, measured by income and
education, though the direction of the response is unclear. Consumer
response may differ by income for several reasons. Mullainathan and
Shafir (2013) argue that poverty consumes mental bandwidth, which
implies that lower-income individuals may pay less attention to the
price change. On the other hand, other evidence suggests that lower-
income individuals may be more responsive to the relative price change.
Low-income individuals who receive public assistance (such as food
stamps or social security) exhibit ``first of the month effects''--
their spending on food decreases as the month progresses (Hastings and
Washington, 2010; Shapiro, 2005). This suggests that they may be credit
constrained and perhaps price reductions could have substantial income
effects. Furthermore, other research suggests that the income
elasticity of body weight is greater for low-income individuals (Akee,
et al., 2013; Schmeiser, 2009).
Second, consumer response may also differ by education. The better
educated tend to demand more health and be more efficient producers of
their own health (Grossman, 1972) and thus may have a more elastic
demand for nutritious food. In addition, the better educated may simply
better understand the treatment or respond to changing prices in
general.
Data and Methods
The Field Experiment
Controlled field studies with random assignment have the potential
to clearly identify causal effects (List, 2009, 2011) and can have high
levels of both internal and external validity (Roe and Just 2009).
Thus, these types of studies can be uniquely effective for measuring
the impact of potential policy instruments.
Identifying Nutritious and Less-Nutritious Foods
Any experiment designed to manipulate the prices of nutritious and
less-nutritious foods faces the challenge of defining those two
categories. We relied upon a supermarket shelf-label nutrition guidance
system that had already been in place in the supermarket for several
years prior to this experiment.* \2\ This proprietary system, called
Guiding Stars, scores foods based on their nutritional value. More
specifically, it takes into account vitamins, minerals, fiber and whole
grains (which raise the score) and saturated fat, trans fat,
cholesterol and added sugar and sodium (which lower the score).
Ultimately, foods are rated on a scale from zero stars (poor
nutritional value) to three stars (best nutritional value), and this
score is displayed on the supermarket shelf label below each food item
(retail price and unit price). Over 60,000 food items are rated. The
few foods that are not rated are new (and thus not yet rated), seasonal
(not consistently available), or have no calorie or nutrient content
(such as dried spices or dried coffee or tea). For more information on
Guiding Stars, see Fischer, et al. (2011).
---------------------------------------------------------------------------
* Editor's note: There is no footnote no. 1 in this working paper,
as submitted.
\2\ Sales data suggest that consumers use and respond to the
Guiding Stars information; see Cawley, Sweeney, Just, et al. (2015).
However, this information was in place well before and throughout the
experiment and is thus not confounded with the treatment effects we
estimate.
---------------------------------------------------------------------------
For our experiment, we defined less-nutritious food as that which
receives zero stars, and defined nutritious food as that which receives
any stars (one, two, or three). An incentive scheme could offer more
finely-tuned subsidies based on whether the item received one, two, or
three stars, but that would also involve the tradeoff of increased
complexity that could cause confusion for study participants. We chose
to make the intervention simple to understand, and divided foods into
those with zero stars (which were made relatively more expensive) and
those with one or more stars (which were made relatively cheaper).\3\
Of the rated food items observed in our data, 29% have at least one
star and are thus classified as nutritious.
---------------------------------------------------------------------------
\3\ The prices of unrated items were not altered.
---------------------------------------------------------------------------
Participation and Incentives
Between May 1 and June 30, 2010, we recruited 239 loyalty card
shoppers to participate in the study. Individuals were recruited via
face-to-face contact at the entrances to two grocery stores in upstate
New York. These stores are part of a regional supermarket chain that is
located in the Northeast U.S. In order to ensure a diverse set of
participants, subjects were recruited at various days and times, as
well as at two different stores of the same chain in neighborhoods of
differing socioeconomic status. In addition, to be eligible for
inclusion in the study, participants had to have children under the age
of 18 years living at home, do at least 75% of their shopping at the
supermarket chain, and do a majority of the household's shopping.
After enrollment, subjects were sent an e-mail with a link to
complete a survey on their household characteristics and shopping
patterns. After repeated requests, fourteen subjects did not complete
the survey and were dropped. One household later attrited from the
study and so we drop data for that household. In 16 households, two
individuals claimed to each do \1/2\ of the household's shopping. Both
were enrolled but purchases were aggregated to the household level. As
a result, we have complete information, survey responses and
expenditure data, for 208 households.
Soon after enrollment, participating households received two
cards.\4\ A scanner card (with the subject's name and photograph) was
used to track purchases at the supermarket checkout lane. A debit card
was used to deliver incentives and subsidies, which were electronically
credited on a weekly basis. We observed households' food purchases
(through their use of the scanner card) for a total of 33 weeks,
including an 8 week baseline period before the relative prices of
nutritious and less-nutritious foods were altered.\5\ To encourage
households to conduct all of their food shopping at the participating
supermarket, during this baseline period, they received a 10% discount
on purchases of all rated food items, defined as any foods rated with
0, 1, 2, or 3 stars.
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\4\ In the 16 households in which two members enrolled in the
study, each enrollee received his/her own set of cards.
\5\ Households signed up 5-8 weeks before the treatment period;
thus, we have baseline data for every household for at least 4 weeks
and up to 8 weeks for some households.
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Treatment Conditions
At the conclusion of the baseline period, subjects were randomized
into one of four groups. The control group (N=52 households) continued
to receive a 10% discount on all rated food items. For the treatment
group (N=156), nutritious food was made 10% cheaper than less-
nutritious food. How this price wedge was framed differed based on the
treatment group into which the subject was randomized. The tax group
(N=51) was told that they received a 15% discount on all rated food
items, but were taxed 10% (and thus received only a 5% discount) on
less-nutritious food. The subsidy group (N=55) was told it received a
5% discount on all rated food items, plus an additional 10% subsidy on
nutritious food, for a total of 15% off nutritious food. The tax/
subsidy group (N=50) was told that it received a 10% discount on all
rated food items, plus an additional 5% subsidy on nutritious food (for
a total subsidy of 15%) but was taxed 5% on less-nutritious food (for a
net subsidy of 5%). In all three treatment conditions, nutritious food
was subsidized 15% and less-nutritious food was subsidized 5%; thus
each group faced a 10% price wedge between nutritious and less-
nutritious food. The only way the treatments differed was in how that
relative price difference was framed.
Households were notified of their respective treatment via e-mail
and phone calls. Out of concern that subjects may not check their e-
mail or voice messages, the enrolled representative from each household
was also individually contacted by phone and notified directly; this
process took 12 days. We removed these 2 weeks from analysis because
some subjects during that time may not have yet been aware of their
treatment condition.
In a voluntary field experiment, it is not possible to impose taxes
on less-nutritious foods greater than the participation incentive, or
subjects would likely buy these foods elsewhere and such expenditures
would not be recorded as part of the study. To address this, the
participation incentive was always greater than the tax imposed,
ensuring that shoppers could not be worse off by shopping at the study
stores. Because the participation incentive was also offered during the
baseline period, we are able to identify the effect of price changes
using the relative price changes between nutritious and less-nutritious
foods that were imposed between the baseline and treatment periods. See
Table 1 for the relative price changes at baseline and during the
treatment period, and details of the framing of the treatment.
To clarify, prices on the supermarket shelves were not altered. The
participating supermarket was understandably unwilling to allow the
researchers to manipulate shelf prices for all of their customers.
Instead, subjects' purchases were tracked using the scanner cards, and
the discounts, net of taxes, were uploaded weekly to the debit card. To
ensure the salience of the price changes, each subject received a
weekly e-mail notifying them of the amount of incentive or subsidy they
had received, and reminding them which foods were taxed and which were
subsidized. We acknowledge that this may affect the generalizability of
these results, an issue we return to in the Discussion. The treatment
period lasted for 25 weeks and ended without prior notice. See Figure 1
for a detailed timeline of the study.
Data
Itemized grocery purchases of each subject were tracked by the
supermarket for the entire 33 weeks of the study using the scanner
cards. The item-level transaction data include: date, quantity of item,
expenditures on item, Guiding Stars score of each item (0, 1, 2, or 3
stars), and the description of the item. These transactions were
aggregated by household and week, with weeks defined as Monday through
Sunday. We merge the information from the baseline survey with the
transaction data.
We focus on two main outcomes: the household's expenditures
(defined before any subsidies or taxes applied by the experiment) and
quantity purchased. Quantity purchased is measured in units, which is a
limited measure because it does not account for size differences. For
example, a \1/2\ gallon and a gallon of milk each count as one unit, as
do two different-sized boxes of the same cereal. Thus, this measure of
quantity is a noisy measure of the quantity of food purchased. We
examine these two outcomes for all food purchases, as well as
separately for nutritious food and less-nutritious food.
If a household did not buy any food in that category in that week,
the values of expenditures and quantity purchased are set to zero. The
exception to this occurred during the first 3 weeks of the baseline
period when households were still being enrolled in the study. During
these 3 weeks, weeks with no expenditures were treated as missing until
the household recorded their first shopping trip.
Hypotheses and Empirical Methods
We test the following hypotheses:
H1: Increasing the price of less-nutritious food relative to
the price of nutritious food will decrease purchases of less-
nutritious food and increase purchases of nutritious food;
H2: Framing the relative price change as a subsidy for
nutritious food will increase the extent to which the price
change increases purchases of nutritious food;
H3: Framing the relative price change as a tax on less-
nutritious food will increase the extent to which the relative
price change decreases purchases of less-nutritious food;
H4: These effects will vary by income and education.
In order to test these hypotheses, we estimate difference-in-
differences models of expenditures and quantities. Randomization into
the treatment and control groups allows for interpretation of the
difference-in-differences estimator as a causal effect of the
treatment. We first estimate these models assuming no framing effects
and thus pool all three treatment conditions--tax, subsidy, and tax/
subsidy--into a single treatment condition. We then subsequently
estimate the models testing for framing effects, with each of the three
frames as a separate treatment.
To estimate the average effect of the price change, ignoring the
possibility of framing effects, we estimate the following two-way fixed
effects model:
(1)
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
The data are aggregated by household (h) and week (w). The difference-
in-differences estimator is b0. This coefficient measures
the change between the baseline and treatment period for the treatment
group relative to the control group. In order to control for time-
invariant unobserved heterogeneity among households, the model controls
for household fixed effects Ih. In order to control for time
effects, such as the seasonal availability of fresh fruits and
vegetables and changes in demand due to holidays, the model controls
for week fixed effects Iw. The OLS regression model is
estimated for all food purchases, as well as separately for purchases
of nutritious food and less-nutritious food. The null hypothesis is
that the 10% price wedge has no impact on purchases: b0=0.
To account for possible correlation in errors for the same household
over time, standard errors are clustered by household.
In order to test whether the framing of the price change affects
consumers' response to the price change, we estimate the following
model, which estimates a separate difference-in-differences effect for
each of the three treatment groups (tax, subsidy, tax and subsidy):
(2)
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
The null hypothesis is that the framing of the treatment as either a
tax on less-nutritious food, a subsidy of nutritious food, or both,
does not alter the treatment effect; i.e., that
b1=b2=b3.
To test whether the treatment effect varies by income, we estimate
models (1) and (2) separately for those whose household income is (a)
below or (b) above 130% of the Federal Poverty Line (FPL), which is the
eligibility threshold for the Supplemental Nutrition Assistance Program
(SNAP) and is close to the eligibility threshold for Medicaid (133% of
FPL).
To test whether the treatment effect varies by education, we
estimate the model separately for those whose educational attainment is
(a) a high school degree or less or (b) some college or more.
We emphasize that, given our overall sample size, we have limited
statistical power for subgroups. When we divide the sample by income,
we have 36 households below and 155 households above 130% of the FPL.
When we divide the sample by education, we have 18 participants with a
high school education or less, and 182 participants with some college
or more education (see Table 2). These subtotals do not sum to our
total of 208 households because of non-response to the questions about
income and education.
Empirical Results
Summary Statistics
Tables 2 and 3 list summary statistics for the study participants,
with columns for the whole sample, control group, all treatment groups
pooled, and each treatment group separately. Table 2 reports sample
sizes for the socioeconomic subgroups. Tables 3a and 3b report summary
statistics for additional household characteristics, such as income,
number of children at home, household size, marital status, and race/
ethnicity, which are all controlled for in our model through the
inclusion of household fixed effects.
The summary statistics indicate that our sample is relatively well
educated (91% have more than a high school education) and white
(93.7%). This is a reflection of the fact that our sample consists of
individuals in upstate New York and the participating supermarket chain
is relatively high-end. By construction, all families have at least one
child under the age of 18 years in the household.
Table 4 lists unconditional weekly expenditures on foods (overall,
all rated, less nutritious, nutritious) for the entire sample and by
group (control, all treatment, each treatment group). Household weekly
food expenditures at this supermarket averaged $89.83 during the
baseline period, and $100.88 during the treatment period. In
comparison, data from the Consumer Expenditure Survey indicate that on
average U.S. households spent $76 per week on food purchased for at-
home consumption in 2013 (BLS, 2015). Notably the BLS estimate is
unconditional, whereas our sample consists of households with at least
one child under the age of 18 years, and are thus likely to be above-
average in terms of food expenditures.
The increase in average weekly food expenditures for all treatment
groups ($10.95) is roughly equal to that for the control group
($11.32); this unconditional difference-in-differences suggests that
the treatment did not significantly affect overall expenditures on
food. The increase in expenditures on nutritious food was also similar
for all treatment groups pooled ($4.69) and the control group ($3.30).
Overall Effect of Relative Price Change
Table 5 lists results of the difference-in-differences models for
expenditures and quantities. Our hypothesis is that the 10% relative
price change increased the quantity demanded of nutritious food, and
decreased the quantity demanded of less-nutritious food. Table 5 shows
that the point estimates of the coefficients are consistent with these
hypotheses, but the coefficients are not statistically significant. For
example, we find that creating a 10% price difference between
nutritious and less-nutritious foods raised spending on nutritious food
by $1.11 per week and lowered spending on less nutritious food by $1.55
per week, neither of which is statistically significant. On net,
spending on all food rated by Guiding Stars (whether nutritious or less
nutritious) fell by $0.44 per week, which was not statistically
significant. In terms of quantities, the 10% relative price difference
increased weekly purchases of nutritious food by 0.95 units and lowered
weekly purchases of less nutritious food by 0.87 units; overall
purchases of foods rated by Guiding Stars rose by .08 units. None of
those changes are statistically significant.
In summary, we are unable to reject the null hypothesis of no
effect of the relative price change on purchases of nutritious and
less-nutritious foods.
Effect of Framing of Relative Price Change
Next we test whether the effect of the relative price change
differed by the way in which it was framed: as a tax on less-nutritious
food, a subsidy for nutritious food, or both. It is possible that,
because of loss aversion, the tax frame may exhibit a greater treatment
effect than the subsidy frame. Moreover, given the difference in
salience, we may see a greater increase in purchases of nutritious food
for the subsidy frame, but a greater decrease in purchases of less-
nutritious food for the tax frame.
Table 6 presents the results of the difference-in-difference models
that estimate separate effects by frame. In no case are the treatment
effects significantly different across frames (whether tax versus
subsidy, tax versus tax/subsidy, or subsidy versus tax/subsidy). In
addition, no estimated treatment effect for nutritious or less-
nutritious food is significantly different from zero. However, some
point estimates are substantial; e.g., the effect of the relative price
change for those in the tax frame to increase their weekly purchases of
nutritious food by $4.52 (relative to a mean of $36.55) and for those
in the tax/subsidy frame to decrease their weekly purchases of less
nutritious food by $4.40 (relative to a mean of $49.59).
In summary, we are unable to reject the null hypothesis of no
framing effect for the relative price change.
Differences by Income and Education
In our next analyses, we test whether the overall price treatment
effects differed by income or education. For the sake of simplicity, we
report results for expenditures (but not those for quantities). Table 7
presents results of the overall price treatment effects separately for
households with incomes below and above 130% of the Federal Poverty
Line.
Although the difference in results across income was not
statistically significant, the point estimates suggest that the
treatment was associated with lower-income households spending $7.03
more per week on nutritious food and $7.11 more per week on less-
nutritious food. In contrast, higher-income households spent $1.27 less
on nutritious food per week and $4.02 less on less-nutritious food per
week. None of these point estimates are statistically significant.
Table 8 presents the results of models estimated separately by
education. Again, we find no statistically significant difference
between the effect of the relative price change for the two
socioeconomic groups. Moreover, the difference in point estimates is
considerably smaller across education groups than across income groups.
We next test whether framing effects differed by income or
education. Table 9 reports results for the model that estimates
treatment effects by frame, with the model estimated separately by
income category. There are large and statistically significant
differences in the effects of the frame by income. Specifically, low-
income households that were given the subsidy frame (i.e., told that
the 10% relative price change represented a subsidy for nutritious
food) significantly increased their purchases of less-nutritious food
(by $21.23 per week). The increase in purchases of nutritious foods was
$11.58, but not statistically significant. Overall, purchases of foods
rated by Guiding Stars rose $32.81 per week on average for this group.
In contrast, higher-income households that were given the subsidy
frame decreased their weekly purchases of both nutritious food ($4.55)
and less-nutritious food ($7.55), although these are not significantly
different from zero. The effects of the price change on less nutritious
foods and all rated foods are, however, significantly different for the
low-income and high-income groups given the subsidy frame.
In addition, within each income group, there is a significant
difference in framing effects. As stated above, the low-income
individuals given the subsidy frame significantly increased their
purchases of less-nutritious food (by $21.23 per week); in contrast,
the low-income individuals given the tax frame decreased their
purchases of less-nutritious food (by $9.04, which is not statistically
significant). That difference across frames is statistically
significant. The responses of the tax and subsidy frame among the low-
income participants also significantly differed for expenditures on all
rated items, unrated items, and all items. They did not significantly
differ in their treatment effect on expenditures on nutritious foods.
Table 10 presents results for models that estimate treatment
effects by frame, with the models estimated separately by education
category. There are no statistically significant differences in framing
effects by education. Moreover, within educational group there are no
statistically significant differences in framing effects; i.e., we
cannot reject the null hypothesis that the effect was the same for each
treatment group or frame.
In summary, we find significant differences in framing effects by
income. Specifically, the treatment effect is much greater for the low-
income households given the subsidy frame than those given the tax
frame; they buy more of even what the relative price change was seeking
to discourage: less-nutritious food.
Extension: Permutation Tests
Given our sample size (208 households' weekly purchases over 8
months) we seek additional confirmation of both the result of
significant treatment effects among low-income households given the
subsidy frame, and the inability to reject the null of no effect for
the overall sample. To that end, we conducted permutation tests
(Kaiser, 2007) in which households were randomly re-labeled as being in
one of the three treatment groups or the control group, after which the
expenditure models were re-estimated. This was repeated 1,000 times and
we compare the statistical significance of the treatment effect in our
primary models to the distribution of treatment effects estimated in
the 1,000 permutations. A p value of (e.g.) 0.05 indicates that only 5%
of the permutations yielded more statistically significant results than
our primary models, which would suggest that the original result was
not due to chance.
The results of the permutation tests are provided in Appendix
Tables 1-3. In general, these results confirm both of our major
findings. First, for the overall sample we cannot reject the null
hypothesis of no effect of the price change treatment. Appendix Table 1
shows that, for both nutritious and less-nutritious foods, 70% or more
of the permutations yielded more statistically significant treatment
effects than the ones estimated in our primary model. Table 2 shows
that the permutation tests are also consistent with our inability to
reject the null hypothesis of no effect of framing for the overall
sample. Appendix Table 3 shows that the permutation test confirms our
finding of a significant positive effect of the treatment for low-
income households given the subsidy frame; specifically, the
permutation test p value is 0.056, indicating that the finding in our
primary model is more significant than 94.4% of the permutations based
on random re-labeling of groups. The result for the purchase of
nutritious foods by the low-income households given the subsidy frame
falls just short of statistical significance (p=.102).
Overall, the results of the permutation test confirm the earlier
results--we cannot reject the null of a zero treatment effect for the
overall sample, and we find evidence that low-income households given
the subsidy frame buy significantly more less-nutritious foods.
Extension: Share of Purchases that was Nutritious
As another extension, we examine the proportion of expenditures on
nutritious foods (the denominator includes expenditures on all rated
foods). Table 11 presents results for the difference-in-differences
model in which the dependent variable is the percent of expenditures
that was on nutritious foods. The effect of the relative price change
was to increase the share of expenditures devoted to nutritious food by
1.08 percentage points, relative to a mean of 42.5%. However, this
increase was not statistically significant. Subsequent columns in the
table list the effects for high and low-income, and the high and low
education groups. In each case the change in the percent of nutritious
purchases resulting from the tax is small and not statistically
significant.
Extension: Purchases of Unrated Foods
As described in the Data section, the Guiding Stars system rates
virtually all foods in the supermarket. Those that are not rated
include items that are new and have simply not yet been rated, or
seasonal and therefore not consistently available. However, foods that
have no calorie content are also not rated. This includes some items
that are relatively uninteresting from a health perspective (e.g.,
dried spices) but it also includes bottled water, alcoholic beverages,
and dried tea and coffee. These are of interest because after the
relative price change consumers may shift away from sugar-sweetened
beverages to these other drink options. In order to test for any such
effects, we estimate difference-in-differences models of expenditures
and quantities purchased in that category. The results appear as
additional columns in each of the earlier tables. We also include a
column for All Items, which includes not just rated foods but also
unrated foods.
Table 5 shows that the main effect of the treatment is a very small
change in weekly expenditures on unrated items ($0.81), which is not
statistically significant. However, the treatment results in an
increase in the quantity of unrated foods purchased per week of 0.66
units, which is statistically significant. Table 6 provides information
on the effect of the framing of the relative price change. In five out
of six cases, the effect of the treatment on purchases of unrated food
items is not statistically significant; the exception is that those
given the subsidy frame purchased 0.92 more units of unrated food per
week. The results in Table 9 indicate that this effect is concentrated
among the lower-income households in the subsidy frame, who increased
their purchases of unrated food items by $5.78 per week.
Extension: Change in Treatment Effects over Time
The dynamics of treatment effects can be interesting; a large
initial effect that falls over time could be due to novelty or
salience, while a small initial effect that increases over time is
consistent with habit formation. To investigate this, we estimated our
model of the overall treatment effect (i.e., ignoring framing effects)
for each week, and plot the results in Figure 1. Although our sample
size precludes us from drawing strong conclusions, the negligible
effect in the first 7 weeks of the treatment, combined with the larger
treatment effects later in the treatment period, are consistent with
gradual habit formation.
Robustness Checks
To verify our initial results, we conduct a variety of additional
robustness checks. First, we re-estimate our models excluding the
baseline data and find very similar results. Second, we estimate our
original difference-in-differences models dropping the weeks with
holidays (Thanksgiving, Christmas, and New Year's); the main difference
is that the treatment effect is significant for low-income households'
spending on nutritious foods (it rises by $9.43 per week). This is
concentrated among the low-income households given the subsidy frame,
who increase their spending on nutritious food by $16.80 per week.
Third, most of the subjects are women, so we drop the men and re-
estimate the models using only the female subjects. The main difference
is that the results for higher-income households become more
significant; e.g., the high-income households in the subsidy frame
decrease their spending on nutritious food ($8.87 less per week), less
nutritious foods ($10.93 less per week), all rated foods ($19.80 less
per week) and all items ($20.45 less per week). Fourth, we sought to
investigate the large treatment effects exhibited by the low-income
households given the subsidy frame. In particular, we investigated
whether these households were buying non-perishables (stocking up for
future consumption) or were buying perishables (for immediate
consumption). Estimating our models separately for expenditures on
perishables and non-perishables, we find that the low-income households
given the subsidy frame generally bought more of everything, but the
increases were statistically significant for perishables that were
nutritious and less-nutritious, and for non-perishables that were less-
nutritious. In other words, the low-income households given the subsidy
frame were not just using the treatment as an opportunity to ``stock
up''; they were also buying more perishables for immediate consumption.
Extension: Subjects' Interpretations of the Relative Price Change
In order to better understand why there might be framing effects,
we examine the results of a survey we administered to study
participants after the treatment period ended. Participants were asked
how they interpreted the treatment. Specifically, they were presented
with seven statements describing the treatment, and were asked to rate
their agreement with each of them on a Likert scale that ranged from 1
(strongly disagree) to 9 (strongly agree). Table 12 presents the
unconditional mean responses for the entire sample as well as the
control group, the entire treatment group, and each treatment group
separately.
One important result that stands out is that participants, no
matter what their frame, tended to interpret the relative price change
as a subsidy for nutritious food rather than a tax on less-nutritious
food. For example, for the sample as a whole, the mean agreement that
the debit card payments were a ``reward for eating healthy food''
averaged 6.2 on the nine-point scale, whereas ``penalty for eating
unhealthy food'' averaged 2.9. In addition, for the sample as a whole,
the mean agreement that it represented a ``discount for eating healthy
foods'' was 6.4 out of 9, whereas the agreement that it was a ``tax on
unhealthy foods'' was 3.4 out of 9.
This is not to say that the framing had no effect on subjects'
perceptions. There was a statistically significant difference in the
mean agreement that the treatment was a ``penalty for eating unhealthy
food'' (3.4 in the tax frame versus 2.4 in the subsidy frame) as well
as in the mean agreement that the treatment was a ``tax on unhealthy
foods'' (3.7 in the tax frame versus 2.8 in the subsidy frame). Thus,
the frame did have a detectable effect on perceptions of the treatment,
but participants in all groups tended to interpret the treatment as
more of a subsidy of nutritious food than a tax on less-nutritious
food.
Extension: Subjects' Interpretations of their Change in Shopping During
Treatment
In the survey conducted after the treatment concluded, subjects
were also asked whether or not participating in the study influenced
their shopping. The unconditional means by group are reported in Table
12. Those in the treatment groups (all pooled) expressed greater
agreement with the statements that they were buying more starred
(nutritious) foods, more healthier foods, and a higher percentage of
healthier foods, but the difference between the treatment and control
groups is not statistically significant in any of those cases.
There are significant differences in the mean response to these
questions by frame. Specifically, those in the tax/subsidy frame tend
to express greater agreement that the study led them to buy more
nutritious foods, buy healthier foods, and buy a higher percentage of
healthier foods, relative to those in the subsidy frame. Notably, we
did not see such a difference in our data in the actual expenditures
and quantities purchased.
Discussion
This paper contributes to the literature on the effects of food
taxes and subsidies through an 8 month field experiment that created a
10% price wedge between nutritious and less-nutritious foods. We find
that, on the whole, expenditures and quantities purchased did not
change significantly in response to the price change. The point
estimates suggest that the treatment group spent slightly less on less-
nutritious food and slightly more on nutritious food, but these changes
were not statistically significant. Some of the point estimates are
substantial in magnitude, and their lack of statistical significance is
due in part to imprecision of the estimates and to limited statistical
power from 208 households.
Although we hypothesized that the framing of the relative price
change as either a subsidy for nutritious food or a tax on less-
nutritious food could alter the treatment effect, we find no
significant differences in effects by frame. We do, however, find
effects of framing by income. Specifically, lower income households to
whom the relative price change was framed as a subsidy bought
significantly more less-nutritious food (and more of all food) than
low-income households to whom it was framed as a tax. Permutation tests
are consistent with these results, suggesting that they are not due to
chance.
One possible explanation for lower-income households buying more of
all food, including the relatively more expensive less-nutritious food,
is that lower-income households may experience a large income effect of
a price decrease. In a related finding, List, et al. (2015) estimate
that a $1 reward for buying any fresh fruits and vegetables caused the
patrons of a grocery store in a low-income neighborhood of Chicago to
double their purchases of produce. Previous research has also
documented that food purchases drop significantly in the course of the
benefit month for low-income households (e.g., Hastings and Washington,
2010, Shaprio, 2005) and that income increases obesity for low-income,
but not other, households (see the review in Cawley, 2015). Another
possibility is that poverty consumes mental bandwidth for low-income
individuals (Mullainathan and Shafir, 2013) or causes distractions
sufficient to result in cognitive deficits (Mani, et al., 2013), such
that households may have misunderstood the subsidy for nutritious food
as a general ``food subsidy.''
Although we hypothesized that better educated individuals might
respond differently to the treatment, we find no evidence of
differences in the treatment effect or in the framing effects by
education.
Taxes on energy-dense foods are arguably the most commonly-
advocated anti-obesity policy. The results of this paper have several
implications for such policies to promote more nutritious diets. First,
taxes may need to be large to change behavior. In the U.S., taxes on
soda pop and snacks average one to four percent (Chriqui, et al.,
2014), but we find no significant impact on expenditures or purchases
from a ten percent relative price change. Second, price changes may
have different impacts by income; we find that subsidies for nutritious
food may lead low-income households to buy more of all food, including
more of the less-nutritious food that the policy is attempting to
discourage.
It should be noted that even if taxes do not change behavior, these
policy instruments can still internalize external costs, thereby
addressing a market failure. Moreover, if consumers do not
significantly alter their purchases, it implies that the tax results in
relatively little deadweight loss and thus is a relatively efficient
way for the government to collect revenue.
Strengths of this study include a randomized controlled field
experiment, with actual consumers making real purchases of actual
products in their usual retail environment. Such controlled field
experiments represent a strong design for estimating casual effects
(List, 2009). The present study is a relatively long experiment of this
type, with an 8 week baseline and 25 week treatment period.
The greatest limitation of the study is the limited statistical
power associated with observing 208 households for 33 weeks; this is
particularly acute when studying subsamples and testing for differences
between income or education groups. In some cases, we estimate
substantial point estimates but because of their imprecision they are
not statistically significant. Given our limits with statistical power,
we cannot rule out price elasticities common in the literature.\6\
However, the permutation tests are consistent with our main results of
a null effect for the overall sample but that low-income households
given the subsidy frame spend more on less-nutritious food. Another
limitation is a lack of data from after the intervention ended;
however, we find no significant main effects of the treatment, so there
is little reason to look for habit persistence after the treatment
ended.
---------------------------------------------------------------------------
\6\ The 95% confidence intervals for the implied price elasticities
of demand are quite large: ^3.5, ^10.3) for nutritious food and (2.5,
6.2) for less-nutritious foods.
---------------------------------------------------------------------------
Readers should exercise caution when generalizing from the results
associated with this relatively white, well-educated and high-income
sample from upstate New York. In addition, although we observe detailed
information on food purchases, we do not observe food consumption,
which would be informative about the health consequences of taxes on
energy-dense foods.
Furthermore, the effects estimated in this paper may be influenced
by the design of the experiment. Consumer responsiveness may have been
attenuated by the fact that the price changes were less salient than
usual. Our relative price changes were not reflected on supermarket
shelves; consumers had to note the number of Guiding Stars for the item
and take into account the subsidy or tax they received. This may have
led to less responsiveness because of the mental cost of calculating
the relative price change, or consumers may have overlooked the price
change at times because it was less salient (Finkelstein, 2009).
In addition, participation and subsidies, minus taxes, were paid
weekly, and this departure from immediacy may have also muted consumer
responsiveness. Given that participants knew they were participating in
a study, they may have perceived the price changes as temporary and not
bothered changing their usual food habits.
In this study consumers were directed to the Guiding Stars
nutrition guidance system to determine the amount of the tax or subsidy
(if any). Thus, there was not only a price effect but also potentially
an effect from nutrition information. This would also be true of any
salient tax placed on energy-dense foods, such as a ``fat tax'' or tax
on sugar-sweetened beverages. It also implies that the consumer
responses we estimate may be greater than those that would be observed
from a tax on certain foods that was implemented simply for revenue
reasons and was not directly linked to the nutrition of the items.
Important directions for future research include estimating the
impacts of greater price changes, testing for changes in treatment
effects over time (they may increase due to habit formation or decrease
due to diminishing salience or novelty), and continuing to refine how
to frame price changes to maximize their intended impact.
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Figure 1: Study Timeline
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Note: Weeks are defined as Monday through Sunday.
Figure 2: Estimated Coefficients for Overall Price Treatment by Week
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Notes: Week 11 is the first week of the intervention period
and begins on Mon, Sep. 20, 2010. Thanksgiving occurred during
week 20 and Christmas occurred during week 24.
Table 1: Comparison of Treatment and Control Groups
------------------------------------------------------------------------
Treatment
Control Treatment Treatment Group 3:
Group Group 1: Group 2: Subsidy and
Subsidy Tax Tax
------------------------------------------------------------------------
Discount on all Food 10% 5% 15% 10%
Items as a Reward
for Participation
Subsidy on -- 10% -- 5%
Nutritious Foods
Tax on Less- -- -- 10% 5%
Nutritious Foods
Reduction in the None 10% 10% 10%
Relative Price of
Nutritious vs Less-
Nutritious Foods
------------------------------------------------------------------------
Table 2: Descriptive Measures of Household Demographic Variables Used in Regression
(standard deviations in parentheses)
----------------------------------------------------------------------------------------------------------------
All
Whole Sample Control Treatment Subsidy Tax Tax/ Subsidy
Groups
----------------------------------------------------------------------------------------------------------------
More than high school 91.00% 92.00% 90.70% 90.60% 91.80% 89.60%
education
St. dev. (0.287) (0.274) (0.292) (0.295) (0.277) (0.309)
N (> HS ed.) 182 46 136 48 45 43
N (5 HS ed.) 18 4 14 5 4 5
Above 130% of FPL 81.20% 75.00% 83.20% 82.40% 82.60% 84.80%
St. dev. (0.392) (0.438) (0.375) (0.385) (0.383) (0.363)
N (Above 130% of FPL) 155 36 119 42 48 39
N (At or below 130% of FPL) 36 12 24 9 8 7
Income > $80,000 31.41% 27.08% 32.87% 25.49% 34.78% 39.13%
St. dev. (0.465) (0.449) (0.471) (0.440) (0.482) (0.493)
N (Inc. > $80K) 60 13 47 13 16 18
N (Inc. <= $80K) 131 35 96 38 30 28
More than one child under 18 58.70% 59.60% 58.40% 54.70% 56.90% 64.00%
St. dev. (0.494) (0.495) (0.494) (0.503) (0.500) (0.485)
N (> 1 child) 121 31 90 29 29 32
N (= 1 child) 85 21 64 24 22 18
----------------------------------------------------------------------------------------------------------------
* p<.1. ** p<0.05. *** p<0.01. Note that the asterisks represent differences of the annotated value from the
corresponding value of the control group at the respective level of significance. FPL stands for Federal
Poverty Line.
Table 3: Additional Household Demographic Measures
a. Food Assistance, Household Size, and Income
(standard deviations in parentheses)
----------------------------------------------------------------------------------------------------------------
All
Whole Sample Control Treatment Subsidy Tax Tax/ Subsidy
Group Groups
----------------------------------------------------------------------------------------------------------------
% Households Enrolled in WIC 4.8% 5.8% 4.5% 1.8% 2.0% 10.2%
(0.215) (0.235) (0.208) (0.135) (0.140) (0.306)
% Households Enrolled in 4.3% 5.8% 3.9% 3.6% 3.9% 4.1%
SNAP
(0.204) (0.235) (0.194) (0.189) (0.196) (0.200)
% Households Not Receiving 89.9% 87.7% 90.7% 94.4% 87.3% 89.8%
Food Assistance
(0.282) (0.318) (0.270) (0.205) (0.297) (0.306)
Average Household Size 3.93 3.92 3.93 3.76 4.04 4.02
(1.076) (1.064) (1.084) (1.027) (1.190) (1.031)
Average Number of Children 2.2 1.8 2.3 3.0 1.9 1.8
Under 18
(3.852) (0.936) (4.412) (7.295) (1.051) (0.889)
% Household Shopping at 83.58 82.09 84.07 83.15 82.24 87.02
Hannaford
(13.894) (15.754) (13.230) (13.687) (14.960) (10.211)
$10K-$20K 9.4% 10.4% 9.0% 11.8% 4.1% 10.9%
(0.291) (0.309) (0.286) (0.325) (0.196) (0.315)
$20K-$30K 19.0% 19.5% 18.9% 19.6% 15.2% 21.7%
(0.392) (0.393) (0.393) (0.401) (0.363) (0.417)
$30K-$40K 9.7% 10.4% 9.4% 7.8% 13.0% 7.6%
(0.294) (0.309) (0.290) (0.272) (0.341) (0.257)
$40K-$50K 9.5% 12.5% 8.4% 3.9% 14.3% 7.6%
(0.288) (0.334) (0.271) (0.196) (0.341) (0.257)
$50K-$60K 12.2% 11.5% 12.4% 10.9% 13.5% 13.0%
(0.322) (0.314) (0.325) (0.303) (0.340) (0.341)
$60K-$70K 10.2% 8.3% 10.8% 12.7% 8.7% 10.9%
(0.301) (0.279) (0.309) (0.329) (0.285) (0.315)
$70K-$80K 4.9% 8.3% 3.7% 3.9% 2.8% 4.3%
(0.213) (0.279) (0.186) (0.196) (0.153) (0.206)
$80K-$90K 11.5% 10.2% 11.9% 21.6% 6.5% 6.5%
(0.315) (0.288) (0.325) (0.415) (0.250) (0.250)
$90K-$100K 4.7% 2.1% 5.5% 0.0% 8.5% 8.7%
(0.204) (0.144) (0.220) (0.000) (0.257) (0.285)
>$100K 6.4% 2.6% 7.7% 5.9% 8.7% 8.7%
(0.244) (0.148) (0.267) (0.238) (0.285) (0.285)
----------------------------------------------------------------------------------------------------------------
* p<0.1. ** p<0.05. *** p<0.01. Note that the asterisks represent differences of the annotated value from the
corresponding value of the control group at the respective level of significance.
Table 3: Additional Household Demographic Measures
b. Marital Status and Race
(standard deviations in parentheses)
----------------------------------------------------------------------------------------------------------------
All
Whole Sample Control Treatment Subsidy Tax Tax/ Subsidy
Group Groups
----------------------------------------------------------------------------------------------------------------
Divorced 5.1% 8.0% 4.1% 5.7% 2.1% 4.3%
(0.220) (0.274) (0.198) (0.233) (0.144) (0.204)
Married 80.2% 74.0% 82.3% 77.2% * 87.3% 83.0%
(0.381) (0.419) (0.366) (0.409) (0.297) (0.380)
Separated 1.5% 2.0% 1.4% 1.9% 2.1% 0.0%
(0.122) (0.141) (0.116) (0.137) (0.144) (0.000)
Widowed 9.6% 12.0% 8.8% 9.4% 4.2% 12.8%
(0.295) (0.328) (0.284) (0.295) (0.202) (0.337)
Single 1.0% 0.0% 1.4% 3.8% 0.0% 0.0%
(0.100) (0.000) (0.116) (0.192) (0.000) (0.000)
African American 1.7% 2.0% 1.6% 1.9% 0.7% 2.1%
(0.125) (0.143) (0.119) (0.137) (0.047) (0.146)
American Indian or Alaska 0.5% 0.0% 0.7% 1.9% 0.0% 0.0%
Native
(0.071) (0.000) (0.082) (0.137) (0.000) (0.000)
Asian 1.5% 2.0% 1.4% 0.0% 0.0% 4.3%
(0.123) (0.143) (0.116) (0.000) (0.000) (0.204)
White 93.7% 91.8% 94.3% 94.2% 94.9% 93.6%
(0.214) (0.236) (0.207) (0.208) (0.162) (0.247)
Hispanic or Latino 0.5% 2.0% * 0.0% 0.0% 0.0% 0.0%
(0.071) (0.141) (0.000) (0.000) (0.000) (0.000)
Not Hispanic or Latino 96.9% 94.0% * 97.9% 98.0% 95.6% ** 100.0%
(0.127) (0.193) (0.094) (0.089) (0.134) (0.000)
----------------------------------------------------------------------------------------------------------------
* p<0.1. ** p<0.05. *** p<0.01. Note that the asterisks represent differences of the annotated value from the
corresponding value of the control group at the respective level of significance.
Table 4: Weekly Expenditures: Unconditional Means by Treatment Group
(standard deviations in parentheses)
----------------------------------------------------------------------------------------------------------------
All
Whole Sample Control Treatment Subsidy Tax Tax/ Subsidy
Group Groups
----------------------------------------------------------------------------------------------------------------
Baseline Period
----------------------------------------------------------------------------------------------------------------
All Foods $89.83 $89.90 $89.81 $99.99 $81.82 $86.76
(116.035) (95.315) (122.488) (119.643) (81.283) (157.529)
All Rated Foods $78.80 $78.25 $79.00 $88.59 $70.25 $77.43
(105.460) (83.229) (112.223) (113.315) (69.960) (143.396)
Foods Rated Less Nutritious $45.65 $44.72 $45.98 $50.73 $41.51 $45.35
(62.311) (48.867) (66.384) (65.884) (43.122) (85.031)
Foods Rated Nutritious $33.15 $33.52 $33.02 $37.86 * $28.74 $32.08
(47.030) (40.335) (49.170) (51.713) (31.500) (60.313)
----------------------------------------------------------------------------------------------------------------
Treatment Period
----------------------------------------------------------------------------------------------------------------
All Foods $100.88 $101.22 $100.76 ** $109.56 $98.97 ** $92.91
(102.566) (108.558) (100.503) (102.659) (97.627) (100.332)
All Rated Foods $88.13 $88.31 $88.08 ** $95.53 $86.33 * $81.66
(89.686) (94.830) (87.917) (89.599) (85.050) (88.394)
Foods Rated Less Nutritious $50.65 $51.49 $50.37 $54.65 $49.37 ** $46.68
(54.582) (57.214) (53.681) (53.898) (53.374) (53.471)
Foods Rated Nutritious $37.48 $36.82 $37.71 ** $40.88 $36.95 $34.98
(40.427) (42.804) (39.606) (41.832) (37.198) (39.259)
----------------------------------------------------------------------------------------------------------------
Because weeks were classified as Monday through Sunday, the baseline period ended with week 8, which is the full
week prior to households receiving notice of their treatment group. In the baseline period, values are set to
missing prior to the first shopping trip in the first 3 weeks. Once all households were enrolled in the study
(by week 4), any missing value was set to zero. Since households received their notices between September 7-
15, weeks including these dates were omitted from the analysis. As a result, the treatment period begins with
week 11, which is after all households received notice of their treatment.
* p<0.1. ** p<0.05. *** p<0.01. Note that the asterisks represent differences of the annotated value from the
corresponding value of the control group at the respective level of significance.
Table 5: Overall Price Effect on Weekly Household Expenditures and Quantities Purchased
(standard errors in parentheses)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Expenditures Quantities
--------------------------------------------------------------------------------------------------------------------------------------------------------
Less All Rated All Less All Rated
Nutritious Nutritious Items Unrated Items Nutritious Nutritious Items Unrated All Items
--------------------------------------------------------------------------------------------------------------------------------------------------------
All Treatment $1.11 ^$1.55 ^$0.44 $0.81 $0.37 0.951 ^0.873 0.078 * 0.661 0.739
Groups
(3.010) (4.042) (6.780) (1.138) (7.606) (1.347) (1.607) (2.822) (0.387) (3.091)
Weekly Dummy
Variables e
N 6,572 6,572 6,572 6,572 6,572 6,572 6,572 6,572 6,572 6,572
Unconditional $36.55 $49.59 $86.14 $11.86 $98.50 16.132 18.853 34.985 3.609 38.744
mean of
dependent
variable
--------------------------------------------------------------------------------------------------------------------------------------------------------
Participants in the intervention conditions were all combined. Regression coefficients were estimated using a fixed effects regression with weekly dummy
variables. For the sake of space, coefficients from the weekly dummy variables were not included in the table. Because weeks were classified as Monday
through Sunday, the baseline period ended with week 8, which is the full week prior to households receiving notice of their treatment group. In the
baseline period, values are set to missing prior to the first shopping trip in the first 3 weeks. Once all households were enrolled in the study (by
week 4), any missing value was set to zero. Since households received their notices between September 7-15, weeks including these dates were omitted
from the analysis. As a result, the treatment period begins with week 11, which is after all households received notice of their treatment.
* p<0.1. ** p<0.05. *** p<0.01.
Table 6: Impact of Price Frame on Expenditures and Quantities Purchased
(standard errors in parentheses)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Expenditures Quantities
--------------------------------------------------------------------------------------------------------------------------------------------------------
Less All Rated All Less All Rated
Nutritious Nutritious Items Unrated Items Nutritious Nutritious Items Unrated All Items
--------------------------------------------------------------------------------------------------------------------------------------------------------
Subsidy ^$0.78 ^$2.29 ^$3.07 $1.60 ^$1.47 0.523 ^1.220 ^0.698 ** 0.917 0.220
(3.655) (4.914) (8.225) (1.376) (9.041) (1.600) (1.884) (3.327) (0.450) (3.627)
Tax $4.52 $1.89 $6.41 ^$0.07 $6.34 2.287 0.896 3.182 0.306 3.489
(3.489) (4.784) (7.908) (1.460) (9.015) (1.564) (1.925) (3.325) (0.461) (3.654)
Tax/Subsidy ^$0.42 ^$4.40 ^$4.82 $0.84 ^$3.98 ^0.002 ^2.384 ^2.386 0.752 ^1.634
(4.371) (5.831) (9.942) (1.466) (11.010 (1.876) (2.293) (4.044) (0.527) (4.399)
)
Weekly Dummy
Variables e
N 6,572 6,572 6,572 6,572 6,572 6,572 6,572 6,572 6,572 6,572
Unconditional $36.55 $49.59 $86.14 $11.86 $98.50 16.132 18.853 34.985 3.609 38.744
mean of
dependent
variable
--------------------------------------------------------------------------------------------------------------------------------------------------------
Participants in the intervention conditions were all combined. Regression coefficients were estimated using a fixed effects regression with weekly dummy
variables. For the sake of space, coefficients from the weekly dummy variables were not included in the table. Because weeks were classified as Monday
through Sunday, the baseline period ended with week 8, which is the full week prior to households receiving notice of their treatment group. In the
baseline period, values are set to missing prior to the first shopping trip in the first 3 weeks. Once all households were enrolled in the study (by
week 4), any missing value was set to zero. Since households received their notices between September 7-15, weeks including these dates were omitted
from the analysis. As a result, the treatment period begins with week 11, which is after all households received notice of their treatment.
* p<0.1. ** p<0.05. *** p<0.01.
a p<0.05 for difference between Subsidy and Tax.
b p<0.05 for difference between Subsidy and Tax/Subsidy.
c p<0.05 for difference between Tax and Tax/Subsidy.
Table 7: Overall Price Effect on Weekly Household Expenditures, by Income
(standard errors in parentheses)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Households at or Below 130% of the Federal Poverty Line Households Above 130% of the Federal Poverty Line
--------------------------------------------------------------------------------------------------------------------------------------------------------
Less All Rated All Less All Rated
Nutritious Nutritious Items Unrated Items Nutritious Nutritious Items Unrated All Items
--------------------------------------------------------------------------------------------------------------------------------------------------------
All Treatment $7.03 $7.11 $14.14 $2.47 $16.61 ^$1.27 ^$4.02 ^$5.29 $0.24 ^$5.05
Groups
(6.010) (9.793) (15.460) (2.597) (17.420 (3.707) (4.543) (7.898) (1.313) (8.893)
)
Weekly Dummy
Variables e
N 1,141 1,141 1,141 1,141 1,141 4,904 4,904 4,904 4,904 4,904
Unconditional $28.28 $41.04 $69.32 $9.17 $78.85 $38.36 $50.70 $89.06 $12.25 $101.81
mean of
dependent
variable
--------------------------------------------------------------------------------------------------------------------------------------------------------
Participants in the intervention conditions were all combined. Regression coefficients were estimated using a fixed effects regression with weekly dummy
variables. For the sake of space, coefficients from the weekly dummy variables were not included in the table. Because weeks were classified as Monday
through Sunday, the baseline period ended with week 8, which is the full week prior to households receiving notice of their treatment group. In the
baseline period, values are set to missing prior to the first shopping trip in the first 3 weeks. Once all households were enrolled in the study (by
week 4), any missing value was set to zero. Since households received their notices between September 7-15, weeks including these dates were omitted
from the analysis. As a result, the treatment period begins with week 11, which is after all households received notice of their treatment.
* p<0.1. ** p<0.05. *** p<0.01.
d p<0.05 difference of estimates for the same type of food (all items, all rated items, etc.) but across demographic comparisons.
Table 8: Overall Price Effect on Weekly Household Expenditures, by Education
(standard errors in parentheses)
--------------------------------------------------------------------------------------------------------------------------------------------------------
High School Education or Less More than High School Education
--------------------------------------------------------------------------------------------------------------------------------------------------------
Less All Rated All Less All Rated
Nutritious Nutritious Items Unrated Items Nutritious Nutritious Items Unrated All Items
--------------------------------------------------------------------------------------------------------------------------------------------------------
All Treatment $2.36 ^$4.02 ^$1.65 $6.18 $4.52 $0.52 ^$2.17 ^$1.65 $0.46 ^$1.19
Groups
(11.190) (20.950) (31.600) (4.130) (34.200 (3.091) (3.925) (6.714) (1.139) (7.528)
)
Weekly Dummy
Variables e
N 567 567 567 567 567 5,759 5,759 5,759 5,759 5,759
Unconditional $25.16 $39.92 $65.08 $8.76 $74.23 $37.73 $50.41 $88.14 $12.05 $100.67
mean of
dependent
variable
--------------------------------------------------------------------------------------------------------------------------------------------------------
Participants in the intervention conditions were all combined. Regression coefficients were estimated using a fixed effects regression with weekly dummy
variables. For the sake of space, coefficients from the weekly dummy variables were not included in the table. Because weeks were classified as Monday
through Sunday, the baseline period ended with week 8, which is the full week prior to households receiving notice of their treatment group. In the
baseline period, values are set to missing prior to the first shopping trip in the first 3 weeks. Once all households were enrolled in the study (by
week 4), any missing value was set to zero. Since households received their notices between September 7-15, weeks including these dates were omitted
from the analysis. As a result, the treatment period begins with week 11, which is after all households received notice of their treatment.
* p<0.1. ** p<0.05. *** p<0.01.
d p<0.05 difference of estimates for the same type of food (all items, all rated items, etc.) but across demographic comparisons.
Table 9: Impact of Price Frames on Weekly Expenditures, by Income
(standard errors in parentheses)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Poverty Income Ratio <= 1.3 Poverty Income Ratio >1.3
--------------------------------------------------------------------------------------------------------------------------------------------------------
Less All Rated All Less All Rated
Nutritious Nutritious Items Unrated Items Nutritious Nutritious Items Unrated All Items
--------------------------------------------------------------------------------------------------------------------------------------------------------
Subsidy 11.58 * a $21.23 * a, d $32.81 ** a $5.78 ** a, d a ^$4.548 * a d ^$7.546 a, d ^$12.09 $0.414 d ^$11.68
$38.59
(6.914) (10.780) (16.990) (2.802) (18.990 (4.434) (5.521) (9.534) (1.608) (10.490)
)
Tax $0.30 a ^$9.037 a ^$8.735 a ^$3.38 a ^$12. a $3.832 $3.62 a $7.451 $0.588 $8.039
11
(8.190) (12.470) (20.380) (4.138) (23.370 (4.180) (5.334) (9.015) (1.540) (10.230)
)
Tax/Subsidy $9.14 $8.14 $17.28 ** $5.13 $22.40 ^$2.831 ^$7.931 ^$10.76 ^$0.327 ^$11.09
(6.874) (9.965) (16.310) (2.039) (17.710 (5.338) (6.790) (11.800) (1.750) (13.080)
)
Weekly Dummy
Variables e
N 1,141 1,141 1,141 1,141 1,141 4,904 4,904 4,904 4,904 4,904
Unconditional $28.28 $41.04 $69.32 $9.17 $78.85 $38.36 $50.70 $89.06 $12.25 $101.81
mean of
dependent
variable
--------------------------------------------------------------------------------------------------------------------------------------------------------
Participants in the intervention conditions were all combined. Regression coefficients were estimated using a fixed effects regression with weekly dummy
variables. For the sake of space, coefficients from the weekly dummy variables were not included in the table. Because weeks were classified as Monday
through Sunday, the baseline period ended with week 8, which is the full week prior to households receiving notice of their treatment group. In the
baseline period, values are set to missing prior to the first shopping trip in the first 3 weeks. Once all households were enrolled in the study (by
week 4), any missing value was set to zero. Since households received their notices between September 7-15, weeks including these dates were omitted
from the analysis. As a result, the treatment period begins with week 11, which is after all households received notice of their treatment.
* p<0.1. ** p<0.05. *** p<0.01.
a p<0.05 for difference between Subsidy and Tax.
b p<0.05 for difference between Subsidy and Tax/Subsidy.
c p<0.05 for difference between Tax and Tax/Subsidy.
d p<0.05 difference of estimates for the same type of food (all items, all rated items, etc.) but across demographic comparisons.
Table 10: Impact of Price Frame on Weekly Expenditures, by Education
(standard errors in parentheses)
--------------------------------------------------------------------------------------------------------------------------------------------------------
High School Education or Less More than High School Education
--------------------------------------------------------------------------------------------------------------------------------------------------------
Less All Rated All Less All Rated
Nutritious Nutritious Items Unrated Items Nutritious Nutritious Items Unrated All Items
--------------------------------------------------------------------------------------------------------------------------------------------------------
Subsidy ^$0.65 ^$3.86 ^$4.51 $7.38 $2.87 ^$0.97 ^$2.71 ^$3.68 $1.37 ^$2.31
(11.440) (21.320) (32.150) (6.381) (34.410 (3.824) (4.986) (8.414) (1.343) (9.226)
)
Tax $2.26 ^$5.53 ^$3.26 * $6.79 $3.53 $4.19 $2.34 $6.53 ^$0.44 $6.09
(12.020) (23.800) (34.630) (3.621) (36.810 (3.536) (4.636) (7.781) (1.523) (8.963)
)
Tax/Subsidy $5.64 ^$2.81 $2.83 $4.35 $7.17 ^$1.81 ^$6.52 ^$8.33 $0.39 ^$7.94
(13.210) (24.060) (36.560) (4.385) (39.780 (4.705) (5.961) (10.400) (1.533) (11.470)
)
Weekly Dummy
Variables e
N 567 567 567 567 567 5,759 5,759 5,759 5,759 5,759
Unconditional $25.16 $39.92 $65.08 $8.76 $74.23 $37.73 $50.41 $88.14 $12.05 $100.67
mean of
dependent
variable
--------------------------------------------------------------------------------------------------------------------------------------------------------
Participants in the intervention conditions were all combined. Regression coefficients were estimated using a fixed effects regression with weekly dummy
variables. For the sake of space, coefficients from the weekly dummy variables were not included in the table. Because weeks were classified as Monday
through Sunday, the baseline period ended with week 8, which is the full week prior to households receiving notice of their treatment group. In the
baseline period, values are set to missing prior to the first shopping trip in the first 3 weeks. Once all households were enrolled in the study (by
week 4), any missing value was set to zero. Since households received their notices between September 7-15, weeks including these dates were omitted
from the analysis. As a result, the treatment period begins with week 11, which is after all households received notice of their treatment.
* p<0.1. ** p<0.05. *** p<0.01.
a p<0.05 for difference between Subsidy and Tax.
b p<0.05 for difference between Subsidy and Tax/Subsidy.
c p<0.05 for difference between Tax and Tax/Subsidy.
d p<0.05 difference of estimates for the same type of food (all items, all rated items, etc.) but across demographic comparisons.
Table 11: Overall Price Effect on Shares of Expenditures on Nutritious Foods, by Income and Education
(standard errors in parentheses)
----------------------------------------------------------------------------------------------------------------
At or Below Above 130% HS Educ. or More than HS
All 130% FPL FPL Less Educ.
----------------------------------------------------------------------------------------------------------------
All Treatments 0.0108 0.00359 0.00834 ^0.0057 0.00928
(0.01) (0.03) (0.01) (0.03) (0.01)
Weekly Dummy Variables
N 4,816 769 3,637 342 4,266
Unconditional Mean Shares 0.425 0.406 0.433 0.369 0.431
----------------------------------------------------------------------------------------------------------------
Shares of less nutritious and nutritious foods were calculated using only rated food purchases, thus the sign of
the share is opposite when comparing nutritious and less nutritious foods. Participants in the intervention
conditions were all combined. Regression coefficients were estimated using a fixed effects regression with
weekly dummy variables. For the sake of space, coefficients for the constants and the weekly dummy variables
were not included in the table. Because weeks were classified as Monday through Sunday, the baseline period
ended with week 8, which is the full week prior to households receiving notice of their treatment group. In
the baseline period, values are set to missing prior to the first shopping trip in the first 3 weeks. Once all
households were enrolled in the study (by week 4), any missing value was set to zero. Since households
received their notices between September 7-15, weeks including these dates were omitted from the analysis. As
a result, the treatment period begins with week 11, which is after all households received notice of their
treatment.
* p<0.1. ** p<0.05. *** p<0.01.
d p<0.05 difference of estimates for the same type of food (all items, all rated items, etc.) but across
demographic comparisons.
Table 12: Results of Post-Experiment Survey
(on 9-point Likert Scale)
----------------------------------------------------------------------------------------------------------------
All
Whole Sample Control Treatment Subsidy Tax Tax/ Subsidy
Group Groups
----------------------------------------------------------------------------------------------------------------
Interpretation of Treatment
----------------------------------------------------------------------------------------------------------------
Penalty for eating unhealthy 2.9 2.6 3.0 a 2.4 a 3.4 3.2
food
(1.937) (1.739) (2.003) (1.662) (2.100) (2.161)
Reward for eating healthy 6.2 6.1 6.3 6.0 6.0 6.9
food
(2.286) (2.515) (2.211) (2.362) (2.394) (1.641)
Tax on unhealthy foods 3.4 2.8 * 3.6 b 2.8 * 3.7 **b 4.4
(2.076) (1.796) (2.141) (1.696) (2.237) (2.218)
Discount for eating healthy 6.4 5.8 * 6.6 6.7 6.2 * 6.9
foods
(2.225) (2.543) (2.077) (2.157) (2.313) (1.595)
Effective in changing what I 4.5 4.2 4.6 4.8 4.2 5.0
usually buy
(2.419) (2.444) (2.413) (2.250) (2.452) (2.568)
----------------------------------------------------------------------------------------------------------------
How much did being a part of the study influence your shopping?
----------------------------------------------------------------------------------------------------------------
Buy more starred foods 5.0 4.5 5.1 b 4.8 c 4.8 b, c 5.9
(2.084) (2.152) (2.048) (2.009) (2.060) (1.950)
Buy more non-starred foods 3.1 3.2 3.1 3.0 3.2 3.0
(1.421) (1.567) (1.373) (1.650) (1.050) (1.401)
Buy healthier food 5.3 4.7 5.5 b 5.0 5.3 b 6.2
(2.146) (2.271) (2.078) (2.048) (2.357) (1.541)
Buy a higher percentage of 5.3 4.8 5.5 b 4.9 5.5 b 6.2
healthy food
(2.200) (2.360) (2.124) (2.043) (2.407) (1.595)
----------------------------------------------------------------------------------------------------------------
In general, over the entire program
----------------------------------------------------------------------------------------------------------------
Shopped healthier at the 3.3 3.1 3.4 3.4 3.1 3.6
beginning than at the end
(1.725) (1.555) (1.784) (1.845) (1.465) (2.077)
----------------------------------------------------------------------------------------------------------------
Note that the asterisks represent differences of the annotated value from the corresponding value of the control
group at the respective level of significance. All responses were based on a 9 point Likert scale from
Strongly Disagree (1) to Strongly Agree (9).* p<0.1. ** p<0.05. *** p<0.01.
a p<0.05 for comparison between Subsidy and Tax groups.
b p<0.05 for comparison between Subsidy and Tax/Subsidy groups.
c p<0.05 for comparison between Tax and Tax/Subsidy groups.
Appendix Table 1: Permutation Tests for Combined Interventions
------------------------------------------------------------------------
Combined 95% Lower 95% Upper
Interventions P-value Confidence Level Confidence Level
------------------------------------------------------------------------
All Households:
Less-Nutritious. 0.700 0.671 0.728
Nutritious...... 0.724 0.695 0.752
At or below 130%
FPL:
Less-Nutritious. 0.481 0.450 0.512
Nutritious...... 0.253 0.226 0.281
Above 130% FPL:
Less-Nutritious. 0.401 0.370 0.432
Nutritious...... 0.714 0.685 0.742
------------------------------------------------------------------------
Appendix Table 2: Permutation Tests for Individual Treatments
------------------------------------------------------------------------
Combined 95% Lower 95% Upper
Interventions P-value Confidence Level Confidence Level
------------------------------------------------------------------------
Less-Nutritious:
Subsidy......... 0.645 0.614 0.675
Tax............. 0.709 0.680 0.737
Tax/Subsidy..... 0.455 0.424 0.486
Nutritious:
Subsidy......... 0.825 0.800 0.848
Tax............. 0.193 0.169 0.219
Tax/Subsidy..... 0.928 0.910 0.943
------------------------------------------------------------------------
Appendix Table 3: Permutation Tests for Separate Interventions When Data
Are Separated Into Income Groups
------------------------------------------------------------------------
Combined 95% Lower 95% Upper
Interventions P-value Confidence Level Confidence Level
------------------------------------------------------------------------
At or Below 130% FPL
------------------------------------------------------------------------
Less-Nutritious:
Subsidy......... 0.056 0.043 0.072
Tax............. 0.480 0.449 0.511
Tax/Subsidy..... 0.448 0.417 0.479
Nutritious:
Subsidy......... 0.102 0.084 0.122
Tax............. 0.969 0.956 0.979
Tax/Subsidy..... 0.204 0.179 0.230
------------------------------------------------------------------------
Above 130% FPL
------------------------------------------------------------------------
Less-Nutritious:
Subsidy......... 0.179 0.156 0.204
Tax............. 0.511 0.480 0.542
Tax/Subsidy..... 0.242 0.216 0.270
Nutritious:
Subsidy......... 0.298 0.270 0.327
Tax............. 0.360 0.330 0.391
Tax/Subsidy..... 0.611 0.580 0.641
------------------------------------------------------------------------
Attachment 1
Excerpt from Slim by Design_Mindless Eating Solutions for Everyday Life
*
---------------------------------------------------------------------------
* Editor's note: The original format of the book, Slim by Design--
Mindless Eating Solutions for Everyday Life, has an entire section
devoted to endnotes for all of the chapters. In this reproduction the
endnotes are set as footnotes.
---------------------------------------------------------------------------
slim by design. Copyright 2014 by Consumer Psych Labs, Inc. All
rights reserved. Printed in the United States of America. No part of
this book may be used or reproduced in any manner whatsoever without
written permission except in the case of brief quotations embodied in
critical articles and reviews. For information address HarperCollins
Publishers, 195 Broadway, New York, NY 10007.
HarperCollins books may be purchased for educational, business, or
sales promotional use. For information please e-mail the Special
Markets Department at [email protected].
First Edition
Designed by Paul Kepple and Ralph Geroni at Headcase Design
Illustrations by Mitch Blunt
Library of Congress Cataloging-in-Publication Data has been applied
for.
ISBN 978-0-06-213652-7
14 15 16 17 18 ov/rrd 10 9 8 7 6 5 4 3 2 1
Contents
Introduction
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
One: Mindless Eating Solutions
Your Food Radius
Nobody Wants Us to Be Fat
Chinese Buffet Confidential
Starting Small to Get Slim
Sixteen Pounds from Happiness
Becoming Slim by Design
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Two: Your Slim-For-Life Home
Fat-Proofing the Rich and Famous
The Syracuse Study
Step One: The Kitchen Makeover
Step Two: Tablescape Redesign
Step Three: Snack-Proofing
Scoring Big at Home
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Three: Restaurant Dining By Design
In Praise of Leftovers
Show Me to a Slim Table
One Antidote for Fast-Food Fever
``Can I Take Your Order?''
Half-Plate Profits
Smaller and Taller
Bread and Water
Faster Food and Happier Meals
What Would Batman Eat?
Transforming a Town
Is Your Favorite Restaurant Making You Fat?
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Four: Supermarket Makeovers
The Desserted Island of Denmark
Half-Cart Solutions
Healthy First and Green Line Guides
Wide Aisles and High Products
Groceries and Gum
Lights, Stars, Numerology!
Using the Half-Plate Rule
The Three Checkouts
Back to Bornholm
How Your Grocery Store Can Make You Slim
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Five: Office Space and Workplace
Move Away from the Desk
Rethinking Corporate Wellness
Break-Room Makeovers
Trimming the Google-Plex of Food
Cafeteria Cuisine
The Company Health Club
Coaching and Weight-Loss Programs
Would You Sign a Health Conduct Code?
Design Your New Boss's Job Description!
Think Summer Camp, Not Boot Camp
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Six: Smarter Lunchrooms
School Lunch 101
When Chocolate Milk Attacks
More Fruit by Design
The Salad Bar Solution
Lunch-Line Redesign, MTV-Style
What's Your Lunchroom Score?
The Lunchtime Report Card
Designing a Smarter Lunchroom Tray
Helping Your School Become Slim by Design
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Seven: Slim By Design for Life
From Can't to CAN
From Me to We
Getting Started
Design Trumps Discussion
Sample Scripts
Make It Happen
Acknowledgments
Notes
Index
* * * * *
Chapter Four
Supermarket Makeovers
You've Never Seen A Kleenex Cam. That's why it works so well--it
sees you, but you don't see it. It's helped us learn why the crazy
things grocery shoppers do aren't as crazy as they seem.
Back in 2001, I asked some clever engineering students at the
University of Illinois at Urbana-Champaign to rig up a small, remotely
controlled movie camera into what looked like an ordinary box of
Kleenex.\1\ Using this invisible camera we could follow shoppers to
learn exactly how they shop. We took our Kleenex Cams and stacked them
on top of ``deserted'' shopping carts, hid them on shelves next to
Fruity Pebbles cereal, and positioned them in our carts so we could
follow shoppers as they moved through the aisles. The Kleenex Cams
showed us what catches a person's eye, what they pick up and put back,
why they buy things they'll never use,\2\ when shopping lists don't
matter, and how they shop differently in the ``smelly'' parts of a
grocery store. Again, these studies were all university approved.\3\
---------------------------------------------------------------------------
\1\ The only remaining photo of the original Kleenex Cam is in this
newspaper article below. By today's tech standards, it's pretty boring,
but back then it was really souped up. Read about it at
SlimByDesign.org/GroceryStores/.
\2\ One interesting category of items that are most likely to
become cabinet castaways are unusual foods that people are buying for a
specific occasion. When that occasion never happens, the food just sits
and sits. This is a neat article on that: Brian Wansink, S. Adam
Brasel, and Stephen Amjad, ``The Mystery of the Cabinet Castaway: Why
We Buy Products We Never Use,'' Journal of Family and Consumer Science
92, no. 1 (2000): 104-8.
\3\ All of these studies are preapproved. Today--compared to twenty
or even 10 years ago--studies to be approved by a university's
Institutional Review Board to make sure that they are safe and to make
sure all of the data is collected anonymously and that no one will ever
know about that day you bought that EPT kit and the two pints of
Chocolate Fudge Swirl. Some studies--like many shopping studies--are
observational, but others might ask a person to complete a
questionnaire at the end of a trip in exchange for a small amount of
money, free food, movie tickets, and so on.
---------------------------------------------------------------------------
But let's back up and set the stage. Our best and worst eating
habits start in a grocery store. Food that's bought here gets moved
into our homes. Food in our homes gets eaten.\4\ If we bought more bags
of fruit and fewer boxes of Froot Loops, we would eventually eat more
of the first and less of the second. Although bad for the Froot Loops
Corporation, it's great for us--and great for grocery stores. The
typical grocery store makes more profit by selling you $10 more fruit
than $10 more Froot Loops. There's a higher markup on fruit, and--
unlike the everlasting box of Froot Loops--fruit spoils, and spoiled
fruit spoils profits. You have to sell it while you can.
---------------------------------------------------------------------------
\4\ That is, about 88 percent of this food will be eaten. The 12
percent that's wasted, however, isn't the candy, chips, and ice cream;
it's typically the spoiled fruit and vegetables, leftovers, and cabinet
castaways. Brian Wansink, ``Abandoned Products and Consumer Waste: How
Did That Get into the Pantry?,'' Choices (October 2001): 46.
---------------------------------------------------------------------------
So if a grocery store makes more by selling healthy foods like
fruit, why don't they do a better job of it? They try--but what they
really need is a healthy dose of redesign.
Our best and worst eating habits start in a grocery store.
We've been following grocery shoppers since 1995, and some things
have changed since then. For one, we no longer have to wrestle with
Kleenex Cams. Our newer cameras are so small they're embedded into
Aquafina water bottles with false bottoms.\5\ The technology is sexier,
but the results are e-x-a-c-t-l-y the same.\6\ Wherever we've done
these studies--corner markets in Philadelphia or warehouse stores in
France, Brazilian superstores or Taiwanese night markets--people pretty
much shop in the same time-stressed, sensory-overwhelmed way. But
knowing what can be done to get them to buy a healthier cartful of food
is good for shoppers, for grocers, and even for governments.
---------------------------------------------------------------------------
\5\ A cool example of all of these hidden cameras in use can be
found at http://www.youtube.com/watch?v=2B0Ncy3Gz24. It's not at a
grocery store but in a lunchroom. Same approach.
\6\ Lots of people visit our Lab (even from way overseas) like it's
some weird trip to Consumer Mecca. Something I've heard a number of
times is ``Wow . . . this isn't really very high-tech!'' No, it isn't.
What we'd like to think, however, is that insights trump glitzy
technology every day of the week. We've got low-definition hidden
cameras, hidden scales, counters, and timers, because we don't need
holograms or brain-scan machines to nail down the reality--not the
theory--of why people do what they do. You don't need infrared sensors
to see someone eating twice as many Cheetos when you change what
they're watching on TV.
---------------------------------------------------------------------------
Wait. Governments?
What jump-started a lot of our recent thinking was a request we
received from the Danish Government. In April 2011, they sent a six-
person delegation out to my Lab. Their mission: to help Danish grocery
stores make it easier for shoppers to shop healthier. Our mission, if
we chose to accept it: develop a healthy supermarket makeover plan that
would be cheap, easy, and profitable for Danish grocery stores to
implement. Our makeover plan had to be profitable for stores because
that's the only way it would work. But here's the cool clincher: They'd
give us an entire island on which to test our plan.
The Desserted Island of Denmark
Bornholm Is A Danish Island with forty-two thousand inhabitants
that sits in the Baltic Sea, one hundred miles east of Copenhagen.\7\
The Government of Denmark wanted us to help change the grocery stores
on the entire island so they could profitably help these islanders shop
healthier. They wanted to turn it from a Dessert Isle into a Salad
Aisle.
---------------------------------------------------------------------------
\7\ Denmark Islands. Denmark actually has a number of little
islands, but none like poor Bornholm. It never gets any peace.
Strategically located in the Baltic Sea, it was occupied by the Germans
during almost all of World War II and the Russians right after that.
And probably by the Vikings way before that.
Anyone who's read or seen H.G. Wells's The Island of Dr. Moreau
knows that islands are a researcher's dream. You can do all sorts of
crazy, mad scientist things on them and not worry about the rest of the
world bothering you. You can change the shopping carts or layout of all
the stores on the island, and if the sales of Crisco and Pixy Stix drop
by 20 percent, you know it's not because people are swimming over to
buy them in Lapland.
Until they came to talk with us, the Danish Government was
considering three types of changes: tax it, take it, or teach it.\8\
But taxing food or taking it away creates pushback. Shoppers don't like
it, grocers don't like it, and so it can often backfire. For instance,
when we did a 6 month study on taxing soft drinks in grocery stores in
Utica, New York, a medium-size city in the United States, we found that
the only people who bought fewer soft drinks were beer-buying
households--and they just bought a lot more beer.\9\ People had to
drink something with their pizza and burgers, and it wasn't going to be
tap water or soy milk. They changed from Coke to Coors.
---------------------------------------------------------------------------
\8\ People--whether public health professionals or politicians--can
often get very dramatic in what they tell grocery stores they should
do. Dramatic, but not always realistic or right.
\9\ This is an interesting paper of unintended consequences: Brian
Wansink et al., ``From Coke to Coors: A Field Study of a Sugar-
Sweetened Beverage Tax and Its Unintended Consequences,'' May 26, 2012,
available at http://ssrn.com/abstract=2079840 or http://dx.doi.org/
10.2139/ssrn.2079840.
---------------------------------------------------------------------------
And teaching doesn't work much better.\10\ As shoppers, we don't
behave the way we're supposed to because (1) we love tasty food, and
(2) we don't like to think very hard. Because of our love for both
tasty food and for mindless shopping, we don't approach grocery
shopping like a nutrition assignment. We just do it and move on to the
next fifty-seven items on our to-do list. With this mindless mindset,
when we're shopping at 5:45 on a Friday evening, we're not about to be
fazed by there being a few more calories in pizza crust than in pita
bread.
---------------------------------------------------------------------------
\10\ This is controversial for me to admit since I'm the immediate
past president of the Society for Nutrition Education and Behavior and
because I was the White House-appointed person (2007-2009) in charge of
promoting the [D]ietary [G]uidelines for the USDA.
Maybe the best way we can change grocery shopping habits is to make
them more mindlessly healthy--make it more convenient, attractive, and
normal to pick up and buy a healthier food.\11\ So here's what we did
in Bornholm. Based on our ``Kleenex Cam'' recordings,\12\ notes,
stopwatch times, and data from thousands of similar shoppers, we
focused on design changes in five areas of the store: carts, layouts,
aisles, signs, and checkout lines. We had two criteria: (1) all the
changes had to make the store more money in a month than they cost to
implement, and (2) they all had to help make people slim by design.
Let's start with a shopping cart.
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\11\ This was one focus of my book Mindless Eating. The basic idea
is that making small changes around you that you don't even really
notice has a tremendous long-term impact on changing behavior and
weight.
\12\ We no longer use the Kleenex Cam but we still call it that. We
now use our bottles, hats, and iPhones.
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Half-Cart Solutions
Here's a Ten-Word Description of how most people shop for
groceries: They throw things in their cart and they check out. What's
the right amount of fruits and vegetables to put in a cart? We don't
really know because we don't really care. Yet imagine what would happen
if every time we put something in our cart we had to ask ourselves
whether it was healthy or not. It would be irritating--for sure--but
after a while we'd think twice about what we casually threw in. Just
stopping and thinking for a split second would be enough to snap us out
of our mindlessly habitual zombie shopping trance.13-14
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\13\ A number of years ago we gave secretaries dishes of chocolate
Kisses that we either placed on their desk or 6 from their desk. We
found that those who had to walk only 6 ate \1/2\ as much candy (100
calories less; four each day instead of nine). Yet when we asked them
if it was because the 6 walk was too far or too much of a hassle,
their answer surprised us. They said instead that the 6 distance gave
them a chance to pause and ask themselves if they were really that
hungry. Half the time they'd answer ``no.'' The key was that
something--that distance--caused them to pause and interrupt their
mindlessness: Brian Wansink, James E. Painter, and Yeon-Kyung Lee,
``The Office Candy Dish: Proximity's Influence on Estimated and Actual
Candy Consumption,'' International Journal of Obesity 30, no. 5 (May
2006): 871-75.
\14\ Anything that stops and makes a person pause--even for a split
second--might be enough to knock them out of their mindless trance and
rethink.
Back to the cart. When most of us shop, fruits and vegetables take
up only 24 percent of our cart.\15\ But suppose your grocery store
sectioned a cart in \1/2\ by taping a piece of yellow duct tape across
the middle interior. And suppose they put a sign in the front of the
cart that recommended that you put all the fruits and vegetables in the
front and all the other foods in the back. This dividing line in the
cart doesn't moralize or lecture. It just encourages shoppers to ask
themselves whether the food in their hand goes in the front or back of
the cart. There's nothing to resist or rage against--they're simply
sorting their food . . . if they want to.
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\15\ The average grocery shopper buys only 24 percent of fruits and
vegetables. Simone French, Melanie Wall, Nathan R. Mitchell, Scott T.
Shimotsu, and Ericka Welsh, ``Annotated Receipts Capture Household Food
Purchases from a Broad Range of Sources,'' International Journal of
Behavioral Nutrition and Physical Activity 6, no. 37 (2009).
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When you use duct tape at home, you become MacGyver. When it's used
to divide your grocery cart, you become healthier.\16\
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\16\ Brian Wansink, C.R. Payne, K.C. Herbst, and D. Soman, ``Part
Carts: Assortment Allocation Cues That Increase Fruit and Vegetable
Purchases,'' Journal of Nutrition Education and Behavior 45 (2013): 4S,
42.
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We made a few dozen of these divided carts to test at supermarkets
in Williamsburg, Virginia, and Toronto, Canada.\17\ When people
finished shopping and returned their souped-up, tricked-out carts, we
gave them a gift card to a local coffee shop if they would answer some
questions and give us their shopping receipt.
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\17\ Brian Wansink, Dilip Soman, Kenneth C. Herbst, and Collin R.
Payne, ``Partitioned Shopping Carts: Assortment Allocation Cues that
Increase Fruit and Vegetable Purchases,'' under review.
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Shoppers with these divided carts spent twice as much on fruits and
vegetables. They also spent more at the store--about 25 percent more.
Not only did this fruit and vegetable divider make them think twice
about what they bought; it also made them believe that buying more
fruits and vegetables was normal. Who knows how much healthy stuff your
neighbor buys? It must be about \1/2\, people think as they throw in
some pears and three more red peppers.
------------------------------------------------------------------------
How Your Grocer Can Help You . . .
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Test the Half-Cart Waters
------------------------------------------------------------------------
Will a divided, half-cart approach be profitable? It can if it can
sell more perishable produce--like fruits and vegetables. All that's
needed is a visual divider in a few of your carts and a sign in the
front that says, ``Put your fruits and vegetables in the front of your
cart.''
If your grocery store doesn't want to bust out the duct tape, they
can use printable mats for the bottom of the cart that make the same
suggestion--fruits and vegetables in the front \1/2\ and everything
else in back (download at SlimByDesign.org).
------------------------------------------------------------------------
The Miracle of Duct Tape
A Half-Cart Solution
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Do it yourself. Divide your cart with your coat, your purse,
or your briefcase. Or bring your own duct tape.
------------------------------------------------------------------------
What You Can Do . . .
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Hints for Half-Cart Shopping
------------------------------------------------------------------------
Your local supermarket might not have divided carts yet, and you
probably don't travel with your own. Here's what you can do . . .
Decide what you want to buy more of. For instance, a shopper
with children might want to be nudged to buy more fruits and
vegetables, and a shopper with high blood pressure might want to buy
more low-sodium foods. A dieter might want to be nudged to buy more
low-carb foods, and a diabetic might want to buy more foods with a
low glycemic index.
Physically divide your cart by putting something across the
middle. This could be a purse, backpack, scarf, briefcase, coat, or a
sleeping child you want to keep an eye on. You can then claim the
front half of . . . our cart for whatever you want to purchase more
of. If that target space isn't full, you'll naturally tend to buy
more to balance things out.
------------------------------------------------------------------------
You're 11 percent more likely to take the first vegetable you
see than the third.
When opening your cupboard, you're three times as likely to
take the first cereal you see as you are the fifth.
Healthy First and Green Line Guides
When You Walk Up To A Buffet, you're 11 percent more likely to take
the first vegetable you see than the third.\18\ When opening your
cupboard, you're three times as likely to take the first cereal you see
as you are the fifth.\19\ The same is true in grocery stores. When you
start shopping, you can't wait to start piling things in your cart. But
after it starts filling up, you become more selective. If stores could
get you to walk by more of the healthy--and profitable--foods first,
they might be able to get you to fill up the cart on the good stuff,
and squeeze out any room for the Ben & Jerry's variety pack.
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\18\ A really robust finding. A great reason why you should also
pass around the salad and green beans to your kids at dinnertime before
you bring out the lasagna. Brian Wansink and David Just, ``Healthy
Foods First: Students Take the First Lunchroom Food 11% More Often than
the Third,'' Journal of Nutrition Education and Behavior 43 (2011):
4S1, S9.
\19\ You can just believe me, or you can read ponderous evidence of
why this happens: Pierre Chandon and Brian Wansink, ``When Are
Stockpiled Products Consumed Faster? A Convenience-Salience Framework
of Postpurchase Consumption Incidence and Quantity,'' Journal of
Marketing Research 39, no. 3 (2002): 321-35.
We spend less than 6 minutes in the fruit and vegetable
---------------------------------------------------------------------------
section.
Most grocery stores in the United States place the fruit and
vegetable section on the far right of the store. It's the first thing
we see and wander over to. The bad news is that many of us spend less
than 6 minutes there.\20\ We pick up some apples and lettuce and then
wander over to the next aisle. But if stores could get us to linger
there a little longer, we'd buy a little bit more.
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\20\ This is a really neat finding, but it seems like it will take
a miracle to get it published. In the meantime, you can find it on
SSRN: Brian Wansink and Kate Stein, ``Eyes in the Aisle: Eye Scanning
and Choice in Grocery Stores,'' 2013.
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The secret might lie in the fact that we're wanderers--we're not
always very deliberate. What if they put a dashed green line that
zigzagged through the produce section, and what if they put floor
decals in front of food shelves that offer healthy meal ideas? Just
like that dashed yellow line on the highway that keeps you mindlessly
on the road and the billboards that keep you mindlessly amused, maybe
putting a dashed green line and floor decals would also have us
wandering the produce section a bit longer.
To test this, we proposed Operation: Green Highway on our mad
scientist island in Denmark. Supermarkets could put a 2" wide dashed
green line through the produce section--around the apples and oranges,
over to the lettuce, past the onions and herbs, and back around to the
berries and kumquats. They could even include some kid-friendly visuals
or floor graphics. If a shopper followed this green highway, he or she
might be tempted to buy more fruits and vegetables.
To test this, we had people initially trace their way through
grocery stores that either did or did not have Health Highway lines.
Did people stay on the line? Of course not, but they would have spent
an average equivalent of 3 more minutes in the produce section. At
about $1/minute, this would mean they could spend as much as $3 more on
fruits and vegetables than they otherwise would have.21-22
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\21\ Would this dashed green line work through the rest of the
store? It could go down some of the healthier aisles--say canned fruits
and vegetables or foods with whole grains--and around much of the
perimeter of the store. Yet to use the quotation from Spinal Tap again,
``It's a fine line between clever and stupid.'' This line might work
well in the produce section, but don't take it overboard. It might be
irritating or too strange in the rest of the store--particularly
because these long aisles might make it look like a highway divider.
\22\ My good colleagues Collin Payne and David Just have early
evidence that this works well when it's first laid out. See Collin R.
Payne and David R. Just, ``Using Floor Decals and Way Finding to
Increase the Sales of Fruits and Vegetables,'' under review.
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But what about the other store aisles? Let's say that you have two
favorite grocery stores: Tops and Hannaford. At Tops, the aisle after
the produce section--let's call it Aisle 2--is the potato chips,
cookies, and soft drinks aisle. At Hannaford, the potato chips,
cookies, and soft drinks are in Aisle 15--the second-to-last aisle in
the store. If you're on a diet, which store should you choose?
We followed 259 shoppers in Washington, D.C., grocery stores to see
if a person shops differently depending on which aisle they're in.\23\
We discovered that most people with shopping carts behave the same way:
They walk through the produce section, then turn and go down Aisle 2
(which leads back toward the front of the store). It almost doesn't
matter what's in the aisle--health food, dog food, or mops. At this
point, shopping's still a fun adventure. But after Aisle 2, shoppers
get mission-oriented and start skipping aisles as they look for only
what they think they need. So, Aisle 2 gets the most love and attention
from the most shoppers.
---------------------------------------------------------------------------
\23\ Wansink and Stein, ``Eyes in the Aisle.''
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So, what's in Aisle 2 at your favorite grocery store? It's often
soft drinks, chips, or cookies as in the Tops store. To make a grocery
store more slim by design, managers could easily load up this aisle
with whatever healthier food is most profitable for them. This might be
store-brand canned vegetables, whole-grain foods, or high-margin lower-
calorie foods. First in sight is first in cart.
------------------------------------------------------------------------
How Your Grocer Can Help You . . .
-------------------------------------------------------------------------
Guiding Angles, Aisles, and Lines
------------------------------------------------------------------------
One way to help shoppers fill up their carts with healthy foods is
to make sure those are the aisles they visit first and stay in longest.
People cherry-pick their favorite fruits and vegetables and quickly
move to the center of the store, but you can keep them in the produce
area longer by angling displays so they guide shoppers through the
store--think of the 30 and 45 angles you used to see in those old-
school pinball games. Also, green lines--Green Highways--seem to nudge
most of us, at least occasionally, to turn in a direction we otherwise
wouldn't have turned in.
Since shoppers are more likely to buy healthy foods when their carts
are empty, stores should load up Aisles 1, 2, and 3 with whatever's
healthiest and most profitable.
------------------------------------------------------------------------
------------------------------------------------------------------------
What You Can Do . . .
-------------------------------------------------------------------------
Wander the Healthy Aisles First
------------------------------------------------------------------------
Following the green line works well if there is a green line. But if
there isn't one, you can always make your own.
Make a point of wheeling through as many of the produce
aisles as possible. Even if it's fast and furious, simply seeing more
fruits and vegetables while your cart is empty makes them more
tempting.
Hit the other healthier aisles--like those with canned and
frozen fruits and vegetables--before you head for the Crunch & Munch
section.
------------------------------------------------------------------------
Wide Aisles and High Products \24\
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\24\ If you want a beleaguered researcher's view of how this works,
here's an op-ed: Kate Stein, ``Shop Faster,'' New York Times, April 15,
2009, p. A29.
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The More Time You Spend in a store, the more you buy. Similarly,
the more time you spend in an aisle, the more you buy.25 In
order for us to buy a healthy food, we need to (1) see it and (2) have
the time to pick it off the shelf.
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\25\ One source for this is Brian Wansink and Aner Tal,
``Correlates of Purchase Quantities in Grocery Stores,'' under review.
But not all shelves are the same. Food placed at eye level is
easier to spot and buy. For instance, kids' foods are placed at their
eye level, so that they can irritate us into buying them (``I want it!
I want it! I want it!'').
This works for Count Chocula and our kids, but would it for kale
chips and us? We returned to our ``I-Spy'' habits and observed 422
people purchasing thousands of products in the Washington, D.C., area.
First we estimated the height of each shopper using a series of pre-
marked shelves they walked by (picture those height-marker decals on
the doors of convenience stores).\26\ We then measured the height of
each product they looked at. Based on where they looked, we could
figure out what percent of the foods they bought were at eye level.\27\
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\26\ Of course this is less accurate than measuring people barefoot
with a German-made stadeometer, but knowing someone's relative height
is probably sufficient. Being able to document that a 6 male is taller
than a 5 5" female is close enough for this calibration. This issue of
precision does raise to mind the comedian Ron White's quote ``I'm a
pretty big guy--between 6 and 6 6"--depending on what convenience
store I'm coming out of.''
\27\ In this study with Kate Stein, we tracked what people put in
their carts but we didn't track them to the cash register. Still,
unless someone changes their mind when in the National Enquirer
checkout line, we assume that what they took, they probably bought.
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If you're shopping in a narrow aisle, 61 percent of everything
you'll buy is within 1 of your eye level--either 1 above or 1
below.\28\ This is useful to know if you're a grocery-store owner who
wants to sell us healthier foods. Smart store managers can put these
profitable healthy foods at eyeball level. If the product is one that's
typically bought by males, it can be placed even 5" higher, since the
average male is that much taller than the average female.
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\28\ And 12" is even a stretch. Most purchased products were within
a 6 range--higher or lower--of eye level for a particular shopper.
This includes 37 percent of what women put in their cart and 44 percent
for men. To stretch the range of products purchased even further, widen
the shopping aisles. If an aisle is narrow--6 or less--61 percent of
the products you buy will be within 12" of eye level. But if you're in
a wider aisle, you look higher and lower. If it's only 2 wider, \1/2\
of what you buy will be outside this eye zone. But wide aisles also
have something else going for them.
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One well-known finding among people watchers is that nothing causes
a person to scoot out of an aisle faster than when someone accidentally
brushes against their behind. In his book Why We Buy, Paco Underhill
refers to this as the ``butt brush.'' \29\ Think of the last time this
happened to you--five seconds later you had pretty much teleported
yourself to another spot in the store. Since brushing against people
probably happens much more in narrow grocery store aisles than wide
ones, people might spend less time and buy fewer items there. Many
grocery store aisles range from 6 to 8 wide. In the Washington, D.C.,
grocery stores mentioned earlier, we measured the width of all the
aisles and timed how long the average shopper spent in them. Indeed,
the wider the aisle, the more they bought. It didn't matter what was
there--canned Brussels sprouts, twenty-pound bags of cat food,
dishwashing liquid--the more time they spent in the aisles, the more
items they bought.\30\
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\29\ Paco Underhill, Why We Buy: The Science of Shopping (New York:
Simon & Shuster, 2000).
\30\ There's also an irritation factor with narrow aisles. If a
person can't see a clear way through an aisle, they might be less
likely to go down it. And if you keep getting interrupted by people as
you're trying to shop because they're scooting by you, you're less
likely to linger.
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Your grocer could put more healthy, high-margin food in wider
aisles and less healthy food in narrower ones. Identifying or creating
healthy food aisles that are wider would be one solution. Another
solution--make sure the healthier foods are at eye level.\31\
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\31\ Kate Stein and Brian Wansink, ``Eye Height and Purchase
Probability,'' under review.
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Eye-Level Shopping Bull's-Eye
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
* 60 percent of what shoppers buy is within 12" of their eye
height.
Slim-By-Design Grocery Shopping
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Groceries and Gum
Most of Us Know that it's a bad personal policy to go shopping on
an empty stomach. We think it's because we buy more food when we're
hungry--but we don't. In our studies of starving shoppers, they buy the
exact same amount of food as stuffed shoppers. They don't buy more, but
they buy worse.\32\ When we're hungry, we buy foods that are convenient
enough to eat right away and will stop our cravings.\33\ We don't go
for broccoli and tilapia; we go for carbs in a box or bag. We go for
one of the ``Four C's'': crackers, chips, cereal, or candy. We want
packages we can open and eat with our right hand while we drive home
with our left.
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\32\ Here's the best proof of why you shouldn't shop when you're
hungry: Brian Wansink, Aner Tal, and Mitsuru Shimizu, ``First Foods
Most: After 18-Hour Fast, People Drawn to Starches First and Vegetables
Last,'' Archives of Internal Medicine 172, no. 12 (June 2012): 961-63.
\33\ This is a current working paper by Brian Wansink and Drew
Hanks, ``Timing, Hunger, and Increased Sales of Convenience Foods.''
Hopefully it will be published in time for our retirement.
When it comes to cravings, our imagination is the problem. The
cravings hit us super-hard when we're hungry because our hunger leads
us to imagine what a food would feel like in our mouth if we were
eating it. If your Girl Scout neighbor asked you to buy Girl Scout
cookies, you'd buy one or two boxes. But if she were to instead ask you
to describe what it's like to eat your favorite Girl Scout cookie, you
would start imagining the texture, taste, and chewing sensation, and
wind up ordering every life-giving box of Samoas she could carry. (Keep
this in mind the next time your daughter wants to win the gold medal in
---------------------------------------------------------------------------
cookie sales.)
Starving shoppers don't buy more, but they buy worse.
Most food cravings--including those that occur when we shop--are
largely mental. As with the Girl Scout cookies, they seem to be caused
when we imagine the sensory details of eating a food we love--we start
imagining the texture, taste, and chewing sensation. But if we could
interrupt our imagination, it might be easier to walk on by.
One way we can interrupt these cravings is by simply chewing gum.
Chewing gum short-circuits our cravings. It makes it too hard to
imagine the sensory details of crunchy chips or creamy ice cream. My
colleague Aner Tal and I discovered this when we gave gum to shoppers
at the start of their shopping trip. When we reconnected with them at
the end of their trip, they rated themselves as less hungry and less
tempted by food--and in another study we found they also bought seven
percent less junk food than those who weren't chewing gum.\34\ If you
shop for groceries just before dinner, make sure the first thing you
buy is gum--and our early findings show that sugarless bubble gum or
mint-flavor might work best.
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\34\ One of the ways we've tested this is by intercepting grocery
shoppers in the parking lot on their way into a store. We ask them to
answer a couple of questions about the store and if we can talk to them
after they shop. If they say yes, we tag their cart so we can catch
them as they check out. At that time, we ask them a few questions about
their experience and if we can have a copy of their shopping receipt. A
second group of people get the exact same treatment, except that
they're also given a piece of sugarless gum as a thank-you. We tag
their cart with a different color tag, and again catch them as they
check out.
Most food cravings--including those that occur when we shop--
are largely mental.
Chewing gum short-circuits our cravings. It makes it too hard
to imagine the sensory details of crunchy chips or creamy ice
cream.
Lights, Stars, Numerology!
Supermarkets Could Make Us slim by design if they only told us what
foods were the healthiest, right? Not really. Supermarkets and food
companies have endlessly experimented with little stickers and icons
that they hoped would help us to eat better. They'd say things like
``Good for You,'' ``Better for You,'' ``Don't Have a Stroke,'' and so
on. The United Kingdom even uses a traffic light--each food has a green
(go), yellow (slow), or red (no) icon on it.
Do you remember these icons? Of course you don't. Most of us
ignored them because they were too confusing, self-serving, or
unconvincing. Oh, and even when people did pay attention to them, they
often backfired. Some people believed the green and yellow foods were a
lot healthier than they actually were and gorged out on them. Then food
companies got tricky and took advantage of this by producing foods that
barely met the minimum requirements for a green or yellow icon. Getting
the healthy icon then became more important than actually coming up
with a healthier product.
Most labeling systems seem to backfire because we ignore them
or we game them.
One exception seems to be the Guiding Stars program. Back in 2005,
an innovative, brilliant, high-end grocery store in New England--
Hannaford Brothers--boldly stuck its neck out by putting bright yellow
stars next to the healthiest foods on their shelves--super-healthy
foods even got three stars. So, did people buy better food? Well,
according to one study, they didn't initially seem to buy any more of
the starred food. But they initially did buy less of the unstarred
foods. They didn't buy more tofu, though this led them to think twice
about the Doritos.\35\
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\35\ This is a great study that shows surprisingly that either
taxing bad foods or subsidizing good foods seems to backfire. When you
subsidize healthy foods, people buy more of both healthy and unhealthy
foods. When you tax unhealthy foods, shoppers by less of both unhealthy
and healthy foods. John Cawley et al., ``How Nutrition Rating Systems
in Supermarkets Impact the Purchases of Healthy and Less Healthy
Foods,'' under review.
---------------------------------------------------------------------------
But here's why most of these labeling systems seem to backfire: (1)
We don't believe them, or (2) we game them. We know an apple gets a
green light, an A+, or a 100 percent rating. And we know a Twinkie gets
a red light, a D^, and a two percent rating. It's the stuff in the
middle that turns us into nonbelievers. If a food gets a rating that
doesn't line up with our intuition, it totally loses credibility. When
the magic formula is too complicated or too secret, we dismiss these
ratings as ridiculous and ignore them.
But worse than our ignoring them is when we game the system. We're
experts at getting around something we don't want to do or believe. If
one type of cracker is rated five points higher than another type of
cracker, we choose it instead of an orange.\36\ Then we end up
rewarding ourselves by eating more of them.\37\
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\36\ This is an award-winning article that opened a lot of eyes
with the health halo concept: Pierre Chandon and Brian Wansink, ``The
Biasing Health Halos of Fast Food Restaurant Health Claims: Lower
Calorie Estimates and Higher Side-Dish Consumption Intentions,''
Journal of Consumer Research 34, no. 3 (October 2007): 301-14.
\37\ There's a ton of evidence here that's compelling, but way too
detailed to talk about in the text. It happens with both low-fat foods
and with foods with healthy names. Knock yourself out reading these two
detailed (but award-winning papers): One's mentioned in the prior note
and the other one is Brian Wansink and Pierre Chandon, ``Can Low-Fat
Nutrition Labels Lead to Obesity?,'' Obesity 14 (September 2006): A49-
50.
------------------------------------------------------------------------
What You Can Do . . .
-------------------------------------------------------------------------
Use Your Intuition First and Their Labels Second
------------------------------------------------------------------------
Relying too much on ratings is confusing and can backfire. Even if
your grocery store is using them, rely first on your common sense and
only use the ratings to break ties between brands--Count Chocula beats
Cap'n Crunch.
But don't celebrate your slightly smarter choice with a double-wide
candy bar. That's the compensation danger in a health halo world.
------------------------------------------------------------------------
Using the Half-Plate Rule
Each Spring, Wegmans, a popular grocery chain in the Northeast,
does a big health promotion push called ``Eat well. Live well.'' From
time to time, we've helped develop new ideas for their stores. In 2009,
they visited our Lab to see if we could help develop a program that
would encourage their own employees to eat more fruit and vegetables.
They were thinking of providing some sort of education or promotion
program. Instead, we were thinking of giving them a simple, visual rule
of thumb. What we told Wegmans worked great for them, and it can work
great for you in the store and even when you get home.
In the good old days when we were kids, eating was easy. Your
grandmother piled dishes of food on the table, you'd take a little of
each, and--ta-da--that was nutrition! Today, the 273-page United States
Dietary Guidelines tips the scale at almost 3 pounds. But there's an
easier way for most people. When I was the executive director in charge
of the Dietary Guidelines and people asked me how they should eat,
although not the official USDA-sanctioned answer, my shortcut answer
was to simply encourage them to use my Lab's Half-Plate Rule.\38\ Half
of their plate had to be filled with fruit, vegetables, or salad, and
the other \1/2\ could be anything they wanted. It could be lamb, a
blueberry muffin, a handful of cheese . . . anything. They could also
take as many plates of food as they wanted. It's just that every time
they went back for seconds or thirds, \1/2\ their plate still had to be
filled with fruit, vegetables, or salad.
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\38\ Wansink, Mindless Eating, pp. 178-9+.
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Half-Plate Healthy
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
* Follow the Half-Plate Rule.
Could a person load up \1/2\ of their plate with Slim Jims and pork
bellies? Sure, but they don't. Giving people freedom--a license to eat
with only one simple guideline--seems to keep them in check. There's
nothing to rebel against, resist, or work around. As a result, they
don't even try. They also don't seem to overeat.\39\ They may want more
pasta and meatballs or another piece of pizza, but if they also have to
balance this with a \1/2\ plate of fruit, vegetables, or salad, many
people decide they don't want it bad enough.\40\
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\39\ Check out the article Brian Wansink and Kathryn Hoy,``Half-
plate Versus MyPlate: The Simpler the System, the Better the
Nutrition,'' forthcoming, and Brian Wansink and Alyssa Niman, ``The
Half-Plate Rule vs. MyPlate vs. Their Plate: The Effect on the Caloric
Intake and Enjoyment of Dinner,'' Journal of Nutrition Education and
Behavior 44, no. 4 (July-August 2012): S33.
\40\ The more latitude we give, the more likely they'll follow our
advice. When rules become just a little too complicated or vague, we
find reasons to stop following them. This was an early problem with
MyPlate. When somebody starts questioning ``Where does my dessert go?''
or ``How am I supposed to eat fruit with dinner,'' the more likely they
are to simply say ``Whatever'' and ignore it.
Using our Half-Plate Rule works amazingly well at home, but
---------------------------------------------------------------------------
only if you also use it when you shop.
Using our Half-Plate Rule works amazingly well at home, but only if
you also use it when you shop.\41\ To use it, you need to have enough
fruits, vegetables, and salad around in the first place. If as you shop
you think about you and your family being half-plate healthy, you'll
buy healthier and you'll also spend more. The first is good for you;
the second is good for the store.\42\
---------------------------------------------------------------------------
\41\ A recap of this done by Jane Andrews, Wegmans dietitian, can
be found at http://rochester.kidsoutandabout.com/node/1901.
\42\ See more at Wansink and Niman, ``The Half-Plate Rule vs.
MyPlate vs. Their Plate.''
---------------------------------------------------------------------------
Wegmans jumped on our idea. Within 2 years, it was rolled out to
all their stores, and you can now get Half-Plate place mats, magnets,
posters. (They renamed it the trademarkable Half-Plate Healthy.) You
can see it in action in any of their stores, and the only place it
works better than in a grocery store is in your home.
Supermarkets don't have to talk about servings of fruits and
vegetables to get the point across. All they need to do is to reinforce
the idea that \1/2\ a plate could hold whatever fruit, vegetables, or
salad a person wanted. They can do this on signs, specials, recipes, or
in-store promotions--and subtly encourage people to fill their cart
with slightly more fruits and vegetables than they typically do.\43\
---------------------------------------------------------------------------
\43\ Learn more about how Wegmans implemented our idea at http://
www.wegmans.com/webapp/wcs/stores/servlet/
ProductDisplay?storeId=10052&partNumber=UNIVERSAL_20235.
------------------------------------------------------------------------
What You Can Do . . .
-------------------------------------------------------------------------
The Half-Plate Rule at Home
------------------------------------------------------------------------
``Fill \1/2\ your plate with fruit, vegetables, or salad, and fill
the other \1/2\ with whatever you want.'' We've given this simple rule
to tens of thousands of people because it works. People often report
back to us that they eat fewer calories and they eat a lot more
``balanced'' diet than they did before. They also say they eat until
they're full but not stuffed.\44\
\44\ Wansink and Hoy, ``Half-plate Versus MyPlate.''
Nobody likes to be told they can't do something. With the Half-Plate
Rule there's nothing you can't eat. You just have to eat an equal
amount of fruit, vegetables, or salad. At some point, getting that
fourth piece of pizza just isn't worth having to eat another \1/2\
plate of salad. But, most important, you're the one who made that
decision.
------------------------------------------------------------------------
After forty-five minutes of seeing food, guess what we want?
It's not a snack-size can of lima beans.
The Three Checkouts
Grocery Shopping Isn't Exactly a trip to Fantasy Island, but the
checkout line can be an exception. It's filled with guilty-pleasure
rewards at the end of the ho-hum errand of shopping. There are bizarre
new gum flavors like mango chutney mint, meal-size candy bars, and
irresistibly tacky tabloids with headlines like ``Cellulite of the
Stars.'' These aisles are entertaining, but if you're with kids, you're
doomed. Kids in grocery checkout lines are like kids in toy stores.
They grab, bug, beg, pout, and scream. And if we cave in to buying pink
marshmallow puff candy shaped like Hello Kitty, we also cave in to
buying something with lots of chocolate--for us. There's usually
nothing in the aisle that we actually need, but after forty-five
minutes of seeing food, guess what we want? It's not a snack-size can
of lima beans. So we buy the Heath bar we swore we'd never buy again,
finish it by the time we leave the parking lot, and shake our head on
the way home . . . just as we did last week.
Mothers shopping with children wanted more foodfree cashier
lines. Fathers shopping with children didn't exist.
One supermarket solution is to set up at least one checkout line so
it's totally candy-free.\45\ Just as large supermarkets have different
lines for ``10 items or less'' or ``cash only,'' some lines could have
candy, others could have healthy snacks, and some could totally be free
of food. The stores could still sell magazines and other crazy things--
like eyeglass repair kits and superglue--but one or two aisles wouldn't
have any food at all.
---------------------------------------------------------------------------
\45\ See Ulla M. Toft, Lise L. Winkler, Charlotte Glumer, and Brian
Wansink (2014), ``Candy Free Checkout Aisles: Decreasing Candy Sales in
Bornholm Island Supermarkets,'' under review.
---------------------------------------------------------------------------
To see what tired shoppers in grocery store parking lots thought of
this idea, we asked, ``If your favorite supermarket had ten checkout
lines, how many should be candy lines, healthy lines, or food-free
lines?'' Here's what we found:
Men shopping alone wanted all candy lines.
Women shopping alone wanted more of the healthy food lines.
Mothers shopping with children wanted more food-free lines.
Fathers shopping with children didn't exist.
An easier first step would be to help convince your local
supermarket manager to start by simply adding a healthy line--perhaps
selling fresh fruit, granola bars, and so on. It might be the one
longer line shoppers wouldn't mind waiting in. When the manager sees
those lines getting longer, he'll quickly make the bigger steps. If he
doesn't, there are other places you can shop.
------------------------------------------------------------------------
How Your Grocer Can Help You . . .
-------------------------------------------------------------------------
What If All the Aisles Were Candy Aisles?
------------------------------------------------------------------------
If you want that food-free checkout experience but all the aisles
are loaded up with Skittles and SweeTarts, here's what you do:
1. Tell the manager that you want to avoid impulse-buying candy
while you're in the checkout line. Ask him or her which of the open
checkouts would be least tempting for a dieter or a shopper with
children.
2. While the manager is thinking, ask if they would consider
putting in a candy-free aisle. You can mention that other stores
(such as Hy-Vee, Wegmans, and HEB) have at least one candy-free
checkout aisle, and you've heard they're popular with both dieters
and parents shopping with kids. If one of those stores you mention
happens to be a nearby competitor, it might not be too many more
trips before you have your candy-free aisle. That will be a good
time to say ``thank you.'' \46\
\46\ More at Ulla M. Toft, Charlotte Glumer, Lise L. Winkler, and Brian
Wansink (2015), ``Food Free Checkout Aisles: A Danish Field Study of
Becoming Slim by Design,'' under review.
------------------------------------------------------------------------
Which of These Would You Like To See at Your Grocery Store?
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Back to Bornholm
After Watching, Coding, and Analyzing Shoppers on the Danish island
of Bornholm, we generated a small list of changes--baby steps--these
grocers could make to profitably help shoppers become slim by design.
We were scheduled to present these ideas to all nine grocery store
managers at the Bornholm Island Hall after they got off work a couple
of days later at seven thirty.
------------------------------------------------------------------------
How Your Grocer Can Help You . . .
-------------------------------------------------------------------------
The Original Slim-By-Design Supermarket Pledge
------------------------------------------------------------------------
When the Danish Government said they'd be willing to try almost
anything we recommended, here's what we first suggested, and here's
what paved the way for the full 100-point Supermarket Scorecard at the
end of this chapter. We asked them to try the three changes that would
be easiest and most profitable for them.
1. Provide divided \1/2\ carts that encourage people to put their
fruits and vegetables in front. The dividers can be made from
paint, duct tape, mats, etc.
2. Angle produce displays and use floor decals (such as green
lines) to guide and keep people shopping longer in the produce
section.
3. Place the healthiest foods in Aisles 1 through 3.
4. Make the healthiest aisles the widest and put healthy products
at eye level or on end-of-aisle displays (endcaps).
5. Use the ``Half-Plate Rule'' promotion.
6. On end-of-aisle displays, combine the regular promotion with a
healthy food complement.
7. Have two or three types of checkout lines: standard, food-free,
and healthy foods only.
------------------------------------------------------------------------
Unfortunately, 2 days later at seven thirty my five-person
delegation of researchers almost equaled the six grocery managers who
actually showed up. Strike one. After starting the presentation with
the only Danish word I knew--``Velkommen'' (welcome)--I told them the
night was all about ``new ways you can sell more of your healthier
foods and make more money.'' We then went on to give a punchy
presentation on seven easy changes that we knew would work well. We had
photos, video clips of shoppers, cool study results, numbers, and funny
stories. It was great . . . except that nobody laughed, asked a
question, moved, or even seemed to blink. It was like Q&A hour in a wax
museum. Strike two.
We generated a small list of changes these grocers could make
to profitably help shoppers become slim by design.
Because there were no signs of life, I idled down my enthusiasm and
wrapped up our presentation a half hour early so my Danish colleagues
could try to salvage the evening. Once they started talking in Danish,
some sort of switch flipped in the managers. They started talking
louder, started to un-Danishly interrupt each other, and then started
arguing. Thinking things were getting out of control, I suggested we
call it a night before they started to break furniture. My Danish
colleagues waved me off and the melee continued. An hour later, things
had slowed down, and the managers thanked us and cleared out. Before we
started cleaning up, I asked my Danish colleagues why they were so
irate. They said, ``Oh, no. They like the changes and they'll make most
of them. The rest of the time they were talking about the other changes
they wanted to make, like having more produce tastings, more pre-
prepared salads, and bundling meat and vegetable specials together.''
After all our supermarket makeovers, does every Bornholmian look
like a sleek, slim, Danish version of Mad Men? As I mentioned earlier,
it's still too soon to say (we're posting updates at
www.SlimByDesign.org/Bornholm), but with every trip I make, all signs
point in the right direction.
One way to tell how well a new idea is working is by how many
people want to jump in and be a part of it. The more changes we made to
the grocery stores in Bornholm, the more other groups got involved.
Before long, a public health advertising campaign was being rolled out,
petitions were launched, and local ordinances were proposed. After the
kitchen smoke clears, it will be difficult to see which of these moved
the dial the most--but the people on the island are buying in to
becoming slim by design.
There's a humbling expression: ``Success has a thousand fathers,
but failure has only one.'' If there are dramatic changes in the foods
these Danes buy, the public health people will say it was because of
their ads, the activists will say it was because of their tireless
petition drives, and charismatic politicians will say it was because of
their bold regulations. But if nothing happens and the whole plan ends
up being a failure, which father will take the blame? It won't be the
public health adviser or the politician. They'll abandon the program in
a heartbeat. Unsuccessful public health campaigns cost lots of money.
Unsuccessful ordinances can cost political careers.
We projected each change would turn a profit within a month
if not immediately.
Yet these supermarket makeovers were cheap and easy to make. Many
were done over a weekend, and we projected each of them would turn a
profit within a month if not immediately. Still, if even one works,
stores will be further ahead than before. On my most recent trip, they
asked me to help expand it to the mainland, so some hidden sales
numbers must be looking pretty good. It's the beauty of being slim by
design.
How Your Grocery Store Can Make You Slim
There Are Dozens of Ways your favorite grocery store could
profitably help you shop a little healthier. In April 2014, I shared
the Bornholm story with some of the innovative American grocery stores
that sponsored some of the studies you've read about throughout this
chapter. They all had clever ideas they were trying out in their stores
to help their customers shop a little healthier, but they were all
doing something different--and often repeating each other's mistakes.
If we could pool together all of my Lab's slim-by-design research
findings with some of the ideas they were successfully experimenting
with, we could make a supermarket scorecard that could help guide all
of them to make profitable healthy changes.
This supermarket scorecard tells shoppers what they should
look for or ask their local grocery manager to do.
Grocery chains are competitive--and not just for shoppers. Even
though a grocery chain in Texas doesn't compete for the same shoppers
as a grocery chain in Chicago, they all want to win awards for Most
Popular, Prettiest, Smartest, or Most Likely to Succeed at their annual
Grocery Store-a-Palooza Award Conference. Because having a scorecard
means there might be yet another new award they could compete on, most
were eager to help develop one. But more important than enabling
grocery chains to compete with each other, this supermarket scorecard
will transparently show them exactly how to compete. Also, it will tell
shoppers what they should look for or ask their local grocery store
manager to do. If all these changes help grocery stores make a little
more money, grocers will want to make the changes. If all these changes
help shoppers shop a little healthier, shoppers will want to hassle
their favorite grocer until he or she makes changes.
Slim-by-Design Grocery Store Self-Assessment Scorecard
------------------------------------------------------------------------
Okay, so your favorite grocery store has great prices, selection,
and convenience, but it might still be making you fat and happy instead
of happy and slim. This scorecard tells you what your store is doing to
help you eat better. Our Lab has been working with top grocery chains
around the nation to help them make you slim by design. You can use a
scorecard like this to compare your favorite grocery stores, but it
will also tell you what you can ask them to do to make you and your
family more slim--and more loyal to their store. Some items on this
scorecard might initially seem to have nothing to do with food--like
having restrooms and a drinking fountain in the front of the store--but
together they will make you less anxious or more comfortable, and
others will slow you down and relax you. In the end, even some of these
nonfood changes can lead you away from impulsively buying Chunky Monkey
ice cream and more toward intelligently buying bananas. This is a
start--every year this scorecard is updated with the best practices and
the best research that helps us shop better (and helps stores make
money). The newest can be found at SlimByDesign.org.
------------------------------------------------------------------------
Entrance
------------------------------------------------------------------------
Assign designated parking spots The first area entered by most
(similar to handicapped spots) for shoppers is the produce section.
pregnant women and mothers with Free healthy samples are near the
infants. entrance.
Offer preprinted shopping lists of There's a small ``grab and go''
basic staples near the entrance. area in the front of the store
Provide information sheets near with a small selection of milk and
the entrance on healthy ways to bread for the in- and-out, or
shop. ``fill-in'' shopper.
Offer healthier foods near the There's a small ``grab and go''
entrance to prime healthy area in the front of the store
shopping. with a small selection of milk and
Two sizes of shopping carts are bread for the in- and-out, or
available. ``fill-in'' shopper.
Handbaskets are available.
Divided shopping carts with a
``place fruits and vegetables
here'' section are provided.
------------------------------------------------------------------------
Services and Signage
------------------------------------------------------------------------
Signs promote seasonal Signs provide ``Did you know?''
combinations of fruits and facts about the health benefits of
vegetables. specific foods.
Educational posters are located There are specific perimeter
around the stores to educate promotions for lean dairy.
people about healthy eating (for There are specific promotions for
example, the Half-Plate Rule). whole-grain products, such as
Local and seasonal foods are bread and pasta.
clearly promoted. Calorie information is available
There is a special section for in the meat section.
organic fruits and/or vegetables. Healthy food apps such as
The organic section is boldly and Fooducate and QR codes are
clearly labeled. promoted.
At least one produce-tasting A kiosk with tear-off recipes is
station is near the entrance. available in the produce section.
A wide range of precut fruits and Combo packs are available that co-
vegetables are available. promote healthy foods (such as
tomatoes and mozzarella).
There are separate in-aisle A guidance system such as Guiding
promotions for canned fruits. Stars or a stoplight approach is
used.
There are separate in-aisle A dietitian is available and
promotions for canned vegetables. visible in the store a couple of
days each week.
There are separate in-aisle Unit pricing ($/oz) is available
promotions for frozen vegetables. where relevant.
There are specific perimeter
promotions for lean meat.
------------------------------------------------------------------------
Layout and Atmosphere
------------------------------------------------------------------------
Relaxing music is played in the Lighting varies throughout the
produce section. store, but is always brightest on
Show price per unit along with the healthier foods.
price per weight for healthy food, Healthy tear-off recipe cards are
for ease of calculation. provided near the fruits and
Floor decals are used for way- vegetables.
finding to healthy sections. Recipe ingredients for the recipe
cards are located next to the
cards.
------------------------------------------------------------------------
Aisles and Shelves
------------------------------------------------------------------------
Some fruits are bundled into Ingredients are organized by
family-size packs. preparation type (stir-fry versus
Some vegetables are bundled into salad)--for example, put
family-size packs. mushrooms, eggplants, and peppers
in a ``stir-fry'' section.
A complementary fresh produce Expiration dates are visible (at
display is available in the meat front of package or on signs).
section (such as one containing Aisles with healthy foods are the
broccoli, peas, cauliflower, and widest.
peppers). Less healthy foods are
A complementary fresh produce inconveniently placed very low or
display is available in the very high on the shelves.
seafood section (such as lemons, Healthier foods are conveniently
tomatoes, beans, and asparagus). placed at eye level.
A complementary fresh produce Aisles with healthy food are
display is available in the frozen brighter than aisles with
food section. unhealthy food.
Displays of single fruits (such as Hard-to-decide-upon foods (``long-
oranges, apples, pears, buy'' items), such as soups,
nectarines, and apricots) are next dressings, and baby foods are
to desserts. located in less busy aisles so
Ready-to-eat fruits and vegetables people are relaxed enough to
are available in variety packs. comparison shop.
------------------------------------------------------------------------
Prepared Food Area
------------------------------------------------------------------------
Fruit is available in all The healthy daily targeted entrees
foodservice areas. have creative or descriptive
Vegetables are available in all names.
food-service areas. Posters displaying healthy foods
A mix of whole fruit options is or a guidance system (such as the
displayed in an attractive bowl or Half-Plate Rule) are visible in
basket. the dining area.
The ``pick me up'' or prepared The cafeteria tracks the
food section has healthy default popularity and frequency of
foods. healthy-option orders to see what
A daily fruit or vegetable option promotions work most effectively.
is bundled into all grab-and-go All promotional signs and posters
meals. are rotated, updated, or changed
A salad bar is available. at least monthly.
All beverage coolers have both Half portions are available for
water and white milk available. all entrees.
Alternative healthy entree options Half portions are available for
(salad bar, yogurt parfaits, and all desserts.
the like) are highlighted on Takeout boxes are available for
posters or signs within all dining leftovers not eaten in the
areas. cafeteria.
The healthy daily targeted entree
is placed as the first one seen in
all dining areas.
------------------------------------------------------------------------
Shopper Comfort and Service
------------------------------------------------------------------------
Restrooms are easily accessible in Health and nutrition games
the front of the store. dominate the playroom.
A drinking fountain is located in A local fitness club is co-
the front of the store. promoted.
There is an area for shoppers to A small discount to a local
sit and relax. fitness club is given to loyalty
There is an area for shoppers to club shoppers.
eat. There is a drive-through where you
can pick up your groceries, if you
call ahead.
There is a supervised playroom for Home delivery is available (for an
children. extra charge).
------------------------------------------------------------------------
Engagement: Employees and Social Media
------------------------------------------------------------------------
The produce-department manager and All employees are trained to
staff are specifically trained to suggest healthy complementary
suggest healthy answers to shopper products when asked about a
questions. particular item.
The meat-department manager and There are plentiful staff in the
staff are trained to suggest meat and produce sections who are
healthy answers to shopper trained to suggest healthy upsells
questions. or substitutes.
The dairy-department manager and Store or chain has an eng aging
staff are trained to suggest website that has a health-related
healthy answers to shopper blog featuring local or seasonal
questions. products.
The bakery-department manager and The website has shopper loyalty
staff are trained to suggest specials.
healthy answers to shopper Tips, features, or videos
questions. involving better shopping and
better living (such as ``Shopping
with Kids'') are available.
------------------------------------------------------------------------
Checkout
------------------------------------------------------------------------
Loyalty programs specifically Receipt provides an indication of
reward fruit and vegetable what percentage of purchases were
consumption. fruits and vegetables, low-fat
Receipts are itemized in meat, and low-fat dairy.
categories or otherwise coded to A default shopping ``starter''
indicate how healthy you're list is made available to each
shopping. shopper at the front of the store
The back of receipts feature with a number of the major staples
coupons for healthy foods. preprinted on it.
There is at least one food-free The same healthy shopping-tips
checkout aisle. brochure available at the
A discount is offered if a certain beginning of the shopping trip is
percentage of purchases are fruits also available at the checkout
and vegetables. register.
Individual containers of precut ``Don't Forget'' signs are placed
fresh fruit are available next to at the register to remind
at least one cashier. customers about certain healthy
Healthy snack options are offered foods.
next to the cashiers. A ``fruits and vegetables only''
Receipt uses loyalty card self-checkout station is provided
information to show how much was for quick purchases of produce.
spent on fruits and vegetables
compared to past trips.
------------------------------------------------------------------------
Scoring Brackets
------------------------------------------------------------------------
70-100--Slim-by-Design Grocery Store--Gold
50-69--Slim-by-Design Grocery Store--Silver
30-49--Slim-by-Design Grocery Store--Bronze
------------------------------------------------------------------------
Attachment 2
Healthy Profits: An Interdisciplinary Retail Framework that Increases
the Sales of Healthy Foods
Brian Wansink a-b, *
---------------------------------------------------------------------------
\a\ Dyson School of Applied Economics and Management, Cornell
University, United States.
\b\ Cornell Food and Brand Lab, Cornell University, United States.
* Correspondence to: 475 H Warren Hall, Cornell University, Ithaca,
NY 14853, United States. Fax: +1 607 255 9984.
E-mail addresses: [email protected], [email protected].
http://dx.doi.org/10.1016/j.jretai.2016.12.007**
---------------------------------------------------------------------------
** Please cite this article in press as: Wansink, Brian, Healthy
Profits: An Interdisciplinary Retail Framework that Increases the Sales
of Healthy Foods, Journal of Retailing (xxx, 2017), http://dx.doi.org/
10.1016/j.jretai.2016.12.007.
---------------------------------------------------------------------------
0022-4359/' 2017 New York University. Published by
Elsevier Inc. All rights reserved.
Abstract
Disruptive layouts, smart carts, suggestive signage, GPS alerts,
and touch-screen preordering all foreshadow an evolution in how healthy
foods will be sold in grocery stores. Although seemingly unrelated,
they will all influence sales by altering either how convenient,
attractive, or normal (CAN) it is to purchase a healthy target food. A
Retail Intervention Matrix shows how a retailer's actions in these
three areas can be redirected to target shoppers based on whether the
shoppers are Health Vigilant, Health Predisposed, or Health
Disinterested. For researchers, this review offers an organizing
framework that integrates marketing, nutrition, psychology, public
health, and behavioral economics to identify next generation research.
For managers, this framework underscores how dozens of small, low cost,
in-store changes are available to each that can surprisingly increase
sales of entire categories of healthy food.
2017 New York University. Published by Elsevier Inc. All
rights reserved.
Introduction
---------------------------------------------------------------------------
Editor's note: The article is in press, consequently, the
endnotes are unnumbered. In the submitted article pdf the referenced
works have the author'(s) name(s) highlighted for hyperlinking, but
they are not linked; therefore, the endnotes are in order as printed
and not in order as referenced.
---------------------------------------------------------------------------
Our best and worst eating habits start in the grocery store.
Although critics claim that retailers are primarily motivated to sell
unhealthy processed food--Froot Loops instead of fruit or fish sticks
instead of fish--the opposite is true for the savvy ones. If the fruit
turns mushy and the fish begins to smell, retailers may lose more money
in sunk inventory costs then they would otherwise gain by selling the
processed versions. Grocers are motivated to sell healthy, profitable
foods. Unfortunately, they do not know how to effectively do so
(Chandon and Wansink 2012; Guthrie 2017; Inman and Nikolova 2016), so
retail fruit and vegetable sales continue to drop (Haywood 2016;
Produce for Better Health 2015).
Each issue of Supermarket News and Progressive Grocer highlights
clever twists on how retailers can increase sales: novel POP displays,
creative cross-promotions, compelling incentive programs, colorful
floor decals, and trendy planogram arrangements. Most of these tactics
are driven by manufacturers of branded, less-than-healthy packaged
goods. In contrast, most of the newest and most creative solutions for
selling unbranded healthy products--such as fish, poultry, fruits, and
vegetables--have been discovered in academia (Johnson, et al., 2012).
Regretfully, however, many of these discoveries are not widely
adopted or used beyond one or two field test stores (Inman 2012).
First, these discoveries appear disorganized or disjoint because
together they use a wide range of interventions to investigate a wide
range of outcomes (such as sales, satisfaction, loyalty, repatronage,
eye-tracking, and so on). This combination is overwhelming to a manager
who is looking for a single solution, such as how to simply sell more
fish. Instead of giving managers a useful toolbox of organized
solutions, what we give them is more like a shoebox full of tax-time
receipts.
The second reason our work is infrequently translated into practice
is because its conclusions are either uncompelling or inconsistent
(Vermeir and Van Kenhove 2005).We tend to focus on interactions or
boundary conditions where an intervention might work with some
customers and with some food categories, but not with others (List,
Samek, and Zhu 2015). For instance, a Traffic Light rating system may
be useful to some shoppers (Dzhogleva, Inman, and Maurer 2013; Grunert,
Bolton, and Raats 2011; Trudel, et al., 2015), but to others it might
be a glaring warning sign that the food will taste bad (Werle, et al.,
2011). Academia thrives on interactions and exceptions, but the rest of
the world runs on main effects.
The future of healthy retailing will be guided by the future of new
research. All of the research in this review has been published or
conducted after 2011 and \1/2\ are still working papers.
They comprise a framework that integrates the newest discoveries in
marketing, health psychology, public health, consumer research,
nutrition, and behavioral economics to identify what might be the most
actionable and compelling new research to influence practice and
theory. First, the framework collapses the myriad of individual
differences among shoppers into a three-segment hierarchy which
summarizes their healthy shopping disposition. Second, it offers a
useful way to organize the receipt box full of findings in a way that
shows how various interventions work (improving convenience,
attractiveness, and norms) and where they can work within grocery
stores (by altering the signage, structure, service mix). Fig. 1
foreshadows how these pieces will combine to eventually create a Retail
Intervention Matrix framework that can organize existing findings and
stimulate useful new insights.
Fig. 1. How and Where Retail Interventions Can Influence Shoppers
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
The Hierarchy of Health Predispostion
Not all shoppers shop alike. Health food enthusiasts shop
differently than mothers shopping with kids; a ``hot'' fast-thinker
shops differently than a ``cold'' slow-thinker; and variety-seekers
shop differently than budget-constrained shoppers (Hui, Huang, et al.,
2013; Verhoef and van Doorn 2016). There will always be an exception or
an untested segment. This sometimes leads our results to appear
frustratingly inconclusive when we have to admit that we do not know
whether our new intervention works the same way with elderly shoppers
as it does with shoppers using SNAP benefits (Guthrie 2017).
One solution is to only view shoppers based on how predisposed they
are to making a healthier shopping decision. We can view them as
belonging to one of three fluid groups that belong to a Hierarchy of
Health Predisposition. The top segment of this hierarchy are Health
Vigilant shoppers (Fig. 2). They are highly informed, conscious of
calories, and are influenced by nutrition information. At the bottom
extreme, Health Disinterested shoppers have little interest in changing
their eating choices because of either the effort, sacrifice, or
perceived futility. The segment in the middle are the Health
Predisposed shoppers. They would prefer to make healthier food choices,
but they have difficulty consistently doing so unless it involves very
little sacrifice. This Predisposed segment is the one that buys the
100-calorie packages of snacks and the sugar-free yogurt. This segment
is larger on New Year's Day than it was in December; it was larger this
past Monday morning than it was during the prior Friday night's
shopping trip.
One reason nutrition guidance systems (such traffic lights or
Guiding Stars) have had only modest influences on the sales of healthy
food (Cawley, et al., 2015; Nikolova and Inman 2015) may be because
they mainly resonate with only the top of the Hierarchy. Health
Disinterested shoppers ignore these programs, and heath predisposed
shoppers inconsistently follow them. If the only segment they reach are
the vigilant shoppers, interventions like this will have hardly any
sizable impact on health since this segment is already shopping in a
healthy way. Even if the same intervention is perfectly targeted at the
bottom portion of the Hierarchy, it would have hardly any impact
because the bottom segment does not care.
Fig. 2. The Hierarchy of Health Predisposition
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
The CAN Approach to Improving Healthy Shopping
Changing widespread eating behavior does not happen by convincing
shoppers that an apple is healthier than a Snickers nor does it happen
by coaching them to improve their imperfect willpower. While these may
be reminders to Health Vigilant shoppers, they will not reliably work
with Health Predisposed shoppers, and almost certainly will not work
with Health Disinterested shoppers. Instead, a more sensible and cost-
effective solution would be to simply make sure that the apple is much
more convenient, attractive, and normal to choose than the Snickers.
Offering an apple sample at the front of the store primes more fruit
sales (Tal and Wansink 2015) and offering an apple display at the
checkout helps pre-empt Snickers sales (Winkler, et al., 2017). Such
changes are effective because they influence passive shoppers and not
just the vigilant ones.
In 2011, Denmark started a public health initiative to reduce
obesity--partly by trying to increase the sales of fish, fruits, and
vegetables (fresh, frozen, and canned) in grocery stores (thereby
hopefully decreasing the sales of less healthy foods). Starting with a
list of dozens of retail changes that were believed to be revenue
positive (see Appendix A), six were selected to be implemented over a 2
year period on the isolated Danish island of Bornholm (population
42,000). The six interventions selected were ones that retailers
believed would both be profitable and easy to implement and maintain:
1. Fruit displays within 10 of the entrance
2. At least one candy-free check-out line
3. Traffic interrupters (displays of healthy foods in the
wideraisles)
4. End-aisle displays of fish
5. Traffic Light (``Green Key'') labeling
6. In-Store Promotions = \1/2\ Plate Rule Guidance System
In combination, these retail interventions were successful because
they made it more convenient, attractive, and normal to purchase fish,
fruits, and vegetables. For instance, putting fruit in an attractive
display made it appear more normal (typical, or reasonable) to take
fruit--partly because it was now also more convenient and looked more
attractive. It was the CAN approach to changing behavior (Wansink
2015). Looking toward the future of retailing, the key to doing this
successfully is to not handicap our imagination by too narrowly
defining what is meant by convenient, attractive, and normal (Bommelaer
and Wansink, 2017).
More Convenient to Select
As Fig. 3 illustrates, a manager can help make healthy foods more
convenient to see, to consider, and to purchase (Desai and Trivedi
2014; Gilbride, Inman, and Stilley 2015). For instance, one of the
biggest barriers to purchasing fish is that many shoppers are not
confident about how to prepare and serve it. With these shoppers, no
nutrition scale or promotion would lead a person to buy more fish until
they understood that it could be integrated into cooking routines that
were familiar and convenient for them. This was similar with tofu and
to counter this, the largest tofu manufacturer in the U.S. launched an
in-store campaign that clearly illustrated that tofu is convenient to
buy and to cook (``Fridge to pan in 10 minutes'' and ``Cooks like
chicken'') which helped increase both shopper confidence and retail
sales (Hsu 2014).
Even when shopping for familiar foods in familiar aisles, small
changes can conveniently guide shoppers to make healthier choices.
Vegetables placed near the front entrances are selected eight percent
more than those that are not (Wilson, et al., 2016), floor decals that
guide people to other vegetable displays increased sales by nine
percent (Payne and Niculeseu 2012), and center-of-aisle ``traffic
interrupter'' displays repeatedly increased 1 day sales of overlooked
vegetables by 400% in Denmark. Convenience also helps explain why about
43% of interior aisle grocery sales are within 12" of eye level (Stein
2018). This ``you buy what you see'' continues all the way to the
checkout where fruit displays can increase short-term sales by 35% (van
Kleef, Often, and van Tripj 2012).
Fig. 3. The CAN Approach To Influencing Shopping Decisions
Along with saving physical effort, convenience can also refer to
saving cognitive effort. This ranges from using easier-to-understand
product category layouts (de Wijk, et al., 2016; van Herpen 2016) to
leveraging technology in the form of GPS alerts or personal shopping
profiles (Sciandra and Inman 2014). Such reminders can guide shoppers
to healthier choices by making it both more cognitively convenient to
select and more convenient to visualize this food being prepared and
eaten at a home meal (Hui, Inman, et al., 2013; Lowe, Souza-Monteiro,
and Fraser 2013).
More Attractive to Select
The second principle of the CAN approach is that the healthy choice
needs to be made more attractive relative to less healthy (but usually
more tastier) options. It could be more attractively named, more
attractive in appearance, more attractively priced (Hampson and
McGoldrick 2013), or it could evoke more attractive taste expectations
than it usually does (Trivedi, Sridhar, and Kumar 2016; Vega Zamora, et
al., 2014). Fruit that is haphazardly piled onto a flat table is less
attractive than fruit that is angled on a display with a colored frame
around it (Stein 2018). Even simply giving a fruit or vegetable a
descriptive name--crisp carrots or Michigan cherries--makes them more
attractive and increases a person's taste expectations (Spence and
Piqueras-Fiszman, 2014) and selection by sixteen percent or more
(Wansink, et al., 2012).
Attractive packaging, descriptive names, color, labels, and
appearance have all been shown to bias evaluations of taste. Food can
also be more attractive simply by being novel (curried pumpkin),
attention-getting (heirloom Indian corn), or even more ethically
attractive (meat-free turkey). Both the sustainability movement and the
``ugly vegetable'' movement have capitalized on ethically-motivated
shoppers who find sustainable products to be more attractive.
Making a food more attractive by altering its price is a popular
tool of behavioral economists, and it takes the standard form of taxes,
subsidies, rebates, coupons, and bundling (Carroll, Samek, and Zepeda
2016). Unfortunately, when price rebates have been offered on fruits
and vegetables, they can sometimes backfire by increasing both the
sales of healthy produce in addition to the sales of unhealthy foods--
especially in low-income households (Cawley, et al., 2016). That is,
the money saved on fruit is then spent on Froot Loops (Cawley, et al.,
2016).
More Normal to Select
Last, many shoppers often prefer to buy the foods they believe are
normal or popular to purchase, serve, and eat. For instance, signs that
told people that chick peas were the favorite bean in that area
(Harlem) shifted 21% of all bean selections over to chick peas (Bhana
2017). This also works with quantities. Shopping cart signs that stated
that the average shopper purchased at least five fruits and vegetables
increased produce sales by ten percent (Payne, et al., 2014). Moreover,
even the size of the store might subtly suggest to a customer how much
is normal to purchase during a shopping trip (Ailawadi, Ma, and Grewal
2016).
Benchmarks provide visual purchase norms. Consider two benchmarks
that increase fruit and vegetable sales. One is the Half-Plate rule
which was originally designed to help consumers operationalize the
spirit of USDA's MyPlate guidance system (Wansink and Tran 2017). The
Half-Plate rule simply states that in order to eat more balanced meals,
\1/2\ of your plate needs to be fruits, vegetables, or salad and the
other \1/2\ can be whatever you wanted. You can have a second or third
helping if you want, but \1/2\ of your plate always has to be fruits,
vegetables, or salad. This was successfully implemented in the leading
grocery chain in the United States (Kell 2016) as ``Half-Plate
Healthy'' because it had been shown to encourage shoppers to buy
``considerably more'' produce (Wansink 2014). After all, if consumers
were going to eat half-plate healthy, they needed to shop half-plate
healthy (see Fig. 4).
Fig. 4. The Half-Plate Rule and the Half-Cart Both Suggest Larger
Portion Size Norms for Fruits and Vegetables
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
One of the reasons the half-plate healthy approach was effective
for this leading retailer was because it offered a simple visual
benchmark about how much fruit and vegetables are the right amount to
eat--half the plate. Similarly, when consumers shop, little thought may
be given as to whether a food is healthy or not. Yet if asked to
categorize and separate each food they buy according to whether it is
either a fruit or a vegetable (versus neither), it forces more mindful
shopping. One set of studies involved shopping carts that had been
physically divided across the middle and were accompanied with a sign
in the front that instructed people to place their fruits and
vegetables (fresh, frozen, or canned) in the front \1/2\ of the cart
and everything else in the back \1/2\. Using this Half-Cart approach
increased the sales of fruits and vegetables by eighteen percent
(Wansink, Payne, and Herbst 2017). In a second set of studies, when the
proportion of the cart allocated to fruits and vegetables was either at
the 35% level or the 65% level, the amount that shoppers spent
increased from $14.97 to $17.54 (Wansink, Soman, and Herbst 2017). When
the same type of dividing lines were added to online shopper order
forms for grocery delivery, the same results were found. The size of
partitions indeed matters to shoppers.
Nearly all healthy interventions in retailing influence shoppers by
increasing how convenient, attractive, or normal it is to purchase one
food instead of another--an apple or a fish instead of crackers and
beef (Bommelaer and Wansink, 2017). By organizing how our discoveries
work, we open up new possibilities of influence. The CAN approach
enables us to organize how our interventions influence shoppers. As
Table 1 foreshadows, the next section shows where they will work best
in a store.
The Signage, Structure, Service Mix: Where Retailers Can Best Change
Behavior
Although nearly all shopping interventions influence shoppers by
altering how convenient, attractive, or normal it is to buy a product,
there are endless ways they can do so. There are also three different
areas where retailers can influence shoppers by using these tools.
Shoppers can be influenced through signage (inside and outside the
store), by the structure of the store (layout and product positioning),
and by the service the store provides (on-line, in-person, or on-site).
This signage, structure, service mix influences different shoppers in
different ways. Improving service might work best for Health Vigilant
shoppers (because they are most likely to seek out the extra
information or assistance). Improving signage might work best for
Health Predisposed shoppers (as well as those who are and Vigilant).
Changing the store's structure might work well for all three segments.
Signage
Signage overlaps with the traditional ``Promotion P'' of the 4-P's
framework. It involves all out-of-store, in-store, and online efforts
that are directed toward influencing what a shopper buys (Kovacheva and
Inman 2014). Outside the store it includes fliers, circulars,
commercials, outdoor advertising, and coupons. Inside the store it
includes posters, signs, shelf-hangers, floor decals, and kiosks as
well as take-home media such as recipes, brochures, and magazines, and
more stylized or person-based media, such as tailored ads, feedback or
messages on shopping receipts (Otterbring, et al., 2014), and GPS
alerts for promotions. On-line it includes the website, on-line tools,
social media, e-mail alerts, sponsored apps, and GPS alerts for
promotions that can be triggered both in and out of the store.
Table 1. How Sample Findings Fit Into the Retail Intervention Matrix
------------------------------------------------------------------------
More convenient More attractive More normal to
to purchase to purchase purchase
------------------------------------------------------------------------
Signage Floor New Signa
decal arrow recipe ideas, co- ge stating
stickers asking promotions, and that garbonzo
people to follow end-of-aisle beans were
the arrows to displays the most
eat more increased canned popular
nutritiously fish sales by beans,
lead to a nine eighteen percent increased
percent increase (Toft, et al., selection by
in produce sales in preparation) fourteen
(Payne, et al., Starring percent
2014) items as more (Bhana 2017)
Joint healthy Shopp
efforts to decreased the ing cart
provide fish purchase of signs stating
dinner recipe unstarred (less that the
cards and healthy foods) average
grilling by two percent shopper
instruction (Cawley, et al., purchased at
brochures were 2015) least five
part of a larger fruits and
campaign that vegetables
increased fish increased
sales by 28% produce sales
(Karevold, Tran, by ten
and Wansink percent
2017) (Payne, et
al., 2014)
Structure A fruit Fruit Visua
display near samples provided lly diving a
cash register to consumers shopping cart
increased sales upon entering in \1/2\ and
35%, even when the store suggesting
product was not increased sales that \1/2\
discounted (van fruit sales by should be
Kleef, Often, seven percent used for
and van Tripj (Tal and Wansink fruits and
2012) 2015) vegetables,
Items People increased
(including were sixteen their sales
produce) that percent more by fourteen
was within 12" likely to percent
of a shopper's purchase a (Wansink,
eye-level product from the Payne, and
comprised over first full aisle Herbst 2017;
43% of all sales they entered Wansink,
(Stein 2018) than any Soman, and
subsequent aisle Herbst 2017;
(Stein 2017) Wansink,
Tran, and
Karevold
2017)
Using
more islands
than aisles
in produce
aisles
increased
shopping time
and items
purchased
(Mukund,
Atakan, and
Wansink 2018)
Service Healthy In-store ``Hal
``Grab and Go'' suggestions by f-Plate
lines in store staff Healthy'' on-
cafeterias led contributed to line planner,
to a 82% increased fish led to higher
increase in sales (Karevold, produce sales
healthy food Tran, and and more
sales (Hanks, et Wansink 2017) balanced
al., 2012) One meals a
Mobil loyalty program Shopp
apps that rewarded fruit ing receipt
indicated what and vegetable ``scorecards'
percent of your purchases by ' showed
food is healthy providing a consumers how
and which were scaled discount the
missing, was based on how percentage of
rating as being much was fruits and
most attractive purchased a vegetables
to in-store purchased in
consumers (Mao this trip
and Atakan 2017) compared with
past trips
(based on
loyalty card
data) a
------------------------------------------------------------------------
a Unpublished findings based on proprietary studies.
Signage builds awareness, offers reminders, changes attitudes,
encourages comparisons, and so on. It can change the perceived
convenience of purchasing healthy foods by making it more convenient or
easy to consider (``Having turkey for dinner sounds good''), by
changing perceptions of how attractive it would be to add organic
parsnips into a routine meal, or changing how normal it would be to
have a full fruit bowl sitting out when the kids return home from
school (see Fig. 5).
Fig. 5. The Signage-Structure-Service Mix
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Structure
The structure of a store includes its layout and where and how
foods are positioned, such as whether the healthier foods are nearest
the door, at eye level, co-promoted with other displayed products, and
whether they are located in the first two aisles where a consumer
shops. But structure also influences people before they even enter the
store. Starting in the entryway, the size and shape of the shopping
carts structurally influences how much is purchased (bigger carts lead
to bigger shopping trips) and what is purchased (divided carts lead to
more fruit and vegetable sales). Any changes related to shopping carts
and hand baskets continue to influence shoppers throughout their entire
shopping trip, but shopping carts have their biggest impact before it
fills up because this makes a shopper's budget constraints more salient
(van Ittersum, et al., 2013).
A store's structure can be changed by using traffic interrupters
and islands (instead of aisles) in the produce section. A recent
analysis of 1,242 shoppers in four different sections of various
grocery stores shows that while purchases in many sections of a grocery
store (such as meat and cereal) begin to level off after 2 minutes of
shopping, the total number of dollars spent in the produce section
continues rising for about 12 min. at a rate of $1.84/min. One
objective for a store, therefore, is to determine how to keep people
shopping in the produce section for up to 12 min. Islands (instead of
aisles) may help. They appear to slow shoppers down which relates to
them spending more money on produce (Mukund, Atakan, and Wansink 2018).
Service
Most obviously, service includes the sunny appearance, helpfulness,
and friendliness of greeters, butchers, and cashiers (Huneke, et al.,
2015; Keeling, McGoldrick, and Sadhu 2013), the cleanliness of the
store, and the restocking and upkeep of shelves (Robinson, et al.,
2016). Yet much of the service that really guides shoppers to healthier
choices is surprisingly less face-to-face. It starts with how
technology can influence the goals and expectations customers have
before they enter the store (Gustafsson, et al., 2016; Hunneman,
Verhoef, and Sloot 2015; Lee 2015), such as when a Health Vigilant
shopper reads a store blog on healthy food substitutes and prints off
the related coupons. Once in the store, service can be efficiently
boosted by new technologies, such as kiosks that give tailored recipes
or a GPS cart-mounted tablet that gives real-time shopping advice
(Block and Platt 2014). Last, service can influence a shopper's comfort
and mood (Atalay and Meloy 2011; Chen, Lee, and Yap 2011). While the
location of the restrooms and drinking fountains or the availability of
near-the-entrance parking for new mothers appears to have little to do
with sales, it increases a person's shopping time and store
satisfaction, and it may indirectly trigger healthier sales (Atakan and
Finch 2018).
Signage, structure, and service are the areas of the store where
the CAN approach can be much more creatively leveraged to sell
healthier foods. Still, aggressively pressuring shoppers to fill their
shopping carts with healthy foods has diminishing returns, especially
as their shopping trip progresses (Biswas, Szocs, and Inman 2016;
Sheehan and van Ittersum 2016; Van der Heide, van Ittersum, and van
Doorn 2016). There is a limit to how much more produce shoppers can be
nudged to take (Toft, et al., in preparation; Trivedi, Gauri, and Ma
2016). Unless total shopping volume rises, a short intervention study
might heroically claim 30% increases in fruit and vegetable purchases,
but a sustained long-term sales increase of three percent would be more
realistic.
Although a long-term increase in sales of three percent for one
intervention is much less exciting than 30%, there is an entire
shopping experience or journey that needs to be taken into account
(Beatty, et al., 2015; Lemon and Verhoef 2016). This gradual healthy
shift in the entire shopping experience could form the habits (Cleeran,
et al., 2016) that can nurture healthier store loyalty and healthier
bodies.
Shaping Future Healthy Shopping
Organizing our findings into a Retail Intervention Matrix helps us
make them more useful to retailers. If we can better see how one of our
new discoveries influences choice (through the CAN Approach), and then
better imagine where it will work best (the signage, structure, service
mix of a store), we can help retailers far more than if we give them a
nuanced, isolated finding. Moreover, knowing that there are three
segments of shoppers with different degrees of health disposition
(Vigilant, Predisposed, and Disinterested), helps us more realistically
point to who we will have an impact on and who we will not.
Thinking Deeper
Within the signage, structure, service mix, much of the
interdisciplinary retailing research focuses on using signage to make a
healthy food more attractive through the way it is positioned or priced
(Shah, et al., 2013). As the upper right corner of Fig. 6 indicates,
what is less known is how signage can be used to establish new purchase
norms or consumption norms (Van Doorn and Verhoef 2015). For instance,
over the past 40 years, foods like yogurt and granola have gone from
being foreign oddities to favorite staples. Knowing what created these
new norms could help engineer sustainable healthy food trends of the
future--regardless of whether they involve tofu or lab-grown meat
(Purdy 2016).
Fig. 6. Where Research Is Most Needed
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
In contrast to signage, changes in ``structure'' have generally
focused only on making a healthy food more convenient: Move the fruit
to front of the store, over to the cash register, to eye level, to an
end-aisle display, and so on. Now it is time for bigger questions such
as how structure can make a healthy food more attractive or more normal
or popular to buy. Again, consider healthy, high-margin,
environmentally sustainable tofu (Groening, Inman, and Ross 2014).
Using a store's structure to make tofu become more popular and trendy
could be surprisingly transforming for retailers, manufacturers, and
consumers.
Service is sometimes too narrowly defined as face-to-face or voice-
to-voice encounters. New technologies both inside and outside the store
give service the most unrealized potential by leveraging eye-tracking,
smart shopping carts, video-tracking, and GPS technology (Hue, et al.,
2013; Nikolova, et al., 2014). Whereas most interventions cannot easily
show which of the three Hierarchy of Health predisposition segments
they impact most, new technologies could show the results of these
interventions by either directly linking them to sales or indirectly
doing so through shopper loyalty cards.
Applying Wider
Some of what we know about improving healthy shopping in grocery
stores has already been systematically adopted into the growing 24-h
lifestyle of convenience stores, corner stores, and mini-stores (Lenard
and Schnare 2016). In 2016, the National Association of Convenience
Stores launched a new toolkit titled, ``Ideas That Work to Grow Better-
for-You Sales,'' and they include evidence-based tactics including (1)
grab them immediately, (2) variety sells produce, (3) use creative
adjectives, (4) remember the convenience factor, (5) have multiple
displays, (6) let your store ``talk,'' (7) direct their feet, and (8)
remind them (Lenard and Schnare 2016). Given this success in C-Stores
(note the fruit baskets that are now near most cash registers), there
are three other retailing frontiers that are deserving of our
attention.
Concessions and Kiosks
Entrenched managers in food concessions and kiosks have long
justified their unhealthy food portfolio mix my reactively claiming
they simply ``sell what people buy.'' Yet they say this without really
having given healthier food much of a chance. Indeed, when a healthier
range of snacks (fruit, chicken sandwiches, granola bars, low-fat
string cheese, trail mix, and so on) were offered alongside existing
concession foods during one Iowa football season, sales of healthy
snacks rose with each high school game until they comprised nine
percent of sales in less than 2 months because of both switching and
new sales (Laroche, et al., 2015). When Disney World followed by
changing the defaults on kids' meals at their kiosks and offering fruit
instead of french fries, it too generated more praise than pushback
(Peters, et al., 2016). Discovering simple, evidence-based steps that
help retail concessions profitably move from selling snack foods to
selling meal substitutes could be game changing (Laroche, et al.,
2017).
On-line Shopping and Delivery
After its initial growing pains, on-line shopping and delivery has
been consistently growing across both North America and Europe. Yet the
new adopters of this service are often families with children who
steadily use the service once a week for a month; use it less
consistently for the next 2 months; and often become inactive after
that. Other than focusing on price promotions or loyalty programs
(Bodur, Klein, and Arora 2015), a better solution would be to determine
how to increase retention in a way that transforms how they eat in the
same way it transformed how they order (Marinova, et al., 2016). The
opportunity to help people transform the way they view themselves (and
their health) because of how they order food could sustain both this
industry and their families (Lund and Marinova 2014).
Food Pantries
Helping food pantry shoppers make healthier decisions has typically
involved research replicated from other contexts (Bhana and Contento
2017; Wilson 2016). There are limited numbers of products in food
pantries and there are binding constraints (such as how much one can
carry, or how much a person is allowed to take from a category such as
pastries). Yet these limitations are precisely why a food pantry is a
rich context for discovery. Without economic considerations, how do
food shoppers behave? If they still take no fruits and vegetables, this
might suggest that subsidizing cucumbers and taxing cupcakes may not
have the intended policy impact that public health policy makers
believe it would have (Bhana 2017; Cawley, et al., 2015). Aside from
being a rich context for research, applying useful insights to food
pantries provides a world of value far from the office.
Why Healthy Field Study Interventions Appear to Fail
Applying this Retail Intervention Matrix framework is enticing. Yet
one frustration when applying our theories deeper and wider is that
health-focused interventions often fail when we move from the lab to
the field (e.g., van Herpen, et al., 2016). We usually believe it was
because of poor implementation by our retail partner, or it was because
of a noisy measurement problem. Instead, there are two reanalyses we
could make ex post to more precisely determine if an intervention was
more effective than it initially appears. We need to analyze the right
people, and we need to analyze the right days of the week.
We Do Not Analyze the Right Consumer Segment
Not all interventions work with all people (recall Fig. 2). An
expensive, digital, in-store calorie education program with a hip
spokesperson and viral social media support will still have no impact
on the top or bottom segments of this hierarchy. This is because the
Health Vigilant Shoppers already know it, and the Health Disinterested
Shoppers do not care. Yet most retail field studies show
disappointingly modest results because they do not try to disaggregate
the data and focus their analysis on the segment it was most intended
to influence. A more targeted analysis could be done by segmenting
shoppers into the Vigilant, Predisposed, or Disinterested segments
based on their purchase history (which is linked to their loyalty
cards) and then reanalyzing each segment.
Different interventions influence different segments (Table 2).
Setting up a study when and where it is most likely to influence a
targeted segment will better help sift out which interventions are
actually working in the way they intended. Aside from segmenting
shoppers based on their loyalty card purchase records, shoppers could
also be segmented or targeted by where they shop (e.g., Whole Foods,
Target, Wal-Mart, the Co-op, and so on). If neither is possible,
shoppers could be targeted by the time of the day or the day of the
week when they shop.
Table 2. A Retail Intervention Matrix of How Scandinavian Retailers
Doubled the Sales of Frozen Fish
------------------------------------------------------------------------
More convenient to More attractive to More normal to
Mix element purchase purchase purchase
------------------------------------------------------------------------
Signage Created Co- Create
recipe cards promoted the fish d ``Native
titled ``Fish in with vegetables Norway'' logos
15'' (min) (suck as leeks to promote
Offered a and broccoli) fish as local
``Grill Tips'' Named Used
flier for the select fish and ``Local
grilling salmon included a map Favorite'' and
showing the part ``Managers
of the world Special''
where it was stickers
caught
Structure Utilized Moved Placed
vertical display fish displays the single
cases; moved fish immediately after servings of
to eye level and vegetables fish and some
processed foods Included of the lower
to the bottom a buffer of priced ``sales
frozen vegetables specials''
between the fish near the
and the beef so highest
people would not traffic edges
make an unfair of the
sensory displays
comparison with
beef
Service Offered Offered Employ
frozen freezer smaller, one- ees were
packages to keep portion servings instructed to
fish frozen until Put suggest the
home markings on the two best
Offered wrapper to show selling types
plastic bags to how much to of fish and
put shrink- prepare for one, the two most
wrapped fish in two, three, or common items
for extra four persons with which
separation E-mail they were
protection from promotions were prepared
other foods in send to loyalty (e.g., rice
the basket card holders, pilaf and
with recipe ideas broccoli)
and web-links to Employ
downloadable ees were
coupons trained to
suggest
additional
items commonly
bought along
with specific
types of fish
------------------------------------------------------------------------
We Do Not Analyze the Right Days of the Week
It is not surprising that people shop much less healthy at the end
of the year--October through December--than they do after January 1st.
The dollar amount of the healthy food we purchase increases 29.4% right
after the first of the year (Pope, et al., 2014). This is not
surprising but it would suggest that if an intervention has any chance
of working, it would be better to test it in mid-January than in mid-
December or even mid-June. In general, a healthy intervention's
effectiveness might continually decline throughout the year. That is,
healthy shopping-focused interventions may be most effective in the
first quarter, moderately effective in the second quarter and third
quarter, and least effective in the fourth quarter.
Yet if shoppers are on their best healthy shopping behavior during
January, something similar may happen the beginning of each week in a
smaller way. After a weekend of indulging, some people might have an
unstated resolution to try and shop better, which makes them more
susceptible to in-store nudges on a Monday than they would have been
the prior Friday night. This Monday Morning Effect has been recently
shown in both in cafeterias and grocery stores (Wansink, Tran, and
Karevold 2017). In a 3 month study of over 15,000 diners, putting fish
first (and beef last) on a buffet line increased fish selections on
Mondays to Wednesdays but had no influence after Wednesday. Analogous
results were found in grocery stores. Among people who made larger
purchases (over $50 USD), interventions were most effective early in
the week (Monday-Wednesday) than on Thursday-Sunday. If a field study
intervention does not seem to have worked, reanalyze the sales results
for only Mondays, Tuesdays, and Wednesdays. It may give a more accurate
assessment of whether the intervention is worth dropping, reporting, or
improving.
Using the Retail Intervention Matrix to Sell More Fish
Until now, the Retail Intervention Matrix has been presented as a
way to organize research findings based on how they work (making
healthier foods more convenient, attractive, or normal) and where they
are implemented in the store (within the signage, structure, service
mix). This framework can be used to organize key findings into a
sensible pattern that is also useful in practice.
For example, a large Scandinavian grocer had the marketing
objective of growing their market share by repositioning itself as the
most environmentally sustainable retailer in Norway. One way they
planned to accomplish this was by increasing their sales of fresh and
frozen fish, which are much more environmentally sustainable than beef,
pork, and lamb. They planned to first increase the variety of fish they
offered (types, sizes, packaging, and so on) and to more actively
promote this fish though advertising campaigns and price promotions. In
addition to these traditional 4-P marketing mix methods of growing this
category, the Retail Intervention Matrix was then used to create a
broader set of interventions that could be used to further push the
sales of fish by focusing on changes in the signage, structure, and
service mix.
All 457 stores in the chain used the traditional marketing mix
approach of altering the variety, packaging, advertising, and price
promotions of fish. Over a 2 year period, these marketing efforts
consistently increased sales by nine percent. Following this, 239
stores selected various additional changes to make (see the Retail
Intervention Matrix for increasing fish sales in Table 2). Because of
these changes, the average store generated 28% more fresh fish sales
per transaction than those stores that had initially changed only the
marketing mix (Karevold, Tran, and Wansink 2017).
This brief example involving Norwegian fish shows one way research
findings can be extrapolated, organized, and presented in a way that is
compelling for mangers who have little time or tolerance for ambiguity
and nuance. Showing how an intervention might work (the CAN approach)
and where it can be implemented (through the signage, structure,
service mix) enabled this retailer to provide a menu of actions or
changes that each of its stores could pick and choose from. Similar
adoptions of retail-based findings are also being explored by an
American consortium of grocers (Borstein 2015) who are assembling an
industry-wide Grocery Retail Scorecard that will show retailers how
they can profitably help their customers shop healthier
(Convergencepolicy.org/scorecard/).
Conclusion
Retailing research in the future will be different than that of the
past. It will be partly judged on whether it delivers fresh, useful
solutions. A common view in the past was that an academic's role was to
generate insights, and the role of managers was to determine how to use
them. In the future, determining and discovering which insights have
the biggest impact will be broadly rewarded. Using the Retail
Intervention Matrix--including the CAN approach and the signage,
structure, service mix--can help determine what is known and what needs
to be discovered. Last, the Hierarchy of Health Predisposition can show
where an intervention can be most effective, most immediately.
Appendix A. An Abbreviated Scorecard To Help Retailers By Organizing
Sample Findings Into the Retail Intervention Matrix a c
------------------------------------------------------------------------
More convenient More attractive More normal to
to purchase to purchase purchase
------------------------------------------------------------------------
Signage Use Use a Displ
display signs to guidance system, ay
draw attention such as Guiding educational
to and promote Stars or a posters
the store's stoplight around the
selection approach, at the store that
seasonal fruits shelf edge encourage
and vegetables Use healthy
with display display signs to eating, such
signs draw attention as the Half-
Provide to and promote Plate Rule
information seasonal fruits Co-
sheets on and vegetables promote
healthier ways with display healthier
to shop near all signs options
entrances Use together in
Directs signs which snack aisles
traffic entering provide ``Did Highl
the store such You Know?'' ight healthy
that most health benefit alternative
shoppers begin facts, positive entree
in the produce messages about options such
section specific as the salad
Provide healthful foods bar on
a circular/ad throughout the posters or
publication store, or both signs within
featuring and Bundle all dining
promoting recipe areas
healthier value ingredients for Place
options at least family meals posters
once per week next to recipe displaying
cards for a healthier
healthy meal foods or a
Make guidance
sure that soda system such
and low-nutrient as the Half-
snacks (i.e., Plate Rule in
chips) are not visible areas
displayed or in the dining
merchandised in area
the produce
section
Structure Offer a Assign Offer
``grab and go'' designated at least
area in the parking spots three
front of the near at least healthier
store with a one entrance for foods for
small selection pregnant women sale at all
of low fat milk, and mothers with entrances to
eggs, 100% infants (similar prime
juice, low-fat to handicapped healthier
yogurt, and spots) d shopping
whole grain Create a Offer
bread for the in- fresh produce pre-printed
and-out shopper display in the shopping
Organize seafood section lists of
ingredients for including items basic staples
a healthy meal such as lemons, near all
by preparation tomatoes, beans, entrances
method, such as and asparagus Offer
a stir-fry Display healthier
section that whole fruits food samples
includes such as oranges, or
mushrooms, apples, pears, demonstration
eggplants, nectarines, and s near at
peppers, and so apricots next to least one
forth prepared entrance and
Place desserts at least once
healthier foods Make per week
conveniently at sure that there Offer
eye level is at least one \1/2\
Make checkout aisle portions for
available one in which the all entrees
percent or fat only food for and desserts
free milk, 100% sale qualifies that are
juice, and water as healthier (no served or pre-
in all mini candy aisle) packaged,
fridges in smaller
checkout aisles containers
Make for self-
sure there is at service
lest one entrees and
checkout aisle desserts, or
[i]n which the both
only food for Make
sale qualifies sure that
as healthier (no takeout boxes
candy aisle) are available
Make for leftovers
sure that all not eaten in
beverage coolers the dining
have both water area
and low-fat non- Offer
flavored milk divided
stocked and shopping
available carts with a
``place
fruits and
vegetables
here''
section
Service Supply Provide Suppl
simple five- calorie y useful tips
ingredient information on related to
recipes as tear- different types preparation,
off cards next and cuts of meat storage, and
to specific in the form of food safety
produce in- posters, in produce
store, on the brochures, or section, via
store's website, labels mobile phone
mobile phone Make app, or both
app, or both sure that the Use a
Make pre- store's website, receipt
cut vegetables mobile app, or program which
available in the both (if they can create an
meat section have one) has itemized list
Provide Shopper Loyalty indicating
an area in the specials that what
store for include deals on percentage of
shoppers to sit healthier items purchases
and relax d Provide were fruits
Provide a loyalty card and
an area in the program which vegetables,
store for rewards low-fat meat,
shoppers to eat customers with and low-fat
d incentives such dairy
Offer a as bonus points Use a
salad bar that or coupons for receipt
includes lower purchasing program that
calorie fruits and uses loyalty
dressings vegetables, card
options such as making healthier information
oil and vinegar choices, or both to show how
Promote Offer a much was
mobile phone discount for spent on
apps that customers if a fruits and
encourage certain vegetables,
healthful eating percentage of and compares
such as purchases are this amount
Fooducate, fruits and to past trips
MyFitnessPal or vegetables
other Barcode/QR Offer at
code scanners least two daily
Offer healthier grab &
tips, features, go breakfast,
or videos lunch, and
involving better dinner options
shopping and
better living on
the store's
website or
social media
outlets
------------------------------------------------------------------------
a Reprinted, with permission, Slim by Design, Wansink (2014).
b Findings are from published papers, working papers, and unpublished
pilot studies (Wansink 2014).
c Comfort measures reduce stress. People make better food decisions when
they are under lower stress conditions.
d Editor's note: No footnote in submitted article.
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The Chairman. I thank our panel, terrific. I want to remind
our Members that I am going to be relatively strict with the 5
minute clock in order to try to get everybody through the
system. So if you want to use most of your 5 minutes to make
editorial comments and ask a question with a second left on the
clock, I will ask our witnesses to submit the answers for the
record. I am just trying to be fair to everybody.
So with that, I will recognize the Chairman of the
Subcommittee on Nutrition for his 5 minutes. G.T.?
Mr. Thompson. Thank you, Mr. Chairman. Thank you, members
of the panel, for everything that you do, supporting the
nutritional needs of American families. It is greatly
appreciated.
This is such a great topic, obviously one that I am
passionate about. We are looking at how Americans find
themselves, and American families, individuals, a lot of
children find themselves in some pretty challenging financial
circumstances, get access to nutrition. Certainly in addition
to personal resources and family support, community programs,
and obviously our TEFAP program and other ways that this
Committee supports those community programs, and then we have
SNAP, the Supplemental Nutritional Assistance Program.
And this is an appropriate place to have this discussion.
Quite frankly, this is the appropriate jurisdiction when you
look, because there are two basic principles. First, nutrition
matters in so many different ways; and second, it is farmers'
feed.
And so my first question is to the gentleman from the
Keystone State, Mr. Weidman. It is good to see you. Thank you
for being here today, and congratulations on The Food Trust's
25th anniversary. We appreciate all that, sir, your
organization does to improve access for those in need to
affordable and nutritious food.
Your testimony mentions that in Pennsylvania, nutrition
education programming is in both urban and rural environments.
I represent the Pennsylvania 5th district. Obviously, on this
topic I am concerned with all Americans, but in the 5th
district, which is very rural by definition, 24 percent of the
land mass of Pennsylvania, how does SNAP-Education reach into
those rural areas?
Mr. Weidman. Thank you, Congressman Thompson.
In Pennsylvania, we have a great SNAP-Ed program. There is
a little variability from state to state in how the program is
operated. In Pennsylvania, it is led by Penn State, and they do
a great job. Because of the extension program, they have a lot
of breadth to cover rural areas. We have stuff happening in
almost every county in Pennsylvania. And it is similar to the
work that we are doing in Pennsylvania, working with children,
working with adults to get them to learn more about where food
comes from, sometimes nutrition science can be confusing to all
of us, so helping them, kind of guide them to make good choices
of the food around them.
One of the programs we work with is the Share Our Strength
Program called Cooking Matters in the supermarkets. It is
really taking seniors and other adults on tours of a grocery
store literally and teaching them about how to shop healthy,
how to shop on a budget. And this kind of work is happening all
around the country. There is a great rural example of SNAP-Ed
happening in New Mexico, the CHILE (Child Health Initiative for
Lifelong Eating and Exercise) Plus Program, and that is doing
work in Head Start centers as well as pre-K, working with kids
and their families, basically, to help them, again, guide them
on making healthier choices, teaching them how to cook healthy
recipes, taste tests, and that is happening on Tribal lands as
well in New Mexico, in addition to other sites. It is at about
80 sites in all in rural areas of New Mexico. And I am sure
there are plenty of other examples.
SNAP-Ed is a great way to get at both this problem of
improving health in urban and rural communities.
Mr. Thompson. It seems like from the testimony and past
discussions I have had with key stakeholders and folks making
sure that nutritional needs are met is really strengthened by a
collaborative process, and obviously with programs like SNAP-
Ed, food insecurity, nutrition incentives, Healthy Food
Financing Initiative, all those, can you expand just in the
short time we have on other types of collaboratives, other
folks who have sat at the table. You mentioned my alma mater,
the great land-grant university of Penn State. Are there other
examples of collaboratives?
Mr. Weidman. Sure.
Mr. Thompson. It seems like a model we should continue to
strengthen.
Mr. Weidman. Yes, There are great examples of
collaboratives, a lot with the grocers. We work with a great
local grocery chain in Pennsylvania called The Fresh Grocer,
and The Fresh Grocer is partnering with us to provide us space
for doing nutrition education, SNAP-Ed programming in their
stores. As Brian said, these stores are a great place to meet
customers where they shop and help guide them to make healthier
choices. That same grocer is also helping us with SNAP
incentives, doing our Philly Food Bucks inside The Fresh
Grocer. Every time a customer spends $5, the grocer is the one
that created the whole technology to put out a coupon, an
electronic coupon for $2 in free fruits and vegetables at that
store. And, again, this is something that we are seeing
nationally, great partners with grocers, with farmers certainly
at all of our farmers' markets in Pennsylvania and around the
country have been great partners.
I also just quickly would mention that the U.S. Chamber of
Commerce today in D.C. is having a conference called the Health
Means Business conference, and they are recognizing
partnerships between nonprofits and the corporate sector aimed
at improving health. GSK has funded a citywide initiative
called Get Hype Philly, working to get youth to be leaders in
making healthy changes in their community. So we are working
with nine other nonprofits and 50,000 kids in Philadelphia with
GSK, and then Campbell Soup Foundation in Camden, New Jersey is
midway through a 10 year initiative working with a number of
groups to improve health and childhood obesity.
Mr. Thompson. That is great, Mr. Weidman. Thank you.
I see my time has expired.
Mr. Weidman. Okay, thank you.
The Chairman. The gentleman's time has expired.
Mr. Scott, 5 minutes.
Mr. David Scott of Georgia. Thank you, Mr. Chairman.
This whole issue, to me, strikes right at the nerve of the
foundation of our great country, which is founded on the
principles of life, liberty, and the pursuit of happiness. And
there is no other area of human endeavor that best manifests
our foundation of life, of liberty, and happiness than our
choice of food.
Think of what makes you happy. I know there are many things
out there individually that make us happy, but none greater
than food. And what bothers me with this is that we want to
make subjections here that are just absolutely not true. Sodas,
candy, sweet things, that is not what makes us obese. It is the
lack of our children exercising. Look at the history of this
country. Look at us 30 years ago, 20 years ago. What has
happened? Our children and us, we don't go and exercise. We
don't have physical education in the schools anymore. But what
we have is this Blackberry, this Facebook, this going on the
Internet. And instead of children going and saying let's go
play basketball or let's hook up a game here, they go in the
basement or they go in their room and they stay hour after hour
on that.
My whole point is this. Food surveillance violates the
basic principles of this great country, and first of all, you
are going to discriminate between a low-income person simply
because for 6 months on average that is all they stay on food
stamps. They are gone. Look at the complexity you are going to
put into the grocery store. Who is going to pick up that extra
cost to have the food police there monitoring, and why?
Now I think that a better way of going about solving many
of these things is to look at how we educate people. You can't
force them. You can't deny them their freedoms to be able to
make choices without violating their pursuit of happiness.
Think about it. When Thomas Jefferson wrote those words, he
said to himself, and he wrote in one particular pamphlet, and
he wrote this to his arch competitor, Alexander Hamilton. And
what he said was, in this way, he said, ``What I have declared
here, my dear Mr. Hamilton, is has come to me these words,
life, liberty, and the pursuit of happiness.'' He said by some
divine providence intervention. In other words, what he was
saying was those words, life, liberty, and the pursuit of
happiness, he came and he wrote that Declaration of
Independence under the inspiration of God Almighty. Let us not
go against that.
Thank you, Mr. Chairman.
The Chairman. The gentleman yields back.
Mr. Crawford, 5 minutes.
Mr. Crawford. Thank you, Mr. Chairman. That is a tough act
to follow, Mr. Scott. I appreciate that.
I represent a part of the country, the Delta region,
probably better than \1/2\ of my district is, and as you can
imagine, working with a high degree of poverty. So many of my
constituents are heavily dependent on SNAP benefits, and the
problem they confront is that over the years, we have seen a
decline in the number of supermarkets. So what we are dealing
with ultimately here, ironically, is one of the most productive
agricultural regions in the country is effectively a food
desert. We have limited access to the healthy foods, so they
rely on convenience stores and things like that. To restrict
SNAP purchases to healthier food products, my question is would
the compliance costs outweigh the benefits of accepting SNAP
benefits at retail locations, or would it encourage SNAP
retailers to offer a wider variety of healthy food products?
And I will just leave that to any or all that want to make a
comment on that.
Ms. Sarasin. It depends, frankly, on how any changes to the
program were structured. Obviously, retailers want very much to
be in areas where they can meet customer needs, and if the
customer base is there and they can be profitable and
successfully meet the needs locally, they will, and they want
to.
The kinds of proposals that we are talking about here will
definitely have an impact on how these companies can function.
The potential increase in the administrative costs for a
program that limits certain products, whatever they are,
whatever kind of products we are talking about, is going to be
oppressive, as I indicated in my testimony, given the sheer
volume of products that are available in supermarkets today,
and the number of new products that are introduced every year.
The creation of a structure to monitor that and determine which
products are in and which ones are out, is going to necessarily
create pressure on the system, and also create pressure at the
retail level for stores that are in existence, for stores that
are being contemplated to be created, and the result could be
that stores can't function profitably any longer in some areas.
It could also be that some stores will have to determine that
the administrative costs are so great that they would have to
leave the SNAP program entirely.
Dr. Schanzenbach. Thank you.
I would echo that. I would be particularly concerned that
these increased regulatory burdens would drive out some of the
smaller retailers, especially in rural areas.
The other thing that I would like to add is that, as an
economist, all of this comes down to supply and demand, and I
have heard a lot of conversation about how do we increase the
demand for healthy foods, whether that is through education,
whether that is through pricing incentives. If people demand
more healthy foods in those areas, those grocery stores are
going to respond by supplying more of them. So that is why I
would like to see the market work in this, and not restrict.
Mr. Weidman. And I would just agree with you of the need
for more grocery stores in the Delta. We are working with the
Michael and Susan Dell Foundation and Hope Enterprises located
in Jackson, Mississippi, to incentivize more grocery stores to
come to the Delta region. I also think the USDA through the
farm bill, the Healthy Food Financing Initiative offers real
opportunities to bring more grocery stores to the region.
Mr. Crawford. Dr. Rachidi, do you want to weigh in on that?
Dr. Rachidi. Sure, just real quickly. If you placed
restrictions on a very narrowly defined product such as
sweetened beverages, it would not be overly burdensome for
retailers, and I agree that it is really a supply and demand
issue. So if you did a restriction on sweetened beverages, for
example, which drove up demand for healthier products because
that is all people could use their SNAP benefits for, you would
hope that the retailers would then respond by providing more
healthy options.
Dr. Schanzenbach. And just respectfully, we think based on
economic theory that that is not what will happen. So many
people are using both SNAP benefits and their own cash, it
won't actually change behavior.
Ms. Sarasin. And if I could also respond to that.
I think we end up on a slippery slope when we start talking
about sweet beverages, because I don't know what that means,
and like most things, the devil is in the details. Because when
we start talking about sweetened beverages, are we talking, I
don't know exactly we are talking about. I mean, there are
juices that bring lots of nutrition that are sweetened
beverages. There are yogurt drinks that bring all kinds of
nutrients to the consumers of them that also have sugar in
them. We need to be careful about how we are discussing these,
because we are talking about a category of products as if we
all understand what that means.
Mr. Crawford. Thank you. My time has expired.
The Chairman. The gentleman's time has expired.
Mr. McGovern, 5 minutes.
Mr. McGovern. Well thank you.
It is safe to say that we all can make better choices and
healthier choices, SNAP recipients and non-SNAP recipients. The
avoidable health care costs that taxpayers pay for non-SNAP
recipients, they get diabetes, heart disease. We all need to do
better. But I don't think by limiting the choices of SNAP
recipients you get there.
In fact, Ms. Rachidi said that we have a new President.
Maybe this is a time to try a pilot project. So when you say
that, I Googled Donald Trump's eating habits, and it is not a
pretty picture. Domino's Pizza, Kentucky Fried Chicken,
McDonald's, Diet Cokes. I mean, maybe we ought to begin with a
pilot project that limits access to unhealthy foods at the
White House, because we all pay for that. The taxpayers pay for
that.
If we are serious about it, this ought to be a bigger
discussion. And one of the things we ought not to do, and this
is out of this Committee's purview, is cut back on the
nutritional standards of the school feeding programs, which
some have suggested. We ought to figure out the things that
have worked. I visited a place in Dorchester, Massachusetts,
called Daily Table. They provide access to nutritional foods at
a lower cost. A lot of these vegetables and foods would
otherwise be discarded by other grocery stores, but people go
there and they can afford to be able to make healthier choices.
I personally think that one of the things that we could do
is increase the SNAP benefit. It is about $1.40 per person per
meal. You can't make a lot of choices in general with that kind
of benefit. Research from the Center on Budget and Policy
Priorities found that increasing SNAP benefits by a mere $30
per month would lower food insecurity, decrease fast food
consumption, and increase vegetable consumption. We have seen
the Healthy Incentives Pilot which found that an ongoing
investment of less than 15 per person per day may result in 25
percent increase in fresh fruit and vegetable consumption. And
out of this pilot came the FINI grants, which are working
across the country to incentivize healthy eating. All very
positive stuff.
I have been to SNAP-Education programs, and I will be
honest with you, the critique I get from some who attend these
programs is that the ability to buy the stuff to have a
healthier diet is difficult, because in their neighborhoods;
they don't have supermarkets. They have to rely on convenience
stores, and there are a lot of issues here that we need to talk
about.
In our school programs, we ought to stress nutrition
education at an earlier age. It is a lot easier to get people
on a healthy pathway when they are younger.
But let me ask, Dr. Schanzenbach, do you support increasing
SNAP benefits? Do you think that would promote healthy eating?
Dr. Schanzenbach. There is good evidence that an increase
in SNAP benefits would increase consumption of healthy foods.
Just like was testified earlier, when people have really tight
budgets, they concentrate on getting the lowest cost calories;
and then, if we expand purchasing power over time, then people
will increase both the quantity and the quality of foods that
they are eating.
We have really good evidence from the Summer Feeding
Program, the Summer EBT Program that says, additional resources
improve nutrition outcomes, and similar, this work that you
cited from the Center on Budget and Policy Priorities suggests
that additional $30 per month would change how people eat and
make them consume more healthy foods.
Mr. McGovern. And I agree with what Ms. Sarasin said about
how do you define a sweetened beverage. Does cranberry juice
fall into that category? There are lots of nutritional benefits
to cranberry juice, but it is a sweetened beverage. And would
you take that off the list?
And again, from my experience talking to people on SNAP, a
lot of times it comes down to the affordability as well as the
access. We have lots of pilot programs going on all across the
country. You mentioned one going on in Pennsylvania, all very,
very positive stuff. We ought to understand that is how you do
it, not by going and telling somebody that we are going to
restrict your choices. I think that is something that we ought
not to be doing here in Washington. But if you want to do a
pilot program, I am happy to cosponsor one at the White House,
because I am worried about our President's eating habits right
now.
So thank you.
The Chairman. The gentleman's time has expired.
Mr. LaMalfa, for 5 minutes. Mr. LaMalfa, for 5 minutes? You
pass? Mr. Davis, 5 minutes.
Mr. Davis. Thank you, Mr. Chairman, and thank you, Mr.
LaMalfa.
Mr. LaMalfa. You are very welcome.
Mr. Davis. I appreciate that. It is always actually great
to follow my colleague, Mr. McGovern, and outside of the
comments about the President's eating habits, I actually agree
with him on some of these issues that maybe we ought to look at
incentives. Having this debate is great for all of us, because
this is isn't a partisan issue. I would challenge my colleague,
Mr. McGovern, to go to some of those restaurants that he named
and I believe he is going to be able to find that he can make
healthy choices at every single one of those restaurants, and
that is what is great about what we have seen in our country,
in our access to healthy foods. The marketplace is demanding
healthier choices, and all of those restaurants that he
mentioned have so many more today at a very much more
affordable cost than what they had even 5, 10 years ago.
So the marketplace is actually helping to supply that
demand for healthier choices, and I agree with Mr. McGovern. I
don't think that we have a role here in being the food police.
And he mentioned some critics of the School Nutrition Program.
Yes, I am one of those, and it is because the lunch ladies tell
me stories about how kids are throwing food away that they are
not eating. We don't have an adequate supply of healthy food to
serve in our school lunches that tastes good. Kids are throwing
it away, so how do we fix that? We do it by actually offering
more healthier choice, but in a way that is less of a top down
approach. Maybe incentivize it.
It is great to see so much testimony about expanding
purchasing options. I was a big supporter of the Double Bucks
Program in the last farm bill, and Dr. Wansink, I was actually
leaving to go to another hearing, but listening to your
testimony, and you talked about how do we have more incentives?
What can we do to incentivize rather than punish? Because I
don't drink cranberry juice. Sorry. It is probably good for me,
Jim.
Mr. McGovern. It is.
Mr. Davis. I don't drink it. It tastes like syrup to me. I
can't handle the sugar content in it. But if I was a SNAP
beneficiary, would I be able to buy cranberry juice and not
what I live off of, Diet Coke or Diet Pepsi? Who is going to
make that choice? It has zero calories. Actually, cranberry
juice has a lot more sugar and a lot more calories. So I don't
know who is going to make those choices, and frankly, I haven't
seen the Federal Government be a good barometer of making
choices like that for the constituents that I serve.
But Dr. Wansink, can you tell us what type of incentive
program would you recommend?
Dr. Wansink. Thank you very much.
Well if we can use schools as a parallel, as was brought
up. One of the ways that we found that it is best to guide kids
to eat healthier in schools is not necessarily nutrition
education programs, because they are costly and they are tough
to get into schools, but instead simply making the healthier
products more convenient, more attractive, more normal. Having
a basket of apples next to the checkout line, making foods
taste better, reducing waste and it also increases how much
people eat. And there are 29,000 schools who are now on that
program.
Now a similar thing can be done in the stores, and you are
right spot on when you say anybody goes in these restaurants
can eat healthy, because there are the options that are now
cheaper than they used to be. Making simple changes in grocery
stores that are incentive compatible with the grocery stores
that are either profit neutral or profitable for them.
Mr. Davis. Haven't they already been doing that?
Dr. Wansink. Not as widespread. I took last year off and
went on sabbatical to implement this in Norway to show that it
could be done as a tested concept, and even making small
changes in these grocery stores, simply having things such as
having fruits and vegetables within 10 of the doorway
increases how much people take by three percent. Making these
changes are things that grocery stores find profitable, but
then it also benefits all of us, not just SNAP beneficiaries.
Mr. Davis. But you wouldn't make the government force the
stores to change?
Dr. Wansink. Absolutely not.
Mr. Davis. Okay.
Dr. Wansink. No, we would make the profit argument to them
that they can make more money making people healthier.
Mr. Davis. Okay, because I know some stores would have to
actually move their Starbucks out of the way to be within that
10 of the door.
Does anybody else on the panel want to address the
incentivization?
Ms. Sarasin. If I could just say that, apart from the
incentive part of it, the thing that retailers are doing is a
lot of the stuff is happening on their own without incentives.
Our most recent data shows that something in the neighborhood
of 95 percent of our member companies have nutritionists and
dieticians onsite in their stores or at corporate headquarters
helping direct what is going on with their customers and
education. So there is a lot of this stuff that is happening
even without the incentives.
Mr. Davis. Thank you. My time has expired.
The Chairman. The gentleman's time has expired.
Ms. Lujan Grisham, 5 minutes.
Ms. Lujan Grisham. Thank you, Mr. Chairman, and I want to
thank my colleagues, Mr. McGovern and Mr. Davis. The focus of
all of our conversations ought to be on the incentives, and I
don't want to lose momentum. And we do really want very
specific ideas. In my state, we have incentives and initiatives
that are both authorized and supported by the farm bill, and
many of those that are solely state or corporate private-public
partnership initiatives. We have programs at WIC, we have
programs at Head Start that are uniquely focused on SNAP-
Education. We have a program called CHILE Plus. For those of
you not from New Mexico, we are the leaders and have the best-
tasting New Mexico chile anywhere. In fact, our state question
is whether it is red or green? But it is the Child Health
Initiative for Lifelong Eating and Exercise, and it really is
focused to integrate both purchasing and education and cooking
and eating healthy that we pushed out into the rural areas.
The core issue is that we want the flexibility for states
and rural communities and communities to really figure out how
to do it, but we need the farm bill to be really clear that
there is not only those incentives in terms of authorizations,
but there is funding and incentives for those funding vehicles.
I did the SNAP challenge, for $30, so I just had a protein
shake. I really think, as nearly a 60 year old woman with a
fairly sedentary public policy lifestyle I am really proud of,
I try to be cognizant of my calorie intakes. I work very hard
at it. Well during my SNAP challenge, I wasn't so good at it,
all right? I ate ramen noodles. I am trying to think of the
other high carbohydrate kinds of foods. I tried to get peanut
butter, high fat, and I couldn't get any organics. I bought one
banana and one apple for my weekly benefit if I was going to
have enough food and $1.50 left over.
Now if I am dealing with average benefits for my whole
family, and God forbid somebody in your family is sick and they
say we want high iron, high protein, you have a teenager who is
playing football. With that SNAP benefit, you can try all you
want to do healthy foods. It is impossible, because unhealthy,
cheaper foods are all you can buy. And I ate it. I stayed true
to doing what I was supposed to do, but it wasn't good.
So if we don't deal with that, in my state, seniors are
about to get their SNAP benefits cut: $33 a month with the
state portion that they are going to cut. I don't know about
you. I am a caregiver for my mom. If I go to the grocery store
for $33, I can't get anything that she ought to be eating.
Anything. So if we don't increase SNAP-Education funding and we
don't really put resources to allow folks to do these
incentives, we can talk about how great they are all the time,
and they are. I agree with that, my colleagues on the other
side of the aisle, we really shouldn't be the food police. We
ought to do incentives. We ought to do something about obesity.
You do something about obesity, you have hundreds of millions
of dollars to put back into economic incentives and farm bill
incentives to grow better food and to do more in the areas that
we all care about on this Committee. How can you get us, one of
the most bipartisan committees, to really think long and hard
about putting the resources where they need to be and seeing
the evidence-based outcomes that we have the research, the
Chairman teases me about research all the time. We have the
research that shows us that you have to educate people.
Do you have ideas to help us get to that agreement about
making sure that there are the resources that allow us to do
the things that you know would make a difference?
Dr. Wansink. I believe if we want to change things really
quick, it is probably not going to be education. It is a nice
long-term solution that is going to take a long time, and it is
not going to be the payoff. Initially educating, in this case,
retailers as to what they could do to guide people to these
healthier options, which are also high margin foods, because
they have to throw them away. If a banana goes bad, a retailer
loses money on it. What they can do to guide people to these
and get people to buy more of them. It is not just going to
benefit SNAP recipients, but it is going to benefit all of us.
Ms. Lujan Grisham. Anybody else? I have 10 seconds. Let's
go.
Dr. Rachidi. Just real quickly, there is really little
evidence that, and I am in favor of incentive programs, but
there is little evidence that incentive programs reduce
consumption of unhealthy foods. And so I advocate for both, and
so if you look at the integrity of the program, you could gain
support for increasing incentive programs and education if you
eliminate some of these other issues like allowing unhealthy
foods to be purchased from the program.
Ms. Lujan Grisham. My time is definitely up, Mr. Chairman.
The Chairman. Thank you.
Mr. Comer, 5 minutes. Mr. Comer, 5 minutes.
Mr. Comer. I was Commissioner of Agriculture in Kentucky
for 4 years, and one of the things that worked really well for
us with being able to provide healthy options to people with
EBT cards was when I first got elected, we only had 21 farmers'
markets that took EBT cards. When I went out of office, all 225
farmers' markets took EBT cards, and a lot of people said well,
that will never be a factor in sales. In a lot of those
farmers' markets, it was over 25 percent of the sales were from
EBT cards because of food deserts, and access to healthy food.
The farmers' markets are a great way, a great option for
people on SNAP because there are no bad foods at a farmers'
market, or I have never seen candy or soft drinks sold at any
Kentucky farmers' market, so I just wanted to share that story.
That was a pretty successful way to get healthy food options to
people that need it because of the obesity problem that we
clearly have.
Shifting gears here, what percentage of people use their
entire monthly SNAP benefits during the first week of the
month? Does anybody know the answer to that?
Ms. Sarasin. I don't know the percentage, but I can tell
you that the data that we have seen shows that a tremendous
amount of it is spent in the first week, and that those
purchases tend to be the largest of the month.
Mr. Comer. Right.
Ms. Sarasin. And the ones that have the most protein and
the things that we would tend to expect that they would buy
first.
Mr. Comer. In talking with retailers across the state about
this issue, several have come up with this suggestion, and they
use this example. The majority of the people that they deal
with use their entire benefit, monthly benefit, the first week
of the month. So if you want to provide milk and things like
that, meat for low-income people to eat healthy, if this is
their only source of revenue for their food, the milk expires
or it is gone. They don't have access to milk or a lot of
proteins that expire. I wonder how feasible it would be to have
a bi-monthly benefit to encourage more people to try to manage
their budgets to where they can have milk for the first half of
the month and the second half of the month, because it is a big
problem. I represent a very poor district and that is something
that just about every retailer that I have talked to has
mentioned that as a suggestion to be more efficient and to help
the people. Because a lot of the people unfortunately don't
have a high level of financial literacy, and that is an issue.
So I just wanted to get your thoughts on that, Ms. Sarasin.
Ms. Sarasin. As a Kentuckian myself, and from the next
county over from you, I can totally understand the discussions
that you have had. And what we find in many states is that they
have gone to not having single dates of the month when the
benefits are available. There are multiple points in the month
when recipients have access to their benefits. And so in the
states where that has happened, it has been very beneficial,
certainly from the retail perspective because it allows us,
instead of having to have such pressure on both our labor pool
on a certain day or in a certain week of the month, but also on
the supply chain issues so that we have enough milk in
different quantities and different styles and different sizes,
for example. Being able to move these things out over the
course of a month would certainly, from a retail perspective,
be a better situation for us.
Mr. Comer. Yes, I am for less government, and I don't like
the nanny state and all that, but is it clearly a problem, and
the obesity issue, it is almost at epidemic levels. The poorer
the county, the higher the obesity rate. And you can see that
when you go into public schools and, unfortunately for the
students in the poorer schools. You can just tell there is a
higher obesity rate in those schools.
Ms. Sarasin. A couple of things. One is the data seems to
indicate that everybody is getting fat, rich kids, poor kids
alike. But to give my perspective on your question about the
twice a month. Something that people raise as a concern there,
especially for people with limited access to places to shop,
breaking this benefit up into twice a month might make it
harder for them to get to the store, because now instead of one
big shopping trip, they have to do multiple. So it is just
something to consider.
Mr. Comer. Thank you, Mr. Chairman.
The Chairman. The gentleman's time has expired.
Ms. Adams, 5 minutes.
Ms. Adams. Thank you, and thank you, Mr. Chairman and
Ranking Member Peterson, for hosting the hearing, and thank you
to the witnesses for being here today.
The SNAP program is very important to those in the 12th
District of North Carolina. I represent that district, and the
folks struggle with food insecurity a lot. We have a lot of
food deserts. But as someone who lives with diabetes, I know
that there will be times when someone that participates in the
SNAP program and has diabetes will need to buy a candy bar to
quickly raise their blood sugar, and they should be able to buy
that candy bar with their SNAP benefits.
Dr. Schanzenbach, could you provide a brief summary of the
findings of your research on the long-term health impacts on
individuals who participated in SNAP as infants and toddlers?
Dr. Schanzenbach. Thank you. So my recent research study
looked at the introduction of the Food Stamp Program, which was
done over the 1960s and 1970s. Congress in its great wisdom
decided to roll it out slowly, and so that gives us an
opportunity to study, if you lived in this county when you were
5 years old versus that county, you had different access to the
Food Stamp Program, as it was then called. And so then we can
tease out well, what happens if people are given access to the
Food Stamp Program.
What we found was a couple of things. First is children are
born healthier if their moms have access to food stamps while
she is pregnant. But then because this happened so long ago, we
were able to follow the children who grew up in these areas
over time. So now they are 40 and 50 years old. What we found
was that we should really be thinking about food stamps as an
investment in children. So we found that access to food stamps
during childhood increased the likelihood that they graduated
from high school by 18 percentage points. Furthermore, we were
able to look at their adult outcomes. We found that they are
healthier in adulthood. We looked at this thing called
metabolic syndrome, which is a clustered association between
obesity, diabetes, high blood pressure, et cetera. What we
found there was more access to food in early life sets up
systems in your body to actually make you less obese in later
life.
Then finally we found that, and this was particularly the
case for women, that people who had access to food stamps in
childhood grew up to be more economically self sufficient. They
are more likely to be employed. They had higher earnings, and
they themselves as adults were less likely to be reliant on
food stamps or welfare programs.
And of course, as an economist, what I think is going on
here is that the children were better able to make investments,
right? They weren't going to school hungry so they could pay
attention in school better and learn more. And so this is very
important evidence, evidence I certainly want the Committee to
know about, to think about this program as an investment.
Ms. Adams. Okay. So would you support a higher SNAP
benefit?
Dr. Schanzenbach. Certainly, it is very important to
preserve the program as it is, so that is sort of always my
first worry. But then I do think with separate evidence that
there is good evidence that increasing the benefit levels will
increase the amount of healthy foods purchased, will reduce
food insecurity, and of course, one out of every five children
in this nation lives in a food-insecure household right now,
and in nine states, it is one out of four children live in a
food-insecure household. I think that is too high for this
great nation of ours.
Ms. Adams. Okay. So why would SNAP restrictions on soft
drinks, for example, be unlikely to change consumption patterns
shared by all Americans?
Dr. Schanzenbach. Sure. So of course, remember that food
stamps benefits are relatively modest, $4.50 per person per
day, and if we think about an average household, which gets
about $250 in food stamp benefits, and then they have to
supplement their food purchases by additional cash resources.
So it is $100, $150 additional. Then on average, households
spend about $12 to $14 a month on soda, right? So $250 SNAP,
$100 in cash, $12 on soda. Be very straightforward that even if
we go through all this red tape and debate what is in a soda
and what is out of a soda and is this sugar sweetened or not,
if we did that, when they get to the checkout line, they would
be able to say, ``Okay, I still want to purchase my soda, my
sugar sweetened beverage. I just need to do it out of this pot
of money instead of that pot of money.'' That is a lot of red
tape to go through to not change behavior.
Ms. Adams. Thank you very much, Mr. Chairman. I yield back.
The Chairman. The gentlelady yields back.
Mr. Yoho, 5 minutes.
Mr. Yoho. Thank you, Mr. Chairman, I appreciate it, and I
appreciate you all being here. This is such an important topic
that we look to reform and make it right, both for the
recipient and for the taxpayers.
Ms. Sarasin, one of the common arguments against
restricting SNAP purchases has been the operational challenges
of implementing restrictions, and if you have gone over that, I
apologize, and if you haven't, with regard to the tech needed
to track the restricted items, what do you see as a hold up on
that, or is there anything that we can do better legislatively,
or leave you guys alone?
Ms. Sarasin. Well as I mentioned in testimony, one of the
things that is challenging is that our cashiers end up being,
to some degree, the food police at checkout time. And as you
are probably aware, that holds up a line.
Mr. Yoho. Yes.
Ms. Sarasin. And if you have ever been in line behind
somebody who is having a challenge like that, it is difficult.
And sometimes, it ends up being a difficulty with some of our
most vulnerable populations, and so it becomes also a stigma
and a problem in that regard.
But when you are operating a business that in general is on
a one to two percent profit margin a year, every second that is
delayed at the checkout line is a problem. Our companies
measure it because they want to keep things moving.
Mr. Yoho. Right.
Ms. Sarasin. It creates a lot of issues for us at checkout,
and just the administrative function of trying to figure out
what is in, what is out, as if we went into the role of trying
to determine that certain things shouldn't be allowed and
certain things should be allowed, it would create real havoc in
our stores.
Mr. Yoho. Let me ask you this, because this has been
brought up to me multiple times, in the big retailers' aisles
that were restricted just to those things so people could go
right there. It would expedite them going in there, buying
those products, bringing them up and checking out with no
confusion. Your thoughts on that, and then the other one is the
financial impact. We hear people saying that on the retail side
that this brings in `X' amount of dollars for us, and we can't
change it because we are dependent upon that. What is the
pushback that you have experienced in your industry?
Ms. Sarasin. Well the real pushback is the administrative
costs of trying to actually facilitate the program. One of the
things that I hear regularly from our companies is that these
programs are some of the most difficult regulatory programs for
them to implement in their stores. And when you are talking
about companies that have to deal with things like the Food
Safety Modernization Act and all of the regulations that go
along with that, if this is a more difficult challenge for
them, that says a lot for what they are dealing with.
The costs associated in the store with doing this on such a
low margin business is significant, and not that there
shouldn't be changes to the program if they are desirable and
if they achieve a policy goal, but just to unilaterally
identify that certain types of products should or should not be
in without a real basis for making the decision is problematic
for us.
Mr. Yoho. Well, you can see how important it is, as many
meetings as we have had on it, and I commend Chairman Conaway
and the Chairwoman of the Nutrition Subcommittee last year,
Jackie Walorski.
Let me ask one other question, and this goes to Mr.
Weidman. How is SNAP-Ed reached in the rural area? And I know
in the State of Florida with the University of Florida, which
is a land-grant, they have an extension office in every county,
67 counties in Florida. And they seem to do a good job of doing
it. The nutritional educational programs, are they different
based on regions? Like we are in Florida. We have a hot, humid
climate. How is it in your area, and then can you do a one size
fits all for nutritional program for the whole nation, or
should it be more regionalized?
Mr. Weidman. Yes, that is a great question, and SNAP-Ed
does great work in rural and urban areas all around the
country. I mentioned earlier a rural program in New Mexico,
CHILE Plus, which is doing great work in pre-K and Head Start
programs. But yet, to your point, the great thing about the
SNAP-Ed program is it does have kind of oversight and guidance
to all of the programs that the different states are doing, but
it allows for local on the ground sort of innovation so that
the right type of nutrition education is happening, based on
region and based on the population that you are serving.
Mr. Yoho. Okay, I appreciate your time. I am out of time,
and thank you. Mr. Chairman, I thank you.
The Chairman. The gentleman yields back.
Mr. Lawson, from Florida, 5 minutes.
Mr. Lawson. Thank you, Mr. Chairman. I would like to thank
all of you all who are here. I was just thinking, I am a
country boy and so I couldn't think of anything more important
on a Friday than RC Cola and a moon pie. And the other day, I
was in the airport in Atlanta and I wanted to have a healthy
choice, and I saw a long line at Subway, but there wasn't a
line at Bojangles', so I tried to make the right decision, but
Bojangles' won out. So I understand.
What I really want to say is that it appears that when they
did this survey, and anyone can answer, the FNS did a survey,
and they said that in order to change the program, put
restriction on the program, that it could cost as much as $400
million or $600 million to administer the program. And I know
that would be dollars well spent if you put that into the
program, and people are going to do different things. And I
have seen people go into these convenience stores, and even
standing in line when they were making purchases, and saw that
it was very difficult and they didn't really want to be there.
But what I would say to you, and this question will go to
anyone, is that in my state, we have an organization like Farm
Share and Frenchtown Farmers Market that carry a similar
initiative to alleviate hunger. From your success with Food
Bucks programs and with nonprofit, how can I as a Congressman
assist other food banks and various organizations to help be
successful in this way and get this message out? Because you
talk about the educational aspects of it, rural and urban. What
can we do, because, you want to see this program continue, and
I don't know whether the young people know about RC and a moon
pie, but I want to make sure that it happens to all of us. But
what can we do as legislators to help in those areas? Anyone
can answer that.
Mr. Weidman. I will. As I said in my testimony, I really
think what is working is this comprehensive approach that
includes nutrition education, and the SNAP-Ed program is doing
a great job at that. Through incentives like the new FINI
Program, and I really appreciate Congress for launching the
FINI Program. We, for years, have been hearing that you get the
farmers' market in the neighborhood or if you get a grocery
store in the neighborhood, what about price, and that can be an
issue. And we have heard that today. The FINI Program does a
great job of both, making healthy foods more affordable, and
also allowing for innovation, again, at the local level in
places all around the country. And then last, actually getting
the stores located in areas so that people don't have to take
three buses to get to the grocery store. And I really
appreciate, again, the leadership of Congresswoman Fudge and
many others on this Committee for their support of the Healthy
Food Financing Initiative, which is a proven model that was
launched in Pennsylvania, working in partnership with the
grocers and other food retailers, to locate in under-served
urban and rural areas, create jobs, and provide access to
healthy food.
Mr. Lawson. Okay, and I have one more question for, is it
Raskins?
Ms. Sarasin. Sarasin.
Mr. Lawson. Ms. Sarasin, okay. I'm seeing things--dyslexic.
But why in the grocery stores are all the candies and stuff
right up by the cash registers? It feels good to look at all of
it, but I just ask that question, you know what I mean? Once
you missed it you got it again. Once you miss it down in the
candy aisle, it is back up there at the cash register.
Ms. Sarasin. What you will find is that increasingly in our
stores, while there are still aisles with candy right up front,
increasingly there are stores that have lots of other things
right up front as well. Mr. Wansink referred earlier to the
increasing incidents of bowls of fruit and other healthy
products that are available at checkout for consumers who are
interested in having them.
So from a retail perspective, our role is to provide the
best service and create the best experience with the product
lines that our customers seek, and at a price that they can
afford, and hopefully as conveniently as possible. So that is
what we strive to do for all of our customers, whether they be
SNAP beneficiaries or others. And so we have this constant
balance going on of trying to make sure that we are meeting all
of these needs, and for some people, having a sweet treat as
they walk out of the store is important. For others, it is
other kinds of products. They would rather have a piece of
fruit or they would rather have a yogurt as they walk out the
door.
So our goal is to try to provide a balance of products for
all of our customers, depending on what they are looking for.
The Chairman. The gentleman's time has expired.
Mr. LaMalfa, 5 minutes.
Mr. LaMalfa. Thank you, Mr. Chairman.
So today we are talking about the SNAP program,
Supplemental Nutrition Assistance Program. Supplemental meaning
in addition to what might be someone's personal income, or
other forms of aid a family might be receiving. Nutrition,
generally thought of as something good for the body, making you
healthier, stronger. Assistance, the idea that someone else is
probably paying for this to help people.
Ms. Sarasin, you talked repeatedly about how what basically
a hassle this will be for stores to have the system in place to
differentiate between more of these food products, so do people
that come through the checkout line that are SNAP users not
have other products that are ineligible for SNAP very
frequently, such as house cleaning items, toiletries, other
things that they are paying for that are not eligible? Is there
anything that is not eligible for SNAP, I guess, that would
have to cause a second transaction at the checkout counter?
Ms. Sarasin. Yes. Yes, there are many types of products
that are not----
Mr. LaMalfa. Tobacco, alcohol, like that?
Ms. Sarasin. Alcohol and tobacco are not SNAP eligible.
Mr. LaMalfa. Okay.
Ms. Sarasin. SNAP is applicable to food products.
Mr. LaMalfa. Yes. So if you have someone in line that is
making one trip to the store, they are buying all the needs for
their household for the next week or 2. They are buying
multiple items. Some are eligible, some are not.
Ms. Sarasin. Correct.
Mr. LaMalfa. So if we were to have this discussion about
things that are nutritional and we have items on the list that
maybe are now eligible for SNAP but determined somehow to not
be nutritional, is it really that much tougher to differentiate
between soda pop and tobacco?
Ms. Sarasin. The challenge is in how you are defining soda
pop or how you are defining nutrition or how you are defining a
healthy product. We have had a lot----
Mr. LaMalfa. Well shouldn't we try, because we are having
all this effort made in recent years over fighting obesity and
kind of differentiating between what things are contributing to
obesity and what are not?
Ms. Sarasin. We have had testimony this morning that has
provided the evidence that doing so is going to be at great
cost, and that the ultimate benefit----
Mr. LaMalfa. It is great cost to the people that are the
assistance part of this program, and it is also of great cost
to the people, for lack of maybe knowledge or the idea that the
government is incentivizing it, sending them home with candy
bars and soda pop. So maybe it is worth the trouble.
Let me shift to Dr. Rachidi here. I thank you for appearing
as well. When we talk about the SNAP program's intention to
alleviate hunger and malnutrition, and permit low-income
households to obtain a more nutritious diet through normal
sources, that is in statute, so with these aims and the idea
that we are approaching nearly ten percent of beverages are
accounting for expenditure, as was mentioned, we don't have
data to determine how the restriction should impact the
program, but we should at least try.
The recent USDA study was troubling, and I think kind of a
red flag for a lot of folks. A couple thoughts for you on that
is you discussed a study also that evaluated the impact of a
hybrid pilot of incentives and restrictions. So do you think
this could be a feasible demonstration we could take more
widely for entire states, and with some more cooperation from
USDA, which seems to want to shut down states from making their
own determination? Please expound upon that.
Dr. Rachidi. Yes, I definitely think it is something that
should be tested, and at the state level or the local level.
Like I mentioned, we tried to do it in 2011 in New York City.
The USDA at the time denied it, as they denied a few other
states that had----
Mr. LaMalfa. What do you think the USDA's incentive is to
deny these possible studies and the learning we can get from
that at state level or New York City level?
Dr. Rachidi. I think there is a general aversion to
restrictions, as we have heard today, and that is part of it.
An additional reason that was given to us was also that it is,
they felt that our evaluation was not going to be rigorous
enough, which we did not----
Mr. LaMalfa. Do you think we have rigor now in separating
these----
Dr. Rachidi. Meaning that the evaluation design was not
rigorous enough that in the end, even with an evaluation, we
still wouldn't have been able to tell if it was effective or
not. Which we didn't necessarily agree with, but that was one
of the reasons. And the other reason was what we have also
heard today about the difficulties in defining what is a
sweetened beverage or not. We actually came up with what we
thought was a pretty clear definition, which is it excludes
juice, 100 percent juice, and any other beverage that has 10
calories per 8 ounces is a sweetened beverage, with a few
exclusions like Pedialyte, for example. But it was a pretty
straightforward definition.
Mr. LaMalfa. So we have super computers that could probably
program this in at the register and not make it that tough,
right?
Dr. Rachidi. Exactly, and we talked to retailers in New
York City, and there have been other retailers that we have
talked to through other efforts that have said exactly what you
said. They already restrict alcoholic beverages, for example,
non-food products, and this would just be one more thing to add
to the list.
Mr. LaMalfa. Thank you.
The Chairman. The gentleman's time has expired.
Mr. O'Halleran?
Mr. O'Halleran. Thank you, Mr. Chairman. I just have a
couple of brief statements. I will have plenty of questions for
the record.
But one of the statements I heard today was this pot
instead of that pot, and another one was three buses. And my
district is kind of a little bit different. It is a district
the size of Pennsylvania. It has 12 Native American
reservations on it, and some of the kids go to school on a bus
2 hours one way. Some of them have anywhere from a 50 percent
to an 80 percent unemployment rate. And sometimes, people can't
get out of their homes after a big storm because of the
condition of the roads to get to the store. So we have the
urban setting, the rural setting, and then we have these very
rural settings. And I am just trying caution us that as we look
at this whole problem, the cost of stores is an important
aspect to me, because in my area, stores are very far apart,
obviously, and the food that is in those stores is much more
limited in scope than other stores in urban areas. We also have
the concern that the education level on nutrition is very low,
and I appreciate the cooking classes and everything else, but
it is kind of hard to get to a cooking class if you are 2 hours
away from the nearest class.
And so between the quality of the merchandise, the concern
I have for the distances traveled, the unacceptable
unemployment rates, I just want to just caution everybody when
we start to think about this a little more that the entire
process, and I don't think there is anybody here that doesn't
care about nutrition for our families and our children, but we
also have to understand the realities of life in some areas of
America.
Thank you. I yield back.
The Chairman. The gentleman yields back.
Mr. Marshall, 5 minutes.
Mr. Marshall. Thank you, Mr. Chairman. My first question is
for Dr. Rachidi.
As you may know, I am an obstetrician and very familiar
with WIC programs. Of all the things that my patients and
nurses seem to think is a good thing, is WIC. What can we learn
from WIC that we could apply to SNAP? What makes it successful?
Tell me what we are doing differently between the two programs
briefly, if you could?
Dr. Rachidi. Well sure. Real briefly, I mean, WIC has a set
of products that are eligible products to be purchased, and so
there is a list that is put together and it is intended to be
healthy products, and also they cater towards infants and new
mothers and pregnant women. SNAP, on the other hand, does not
have that. There are a few restrictions as we have heard today,
alcoholic beverages, non-food items, hot prepared foods, but in
general, there are no restrictions on what can be purchased
with SNAP benefits.
Mr. Marshall. Tell us a little bit about that education,
what is going on with those pregnant women and breastfeeding
moms that WIC is doing that seems to me to be so beneficial?
Dr. Rachidi. Yes, so WIC also has a large education
component, and again, it is a little bit of a different program
because it is focused on new mothers and infants primarily, and
young children. The education efforts are very much geared
towards that, but also very much geared towards nutrition.
On the SNAP side, as we have also heard today, there is a
nutrition and education program, and it is very different
across the states. States can choose how to implement it. Some
choose to have very robust programs. Some choose to have maybe
not so robust, but reach a lot of people, and so it is just a
little bit different program than WIC.
Mr. Marshall. Okay. Dr. Wansink, I guess my next question
is for you.
Certainly, I am concerned about health and diabetes and
obesity and these things, but my question for you is: have any
of the current educational or in city-based efforts resulted in
large scale changes, in your opinion, large scale changes in
dietary habits? Is it working?
Dr. Wansink. There is some of this going on that is very
good that has been effective, and back when I was Executive
Director for the Center for Nutrition Policy and Promotion, I
kind of said, this is too big of a thing for the government to
figure out, because government can't be where everybody
purchases and prepares food everywhere they work and they play,
but all of the things around us can, the companies and things
like this. So we started a program called Partnering with
MyPyramid. It's now called Partnering with MyPlate. And the
idea was to give credit and incentives to any company or any
nonprofit that would help make it easier for people to move
toward eating following the Dietary Guidelines. It was
tremendously successful under the last year of President Bush's
term, and it still is in place but it is not being encouraged
as much as it could be. And that would be great, because it
would enlist everybody to help more people eating toward the
Dietary Guidelines.
Mr. Marshall. Okay. I am going to stick with this theme of
lifestyle changes a little bit, and this is probably your
questioning, Dr. Wansink.
In my lifetime experiences, as a physician, trying to
change people's lifestyles, when they are pregnant seems to be
their most willing to do it. I have given up trying to convince
people to stop smoking unless they are pregnant or they ask me
about it. Trying to help a newly developed diabetic pregnant
woman to talk to them about diet modifications, they are very
motivated. They start wearing seatbelts. There are reasons that
this woman is motivated for lifestyle changes.
Why are they so motivated, and how can we apply that to
SNAP as well? I just think that pregnant women, by the time
they are 45, it is too late, but when they are 21, there are
opportunities here. So help me with what the next step is for
SNAP to take?
Dr. Wansink. I think that is an outstanding question,
because you are looking at, there is somebody who is doing
something for a bigger cause than themselves, and we see this
with people making changes in their diet, too. They will do it
for a bigger cause and become a vegetarian for a bigger cause,
but not for their health. And in trying to apply some of these
things to SNAP benefits, maybe what we need to do is we need to
start focusing on the impact this has on a person's family or
on their children, and start talking about SNAP benefits not in
terms of, oh, he was going to buy some groceries, but on the
implication this has on their family. And I love the stats that
you had about what happens that graduation rates go up by 18
percent for kids on SNAP benefits----
Mr. Marshall. I am sorry to cut you off, but I appreciate
the answer. My biggest concern is lack of activity as opposed
to calories in. I think that is the biggest problem with
obesity. Do any of you--can you--are we doing anything with
SNAP related to encouraging activity as opposed to playing
video games all day? My time is out. Sorry. I yield back.
The Chairman. The gentleman yields back.
Mr. Panetta, 5 minutes.
Mr. Panetta. Thank you, Mr. Chairman. I appreciate it, and
thanks to all of the witnesses who are here. I appreciate your
testimony, your preparation. I know it took quite a bit of
time, I am sure, to put together your statements today, so
thank you very much. I appreciate that.
My question kind of stems around education. As many Members
are starting to know, and as many people do know, I come from
the salad bowl of the world there on the central coast of
California. But we are looking to change that name actually. We
are going to call it the salad bar of the world. No, I am
serious. The reason they are doing that is because a lot of the
growers and the shippers, what they realized is the people who
work for them weren't eating the same foods that they are
picking. And they realized how to get to them is by getting to
their kids. And so what a lot of our ag companies have done is
donated salad bars, over 100, to the local schools to start
getting our children, including my two daughters, to start
eating more healthy foods, having that salad bar option. And
they are doing that. And what they are seeing is that when
their children start to eat more at schools, those trends go
home and their parents start to develop those same trends, and
that is actually working to a certain extent.
And so my question is how do we continue, besides ag
companies donating salad bars to our schools, how do we
continue to educate our children when it comes to getting them
to eat healthier in our schools? How do we do that?
Ms. Sarasin. A couple of things that the food retailers are
working on, one is a very high percentage of our companies do
in-store tours. I mentioned earlier that about 95 percent of
them have on staff nutritionists and dieticians, and what they
are doing is actually bringing school groups into the stores,
and the nutritionists and the dieticians take the children
through the store, and help them understand about nutrition,
help them understand the kinds of nutrients and vitamins they
get from various products, and the balance that they need to be
trying to achieve in their lives. So that is one thing that has
worked well and will continue to work well.
Another thing that we have done at FMI through our FMI
Foundation is we just had our second annual National Family
Meals Month in September. And the notion of National Family
Meals Month is sort of multi-fold. One is that some of the
societal challenges that we have are improved by having more
frequent family meals, and I am talking now about school
truancy, underage drinking, drug abuse, et cetera. The research
shows that more family meals tends to bring down the incidences
with young children and teenagers. But in addition to that,
what we find is that children who engage with their families at
mealtime, both by cooking, by purchasing the food, by being
involved in preparation and serving, they tend to have a better
understanding of nutrition and diet and health than those that
don't. So we are promoting national family meals within our
organization, but also at store level. And we have had, as I
mentioned, our second annual in September of 2016, so this is
something that we are doing on an annual basis so that our
retailers can actually be engaged with their customers in
helping children engage more with the preparation of food in
their homes.
Mr. Panetta. I appreciate that.
With the FINI Program and the SNAP-Ed program, what do
those entail?
Mr. Weidman. Yes, I was just going to say we work in 100
schools in Pennsylvania, doing SNAP-Ed, nutrition education
work. So teaching kids to try new foods, a lot of it is also
peer-to-peer marketing, so getting kids to be leaders in
changing their school environment, youth-led wellness councils,
and you really find that when the students are kind of
marketing to their peers around healthier eating, that has a
big impact. We also do, to the Congressman's point, our Get
Hype Philly program is about healthy eating and exercise, so
the combination of both of those is really important.
Mr. Panetta. Great. In regards to you, Dr. Wansink, you
talked about middle of the road consumers. You mentioned
signage, service, and structure, is there anything else we can
do to target them? What else can we do?
Dr. Wansink. Well what can be done at a retail level is to
make sure that the foods we want to guide them to are the
healthier foods, and they are being the ones that are most
convenient to purchase, they are most attractive to purchase,
not just by price, but attractively looking, attractively
named, attractively positioned, and then also that are more
normal, because right now it is just not normal to buy a lot of
healthy things at the grocery store, because you feel like you
are kind of a strange person. Simply a lot of placement changes
can make a big difference. Thank you very much for your
questions.
Mr. Panetta. Thank you. Thank you, Mr. Chairman.
The Chairman. The gentleman yields back.
Mr. Faso, 5 minutes.
Mr. Faso. Thank you, Mr. Chairman. I am intrigued, we had a
table here that came from USDA that suggests in 2011 that there
were approximately six billion purchases of sweetened beverages
in 2011. I don't know, do any of the witnesses have an idea of
how much of that six billion would be what we call soda in the
East and my colleagues like Mrs. Hartzler call pop in the West.
Although in western New York, they do call soda pop.
Dr. Rachidi. I believe it is a little more than \1/2\.
Mr. Faso. A little more than \1/2\. And would any of the
witnesses contend to me that soda, sweetened soda has
nutritional value?
This would be for Dr. Schanzenbach, and maybe Dr. Rachidi
as well. I take it by no answer from any of the witnesses that
no one believes soda has nutritional value. What would be the
problem with our, especially if we are looking at more than $3
billion of taxpayer money going to buy something that no one,
as far as I can tell, believes has nutritional value? What
would be the issue in your mind of a carefully designed study
by the USDA to actually analyze this question as to whether if
we had a restriction on certain sugared beverages that it could
result in altered buying habits and dietary consequences and
nutritional consequences for the families, particularly the
children who live in those households where that $3 billion of
taxpayer money is spent to buy soda?
Dr. Schanzenbach. You are asking a researcher if we should
have more research and that is the first thing they teach you
in grad school is yes, I would welcome any sort of a
demonstration program, but I would be quick to add that it
needs to be high quality, and so in particular that includes it
needs to be real randomized controlled trial, and that it also
needs to do a couple of other things. One, it needs to measure
consumption, not just compliance, but how does this change what
people consume, because some of the research out there that
maybe looks at the impact of soda taxes and other things like
that show that yes, people substitute away from soda sometimes,
but what they replace it with isn't necessarily much better.
Mr. Faso. Right, and so how many people do you think would
be appropriate in such a study?
Dr. Schanzenbach. Oh boy. I can't do power calculations on
the fly. I would be happy to submit something.
Mr. Faso. Perhaps you could submit that for the record.
Dr. Schanzenbach. I would be happy to.
Mr. Faso. Ms. Sarasin, at the risk of getting my friends in
the food merchants, and my friend, Mike Rosen, in Albany upset,
the fact is that now that SNAP benefits are in EBT form by and
large for the vast majority of those purchases, the merchants
are able to differentiate among taxable items and non-taxable
items. We had an issue in New York State for years where
certain marshmallows that were used if you put them on a stick
and you roasted them over the fire, those were tax exempt, but
if you bought the small marshmallows, those were taxable. I
realize the administrative complexity argument, but it does
seem to me that we are now at a point where we could be able to
more readily differentiate, just as we do with tobacco and
beer. You can't buy that with food stamps.
Ms. Sarasin. Well as I said in my testimony, could it be
done, yes, probably so. The question is at what cost, and is
the cost of trying to put together a means through which to
define the products that are in clearly, define the products
that are out clearly, such that electronically they could be
contained in a system and therefore would be able
electronically to be able to segregate? Absolutely, that would
certainly help, but again, we are talking about many tens of
thousands of products that would have to be done every year,
and the infrastructure to be able to make those determinations.
Mr. Faso. My point would be that we have these wonderful
academic researchers and experts. Perhaps we could design a
study that was statistically valid and which would consider the
difficulty that the food merchants have, but also get to the
core of the fact that when we were kids, the only time we ever
had soda or pop was when it was someone's birthday. And when I
see $6 billion, perhaps $3 billion of taxpayer dollars being
spent on soda, which has no nutritional value, in a program
that is called Supplemental Nutrition Assistance, something is
wrong.
Thank you, Mr. Chairman.
The Chairman. The gentleman's time has expired.
Mr. Soto, 5 minutes.
Mr. Soto. Thank you, Mr. Chairman.
In Florida, we have our Fresh From Florida Program, which
has tried to cue in local farmers with our schools, which has
had some pretty good success. In listening to your testimony,
it appears that most of you are encouraging us to have
incentives, to have a carrot rather than a stick, pun intended
on that--and to have greater access to folks in food deserts
rather than desserts. And I agree with both those things.
I did, however, read a Washington Post article this morning
that went right into this issue, and they had a conclusion that
a SNAP purged of sodas or candy or both could be less
vulnerable to cuts, and supporters can seek full funding. That
every dollar for SNAP would help nurse the poor, just as
Congress intended. And it got me thinking, first, how many of
you by a show of hands would support a ban on soda and candy?
Go ahead, how many? Okay, we have one. How many of you believe
that it would save money if we banned these two products? Raise
your hand. Okay.
And so I think that is what my main quandary is now is
whether or not the real goal is to have these sorts of bans to
get people to eat healthier, whether the real goal would be to
try to save money to expand a lot of the pilot programs that
you all have discussed. And I am one who doesn't want food
police or a big brother society or any of these other things
that we are all so worried about. And so it would be great in
the time I have remaining for you all to either support or not
the concept of whether this would save money, and why? And I
would like to hear from all of you on it.
Dr. Rachidi. Well I guess I will start.
In terms of saving money, just the opportunity or the
potential to save medical-related expenses, especially on the
public health side, Medicaid/Medicare, I think that there is
potential there. And then----
Mr. Soto. Excuse me, I didn't mean to interrupt. Just with
regard to the SNAP program, whether we would save money in SNAP
funds.
Dr. Rachidi. Right. Well, I don't know if this is exactly
what you are getting at, but in terms of the article this
morning, again, I look at it as a program integrity issue. It
is difficult to talk about expanding SNAP benefits, for
example, when that ten percent of SNAP benefits are spent on
sweetened beverages which have no nutritional value and do
nothing to further the goals of the program.
Dr. Schanzenbach. I think that this won't save SNAP
dollars. In fact, as I testified earlier, it will increase the
administrative cost of the program to no benefit. My
professional opinion as an economist, I don't think it is going
to change behavior.
Ms. Sarasin. And as I have said before, I don't think it is
going to save money either. The administrative costs associated
with making these determinations in the context of USDA would
be astronomical.
Mr. Weidman. We recommend an access to healthy food
incentives and nutrition education, and we think that approach
is the best way to create jobs, lift people out of poverty so
they don't need SNAP, and reduce healthcare costs.
Dr. Wansink. There are easier ways to get at that
objective, and I don't think just cutting that is going to have
the benefits we want.
Mr. Soto. Now my next question is what would be the
administrative costs, knowing that we already ban alcohol, and
that seems to be something that hasn't mushroomed costs.
Dr. Rachidi. When I hear the discussion about how the cost
would be astronomical, I don't quite understand how that could
be with items, for example, like sweetened beverages that are
very straightforward. I understand moving more towards a WIC
model, how that could potentially increase administrative
costs, but the things that I am talking about I don't see how
that would increase administrative costs.
Mr. Soto. And this is a reference just to a ban on candy
and soda, no other items.
Dr. Schanzenbach. So I guess I would add to that that
restricting alcoholic beverages, that is sort of a different
product category and it is real easy for the person who is
checking you out to know oh, this is a bottle of wine and not
something else. But when it comes to something like sugar
sweetened beverages, what we saw in the New York pilot proposal
was it is really hard to decide how to define this. For
example, two what I would call similar beverages, V8 you could
still purchase, but V8 Splash, which is the same sort of thing
but it has a little kiwi fruit in it, was not eligible. I think
that it gets to something that is very complicated at the
store, and it is going to cause confusion. Do we have great
estimates of how much it will cost? We have some evidence from
the Healthy Incentives Pilot that maybe $5 billion a year,
something like that.
The Chairman. The gentleman's time has expired.
Mr. Arrington, 5 minutes.
Mr. Arrington. Thank you, Mr. Chairman. I admittedly come
to the table to discuss as with tension between the consumers'
freedom to choose what they purchase to eat, and our
responsibility as stewards of taxpayer money to guide in the
most responsible way. And I must say, I am undecided, quite
frankly, and I am sorry I couldn't get all your testimonies and
be a part of the discussion. I had another hearing.
Dr. Rachidi, I understand that you ran the SNAP program for
New York City and that you requested a waiver so that you could
apply restrictions to people on SNAP and their purchases. Why
were you denied that flexibility?
Dr. Rachidi. And just to be clear, I didn't run the
program, but I was the director for policy, and so we proposed
the restriction.
But ultimately, what we were told in terms of being denied
was related to the evaluation design and that it wasn't
rigorous enough to be able to conclude whether a restriction
would be effective or not. And that was the main reason that
was given, and then given that other states in the past had
also proposed similar things, we suspected it was just a
general aversion to wanting to do any type of restrictions.
Mr. Arrington. Have they granted--go ahead.
Ms. Sarasin. If I could, just one comment that I don't
think has been mentioned today and it is worth mentioning in
the context of waivers for various reasons. This Committee
several years ago under the leadership of Mr. Goodlatte spent
an awful lot of time and energy working toward a state by state
interoperability type of process with SNAP. In this mobile
society that we are in right now, there has been the need for
SNAP recipients to be able to use their benefits where they
find themselves, and so with EBT cards, et cetera, that has
been facilitated, so these waivers have created a tension
within USDA as well, because once you start doing waivers
piecemeal around the country, the interoperability that this
Committee spent so much time trying to achieve is compromised.
Mr. Arrington. When is the last time the USDA has granted a
waiver for such restrictions?
Dr. Rachidi. They have not.
Mr. Arrington. Ever, okay. Yes?
Dr. Schanzenbach. But, if you wanted to do a real
demonstration project, we would just really need to make sure
that it is set up so that we can learn something from it. Not
only studying the impact on consumption, which I will let you
know I have a prediction what that will be, but also the impact
on retailers and others. It is going to cost you if you elect
to do it.
Mr. Arrington. Yes, that is a good idea and it is fair to
include all stakeholders, with states bearing much of the cost
in healthcare, or let's just say significant costs for
healthcare of their citizens, why not enter another freedom to
choose? Why not block grant SNAP, let states choose if they
want to go higher with support and supplemental support and
work any reforms they want in on work requirements and other
requirements and other reforms that have been discussed, not
for this hearing? And then let them decide if they find it
useful and meaningful to restrict purchases based on the
nutritional value? Let states do that. Has that been discussed,
and what are your thoughts about that?
Dr. Schanzenbach. So my grave concern around a potential
block grant is that one of the things that makes SNAP most
successful, especially to the broader economy, is that it is
designed to respond quickly to changing economic conditions and
to times of need. So the program, as you saw during the great
recession, expanded in response to the greater need that we
saw. It is starting to come back down as the economy is
starting to get a foothold.
You may be aware that the dollars that we spend in SNAP
also they are very promptly spent and they are spent in the
local communities, and so they provide an economic stimulus to
the whole area. For every dollar that we spend, at the height
of the great recession we got $1.74 in local economic activity
because of this. A block grant takes that important aspect of
this program off the table. I think it would be a mistake.
Mr. Arrington. So it seems to me that in terms of who is
more nimble, the Federal Government, Federal program or a state
and local government and program, I am going to put my money on
the state and local program in terms of nimbleness. I don't
think we have anything to compare it to with respect to this
specific program, but I bet there are other ways to compare it.
I am running out of time.
The Chairman. The gentleman's time has expired.
Mr. Evans. Mr. Evans, 5 minutes.
Mr. Evans. Thank you, Mr. Chairman.
One question that I have, and maybe all of you can deal
with this, my inquiry is what is the impact a reduction in SNAP
would mean for retailers from a job perspective? Can someone
shed light on the impact of jobs and a reduction of SNAP would
create?
Mr. Weidman. One of the things that we have been doing
around the country since we started in Pennsylvania with the
Fresh Food Financing Initiative is convening groups that
include grocers, but other stakeholders around the issue of
access to healthy food and grocery store access. That is one
thing that we heard loud and clear is that in order to have a
successful enterprise in low-income communities, SNAP has
become a very critical component there. So in our view,
reductions to SNAP is not only going to result in more hunger
and less food on the table for American families who are
struggling with hard times, but it is going to have an economic
effect. Oftentimes grocery stores are the anchor in a
community, so if the grocery store closes down, that can have a
domino effect, affecting other retail in the community. This
happens in rural small towns and urban neighborhoods.
Mr. Evans. Is anybody as, with the national retailer, able
to quantify it in some way what you think it means in terms of
numbers?
Dr. Schanzenbach. Sure. During normal economic times, every
dollar that we spend on SNAP returns about $1.25 to the local
area, so I would think the way to think about it during normal
economic times, although this would be worse during downturns,
but during normal economic times if we took $1 away from SNAP,
we would expect to see a reduction of $1.25 in local economic
activity.
Mr. Evans. Can each of you shed light from your perspective
on what a SNAP benefit impact would be on recipients?
Dr. Schanzenbach. Sure. We have strong predictions that if
benefits were reduced, I would predict that we would see an
increase in food insecurity. Currently one out of every five
children in this great nation lives in a food-insecure
household. I also think that, just the opposite of what I
talked about before, having fewer dollars to spend at the
grocery store means that people are going to substitute towards
cheaper forms of calories, and that is exactly the opposite of
the direction that we like to see people go. We like to see
people eat healthier foods, which tend to be more expensive per
calorie.
Mr. Evans. Thank you, Mr. Chairman.
The Chairman. The gentleman yields back.
Mr. Allen, 5 minutes.
Mr. Allen. Thank you, Mr. Chairman, and the reason I an the
last one to ask questions is because I was in a conference
meeting this morning talking about spiraling cost of healthcare
in this country. And as I look at, statistically, at the growth
of this program from 17 million people in 2000 to over 40
million people today, and the fact that this program was
initially started during World War II, because I am military,
our generals felt like they didn't have the nourishment that
they needed to battle the enemy. So we have seen tremendous
growth in this program, and then we see tremendous growth in
the cost of healthcare.
We are talking about nutrition, and then what is that doing
to healthcare? Do we have any studies that tell us, okay, are
they related, and if they are related, how do we fix this?
Dr. Schanzenbach. To be sure, obesity rates have
skyrocketed, not just among the poor, but all across the
distribution. And there are studies, we could nitpick them, but
common sense dictates that this increase in obesity that we
have seen across the income distribution has real ramifications
for the cost of healthcare.
Mr. Allen. Obviously, the retailers have a stake in this,
the producers, our farmers obviously have a stake in this. We
have talked about some options here available to us, but it
sounds like to me we better fix this problem because when you
look, for example, at Medicaid costs, I mean, it is
skyrocketing and the number of people on Medicaid is
skyrocketing. And it is because folks are having health
problems because of, it may be other factors, but a large part
of it is nutrition.
Doctor, would you like to comment on what your thoughts
are? I mean, how do we fix this?
Dr. Wansink. Yes, absolutely. We have all the health
concerns that we face, diet-related disease and obesity are the
only ones that we can deal with and change immediately. Now you
bring up a great point that most grocery stores, maybe they
don't really care that much about the shoppers who are there,
and to use a health motivation to try to encourage them to get
people to buy more fruits and vegetables wouldn't be the right
way to do it. But instead, it is aligned in their interest to
get more people to buy fresh fruits and vegetables, lean meat
and dairy, things like this because when that stuff goes bad,
they actually lose money. The margin on it might be thin at the
register, but the loss is huge compared to Fruity Pebbles if
they don't sell it. Being able to show them that these are easy
ways that we can help you get that stuff moving through your
store is going to be a win/win situation, just like it was with
convenience stores when the Association of Convenience Stores
started giving their members ways that they could accelerate
sales of healthy foods.
Mr. Allen. I am sure you would like to respond to that.
Ms. Sarasin. Yes, I would like to respond to that. Thank
you.
I think the notion that food retailers don't care about the
health of their customers is just incorrect.
Dr. Wansink. We will----
Ms. Sarasin. It is incorrect. Just not factually correct.
Of course we care about the health of our customers, and of
course we are doing things to try to enhance the health of our
customers. And we do that every single day, and in my longer
testimony, there are multiple examples of the things that we do
in store, in our communities, and across the board to try to
make sure that we are doing everything we can to meet the needs
of our customers.
So while the convenience stores are relatively new to this
process and apparently are doing some good things, that is
wonderful, but your broad line grocers have been engaged in
this process for decades in trying to assist their customers in
meeting their dietary needs, and they do it by bringing in
nutritionists and dieticians and other professionals in the
store to work with their customers on a daily basis to meet
those needs, and will continue to do so.
Mr. Allen. And of course, we have the food deserts that we
have to deal with now. We had testimony here with Amazon, which
is becoming a big player in the grocery market. Obviously, we
have to come up with a solution to this issue, and so thank you
for your help here today, and hopefully we can get our arms
around this and solve this problem.
The Chairman. The gentleman's time has expired.
I want to thank our witnesses. The great news about this
Committee, and today's hearing is a terrific example of it, is
that if you took the names off the questions and the comments
made, you would be hard pressed to determine which were
Republicans and which were Democrats. You all have given us
great information. The panel has given us terrific information
to chew on. This is not the last conversation we will have on
SNAP restrictions. I have some folks who feel really strongly
about both sides, and the Committee will work its will when we
get to this point and place, but this is an important
conversation to have had today. You have been incredibly
respectful and I appreciate everyone's participation, and I
wish more of our work here in the House was as nonpartisan as
this is. Not a person here doesn't care about nutrition. Not a
person here doesn't care that people eat healthy and that they
exercise, and that they make good decisions.
I was particularly informed by the triangle from Dr.
Wansink. I wish it was reversed. I wish the health vigilant was
the big piece and that the health-disinterested, or the ones
who don't care, was the smaller piece of that triangle, but
that is correct. There are far more people in America who
really don't care. And then there is that group that we can
hit, that can change their habits. It is a convenience issue.
It is an opportunity to have their kids tell them to do it.
So this program is important, and what they spend their
benefit on is important. I am not convinced that the more
decisions we make on people's behalf doesn't make them less
capable of making good decisions on their own, so it takes
education. Somebody said in their testimony there is no silver
bullet to fixing this issue. Sugar drinks have a clear impact
on people's health, but if we eliminated them off the face of
the Earth, I don't know that obesity rates would be any
different than they are right now. There are some other
systemic changes that have to go on in people's choices and the
way they conduct their lives to make this happen.
Under the Rules of the Committee, the record of today's
hearing will remain open for 10 calendar days to receive
additional material and supplementary written responses from
the witnesses to any question posed by a Member.
This hearing of the Committee on Agriculture is adjourned.
Thank you.
[Whereupon, at 12:20 p.m., the Committee was adjourned.]
[Material submitted for inclusion in the record follows:]
Submitted Report by Hon. K. Michael Conaway, a Representative in
Congress from Texas
Foods Typically Purchased by Supplemental Nutrition Assistance Program
(SNAP) Households
November 2016
Nutrition Assistance Program Report
Food and Nutrition Service, Office of Policy Support
Authors: Submitted to:
Steven Garasky Office of Policy Support,
Kassim Mbwana Food and Nutrition Service,
Andres Romualdo 3101 Park Center Drive,
Alex Tenaglio Alexandria, VA 22302-1500
Manan Roy Project Officer:
Submitted by: Sarah Zapolsky
IMPAQ International, LLC,
10420 Little Patuxent Parkway,
Suite 300,
Columbia, MD 21044
Project Director:
Steven Garasky
This study was conducted under Contract number GS-10-F-0240U with
the Food and Nutrition Service, United States Department of
Agriculture.
This report is available on the Food and Nutrition website: http://
www.fns.usda.gov/research-and-analysis.
Suggested Citation:
Garasky, Steven, Kassim Mbwana, Andres Romualdo, Alex Tenaglio and
Manan Roy. Foods Typically Purchased by SNAP Households. Prepared by
IMPAQ International, LLC for USDA, Food and Nutrition Service, November
2016.
Table of Contents
Executive Summary
Purpose and Overview
Methodology
Data Overview
Identifying SNAP Households and Creating Analysis
Categories
Data Caveats and Limitations
Key Findings
Food Items Purchased by SNAP Households
Chapter 1. Introduction and Background
1.1 Introduction
1.2 Background
1.3 Research Questions
1.4 Challenges of Collecting Point-of-Sale Data
Chapter 2. Methodology
2.1 Data Overview
2.2 Identification of SNAP Households and Creation of
Analysis Categories
2.3 Data Caveats and Limitations
Chapter 3. Findings: Top Expenditures by SNAP and Non-SNAP
Households
3.1 Distribution of Expenditures by Summary Categories
3.2 Distribution of Expenditures by Commodities
3.3 Distribution of Expenditures by Subcommodities
3.4 Distribution of Expenditures by Household Demographics,
Store Characteristics, Type of Resource Used, and Month of
Purchase
Chapter 4. Findings: Top Expenditures by USDA Food Pattern
Categories
4.1 Top Expenditures for Dairy
4.2 Top Expenditures for Fruits
4.3 Top Expenditures for Grains
4.4 Top Expenditures for Oils
4.5 Top Expenditures for Protein Foods
4.6 Top Expenditures for Solid Fats and Added Sugars (SoFAS)
4.7 Top Expenditures for Vegetables
4.8 Top Expenditures for Composite Foods
4.9 Top Expenditures for Other Subcommodities
Chapter 5. Conclusion
Appendix A. Top Purchases by Expenditure for SNAP and Non-SNAP
Households *
---------------------------------------------------------------------------
* Editor's note: the report entitled, Foods Typically Purchased By
Supplemental Nutrition Assistance Program (SNAP) Households and Foods
Typically Purchased By Supplemental Nutrition Assistance Program (SNAP)
Households--Appendices are two different documents. For purposes of
publication in this hearing they are treated as one document.
---------------------------------------------------------------------------
Appendix B. Crosswalk of Top 1000 Subcommodities to Summary
Categories
Appendix C. Crosswalk of Subcommodities to USDA Food Pattern
Categories
Appendix D. Top 100 Subcommodities for SNAP Households by
Expenditure for Each USDA Food Pattern Category
Appendix E. Top 100 Subcommodities for SNAP Households by
Expenditure by Demographic and Store Characteristics
Table of Exhibits
Exhibit 1: SNAP and Non-SNAP Household Food Expenditure Patterns
Exhibit 2: Conceptual Map for Identification of SNAP Households in
the POS Data
Exhibit 3: Summary of SNAP and Non-SNAP Household Food Expenditures
in the Dataset by Subcommodity
Exhibit 4: Aggregating Food Items
Exhibit 5: Summary Categories by Expenditure
Exhibit 6: Top 100 Commodities for SNAP Households by Expenditure
Exhibit 7: Top 100 Subcommodities for SNAP Households by
Expenditure
Exhibit 8: Top 25 SNAP Household Dairy Subcommodity Expenditures
Exhibit 9: Top 25 SNAP Household Fruit Subcommodity Expenditures
Exhibit 10: Top 25 SNAP Household Grains Subcommodity Expenditures
Exhibit 11: Oils Subcommodity Expenditures
Exhibit 12: Top 25 SNAP Household Protein Foods Subcommodity
Expenditures
Exhibit 13: Top 25 SNAP Household Solid Fats and Added Sugars
(SoFAS) Subcommodity Expenditures
Exhibit 14: Solid Fats and Added Sugars (SoFAS) Expenditures by
Subcategory
Exhibit 15: Top 25 SNAP Household Vegetables Subcommodity
Expenditures
Exhibit 16: Top 25 SNAP Household Composite Subcommodity
Expenditures
Exhibit 17: Composite Expenditures by Subcategory
Exhibit 18: Top 25 SNAP Household Other Subcommodity Expenditures
Exhibit 19: Other Expenditures by Subcategory
Exhibit 20: SNAP and Non-SNAP Household Food Expenditure Patterns
Executive Summary
Purpose and Overview
The Food and Nutrition Service (FNS) awarded a contract to IMPAQ
International, LLC, to determine what foods are typically purchased by
households receiving Supplemental Nutrition Assistance Program (SNAP)
benefits. This study examined point-of-sale (POS) food purchase data to
determine for what foods SNAP households have the largest expenditures,
including both SNAP benefits and other resources, and how their
expenditures compare to those made by non-SNAP households.
SNAP, administered by FNS, is the nation's largest nutrition
assistance program. In 2011, SNAP participants redeemed over $71
billion in SNAP benefits in more than 230,000 SNAP-authorized
stores.\1\ Given the magnitude of SNAP, FNS has a sustained interest in
understanding the effects of the program. To date, FNS has studied SNAP
household food consumption and expenditures using national surveys that
generally rely on consumers to recall what they ate or to report or
scan every purchase. This previous research has shown that the
similarities in food purchases, consumption patterns, and dietary
outcomes among low-income families and higher-income households are
more striking than the differences.\2\
---------------------------------------------------------------------------
\1\ USDA FNS. (2011). Supplemental Nutrition Assistance Program
2011 Annual Report. Benefit Redemption Division. Available at http://
www.fns.usda.gov/snap/retailers/pdfs/2011-annual-report-revised.pdf.
\2\ See, for example, Office of Research and Analysis (2012).
Building a Healthy America: A Profile of the Supplemental Nutrition
Assistance Program. Food and Nutrition Service, USDA (available on line
at www.fns.usda.gov/ora/MENU/Published/snap/FILES/Other/BuildingHealthy
America.pdf).
---------------------------------------------------------------------------
By using POS data to compare the purchases of SNAP households to
those of non-SNAP households, the current study provides more detail on
food expenditure patterns than previous studies. This study examines
two major questions:
What food items are purchased by SNAP households?
How do foods purchased by SNAP households compare to food
purchased by non-SNAP households?
Methodology
Data Overview
POS transaction data from January 1, 2011 through December 31, 2011
from a leading grocery retailer were examined for this study.\3\ The
majority of stores from which the data came would be classified as
grocery stores, supermarkets, and combination food and drug stores per
FNS Retailer Policy and Management Division food retailer
definitions.\4\ On average, each of the 12 monthly data files contained
over one billion records of food items purchased by 26.5 million
households, reflecting 127 million unique transactions. Each monthly
data file included an average of 3.2 million SNAP households,
identified using the methodology described below. Total expenditures on
all SNAP-eligible food items in the dataset by SNAP and non-SNAP
households over the 12 months were $39.0 billion, or approximately $3.3
billion per month. SNAP households spent approximately $555 million on
SNAP-eligible items each month in this dataset, using both SNAP
benefits and other resources such as cash or credit cards.\5\
---------------------------------------------------------------------------
\3\ Per the data sharing agreement between the data provider and
IMPAQ, a description of the source of these data must be limited to the
following: ``From a leading U.S. grocery retailer data examining POS
transactions from January 1, 2011 through December 31, 2011 across
approximately 11 million SNAP households. The majority of stores would
be classified as grocery stores, supermarkets, and combination food and
drug stores per USDA/FNS food retailer definitions.''
\4\ Stores that opened or closed during 2011 were not included in
these analyses.
\5\ By way of comparison, in FY 2011, 21.1 million households
participated in SNAP in an average month (http://www.fns.usda.gov/ora/
MENU/Published/snap/FILES/Participation/2011Characteristics.pdf) and
redeemed $6.0 billion in benefits in an average month (http://
www.fns.usda.gov/snap/retailers/pdfs/2011-annual-report-revised.pdf).
---------------------------------------------------------------------------
Identifying SNAP Households and Creating Analysis Categories
SNAP households were identified in the data for each month. This
identification was performed monthly because, in any given month, some
households enter or leave the program. The analysis identified SNAP
households each month by first identifying any transaction in which
SNAP electronic benefit transfer (EBT) was used to pay for at least \1/
2\ of the value of the purchase and designating the household that made
that transaction as a SNAP household.\6\ It then linked all other
transactions made by that household during that month to estimate total
monthly spending by SNAP households. All other transactions in these
stores were designated as non-SNAP household purchases.\7\
---------------------------------------------------------------------------
\6\ SNAP transactions in which SNAP EBT was not the majority tender
were not identifiable in the data.
\7\ Some of these transactions may, in fact, have included SNAP
purchases. Some SNAP households may never have presented EBT as the
majority tender in any transaction, for example.
---------------------------------------------------------------------------
IMPAQ analyzed SNAP-eligible food items given the focus of the
study. Per the Food and Nutrition Act of 2008 (the Act), eligible food
includes any food or food product for home consumption, as well as
seeds and plants which produce food for consumption. The Act precludes
alcoholic beverages, tobacco products, hot food and any food sold for
on-premises consumption from being purchased with SNAP benefits.\8\ The
unit of analysis for the study was a food-related subcommodity, with
subcommodities and commodities defined by the data provider. Each
subcommodity typically consisted of multiple food items, often
distinguished by brand or package size, identified by a Universal
Product Code (UPC) or a Price Look Up (PLU) code. Each commodity was an
aggregation of similar subcommodities. The ``apples'' commodity group,
for example, combined different varieties (Gala, Fuji, Honeycrisp) and
forms (bagged, bulk) that were presented separately as subcommodities.
---------------------------------------------------------------------------
\8\ See http://www.fns.usda.gov/snap/retailers/eligible.htm for
more details.
---------------------------------------------------------------------------
Although subcommodities and commodities provide adequate comparison
reference points, these groupings were designed to help retailers
classify purchases for their own needs (e.g., marketing purposes).
Therefore, this study analyzed purchases at two higher levels of
aggregation. Thirty summary categories were created--for example, meat/
poultry/seafood, fruits, vegetables, and frozen prepared foods--to be
roughly analogous to the major sections or departments in a typical
grocery store. These categories were constructed to enhance discussion
of similarities and differences between purchasing patterns of SNAP and
non-SNAP households. Appendix B provides a crosswalk of subcommodities
to summary categories.
IMPAQ also mapped food subcommodities to USDA Food Pattern
categories (dairy, fruits, grains, oils, protein foods, solid fats and
added sugars (SoFAS), and vegetables). Not all subcommodities could be
classified into a single Food Pattern category. Subcommodities
incorporating multiple food categories, such as foods packaged as
complete meals, were classified as composite foods. In addition, some
subcommodities did not contain any Food Pattern categories, or the
labels were not descriptive enough to permit categorization even with
the addition of the composite category. A ninth category, other, was
created to capture such subcommodities. ``Other'' captured all items
that could not be classified using USDA Food Patterns, such as water,
isotonic drinks, and baby food.
Data Caveats and Limitations
Although POS data provide a wealth of information on the food
purchase patterns of SNAP households, some limitations existed in the
data analyzed for this study. The data used for this study captured
only transactions completed at a specific set of retail outlets. As
stated before, the majority of stores from which the data came would be
classified as grocery stores, supermarkets, and combination food and
drug stores per FNS Retailer Policy and Management Division food
retailer definitions.\9\ Purchases made at other SNAP-authorized
retailers or other venues (e.g., farmers['] markets) were not included
in these data. On average, SNAP households in the data spent
approximately $229 per month on SNAP-eligible foods using a combination
of SNAP benefits, cash and other forms of payment.\10\ In contrast, the
national average monthly SNAP benefit per household was $284 in FY
2011.\11\ Therefore, although these data account for a significant
proportion of SNAP-eligible food expenditures by SNAP households, they
do not include all SNAP benefit expenditures.
---------------------------------------------------------------------------
\9\ Stores that opened or closed during 2011 were not included in
these analyses.
\10\ On average, SNAP households in the data made 8.5 transactions
per month. The average total expenditure on SNAP-eligible foods per
transaction was $26.99.
\11\ http://www.fns.usda.gov/pd/19SNAPavg$HH.htm.
---------------------------------------------------------------------------
SNAP transactions were identified as those for which a SNAP EBT
card was the majority tender. Because some transactions included both
SNAP and cash or credit tenders, these data could not differentiate
between items purchased with SNAP benefits and those purchased with
other funds. These data, therefore, represent food purchases made by
SNAP households, rather than the foods purchased with SNAP EBT
specifically.
Rankings of expenditure categories depend in part on the level of
food item aggregation (whether at the Food Pattern, summary, commodity
or subcommodity levels). As discussed above, the data provider
aggregated food items into subcommodities and commodities, considering
other factors outside of the needs of this particular analysis. These
classifications at times presented analytic challenges that may have
affected the rank ordering of purchases. For example, subcommodity
groups categorized as ``composite'' or ``other'' for these analyses
likely included food items that would more appropriately be included in
one of the Food Pattern categories had more information been available.
Similarly, some distinctions of potential nutritional importance were
not available in these data. For example, it was not possible to
distinguish between fat-free or low-fat varieties of some dairy
products, such as fluid milk or yogurt, from whole milk varieties.
Key Findings
Food Items Purchased by SNAP Households
Overall, the findings from this study indicate that SNAP households
and non-SNAP households purchased similar foods in the retail outlets
in these data. Exhibits 1 and 2 summarize the findings.
There were no major differences in the expenditure patterns
of SNAP and non-SNAP households, no matter how the data were
categorized. Similar to most American households:
About 40 of every dollar of food expenditures by SNAP
households was spent on basic items such as meat, fruits,
vegetables, milk, eggs, and bread.
Another 20 out of every dollar was spent on sweetened
beverages, desserts, salty snacks, candy and sugar.
The remaining 40 were spent on a variety of items
such as cereal, prepared foods, dairy products, rice, and
beans.
The top ten summary categories and the top seven commodities
by expenditure were the same for SNAP and non-SNAP households,
although ranked in slightly different orders.
Expenditure shares for each of the USDA Food Pattern
categories (dairy, fruits, grains, oils, protein foods, solid
fats and added sugars (SoFAS), and vegetables) varied by no
more than 3 per dollar when comparing SNAP and non-SNAP
households. Protein foods represented the largest expenditure
share for both household types, while proportionally more was
spent on fruits and vegetables than on SoFAS, grains, or dairy.
Less healthy food items were common purchases for both SNAP
and non-SNAP households. Sweetened beverages, prepared desserts
and salty snacks were among the top ten summary categories for
both groups. Expenditures were greater for sweetened beverages
compared to all milk for both groups, as well.
Expenditures were concentrated in a relatively small number
of similar food-item categories. The top five summary groups
totaled \1/2\ (50%) of the expenditures for SNAP households and
nearly \1/2\ (47%) for non-SNAP households. Twenty-five
commodities accounted for over forty percent of the food
expenditures in these data with SNAP and non-SNAP households
having 20 of them in common. The top 25 subcommodities for SNAP
households and non-SNAP households, respectively, accounted for
between \1/5\ to \1/4\ of total food expenditures for each
group with 16 subcommodities in common for the two groups.
Exhibit 1: SNAP and Non-SNAP Household Food Expenditure Patterns
------------------------------------------------------------------------
Finding SNAP Households Non-SNAP Households
------------------------------------------------------------------------
Total annual $6.7 billion $32.3 billion
expenditures on SNAP-
eligible foods in
dataset
Percentage of all 12% 88%
transactions by all
households
Percentage of total 17% 83%
annual expenditures
by all households
Top 1,000 subcommodity 99% 98%
(of 1,792)
expenditures as a
percentage of all
expenditures
Top 100 subcommodity 51% 46%
expenditures as a
percentage of all
expenditures
Top 25 subcommodity 25% 21%
expenditures as a
percentage of all
expenditures
Top 25 commodity (of 45% 41%
238) expenditures as
a percentage of all
expenditures
Top 10 summary Meat/Poultry/Seafood Meat/Poultry/Seafood
categories (of 30) by
expenditure
Sweetened Beverages Vegetables
Vegetables High-fat Dairy/Cheese
Frozen Prepared Foods Fruits
Prepared Desserts Sweetened Beverages
High-fat Dairy/Cheese Prepared Desserts
Bread and Crackers Bread and Crackers
Fruits Frozen Prepared Foods
Milk Milk
Salty Snacks Salty Snacks
Top 10 commodities (of Soft Drinks Fluid Milk Products
238) by expenditure
Fluid Milk Products Soft Drinks
Beef Grinds Cheese
Bag Snacks Baked Breads
Cheese Bag Snacks
Baked Breads Beef Grinds
Cold Cereal Cold Cereal
Chicken Fresh Candy--Packaged
Frozen Handhelds and Coffee and Creamers
Snacks
Lunchmeat Ice Cream, Ice Milk,
and Sherbets
Top 10 subcommodities Fluid Milk/White Only Fluid Milk/White Only
(of 1,792) by
expenditure
Soft Drinks 12-18 pack Soft Drinks 12-18 pack
Lean Beef Shredded Cheese
Kids' Cereal Chicken Breast--
Boneless
Shredded Cheese Frozen Premium
Nutritional Meals
2-Liter Soft Drink Pure Orange Juice--
Dairy Case
Potato Chips Lean Beef
Primal Beef Potato Chips
Lunchmeat--Deli fresh Large Eggs
Infant Formula/Starter Bananas
Solution
USDA Food Pattern
categories, by
expenditure:
Dairy 9% 10%
Fruits 6% 9%
Grains 12% 13%
Oils 2% 2%
Protein 23% 20%
Foods
Solid Fats 13% 12%
and Added Sugars
Vegetables 8% 10%
Composite 19% 16%
Other 8% 8%
------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ
International, LLC, 2016.
Chapter 1. Introduction and Background
1.1 Introduction
The Food and Nutrition Service (FNS) awarded a contract to IMPAQ
International, LLC, to determine what foods are typically purchased by
households receiving Supplemental Nutrition Assistance Program (SNAP)
benefits. More specifically, this study examined POS food purchase data
to determine for what foods SNAP households have the largest
expenditures, including both SNAP benefits and other resources, and how
these expenditures compare to those made by non-SNAP households.
1.2 Background
The mission of FNS is to provide children and needy families with
improved access to food and a more healthful diet through a range of
nutrition assistance programs and comprehensive nutrition education
efforts. SNAP, administered by FNS, is the nation's largest nutrition
assistance program, providing benefits to more than 15% of the U.S.
population. In 2011, SNAP participants redeemed over $71 billion in
SNAP benefits in more than 230,000 SNAP-authorized stores.\12\ Total
program costs in FY 2011 were nearly $76 billion.\13\ Given the
magnitude of SNAP, FNS has a sustained interest in understanding the
effects of the program.
---------------------------------------------------------------------------
\12\ USDA FNS. (2011). Supplemental Nutrition Assistance Program
2011 Annual Report. Benefit Redemption Division. Available at http://
www.fns.usda.gov/snap/retailers/pdfs/2011-annual-report-revised.pdf.
\13\ http://www.fns.usda.gov/pd/SNAPsummary.htm.
---------------------------------------------------------------------------
SNAP aims to alleviate hunger and improve the nutritional status of
participants by increasing the resources available to households to
purchase food. Paradoxically, one-in-six people in the U.S. experiences
food insecurity,\14\ while \2/3\ of adults and \1/3\ of children are
overweight or obese.\15\ These public health problems
disproportionately affect low-income populations.\16\ While no evidence
exists that SNAP participation causes obesity, the high rates of
obesity and food insecurity among low-income Americans underscore the
importance of exploring ways to continue to employ SNAP strategically
as a tool to promote healthier nutrition, as well as to reduce obesity
rates among program participants of whom nearly 50% are children.
---------------------------------------------------------------------------
\14\ Coleman-Jensen, A., Nord, M., Andrews, M., & Carlson, S.
(2011). Household food security in the United States in 2010. Economic
Research Report, No. ERR-125. Available at http://www.ers.usda.gov/
media/884525/err141.pdf.
\15\ Flegal, K.M., Carroll, M.D., Ogden, C.L., & Curtin, L.R.
(2010). ``Prevalence and trends in obesity among U.S. adults, 1999-
2008,'' Journal of the American Medical Association, 303, 235-241;
Burgstahler, R., Gundersen, C., & Garasky, S. (forthcoming). ``The
Supplemental Nutrition Assistance Program, financial stress, and
childhood obesity.'' Agricultural and Resource Economics Review;
Eisenmann, J.C., Gundersen, C., Lohman, B.J., Garasky, S., & Stewart,
S.D. (2011). ``Is food insecurity related to overweight and obesity in
children and adolescents? A summary of studies, 1995-2009.'' Obesity
Reviews, 12, e73-e83; Lohman, B.J., Stewart, S., Gundersen, C.,
Garasky, S., & Eisenmann, J.C. (2009). ``Adolescent overweight and
obesity: Links to food insecurity and individual, maternal, and family
stressors.'' Journal of Adolescent Health, 45, 230-237; Gundersen, C.,
Garasky, S., & Lohman, B.J. (2009) ``Food insecurity is not associated
with childhood obesity as assessed using multiple measures of
obesity.'' Journal of Nutrition, 139, 1173-1178.
\16\ Trust for America's Health. (2011). F as in fat: How obesity
threatens America's future. Available at http://healthyamericans.org/
reports/obesity2010/Obesity2010Report.pdf.
---------------------------------------------------------------------------
1.3 Research Questions
The project addressed two key research questions.
Research Question 1: What food items are purchased by SNAP
households? Specifically, the study examined SNAP household food
expenditure data by four categorizations: U.S. Department of
Agriculture (USDA) Food Pattern categories, ``summary categories,''
commodities, and subcommodities.
Research Question 2: How do foods purchased by SNAP households
compare to purchases made by non-SNAP households? Analyses paralleled
those for Research Question 1, but for non-SNAP households. Comparisons
were then drawn between the food expenditures of SNAP and non-SNAP
households.
1.4 Challenges of Collecting Point-of-Sale Data
Understanding the food choices and purchasing patterns of SNAP
participants is an important part of promoting healthy choices. FNS
analyzes various extant data that describe the diets and food
purchasing patterns of SNAP households. For example, The National
Health and Nutrition Examination Survey is an annual nationally
representative survey of approximately 5,000 respondents that collects,
among other data, dietary behavior and 24-hour dietary recall data.\17\
The Nielsen Homescan data include a panel of households that records
grocery purchases using a scanning device.\18\ Panelists scan the
barcodes of the products they purchase, recording information such as
price and quantity. The Consumer Expenditure Survey gathers expenditure
information from participants every 3 months over a 15 month period
through interviews and a diary survey.\19\ The interview is designed to
gather expenditure data on items that are easy to recall, while the
diary survey records purchases made each day during a 2 week period.
---------------------------------------------------------------------------
\17\ http://www.cdc.gov/nchs/tutorials/Dietary/SurveyOrientation/
intro.htm.
\18\ http://www.ncppanel.com.
\19\ http://www.bls.gov/cex.
---------------------------------------------------------------------------
An outstanding question is whether food purchase data collected at
the point-of-sale offers a different or more detailed perspective on
the food choices of SNAP and other households. Ideally, retail data on
SNAP electronic benefit transfer (EBT) purchases would be collected in
a timely manner--preferably at the point of sale--and with sufficient
sample size to be nationally representative. To date, there have been
numerous challenges to collecting such retail data:
The immense volume of SNAP retail data--in FY 2011, over $71
billion in SNAP benefits were redeemed at over 230,000
participating stores, farmers['] markets and other venues
authorized to accept SNAP benefits.\20\ These transactions
represent billions of food items purchased each month via an
estimated 250 million or more unique transactions.
---------------------------------------------------------------------------
\20\ Supplemental Nutrition Assistance Program, USDA FNS Benefit
Redemption Division 2011 Annual Report. Available from http://
www.fns.usda.gov/snap/retailers/pdfs/2011-annual-report-revised.pdf.
The wide variety of food products and package sizes sold by
the over 230,000 SNAP-authorized retailers--roughly 40,000
items in larger stores \21\--and the diverse ways retailers
identify and track these items.
---------------------------------------------------------------------------
\21\ http://www.fmi.org/facts_figs/?fuseaction=superfact.
Industry reluctance to share detailed sales data, a key
---------------------------------------------------------------------------
competitive tool for food marketers.
Equipment and system changes needed to capture item-level
data at SNAP-approved stores. The numerous cash register
technologies currently in use vary in their sophistication and
their ability to collect item-level data. Data transmission and
storage are also important issues.
Distinguishing between SNAP and non-SNAP transactions and
purchases, given that SNAP households at times combine SNAP
benefits and their own funds when making purchases.
The current study provides a snapshot of food purchasing patterns
using POS data from a set of retailers to compare expenditures on SNAP-
eligible food items made by SNAP and non-SNAP households.
Chapter 2. Methodology
2.1 Data Overview
POS transaction data from January 1, 2011 through December 31, 2011
from a leading grocery retailer were examined in this study.\22\ The
majority of stores from which the data came would be classified as
grocery stores, supermarkets, and combination food and drug stores per
FNS Retailer Policy and Management Division food retailer
definitions.\23\ On average, each of the 12 monthly data files
contained over one billion records of food items purchased by 26.5
million households, reflecting 127 million unique transactions. Each
monthly data file included an average of 3.2 million SNAP households,
identified using the methodology described below. Total expenditures on
all SNAP-eligible food items in the dataset by SNAP and non-SNAP
households over the 12 months were $39.0 billion, or approximately $3.3
billion per month. SNAP households expended approximately $555 million
on SNAP-eligible food items each month in this dataset, using both SNAP
benefits and other resources such as cash or credit cards.\24\
---------------------------------------------------------------------------
\22\ Per the data sharing agreement between the data provider and
IMPAQ, a description of the source of these data must be limited to the
following: ``From a leading U.S. grocery retailer data examining POS
transactions from January 1, 2011 through December 31, 2011 across
approximately 11 million SNAP households. The majority of stores would
be classified as grocery stores, supermarkets, and combination food and
drug stores per USDA/FNS food retailer definitions.''
\23\ Stores that opened or closed during 2011 were not included in
these analyses.
\24\ By way of comparison, in FY 2011, 21.1 million households
participated in SNAP in an average month (http://www.fns.usda.gov/ora/
MENU/Published/snap/FILES/Participation/2011Characteristics.pdf) and
redeemed $6.0 billion in benefits in an average month (http://
www.fns.usda.gov/snap/retailers/pdfs/2011-annual-report-revised.pdf).
---------------------------------------------------------------------------
2.2 Identification of SNAP Households and Creation of Analysis
Categories
SNAP households were identified in the data for each month. This
identification was performed monthly because, in any given month, some
households enter or leave the program. The analysis identified SNAP
households each month by first identifying any transaction in which
SNAP EBT was used to pay for at least \1/2\ of the value of the
purchase and designating the household that made that transaction as a
SNAP household.\25\ It then linked all other transactions made by that
household during that month to estimate total monthly spending by SNAP
households. All other transactions in these stores were designated as
non-SNAP household purchases.\26\ Exhibit 2 illustrates the
identification of SNAP households.
---------------------------------------------------------------------------
\25\ SNAP transactions in which SNAP EBT was not the majority
tender were not identifiable in the data.
\26\ Some of these transactions may, in fact, have included SNAP
purchases. Some SNAP households may never have presented EBT as the
majority tender in any transaction, for example.
---------------------------------------------------------------------------
Exhibit 2: Conceptual Map for Identification of SNAP Households in the
POS Data
IMPAQ analyzed SNAP-eligible food items given the focus of the
study. Per the Food and Nutrition Act of 2008 (the Act), eligible food
include any food or food product for home consumption, as well as seeds
and plants which produce food for consumption. The Act precludes
alcoholic beverages, tobacco products, hot food and any food sold for
on-premises consumption from being purchased with SNAP benefits.\27\
The unit of analysis for the study was a food-related subcommodity,
with subcommodities and commodities defined by the data provider. Each
subcommodity typically consisted of multiple food items, often
distinguished by brand or package size, identified by a Universal
Product Code (UPC) or a Price Look Up (PLU) code. Each commodity was an
aggregation of similar subcommodities. The ``apples'' commodity group,
for example, combined different varieties (Gala, Fuji, Honeycrisp) and
forms (bagged, bulk) that were presented separately as subcommodities.
The decision to rely on subcommodity groupings follows procedures
established in published studies.\28\ These studies prefer
subcommodity-level analyses over item-level analyses because UPCs and
PLUs assigned by manufacturers and retailers can change over time.
Additionally, the same food item may be sold in multiple forms with
different brands and labels, each with its own unique UPC.\29\
---------------------------------------------------------------------------
\27\ See http://www.fns.usda.gov/snap/retailers/eligible.htm for
more details.
\28\ For examples, see Hamilton, S., et al. (2007). ``Food and
nutrient availability in New Zealand: An analysis of supermarket sales
data.'' Public Health Nutrition, 10(12): 1448-1455; Van Wave, T.W., &
Decker, M. (2003). ``Secondary analysis of a marketing research
database reveals patterns in dairy product purchases over time.''
Journal of American Dietetic Association, 103(4), 445-453.
\29\ Baxter, J., et al. (1996). Experiences in using computerized
sales data to evaluate a nutrition intervention program. Journal of
Nutrition Education, 28, 443-445.
---------------------------------------------------------------------------
Exhibit 3 details expenditures on SNAP-eligible food items in the
dataset. As can be seen, expenditures on all 1,792 subcommodities in
the dataset sum up to $6.7 billion and $32.3 billion for SNAP and non-
SNAP households, respectively. Notably, expenditures on the top 1,000
subcommodities account for 99% of expenditures for SNAP households and
98% for non-SNAP households. For this reason, all subsequent analyses
and tables in the report are generated using the top 1,000
subcommodities.
Exhibit 3: Summary of SNAP and Non-SNAP Household Food Expenditures in
the Dataset by Subcommodity
------------------------------------------------------------------------
Non-SNAP
Finding SNAP Households Households
------------------------------------------------------------------------
Total annual expenditures on SNAP- $6.7 billion $32.3 billion
eligible foods in dataset
Percentage of all transactions by 12% 88%
all households
Percentage of total annual 17% 83%
expenditures by all households
Top 1,000 (of 1,792) subcommodity 99% 98%
expenditures as a percentage of
all expenditures
Top 100 (of 1,792) subcommodity 51% 46%
expenditures as a percentage of
all expenditures
Top 25 (of 1,792) subcommodity 25% 21%
expenditures as a percentage of
all expenditures
Top 25 commodity (of 238) 45% 41%
expenditures as a percentage of
all expenditures
-------------------------------------
Total annual expenditures on top $6.5805 billion $31.5138 billion
1,000 subcommodities
------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ
International, LLC, 2016.
The data provider aggregated the subcommodities to commodities. The
top 1,000 subcommodities represented 238 commodities. Although
subcommodities and commodities provide adequate comparison reference
points, these groupings were designed to help retailers classify
purchases for their own needs (e.g., marketing purposes). Therefore,
this study analyzed purchases at two higher levels of aggregation.
Thirty summary categories were created--for example, meat/poultry/
seafood, fruits, vegetables, cereal, candy, and frozen prepared foods--
to be roughly analogous to the major sections or departments in a
typical grocery store. These categories were constructed to enhance
discussion of similarities and differences between the purchasing
patterns of SNAP and non-SNAP households. Appendix B provides a
crosswalk of subcommodities to summary categories.
IMPAQ also mapped food subcommodities to USDA Food Pattern
categories (dairy, fruits, grains, oils, protein foods, solid fats and
added sugars (SoFAS), and vegetables).\30\ A crosswalk of
subcommodities to USDA Food Pattern categories can be found in Appendix
C. Relative to the 30 summary categories, there are only seven USDA
Food Pattern categories. As a result, more subcommodities were included
in each Food Pattern category, on average, relative to the summary
categories which at times lead to differing results for categories with
the same name. For example, for the USDA Food Patterns analysis, 100%
pure orange juice was classified as a fruit. Juice, however, is a
specific category among the summary categories. Therefore, expenditures
on 100% orange juice were not included as fruit expenditures for the
summary categories analysis as they were for the Food Patterns
analysis. Readers should keep this in mind when comparing results for
categories such as fruits or vegetables across analyses.
---------------------------------------------------------------------------
\30\ USDA Center for Nutrition Policy and Promotion Food Patterns
(http://www.cnpp.usda.gov/USDAFoodPatterns.htm).
---------------------------------------------------------------------------
Not all subcommodities could be classified into single Food Pattern
categories. Subcommodities incorporating multiple food categories, such
as foods packaged as complete meals, were classified as composite
foods. In addition, some subcommodities did not fit any Food Pattern
categories, or the labels were not descriptive enough to permit
categorization even with the addition of the composite category. A
ninth category, other, was created to capture such subcommodities.
``Other'' captured all items that could not be classified using USDA
Food Patterns, such as water, isotonic drinks, and baby food. Exhibit 4
describes the aggregations of food items used for these analyses, using
fluid milk products as an example.
Exhibit 4: Aggregating Food Items
Note: The vast majority of commodities included
subcommodities that could be mapped to a single summary
category as shown above. However, a small number of commodities
included subcommodities that did not map to the same summary
category. For example, the commodity group Authentic Hispanic
Foods and Products included authentic vegetables and foods,
Hispanic carbonated beverages, and authentic pasta/rice/beans
subcommodities which mapped to the vegetables, sweetened
beverages, and rice summary categories, respectively. The top
1,000 subcommodities accounted for 99% of all expenditures on
SNAP-eligible food items in the dataset for SNAP households and
98% of all expenditures on SNAP-eligible food items by non-SNAP
households.
2.3 Data Caveats and Limitations
Although POS data provide a wealth of information on the food
purchase patterns of SNAP households, some limitations existed in the
data analyzed for this study. The data used for this study captured
only transactions completed at a specific set of retail outlets. As
stated before, the majority of stores from which the data came would be
classified as grocery stores, supermarkets, and combination food and
drug stores per FNS Retailer Policy and Management Division food
retailer definitions.\31\ Purchases made at other SNAP-authorized
retailers or other venues (e.g., farmers['] markets) were not included
in these data. On average, SNAP households in the data spent
approximately $229 per month on SNAP-eligible foods using a combination
of SNAP benefits, cash and other forms of payment.\32\ In contrast, the
national average monthly SNAP benefit per household was $284 in FY
2011.\33\ Therefore, although these data account for a significant
proportion of SNAP-eligible food expenditures by SNAP households, they
do not include all SNAP benefit expenditures.
---------------------------------------------------------------------------
\31\ Stores that opened or closed during 2011 were not included in
these analyses.
\32\ On average, SNAP households in the data made 8.5 transactions
per month. The average total expenditure on SNAP-eligible foods per
transaction was $26.99.
\33\ http://www.fns.usda.gov/pd/19SNAPavg$HH.htm.
---------------------------------------------------------------------------
SNAP transactions were identified as those for which a SNAP EBT
card was the majority tender. Because some transactions included both
SNAP and cash or credit tenders, these data could not differentiate
between items purchased with SNAP benefits and those purchased with
other funds. These data, therefore, represent food purchases made by
SNAP households rather than the foods purchased with SNAP EBT.
Rankings of expenditure categories depend in part on the level of
food item aggregation (whether at the Food Pattern category, summary
category, commodity or subcommodity levels). As discussed above, the
data provider aggregated food items into subcommodities and commodities
considering other factors outside of the needs of this particular
analysis. These classifications at times presented analytic challenges
that may have affected the rank ordering of expenditures. For example,
subcommodity groups categorized as ``composite'' or ``other'' for these
analyses likely included food items that would more appropriately be
included in one of the Food Pattern categories had more information
been available. Similarly, some distinctions of potential nutritional
importance were not available in these data. For example, it was not
possible to distinguish between fat-free or low-fat varieties of some
dairy products, such as fluid milk or yogurt, from whole milk
varieties.
Chapter 3. Findings: Top Expenditures by SNAP and Non-SNAP Households
------------------------------------------------------------------------
-------------------------------------------------------------------------
Key Findings
There were no major differences in the expenditure patterns
of SNAP and non-SNAP households, no matter how the data were
categorized. Similar to most American households:
About 40 of every dollar of food expenditures by SNAP
households was spent on basic items such as meat, fruits,
vegetables, milk, eggs, and bread.
Another 20 out of every dollar was spent on sweetened
beverages, desserts, salty snacks, candy and sugar.
The remaining 40 were spent on a variety of items such as
cereal, prepared foods, dairy products, rice, and beans.
The top ten summary categories and the top seven
commodities by expenditure were the same for SNAP and non-SNAP
households, although ranked in slightly different orders.
Less healthy food items were common purchases for both SNAP
and non-SNAP households. Sweetened beverages, prepared desserts and
salty snacks were among the top ten summary categories for both
groups. Expenditures were greater for sweetened beverages compared
to all milk for both groups, as well.
Expenditures were concentrated in a relatively small number
of similar food-item categories. The top five summary groups
totaled \1/2\ (50%) of the expenditures for SNAP households and
nearly \1/2\ (47%) for non-SNAP households. Twenty-five commodities
accounted for nearly \1/2\ of the food expenditures in these data
with SNAP and non-SNAP households having 20 of them in common. The
top 25 subcommodities for SNAP households and non-SNAP households,
respectively, accounted for over \1/5\ of food expenditures for
each group with 16 subcommodities in common for the two groups.
------------------------------------------------------------------------
3.1 Distribution of Expenditures by Summary Categories
Exhibit 5 provides an overview of expenditures by the summary
categories described in Chapter 2. In general, SNAP and non-SNAP
household expenditure rankings and proportions were similar.
Expenditures on basic or staple foods (meat/poultry/seafood, fruits,
vegetables, milk, eggs and bread/crackers) comprised over 40 of every
food purchase dollar for both SNAP and non-SNAP households (41 and
44 /dollar, respectively). Another 20 per dollar was spent on less
healthy foods such as sweetened beverages, prepared desserts, salty
snacks, candy and sugars by both household groups (SNAP households--
23 ; non-SNAP households--20 ).
Expenditures were generally concentrated in a small number of
summary groups for both SNAP and non-SNAP households. The top five
groups total \1/2\ (50%) of the expenditures for SNAP households and
nearly \1/2\ (47%) for non-SNAP households. The top three categories by
expenditures for SNAP households were meat/poultry/seafood, sweetened
beverages, and vegetables. The top three categories for non-SNAP
households were meat/poultry/seafood, vegetables, and high fat dairy/
cheese; sweetened beverages ranked fifth. Both SNAP and non-SNAP
households spent a greater proportion of total expenditures on meat,
poultry and seafood than any other category. Both household groups
spent more on fruits and vegetables than on prepared foods, and more on
sweetened beverages than on milk.
Exhibit 5: Summary Categories by Expenditure
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
---------------------------------------------------------------------------
Summary Category $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
Meat, Poultry and Seafood 1 $1,262.9 19.19% 1 $5,016.3 15.92%
Sweetened Beverages 2 $608.7 9.25% 5 $2,238.8 7.10%
Vegetables 3 $473.4 7.19% 2 $2,873.9 9.12%
Frozen Prepared Foods 4 $455.2 6.92% 8 $1,592.3 5.05%
Prepared Desserts 5 $453.8 6.90% 6 $2,021.2 6.41%
High Fat Dairy/Cheese 6 $427.8 6.50% 3 $2,483.2 7.88%
Bread and Crackers 7 $354.9 5.39% 7 $1,978.2 6.28%
Fruits 8 $308.2 4.68% 4 $2,271.2 7.21%
Milk 9 $232.7 3.54% 9 $1,211.0 3.84%
Salty Snacks 10 $225.6 3.43% 10 $969.7 3.08%
Prepared Foods 11 $202.2 3.07% 14 $707.0 2.24%
Cereal 12 $186.9 2.84% 11 $933.9 2.96%
Condiments and Seasoning 13 $174.6 2.65% 12 $878.9 2.79%
Fats and Oils 14 $155.1 2.36% 13 $766.9 2.43%
Candy 15 $138.2 2.10% 15 $701.4 2.23%
Baby Food 16 $126.8 1.93% 27 $198.2 0.63%
Juices 17 $110.4 1.68% 16 $605.4 1.92%
Coffee and Tea 18 $83.4 1.27% 17 $568.8 1.80%
Bottled Water 19 $78.1 1.19% 22 $377.4 1.20%
Eggs 20 $73.8 1.12% 21 $388.2 1.23%
Other Dairy Products 21 $69.8 1.06% 18 $549.5 1.74%
Pasta, Cornmeal, Other Cereal 22 $66.4 1.01% 23 $281.5 0.89%
Products
Soups 23 $62.7 0.95% 20 $414.1 1.31%
Sugars 24 $60.9 0.93% 24 $260.3 0.83%
Nuts and Seeds 25 $53.2 0.81% 19 $445.9 1.41%
Beans 26 $38.3 0.58% 25 $234.5 0.74%
Rice 27 $30.1 0.46% 28 $131.0 0.42%
Jams, Jellies, Preserves and Other 28 $29.1 0.44% 29 $117.5 0.37%
Sweets
Flour and Prepared Flour Mixes 29 $18.7 0.28% 30 $94.9 0.30%
Miscellaneous 30 $18.6 0.28% 26 $202.6 0.64%
---------------------------- ---------------------------
Total Summary Category $6,580.5 100% $31,513.8 100%
Expenditures (Top 1,000
subcommodities)
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
3.2 Distribution of Expenditures by Commodities
Exhibit 6 examines expenditures at the commodity level, listing the
top 100 commodities by expenditure for SNAP households while providing
corresponding rankings of these commodities for non-SNAP households.
The top 100 commodities accounted for nearly all expenditures for both
SNAP (87%) and non-SNAP (82%) households. The top 25 SNAP household
commodities accounted for nearly \1/2\ (46%) of the food expenditures
for SNAP households; the top 25 commodities for non-SNAP households
accounted for 42%. Among the top 25 commodities, the two households
groups had 20 in common.
The top two commodities were the same for SNAP and non-SNAP
households, namely soft drinks and fluid milk products, although the
order was reversed with soft drinks ranked first for SNAP households
compared to fluid milk products for non-SNAP households. However, while
expenditure proportions were similar for fluid milk products across the
two household types (4 per dollar), expenditure proportions on soft
drinks were slightly higher for SNAP households compared to non-SNAP
households (5 versus 4 per dollar). Overall, the expenditure rankings
and patterns should be assessed with caution as a small difference in
the expenditure share of a commodity can lead to a major difference in
the ranking of the commodity. For example, among SNAP households, the
difference in expenditure shares between lunchmeat, ranked tenth, and
aseptic juice, ranked sixty-ninth, is approximately 1 per dollar.
Exhibit 6: Top 100 Commodities for SNAP Households by Expenditure
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
---------------------------------------------------------------------------
Commodity $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
Soft drinks 1 $357.7 5.44% 2 $1,263.3 4.01%
Fluid milk products 2 $253.7 3.85% 1 $1,270.3 4.03%
Beef grinds 3 $201.0 3.05% 6 $621.1 1.97%
Bag snacks 4 $199.3 3.03% 5 $793.9 2.52%
Cheese 5 $186.4 2.83% 3 $948.9 3.01%
Baked breads 6 $163.7 2.49% 4 $874.8 2.78%
Cold cereal 7 $139.2 2.12% 7 $583.9 1.85%
Chicken fresh 8 $121.4 1.85% 11 $477.8 1.52%
Frozen handhelds and snacks 9 $101.5 1.54% 47 $214.6 0.68%
Lunchmeat 10 $99.4 1.51% 17 $386.1 1.23%
Candy--packaged 11 $96.2 1.46% 8 $527.7 1.67%
Infant formula 12 $95.7 1.45% 80 $124.8 0.40%
Frozen pizza 13 $90.2 1.37% 23 $305.7 0.97%
Refrigerated juices/drinks 14 $88.5 1.35% 14 $412.8 1.31%
Ice cream, ice milk, sherbets 15 $86.0 1.31% 10 $481.8 1.53%
Coffee and creamers 16 $82.3 1.25% 9 $519.4 1.65%
Cookies 17 $78.2 1.19% 16 $408.3 1.30%
Water--(sparkling and still) 18 $77.0 1.17% 18 $379.2 1.20%
Shelf stable juice 19 $73.1 1.11% 28 $282.2 0.90%
Eggs/muffins/potatoes 20 $72.0 1.09% 20 $358.7 1.14%
Frozen single serving premium meals 21 $68.6 1.04% 12 $447.1 1.42%
Cakes 22 $68.2 1.04% 38 $240.9 0.76%
Bacon 23 $66.1 1.00% 27 $283.2 0.90%
Traditional Mexican foods 24 $62.6 0.95% 25 $286.9 0.91%
Yogurt 25 $59.9 0.91% 13 $442.3 1.40%
Salad dressing and sandwich spreads 26 $59.7 0.91% 30 $280.9 0.89%
Dinner sausage 27 $59.3 0.90% 46 $222.6 0.71%
Frozen prepared chicken 28 $58.6 0.89% 74 $136.4 0.43%
Baked sweet goods 29 $57.5 0.87% 62 $159.6 0.51%
Beef loins 30 $56.3 0.86% 31 $280.3 0.89%
Chicken frozen 31 $54.8 0.83% 85 $123.0 0.39%
Deli meat: bulk 32 $54.6 0.83% 15 $411.0 1.30%
Frozen multi-serve meals 33 $53.0 0.81% 54 $183.5 0.58%
Dinner mixes-dry 34 $51.8 0.79% 72 $140.3 0.45%
Frozen breakfast foods 35 $51.3 0.78% 55 $180.9 0.57%
Crackers and misc baked food 36 $50.9 0.77% 21 $323.7 1.03%
Frozen novelties-water ice 37 $50.7 0.77% 43 $229.7 0.73%
Margarines 38 $50.3 0.76% 24 $303.0 0.96%
Condiments and sauces 39 $49.8 0.76% 52 $187.2 0.59%
Potatoes 40 $48.8 0.74% 34 $265.2 0.84%
Frozen vegetable and veg dish 41 $48.2 0.73% 33 $266.9 0.85%
Hot dogs 42 $45.5 0.69% 63 $158.4 0.50%
Can vegetables--shelf stable 43 $45.3 0.69% 50 $191.7 0.61%
Shortening and oil 44 $44.6 0.68% 57 $174.2 0.55%
Sugars and sweeteners 45 $43.3 0.66% 60 $162.4 0.52%
Isotonic drinks 46 $42.8 0.65% 53 $185.3 0.59%
Salad mix 47 $42.8 0.65% 22 $319.4 1.01%
Milk by-products 48 $42.5 0.65% 32 $268.9 0.85%
Pork boneless loin/rib 49 $41.5 0.63% 58 $168.0 0.53%
Convenience breakfasts and wholesome 50 $41.1 0.62% 45 $226.1 0.72%
snacks
Frozen single serve economy meals 51 $40.9 0.62% 109 $80.7 0.26%
Refrigerated dough products 52 $40.5 0.62% 56 $176.6 0.56%
Beef round 53 $40.4 0.61% 75 $134.2 0.43%
Dry bean vegetables and rice 54 $39.9 0.61% 59 $166.1 0.53%
Convenient meals 55 $38.7 0.59% 108 $81.0 0.26%
Tomatoes 56 $38.3 0.58% 35 $261.7 0.83%
Candy--checklane 57 $37.9 0.58% 64 $154.0 0.49%
Berries 58 $37.4 0.57% 19 $373.5 1.19%
Grapes 59 $36.1 0.55% 39 $235.7 0.75%
Bananas 60 $36.1 0.55% 36 $261.4 0.83%
Peanut 61 $36.0 0.55% 42 $231.0 0.73%
Pork thin meats 62 $35.0 0.53% 93 $106.8 0.34%
Citrus 63 $34.3 0.52% 37 $251.7 0.80%
Breakfast sausage 64 $34.2 0.52% 79 $126.7 0.40%
Dry sauce, gravy, potatoes, stuffing 65 $34.0 0.52% 87 $119.2 0.38%
Salad and dips 66 $33.9 0.52% 40 $235.3 0.75%
Apples 67 $33.7 0.51% 29 $281.7 0.89%
Meat--shelf stable 68 $33.3 0.51% 91 $109.2 0.35%
Aseptic juice 69 $33.1 0.50% 112 $78.9 0.25%
Sweet goods 70 $32.5 0.49% 66 $152.9 0.49%
Frozen potatoes 71 $32.2 0.49% 95 $104.5 0.33%
Meat frozen 72 $31.9 0.48% 120 $69.9 0.22%
Baby foods 73 $30.6 0.46% 121 $67.8 0.22%
Vegetables salad 74 $30.0 0.46% 44 $228.6 0.73%
Beef: thin meats 75 $30.0 0.46% 78 $127.7 0.41%
Seafood--shrimp 76 $29.8 0.45% 84 $123.1 0.39%
Canned soups 77 $29.7 0.45% 65 $153.6 0.49%
Baking mixes 78 $28.3 0.43% 69 $148.1 0.47%
Pasta and pizza sauce 79 $27.6 0.42% 99 $96.7 0.31%
Dry noodles and pasta 80 $27.5 0.42% 71 $141.5 0.45%
Can seafood--shelf stable 81 $26.5 0.40% 77 $132.3 0.42%
Rts/micro soup/broth 82 $26.0 0.40% 48 $200.8 0.64%
Canned pasta and microwave food 83 $25.9 0.39% 135 $56.7 0.18%
Smoked hams 84 $25.7 0.39% 92 $108.8 0.35%
Nuts 85 $25.6 0.39% 41 $234.2 0.74%
Value-added fruit 86 $25.3 0.38% 70 $146.6 0.47%
Can beans 87 $24.0 0.36% 82 $123.3 0.39%
Dry/ramen bouillon 88 $21.7 0.33% 133 $61.0 0.19%
Powder and crystal drink mix 89 $21.6 0.33% 119 $75.2 0.24%
Rtd tea/new age juice 90 $21.5 0.33% 103 $93.8 0.30%
Baking needs 91 $21.3 0.32% 51 $188.9 0.60%
Can fruit/jar applesauce 92 $20.9 0.32% 96 $104.0 0.33%
Spices and extracts 93 $20.4 0.31% 86 $121.9 0.39%
Energy drinks 94 $20.1 0.30% 102 $94.1 0.30%
Onions 95 $20.0 0.30% 81 $123.5 0.39%
Tropical fruit 96 $19.8 0.30% 61 $160.1 0.51%
Bagels and cream cheese 97 $19.8 0.30% 83 $123.2 0.39%
Frozen bread/dough 98 $19.7 0.30% 114 $77.7 0.25%
Rolls 99 $18.9 0.29% 88 $113.9 0.36%
Hot cereal 100 $18.9 0.29% 100 $96.1 0.30%
---------------------------- ---------------------------
Expenditures on Listed Commodities $5,700.3 86.62% $25,800.4 81.93%
============================ ===========================
Expenditures on Top 1,000 $6,580.5 100% $31,513.8 100%
Subcommodities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: The table lists the top 100 commodities for SNAP households and the corresponding rankings of these
commodities for non-SNAP households. Columns may not sum to total shown due to rounding.
3.3 Distribution of Expenditures by Subcommodities
Exhibit 7 presents the top 100 subcommodities purchased by SNAP
households, along with corresponding expenditures and ranks of these
subcommodities for non-SNAP households.\34\ These 100 subcommodities
accounted for over \1/2\ (51%) of the food expenditures in these data
for SNAP households. Comparatively, the food purchases of non-SNAP
households on these 100 subcommodities represented only 43% of their
total expenditures. As expected, the level of detail provided by the
subcommodity classifications resulted in relatively small proportions
of total expenditures being spent on any single subcommodity.
Individually, only six subcommodities represented more than 1% of the
expenditures of SNAP households. As with the commodity rankings, a
small difference in the expenditure share of a subcommodity translated
into a substantial difference in its ranking. For example, among SNAP
households, the difference in shares of expenditures between potato
chips, ranked seventh, and bananas, ranked thirty-fifth, is less than
\1/2\ of one percentage point.
---------------------------------------------------------------------------
\34\ See Appendix A for the commodity that corresponds to each
subcommodity for the top 1,000 subcommodities.
---------------------------------------------------------------------------
The top two subcommodities purchased by SNAP households, fluid
milk/white only and carbonated soft drinks in 12-18 can packages, were
the top subcommodities for non-SNAP households as well. An interesting
difference in rankings of subcommodities between SNAP households and
non-SNAP households was for infant formula/starter solution. This
subcommodity ranked tenth among SNAP households. The majority of these
formula purchases were made when SNAP EBT was not the majority tender
(results not presented here), perhaps because WIC (Special Supplemental
Nutrition Program for Women, Infants, and Children) benefits were used.
Infant formula/starter solution purchases ranked well out of the top
100 for non-SNAP households, at 190.
Exhibit 7: Top 100 Subcommodities for SNAP Households by Expenditure
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
---------------------------------------------------------------------------
Subcommodity $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/White Only 1 $191.1 2.90% 1 $853.8 2.71%
Soft Drinks 12/18 &15pk Can Car 2 $164.6 2.50% 2 $601.2 1.91%
Lean [Beef] 3 $112.4 1.71% 7 $257.9 0.82%
Kids' Cereal 4 $78.1 1.19% 20 $186.4 0.59%
Shredded Cheese 5 $74.7 1.14% 3 $342.0 1.09%
Soft Drink 2 Liter Btl Carb Incl 6 $70.9 1.08% 12 $230.1 0.73%
Potato Chips 7 $64.4 0.98% 8 $253.2 0.80%
Primal [Beef] 8 $62.4 0.95% 14 $219.8 0.70%
Lunchmeat--Deli Fresh 9 $55.8 0.85% 11 $242.6 0.77%
Infant Formula Starter/Solution 10 $54.2 0.82% 190 $45.3 0.14%
Eggs--Large 11 $52.1 0.79% 9 $251.6 0.80%
Chicken Breast Boneless 12 $49.6 0.75% 4 $292.9 0.93%
Still Water Drinking/Mineral Water 13 $48.8 0.74% 19 $187.7 0.60%
Mainstream White Bread 14 $48.0 0.73% 39 $136.8 0.43%
Tortilla/Nacho Chips 15 $47.4 0.72% 17 $209.0 0.66%
Snacks/Appetizers 16 $44.6 0.68% 65 $100.5 0.32%
American Single Cheese 17 $44.1 0.67% 41 $136.6 0.43%
Frozen Single Serve Premium 18 $43.8 0.67% 24 $175.4 0.56%
Traditional Meals
Dairy Case 100% Pure Juice--Orange 19 $43.5 0.66% 6 $269.0 0.85%
Snack Cake--Multi-Pack 20 $41.6 0.63% 63 $101.7 0.32%
Enhanced [Pork Boneless Loin/Rib] 21 $41.5 0.63% 27 $168.0 0.53%
Unflavored Can Coffee 22 $41.3 0.63% 18 $198.0 0.63%
Frozen Single Serve Economy Meals 23 $40.9 0.62% 81 $80.7 0.26%
All
Bacon--Trad 16oz Or Less 24 $40.7 0.62% 29 $157.6 0.50%
Soft Drinks 20pk & 24pk Can Carb 25 $39.7 0.60% 60 $106.4 0.34%
Pizza/Premium 26 $39.7 0.60% 32 $153.3 0.49%
Mainstream Variety Breads 27 $38.4 0.58% 26 $173.2 0.55%
Sugar 28 $36.9 0.56% 55 $112.7 0.36%
All Family Cereal 29 $36.2 0.55% 16 $214.9 0.68%
Sandwiches and Handhelds 30 $35.9 0.54% 91 $73.6 0.23%
Potatoes Russet (Bulk & Bag) 31 $35.8 0.54% 30 $154.5 0.49%
Natural Cheese Chunks 32 $35.3 0.54% 15 $216.1 0.69%
Ribs [Pork] 33 $35.0 0.53% 59 $106.8 0.34%
Convenient Meals--Kids Meal 34 $34.2 0.52% 96 $69.7 0.22%
Bananas 35 $34.2 0.52% 10 $242.7 0.77%
Soft Drink Mlt-Pk Btl Carb 36 $34.0 0.52% 25 $173.6 0.55%
Premium [Ice Cream & Sherbert] 37 $31.2 0.47% 13 $226.0 0.72%
Isotonic Drinks Single Serve 38 $30.5 0.46% 47 $119.5 0.38%
Frozen Chicken--White Meat 39 $30.0 0.46% 66 $99.8 0.32%
Condensed Soup 40 $29.7 0.45% 31 $153.6 0.49%
Pourable Salad Dressings 41 $29.0 0.44% 37 $139.4 0.44%
Choice Beef 42 $28.4 0.43% 40 $136.6 0.43%
Select Beef 43 $27.9 0.42% 36 $143.7 0.46%
Soft Drink Single Srv Btl Carb 44 $27.8 0.42% 94 $71.4 0.23%
Frozen Family Style Entrees 45 $27.6 0.42% 77 $83.5 0.26%
Mayonnaise & Whipped Dressing 46 $27.3 0.41% 48 $119.1 0.38%
Frozen Bag Vegetables--Plain 47 $25.7 0.39% 42 $131.9 0.42%
Traditional [Ice Cream and Sherbert] 48 $25.6 0.39% 49 $118.7 0.38%
Hot Dogs--Base Meat 49 $25.1 0.38% 138 $56.8 0.18%
Adult Cereal 50 $24.9 0.38% 21 $182.6 0.58%
Frozen Single Serve Premium 51 $24.7 0.38% 5 $271.6 0.86%
Nutritional Meals
Macaroni and Cheese Dinners 52 $24.3 0.37% 125 $59.7 0.19%
Aseptic Pack Juice and Drinks 53 $24.2 0.37% 134 $57.1 0.18%
Refrigerated Coffee Creamers 54 $24.1 0.37% 34 $147.2 0.47%
Choice Beef 55 $24.0 0.37% 92 $72.5 0.23%
Mexican Soft Tortillas and Wraps 56 $23.7 0.36% 54 $113.1 0.36%
Strawberries 57 $23.5 0.36% 22 $178.4 0.57%
Margarine: Tubs and Bowls 58 $23.4 0.36% 64 $100.9 0.32%
Mainstream [Pasta & Pizza] 59 $23.0 0.35% 80 $81.0 0.26%
Chicken Wings 60 $22.2 0.34% 300 $28.6 0.09%
Can Pasta 61 $22.2 0.34% 179 $47.7 0.15%
Frozen Chicken--Wings 62 $22.2 0.34% 452 $17.4 0.06%
Lunchmeat--Bologna/Sausage 63 $21.8 0.33% 121 $60.9 0.19%
Multi-Pack Bag Snacks 64 $21.6 0.33% 199 $43.4 0.14%
Candy Bags-Chocolate 65 $21.5 0.33% 33 $147.5 0.47%
Sweet Goods: Donuts 66 $21.3 0.32% 78 $82.3 0.26%
Tuna 67 $21.1 0.32% 57 $109.9 0.35%
Vegetable Oil 68 $20.5 0.31% 246 $35.4 0.11%
Frozen French Fries 69 $20.5 0.31% 163 $50.3 0.16%
Peanut Butter 70 $20.4 0.31% 43 $127.8 0.41%
Pizza/Economy 71 $19.8 0.30% 192 $45.1 0.14%
Butter 72 $19.6 0.30% 23 $175.6 0.56%
Meat: Turkey Bulk 73 $19.3 0.29% 28 $159.6 0.51%
Frozen Breakfast Sandwiches 74 $19.1 0.29% 142 $55.7 0.18%
Frozen Meat--Beef 75 $19.0 0.29% 185 $46.3 0.15%
Frozen Skillet Meals 76 $18.8 0.29% 83 $79.3 0.25%
Value Forms/18oz and Larger 77 $18.6 0.28% 209 $42.6 0.14%
[Chicken]
Cakes: Birthday/Celebration 78 $18.6 0.28% 164 $50.3 0.16%
Sandwich Cookies 79 $18.0 0.27% 93 $71.8 0.23%
Pizza/Traditional 80 $17.9 0.27% 111 $64.1 0.20%
Fruit Snacks 81 $17.6 0.27% 202 $43.2 0.14%
Rts Soup: Chunky/Homestyle 82 $17.6 0.27% 46 $119.9 0.38%
Sour Creams 83 $17.5 0.27% 70 $95.2 0.30%
Waffles/Pancakes/French Toast 84 $17.3 0.26% 90 $77.4 0.25%
Chicken Drums 85 $17.3 0.26% 270 $31.5 0.10%
Cream Cheese 86 $17.2 0.26% 51 $115.5 0.37%
Angus [Beef] 87 $17.1 0.26% 61 $103.8 0.33%
Bagged Cheese Snacks 88 $17.1 0.26% 157 $52.0 0.16%
Salsa and Dips 89 $17.1 0.26% 135 $57.0 0.18%
Sandwiches--(Cold) 90 $16.9 0.26% 106 $67.7 0.21%
Ramen Noodles/Ramen Cups 91 $16.7 0.25% 304 $28.1 0.09%
Cheese Crackers 92 $16.5 0.25% 72 $90.2 0.29%
Dinner Sausage--Links Pork 93 $16.4 0.25% 233 $37.6 0.12%
Candy Bars (Singles) 94 $16.3 0.25% 146 $54.9 0.17%
Hamburger Buns 95 $16.2 0.25% 95 $70.2 0.22%
Hot Dog Buns 96 $16.2 0.25% 117 $62.2 0.20%
Spring Water 97 $16.2 0.25% 69 $95.6 0.30%
Dairy Case Juice Drink Under 10oz 98 $16.0 0.24% 177 $48.0 0.15%
Flavored Milk 99 $16.0 0.24% 128 $59.4 0.19%
Sweet Goods--Full Size 100 $15.8 0.24% 133 $57.9 0.18%
---------------------------- ---------------------------
Expenditures on Listed $3,372.2 51.01% $13,390.0 42.14%
Subcommodities
============================ ===========================
Expenditures on Top 1,000 $6,580.5 100% $31,513.8 100%
subcommodities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: The table lists the top 100 subcommodities for SNAP households and the corresponding rankings of these
subcommodities for non-SNAP households. Columns may not sum to total shown due to rounding.
3.4 Distribution of Expenditures by Household Demographics, Store
Characteristics, Type of Resource Used, and Month of Purchase
In addition to analyzing purchase patterns as a whole, IMPAQ also
analyzed the POS purchase data by household demographic and store
characteristic subgroups based on information from the data provider.
Appendix E provides these analyses. More transactions in these data
were made by households without children than by households with
children. In addition, a larger proportion of transactions were made at
retail outlets in metropolitan areas than in rural or suburban areas;
\35\ at larger stores rather than smaller ones; \36\ and in counties
with 10-20% poverty rates, the median of the three poverty rate
categories into which the counties in which the stores were located
were classified.\37\ Compared to non-SNAP household transactions, SNAP
household transactions were more likely to be made by households headed
by adults 19-44 years of age, in stores located in the Midwest, and in
medium-sized grocery stores. A larger proportion of SNAP household
transactions than of non-SNAP household transactions took place in the
most impoverished counties (counties with poverty rates greater than
20%). Notably, the distribution of transactions by household
demographic and store characteristics was relatively consistent whether
SNAP households used SNAP benefits or other resources.
---------------------------------------------------------------------------
\35\ USDA Economic Research Service Urban Influence Codes (http://
www.ers.usda.gov/data-products/urban-influence-codes.aspx).
\36\ Following Food Marketing Institute conventions from http://
www.fmi.org/about/ and http://www.fmi.org/facts--figs/
?fuseaction=superfact and FNS Retailer Policy and Management Division
food retailer definitions from http://www.fns.usda.gov/snap/retailers/
pdfs/2012-annual-report.pdf.
\37\ Census Bureau data from http://www.census.gov/did/www/saipe/
county.html.
---------------------------------------------------------------------------
In addition to analyzing the POS data for the full year, analyses
were completed at the monthly level to investigate monthly or seasonal
patterns in purchases. There was little month-to-month variation in
expenditure patterns for either SNAP or non-SNAP households. A notable
exception was that for both household types expenditure shares for
vegetables were 2-3 percentage points lower during the summer months,
while expenditure shares for fruits were 2-3 percentage points higher
(data not shown).
Chapter 4. Findings: Top Expenditures by USDA Food Pattern Categories
------------------------------------------------------------------------
-------------------------------------------------------------------------
Key Findings
Overall, there were few differences between SNAP and non-
SNAP household expenditures by USDA Food Pattern categories.
Expenditure shares for each of the USDA Food Pattern categories
(dairy, fruits, grains, oils, protein foods, solid fats and added
sugars (SoFAS), and vegetables) varied by no more than 3 per
dollar when comparing SNAP and non-SNAP households.
Protein foods represented the largest expenditure share for
both household types, while proportionally more was spent on fruits
and vegetables than on solid fats and added sugars, grains or
dairy.
------------------------------------------------------------------------
SNAP and Non-SNAP Household Expenditures by USDA Food Pattern
Categories
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
SNAP Households Non-SNAP Households
Source: Foods Typically Purchased by SNAP Households, IMPAQ
International, LLC, 2016.
4.1 Top Expenditures for Dairy
There are few differences in dairy expenditure patterns between
SNAP households and non-SNAP households. Shown in Exhibit 8, the top
four dairy subcommodities for both household groups were identical--
fluid milk/white only, shredded cheese, American single cheese, and
natural cheese chunks. These top four accounted for 60% of all dairy
expenditures for SNAP households and 47% for non-SNAP households. The
biggest driver of the proportional difference was the purchase of fluid
milk/white only. Fluid white milk was the top subcommodity representing
33% of all dairy expenditures by SNAP households. In comparison, this
subcommodity accounted for 26% of non-SNAP household dairy
expenditures. Overall, 23 dairy subcommodities in the top 25 for SNAP
households were also among the top 25 for non-SNAP households. The top
25 dairy subcommodities for SNAP households represented almost all
dairy expenditures, 93%, while these 25 subcommodities represented 85%
of dairy expenditures for non-SNAP households.
Exhibit 8: Top 25 SNAP Household Dairy Subcommodity Expenditures
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
---------------------------------------------------------------------------
Dairy Subcommodity $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/White Only 1 $191.1 33.25% 1 $853.8 25.69%
Shredded Cheese 2 $74.7 13.00% 2 $342.0 10.29%
American Single Cheese 3 $44.1 7.67% 4 $136.6 4.11%
Natural Cheese Chunks 4 $35.3 6.14% 3 $216.1 6.50%
Bagged Cheese Snacks 5 $17.1 2.98% 16 $52.0 1.56%
Flavored Fluid Milk 6 $16.0 2.78% 14 $59.4 1.79%
String Cheese 7 $15.1 2.63% 9 $99.0 2.98%
Yogurt/Kids 8 $14.0 2.44% 20 $42.4 1.28%
Cottage Cheese 9 $13.9 2.42% 7 $108.8 3.27%
Natural Cheese Slices 10 $13.4 2.33% 6 $113.2 3.41%
Yogurt/Single Serving Regular 11 $11.0 1.91% 11 $69.0 2.07%
Loaf Cheese 12 $10.9 1.90% 23 $38.1 1.15%
Yogurt/Single Serve Light 13 $10.2 1.78% 8 $103.1 3.10%
Yogurt/Pro Active Health 14 $7.4 1.29% 13 $63.5 1.91%
Yogurt/Adult Multi-Packs 15 $7.2 1.25% 19 $42.5 1.28%
Specialty/Lactose Free Milk 16 $6.7 1.17% 17 $48.4 1.46%
Grated Cheese 17 $6.2 1.08% 25 $33.6 1.01%
Bulk Semi-Hard (Cheese) 18 $6.1 1.05% 18 $44.0 1.32%
Fluid Milk 19 $5.9 1.02% 5 $113.3 3.41%
Canned Milk 20 $5.5 0.96% 27 $27.9 0.84%
Yogurt/Specialty Greek 21 $5.0 0.86% 10 $77.4 2.33%
Half & Half 22 $4.4 0.77% 15 $54.6 1.64%
Yogurt/Large Size (16oz or More) 23 $4.4 0.76% 22 $40.4 1.22%
Miscellaneous Cheese 24 $3.8 0.67% 21 $42.1 1.27%
Bulk Processed (Cheese) 25 $3.4 0.59% 29 $19.8 0.60%
---------------------------- ---------------------------
Sum of Listed Dairy Expenditures $532.9 92.70% $2,841.0 85.49%
============================ ===========================
Total Dairy Expenditures Among $571.2 100% $3,257.4 100%
Top 1,000 Subcommodities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: The table lists the top 25 dairy subcommodities for SNAP households and the corresponding ranking of these
subcommodities for non-SNAP households. Columns may not sum to total shown due to rounding.
4.2 Top Expenditures for Fruits
The top 25 fruit subcommodities by expenditure for SNAP households
included whole fruits as well as 100% fruit juices, as shown in Exhibit
9 below. The top fruit subcommodity for both SNAP and non-SNAP
households was 100% orange juice. This top fruit subcommodity
represented 10% of all SNAP household fruit expenditures, 9% for non-
SNAP households. Bananas and strawberries rank second and third,
respectively, for both household groups. Together, the top three fruit
subcommodities account for about \1/4\ (24%) of the fruit expenditures
for both SNAP and non-SNAP households. The top 25 SNAP household fruit
subcommodities accounted for 71% of all SNAP household fruit
expenditures. These 25 subcommodities accounted for 66% of fruit
expenditures for non-SNAP households. Twenty-one of the top 25 fruit
subcommodities for SNAP households were also in the top 25 for non-SNAP
households.
Exhibit 9: Top 25 SNAP Household Fruit Subcommodity Expenditures
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
---------------------------------------------------------------------------
Fruit Subcommodity $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
100% Pure Juice--Orange; Dairy Case 1 $43.5 10.18% 1 $269.0 9.35%
Bananas 2 $34.2 8.00% 2 $242.7 8.43%
Strawberries 3 $23.5 5.48% 3 $178.4 6.20%
Fruit Snacks 4 $17.6 4.13% 17 $43.2 1.50%
Grapes Red 5 $15.8 3.70% 4 $121.7 4.23%
Grapes White 6 $15.5 3.61% 6 $84.9 2.95%
Apple Juice & Cider (Over 50% Pure 7 $13.3 3.11% 14 $45.8 1.59%
Juice)
Instore Cut Fruit 8 $13.2 3.09% 5 $85.8 2.98%
Oranges Navels 9 $12.6 2.94% 8 $79.3 2.75%
Fruit Cup 10 $10.6 2.47% 19 $42.7 1.49%
Blended Juice & Combinations 11 $9.3 2.17% 29 $29.6 1.03%
Clementines 12 $8.8 2.06% 9 $78.6 2.73%
Melons Instore Cut 13 $8.2 1.93% 18 $42.8 1.49%
Watermelon Seedless Whole 14 $7.9 1.84% 16 $43.9 1.53%
Cherries Red 15 $6.9 1.61% 11 $56.7 1.97%
Apples Gala (Bulk & Bag) 16 $6.6 1.54% 10 $69.3 2.41%
Cranapple/Cran Grape Juice 17 $6.1 1.43% 31 $27.3 0.95%
Apples Red Delicious (Bulk & Bag) 18 $5.8 1.35% 23 $35.2 1.22%
100% Pure Juice--Other; Dairy Case 19 $5.4 1.26% 25 $32.3 1.12%
Cantaloupe Whole 20 $5.3 1.24% 15 $44.4 1.54%
Blueberries 21 $5.1 1.19% 7 $79.4 2.76%
Pineapple 22 $4.9 1.15% 33 $24.0 0.83%
Peaches Yellow Flesh 23 $4.8 1.13% 22 $35.6 1.24%
Grape Juice (Over 50% Juice) 24 $4.8 1.12% 44 $17.1 0.60%
Lemons 25 $4.6 1.08% 24 $33.6 1.17%
---------------------------- ---------------------------
Sum of Listed Fruit Expenditures $294.3 68.81% $1,843.4 64.06%
============================ ===========================
Total Fruit Expenditures Among $416.8 100% $2,772.4 100%
Top 1,000 Subcommodities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: The table lists the top 25 fruit subcommodities for SNAP households and the corresponding rankings of
these subcommodities for non-SNAP households. Columns may not sum to total shown due to rounding.
4.3 Top Expenditures for Grains
Exhibit 10 details the top 25 grain subcommodities purchased by
SNAP households. Cereals are a popular purchase among grain
subcommodities for both SNAP and non-SNAP households. The top grain
subcommodity for SNAP households was kids cereal, representing almost
10% of all grain expenditures. Kids cereal, ranked third for non-SNAP
households. All family cereal was ranked first for non-SNAP households
and fifth for SNAP households. Adult cereals were also common purchases
ranking sixth for SNAP households and fourth for non-SNAP households.
The top 25 grain subcommodities purchased by SNAP households made up
67% of their grain expenditures. Comparatively, these 25 subcommodities
comprised 57% of expenditures on grains subcommodities for non-SNAP
households. Ninteen subcommodities in the top 25 for SNAP households
were also among the top 25 for non-SNAP households.
Exhibit 10: Top 25 SNAP Household Grains Subcommodity Expenditures
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
---------------------------------------------------------------------------
Grains Subcommodity $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
Kids Cereal 1 $78.1 9.88% 3 $186.4 4.51%
Mainstream White Bread 2 $48.0 6.07% 7 $136.8 3.31%
Tortilla/Nacho Chips 3 $47.4 5.99% 2 $209.0 5.05%
Mainstream Variety Breads 4 $38.4 4.86% 5 $173.2 4.19%
All Family Cereal 5 $36.2 4.58% 1 $214.9 5.20%
Adult Cereal 6 $24.9 3.15% 4 $182.6 4.42%
Mexican Soft Tortillas and Wraps 7 $23.7 3.00% 8 $113.1 2.74%
Waffles/Pancakes/French Toast 8 $17.3 2.19% 13 $77.4 1.87%
Ramen Noodles/Ramen Cups 9 $16.7 2.12% 43 $28.1 0.68%
Cheese Crackers 10 $16.5 2.08% 10 $90.2 2.18%
Hamburger Buns 11 $16.2 2.05% 14 $70.2 1.70%
Hot Dog Buns 12 $16.2 2.05% 18 $62.2 1.50%
Refrigerated Biscuits 13 $14.7 1.86% 30 $45.2 1.09%
Butter Spray Crackers 14 $14.6 1.85% 15 $68.7 1.66%
Toaster Pastries 15 $14.0 1.77% 27 $47.6 1.15%
Rice Side Dish Mixes Dry 16 $14.0 1.76% 28 $46.7 1.13%
Popcorn--Microwave 17 $13.1 1.65% 17 $63.4 1.53%
Long Cut Pasta 18 $13.0 1.64% 19 $60.4 1.46%
Granola Bars 19 $12.8 1.61% 11 $88.9 .15%
Premium Bread 20 $12.3 1.55% 6 $144.7 3.50%
Cereal Bars 21 $10.9 1.38% 12 $78.4 1.90%
Short Cut Pasta 22 $9.9 1.25% 21 $56.2 1.36%
Rolls: Dinner 23 $9.5 1.21% 23 $50.5 1.22%
Frozen Garlic Toast 24 $9.1 1.16% 44 $27.8 0.67%
Corn Chips 25 $9.1 1.15% 29 $45.6 1.10%
---------------------------- ---------------------------
Sum of Listed Grain Expenditures $536.6 67.86% $2,368.4 57.27%
============================ ===========================
Total Grain Expenditures Among $783.8 100% $4,049.9 100%
Top 1,000 Subcommodities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: The table lists the top 25 grain subcommodities for SNAP households and the corresponding ranking of these
subcommodities for non-SNAP households. Columns may not sum to total shown due to rounding.
4.4 Top Expenditures for Oils
The top oils subcommodity expenditures are shown in Exhibit 11.
Pourable salad dressings was the top oils subcommodity by expenditure
for both SNAP and non-SNAP households, accounting for nearly \1/4\ of
their total expenditures on oils. The second and third ranked oils
subcommodities, mayonnaise/whipped dressing and margarine in tubs and
bowls, were the same for both household groups, as well.
Exhibit 11: Oils Subcommodity Expenditures
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
---------------------------------------------------------------------------
Oils Subcommodity $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
Pourable Salad Dressings 1 $29.0 22.71% 1 $139.4 24.28%
Mayonnaise and Whipped Dressing 2 $27.3 21.34% 2 $119.1 20.73%
Margarine: Tubs and Bowls 3 $23.4 18.37% 3 $100.9 17.56%
Vegetable Oils 4 $20.5 16.07% 5 $35.4 6.16%
Canola Oils 5 $8.3 6.49% 6 $29.3 5.10%
Olive Oils 6 $7.3 5.69% 4 $63.8 11.11%
Cooking Sprays 7 $3.2 2.49% 7 $21.0 3.65%
Dressing Creamy 8 $1.6 1.23% 8 $14.5 2.53%
Sandwich/Horseradish and Tartar 9 $1.4 1.14% 10 $7.2 1.26%
Sauce
Corn Oils 10 $1.3 1.01% 14 $4.1 0.71%
Cooking Oils: Peanut/Safflower 11 $1.1 0.89% 11 $6.7 1.17%
Dressing Blue Cheese 12 $0.9 0.71% 9 $9.5 1.65%
Margarine: Squeeze 13 $0.6 0.44% 13 $4.2 0.74%
---------------------------- ---------------------------
Sum of Listed Oils Expenditures $125.9 98.58% $555.0 96.65%
============================ ===========================
Total Oils Expenditures Among $125.9 100% $555.0 100%
the Top 1,000 Subcommodities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: The data included only 13 oils subcommodities in the top 1,000 subcommodities. Columns may not sum to
total shown due to rounding.
4.5 Top Expenditures for Protein Foods
The top 25 protein foods subcommodities based on expenditures of
SNAP households are shown in Exhibit 12. For SNAP households, the top
25 represented over \1/2\ (54%) of all protein foods expenditures.
These same 25 subcommodities comprised 48% of the protein foods
expenditures for non-SNAP households. The top five subcommodities were
the same for both household groups, although in slightly different
order and accounted for \1/5\ of all protein expenditures for both
households. The protein foods included in the top five were beef,
lunchmeat, eggs and chicken. Lean ground beef was the top protein foods
subcommodity by expenditure for SNAP households, totaling just over 7%
of all protein foods expenditures. The top protein foods subcommodity
for non-SNAP households was boneless chicken breasts at 5% of their
expenditures. Eighteen of the SNAP household top 25 subcommodities were
also ranked in the top 25 for non-SNAP households.
Exhibit 12: Top 25 SNAP Household Protein Foods Subcommodity Expenditures
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
---------------------------------------------------------------------------
Protein Foods Subcommodity $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
Lean Ground Beef 1 $112.4 7.38% 2 $257.9 4.03%
Primal Ground Beef 2 $62.4 4.10% 5 $219.8 3.43%
Lunchmeat--Deli Fresh 3 $55.8 3.67% 4 $242.6 3.79%
Eggs--Large 4 $52.1 3.43% 3 $251.6 .93%
Chicken Breast Boneless 5 $49.6 3.26% 1 $292.9 4.57%
Enhanced Pork Boneless Loin/Rib 6 $41.5 2.73% 6 $168.0 2.62%
Bacon--Trad 16oz Or Less 7 $40.7 2.68% 8 $157.6 2.46%
Ribs (Pork) 8 $35.0 2.30% 15 $106.8 1.67%
Frozen Chicken--White Meat 9 $30.0 1.97% 17 $99.8 1.56%
Choice Beef (Loins) 10 $28.4 1.87% 11 $136.6 2.13%
Select Beef 11 $27.9 1.83% 9 $143.7 2.24%
Hot Dogs--Base Meat 12 $25.1 1.65% 27 $56.8 0.89%
Choice Beef (Rounds) 13 $24.0 1.58% 20 $72.5 1.13%
Chicken Wings 14 $22.2 1.46% 58 $28.6 0.45%
Frozen Chicken--Wings 15 $22.2 1.46% 97 $17.4 0.27%
Lunchmeat--Bologna/Sausage 16 $21.8 1.43% 24 $60.9 0.95%
Tuna 17 $21.1 1.39% 14 $109.9 1.72%
Peanut Butter 18 $20.4 1.34% 12 $127.8 1.99%
Meat: Turkey Bulk 19 $19.3 1.27% 7 $159.6 2.49%
Frozen Meat--Beef 20 $19.0 1.25% 34 $46.3 0.72%
Value Forms/18oz & Larger 21 $18.6 1.22% 41 $42.6 0.67%
Chicken Drumsticks 22 $17.3 1.14% 49 $31.5 0.49%
Angus Beef 23 $17.1 1.13% 16 $103.8 1.62%
Dinner Sausage--Links Pork Ckd 24 $16.4 1.08% 45 $37.6 0.59%
Meat: Ham Bulk 25 $15.3 1.00% 13 $115.9 1.81%
---------------------------- ---------------------------
Sum of Listed Protein Foods $815.7 53.62% $3,088.3 48.22%
Expenditures
============================ ===========================
Total Protein Foods Expenditures $1,512.2 100% $6,288.8 100%
Among Top 1,000 Subcommodities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: The table lists the top 25 protein foods subcommodities for SNAP households and the corresponding ranking
of these subcommodities for non-SNAP households. Columns may not sum to total shown due to rounding.
4.6 Top Expenditures for Solid Fats and Added Sugars (SoFAS)
The top 25 SoFAS subcommodities by expenditure for SNAP households
are shown in Exhibit 13. Twenty two subcommodities in the top 25 for
SNAP households were also among the top 25 for non-SNAP households. In
addition, the top two subcommodities were the same. They were
carbonated soft drinks packaged as 12-18 pack cans and 2-liter bottles.
These two subcommodities represented approximately \1/4\ of the SoFAS
expenditures for both types of households. Sugar, ranked fourth, was
the highest ranked non-beverage SoFAS subcommodity for SNAP households.
It was eighth ranked for non-SNAP households. Butter ranked higher
(third) for non-SNAP households compared to tenth for SNAP households.
Overall, the top 25 SNAP household SoFAS subcommodities in Exhibit 13
totaled 75% of SNAP household SoFAS expenditures. These 25
subcommodities totaled 71% of the SoFAS expenditures for non-SNAP
households.
Exhibit 13: Top 25 SNAP Household Solid Fats and Added Sugars (SoFAS) Subcommodity Expenditures
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
Solid Fats and Added Sugars (SoFAS) ---------------------------------------------------------------------------
Subcommodity $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
Soft Drinks 12/18 & 15pk Can Car 1 $164.6 18.86% 1 $601.2 16.11%
Soft Drinks 2 Liter Btl Carb Incl 2 $70.9 8.12% 2 $230.1 6.17%
Soft Drinks 20pk & 24pk Can Carb 3 $39.7 4.55% 9 $106.4 2.85%
Sugar 4 $36.9 4.23% 8 $112.7 3.02%
Soft Drink Mlt-Pk Btl Carb 5 $34.0 3.90% 4 $173.6 4.65%
Soft Drink Single Serve Btl Carb 6 $27.8 3.18% 11 $71.4 1.91%
Aseptic Pack Juice And Drinks 7 $24.2 2.78% 16 $57.1 1.53%
Refrigerated Coffee Creamers 8 $24.1 2.76% 6 $147.2 3.95%
Candy Bags-Chocolate 9 $21.5 2.46% 5 $147.5 3.95%
Butter 10 $19.6 2.24% 3 $175.6 4.71%
Sour Creams 11 $17.5 2.00% 10 $95.2 2.55%
Cream Cheese 12 $17.2 1.97% 7 $115.5 3.10%
Candy Bars (Singles) 13 $16.3 1.87% 18 $54.9 1.47%
Dairy Case Juice Drink Under 10 Oz 14 $16.0 1.83% 22 $48.0 1.29%
Candy Bars (Multi Pack) 15 $15.6 1.79% 12 $69.6 1.86%
Tea Sweetened 16 $13.9 1.59% 13 $68.7 1.84%
Chewing Gum 17 $13.2 1.51% 14 $68.3 1.83%
Candy Bags-Non Chocolate 18 $12.6 1.44% 19 $54.9 1.47%
Molasses and Syrups 19 $11.7 1.34% 15 $58.7 1.57%
Dairy Case Citrus Punch/OJ Subs 20 $11.0 1.26% 27 $34.4 0.92%
Fruit Drinks: Canned & Glass 21 $10.6 1.21% 60 $10.9 0.29%
Non Dairy Creamer 22 $10.5 1.20% 25 $35.4 0.95%
Seasonal Miscellaneous 23 $9.2 1.05% 23 $46.9 1.26%
Dairy Case Tea With Sugar 24 $8.4 0.96% 36 $23.1 0.62%
Seasonal Candy Bags-Chocolate 25 $7.9 0.90% 20 $54.8 1.47%
---------------------------- ---------------------------
Sum of Listed SoFAS Expenditures $655.0 75.00% $2,662.3 71.34%
============================ ===========================
Total SoFAS Expenditures Among $864.1 100% $3,673.1 100%
Top 1,000 Subcommodities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: The table lists the top 25 SoFAS subcommodities for SNAP households and the corresponding ranking of these
subcommodities for non-SNAP households. Columns may not sum to total shown due to rounding.
SoFAS were divided into three broad subcategories to inform the
analyses: butter/cream/solid fats, candy/sweets, and sweetened
beverages.\38\ The distribution of these subcategories for both
household types is shown in Exhibit 14. As a share of total SoFAS
expenditures, sweetened beverage expenditures were more than ten
percentage points higher in SNAP households than non-SNAP households.
In contrast, non-SNAP households spent a larger share of their SoFAS
expenditures on the butter/cream/solid fats and candy/sweets
subcategories.
---------------------------------------------------------------------------
\38\ Fruit drinks that are over 50% juice are categorized as
fruits. All other fruit drinks are categorized as SoFAS. In our
discussion, fruit drinks that are less than 50% juice are grouped into
``sweetened beverages.''
---------------------------------------------------------------------------
Exhibit 14: Solid Fats and Added Sugars (SoFAS) Expenditures by
Subcategory
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
SNAP Households Non-SNAP Households
Source: Foods Typically Purchased by SNAP Households, IMPAQ
International, LLC, 2016.
4.7 Top Expenditures for Vegetables
As shown in Exhibit 15, russet potatoes and plain frozen bag
vegetables were the top two vegetable subcommodities by expenditure
purchased by SNAP and non-SNAP households. Overall, 18 of the top 25
vegetable subcommodities for SNAP households were among the top 25 for
non-SNAP households. The top 25 SNAP household subcommodities comprised
56% of total vegetable expenditures for SNAP households. These same 25
subcommodities comprised 47% of total vegetable expenditures for non-
SNAP households. The top 25 subcommodities for both SNAP and non-SNAP
households for this Food Pattern category included a range of
vegetables such as potatoes, avocados, green beans, corn, lettuce and
cucumbers to name a few.
Exhibit 15: Top 25 SNAP Household Vegetables Subcommodity Expenditures
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
---------------------------------------------------------------------------
Vegetables Subcommodity $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
Potatoes Russet (Bulk & Bag) 1 $35.8 6.74% 1 $154.5 4.60%
Frozen Bag Vegetables--Plain 2 $25.7 4.85% 2 $131.9 3.93%
Mainstream Pasta & Pizza Sauce 3 $23.0 4.33% 6 $81.0 2.41%
Frozen French Fries 4 $20.5 3.86% 19 $50.3 1.50%
Avocado 5 $13.4 2.52% 4 $112.6 3.35%
Blends Salad Mix 6 $13.1 2.47% 3 $124.0 3.69%
Green Beans: Fs/Whl/Cut 7 $12.8 2.41% 15 $53.1 1.58%
Potatoes: Dry 8 $12.3 2.31% 33 $32.3 0.96%
Corn 9 $12.1 2.28% 22 $44.0 1.31%
Head Lettuce 10 $11.6 2.18% 13 $55.5 1.65%
Frozen Steamable Vegetables 11 $10.5 1.98% 5 $81.4 2.42%
Mexican Sauces and Picante Sauce 12 $10.2 1.93% 9 $62.3 1.85%
Tomatoes Diced 13 $9.5 1.79% 11 $59.9 1.78%
Tomatoes Hothouse On The Vine 14 $9.2 1.74% 7 $77.7 2.31%
Onions Yellow (Bulk & Bag) 15 $8.7 1.65% 27 $39.3 1.17%
Cucumbers 16 $8.2 1.55% 12 $58.9 1.75%
Vegetable Salads--Prepack 17 $7.8 1.48% 29 $36.6 1.09%
Peppers Green Bell 18 $7.8 1.47% 25 $41.5 1.24%
Regular Garden 19 $7.8 1.46% 35 $31.9 0.95%
Roma Tomatoes (Bulk/Pkg) 20 $7.5 1.41% 26 $39.6 1.18%
Carrots Mini Peeled 21 $7.0 1.32% 10 $61.4 1.83%
Onions Sweet (Bulk & Bag) 22 $6.2 1.16% 20 $47.4 1.41%
Celery 23 $5.9 1.11% 17 $51.2 1.52%
Tomatoes Vine Ripe Bulk 24 $5.7 1.07% 51 $22.5 0.67%
Garden Plus Salad Mix 25 $5.5 1.03% 36 $31.8 0.95%
---------------------------- ---------------------------
Sum of Listed Vegetable $297.7 56.10% $1,582.6 47.10%
Expenditures
============================ ===========================
Total Vegetable Expenditures $520.5 100% $3,251.8 100%
Among Top 1,000 Subcommodities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: The table lists the top 25 vegetable subcommodities for SNAP households and the corresponding ranking of
these subcommodities for non-SNAP households. Columns may not sum to total shown due to rounding.
4.8 Top Expenditures for Composite Foods
Composite foods include those subcommodities that contain more than
one USDA Food Pattern category. As a result, they could not be assigned
specifically to a single category. For example, composite foods include
both dairy and grains (macaroni and cheese), dairy and SoFAS (ice
cream), vegetables and oils (potato chips), or protein foods,
vegetables and grains (frozen meals). The top 25 composite foods
subcommodities based on the expenditures of SNAP households are
presented in Exhibit 16. Potato chips were the top composite
subcommodity by expenditure for SNAP households, representing 5% of
their overall expenditures on composite items. Potato chips were ranked
second for non-SNAP households. Overall, expenditures on composite
subcommodities were similar for SNAP and non-SNAP households with 19
subcommodities in the top 25 for both groups. The top 25 SNAP household
subcommodities shown in Exhibit 16 represented 58% of all SNAP
household composite foods expenditures, while expenditures on these 25
subcommodities by non-SNAP households accounted for 51% of their total
composite foods expenditures.
Exhibit 16: Top 25 SNAP Household Composite Subcommodity Expenditures
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
---------------------------------------------------------------------------
Composite Subcommodity $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
Potato Chips 1 $64.4 5.19% 2 $253.2 4.88%
Snacks/Appetizers 2 $44.6 3.59% 10 $100.5 1.94%
Frozen Single Serve Premium 3 $43.8 3.53% 4 $175.4 3.38%
Traditional Meals
Snack Cake--Multi Pack 4 $41.6 3.36% 9 $101.7 1.96%
Frozen Single Serve Economy Meals 5 $40.9 3.30% 15 $80.7 1.56%
Pizza/Premium 6 $39.7 3.20% 6 $153.3 2.95%
Sandwiches and Handhelds 7 $35.9 2.89% 17 $73.6 1.42%
Convenient Meals--Kids Meal 8 $34.2 2.76% 19 $69.7 1.34%
Premium (Ice Cream & Sherbert) 9 $31.2 2.52% 3 $226.0 4.35%
Condensed Soup 10 $29.7 2.39% 5 $153.6 2.96%
Frozen Family Style Entrees 11 $27.6 2.23% 13 $83.5 1.61%
Traditional 12 $25.6 2.07% 8 $118.7 2.29%
Frozen Single Serve Premium 13 $24.7 1.99% 1 $271.6 5.23%
Nutritional Meals
Macaroni and Cheese Dinners 14 $24.3 1.96% 24 $59.7 1.15%
Can Pasta 15 $22.2 1.79% 36 $47.7 0.92%
Multi-Pack Bag Snacks 16 $21.6 1.74% 38 $43.4 0.84%
Sweet Goods: Donuts 17 $21.3 1.72% 14 $82.3 1.58%
Pizza/Economy 18 $19.8 1.60% 37 $45.1 0.87%
Frozen Breakfast Sandwiches 19 $19.1 1.54% 29 $55.7 1.07%
Frozen Skillet Meals 20 $18.8 1.51% 16 $79.3 1.53%
Cakes: Birthday/Celebration 21 $18.6 1.50% 33 $50.3 0.97%
Sandwich Cookies 22 $18.0 1.45% 18 $71.8 1.38%
Pizza/Traditional 23 $17.9 1.44% 22 $64.1 1.24%
Rts Soup: Chunky/Homestyle 24 $17.6 1.42% 7 $119.9 2.31%
Salsa and Dips 25 $17.1 1.38% 28 $57.0 1.10%
---------------------------- ---------------------------
Sum of Listed Composite $720.5 58.07% $2,637.7 50.83%
Expenditures
============================ ===========================
Total Composite Expenditures $1,235.4 100% $5,132.0 100%
Among Top 1,000 Subcommodities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: The table lists the top 25 composite subcommodities for SNAP households and the corresponding ranking of
these subcommodities for non-SNAP households. Columns may not sum to total shown due to rounding.
The composite subcommodities were further categorized as snacks,
soups, desserts, and entree/meal items to inform the analyses. Exhibit
17 suggests some differences in SNAP and non-SNAP household expenditure
distributions on these subgroups. SNAP households spent a larger share
of their composite expenditures on entree/meal subcommodities, while
non-SNAP households spent larger shares on desserts and soup.
Expenditures on snacks were not very different across the two groups.
Exhibit 17: Composite Expenditures by Subcategory
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
SNAP Households Non-SNAP Households
Source: Foods Typically Purchased by SNAP Households, IMPAQ
International, LLC, 2016.
4.9 Top Expenditures for Other Subcommodities
Some subcommodities did not contain any USDA Food Pattern
categories, or the subcommodity labels were not descriptive enough to
permit categorization even with the addition of the composite category.
As a result, a ninth category, other, was created to capture such
subcommodities. ``Other'' included subcommodities such as water,
isotonic drinks, and baby food. The top 25 other subcommodities based
on the expenditures of SNAP households are shown in Exhibit 18 and
accounted for 66% of their overall other subcommodity expenditures.
These subcommodities accounted for 54% of all other expenditures for
non-SNAP households. Overall, expenditures on other subcommodities were
similar for SNAP and non-SNAP households with 19 subcommodities in
common in the top 25 for both groups. The top other subcommodity
purchased by SNAP households was infant formula/starter solution,
accounting for almost 10% of the total SNAP household expenditures on
these items. Subcommodities reflecting drinking water and coffee were
ranked second and third, respectively. Coffee subcommodities were
ranked first and third for non-SNAP households with the same water
subcommodity that was ranked second for SNAP households ranked second
for non-SNAP households, as well. Interestingly, infant formula/starter
solution that was ranked first for SNAP households was ranked 14th for
non-SNAP households.
Exhibit 18: Top 25 SNAP Household Other Subcommodity Expenditures
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
---------------------------------------------------------------------------
Other Subcommodity $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
Infant Formula/Starter Solution 1 $54.2 9.60% 14 $45.3 1.70%
Still Water Drinking/Mineral Water 2 $48.8 8.64% 2 $187.7 7.03%
Unflavored Can Coffee 3 $41.3 7.32% 1 $198.0 7.41%
Isotonic Drinks Single Serve 4 $30.5 5.40% 4 $119.5 4.47%
Spring Water 5 $16.2 2.87% 5 $95.6 3.58%
Traditional Spices 6 $14.1 2.49% 8 $61.2 2.29%
Bbq Sauce 7 $12.3 2.17% 16 $38.6 1.45%
Baby Food--Beginner 8 $11.7 2.07% 21 $28.1 1.05%
Non-Carb Water Flavor--Drink/Mnr 9 $11.6 2.05% 7 $63.4 2.37%
Catsup 10 $11.5 2.03% 15 $41.5 1.55%
Sauce Mixes/Gravy Mixes Dry 11 $11.5 2.03% 13 $46.7 1.75%
Baby Food Junior/All Brands 12 $11.2 1.98% 22 $27.5 1.03%
Isotonic Drinks Multi-Pack 13 $10.8 1.92% 9 $58.1 2.17%
Ice--Crushed/Cubed 14 $9.3 1.65% 11 $49.9 1.87%
Unflavored Bag Coffee 15 $8.5 1.50% 3 $137.3 5.14%
Infant Formula Specialty 16 $8.4 1.49% 71 $9.1 0.34%
Infant Formula Starter Large 17 $8.3 1.46% 30 $22.8 0.85%
Steak & Worchester Sauce 18 $8.2 1.44% 25 $26.7 1.00%
Unflavored Instant Coffee 19 $7.6 1.34% 23 $27.3 1.02%
Non-Dairy Milk 20 $7.1 1.25% 6 $67.7 2.53%
Unsweetened Envelope (Powder Drink 21 $7.0 1.25% 88 $6.2 0.23%
Mix)
Malted Milk/Syrup/Powders/Eggnog 22 $6.9 1.23% 28 $25.3 0.95%
Still Water Flavored Drink/Mineral 23 $6.3 1.11% 17 $38.1 1.43%
Water
Infant Formula Toddler 24 $6.0 1.06% 55 $12.4 0.46%
Mexican Seasoning Mixes 25 $5.9 1.05% 33 $20.6 0.77%
---------------------------- ---------------------------
Sum of Listed Other Expenditures $374.8 66.40% $1,454.7 54.44%
============================ ===========================
Total Other Expenditures Among $550.7 100% $2,533.2 100%
Top 1,000 Subcommodities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: The table lists the top 25 ``other'' subcommodities for SNAP households and the corresponding ranking of
these subcommodities for non-SNAP households. Columns may not sum to total shown due to rounding.
All other subcommodities were divided into the following six
subcategories for additional analysis: condiments; infant formula/baby
food; seasoning/baking needs; supplements/meal replacements/energy
drinks; unsweetened beverages; and miscellaneous. Exhibit 19 shows that
SNAP households spent a notably larger share--about 15 percentage
points more than non-SNAP households--on infant formulas and baby foods
in these data. Non-SNAP households spent a larger share on unsweetened
beverages.
Exhibit 19: Other Expenditures by Subcategory
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
SNAP Households Non-SNAP Households
Source: Foods Typically Purchased by SNAP Households, IMPAQ
International, LLC, 2016.
Chapter 5. Conclusion
IMPAQ analyzed point-of-sale transaction data from January 1, 2011
through December 31, 2011 from a leading grocery retailer to understand
what food items are typically purchased by SNAP households and how
these purchases compare to those made by non-SNAP households. The
majority of stores from which the data came would be classified as
grocery stores, supermarkets, and combination food and drug stores per
FNS Retailer Policy and Management Division food retailer
definitions.\39\ Expenditures on SNAP-eligible food items were examined
at four levels: by USDA Food Pattern categories, summary categories,
commodities, and subcommodities, as shown in Exhibit 20.
---------------------------------------------------------------------------
\39\ Stores that opened or closed during 2011 were not included in
these analyses.
---------------------------------------------------------------------------
Overall, the findings from this study indicate that SNAP households
and non-SNAP households purchased similar foods in the retail outlets
in these data. The findings hold true after assessing food expenditure
patterns of SNAP and non-SNAP households using multiple categorization
methods. Both groups of households spent about 40 of every dollar of
food expenditures on basic items such as meat, fruits, vegetables,
milk, eggs, and bread. Another 20 out of every dollar was spent on
sweetened beverages, desserts, salty snacks, candy and sugar. The
remaining 40 were spent on a variety of items such as cereal, prepared
foods, dairy products, rice, and beans.
Exhibit 20: SNAP and Non-SNAP Household Food Expenditure Patterns
------------------------------------------------------------------------
Finding SNAP Households Non-SNAP Households
------------------------------------------------------------------------
Total annual $6.7 billion $32.3 billion
expenditures on SNAP-
eligible foods in
dataset
Percentage of all 12% 88%
transactions by all
households
Percentage of total 17% 83%
annual expenditures
by all households
Top 1,000 (of 1,792) 99% 98%
subcommodity
expenditures as a
percentage of all
expenditures
Top 100 subcommodity 51% 46%
expenditures as a
percentage of all
expenditures
Top 25 subcommodity 25% 21%
expenditures as a
percentage of all
expenditures
Top 25 commodity (of 45% 41%
238) expenditures as
a percentage of all
expenditures
Top 10 summary Meat, Poultry and Meat, Poultry and
categories (of 30) by Seafood Seafood
expenditure
Sweetened Beverages Vegetables
Vegetables High-fat Dairy/Cheese
Frozen Prepared Foods Fruits
Prepared Desserts Sweetened Beverages
High-fat Dairy/Cheese Prepared Desserts
Bread and Crackers Bread and Crackers
Fruits Frozen Prepared Foods
Milk Milk
Salty Snacks Salty Snacks
Top 10 commodities (of Soft Drinks Fluid Milk Products
238) by expenditure
Fluid Milk Products Soft Drinks
Beef Grinds Cheese
Bag Snacks Baked Breads
Cheese Bag Snacks
Baked Breads Beef Grinds
Cold Cereal Cold Cereal
Chicken Fresh Candy--Packaged
Frozen Handhelds and Coffee and Creamers
Snacks
Lunchmeat Ice Cream, Ice Milk,
and Sherbets
Top 10 subcommodities Fluid Milk/White Only Fluid Milk/White Only
(of 1,792) by
expenditure
Soft Drinks 12-18 pack Soft Drinks 12-18 pack
Lean Beef Shredded Cheese
Kids' Cereal Chicken Breast--
Boneless
Shredded Cheese Frozen Premium
Nutritional Meals
2-Liter Soft Drink Pure Orange Juice--
Dairy Case
Potato Chips Lean Beef
Primal Beef Potato Chips
Lunchmeat--Deli fresh Large Eggs
Infant Formula/Starter Bananas
Solution
------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ
International, LLC, 2016.
* All SNAP totals represent purchases by SNAP households in the dataset,
not SNAP dollars.
In summary, after assessing food expenditure patterns of SNAP households
and non-SNAP households using multiple categorization methods, both
household types made similar food expenditures in 2011 from the retail
outlets included in these data.
Appendix A: Top Purchases by Expenditure for SNAP and Non-SNAP
Households
Exhibit A-1: All Commodities
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
---------------------------------------------------------------------------
Commodity $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
Soft drinks 1 $357.7 5.44% 2 $1,263.3 4.01%
Fluid milk products 2 $253.7 3.85% 1 $1,270.3 4.03%
Beef: grinds 3 $201.0 3.05% 6 $621.1 1.97%
Bag snacks 4 $199.3 3.03% 5 $793.9 2.52%
Cheese 5 $186.4 2.83% 3 $948.9 3.01%
Baked breads 6 $163.7 2.49% 4 $874.8 2.78%
Cold cereal 7 $139.2 2.12% 7 $583.9 1.85%
Chicken fresh 8 $121.4 1.85% 11 $477.8 1.52%
Frozen handhelds & snacks 9 $101.5 1.54% 47 $214.6 0.68%
Lunchmeat 10 $99.4 1.51% 17 $386.1 1.23%
Candy--packaged 11 $96.2 1.46% 8 $527.7 1.67%
Infant formula 12 $95.7 1.45% 80 $124.8 0.40%
Frozen pizza 13 $90.2 1.37% 23 $305.7 0.97%
Refrigerated juices/drinks 14 $88.5 1.35% 14 $412.8 1.31%
Ice cream ice milk & sherbets 15 $86.0 1.31% 10 $481.8 1.53%
Coffee & creamers 16 $82.3 1.25% 9 $519.4 1.65%
Cookies 17 $78.2 1.19% 16 $408.3 1.30%
Water--(sparkling & still) 18 $77.0 1.17% 18 $379.2 1.20%
Shelf stable juice 19 $73.1 1.11% 28 $282.2 0.90%
Eggs/muffins/potatoes 20 $72.0 1.09% 20 $358.7 1.14%
Frozen ss premium meals 21 $68.6 1.04% 12 $447.1 1.42%
Cakes 22 $68.2 1.04% 38 $240.9 0.76%
Bacon 23 $66.1 1.00% 27 $283.2 0.90%
Traditional Mexican foods 24 $62.6 0.95% 25 $286.9 0.91%
Yogurt 25 $59.9 0.91% 13 $442.3 1.40%
Salad dressing & sandwich spreads 26 $59.7 0.91% 30 $280.9 0.89%
Dinner sausage 27 $59.3 0.90% 46 $222.6 0.71%
Frozen prepared chicken 28 $58.6 0.89% 74 $136.4 0.43%
Baked sweet goods 29 $57.5 0.87% 62 $159.6 0.51%
Beef loins 30 $56.3 0.86% 31 $280.3 0.89%
Chicken frozen 31 $54.8 0.83% 85 $123.0 0.39%
Deli meat: bulk 32 $54.6 0.83% 15 $411.0 1.30%
Frozen multi serve 33 $53.0 0.81% 54 $183.5 0.58%
Dinner mixes--dry 34 $51.8 0.79% 72 $140.3 0.45%
Frozen breakfast foods 35 $51.3 0.78% 55 $180.9 0.57%
Crackers & misc baked food 36 $50.9 0.77% 21 $323.7 1.03%
Frozen novelties--water ice 37 $50.7 0.77% 43 $229.7 0.73%
Margarines 38 $50.3 0.76% 24 $303.0 0.96%
Condiments & sauces 39 $49.8 0.76% 52 $187.2 0.59%
Potatoes 40 $48.8 0.74% 34 $265.2 0.84%
Frozen vegetable & veg dish 41 $48.2 0.73% 33 $266.9 0.85%
Hot dogs 42 $45.5 0.69% 63 $158.4 0.50%
Can vegetables--shelf stable 43 $45.3 0.69% 50 $191.7 0.61%
Shortening & oil 44 $44.6 0.68% 57 $174.2 0.55%
Sugars & sweeteners 45 $43.3 0.66% 60 $162.4 0.52%
Isotonic drinks 46 $42.8 0.65% 53 $185.3 0.59%
Salad mix 47 $42.8 0.65% 22 $319.4 1.01%
Milk by-products 48 $42.5 0.65% 32 $268.9 0.85%
Pork boneless loin/rib 49 $41.5 0.63% 58 $168.0 0.53%
Cnv breakfast & wholesome snacks 50 $41.1 0.62% 45 $226.1 0.72%
Frozen ss economy meals 51 $40.9 0.62% 109 $80.7 0.26%
Refrigerated dough products 52 $40.5 0.62% 56 $176.6 0.56%
Beef: round 53 $40.4 0.61% 75 $134.2 0.43%
Dry bean veg & rice 54 $39.9 0.61% 59 $166.1 0.53%
Convenient meals 55 $38.7 0.59% 108 $81.0 0.26%
Tomatoes 56 $38.3 0.58% 35 $261.7 0.83%
Candy--checklane 57 $37.9 0.58% 64 $154.0 0.49%
Berries 58 $37.4 0.57% 19 $373.5 1.19%
Grapes 59 $36.1 0.55% 39 $235.7 0.75%
Bananas 60 $36.1 0.55% 36 $261.4 0.83%
Peanut butter/jelly/jams & honey 61 $36.0 0.55% 42 $231.0 0.73%
Pork thin meats 62 $35.0 0.53% 93 $106.8 0.34%
Citrus 63 $34.3 0.52% 37 $251.7 0.80%
Breakfast sausage 64 $34.2 0.52% 79 $126.7 0.40%
Dry sauce/gravy/potatoes/stuffing 65 $34.0 0.52% 87 $119.2 0.38%
Salad & dips 66 $33.9 0.52% 40 $235.3 0.75%
Apples 67 $33.7 0.51% 29 $281.7 0.89%
Meat--shelf stable 68 $33.3 0.51% 91 $109.2 0.35%
Aseptic juice 69 $33.1 0.50% 112 $78.9 0.25%
Sweet goods 70 $32.5 0.49% 66 $152.9 0.49%
Frozen potatoes 71 $32.2 0.49% 95 $104.5 0.33%
Meat frozen 72 $31.9 0.48% 120 $69.9 0.22%
Baby foods 73 $30.6 0.46% 121 $67.8 0.22%
Vegetables salad 74 $30.0 0.46% 44 $228.6 0.73%
Beef: thin meats 75 $30.0 0.46% 78 $127.7 0.41%
Seafood--shrimp 76 $29.8 0.45% 84 $123.1 0.39%
Canned soups 77 $29.7 0.45% 65 $153.6 0.49%
Baking mixes 78 $28.3 0.43% 69 $148.1 0.47%
Pasta & pizza sauce 79 $27.6 0.42% 99 $96.7 0.31%
Dry noodles & pasta 80 $27.5 0.42% 71 $141.5 0.45%
Can seafood--shelf stable 81 $26.5 0.40% 77 $132.3 0.42%
Rts/micro soup/broth 82 $26.0 0.40% 48 $200.8 0.64%
Canned pasta & mwv fd-shlf stbl 83 $25.9 0.39% 135 $56.7 0.18%
Smoked hams 84 $25.7 0.39% 92 $108.8 0.35%
Nuts 85 $25.6 0.39% 41 $234.2 0.74%
Value-added fruit 86 $25.3 0.38% 70 $146.6 0.47%
Can beans 87 $24.0 0.36% 82 $123.3 0.39%
Dry/ramen bouillon 88 $21.7 0.33% 133 $61.0 0.19%
Powder & crystal drink mix 89 $21.6 0.33% 119 $75.2 0.24%
Rtd tea/new age juice 90 $21.5 0.33% 103 $93.8 0.30%
Baking needs 91 $21.3 0.32% 51 $188.9 0.60%
Can fruit/jar applesauce 92 $20.9 0.32% 96 $104.0 0.33%
Spices & extracts 93 $20.4 0.31% 86 $121.9 0.39%
Energy drinks 94 $20.1 0.30% 102 $94.1 0.30%
Onions 95 $20.0 0.30% 81 $123.5 0.39%
Tropical fruit 96 $19.8 0.30% 61 $160.1 0.51%
Bagels & cream cheese 97 $19.8 0.30% 83 $123.2 0.39%
Frozen bread/dough 98 $19.7 0.30% 114 $77.7 0.25%
Rolls 99 $18.9 0.29% 88 $113.9 0.36%
Hot cereal 100 $18.9 0.29% 100 $96.1 0.30%
Tomato products-shelf stable 101 $18.8 0.29% 90 $112.5 0.36%
Bread 102 $18.7 0.28% 49 $194.7 0.62%
Frozen desserts 103 $18.7 0.28% 107 $82.9 0.26%
Chicken & poultry 104 $18.7 0.28% 140 $50.3 0.16%
Refrigerated dairy case 105 $18.6 0.28% 26 $284.7 0.90%
Dry cheese 106 $18.5 0.28% 111 $79.1 0.25%
Stone fruit 107 $18.3 0.28% 73 $138.6 0.44%
Molasses/syrups/pancake mixes 108 $17.9 0.27% 110 $80.6 0.26%
Peppers 109 $17.7 0.27% 76 $133.4 0.42%
Fruit snacks 110 $17.6 0.27% 152 $43.2 0.14%
Vegetables cooking bulk 111 $17.3 0.26% 68 $150.6 0.48%
Sandwiches 112 $16.9 0.26% 124 $67.7 0.21%
Service case meat 113 $16.8 0.26% 97 $101.4 0.32%
Melons 114 $16.7 0.25% 89 $113.2 0.36%
Popcorn 115 $15.3 0.23% 117 $76.6 0.24%
Warehouse snacks 116 $14.7 0.22% 125 $67.1 0.21%
Dry mix desserts 117 $14.7 0.22% 128 $65.0 0.21%
Single serve fruit/applesauce 118 $14.6 0.22% 127 $65.4 0.21%
Frozen seafood 119 $13.8 0.21% 155 $41.0 0.13%
Flour & meals 120 $13.8 0.21% 126 $65.7 0.21%
Pickle/relish/pckld veg & olives 121 $13.5 0.21% 106 $83.1 0.26%
Turkey grinds 122 $13.1 0.20% 113 $78.0 0.25%
Bulk service case cheese 123 $12.5 0.19% 104 $87.1 0.28%
Pies 124 $12.3 0.19% 123 $67.7 0.21%
Water 125 $12.3 0.19% 122 $67.8 0.22%
Sushi 126 $11.8 0.18% 94 $104.6 0.33%
Teas 127 $11.4 0.17% 116 $76.9 0.24%
Authentic Hispanic foods & products 128 $11.0 0.17% 165 $31.7 0.10%
Cookie/cracker multi-pks 129 $10.9 0.16% 136 $52.7 0.17%
Carrots 130 $10.6 0.16% 98 $97.3 0.31%
Pork shoulder 131 $10.5 0.16% 164 $32.1 0.10%
Cocoa mixes 132 $10.4 0.16% 153 $43.0 0.14%
Juices super premium 133 $10.3 0.16% 130 $63.2 0.20%
Snack meat 134 $10.3 0.16% 147 $47.9 0.15%
Seafood--catfish 135 $9.8 0.15% 191 $17.6 0.06%
Turkey frozen 136 $9.7 0.15% 138 $51.8 0.16%
Specialty cheese pre pack 137 $9.6 0.15% 67 $152.4 0.48%
Smoked pork 138 $9.4 0.14% 156 $39.2 0.12%
Frozen ice 139 $9.3 0.14% 142 $49.9 0.16%
Seafood--crab 140 $9.2 0.14% 182 $24.5 0.08%
Mushrooms 141 $9.1 0.14% 105 $85.7 0.27%
Value-added vegetables 142 $9.0 0.14% 115 $77.0 0.24%
Seafood--value-added seafood 143 $8.9 0.14% 178 $25.6 0.08%
Sweet goods & snacks 144 $8.6 0.13% 146 $48.3 0.15%
Meat snacks 145 $8.5 0.13% 170 $29.3 0.09%
Single serve/vending--salty snacks 146 $8.4 0.13% 197 $15.8 0.05%
Traditional Asian foods 147 $8.3 0.13% 134 $59.8 0.19%
Frozen juice and smoothies 148 $7.7 0.12% 150 $44.9 0.14%
Broccoli/cauliflower 149 $7.4 0.11% 118 $76.5 0.24%
Beef: rib 150 $7.3 0.11% 151 $43.3 0.14%
Refrigerated desserts 151 $7.0 0.11% 143 $49.5 0.16%
Croutons/bread stick & salad top 152 $6.9 0.11% 171 $29.1 0.09%
Dietary aid product/med liq nutr 153 $6.8 0.10% 132 $62.9 0.20%
Dressings/dips 154 $6.6 0.10% 139 $51.7 0.16%
Party tray 155 $6.6 0.10% 154 $42.6 0.14%
Corn 156 $6.5 0.10% 149 $45.3 0.14%
Canned & dry milk 157 $6.1 0.09% 163 $33.1 0.10%
Fitness & diet 158 $5.8 0.09% 101 $95.8 0.30%
Juice 159 $5.8 0.09% 148 $46.2 0.15%
Single serve sweet goods 160 $5.7 0.09% 196 $16.2 0.05%
Refrigerated hispanic grocery 161 $5.7 0.09% 177 $26.5 0.08%
Enhancements (Pickles/Spreads) 162 $5.6 0.08% 174 $27.3 0.09%
Convenience/snacking 163 $5.5 0.08% 173 $28.5 0.09%
Dried fruit 164 $5.4 0.08% 137 $52.6 0.17%
Seafood--salmon-farm raised 165 $5.0 0.08% 144 $48.8 0.15%
Frozen whipped topping 166 $5.0 0.08% 167 $30.9 0.10%
Deli meat: presliced 167 $4.9 0.07% 129 $63.8 0.20%
Herbs/garlic 168 $4.8 0.07% 141 $50.0 0.16%
Seafood--party trays 169 $4.8 0.07% 181 $24.8 0.08%
Salad bar 170 $4.5 0.07% 188 $18.2 0.06%
Seafood--salmon--wild caught 171 $4.5 0.07% 158 $36.7 0.12%
Frozen fruits 172 $4.3 0.07% 145 $48.6 0.15%
Single serve/vending--cookie/cracker 173 $4.1 0.06% 211 $9.1 0.03%
Chicken specialty/natural 174 $3.8 0.06% 166 $31.5 0.10%
Cereals 175 $3.8 0.06% 131 $63.0 0.20%
Pork offal 176 $3.5 0.05% 232 $4.2 0.01%
Pears 177 $3.5 0.05% 162 $33.6 0.11%
Frozen meatless 178 $3.3 0.05% 169 $30.0 0.10%
Seafood--tilapia 179 $3.2 0.05% 194 $16.4 0.05%
Non-dairy/dairy aseptic 180 $3.1 0.05% 168 $30.5 0.10%
Refrigerated italian 181 $2.9 0.04% 159 $36.6 0.12%
Rice cakes 182 $2.8 0.04% 184 $22.4 0.07%
Vinegar & cooking wines 183 $2.8 0.04% 176 $27.2 0.09%
Seafood--salad/dip/sce/cond 184 $2.8 0.04% 223 $6.2 0.02%
Refrigerated vegetarian 185 $2.8 0.04% 180 $24.8 0.08%
Cake decor 186 $2.7 0.04% 199 $15.4 0.05%
Frozen pasta 187 $2.6 0.04% 193 $16.9 0.05%
Syrups toppings & cones 188 $2.6 0.04% 202 $14.1 0.04%
Snacks 189 $2.6 0.04% 157 $37.6 0.12%
Trail mix & snacks 190 $2.5 0.04% 189 $18.1 0.06%
Snack 191 $2.5 0.04% 160 $35.6 0.11%
Prepared/pdgd foods 192 $2.3 0.04% 161 $34.1 0.11%
Turkey fresh 193 $2.3 0.04% 192 $17.0 0.05%
Condiments 194 $2.3 0.03% 175 $27.2 0.09%
Seafood--fin fish other 195 $2.2 0.03% 225 $5.8 0.02%
Seafood--lobster 196 $2.2 0.03% 204 $13.0 0.04%
Pre-slice service case cheese 197 $2.1 0.03% 172 $28.6 0.09%
Spices/jarred garlic 198 $2.1 0.03% 205 $12.4 0.04%
Vegetables cooking packaged 199 $2.0 0.03% 187 $18.3 0.06%
Mixers 200 $1.9 0.03% 195 $16.4 0.05%
Poultry other 201 $1.8 0.03% 219 $6.7 0.02%
Pork bone in loin/rib 202 $1.8 0.03% 214 $7.6 0.02%
Turkey offal 203 $1.6 0.02% 235 $2.0 0.01%
Organics fruit & vegetables 204 $1.6 0.02% 185 $22.2 0.07%
Frozen ethnic 205 $1.6 0.02% 218 $6.7 0.02%
Lamb 206 $1.6 0.02% 207 $11.4 0.04%
Seasonal 207 $1.5 0.02% 209 $10.3 0.03%
Chicken offal 208 $1.5 0.02% 230 $4.3 0.01%
Turkey smoked 209 $1.5 0.02% 234 $2.5 0.01%
Seafood--cod 210 $1.5 0.02% 206 $12.0 0.04%
Frozen meat alternatives 211 $1.5 0.02% 203 $13.6 0.04%
Soup 212 $1.4 0.02% 179 $25.4 0.08%
Authentic central american fds 213 $1.4 0.02% 227 $5.5 0.02%
Cereal bars 214 $1.4 0.02% 183 $23.6 0.07%
Frozen entrees 215 $1.4 0.02% 186 $21.5 0.07%
Authentic asian foods 216 $1.4 0.02% 208 $11.3 0.04%
Bulk food 217 $1.3 0.02% 190 $18.0 0.06%
Baking 218 $1.2 0.02% 201 $14.6 0.05%
Random weight meat products 219 $1.1 0.02% 233 $4.0 0.01%
Processed (dry mixes/squeezed fruit) 220 $1.0 0.02% 222 $6.2 0.02%
Mediterranean bar 221 $1.0 0.02% 198 $15.5 0.05%
Chicken grinds 222 $0.9 0.01% 217 $6.9 0.02%
Chilled ready meals 223 $0.9 0.01% 231 $4.2 0.01%
Dry tea/coffee/coco mixes 224 $0.9 0.01% 210 $9.2 0.03%
Crackers 225 $0.8 0.01% 200 $14.6 0.05%
Seafood--trout 226 $0.7 0.01% 224 $6.0 0.02%
Beverages 227 $0.7 0.01% 215 $7.6 0.02%
Seafood--scallops 228 $0.6 0.01% 221 $6.4 0.02%
Baby food 229 $0.6 0.01% 226 $5.5 0.02%
Deli specialties (retail pk) 230 $0.6 0.01% 228 $5.3 0.02%
Buffalo 231 $0.5 0.01% 213 $8.3 0.03%
Seafood--smoked seafood 232 $0.5 0.01% 212 $8.4 0.03%
Pork grinds 233 $0.5 0.01% 229 $4.3 0.01%
Authentic italian foods 234 $0.5 0.01% 216 $7.4 0.02%
Bakery party trays 235 $0.4 0.01% 236 $1.9 0.01%
Candy 236 $0.4 0.01% 220 $6.5 0.02%
Authentic caribbean foods 237 $0.4 0.01% 238 $1.1 0.00%
Seafood--shellfish other 238 $0.4 0.01% 237 $1.3 0.00%
---------------------------- ---------------------------
Totals $6,580.5 100% $31,513.8 100%
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
Exhibit A-2: Top 1,000 Subcommodities by Expenditures of SNAP Households
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Expenditures
---------------------------------------------------------------------------
Commodity Subcommodity $ in % of $ in % of
Rank Millions Expenditures Rank Millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk Milk/White Only 1 $191.1 2.90% 1 $853.8 2.71%
Products
Soft Drinks Soft Drinks 12/18 2 $164.6 2.50% 2 $601.2 1.91%
& 15pk Can Car
Beef: Grinds Lean [Beef] 3 $112.4 1.71% 7 $257.9 0.82%
Cold Cereal Kids Cereal 4 $78.1 1.19% 20 $186.4 0.59%
Cheese Shredded Cheese 5 $74.7 1.14% 3 $342.0 1.09%
Soft Drinks Sft Drnk 2 Liter 6 $70.9 1.08% 12 $230.1 0.73%
Btl Carb Incl
Bag Snacks Potato Chips 7 $64.4 0.98% 8 $253.2 0.80%
Beef: Grinds Primal [Beef] 8 $62.4 0.95% 14 $219.8 0.70%
Lunchmeat Lunchmeat--Deli 9 $55.8 0.85% 11 $242.6 0.77%
Fresh
Infant Formula Infant Formula 10 $54.2 0.82% 190 $45.3 0.14%
Starter/Solution
Eggs/Muffins/ Eggs--Large 11 $52.1 0.79% 9 $251.6 0.80%
Potatoes
Chicken Fresh Chicken Breast 12 $49.6 0.75% 4 $292.9 0.93%
Boneless
Water--(Sparklin Still Water 13 $48.8 0.74% 19 $187.7 0.60%
g & Still) Drnking/Mnrl
Water
Baked Breads Mainstream White 14 $48.0 0.73% 39 $136.8 0.43%
Bread
Bag Snacks Tortilla/Nacho 15 $47.4 0.72% 17 $209.0 0.66%
Chips
Frozen Handhelds Snacks/Appetizers 16 $44.6 0.68% 65 $100.5 0.32%
& Snacks
Cheese American Single 17 $44.1 0.67% 41 $136.6 0.43%
Cheese
Frzn Ss Premium Fz Ss Prem 18 $43.8 0.67% 24 $175.4 0.56%
Meals Traditional Meals
Refrgratd Juices/ Dairy Case 100% 19 $43.5 0.66% 6 $269.0 0.85%
Drinks Pure Juice--O
Baked Sweet Snack Cake--Multi 20 $41.6 0.63% 63 $101.7 0.32%
Goods Pack
Pork Boneless Enhanced [Pork 21 $41.5 0.63% 27 $168.0 0.53%
Loin/Rib Boneless Loin/
Rib]
Coffee & Unflavored Can 22 $41.3 0.63% 18 $198.0 0.63%
Creamers Coffee
Frzn Ss Economy Fz Ss Economy 23 $40.9 0.62% 81 $80.7 0.26%
Meals Meals All
Bacon Bacon--Trad 16oz 24 $40.7 0.62% 29 $157.6 0.50%
Or Less
Soft Drinks Soft Drinks 20pk & 25 $39.7 0.60% 60 $106.4 0.34%
24pk Can Carb
Frozen Pizza Pizza/Premium 26 $39.7 0.60% 32 $153.3 0.49%
Baked Breads Mainstream Variety 27 $38.4 0.58% 26 $173.2 0.55%
Breads
Sugars & Sugar 28 $36.9 0.56% 55 $112.7 0.36%
Sweeteners
Cold Cereal All Family Cereal 29 $36.2 0.55% 16 $214.9 0.68%
Frozen Handhelds Sandwiches & 30 $35.9 0.54% 91 $73.6 0.23%
& Snacks Handhelds
Potatoes Potatoes Russet 31 $35.8 0.54% 30 $154.5 0.49%
(Bulk & Bag)
Cheese Natural Cheese 32 $35.3 0.54% 15 $216.1 0.69%
Chunks
Pork Thin Meats Ribs [Pork] 33 $35.0 0.53% 59 $106.8 0.34%
Convenient Meals Convenient Meals-- 34 $34.2 0.52% 96 $69.7 0.22%
Kids Meal C
Bananas Bananas 35 $34.2 0.52% 10 $242.7 0.77%
Soft Drinks Sft Drnk Mlt-Pk 36 $34.0 0.52% 25 $173.6 0.55%
Btl Carb (Excp)
Ice Cream Ice Premium [Ice Cream 37 $31.2 0.47% 13 $226.0 0.72%
Milk & Sherbets & Sherbert]
Isotonic Drinks Isotonic Drinks 38 $30.5 0.46% 47 $119.5 0.38%
Single Serve
Chicken Frozen Frzn Chicken--Wht 39 $30.0 0.46% 66 $99.8 0.32%
Meat
Canned Soups Condensed Soup 40 $29.7 0.45% 31 $153.6 0.49%
Salad Dresing & Pourable Salad 41 $29.0 0.44% 37 $139.4 0.44%
Sandwich Dressings
Spreads
Beef: Loins Choice Beef 42 $28.4 0.43% 40 $136.6 0.43%
Beef: Loins Select Beef 43 $27.9 0.42% 36 $143.7 0.46%
Soft Drinks Sft Drnk Sngl Srv 44 $27.8 0.42% 94 $71.4 0.23%
Btl Carb (Ex)
Frzn Multi Serve Fz Family Style 45 $27.6 0.42% 77 $83.5 0.26%
Entrees
Salad Dresing & Mayonnaise & 46 $27.3 0.41% 48 $119.1 0.38%
Sandwich Whipped Dressing
Spreads
Frozen Vegetable Fz Bag Vegetables-- 47 $25.7 0.39% 42 $131.9 0.42%
& Veg Dish Plain
Ice Cream Ice Traditional [Ice 48 $25.6 0.39% 49 $118.7 0.38%
Milk & Sherbets Cream & Sherbert]
Hot Dogs Hot Dogs--Base 49 $25.1 0.38% 138 $56.8 0.18%
Meat
Cold Cereal Adult Cereal 50 $24.9 0.38% 21 $182.6 0.58%
Frzn Ss Premium Fz Ss Prem 51 $24.7 0.38% 5 $271.6 0.86%
Meals Nutritional Meals
Dinner Mixes-Dry Macaroni & Cheese 52 $24.3 0.37% 125 $59.7 0.19%
Dnrs
Aseptic Juice Aseptic Pack Juice 53 $24.2 0.37% 134 $57.1 0.18%
And Drinks
Fluid Milk Refrigerated 54 $24.1 0.37% 34 $147.2 0.47%
Products Coffee Creamers
Beef: Round Choice Beef 55 $24.0 0.37% 92 $72.5 0.23%
Traditional Mexican Soft 56 $23.7 0.36% 54 $113.1 0.36%
Mexican Foods Tortillas And
Wraps
Berries Strawberries 57 $23.5 0.36% 22 $178.4 0.57%
Margarines Margarine: Tubs 58 $23.4 0.36% 64 $100.9 0.32%
And Bowls
Pasta & Pizza Mainstream [Pasta 59 $23.0 0.35% 80 $81.0 0.26%
Sauce & Pizza Sauce]
Chicken Fresh Chicken Wings 60 $22.2 0.34% 300 $28.6 0.09%
Canned Pasta & Can Pasta 61 $22.2 0.34% 179 $47.7 0.15%
Mwv Fd-Shlf
Stbl
Chicken Frozen Frzn Chicken-- 62 $22.2 0.34% 452 $17.4 0.06%
Wings
Lunchmeat Lunchmeat--Bologna/ 63 $21.8 0.33% 121 $60.9 0.19%
Sausage
Bag Snacks Mult Pk Bag Snacks 64 $21.6 0.33% 199 $43.4 0.14%
Candy--Packaged Candy Bags- 65 $21.5 0.33% 33 $147.5 0.47%
Chocolate
Sweet Goods Sw Gds: Donuts 66 $21.3 0.32% 78 $82.3 0.26%
Can Seafood-- Tuna 67 $21.1 0.32% 57 $109.9 0.35%
Shelf Stable
Shortening & Oil Vegetable Oil 68 $20.5 0.31% 246 $35.4 0.11%
Frozen Potatoes Frzn French Fries 69 $20.5 0.31% 163 $50.3 0.16%
Peanut Butter/ Peanut Butter 70 $20.4 0.31% 43 $127.8 0.41%
Jelly/Jams &
Honey
Frozen Pizza Pizza/Economy 71 $19.8 0.30% 192 $45.1 0.14%
Margarines Butter 72 $19.6 0.30% 23 $175.6 0.56%
Deli Meat: Bulk Meat: Turkey Bulk 73 $19.3 0.29% 28 $159.6 0.51%
Frozen Breakfast Frzn Breakfast 74 $19.1 0.29% 142 $55.7 0.18%
Foods Sandwiches
Meat Frozen Frzn Meat--Beef 75 $19.0 0.29% 185 $46.3 0.15%
Frzn Multi Serve Fz Skillet Meals 76 $18.8 0.29% 83 $79.3 0.25%
Frzn Prepared Value Forms/18oz 77 $18.6 0.28% 209 $42.6 0.14%
Chicken And Larger
[Chicken]
Cakes Cakes: Birthday/ 78 $18.6 0.28% 164 $50.3 0.16%
Celebration
Cookies Sandwich Cookies 79 $18.0 0.27% 93 $71.8 0.23%
Frozen Pizza Pizza/Traditional 80 $17.9 0.27% 111 $64.1 0.20%
Fruit Snacks Fruit Snacks 81 $17.6 0.27% 202 $43.2 0.14%
Rts/Micro Soup/ Soup: Chunky/ 82 $17.6 0.27% 46 $119.9 0.38%
Broth Rts Homestyle
Milk By-Products Sour Creams 83 $17.5 0.27% 70 $95.2 0.30%
Frozen Breakfast Waffles/Pancakes/ 84 $17.3 0.26% 90 $77.4 0.25%
Foods French Toast
Chicken Fresh Chicken Drums 85 $17.3 0.26% 270 $31.5 0.10%
Bagels & Cream Cream Cheese 86 $17.2 0.26% 51 $115.5 0.37%
Cheese
Beef: Grinds Angus [Beef] 87 $17.1 0.26% 61 $103.8 0.33%
Bag Snacks Bagged Cheese 88 $17.1 0.26% 157 $52.0 0.16%
Snacks
Bag Snacks Salsa & Dips 89 $17.1 0.26% 135 $57.0 0.18%
Sandwiches Sandwiches--(Cold) 90 $16.9 0.26% 106 $67.7 0.21%
Dry/Ramen Ramen Noodles/ 91 $16.7 0.25% 304 $28.1 0.09%
Bouillon Ramen Cups
Crackers & Misc Cheese Crackers 92 $16.5 0.25% 72 $90.2 0.29%
Baked Food
Dinner Sausage Dnr Sausage--Links 93 $16.4 0.25% 233 $37.6 0.12%
Pork Ckd
Candy--Checklane Candy Bars 94 $16.3 0.25% 146 $54.9 0.17%
(Singles)
Baked Breads Hamburger Buns 95 $16.2 0.25% 95 $70.2 0.22%
Baked Breads Hot Dog Buns 96 $16.2 0.25% 117 $62.2 0.20%
Water--(Sparklin Spring Water 97 $16.2 0.25% 69 $95.6 0.30%
g & Still)
Refrgratd Juices/ Dairy Case Juice 98 $16.0 0.24% 177 $48.0 0.15%
Drinks Drnk Under 10 oz
Fluid Milk Flavored Milk 99 $16.0 0.24% 128 $59.4 0.19%
Products
Baked Sweet Sweet Goods--Full 100 $15.8 0.24% 133 $57.9 0.18%
Goods Size
Grapes Grapes Red 101 $15.8 0.24% 45 $121.7 0.39%
Candy--Packaged Candy Bars (Multi 102 $15.6 0.24% 97 $69.6 0.22%
Pack)
Grapes Grapes White 103 $15.5 0.23% 76 $84.9 0.27%
Cookies Tray Pack/Choc 104 $15.3 0.23% 153 $53.9 0.17%
Chip Cookies
Deli Meat: Bulk Meat: Ham Bulk 105 $15.3 0.23% 50 $115.9 0.37%
Cheese String Cheese 106 $15.1 0.23% 67 $99.0 0.31%
Breakfast Bkfst Sausage-- 107 $15.1 0.23% 119 $61.4 0.19%
Sausage Fresh Rolls
Seafood--Shrimp Shrimp--Raw 108 $15.0 0.23% 99 $69.0 0.22%
Seafood--Shrimp Shrimp--Cooked 109 $14.8 0.22% 152 $54.0 0.17%
Refrgrated Dough Refrigerated 110 $14.7 0.22% 191 $45.2 0.14%
Products Biscuits
Crackers & Misc Butter Spray 111 $14.6 0.22% 101 $68.7 0.22%
Baked Food Cracker
Frozen Sticks/Enrobed 112 $14.2 0.22% 126 $59.7 0.19%
Novelties--Wate [Frozen
r Ice Novelties]
Spices & Traditional Spices 113 $14.1 0.21% 120 $61.2 0.19%
Extracts
Frozen Water Ice [Frozen 114 $14.0 0.21% 160 $50.6 0.16%
Novelties--Wate Novelties]
r Ice
Yogurt Yogurt/Kids 115 $14.0 0.21% 212 $42.4 0.13%
Cnv Breakfast & Toaster Pastries 116 $14.0 0.21% 180 $47.6 0.15%
Wholesome Snks
Dry Bean Veg & Rice Side Dish 117 $14.0 0.21% 184 $46.7 0.15%
Rice Mixes Dry
Ice Cream Ice Pails [Ice Cream & 118 $13.9 0.21% 250 $35.1 0.11%
Milk & Sherbets Sherbert]
Milk By-Products Cottage Cheese 119 $13.9 0.21% 58 $108.8 0.35%
Rtd Tea/New Age Tea Sweetened 120 $13.9 0.21% 102 $68.7 0.22%
Juice
Can Beans Prepared Beans-- 121 $13.4 0.20% 145 $55.3 0.18%
Baked W/Pork
Cheese Natural Cheese 122 $13.4 0.20% 53 $113.2 0.36%
Slices
Tropical Fruit Avocado 123 $13.4 0.20% 56 $112.6 0.36%
Meat--Shelf Chili: Canned 124 $13.3 0.20% 206 $42.8 0.14%
Stable
Shelf Stable Apple Juice & 125 $13.3 0.20% 187 $45.8 0.15%
Juice Cider (Over 50%)
Value-Added Instore Cut Fruit 126 $13.2 0.20% 74 $85.8 0.27%
Fruit
Candy--Checklane Chewing Gum 127 $13.2 0.20% 103 $68.3 0.22%
Salad Mix Blends [Salad Mix] 128 $13.1 0.20% 44 $124.0 0.39%
Popcorn Popcorn--Microwave 129 $13.1 0.20% 114 $63.4 0.20%
Turkey Grinds Ground Turkey 130 $13.1 0.20% 87 $78.0 0.25%
Dinner Sausage Dnr Sausage--Links 131 $13.0 0.20% 132 $58.0 0.18%
Fresh
Dinner Mixes-Dry Skillet Dinners 132 $13.0 0.20% 332 $25.8 0.08%
Dry Noodles & Long Cut Pasta 133 $13.0 0.20% 122 $60.4 0.19%
Pasta
Chicken Fresh Whole Chicken 134 $12.9 0.20% 136 $56.9 0.18%
(Roasters/Fryer)
Frozen Pizza Pizza/Single Serve/ 135 $12.8 0.19% 203 $43.2 0.14%
Microwave
Can Vegetables-- Green Beans: Fs/ 136 $12.8 0.19% 155 $53.1 0.17%
Shelf Stable Whl/Cut
Cnv Breakfast & Granola Bars 137 $12.8 0.19% 73 $88.9 0.28%
Wholesome Snks
Candy--Packaged Candy Bags-Non 138 $12.6 0.19% 147 $54.9 0.17%
Chocolate
Citrus Oranges Navels All 139 $12.6 0.19% 84 $79.3 0.25%
Baked Breads Premium Bread 140 $12.3 0.19% 35 $144.7 0.46%
Dry Sce/Gravy/ Potatoes: Dry 141 $12.3 0.19% 262 $32.3 0.10%
Potatoes/
Stuffng
Condiments & Bbq Sauce 142 $12.3 0.19% 226 $38.6 0.12%
Sauces
Chicken Fresh Chicken Thighs 143 $12.2 0.19% 165 $50.0 0.16%
Dinner Sausage Dnr Sausage--Pork 144 $12.1 0.18% 227 $38.2 0.12%
Rope Ckd
Can Vegetables-- Corn 145 $12.1 0.18% 197 $44.0 0.14%
Shelf Stable
Bacon Bacon--Trad 146 $12.0 0.18% 193 $44.6 0.14%
Greater Than 16oz
Ice Cream Ice Super Premium 147 $11.8 0.18% 71 $91.1 0.29%
Milk & Sherbets Pints [Ice Cream
& Sherbert]
Baby Foods Baby Food-- 148 $11.7 0.18% 303 $28.1 0.09%
Beginner
Molasses/Syrups/ Molasses & Syrups 149 $11.7 0.18% 130 $58.7 0.19%
Pancake Mixes
Water Non-Carb Water 150 $11.6 0.18% 115 $63.4 0.20%
Flvr--Drnk/Mnr
Vegetables Salad Head Lettuce 151 $11.6 0.18% 143 $55.5 0.18%
Condiments & Catsup 152 $11.5 0.17% 216 $41.5 0.13%
Sauces
Dry Sce/Gravy/ Sauce Mixes/Gravy 153 $11.5 0.17% 183 $46.7 0.15%
Potatoes/ Mixes Dry
Stuffng
Beef: Thin Meats Soup/Stew 154 $11.2 0.17% 195 $44.1 0.14%
Baby Foods Baby Food Junior/ 155 $11.2 0.17% 311 $27.5 0.09%
All Brands
Frzn Prepared Whole Muscle 156 $11.1 0.17% 285 $29.9 0.09%
Chicken Breaded/18oz
Cakes Cakes: Cupcakes 157 $11.1 0.17% 247 $35.3 0.11%
Refrgratd Juices/ Dairy Case Citrus 158 $11.0 0.17% 254 $34.4 0.11%
Drinks Pnch/Oj Subs
Yogurt Yogurt/Ss Regular 159 $11.0 0.17% 100 $69.0 0.22%
Dry Cheese Loaf Cheese 160 $10.9 0.17% 229 $38.1 0.12%
Frozen Handhelds Corn Dogs 161 $10.9 0.17% 401 $20.6 0.07%
& Snacks
Cnv Breakfast & Cereal Bars 162 $10.9 0.17% 86 $78.4 0.25%
Wholesome Snks
Isotonic Drinks Isotonic Drinks 163 $10.8 0.16% 131 $58.1 0.18%
Multi-Pack
Cookies Cookies: Regular 164 $10.8 0.16% 127 $59.6 0.19%
Shelf Stable Fruit Drinks: 165 $10.6 0.16% 617 $10.9 0.03%
Juice Canned & Glass
Single Serve Fruit Cup 166 $10.6 0.16% 207 $42.7 0.14%
Fruit/
Applesauce
Can Beans Variety Beans-- 167 $10.5 0.16% 104 $68.0 0.22%
Kidney/Pinto
Frozen Vegetable Frzn Steamable 168 $10.5 0.16% 79 $81.4 0.26%
& Veg Dish Vegetables
Coffee & Non Dairy Creamer 169 $10.5 0.16% 244 $35.4 0.11%
Creamers
Beef: Thin Meats Cubed Meats [Beef] 170 $10.5 0.16% 286 $29.8 0.09%
Hot Dogs Hot Dogs--Base 171 $10.3 0.16% 171 $49.4 0.16%
Beef
Yogurt Yogurt/Ss Light 172 $10.2 0.16% 62 $103.1 0.33%
Traditional Mexican Sauces And 173 $10.2 0.16% 116 $62.3 0.20%
Mexican Foods Picante Sauce
Frozen Handhelds Burritos 174 $10.2 0.15% 406 $20.0 0.06%
& Snacks
Eggs/Muffins/ Eggs--Medium 175 $10.1 0.15% 394 $21.0 0.07%
Potatoes
Dry Noodles & Short Cut Pasta 176 $9.9 0.15% 140 $56.2 0.18%
Pasta
Dinner Mixes-Dry Microwave Dinners 177 $9.8 0.15% 220 $39.9 0.13%
Cakes Cakes: Layers 178 $9.8 0.15% 228 $38.2 0.12%
Pork Shoulder Butts [Pork 179 $9.7 0.15% 292 $29.2 0.09%
Shoulder]
Frzn Prepared Boneless Snack/ 180 $9.6 0.15% 384 $21.5 0.07%
Chicken 18oz And Larger
Rolls Rolls: Dinner 181 $9.5 0.14% 161 $50.5 0.16%
Chicken & Chix: Value-Added 182 $9.5 0.14% 323 $26.7 0.08%
Poultry (Cold)
Tomato Products- Tomatoes Diced 183 $9.5 0.14% 123 $59.9 0.19%
Shelf Stable
Frozen Ice Ice--Crushed/Cubed 184 $9.3 0.14% 166 $49.9 0.16%
Beef: Round Angus [Beef] 185 $9.3 0.14% 271 $31.4 0.10%
Shelf Stable Blended Juice & 186 $9.3 0.14% 287 $29.6 0.09%
Juice Combinations
Sushi Sushi--In Store 187 $9.2 0.14% 75 $85.4 0.27%
Prepared
Tomatoes Tomatoes Hothouse 188 $9.2 0.14% 88 $77.7 0.25%
On The Vine
Candy--Packaged Seasonal 189 $9.2 0.14% 182 $46.9 0.15%
Miscellaneous
[Candy]
Frozen Bread/ Frzn Garlic Toast 190 $9.1 0.14% 307 $27.8 0.09%
Dough
Warehouse Snacks Canister Snacks 191 $9.1 0.14% 241 $36.4 0.12%
Beef: Grinds Patties [Beef] 192 $9.1 0.14% 221 $39.7 0.13%
Bag Snacks Corn Chips 193 $9.1 0.14% 188 $45.6 0.14%
Hot Cereal Instant Oatmeal 194 $8.9 0.14% 218 $41.1 0.13%
Breakfast Bkfst Sausage-- 195 $8.9 0.14% 325 $26.3 0.08%
Sausage Fresh Links
Crackers & Misc Snack Crackers 196 $8.9 0.14% 68 $98.6 0.31%
Baked Food
Citrus Clementines 197 $8.8 0.13% 85 $78.6 0.25%
Frzn Prepared Bone-In Wings 198 $8.8 0.13% 586 $12.0 0.04%
Chicken
Onions Onions Yellow 199 $8.7 0.13% 225 $39.3 0.12%
(Bulk & Bag)
Dry Mix Desserts Pudding & Gelatin 200 $8.7 0.13% 310 $27.6 0.09%
Cups/Cans
Coffee & Unflavored Bag 201 $8.5 0.13% 38 $137.3 0.44%
Creamers Coffee
Refrgratd Juices/ Dairy Case Tea 202 $8.4 0.13% 364 $23.1 0.07%
Drinks With Sugar
Infant Formula Infant Formula 203 $8.4 0.13% 687 $9.1 0.03%
Specialty
Ss/Vending-- Salty Snacks 204 $8.4 0.13% 480 $15.8 0.05%
Salty Snacks Vending
Shortening & Oil Canola Oils 205 $8.3 0.13% 291 $29.3 0.09%
Infant Formula Infant Formula 206 $8.3 0.13% 368 $22.8 0.07%
Starter Large
Value-Added Melons Instore Cut 207 $8.2 0.13% 205 $42.8 0.14%
Fruit
Vegetables Salad Cucumbers 208 $8.2 0.13% 129 $58.9 0.19%
Smoked Hams Hams--Half/Port 209 $8.2 0.12% 282 $30.0 0.10%
Bone-In
Crackers & Misc Saltine/Oyster 210 $8.2 0.12% 204 $43.1 0.14%
Baked Food
Condiments & Steak & Worchester 211 $8.2 0.12% 321 $26.7 0.08%
Sauces Sauce
Cookie/Cracker Multi-Pack 212 $8.0 0.12% 217 $41.3 0.13%
Multi-Pks Crackers
Frozen Cones [Frozen 213 $7.9 0.12% 273 $31.2 0.10%
Novelties--Wate Novelties]
r Ice
Deli Meat: Bulk Meat: Beef Bulk 214 $7.9 0.12% 154 $53.4 0.17%
Melons Watermelon 215 $7.9 0.12% 198 $43.9 0.14%
Seedless Whole
Candy--Packaged Seasonal Candy 216 $7.9 0.12% 148 $54.8 0.17%
Bags--Chocolate
Salad & Dips Vegetable Salads-- 217 $7.8 0.12% 238 $36.6 0.12%
Prepack
Baked Breads Bagels 218 $7.8 0.12% 108 $66.9 0.21%
Peppers Peppers Green Bell 219 $7.8 0.12% 215 $41.5 0.13%
Salad Mix Regular Garden 220 $7.8 0.12% 265 $31.9 0.10%
Salad
Energy Drinks Energy Drink-- 221 $7.7 0.12% 327 $26.3 0.08%
Single Serve
Smoked Hams Hams--Spiral 222 $7.6 0.12% 240 $36.5 0.12%
Coffee & Unflavored Instant 223 $7.6 0.12% 316 $27.3 0.09%
Creamers Coffee
Tomatoes Roma Tomatoes 224 $7.5 0.11% 222 $39.6 0.13%
(Bulk/Pkg)
Cookies Vanilla Wafer/Kids 225 $7.5 0.11% 236 $36.7 0.12%
Cookies
Frozen Ice Cream 226 $7.4 0.11% 354 $24.2 0.08%
Novelties--Wate Sandwiches
r Ice
Hot Dogs Hot Dogs--Premium 227 $7.4 0.11% 208 $42.7 0.14%
Yogurt Yogurt/Pro Active 228 $7.4 0.11% 113 $63.5 0.20%
Health
Snack Meat Snack Meat-- 229 $7.4 0.11% 263 $32.1 0.10%
Pepperoni
Cakes Cakes: Creme/ 230 $7.4 0.11% 333 $25.8 0.08%
Pudding
Meat Frozen Frzn Meat-- 231 $7.3 0.11% 602 $11.3 0.04%
Breakfast Sausage
Beef: Rib Angus [Beef] 232 $7.3 0.11% 200 $43.3 0.14%
Shortening & Oil Olive Oil 233 $7.3 0.11% 112 $63.8 0.20%
Dry Bean Veg & Noodle Side Dish 234 $7.3 0.11% 390 $21.1 0.07%
Rice Mixes
Yogurt Yogurt/Adult Multi- 235 $7.2 0.11% 210 $42.5 0.14%
Packs
Dry Bean Veg & Rice--Dry Bag And 236 $7.1 0.11% 255 $33.9 0.11%
Rice Box
Energy Drinks Energy Drink-- 237 $7.1 0.11% 224 $39.5 0.13%
Single Serve
Baked Breads Sandwich Buns 238 $7.1 0.11% 137 $56.8 0.18%
Refrigerated Non-Dairy Milks 239 $7.1 0.11% 105 $67.7 0.21%
Dairy Case
Beef: Round Select Beef 240 $7.1 0.11% 278 $30.4 0.10%
Powder & Crystal Unsweetened 241 $7.0 0.11% 802 $6.2 0.02%
Drink Mix Envelope [Powder
Drink Mix]
Refrigerated Refrigerated 242 $7.0 0.11% 170 $49.5 0.16%
Desserts Pudding
Carrots Carrots Mini 243 $7.0 0.11% 118 $61.4 0.19%
Peeled
Baking Mixes Layer Cake Mix 244 $7.0 0.11% 251 $35.1 0.11%
Cocoa Mixes Malted Mlk/Syrup/ 245 $6.9 0.11% 339 $25.3 0.08%
Pwdrs (Eggnog)
Stone Fruit Cherries Red 246 $6.9 0.10% 139 $56.7 0.18%
Frzn Seafood Frz Coated Fish 247 $6.9 0.10% 389 $21.1 0.07%
Fillets
Meat Snacks Jerky/Nuggets/ 248 $6.8 0.10% 334 $25.8 0.08%
Tenders
Dry Bean Veg & Rice--Instant & 249 $6.8 0.10% 231 $38.0 0.12%
Rice Microwave
Seafood--Catfish Catfish--Fillet 250 $6.8 0.10% 544 $13.1 0.04%
Refrgrated Dough Refrigerated 251 $6.8 0.10% 296 $28.8 0.09%
Products Cookies-Brand
Fluid Milk Specialty/Lactose 252 $6.7 0.10% 175 $48.4 0.15%
Products Free Milk
Peanut Butter/ Preserves/Jam/ 253 $6.7 0.10% 141 $56.2 0.18%
Jelly/Jams & Marmalade
Honey
Margarines Margarine Stick 254 $6.7 0.10% 376 $22.3 0.07%
Rts/Micro Soup/ Broth 255 $6.7 0.10% 109 $65.6 0.21%
Broth
Rtd Tea/New Age Juice (Under 10% 256 $6.7 0.10% 374 $22.4 0.07%
Juice Juice)
Apples Apples Gala (Bulk 257 $6.6 0.10% 98 $69.3 0.22%
& Bag)
Chicken Fresh Chicken Legs/ 258 $6.6 0.10% 536 $13.5 0.04%
Quarters
Frozen Breakfast Frzn Breakfast 259 $6.5 0.10% 420 $19.0 0.06%
Foods Pastry
Flour & Meals Flour: White & 260 $6.4 0.10% 297 $28.8 0.09%
Self Rising
Seafood--Value- Seafood Value- 261 $6.4 0.10% 459 $16.9 0.05%
Added Added Breaded
Shrimp
Sugars & Sweeteners 262 $6.4 0.10% 168 $49.8 0.16%
Sweeteners
Baking Mixes Frosting 263 $6.3 0.10% 318 $27.0 0.09%
Pies Pies: Fruit/Nut 264 $6.3 0.10% 223 $39.6 0.13%
Molasses/Syrups/ Pancake Mixes 265 $6.3 0.10% 379 $21.9 0.07%
Pancake Mixes
Water--(Sparklin Still Water Flvrd 266 $6.3 0.10% 230 $38.1 0.12%
g & Still) Drnk/Mnrl Wtr
Bag Snacks Pretzels 267 $6.2 0.09% 144 $55.4 0.18%
Dry Cheese Grated Cheese 268 $6.2 0.09% 256 $33.6 0.11%
Onions Onions Sweet (Bulk 269 $6.2 0.09% 181 $47.4 0.15%
& Bag)
Shelf Stable Cranapple/Cran 270 $6.1 0.09% 315 $27.3 0.09%
Juice Grape Juice
Frzn Seafood Frz Fishsticks/ 271 $6.1 0.09% 506 $14.7 0.05%
Tenders/Nuggets
Seafood--Crab Crab--Snow 272 $6.1 0.09% 598 $11.4 0.04%
Bread Bread:Italian/ 273 $6.1 0.09% 172 $49.0 0.16%
French
Bulk Service Bulk Semi-Hard 274 $6.1 0.09% 196 $44.0 0.14%
Case Cheese Cheese
Baking Mixes Muffin & Corn 275 $6.0 0.09% 295 $28.9 0.09%
Bread Mix
Chicken & Chix: Frd 8pc/Cut 276 $6.0 0.09% 558 $12.7 0.04%
Poultry Up (Cold)
Infant Formula Infant Formula 277 $6.0 0.09% 570 $12.4 0.04%
Toddler
Vegetables Celery 278 $5.9 0.09% 158 $51.2 0.16%
Cooking Bulk
Traditional Mexican Seasoning 279 $5.9 0.09% 402 $20.6 0.07%
Mexican Foods Mixes
Refrigerated Fluid Milk 280 $5.9 0.09% 52 $113.3 0.36%
Dairy Case
Soft Drinks Soft Drinks Can 281 $5.9 0.09% 592 $11.5 0.04%
Non-Carb
Condiments & Hot Sauce 282 $5.8 0.09% 466 $16.4 0.05%
Sauces
Apples Apples Red 283 $5.8 0.09% 248 $35.2 0.11%
Delicious (Bulk &
Bag)
Single Serve Snack Cake--Single 284 $5.7 0.09% 470 $16.2 0.05%
Sweet Goods Serve
Milk By-Products Refrig Dips 285 $5.7 0.09% 350 $24.7 0.08%
Tomatoes Tomatoes Vine Ripe 286 $5.7 0.09% 373 $22.5 0.07%
Bulk
Bag Snacks Brand Snacks 287 $5.6 0.09% 176 $48.1 0.15%
Refrgrated Dough Refrigerated 288 $5.5 0.08% 312 $27.5 0.09%
Products Specialty Rolls
Canned & Dry Canned Milk 289 $5.5 0.08% 305 $27.9 0.09%
Milk
Coffee & Ready To Drink 290 $5.5 0.08% 403 $20.5 0.06%
Creamers Coffee
Salad Mix Garden Plus [Salad 291 $5.5 0.08% 267 $31.8 0.10%
Mix]
Cookies Cookies: Holiday/ 292 $5.5 0.08% 320 $26.8 0.08%
Special Occas
Bag Snacks Misc Bag Snacks 293 $5.5 0.08% 591 $11.5 0.04%
Refrgratd Juices/ 100% Pure Juice 294 $5.4 0.08% 261 $32.3 0.10%
Drinks Dairy Other
Case
Refrgrated Dough Refrigerated 295 $5.4 0.08% 274 $31.2 0.10%
Products Crescent Rolls
Teas Tea Bags & Bulk 296 $5.4 0.08% 317 $27.2 0.09%
Tea
Aseptic Juice Aseptic Pack Juice 297 $5.3 0.08% 449 $17.5 0.06%
And Drinks
Infant Formula Infant Formula 298 $5.3 0.08% 497 $15.2 0.05%
Solutions Large
Vegetables Cabbage 299 $5.3 0.08% 340 $25.1 0.08%
Cooking Bulk
Melons Cantaloupe Whole 300 $5.3 0.08% 194 $44.4 0.14%
Dry Sce/Gravy/ Stuffing Mixes 301 $5.3 0.08% 378 $22.1 0.07%
Potatoes/
Stuffng
Frozen Desserts Frozen Fruit Pies 302 $5.3 0.08% 359 $23.7 0.08%
& Cobblers
Frozen Potatoes Frzn Tater Tots/ 303 $5.2 0.08% 424 $18.8 0.06%
Other Extruded
Traditional Mexican Taco/ 304 $5.2 0.08% 417 $19.1 0.06%
Mexican Foods Tostado/Shells
Broccoli/ Broccoli Whole & 305 $5.2 0.08% 156 $52.0 0.16%
Cauliflower Crowns
Tomato Products- Tomato Sauce 306 $5.1 0.08% 353 $24.2 0.08%
Shelf Stable
Candy--Checklane Candy Bars 307 $5.1 0.08% 476 $15.9 0.05%
(Singles)
Lunchmeat Lunchmeat--Chop/ 308 $5.1 0.08% 583 $12.1 0.04%
Form Pltry
Vegetables Salad Variety Lettuce 309 $5.1 0.08% 110 $65.2 0.21%
Berries Blueberries 310 $5.1 0.08% 82 $79.4 0.25%
Shelf Stable Cranberry Juice 311 $5.0 0.08% 371 $22.6 0.07%
Juice (50% And Under)
Seafood--Salmon- Salmon Fr-- 312 $5.0 0.08% 173 $48.8 0.15%
Farm Raised Atlantic
Tomatoes Tomatoes Hot House 313 $5.0 0.08% 280 $30.3 0.10%
Bulk
Yogurt Yogurt/Specialty 314 $5.0 0.08% 89 $77.4 0.25%
Greek
Frozen Whipped Frzn Whipped 315 $5.0 0.08% 276 $30.9 0.10%
Topping Topping
Can Fruit/Jar Pineapple 316 $4.9 0.07% 357 $24.0 0.08%
Applesauce
Frozen Desserts Frozen Cream Pies 317 $4.9 0.07% 423 $18.9 0.06%
Infant Formula Infant Formula 318 $4.9 0.07% 954 $3.9 0.01%
Concentrate
Stone Fruit Peaches Yellow 319 $4.8 0.07% 243 $35.6 0.11%
Flesh
Sweet Goods Sw Gds: Sw Rolls/ 320 $4.8 0.07% 319 $26.9 0.09%
Dan
Potatoes Potatoes Sweet & 321 $4.8 0.07% 234 $37.1 0.12%
Yams
Seafood--Party Party Tray--Shrimp 322 $4.8 0.07% 347 $24.8 0.08%
Trays
Shelf Stable Blended Juice & 323 $4.8 0.07% 365 $22.9 0.07%
Juice Combinations
Baking Mixes Brownie Mix 324 $4.8 0.07% 313 $27.5 0.09%
Shelf Stable Grape Juice (Over 325 $4.8 0.07% 455 $17.1 0.05%
Juice 50% Juice)
Frzn Prepared Fz Meal Kits/ 326 $4.8 0.07% 578 $12.2 0.04%
Chicken Stuffed/Other
Peanut Butter/ Jelly 327 $4.7 0.07% 439 $18.1 0.06%
Jelly/Jams &
Honey
Smoked Pork Ham Steaks/Cubes/ 328 $4.7 0.07% 324 $26.3 0.08%
Slices
Tomatoes Tomatoes Grape 329 $4.7 0.07% 150 $54.6 0.17%
Traditional Mexican Beans/ 330 $4.7 0.07% 393 $21.0 0.07%
Mexican Foods Refried
Citrus Lemons 331 $4.6 0.07% 257 $33.6 0.11%
Can Fruit/Jar Peaches 332 $4.6 0.07% 387 $21.3 0.07%
Applesauce
Frozen Potatoes Frzn Hashbrown 333 $4.6 0.07% 348 $24.8 0.08%
Potatoes
Dry Noodles & Noodles Dry 334 $4.5 0.07% 344 $24.9 0.08%
Pasta
Salad Bar Salad Bar Other 335 $4.5 0.07% 438 $18.2 0.06%
Corn Corn Bulk 336 $4.5 0.07% 260 $32.5 0.10%
Sweet Goods Sw Gds: Muffins 337 $4.5 0.07% 266 $31.8 0.10%
Frozen Breakfast Frzn Breakfast 338 $4.5 0.07% 473 $16.2 0.05%
Foods Entrees
Eggs/Muffins/ Eggs--X-Large 339 $4.5 0.07% 232 $37.9 0.12%
Potatoes
Convenient Meals Convenient Meals-- 340 $4.5 0.07% 603 $11.2 0.04%
Adult Meal
Bacon Bacon--Poultry 341 $4.5 0.07% 435 $18.4 0.06%
Smoked Hams Hams--Whole 342 $4.5 0.07% 510 $14.6 0.05%
Boneless
Fluid Milk Half & Half 343 $4.4 0.07% 149 $54.6 0.17%
Products
Deli Meat: Bulk Meat Bulk: 344 $4.4 0.07% 302 $28.3 0.09%
Specialty Dry
Meats
Frozen Vegetable Fz Box Vegetables-- 345 $4.4 0.07% 349 $24.7 0.08%
& Veg Dish Value-Added
Apples Apples Granny 346 $4.4 0.07% 277 $30.9 0.10%
Smith (Bulk &
Bag)
Baking Needs Bits & Morsels 347 $4.4 0.07% 162 $50.3 0.16%
[Baking Needs]
Meat--Shelf Chunk Meats--Chix/ 348 $4.4 0.07% 338 $25.3 0.08%
Stable Ham/Etc.
Yogurt Yogurt/Large Size 349 $4.4 0.07% 219 $40.4 0.13%
(16oz Or Larger)
Energy Drinks Energy Drink-- 350 $4.3 0.07% 421 $19.0 0.06%
Multi-Pack
Frozen Fruits Frozen Fruit 351 $4.3 0.07% 174 $48.6 0.15%
Turkey Frozen Whole Toms (Over 352 $4.3 0.06% 407 $20.0 0.06%
16lbs) [Turkey]
Lunchmeat Lunchmeat--Whole 353 $4.2 0.06% 413 $19.7 0.06%
Muscle Pltry
Dry Bean Veg & Dry Beans/Peas/ 354 $4.2 0.06% 425 $18.8 0.06%
Rice Barley: Bag &
Bulk
Frozen Adult Premium 355 $4.2 0.06% 151 $54.5 0.17%
Novelties--Wate [Frozen
r Ice Novelties]
Traditional Mexican Dinners 356 $4.2 0.06% 597 $11.4 0.04%
Mexican Foods And Foods
Salad Mix Kits [Salad Mix] 357 $4.2 0.06% 258 $33.5 0.11%
Cookies Premium Cookies 358 $4.2 0.06% 269 $31.5 0.10%
Peanut Butter/ Honey 359 $4.1 0.06% 294 $28.9 0.09%
Jelly/Jams &
Honey
Pickle/Relish/ Ripe Olives 360 $4.1 0.06% 337 $25.3 0.08%
Pckld Veg &
Olives
Bacon Bacon--Pre-Cooked 361 $4.1 0.06% 346 $24.8 0.08%
Rolls Rolls: Sandwich 362 $4.1 0.06% 322 $26.7 0.08%
Potatoes Potatoes Red (Bulk 363 $4.1 0.06% 264 $32.0 0.10%
& Bag)
Croutons/Bread Salad Toppers 364 $4.1 0.06% 500 $15.1 0.05%
Stick & Salad
Top
Candy--Packaged Gum (Packaged) 365 $4.1 0.06% 331 $25.9 0.08%
Baking Needs Baking Nuts 366 $4.1 0.06% 201 $43.2 0.14%
Soft Drinks Soft Drinks 6pk 367 $4.1 0.06% 308 $27.8 0.09%
Can Carb
Single Serve Applesauce Cup 368 $4.1 0.06% 370 $22.6 0.07%
Fruit/
Applesauce
Dry Sce/Gravy/ Gravy Can/Glass 369 $4.0 0.06% 485 $15.7 0.05%
Potatoes/
Stuffng
Cookies Graham Crackers 370 $4.0 0.06% 342 $24.9 0.08%
Candy--Packaged Miscellaneous 371 $4.0 0.06% 418 $19.0 0.06%
Candy
Frozen Vegetable Frzn Corn On The 372 $4.0 0.06% 708 $8.4 0.03%
& Veg Dish Cob
Cookies Chocolate Covered 373 $4.0 0.06% 432 $18.5 0.06%
Cookies
Value-Added Vegetable Party 374 $4.0 0.06% 341 $25.1 0.08%
Vegetables Tray
Value-Added Cut Vegetables All 375 $4.0 0.06% 213 $42.2 0.13%
Vegetables Other
Deli Meat: Bulk Bologna/Loaves/ 376 $4.0 0.06% 415 $19.2 0.06%
Franks
Condiments & Marinades 377 $3.9 0.06% 434 $18.4 0.06%
Sauces
Nuts Pistachios 378 $3.9 0.06% 293 $29.1 0.09%
Service Case Seasoned Poultry 379 $3.9 0.06% 463 $16.5 0.05%
Meat
Salad & Dips Protein Salads-- 380 $3.9 0.06% 326 $26.3 0.08%
Bulk
Hot Cereal Standard Oatmeal 381 $3.9 0.06% 284 $29.9 0.09%
Cheese Miscellaneous 382 $3.8 0.06% 214 $42.1 0.13%
Cheese
Salad & Dips Vegetable Salads-- 383 $3.8 0.06% 275 $31.0 0.10%
Bulk
Shelf Stable Veg Juice (Except 384 $3.8 0.06% 279 $30.4 0.10%
Juice Tomato)
Juices Super Juices Superfoods/ 385 $3.8 0.06% 367 $22.8 0.07%
Premium Enhanced
Breakfast Bkfst Sausage-- 386 $3.8 0.06% 651 $9.8 0.03%
Sausage Fresh Patties
Vegetables Asparagus 387 $3.8 0.06% 159 $50.7 0.16%
Cooking Bulk
Baby Foods Baby Food Cereals 388 $3.8 0.06% 756 $7.1 0.02%
Baked Breads English Muffins/ 389 $3.8 0.06% 169 $49.5 0.16%
Waffles
Baked Breads Main Meal Bread 390 $3.8 0.06% 252 $34.9 0.11%
Juice Non-Carb Jce (Over 391 $3.8 0.06% 268 $31.7 0.10%
50% Juice)
Deli Meat: Bulk Meat: Chicken Bulk 392 $3.7 0.06% 253 $34.6 0.11%
Breakfast Bkfst Sausage-- 393 $3.7 0.06% 385 $21.4 0.07%
Sausage Precooked
Dietary Aid Diet Cntrl Liqs 394 $3.7 0.06% 281 $30.3 0.10%
Prdct/Med Liq Nutritional
Nutr
Refrgratd Juices/ Fruit Drinks 395 $3.7 0.06% 1,041 $2.8 0.01%
Drinks Dairy
Case
Dinner Sausage Dnr Sausage--Beef 396 $3.7 0.06% 577 $12.2 0.04%
Rope Ckd
Canned Pasta & Microwavable Cups 397 $3.7 0.06% 690 $9.0 0.03%
Mwv Fd-Shlf
Stbl
Turkey Frozen Whole Hens (Under 398 $3.6 0.06% 419 $19.0 0.06%
16lbs) [Turkey]
Cakes Cakes: Cheesecake 399 $3.6 0.06% 507 $14.7 0.05%
Enhancements Enhancements--Pick 400 $3.6 0.06% 410 $19.8 0.06%
(Pickles/ les/Kraut
Spreads)
Tomatoes Tomatoes Vine Ripe 401 $3.6 0.06% 743 $7.3 0.02%
Pkg
Peppers Peppers Red Bell 402 $3.6 0.05% 211 $42.5 0.13%
Dinner Sausage Dnr Sausage--Other 403 $3.6 0.05% 381 $21.6 0.07%
Forms
Pork Offal External Fresh 404 $3.5 0.05% 937 $4.2 0.01%
Pasta & Pizza Value [Pasta & 405 $3.5 0.05% 657 $9.7 0.03%
Sauce Pizza Sauce]
Aseptic Juice Aseptic Pack Juice 406 $3.5 0.05% 934 $4.2 0.01%
And Drinks
Berries Raspberries 407 $3.5 0.05% 186 $45.8 0.15%
Beef: Thin Meats Corned Beef 408 $3.5 0.05% 461 $16.9 0.05%
Party Tray Deli Tray: Meat 409 $3.5 0.05% 383 $21.5 0.07%
And Cheese
Can Vegetables-- Peas/Green 410 $3.5 0.05% 504 $14.7 0.05%
Shelf Stable
Dry/Ramen Dry Soup 411 $3.5 0.05% 362 $23.3 0.07%
Bouillon
Can Vegetables-- Spinach & Greens 412 $3.5 0.05% 765 $7.0 0.02%
Shelf Stable
Frzn Multi Serve Fz Meatballs 413 $3.5 0.05% 447 $17.7 0.06%
Milk By-Products Aerosol Toppings 414 $3.5 0.05% 351 $24.5 0.08%
[Milk By-
Products]
Baked Breads Dinner Rolls 415 $3.5 0.05% 513 $14.5 0.05%
Cocoa Mixes Hot Chocolate/ 416 $3.5 0.05% 445 $17.8 0.06%
Cocoa Mix
Infant Formula Infant Formula 417 $3.5 0.05% 768 $6.9 0.02%
Ready To Use
Powder & Crystal Sugar Free 418 $3.5 0.05% 391 $21.1 0.07%
Drink Mix Canister [Powder
Drink Mix]
Cnv Breakfast & Treats [Breakfast] 419 $3.5 0.05% 605 $11.2 0.04%
Wholesome Snks
Smoked Hams Hams--Half/Port 420 $3.4 0.05% 392 $21.0 0.07%
Boneless
Fitness & Diet Fitness & Diet-- 421 $3.4 0.05% 124 $59.8 0.19%
Bars W/Flour
Refrgrated Dough Refrigerated 422 $3.4 0.05% 551 $12.9 0.04%
Products Cookie Dough
Grapes Grapes Black/Blue 423 $3.4 0.05% 380 $21.8 0.07%
Bulk Service Bulk Processed 424 $3.4 0.05% 411 $19.8 0.06%
Case Cheese [Cheese]
Candy--Packaged Seasonal Candy 425 $3.4 0.05% 462 $16.6 0.05%
Box--Chocolate
Coffee & Coffee Pods/ 426 $3.4 0.05% 167 $49.8 0.16%
Creamers Singles/Filter
Pack
Can Fruit/Jar Fruit Cocktail/ 427 $3.4 0.05% 569 $12.5 0.04%
Applesauce Fruit Salad
Peppers Peppers Other Bell 428 $3.4 0.05% 301 $28.4 0.09%
Mushrooms Mushrooms White 429 $3.3 0.05% 306 $27.8 0.09%
Sliced Pkg
Lunchmeat Lunchmeat--Chip 430 $3.3 0.05% 653 $9.7 0.03%
Meat
Soft Drinks Sft Drnk 1 Liter 431 $3.3 0.05% 716 $8.2 0.03%
Btl Carb
Cakes Cakes: Fancy/ 432 $3.3 0.05% 451 $17.4 0.06%
Service Case
Salad Mix Shredded Lettuce 433 $3.3 0.05% 616 $10.9 0.03%
Powder & Crystal Sugar Free Sticks 434 $3.3 0.05% 426 $18.8 0.06%
Drink Mix [Powder Drink
Mix]
Dinner Mixes-Dry Package Dinners/ 435 $3.3 0.05% 664 $9.5 0.03%
Pasta Salads
Cakes Cakes: Layers/ 436 $3.3 0.05% 565 $12.5 0.04%
Sheets Novelties
Flour & Meals Breadings/Coatings/ 437 $3.2 0.05% 474 $16.0 0.05%
Crumbs
Pies Pies: Pumpkin/ 438 $3.2 0.05% 545 $13.1 0.04%
Custard
Refrigerated Yogurt 439 $3.2 0.05% 107 $67.0 0.21%
Dairy Case
Apples Mixed Fruit Bags 440 $3.2 0.05% 829 $5.7 0.02%
Shelf Stable Fruit Drinks: 441 $3.2 0.05% 870 $5.0 0.02%
Juice Canned & Glass
Dry Mix Desserts Puddings Dry 442 $3.2 0.05% 400 $20.8 0.07%
Can Seafood-- Salmon 443 $3.2 0.05% 534 $13.6 0.04%
Shelf Stable
Shortening & Oil Cooking Sprays 444 $3.2 0.05% 396 $21.0 0.07%
Meat--Shelf Sandwich Sauce 445 $3.2 0.05% 733 $7.7 0.02%
Stable (Manwich)
Bread Bread: Specialty 446 $3.2 0.05% 366 $22.9 0.07%
Seafood--Tilapia Tilapia--Fillet 447 $3.2 0.05% 465 $16.4 0.05%
Frzn Multi Serve Frzn Burgers 448 $3.2 0.05% 1,010 $3.1 0.01%
Convenience/ Jarred Fruit 449 $3.1 0.05% 511 $14.6 0.05%
Snacking Single Serve
Powder & Crystal Soft Drink 450 $3.1 0.05% 723 $7.9 0.03%
Drink Mix Canisters
Frozen Breakfast Frzn Breakfast 451 $3.1 0.05% 647 $9.8 0.03%
Foods Sausage
Ss/Vending-- Vendor Size/Single 452 $3.1 0.05% 770 $6.8 0.02%
Cookie/Cracker Serve Cookie
Water--(Sparklin Sparkling Water-- 453 $3.1 0.05% 355 $24.1 0.08%
g & Still) Flvrd Sweet
Service Case Stuffed/Mixed Beef 454 $3.1 0.05% 416 $19.2 0.06%
Meat
Meat--Shelf Vienna Sausage 455 $3.1 0.05% 867 $5.1 0.02%
Stable
Mushrooms Mushrooms White 456 $3.1 0.05% 288 $29.6 0.09%
Whole Pkg
Teas Tea Bags/Herbal 457 $3.1 0.05% 272 $31.2 0.10%
Meat Frozen Frzn Meat--Offals 458 $3.0 0.05% 1,053 $2.6 0.01%
Bulk Service Bulk Semi-Soft 459 $3.0 0.05% 363 $23.3 0.07%
Case Cheese
Bag Snacks Bagged Popped 460 $3.0 0.05% 566 $12.5 0.04%
Popcorn
Condiments & Yellow Mustard 461 $3.0 0.05% 571 $12.4 0.04%
Sauces
Vegetables Salad Green Onions 462 $3.0 0.05% 361 $23.5 0.07%
Frozen Bread/ Frzn Dinner Rolls 463 $3.0 0.05% 398 $20.9 0.07%
Dough
Baking Needs Marshmallows 464 $3.0 0.05% 467 $16.4 0.05%
Warehouse Snacks Snack Mix 465 $3.0 0.05% 450 $17.5 0.06%
Fluid Milk Whipping Cream 466 $3.0 0.04% 249 $35.2 0.11%
Products
Dried Fruit Raisins 467 $2.9 0.04% 330 $26.0 0.08%
Dinner Sausage Dnr Sausage--Links 468 $2.9 0.04% 722 $8.0 0.03%
Beef Ckd
Rolls Rolls: Croissants/ 469 $2.9 0.04% 464 $16.5 0.05%
Breadsticks
Lunchmeat Lunchmeat--Brauns/ 470 $2.9 0.04% 632 $10.3 0.03%
Liver/Loave
Cookie/Cracker Multi-Pack Cookies 471 $2.9 0.04% 596 $11.4 0.04%
Multi-Pks
Snack Meat Snack Meat--Salami/ 472 $2.9 0.04% 481 $15.8 0.05%
Smr Sausage
Shortening & Oil Solid Shortening 473 $2.9 0.04% 525 $14.0 0.04%
Salad Mix Salad Bowls 474 $2.9 0.04% 572 $12.3 0.04%
Hot Cereal Grits 475 $2.8 0.04% 774 $6.7 0.02%
Cereals Cereal--Cold 476 $2.8 0.04% 178 $47.8 0.15%
Frozen Vegetable Fz Bag Vegetables-- 477 $2.8 0.04% 505 $14.7 0.05%
& Veg Dish Value-Added
Traditional Asian Other Sauces/ 478 $2.8 0.04% 422 $18.9 0.06%
Asian Foods Marinade
Frozen Cups/Push Ups/ 479 $2.8 0.04% 661 $9.6 0.03%
Novelties--Wate Other [Frozen
r Ice Novelties]
Refrigerated Refrigerated 480 $2.8 0.04% 669 $9.4 0.03%
Hispanic Tortillas
Grocery
Frzn Prepared Whole Muscle 481 $2.8 0.04% 555 $12.8 0.04%
Chicken Unbreaded Chicken
Meat--Shelf Luncheon Meat 482 $2.8 0.04% 693 $8.9 0.03%
Stable (Spam)
Frzn Prepared Boneless Snack/ 483 $2.8 0.04% 836 $5.5 0.02%
Chicken Value/Small
Croutons/Bread Croutons 484 $2.8 0.04% 526 $14.0 0.04%
Stick & Salad
Top
Apples Apples Other (Bulk 485 $2.8 0.04% 314 $27.4 0.09%
& Bag)
Apples Apples Fuji (Bulk 486 $2.8 0.04% 242 $36.2 0.11%
& Bag)
Apples Apples Gold 487 $2.8 0.04% 443 $17.9 0.06%
Delicious (Bulk &
Bag)
Salad & Dips Sal: Hommus 488 $2.8 0.04% 189 $45.4 0.14%
Dinner Sausage Dnr Sausage-- 489 $2.7 0.04% 562 $12.7 0.04%
Cocktails
Can Vegetables-- Mushrooms Cnd & 490 $2.7 0.04% 521 $14.3 0.05%
Shelf Stable Glass
Frozen Desserts Frzn Pie Shells/ 491 $2.7 0.04% 475 $16.0 0.05%
Pastry Shell
Lunchmeat Lunchmeat--Variety 492 $2.7 0.04% 677 $9.3 0.03%
Pack
Frozen Desserts Frozen Cakes/ 493 $2.7 0.04% 611 $11.0 0.03%
Desserts
Pickle/Relish/ Peppers 494 $2.7 0.04% 537 $13.5 0.04%
Pckld Veg &
Olives
Cakes Cakes: Angel Fds/ 495 $2.7 0.04% 440 $18.1 0.06%
Cke Rolls
Berries Blackberries 496 $2.7 0.04% 283 $29.9 0.09%
Frozen Bread/ Frzn Garlic Bread 497 $2.7 0.04% 608 $11.1 0.04%
Dough
Traditional Mexican Enchilada 498 $2.7 0.04% 532 $13.7 0.04%
Mexican Foods Sauce
Fluid Milk Egg Nog/Boiled 499 $2.7 0.04% 539 $13.3 0.04%
Products Custard
Hot Dogs Hot Dogs--Base 500 $2.7 0.04% 667 $9.4 0.03%
Poultry
Beef: Thin Meats Brisket [Beef] 501 $2.7 0.04% 446 $17.8 0.06%
Cookies Wellness/Portion 502 $2.7 0.04% 358 $23.8 0.08%
Control [Cookies]
Baking Needs Pie Filling/ 503 $2.7 0.04% 345 $24.8 0.08%
Mincemeat/Glazes
Soft Drinks Tea Can With 504 $2.7 0.04% 807 $6.1 0.02%
Sweetener/Sugar
Citrus Limes 505 $2.7 0.04% 369 $22.7 0.07%
Warehouse Snacks Misc Snacks 506 $2.6 0.04% 541 $13.2 0.04%
Traditional Mexican Taco Sauce 507 $2.6 0.04% 761 $7.0 0.02%
Mexican Foods
Soft Drinks Soft Drink Bottle 508 $2.6 0.04% 887 $4.7 0.02%
Non-Carb
Seafood--Salmon- Salmon Wc--Pink 509 $2.6 0.04% 612 $11.0 0.03%
Wild Caught
Frozen Bread/ Frzn Biscuits 510 $2.6 0.04% 550 $12.9 0.04%
Dough
Frzn Pasta Frozen Pasta 511 $2.6 0.04% 458 $16.9 0.05%
Chicken Frozen Frzn Chicken--Drk 512 $2.6 0.04% 818 $5.9 0.02%
Meat
Syrups Toppings Ice Cream Toppings 513 $2.6 0.04% 524 $14.1 0.04%
& Cones
Candy--Packaged Seasonal Candy 514 $2.6 0.04% 502 $14.9 0.05%
Bags Non-
Chocolate
Salad & Dips Pasta/Grain 515 $2.6 0.04% 631 $10.3 0.03%
Salads--Prepack
Cakes Cakes: Ice Cream 516 $2.6 0.04% 700 $8.6 0.03%
Nuts Mixed Nuts 517 $2.6 0.04% 309 $27.6 0.09%
Sushi Sushi--Prepackaged 518 $2.6 0.04% 414 $19.2 0.06%
Pickle/Relish/ Green Olives 519 $2.6 0.04% 483 $15.8 0.05%
Pckld Veg &
Olives
Candy--Packaged Candy Bars Multi 520 $2.6 0.04% 695 $8.8 0.03%
Pack W/Flour
Stone Fruit Nectarines Yellow 521 $2.5 0.04% 430 $18.6 0.06%
Flesh
Onions Onions Red (Bulk & 522 $2.5 0.04% 397 $20.9 0.07%
Bag)
Flour & Meals Cornmeal 523 $2.5 0.04% 746 $7.3 0.02%
Tropical Fruit Pineapple Whole & 524 $2.5 0.04% 377 $22.1 0.07%
Peel/Cored
Bagels & Cream Refrigerated 525 $2.5 0.04% 731 $7.7 0.02%
Cheese Bagels
Onions Onions White (Bulk 526 $2.5 0.04% 482 $15.8 0.05%
& Bag)
Meat Frozen Frzn Meat--Turkey 527 $2.5 0.04% 652 $9.7 0.03%
Pickle/Relish/ Relishes 528 $2.5 0.04% 590 $11.6 0.04%
Pckld Veg &
Olives
Candy--Packaged Candy Bags-- 529 $2.5 0.04% 496 $15.2 0.05%
Chocolate W/Flour
Nuts Cashews 530 $2.5 0.04% 437 $18.3 0.06%
Cakes Cakes:Birthday/ 531 $2.5 0.04% 684 $9.1 0.03%
Celebration Lay
Smoked Pork Smoked Offal 532 $2.4 0.04% 940 $4.1 0.01%
[Pork]
Apples Apples Honeycrisp 533 $2.4 0.04% 235 $36.9 0.12%
Sweet Goods & Sw Gds: Swt/Flvrd 534 $2.4 0.04% 528 $13.9 0.04%
Snacks Loaves
Fluid Milk Buttermilk 535 $2.4 0.04% 478 $15.9 0.05%
Products
Cakes Cakes: Sheet 536 $2.4 0.04% 750 $7.2 0.02%
Cookies Cookies: Gourmet 537 $2.4 0.04% 399 $20.8 0.07%
Citrus Grapefruit 538 $2.4 0.04% 388 $21.2 0.07%
Coffee & Flavored Bag 539 $2.4 0.04% 328 $26.2 0.08%
Creamers Coffee
Stone Fruit Plums 540 $2.4 0.04% 543 $13.1 0.04%
Refrigerated Refrigerated Pasta 541 $2.4 0.04% 290 $29.3 0.09%
Italian
Spices & Gourmet Spices 542 $2.4 0.04% 259 $33.2 0.11%
Extracts
Baked Breads Diet/Light Bread 543 $2.4 0.04% 356 $24.0 0.08%
Bacon Bacon--Trad Center 544 $2.3 0.04% 395 $21.0 0.07%
Cut
Salad & Dips Pasta/Grain 545 $2.3 0.04% 460 $16.9 0.05%
Salads--Bulk
Rice Cakes Mini-Cakes 546 $2.3 0.04% 454 $17.2 0.05%
Authentic Authentic Sauces/ 547 $2.3 0.03% 678 $9.2 0.03%
Hispanic Fds & Salsa/Picante
Product
Ice Cream Ice Premium Pints [Ice 548 $2.3 0.03% 787 $6.5 0.02%
Milk & Sherbets Cream & Sherbert]
Can Fruit/Jar Mandarin Oranges/ 549 $2.3 0.03% 564 $12.6 0.04%
Applesauce Citrus Sect
Baby Foods Baby Juices 550 $2.3 0.03% 1013 $3.1 0.01%
Salad Mix Salad Mix Blends 551 $2.3 0.03% 239 $36.5 0.12%
Organic
Salad & Dips Salad: Lettuce 552 $2.2 0.03% 576 $12.2 0.04%
Baked Breads Fruit/Breakfast 553 $2.2 0.03% 427 $18.7 0.06%
Bread
Seafood--Salad/ Breading [Seafood] 554 $2.2 0.03% 966 $3.7 0.01%
Dip/Sce/Cond
Seafood--Finfish Finfish--Other 555 $2.2 0.03% 826 $5.8 0.02%
Other
Frozen Bread/ Frzn Breadsticks 556 $2.2 0.03% 871 $5.0 0.02%
Dough
Bag Snacks Pork Skins/ 557 $2.2 0.03% 804 $6.2 0.02%
Cracklins
Frozen Juice And Frzn Conc Allieds 558 $2.2 0.03% 638 $10.1 0.03%
Smoothies Over 50% Juice
Broccoli/ Cauliflower Whole 559 $2.2 0.03% 352 $24.5 0.08%
Cauliflower
Mushrooms Mushrooms 560 $2.2 0.03% 372 $22.6 0.07%
Portabella
Tropical Fruit Mango 561 $2.2 0.03% 522 $14.1 0.04%
Seafood--Lobster Lobster--Tails 562 $2.2 0.03% 546 $13.0 0.04%
Can Fruit/Jar Apple Sauce 563 $2.2 0.03% 530 $13.8 0.04%
Applesauce (Excludes Cup)
Traditional Mexican Peppers 564 $2.2 0.03% 487 $15.7 0.05%
Mexican Foods Chilies
Candy--Checklane Mints/Candy & 565 $2.1 0.03% 582 $12.1 0.04%
Breath
Citrus Tangerines & 566 $2.1 0.03% 600 $11.3 0.04%
Tangelos
Juices Super Juices Smoothies/ 567 $2.1 0.03% 613 $11.0 0.03%
Premium Blended
Can Vegetables-- Fried Onions 568 $2.1 0.03% 574 $12.3 0.04%
Shelf Stable
Carrots Carrots Bagged 569 $2.0 0.03% 453 $17.2 0.05%
Eggs/Muffins/ Eggs--Jumbo 570 $2.0 0.03% 548 $13.0 0.04%
Potatoes
Potatoes Potatoes Gourmet 571 $2.0 0.03% 405 $20.3 0.06%
Can Vegetables-- Sweet Potatoes 572 $2.0 0.03% 777 $6.7 0.02%
Shelf Stable
Seafood--Value- Value-Added Shrimp 573 $2.0 0.03% 840 $5.4 0.02%
Added Seafood
Baked Breads Rye Breads 574 $2.0 0.03% 375 $22.3 0.07%
Salad Dresing & Dry Salad Dressing 575 $2.0 0.03% 498 $15.1 0.05%
Sandwich & Dip Mixes
Spreads
Condiments & Mustard--All Other 576 $2.0 0.03% 436 $18.3 0.06%
Sauces
Fluid Milk Organic Milk 577 $2.0 0.03% 245 $35.4 0.11%
Products
Dry Mix Desserts Gelatin 578 $2.0 0.03% 517 $14.3 0.05%
Nuts Sunflower/Other 579 $1.9 0.03% 656 $9.7 0.03%
Seeds
Vinegar & Vinegar/White & 580 $1.9 0.03% 515 $14.4 0.05%
Cooking Wines Cider
Dinner Sausage Dnr Sausage-- 581 $1.9 0.03% 618 $10.9 0.03%
Poultry Rope Ckd
Corn Corn Is Packaged 582 $1.9 0.03% 556 $12.8 0.04%
Candy--Packaged Miscellaneous 583 $1.9 0.03% 607 $11.2 0.04%
Candy
Milk By-Products Ricotta Cheese 584 $1.9 0.03% 490 $15.6 0.05%
Hot Cereal Other Hot Cereal 585 $1.9 0.03% 628 $10.3 0.03%
Frozen Juice And Frzn Oj&Oj 586 $1.9 0.03% 472 $16.2 0.05%
Smoothies Substitutes (Over
50%)
Sweet Goods & Sw Gds: Brownie/ 587 $1.9 0.03% 606 $11.2 0.04%
Snacks Bar Cookie
Rolls Rolls: Bagels 588 $1.9 0.03% 494 $15.4 0.05%
Melons Watermelon 589 $1.9 0.03% 477 $15.9 0.05%
Personal
Nuts Pecans Shelled 590 $1.9 0.03% 448 $17.6 0.06%
Infant Formula Baby Isotonic 591 $1.9 0.03% 878 $4.9 0.02%
Drinks
Mixers Cocktail Mixes- 592 $1.9 0.03% 468 $16.4 0.05%
Fluid: Add Liq
Bananas Bananas Organic 593 $1.9 0.03% 428 $18.7 0.06%
Seafood--Crab Crab--King 594 $1.9 0.03% 725 $7.9 0.02%
Bacon Bacon--Other 595 $1.9 0.03% 655 $9.7 0.03%
Can Fruit/Jar Pears 596 $1.9 0.03% 646 $10.0 0.03%
Applesauce
Baking Mixes Biscuit Flour & 597 $1.9 0.03% 529 $13.8 0.04%
Mixes
Chicken Chicken Breast 598 $1.9 0.03% 343 $24.9 0.08%
Specialty/ Boneless
Natural
Sweet Goods Sw Gds: Coffee 599 $1.8 0.03% 588 $11.9 0.04%
Cakes
Refrigerated Eggs 600 $1.8 0.03% 289 $29.5 0.09%
Dairy Case
Condiments & Wing Sauce 601 $1.8 0.03% 872 $5.0 0.02%
Sauces
Seafood--Salmon- Salmon Wc--Sockeye 602 $1.8 0.03% 335 $25.7 0.08%
Wild Caught
Baking Needs Pie Crust Mixes & 603 $1.8 0.03% 676 $9.3 0.03%
Shells
Salad Mix Salad Spinach 604 $1.8 0.03% 442 $17.9 0.06%
Eggs/Muffins/ Eggs Substitute 605 $1.8 0.03% 329 $26.2 0.08%
Potatoes
Crackers & Misc Aerosol Cheese 606 $1.8 0.03% 857 $5.2 0.02%
Baked Food
Poultry Other Cornish Hen 607 $1.8 0.03% 773 $6.7 0.02%
Tomato Products- Tomato Paste 608 $1.8 0.03% 633 $10.2 0.03%
Shelf Stable
Turkey Frozen Turkey Breast Bone 609 $1.8 0.03% 553 $12.8 0.04%
In
Sweet Goods & Sw Gds: Puff 610 $1.8 0.03% 573 $12.3 0.04%
Snacks Pastry
Seafood--Catfish Catfish--Whole 611 $1.8 0.03% 1,055 $2.6 0.01%
Cake Decor Cake Decors & 612 $1.8 0.03% 645 $10.0 0.03%
Icing
Convenience/ Convenience/ 613 $1.8 0.03% 670 $9.4 0.03%
Snacking Snacking Fruit
Salad & Dips Sal: Salsa/Dips 614 $1.8 0.03% 730 $7.7 0.02%
Bulk
Pork Bone In Dry [Pork Bone In 615 $1.8 0.03% 734 $7.6 0.02%
Loin/Rib Loin/Rib]
Authentic Authentic Pasta/ 616 $1.7 0.03% 884 $4.8 0.02%
Hispanic Fds & Rice/Beans
Product
Spices & Pure Extracts 617 $1.7 0.03% 493 $15.4 0.05%
Extracts
Powder & Crystal Enhanced Stick 618 $1.7 0.03% 621 $10.7 0.03%
Drink Mix [Powder Drink
Mix]
Bread Bread: Artisan 619 $1.7 0.03% 237 $36.7 0.12%
Infant Formula Infant Formula Soy 620 $1.7 0.03% 1,270 $1.1 0.00%
Base
Juices Super Juices Proteins 621 $1.7 0.03% 640 $10.1 0.03%
Premium
Salad & Dips Sal: Dip Prepack 622 $1.7 0.03% 584 $12.1 0.04%
[Salad & Dips]
Dietary Aid Diet Energy Drinks 623 $1.7 0.03% 554 $12.8 0.04%
Prdct/Med Liq
Nutr
Nuts Peanuts All 624 $1.7 0.03% 594 $11.5 0.04%
Rts/Micro Soup/ Microwavable Soups 625 $1.7 0.03% 495 $15.3 0.05%
Broth
Service Case Marinated Pork 626 $1.7 0.03% 519 $14.3 0.05%
Meat
Chicken & Chix: Baked 8pc 627 $1.7 0.03% 837 $5.5 0.02%
Poultry Cut Up (Cold)
Vegetables Beans 628 $1.7 0.03% 457 $16.9 0.05%
Cooking Bulk
Baby Foods Baby Spring Waters 629 $1.7 0.03% 1,128 $2.0 0.01%
Shelf Stable Tomato Juice (Over 630 $1.7 0.03% 662 $9.6 0.03%
Juice 50% Jce)
Authentic Authentic 631 $1.7 0.03% 998 $3.2 0.01%
Hispanic Fds & Vegetables And
Product Foods
Meat Snacks Meat Sticks/Bites 632 $1.7 0.03% 972 $3.6 0.01%
Refrigerated Hispanic Cheese 633 $1.7 0.03% 769 $6.9 0.02%
Hispanic
Grocery
Can Fruit/Jar Cranberry Sauce 634 $1.7 0.03% 642 $10.0 0.03%
Applesauce
Fitness & Diet Fitness & Diet-- 635 $1.7 0.03% 298 $28.7 0.09%
Bars W/O Flour
Pies Pies: Cream/ 636 $1.6 0.02% 728 $7.8 0.02%
Meringue
Berries Strawberries 637 $1.6 0.02% 386 $21.4 0.07%
Organic
Candy--Packaged Novelty Candy 638 $1.6 0.02% 827 $5.7 0.02%
Party Tray Deli Tray: 639 $1.6 0.02% 636 $10.2 0.03%
Sandwiches
Value-Added Cut Fruit All 640 $1.6 0.02% 704 $8.5 0.03%
Fruit Other Prepack
Nuts Walnuts Shelled 641 $1.6 0.02% 431 $18.5 0.06%
Turkey Offal External [Turkey] 642 $1.6 0.02% 1,133 $2.0 0.01%
Flour & Meals Flour: Misc/ 643 $1.6 0.02% 533 $13.6 0.04%
Specialty/Blend
Frozen Ethnic Frozen 644 $1.6 0.02% 771 $6.7 0.02%
Internaional
[Ethnic Foods]
Deli Meat: Deli Meat: 645 $1.6 0.02% 336 $25.5 0.08%
Presliced Specialty Dry
Meats
Dressings/Dips Dressing Creamy 646 $1.6 0.02% 512 $14.5 0.05%
Spices & Table Salt/Popcorn 647 $1.6 0.02% 698 $8.6 0.03%
Extracts Salt
Meat--Shelf Hash: Canned 648 $1.6 0.02% 863 $5.1 0.02%
Stable [Meat]
Water--(Sparklin Distilled Water 649 $1.6 0.02% 579 $12.2 0.04%
g & Still)
Frozen Desserts Frzn Pastry & 650 $1.6 0.02% 694 $8.8 0.03%
Cookies
Potatoes Potatoes Gold 651 $1.6 0.02% 503 $14.8 0.05%
(Bulk & Bag)
Herbs/Garlic Garlic Whole 652 $1.6 0.02% 557 $12.7 0.04%
Cloves
Salad Mix Coleslaw 653 $1.6 0.02% 589 $11.9 0.04%
Apples Caramel/Candy 654 $1.6 0.02% 985 $3.4 0.01%
Apples
Nuts Almonds Shelled 655 $1.5 0.02% 412 $19.8 0.06%
Service Case Marinated Poultry 656 $1.5 0.02% 702 $8.5 0.03%
Meat
Carrots Carrots Bagged 657 $1.5 0.02% 429 $18.6 0.06%
Organic
Frozen Desserts Single Serv/ 658 $1.5 0.02% 898 $4.6 0.01%
Portion Control
Seasonal Pumpkins 659 $1.5 0.02% 626 $10.3 0.03%
Chicken Offal Internal [Chicken 660 $1.5 0.02% 929 $4.3 0.01%
Offal]
Specialty Cheese Specialty Ppk 661 $1.5 0.02% 299 $28.7 0.09%
Pre Pack Cheese Hard/
Grated
Pears Pears Bartlett 662 $1.5 0.02% 486 $15.7 0.05%
Meat--Shelf Beef Stew 663 $1.5 0.02% 897 $4.6 0.01%
Stable
Bread Bread: Pita/Pocket/ 664 $1.5 0.02% 523 $14.1 0.04%
Flatbrd
Chicken & Chix: Rotisserie 665 $1.5 0.02% 848 $5.4 0.02%
Poultry Cold
Dry/Ramen Bouillon 666 $1.5 0.02% 663 $9.6 0.03%
Bouillon
Nuts Trail Mix 667 $1.5 0.02% 610 $11.0 0.03%
Enhancements Enhancements--Sala 668 $1.5 0.02% 858 $5.2 0.02%
(Pickles/ ds/Spreads
Spreads)
Smoked Pork Bacon--Belly/Jowl 669 $1.5 0.02% 783 $6.6 0.02%
Seafood--Cod Cod--Fillet 670 $1.5 0.02% 587 $12.0 0.04%
Refrgrated Dough Refrigerated 671 $1.5 0.02% 834 $5.5 0.02%
Products Cookies--Seasonal
Traditional Asian Soy Sauce 672 $1.5 0.02% 630 $10.3 0.03%
Asian Foods
Salad Dresing & Sand/Horseradish & 673 $1.4 0.02% 749 $7.2 0.02%
Sandwich Tartar Sauce
Spreads
Refrgrated Dough Refrigerated Pie 674 $1.4 0.02% 538 $13.5 0.04%
Products Crust
Frozen Juice And Frzn Fruit Drinks 675 $1.4 0.02% 685 $9.1 0.03%
Smoothies (Under 10% Juice)
Sweet Goods & Sw Gds: Specialty 676 $1.4 0.02% 784 $6.6 0.02%
Snacks Desserts
Dinner Mixes-Dry Pizza Mix Dry 677 $1.4 0.02% 845 $5.4 0.02%
Authentic Central American 678 $1.4 0.02% 838 $5.5 0.02%
Central Foods
American Fds
Cereal Bars Breakfast Bars/ 679 $1.4 0.02% 360 $23.6 0.07%
Tarts/Scones
Service Case Seasoned Beef 680 $1.4 0.02% 724 $7.9 0.03%
Meat
Herbs/Garlic Herbs Cilanto 681 $1.4 0.02% 637 $10.1 0.03%
Value-Added Fruit Party Tray 682 $1.4 0.02% 785 $6.5 0.02%
Fruit Prepack
Dried Fruit Dried Fruit--Other 683 $1.4 0.02% 491 $15.6 0.05%
Non-Dairy/Dairy Aseptic Milk 684 $1.4 0.02% 535 $13.6 0.04%
Aseptic
Eggs/Muffins/ Misc Dairy 685 $1.4 0.02% 686 $9.1 0.03%
Potatoes Refigerated
Shelf Stable Pineapple Juice 686 $1.4 0.02% 788 $6.4 0.02%
Juice (Over 50% Juice)
Frozen Entrees Meatless/ 687 $1.4 0.02% 382 $21.5 0.07%
Vegetarian
Powder & Crystal Sugar Sweetened 688 $1.4 0.02% 1,071 $2.5 0.01%
Drink Mix Sticks
Lunchmeat Lunchmeat--Other 689 $1.4 0.02% 951 $3.9 0.01%
Dietary Aid Diet Cntrl Bars 690 $1.4 0.02% 409 $19.9 0.06%
Prdct/Med Liq Nutritional
Nutr
Popcorn Popcorn--Other 691 $1.4 0.02% 641 $10.0 0.03%
Salad & Dips Sal: Desserts- 692 $1.4 0.02% 906 $4.5 0.01%
Prepack
Dry Cheese Misc Dry Cheese 693 $1.4 0.02% 739 $7.3 0.02%
Shelf Stable Cranberry Juice 694 $1.4 0.02% 706 $8.4 0.03%
Juice (Over 50% Juice)
Baking Mixes Cookies Mix 695 $1.4 0.02% 699 $8.6 0.03%
Frozen Potatoes Frzn Baked/Stuffed/ 696 $1.3 0.02% 689 $9.0 0.03%
Mashed
Turkey Fresh Whole Hen (Under 697 $1.3 0.02% 658 $9.7 0.03%
16lbs) [Turkey]
Vegetables Broccoli/ 698 $1.3 0.02% 567 $12.5 0.04%
Cooking Cauliflower
Packaged Processed
Dressings/Dips Dips Caramel/Fruit 699 $1.3 0.02% 819 $5.9 0.02%
Glazes
Dressings/Dips Dips Guacamole/ 700 $1.3 0.02% 563 $12.6 0.04%
Salsa/Queso
Meat--Shelf Hot Dog Chili 701 $1.3 0.02% 1,063 $2.6 0.01%
Stable Sauce
Breakfast Bkfst Sausage-- 702 $1.3 0.02% 986 $3.4 0.01%
Sausage Bkfast Side
Traditional Asian Noodles/Rice 703 $1.3 0.02% 623 $10.5 0.03%
Asian Foods
Deli Meat: Deli Meat: Semi- 704 $1.3 0.02% 674 $9.3 0.03%
Presliced Dry Sausage
Breakfast Bkfst Sausage-- 705 $1.3 0.02% 916 $4.4 0.01%
Sausage Other Forms
Shortening & Oil Corn Oil 706 $1.3 0.02% 943 $4.1 0.01%
Nuts Almonds 707 $1.3 0.02% 404 $20.5 0.06%
Hot Cereal Instant Breakfast 708 $1.3 0.02% 718 $8.1 0.03%
Traditional Asian Foods And 709 $1.3 0.02% 793 $6.3 0.02%
Asian Foods Meals
Can Vegetables-- Mixed Vegetables 710 $1.3 0.02% 905 $4.5 0.01%
Shelf Stable
Authentic Authentic Peppers 711 $1.3 0.02% 910 $4.5 0.01%
Hispanic Fds &
Product
Dinner Sausage Dnr Sausage--Links 712 $1.3 0.02% 766 $7.0 0.02%
Poultry Ck
Snack Tortilla Chips 713 $1.3 0.02% 408 $19.9 0.06%
Salad & Dips Sal: Salsa Prepack 714 $1.3 0.02% 531 $13.7 0.04%
Fluid Milk Soy Milk 715 $1.3 0.02% 753 $7.1 0.02%
Products
Bread Bread: Sweet/ 716 $1.3 0.02% 707 $8.4 0.03%
Breakfast
Bulk Food Trail Mix/Nuts 717 $1.3 0.02% 441 $18.0 0.06%
Bulk
Service Case Seasoned Pork 718 $1.3 0.02% 744 $7.3 0.02%
Meat
Refrigerated Vegetarian Meats 719 $1.3 0.02% 625 $10.4 0.03%
Vegetarian
Candy--Packaged Seasonal 720 $1.2 0.02% 754 $7.1 0.02%
Miscellaneous W/
Flour [Candy]
Teas Tea Bags/Green 721 $1.2 0.02% 604 $11.2 0.04%
Chicken Chicken Wings 722 $1.2 0.02% 1,111 $2.1 0.01%
Specialty/
Natural
Refrgrated Dough Refrigerated 723 $1.2 0.02% 634 $10.2 0.03%
Products Breads
Shelf Stable Lemon Juice & Lime 724 $1.2 0.02% 727 $7.8 0.02%
Juice Juice
Specialty Cheese Specialty Ppk 725 $1.2 0.02% 469 $16.2 0.05%
Pre Pack Cheese Spreads
Baking Flours/Grains/ 726 $1.2 0.02% 509 $14.6 0.05%
Sugar
Smoked Hams Hams--Dry Cured/ 727 $1.2 0.02% 917 $4.4 0.01%
Country
Coffee & Specialty Instant 728 $1.2 0.02% 732 $7.7 0.02%
Creamers Coffee W/Swe
Cookies Fruit Filled 729 $1.2 0.02% 601 $11.3 0.04%
Cookies
Traditional Mexican Con Queso 730 $1.2 0.02% 1,009 $3.1 0.01%
Mexican Foods
Nuts Dry Roast Peanuts 731 $1.2 0.02% 479 $15.9 0.05%
Can Seafood-- Sardines 732 $1.2 0.02% 822 $5.8 0.02%
Shelf Stable
Service Case Stuffed/Mixed 733 $1.2 0.02% 717 $8.2 0.03%
Meat Poultry
Citrus Oranges Non Navel 734 $1.2 0.02% 868 $5.0 0.02%
All
Seafood--Catfish Catfish--Nuggets 735 $1.2 0.02% 1,151 $1.8 0.01%
Snack Soy/Rice Snacks 736 $1.2 0.02% 488 $15.7 0.05%
Bread Bread: Sourdough 737 $1.2 0.02% 456 $17.1 0.05%
Refrigerated Misc Hispanic 738 $1.2 0.02% 635 $10.2 0.03%
Hispanic Grocery
Grocery
Prepared/Pdgd Boxed Prepared/ 739 $1.2 0.02% 489 $15.6 0.05%
Foods Entree/Dry Prep
Shelf Stable Prune Juice (Over 740 $1.2 0.02% 711 $8.3 0.03%
Juice 50% Juice)
Specialty Cheese Specialty Ppk 741 $1.2 0.02% 433 $18.5 0.06%
Pre Pack Cheese Feta
Teas Instant Tea & Tea 742 $1.1 0.02% 914 $4.4 0.01%
Mix (W/Sugar)
Pre-Slice Pre-Sliced Semi- 743 $1.1 0.02% 514 $14.4 0.05%
Service Case Soft Cheese
Cheese
Shortening & Oil Cooking Oil: 744 $1.1 0.02% 775 $6.7 0.02%
Peanut/Safflower
Authentic Hispanic Cookies 745 $1.1 0.02% 1,152 $1.8 0.01%
Hispanic Fds & Crackers
Product
Can Vegetables-- Carrots 746 $1.1 0.02% 900 $4.5 0.01%
Shelf Stable
Juice Drinks-- Juice (Over 50% 747 $1.1 0.02% 659 $9.7 0.03%
Carb juice)
Juices Super Juice Single Blend 748 $1.1 0.02% 673 $9.4 0.03%
Premium
Nuts Oil Roast Peanuts 749 $1.1 0.02% 615 $10.9 0.03%
Beef: Thin Meats Skirt [Beef] 750 $1.1 0.02% 798 $6.3 0.02%
Nuts Nuts Other 751 $1.1 0.02% 593 $11.5 0.04%
Peppers Peppers Yellow 752 $1.1 0.02% 599 $11.4 0.04%
Bell
Baking Needs Baking Powder & 753 $1.1 0.02% 715 $8.2 0.03%
Soda
Frzn Meatless Meatless Burgers 754 $1.1 0.02% 639 $10.1 0.03%
Candy--Checklane Misc Checklane 755 $1.1 0.02% 1,052 $2.6 0.01%
Candy
Pears Pears Anjou 756 $1.1 0.02% 649 $9.8 0.03%
Powder & Crystal Fluid Pouch 757 $1.1 0.02% 781 $6.6 0.02%
Drink Mix [Powder Drink
Mix]
Pasta & Pizza Pizza Sauce 758 $1.1 0.02% 810 $6.1 0.02%
Sauce
Spices/Jarred Garlic Jar 759 $1.1 0.02% 729 $7.7 0.02%
Garlic
Sweet Goods & Sweet Goods: Candy 760 $1.1 0.02% 920 $4.4 0.01%
Snacks
Soft Drinks Tea Bottles With 761 $1.1 0.02% 1,148 $1.9 0.01%
Sweetener/Sugar
Random Weight Lunch Meats 762 $1.1 0.02% 947 $4.0 0.01%
Meat Products
Authentic Hispanic 763 $1.1 0.02% 979 $3.5 0.01%
Hispanic Fds & Carbonated
Product Beverages
Isotonic Drinks Isotonic Drinks 764 $1.1 0.02% 889 $4.7 0.01%
Multi-Serve
Juices Super Juices Antioxidant/ 765 $1.0 0.02% 719 $8.1 0.03%
Premium Wellness
Spices/Jarred Spices & 766 $1.0 0.02% 892 $4.6 0.01%
Garlic Seasonings
Trail Mix & Trail Mixes/Snack 767 $1.0 0.02% 650 $9.8 0.03%
Snacks
Lunchmeat Lunchmeat--Natural/ 768 $1.0 0.02% 559 $12.7 0.04%
Organic
Lunchmeat Lunchmeat--Peggabl 769 $1.0 0.02% 877 $4.9 0.02%
e Deli Fresh
Bread Bread: Tortillas/ 770 $1.0 0.02% 648 $9.8 0.03%
Wraps
Ice Cream Ice Quarts [Ice Cream 771 $1.0 0.02% 924 $4.3 0.01%
Milk & Sherbets & Sherbert]
Infant Formula Infant Formula Up 772 $1.0 0.02% 1,015 $3.0 0.01%
Age
Tropical Fruit Kiwi Fruit 773 $1.0 0.02% 764 $7.0 0.02%
Peppers Peppers Jalapeno 774 $1.0 0.02% 911 $4.4 0.01%
Tomatoes Tomatoes Cherry 775 $1.0 0.02% 580 $12.1 0.04%
Trail Mix & Candy W/O Flour 776 $1.0 0.02% 844 $5.4 0.02%
Snacks
Condiments Oils/Vinegar 777 $1.0 0.02% 643 $10.0 0.03%
Value-Added Instore Cut 778 $1.0 0.02% 654 $9.7 0.03%
Vegetables Vegetables
Candy--Packaged Candy Boxed 779 $1.0 0.02% 852 $5.3 0.02%
Chocolates W/
Flour
Dried Fruit Dried Plums 780 $1.0 0.02% 609 $11.0 0.03%
Shelf Stable Apple Juice & 781 $1.0 0.02% 1,024 $3.0 0.01%
Juice Cider (50% And
Under)
Pre-Slice Pre-Sliced Semi- 782 $1.0 0.02% 520 $14.3 0.05%
Service Case Hard [Cheese]
Cheese
Tomato Products- Tomato Stewed 783 $1.0 0.02% 790 $6.4 0.02%
Shelf Stable
Nuts Misc Snack Nuts 784 $1.0 0.02% 726 $7.8 0.02%
Beef: Thin Meats Flank [Beef] 785 $1.0 0.02% 547 $13.0 0.04%
Cookies Cookies: Message 786 $1.0 0.02% 876 $4.9 0.02%
Baking Mixes Miscellaneous 787 $1.0 0.02% 752 $7.2 0.02%
Package Mixes
Mediterranean Sal: Olives/ 788 $1.0 0.02% 492 $15.5 0.05%
Bar Pickles--Bulk
Dry Sce/Gravy/ Cooking Bags With 789 $1.0 0.01% 1,078 $2.4 0.01%
Potatoes/ Spices/Season
Stuffng
Stone Fruit Cherries Ranier 790 $1.0 0.01% 691 $9.0 0.03%
Energy Drinks Energy Drink-- 791 $1.0 0.01% 671 $9.4 0.03%
Multi-Pack
Meat--Shelf Beef/Pork--Dried 792 $1.0 0.01% 990 $3.3 0.01%
Stable Sliced
Cookies Cookies/Sweet 793 $1.0 0.01% 542 $13.1 0.04%
Goods
Turkey Fresh Whole Tom (Over 794 $1.0 0.01% 747 $7.3 0.02%
16lbs) [Turkey]
Ss/Vending-- Vending Size/Sngl 795 $1.0 0.01% 1,090 $2.3 0.01%
Cookie/Cracker Serve Cracker
Can Vegetables-- White Potatoes 796 $1.0 0.01% 927 $4.3 0.01%
Shelf Stable
Can Seafood-- Oysters 797 $0.9 0.01% 1,025 $3.0 0.01%
Shelf Stable
Dressings/Dips Dips Veggie 798 $0.9 0.01% 740 $7.3 0.02%
Snacks Snacks: Pita Chips 799 $0.9 0.01% 484 $15.7 0.05%
Candy--Packaged Candy Boxed 800 $0.9 0.01% 772 $6.7 0.02%
Chocolates
Chicken Grinds Ground Chicken 801 $0.9 0.01% 767 $6.9 0.02%
Candy--Packaged Seasonal Candy Box 802 $0.9 0.01% 949 $4.0 0.01%
Non-Chocola
Frozen Meat Alternatives Soy/ 803 $0.9 0.01% 688 $9.0 0.03%
Tofu
Can Vegetables-- Kraut & Cabbage 804 $0.9 0.01% 814 $6.0 0.02%
Shelf Stable
Cereals Granola 805 $0.9 0.01% 501 $15.1 0.05%
Baking Needs Cooking Chocolate 806 $0.9 0.01% 627 $10.3 0.03%
(Ex Smi-Swt)
Candy--Packaged Candy Box Non- 807 $0.9 0.01% 953 $3.9 0.01%
Chocolate
Dinner Sausage Dnr Sausage-- 808 $0.9 0.01% 585 $12.1 0.04%
Natural/Organic
Dressings/Dips Dressing Blue 809 $0.9 0.01% 666 $9.5 0.03%
Cheese
Herbs/Garlic Herbs Fresh Other 810 $0.9 0.01% 518 $14.3 0.05%
Organic
Shelf Stable Tomato Juice (50% 811 $0.9 0.01% 975 $3.5 0.01%
Juice And Under)
Popcorn Caramel Coated 812 $0.9 0.01% 1,006 $3.1 0.01%
Snacks
Deli Meat: Deli Meat: Turkey 813 $0.9 0.01% 516 $14.3 0.05%
Presliced
Cake Decor Cake Decors-- 814 $0.9 0.01% 841 $5.4 0.02%
Candies
Specialty Cheese Specialty Ppk 815 $0.9 0.01% 471 $16.2 0.05%
Pre Pack Cheese Mozzarell
Shelf Stable Cranapple/Cran 816 $0.9 0.01% 797 $6.3 0.02%
Juice Grape Juice
Rtd Tea/New Age Juice (Over 50% 817 $0.9 0.01% 1,047 $2.7 0.01%
Juice Juice)
Crackers & Misc Specialty Crackers 818 $0.9 0.01% 444 $17.8 0.06%
Baked Food
Salad & Dips Salad Bar 819 $0.9 0.01% 644 $10.0 0.03%
Service Case Marinated Beef 820 $0.9 0.01% 782 $6.6 0.02%
Meat
Juice Non-Carb Jce 821 $0.9 0.01% 880 $4.8 0.02%
(Under 50% Juice)
Organics Fruit & Organic Salad Mix 822 $0.9 0.01% 499 $15.1 0.05%
Vegetables
Chilled Ready Store Brand 823 $0.9 0.01% 932 $4.2 0.01%
Meals
Frzn Meatless Meatless Breakfast 824 $0.9 0.01% 697 $8.6 0.03%
Dry Tea/Coffee/ Tea Bags 825 $0.9 0.01% 681 $9.2 0.03%
Coco Mixes (Supplement)
Melons Watermelon W/Seeds 826 $0.9 0.01% 1,019 $3.0 0.01%
Whole
Dry Mix Desserts Misc: Cheesecake/ 827 $0.9 0.01% 1,087 $2.3 0.01%
Mousse Mixes
Value-Added Parfait Cups 828 $0.8 0.01% 1,032 $2.9 0.01%
Fruit Instore
Vinegar & Specialty Vinegar 829 $0.8 0.01% 552 $12.9 0.04%
Cooking Wines
Pork Shoulder Fresh Hams 830 $0.8 0.01% 1,030 $2.9 0.01%
Specialty Cheese Specialty Ppk 831 $0.8 0.01% 815 $6.0 0.02%
Pre Pack Cheese Processed
Turkey Smoked Turkey Wings 832 $0.8 0.01% 1,228 $1.3 0.00%
Frzn Seafood Frz Non-Coated 833 $0.8 0.01% 860 $5.2 0.02%
Fish Fillets
Vegetables Salad Radish 834 $0.8 0.01% 713 $8.3 0.03%
Cookies Specialty Cookies 835 $0.8 0.01% 622 $10.7 0.03%
Traditional Traditional Thai 836 $0.8 0.01% 710 $8.3 0.03%
Asian Foods Foods
Yogurt Yogurt/Adult 837 $0.8 0.01% 958 $3.8 0.01%
Drinks
Specialty Cheese Specialty Ppk 838 $0.8 0.01% 527 $13.9 0.04%
Pre Pack Cheese Cheddar
Peppers Peppers All Other 839 $0.8 0.01% 864 $5.1 0.02%
Pickle/Relish/ Pickld Veg/Peppers/ 840 $0.8 0.01% 820 $5.9 0.02%
Pckld Veg & Etc.
Olives
Candy--Packaged Candy Bags-Non 841 $0.8 0.01% 965 $3.7 0.01%
Chocolate W/Flour
Frozen Juice And Frzn Conc Under 842 $0.8 0.01% 983 $3.4 0.01%
Smoothies 50% Juice
Pickle/Relish/ Specialty Olives 843 $0.8 0.01% 614 $11.0 0.03%
Pckld Veg &
Olives
Salad & Dips Sal: Desserts-- 844 $0.8 0.01% 890 $4.7 0.01%
Bulk
Authentic Asian Authentic Japanese 845 $0.8 0.01% 755 $7.1 0.02%
Foods Foods
Crackers Crackers 846 $0.8 0.01% 508 $14.6 0.05%
Smoked Pork Smoked Picnics 847 $0.8 0.01% 1,105 $2.2 0.01%
[Pork]
Condiments Nut Butters/Peanut 848 $0.8 0.01% 549 $12.9 0.04%
Butter
Tomato Products- Tomatoes/Whole 849 $0.8 0.01% 865 $5.1 0.02%
Shelf Stable
Party Tray Deli Tray: Appetizers & 850 $0.8 0.01% 957 $3.9 0.01%
Hors D'oe
Soup Cans Soup/Chili 851 $0.8 0.01% 561 $12.7 0.04%
Service Case Kabobs Beef 852 $0.8 0.01% 843 $5.4 0.02%
Meat
Vegetables Salad Variety Lettuce 853 $0.8 0.01% 568 $12.5 0.04%
Organic
Melons Honeydew Whole 854 $0.8 0.01% 817 $5.9 0.02%
Grapes Grapes Red Globe 855 $0.8 0.01% 980 $3.5 0.01%
Condiments & Chili Sauce/ 856 $0.7 0.01% 813 $6.0 0.02%
Sauces Cocktail Sauce
Tropical Fruit Pomegranates 857 $0.7 0.01% 926 $4.3 0.01%
Organics Fruit & Organic Value- 858 $0.7 0.01% 762 $7.0 0.02%
Vegetables Added Vegetables
Grapes Grapes Other 859 $0.7 0.01% 960 $3.8 0.01%
Chicken Fresh Mixed Packs 860 $0.7 0.01% 923 $4.3 0.01%
[Chicken]
Nuts Nuts Inshell 861 $0.7 0.01% 894 $4.6 0.01%
Authentic Hispanic Juice 862 $0.7 0.01% 1,123 $2.0 0.01%
Hispanic Fds & Under 50% Juice
Product
Coffee & Flavored Can 863 $0.7 0.01% 823 $5.8 0.02%
Creamers Coffee
Prepared/Pdgd Vegetables/Dry 864 $0.7 0.01% 575 $12.2 0.04%
Foods Beans
Bread Bread: Rye/ 865 $0.7 0.01% 720 $8.1 0.03%
Cocktail
Baking Needs Maraschino 866 $0.7 0.01% 944 $4.1 0.01%
Cherries
Seafood--Crab Crab--Dungy 867 $0.7 0.01% 952 $3.9 0.01%
Bread Whole Grain Bread 868 $0.7 0.01% 680 $9.2 0.03%
Smoked Hams Hams--Whole Bone- 869 $0.7 0.01% 1,092 $2.3 0.01%
In
Apples Apples Braeburn 870 $0.7 0.01% 668 $9.4 0.03%
(Bulk & Bag)
Shelf Stable Grapefruit Juice 871 $0.7 0.01% 939 $4.1 0.01%
Juice (Over 50% Juice)
Water Fortified/Water 872 $0.7 0.01% 913 $4.4 0.01%
Meat--Shelf Potted Meats And 873 $0.7 0.01% 1,103 $2.2 0.01%
Stable Spreads
Water--(Sparklin Sparkling Water-- 874 $0.7 0.01% 581 $12.1 0.04%
g & Still) Unflavored
Seafood--Trout Steelhead Fr 875 $0.7 0.01% 812 $6.0 0.02%
[Trout]
Can Vegetables-- Beets 876 $0.7 0.01% 825 $5.8 0.02%
Shelf Stable
Frozen Juice And Smoothies-Frozen 877 $0.7 0.01% 950 $4.0 0.01%
Smoothies
Frozen Breakfast Frzn Bagels 878 $0.7 0.01% 1,035 $2.9 0.01%
Foods
Party Tray Deli Tray: Fruit And 879 $0.7 0.01% 758 $7.1 0.02%
Vegetable
Chicken Whole Chicken 880 $0.7 0.01% 902 $4.5 0.01%
Specialty/ (Roasters/Fryer)
Natural
Bread Bread: Wheat/Whl 881 $0.7 0.01% 629 $10.3 0.03%
Grain
Non-Dairy/Dairy Soy Beverage 882 $0.7 0.01% 849 $5.3 0.02%
Aseptic
Fitness & Diet Fitness & Diet- 883 $0.7 0.01% 741 $7.3 0.02%
Powder Ntrtnl
Frzn Meatless Meatless Poultry 884 $0.7 0.01% 799 $6.2 0.02%
Pies Pies: Sugar Free 885 $0.7 0.01% 904 $4.5 0.01%
Dinner Sausage Dnr Sausage--Fresh 886 $0.7 0.01% 918 $4.4 0.01%
Poultry
Spices & Imitation Extracts 887 $0.7 0.01% 973 $3.5 0.01%
Extracts
Beverages Can/Btl Carb Beve 888 $0.7 0.01% 736 $7.6 0.02%
50% And Under
Vegetables Vegetables Cooking 889 $0.7 0.01% 821 $5.9 0.02%
Cooking Packaged
Packaged
Frozen Vegetable Fz Box Vegetables-- 890 $0.7 0.01% 824 $5.8 0.02%
& Veg Dish Plain
Soup Broths 891 $0.7 0.01% 560 $12.7 0.04%
Bread Bread: Brand 892 $0.7 0.01% 679 $9.2 0.03%
Can Vegetables-- Peas Fresh Pack/ 893 $0.7 0.01% 978 $3.5 0.01%
Shelf Stable Crowder
Snacks Snacks: Salty 894 $0.7 0.01% 703 $8.5 0.03%
Salad & Dips Protein Salads-- 895 $0.6 0.01% 946 $4.0 0.01%
Prepack
Turkey Smoked Turkey Drums 896 $0.6 0.01% 1,250 $1.2 0.00%
Apples Apples Gala (Bulk 897 $0.6 0.01% 672 $9.4 0.03%
& Bag) Organic
Stone Fruit Peaches White 898 $0.6 0.01% 833 $5.5 0.02%
Flesh
Tomatoes Tomatoes--Other 899 $0.6 0.01% 1,003 $3.2 0.01%
Service Case Kabobs Poultry 900 $0.6 0.01% 879 $4.9 0.02%
Meat
Frzn Meatless Meatless 901 $0.6 0.01% 869 $5.0 0.02%
Miscellaneous
Seafood--Scallop Scallops--Sea 902 $0.6 0.01% 791 $6.4 0.02%
s
Convenience/ Jarred Fruit Multi 903 $0.6 0.01% 901 $4.5 0.01%
Snacking Serve
Traditional Asian Vegetables 904 $0.6 0.01% 847 $5.4 0.02%
Asian Foods
Shelf Stable Cranapple/Cran 905 $0.6 0.01% 760 $7.0 0.02%
Juice Grape Juice
Frozen Juice And Cocktail Mixes-Frz 906 $0.6 0.01% 1,107 $2.2 0.01%
Smoothies
Shelf Stable Grapefruit Juice 907 $0.6 0.01% 1,007 $3.1 0.01%
Juice (50% And Under)
Tomato Products- Tomato Crushed 908 $0.6 0.01% 780 $6.6 0.02%
Shelf Stable
Condiments & Misc Meat Sauces 909 $0.6 0.01% 962 $3.7 0.01%
Sauces
Shelf Stable Blended Juice & 910 $0.6 0.01% 1,022 $3.0 0.01%
Juice Combinations
Coffee & Bulk Coffee 911 $0.6 0.01% 701 $8.6 0.03%
Creamers
Specialty Cheese Specialty Ppk 912 $0.6 0.01% 595 $11.4 0.04%
Pre Pack Cheese Semi Soft
Non-Dairy/Dairy Nut Milk 913 $0.6 0.01% 763 $7.0 0.02%
Aseptic
Specialty Cheese Specialty Ppk 914 $0.6 0.01% 620 $10.8 0.03%
Pre Pack Cheese Soft &
Ripe
Authentic Authentic Soups/ 915 $0.6 0.01% 1,200 $1.5 0.00%
Hispanic Fds & Bouillons
Product
Authentic Asian Authentic Chinese 916 $0.6 0.01% 931 $4.2 0.01%
Foods Foods
Baby Food Baby Food 917 $0.6 0.01% 835 $5.5 0.02%
Deli Meat: Deli Meat: Ham 918 $0.6 0.01% 665 $9.5 0.03%
Presliced
Bacon Bacon--Natural/ 919 $0.6 0.01% 759 $7.1 0.02%
Organic
Frozen Potatoes Frzn Onion Rings 920 $0.6 0.01% 1,177 $1.6 0.01%
Margarines Margarine: Squeeze 921 $0.6 0.01% 930 $4.2 0.01%
Deli Specialties Dl Spec: Dry/ 922 $0.6 0.01% 850 $5.3 0.02%
(Retail Pk) Refrig Pastas
Seafood--Crab Crab--Other 923 $0.6 0.01% 1,213 $1.4 0.00%
Specialty Cheese Specialty Ppk 924 $0.6 0.01% 619 $10.8 0.03%
Pre Pack Cheese Blue/Gorg
Tomatoes Tomatoes Others 925 $0.6 0.01% 808 $6.1 0.02%
Organic
Teas Instant Tea & Tea 926 $0.6 0.01% 1,038 $2.9 0.01%
Mix
Refrigerated Vegetarian Misc 927 $0.6 0.01% 963 $3.7 0.01%
Vegetarian
Canned & Dry Non Fat Dry Milk 928 $0.6 0.01% 859 $5.2 0.02%
Milk
Refrigerated Kefir 929 $0.6 0.01% 751 $7.2 0.02%
Dairy Case
Coffee & Specialty Instant 930 $0.6 0.01% 1,043 $2.8 0.01%
Creamers Coffee
Can Vegetables-- Artichokes 931 $0.6 0.01% 682 $9.1 0.03%
Shelf Stable
Soft Drinks Mixers (Tonic 932 $0.5 0.01% 540 $13.2 0.04%
Water/Gngr Ale)
Refrigerated Refrigerated Pasta 933 $0.5 0.01% 742 $7.3 0.02%
Italian Sauce
Baking Needs Baking Cocoa 934 $0.5 0.01% 851 $5.3 0.02%
Vegetables Salad Spinach Bulk 935 $0.5 0.01% 883 $4.8 0.02%
Infant Formula Infant Formula 936 $0.5 0.01% 1,455 $0.3 0.00%
Milk Base
Seafood--Salad/ Dips/Spreads 937 $0.5 0.01% 1,069 $2.5 0.01%
Dip/Sce/Cond
Authentic Hispanic Baking 938 $0.5 0.01% 1,233 $1.3 0.00%
Hispanic Fds & Needs
Product
Baking Needs Marshmallow Creme 939 $0.5 0.01% 977 $3.5 0.01%
Buffalo Grinds [Buffalo] 940 $0.5 0.01% 712 $8.3 0.03%
Baking Needs Yeast: Dry 941 $0.5 0.01% 816 $5.9 0.02%
Lamb Round/Leg [Lamb] 942 $0.5 0.01% 936 $4.2 0.01%
Seafood--Smoked Seafood Smoked 943 $0.5 0.01% 709 $8.4 0.03%
Salmon
Processed Packaged Dry Mixes 944 $0.5 0.01% 1,039 $2.9 0.01%
Frozen Meat Micro Protein 945 $0.5 0.01% 899 $4.6 0.01%
Alternatives [Meats]
Refrgrated Dough Misc Refrig Dough 946 $0.5 0.01% 1,162 $1.7 0.01%
Products Products
Deli Meat: Deli Meat: Beef 947 $0.5 0.01% 862 $5.2 0.02%
Presliced
Vegetables Celery Organic 948 $0.5 0.01% 779 $6.6 0.02%
Cooking Bulk
Cakes Cakes: Creme/ 949 $0.5 0.01% 1,171 $1.7 0.01%
Pudding Novelties
Lamb Loin [Lamb] 950 $0.5 0.01% 882 $4.8 0.02%
Refrgratd Juices/ Dairy Case Tea No 951 $0.5 0.01% 1,002 $3.2 0.01%
Drinks Sugar Or Sweetner
Baking Needs Coconut [Baking 952 $0.5 0.01% 873 $4.9 0.02%
Needs]
Salad Mix Salad Spinach 953 $0.5 0.01% 696 $8.7 0.03%
Organic
Pork Grinds Ground Pork 954 $0.5 0.01% 928 $4.3 0.01%
Processed Squeeze Lemons/ 955 $0.5 0.01% 988 $3.3 0.01%
Limes
Lamb Chuck/Shoulder 956 $0.5 0.01% 1,083 $2.4 0.01%
[Lamb]
Berries Raspberries 957 $0.5 0.01% 683 $9.1 0.03%
Organic
Rolls Rolls: Biscuits/ 958 $0.5 0.01% 886 $4.7 0.02%
Eng Muffins
Snacks Snacks: Tortilla 959 $0.5 0.01% 874 $4.9 0.02%
Chips
Condiments Honey/Syrup 960 $0.5 0.01% 921 $4.3 0.01%
Rice Cakes Large Cakes 961 $0.5 0.01% 855 $5.2 0.02%
Authentic Italian Vegetables 962 $0.5 0.01% 738 $7.4 0.02%
Italian Foods
Dressings/Dips Dips Fruit And 963 $0.5 0.01% 1,149 $1.9 0.01%
Chocolate
Potatoes Potatoes Other 964 $0.5 0.01% 789 $6.4 0.02%
Organic
Juices Super Juices (50% And 965 $0.5 0.01% 1,141 $1.9 0.01%
Premium Under Juice)
Specialty Cheese Specialty Ppk 966 $0.5 0.01% 1,192 $1.5 0.00%
Pre Pack Cheese Hispanic
Seafood--Value- Seafood Value- 967 $0.5 0.01% 997 $3.2 0.01%
Added Added Crab
Service Case Stuffed/Mixed Pork 968 $0.5 0.01% 1,051 $2.7 0.01%
Meat
Herbs/Garlic Sprouts 969 $0.5 0.01% 955 $3.9 0.01%
Pears Pears Bosc 970 $0.5 0.01% 922 $4.3 0.01%
Meat--Shelf Corn Beef 971 $0.5 0.01% 1,169 $1.7 0.01%
Stable
Refrigerated Non-Dairy Cheese 972 $0.5 0.01% 893 $4.6 0.01%
Vegetarian
Isotonic Drinks Sports Drink N/ 973 $0.5 0.01% 1,017 $3.0 0.01%
Supplmnt Milk
Soft Drinks Seltzer Unflavored 974 $0.5 0.01% 757 $7.1 0.02%
Refrigerated Tofu 975 $0.5 0.01% 809 $6.1 0.02%
Vegetarian
Berries Blueberries 976 $0.5 0.01% 660 $9.6 0.03%
Organic
Trail Mix & Candy W/Flour 977 $0.5 0.01% 1,027 $2.9 0.01%
Snacks
Cakes Cakes: Cheesecake 978 $0.5 0.01% 1,115 $2.1 0.01%
Novelties
Water--(Sparklin Sparkling Water-- 979 $0.5 0.01% 675 $9.3 0.03%
g & Still) Flvrd Unsweetened
Powder & Crystal Breakfast Crystals 980 $0.5 0.01% 1,209 $1.4 0.00%
Drink Mix
Non-Dairy/Dairy Rice Beverage 981 $0.5 0.01% 891 $4.6 0.01%
Aseptic
Pies Pies: Tarts/Minis/ 982 $0.5 0.01% 1,045 $2.7 0.01%
Crstdas
Specialty Cheese Specialty Ppk 983 $0.5 0.01% 721 $8.0 0.03%
Pre Pack Cheese Gouda &
Eda
Enhancements Enhancements--Spic 984 $0.5 0.01% 1,082 $2.4 0.01%
(Pickles/ es/Sauces
Spreads)
Snacks Snacks: Crackers/ 985 $0.5 0.01% 705 $8.4 0.03%
Cookies
Baking Needs Corn Starch 986 $0.5 0.01% 1,062 $2.6 0.01%
Candy--Packaged Bulk Candy 987 $0.5 0.01% 1,031 $2.9 0.01%
Prepared/Pdgd Pasta/Ramen 988 $0.5 0.01% 801 $6.2 0.02%
Foods
Specialty Cheese Specialty Ppk 989 $0.5 0.01% 624 $10.4 0.03%
Pre Pack Cheese Goat Milk
Herbs/Garlic Herbs Basil 990 $0.4 0.01% 692 $9.0 0.03%
Organic
Bakery Party Party Trays: Cakes 991 $0.4 0.01% 1,147 $1.9 0.01%
Trays
Mushrooms Mushrooms White 992 $0.4 0.01% 830 $5.7 0.02%
Bulk
Candy Candy/Chocolate 993 $0.4 0.01% 786 $6.5 0.02%
Candy--Packaged Seasonal Candy 994 $0.4 0.01% 999 $3.2 0.01%
Bags-Chocolate
Tomatoes Tomatoes Cocktail 995 $0.4 0.01% 714 $8.3 0.03%
Pears Pears Asian 996 $0.4 0.01% 961 $3.8 0.01%
Authentic Caribbean Foods 997 $0.4 0.01% 1,273 $1.1 0.00%
Caribbean Foods
Dry Bean Veg & Misc Grain Mixes 998 $0.4 0.01% 735 $7.6 0.02%
Rice
Can Vegetables-- Peas & Onions/Peas 999 $0.4 0.01% 1,136 $1.9 0.01%
Shelf Stable & Carrot
Seafood--Shellfi Other Shellfish-- 1,000 $0.4 0.01% 1,225 $1.3 0.00%
sh Other
-----------------------------------------------------------------
Top 1,000 $6,580.5 100% $31,513.8 100%
Totals
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
EAppendix B. Crosswalk of Top 1,000 Subcommodities to Summary Categories
------------------------------------------------------------------------
Commodity Subcommodity Summary Category
------------------------------------------------------------------------
Baby Food Baby Food Baby food
Baby Foods Baby Food--Beginner Baby food
Baby Foods Baby Food Junior/All Brands
Baby food
Baby Foods Baby Food Cereals Baby food
Baby Foods Baby Juices Baby food
Baby Foods Baby Spring Waters Baby food
Infant Formula Infant Formula Starter/Solutio Baby
food
Infant Formula Infant Formula Specialty Baby food
Infant Formula Infant Formula Starter Large P Baby
food
Infant Formula Infant Formula Toddler Baby food
Infant Formula Infant Formula Solutions Large Baby
food
Infant Formula Infant Formula Concentrate Baby food
Infant Formula Infant Formula Ready To Use Baby
food
Infant Formula Baby Isotonic Drinks Baby food
Infant Formula Infant Formula Soy Base Baby food
Infant Formula Infant Formula Up Age Baby food
Infant Formula Infant Formula Milk Base Baby food
Can Beans Prepared Beans--Baked W/ Beans
Pork
Can Beans Variety Beans--Kidney/ Beans
Pinto/E
Dry Bean Veg & Rice Dry Beans/Peas/Barley: Beans
Bag & B
Frozen Meat Soy/Tofu Beans
Alternatives
Salad & Dips Sal: Hommus Beans
Traditional Mexican Mexican Beans/Refried Beans
Foods
Vegetables Cooking Beans Beans
Bulk
Frozen Ice Ice--Crushed/Cubed Bottled water
Water Fortified/Water Bottled water
Water--(Sparkling & Still Water Drnking/Mnrl Bottled water
Still) Water
Water--(Sparkling & Spring Water Bottled water
Still)
Water--(Sparkling & Distilled Water Bottled water
Still)
Water--(Sparkling & Sparkling Water-- Bottled water
Still) Unflavored
Water--(Sparkling & Sparkling Water--Flvrd Bottled water
Still) Unswee
Bagels & Cream Cheese Refrigerated Bagels Bread and Crackers
Baked Breads Mainstream White Bread Bread and Crackers
Baked Breads Mainstream Variety Breads Bread and Crackers
Baked Breads Hamburger Buns Bread and Crackers
Baked Breads Hot Dog Buns Bread and Crackers
Baked Breads Premium Bread Bread and Crackers
Baked Breads Bagels Bread and Crackers
Baked Breads Sandwich Buns Bread and Crackers
Baked Breads English Muffins/Waffles Bread and Crackers
Baked Breads Main Meal Bread Bread and Crackers
Baked Breads Dinner Rolls Bread and Crackers
Baked Breads Diet/Light Bread Bread and Crackers
Baked Breads Fruit/Breakfast Bread Bread and Crackers
Baked Breads Rye Breads Bread and Crackers
Baking Mixes Biscuit Flour & Mixes Bread and Crackers
Bread Bread: Italian/French Bread and Crackers
Bread Bread: Specialty Bread and Crackers
Bread Bread: Artisan Bread and Crackers
Bread Bread: Pita/Pocket/ Bread and Crackers
Flatbrd
Bread Bread: Sweet/Breakfast Bread and Crackers
Bread Bread: Sourdough Bread and Crackers
Bread Bread: Tortillas/Wraps Bread and Crackers
Bread Bread: Rye/Cocktail Bread and Crackers
Bread Whole Grain Bread Bread and Crackers
Bread Bread: Wheat/Whl Grain Bread and Crackers
Bread Bread: Brand Bread and Crackers
Cookie/Cracker Multi- Multi-Pack Crackers Bread and Crackers
Pks
Crackers Crackers Bread and Crackers
Crackers & Misc Baked Cheese Crackers Bread and Crackers
Food
Crackers & Misc Baked Butter Spray Cracker Bread and Crackers
Food
Crackers & Misc Baked Snack Crackers Bread and Crackers
Food
Crackers & Misc Baked Saltine/Oyster Bread and Crackers
Food
Crackers & Misc Baked Specialty Crackers Bread and Crackers
Food
Croutons/Bread Croutons Bread and Crackers
Stick&Salad Top
Dry Sce/Gravy/Potatoes/ Stuffing Mixes Bread and Crackers
Stuffng
Frozen Bread/Dough Frzn Garlic Toast Bread and Crackers
Frozen Bread/Dough Frzn Dinner Rolls Bread and Crackers
Frozen Bread/Dough Frzn Garlic Bread Bread and Crackers
Frozen Bread/Dough Frzn Biscuits Bread and Crackers
Frozen Bread/Dough Frzn Breadsticks Bread and Crackers
Frozen Breakfast Foods Frzn Bagels Bread and Crackers
Refrgrated Dough Refrigerated Biscuits Bread and Crackers
Products
Refrgrated Dough Refrigerated Specialty Bread and Crackers
Products Rolls
Refrgrated Dough Refrigerated Crescent Bread and Crackers
Products Rolls
Refrgrated Dough Refrigerated Breads Bread and Crackers
Products
Refrgrated Dough Misc Refrig Dough Bread and Crackers
Products Products
Refrigerated Hispanic Refrigerated Tortillas Bread and Crackers
Grocery
Rice Cakes Mini-Cakes Bread and Crackers
Rice Cakes Large Cakes Bread and Crackers
Rolls Rolls: Dinner Bread and Crackers
Rolls Rolls: Sandwich Bread and Crackers
Rolls Rolls: Croissants/ Bread and Crackers
Breadsticks
Rolls Rolls: Bagels Bread and Crackers
Rolls Rolls: Biscuits/Eng Bread and Crackers
Muffins
Ss/Vending--Cookie/ Vending Size/Sngl Serve Bread and Crackers
Cracker Cracke
Traditional Mexican Mexican Soft Tortillas Bread and Crackers
Foods And Wra
Traditional Mexican Mexican Taco/Tostado/ Bread and Crackers
Foods Shells
Apples Caramel/Candy Apples Candy
Candy Candy/Chocolate Candy
Candy--Checklane Candy Bars (Singles) Candy
(Including)
Candy--Checklane Chewing Gum Candy
Candy--Checklane Candy Bars (Singles) Candy
(Including)
Candy--Checklane Mints/Candy & Breath (Not Candy
Life)
Candy--Checklane Misc Checklane Candy Candy
Candy--Checklane Mints/Candy & Breath (Not Candy
Life)
Candy--Packaged Candy Bags-Chocolate Candy
Candy--Packaged Candy Bars (Multi Pack) Candy
Candy--Packaged Candy Bags-Non Chocolate Candy
Candy--Packaged Seasonal Miscellaneous Candy
[Candy]
Candy--Packaged Seasonal Candy Bags- Candy
Chocolate
Candy--Packaged Gum (Packaged) Candy
Candy--Packaged Miscellaneous Candy Candy
(Including)
Candy--Packaged Seasonal Candy Box- Candy
Chocolate
Candy--Packaged Seasonal Candy Bags Non- Candy
Chocol
Candy--Packaged Candy Bars Multi Pack W/ Candy
Flour
Candy--Packaged Candy Bags-Chocolate W/ Candy
Flour
Candy--Packaged Miscellaneous Candy Candy
(Including)
Candy--Packaged Novelty Candy Candy
Candy--Packaged Seasonal Miscellaneous W/ Candy
Flour
Candy--Packaged Candy Boxed Chocolates W/ Candy
Flour
Candy--Packaged Candy Boxed Chocolates Candy
Candy--Packaged Seasonal Candy Box Non- Candy
Chocola
Candy--Packaged Candy Box Non-Chocolate Candy
Candy--Packaged Candy Bags-Non Chocolate Candy
W/Flo
Candy--Packaged Bulk Candy Candy
Candy--Packaged Seasonal Candy Bags- Candy
Chocolate
Candy--Packaged Seasonal Candy Bags Non- Candy
Chocol
Candy--Packaged Seasonal Candy Box Non- Candy
Chocola
Sweet Goods & Snacks Sweet Goods: Candy Candy
Trail Mix & Snacks Candy W/O Flour Candy
Trail Mix & Snacks Candy W/Flour Candy
Cereal Bars Breakfast Bars/Tarts/ Cereal
Scones
Cereals Cereal--Cold Cereal
Cereals Granola Cereal
Cnv Breakfast & Granola Bars Cereal
Wholesome Snks
Cnv Breakfast & Cereal Bars Cereal
Wholesome Snks
Cold Cereal Kids Cereal Cereal
Cold Cereal All Family Cereal Cereal
Cold Cereal Adult Cereal Cereal
Hot Cereal Instant Oatmeal Cereal
Hot Cereal Standard Oatmeal Cereal
Hot Cereal Grits Cereal
Hot Cereal Other Hot Cereal Cereal
Hot Cereal Instant Breakfast Cereal
Coffee & Creamers Unflavored Can Coffee Coffee and tea
Coffee & Creamers Unflavored Bag Coffee Coffee and tea
Coffee & Creamers Unflavored Instant Coffee Coffee and tea
Coffee & Creamers Ready To Drink Coffee Coffee and tea
Coffee & Creamers Coffee Pods/Singles/ Coffee and tea
Filter Pac
Coffee & Creamers Flavored Bag Coffee Coffee and tea
Coffee & Creamers Specialty Instant Coffee Coffee and tea
W/Swe
Coffee & Creamers Flavored Can Coffee Coffee and tea
Coffee & Creamers Bulk Coffee Coffee and tea
Coffee & Creamers Specialty Instant Coffee Coffee and tea
W/O S
Dry Tea/Coffee/Coco Tea Bags (Supplement) Coffee and tea
Mixes
Refrgratd Juices/ Tea No Sugar Or Swe Coffee and tea
Drinks Dairy Case
Teas Tea Bags & Bulk Tea Coffee and tea
Teas Tea Bags/Herbal Coffee and tea
Teas Tea Bags/Green Coffee and tea
Teas Instant Tea & Tea Mix Coffee and tea
Authentic Hispanic Fds Authentic Sauces/Salsa/ Condiments and
& Product Picante seasoning
Bag Snacks Salsa & Dips Condiments and
seasoning
Can Vegetables--Shelf Fried Onions Condiments and
Stable seasoning
Condiments Oils/Vinegar Condiments and
seasoning
Condiments & Sauces Bbq Sauce Condiments and
seasoning
Condiments & Sauces Catsup Condiments and
seasoning
Condiments & Sauces Steak & Worchester Sauce Condiments and
seasoning
Condiments & Sauces Hot Sauce Condiments and
seasoning
Condiments & Sauces Marinades Condiments and
seasoning
Condiments & Sauces Yellow Mustard Condiments and
seasoning
Condiments & Sauces Mustard--All Other Condiments and
seasoning
Condiments & Sauces Wing Sauce Condiments and
seasoning
Condiments & Sauces Chili Sauce/Cocktail Condiments and
Sauce seasoning
Condiments & Sauces Misc Meat Sauces Condiments and
seasoning
Croutons/Bread Stick & Salad Toppers Condiments and
Salad Top seasoning
Dressings/Dips Dips Guacamole/Salsa/ Condiments and
Queso seasoning
Dressings/Dips Dips Veggie Condiments and
seasoning
Dressings/Dips Dips Fruit And Chocolate Condiments and
seasoning
Dry Sce/Gravy/Potatoes/ Sauce Mixes/Gravy Mixes Condiments and
Stuffng Dry seasoning
Dry Sce/Gravy/Potatoes/ Gravy Can/Glass Condiments and
Stuffng seasoning
Dry Sce/Gravy/Potatoes/ Cooking Bags With Spices/ Condiments and
Stuffng Seaso seasoning
Enhancements Enhancements--Pickles/ Condiments and
Kraut seasoning
Enhancements Enhancements--Salads/ Condiments and
Spreads seasoning
Enhancements Enhancements--Spices/ Condiments and
Sauces seasoning
Herbs/Garlic Herbs Cilanto Condiments and
seasoning
Herbs/Garlic Herbs Fresh Other Organic Condiments and
seasoning
Herbs/Garlic Herbs Basil Organic Condiments and
seasoning
Mediterranean Bar Sal: Olives/Pickles--Bulk Condiments and
seasoning
Mediterranean Bar Sal: Olives/Pickles--Bulk Condiments and
seasoning
Pickle/Relish/Pckld Ripe Olives Condiments and
Veg & Olives seasoning
Pickle/Relish/Pckld Peppers Condiments and
Veg & Olives seasoning
Pickle/Relish/Pckld Green Olives Condiments and
Veg & Olives seasoning
Pickle/Relish/Pckld Relishes Condiments and
Veg & Olives seasoning
Pickle/Relish/Pckld Pickld Veg/Peppers/Etc. Condiments and
Veg & Olives seasoning
Pickle/Relish/Pckld Specialty Olives Condiments and
Veg & Olives seasoning
Refrigerated Italian Refrigerated Pasta Sauce Condiments and
seasoning
Salad & Dips Sal: Salsa/Dips Bulk Condiments and
seasoning
Salad & Dips Sal: Dip Prepack Condiments and
seasoning
Salad & Dips Sal: Salsa Prepack Condiments and
seasoning
Salad Dresing & Dry Salad Dressing & Dip Condiments and
Sandwich Spreads Mixes seasoning
Seafood--Salad/Dip/Sce/ Dips/Spreads Condiments and
Cond seasoning
Spices & Extracts Traditional Spices Condiments and
seasoning
Spices & Extracts Gourmet Spices Condiments and
seasoning
Spices & Extracts Pure Extracts Condiments and
seasoning
Spices & Extracts Table Salt/Popcorn Salt/ Condiments and
Ice Cr seasoning
Spices & Extracts Imitation Extracts Condiments and
seasoning
Spices/Jarred Garlic Spices & Seasonings Condiments and
seasoning
Traditional Asian Asian Other Sauces/ Condiments and
Foods Marinad seasoning
Traditional Asian Asian Soy Sauce Condiments and
Foods seasoning
Traditional Mexican Mexican Sauces And Condiments and
Foods Picante Sau seasoning
Traditional Mexican Mexican Seasoning Mixes Condiments and
Foods seasoning
Traditional Mexican Mexican Taco Sauce Condiments and
Foods seasoning
Vinegar & Cooking Vinegar/White & Cider Condiments and
Wines seasoning
Vinegar & Cooking Specialty Vinegar Condiments and
Wines seasoning
Eggs/Muffins/Potatoes Eggs--Large Eggs
Eggs/Muffins/Potatoes Eggs--Medium Eggs
Eggs/Muffins/Potatoes Eggs--X-Large Eggs
Eggs/Muffins/Potatoes Eggs--Jumbo Eggs
Eggs/Muffins/Potatoes Eggs Substitute Eggs
Eggs/Muffins/Potatoes Misc Dairy Refigerated Eggs
Refrigerated Dairy Eggs Eggs
Case
Dressings/Dips Creamy Fats and oils
Dressing
Dressings/Dips Blue Cheese Fats and oils
Dressing
Margarines Margarine: Tubs And Bowls Fats and oils
Margarines Butter Fats and oils
Margarines Margarine Stick Fats and oils
Margarines Margarine: Squeeze Fats and oils
Salad Dresing & Pourable Salad Dressings Fats and oils
Sandwich Spreads
Salad Dresing & Mayonnaise & Whipped Fats and oils
Sandwich Spreads Dressing
Salad Dresing & Sand/Horseradish & Tartar Fats and oils
Sandwich Spreads Sauce
Shortening & Oil Vegetable Oil Fats and oils
Shortening & Oil Canola Oils Fats and oils
Shortening & Oil Olive Oil Fats and oils
Shortening & Oil Cooking Sprays Fats and oils
Shortening & Oil Solid Shortening Fats and oils
Shortening & Oil Corn Oil Fats and oils
Shortening & Oil Cooking Oil: Peanut/ Fats and oils
Safflower/
Baking Flours/Grains/Sugar Flour and prepared
flour mixes
Flour & Meals Flour: White & Self Flour and prepared
Rising flour mixes
Flour & Meals Breadings/Coatings/Crumbs Flour and prepared
flour mixes
Flour & Meals Flour: Misc/Specialty/ Flour and prepared
Blend Et flour mixes
Molasses/Syrups/ Pancake Mixes Flour and prepared
Pancake Mixes flour mixes
Frozen Breakfast Foods Frzn Breakfast Sandwiches Frozen prepared foods
Frozen Breakfast Foods Waffles/Pancakes/French Frozen prepared foods
Toast
Frozen Breakfast Foods Frzn Breakfast Entrees Frozen prepared foods
Frozen Entrees Meatless/Vegetarian Frozen prepared foods
Frozen Ethnic Frozen International Frozen prepared foods
[Ethnic Food]
Frozen Handhelds & Snacks/Appetizers Frozen prepared foods
Snacks
Frozen Handhelds & Sandwiches & Handhelds Frozen prepared foods
Snacks
Frozen Handhelds & Corn Dogs Frozen prepared foods
Snacks
Frozen Handhelds & Burritos Frozen prepared foods
Snacks
Frozen Meat Micro Protein [Meat] Frozen prepared foods
Alternatives
Frozen Pizza Pizza/Premium Frozen prepared foods
Frozen Pizza Pizza/Economy Frozen prepared foods
Frozen Pizza Pizza/Traditional Frozen prepared foods
Frozen Pizza Pizza/Single Serve/ Frozen prepared foods
Microwave
Frzn Meatless Meatless Burgers Frozen prepared foods
Frzn Meatless Meatless Breakfast Frozen prepared foods
Frzn Meatless Meatless Poultry Frozen prepared foods
Frzn Meatless Meatless Miscellaneous Frozen prepared foods
Frzn Multi Serve Fz Family Style Entrees Frozen prepared foods
Frzn Multi Serve Fz Skillet Meals Frozen prepared foods
Frzn Multi Serve Fz Meatballs Frozen prepared foods
Frzn Pasta Frozen Pasta Frozen prepared foods
Frzn Prepared Chicken Whole Muscle Breaded/18oz Frozen prepared foods
And
Frzn Prepared Chicken Boneless Snack/18oz And Frozen prepared foods
Larger
Frzn Prepared Chicken Bone-In Wings Frozen prepared foods
Frzn Prepared Chicken Fz Meal Kits/Stuffed/ Frozen prepared foods
Other
Frzn Prepared Chicken Whole Muscle Unbreaded Frozen prepared foods
Frzn Prepared Chicken Boneless Snack/Value/ Frozen prepared foods
Small
Frzn Seafood Frz Coated Fish Fillets Frozen prepared foods
Frzn Seafood Frz Fishsticks/Tenders/ Frozen prepared foods
Nuggets
Frzn Seafood Frz Non-Coated Fish Frozen prepared foods
Fillets
Frzn Ss Economy Meals Fz Ss Economy Meals All Frozen prepared foods
Frzn Ss Premium Meals Fz Ss Prem Traditional Frozen prepared foods
Meals
Frzn Ss Premium Meals Fz Ss Prem Nutritional Frozen prepared foods
Meals
Apples Apples Gala (Bulk & Bag) Fruits
Apples Apples Red Delicious Fruits
(Bulk & Bag)
Apples Apples Granny Smith (Bulk Fruits
& Bag)
Apples Mixed Fruit Bags Fruits
Apples Apples Other (Bulk & Bag) Fruits
Apples Apples Fuji (Bulk & Bag) Fruits
Apples Apples Gold Delicious Fruits
(Bulk & Bag)
Apples Apples Honeycrisp Fruits
Apples Apples Braeburn (Bulk & Fruits
Bag)
Apples Apples Gala (Bulk & Bag) Fruits
Organic
Apples Apples Red Delicious Fruits
(Bulk & Bag)
Apples Apples Granny Smith (Bulk Fruits
& Bag)
Apples Apples Gold Delicious Fruits
(Bulk & Bag)
Bananas Bananas Fruits
Bananas Bananas Organic Fruits
Berries Strawberries Fruits
Berries Blueberries Fruits
Berries Raspberries Fruits
Berries Blackberries Fruits
Berries Strawberries Organic Fruits
Berries Raspberries Organic Fruits
Berries Blueberries Organic Fruits
Can Fruit/Jar Pineapple Fruits
Applesauce
Can Fruit/Jar Peaches Fruits
Applesauce
Can Fruit/Jar Fruit Cocktail/Fruit Fruits
Applesauce Salad
Can Fruit/Jar Mandarin Oranges/Citrus Fruits
Applesauce Sect
Can Fruit/Jar Apple Sauce (Excludes Fruits
Applesauce Cup)
Can Fruit/Jar Pears Fruits
Applesauce
Can Fruit/Jar Cranberry Sauce Fruits
Applesauce
Citrus Oranges Navels All Fruits
Citrus Clementines Fruits
Citrus Lemons Fruits
Citrus Limes Fruits
Citrus Grapefruit Fruits
Citrus Tangerines & Tangelos Fruits
Citrus Oranges Non Navel All Fruits
Convenience/Snacking Jarred Fruit Single Serve Fruits
Convenience/Snacking Convenience/Snacking Fruits
Fruit Pro
Convenience/Snacking Jarred Fruit Multi Serve Fruits
Dried Fruit Raisins Fruits
Dried Fruit Dried Fruit--Other Fruits
Dried Fruit Dried Plums Fruits
Frozen Fruits Frozen Fruit Fruits
Grapes Grapes Red Fruits
Grapes Grapes White Fruits
Grapes Grapes Black/Blue Fruits
Grapes Grapes Red Globe Fruits
Grapes Grapes Other Fruits
Melons Watermelon Seedless Whole Fruits
Melons Cantaloupe Whole Fruits
Melons Watermelon Personal Fruits
Melons Watermelon W/Seeds Whole Fruits
Melons Honeydew Whole Fruits
Pears Pears Bartlett Fruits
Pears Pears Anjou Fruits
Pears Pears Bosc Fruits
Single Serve Fruit/ Fruit Cup Fruits
Applesauce
Single Serve Fruit/ Applesauce Cup Fruits
Applesauce
Stone Fruit Cherries Red Fruits
Stone Fruit Peaches Yellow Flesh Fruits
Stone Fruit Nectarines Yellow Flesh Fruits
Stone Fruit Plums Fruits
Stone Fruit Cherries Ranier Fruits
Stone Fruit Peaches White Flesh Fruits
Tropical Fruit Avocado Fruits
Tropical Fruit Pineapple Whole&Peel/ Fruits
Cored
Tropical Fruit Mango Fruits
Tropical Fruit Kiwi Fruit Fruits
Tropical Fruit Pomegranates Fruits
Value-Added Fruit Instore Cut Fruit Fruits
Value-Added Fruit Melons Instore Cut Fruits
Value-Added Fruit Cut Fruit All Other Fruits
Prepack
Value-Added Fruit Fruit Party Tray Prepack Fruits
Bagels & Cream Cheese Cream Cheese High fat dairy/cheese
Bulk Service Case Bulk Semi-Hard [Cheese] High fat dairy/cheese
Cheese
Bulk Service Case Bulk Processed [Cheese] High fat dairy/cheese
Cheese
Bulk Service Case Bulk Semi-Soft [Cheese] High fat dairy/cheese
Cheese
Cheese Shredded Cheese High fat dairy/cheese
Cheese American Single Cheese High fat dairy/cheese
Cheese Natural Cheese Chunks High fat dairy/cheese
Cheese String Cheese High fat dairy/cheese
Cheese Natural Cheese Slices High fat dairy/cheese
Cheese Miscellaneous Cheese High fat dairy/cheese
Coffee & Creamers Non Dairy Creamer High fat dairy/cheese
Crackers & Misc Baked Aerosol Cheese High fat dairy/cheese
Food
Dry Cheese Loaf Cheese High fat dairy/cheese
Dry Cheese Grated Cheese High fat dairy/cheese
Dry Cheese Misc Dry Cheese High fat dairy/cheese
Fluid Milk Products Refrigerated Coffee High fat dairy/cheese
Creamers
Fluid Milk Products Half & Half High fat dairy/cheese
Fluid Milk Products Whipping Cream High fat dairy/cheese
Fluid Milk Products Egg Nog/Boiled Custard High fat dairy/cheese
Fluid Milk Products Buttermilk High fat dairy/cheese
Ice Cream Ice Milk & Premium [Ice Cream & High fat dairy/cheese
Sherbets Sherbert]
Ice Cream Ice Milk & Traditional [Ice Cream & High fat dairy/cheese
Sherbets Sherbert]
Ice Cream Ice Milk & Pails [Ice Cream & High fat dairy/cheese
Sherbets Sherbert]
Ice Cream Ice Milk & Super Premium Pints [Ice High fat dairy/cheese
Sherbets Cream & Sherbert]
Ice Cream Ice Milk & Premium Pints [Ice Cream High fat dairy/cheese
Sherbets & Sherbert]
Ice Cream Ice Milk & Quarts [Ice Cream & High fat dairy/cheese
Sherbets Sherbert]
Milk By-Products Sour Creams High fat dairy/cheese
Milk By-Products Cottage Cheese High fat dairy/cheese
Milk By-Products Refrig Dips High fat dairy/cheese
Milk By-Products Aerosol Toppings [Milk By- High fat dairy/cheese
Products]
Milk By-Products Ricotta Cheese High fat dairy/cheese
Pre-Slice Service Case Pre-Sliced Semi-Soft High fat dairy/cheese
Cheese [Cheese]
Pre-Slice Service Case Pre-Sliced Semi-Hard High fat dairy/cheese
Cheese [Cheese]
Refrigerated Hispanic Hispanic Cheese High fat dairy/cheese
Grocery
Specialty Cheese Pre Specialty Ppk Cheese Hard/ High fat dairy/cheese
Pack Grat
Specialty Cheese Pre Specialty Ppk Cheese High fat dairy/cheese
Pack Spreads
Specialty Cheese Pre Specialty Ppk Cheese Feta High fat dairy/cheese
Pack
Specialty Cheese Pre Specialty Ppk Cheese High fat dairy/cheese
Pack Mozzarell
Specialty Cheese Pre Specialty Ppk Cheese High fat dairy/cheese
Pack Processed
Specialty Cheese Pre Specialty Ppk Cheese High fat dairy/cheese
Pack Cheddar & C
Specialty Cheese Pre Specialty Ppk Cheese Semi High fat dairy/cheese
Pack Soft
Specialty Cheese Pre Specialty Ppk Cheese Soft High fat dairy/cheese
Pack & Ripe
Specialty Cheese Pre Specialty Ppk Cheese Blue/ High fat dairy/cheese
Pack Gorg
Specialty Cheese Pre Specialty Ppk Cheese High fat dairy/cheese
Pack Hispanic
Specialty Cheese Pre Specialty Ppk Cheese High fat dairy/cheese
Pack Gouda & Eda
Specialty Cheese Pre Specialty Ppk Cheese Goat High fat dairy/cheese
Pack Milk
Traditional Mexican Mexican Con Queso High fat dairy/cheese
Foods
Fruit Snacks Fruit Snacks Jams, jellies,
preserves and other
sweets
Peanut Butter/Jelly/ Preserves/Jam/Marmalade Jams, jellies,
Jams & Honey preserves and other
sweets
Peanut Butter/Jelly/ Jelly Jams, jellies,
Jams & Honey preserves and other
sweets
Aseptic Juice Aseptic Pack Juice And Juices
Drinks
Frozen Juice And Frzn Conc Allieds Over Juices
Smoothies 50% Jui
Frozen Juice And Frzn Oj & Oj Substitutes Juices
Smoothies (Over 5
Juice Non-Carb Jce(Over 50% Juices
Jce)
Juice Drinks--Carb Juice (Over Juices
50%)
Juices Super Premium Juices Superfoods/ Juices
Enhanced
Juices Super Premium Juices Proteins Juices
Juices Super Premium Juice Single Blend Juices
Processed Squeeze Lemons/Limes Juices
Refrgratd Juices/ Dairy Case 100% Pure Juices
Drinks Juice--O
Refrgratd Juices/ Dairy Case 100% Pure Juices
Drinks Juice Oth
Rtd Tea/New Age Juice Juice (Over 50% Juice) Juices
Shelf Stable Juice Apple Juice & Cider (Over Juices
50%)
Shelf Stable Juice Blended Juice & Juices
Combinations (Ov)
Shelf Stable Juice Grape Juice (Over 50% Juices
Juice)
Shelf Stable Juice Veg Juice (Except Tomato) Juices
(Ove)
Shelf Stable Juice Tomato Juice (Over 50% Juices
Jce)
Shelf Stable Juice Pineapple Juice (Over 50% Juices
Juic)
Shelf Stable Juice Cranberry Juice (Over 50% Juices
Jce)
Shelf Stable Juice Lemon Juice & Lime Juice Juices
(Over)
Shelf Stable Juice Prune Juice (Over 50% Juices
Juice)
Shelf Stable Juice Cranapple/Cran Grape Juices
Juice (Ov)
Shelf Stable Juice Grapefruit Juice (Over Juices
50% Jui)
Shelf Stable Juice Cranapple/Cran Grape Juices
Juice (Un)
Shelf Stable Juice Grapefruit Juice (50% And Juices
Unde)
Bacon Bacon--Trad 16oz Or Less Meat/Poultry/Seafood
Bacon Bacon--Trad Greater Than Meat/Poultry/Seafood
16oz
Bacon Bacon--Poultry Meat/Poultry/Seafood
Bacon Bacon--Pre-Cooked Meat/Poultry/Seafood
Bacon Bacon--Trad Center Cut Meat/Poultry/Seafood
Bacon Bacon--Other Meat/Poultry/Seafood
Bacon Bacon--Natural/Organic Meat/Poultry/Seafood
Beef: Grinds Lean [Beef] Meat/Poultry/Seafood
Beef: Grinds Primal [Beef] Meat/Poultry/Seafood
Beef: Grinds Angus [Beef] Meat/Poultry/Seafood
Beef: Grinds Patties [Beef] Meat/Poultry/Seafood
Beef: Loins Choice Beef Meat/Poultry/Seafood
Beef: Loins Select Beef Meat/Poultry/Seafood
Beef: Rib Angus [Beef] Meat/Poultry/Seafood
Beef: Round Choice Beef Meat/Poultry/Seafood
Beef: Round Angus Beef Meat/Poultry/Seafood
Beef: Round Select Beef Meat/Poultry/Seafood
Beef: Thin Meats Soup/Stew Meat/Poultry/Seafood
Beef: Thin Meats Cubed Meats [Beef] Meat/Poultry/Seafood
Beef: Thin Meats Corned Beef Meat/Poultry/Seafood
Beef: Thin Meats Brisket [Beef] Meat/Poultry/Seafood
Beef: Thin Meats Skirt [Beef] Meat/Poultry/Seafood
Beef: Thin Meats Flank [Beef] Meat/Poultry/Seafood
Breakfast Sausage Bkfst Sausage--Fresh Meat/Poultry/Seafood
Rolls
Breakfast Sausage Bkfst Sausage--Fresh Meat/Poultry/Seafood
Links
Breakfast Sausage Bkfst Sausage--Fresh Meat/Poultry/Seafood
Patties
Breakfast Sausage Bkfst Sausage--Precooked Meat/Poultry/Seafood
Breakfast Sausage Bkfst Sausage--Bkfast Meat/Poultry/Seafood
Side Di
Breakfast Sausage Bkfst Sausage--Other Meat/Poultry/Seafood
Forms
Buffalo Grinds [Buffalo] Meat/Poultry/Seafood
Can Seafood--Shelf Tuna Meat/Poultry/Seafood
Stable
Can Seafood--Shelf Salmon Meat/Poultry/Seafood
Stable
Can Seafood--Shelf Sardines Meat/Poultry/Seafood
Stable
Can Seafood--Shelf Oysters Meat/Poultry/Seafood
Stable
Chicken & Poultry Chix: Value-Added (Cold) Meat/Poultry/Seafood
Chicken & Poultry Chix: Frd 8pc/Cut Up Meat/Poultry/Seafood
(Cold)
Chicken & Poultry Chix: Baked 8pc Cut Up Meat/Poultry/Seafood
(Cold)
Chicken & Poultry Chix: Rotisserie Cold Meat/Poultry/Seafood
Chicken Fresh Chicken Breast Boneless Meat/Poultry/Seafood
Chicken Fresh Chicken Wings Meat/Poultry/Seafood
Chicken Fresh Chicken Drums Meat/Poultry/Seafood
Chicken Fresh Whole Chicken (Roasters/ Meat/Poultry/Seafood
Fryer)
Chicken Fresh Chicken Thighs Meat/Poultry/Seafood
Chicken Fresh Chicken Legs/Quarters Meat/Poultry/Seafood
Chicken Fresh Mixed Packs [Chicken] Meat/Poultry/Seafood
Chicken Frozen Frzn Chicken--Wht Meat Meat/Poultry/Seafood
Chicken Frozen Frzn Chicken--Wings Meat/Poultry/Seafood
Chicken Frozen Frzn Chicken--Drk Meat Meat/Poultry/Seafood
Chicken Grinds Ground Chicken Meat/Poultry/Seafood
Chicken Offal Internal Chicken Offal Meat/Poultry/Seafood
Chicken Specialty/ Chicken Breast Boneless Meat/Poultry/Seafood
Natural
Chicken Specialty/ Chicken Wings Meat/Poultry/Seafood
Natural
Chicken Specialty/ Whole Chicken (Roasters/ Meat/Poultry/Seafood
Natural Fryer)
Deli Meat: Bulk Meat: Turkey Bulk Meat/Poultry/Seafood
Deli Meat: Bulk Meat: Ham Bulk Meat/Poultry/Seafood
Deli Meat: Bulk Meat: Beef Bulk Meat/Poultry/Seafood
Deli Meat: Bulk Meat Bulk: Specialty Dry Meat/Poultry/Seafood
Meats
Deli Meat: Bulk Bologna/Loaves/Franks Meat/Poultry/Seafood
Deli Meat: Bulk Meat: Chicken Bulk Meat/Poultry/Seafood
Deli Meat: Presliced Deli Meat: Specialty Dry Meat/Poultry/Seafood
Meats
Deli Meat: Presliced Deli Meat: Semi-Dry Meat/Poultry/Seafood
Sausage
Deli Meat: Presliced Deli Meat: Turkey Meat/Poultry/Seafood
Deli Meat: Presliced Deli Meat: Ham Meat/Poultry/Seafood
Deli Meat: Presliced Deli Meat: Beef Meat/Poultry/Seafood
Dinner Sausage Dnr Sausage--Links Pork Meat/Poultry/Seafood
Ckd/S
Dinner Sausage Dnr Sausage--Links Fresh Meat/Poultry/Seafood
Dinner Sausage Dnr Sausage--Pork Rope Meat/Poultry/Seafood
Ckd/Sm
Dinner Sausage Dnr Sausage--Beef Rope Meat/Poultry/Seafood
Ckd/Sm
Dinner Sausage Dnr Sausage--Other Forms Meat/Poultry/Seafood
Dinner Sausage Dnr Sausage--Links Beef Meat/Poultry/Seafood
Ckd/S
Dinner Sausage Dnr Sausage--Poultry Rope Meat/Poultry/Seafood
Ckd
Dinner Sausage Dnr Sausage--Links Meat/Poultry/Seafood
Poultry Ck
Dinner Sausage Dnr Sausage--Natural/ Meat/Poultry/Seafood
Organic
Dinner Sausage Dnr Sausage--Fresh Meat/Poultry/Seafood
Poultry
Frozen Breakfast Foods Frzn Breakfast Sausage Meat/Poultry/Seafood
Frzn Multi Serve Frzn Burgers Meat/Poultry/Seafood
Frzn Prepared Chicken Value Forms/18oz And Meat/Poultry/Seafood
Larger [Chicken]
Hot Dogs Hot Dogs--Base Meat Meat/Poultry/Seafood
Hot Dogs Hot Dogs--Base Beef Meat/Poultry/Seafood
Hot Dogs Hot Dogs--Premium Meat/Poultry/Seafood
Hot Dogs Hot Dogs--Base Poultry Meat/Poultry/Seafood
Lamb Round/Leg [Lamb] Meat/Poultry/Seafood
Lamb Loin [Lamb] Meat/Poultry/Seafood
Lamb Chuck/Shoulder [Lamb] Meat/Poultry/Seafood
Lunchmeat Lunchment--Deli Fresh Meat/Poultry/Seafood
Lunchmeat Lunchment--Bologna/ Meat/Poultry/Seafood
Sausage
Lunchmeat Lunchmeat--Chop/Form Meat/Poultry/Seafood
Pltry & Ha
Lunchmeat Lunchmeat--Whole Muscle Meat/Poultry/Seafood
Pltry
Lunchmeat Lunchmeat--Chip Meat Meat/Poultry/Seafood
Lunchmeat Lunchmeat--Brauns/Liver/ Meat/Poultry/Seafood
Loave
Lunchmeat Lunchmeat--Variety Pack Meat/Poultry/Seafood
Lunchmeat Lunchmeat--Other Meat/Poultry/Seafood
Lunchmeat Lunchment--Natural/ Meat/Poultry/Seafood
Organic
Lunchmeat Lunchmeat--Peggable Deli Meat/Poultry/Seafood
Fres
Meat Frozen Frzn Meat--Beef Meat/Poultry/Seafood
Meat Frozen Frzn Meat--Breakfast Meat/Poultry/Seafood
Sausage
Meat Frozen Frzn Meat--Offals Meat/Poultry/Seafood
Meat Frozen Frzn Meat--Turkey Meat/Poultry/Seafood
Meat Snacks Jerky/Nuggets/Tenders Meat/Poultry/Seafood
Meat Snacks Meat Sticks/Bites Meat/Poultry/Seafood
Party Tray Deli Tray: Meat And Cheese Meat/Poultry/Seafood
Pork Bone In Loin/Rib Dry [Pork Bone In Loin/ Meat/Poultry/Seafood
Rib]
Pork Boneless Loin/Rib Enhanced [Pork Boneless Meat/Poultry/Seafood
Loin/Rib]
Pork Grinds Ground Pork Meat/Poultry/Seafood
Pork Offal External Fresh [Pork Meat/Poultry/Seafood
Offal]
Pork Shoulder Butts [Pork Shoulder] Meat/Poultry/Seafood
Pork Shoulder Fresh Hams Meat/Poultry/Seafood
Pork Thin Meats Ribs [Pork] Meat/Poultry/Seafood
Poultry Other Cornish Hen Meat/Poultry/Seafood
Random Weight Meat Lunch Meats Meat/Poultry/Seafood
Products
Seafood--Catfish Catfish--Fillet Meat/Poultry/Seafood
Seafood--Catfish Catfish--Whole Meat/Poultry/Seafood
Seafood--Catfish Catfish--Nuggets Meat/Poultry/Seafood
Seafood--Cod Cod--Fillet Meat/Poultry/Seafood
Seafood--Crab Crab--Snow Meat/Poultry/Seafood
Seafood--Crab Crab--King Meat/Poultry/Seafood
Seafood--Crab Crab--Dungy Meat/Poultry/Seafood
Seafood--Crab Crab--Other Meat/Poultry/Seafood
Seafood--Finfish Other Finfish--Other Meat/Poultry/Seafood
Seafood--Finfish Other Finfish--Other Meat/Poultry/Seafood
Seafood--Lobster Lobster--Tails Meat/Poultry/Seafood
Seafood--Party Trays Party Tray--Shrimp Meat/Poultry/Seafood
Seafood--Salmon-Farm Salmon Fr--Altantic Meat/Poultry/Seafood
Raised
Seafood--Salmon-Wild Salmon Wc--Pink Meat/Poultry/Seafood
Caught
Seafood--Salmon-Wild Salmon Wc--Sockeye Meat/Poultry/Seafood
Caught
Seafood--Scallops Scallops--Sea Meat/Poultry/Seafood
Seafood--Shrimp Shrimp--Raw Meat/Poultry/Seafood
Seafood--Shrimp Shrimp--Cooked Meat/Poultry/Seafood
Seafood--Smoked Smoked Salmon Meat/Poultry/Seafood
Seafood
Seafood--Tilapia Tilapia--Fillet Meat/Poultry/Seafood
Seafood--Trout Steelhead Fr [Trout] Meat/Poultry/Seafood
Seafood--Value-Added Value-Added Breaded Meat/Poultry/Seafood
Seafood Shrimp
Seafood--Value-Added Value-Added Shrimp Meat/Poultry/Seafood
Seafood
Seafood--Value-Added Value-Added Crab Meat/Poultry/Seafood
Seafood
Service Case Meat Seasoned Poultry Meat/Poultry/Seafood
Service Case Meat Stuffed/Mixed Beef Meat/Poultry/Seafood
Service Case Meat Marinated Pork Meat/Poultry/Seafood
Service Case Meat Marinated Poultry Meat/Poultry/Seafood
Service Case Meat Seasoned Beef Meat/Poultry/Seafood
Service Case Meat Seasoned Pork Meat/Poultry/Seafood
Service Case Meat Stuffed/Mixed Poultry Meat/Poultry/Seafood
Service Case Meat Marinated Beef Meat/Poultry/Seafood
Service Case Meat Kabobs Beef Meat/Poultry/Seafood
Service Case Meat Kabobs Poultry Meat/Poultry/Seafood
Service Case Meat Stuffed/Mixed Pork Meat/Poultry/Seafood
Smoked Hams Hams--Half/Port Bone-In Meat/Poultry/Seafood
Smoked Hams Hams--Spiral Meat/Poultry/Seafood
Smoked Hams Hams--Whole Boneless Meat/Poultry/Seafood
Smoked Hams Hams--Half/Port Boneless Meat/Poultry/Seafood
Smoked Hams Hams--Dry Cured/Country Meat/Poultry/Seafood
Smoked Hams Hams--Whole Bone-In Meat/Poultry/Seafood
Smoked Pork Ham Steaks/Cubes/Slices Meat/Poultry/Seafood
Smoked Pork Smoked Offal [Pork] Meat/Poultry/Seafood
Smoked Pork Bacon--Belly/Jowl Meat/Poultry/Seafood
Smoked Pork Smoked Picnics [Pork] Meat/Poultry/Seafood
Snack Meat Snack Meat--Pepperoni Meat/Poultry/Seafood
Snack Meat Snack Meat--Salami/Smr Meat/Poultry/Seafood
Sausag
Turkey Fresh Whole Hen (Under 16lbs) Meat/Poultry/Seafood
[Turkey]
Turkey Fresh Whole Tom (Over 16lbs) Meat/Poultry/Seafood
[Turkey]
Turkey Frozen Whole Toms (Over 16lbs) Meat/Poultry/Seafood
[Turkey]
Turkey Frozen Whole Hens (Under 16lbs) Meat/Poultry/Seafood
[Turkey]
Turkey Frozen Turkey Breast Bone In Meat/Poultry/Seafood
Turkey Grinds Ground Turkey Meat/Poultry/Seafood
Turkey Offal External [Turkey Offal] Meat/Poultry/Seafood
Turkey Smoked Turkey Wings Meat/Poultry/Seafood
Turkey Smoked Turkey Drums Meat/Poultry/Seafood
Fluid Milk Products Fluid Milk/White Only Milk
Fluid Milk Products Flavored Milk Milk
Fluid Milk Products Specialty/Lactose Free Milk
Milk
Fluid Milk Products Organic Milk Milk
Fluid Milk Products Soy Milk Milk
Non-Dairy/Dairy Aseptic Milk Milk
Aseptic
Non-Dairy/Dairy Soy Beverage Milk
Aseptic
Non-Dairy/Dairy Nut Milk Milk
Aseptic
Non-Dairy/Dairy Rice Beverage Milk
Aseptic
Refrigerated Dairy Non-Dairy Milks Milk
Case
Refrigerated Dairy Fluid Milk Milk
Case
Authentic Asian Foods Authentic Japanese Foods Miscellaneous
Authentic Asian Foods Authentic Chinese Foods Miscellaneous
Authentic Central Central American Foods Miscellaneous
American Fds
Authentic Hispanic Fds Hispanic Baking Needs Miscellaneous
& Product
Baking Needs Baking Powder & Soda Miscellaneous
Baking Needs Yeast: Dry Miscellaneous
Baking Needs Corn Starch Miscellaneous
Dietary Aid Prdct/Med Diet Cntrl Liqs Miscellaneous
Liq Nutr Nutritional
Dietary Aid Prdct/Med Diet Energy Drinks Miscellaneous
Liq Nutr
Dietary Aid Prdct/Med Diet Cntrl Bars Miscellaneous
Liq Nutr Nutritional
Fitness & Diet Fitness & Diet--Bars W/ Miscellaneous
Flour
Fitness & Diet Fitness & Diet--Bars W/O Miscellaneous
Flour
Fitness & Diet Fitness & Diet--Powder Miscellaneous
Ntrtnl
Refrigerated Hispanic Misc Hispanic Grocery Miscellaneous
Grocery
Baking Needs Baking Nuts Nuts and seeds
Bulk Food Trail Mix/Nuts Bulk Nuts and seeds
Condiments Nut Butters/Peanut Butter Nuts and seeds
Nuts Pistachios Nuts and seeds
Nuts Mixed Nuts Nuts and seeds
Nuts Cashews Nuts and seeds
Nuts Sunflower/Other Seeds Nuts and seeds
Nuts Pecans Shelled Nuts and seeds
Nuts Peanuts All Nuts and seeds
Nuts Walnuts Shelled Nuts and seeds
Nuts Almonds Shelled Nuts and seeds
Nuts Trail Mix Nuts and seeds
Nuts Almonds Nuts and seeds
Nuts Dry Roast Peanuts Nuts and seeds
Nuts Oil Roast Peanuts Nuts and seeds
Nuts Nuts Other Nuts and seeds
Nuts Misc Snack Nuts Nuts and seeds
Nuts Nuts Inshell Nuts and seeds
Peanut Butter/Jelly/ Peanut Butter Nuts and seeds
Jams & Honey
Trail Mix & Snacks Trail Mixes/Snack Nuts and seeds
Canned & Dry Milk Canned Milk Other dairy products
Canned & Dry Milk Non Fat Dry Milk Other dairy products
Refrigerated Dairy Yogurt Other dairy products
Case
Refrigerated Dairy Kefir Other dairy products
Case
Yogurt Yogurt/Kids Other dairy products
Yogurt Yogurt/Ss Regular Other dairy products
Yogurt Yogurt/Ss Light Other dairy products
Yogurt Yogurt/Pro Active Health Other dairy products
Yogurt Yogurt/Adult Multi-Packs Other dairy products
Yogurt Yogurt/Specialty Greek Other dairy products
Yogurt Yogurt/Large Size (16oz Other dairy products
Or Lar)
Yogurt Yogurt/Adult Drinks Other dairy products
Deli Specialties Dl Spec: Dry/Refrig Pasta, cornmeal,
(Retail Pk) Pastas other cereal
products
Dry Bean Veg & Rice Noodle Side Dish Mixes Pasta, cornmeal,
other cereal
products
Dry Noodles & Pasta Long Cut Pasta Pasta, cornmeal,
other cereal
products
Dry Noodles & Pasta Short Cut Pasta Pasta, cornmeal,
other cereal
products
Dry Noodles & Pasta Noodles Dry Pasta, cornmeal,
other cereal
products
Dry/Ramen Bouillon Ramen Noodles/Ramen Cups Pasta, cornmeal,
other cereal
products
Flour & Meals Cornmeal Pasta, cornmeal,
other cereal
products
Prepared/Pdgd Foods Pasta/Ramen Pasta, cornmeal,
other cereal
products
Refrigerated Italian Refrigerated Pasta Pasta, cornmeal,
other cereal
products
Salad & Dips Pasta/Grain Salads-- Pasta, cornmeal,
Prepack other cereal
products
Salad & Dips Pasta/Grain Salads--Bulk Pasta, cornmeal,
other cereal
products
Seafood--Salad/Dip/Sce/ Breading Pasta, cornmeal,
Cond other cereal
products
Traditional Asian Asian Noodles/Rice Pasta, cornmeal,
Foods other cereal
products
Authentic Hispanic Fds Hispanic Cookies/Crackers Prepared Desserts
& Product
Baked Sweet Goods Snack Cake--Multi Pack Prepared Desserts
Baked Sweet Goods Sweet Goods--Full Size Prepared Desserts
Bakery Party Trays Party Trays: Cakes Prepared Desserts
Baking Mixes Layer Cake Mix Prepared Desserts
Baking Mixes Frosting Prepared Desserts
Baking Mixes Muffin & Corn Bread Mix Prepared Desserts
Baking Mixes Brownie Mix Prepared Desserts
Baking Mixes Cookies Mix Prepared Desserts
Baking Mixes Miscellaneous Package Prepared Desserts
Mixes
Baking Needs Bits & Morsels [Baking Prepared Desserts
Needs]
Baking Needs Marshmallows Prepared Desserts
Baking Needs Pie Filling/Mincemeat/ Prepared Desserts
Glazes
Baking Needs Pie Crust Mixes & Shells Prepared Desserts
Baking Needs Cooking Chocolate (Ex: Prepared Desserts
Smi-Swt)
Baking Needs Maraschino Cherries Prepared Desserts
Baking Needs Baking Cocoa Prepared Desserts
Baking Needs Marshmallow Creme Prepared Desserts
Baking Needs Coconut [Baking Needs] Prepared Desserts
Cake Decor Cake Decors & Icing Prepared Desserts
Cake Decor Cake Decors--Candies Prepared Desserts
Cakes Cakes: Birthday/ Prepared Desserts
Celebration Sh
Cakes Cakes: Cupcakes Prepared Desserts
Cakes Cakes: Layers Prepared Desserts
Cakes Cakes: Creme/Pudding Prepared Desserts
Cakes Cakes: Cheesecake Prepared Desserts
Cakes Cakes: Fancy/Service Case Prepared Desserts
Cakes Cakes: Layers/Sheets Prepared Desserts
Novelties
Cakes Cakes: Angel Fds/Cke Prepared Desserts
Rolls
Cakes Cakes: Ice Cream Prepared Desserts
Cakes Cakes: Birthday/ Prepared Desserts
Celebration Lay
Cakes Cakes: Sheet Prepared Desserts
Cakes Cakes: Creme/Pudding Prepared Desserts
Novelties
Cakes Cakes: Cheesecake Prepared Desserts
Novelties
Cnv Breakfast & Toaster Pastries Prepared Desserts
Wholesome Snks
Cnv Breakfast & Treats Prepared Desserts
Wholesome Snks
Cookie/Cracker Multi- Multi-Pack Cookies Prepared Desserts
Pks
Cookies Sandwich Cookies Prepared Desserts
Cookies Tray Pack/Choc Chip Prepared Desserts
Cookies
Cookies Cookies: Regular Prepared Desserts
Cookies Vanilla Wafer/Kids Prepared Desserts
Cookies
Cookies Cookies: Holiday/Special Prepared Desserts
Occas
Cookies Premium Cookies (Ex: Prepared Desserts
Pepperidg)
Cookies Graham Crackers Prepared Desserts
Cookies Chocolate Covered Cookies Prepared Desserts
Cookies Wellness/Portion Control Prepared Desserts
[Cookies]
Cookies Cookies: Gourmet Prepared Desserts
Cookies Fruit Filled Cookies Prepared Desserts
Cookies Cookies: Message Prepared Desserts
Cookies Cookies/Sweet Goods Prepared Desserts
Cookies Specialty Cookies Prepared Desserts
Dry Mix Desserts Pudding & Gelatin Cups/ Prepared Desserts
Cans
Dry Mix Desserts Puddings Dry Prepared Desserts
Dry Mix Desserts Gelatin Prepared Desserts
Dry Mix Desserts Misc: Cheesecake/Mousse Prepared Desserts
Mixes
Frozen Breakfast Foods Frzn Breakfast Pastry Prepared Desserts
Frozen Desserts Frozen Fruit Pies & Prepared Desserts
Cobblers
Frozen Desserts Frozen Cream Pies Prepared Desserts
Frozen Desserts Frzn Pie Shells/Pastry Prepared Desserts
Shell/F
Frozen Desserts Frozen Cakes/Desserts Prepared Desserts
Frozen Desserts Frzn Pastry & Cookies Prepared Desserts
Frozen Desserts Single Serv/Portion Prepared Desserts
Control
Frozen Novelties-- Sticks/Enrobed [Frozen Prepared Desserts
Water Ice Novelties]
Frozen Novelties-- Water Ice [Frozen Prepared Desserts
Water Ice Novelties]
Frozen Novelties-- Cones [Frozen Novelties] Prepared Desserts
Water Ice
Frozen Novelties-- Ice Cream Sandwiches Prepared Desserts
Water Ice
Frozen Novelties-- Adult Premium [Frozen Prepared Desserts
Water Ice Novelties]
Frozen Novelties-- Cups/Push Ups/Other Prepared Desserts
Water Ice [Frozen Novelties]
Frozen Whipped Topping Frzn Whipped Topping Prepared Desserts
Pies Pies: Fruit/Nut Prepared Desserts
Pies Pies: Pumpkin/Custard Prepared Desserts
Pies Pies: Cream/Meringue Prepared Desserts
Pies Pies: Sugar Free Prepared Desserts
Pies Pies: Tarts/Minis/Crstdas Prepared Desserts
Refrgrated Dough Refrigerated Cookies-- Prepared Desserts
Products Break N B
Refrgrated Dough Refrigerated Cookie Dough Prepared Desserts
Products
Refrgrated Dough Refrigerated Cookies-- Prepared Desserts
Products Seasonal
Refrgrated Dough Refrigerated Pie Crust Prepared Desserts
Products
Refrigerated Desserts Refrigerated Pudding Prepared Desserts
Salad & Dips Sal: Desserts--Prepack Prepared Desserts
Salad & Dips Sal: Desserts--Bulk Prepared Desserts
Single Serve Sweet Snack Cake--Single Serve Prepared Desserts
Goods
Ss/Vending--Cookie/ Vendor Size/Single Serve Prepared Desserts
Cracker Cooki
Sweet Goods Sw Gds: Donuts Prepared Desserts
Sweet Goods Sw Gds: Sw Rolls/Dan Prepared Desserts
Sweet Goods Sw Gds: Muffins Prepared Desserts
Sweet Goods Sw Gds: Coffee Cakes Prepared Desserts
Sweet Goods & Snacks Sw Gds: Swt/Flvrd Loaves Prepared Desserts
Sweet Goods & Snacks Sw Gds: Brownie/Bar Prepared Desserts
Cookie
Sweet Goods & Snacks Sw Gds: Puff Pastry Prepared Desserts
Sweet Goods & Snacks Sw Gds: Specialty Prepared Desserts
Desserts
Syrups Toppings & Ice Cream Toppings Prepared Desserts
Cones
Value-Added Fruit Parfait Cups Instore Prepared Desserts
Canned Pasta & Mwv Fd- Can Pasta Prepared Foods
Shlf Stbl
Canned Pasta & Mwv Fd- Microwavable Cups [Pasta] Prepared Foods
Shlf Stbl
Chilled Ready Meals Store Brand Prepared Foods
Convenient Meals Convenient Meals--Kids Prepared Foods
Meal C
Convenient Meals Convenient Meals--Adult Prepared Foods
Meal
Dinner Mixes--Dry Macaroni & Cheese Dnrs Prepared Foods
Dinner Mixes--Dry Skillet Dinners Prepared Foods
Dinner Mixes--Dry Microwave Dinners Prepared Foods
Dinner Mixes--Dry Package Dinners/Pasta Prepared Foods
Salads
Dinner Mixes--Dry Pizza Mix Dry Prepared Foods
Dinner Sausage Dnr Sausage--Cocktails Prepared Foods
Meat--Shelf Stable Chili: Canned Prepared Foods
Meat--Shelf Stable Chunk Meats--Chix/Ham/ Prepared Foods
Etc.
Meat--Shelf Stable Sandwich Sauce (Manwich) Prepared Foods
Meat--Shelf Stable Vienna Sausage Prepared Foods
Meat--Shelf Stable Luncheon Meat (Spam) Prepared Foods
Meat--Shelf Stable Hash: Canned Prepared Foods
Meat--Shelf Stable Beef Stew Prepared Foods
Meat--Shelf Stable Hot Dog Chili Sauce Prepared Foods
Meat--Shelf Stable Beef/Pork--Dried Sliced W/ Prepared Foods
Gra
Meat--Shelf Stable Potted Meats And Spreads Prepared Foods
Meat--Shelf Stable Corn Beef Prepared Foods
Party Tray Deli Tray: Sandwiches Prepared Foods
Party Tray Deli Tray: Prepared Foods
Appetizers&Hors D'oe
Prepared/Pdgd Foods Boxed Prepared/Entree/Dry Prepared Foods
Prep
Prepared/Pdgd Foods Vegetables/Dry Beans Prepared Foods
Refrigerated Vegetarian Meats Prepared Foods
Vegetarian
Refrigerated Vegetarian Misc Prepared Foods
Vegetarian
Refrigerated Non-Dairy Cheese Prepared Foods
Vegetarian
Refrigerated Tofu Prepared Foods
Vegetarian
Salad & Dips Protein Salads--Bulk Prepared Foods
Salad & Dips Protein Salads--Prepack Prepared Foods
Sandwiches Sandwiches--(Cold) Prepared Foods
Sushi Sushi--In Store Prepared Prepared Foods
Sushi Sushi--Prepackaged Prepared Foods
Traditional Asian Asian Foods And Meals Prepared Foods
Foods
Traditional Asian Traditional Thai Foods Prepared Foods
Foods
Traditional Mexican Mexican Dinners And Foods Prepared Foods
Foods
Traditional Mexican Mexican Enchilada Sauce Prepared Foods
Foods
Authentic Hispanic Fds Authentic Pasta/Rice/ Rice
& Product Beans
Dry Bean Veg & Rice Rice Side Dish Mixes Dry Rice
Dry Bean Veg & Rice Rice--Dry Bag And Box Rice
Dry Bean Veg & Rice Rice--Instant & Microwave Rice
Bag Snacks Potato Chips Salty snacks
Bag Snacks Tortilla/Nacho Chips Salty snacks
Bag Snacks Mult Pk Bag Snacks Salty snacks
Bag Snacks Bagged Cheese Snacks Salty snacks
Bag Snacks Corn Chips Salty snacks
Bag Snacks Pretzels Salty snacks
Bag Snacks Store Brand Salty snacks
Bag Snacks Misc Bag Snacks Salty snacks
Bag Snacks Bagged Popped Popcorn Salty snacks
Bag Snacks Pork Skins/Cracklins Salty snacks
Popcorn Popcorn--Microwave Salty snacks
Popcorn Popcorn--Other Salty snacks
Popcorn Caramel Coated Snacks Salty snacks
Snack Tortilla Chips Salty snacks
Snack Soy/Rice Snacks Salty snacks
Snacks Snacks: Pita Chips Salty snacks
Snacks Snacks: Salty Salty snacks
Snacks Snacks: Tortilla Chips Salty snacks
Snacks Snacks: Crackers/Cookies Salty snacks
Ss/Vending--Salty Salty Snacks Vending Salty snacks
Snacks
Warehouse Snacks Canister Snacks Salty snacks
Warehouse Snacks Snack Mix Salty snacks
Warehouse Snacks Misc Snacks Salty snacks
Authentic Hispanic Fds Authentic Soups/Bouillons Soup
& Product
Canned Soups Condensed Soup Soup
Dry/Ramen Bouillon Dry Soup Soup
Dry/Ramen Bouillon Bouillon Soup
Rts/Micro Soup/Broth Rts Soup: Chunky/ Soup
Homestyle/Et
Rts/Micro Soup/Broth Broth Soup
Rts/Micro Soup/Broth Microwavable Soups Soup
Soup Cans Soup/Chili Soup
Soup Broths Soup
Condiments Honey/Syrup Sugars
Dressings/Dips Dips Caramel/Fruit Glazes Sugars
Molasses/Syrups/ Molasses & Syrups Sugars
Pancake Mixes
Peanut Butter/Jelly/ Honey Sugars
Jams & Honey
Sugars & Sweeteners Sugar Sugars
Sugars & Sweeteners Sweeteners Sugars
Aseptic Juice Aseptic Pack Juice And Sweetened Beverages
Drinks
Aseptic Juice Aseptic Pack Juice And Sweetened Beverages
Drinks
Authentic Hispanic Fds Hispanic Carbonated Sweetened Beverages
& Product Beverages
Authentic Hispanic Fds Hispanic Juice Under 50% Sweetened Beverages
& Product Juice
Beverages Can/Btl Carb Beve 50% And Sweetened Beverages
Unde
Cocoa Mixes Malted Mlk/Syrup/Pwdrs Sweetened Beverages
(Eggnog)
Cocoa Mixes Hot Chocolate/Cocoa Mix Sweetened Beverages
Energy Drinks Energy Drink--Single Sweetened Beverages
Serve
Energy Drinks Energy Drink--Single Sweetened Beverages
Serve (N)
Energy Drinks Energy Drink--Multi-Pack Sweetened Beverages
Energy Drinks Energy Drink--Multi-Pack Sweetened Beverages
(Non)
Frozen Juice And Frzn Fruit Drinks (Under Sweetened Beverages
Smoothies 10% J)
Frozen Juice And Frzn Conc Under 50% Juice Sweetened Beverages
Smoothies
Frozen Juice And Smoothies-Frz Sweetened Beverages
Smoothies
Frozen Juice And Cocktail Mixes-Frz Sweetened Beverages
Smoothies
Isotonic Drinks Isotonic Drinks Single Sweetened Beverages
Serve
Isotonic Drinks Isotonic Drinks Multi- Sweetened Beverages
Pack
Isotonic Drinks Isotonic Drinks Multi- Sweetened Beverages
Serve
Isotonic Drinks Sports Drink N/Supplmnt Sweetened Beverages
Milk/M
Juice Non-Carb Jce (Under 50% Sweetened Beverages
Jce)
Juices Super Premium Juices Smoothies/Blended Sweetened Beverages
Juices Super Premium Juices Antioxidant/ Sweetened Beverages
Wellness
Juices Super Premium Juices (50% And Under Sweetened Beverages
Juice)
Mixers Cocktail Mixes--Fluid: Add Liq Sweetened Beverages
Powder & Crystal Drink Unsweetened Envelope Sweetened Beverages
Mix [Powder Drink Mix]
Powder & Crystal Drink Sugar Free Canister Sweetened Beverages
Mix [Powder Drink Mix]
Powder & Crystal Drink Sugar Free Sticks [Powder Sweetened Beverages
Mix Drink Mix]
Powder & Crystal Drink Soft Drink Canisters Sweetened Beverages
Mix
Powder & Crystal Drink Enhanced Stick [Powder Sweetened Beverages
Mix Drink Mix]
Powder & Crystal Drink Sugar Sweetened Sticks Sweetened Beverages
Mix
Powder & Crystal Drink Fluid Pouch [Powder Drink Sweetened Beverages
Mix Mix]
Powder & Crystal Drink Breakfast Crystals Sweetened Beverages
Mix
Processed Packaged Dry Mixes Sweetened Beverages
Refrgratd Juices/ Dairy Case Juice Drnk Sweetened Beverages
Drinks Under 10
Refrgratd Juices/ Dairy Case Citrus Pnch/Oj Sweetened Beverages
Drinks Subs
Refrgratd Juices/ Dairy Case Tea With Sugar Sweetened Beverages
Drinks Or S
Refrgratd Juices/ Dairy Case Fruit Drinks Sweetened Beverages
Drinks (No Ju)
Rtd Tea/New Age Juice Tea Sweetened Sweetened Beverages
Rtd Tea/New Age Juice Juice (Under 10% Juice) Sweetened Beverages
Shelf Stable Juice Fruit Drinks: Canned & Sweetened Beverages
Glass
Shelf Stable Juice Cranapple/Cran Grape Sweetened Beverages
Juice (50)
Shelf Stable Juice Cranberry Juice (50% And Sweetened Beverages
Under)
Shelf Stable Juice Blended Juice & Sweetened Beverages
Combinations (50)
Shelf Stable Juice Fruit Drinks: Canned & Sweetened Beverages
Glass
Shelf Stable Juice Apple Juice & Cider (50% Sweetened Beverages
And U)
Shelf Stable Juice Tomato Juice (50% And Sweetened Beverages
Under)
Shelf Stable Juice Blended Juice & Sweetened Beverages
Combinations (Un)
Shelf Stable Juice Fruit Drinks: Canned & Sweetened Beverages
Glass
Soft Drinks Soft Drinks 12/18 & 15pk Sweetened Beverages
Can Car
Soft Drinks Sft Drnk 2 Liter Btl Carb Sweetened Beverages
Incl
Soft Drinks Soft Drinks 20pk & 24pk Sweetened Beverages
Can Carb
Soft Drinks Sft Drnk Mlt-Pk Btl Carb Sweetened Beverages
(Excp)
Soft Drinks Sft Drnk Sngl Srv Btl Sweetened Beverages
Carb (Ex)
Soft Drinks Soft Drinks Can Non-Carb Sweetened Beverages
(Exce)
Soft Drinks Soft Drinks 6pk Can Carb Sweetened Beverages
(Exp)
Soft Drinks Sft Drnk 1 Liter Btl Carb Sweetened Beverages
(Exc)
Soft Drinks Tea Can With Sweetener/ Sweetened Beverages
Sugar
Soft Drinks Soft Drink Bottle Non- Sweetened Beverages
Carb (Ex)
Soft Drinks Tea Bottles With Sweetened Beverages
Sweetener/Sug
Soft Drinks Mixers (Tonic Water/Gngr Sweetened Beverages
Ale)
Soft Drinks Seltzer Unflavored Sweetened Beverages
Teas Instant Tea & Tea Mix (W/ Sweetened Beverages
Sugar)
Water Non-Carb Water Flvr--Drnk/ Sweetened Beverages
Mnr
Water--(Sparkling & Still Water Flvrd Drnk/ Sweetened Beverages
Still) Mnrl Wt
Water--(Sparkling & Sparkling Water--Flvrd Sweetened Beverages
Still) Sweet
Authentic Hispanic Fds Authentic Vegetables And Vegetables
& Product Foods
Authentic Hispanic Fds Authentic Peppers Vegetables
& Product
Authentic Italian Italian Vegetables Vegetables
Foods
Broccoli/Cauliflower Broccoli Whole & Crowns Vegetables
Broccoli/Cauliflower Cauliflower Whole Vegetables
Can Vegetables--Shelf Green Beans: Fs/Whl/Cut Vegetables
Stable
Can Vegetables--Shelf Corn Vegetables
Stable
Can Vegetables--Shelf Peas/Green Vegetables
Stable
Can Vegetables--Shelf Spinach & Greens Vegetables
Stable
Can Vegetables--Shelf Mushrooms Cnd & Glass Vegetables
Stable
Can Vegetables--Shelf Sweet Potatoes Vegetables
Stable
Can Vegetables--Shelf Mixed Vegetables Vegetables
Stable
Can Vegetables--Shelf Carrots Vegetables
Stable
Can Vegetables--Shelf White Potatoes Vegetables
Stable
Can Vegetables--Shelf Kraut & Cabbage Vegetables
Stable
Can Vegetables--Shelf Beets Vegetables
Stable
Can Vegetables--Shelf Peas Fresh Pack/Crowder Vegetables
Stable
Can Vegetables--Shelf Artichokes Vegetables
Stable
Carrots Carrots Mini Peeled Vegetables
Carrots Carrots Bagged Vegetables
Carrots Carrots Bagged Organic Vegetables
Corn Corn Bulk Vegetables
Corn Corn Is Packaged Vegetables
Dry Sce/Gravy/Potatoes/ Potatoes: Dry Vegetables
Stuffng
Frozen Potatoes Frzn French Fries Vegetables
Frozen Potatoes Frzn Tater Tots/Other Vegetables
Extruded
Frozen Potatoes Frzn Hashbrown Potatoes Vegetables
Frozen Potatoes Frzn Baked/Stuffed/Mashed Vegetables
& Spec
Frozen Potatoes Frzn Onion Rings Vegetables
Frozen Vegetable & Veg Fz Bag Vegetables--Plain Vegetables
Dish
Frozen Vegetable & Veg Frzn Steamable Vegetables Vegetables
Dish
Frozen Vegetable & Veg Fz Box Vegetables--Value- Vegetables
Dish Added
Frozen Vegetable & Veg Frzn Corn On The Cob Vegetables
Dish
Frozen Vegetable & Veg Fz Bag Vegetables--Value- Vegetables
Dish Added
Frozen Vegetable & Veg Fz Box Vegetables--Plain Vegetables
Dish
Herbs/Garlic Garlic Whole Cloves Vegetables
Herbs/Garlic Sprouts Vegetables
Mushrooms Mushrooms White Sliced Vegetables
Pkg
Mushrooms Mushrooms White Whole Pkg Vegetables
Mushrooms Mushrooms Portabella Vegetables
Mushrooms Mushrooms White Bulk Vegetables
Onions Onions Yellow (Bulk & Vegetables
Bag)
Onions Onions Sweet (Bulk & Bag) Vegetables
Onions Onions Red (Bulk & Bag) Vegetables
Onions Onions White (Bulk & Bag) Vegetables
Organics Fruit & Organic Salad Mix Vegetables
Vegetables
Organics Fruit & Organic Value-Added Vegetables
Vegetables Vegetables
Party Tray Deli Tray: Fruit And Vegetables
Vegetable
Pasta & Pizza Sauce Mainstream [Pasta & Pizza Vegetables
Sauce]
Pasta & Pizza Sauce Value [Pasta & Pizza Vegetables
Sauce]
Pasta & Pizza Sauce Pizza Sauce Vegetables
Peppers Peppers Green Bell Vegetables
Peppers Peppers Red Bell Vegetables
Peppers Peppers Other Bell Vegetables
Peppers Peppers Yellow Bell Vegetables
Peppers Peppers Jalapeno Vegetables
Peppers Peppers All Other Vegetables
Potatoes Potatoes Russet (Bulk & Vegetables
Bag)
Potatoes Potatoes Sweet & Yams Vegetables
Potatoes Potatoes Red (Bulk & Bag) Vegetables
Potatoes Potatoes Gourmet Vegetables
Potatoes Potatoes Gold (Bulk & Vegetables
Bag)
Potatoes Potatoes Other Organic Vegetables
Salad & Dips Vegetable Salads--Prepack Vegetables
Salad & Dips Vegetable Salads--Bulk Vegetables
Salad & Dips Salad: Lettuce Vegetables
Salad & Dips Salad Bar Vegetables
Salad Bar Salad Bar Other Vegetables
Salad Mix Blends [Salad Mix] Vegetables
Salad Mix Regular Garden Vegetables
Salad Mix Garden Plus [Salad Mix] Vegetables
Salad Mix Kits [Salad Mix] Vegetables
Salad Mix Shredded Lettuce Vegetables
Salad Mix Salad Bowls Vegetables
Salad Mix Salad Mix Blends Organic Vegetables
Salad Mix Salad Spinach Vegetables
Salad Mix Coleslaw Vegetables
Salad Mix Salad Spinach Organic Vegetables
Seasonal Pumpkins Vegetables
Spices/Jarred Garlic Garlic Jar Vegetables
Tomato Products--Shelf Tomatoes Diced Vegetables
Stable
Tomato Products--Shelf Tomato Sauce Vegetables
Stable
Tomato Products--Shelf Tomato Paste Vegetables
Stable
Tomato Products--Shelf Tomato Stewed Vegetables
Stable
Tomato Products--Shelf Tomatoes/Whole Vegetables
Stable
Tomato Products--Shelf Tomato Crushed Vegetables
Stable
Tomatoes Tomatoes Hothouse On The Vegetables
Vine
Tomatoes Roma Tomatoes (Bulk/Pkg) Vegetables
Tomatoes Tomatoes Vine Ripe Bulk Vegetables
Tomatoes Tomatoes Hot House Bulk Vegetables
Tomatoes Tomatoes Grape Vegetables
Tomatoes Tomatoes Vine Ripe Pkg Vegetables
Tomatoes Tomatoes Cherry Vegetables
Tomatoes Tomatoes--Other Vegetables
Tomatoes Tomatoes Others Organic Vegetables
Tomatoes Tomatoes Cocktail Vegetables
Traditional Asian Asian Vegetables Vegetables
Foods
Traditional Mexican Mexican Peppers Chilies Vegetables
Foods
Value-Added Vegetables Vegetable Party Tray Vegetables
Value-Added Vegetables Cut Vegetables All Other Vegetables
Value-Added Vegetables Instore Cut Vegetables Vegetables
Vegetables Cooking Celery Vegetables
Bulk
Vegetables Cooking Cabbage Vegetables
Bulk
Vegetables Cooking Asparagus Vegetables
Bulk
Vegetables Cooking Celery Organic Vegetables
Bulk
Vegetables Cooking Broccoli/Cauliflower Vegetables
Packaged Processed
Vegetables Cooking Vegetables Cooking Vegetables
Packaged Packaged
Vegetables Salad Head Lettuce Vegetables
Vegetables Salad Cucumbers Vegetables
Vegetables Salad Variety Lettuce Vegetables
Vegetables Salad Green Onions Vegetables
Vegetables Salad Radish Vegetables
Vegetables Salad Variety Lettuce Organic Vegetables
Vegetables Salad Spinach Bulk Vegetables
------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ
International, LLC, 2016.
Appendix C. Crosswalk of Subcommodities to USDA Food Pattern Categories
----------------------------------------------------------------------------------------------------------------
USDA Food SoFAS Composite Other
Commodity Subcommodity Pattern Subcategories Subcategories Subcategories
----------------------------------------------------------------------------------------------------------------
Aseptic Juice Kids Milk Drinks-- Dairy
Aseptic
Bag Snacks Bagged Cheese Dairy
Snacks
Bulk Service Bulk Processed Dairy
Case Cheese [Cheese]
Bulk Service Bulk Semi-Hard Dairy
Case Cheese [Cheese]
Bulk Service Bulk Semi-Soft Dairy
Case Cheese [Cheese]
Bulk Service Cheese: Dairy
Case Cheese Cheeseballs/
Spreads
Bulk Service Cheese: Specialty Dairy
Case Cheese Bulk
Bulk Service Cheese: Specialty Dairy
Case Cheese Prepack
Bulk Service Service Case Dairy
Case Cheese Natural [Cheese]
Bulk Service Service Case Dairy
Case Cheese Natural
Prepackage
[Cheese]
Bulk Service Service Case Dairy
Case Cheese Processed Prepack
[Cheese]
Canned & Dry Aseptic Milk & Dairy
Milk Milk Drinks
Canned & Dry Canned Milk Dairy
Milk
Canned & Dry Non Fat Dry Milk Dairy
Milk
Cheese American Single Dairy
Cheese
Cheese Miscellaneous Dairy
Cheese
Cheese Natural Cheese Dairy
Chunks
Cheese Natural Cheese Dairy
Random Wt
Cheese Natural Cheese Dairy
Slices
Cheese Shredded Cheese Dairy
Cheese String Cheese Dairy
Crackers & Misc Aerosol Cheese Dairy
Baked Food
Cubes/ Cubes Cheese Dairy
Prepackage
Cheese
Cubes/ Prepackage Cheese Dairy
Prepackage
Cheese
Dry Cheese Grated Cheese Dairy
Dry Cheese Loaf Cheese Dairy
Dry Cheese Misc Dry Cheese Dairy
Fluid Milk Buttermilk Dairy
Products
Fluid Milk Egg Nog/Boiled Dairy
Products Custard
Fluid Milk Flavored Milk Dairy
Products
Fluid Milk Fluid Milk/White Dairy
Products Only
Fluid Milk Half & Half Dairy
Products
Fluid Milk Organic Milk Dairy
Products
Fluid Milk Soy Milk Dairy
Products
Fluid Milk Specialty/Lactose Dairy
Products Free Milk
Milk By- Cottage Cheese Dairy
Products
Milk By- Ricotta Cheese Dairy
Products
Non-Dairy/Dairy Aseptic Milk Dairy
Aseptic
Non-Dairy/Dairy Soy Beverage Dairy
Aseptic
Pre-Slice Pre-Sliced Dairy
Service Case Processed
Cheese [Cheese]
Pre-Slice Pre-Sliced Semi- Dairy
Service Case Hard [Cheese]
Cheese
Pre-Slice Pre-Sliced Semi- Dairy
Service Case Soft [Cheese]
Cheese
Refrigerated Cheese Spreads Dairy
Dairy Case
Refrigerated Dairy Cheese Dairy
Dairy Case
Refrigerated Fluid Milk Dairy
Dairy Case
Refrigerated Kefir Dairy
Dairy Case
Refrigerated Yogurt Dairy
Dairy Case
Refrigerated Hispanic Cheese Dairy
Hispanic
Grocery
Service Sv Bev: Milk/Milk Dairy
Beverage Products
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Blue
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Cheddar
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Cheeseba
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Feta
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Fresh
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Gift Pac
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Goat
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Gouda &
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Hard
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Hispanic
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Mozzarel
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Semi-Sof
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Smallwar
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Soft Rip
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Spreads
Specialty Bulk Specialty Bulk Dairy
Cheese Cheese Swiss
Specialty Ppk Cheese Shoppe Dairy
Cheese Pre
Pack
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Blue/Gorg
Pack
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Cheddar &
Pack C
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Feta
Pack
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Fresh
Pack
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Gift Pack
Pack
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Goat Milk
Pack
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Gouda &
Pack Eda
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Hard/Grat
Pack
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Hispanic
Pack
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Mozzarell
Pack
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Processed
Pack
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Semi Soft
Pack
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Soft &
Pack Ripe
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Spreads
Pack
Specialty Specialty Ppk Dairy
Cheese Pre Cheese Swiss
Pack
Specialty Specialty Ppk Dairy
Cheese Pre Cheese: Smallwar
Pack
Traditional Mexican Con Queso Dairy
Mexican Foods
Yogurt Yogurt/Adult Dairy
Drinks
Yogurt Yogurt/Adult Multi- Dairy
Packs
Yogurt Yogurt/Kids Dairy
Yogurt Yogurt/Large Size Dairy
(16oz Or Lar)
Yogurt Yogurt/Pro Active Dairy
Health
Yogurt Yogurt/Specialty Dairy
Greek
Yogurt Yogurt/Ss Light Dairy
Yogurt Yogurt/Ss Regular Dairy
Apples Apples Braeburn Fruit
(Bulk & Bag)
Apples Apples Braeburn Fruit
(Bulk & Bag) Org
Apples Apples Fuji (Bulk Fruit
& Bag)
Apples Apples Fuji (Bulk Fruit
& Bag) Organic
Apples Apples Gala (Bulk Fruit
& Bag)
Apples Apples Gala (Bulk Fruit
& Bag) Organic
Apples Apples Gold Fruit
Delicious (Bulk &
Bag)
Apples Apples Gold Fruit
Delicious (Bulk &
Bag)
Apples Apples Granny Fruit
Smith (Bulk &
Bag)
Apples Apples Granny Fruit
Smith (Bulk &
Bag)
Apples Apples Honeycrisp Fruit
Apples Apples Honeycrisp Fruit
Organic
Apples Apples Other (Bulk Fruit
& Bag)
Apples Apples Other (Bulk Fruit
& Bag) Organic
Apples Apples Red Fruit
Delicious (Bulk &
Bag)
Apples Apples Red Fruit
Delicious (Bulk &
Bag)
Apples Caramel/Candy Fruit
Apples
Apples Mixed Fruit Bags Fruit
Authentic Hispanic Juices Fruit
Hispanic Foods Over 50% Juice
& Products
Baking Needs Maraschino Fruit
Cherries
Bananas Bananas Fruit
Bananas Bananas Organic Fruit
Bananas Bananas: Variety Fruit
Berries Berries Other Fruit
Berries Berries Other Fruit
Organic
Berries Blackberries Fruit
Berries Blackberries Fruit
Organic
Berries Blueberries Fruit
Berries Blueberries Fruit
Organic
Berries Cranberries Fruit
Berries Cranberries Fruit
Organic
Berries Raspberries Fruit
Berries Raspberries Fruit
Organic
Berries Strawberries Fruit
Berries Strawberries Fruit
Organic
Beverages Can/Btl Beverage Fruit
Over 50% Juice
Bulk Food Fruit Bulk Fruit
Bulk Food Fruit W/Sweetener Fruit
Can Fruit/Jar Apple Sauce Fruit
Applesauce (Excludes Cup)
Can Fruit/Jar Apples/Crabapples Fruit
Applesauce
Can Fruit/Jar Berries/Cnd (Blu/ Fruit
Applesauce Blk/Rasp)
Can Fruit/Jar Cherries (Except Fruit
Applesauce Maraschino)
Can Fruit/Jar Cranberry Sauce Fruit
Applesauce
Can Fruit/Jar Fruit Cocktail/ Fruit
Applesauce Fruit Salad
Can Fruit/Jar Mandarin Oranges/ Fruit
Applesauce Citrus Sect
Can Fruit/Jar Misc. Cnd Fruit Fruit
Applesauce (Grapes/Figs)
Can Fruit/Jar Peaches Fruit
Applesauce
Can Fruit/Jar Pears Fruit
Applesauce
Can Fruit/Jar Pineapple Fruit
Applesauce
Citrus Citrus--Other Fruit
Citrus Citrus Other Fruit
Organic
Citrus Clementines Fruit
Citrus Clementines Fruit
Organic
Citrus Grapefruit Fruit
Citrus Grapefruit Organic Fruit
Citrus Lemons Fruit
Citrus Lemons Organic Fruit
Citrus Limes Fruit
Citrus Limes Organic Fruit
Citrus Oranges Navels All Fruit
Citrus Oranges Navels All Fruit
Organic
Citrus Oranges Non Navel Fruit
All
Citrus Oranges Non Navel Fruit
All Organic
Citrus Tangerines & Fruit
Tangelos
Citrus Tangerines & Fruit
Tangelos Organic
Coffee Shop Sv Bev: Bev/Juice Fruit
50-100% Jce
Coffee Shop Sv Bev: Bev/Juice Fruit
50-100% Jce
Convenience/ Convenience/ Fruit
Snacking Snacking Fruit
Pro
Convenience/ Jarred Fruit Multi Fruit
Snacking Serve
Convenience/ Jarred Fruit Fruit
Snacking Single Serve
Convenience/ Squeeze Fruits Fruit
Snacking
Dried Fruit Dates Fruit
Dried Fruit Dried Fruit--Other Fruit
Dried Fruit Dried Fruit Fruit
Cranberries
Dried Fruit Dried Fruit Other Fruit
Organic
Dried Fruit Dried Fruit W/ Fruit
Sweetener
Dried Fruit Dried Plums Fruit
Dried Fruit Glace Fruit Fruit
Dried Fruit Raisins Fruit
Frozen Juice Over 50% Fruit
Breakfast Juice
Frozen Fruits Frozen Fruit Fruit
Frozen Juice Frzn Conc Allieds Fruit
And Smoothies Over 50% Juice
Frozen Juice Frozen Oj & Oj Fruit
And Smoothies Substitutes
Fruit Snacks Fruit Snacks Fruit
Gift & Fruit Fruit Baskets Fruit
Baskets
Gift & Fruit In Store Made Fruit
Baskets Fruit Baskets
Gift & Fruit Ready To Sell Fruit
Baskets Fruit Baskets
Grapes Grapes Black/Blue Fruit
Grapes Grapes Black/Blue Fruit
Organic
Grapes Grapes Other Fruit
Grapes Grapes Other Fruit
Organic
Grapes Grapes Red Fruit
Grapes Grapes Red Globe Fruit
Grapes Grapes Red Globe Fruit
Organic
Grapes Grapes Red Organic Fruit
Grapes Grapes White Fruit
Grapes Grapes White Fruit
Organic
Grapes Grapes Wine Fruit
Juice Drinks--Carb Juice Fruit
(Over 50% Juice)
Juice Non-Carb Jce(Over Fruit
50% Juice)
Juices Super Cider Fruit
Premium
Juices Super Juice Single Blend Fruit
Premium
Juices Super Juices Organic Fruit
Premium (Over 50% Juice)
Melons Cantaloupe Whole Fruit
Melons Cantaloupe Whole Fruit
Organic
Melons Honeydew Whole Fruit
Melons Honeydew Whole Fruit
Organic
Melons Melons Whole Other Fruit
Melons Melons Whole Other Fruit
Organic
Melons Watermelon Fruit
Personal
Melons Watermelon Fruit
Personal Organic
Melons Watermelon Fruit
Seedless Whole
Melons Watermelon Fruit
Seedless Whole
Organic
Melons Watermelon W/Seeds Fruit
Whole
Packaged Dried Fruit Fruit
Natural Snacks
Packaged Dried Fruit W/ Fruit
Natural Snacks Sweetener
Peanut Butter/ Apple Butter/Fruit Fruit
Jelly/Jams & Butter
Honey
Pears Pears Anjou Fruit
Pears Pears Anjou Fruit
Organic
Pears Pears Asian Fruit
Pears Pears Asian Fruit
Organic
Pears Pears Bartlett Fruit
Pears Pears Bartlett Fruit
Organic
Pears Pears Bosc Fruit
Pears Pears Bosc Organic Fruit
Pears Pears Other Fruit
Pears Pears Other Fruit
Organic
Pears Pears Red Fruit
Prepared/Pdgd Apple Sauce/ Fruit
Foods Pudding
Prepared/Pdgd Canned Fruit Fruit
Foods
Processed Jarred Fruit Fruit
Processed Juice Fruit
Processed Squeeze Lemons/ Fruit
Limes
Refrgratd Dairy Case 100% Fruit
Juices/Drinks Pure Juice--
Orange
Refrgratd Dairy Case 100% Fruit
Juices/Drinks Pure Juice Other
Refrigerated Nut Refrig Juice Fruit
Dairy Case Over 50%
Rtd Tea/New Age Juice (100% Juice) Fruit
Juice
Rtd Tea/New Age Juice (Over 50% Fruit
Juice Juice)
Salad Bar Salad Bar Fresh Fruit
Fruit
Seasonal Fruit Baskets Fruit
Service Sv Bev: Bev/Juice Fruit
Beverage 50-100% Juice
Shelf Stable Apple Juice & Fruit
Juice Cider (Over 50%
Juice)
Shelf Stable Blended Juice & Fruit
Juice Combinations
Shelf Stable Cranapple/Cran Fruit
Juice Grape Juice
Shelf Stable Cranapple/Cran Fruit
Juice Grape Juice
Shelf Stable Cranberry Juice Fruit
Juice (Over 50% Juice)
Shelf Stable Grape Juice (Over Fruit
Juice 50% Juice)
Shelf Stable Grapefruit Juice Fruit
Juice (Over 50% Juice)
Shelf Stable Lemon Juice & Lime Fruit
Juice Juice (Over 50%
Juice)
Shelf Stable Nectars (Over 50% Fruit
Juice Juice)
Shelf Stable Orange Juice (Over Fruit
Juice 50% Juice)
Shelf Stable Other Citrus Fruit
Juice Juices (50% And
Under Juice)
Shelf Stable Other Citrus Fruit
Juice Juices (Over 50%
Juice)
Shelf Stable Pineapple Juice Fruit
Juice (Over 50% Juice)
Shelf Stable Prune Juice (Over Fruit
Juice 50% Juice)
Single Serve Applesauce Cup Fruit
Fruit/
Applesauce
Single Serve Applesauce Pouch Fruit
Fruit/
Applesauce
Single Serve Fruit Cup Fruit
Fruit/
Applesauce
Stone Fruit Apricots Fruit
Stone Fruit Cherries Ranier Fruit
Stone Fruit Cherries Red Fruit
Stone Fruit Cherries Red Fruit
Organic
Stone Fruit Nectarines White Fruit
Flesh
Stone Fruit Nectarines Yellow Fruit
Flesh
Stone Fruit Nectarines Yellow Fruit
Flesh Organic
Stone Fruit Peaches White Fruit
Flesh
Stone Fruit Peaches White Fruit
Flesh Organic
Stone Fruit Peaches Yellow Fruit
Flesh
Stone Fruit Peaches Yellow Fruit
Flesh Organic
Stone Fruit Plums Fruit
Stone Fruit Plums Organic Fruit
Stone Fruit Pluots Fruit
Stone Fruit Stone Fruit Other Fruit
Organic
Tropical Fruit Kiwi Fruit Fruit
Tropical Fruit Kiwi Fruit Organic Fruit
Tropical Fruit Mango Fruit
Tropical Fruit Mango Organic Fruit
Tropical Fruit Papaya Fruit
Tropical Fruit Pineapple Whole & Fruit
Peel/Cored
Tropical Fruit Pineapple Whole & Fruit
Peel/Cored
Organic
Tropical Fruit Pomegranates Fruit
Tropical Fruit Pomegranates Fruit
Organic
Tropical Fruit Tropical Fruit-- Fruit
Other
Tropical Fruit Tropical Fruit Fruit
Other Organic
Unknown Frozen Fruit Fruit
Value-Added Cut Fruit All Fruit
Fruit Other Prepack
Value-Added Fruit Party Tray Fruit
Fruit Prepack
Value-Added Instore Cut Fruit Fruit
Fruit
Value-Added Melon Halves/ Fruit
Fruit Quarters Prepack
Value-Added Melons Instore Cut Fruit
Fruit
Value-Added Pineapple Wedge/ Fruit
Fruit Sliced/Chunks
Value-Added Value-Added Fruit Fruit
Fruit Organic
Authentic Hispanic Tostados Grains
Hispanic Fds & & Tortillas
Product
Bag Snacks Bagged Popped Grains
Popcorn
Bag Snacks Bagged Popped Grains
Popcorn W/
Sweetener
Bag Snacks Corn Chips Grains
Bag Snacks Pretzel W/Sweetner Grains
Bag Snacks Pretzels Grains
Bag Snacks Tortilla/Nacho Grains
Chips
Bagels & Cream Refrigerated Grains
Cheese Bagels
Baked Breads Bagels Grains
Baked Breads Diet/Light Bread Grains
Baked Breads Dinner Rolls Grains
Baked Breads English Muffins/ Grains
Waffles
Baked Breads Fruit/Breakfast Grains
Bread
Baked Breads Hamburger Buns Grains
Baked Breads Hot Dog Buns Grains
Baked Breads Main Meal Bread Grains
Baked Breads Mainstream Variety Grains
Breads
Baked Breads Mainstream White Grains
Bread
Baked Breads Pita/Tortillas Grains
Baked Breads Premium Bread Grains
Baked Breads Rye Breads Grains
Baked Breads Sandwich Buns Grains
Bakery Party Trays Party Trays: Grains
Rolls
Baking Mixes Biscuit Flour & Grains
Mixes
Baking Mixes Muffin & Corn Grains
Bread Mix
Baking Needs Corn Starch Grains
Bread All Other Bread Grains
Bread Bread--Ingredients Grains
Bread Bread Snacks Grains
Bread Bread: Diet/ Grains
Organic
Bread Bread: Kosher Grains
Bread Bread: Artisan Grains
Bread Bread: Italian/ Grains
French
Bread Bread: Pita/Pocket/ Grains
Flatbrd
Bread Bread: Retail Grains
Seasonings
Bread Bread: Rye/ Grains
Cocktail
Bread Bread: Sourdough Grains
Bread Bread: Specialty Grains
Bread Bread: Sweet/ Grains
Breakfast
Bread Bread: Brand Grains
Bread Bread: Tortillas/ Grains
Wraps
Bread Bread: Wheat/Whl Grains
Grain
Bread Bread: White Loaf Grains
Bread Gluten Free Grains
Bread Whole Grain Bread Grains
Bulk Food Cereal Bulk Grains
Cereal Bars Breakfast Bars/ Grains
Tarts/Scones
Cereals Cereal--Cold Grains
Cereals Cereal--Hot Grains
Cereals Grains Grains
Cereals Granola Grains
Cnv Breakfast & Cereal Bars Grains
Wholesome Snks
Cnv Breakfast & Granola Bars Grains
Wholesome Snks
Cnv Breakfast & Toaster Pastries Grains
Wholesome Snks
Coffee Shop Coffee Shop: Grains
Sweet Goods & Bagged Snacks
Rtl
Cold Cereal Adult Cereal Grains
Cold Cereal All Family Cereal Grains
Cold Cereal Kids Cereal Grains
Cold Cereal Misc. Cereal Grains
Cookie/Cracker Multi-Pack Grains
Multi-Pks Crackers
Cookies Graham Crackers Grains
Crackers Crackers Grains
Crackers & Misc Butter Spray Grains
Baked Food Cracker
Crackers & Misc Cheese Crackers Grains
Baked Food
Crackers & Misc Saltine/Oyster Grains
Baked Food
Crackers & Misc Snack Crackers Grains
Baked Food
Crackers & Misc Specialty Crackers Grains
Baked Food
Croutons/Bread Bread Sticks Grains
Stick & Salad
Toppings
Croutons/Bread Croutons Grains
Stick & Salad
Toppings
Croutons/Bread Salad Toppers Grains
Stick & Salad
Toppings
Deli Dl Spec: Dry/ Grains
Specialties Refrig Pastas
(Retail Pk)
Dietary Aid Diabetic Dry Grains
Prdct/Med Liq Cereal
Nutr
Dinner Mixes-- Pizza Mix Dry Grains
Dry
Dry Bean Veg & Misc Grain Mixes Grains
Rice
Dry Bean Veg & Noodle Side Dish Grains
Rice Mixes
Dry Bean Veg & Rice--Dry Bag And Grains
Rice Box
Dry Bean Veg & Rice--Instant & Grains
Rice Microwave
Dry Bean Veg & Rice Side Dish Grains
Rice Mixes Dry
Dry Noodles & Long Cut Pasta Grains
Pasta
Dry Noodles & Noodles Dry Grains
Pasta
Dry Noodles & Short Cut Pasta Grains
Pasta
Dry Noodles & Specialty Pasta Grains
Pasta
Dry Sauce/Gravy/ Stuffing Mixes Grains
Potatoes/
Stuffing
Dry/Ramen Ramen Noodles/ Grains
Bouillon Ramen Cups
Eggs/Muffins/ Refrigerated Grains
Potatoes English Muffins
Flour & Meals Breadings/Coatings/ Grains
Crumbs
Flour & Meals Cornmeal Grains
Flour & Meals Flour: Misc/ Grains
Specialty/Blend
Et
Flour & Meals Flour: White & Grains
Self Rising
Frozen Bread Allergen Free Grains
And Desserts [Frozen Bread]
Frozen Bread Breads Grains
And Desserts
Frozen Bread Muffins/Bagels Grains
And Desserts
Frozen Bread Rolls Grains
And Desserts
Frozen Bread Sprouted Breads Grains
And Desserts
Frozen Bread/ Frzn Biscuits Grains
Dough
Frozen Bread/ Frzn Bread Dough Grains
Dough
Frozen Bread/ Frzn Breadsticks Grains
Dough
Frozen Bread/ Frzn Dinner Rolls Grains
Dough
Frozen Bread/ Frzn Garlic Bread Grains
Dough
Frozen Bread/ Frzn Garlic Toast Grains
Dough
Frozen Bread/ Frzn Sweet Rolls & Grains
Dough Muffins
Frozen Pancakes/French Grains
Breakfast Toast
Frozen Waffles Grains
Breakfast
Frozen Frzn Bagels Grains
Breakfast
Foods
Frozen Frzn Breakfast Grains
Breakfast Pastry
Foods
Frozen Waffles/Pancakes/ Grains
Breakfast French Toast
Foods
Frzn Pasta Frozen Pasta Grains
Hot Cereal Grits Grains
Hot Cereal Instant Breakfast Grains
Hot Cereal Instant Oatmeal Grains
Hot Cereal Other Hot Cereal Grains
Hot Cereal Standard Oatmeal Grains
Kosher Foods Kosher Matzas Grains
And Products
Kosher Foods Kosher Noodles And Grains
And Products Rice
Meat--Shelf Tamales Grains
Stable
Molasses/Syrups/ Pancake Mixes Grains
Pancake Mixes
Multicultural Rice Bulk/Bag Grains
Products
Non-Dairy/Dairy Rice Beverage Grains
Aseptic
Pies Pies: Sugar Free Grains
Popcorn Caramel Coated Grains
Snacks
Popcorn Popcorn--Microwave Grains
Popcorn Popcorn--Other Grains
Prepared/Pdgd Grains Grains
Foods
Prepared/Pdgd Pasta/Ramen Grains
Foods
Refrgrated Misc Refrig Dough Grains
Dough Products Products
Refrgrated Refrigerated Grains
Dough Products Biscuits
Refrgrated Refrigerated Grains
Dough Products Breads
Refrgrated Refrigerated Grains
Dough Products Crescent Rolls
Refrgrated Refrigerated Grains
Dough Products Specialty Rolls
Refrigerated Refrigerated Grains
Hispanic Tortillas
Grocery
Refrigerated Refrigerated Pasta Grains
Italian
Rice Cakes Large--Rice Cakes Grains
Rice Cakes Large Cakes Grains
Rice Cakes Mini--Rice Cakes Grains
Rice Cakes Mini-Cakes Grains
Rice Cakes Other--Rice Cakes Grains
Rolls Rolls: Bagels Grains
Rolls Rolls: Bagels-- Grains
Less Than 6
Rolls Rolls: Biscuits/ Grains
Eng Muffins
Rolls Rolls: Croissants/ Grains
Breadsticks
Rolls Rolls: Diet/ Grains
Organic
Rolls Rolls: Dinner Grains
Rolls Rolls: Kosher Grains
Rolls Rolls: Sandwich Grains
Salad & Dips Salads--Bulk Grains
Pasta/Grain
Salad & Dips Salads--Prepack Grains
Pasta/Grain
Salad Toppings Croutons Organic Grains
Seafood--Salad/ Breading Grains
Dip/Sce/Cond
Snack Popcorn Grains
Snack Popcorn W/ Grains
Sweetener
Snack Tortilla Chips Grains
Snacks Snacks: Crackers/ Grains
Cookies
Snacks Snacks: Bagel Grains
Chips
Snacks Snacks: Pita Chips Grains
Snacks Snacks: Tortilla Grains
Chips
Specialty Gourmet Crackers Grains
Cheese Pre
Pack
Ss/Vending-- Vending Size/Sngl Grains
Cookie/Cracker Serve Cracke
Syrups Toppings Cones [Frozen Grains
& Cones Novelties]
Traditional Asian Noodles/Rice Grains
Asian Foods
Traditional Mexican Soft Grains
Mexican Foods Tortillas And Wra
Traditional Mexican Taco/ Grains
Mexican Foods Tostado/Shells
Unknown Frozen Bread Grains
Unknown Frozen Convenience/ Grains
Pockets
Bacon Bacon--Natural/ Protein Foods
Organic
Bacon Bacon--Other Protein Foods
Bacon Bacon--Poultry Protein Foods
Bacon Bacon--Pre-Cooked Protein Foods
Bacon Bacon--Trad 16oz Protein Foods
Or Less
Bacon Bacon--Trad Center Protein Foods
Cut
Bacon Bacon--Trad Protein Foods
Greater Than 16oz
Baking Needs Baking Nuts Protein Foods
Beef: Chuck/ Choice Beef Protein Foods
Shoulder
Beef: Chuck/ Natural Beef Protein Foods
Shoulder
Beef: Chuck/ Organic Beef Protein Foods
Shoulder
Beef: Grinds Angus [Beef] Protein Foods
Beef: Grinds Lean [Beef] Protein Foods
Beef: Grinds Natural [Beef] Protein Foods
Beef: Grinds Organic [Beef] Protein Foods
Beef: Grinds Patties [Beef] Protein Foods
Beef: Grinds Primal [Beef] Protein Foods
Beef: Grinds Sausage [Beef] Protein Foods
Beef: Loins Choice Beef Protein Foods
Beef: Loins Select Beef Protein Foods
Beef: Offal External [Beef Protein Foods
Offal]
Beef: Rib Angus Beef Protein Foods
Beef: Rib Prime Beef Protein Foods
Beef: Round Angus Beef Protein Foods
Beef: Round Choice Beef Protein Foods
Beef: Round Natural Beef Protein Foods
Beef: Round Organic Beef Protein Foods
Beef: Round Prime Beef Protein Foods
Beef: Round Select Beef Protein Foods
Beef: Thin Brisket Protein Foods
Meats
Beef: Thin Corned Beef Protein Foods
Meats
Beef: Thin Cubed Meats [Beef] Protein Foods
Meats
Beef: Thin Flank [Beef] Protein Foods
Meats
Beef: Thin Lifter Meat [Beef] Protein Foods
Meats
Beef: Thin Skirt [Beef] Protein Foods
Meats
Beef: Thin Soup/Stew Protein Foods
Meats
Breakfast Bkfst Sausage-- Protein Foods
Sausage Bkfast Side Di
Breakfast Bkfst Sausage-- Protein Foods
Sausage Fresh Links
Breakfast Bkfst Sausage-- Protein Foods
Sausage Fresh Patties
Breakfast Bkfst Sausage-- Protein Foods
Sausage Fresh Rolls
Breakfast Bkfst Sausage-- Protein Foods
Sausage Other Forms
Breakfast Bkfst Sausage-- Protein Foods
Sausage Precooked
Buffalo Chuck/Shoulder Protein Foods
[Buffalo]
Buffalo Grinds [Buffalo] Protein Foods
Buffalo Loin [Buffalo] Protein Foods
Buffalo Natural [Buffalo] Protein Foods
Buffalo Rib [Buffalo] Protein Foods
Buffalo Round/Leg Protein Foods
[Buffalo]
Buffalo Thin Meats Protein Foods
[Buffalo]
Bulk Food Nuts Bulk W/ Protein Foods
Sweetener
Bulk Food Trail Mix/Nuts Protein Foods
Bulk
Can Beans Prepared Beans-- Protein Foods
Baked W/Pork
Can Beans Variety Beans-- Protein Foods
Kidney/Pinto/E
Can Seafood-- Anchovies Protein Foods
Shelf Stable
Can Seafood-- Caviar Protein Foods
Shelf Stable
Can Seafood-- Clam Juice Protein Foods
Shelf Stable
Can Seafood-- Clams Protein Foods
Shelf Stable
Can Seafood-- Crabmeat Protein Foods
Shelf Stable
Can Seafood-- Kipper Snack Protein Foods
Shelf Stable
Can Seafood-- Mackerel Protein Foods
Shelf Stable
Can Seafood-- Misc. Cnd Seafoods Protein Foods
Shelf Stable (Crab/Etc.)
Can Seafood-- Oysters Protein Foods
Shelf Stable
Can Seafood-- Salmon Protein Foods
Shelf Stable
Can Seafood-- Sardines Protein Foods
Shelf Stable
Can Seafood-- Shrimp Protein Foods
Shelf Stable
Can Seafood-- Tuna Protein Foods
Shelf Stable
Chicken & Chix/Poultry Protein Foods
Poultry Ingredients
Chicken & Chix: Baked 8pc Protein Foods
Poultry Cut Up (Cold)
Chicken & Chix: Chicken Protein Foods
Poultry Dinners/Snacks C
Chicken & Chix: Chicken Protein Foods
Poultry Dinners/Snacks H
Chicken & Chix: Kosher Protein Foods
Poultry (Cold)
Chicken & Chix: Rotisserie Protein Foods
Poultry Cold
Chicken & Chix: Frd 8pc/Cut Protein Foods
Poultry Up (Cold)
Chicken & Chix: Value-Added Protein Foods
Poultry (Cold)
Chicken Fresh Chicken Breast Protein Foods
Boneless
Chicken Fresh Chicken Drums Protein Foods
Chicken Fresh Chicken Legs/ Protein Foods
Quarters
Chicken Fresh Chicken Thighs Protein Foods
Chicken Fresh Chicken Wings Protein Foods
Chicken Fresh Mixed Packs Protein Foods
Chicken Fresh Whole Chicken Protein Foods
(Roasters/Fryer)
Chicken Frozen Chicken--Frz Iqf-- Protein Foods
Raw
Chicken Frozen Frzn Chicken--Drk Protein Foods
Meat
Chicken Frozen Frzn Chicken--Wht Protein Foods
Meat
Chicken Frozen Frzn Chicken-- Protein Foods
Wings
Chicken Frozen Whole/Cutup Protein Foods
[Chicken]
Chicken Frozen Chicken Breast Protein Foods
(Rw) Bone In
Chicken Frozen Chicken Breast Protein Foods
(Rw) Boneless
Chicken Frozen Chicken Drums Protein Foods
(Rw)
Chicken Frozen Chicken Legs/ Protein Foods
(Rw) Quarters
Chicken Frozen Chicken Thighs Protein Foods
(Rw)
Chicken Frozen Chicken Wings Protein Foods
(Rw)
Chicken Frozen Whole Chicken Protein Foods
(Rw) (Roasters/Fryer)
Chicken Grinds Ground Chicken Protein Foods
Chicken Offal External [Chicken Protein Foods
Offal]
Chicken Offal Internal [Chicken Protein Foods
Offal]
Chicken Organic Chicken Breast Protein Foods
Bone In
Chicken Smoked Chicken Breast Protein Foods
Bone In
Chicken Chicken Breast Protein Foods
Specialty/ Bone In
Natural
Chicken Chicken Breast Protein Foods
Specialty/ Boneless
Natural
Chicken Chicken Drums Protein Foods
Specialty/
Natural
Chicken Chicken Legs/ Protein Foods
Specialty/ Quarters
Natural
Chicken Chicken Thighs Protein Foods
Specialty/
Natural
Chicken Chicken Wings Protein Foods
Specialty/
Natural
Chicken Mixed Packs Protein Foods
Specialty/ [Chicken]
Natural
Chicken Whole Chicken Protein Foods
Specialty/ (Roasters/Fryer)
Natural
Condiments Nut Butters/Peanut Protein Foods
Butter
Deli Meat: Bulk Bologna/Loaves/ Protein Foods
Franks
Deli Meat: Bulk Meat Bulk: Protein Foods
Specialty Dry
Meats
Deli Meat: Bulk Meat: Bacon Protein Foods
Deli Meat: Bulk Meat: Beef Bulk Protein Foods
Deli Meat: Bulk Meat: Chicken Bulk Protein Foods
Deli Meat: Bulk Meat: Gift Pack Protein Foods
Deli Meat: Bulk Meat: Ham Ppk/ Protein Foods
Prslc
Deli Meat: Bulk Meat: Pates/Mousse Protein Foods
Deli Meat: Bulk Meat: Saus Dry Ppk/ Protein Foods
Prslc
Deli Meat: Bulk Meat: Turkey Bulk Protein Foods
Deli Meat: Bulk Meat:Ham Bulk Protein Foods
Deli Meat: Bulk Meat: Lnchmt Ppk/ Protein Foods
Prslc
Deli Meat: Deli Meat: Bacon Protein Foods
Other
Deli Meat: Deli Meat: Kosher Protein Foods
Other
Deli Meat: Deli Meat: Pates/ Protein Foods
Other Mousse
Deli Meat: Deli Meat: Protein Foods
Other Shippers/Gift
Packs
Deli Meat: Deli Meat: Beef Protein Foods
Presliced
Deli Meat: Deli Meat: Bologna/ Protein Foods
Presliced Loaves/Fran
Deli Meat: Deli Meat: Chicken Protein Foods
Presliced
Deli Meat: Deli Meat: Ham Protein Foods
Presliced
Deli Meat: Deli Meat: Semi- Protein Foods
Presliced Dry Sausage
Deli Meat: Deli Meat: Protein Foods
Presliced Specialty Dry
Meats
Deli Meat: Deli Meat: Turkey Protein Foods
Presliced
Dinner Sausage Dnr Sausage--Beef Protein Foods
Rope Ckd/Sm
Dinner Sausage Dnr Sausage-- Protein Foods
Cocktails
Dinner Sausage Dnr Sausage--Fresh Protein Foods
Poultry
Dinner Sausage Dnr Sausage--Links Protein Foods
Beef Ckd
Dinner Sausage Dnr Sausage--Links Protein Foods
Fresh
Dinner Sausage Dnr Sausage--Links Protein Foods
Pork Ckd
Dinner Sausage Dnr Sausage--Links Protein Foods
Poultry Ck
Dinner Sausage Dnr Sausage-- Protein Foods
Natural/Organic
Dinner Sausage Dnr Sausage--Other Protein Foods
Forms
Dinner Sausage Dnr Sausage--Pork Protein Foods
Rope Ckd/Sm
Dinner Sausage Dnr Sausage-- Protein Foods
Poultry Rope Ckd
Dinner Sausage Dnr Saus-Rope/Link- Protein Foods
Smkd/Preckd
Eggs/Muffins/ Eggs--Jumbo Protein Foods
Potatoes
Eggs/Muffins/ Eggs--Large Protein Foods
Potatoes
Eggs/Muffins/ Eggs--Medium Protein Foods
Potatoes
Eggs/Muffins/ Eggs--Small Protein Foods
Potatoes
Eggs/Muffins/ Eggs--X-Large Protein Foods
Potatoes
Eggs/Muffins/ Eggs Substitute Protein Foods
Potatoes
Eggs/Muffins/ Specialty Eggs Protein Foods
Potatoes
Exotic Goat Protein Food
Exotic Rabbit Protein Foods
Frozen Frzn Breakfast Protein Foods
Breakfast Sausage
Foods
Frozen Frzn Egg Protein Foods
Breakfast Substitutes
Foods
Frozen Entrees Meat Protein Protein Foods
Frozen Meat Frozen Meat Protein Foods
Frozen Meat Alternatives Meat Protein Foods
Frozen Meat Alternatives Soy/ Protein Foods
Tofu
Frzn Multi Frzn Burgers Protein Foods
Serve
Frzn Multi Fz Bbq Protein Foods
Serve
Frzn Multi Fz Meatballs Protein Foods
Serve
Frzn Prepared Bone-In Wings Protein Foods
Chicken
Frzn Prepared Boneless Snack/ Protein Foods
Chicken 18oz And Larger
Frzn Prepared Boneless Snack/ Protein Foods
Chicken Value/Small
Frzn Prepared Value Forms/18oz Protein Foods
Chicken And Larger
[Chicken]
Frzn Prepared Whole Muscle Protein Foods
Chicken Breaded/18oz And
Frzn Prepared Whole Muscle Protein Foods
Chicken Unbreaded
Frzn Seafood Frz Coated Fish Protein Foods
Fillets
Frzn Seafood Frz Fishsticks/ Protein Foods
Tenders/Nuggets
Frzn Seafood Frz Non-Coated Protein Foods
Fish Fillets
Frzn Seafood Frz Seafood Protein Foods
Entrees
Frzn Seafood Frzn Misc Seafood Protein Foods
Hot Dogs Hot Dogs--Base Protein Foods
Beef
Hot Dogs Hot Dogs--Base Protein Foods
Meat
Hot Dogs Hot Dogs--Base Protein Foods
Poultry
Hot Dogs Hot Dogs--Premium Protein Foods
Hot Dogs Hot Dogs-Rw-All Protein Foods
Kosher Beef Protein Foods
Kosher Chicken Protein Foods
Kosher Lamb Protein Foods
Kosher Turkey Protein Foods
Kosher Veal Protein Foods
Kosher Foods Kosher Seafood Protein Foods
And Products
Lamb Chuck/Shoulder Protein Foods
[Lamb]
Lamb Grinds [Lamb] Protein Foods
Lamb Loin [Lamb] Protein Foods
Lamb Offals [Lamb] Protein Foods
Lamb Rib [Lamb] Protein Foods
Lamb Round/Leg [Lamb] Protein Foods
Lamb Thin Meats [Lamb] Protein Foods
Lunchmeat Lunchmeat--Brauns/ Protein Foods
Liver/Loave
Lunchmeat Lunchmeat--Chip Protein Foods
Meat
Lunchmeat Lunchmeat--Chop/ Protein Foods
Form Pltry & Ha
Lunchmeat Lunchmeat--Other Protein Foods
Lunchmeat Lunchmeat--Peggabl Protein Foods
e Deli Fres
Lunchmeat Lunchmeat--Variety Protein Foods
Pack
Lunchmeat Lunchmeat--Whole Protein Foods
Muscle Pltry
Lunchmeat Lunchmeat--Rw-All Protein Foods
Lunchmeat Lunchmeat--Bologna/ Protein Foods
Sausage
Lunchmeat Lunchmeat--Deli Protein Foods
Fresh
Lunchmeat Lunchmeat--Natural/ Protein Foods
Organic
Meat--Shelf Beef Stew Protein Foods
Stable
Meat--Shelf Beef/Pork--Dried Protein Foods
Stable Sliced W/Gra
Meat--Shelf Chicken & Protein Foods
Stable Dumplings
Meat--Shelf Chili: Canned Protein Foods
Stable
Meat--Shelf Chunk Meats--Chix/ Protein Foods
Stable Ham/Etc.
Meat--Shelf Corn Beef Protein Foods
Stable
Meat--Shelf Hash: Canned Protein Foods
Stable
Meat--Shelf Hot Dog Chili Protein Foods
Stable Sauce
Meat--Shelf Luncheon Meat Protein Foods
Stable (Spam)
Meat--Shelf Misc Cnd Meats Protein Foods
Stable
Meat--Shelf Potted Meats And Protein Foods
Stable Spreads
Meat--Shelf Sandwich Sauce Protein Foods
Stable (Manwich)
Meat--Shelf Vienna Sausage Protein Foods
Stable
Meat Frozen Frzn Meat--Beef Protein Foods
Meat Frozen Frzn Meat-- Protein Foods
Breakfast Sausage
Meat Frozen Frzn Meat--Exotic Protein Foods
Meat Frozen Frzn Meat--Natural/ Protein Foods
Organic
Meat Frozen Frzn Meat--Offals Protein Foods
Meat Frozen Frzn Meat--Pork Protein Foods
Meat Frozen Frzn Meat--Turkey Protein Foods
Meat Frozen Meat--Misc-Misc Protein Foods
Meat Snacks Jerky/Nuggets/ Protein Foods
Tenders
Meat Snacks Meat Sticks/Bites Protein Foods
Nat Foods-- Ntrn Refrig Meat: Protein Foods
Refrigerated Breakfast Me
Meat
Nat Foods-- Ntrn Refrig Meat: Protein Foods
Refrigerated Hot Dogs/Sau
Meat
Nat Foods-- Ntrn Refrig Meat: Protein Foods
Refrigerated Lunchmeat
Meat
Non-Dairy/Dairy Nut Milk Protein Foods
Aseptic
Nuts Almonds Protein Foods
Nuts Almonds Shelled Protein Foods
Nuts Almonds W/ Protein Foods
Sweetener
Nuts Cashews Protein Foods
Nuts Cashews W/ Protein Foods
Sweetener
Nuts Dry Roast Peanuts Protein Foods
Nuts Dry Roast Peanuts Protein Foods
W/Sweetener
Nuts Misc Snack Nuts Protein Foods
Nuts Misc Snacks Nuts W/ Protein Foods
Sweetener
Nuts Mixed Nuts Protein Foods
Nuts Mixed Nuts W/ Protein Foods
Sweetener
Nuts Nuts Inshell Protein Foods
Nuts Nuts Other Protein Foods
Nuts Nuts Other Organic Protein Foods
Nuts Nuts Sugar Coated Protein Foods
All
Nuts Oil Roast Peanuts Protein Foods
Nuts Oil Roast Peanuts Protein Foods
W/Sweetener
Nuts Peanuts All Protein Foods
Nuts Pecans Shelled Protein Foods
Nuts Pecans W/Sweetener Protein Foods
Nuts Pistachios Protein Foods
Nuts Sunflower/Other Protein Foods
Seeds
Nuts Sunflower/Other Protein Foods
Seeds W/Sweete
Nuts Trail Mix Protein Foods
Nuts Walnuts Shelled Protein Foods
Packaged Nuts Protein Foods
Natural Snacks
Packaged Nuts W/Sweetener Protein Foods
Natural Snacks
Peanut Butter/ Peanut Butter Protein Foods
Jelly/Jams &
Honey
Pkgd Meat Corp Only Pkgd Meat Protein Foods
Use Corp Use Only
Pork Bone In Dry [Pork Bone In Protein Foods
Loin/Rib Loin/Rib]
Pork Boneless Enhanced [Pork Protein Foods
Loin/Rib Boneless Loin/
Rib]
Pork Boneless Natural [Pork Protein Foods
Loin/Rib Boneless Loin/
Rib]
Pork Grinds Ground Pork Protein Foods
Pork Offal External Fresh Protein Foods
[Pork Offal]
Pork Offal Internal Fresh Protein Foods
[Pork Offal]
Pork Shoulder Butts [Pork Protein Foods
Shoulder]
Pork Shoulder Fresh Hams Protein Foods
Pork Thin Meats Kabobs [Pork] Protein Foods
Pork Thin Meats Organics [Pork] Protein Foods
Pork Thin Meats Ribs [Pork] Protein Foods
Pork Thin Meats Stir Fry/Strips/ Protein Foods
Fajitas [Pork]
Poultry Other Capons Protein Foods
Poultry Other Cornish Hen Protein Foods
Poultry Other Ducks Protein Foods
Poultry Other Geese Protein Foods
Poultry Other Poultry/Other Protein Foods
Prepared/Pdgd Meat--Can/Pouch Protein Foods
Foods
Processed Beans Dried Protein Foods
Random Weight Lunch Meats Protein Foods
Meat Products
Refrigerated Eggs Protein Foods
Dairy Case
Refrigerated Non-Dairy Cheese Protein Foods
Vegetarian
Refrigerated Tofu Protein Foods
Vegetarian
Refrigerated Vegetarian Meats Protein Foods
Vegetarian
Restricted Diet Pnut Btr/Jelly Protein Foods
Salad & Dips Protein Salads-- Protein Foods
Bulk
Salad & Dips Protein Salads-- Protein Foods
Prepack
Seafood--Catfis Catfish--Fillet Protein Foods
h
Seafood--Catfis Catfish--Nuggets Protein Foods
h
Seafood--Catfis Catfish--Other Protein Foods
h Form
Seafood--Catfis Catfish--Whole Protein Foods
h
Seafood--Cod Cod--Fillet Protein Foods
Seafood--Cod Cod--Other Form Protein Foods
Seafood--Cod Cod--Whole Protein Foods
Seafood--Crab Crab--Dungy Protein Foods
Seafood--Crab Crab--King Protein Foods
Seafood--Crab Crab--Other Protein Foods
Seafood--Crab Crab--Snow Protein Foods
Seafood--Exotic Exotic--Mahi Mahi Protein Foods
Seafood--Exotic Exotic--Other Protein Foods
Seafood--Exotic Exotic--Red Protein Foods
Snapper
Seafood--Exotic Exotic--Shark Protein Foods
Seafood--Exotic Exotic--Swordfish Protein Foods
Seafood--Exotic Exotic--Tuna Protein Foods
Seafood--Finfis Finfish--Halibut Protein Foods
h Other
Seafood--Finfis Finfish--Other Protein Foods
h Other
Seafood--Finfis Finfish--Other Protein Foods
h Other
Seafood--Finfis Finfish--Rockfish Protein Foods
h Other
Seafood--Finfis Finfish--Sole/ Protein Foods
h Other Flounder
Seafood--Finfis Finfish--Sole/ Protein Foods
h Other Flounder
Seafood--Imitat Imitation Crab Protein Foods
ion Seafood
Seafood--Imitat Imitation Other Protein Foods
ion Seafood
Seafood--Imitat Imitation Shrimp Protein Foods
ion Seafood
Seafood--Lobste Lobster--Live Protein Foods
r
Seafood--Lobste Lobster--Meat Protein Foods
r
Seafood--Lobste Lobster--Other Protein Foods
r Form
Seafood--Lobste Lobster--Tails Protein Foods
r
Seafood--Oyster Oyster--Bulk Protein Foods
Seafood--Oyster Oyster--Cup Protein Foods
(Packaged)
Seafood--Oyster Oyster--Cup Protein Foods
(Packaged)
Seafood--Party Party Tray--Shrimp Protein Foods
Trays
Seafood--Salmon- Salmon Fr-- Protein Foods
Farm Raised Altantic
Seafood--Salmon- Salmon Fr--Other Protein Foods
Farm Raised Form
Seafood--Salmon- Salmon Fr-- Protein Foods
Farm Raised Atlantic
Seafood--Salmon- Salmon Fr--Coho Protein Foods
Farm Raised
Seafood--Salmon- Salmon Fr--King Protein Foods
Farm Raised
Seafood--Salmon- Seafood--Fre- Protein Foods
Farm Raised Catfish
Seafood--Salmon- Seafood--Fre-Misc Protein Foods
Farm Raised
Seafood--Salmon- Seafood--Fre-Raw Protein Foods
Farm Raised Finfish--Other
Seafood--Salmon- Salmon Wc--Other Protein Foods
Wild Caught Form
Seafood--Salmon- Salmon Wc--Coho Protein Foods
Wild Caught
Seafood--Salmon- Salmon Wc--King Protein Foods
Wild Caught
Seafood--Salmon- Salmon Wc--Pink Protein Foods
Wild Caught
Seafood--Salmon- Salmon Wc-- Protein Foods
Wild Caught Silverbrite
Seafood--Salmon- Salmon Wc-- Protein Foods
Wild Caught Silverbrite
Seafood--Salmon- Salmon Wc--Sockeye Protein Foods
Wild Caught
Seafood--Scallo Scallops--Bay Protein Foods
ps
Seafood--Scallo Scallops--Sea Protein Foods
ps
Seafood--Shellf Shellfish--Clams Protein Foods
ish Other
Seafood--Shellf Shellfish--Clams Protein Foods
ish Other
Seafood--Shellf Shellfish--Mussles Protein Foods
ish Other
Seafood--Shellf Shellfish--Other Protein Foods
ish Other
Seafood--Shrimp Shrimp--Cooked Protein Foods
Seafood--Shrimp Shrimp--Natural/ Protein Foods
Organic
Seafood--Shrimp Shrimp--Raw Protein Foods
Seafood--Smoked Smoked Other Protein Foods
Seafood
Seafood--Smoked Smoked Salmon Protein Foods
Seafood
Seafood--Tilapi Tilapia--Fillet Protein Foods
a
Seafood--Tilapi Tilapia--Other Protein Foods
a Form
Seafood--Tilapi Tilapia--Whole Protein Foods
a
Seafood--Trout Steelhead Fr Protein Foods
Seafood--Trout Trout--Fillet Protein Foods
Seafood--Trout Trout--Whole Protein Foods
Seafood--Value- Value-Added Protein Foods
Added Seafood Catfish
Seafood--Value- Value-Added In- Protein Foods
Added Seafood Store Cooked Ho
Seafood--Value- Value-Added Protein Foods
Added Seafood Breaded Shrimp
Seafood--Value- Value-Added Crab Protein Foods
Added Seafood
Seafood--Value- Value-Added Protein Foods
Added Seafood Finfish
Seafood--Value- Value-Added In- Protein Foods
Added Seafood Store Cooked Co
Seafood--Value- Value-Added Kabobs Protein Foods
Added Seafood
Seafood--Value- Value-Added Other Protein Foods
Added Seafood
Seafood--Value- Value-Added Salmon Protein Foods
Added Seafood
Seafood--Value- Value-Added Shrimp Protein Foods
Added Seafood
Seafood--Value- Value-Added Protein Foods
Added Seafood Tilapia
Seafood--Salad/ Herring Protein Foods
Dips/Sce/Cond
Service Case Cooked Protein Foods
Meat
Service Case Ingredients Protein Foods
Meat
Service Case Kabobs Beef Protein Foods
Meat
Service Case Kabobs Pork Protein Foods
Meat
Service Case Kabobs Poultry Protein Foods
Meat
Service Case Marinated Beef Protein Foods
Meat
Service Case Marinated Pork Protein Foods
Meat
Service Case Marinated Poultry Protein Foods
Meat
Service Case Seasoned Protein Foods
Meat
Service Case Seasoned Beef Protein Foods
Meat
Service Case Seasoned Pork Protein Foods
Meat
Service Case Seasoned Poultry Protein Foods
Meat
Service Case Stuffed/Mixed Beef Protein Foods
Meat
Service Case Stuffed/Mixed Pork Protein Foods
Meat
Service Case Stuffed/Mixed Protein Foods
Meat Poultry
Smoked Hams Hams--Canned Protein Foods
Smoked Hams Hams--Dry Cured/ Protein Foods
Country
Smoked Hams Hams--Half/Port Protein Foods
Bone-In
Smoked Hams Hams--Half/Port Protein Foods
Boneless
Smoked Hams Hams--Spiral Protein Foods
Smoked Hams Hams--Whole Bone- Protein Foods
In
Smoked Hams Hams--Whole Protein Foods
Boneless
Smoked Pork Bacon--Belly/Jowl Protein Foods
Smoked Pork Ham Steaks/Cubes/ Protein Foods
Slices
Smoked Pork Smoked Chops Bone- Protein Foods
In [Pork]
Smoked Pork Smoked Chops Protein Foods
Boneless [Pork]
Smoked Pork Smoked Offal Protein Foods
[Pork]
Smoked Pork Smoked Picnics Protein Foods
[Pork]
Snack Meat Grnd/Patty--Chuck Protein Foods
Snack Meat Snack Meat--Other Protein Foods
Snack Meat Snack Meat-- Protein Foods
Pepperoni
Snack Meat Snack Meat--Salami/ Protein Foods
Smr Sausag
Snacks Snacks: Deli Nuts Protein Foods
Ss/Vending-- Tube Nuts Protein Foods
Salty Snacks
Ss/Vending-- Tube Nuts W/ Protein Foods
Salty Snacks Sweetener
Turkey Fresh Turkey Legs Protein Foods
Turkey Fresh Whole Hen (Under Protein Foods
16lbs) [Turkey]
Turkey Fresh Whole Tom (Over Protein Foods
16lbs) [Turkey]
Turkey Frozen Turkey Breast Bone Protein Foods
In
Turkey Frozen Turkey Breast Protein Foods
Boneless
Turkey Frozen Turkey Halves/ Protein Foods
Quarters
Turkey Frozen Turkey Thighs Protein Foods
Turkey Frozen Whole Hens (Under Protein Foods
16lbs) [Turkey]
Turkey Frozen Whole Toms (Over Protein Foods
16lbs) [Turkey]
Turkey Grinds Ground Turkey Protein Foods
Turkey Offal External [Turkey Protein Foods
Offal]
Turkey Offal Internal [Turkey Protein Foods
Offal]
Turkey Organic Whole Hens (Under Protein Foods
15lbs) [Turkey]
Turkey Organic Whole Toms (Over Protein Foods
15lbs) [Turkey]
Turkey Smoked Turkey Drums Protein Foods
Turkey Smoked Turkey Wings Protein Foods
Turkey Whole Hens (Under Protein Foods
Specialty 15lbs) [Turkey]
Natural
Turkey Whole Toms (Over Protein Foods
Specialty 15lbs) [Turkey]
Natural
Unknown Beef--Boneless- Protein Foods
Choice
Unknown Beef--Grinds Protein Foods
Unknown Breast--Bone-In Protein Foods
(Frz)
Unknown Frozen Burgers Protein Foods
Unknown Frozen Meat Protein Foods
Unknown Frozen Meat Protein Foods
(Vegetarian)
Unknown Ham--Bone-In Whole Protein Foods
Unknown Ham--Boneless Half/ Protein Foods
Port
Unknown Marinated Protein Foods
Unknown Meal Sol-- Protein Foods
Precooked Meats
Unknown Meal Sol--Raw Protein Foods
Frthr Preprd Mt
Unknown Meat Frz--Misc Protein Foods
Unknown Seafood--Frz--Rw-- Protein Foods
All
Unknown Smkd Ham Country-- Protein Foods
All
Unknown Turkey--Grinds Protein Foods
Unknown Turkey--Other Protein Foods
Parts/Pieces--Fre
Unknown Whole--Tom (16 Lbs Protein Foods
& Over Frz
Veal Whole/Half [Veal] Protein Foods
Nuts Pecans Protein Foods
Authentic Authentic Peppers Vegetables
Hispanic Fds &
Product
Authentic Authentic Sauces/ Vegetables
Hispanic Fds & Salsa/Picante
Product
Authentic Authentic Vegetables
Hispanic Fds & Vegetables And
Product Foods
Authentic Italian Vegetables Vegetables
Italian Foods
Broccoli/ Brocco--Flower Vegetables
Cauliflower
Broccoli/ Broccoli Whole & Vegetables
Cauliflower Crowns Organi
Broccoli/ Broccoli Vegetables
Cauliflower Whole&Crowns
Broccoli/ Cauliflower Whole Vegetables
Cauliflower
Broccoli/ Cauliflower Whole Vegetables
Cauliflower Organic
Can Vegetables-- Artichokes Vegetables
Shelf Stable
Can Vegetables-- Beans/Wax/Shellies Vegetables
Shelf Stable
Can Vegetables-- Beets Vegetables
Shelf Stable
Can Vegetables-- Carrots Vegetables
Shelf Stable
Can Vegetables-- Corn Vegetables
Shelf Stable
Can Vegetables-- Fried Onions Vegetables
Shelf Stable
Can Vegetables-- Green Beans: Fs/ Vegetables
Shelf Stable Whl/Cut
Can Vegetables-- Hominy Vegetables
Shelf Stable
Can Vegetables-- Kraut & Cabbage Vegetables
Shelf Stable
Can Vegetables-- Lima Beans Vegetables
Shelf Stable
Can Vegetables-- Miscellaneous Vegetables
Shelf Stable Vegetables
Can Vegetables-- Mixed Vegetables Vegetables
Shelf Stable
Can Vegetables-- Mushrooms Cnd & Vegetables
Shelf Stable Glass
Can Vegetables-- Peas & Onions/Peas Vegetables
Shelf Stable & Carrot
Can Vegetables-- Peas Fresh Pack/ Vegetables
Shelf Stable Crowder
Can Vegetables-- Peas/Green Vegetables
Shelf Stable
Can Vegetables-- Pimentos Vegetables
Shelf Stable
Can Vegetables-- Salads Cnd (Bean/ Vegetables
Shelf Stable Potato)
Can Vegetables-- Spinach & Greens Vegetables
Shelf Stable
Can Vegetables-- Squash Vegetables
Shelf Stable
Can Vegetables-- Sweet Potatoes Vegetables
Shelf Stable
Can Vegetables-- White Potatoes Vegetables
Shelf Stable
Carrots Carrots--Bulk Vegetables
Carrots Carrots Bagged Vegetables
Carrots Carrots Bagged Vegetables
Organic
Carrots Carrots Bulk Vegetables
Organic
Carrots Carrots Mini Vegetables
Peeled
Carrots Carrots Mini Vegetables
Peeled Organic
Condiments Salsa/Dips Vegetables
Convenience/ Convenience/ Vegetables
Snacking Snacking Organic
Convenience/ Convenience/ Vegetables
Snacking Snacking
Vegetable
Corn Corn Bulk Vegetables
Corn Corn Is Packaged Vegetables
Corn Corn Organic Vegetables
Corn Corn Packaged Vegetables
Corn Corn White Vegetables
Dry Sauce/Gravy/ Potatoes: Dry Vegetables
Potatoes/
Stuffing
Frozen Potatoes Frzn Baked/Stuffed/ Vegetables
Mashed & Spec
Frozen Potatoes Frzn French Fries Vegetables
Frozen Potatoes Frzn Hashbrown Vegetables
Potatoes
Frozen Potatoes Frzn Onion Rings Vegetables
Frozen Potatoes Frzn Tater Tots/ Vegetables
Other Extruded
Frozen Frzn Breaded Vegetables
Vegetable & Vegetables
Veg Dish
Frozen Frzn Corn On The Vegetables
Vegetable & Cob
Veg Dish
Frozen Frzn Organic Vegetables
Vegetable & Vegetables
Veg Dish
Frozen Frzn Steamable Vegetables
Vegetable & Vegetables
Veg Dish
Frozen Fz Bag Vegetables-- Vegetables
Vegetable & Plain
Veg Dish
Frozen Fz Bag Vegetables-- Vegetables
Vegetable & Value-Added
Veg Dish
Frozen Fz Box Vegetables-- Vegetables
Vegetable & Plain
Veg Dish
Frozen Fz Box Vegetables-- Vegetables
Vegetable & Value-Added
Veg Dish
Frozen Bag Vegetables Vegetables
Vegetables And
Potatoes
Frozen Box Vegetables Vegetables
Vegetables And
Potatoes
Frozen Edamame Vegetables
Vegetables And
Potatoes
Frozen Potatoes Vegetables
Vegetables And
Potatoes
Fruit & Veg Herbs (Outdoor) Vegetables
Plants
(Outdoor)
Fruit & Veg Vegetable Vegetables
Plants
(Outdoor)
Herbs/Garlic Garlic Whole Vegetables
Cloves
Herbs/Garlic Garlic Whole Vegetables
Cloves Organic
Herbs/Garlic Herbs Basil Vegetables
Herbs/Garlic Herbs Basil Vegetables
Organic
Herbs/Garlic Herbs Cilanto Vegetables
Herbs/Garlic Herbs Cilantro Vegetables
Organic
Herbs/Garlic Herbs Fresh Other Vegetables
Herbs/Garlic Herbs Fresh Other Vegetables
Organic
Herbs/Garlic Herbs Parsley Vegetables
Herbs/Garlic Herbs Parsley Vegetables
Organic
Herbs/Garlic Sprouts Vegetables
Kosher Foods Kosher Potato Vegetables
And Products Vegetable
Mushrooms Mushrooms Dried Vegetables
Mushrooms Mushrooms Other Vegetables
Mushrooms Mushrooms Others Vegetables
Organic
Mushrooms Mushrooms Vegetables
Portabella
Mushrooms Mushrooms White Vegetables
Bulk
Mushrooms Mushrooms White Vegetables
Sliced Pkg
Mushrooms Mushrooms White Vegetables
Whole Pkg
Mushrooms Mushrooms White Vegetables
Whole Pkg Organic
Onions Onions Gourmet Vegetables
Onions Onions Other Vegetables
Onions Onions Other Vegetables
Organic
Onions Onions Red (Bulk & Vegetables
Bag)
Onions Onions Sweet (Bulk Vegetables
& Bag)
Onions Onions White (Bulk Vegetables
& Bag)
Onions Onions Yellow Vegetables
(Bulk & Bag)
Organics Fruit Organic Broccoli/ Vegetables
& Vegetables Cauliflower
Organics Fruit Organic Fruit/Veg Vegetables
& Vegetables Instore Proc
Organics Fruit Organic Other Vegetables
& Vegetables
Organics Fruit Organic Processed Vegetables
& Vegetables
Organics Fruit Organic Salad Mix Vegetables
& Vegetables
Organics Fruit Organic Value- Vegetables
& Vegetables Added Vegetables
Organics Fruit Organic Vegetables Vegetables
& Vegetables Salad
Pasta & Pizza Mainstream [Pasta Vegetables
Sauce & Pizza Sauce]
Pasta & Pizza Pizza Sauce Vegetables
Sauce
Pasta & Pizza Specialty Italian Vegetables
Sauce Sauce
Pasta & Pizza Value [Pasta & Vegetables
Sauce Pizza Sauce]
Peppers Peppers All Other Vegetables
Peppers Peppers All Others Vegetables
Organic
Peppers Peppers Green Bell Vegetables
Peppers Peppers Green Bell Vegetables
Organic
Peppers Peppers Jalapeno Vegetables
Peppers Peppers Mini Sweet Vegetables
Packaged
Peppers Peppers Other Bell Vegetables
Peppers Peppers Other Bell Vegetables
Organic
Peppers Peppers Red Bell Vegetables
Peppers Peppers Red Bell Vegetables
Organic
Peppers Peppers Serrano Vegetables
Peppers Peppers Yellow Vegetables
Bell
Peppers Peppers Yellow Vegetables
Bell Organic
Potatoes Potatoes Gold Vegetables
(Bulk & Bag)
Potatoes Potatoes Gourmet Vegetables
Potatoes Potatoes Other Vegetables
Potatoes Potatoes Other Vegetables
Organic
Potatoes Potatoes Red (Bulk Vegetables
& Bag)
Potatoes Potatoes Russet Vegetables
(Bulk & Bag)
Potatoes Potatoes Vegetables
Sweet&Yams
Potatoes Potatoes White Vegetables
(Bulk & Bag)
Prepared/Pdgd Vegetables/Dry Vegetables
Foods Beans
Processed Jarred Vegetables Vegetables
Refrigerated Refrigerated Pasta Vegetables
Italian Sauce
Salad & Dips Sal: Hommus Vegetables
Salad & Dips Sal: Salsa/Dips Vegetables
Bulk
Salad & Dips Sal: Salsa Prepack Vegetables
Salad & Dips Salad Bar Vegetables
Salad & Dips Salad: Ingredients Vegetables
Salad & Dips Salad: Lettuce Vegetables
Salad & Dips Vegetable Salads-- Vegetables
Bulk
Salad & Dips Vegetable Salads-- Vegetables
Prepack
Salad Bar Processed Salad Vegetables
Salad Mix Blends [Salad Mix] Vegetables
Salad Mix Coleslaw Vegetables
Salad Mix Garden Plus [Salad Vegetables
Mix]
Salad Mix Kits [Salad Mix] Vegetables
Salad Mix Regular Garden Vegetables
[Salad Mix]
Salad Mix Salad Bowls Vegetables
Salad Mix Salad Mix Blends Vegetables
Organic
Salad Mix Salad Mix Kits Vegetables
Organic
Salad Mix Salad Mix Other Vegetables
Salad Mix Salad Spinach Vegetables
Salad Mix Salad Spinach Vegetables
Organic
Salad Mix Shredded Lettuce Vegetables
Seasonal Pumpkins Vegetables
Shelf Stable Tomato Juice (50% Vegetables
Juice And Under)
Shelf Stable Tomato Juice (Over Vegetables
Juice 50% Juice)
Shelf Stable Veg Juice (Except Vegetables
Juice Tomato) (50% And
Under)
Shelf Stable Veg Juice (Except Vegetables
Juice Tomato) (Over 50%
Juice)
Snack Salsa Vegetables
Spices/Jarred Garlic Jar Vegetables
Garlic
Spices/Jarred Garlic Jar Organic Vegetables
Garlic
Spices/Jarred Herbs Dried Vegetables
Garlic
Spices/Jarred Herbs Squeeze Tube Vegetables
Garlic Organic
Spices/Jarred Peppers Dried Vegetables
Garlic
Tomato Tomato Stewed Vegetables
Products--Shel
f
Tomato Tomato Paste Vegetables
Products--Shel
f Stable
Tomato Tomatoes Diced Vegetables
Products--Shel
f Stable
Tomato Tomato Crushed Vegetables
Products--Shel
f Stable
Tomato Tomato Puree Vegetables
Products--Shel
f Stable
Tomato Tomato Sauce Vegetables
Products--Shel
f Stable
Tomato Tomato Sun Dried Vegetables
Products--Shel
f Stable
Tomato Tomatoes/Whole Vegetables
Products--Shel
f Stable
Tomatoes Roma Tomatoes Vegetables
(Bulk/Pkg)
Tomatoes Tomatoes Cherry Vegetables
Tomatoes Tomatoes Cherry Vegetables
Organic
Tomatoes Tomatoes Cocktail Vegetables
Tomatoes Tomatoes Grape Vegetables
Tomatoes Tomatoes Grape Vegetables
Organic
Tomatoes Tomatoes Hot House Vegetables
Bulk
Tomatoes Tomatoes Hothouse Vegetables
On The Vine
Tomatoes Tomatoes Hothouse Vegetables
Pkg
Tomatoes Tomatoes Others Vegetables
Organic
Tomatoes Tomatoes Snacking Vegetables
Colored
Tomatoes Tomatoes Vine Ripe Vegetables
Bulk
Tomatoes Tomatoes Vine Ripe Vegetables
Pkg
Tomatoes Tomatoes--Other Vegetables
Traditional Asian Vegetables Vegetables
Asian Foods
Traditional Mexican Beans/ Vegetables
Mexican Foods Refried
Traditional Mexican Enchilada Vegetables
Mexican Foods Sauce
Traditional Mexican Peppers Vegetables
Mexican Foods Chilies
Traditional Mexican Sauces And Vegetables
Mexican Foods Picante Sau
Tropical Fruit Avocado Vegetables
Tropical Fruit Avocado Organic Vegetables
Unknown Frozen Vegetables Vegetables
Value-Added Celery Chopped/ Vegetables
Vegetables Sticks
Value-Added Cut Vegetables All Vegetables
Vegetables Other
Value-Added Instore Cut Vegetables
Vegetables Vegetables
Value-Added Onions Processed Vegetables
Vegetables
Value-Added Vegetable Party Vegetables
Vegetables Tray
Vegetables Asparagus Vegetables
Cooking Bulk
Vegetables Beans Vegetables
Cooking Bulk
Vegetables Beans Organic Vegetables
Cooking Bulk
Vegetables Cabbage Vegetables
Cooking Bulk
Vegetables Cabbage Organic Vegetables
Cooking Bulk
Vegetables Celery Vegetables
Cooking Bulk
Vegetables Celery Organic Vegetables
Cooking Bulk
Vegetables Greens Bulk Vegetables
Cooking Bulk
Vegetables Greens Bulk Vegetables
Cooking Bulk Organic
Vegetables Hard Squash Vegetables
Cooking Bulk
Vegetables Organic Vegetables Vegetables
Cooking Bulk All Others
Vegetables Squash Other Vegetables
Cooking Bulk
Vegetables Squash Other Vegetables
Cooking Bulk Organic
Vegetables Vegetables All Vegetables
Cooking Bulk Other
Vegetables Vegetables Cooking Vegetables
Cooking Packaged Organic
Packaged
Vegetables Broccoli/ Vegetables
Cooking Cauliflower
Packaged Processed
Vegetables Potatoes/Onions Vegetables
Cooking Processed
Packaged
Vegetables Vegetables Cooking Vegetables
Cooking Packaged
Packaged
Vegetables Cucumbers Vegetables
Salad
Vegetables Cucumbers Organic Vegetables
Salad
Vegetables Green Onions Vegetables
Salad
Vegetables Green Onions Vegetables
Salad Organic
Vegetables Head Lettuce Vegetables
Salad
Vegetables Head Lettuce Vegetables
Salad Organic
Vegetables Radish Vegetables
Salad
Vegetables Radishes Organic Vegetables
Salad
Vegetables Spinach Bulk Vegetables
Salad
Vegetables Spring Mix Bulk Vegetables
Salad
Vegetables Variety Lettuce Vegetables
Salad
Vegetables Variety Lettuce Vegetables
Salad Organic
Authentic Italian Oils And Oils
Italian Foods Dressings
Deli Dl Spec: Sauces/ Oils
Specialties Sld Dressings
(Retail Pk)
Dressings/Dips Dressing Blue Oils
Cheese
Dressings/Dips Dressing Cole Slaw Oils
Dressings/Dips Dressing Creamy Oils
Dressings/Dips Dressing Ginger Oils
Dressings/Dips Dressing Organics Oils
Dressings/Dips Dressing Oils
Vinegarette
Dressings/Dips Dressing Yogurt Oils
Based
Margarines Margarine: Squeeze Oils
Margarines Margarine: Tubs Oils
And Bowls
Processed Dressings Oils
Salad Dresing & Mayonnaise & Oils
Sandwich Whipped Dressing
Spreads
Salad Dresing & Pourable Salad Oils
Sandwich Dressings
Spreads
Salad Dresing & Sand/Horseradish & Oils
Sandwich Tartar Sauce
Spreads
Shortening & Canola Oils Oils
Oil
Shortening & Cooking Oil: Oils
Oil Peanut/Safflower
Shortening & Cooking Sprays Oils
Oil
Shortening & Corn Oil Oils
Oil
Shortening & Misc Oils Oils
Oil
Shortening & Olive Oil Oils
Oil
Shortening & Vegetable Oil Oils
Oil
Aseptic Juice Aseptic Pack Juice Solid Fats & sweetened beverage
And Drinks Added Sugar
Aseptic Juice Aseptic Pack Juice Solid Fats & sweetened beverage
And Drinks Added Sugar
Aseptic Juice Aseptic Pack Juice Solid Fats & sweetened beverage
And Drinks Added Sugar
Authentic Central American Solid Fats & candy/sweet
Central Candy W/O Flour Added Sugar
American Fds
Authentic Central American Solid Fats & sweetened beverage
Central Carbonated Bev Added Sugar
American Fds
Authentic Hispanic Solid Fats & sweetened beverage
Hispanic Fds & Carbonated Added Sugar
Product Beverages
Authentic Authentic Dry Solid Fats & sweetened beverage
Hispanic Fds & Beverages W/ Added Sugar
Product Sweetener
Authentic Hispanic Juice Solid Fats & sweetened beverage
Hispanic Fds & Under 50% Juice Added Sugar
Product
Authentic South South American Solid Fats & candy/sweet
American Fds Candy W/O Flour Added Sugar
Bag Snacks Pork Skins/ Solid Fats & butter/cream/solid
Cracklins Added Sugar fat
Bagels & Cream Cream Cheese Solid Fats & butter/cream/solid
Cheese Added Sugar fat
Baking Chocolate Chips & Solid Fats & candy/sweet
Bars (Sweete) Added Sugar
Baking Mixes Frosting Solid Fats & candy/sweet
Added Sugar
Baking Needs Coconut [Baking Solid Fats & butter/cream/solid
Needs] Added Sugar fat
Baking Needs Marshmallow Creme Solid Fats & candy/sweet
Added Sugar
Baking Needs Marshmallows Solid Fats & candy/sweet
Added Sugar
Beverages Can/Btl Carb Beve Solid Fats & sweetened beverage
50% And Under Added Sugar
Beverages Can/Btl N/Carb Solid Fats & sweetened beverage
Beve 50% And Added Sugar
Under
Beverages Tea (Canned/ Solid Fats & sweetened beverage
Bottled) W/ Added Sugar
Sweetener
Bulk Food Candy Bulk Solid Fats & candy/sweet
Added Sugar
Bulk Food Candy Bulk W/Flour Solid Fats & candy/sweet
Added Sugar
Cake Decor Cake Decors-- Solid Fats & candy/sweet
Candies Added Sugar
Cake Decor Cake Decors & Solid Fats & candy/sweet
Icing Added Sugar
Candy Candy W/Flour Solid Fats & candy/sweet
Added Sugar
Candy Candy/Chocolate Solid Fats & candy/sweet
Added Sugar
Candy--Checklan Candy Bars Solid Fats & candy/sweet
e (Singles) Added Sugar
(Including)
Candy--Checklan Candy Bars Solid Fats & candy/sweet
e (Singles) Added Sugar
(Including)
Candy--Checklan Chewing Gum Solid Fats & candy/sweet
e Added Sugar
Candy--Checklan Mints/Candy & Solid Fats & candy/sweet
e Breath (Not Added Sugar
Lifesavers)
Candy--Checklan Mints/Candy & Solid Fats & candy/sweet
e Breath (Not Added Sugar
Lifesavers)
Candy--Checklan Misc Checklane Solid Fats & candy/sweet
e Candy Added Sugar
Candy--Packaged Bulk Candy Solid Fats & candy/sweet
Added Sugar
Candy--Packaged Bulk Candy W/Flour Solid Fats & candy/sweet
Added Sugar
Candy--Packaged Candy & Breath Solid Fats & candy/sweet
Mints (Pkgd) Added Sugar
Candy--Packaged Candy & Breath Solid Fats & candy/sweet
Mints (Pkgd) Added Sugar
Candy--Packaged Candy Bags-- Solid Fats & candy/sweet
Chocolate Added Sugar
Candy--Packaged Candy Bags-- Solid Fats & candy/sweet
Chocolate W/Flour Added Sugar
Candy--Packaged Candy Bags--Non Solid Fats & candy/sweet
Chocolate Added Sugar
Candy--Packaged Candy Bags--Non Solid Fats & candy/sweet
Chocolate W/Flour Added Sugar
Candy--Packaged Candy Bars (Multi Solid Fats & candy/sweet
Pack) Added Sugar
Candy--Packaged Candy Bars Multi Solid Fats & candy/sweet
Pack W/Flour Added Sugar
Candy--Packaged Candy Box Non- Solid Fats & candy/sweet
Chocolate Added Sugar
Candy--Packaged Candy Box Non- Solid Fats & candy/sweet
Chocolate W/Flour Added Sugar
Candy--Packaged Candy Boxed Solid Fats & candy/sweet
Chocolates Added Sugar
Candy--Packaged Candy Boxed Solid Fats & candy/sweet
Chocolates W/ Added Sugar
Flour
Candy--Packaged Candy Refrigerated Solid Fats & candy/sweet
Added Sugar
Candy--Packaged Gum (Packaged) Solid Fats & candy/sweet
Added Sugar
Candy--Packaged Hispanic Candy Solid Fats & candy/sweet
Added Sugar
Candy--Packaged Miscellaneous Solid Fats & candy/sweet
Candy Added Sugar
Candy--Packaged Miscellaneous Solid Fats & candy/sweet
Candy Added Sugar
Candy--Packaged Novelty Candy Solid Fats & candy/sweet
Added Sugar
Candy--Packaged Novelty Candy W/ Solid Fats & candy/sweet
Flour Added Sugar
Candy--Packaged Novelty Candy-- Solid Fats & candy/sweet
Taxable Added Sugar
Candy--Packaged Seasonal Candy Solid Fats & candy/sweet
Bags Non- Added Sugar
Chocolate
Candy--Packaged Seasonal Candy Solid Fats & candy/sweet
Bags Non- Added Sugar
Chocolate
Candy--Packaged Seasonal Candy Solid Fats & candy/sweet
Bags--Chocolate Added Sugar
Candy--Packaged Seasonal Candy Solid Fats & candy/sweet
Bags--Chocolate Added Sugar
Candy--Packaged Seasonal Candy Box Solid Fats & candy/sweet
Non-Chocolate Added Sugar
Candy--Packaged Seasonal Candy Box Solid Fats & candy/sweet
Non-Chocolate Added Sugar
Candy--Packaged Seasonal Candy Solid Fats & candy/sweet
Box--Chocolate Added Sugar
Candy--Packaged Seasonal Candy Solid Fats & candy/sweet
Box--Chocolate W/ Added Sugar
Flour
Candy--Packaged Seasonal Solid Fats & candy/sweet
Miscellaneous Added Sugar
[Candy]
Candy--Packaged Seasonal Solid Fats & candy/sweet
Miscellaneous W/ Added Sugar
Flour
Cocoa Mixes Hot Chocolate/ Solid Fats & sweetened beverage
Cocoa Mix Added Sugar
Cocoa Mixes Malted Mlk/Syrup/ Solid Fats & sweetened beverage
Pwdrs (Eggnog) Added Sugar
Coffee & Coffee Sweeteners Solid Fats & sweetened beverage
Creamers Added Sugar
Coffee & Non Dairy Creamer Solid Fats & sweetened beverage
Creamers Added Sugar
Coffee Shop Coffee Shop: Candy Solid Fats & candy/sweet
Sweet Goods & Added Sugar
Rtl
Condiments Honey/Syrup Solid Fats & candy/sweet
Added Sugar
Condiments Jellies/Preserves/ Solid Fats & candy/sweet
Apple Butter Added Sugar
Deli Dl Spec: Jellies/ Solid Fats & Sugar candy/sweet
Specialties Toppings Added
(Retail Pk)
Dressings/Dips Dips Caramel/Fruit Solid Fats & candy/sweet
Glazes Added Sugar
Dressings/Dips Dips Fruit And Solid Fats & candy/sweet
Chocolate Added Sugar
Dry Mix Desserts Topping Solid Fats & butter/cream/solid
Mixes/Whip Added Sugar fat
Topping
Dry Tea/Coffee/ Coco Mix Solid Fats & sweetened beverage
Coco Mixes Added Sugar
Dry Tea/Coffee/ Tea Concentrate W/ Solid Fats & sweetened beverage
Coco Mixes Sweetener/Su Added Sugar
Dry Tea/Coffee/ Tea Rtd With Solid Fats & sweetened beverage
Coco Mixes Sweetener/Sugar Added Sugar
Energy Drinks Energy Drink-- Solid Fats & sweetened beverage
Multi-Pack Added Sugar
Energy Drinks Energy Drink-- Solid Fats & sweetened beverage
Multi-Pack (Non) Added Sugar
Energy Drinks Energy Drink-- Solid Fats & sweetened beverage
Single Serve Added Sugar
Energy Drinks Energy Drink-- Solid Fats & sweetened beverage
Single Serve Added Sugar
European Foods British Carbonated Solid Fats & sweetened beverage
Beverages Added Sugar
European Foods European Solid Fats & sweetened beverage
Carbonated Added Sugar
Beverages
Fluid Milk Refrigerated Solid Fats & butter/cream/solid
Products Coffee Creamers Added Sugar fat
Fluid Milk Whipping Cream Solid Fats & butter/cream/solid
Products Added Sugar fat
Frozen Frzn Non-Dairy Solid Fats & butter/cream/solid
Breakfast Creamers Added Sugar fat
Foods
Frozen Juice Cocktail Mixes-Frz Solid Fats & sweetened beverage
And Smoothies Added Sugar
Frozen Juice Frzn Conc Under Solid Fats & sweetened beverage
And Smoothies 50% Juice Added Sugar
Frozen Juice Frzn Fruit Drinks Solid Fats & sweetened beverage
And Smoothies (Under 10% J) Added Sugar
Frozen Whipped Frzn Whipped Solid Fats & butter/cream/solid
Topping Topping Added Sugar fat
Gift & Fruit Candy Arrangements Solid Fats & candy/sweet
Baskets Food Only Added Sugar
Juice Drinks--Carb Juice Solid Fats & sweetened beverage
(Under 50%) Added Sugar
Juice Non-Carb Jce Solid Fats & sweetened beverage
(Under 50% Juice) Added Sugar
Juices Super Juices (50% And Solid Fats & sweetened beverage
Premium Under Juice) Added Sugar
Juices Super Juices Organic Solid Fats & sweetened beverage
Premium (50% And Under) Added Sugar
Juices Super Juices Smoothies/ Solid Fats & sweetened beverages
Premium Blended Added Sugar
Juices Super Juices Superfoods/ Solid Fats & sweetened beverages
Premium Enhanced Added Sugar
Juices Super Juices/Smoothies Solid Fats & sweetened beverages
Premium Instore Produ Added Sugar
Kosher Foods Kosher Beverage Solid Fats & sweetened beverages
And Products Added Sugar
Kosher Foods Kosher Candy Solid Fats & candy/sweet
And Products Added Sugar
Kosher Foods Kosher Carbonated Solid Fats & sweetened beverage
And Products Soft Drinks Added Sugar
Margarines Butter Solid Fats & butter/cream/solid
Added Sugar fat
Margarines Margarine Stick Solid Fats & butter/cream/solid
Added Sugar fat
Milk By- Aerosol Toppings Solid Fats & butter/cream/solid
Products [Milk By- Added Sugar fat
Products]
Milk By- Refrig Dips Solid Fats & butter/cream/solid
Products Added Sugar fat
Milk By- Sour Creams Solid Fats & butter/cream/solid
Products Added Sugar fat
Mixers Cocktail Mixes-- Solid Fats & sweetened beverage
Dry Added Sugar
Mixers Cocktail Mixes-- Solid Fats & sweetened beverage
Fluid: Add Liq Added Sugar
Molasses/Syrups/ Molasses & Syrups Solid Fats & candy/sweet
Pancake Mixes Added Sugar
Packaged Candy Solid Fats & candy/sweet
Natural Snacks Added Sugar
Peanut Butter/ Preserves/Jam/ Solid Fats & candy/sweet
Jelly/Jams & Marmalade Added Sugar
Honey
Peanut Butter/ Honey Solid Fats & candy/sweet
Jelly/Jams & Added Sugar
Honey
Peanut Butter/ Jelly Solid Fats & candy/sweet
Jelly/Jams & Added Sugar
Honey
Powder & Breakfast Crystals Solid Fats & sweetened beverage
Crystal Drink Added Sugar
Mix
Powder & Enhanced Stick Solid Fats & sweetened beverage
Crystal Drink [Powder Drink Added Sugar
Mix Mix]
Powder & Fluid Pouch Solid Fats & sweetened beverage
Crystal Drink [Powder Drink Added Sugar
Mix Mix]
Powder & Soft Drink Solid Fats & sweetened beverage
Crystal Drink Canisters [Powder Added Sugar
Mix Drink Mix]
Powder & Sugar Sweetened Solid Fats & candy/sweet
Crystal Drink Envelopes Added Sugar
Mix
Powder & Sugar Sweetened Solid Fats & candy/sweet
Crystal Drink Sticks Added Sugar
Mix
Processed Dips Solid Fats & butter/cream/solid
Added Sugar fat
Processed Packaged Dry Solid Fats & sweetened beverages
Smoothie Mix Added Sugar
Refrgratd Dairy Case Citrus Solid Fats & sweetened beverage
Juices/Drinks Pnch/Oj Subs Added Sugar
Refrgratd Dairy Case Fruit Solid Fats & sweetened beverage
Juices/Drinks Drinks (No Ju) Added Sugar
Refrgratd Dairy Case Juice Solid Fats & sweetened beverage
Juices/Drinks Drnk Under 10 Added Sugar
Refrgratd Dairy Case Tea Solid Fats & sweetened beverage
Juices/Drinks With Sugar Or S Added Sugar
Refrigerated Ntrn Refrig Juice Solid Fats & sweetened beverage
Dairy Case Under 50% Added Sugar
Refrigerated Sour Cream/Cottage Solid Fats & butter/cream/solid
Dairy Case Cheese Added Sugar fat
Refrigerated Tea With Sweetener/ Solid Fats & sweetened beverage
Dairy Case Sugar Added Sugar
Rtd Tea/New Age Juice (Under 10% Solid Fats & sweetened beverage
Juice Juice) Added Sugar
Rtd Tea/New Age Juice (Under 50% Solid Fats & sweetened beverage
Juice Juice) Added Sugar
Rtd Tea/New Age Tea Sweetened Solid Fats & sweetened beverage
Juice Added Sugar
Service Sv Bev: Bev/Juic Solid Fats & sweetened beverage
Beverage 10-50% Juice Added Sugar
Shelf Stable Apple Juice & Solid Fats & sweetened beverage
Juice Cider (50% And Added Sugar
Under Juice)
Shelf Stable Apple Juice & Solid Fats & sweetened beverage
Juice Cider (Under 10% Added Sugar
Juice)
Shelf Stable Blended Juice & Solid Fats & sweetened beverage
Juice Combinations Added Sugar
Shelf Stable Blended Juice & Solid Fats & sweetened beverage
Juice Combinations Added Sugar
Shelf Stable Cranapple/Cran Solid Fats & sweetened beverage
Juice Grape Juice Added Sugar
Shelf Stable Cranberry Juice Solid Fats & sweetened beverage
Juice (50% And Under Added Sugar
Juice)
Shelf Stable Fruit Drinks: Solid Fats & sweetened beverage
Juice Canned & Glass Added Sugar
Shelf Stable Fruit Drinks: Solid Fats & sweetened beverage
Juice Canned & Glass Added Sugar
Shelf Stable Fruit Drinks: Solid Fats & sweetened beverage
Juice Canned & Glass Added Sugar
Shelf Stable Fruit Drinks: Solid Fats & sweetened beverage
Juice Canned & Glass Added Sugar
Shelf Stable Grape Juice (50% Solid Fats & sweetened beverage
Juice And Under Juice) Added Sugar
Shelf Stable Grapefruit Juice Solid Fats & sweetened beverage
Juice (50% And Under Added Sugar
Juice)
Shelf Stable Lemon Juice & Lime Solid Fats & sweetened beverage
Juice Juice (50% And Added Sugar
Under Juice)
Shelf Stable Lemon Juice & Lime Solid Fats & sweetened beverage
Juice Juice Added Sugar
Shelf Stable Nectars (50% And Solid Fats & sweetened beverage
Juice Under Juice) Added Sugar
Shelf Stable Prune Juice (50% Solid Fats & sweetened beverage
Juice And Under Juice) Added Sugar
Shortening & Solid Shortening Solid Fats & butter/cream/solid
Oil Added Sugar fat
Soft Drinks Mixers (Tonic Solid Fats & sweetened beverage
Water/Gngr Ale) Added Sugar
Soft Drinks Mixers (Tonic Wtr/ Solid Fats & sweetened beverage
Gngr Ale) Added Sugar
Soft Drinks Sft Drnk 1 Liter Solid Fats & sweetened beverage
Btl Carb Added Sugar
Soft Drinks Sft Drnk 2 Liter Solid Fats & sweetened beverage
Btl Carb Incl Added Sugar
Soft Drinks Sft Drnk 3 Liter Solid Fats & sweetened beverage
Btl Carb Added Sugar
Soft Drinks Sft Drnk Misc Btl Solid Fats & sweetened beverage
(Any Btl) Added Sugar
Soft Drinks Sft Drnk Misc Can Solid Fats & sweetened beverage
(Ex: 4/8/18pk) Added Sugar
Soft Drinks Sft Drnk Mlt-Pk Solid Fats & sweetened beverage
Btl Carb Added Sugar
Soft Drinks Sft Drnk Sngl Srv Solid Fats & sweetened beverage
Btl Carb Added Sugar
Soft Drinks Soft Drink Bottle Solid Fats & sweetened beverage
Non-Carb Added Sugar
Soft Drinks Soft Drinks 12/18 Solid Fats & sweetened beverage
& 15pk Can Car Added Sugar
Soft Drinks Soft Drinks 20pk & Solid Fats & sweetened beverage
24pk Can Carb Added Sugar
Soft Drinks Soft Drinks 6pk Solid Fats & sweetened beverage
Can Carb Added Sugar
Soft Drinks Soft Drinks Bottle Solid Fats & sweetened beverage
Returnable Added Sugar
Soft Drinks Soft Drinks Can Solid Fats & sweetened beverage
Non-Carb Added Sugar
Soft Drinks Soft Drinks Single Solid Fats & sweetened beverage
Cans Carb Added Sugar
Soft Drinks Tea Bottles With Solid Fats & sweetened beverage
Sweetener/Sugar Added Sugar
Soft Drinks Tea Can With Solid Fats & sweetened beverage
Sweetener/Sugar Added Sugar
Sugars & Sugar Solid Fats & candy/sweet
Sweeteners Added Sugar
Sugars & Sweeteners Solid Fats & candy/sweet
Sweeteners Added Sugar
Sweet Goods & Sweet Goods: Candy Solid Fats & candy/sweet
Snacks Added Sugar
Sweet Goods & Sweet Goods: Candy Solid Fats & candy/sweet
Snacks W/Flour Added Sugar
Syrups Toppings Ice Cream Toppings Solid Fats & candy/sweet
& Cones Added Sugar
Teas Instant Tea & Tea Solid Fats & sweetened beverage
Mix (W/Sugar) Added Sugar
Traditional Mexican Candy Solid Fats & candy/sweet
Mexican Foods Added Sugar
Trail Mix & Candy W/Flour Solid Fats & candy/sweet
Snacks Added Sugar
Trail Mix & Candy W/O Flour Solid Fats & candy/sweet
Snacks Added Sugar
Trail Mix & Candy W/O Flour Solid Fats & candy/sweet
Snacks Organic Added Sugar
Water Carb Water--Flvrd Solid Fats & sweetened beverage
Sweetened Added Sugar
Water Energy Drinks Solid Fats & sweetened beverage
Added Sugar
Authentic Authentic Pasta/ Composite entree/meal
Hispanic Fds & Rice/Beans
Product
Authentic Authentic Soups/ Composite soup
Hispanic Fds & Bouillons
Product
Authentic Hispanic Cookies/ Composite desserts
Hispanic Fds & Crackers
Product
Authentic Italian Pasta And Composite entree/meal
Italian Foods Pasta Sauce
Bag Snacks Store Brand Composite snacks
Bag Snacks Misc Bag Snacks Composite snacks
Bag Snacks Mult Pk Bag Snacks Composite snacks
Bag Snacks Potato Chips Composite snacks
Bag Snacks Salsa & Dips Composite snacks
Baked Sweet Snack Cake--Multi Composite desserts
Goods Pack
Baked Sweet Sweet Goods--Full Composite desserts
Goods Size
Bakery Party Composite desserts
Trays
Bakery Party Party Trays: Composite desserts
Trays Breakfast Sweets
Bakery Party Party Trays: Cakes Composite desserts
Trays
Bakery Party Party Trays: Composite desserts
Trays Cookies--Rolls
Baking Mixes Brownie Mix Composite desserts
Baking Mixes Cookies Mix Composite desserts
Baking Mixes Layer Cake Mix Composite desserts
Baking Mixes Microwavable Cake Composite desserts
Mix
Baking Needs Pie Crust Mixes & Composite desserts
Shells
Baking Needs Pie Filling/ Composite desserts
Mincemeat/Glazes
Bulk Food Grain/Beans Bulk Composite entree/meal
Bulk Food Misc Bulk Snacks Composite snacks
Sweetened
Bulk Food Snacks Bulk Composite snacks
Cakes Cakes Ingredients Composite desserts
Cakes Cakes: Angel Fds/ Composite desserts
Cke Rolls
Cakes Cakes: Angl Fd/ Composite desserts
Roll Novelties
Cakes Cakes: Birthday/ Composite desserts
Celebration Sheet
Cakes Cakes: Cheesecake Composite desserts
Cakes Cakes: Cheesecake Composite desserts
Novelties
Cakes Cakes: Cndles/Retl Composite desserts
Accss
Cakes Cakes: Creme/ Composite desserts
Pudding
Cakes Cakes: Creme/ Composite desserts
Pudding Novelties
Cakes Cakes: Cupcakes Composite desserts
Cakes Cakes: Fancy/ Composite desserts
Service Case
Cakes Cakes: Ice Cream Composite desserts
Cakes Cakes: Kosher Composite desserts
Cakes Cakes: Layers Composite desserts
Cakes Cakes: Layers/ Composite desserts
Sheets Novelties
Cakes Cakes: Novelties Composite desserts
Cakes Cakes: Pound Composite desserts
Cakes Cakes: Pound Cake Composite desserts
Novelties
Cakes Cakes: Sheet Composite desserts
Cakes Cakes: Birthday/ Composite desserts
Celebration Layer
Cakes Cakes: Wedding/ Composite desserts
Designer Series
Canned Pasta & Can Pasta Composite entree/meal
Mwv Fd--Shlf
Stbl
Canned Pasta & Microwavable Cups Composite entree/meal
Mwv Fd--Shlf [Canned Pasta]
Stbl
Canned Pasta & Microwavable Trays Composite entree/meal
Mwv Fd--Shlf [Canned Pasta]
Stbl
Canned Soups Condensed Soup Composite soup
Chilled Ready Store Brand Composite entree/meal
Meals
Chilled Ready Fresh Meals Composite entree/meal
Meals
Chilled Ready Fresh Side Dishes Composite entree/meal
Meals
Cnv Breakfast & Treats Composite snacks
Wholesome Snks
Convenient Convenient Meals-- Composite entree/meal
Meals Adult Meal
Convenient Convenient Meals-- Composite entree/meal
Meals Kids Meal
Cookie/Cracker Multi-Pack Cookies Composite desserts
Multi-Pks
Cookies Chocolate Covered Composite desserts
Cookies
Cookies Cookies/Sweet Composite desserts
Goods
Cookies Cookies: Gourmet Composite desserts
Cookies Cookies: Holiday/ Composite desserts
Special Occas
Cookies Cookies: Kosher Composite desserts
Cookies Cookies: Less Than Composite desserts
6
Cookies Cookies: Message Composite desserts
Cookies Cookies: Party Composite desserts
Cookies Cookies: Regular Composite desserts
Cookies Fruit Filled Composite desserts
Cookies
Cookies Premium Cookies Composite desserts
(Ex: Pepperidge)
Cookies Sandwich Cookies Composite desserts
Cookies Specialty Cookies Composite desserts
Cookies Tray Pack/Choc Composite desserts
Chip Cookies
Cookies Vanilla Wafer/Kids Composite desserts
Cookies
Cookies Wellness/Portion Composite desserts
Control [Cookies]
Dinner Mixes-- Macaroni & Cheese Composite entree/meal
Dry Dnrs
Dinner Mixes-- Microwave Dinners Composite entree/meal
Dry
Dinner Mixes-- Package Dinners Composite entree/meal
Dry Meat Included
Dinner Mixes-- Package Dinners W/ Composite entree/meal
Dry O Meat
Dinner Mixes-- Package Dinners/ Composite entree/meal
Dry Pasta Salads
Dinner Mixes-- Skillet Dinners Composite entree/meal
Dry
Dressings/Dips Dips Guacamole/ Composite snacks
Salsa/Queso
Dressings/Dips Dips Organic Composite snacks
Dressings/Dips Dips Veggie Composite snacks
Dry Bean Veg & Dry Beans/Peas/ Composite entree/meal
Rice Barley: Bag & B
Dry Mix Freeze Mixes/Pwdrs/ Composite desserts
Desserts Liquids
Dry Mix Misc: Cheesecake/ Composite desserts
Desserts Mousse Mixes
Dry Mix Pudding & Gelatin Composite desserts
Desserts Cups/Cans
Dry Mix Puddings Dry Composite desserts
Desserts
Dry/Ramen 12 Pack Soup/Case Composite soup
Bouillon Soup/Etc.
Dry/Ramen Bouillon Composite soup
Bouillon
Dry/Ramen Dry Soup Composite soup
Bouillon
Fitness & Diet Fitness & Diet-- Composite snacks
Bars (Supplement)
Fitness & Diet Fitness & Diet-- Composite snacks
Bars W/Flour
Fitness & Diet Fitness & Diet-- Composite snacks
Bars W/O Flour
Frozen Bread Desserts Composite desserts
And Desserts
Frozen Donuts Composite desserts
Breakfast
Frozen Meals/Sandwichs Composite entree/meal
Breakfast
Frozen Foods Frzn Composite entree/meal
Breakfast Breakfast Entrees
Frozen Foods Frzn Composite entree/meal
Breakfast Breakfast
Sandwiches
Frozen Desserts Frozen Cakes/ Composite desserts
Desserts
Frozen Desserts Frozen Cream Pies Composite desserts
Frozen Desserts Frozen Fruit Pies Composite desserts
& Cobblers
Frozen Desserts Frzn Composite desserts
Pastry&Cookies
Frozen Desserts Frzn Pie Shells/ Composite desserts
Pastry Shell/F
Frozen Desserts Single Serv/ Composite desserts
Portion Control
Frozen Entrees Bowls Composite entree/meal
Frozen Entrees Meatless/ Composite entree/meal
Vegetarian
Frozen Entrees Pasta/Skillet Composite entree/meal
Meals
Frozen Entrees Soup Composite soup
Frozen Burritos Composite entree/meal
Handhelds &
Snacks
Frozen Corn Dogs Composite snacks
Handhelds &
Snacks
Frozen Sandwiches & Composite entree/meal
Handhelds & Handhelds
Snacks
Frozen Snacks/Appetizers Composite snacks
Handhelds &
Snacks
Frozen Ice Almond Composite desserts
Cream &
Novelties
Frozen Ice Ice Cream Composite desserts
Cream &
Novelties
Frozen Ice Novelties--Dairy Composite desserts
Cream &
Novelties
Frozen Ice Novelties--Non Composite desserts
Cream & Dairy
Novelties
Frozen Ice Novelties--Water Composite desserts
Cream & Base
Novelties
Frozen Ice Rice Composite desserts
Cream &
Novelties
Frozen Ice Soy Composite desserts
Cream &
Novelties
Frozen Ice Yogurt/Sorbet And Composite desserts
Cream & Kefir
Novelties
Frozen Juice Smoothies--Frz Composite desserts
And Smoothies
Frozen Adult Premium Composite desserts
Novelties--Wat [Frozen
er Ice Novelties]
Frozen Cones [Frozen Composite desserts
Novelties--Wat Novelties]
er Ice
Frozen Cups/Push Ups/ Composite desserts
Novelties--Wat Other [Frozen
er Ice Novelties]
Frozen Ice Cream Composite desserts
Novelties--Wat Sandwiches
er Ice
Frozen Sticks/Enrobed Composite desserts
Novelties--Wat [Frozen
er Ice Novelties]
Frozen Water Ice [Frozen Composite desserts
Novelties--Wat Novelties]
er Ice
Frozen Pizza Meatless/ Composite entree/meal
Vegetarian
Frozen Pizza Pizza/Economy Composite entree/meal
Frozen Pizza Pizza/Premium Composite entree/meal
Frozen Pizza Pizza/Single Serve/ Composite entree/meal
Microwave
Frozen Pizza Pizza/Traditional Composite entree/meal
Frozen Pizza Pizza/Value Composite entree/meal
Frozen Pizza Single Serve Composite entree/meal
Frozen Snacks Burritos--Meatless/ Composite entree/meal
And Vegetarian
Frozen Snacks Appetizers Composite snacks
And Handhelds
Frozen Snacks Burritos--Meat Composite entree/meal
And Handhelds Protein
Frozen Snacks Wraps/Handhelds-- Composite entree/meal
And Handhelds Meat
Frozen Snacks Wraps/Handhelds-- Composite entree/meal
And Handhelds Meatless
Frozen Meals Composite entree/meal
Vegetables And
Potatoes
Frzn Meatless Meatless Breakfast Composite entree/meal
Frzn Meatless Meatless Burgers Composite entree/meal
Frzn Meatless Meatless Entrees Composite entree/meal
Frzn Meatless Meatless Meal Composite entree/meal
Starters
Frzn Meatless Meatless Composite entree/meal
Miscellaneous
Frzn Meatless Meatless Poultry Composite entree/meal
Frzn Meatless Meatless Snacks Composite snacks
Frzn Multi Fz Crockpots/Soups Composite soup
Serve
Frzn Multi Fz Family Style Composite entree/meal
Serve Entrees
Frzn Multi Fz Skillet Meals Composite entree/meal
Serve
Frzn Prepared Fz Meal Kits/ Composite entree/meal
Chicken Stuffed/Other
Frzn Ss Economy Fz Ss Economy Composite entree/meal
Meals Meals All
Frzn Ss Premium Fz Regional/Other Composite entree/meal
Meals
Frzn Ss Premium Fz Ss Prem Composite entree/meal
Meals Nutritional Meals
Frzn Ss Premium Fz Ss Prem Composite entree/meal
Meals Traditional Meals
Gift & Fruit Snack Packs W/Soda Composite snacks
Baskets
Ice Cream Ice Pails [Ice Cream & Composite desserts
Milk & Sherbert]
Sherbets
Ice Cream Ice Premium [Ice Cream Composite desserts
Milk & & Sherbert]
Sherbets
Ice Cream Ice Premium Pints [Ice Composite desserts
Milk & Cream & Sherbert]
Sherbets
Ice Cream Ice Quarts [Ice Cream Composite desserts
Milk & & Sherbert]
Sherbets
Ice Cream Ice Super Premium Composite desserts
Milk & Pints [Ice Cream
Sherbets & Sherbert]
Ice Cream Ice Traditional [Ice Composite desserts
Milk & Cream & Sherbert]
Sherbets
Kosher Foods Kosher Snacks Composite snacks
And Products
Kosher Foods Kosher Soups Composite soup
And Products
Packaged Trail Mixes Composite snacks
Natural Snacks
Party Tray Deli Tray-- Composite entree/meal
Includes Non-
Foods
Party Tray Deli Tray: Composite entree/meal
Appetizers & Hors
D'oe
Party Tray Deli Tray: Chicken Composite entree/meal
Party Tray Deli Tray: Fruit Composite entree/meal
And Vegetable
Party Tray Deli Tray: Meat Composite entree/meal
And Cheese
Party Tray Deli Tray: Composite entree/meal
Sandwiches
Party Tray Deli Trays: Hot Composite entree/meal
Pies Pie Ingredients Composite desserts
Pies Pies: Cream/ Composite desserts
Meringue
Pies Pies: Fruit/Nut Composite desserts
Pies Pies: Kosher Composite desserts
Pies Pies: Pumpkin/ Composite desserts
Custard
Pies Pies: Tarts/Minis/ Composite desserts
Crstdas
Prepared/Pdgd Boxed Prepared/ Composite entree/meal
Foods Entree/Dry Prep
Refrgrated Refrigerated Composite desserts
Dough Products Cookie Dough
Refrgrated Refrigerated Composite desserts
Dough Products Cookies--Brand
Refrgrated Refrigerated Composite desserts
Dough Products Cookies--Seasonal
Refrgrated Refrigerated Pie Composite desserts
Dough Products Crust
Refrigerated Refrigerated Composite desserts
Desserts Pudding
Restricted Diet Cookies Composite desserts
Rts/Micro Soup/ Broth Composite soup
Broth
Rts/Micro Soup/ Microwavable Soups Composite soup
Broth
Rts/Micro Soup/ Rts Soup: Chunky/ Composite soup
Broth Homestyle/Et
Salad & Dips Sal: Desserts-- Composite desserts
Bulk
Salad & Dips Sal: Desserts-- Composite desserts
Prepack
Salad Bar Soups Composite soup
Sandwiches Sandwich Composite entree/meals
Ingredients
Sandwiches Sandwiches--(Cold) Composite entree/meals
Sandwiches Sandwiches: Kosher Composite entree/meals
(Cold)
Seafood--Party Party Tray Other Composite entree/meal
Trays
Seafood--Party Party Tray Other Composite entree/meal
Trays
Seafood--Salad/ Salads Composite entree/meal
Dips/Sce/Cond
Service Case Side Dishes Composite entree/meal
Meat
Service Case Stuffed/Mixed Composite entree/meal
Meat
Single Serve Single Serve Composite desserts
Items Desserts
Single Serve Single Serve Composite snacks
Items Snacks
Single Serve Snack Cake--Single Composite desserts
Sweet Goods Serve
Snack Nuts/Trail Mix/ Composite snacks
Dried Fruit
Snack Soy/Rice Snacks Composite snacks
Snack Specialty Chips Composite snacks
Snacks Snacks: Dry Composite snacks
Snacks Snacks: Gift Packs Composite snacks
Snacks Snacks: Salty Composite snacks
Snacks Snacks:Chippery Composite snacks
Soup Asceptic Composite soup
Soup Broths Composite soup
Soup Cans Soup/Chili Composite soup
Soup Cups Composite soup
Ss/Vending-- Vendor Size/Single Composite desserts
Cookie/Cracker Serve Cookie
Ss/Vending-- Salty Snacks Composite snacks
Salty Snacks Vending
Ss/Vending-- Salty Snacks W/ Composite snacks
Salty Snacks Sweetener Vending
Sushi Sushi--In Store Composite entree/meal
Prepared
Sushi Sushi--Kosher Composite entree/meal
Sushi Sushi--Prepackaged Composite entree/meal
Sushi Sushi: In Store Composite entree/meal
Prepared (Hot)
Sushi Sushi: Ingredients Composite entree/meal
Sushi Sushi: In-Store Composite entree/meal
Prepared (Dine)
Sushi Sushi: Smallwares Composite entree/meal
Sweet Goods Sw Gds: Kosher Composite desserts
Breakfast
Sweet Goods Sw Gds: Muffins Composite desserts
Sweet Goods Sw Gds: Sw Rolls/ Composite desserts
Dan
Sweet Goods Sw Gds: Coffee Composite desserts
Cakes
Sweet Goods Sw Gds: Donuts Composite desserts
Sweet Goods Sw Gds: Donuts-- Composite desserts
Less Than 6
Sweet Goods Sw Gds: Muffins-- Composite desserts
Lss Thn 6
Sweet Goods Swt Gds Composite desserts
Ingredients
Sweet Goods & Sw Gds: Brownie/ Composite desserts
Snacks Bar Cookie
Sweet Goods & Sw Gds: Kosher Composite desserts
Snacks
Sweet Goods & Sw Gds: Puff Composite desserts
Snacks Pastry
Sweet Goods & Sw Gds: Specialty Composite desserts
Snacks Desserts
Sweet Goods & Sw Gds: Swt/Flvrd Composite desserts
Snacks Loaves
Traditional Asian Foods And Composite entree/meal
Asian Foods Meals
Traditional Mexican Dinners Composite entree/meal
Mexican Foods And Foods
Trail Mix & Trail Mixes/Snack Composite snacks
Snacks
Trail Mix & Trail Mixes/Snacks Composite snacks
Snacks Organic
Unknown Frozen Breakfast Composite entree/meal
Unknown Frozen Dessert Composite desserts
(Ice Cream Cake)
Unknown Frozen Entrees Composite entree/meal
Unknown Frozen Ice Cream Composite desserts
Unknown Frozen Side Dish Composite entree/meal
Value-Added Parfait Cups Composite desserts
Fruit Instore
Warehouse Canister Snacks Composite snacks
Snacks
Warehouse Misc Snacks Composite snacks
Snacks
Warehouse Misc Snacks W/ Composite snacks
Snacks Sweetener
Warehouse Snack Mix Composite snacks
Snacks
Authentic Asian Authentic Chinese Other misc
Foods Foods
Authentic Asian Authentic Japanese Other misc
Foods Foods
Authentic Asian Authentic Thai Other misc
Foods Foods
Authentic Asian Other Authentic Other misc
Foods Asian Foods
Authentic Caribbean Foods Other misc
Caribbean
Foods
Authentic Central American Other misc
Central Foods
American Fds
Authentic Hispanic Baking Other seasoning/baking
Hispanic Fds & Needs need
Product
Authentic Authentic Dry Other unsweetened
Hispanic Fds & Beverages W/O beverage
Product Sweetener
Authentic Hispanic Other condiments
Hispanic Fds & Condiments
Product
Authentic Hispanic Spices Other seasoning/baking
Hispanic Fds & And Seasonings need
Product
Authentic Other Italian Other misc
Italian Foods Foods
Authentic South South American Other misc
American Fds Foods
Baby Food Baby Cereal Other infant formula/
baby food
Baby Food Baby Crackers Other infant formula/
baby food
Baby Food Baby Food Other infant formula/
baby food
Baby Food Baby Formula Other infant formula/
baby food
Baby Food Baby Misc Other infant formula/
baby food
Baby Foods Baby Food-- Other infant formula/
Beginner baby food
Baby Foods Baby Food Cereals Other infant formula/
baby food
Baby Foods Baby Food Junior/ Other infant formula/
All Brands baby food
Baby Foods Baby Juices Other infant formula/
baby food
Baby Foods Baby Spring Waters Other infant formula/
baby food
Baking Flours/Grains/ Other seasoning/baking
Sugar need
Baking Mixes Other seasoning/baking
need
Baking Spices Other seasoning/baking
need
Baking Mixes Microwave Mixes: Other seasoning/baking
All Other need
Baking Mixes Miscellaneous Other seasoning/baking
Package Mixes need
Baking Needs Baking Cocoa Other seasoning/baking
need
Baking Needs Baking Powder & Other seasoning/baking
Soda need
Baking Needs Bits & Morsels Other seasoning/baking
[Baking Needs] need
Baking Needs Cooking Chocolate Other seasoning/baking
(Ex.: Smi-Swt) need
Baking Needs Cooking Chocolate Other seasoning/baking
Unsweetened need
Baking Needs Yeast: Dry Other seasoning/baking
need
Beverages Tea Unsweetened Other unsweetened
(Can/Bottle) beverage
Bulk Food Bulk Spices Other seasoning/baking
need
Bulk Food Coffee & Tea Bulk Other unsweetened
beverage
Bulk Food Misc Bulk Other misc
Coffee & Bulk Coffee Other unsweetened
Creamers beverage
Coffee & Coffee Pods/ Other unsweetened
Creamers Singles/Filter beverage
Pac
Coffee & Flavored Bag Other unsweetened
Creamers Coffee beverage
Coffee & Flavored Can Other unsweetened
Creamers Coffee beverage
Coffee & Flavored Instant Other unsweetened
Creamers Coffee beverage
Coffee & Ready To Drink Other unsweetened
Creamers Coffee beverage
Coffee & Ready To Drink Other unsweetened
Creamers Coffee Suppleme beverage
Coffee & Specialty Instant Other unsweetened
Creamers Coffee W/O S beverage
Coffee & Specialty Instant Other unsweetened
Creamers Coffee W/Swe beverage
Coffee & Unflavored Bag Other unsweetened
Creamers Coffee beverage
Coffee & Unflavored Can Other unsweetened
Creamers Coffee beverage
Coffee & Unflavored Instant Other unsweetened
Creamers Coffee beverage
Coffee Shop Sv Bev: Inged/ Other unsweetened
Portion Pk beverage
Coffee Shop Sv Bev: Carb Wat- Other unsweetened
Flv/Unflv beverage
Coffee Shop Coff Shop: Instant Other unsweetened
Sweet Goods & Retail Pack beverage
Rtl
Coffee Shop Coff Shop: Retail Other unsweetened
Sweet Goods & Pack Beverag beverage
Rtl
Coffee Shop Coff Shop: Whole Other unsweetened
Sweet Goods & Bean Retail P beverage
Rtl
Condiments Ketchup/Mustard/ Other condiments
Bbq Sce/Marina
Condiments Oils/Vinegar Other condiments
Condiments Pickles/Olives/ Other condiments
Kraut
Condiments & Bbq Sauce Other condiments
Sauces
Condiments & Catsup Other condiments
Sauces
Condiments & Chili Sauce/ Other condiments
Sauces Cocktail Sauce
Condiments & Hot Sauce Other condiments
Sauces
Condiments & Marinades Other condiments
Sauces
Condiments & Misc Meat Sauces Other condiments
Sauces
Condiments & Mustard--All Other Other condiments
Sauces
Condiments & Steak & Worchester Other condiments
Sauces Sauce
Condiments & Wing Sauce Other condiments
Sauces
Condiments & Yellow Mustard Other condiments
Sauces
Deli Dl Spec: Beverages Other unsweetened
Specialties beverage
(Retail Pk)
Deli Dl Spec: Must/Oils/ Other condiments
Specialties Vinegars
(Retail Pk)
Deli/Bakery Deli/Bakery Other misc
Discontnued Discontinued
Items Items
Dietary Aid Diet Cntrl Liqs Other supplements/meal
Prdct/Med Liq Supplement replacements/
Nutr energy drinks
Dietary Aid Diet Cntrl Powders Other supplements/meal
Prdct/Med Liq Nutritional replacements/
Nutr energy drinks
Dietary Aid Diet Control Water Other supplements/meal
Prdct/Med Liq replacements/
Nutr energy drinks
Dietary Aid Diet Cntrl Bars Other supplements/meal
Prdct/Med Liq (Supplement) replacements/
Nutr energy drinks
Dietary Aid Diet Cntrl Bars Other supplements/meal
Prdct/Med Liq Nutritional replacements/
Nutr energy drinks
Dietary Aid Diet Cntrl Bars Other supplements/meal
Prdct/Med Liq Nutritional W/ replacements/
Nutr energy drinks
Dietary Aid Diet Cntrl Liqs Other supplements/meal
Prdct/Med Liq Nutritional replacements/
Nutr energy drinks
Dietary Aid Diet Energy Drinks Other supplements/meal
Prdct/Med Liq replacements/
Nutr energy drinks
Dietary Aid Powder Nutrition Other supplements/meal
Prdct/Med Liq Products replacements/
Nutr energy drinks
Dry Mix Desserts Gelatin Other seasoning/baking
need
Dry Tea/Coffee/ Coffee Ground Other unsweetened
Coco Mixes beverage
Dry Tea/Coffee/ Coffee Whole Bean Other unsweetened
Coco Mixes beverage
Dry Tea/Coffee/ Tea Bags Other unsweetened
Coco Mixes (Supplement) beverage
Dry Tea/Coffee/ Tea Dry Other unsweetened
Coco Mixes beverage
Dry Sauce/Gravy/ Cooking Bags With Other seasoning/baking
Potatoes/ Spices/Seaso need
Stuffing
Dry Sauce/Gravy/ Gravy Can/Glass Other seasoning/baking
Potatoes/ need
Stuffing
Dry Sauce/Gravy/ Sauce Mixes/Gravy Other seasoning/baking
Potatoes/ Mixes Dry need
Stuffing
Eggs/Muffins/ Misc Dairy Other misc
Potatoes Refigerated
Enhancements Enhancements--Othe Other supplements/meal
r replacements/
energy drinks
Enhancements Enhancements--Pick Other condiments
led Items
Enhancements Enhancements--Pick Other condiments
les/Kraut
Enhancements Enhancements--Sala Other condiments
ds/Spreads
Enhancements Enhancements--Spic Other seasoning/baking
es/Sauces need
European Foods British Foods Other misc
European Foods French Foods Other misc
European Foods German Foods Other misc
European Foods Mediterranean/ Other misc
Greek Foods
European Foods Other Ethnic Foods Other misc
European Foods Polish Foods Other misc
European Foods Scandinavian Foods Other misc
Fitness & Diet Fitness & Diet Other supplements/meal
Energy Drinks F/S replacements/
energy drinks
Fitness & Diet Fitness & Diet Other supplements/meal
Energy Drinks Non replacements/
energy drinks
Fitness & Diet Fitness & Diet Other supplements/meal
Isotonic Drinks replacements/
energy drinks
Fitness & Diet Fitness & Diet Other supplements/meal
Isotonic Drinks replacements/
energy drinks
Fitness & Diet Fitness & Diet-- Other supplements/meal
Liq (Supplement) replacements/
energy drinks
Fitness & Diet Fitness & Diet-- Other supplements/meal
Liq Ntrtnl replacements/
energy drinks
Fitness & Diet Fitness & Diet-- Other supplements/meal
Powder replacements/
(Supplement) energy drinks
Fitness & Diet Fitness & Diet-- Other supplements/meal
Powder Ntrtnl replacements/
energy drinks
Fitness & Diet Fitness/Diet--Meal Other supplements/meal
Replacement replacements/
energy drinks
Frozen Ethnic Frozen Other misc
Internaional
Frozen Ethnic Frozen Kosher Other misc
Frozen Meat Alternatives Micro Other supplements/meal
Protein replacements/
energy drinks
Frzn Multi Frozen Other Other misc
Serve
Gift & Fruit Gift Baskets W/ Other misc
Baskets Food
Gift & Fruit Snack Packs W/Food Other misc
Baskets
Indian Foods Authentic Indian Other misc
Foods
Infant Formula Baby Isotonic Other infant formula/
Drinks baby food
Infant Formula Infant Formula Other infant formula/
Concentrate baby food
Infant Formula Infant Formula Other infant formula/
Milk Base baby food
Infant Formula Infant Formula Other infant formula/
Ready To Use baby food
Infant Formula Infant Formula Other infant formula/
Solutions Large baby food
Infant Formula Infant Formula Soy Other infant formula/
Base baby food
Infant Formula Infant Formula Other infant formula/
Specialty baby food
Infant Formula Infant Formula Other infant formula/
Starter Large Pk baby food
Infant Formula Infant Formula Other infant formula/
Starter/Solution baby food
Infant Formula Infant Formula Other infant formula/
Toddler baby food
Infant Formula Infant Formula Up Other infant formula/
Age baby food
Isotonic Drinks Isotonic Drinks Other supplements/meal
Multi-Pack replacements/
energy drinks
Isotonic Drinks Isotonic Drinks Other supplements/meal
Multi-Serve replacements/
energy drinks
Isotonic Drinks Isotonic Drinks Other supplements/meal
Powdered replacements/
energy drinks
Isotonic Drinks Isotonic Drinks Other supplements/meal
Single Serve replacements/
energy drinks
Isotonic Drinks Sports Bars Other supplements/meal
replacements/
energy drinks
Isotonic Drinks Sports Drink N/ Other supplements/meal
Supplmnt Milk replacements/
energy drinks
Isotonic Drinks Sports Drink Other supplements/meal
Supplement replacements/
energy drinks
Juices Super Juices Antioxidant/ Other supplements/meal
Premium Wellness replacements/
energy drinks
Juices Super Juices Proteins Other supplements/meal
Premium replacements/
energy drinks
Kosher Exotic [Kosher Other misc
Foods]
Kosher Further Prepared Other misc
Kosher Foods Kosher Baking Other seasoning/baking
And Products Needs need
Kosher Foods Kosher Condiments Other condiments
And Products
Kosher Foods Passover Products Other misc
And Products
Mediterranean Sal: Olives/ Other condiments
Bar Pickles--Bulk
Mediterranean Sal: Olives/ Other condiments
Bar Pickles--Bulk
Mediterranean Sal: Olives/ Other condiments
Bar Pickls--Prpck
Mediterranean Sal: Olives/ Other condiments
Bar Pickls--Prpck
Mixers Margarita Salt/ Other condiments
Sugar/Misc
Multicultural Asian Processed Other misc
Products
Multicultural Hispanic Processed Other misc
Products Produce
Non-Dairy/Dairy Aseptic Soy/Rice Other misc
Powder
Pickle/Relish/ Green Olives Other condiments
Pckld Veg &
Olives
Pickle/Relish/ Peppers Other condiments
Pckld Veg &
Olives
Pickle/Relish/ Pickld Veg/Peppers/ Other condiments
Pckld Veg & Etc.
Olives
Pickle/Relish/ Relishes Other condiments
Pckld Veg &
Olives
Pickle/Relish/ Ripe Olives Other condiments
Pckld Veg &
Olives
Pickle/Relish/ Specialty Olives Other condiments
Pckld Veg &
Olives
Powder & Sugar Free Other unsweetened
Crystal Drink Canister [Powder beverage
Mix Drink Mix]
Powder & Sugar Free Sticks Other unsweetened
Crystal Drink [Powder Drink beverage
Mix Mix]
Powder & Tea Other unsweetened
Crystal Drink beverage
Mix
Powder & Unsweetened Other unsweetened
Crystal Drink Envelope [Powder beverage
Mix Drink Mix]
Prepared/Pdgd Prepared/Pkgd Food Other misc
Foods Misc
Processed Packaged Dry Mixes Other misc
Processed Processed Other Other misc
Refrgratd Dairy Case Tea No Other unsweetened
Juices/Drinks Sugar Or Swe beverage
Refrigerated Non-Dairy Milks Other misc
Dairy Case
Refrigerated Tea W/O Sweetener/ Other unsweetened
Dairy Case Sugar beverage
Refrigerated Misc: Herring/ Other condiments
Grocery Pickles/Horserad
Refrigerated Refrigerated Other misc
Grocery Kosher Products
Refrigerated Hispanic Cultured Other misc
Hispanic Products
Grocery
Refrigerated Misc Hispanic Other misc
Hispanic Grocery
Grocery
Refrigerated Refrigerated Other misc
Hispanic Hispanic Drinks
Grocery
Refrigerated Vegetarian Misc Other misc
Vegetarian
Restricted Diet Baking Other seasoning/baking
need
Restricted Diet Beverage Other supplements/meal
replacements/
energy drinks
Restricted Diet Breakfast Foods Other supplements/meal
replacements/
energy drinks
Restricted Diet Diet Bars/Diet Other supplements/meal
Liquid Meals replacements/
energy drinks
Restricted Diet Misc Diet Other supplements/meal
replacements/
energy drinks
Rtd Tea/New Age Sparkling Tea Other unsweetened
Juice beverage
Rtd Tea/New Age Tea Unsweetened Other unsweetened
Juice beverage
Salad & Dips Sal: Kosher Other misc
Salad & Dips Sal:Dip Prepack Other condiments
Salad Bar Condiments/ Other condiments
Supplies
Salad Bar Salad Bar Other Other misc
Salad Dresing & Dry Salad Dressing Other condiments
Sandwich & Dip Mixes
Spreads
Seafood--Salad/ Dips/Spreads Other condiments
Dip/Sce/Cond
Seafood--Salad/ Sauces Other condiments
Dip/Sce/Cond
Seafood--Salad/ Other Pkgd Dip/ Other condiments
Dips/Sce/Cond Sauce/Condiment
Seafood--Salad/ Sauces Other condiments
Dips/Sce/Cond
Seafood--Salad/ Spices/Marinades Other condiments
Dips/Sce/Cond
Service Sv Bev: Coffee Other unsweetened
Beverage beverage
Service Sv Bev: Flav Tea Other unsweetened
Beverage Products beverage
Service Sv Bev: N/Carb Flv Other unsweetened
Beverage Frk/Minwtr beverage
Service Sv Bev: Spring Other unsweetened
Beverage Water beverage
Shelf Stable Tea Bottles Other unsweetened
Juice beverage
Soft Drinks Club Soda Other unsweetened
beverage
Soft Drinks Misc Items For Other unsweetened
Soft Drinks beverage
Soft Drinks Seltzer Unflavored Other unsweetened
beverage
Soft Drinks Unswntd Flavored Other unsweetened
Seltzer Water beverage
Spices & Food Colorings Other seasoning/baking
Extracts need
Spices & Gourmet Spices Other seasoning/baking
Extracts need
Spices & Imitation Extracts Other seasoning/baking
Extracts need
Spices & Pure Extracts Other seasoning/baking
Extracts need
Spices & Salt Substitutes Other seasoning/baking
Extracts need
Spices & Spices & Other seasoning/baking
Extracts Seasonings need
Spices & Table Salt/Popcorn Other seasoning/baking
Extracts Salt/Ice Cr need
Spices & Traditional Spices Other seasoning/baking
Extracts need
Spices/Jarred Spices & Other seasoning/baking
Garlic Seasonings need
Spices/Jarred Spices & Other seasoning/baking
Garlic Seasonings need
Organic
Teas Bulk Tea Other unsweetened
beverage
Teas Instant Tea & Tea Other unsweetened
Mix beverage
Teas Supplemental Tea Other unsweetened
beverage
Teas Tea Bags & Bulk Other unsweetened
Tea beverage
Teas Tea Bags/Chai Other unsweetened
beverage
Teas Tea Bags/Green Other unsweetened
beverage
Teas Tea Bags/Herbal Other unsweetened
beverage
Traditional Asian Other Sauces/ Other seasoning/baking
Asian Foods Marinad need
Traditional Asian Soy Sauce Other seasoning/baking
Asian Foods need
Traditional Traditional Thai Other misc
Asian Foods Foods
Traditional Mexican Seasoning Other seasoning/baking
Mexican Foods Mixes need
Traditional Mexican Taco Sauce Other condiments
Mexican Foods
Unknown Frozen Misc Other misc
Vinegar & Cooking Wines Other seasoning/baking
Cooking Wines need
Vinegar & Specialty Vinegar Other seasoning/baking
Cooking Wines need
Vinegar & Vinegar/White & Other seasoning/baking
Cooking Wines Cider need
Water Carb Water Unflvrd Other unsweetened
beverage
Water Carb Water--Flvrd Other unsweetened
Unsweetened beverage
Water Fortified/Water Other unsweetened
beverage
Water Non-Carb Water Other unsweetened
Flvr--Drnk/Mnr beverage
Water Non-Carb Water Other unsweetened
Flvr--Unsweetened beverage
Water--(Sparkli Distilled Water Other unsweetened
ng & Still) beverage
Water--(Sparkli Sparkling Water-- Other unsweetened
ng & Still) Flvrd Sweet beverage
Water--(Sparkli Sparkling Water-- Other unsweetened
ng & Still) Flvrd Unsweetened beverage
Water--(Sparkli Sparkling Water-- Other unsweetened
ng & Still) Unflavored beverage
Water--(Sparkli Spring Water Other unsweetened
ng & Still) beverage
Water--(Sparkli Still Water Other unsweetened
ng & Still) Drnking/Mnrl beverage
Water
Water--(Sparkli Still Water Flvrd Other unsweetened
ng & Still) Drnk/Mnrl Wt beverage
Water--(Sparkli Still Water Flvrd Other unsweetened
ng & Still) Unsweetened beverage
Water--(Sparkli Water--Supplies Other unsweetened
ng & Still) beverage
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Appendix D. Top 100 Subcommodities for SNAP Households By Expenditure
for Each USDA Food Pattern Category
Exhibit D-1: Dairy
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
--------------------------------- Expenditures -------------------------------
Dairy ---------------------------------
Subcommodity Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $191.1 33.25% 1 $853.8 25.69% 1 $1,044.9 26.80%
White Only
Shredded 2 $74.7 13.00% 2 $342.0 10.29% 2 $416.7 10.69%
Cheese
American 3 $44.1 7.67% 4 $136.6 4.11% 4 $180.7 4.63%
Single Cheese
Natural Cheese 4 $35.3 6.14% 3 $216.1 6.50% 3 $251.4 6.45%
Chunks
Bagged Cheese 5 $17.1 2.98% 16 $52.0 1.56% 15 $69.1 1.77%
Snacks
Flavored Milk 6 $16.0 2.78% 14 $59.4 1.79% 12 $75.4 1.93%
String Cheese 7 $15.1 2.63% 9 $99.0 2.98% 8 $114.1 2.93%
Yogurt/Kids 8 $14.0 2.44% 20 $42.4 1.28% 17 $56.5 1.45%
Cottage Cheese 9 $13.9 2.42% 7 $108.8 3.27% 6 $122.7 3.15%
Natural Cheese 10 $13.4 2.33% 6 $113.2 3.41% 5 $126.6 3.25%
Slices
Yogurt/Ss 11 $11.0 1.91% 11 $69.0 2.07% 11 $79.9 2.05%
Regular
Loaf Cheese 12 $10.9 1.90% 23 $38.1 1.15% 21 $49.1 1.26%
Yogurt/Ss 13 $10.2 1.78% 8 $103.1 3.10% 9 $113.3 2.91%
Light
Yogurt/Pro 14 $7.4 1.29% 13 $63.5 1.91% 13 $70.9 1.82%
Active Health
Yogurt/Adult 15 $7.2 1.25% 19 $42.5 1.28% 20 $49.7 1.28%
Multi-Packs
Specialty/ 16 $6.7 1.17% 17 $48.4 1.46% 18 $55.1 1.41%
Lactose Free
Milk
Grated Cheese 17 $6.2 1.08% 25 $33.6 1.01% 24 $39.9 1.02%
Bulk Semi-Hard 18 $6.1 1.05% 18 $44.0 1.32% 19 $50.1 1.28%
[Cheese]
Fluid Milk 19 $5.9 1.02% 5 $113.3 3.41% 7 $119.2 3.06%
Canned Milk 20 $5.5 0.96% 27 $27.9 0.84% 26 $33.4 0.86%
Yogurt/ 21 $5.0 0.86% 10 $77.4 2.33% 10 $82.4 2.11%
Specialty
Greek
Half & Half 22 $4.4 0.77% 15 $54.6 1.64% 16 $59.1 1.52%
Yogurt/Large 23 $4.4 0.76% 22 $40.4 1.22% 23 $44.8 1.15%
Size (16oz Or
Lar)
Miscellaneous 24 $3.8 0.67% 21 $42.1 1.27% 22 $45.9 1.18%
Cheese
Bulk Processed 25 $3.4 0.59% 29 $19.8 0.60% 29 $23.2 0.59%
[Cheese]
Yogurt 26 $3.2 0.56% 12 $67.0 2.02% 14 $70.2 1.80%
Bulk Semi-Soft 27 $3.0 0.53% 28 $23.3 0.70% 28 $26.3 0.68%
[Cheese]
Egg Nog/Boiled 28 $2.7 0.47% 39 $13.3 0.40% 35 $16.0 0.41%
Custard
Buttermilk 29 $2.4 0.42% 33 $15.9 0.48% 31 $18.3 0.47%
Organic Milk 30 $2.0 0.34% 24 $35.4 1.06% 25 $37.3 0.96%
Ricotta Cheese 31 $1.9 0.33% 34 $15.6 0.47% 32 $17.5 0.45%
Aerosol Cheese 32 $1.8 0.31% 54 $5.2 0.16% 51 $7.0 0.18%
Hispanic 33 $1.7 0.29% 50 $6.9 0.21% 45 $8.6 0.22%
Cheese
Specialty Ppk 34 $1.5 0.27% 26 $28.7 0.86% 27 $30.2 0.78%
Cheese Hard/
Grat
Aseptic Milk 35 $1.4 0.24% 38 $13.6 0.41% 38 $15.0 0.38%
Misc Dry 36 $1.4 0.24% 46 $7.3 0.22% 44 $8.7 0.22%
Cheese
Soy Milk 37 $1.3 0.22% 49 $7.1 0.22% 47 $8.4 0.22%
Specialty Ppk 38 $1.2 0.21% 31 $16.2 0.49% 33 $17.5 0.45%
Cheese
Spreads
Mexican Con 39 $1.2 0.21% 63 $3.1 0.09% 61 $4.3 0.11%
Queso
Specialty Ppk 40 $1.2 0.20% 30 $18.5 0.56% 30 $19.6 0.50%
Cheese Feta
Pre-Sliced 41 $1.1 0.20% 35 $14.4 0.43% 36 $15.5 0.40%
Semi-Soft
[Cheese]
Pre-Sliced 42 $1.0 0.18% 36 $14.3 0.43% 37 $15.3 0.39%
Semi-Hard
[Cheese]
Specialty Ppk 43 $0.9 0.15% 32 $16.2 0.49% 34 $17.1 0.44%
Cheese
Mozzarell
Specialty Ppk 44 $0.8 0.15% 52 $6.0 0.18% 52 $6.8 0.17%
Cheese
Processed
Yogurt/Adult 45 $0.8 0.14% 60 $3.8 0.12% 60 $4.7 0.12%
Drinks
Specialty Ppk 46 $0.8 0.14% 37 $13.9 0.42% 39 $14.7 0.38%
Cheese
Cheddar & C
Soy Beverage 47 $0.7 0.12% 53 $5.3 0.16% 54 $6.0 0.15%
Specialty Ppk 48 $0.6 0.10% 40 $11.4 0.34% 40 $12.0 0.31%
Cheese Semi
Soft
Specialty Ppk 49 $0.6 0.10% 42 $10.8 0.32% 41 $11.4 0.29%
Cheese Soft &
Ripe
Specialty Ppk 50 $0.6 0.10% 41 $10.8 0.33% 42 $11.4 0.29%
Cheese Blue/
Gorg
Non Fat Dry 51 $0.6 0.10% 55 $5.2 0.16% 55 $5.7 0.15%
Milk
Kefir 52 $0.6 0.10% 48 $7.2 0.22% 48 $7.8 0.20%
Specialty Ppk 53 $0.5 0.09% 68 $1.5 0.05% 68 $2.0 0.05%
Cheese
Hispanic
Specialty Ppk 54 $0.5 0.08% 44 $8.0 0.24% 46 $8.5 0.22%
Cheese Gouda
& Eda
Specialty Ppk 55 $0.5 0.08% 43 $10.4 0.31% 43 $10.9 0.28%
Cheese Goat
Milk
-------------------------------------------------------------------------------------------------
Total Dairy $571.2 99.37% $3,989.3 98.04% $4,767.6 98.22%
Expenditure
s * Among
Top 1,000
Subcommodit
ies
=================================================================================================
Total $574.9 100% $3,323.6 100% $3,898.5 100%
Dairy
Expenditu
res Among
1,792
Subcommod
ities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Only 55 dairy subcommodities among the top 1,000 subcommodities.
Exhibit D-2: Fruit
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
--------------------------------- Expenditures -------------------------------
Fruit ---------------------------------
Subcommodity Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Dairy Case 1 $43.5 10.18% 1 $269.0 9.35% 1 $312.6 9.46%
100% Pure
Juice--O
Bananas 2 $34.2 8.00% 2 $242.7 8.43% 2 $276.9 8.38%
Strawberries 3 $23.5 5.48% 3 $178.4 6.20% 3 $201.9 6.11%
Fruit Snacks 4 $17.6 4.13% 17 $43.2 1.50% 12 $60.8 1.84%
Grapes Red 5 $15.8 3.70% 4 $121.7 4.23% 4 $137.5 4.16%
Grapes White 6 $15.5 3.61% 6 $84.9 2.95% 5 $100.4 3.04%
Apple Juice & 7 $13.3 3.11% 14 $45.8 1.59% 13 $59.0 1.79%
Cider (Over
50%)
Instore Cut 8 $13.2 3.09% 5 $85.8 2.98% 6 $99.0 3.00%
Fruit
Oranges Navels 9 $12.6 2.94% 8 $79.3 2.75% 7 $91.8 2.78%
All
Fruit Cup 10 $10.6 2.47% 19 $42.7 1.49% 14 $53.3 1.61%
Blended Juice 11 $9.3 2.17% 29 $29.6 1.03% 24 $38.9 1.18%
&
Combinations
(Ov)
Clementines 12 $8.8 2.06% 9 $78.6 2.73% 8 $87.5 2.65%
Melons Instore 13 $8.2 1.93% 18 $42.8 1.49% 17 $51.1 1.55%
Cut
Watermelon 14 $7.9 1.84% 16 $43.9 1.53% 16 $51.8 1.57%
Seedless
Whole
Cherries Red 15 $6.9 1.61% 11 $56.7 1.97% 11 $63.6 1.93%
Apples Gala 16 $6.6 1.54% 10 $69.3 2.41% 10 $75.9 2.30%
(Bulk & Bag)
Cranapple/Cran 17 $6.1 1.43% 31 $27.3 0.95% 29 $33.4 1.01%
Grape Juice
(50)
Apples Red 18 $5.8 1.35% 23 $35.2 1.22% 20 $41.0 1.24%
Delicious
(Bulk & Bag)
Dairy Case 19 $5.4 1.26% 25 $32.3 1.12% 26 $37.7 1.14%
100% Pure
Juice Oth
Cantaloupe 20 $5.3 1.24% 15 $44.4 1.54% 18 $49.7 1.50%
Whole
Blueberries 21 $5.1 1.19% 7 $79.4 2.76% 9 $84.5 2.56%
Pineapple 22 $4.9 1.15% 33 $24.0 0.83% 33 $28.9 0.87%
Peaches Yellow 23 $4.8 1.13% 22 $35.6 1.24% 21 $40.5 1.22%
Flesh
Grape Juice 24 $4.8 1.12% 44 $17.1 0.60% 41 $21.9 0.66%
(Over 50%
Juice)
Lemons 25 $4.6 1.08% 24 $33.6 1.17% 25 $38.2 1.16%
Peaches 26 $4.6 1.07% 39 $21.3 0.74% 35 $25.9 0.78%
Apples Granny 27 $4.4 1.03% 27 $30.9 1.07% 28 $35.3 1.07%
Smith (Bulk &
Bag)
Frozen Fruit 28 $4.3 1.01% 12 $48.6 1.69% 15 $52.9 1.60%
Applesauce Cup 29 $4.1 0.95% 35 $22.6 0.79% 34 $26.7 0.81%
Non-Carb Jce 30 $3.8 0.88% 26 $31.7 1.10% 27 $35.4 1.07%
(Over 50%
Jce)
Raspberries 31 $3.5 0.83% 13 $45.8 1.59% 19 $49.3 1.49%
Grapes Black/ 32 $3.4 0.80% 37 $21.8 0.76% 37 $25.2 0.76%
Blue
Fruit Cocktail/ 33 $3.4 0.79% 54 $12.5 0.43% 52 $15.8 0.48%
Fruit Salad
Mixed Fruit 34 $3.2 0.75% 79 $5.7 0.20% 73 $8.9 0.27%
Bags
Jarred Fruit 35 $3.1 0.73% 49 $14.6 0.51% 47 $17.7 0.54%
Single Serve
Raisins 36 $2.9 0.69% 32 $26.0 0.90% 32 $28.9 0.87%
Apples Other 37 $2.8 0.66% 30 $27.4 0.95% 31 $30.2 0.91%
(Bulk & Bag)
Apples Fuji 38 $2.8 0.65% 21 $36.2 1.26% 23 $39.0 1.18%
(Bulk & Bag)
Apples Gold 39 $2.8 0.65% 43 $17.9 0.62% 43 $20.7 0.62%
Delicious
(Bulk & Bag)
Blackberries 40 $2.7 0.63% 28 $29.9 1.04% 30 $32.6 0.99%
Limes 41 $2.7 0.62% 34 $22.7 0.79% 36 $25.3 0.77%
Nectarines 42 $2.5 0.60% 42 $18.6 0.64% 42 $21.1 0.64%
Yellow Flesh
Pineapple 43 $2.5 0.59% 36 $22.1 0.77% 38 $24.6 0.75%
Whole & Peel/
Cored
Apples 44 $2.4 0.57% 20 $36.9 1.28% 22 $39.4 1.19%
Honeycrisp
Grapefruit 45 $2.4 0.56% 40 $21.2 0.74% 39 $23.6 0.71%
Plums 46 $2.4 0.56% 52 $13.1 0.46% 53 $15.5 0.47%
Mandarin 47 $2.3 0.53% 53 $12.6 0.44% 54 $14.8 0.45%
Oranges/
Citrus Sect
Frzn Conc 48 $2.2 0.52% 57 $10.1 0.35% 56 $12.3 0.37%
Allieds Over
50% Jui
Mango 49 $2.2 0.52% 50 $14.1 0.49% 50 $16.3 0.49%
Apple Sauce 50 $2.2 0.51% 51 $13.8 0.48% 51 $16.0 0.48%
(Excludes
Cup)
Tangerines & 51 $2.1 0.49% 55 $11.3 0.39% 55 $13.4 0.41%
Tangelos
Frzn Oj & Oj 52 $1.9 0.44% 45 $16.2 0.56% 45 $18.1 0.55%
Substitutes
(Over 5)
Watermelon 53 $1.9 0.44% 46 $15.9 0.55% 46 $17.8 0.54%
Personal
Bananas 54 $1.9 0.44% 41 $18.7 0.65% 44 $20.6 0.62%
Organic
Pears 55 $1.9 0.43% 59 $10.0 0.35% 58 $11.8 0.36%
Convenience/ 56 $1.8 0.41% 64 $9.4 0.33% 60 $11.2 0.34%
Snacking
Fruit Pro
Cranberry 57 $1.7 0.39% 58 $10.0 0.35% 59 $11.6 0.35%
Sauce
Strawberries 58 $1.6 0.38% 38 $21.4 0.74% 40 $23.0 0.70%
Organic
Cut Fruit All 59 $1.6 0.38% 69 $8.5 0.29% 65 $10.1 0.31%
Other Prepack
Caramel/Candy 60 $1.6 0.36% 94 $3.4 0.12% 84 $4.9 0.15%
Apples
Pears Bartlett 61 $1.5 0.35% 47 $15.7 0.55% 48 $17.2 0.52%
Fruit Party 62 $1.4 0.33% 74 $6.5 0.23% 75 $7.9 0.24%
Tray Prepack
Dried Fruit-- 63 $1.4 0.33% 48 $15.6 0.54% 49 $17.0 0.51%
Other
Pineapple 64 $1.4 0.33% 75 $6.4 0.22% 76 $7.8 0.24%
Juice (Over
50% Juic)
Cranberry 65 $1.4 0.32% 70 $8.4 0.29% 69 $9.8 0.30%
Juice (Over
50% Jce)
Lemon Juice & 66 $1.2 0.29% 72 $7.8 0.27% 72 $9.0 0.27%
Lime Juice
(Over)
Oranges Non 67 $1.2 0.28% 81 $5.0 0.18% 80 $6.2 0.19%
Navel All
Prune Juice 68 $1.2 0.27% 71 $8.3 0.29% 71 $9.5 0.29%
(Over 50%
Juice)
Drinks--Carb 69 $1.1 0.26% 61 $9.7 0.34% 62 $10.8 0.33%
Juice (Over
50%)
Juice Single 70 $1.1 0.26% 66 $9.4 0.33% 63 $10.5 0.32%
Blend
Pears Anjou 71 $1.1 0.26% 60 $9.8 0.34% 61 $10.9 0.33%
Kiwi Fruit 72 $1.0 0.24% 73 $7.0 0.24% 74 $8.0 0.24%
Dried Plums 73 $1.0 0.24% 56 $11.0 0.38% 57 $12.0 0.36%
Cherries 74 $1.0 0.23% 68 $9.0 0.31% 68 $10.0 0.30%
Ranier
Cranapple/Cran 75 $0.9 0.21% 77 $6.3 0.22% 77 $7.2 0.22%
Grape Juice
(Ov)
Juice (Over 76 $0.9 0.21% 100 $2.7 0.09% 98 $3.6 0.11%
50% Juice)
Watermelon W/ 77 $0.9 0.20% 98 $3.0 0.11% 93 $3.9 0.12%
Seeds Whole
Honeydew Whole 78 $0.8 0.18% 78 $5.9 0.21% 79 $6.7 0.20%
Grapes Red 79 $0.8 0.18% 92 $3.5 0.12% 91 $4.2 0.13%
Globe
Pomegranates 80 $0.7 0.17% 85 $4.3 0.15% 83 $5.0 0.15%
Grapes Other 81 $0.7 0.17% 89 $3.8 0.13% 89 $4.6 0.14%
Maraschino 82 $0.7 0.17% 88 $4.1 0.14% 87 $4.8 0.14%
Cherries
Apples 83 $0.7 0.17% 63 $9.4 0.33% 64 $10.1 0.31%
Braeburn
(Bulk & Bag)
Grapefruit 84 $0.7 0.17% 86 $4.1 0.14% 85 $4.8 0.15%
Juice (Over
50% Jui)
Apples Gala 85 $0.6 0.15% 65 $9.4 0.33% 67 $10.0 0.30%
(Bulk & Bag)
Organic
Peaches White 86 $0.6 0.15% 80 $5.5 0.19% 81 $6.2 0.19%
Flesh
Jarred Fruit 87 $0.6 0.14% 82 $4.5 0.16% 82 $5.1 0.16%
Multi Serve
Squeeze Lemons/ 88 $0.5 0.12% 95 $3.3 0.12% 94 $3.9 0.12%
Limes
Raspberries 89 $0.5 0.12% 67 $9.1 0.32% 70 $9.6 0.29%
Organic
Pears Bosc 90 $0.5 0.11% 84 $4.3 0.15% 86 $4.8 0.14%
Blueberries 91 $0.5 0.11% 62 $9.6 0.33% 66 $10.1 0.30%
Organic
Pears Asian 92 $0.4 0.10% 90 $3.8 0.13% 92 $4.2 0.13%
------------------------- --------------------------------------------------------
Total Fruit $416.8 97.49% $2,772.4 96.36% $3,189.2 96.54%
Expenditure
s * Among
Top 1,000
subcommodit
ies
========================= ========================================================
Total $427.6 100% $2,877.2 100% $3,304.8 100%
Fruit
Expenditu
res Among
1,792
Subcommod
ities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
*Only 92 fruit subcommodities among top 1,000 subcommodities.
Exhibit D-3: Grains
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
--------------------------------- Expenditures -------------------------------
Grain ---------------------------------
Subcommodity Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Kids Cereal 1 $78.1 9.88% 3 $186.4 4.51% 1 $264.5 5.37%
Mainstream 2 $48.0 6.07% 7 $136.8 3.31% 6 $184.7 3.75%
White Bread
Tortilla/Nacho 3 $47.4 5.99% 2 $209.0 5.05% 2 $256.4 5.21%
Chips
Mainstream 4 $38.4 4.86% 5 $173.2 4.19% 4 $211.7 4.30%
Variety
Breads
All Family 5 $36.2 4.58% 1 $214.9 5.20% 3 $251.1 5.10%
Cereal
Adult Cereal 6 $24.9 3.15% 4 $182.6 4.42% 5 $207.5 4.21%
Mexican Soft 7 $23.7 3.00% 8 $113.1 2.74% 8 $136.8 2.78%
Tortillas And
Wra
Waffles/ 8 $17.3 2.19% 13 $77.4 1.87% 12 $94.7 1.92%
Pancakes/
French Toast
Ramen Noodles/ 9 $16.7 2.12% 43 $28.1 0.68% 34 $44.8 0.91%
Ramen Cups
Cheese 10 $16.5 2.08% 10 $90.2 2.18% 10 $106.7 2.17%
Crackers
Hamburger Buns 11 $16.2 2.05% 14 $70.2 1.70% 14 $86.4 1.75%
Hot Dog Buns 12 $16.2 2.05% 18 $62.2 1.50% 16 $78.4 1.59%
Refrigerated 13 $14.7 1.86% 30 $45.2 1.09% 26 $59.9 1.22%
Biscuits
Butter Spray 14 $14.6 1.85% 15 $68.7 1.66% 15 $83.3 1.69%
Cracker
Toaster 15 $14.0 1.77% 27 $47.6 1.15% 23 $61.6 1.25%
Pastries
Rice Side Dish 16 $14.0 1.76% 28 $46.7 1.13% 24 $60.6 1.23%
Mixes Dry
Popcorn--Micro 17 $13.1 1.65% 17 $63.4 1.53% 17 $76.5 1.55%
wave
Long Cut Pasta 18 $13.0 1.64% 19 $60.4 1.46% 19 $73.4 1.49%
Granola Bars 19 $12.8 1.61% 11 $88.9 2.15% 11 $101.7 2.06%
Premium Bread 20 $12.3 1.55% 6 $144.7 3.50% 7 $157.0 3.19%
Cereal Bars 21 $10.9 1.38% 12 $78.4 1.90% 13 $89.3 1.81%
Short Cut 22 $9.9 1.25% 21 $56.2 1.36% 20 $66.1 1.34%
Pasta
Rolls: Dinner 23 $9.5 1.21% 23 $50.5 1.22% 25 $60.1 1.22%
Frzn Garlic 24 $9.1 1.16% 44 $27.8 0.67% 39 $36.9 0.75%
Toast
Corn Chips 25 $9.1 1.15% 29 $45.6 1.10% 28 $54.7 1.11%
Instant 26 $8.9 1.13% 33 $41.1 0.99% 32 $50.0 1.02%
Oatmeal
Snack Crackers 27 $8.9 1.13% 9 $98.6 2.39% 9 $107.6 2.18%
Saltine/Oyster 28 $8.2 1.03% 31 $43.1 1.04% 30 $51.3 1.04%
Multi-Pack 29 $8.0 1.01% 32 $41.3 1.00% 33 $49.3 1.00%
Crackers
Bagels 30 $7.8 0.99% 16 $66.9 1.62% 18 $74.7 1.52%
Noodle Side 31 $7.3 0.92% 53 $21.1 0.51% 49 $28.4 0.58%
Dish Mixes
Rice--Dry Bag 32 $7.1 0.90% 37 $33.9 0.82% 36 $41.1 0.83%
And Box
Sandwich Buns 33 $7.1 0.90% 20 $56.8 1.37% 21 $63.9 1.30%
Rice--Instant 34 $6.8 0.86% 34 $38.0 0.92% 35 $44.8 0.91%
& Microwave
Frzn Breakfast 35 $6.5 0.82% 57 $19.0 0.46% 52 $25.4 0.52%
Pastry
Flour: White & 36 $6.4 0.81% 42 $28.8 0.70% 41 $35.2 0.71%
Self Rising
Pretzels 37 $6.2 0.79% 22 $55.4 1.34% 22 $61.6 1.25%
Bread: Italian/ 38 $6.1 0.77% 25 $49.0 1.19% 27 $55.1 1.12%
French
Muffin & Corn 39 $6.0 0.76% 41 $28.9 0.70% 42 $34.9 0.71%
Bread Mix
Refrigerated 40 $5.5 0.70% 45 $27.5 0.66% 44 $33.0 0.67%
Specialty
Rolls
Refrigerated 41 $5.4 0.68% 38 $31.2 0.76% 40 $36.6 0.74%
Crescent
Rolls
Mexican Taco/ 42 $5.2 0.66% 56 $19.1 0.46% 55 $24.3 0.49%
Tostado/
Shells
Noodles Dry 43 $4.5 0.58% 48 $24.9 0.60% 47 $29.4 0.60%
Rolls: 44 $4.1 0.52% 46 $26.7 0.65% 46 $30.9 0.63%
Sandwich
Salad Toppers 45 $4.1 0.52% 68 $15.1 0.37% 64 $19.2 0.39%
Graham 46 $4.0 0.51% 47 $24.9 0.60% 48 $29.0 0.59%
Crackers
Standard 47 $3.9 0.49% 39 $29.9 0.72% 43 $33.8 0.69%
Oatmeal
English 48 $3.8 0.48% 24 $49.5 1.20% 29 $53.3 1.08%
Muffins/
Waffles
Main Meal 49 $3.8 0.48% 36 $34.9 0.84% 37 $38.7 0.79%
Bread
Dinner Rolls 50 $3.5 0.44% 71 $14.5 0.35% 67 $18.0 0.36%
Breadings/ 51 $3.2 0.41% 65 $16.0 0.39% 62 $19.3 0.39%
Coatings/
Crumbs
Bread: 52 $3.2 0.40% 51 $22.9 0.55% 51 $26.0 0.53%
Specialty
Bagged Popped 53 $3.0 0.38% 77 $12.5 0.30% 75 $15.5 0.32%
Popcorn
Frzn Dinner 54 $3.0 0.38% 54 $20.9 0.50% 56 $23.9 0.48%
Rolls
Rolls: 55 $2.9 0.37% 64 $16.5 0.40% 61 $19.4 0.39%
Croissants/
Breadsticks
Grits 56 $2.8 0.36% 96 $6.7 0.16% 92 $9.6 0.19%
Cereal--Cold 57 $2.8 0.36% 26 $47.8 1.16% 31 $50.7 1.03%
Refrigerated 58 $2.8 0.36% 86 $9.4 0.23% 80 $12.3 0.25%
Tortillas
Croutons 59 $2.8 0.36% 73 $14.0 0.34% 69 $16.8 0.34%
Frzn Garlic 60 $2.7 0.34% 78 $11.1 0.27% 78 $13.8 0.28%
Bread
Frzn Biscuits 61 $2.6 0.33% 76 $12.9 0.31% 74 $15.6 0.32%
Frozen Pasta 62 $2.6 0.33% 62 $16.9 0.41% 59 $19.6 0.40%
Pasta/Grain 63 $2.6 0.33% 82 $10.3 0.25% 79 $12.9 0.26%
Salads--Prepa
ck
Cornmeal 64 $2.5 0.32% 95 $7.3 0.18% 90 $9.8 0.20%
Refrigerated 65 $2.5 0.32% 93 $7.7 0.19% 87 $10.2 0.21%
Bagels
Refrigerated 66 $2.4 0.30% 40 $29.3 0.71% 45 $31.7 0.64%
Pasta
Diet/Light 67 $2.4 0.30% 49 $24.0 0.58% 50 $26.3 0.53%
Bread
Pasta/Grain 68 $2.3 0.30% 63 $16.9 0.41% 63 $19.3 0.39%
Salads--Bulk
Mini-Cakes 69 $2.3 0.30% 60 $17.2 0.42% 60 $19.5 0.40%
Fruit/ 70 $2.2 0.28% 58 $18.7 0.45% 58 $21.0 0.43%
Breakfast
Bread
Breading 71 $2.2 0.28% 114 $3.7 0.09% 104 $5.9 0.12%
Frzn 72 $2.2 0.28% 106 $5.0 0.12% 97 $7.2 0.15%
Breadsticks
Rye Breads 73 $2.0 0.25% 52 $22.3 0.54% 54 $24.3 0.49%
Other Hot 74 $1.9 0.24% 80 $10.3 0.25% 81 $12.2 0.25%
Cereal
Rolls: Bagels 75 $1.9 0.24% 67 $15.4 0.37% 68 $17.3 0.35%
Biscuit Flour 76 $1.9 0.23% 74 $13.8 0.33% 72 $15.7 0.32%
& Mixes
Bread: Artisan 77 $1.7 0.22% 35 $36.7 0.89% 38 $38.4 0.78%
Flour: Misc/ 78 $1.6 0.20% 75 $13.6 0.33% 77 $15.2 0.31%
Specialty/
Blend Et
Bread: Pita/ 79 $1.5 0.19% 72 $14.1 0.34% 73 $15.6 0.32%
Pocket/
Flatbrd
Pizza Mix Dry 80 $1.4 0.18% 102 $5.4 0.13% 98 $6.8 0.14%
Breakfast Bars/ 81 $1.4 0.18% 50 $23.6 0.57% 53 $25.0 0.51%
Tarts/Scones
Popcorn--Other 82 $1.4 0.17% 84 $10.0 0.24% 84 $11.4 0.23%
Asian Noodles/ 83 $1.3 0.17% 79 $10.5 0.25% 82 $11.8 0.24%
Rice
Instant 84 $1.3 0.16% 91 $8.1 0.20% 93 $9.4 0.19%
Breakfast
Tortilla Chips 85 $1.3 0.16% 55 $19.9 0.48% 57 $21.2 0.43%
Bread: Sweet/ 86 $1.3 0.16% 90 $8.4 0.20% 91 $9.7 0.20%
Breakfast
Refrigerated 87 $1.2 0.16% 83 $10.2 0.25% 83 $11.5 0.23%
Breads
Bread: 88 $1.2 0.15% 61 $17.1 0.41% 66 $18.3 0.37%
Sourdough
Bread: 89 $1.0 0.13% 85 $9.8 0.24% 86 $10.8 0.22%
Tortillas/
Wraps
Vending Size/ 90 $1.0 0.12% 124 $2.3 0.06% 120 $3.3 0.07%
Sngl Serve
Cracke
Snacks: Pita 91 $0.9 0.12% 66 $15.7 0.38% 70 $16.7 0.34%
Chips
Granola 92 $0.9 0.12% 69 $15.1 0.37% 71 $16.0 0.33%
Caramel Coated 93 $0.9 0.11% 118 $3.1 0.08% 115 $4.0 0.08%
Snacks
Specialty 94 $0.9 0.11% 59 $17.8 0.43% 65 $18.7 0.38%
Crackers
Crackers 95 $0.8 0.10% 70 $14.6 0.35% 76 $15.4 0.31%
Bread: Rye/ 96 $0.7 0.09% 92 $8.1 0.20% 95 $8.8 0.18%
Cocktail
Whole Grain 97 $0.7 0.09% 88 $9.2 0.22% 88 $9.9 0.20%
Bread
Frzn Bagels 98 $0.7 0.09% 120 $2.9 0.07% 119 $3.6 0.07%
Bread: Wheat/ 99 $0.7 0.09% 81 $10.3 0.25% 85 $11.0 0.22%
Whl Grain
Pies: Sugar 100 $0.7 0.09% 111 $4.5 0.11% 111 $5.2 0.11%
Free
------------------------- --------------------------------------------------------
Top 100 $778.3 98.43% $3,989.3 96.47% $4,767.6 96.79%
Grain
Expenditure
s *
------------------------- --------------------------------------------------------
Total Grain $783.8 99.13% $4,049.9 96.28% $4,833.8 98.63%
Expenditure
s Among Top
1,000
Subcommodit
ies
========================= ========================================================
Total $790.7 100% $4,135.0 100% $4,925.7 100%
Grain
Expenditu
res Among
1,792
Subcommod
ities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit D-4: Oils
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
--------------------------------- Expenditures -------------------------------
Oil ---------------------------------
Subcommodity Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Pourable Salad 1 $29.0 22.71% 1 $139.4 24.28% 1 $168.4 23.99%
Dressings
Mayonnaise & 2 $27.3 21.34% 2 $119.1 20.73% 2 $146.3 20.84%
Whipped
Dressing
Margarine: 3 $23.4 18.37% 3 $100.9 17.56% 3 $124.3 17.71%
Tubs And
Bowls
Vegetable Oil 4 $20.5 16.07% 5 $35.4 6.16% 5 $55.9 7.96%
Canola Oils 5 $8.3 6.49% 6 $29.3 5.10% 6 $37.6 5.35%
Olive Oil 6 $7.3 5.69% 4 $63.8 11.11% 4 $71.1 10.12%
Cooking Sprays 7 $3.2 2.49% 7 $21.0 3.65% 7 $24.1 3.44%
Dressing 8 $1.6 1.23% 8 $14.5 2.53% 8 $16.1 2.30%
Creamy
Sand/ 9 $1.4 1.14% 10 $7.2 1.26% 10 $8.7 1.23%
Horseradish &
Tartar Sauce
Corn Oil 10 $1.3 1.01% 14 $4.1 0.71% 12 $5.4 0.77%
Cooking Oil: 11 $1.1 0.89% 11 $6.7 1.17% 11 $7.8 1.12%
Peanut/
Safflower/
Dressing Blue 12 $0.9 0.71% 9 $9.5 1.65% 9 $10.4 1.48%
Cheese
Margarine: 13 $0.6 0.44% 13 $4.2 0.74% 14 $4.8 0.68%
Squeeze
------------------------- --------------------------------------------------------
Total Oil $125.9 98.58% $555.0 96.65% $680.9 96.99%
Expenditure
s * Among
Top 1,000
Subcommodit
ies
========================= ========================================================
Total Oil $127.0 100% $574.4 100% $702.1 100%
Expenditu
res Among
1,792
Subcommod
ities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Only 13 oil subcommodities among the top 1,000 subcommodities.
Exhibit D-5: Protein Foods
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
--------------------------------- Expenditures -------------------------------
Protein Foods ---------------------------------
Subcommodity Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Lean [Beef] 1 $112.4 7.38% 2 $257.9 4.03% 1 $370.3 4.67%
Primal [Beef] 2 $62.4 4.10% 5 $219.8 3.43% 5 $282.2 3.56%
Lunchment--Del 3 $55.8 3.67% 4 $242.6 3.79% 4 $298.4 3.76%
i Fresh
Eggs--Large 4 $52.1 3.43% 3 $251.6 3.93% 3 $303.7 3.83%
Chicken Breast 5 $49.6 3.26% 1 $292.9 4.57% 2 $342.5 4.32%
Boneless
Enhanced [Pork 6 $41.5 2.73% 6 $168.0 2.62% 6 $209.5 2.64%
Boneless Loin/
Rib]
Bacon--Trad 7 $40.7 2.68% 8 $157.6 2.46% 7 $198.3 2.50%
16oz Or Less
Ribs [Pork] 8 $35.0 2.30% 15 $106.8 1.67% 13 $141.8 1.79%
Frzn Chicken-- 9 $30.0 1.97% 17 $99.8 1.56% 16 $129.8 1.64%
Wht Meat
Choice Beef 10 $28.4 1.87% 11 $136.6 2.13% 10 $165.1 2.08%
(Loins)
Select Beef 11 $27.9 1.83% 9 $143.7 2.24% 9 $171.5 2.16%
Hot Dogs--Base 12 $25.1 1.65% 27 $56.8 0.89% 23 $81.9 1.03%
Meat
Choice Beef 13 $24.0 1.58% 20 $72.5 1.13% 19 $96.5 1.22%
(Rounds)
Chicken Wings 14 $22.2 1.46% 58 $28.6 0.45% 40 $50.9 0.64%
Frzn Chicken-- 15 $22.2 1.46% 97 $17.4 0.27% 52 $39.5 0.50%
Wings
Lunchment--Bol 16 $21.8 1.43% 24 $60.9 0.95% 22 $82.7 1.04%
ogna/Sausage
Tuna 17 $21.1 1.39% 14 $109.9 1.72% 15 $131.0 1.65%
Peanut Butter 18 $20.4 1.34% 12 $127.8 1.99% 12 $148.2 1.87%
Meat: Turkey 19 $19.3 1.27% 7 $159.6 2.49% 8 $178.9 2.26%
Bulk
Frzn Meat-- 20 $19.0 1.25% 34 $46.3 0.72% 30 $65.2 0.82%
Beef
Value Forms/ 21 $18.6 1.22% 41 $42.6 0.67% 33 $61.2 0.77%
18oz And
Larger
[Chicken]
Chicken Drums 22 $17.3 1.14% 49 $31.5 0.49% 44 $48.8 0.62%
Angus [Beef] 23 $17.1 1.13% 16 $103.8 1.62% 17 $120.9 1.53%
Dnr Sausage-- 24 $16.4 1.08% 45 $37.6 0.59% 38 $54.1 0.68%
Links Pork
Ckd/S
Meat: Ham Bulk 25 $15.3 1.00% 13 $115.9 1.81% 14 $131.2 1.65%
Bkfst Sausage-- 26 $15.1 0.99% 23 $61.4 0.96% 25 $76.5 0.96%
Fresh Rolls
Shrimp--Raw 27 $15.0 0.99% 21 $69.0 1.08% 21 $84.1 1.06%
Shrimp--Cooked 28 $14.8 0.97% 29 $54.0 0.84% 28 $68.8 0.87%
Prepared 29 $13.4 0.88% 28 $55.3 0.86% 29 $68.7 0.87%
Beans--Baked
W/Pork
Chili: Canned 30 $13.3 0.88% 39 $42.8 0.67% 36 $56.1 0.71%
Ground Turkey 31 $13.1 0.86% 19 $78.0 1.22% 20 $91.1 1.15%
Dnr Sausage-- 32 $13.0 0.86% 25 $58.0 0.91% 26 $71.1 0.90%
Links Fresh
Whole Chicken 33 $12.9 0.85% 26 $56.9 0.89% 27 $69.8 0.88%
(Roasters/
Fryer)
Chicken Thighs 34 $12.2 0.80% 31 $50.0 0.78% 31 $62.2 0.78%
Dnr Sausage-- 35 $12.1 0.80% 43 $38.2 0.60% 42 $50.4 0.64%
Pork Rope Ckd/
Sm
Bacon--Trad 36 $12.0 0.79% 35 $44.6 0.70% 35 $56.6 0.71%
Greater Than
16oz
Soup/Stew 37 $11.2 0.74% 36 $44.1 0.69% 37 $55.3 0.70%
Whole Muscle 38 $11.1 0.73% 53 $29.9 0.47% 49 $41.0 0.52%
Breaded/18oz
And
Variety Beans-- 39 $10.5 0.69% 22 $68.0 1.06% 24 $78.5 0.99%
Kidney/Pinto/
E
Cubed Meats 40 $10.5 0.69% 54 $29.8 0.46% 51 $40.3 0.51%
[Beef]
Hot Dogs--Base 41 $10.3 0.68% 32 $49.4 0.77% 34 $59.8 0.75%
Beef
Eggs--Medium 42 $10.1 0.66% 81 $21.0 0.33% 64 $31.1 0.39%
Butts [Pork 43 $9.7 0.63% 56 $29.2 0.46% 54 $38.8 0.49%
Shoulder]
Boneless Snack/ 44 $9.6 0.63% 77 $21.5 0.33% 65 $31.1 0.39%
18oz And
Larger
Chix: Value- 45 $9.5 0.63% 62 $26.7 0.42% 58 $36.2 0.46%
Added (Cold)
Angus [Beef] 46 $9.3 0.61% 50 $31.4 0.49% 50 $40.6 0.51%
Patties [Beef] 47 $9.1 0.60% 42 $39.7 0.62% 45 $48.8 0.61%
Bkfst Sausage-- 48 $8.9 0.59% 64 $26.3 0.41% 59 $35.3 0.44%
Fresh Links
Bone-In Wings 49 $8.8 0.58% 123 $12.0 0.19% 94 $20.8 0.26%
Hams--Half/ 50 $8.2 0.54% 52 $30.0 0.47% 56 $38.2 0.48%
Port Bone-In
Meat: Beef 51 $7.9 0.52% 30 $53.4 0.83% 32 $61.3 0.77%
Bulk
Hams--Spiral 52 $7.6 0.50% 46 $36.5 0.57% 47 $44.1 0.56%
Hot Dogs-- 53 $7.4 0.49% 40 $42.7 0.67% 43 $50.1 0.63%
Premium
Snack Meat-- 54 $7.4 0.48% 48 $32.1 0.50% 53 $39.5 0.50%
Pepperoni
Frzn Meat-- 55 $7.3 0.48% 128 $11.3 0.18% 109 $18.6 0.23%
Breakfast
Sausage
Angus [Beef] 56 $7.3 0.48% 37 $43.3 0.68% 41 $50.7 0.64%
Select Beef 57 $7.1 0.46% 51 $30.4 0.47% 57 $37.5 0.47%
Frz Coated 58 $6.9 0.45% 79 $21.1 0.33% 74 $28.0 0.35%
Fish Fillets
Jerky/Nuggets/ 59 $6.8 0.45% 67 $25.8 0.40% 62 $32.6 0.41%
Tenders
Catfish--Fille 60 $6.8 0.45% 110 $13.1 0.20% 102 $19.9 0.25%
t
Chicken Legs/ 61 $6.6 0.43% 109 $13.5 0.21% 101 $20.1 0.25%
Quarters
Value-Added 62 $6.4 0.42% 98 $16.9 0.26% 86 $23.3 0.29%
Breaded
Shrimp
Pancake Mixes 63 $6.3 0.41% 65 $21.9 0.34% 68 $28.1 0.35%
Frz Fishsticks/ 64 $6.1 0.40% 104 $14.7 0.23% 95 $20.8 0.26%
Tenders/
Nuggets
Crab--Snow 65 $6.1 0.40% 127 $11.4 0.18% 110 $17.5 0.22%
Chix: Frd 8pc/ 66 $6.0 0.39% 117 $12.7 0.20% 107 $18.7 0.24%
Cut Up (Cold)
Lunchmeat--Cho 67 $5.1 0.34% 121 $12.1 0.19% 111 $17.2 0.22%
p/Form Pltry
& Ha
Salmon Fr-- 68 $5.0 0.33% 33 $48.8 0.76% 39 $53.8 0.68%
Altantic
Party Tray-- 69 $4.8 0.32% 73 $24.8 0.39% 71 $29.6 0.37%
Shrimp
Ham Steaks/ 70 $4.7 0.31% 63 $26.3 0.41% 66 $31.0 0.39%
Cubes/Slices
Eggs--X-Large 71 $4.5 0.29% 44 $37.9 0.59% 48 $42.4 0.54%
Bacon--Poultry 72 $4.5 0.29% 91 $18.4 0.29% 88 $22.9 0.29%
Hams--Whole 73 $4.5 0.29% 105 $14.6 0.23% 106 $19.1 0.24%
Boneless
Meat Bulk: 74 $4.4 0.29% 59 $28.3 0.44% 61 $32.8 0.41%
Specialty Dry
Meats
Chunk Meats-- 75 $4.4 0.29% 70 $25.3 0.40% 70 $29.7 0.37%
Chix/Ham/Etc.
Whole Toms 76 $4.3 0.28% 84 $20.0 0.31% 83 $24.2 0.31%
(Over 16lbs)
[Turkey]
Lunchmeat--Who 77 $4.2 0.28% 86 $19.7 0.31% 84 $24.0 0.30%
le Muscle
Pltry
Bacon--Pre- 78 $4.1 0.27% 72 $24.8 0.39% 72 $28.9 0.36%
Cooked
Baking Nuts 79 $4.1 0.27% 38 $43.2 0.67% 46 $47.3 0.60%
Bologna/Loaves/ 80 $4.0 0.26% 87 $19.2 0.30% 87 $23.1 0.29%
Franks
Pistachios 81 $3.9 0.26% 57 $29.1 0.45% 60 $33.0 0.42%
Seasoned 82 $3.9 0.26% 100 $16.5 0.26% 99 $20.4 0.26%
Poultry
Protein 83 $3.9 0.26% 65 $26.3 0.41% 69 $30.2 0.38%
Salads--Bulk
Bkfst Sausage-- 84 $3.8 0.25% 136 $9.8 0.15% 126 $13.6 0.17%
Fresh Patties
Meat: Chicken 85 $3.7 0.25% 47 $34.6 0.54% 55 $38.4 0.48%
Bulk
Bkfst Sausage-- 86 $3.7 0.25% 78 $21.4 0.33% 80 $25.2 0.32%
Precooked
Dnr Sausage-- 87 $3.7 0.24% 120 $12.2 0.19% 115 $15.9 0.20%
Beef Rope Ckd/
Sm
Whole Hens 88 $3.6 0.24% 89 $19.0 0.30% 89 $22.6 0.29%
(Under 16lbs)
[Turkey]
Dnr Sausage-- 89 $3.6 0.24% 76 $21.6 0.34% 81 $25.2 0.32%
Other Forms
External Fresh 90 $3.5 0.23% 204 $4.2 0.06% 169 $7.7 0.10%
[Pork Offal]
Corned Beef 91 $3.5 0.23% 99 $16.9 0.26% 98 $20.4 0.26%
Fz Meatballs 92 $3.5 0.23% 95 $17.7 0.28% 93 $21.1 0.27%
Hams--Half/ 93 $3.4 0.23% 80 $21.0 0.33% 82 $24.5 0.31%
Port Boneless
Lunchmeat--Chi 94 $3.3 0.22% 138 $9.7 0.15% 130 $13.1 0.16%
p Meat
Salmon 95 $3.2 0.21% 108 $13.6 0.21% 113 $16.8 0.21%
Sandwich Sauce 96 $3.2 0.21% 156 $7.7 0.12% 146 $10.8 0.14%
Tilapia--Fille 97 $3.2 0.21% 101 $16.4 0.26% 103 $19.6 0.25%
t
Frozen Burgers 98 $3.2 0.21% 217 $3.1 0.05% 185 $6.3 0.08%
Frozen 99 $3.1 0.20% 135 $9.8 0.15% 132 $12.9 0.16%
Breakfast
Sausage
Stuffed/Mixed 100 $3.1 0.20% 88 $19.2 0.30% 90 $22.3 0.28%
Beef
------------------------- --------------------------------------------------------
Top 100 $1,342.3 87.82% $5,249.5 81.66% $6,591.7 82.84%
Protein
Foods
Expenditure
s *
------------------------- --------------------------------------------------------
Total $1,512.2 98.95% $6,288.8 97.83% $7,801.0 98.04%
Protein
Foods
Expenditure
s Among Top
1,000
Subcommodit
ies
========================= ========================================================
Total $1,528.3 100% $6,428.5 100% $7,956.9 100%
Protein
Foods
Expenditu
res Among
1,792
Subcommod
ities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit D-6: Saturated Fats and Added Sugars (SoFAS)
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
--------------------------------- Expenditures -------------------------------
(SoFAS) ---------------------------------
Subcommodity Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Soft Drinks 12/ 1 $164.6 18.86% 1 $601.2 16.11% 1 $765.8 16.63%
18 & 15pk Can
Car
Sft Drnk 2 2 $70.9 8.12% 2 $230.1 6.17% 2 $301.0 6.54%
Liter Btl
Carb Incl
Soft Drinks 3 $39.7 4.55% 9 $106.4 2.85% 8 $146.1 3.17%
20pk & 24pk
Can Carb
Sugar 4 $36.9 4.23% 8 $112.7 3.02% 7 $149.6 3.25%
Sft Drnk Mlt- 5 $34.0 3.90% 4 $173.6 4.65% 3 $207.6 4.51%
Pk Btl Carb
(Excp)
Sft Drnk Sngl 6 $27.8 3.18% 11 $71.4 1.91% 11 $99.2 2.15%
Srv Btl Carb
(Ex)
Aseptic Pack 7 $24.2 2.78% 16 $57.1 1.53% 15 $81.4 1.77%
Juice And
Drinks
Refrigerated 8 $24.1 2.76% 6 $147.2 3.95% 5 $171.3 3.72%
Coffee
Creamers
Candy Bags-- 9 $21.5 2.46% 5 $147.5 3.95% 6 $169.1 3.67%
Chocolate
Butter 10 $19.6 2.24% 3 $175.6 4.71% 4 $195.2 4.24%
Sour Creams 11 $17.5 2.00% 10 $95.2 2.55% 10 $112.7 2.45%
Cream Cheese 12 $17.2 1.97% 7 $115.5 3.10% 9 $132.7 2.88%
Candy Bars 13 $16.3 1.87% 18 $54.9 1.47% 16 $71.3 1.55%
(Singles)
(Including)
Dairy Case 14 $16.0 1.83% 22 $48.0 1.29% 19 $64.0 1.39%
Juice Drnk
Under 10
Candy Bars 15 $15.6 1.79% 12 $69.6 1.86% 12 $85.2 1.85%
(Multi Pack)
Tea Sweetened 16 $13.9 1.59% 13 $68.7 1.84% 13 $82.6 1.79%
Chewing Gum 17 $13.2 1.51% 14 $68.3 1.83% 14 $81.5 1.77%
Candy Bags-- 18 $12.6 1.44% 19 $54.9 1.47% 18 $67.5 1.46%
Non Chocolate
Molasses & 19 $11.7 1.34% 15 $58.7 1.57% 17 $70.4 1.53%
Syrups
Dairy Case 20 $11.0 1.26% 27 $34.4 0.92% 26 $45.4 0.99%
Citrus Pnch/
Oj Subs
Fruit Drinks: 21 $10.6 1.21% 60 $10.9 0.29% 46 $21.5 0.47%
Canned &
Glass
Non Dairy 22 $10.5 1.20% 25 $35.4 0.95% 25 $45.9 1.00%
Creamer
Seasonal 23 $9.2 1.05% 23 $46.9 1.26% 23 $56.0 1.22%
Miscellaneous
[Candy]
Dairy Case Tea 24 $8.4 0.96% 36 $23.1 0.62% 33 $31.5 0.68%
With Sugar Or
S
Seasonal Candy 25 $7.9 0.90% 20 $54.8 1.47% 21 $62.7 1.36%
Bags--Chocola
te
Energy Drink-- 26 $7.7 0.88% 32 $26.3 0.70% 29 $33.9 0.74%
Single Serve
Energy Drink-- 27 $7.1 0.82% 24 $39.5 1.06% 24 $46.7 1.01%
Single Serve
(N)
Preserves/Jam/ 28 $6.7 0.77% 17 $56.2 1.51% 20 $63.0 1.37%
Marmalade
Margarine 29 $6.7 0.77% 41 $22.3 0.60% 37 $29.0 0.63%
Stick
Juice (Under 30 $6.7 0.76% 40 $22.4 0.60% 36 $29.1 0.63%
10% Juice)
Sweeteners 31 $6.4 0.73% 21 $49.8 1.33% 22 $56.2 1.22%
Frosting 32 $6.3 0.72% 31 $27.0 0.72% 30 $33.4 0.72%
Soft Drinks 33 $5.9 0.67% 57 $11.5 0.31% 54 $17.4 0.38%
Can Non-Carb
(Exce)
Refrig Dips 34 $5.7 0.66% 34 $24.7 0.66% 34 $30.4 0.66%
Aseptic Pack 35 $5.3 0.61% 46 $17.5 0.47% 44 $22.9 0.50%
Juice And
Drinks
Candy Bars 36 $5.1 0.59% 50 $15.9 0.43% 48 $21.1 0.46%
(Singles)
(Including)
Cranberry 37 $5.0 0.58% 39 $22.6 0.61% 40 $27.6 0.60%
Juice (50%
And Under)
Frzn Whipped 38 $5.0 0.57% 28 $30.9 0.83% 28 $35.9 0.78%
Topping
Blended Juice 39 $4.8 0.55% 37 $22.9 0.61% 39 $27.7 0.60%
&
Combinations
(50)
Jelly 40 $4.7 0.54% 44 $18.1 0.48% 45 $22.8 0.50%
Energy Drink-- 41 $4.3 0.49% 43 $19.0 0.51% 42 $23.3 0.51%
Multi-Pack
Honey 42 $4.1 0.48% 29 $28.9 0.78% 31 $33.1 0.72%
Gum (Packaged) 43 $4.1 0.47% 33 $25.9 0.69% 35 $30.0 0.65%
Soft Drinks 44 $4.1 0.47% 30 $27.8 0.74% 32 $31.9 0.69%
6pk Can Carb
(Exp)
Miscellaneous 45 $4.0 0.46% 42 $19.0 0.51% 43 $23.0 0.50%
Candy
(Including)
Juices 46 $3.8 0.44% 38 $22.8 0.61% 41 $26.6 0.58%
Superfoods/
Enhanced
Dairy Case 47 $3.7 0.42% 102 $2.8 0.08% 80 $6.5 0.14%
Fruit Drinks
(No Ju)
Aseptic Pack 48 $3.5 0.41% 87 $4.2 0.11% 72 $7.7 0.17%
Juice And
Drinks
Aerosol 49 $3.5 0.40% 35 $24.5 0.66% 38 $28.0 0.61%
Toppings
[Milk By-
Products]
Hot Chocolate/ 50 $3.5 0.40% 45 $17.8 0.48% 47 $21.2 0.46%
Cocoa Mix
Seasonal Candy 51 $3.4 0.39% 47 $16.6 0.45% 49 $20.0 0.43%
Box--Chocolat
e
Sft Drnk 1 52 $3.3 0.38% 65 $8.2 0.22% 63 $11.5 0.25%
Liter Btl
Carb (Exc)
Fruit Drinks: 53 $3.2 0.37% 80 $5.0 0.13% 71 $8.2 0.18%
Canned &
Glass
Soft Drink 54 $3.1 0.36% 66 $7.9 0.21% 65 $11.1 0.24%
Canisters
Marshmallows 55 $3.0 0.34% 48 $16.4 0.44% 50 $19.4 0.42%
Whipping Cream 56 $3.0 0.34% 26 $35.2 0.94% 27 $38.1 0.83%
Solid 57 $2.9 0.33% 54 $14.0 0.38% 55 $16.9 0.37%
Shortening
Tea Can With 58 $2.7 0.31% 74 $6.1 0.16% 67 $8.8 0.19%
Sweetener/
Sugar
Soft Drink 59 $2.6 0.30% 83 $4.7 0.13% 76 $7.4 0.16%
Bottle Non-
Carb (Ex)
Ice Cream 60 $2.6 0.30% 53 $14.1 0.38% 56 $16.7 0.36%
Toppings
Seasonal Candy 61 $2.6 0.30% 52 $14.9 0.40% 53 $17.5 0.38%
Bags Non-
Chocol
Candy Bars 62 $2.6 0.29% 64 $8.8 0.23% 64 $11.3 0.25%
Multi Pack W/
Flour
Candy Bags-- 63 $2.5 0.29% 51 $15.2 0.41% 52 $17.7 0.38%
Chocolate W/
Flour
Pork Skins/ 64 $2.2 0.26% 73 $6.2 0.17% 68 $8.4 0.18%
Cracklins
Mints/Candy & 65 $2.1 0.25% 56 $12.1 0.32% 57 $14.3 0.31%
Breath (Not
Life)
Juices 66 $2.1 0.24% 59 $11.0 0.29% 60 $13.1 0.28%
Smoothies/
Blended
Miscellaneous 67 $1.9 0.22% 58 $11.2 0.30% 59 $13.1 0.28%
Candy
(Including)
Cocktail 68 $1.9 0.22% 49 $16.4 0.44% 51 $18.3 0.40%
Mixes--Fluid:
Add Liq
Cake Decors & 69 $1.8 0.20% 62 $10.0 0.27% 62 $11.7 0.25%
Icing
Enhanced Stick 70 $1.7 0.20% 61 $10.7 0.29% 61 $12.5 0.27%
[Powder Drink
Mix]
Novelty Candy 71 $1.6 0.19% 76 $5.7 0.15% 77 $7.4 0.16%
Sugar 72 $1.4 0.16% 104 $2.5 0.07% 96 $3.9 0.08%
Sweetened
Sticks
Dips Caramel/ 73 $1.3 0.15% 75 $5.9 0.16% 78 $7.2 0.16%
Fruit Glazes
Seasonal 74 $1.2 0.14% 68 $7.1 0.19% 69 $8.4 0.18%
Miscellaneous
W/Flour
Instant Tea & 75 $1.1 0.13% 84 $4.4 0.12% 85 $5.6 0.12%
Tea Mix (W/
Sugar)
Misc Checklane 76 $1.1 0.13% 103 $2.6 0.07% 97 $3.7 0.08%
Candy
Fluid Pouch 77 $1.1 0.13% 71 $6.6 0.18% 73 $7.7 0.17%
[Powder Drink
Mix]
Sweet Goods: 78 $1.1 0.12% 85 $4.4 0.12% 87 $5.4 0.12%
Candy
Tea Bottles 79 $1.1 0.12% 114 $1.9 0.05% 105 $3.0 0.06%
With
Sweetener/Sug
Hispanic 80 $1.1 0.12% 93 $3.5 0.09% 92 $4.6 0.10%
Carbonated
Beverages
Candy W/O 81 $1.0 0.12% 78 $5.4 0.15% 81 $6.5 0.14%
Flour
Candy Boxed 82 $1.0 0.12% 79 $5.3 0.14% 83 $6.3 0.14%
Chocolates W/
Flour
Apple Juice & 83 $1.0 0.12% 98 $3.0 0.08% 95 $4.0 0.09%
Cider (50%
And U)
Energy Drink-- 84 $1.0 0.11% 63 $9.4 0.25% 66 $10.4 0.22%
Multi-Pack
(Non)
Candy Boxed 85 $0.9 0.11% 70 $6.7 0.18% 74 $7.7 0.17%
Chocolates
Seasonal Candy 86 $0.9 0.11% 89 $4.0 0.11% 88 $4.9 0.11%
Box Non-
Chocola
Candy Box Non-- 87 $0.9 0.11% 90 $3.9 0.10% 89 $4.8 0.10%
Chocolate
Cake Decors-- 88 $0.9 0.10% 77 $5.4 0.15% 82 $6.3 0.14%
Candies
Non-Carb Jce 89 $0.9 0.10% 82 $4.8 0.13% 84 $5.7 0.12%
(Under 50%
Jce)
Candy Bags-- 90 $0.8 0.09% 91 $3.7 0.10% 93 $4.5 0.10%
Non Chocolate
W/Flo
Hispanic Juice 91 $0.7 0.08% 113 $2.0 0.07% 109 $2.7 0.05%
Under 50%
Juice
Can/Btl Carb 92 $0.7 0.08% 67 $7.6 0.20% 70 $8.3 0.18%
Beve 50% And
Unde
Cranapple/Cran 93 $0.6 0.07% 69 $7.0 0.19% 75 $7.6 0.17%
Grape Juice
(Un)
Grapefruit 94 $0.6 0.07% 96 $3.1 0.08% 98 $3.7 0.08%
Juice (50%
And Unde)
Blended Juice 95 $0.6 0.07% 97 $3.0 0.08% 100 $3.6 0.08%
&
Combinations
(Un)
Mixers (Tonic 96 $0.5 0.06% 55 $13.2 0.35% 58 $13.7 0.30%
Water/Gngr
Ale) Un
Marshmallow 97 $0.5 0.06% 92 $3.5 0.09% 94 $4.1 0.09%
Creme
Coconut 98 $0.5 0.06% 81 $4.9 0.13% 86 $5.5 0.12%
[Baking
Needs]
Honey/Syrup 99 $0.5 0.06% 86 $4.3 0.11% 90 $4.8 0.10%
Dips Fruit And 100 $0.5 0.06% 106 $1.9 0.05% 112 $2.4 0.04%
Chocolate
------------------------- --------------------------------------------------------
Top 100 $862.5 98.70% $3,660.7 97.93% $4,523.2 98.05%
SoFAS
Expenditure
s *
------------------------- --------------------------------------------------------
Total SoFAS $864.1 98.96% $3,673.1 98.42% $4,537.3 98.53%
Expenditure
s Among Top
1,000
Subcommodit
ies
========================= ========================================================
Total $873.2 100% $3,731.9 100% $4,605.0 100%
SoFAS
Expenditu
res Among
1,792
Subcommod
ities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit D-7: Vegetables
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
--------------------------------- Expenditures -------------------------------
Vegetable ---------------------------------
Subcommodity Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Potatoes 1 $35.8 6.74% 1 $154.5 4.60% 1 $190.2 4.89%
Russet (Bulk
& Bag)
Fz Bag 2 $25.7 4.85% 2 $131.9 3.93% 2 $157.7 4.05%
Vegetables--P
lain
Mainstream 3 $23.0 4.33% 6 $81.0 2.41% 5 $103.9 2.67%
[Pasta &
Pizza Sauce]
Frzn French 4 $20.5 3.86% 19 $50.3 1.50% 9 $70.8 1.82%
Fries
Avocado 5 $13.4 2.52% 4 $112.6 3.35% 4 $126.0 3.24%
Blends [Salad 6 $13.1 2.47% 3 $124.0 3.69% 3 $137.1 3.52%
Mix]
Green Beans: 7 $12.8 2.41% 15 $53.1 1.58% 15 $65.9 1.69%
Fs/Whl/Cut
Potatoes: Dry 8 $12.3 2.31% 33 $32.3 0.96% 28 $44.6 1.15%
Corn 9 $12.1 2.28% 22 $44.0 1.31% 19 $56.0 1.44%
Head Lettuce 10 $11.6 2.18% 13 $55.5 1.65% 14 $67.1 1.72%
Frzn Steamable 11 $10.5 1.98% 5 $81.4 2.42% 6 $91.9 2.36%
Vegetables
Mexican Sauces 12 $10.2 1.93% 9 $62.3 1.85% 8 $72.5 1.86%
And Picante
Sau
Tomatoes Diced 13 $9.5 1.79% 11 $59.9 1.78% 11 $69.4 1.79%
Tomatoes 14 $9.2 1.74% 7 $77.7 2.31% 7 $86.9 2.23%
Hothouse On
The Vine
Onions Yellow 15 $8.7 1.65% 27 $39.3 1.17% 24 $48.1 1.24%
(Bulk & Bag)
Cucumbers 16 $8.2 1.55% 12 $58.9 1.75% 13 $67.1 1.73%
Vegetable 17 $7.8 1.48% 29 $36.6 1.09% 29 $44.4 1.14%
Salads--Prepa
ck
Peppers Green 18 $7.8 1.47% 25 $41.5 1.24% 22 $49.3 1.27%
Bell
Regular Garden 19 $7.8 1.46% 35 $31.9 0.95% 31 $39.6 1.02%
Roma Tomatoes 20 $7.5 1.41% 26 $39.6 1.18% 25 $47.1 1.21%
(Bulk/Pkg)
Carrots Mini 21 $7.0 1.32% 10 $61.4 1.83% 12 $68.5 1.76%
Peeled
Onions Sweet 22 $6.2 1.16% 20 $47.4 1.41% 21 $53.6 1.38%
(Bulk & Bag)
Celery 23 $5.9 1.11% 17 $51.2 1.52% 18 $57.1 1.47%
Tomatoes Vine 24 $5.7 1.07% 51 $22.5 0.67% 48 $28.2 0.72%
Ripe Bulk
Garden Plus 25 $5.5 1.03% 36 $31.8 0.95% 34 $37.2 0.96%
[Salad Mix]
Cabbage 26 $5.3 1.00% 43 $25.1 0.75% 43 $30.5 0.78%
Frzn Tater 27 $5.2 0.99% 55 $18.8 0.56% 53 $24.1 0.62%
Tots/Other
Extruded
Broccoli Whole 28 $5.2 0.97% 16 $52.0 1.55% 17 $57.1 1.47%
& Crowns
Tomato Sauce 29 $5.1 0.96% 48 $24.2 0.72% 45 $29.3 0.75%
Variety 30 $5.1 0.96% 8 $65.2 1.94% 10 $70.3 1.81%
Lettuce
Tomatoes Hot 31 $5.0 0.94% 39 $30.3 0.90% 37 $35.3 0.91%
House Bulk
Potatoes Sweet 32 $4.8 0.91% 28 $37.1 1.11% 30 $41.9 1.08%
& Yams
Tomatoes Grape 33 $4.7 0.88% 14 $54.6 1.63% 16 $59.3 1.52%
Mexican Beans/ 34 $4.7 0.88% 52 $21.0 0.63% 51 $25.6 0.66%
Refried
Frzn Hashbrown 35 $4.6 0.86% 45 $24.8 0.74% 44 $29.3 0.75%
Potatoes
Corn Bulk 36 $4.5 0.85% 32 $32.5 0.97% 35 $37.1 0.95%
Fz Box 37 $4.4 0.83% 46 $24.7 0.73% 47 $29.1 0.75%
Vegetables--V
alue-Added
Kits [Salad 38 $4.2 0.79% 31 $33.5 1.00% 33 $37.6 0.97%
Mix]
Potatoes Red 39 $4.1 0.78% 34 $32.0 0.95% 36 $36.1 0.93%
(Bulk & Bag)
Frzn Corn On 40 $4.0 0.75% 94 $8.4 0.25% 83 $12.4 0.32%
The Cob
Vegetable 41 $4.0 0.75% 44 $25.1 0.75% 46 $29.1 0.75%
Party Tray
Cut Vegetables 42 $4.0 0.75% 24 $42.2 1.26% 26 $46.2 1.19%
All Other
Vegetable 43 $3.8 0.72% 37 $31.0 0.92% 38 $34.8 0.89%
Salads--Bulk
Veg Juice 44 $3.8 0.72% 38 $30.4 0.91% 39 $34.2 0.88%
(Except
Tomato) (Ove)
Asparagus 45 $3.8 0.72% 18 $50.7 1.51% 20 $54.5 1.40%
Tomatoes Vine 46 $3.6 0.68% 101 $7.3 0.22% 89 $10.9 0.28%
Ripe Pkg
Peppers Red 47 $3.6 0.68% 23 $42.5 1.27% 27 $46.1 1.19%
Bell
Value (Pasta 48 $3.5 0.67% 87 $9.7 0.29% 78 $13.2 0.34%
Tomato Sauce)
Peas/Green 49 $3.5 0.66% 64 $14.7 0.44% 61 $18.2 0.47%
Spinach & 50 $3.5 0.66% 103 $7.0 0.21% 92 $10.5 0.27%
Greens
Peppers Other 51 $3.4 0.63% 41 $28.4 0.85% 41 $31.8 0.82%
Bell
Mushrooms 52 $3.3 0.63% 42 $27.8 0.83% 42 $31.2 0.80%
White Sliced
Pkg
Shredded 53 $3.3 0.62% 81 $10.9 0.32% 75 $14.2 0.36%
Lettuce
Mushrooms 54 $3.1 0.58% 40 $29.6 0.88% 40 $32.7 0.84%
White Whole
Pkg
Green Onions 55 $3.0 0.57% 49 $23.5 0.70% 50 $26.5 0.68%
Salad Bowls 56 $2.9 0.54% 74 $12.3 0.37% 69 $15.2 0.39%
Fz Bag 57 $2.8 0.54% 65 $14.7 0.44% 63 $17.6 0.45%
Vegetables--V
alue-Added
Sal: Hommus 58 $2.8 0.52% 21 $45.4 1.35% 23 $48.2 1.24%
Mushrooms Cnd 59 $2.7 0.52% 67 $14.3 0.42% 64 $17.0 0.44%
& Glass
Mexican 60 $2.7 0.51% 69 $13.7 0.41% 66 $16.4 0.42%
Enchilada
Sauce
Onions Red 61 $2.5 0.48% 53 $20.9 0.62% 54 $23.5 0.60%
(Bulk & Bag)
Onions White 62 $2.5 0.47% 60 $15.8 0.47% 60 $18.3 0.47%
(Bulk & Bag)
Authentic 63 $2.3 0.43% 89 $9.2 0.27% 87 $11.5 0.30%
Sauces/Salsa/
Picante
Salad Mix 64 $2.3 0.43% 30 $36.5 1.09% 32 $38.8 1.00%
Blends
Organic
Salad: Lettuce 65 $2.2 0.42% 77 $12.2 0.36% 72 $14.5 0.37%
Cauliflower 66 $2.2 0.42% 47 $24.5 0.73% 49 $26.8 0.69%
Whole
Mushrooms 67 $2.2 0.42% 50 $22.6 0.67% 52 $24.8 0.64%
Portabella
Mexican 68 $2.2 0.41% 61 $15.7 0.47% 62 $17.9 0.46%
Peppers
Chilies
Fried Onions 69 $2.1 0.39% 75 $12.3 0.37% 73 $14.3 0.37%
Carrots Bagged 70 $2.0 0.39% 58 $17.2 0.51% 58 $19.2 0.49%
Potatoes 71 $2.0 0.38% 54 $20.3 0.60% 55 $22.3 0.57%
Gourmet
Sweet Potatoes 72 $2.0 0.38% 104 $6.7 0.20% 101 $8.7 0.22%
Corn Is 73 $1.9 0.36% 70 $12.8 0.38% 71 $14.7 0.38%
Packaged
Salad Spinach 74 $1.8 0.34% 57 $17.9 0.53% 57 $19.7 0.51%
Tomato Paste 75 $1.8 0.34% 83 $10.2 0.30% 84 $12.0 0.31%
Sal: Salsa/ 76 $1.8 0.33% 98 $7.7 0.23% 95 $9.5 0.24%
Dips Bulk
Beans 77 $1.7 0.32% 59 $16.9 0.50% 59 $18.6 0.48%
Tomato Juice 78 $1.7 0.32% 88 $9.6 0.28% 88 $11.2 0.29%
(Over 50%
Jce)
Authentic 79 $1.7 0.32% 136 $3.2 0.10% 128 $4.9 0.13%
Vegetables
And Foods
Potatoes Gold 80 $1.6 0.29% 63 $14.8 0.44% 65 $16.4 0.42%
(Bulk & Bag)
Garlic Whole 81 $1.6 0.29% 71 $12.7 0.38% 74 $14.3 0.37%
Cloves
Coleslaw 82 $1.6 0.29% 79 $11.9 0.35% 77 $13.5 0.35%
Carrots Bagged 83 $1.5 0.29% 56 $18.6 0.55% 56 $20.2 0.52%
Organic
Pumpkins 84 $1.5 0.29% 82 $10.3 0.31% 85 $11.9 0.31%
Herbs Cilanto 85 $1.4 0.26% 84 $10.1 0.30% 86 $11.5 0.30%
Frzn Baked/ 86 $1.3 0.25% 91 $9.0 0.27% 93 $10.4 0.27%
Stuffed/
Mashed & Spec
Broccoli/ 87 $1.3 0.25% 72 $12.5 0.37% 76 $13.8 0.36%
Cauliflower
Processed
Mixed 88 $1.3 0.24% 124 $4.5 0.13% 119 $5.8 0.15%
Vegetables
Authentic 89 $1.3 0.24% 125 $4.5 0.13% 120 $5.7 0.15%
Peppers
Sal: Salsa 90 $1.3 0.24% 68 $13.7 0.41% 70 $15.0 0.38%
Prepack
Carrots 91 $1.1 0.21% 123 $4.5 0.14% 121 $5.7 0.15%
Peppers Yellow 92 $1.1 0.21% 80 $11.4 0.34% 82 $12.5 0.32%
Bell
Pizza Sauce 93 $1.1 0.21% 110 $6.1 0.18% 107 $7.2 0.18%
Garlic Jar 94 $1.1 0.21% 97 $7.7 0.23% 99 $8.8 0.23%
Peppers 95 $1.0 0.19% 126 $4.4 0.13% 125 $5.5 0.14%
Jalapeno
Tomatoes 96 $1.0 0.19% 78 $12.1 0.36% 80 $13.1 0.34%
Cherry
Instore Cut 97 $1.0 0.19% 86 $9.7 0.29% 91 $10.7 0.28%
Vegetables
Tomato Stewed 98 $1.0 0.19% 108 $6.4 0.19% 105 $7.4 0.19%
White Potatoes 99 $1.0 0.18% 128 $4.3 0.13% 127 $5.2 0.13%
Sauerkraut and 100 $0.9 0.17% 111 $6.0 0.18% 109 $6.9 0.18%
Cabbage
------------------------- --------------------------------------------------------
Top 100 $500.7 94.36% $3,035.6 90.37% $3,536.4 90.91%
Vegetable
Expenditure
s *
------------------------- --------------------------------------------------------
Total $520.5 98.08% $3,251.8 96.80% $3,772.3 96.97%
Vegetable
Expenditure
s Among Top
1,000
Subcommodit
ies
========================= ========================================================
Total $530.7 100% $3,359.3 100% $3,890.0 100%
Vegetable
Expenditu
res Among
1,792
Subcommod
ities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit D-8: Composite Foods
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
--------------------------------- Expenditures -------------------------------
Composite ---------------------------------
Subcommodity Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Potato Chips 1 $64.4 5.19% 2 $253.2 4.88% 1 $317.6 4.94%
Snacks/ 2 $44.6 3.59% 10 $100.5 1.94% 7 $145.0 2.26%
Appetizers
Fz Ss Prem 3 $43.8 3.53% 4 $175.4 3.38% 4 $219.3 3.41%
Traditional
Meals
Snack Cake-- 4 $41.6 3.36% 9 $101.7 1.96% 9 $143.3 2.23%
Multi Pack
Fz Ss Economy 5 $40.9 3.30% 15 $80.7 1.56% 11 $121.6 1.89%
Meals All
Pizza/Premium 6 $39.7 3.20% 6 $153.3 2.95% 5 $193.0 3.00%
Sandwiches & 7 $35.9 2.89% 17 $73.6 1.42% 13 $109.4 1.70%
Handhelds
Convenient 8 $34.2 2.76% 19 $69.7 1.34% 14 $104.0 1.62%
Meals--Kids
Meal C
Premium [Ice 9 $31.2 2.52% 3 $226.0 4.35% 3 $257.2 4.00%
Cream &
Sherbert]
Condensed Soup 10 $29.7 2.39% 5 $153.6 2.96% 6 $183.2 2.85%
Fz Family 11 $27.6 2.23% 13 $83.5 1.61% 12 $111.1 1.73%
Style Entrees
Traditional 12 $25.6 2.07% 8 $118.7 2.29% 8 $144.4 2.25%
[Ice Cream &
Sherbert]
Fz Ss Prem 13 $24.7 1.99% 1 $271.6 5.23% 2 $296.3 4.61%
Nutritional
Meals
Macaroni & 14 $24.3 1.96% 24 $59.7 1.15% 21 $84.0 1.31%
Cheese Dnrs
Can Pasta 15 $22.2 1.79% 36 $47.7 0.92% 29 $69.9 1.09%
Mult Pk Bag 16 $21.6 1.74% 38 $43.4 0.84% 32 $65.0 1.01%
Snacks
Sw Gds: Donuts 17 $21.3 1.72% 14 $82.3 1.58% 15 $103.6 1.61%
Pizza/Economy 18 $19.8 1.60% 37 $45.1 0.87% 33 $65.0 1.01%
Frzn Breakfast 19 $19.1 1.54% 29 $55.7 1.07% 23 $74.8 1.16%
Sandwiches
Fz Skillet 20 $18.8 1.51% 16 $79.3 1.53% 17 $98.1 1.53%
Meals
Cakes: 21 $18.6 1.50% 33 $50.3 0.97% 31 $68.9 1.07%
Birthday/
Celebration
Sh
Sandwich 22 $18.0 1.45% 18 $71.8 1.38% 19 $89.8 1.40%
Cookies
Pizza/ 23 $17.9 1.44% 22 $64.1 1.24% 22 $82.0 1.27%
Traditional
Rts Soup: 24 $17.6 1.42% 7 $119.9 2.31% 10 $137.5 2.14%
Chunky/
Homestyle/Et
Salsa & Dips 25 $17.1 1.38% 28 $57.0 1.10% 24 $74.1 1.15%
Sandwiches--(C 26 $16.9 1.36% 20 $67.7 1.30% 20 $84.6 1.32%
old)
Sweet Goods-- 27 $15.8 1.28% 27 $57.9 1.12% 26 $73.8 1.15%
Full Size
Tray Pack/Choc 28 $15.3 1.23% 31 $53.9 1.04% 30 $69.2 1.08%
Chip Cookies
Sticks/Enrobed 29 $14.2 1.14% 25 $59.7 1.15% 25 $73.9 1.15%
[Frozen
Novelties]
Water Ice 30 $14.0 1.13% 32 $50.6 0.97% 34 $64.6 1.00%
[Frozen
Novelties]
Pails [Ice 31 $13.9 1.12% 46 $35.1 0.68% 41 $49.1 0.76%
Cream &
Sherbert]
Skillet 32 $13.0 1.05% 57 $25.8 0.50% 49 $38.9 0.60%
Dinners
Pizza/Single 33 $12.8 1.03% 39 $43.2 0.83% 38 $56.0 0.87%
Serve/
Microwave
Super Premium 34 $11.8 0.95% 11 $91.1 1.76% 16 $103.0 1.60%
Pints [Ice
Cream &
Sherbert]
Cakes: 35 $11.1 0.89% 45 $35.3 0.68% 43 $46.3 0.72%
Cupcakes
Corn Dogs 36 $10.9 0.88% 68 $20.6 0.40% 59 $31.5 0.49%
Cookies: 37 $10.8 0.87% 26 $59.6 1.15% 28 $70.4 1.09%
Regular
Burritos 38 $10.2 0.82% 69 $20.0 0.39% 61 $30.1 0.47%
Microwave 39 $9.8 0.79% 40 $39.9 0.77% 40 $49.8 0.77%
Dinners
Cakes: Layers 40 $9.8 0.79% 42 $38.2 0.74% 42 $48.1 0.75%
Sushi--In 41 $9.2 0.74% 12 $85.4 1.64% 18 $94.6 1.47%
Store
Prepared
Canister 42 $9.1 0.73% 44 $36.4 0.70% 45 $45.5 0.71%
Snacks
Pudding & 43 $8.7 0.70% 53 $27.6 0.53% 51 $36.3 0.56%
Gelatin Cups/
Cans
Salty Snacks 44 $8.4 0.67% 80 $15.8 0.31% 67 $24.2 0.38%
Vending
Cones [Frozen 45 $7.9 0.64% 50 $31.2 0.60% 48 $39.2 0.61%
Novelties]
Vanilla Wafer/ 46 $7.5 0.60% 43 $36.7 0.71% 46 $44.2 0.69%
Kids Cookies
Ice Cream 47 $7.4 0.60% 60 $24.2 0.47% 58 $31.6 0.49%
Sandwiches
Cakes: Creme/ 48 $7.4 0.59% 58 $25.8 0.50% 54 $33.2 0.52%
Pudding
Refrigerated 49 $7.0 0.57% 34 $49.5 0.95% 37 $56.5 0.88%
Pudding
Layer Cake Mix 50 $7.0 0.56% 47 $35.1 0.68% 47 $42.1 0.65%
Refrigerated 51 $6.8 0.55% 51 $28.8 0.56% 53 $35.6 0.55%
Cookies--Bran
d
Broth 52 $6.7 0.54% 21 $65.6 1.26% 27 $72.3 1.12%
Pies: Fruit/ 53 $6.3 0.51% 41 $39.6 0.76% 44 $45.9 0.71%
Nut
Snack Cake-- 54 $5.7 0.46% 77 $16.2 0.31% 74 $22.0 0.34%
Single Serve
Better For You 55 $5.6 0.45% 35 $48.1 0.93% 39 $53.7 0.84%
Snacks
Cookies: 56 $5.5 0.44% 56 $26.8 0.52% 56 $32.2 0.50%
Holiday/
Special Occas
Misc Bag 57 $5.5 0.44% 98 $11.5 0.22% 83 $17.0 0.26%
Snacks
Frozen Fruit 58 $5.3 0.43% 62 $23.7 0.46% 62 $28.9 0.45%
Pies &
Cobblers
Frozen Cream 59 $4.9 0.39% 71 $18.9 0.36% 69 $23.8 0.37%
Pies
Sw Gds: Sw 60 $4.8 0.39% 55 $26.9 0.52% 57 $31.7 0.49%
Rolls/Dan
Brownie Mix 61 $4.8 0.39% 54 $27.5 0.53% 55 $32.3 0.50%
Fz Meal Kits/ 62 $4.8 0.38% 96 $12.2 0.23% 84 $16.9 0.26%
Stuffed/Other
Sw Gds: 63 $4.5 0.36% 48 $31.8 0.61% 50 $36.3 0.57%
Muffins
Frzn Breakfast 64 $4.5 0.36% 78 $16.2 0.31% 78 $20.7 0.32%
Entrees
Convenient 65 $4.5 0.36% 102 $11.2 0.22% 92 $15.7 0.24%
Meals--Adult
Meal
Dry Beans/Peas/ 66 $4.2 0.34% 72 $18.8 0.36% 71 $23.1 0.36%
Barley: Bag &
B
Adult Premium 67 $4.2 0.34% 30 $54.5 1.05% 36 $58.7 0.91%
[Frozen
Novelties]
Mexican 68 $4.2 0.34% 100 $11.4 0.22% 93 $15.6 0.24%
Dinners And
Foods
Premium 69 $4.2 0.33% 49 $31.5 0.61% 52 $35.7 0.55%
Cookies (Ex:
Pepperidg)
Chocolate 70 $4.0 0.32% 73 $18.5 0.36% 73 $22.5 0.35%
Covered
Cookies
Microwavable 71 $3.7 0.29% 116 $9.0 0.17% 106 $12.7 0.20%
Cups
Cakes: 72 $3.6 0.29% 84 $14.7 0.28% 81 $18.3 0.28%
Cheesecake
Deli Tray: 73 $3.5 0.28% 65 $21.5 0.41% 66 $25.0 0.39%
Meat And
Cheese
Dry Soup 74 $3.5 0.28% 63 $23.3 0.45% 64 $26.8 0.42%
Treats 75 $3.5 0.28% 103 $11.2 0.22% 95 $14.6 0.23%
Fitness & 76 $3.4 0.28% 23 $59.8 1.15% 35 $63.2 0.98%
Diet--Bars W/
Flour
Refrigerated 77 $3.4 0.28% 90 $12.9 0.25% 89 $16.3 0.25%
Cookie Dough
Cakes: Fancy/ 78 $3.3 0.27% 76 $17.4 0.34% 77 $20.7 0.32%
Service Case
Package 79 $3.3 0.26% 112 $9.5 0.18% 105 $12.7 0.20%
Dinners/Pasta
Salads
Cakes: Layers/ 80 $3.3 0.26% 94 $12.5 0.24% 91 $15.8 0.25%
Sheets
Novelties
Pies: Pumpkin/ 81 $3.2 0.26% 89 $13.1 0.25% 87 $16.3 0.25%
Custard
Puddings Dry 82 $3.2 0.26% 67 $20.8 0.40% 68 $23.9 0.37%
Vendor Size/ 83 $3.1 0.25% 126 $6.8 0.13% 120 $9.9 0.15%
Single Serve
Cooki
Snack Mix 84 $3.0 0.24% 75 $17.5 0.34% 79 $20.5 0.32%
Multi-Pack 85 $2.9 0.23% 99 $11.4 0.22% 96 $14.3 0.22%
Cookies
Cups/Push Ups/ 86 $2.8 0.23% 110 $9.6 0.18% 108 $12.4 0.19%
Other
Frzn Pie 87 $2.7 0.22% 79 $16.0 0.31% 80 $18.7 0.29%
Shells/Pastry
Shell/F
Frozen Cakes/ 88 $2.7 0.22% 105 $11.0 0.21% 101 $13.7 0.21%
Desserts
Cakes: Angel 89 $2.7 0.22% 74 $18.1 0.35% 76 $20.8 0.32%
Fds/Cke Rolls
Wellness/ 90 $2.7 0.22% 61 $23.8 0.46% 65 $26.5 0.41%
Portion
Control
Pie Filling/ 91 $2.7 0.22% 59 $24.8 0.48% 63 $27.5 0.43%
Mincemeat/
Glazes
Misc Snacks 92 $2.6 0.21% 87 $13.2 0.25% 90 $15.8 0.25%
Cakes: Ice 93 $2.6 0.21% 120 $8.6 0.17% 113 $11.2 0.17%
Cream
Sushi--Prepack 94 $2.6 0.21% 70 $19.2 0.37% 75 $21.8 0.34%
aged
Cakes: 95 $2.5 0.20% 114 $9.1 0.18% 110 $11.6 0.18%
Birthday/
Celebration
Lay
Sw Gds: Swt/ 96 $2.4 0.20% 85 $13.9 0.27% 88 $16.3 0.25%
Flvrd Loaves
Cakes: Sheet 97 $2.4 0.19% 124 $7.2 0.14% 121 $9.6 0.15%
Cookies: 98 $2.4 0.19% 66 $20.8 0.40% 70 $23.2 0.36%
Gourmet
Premium Pints 99 $2.3 0.18% 128 $6.5 0.13% 125 $8.8 0.14%
[Ice Cream &
Sherbert]
Sw Gds: 100 $1.9 0.15% 104 $11.2 0.22% 104 $13.1 0.20%
Brownie/Bar
Cookie
------------------------- --------------------------------------------------------
Top 100 $1,179.3 95.05% $4,717.8 90.90% $5,897.1 91.70%
Composite
Expenditure
s *
------------------------- --------------------------------------------------------
Total $1,235.4 99.57% $5,132.0 98.88% $6,367.4 99.01%
Composite
Expenditure
s Among Top
1,000
Subcommodit
ies
========================= ========================================================
Total $1,240.7 100% $5,190.0 100% $6,430.7 100%
Composite
Expenditu
res Among
1,792
Subcommod
ities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit D-9: Other Subcommodities
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
--------------------------------- Expenditures -------------------------------
Other ---------------------------------
Subcommodity Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Infant Formula 1 $54.2 9.60% 14 $45.3 1.70% 6 $99.5 3.07%
Starter/
Solution
Still Water 2 $48.8 8.64% 2 $187.7 7.03% 2 $236.5 7.31%
Drnking/Mnrl
Water
Unflavored Can 3 $41.3 7.32% 1 $198.0 7.41% 1 $239.3 7.39%
Coffee
Isotonic 4 $30.5 5.40% 4 $119.5 4.47% 3 $150.0 4.63%
Drinks Single
Serve
Spring Water 5 $16.2 2.87% 5 $95.6 3.58% 5 $111.8 3.45%
Traditional 6 $14.1 2.49% 8 $61.2 2.29% 7 $75.2 2.32%
Spices
Bbq Sauce 7 $12.3 2.17% 16 $38.6 1.45% 16 $50.9 1.57%
Baby Food-- 8 $11.7 2.07% 21 $28.1 1.05% 18 $39.8 1.23%
Beginner
Non-Carb Water 9 $11.6 2.05% 7 $63.4 2.37% 8 $74.9 2.32%
Flvr--Drnk/
Mnr
Catsup 10 $11.5 2.03% 15 $41.5 1.55% 15 $53.0 1.64%
Sauce Mixes/ 11 $11.5 2.03% 13 $46.7 1.75% 12 $58.2 1.80%
Gravy Mixes
Dry
Baby Food 12 $11.2 1.98% 22 $27.5 1.03% 19 $38.7 1.20%
Junior/All
Brands
Isotonic 13 $10.8 1.92% 9 $58.1 2.17% 10 $68.9 2.13%
Drinks Multi-
Pack
Ice--Crushed/ 14 $9.3 1.65% 11 $49.9 1.87% 11 $59.2 1.83%
Cubed
Unflavored Bag 15 $8.5 1.50% 3 $137.3 5.14% 4 $145.8 4.50%
Coffee
Infant Formula 16 $8.4 1.49% 71 $9.1 0.34% 47 $17.5 0.54%
Specialty
Infant Formula 17 $8.3 1.46% 30 $22.8 0.85% 27 $31.0 0.96%
Starter Large
P
Steak & 18 $8.2 1.44% 25 $26.7 1.00% 21 $34.9 1.08%
Worchester
Sauce
Unflavored 19 $7.6 1.34% 23 $27.3 1.02% 22 $34.8 1.08%
Instant
Coffee
Non-Dairy 20 $7.1 1.25% 6 $67.7 2.53% 9 $74.8 2.31%
Milks
Unsweetened 21 $7.0 1.25% 88 $6.2 0.23% 61 $13.3 0.41%
Envelope
[Powder Drink
Mix]
Malted Mlk/ 22 $6.9 1.23% 28 $25.3 0.95% 26 $32.2 1.00%
Syrup/Pwdrs
(Eggnog)
Still Water 23 $6.3 1.11% 17 $38.1 1.43% 17 $44.4 1.37%
Flvrd Drnk/
Mnrl Wt
Infant Formula 24 $6.0 1.06% 55 $12.4 0.46% 45 $18.4 0.57%
Toddler
Mexican 25 $5.9 1.05% 33 $20.6 0.77% 32 $26.5 0.82%
Seasoning
Mixes
Hot Sauce 26 $5.8 1.03% 42 $16.4 0.61% 38 $22.2 0.69%
Ready To Drink 27 $5.5 0.98% 34 $20.5 0.77% 33 $26.0 0.80%
Coffee
Tea Bags & 28 $5.4 0.95% 24 $27.2 1.02% 25 $32.5 1.01%
Bulk Tea
Infant Formula 29 $5.3 0.95% 47 $15.2 0.57% 42 $20.5 0.63%
Solutions
Large
Stuffing Mixes 30 $5.3 0.94% 31 $22.1 0.83% 30 $27.4 0.85%
Infant Formula 31 $4.9 0.86% 111 $3.9 0.15% 82 $8.8 0.27%
Concentrate
Salad Bar 32 $4.5 0.81% 41 $18.2 0.68% 36 $22.8 0.70%
Other
Bits & Morsels 33 $4.4 0.77% 10 $50.3 1.88% 13 $54.7 1.69%
[Baking
Needs]
Ripe Olives 34 $4.1 0.73% 27 $25.3 0.95% 28 $29.5 0.91%
Gravy Can/ 35 $4.0 0.72% 44 $15.7 0.59% 44 $19.8 0.61%
Glass
Marinades 36 $3.9 0.70% 39 $18.4 0.69% 37 $22.4 0.69%
Baby Food 37 $3.8 0.67% 82 $7.1 0.27% 70 $10.9 0.34%
Cereals
Diet Cntrl 38 $3.7 0.66% 20 $30.3 1.13% 24 $34.0 1.05%
Liqs
Nutritional
Enhancements-- 39 $3.6 0.64% 36 $19.8 0.74% 35 $23.4 0.72%
Pickles/Kraut
Infant Formula 40 $3.5 0.61% 85 $6.9 0.26% 72 $10.4 0.32%
Ready To Use
Sugar Free 41 $3.5 0.61% 32 $21.1 0.79% 34 $24.5 0.76%
Canister
[Powder Drink
Mix]
Coffee Pods/ 42 $3.4 0.60% 12 $49.8 1.87% 14 $53.2 1.65%
Singles/
Filter Pac
Sugar Free 43 $3.3 0.58% 38 $18.8 0.70% 39 $22.1 0.68%
Sticks
[Powder Drink
Mix]
Sparkling 44 $3.1 0.55% 29 $24.1 0.90% 31 $27.2 0.84%
Water--Flvrd
Sweet
Tea Bags/ 45 $3.1 0.54% 19 $31.2 1.17% 23 $34.3 1.06%
Herbal
Yellow Mustard 46 $3.0 0.53% 56 $12.4 0.46% 55 $15.4 0.48%
Asian Other 47 $2.8 0.50% 37 $18.9 0.71% 40 $21.8 0.67%
Sauces/
Marinad
Peppers 48 $2.7 0.48% 52 $13.5 0.50% 53 $16.2 0.50%
Mexican Taco 49 $2.6 0.47% 84 $7.0 0.26% 76 $9.7 0.30%
Sauce
Green Olives 50 $2.6 0.46% 43 $15.8 0.59% 46 $18.3 0.57%
Relishes 51 $2.5 0.44% 60 $11.6 0.43% 57 $14.1 0.44%
Flavored Bag 52 $2.4 0.42% 26 $26.2 0.98% 29 $28.6 0.88%
Coffee
Gourmet Spices 53 $2.4 0.42% 18 $33.2 1.24% 20 $35.6 1.10%
Baby Juices 54 $2.3 0.40% 118 $3.1 0.11% 105 $5.3 0.16%
Dry Salad 55 $2.0 0.35% 48 $15.1 0.57% 49 $17.1 0.53%
Dressing &
Dip Mixes
Mustard--All 56 $2.0 0.35% 40 $18.3 0.69% 43 $20.3 0.63%
Other
Gelatin 57 $2.0 0.35% 51 $14.3 0.54% 52 $16.3 0.50%
Vinegar/White 58 $1.9 0.34% 50 $14.4 0.54% 51 $16.3 0.50%
& Cider
Baby Isotonic 59 $1.9 0.33% 101 $4.9 0.18% 92 $6.8 0.21%
Drinks
Wing Sauce 60 $1.8 0.33% 100 $5.0 0.19% 91 $6.8 0.21%
Pure Extracts 61 $1.7 0.31% 46 $15.4 0.58% 48 $17.2 0.53%
Infant Formula 62 $1.7 0.31% 161 $1.1 0.04% 135 $2.8 0.09%
Soy Base
Juices 63 $1.7 0.30% 66 $10.1 0.38% 64 $11.8 0.36%
Proteins
Sal: Dip 64 $1.7 0.30% 59 $12.1 0.45% 58 $13.8 0.43%
Prepack
Diet Energy 65 $1.7 0.30% 54 $12.8 0.48% 56 $14.5 0.45%
Drinks
Baby Spring 66 $1.7 0.30% 138 $2.0 0.07% 119 $3.7 0.11%
Waters
Frozen 67 $1.6 0.28% 86 $6.7 0.25% 83 $8.3 0.26%
Internaional
Table Salt/ 68 $1.6 0.28% 72 $8.6 0.32% 73 $10.2 0.31%
Popcorn Salt/
Ice Cr
Distilled 69 $1.6 0.28% 57 $12.2 0.46% 59 $13.7 0.42%
Water
Enhancements-- 70 $1.5 0.26% 99 $5.2 0.19% 95 $6.6 0.21%
Salads/
Spreads
Asian Soy 71 $1.5 0.26% 64 $10.3 0.39% 66 $11.7 0.36%
Sauce
Central 72 $1.4 0.25% 94 $5.5 0.21% 90 $6.9 0.21%
American
Foods
Misc Dairy 73 $1.4 0.25% 70 $9.1 0.34% 71 $10.5 0.32%
Refigerated
Diet Cntrl 74 $1.4 0.24% 35 $19.9 0.74% 41 $21.3 0.66%
Bars
Nutritional
Tea Bags/Green 75 $1.2 0.22% 61 $11.2 0.42% 63 $12.5 0.38%
Flours/Grains/ 76 $1.2 0.22% 49 $14.6 0.55% 54 $15.9 0.49%
Sugar
Specialty 77 $1.2 0.22% 77 $7.7 0.29% 81 $8.9 0.27%
Instant
Coffee W/Swe
Misc Hispanic 78 $1.2 0.21% 65 $10.2 0.38% 67 $11.4 0.35%
Grocery
Baking Powder 79 $1.1 0.20% 75 $8.2 0.31% 77 $9.4 0.29%
& Soda
Isotonic 80 $1.1 0.19% 103 $4.7 0.18% 103 $5.7 0.18%
Drinks Multi-
Serve
Juices 81 $1.0 0.19% 76 $8.1 0.30% 78 $9.2 0.28%
Antioxidant/
Wellness
Spices & 82 $1.0 0.19% 104 $4.6 0.17% 104 $5.7 0.17%
Seasonings
Infant Formula 83 $1.0 0.18% 119 $3.0 0.11% 117 $4.1 0.13%
Up Age
Oils/Vinegar 84 $1.0 0.18% 67 $10.0 0.37% 69 $11.0 0.34%
Miscellaneous 85 $1.0 0.18% 80 $7.2 0.27% 84 $8.2 0.25%
Package Mixes
Sal: Olives/ 86 $1.0 0.18% 45 $15.5 0.58% 50 $16.5 0.51%
Pickles--Bulk
Cooking Bags 87 $1.0 0.17% 132 $2.4 0.09% 124 $3.4 0.10%
With Spices/
Seaso
Cooking 88 $0.9 0.16% 63 $10.3 0.39% 68 $11.2 0.35%
Chocolate
(Ex: Smi-Swt)
Tea Bags 89 $0.9 0.15% 69 $9.2 0.34% 74 $10.0 0.31%
(Supplement)
Specialty 90 $0.8 0.15% 53 $12.9 0.48% 60 $13.7 0.42%
Vinegar
Traditional 91 $0.8 0.14% 74 $8.3 0.31% 80 $9.1 0.28%
Thai Foods
Pickld Veg/ 92 $0.8 0.14% 91 $5.9 0.22% 94 $6.7 0.21%
Peppers/Etc.
Specialty 93 $0.8 0.14% 62 $11.0 0.41% 65 $11.7 0.36%
Olives
Authentic 94 $0.8 0.14% 81 $7.1 0.27% 86 $7.9 0.24%
Japanese
Foods
Chili Sauce/ 95 $0.7 0.13% 89 $6.0 0.22% 93 $6.7 0.21%
Cocktail
Sauce
Flavored Can 96 $0.7 0.13% 92 $5.8 0.22% 96 $6.5 0.20%
Coffee
Fortified/ 97 $0.7 0.13% 108 $4.4 0.17% 107 $5.1 0.16%
Water
Sparkling 98 $0.7 0.12% 58 $12.1 0.45% 62 $12.8 0.40%
Water--Unflav
ored
Fitness & 99 $0.7 0.12% 78 $7.3 0.27% 85 $8.0 0.25%
Diet--Powder
Ntrtnl
Imitation 100 $0.7 0.12% 115 $3.5 0.13% 116 $4.2 0.13%
Extracts
------------------------- --------------------------------------------------------
Top 100 $540.1 95.68% $2,453.1 91.80% $2,993.1 92.48%
Other
Expenditure
s *
------------------------- --------------------------------------------------------
Total Other $550.7 97.56% $2,533.2 94.80% $3,083.9 95.28%
Expenditure
s Among Top
1,000
Subcommodit
ies
========================= ========================================================
Total $564.5 100% $2,672.1 100% $3,236.6 100%
Other
Expenditu
res Among
1,792
Subcommod
ities
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Appendix E. Top 100 Subcommodities for SNAP Households by Expenditure
by Demographic and Store Characteristics
Exhibit E-1: Top 100 Subcommodities for SNAP Households by Expenditure: Household Head Age 19-44 Year Olds
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $30.7 0.47% 1 $143.7 0.46% 1 $174.3 0.46%
White Only
Soft Drinks 12/ 2 $25.1 0.38% 2 $95.9 0.30% 2 $121.0 0.32%
18 & 15pk Can
Car
Lean [Beef] 3 $17.2 0.26% 8 $42.7 0.14% 5 $59.9 0.16%
Kids Cereal 4 $13.8 0.21% 5 $44.6 0.14% 6 $58.4 0.15%
Shredded 5 $13.0 0.20% 3 $67.1 0.21% 3 $80.1 0.21%
Cheese
Sft Drnk 2 6 $11.3 0.17% 13 $39.1 0.12% 8 $50.5 0.13%
Liter Btl
Carb Incl
Potato Chips 7 $10.1 0.15% 11 $39.4 0.13% 9 $49.5 0.13%
Primal [Beef] 8 $10.0 0.15% 16 $35.6 0.11% 14 $45.5 0.12%
Infant Formula 9 $9.8 0.15% 150 $9.2 0.03% 73 $19.0 0.05%
Starter/
Solutio
Lunchment--Del 10 $8.9 0.14% 6 $43.0 0.14% 7 $51.9 0.14%
i Fresh
Chicken Breast 11 $8.4 0.13% 4 $54.2 0.17% 4 $62.5 0.16%
Boneless
Tortilla/Nacho 12 $8.2 0.13% 10 $41.1 0.13% 10 $49.3 0.13%
Chips
Eggs--Large 13 $7.8 0.12% 12 $39.4 0.13% 12 $47.2 0.12%
Snacks/ 14 $7.7 0.12% 45 $20.6 0.07% 32 $28.3 0.07%
Appetizers
Still Water 15 $7.6 0.12% 20 $30.5 0.10% 18 $38.1 0.10%
Drnking/Mnrl
Water
Mainstream 16 $7.4 0.11% 31 $23.4 0.07% 25 $30.8 0.08%
White Bread
American 17 $7.0 0.11% 34 $22.8 0.07% 26 $29.8 0.08%
Single Cheese
Dairy Case 18 $6.8 0.10% 9 $41.4 0.13% 11 $48.2 0.13%
100% Pure
Juice--O
Enhanced [Pork 19 $6.6 0.10% 24 $27.1 0.09% 23 $33.6 0.09%
Boneless Loin/
Rib]
Pizza/Premium 20 $6.5 0.10% 22 $28.2 0.09% 20 $34.8 0.09%
Snack Cake-- 21 $6.5 0.10% 57 $18.9 0.06% 40 $25.5 0.07%
Multi Pack
Fz Ss Economy 22 $6.3 0.10% 90 $13.6 0.04% 72 $19.9 0.05%
Meals All
Convenient 23 $6.2 0.09% 48 $20.3 0.06% 38 $26.6 0.07%
Meals--Kids
Meal C
All Family 24 $6.2 0.09% 14 $37.6 0.12% 15 $43.8 0.11%
Cereal
Fz Ss Prem 25 $6.1 0.09% 52 $19.7 0.06% 39 $25.8 0.07%
Traditional
Meals
Sandwiches & 26 $6.0 0.09% 77 $14.9 0.05% 64 $20.9 0.05%
Handhelds
Soft Drinks 27 $6.0 0.09% 61 $17.9 0.06% 48 $23.9 0.06%
20pk & 24pk
Can Carb
Bacon--Trad 28 $6.0 0.09% 30 $23.5 0.07% 29 $29.4 0.08%
16oz Or Less
Mainstream 29 $5.8 0.09% 23 $28.0 0.09% 22 $33.8 0.09%
Variety
Breads
Sugar 30 $5.6 0.09% 62 $17.9 0.06% 50 $23.5 0.06%
Natural Cheese 31 $5.6 0.08% 17 $34.5 0.11% 17 $40.1 0.11%
Chunks
Unflavored Can 32 $5.5 0.08% 32 $23.3 0.07% 30 $28.8 0.08%
Coffee
Frzn Chicken-- 33 $5.4 0.08% 51 $19.9 0.06% 42 $25.2 0.07%
Wht Meat
Potatoes 34 $5.3 0.08% 37 $22.4 0.07% 35 $27.7 0.07%
Russet (Bulk
& Bag)
Bananas 35 $5.2 0.08% 15 $37.0 0.12% 16 $42.2 0.11%
Isotonic 36 $5.1 0.08% 33 $22.9 0.07% 34 $28.0 0.07%
Drinks Single
Serve
Ribs [Pork] 37 $5.1 0.08% 78 $14.8 0.05% 71 $19.9 0.05%
Sft Drnk Mlt- 38 $5.0 0.08% 35 $22.6 0.07% 36 $27.6 0.07%
Pk Btl Carb
(Excp)
Premium [Ice 39 $4.7 0.07% 18 $32.9 0.10% 19 $37.6 0.10%
Cream &
Sherbert]
Sft Drnk Sngl 40 $4.7 0.07% 89 $13.7 0.04% 77 $18.4 0.05%
Srv Btl Carb
(Ex)
Pourable Salad 41 $4.7 0.07% 36 $22.4 0.07% 37 $27.1 0.07%
Dressings
Condensed Soup 42 $4.6 0.07% 29 $24.0 0.08% 31 $28.6 0.08%
Choice Beef 43 $4.5 0.07% 86 $14.0 0.04% 76 $18.5 0.05%
Fz Family 44 $4.5 0.07% 82 $14.3 0.05% 74 $18.8 0.05%
Style Entrees
Aseptic Pack 45 $4.4 0.07% 66 $16.9 0.05% 61 $21.3 0.06%
Juice And
Drinks
Select Beef 46 $4.3 0.06% 46 $20.5 0.07% 45 $24.8 0.07%
Macaroni & 47 $4.2 0.06% 92 $13.5 0.04% 82 $17.7 0.05%
Cheese Dnrs
Choice Beef 48 $4.1 0.06% 63 $17.8 0.06% 56 $21.9 0.06%
Mainstream 49 $4.0 0.06% 70 $16.1 0.05% 67 $20.1 0.05%
[Pasta &
Pizza Sauce]
Mayonnaise & 50 $4.0 0.06% 67 $16.8 0.05% 65 $20.8 0.05%
Whipped
Dressing
Fz Ss Prem 51 $4.0 0.06% 7 $42.9 0.14% 13 $46.9 0.12%
Nutritional
Meals
Refrigerated 52 $4.0 0.06% 26 $25.8 0.08% 27 $29.7 0.08%
Coffee
Creamers
Fz Bag 53 $3.9 0.06% 54 $19.4 0.06% 51 $23.3 0.06%
Vegetables--P
lain
Hot Dogs--Base 54 $3.9 0.06% 137 $9.8 0.03% 113 $13.6 0.04%
Meat
Strawberries 55 $3.8 0.06% 19 $30.7 0.10% 21 $34.5 0.09%
Adult Cereal 56 $3.8 0.06% 25 $25.8 0.08% 28 $29.6 0.08%
Can Pasta 57 $3.8 0.06% 119 $10.8 0.03% 102 $14.6 0.04%
Mexican Soft 58 $3.8 0.06% 39 $21.7 0.07% 41 $25.4 0.07%
Tortillas And
Wra
Traditional 59 $3.8 0.06% 69 $16.2 0.05% 70 $19.9 0.05%
[Ice Cream &
Sherbert]
Choice Beef 60 $3.7 0.06% 124 $10.6 0.03% 104 $14.3 0.04%
Mult Pk Bag 61 $3.6 0.05% 132 $10.0 0.03% 114 $13.6 0.04%
Snacks
Pizza/Economy 62 $3.5 0.05% 128 $10.3 0.03% 111 $13.7 0.04%
Margarine: 63 $3.5 0.05% 88 $13.8 0.04% 84 $17.3 0.05%
Tubs And
Bowls
Frzn Chicken-- 64 $3.4 0.05% 441 $3.0 0.01% 269 $6.4 0.02%
Wings
Frzn French 65 $3.4 0.05% 143 $9.6 0.03% 119 $13.0 0.03%
Fries
Peanut Butter 66 $3.4 0.05% 40 $21.4 0.07% 44 $24.8 0.07%
Candy Bags-- 67 $3.4 0.05% 42 $20.8 0.07% 47 $24.2 0.06%
Chocolate
Value Forms/ 68 $3.3 0.05% 120 $10.7 0.03% 108 $13.9 0.04%
18oz And
Larger
[Chicken]
Fruit Snacks 69 $3.3 0.05% 104 $12.1 0.04% 94 $15.4 0.04%
Sw Gds: Donuts 70 $3.2 0.05% 98 $12.5 0.04% 92 $15.7 0.04%
Meat: Turkey 71 $3.2 0.05% 21 $28.5 0.09% 24 $31.8 0.08%
Bulk
Frzn Meat-- 72 $3.2 0.05% 161 $8.8 0.03% 139 $12.0 0.03%
Beef
Chicken Wings 73 $3.1 0.05% 350 $4.0 0.01% 247 $7.2 0.02%
Frzn Breakfast 74 $3.1 0.05% 125 $10.5 0.03% 115 $13.6 0.04%
Sandwiches
Tuna 75 $3.1 0.05% 74 $15.6 0.05% 75 $18.8 0.05%
Waffles/ 76 $3.1 0.05% 59 $18.2 0.06% 62 $21.3 0.06%
Pancakes/
French Toast
Cakes: 77 $3.1 0.05% 152 $9.2 0.03% 136 $12.2 0.03%
Birthday/
Celebration
Sh
Sour Creams 78 $3.0 0.05% 64 $17.5 0.06% 66 $20.5 0.05%
Cheese 79 $3.0 0.05% 44 $20.7 0.07% 49 $23.7 0.06%
Crackers
Fz Skillet 80 $3.0 0.05% 97 $12.6 0.04% 93 $15.6 0.04%
Meals
Vegetable Oil 81 $3.0 0.05% 253 $5.7 0.02% 196 $8.7 0.02%
Lunchment--Bol 82 $3.0 0.05% 177 $8.1 0.03% 149 $11.1 0.03%
ogna/Sausage
Pizza/ 83 $3.0 0.05% 101 $12.3 0.04% 97 $15.3 0.04%
Traditional
Cream Cheese 84 $3.0 0.04% 49 $20.3 0.06% 53 $23.2 0.06%
Sandwich 85 $2.9 0.04% 100 $12.4 0.04% 95 $15.4 0.04%
Cookies
Butter 86 $2.9 0.04% 27 $25.1 0.08% 33 $28.0 0.07%
Ramen Noodles/ 87 $2.9 0.04% 258 $5.6 0.02% 208 $8.5 0.02%
Ramen Cups
String Cheese 88 $2.8 0.04% 38 $22.0 0.07% 46 $24.7 0.06%
Bagged Cheese 89 $2.7 0.04% 153 $9.0 0.03% 142 $11.7 0.03%
Snacks
Salsa & Dips 90 $2.7 0.04% 136 $9.8 0.03% 129 $12.5 0.03%
Toaster 91 $2.7 0.04% 107 $11.8 0.04% 103 $14.5 0.04%
Pastries
Hot Dog Buns 92 $2.7 0.04% 110 $11.2 0.04% 109 $13.9 0.04%
Hamburger Buns 93 $2.7 0.04% 103 $12.2 0.04% 100 $14.9 0.04%
Rts Soup: 94 $2.7 0.04% 65 $17.4 0.06% 68 $20.0 0.05%
Chunky/
Homestyle/Et
Flavored Milk 95 $2.6 0.04% 118 $10.8 0.03% 116 $13.4 0.04%
Candy Bars 96 $2.6 0.04% 158 $8.9 0.03% 146 $11.5 0.03%
(Singles)
(Including)
Yogurt/Kids 97 $2.6 0.04% 80 $14.4 0.05% 85 $17.0 0.04%
Angus [Beef] 98 $2.6 0.04% 75 $15.3 0.05% 80 $17.9 0.05%
Chicken Drums 99 $2.5 0.04% 297 $4.8 0.02% 241 $7.3 0.02%
Sweet Goods-- 100 $2.5 0.04% 145 $9.5 0.03% 137 $12.0 0.03%
Full Size
------------------------- ----------------------------------------------------------
Top 100 $537.8 8.17% $2,251.0 7.14% $2,788.8 7.32%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-2: Top 100 Subcommodities for SNAP Households by Expenditure: Household Head Age 45-64 Year Olds
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $41.3 0.63% 1 $258.9 0.82% 1 $300.1 0.79%
White Only
Soft Drinks 12/ 2 $36.6 0.56% 2 $197.3 0.63% 2 $233.9 0.61%
18 & 15pk Can
Car
Lean [Beef] 3 $22.4 0.34% 8 $77.3 0.25% 5 $99.7 0.26%
Shredded 4 $16.7 0.25% 3 $112.7 0.36% 6 $129.4 0.34%
Cheese
Sft Drnk 2 5 $15.6 0.24% 14 $70.8 0.22% 3 $86.4 0.23%
Liter Btl
Carb Incl
Kids Cereal 6 $15.0 0.23% 27 $52.7 0.17% 8 $67.7 0.18%
Primal [Beef] 7 $14.6 0.22% 11 $74.6 0.24% 9 $89.2 0.23%
Potato Chips 8 $14.6 0.22% 6 $85.6 0.27% 14 $100.2 0.26%
Lunchment--Del 9 $12.2 0.19% 9 $76.8 0.24% 73 $89.1 0.23%
i Fresh
Eggs--Large 10 $11.3 0.17% 10 $75.4 0.24% 7 $86.7 0.23%
Chicken Breast 11 $11.1 0.17% 4 $95.0 0.30% 4 $106.1 0.28%
Boneless
Unflavored Can 12 $10.2 0.16% 18 $64.2 0.20% 10 $74.4 0.20%
Coffee
Mainstream 13 $10.2 0.15% 44 $40.6 0.13% 12 $50.8 0.13%
White Bread
Fz Ss Prem 14 $10.1 0.15% 26 $53.5 0.17% 32 $63.7 0.17%
Traditional
Meals
Tortilla/Nacho 15 $10.0 0.15% 17 $67.1 0.21% 18 $77.1 0.20%
Chips
Still Water 16 $9.9 0.15% 22 $56.0 0.18% 25 $65.9 0.17%
Drnking/Mnrl
Water
Infant Formula 17 $9.8 0.15% 363 $7.7 0.02% 26 $17.4 0.05%
Starter/
Solutio
Dairy Case 18 $9.7 0.15% 7 $80.7 0.26% 11 $90.4 0.24%
100% Pure
Juice--O
American 19 $9.4 0.14% 42 $41.5 0.13% 23 $50.9 0.13%
Single Cheese
Bacon--Trad 20 $9.1 0.14% 30 $50.1 0.16% 20 $59.2 0.16%
16oz Or Less
Enhanced [Pork 21 $9.0 0.14% 24 $54.8 0.17% 40 $63.9 0.17%
Boneless Loin/
Rib]
Snacks/ 22 $8.9 0.14% 64 $32.2 0.10% 72 $41.1 0.11%
Appetizers
Snack Cake-- 23 $8.8 0.13% 66 $31.8 0.10% 38 $40.5 0.11%
Multi Pack
Sft Drnk Mlt- 24 $8.6 0.13% 19 $61.3 0.19% 15 $69.9 0.18%
Pk Btl Carb
(Excp)
Mainstream 25 $8.4 0.13% 29 $50.8 0.16% 39 $59.2 0.16%
Variety
Breads
Fz Ss Economy 26 $8.3 0.13% 104 $22.2 0.07% 64 $30.6 0.08%
Meals All
Pizza/Premium 27 $8.3 0.13% 34 $48.7 0.15% 48 $57.0 0.15%
Natural Cheese 28 $8.3 0.13% 15 $69.9 0.22% 29 $78.2 0.21%
Chunks
All Family 29 $8.1 0.12% 16 $68.0 0.22% 22 $76.1 0.20%
Cereal
Soft Drinks 30 $8.1 0.12% 62 $33.3 0.11% 50 $41.5 0.11%
20pk & 24pk
Can Carb
Potatoes 31 $8.1 0.12% 31 $49.4 0.16% 17 $57.5 0.15%
Russet (Bulk
& Bag)
Bananas 32 $7.9 0.12% 12 $74.3 0.24% 30 $82.3 0.22%
Sugar 33 $7.7 0.12% 57 $35.2 0.11% 42 $42.9 0.11%
Ribs [Pork] 34 $7.7 0.12% 59 $34.9 0.11% 35 $42.6 0.11%
Premium [Ice 35 $7.4 0.11% 13 $73.2 0.23% 16 $80.6 0.21%
Cream &
Sherbert]
Condensed Soup 36 $7.2 0.11% 33 $49.0 0.16% 34 $56.2 0.15%
Sandwiches & 37 $7.1 0.11% 100 $22.5 0.07% 71 $29.5 0.08%
Handhelds
Fz Ss Prem 38 $6.7 0.10% 5 $91.3 0.29% 36 $98.0 0.26%
Nutritional
Meals
Convenient 39 $6.6 0.10% 143 $18.0 0.06% 19 $24.6 0.06%
Meals--Kids
Meal C
Isotonic 40 $6.6 0.10% 54 $36.0 0.11% 77 $42.6 0.11%
Drinks Single
Serve
Select Beef 41 $6.6 0.10% 32 $49.3 0.16% 37 $55.9 0.15%
Frzn Chicken-- 42 $6.5 0.10% 65 $32.0 0.10% 31 $38.5 0.10%
Wht Meat
Choice Beef 43 $6.5 0.10% 70 $30.7 0.10% 76 $37.2 0.10%
Choice Beef 44 $6.5 0.10% 39 $45.3 0.14% 74 $51.8 0.14%
Pourable Salad 45 $6.5 0.10% 37 $46.3 0.15% 61 $52.7 0.14%
Dressings
Traditional 46 $6.2 0.09% 52 $37.1 0.12% 45 $43.3 0.11%
[Ice Cream &
Sherbert]
Fz Bag 47 $6.2 0.09% 40 $42.0 0.13% 82 $48.2 0.13%
Vegetables--P
lain
Mayonnaise & 48 $6.0 0.09% 49 $38.0 0.12% 56 $44.0 0.12%
Whipped
Dressing
Refrigerated 49 $5.9 0.09% 35 $48.1 0.15% 67 $54.0 0.14%
Coffee
Creamers
Fz Family 50 $5.8 0.09% 80 $26.5 0.08% 65 $32.3 0.08%
Style Entrees
Sft Drnk Sngl 51 $5.7 0.09% 111 $21.2 0.07% 13 $26.9 0.07%
Srv Btl Carb
(Ex)
Adult Cereal 52 $5.6 0.08% 21 $57.0 0.18% 27 $62.6 0.16%
Butter 53 $5.4 0.08% 20 $60.1 0.19% 51 $65.5 0.17%
Strawberries 54 $5.4 0.08% 25 $54.8 0.17% 113 $60.1 0.16%
Candy Bags-- 55 $5.2 0.08% 28 $50.9 0.16% 21 $56.1 0.15%
Chocolate
Hot Dogs--Base 56 $5.1 0.08% 161 $16.6 0.05% 28 $21.7 0.06%
Meat
Margarine: 57 $5.1 0.08% 71 $30.5 0.10% 102 $35.6 0.09%
Tubs And
Bowls
Choice Beef 58 $5.1 0.08% 99 $22.6 0.07% 41 $27.7 0.07%
Mainstream 59 $4.9 0.07% 87 $25.0 0.08% 70 $29.9 0.08%
[Pasta &
Pizza Sauce]
Tuna 60 $4.8 0.07% 58 $35.1 0.11% 104 $39.9 0.10%
Lunchment--Bol 61 $4.7 0.07% 138 $18.5 0.06% 114 $23.2 0.06%
ogna/Sausage
Meat: Turkey 62 $4.7 0.07% 23 $55.8 0.18% 111 $60.5 0.16%
Bulk
Macaroni & 63 $4.7 0.07% 154 $17.1 0.05% 84 $21.8 0.06%
Cheese Dnrs
Peanut Butter 64 $4.7 0.07% 45 $40.5 0.13% 269 $45.1 0.12%
Aseptic Pack 65 $4.5 0.07% 194 $14.2 0.04% 119 $18.7 0.05%
Juice And
Drinks
Chicken Wings 66 $4.5 0.07% 346 $8.1 0.03% 44 $12.6 0.03%
Mexican Soft 67 $4.5 0.07% 63 $33.0 0.10% 47 $37.5 0.10%
Tortillas And
Wra
Can Pasta 68 $4.4 0.07% 206 $13.4 0.04% 108 $17.9 0.05%
Sw Gds: Donuts 69 $4.4 0.07% 91 $23.6 0.07% 94 $27.9 0.07%
Frzn French 70 $4.3 0.07% 166 $16.2 0.05% 92 $20.5 0.05%
Fries
Angus [Beef] 71 $4.3 0.07% 53 $36.2 0.11% 24 $40.5 0.11%
Rts Soup: 72 $4.2 0.06% 48 $38.2 0.12% 139 $42.4 0.11%
Chunky/
Homestyle/Et
Fz Skillet 73 $4.1 0.06% 85 $25.2 0.08% 247 $29.4 0.08%
Meals
Cream Cheese 74 $4.1 0.06% 51 $37.6 0.12% 115 $41.7 0.11%
Frzn Chicken-- 75 $4.1 0.06% 514 $4.8 0.02% 75 $8.9 0.02%
Wings
Mult Pk Bag 76 $4.1 0.06% 208 $13.4 0.04% 62 $17.5 0.05%
Snacks
Frzn Breakfast 77 $4.0 0.06% 147 $17.7 0.06% 136 $21.6 0.06%
Sandwiches
Sandwich 78 $3.9 0.06% 94 $23.3 0.07% 66 $27.2 0.07%
Cookies
Vegetable Oil 79 $3.9 0.06% 279 $9.8 0.03% 49 $13.7 0.04%
Sour Creams 80 $3.9 0.06% 67 $31.0 0.10% 93 $34.9 0.09%
Frzn Meat-- 81 $3.9 0.06% 180 $15.2 0.05% 196 $19.1 0.05%
Beef
Meat: Ham Bulk 82 $3.9 0.06% 46 $40.3 0.13% 149 $44.1 0.12%
Pizza/ 83 $3.8 0.06% 125 $19.6 0.06% 97 $23.4 0.06%
Traditional
Hamburger Buns 84 $3.8 0.06% 93 $23.5 0.07% 53 $27.2 0.07%
Pizza/Economy 85 $3.8 0.06% 238 $11.7 0.04% 95 $15.5 0.04%
Flavored Milk 86 $3.7 0.06% 116 $20.3 0.06% 33 $24.0 0.06%
Cheese 87 $3.7 0.06% 74 $29.0 0.09% 46 $32.7 0.09%
Crackers
Candy Bars 88 $3.6 0.05% 96 $22.9 0.07% 142 $26.5 0.07%
(Multi Pack)
Value Forms/ 89 $3.6 0.05% 240 $11.6 0.04% 129 $15.2 0.04%
18oz And
Larger
[Chicken]
Grapes Red 90 $3.6 0.05% 50 $37.6 0.12% 103 $41.2 0.11%
Hot Dog Buns 91 $3.6 0.05% 122 $19.7 0.06% 109 $23.3 0.06%
Waffles/ 92 $3.6 0.05% 105 $22.1 0.07% 100 $25.6 0.07%
Pancakes/
French Toast
Spring Water 93 $3.6 0.05% 73 $29.4 0.09% 68 $32.9 0.09%
Sweet Goods-- 94 $3.6 0.05% 144 $18.0 0.06% 116 $21.5 0.06%
Full Size
Cottage Cheese 95 $3.5 0.05% 56 $35.4 0.11% 146 $38.9 0.10%
Cakes: 96 $3.5 0.05% 190 $14.6 0.05% 85 $18.2 0.05%
Birthday/
Celebration
Sh
Bkfst Sausage-- 97 $3.5 0.05% 117 $20.2 0.06% 80 $23.7 0.06%
Fresh Rolls
Dnr Sausage-- 98 $3.5 0.05% 242 $11.5 0.04% 241 $15.0 0.04%
Links Pork
Ckd/S
Candy Bars 99 $3.5 0.05% 155 $17.1 0.05% 137 $20.5 0.05%
(Singles)
(Including)
Fruit Snacks 100 $3.5 0.05% 224 $12.2 0.04% 203 $15.6 0.04%
------------------------- ----------------------------------------------------------
Top 100 $731.2 11.09% $4,237.7 13.52% $5,004.7 13.17%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-3: Top 100 Subcommodities for SNAP Households by Expenditure: Household Head Age 65 Years or Older
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $12.6 0.19% 1 $109.6 0.35% 1 $122.2 0.32%
White Only
Soft Drinks 12/ 2 $10.9 0.17% 2 $69.4 0.22% 2 $80.3 0.21%
18 & 15pk Can
Car
Lean [Beef] 3 $6.3 0.10% 18 $26.1 0.08% 12 $32.4 0.09%
Sft Drnk 2 4 $4.2 0.06% 29 $21.4 0.07% 24 $25.6 0.07%
Liter Btl
Carb Incl
Primal [Beef] 5 $4.2 0.06% 15 $27.5 0.09% 13 $31.7 0.08%
Shredded 6 $4.2 0.06% 10 $29.8 0.09% 10 $34.0 0.09%
Cheese
Potato Chips 7 $4.0 0.06% 13 $28.8 0.09% 11 $32.7 0.09%
Kids Cereal 8 $3.8 0.06% 72 $10.7 0.03% 59 $14.5 0.04%
Eggs--Large 9 $3.6 0.06% 8 $32.7 0.10% 8 $36.4 0.10%
Unflavored Can 10 $3.5 0.05% 6 $35.6 0.11% 5 $39.1 0.10%
Coffee
Fz Ss Prem 11 $3.4 0.05% 9 $31.9 0.10% 9 $35.3 0.09%
Traditional
Meals
Lunchment--Del 12 $3.4 0.05% 19 $24.6 0.08% 19 $28.0 0.07%
i Fresh
Mainstream 13 $3.2 0.05% 40 $16.9 0.05% 36 $20.1 0.05%
White Bread
Dairy Case 14 $3.1 0.05% 3 $38.6 0.12% 3 $41.7 0.11%
100% Pure
Juice--O
Bacon--Trad 15 $2.9 0.04% 24 $23.1 0.07% 23 $26.0 0.07%
16oz Or Less
Chicken Breast 16 $2.8 0.04% 17 $26.2 0.08% 18 $29.0 0.08%
Boneless
Bananas 17 $2.7 0.04% 4 $37.1 0.12% 4 $39.8 0.10%
American 18 $2.7 0.04% 38 $17.4 0.06% 35 $20.1 0.05%
Single Cheese
Enhanced [Pork 19 $2.7 0.04% 26 $22.9 0.07% 25 $25.6 0.07%
Boneless Loin/
Rib]
Mainstream 20 $2.7 0.04% 27 $22.6 0.07% 28 $25.3 0.07%
Variety
Breads
Sft Drnk Mlt- 21 $2.6 0.04% 20 $24.5 0.08% 20 $27.1 0.07%
Pk Btl Carb
(Excp)
Still Water 22 $2.6 0.04% 49 $15.2 0.05% 43 $17.8 0.05%
Drnking/Mnrl
Water
Potatoes 23 $2.6 0.04% 25 $22.9 0.07% 27 $25.5 0.07%
Russet (Bulk
& Bag)
Snack Cake-- 24 $2.6 0.04% 68 $11.4 0.04% 62 $14.0 0.04%
Multi Pack
Natural Cheese 25 $2.5 0.04% 14 $28.3 0.09% 16 $30.8 0.08%
Chunks
All Family 26 $2.5 0.04% 12 $29.0 0.09% 14 $31.5 0.08%
Cereal
Fz Ss Economy 27 $2.5 0.04% 87 $9.3 0.03% 73 $11.8 0.03%
Meals All
Premium [Ice 28 $2.5 0.04% 7 $35.5 0.11% 6 $38.0 0.10%
Cream &
Sherbert]
Tortilla/Nacho 29 $2.5 0.04% 48 $15.6 0.05% 41 $18.0 0.05%
Chips
Condensed Soup 30 $2.4 0.04% 22 $24.5 0.08% 21 $26.8 0.07%
Soft Drinks 31 $2.3 0.04% 82 $9.7 0.03% 70 $12.1 0.03%
20pk & 24pk
Can Carb
Sugar 32 $2.3 0.04% 51 $15.1 0.05% 47 $17.5 0.05%
Traditional 33 $2.3 0.03% 23 $23.2 0.07% 26 $25.5 0.07%
[Ice Cream &
Sherbert]
Ribs [Pork] 34 $2.3 0.03% 57 $13.4 0.04% 53 $15.6 0.04%
Snacks/ 35 $2.2 0.03% 144 $6.6 0.02% 112 $8.9 0.02%
Appetizers
Infant Formula 36 $2.2 0.03% 583 $1.4 0.00% 336 $3.6 0.01%
Starter/
Solutio
Pizza/Premium 37 $2.2 0.03% 59 $12.7 0.04% 57 $14.9 0.04%
Select Beef 38 $2.1 0.03% 35 $17.9 0.06% 37 $19.9 0.05%
Fz Ss Prem 39 $2.0 0.03% 5 $35.8 0.11% 7 $37.8 0.10%
Nutritional
Meals
Fz Bag 40 $2.0 0.03% 32 $19.9 0.06% 31 $21.9 0.06%
Vegetables--P
lain
Choice Beef 41 $1.9 0.03% 60 $12.7 0.04% 58 $14.6 0.04%
Mayonnaise & 42 $1.9 0.03% 34 $18.2 0.06% 34 $20.1 0.05%
Whipped
Dressing
Choice Beef 43 $1.9 0.03% 36 $17.7 0.06% 38 $19.6 0.05%
Adult Cereal 44 $1.9 0.03% 11 $29.4 0.09% 15 $31.2 0.08%
Butter 45 $1.9 0.03% 16 $27.4 0.09% 17 $29.3 0.08%
Margarine: 46 $1.8 0.03% 33 $18.5 0.06% 33 $20.4 0.05%
Tubs And
Bowls
Pourable Salad 47 $1.8 0.03% 39 $16.9 0.05% 39 $18.7 0.05%
Dressings
Sandwiches & 48 $1.8 0.03% 219 $4.9 0.02% 165 $6.7 0.02%
Handhelds
Strawberries 49 $1.7 0.03% 21 $24.5 0.08% 22 $26.1 0.07%
Candy Bags-- 50 $1.6 0.02% 28 $22.4 0.07% 29 $24.1 0.06%
Chocolate
Convenient 51 $1.6 0.02% 324 $3.4 0.01% 240 $5.0 0.01%
Meals--Kids
Meal C
Refrigerated 52 $1.6 0.02% 45 $16.1 0.05% 44 $17.7 0.05%
Coffee
Creamers
Frzn Chicken-- 53 $1.6 0.02% 96 $8.8 0.03% 86 $10.4 0.03%
Wht Meat
Lunchment--Bol 54 $1.6 0.02% 84 $9.5 0.03% 78 $11.1 0.03%
ogna/Sausage
Fz Family 55 $1.6 0.02% 90 $9.0 0.03% 85 $10.6 0.03%
Style Entrees
Isotonic 56 $1.6 0.02% 123 $7.4 0.02% 108 $9.0 0.02%
Drinks Single
Serve
Choice Beef 57 $1.5 0.02% 80 $9.8 0.03% 76 $11.3 0.03%
Sw Gds: Donuts 58 $1.5 0.02% 62 $12.5 0.04% 61 $14.1 0.04%
Hot Dogs--Base 59 $1.5 0.02% 146 $6.6 0.02% 125 $8.1 0.02%
Meat
Peanut Butter 60 $1.4 0.02% 44 $16.1 0.05% 45 $17.6 0.05%
Sft Drnk Sngl 61 $1.4 0.02% 237 $4.5 0.01% 195 $6.0 0.02%
Srv Btl Carb
(Ex)
Tuna 62 $1.4 0.02% 54 $13.9 0.04% 55 $15.3 0.04%
Angus [Beef] 63 $1.4 0.02% 50 $15.2 0.05% 51 $16.6 0.04%
Cottage Cheese 64 $1.3 0.02% 31 $20.3 0.06% 32 $21.6 0.06%
Rts Soup: 65 $1.3 0.02% 41 $16.6 0.05% 42 $17.9 0.05%
Chunky/
Homestyle/Et
Chicken Wings 66 $1.3 0.02% 405 $2.6 0.01% 310 $3.9 0.01%
Meat: Turkey 67 $1.2 0.02% 47 $15.9 0.05% 48 $17.1 0.04%
Bulk
Mainstream 68 $1.2 0.02% 142 $6.9 0.02% 126 $8.1 0.02%
[Pasta &
Pizza Sauce]
Grapes Red 69 $1.2 0.02% 37 $17.4 0.06% 40 $18.6 0.05%
Macaroni & 70 $1.2 0.02% 277 $4.0 0.01% 231 $5.2 0.01%
Cheese Dnrs
Mexican Soft 71 $1.2 0.02% 115 $7.9 0.03% 106 $9.1 0.02%
Tortillas And
Wra
Frzn Breakfast 72 $1.2 0.02% 165 $6.1 0.02% 150 $7.2 0.02%
Sandwiches
Cream Cheese 73 $1.1 0.02% 55 $13.9 0.04% 56 $15.0 0.04%
Can Pasta 74 $1.1 0.02% 321 $3.4 0.01% 268 $4.6 0.01%
Sweet Goods-- 75 $1.1 0.02% 93 $8.9 0.03% 90 $10.1 0.03%
Full Size
Meat: Ham Bulk 76 $1.1 0.02% 46 $15.9 0.05% 49 $17.0 0.04%
Bkfst Sausage-- 77 $1.1 0.02% 105 $8.3 0.03% 97 $9.5 0.02%
Fresh Rolls
Fz Skillet 78 $1.1 0.02% 83 $9.6 0.03% 82 $10.8 0.03%
Meals
Vegetable Oil 79 $1.1 0.02% 305 $3.6 0.01% 258 $4.7 0.01%
Frzn French 80 $1.1 0.02% 234 $4.6 0.01% 212 $5.7 0.01%
Fries
Sandwich 81 $1.1 0.02% 102 $8.4 0.03% 96 $9.5 0.03%
Cookies
Candy Bars 82 $1.1 0.02% 78 $9.9 0.03% 79 $11.0 0.03%
(Multi Pack)
Butter Spray 83 $1.1 0.02% 69 $10.9 0.03% 71 $12.0 0.03%
Cracker
Premium Bread 84 $1.1 0.02% 30 $21.2 0.07% 30 $22.3 0.06%
Aseptic Pack 85 $1.1 0.02% 420 $2.5 0.01% 343 $3.6 0.01%
Juice And
Drinks
Sticks/Enrobed 86 $1.1 0.02% 76 $10.2 0.03% 77 $11.3 0.03%
[Frozen
Novelties]
Sour Creams 87 $1.1 0.02% 71 $10.7 0.03% 72 $11.8 0.03%
Waffles/ 88 $1.1 0.02% 111 $8.1 0.03% 102 $9.2 0.02%
Pancakes/
French Toast
Spring Water 89 $1.1 0.02% 73 $10.3 0.03% 75 $11.3 0.03%
Hamburger Buns 90 $1.1 0.02% 116 $7.9 0.02% 110 $8.9 0.02%
Mult Pk Bag 91 $1.0 0.02% 408 $2.6 0.01% 341 $3.6 0.01%
Snacks
Frzn Chicken-- 92 $1.0 0.02% 654 $1.2 0.00% 479 $2.2 0.01%
Wings
Flavored Milk 93 $1.0 0.02% 178 $5.8 0.02% 161 $6.8 0.02%
Refrigerated 94 $1.0 0.02% 164 $6.2 0.02% 151 $7.2 0.02%
Biscuits
Grapes White 95 $1.0 0.02% 70 $10.8 0.03% 74 $11.8 0.03%
Dnr Sausage-- 96 $1.0 0.02% 284 $3.9 0.01% 249 $4.9 0.01%
Links Pork
Ckd/S
Pizza/Economy 97 $1.0 0.02% 357 $3.0 0.01% 305 $4.0 0.01%
Frzn Meat-- 98 $1.0 0.01% 279 $3.9 0.01% 248 $4.9 0.01%
Beef
Pizza/ 99 $1.0 0.01% 211 $5.1 0.02% 184 $6.1 0.02%
Traditional
Candy Bars 100 $1.0 0.01% 167 $6.0 0.02% 154 $7.0 0.02%
(Singles)
------------------------- ----------------------------------------------------------
Top 100 $213.1 3.29% $1,664.6 5.23% $1,877.6 4.94%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-4: Top 100 Subcommodities for SNAP Households by Expenditure: Households with Children Present
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $33.9 0.52% 1 $190.0 0.60% 1 $223.9 0.59%
White Only
Soft Drinks 12/ 2 $28.4 0.43% 2 $128.5 0.41% 2 $156.9 0.41%
18 & 15pk Can
Car
Lean [Beef] 3 $17.5 0.27% 10 $51.4 0.16% 5 $68.9 0.18%
Kids Cereal 4 $14.0 0.21% 7 $53.4 0.17% 6 $67.4 0.18%
Shredded 5 $13.9 0.21% 3 $82.7 0.26% 3 $96.7 0.25%
Cheese
Sft Drnk 2 6 $12.4 0.19% 12 $49.7 0.16% 9 $62.2 0.16%
Liter Btl
Carb Incl
Primal [Beef] 7 $11.4 0.17% 13 $49.7 0.16% 10 $61.0 0.16%
Potato Chips 8 $11.3 0.17% 5 $55.3 0.18% 7 $66.6 0.17%
Lunchment--Del 9 $9.6 0.15% 8 $53.4 0.17% 8 $63.0 0.17%
i Fresh
Chicken Breast 10 $8.9 0.14% 4 $65.2 0.21% 4 $74.1 0.19%
Boneless
Infant Formula 11 $8.7 0.13% 258 $7.1 0.02% 127 $15.8 0.04%
Starter/
Solutio
Tortilla/Nacho 12 $8.5 0.13% 11 $50.4 0.16% 12 $58.9 0.15%
Chips
Eggs--Large 13 $8.5 0.13% 14 $49.1 0.16% 13 $57.6 0.15%
Mainstream 14 $8.3 0.13% 32 $31.1 0.10% 28 $39.4 0.10%
White Bread
Snacks/ 15 $8.2 0.12% 41 $27.4 0.09% 34 $35.6 0.09%
Appetizers
Still Water 16 $7.8 0.12% 21 $37.7 0.12% 19 $45.5 0.12%
Drnking/Mnrl
Water
American 17 $7.5 0.11% 36 $28.9 0.09% 33 $36.4 0.10%
Single Cheese
Dairy Case 18 $7.5 0.11% 6 $53.5 0.17% 11 $61.0 0.16%
100% Pure
Juice--O
Snack Cake-- 19 $7.2 0.11% 47 $25.7 0.08% 41 $32.9 0.09%
Multi Pack
Enhanced [Pork 20 $7.2 0.11% 22 $36.4 0.12% 21 $43.6 0.11%
Boneless Loin/
Rib]
Fz Ss Prem 21 $7.1 0.11% 39 $27.9 0.09% 36 $35.0 0.09%
Traditional
Meals
Pizza/Premium 22 $6.9 0.11% 27 $34.2 0.11% 24 $41.1 0.11%
Fz Ss Economy 23 $6.9 0.10% 90 $16.7 0.05% 73 $23.5 0.06%
Meals All
All Family 24 $6.8 0.10% 15 $48.9 0.16% 15 $55.7 0.15%
Cereal
Unflavored Can 25 $6.8 0.10% 26 $34.3 0.11% 25 $41.0 0.11%
Coffee
Bacon--Trad 26 $6.8 0.10% 30 $32.1 0.10% 30 $38.8 0.10%
16oz Or Less
Convenient 27 $6.7 0.10% 58 $23.7 0.08% 43 $30.3 0.08%
Meals--Kids
Meal C
Soft Drinks 28 $6.5 0.10% 63 $22.3 0.07% 53 $28.7 0.08%
20pk & 24pk
Can Carb
Mainstream 29 $6.3 0.10% 24 $35.3 0.11% 22 $41.6 0.11%
Variety
Breads
Sandwiches & 30 $6.2 0.09% 79 $18.6 0.06% 67 $24.8 0.07%
Handhelds
Sft Drnk Mlt- 31 $6.2 0.09% 28 $33.7 0.11% 26 $39.9 0.10%
Pk Btl Carb
(Excp)
Natural Cheese 32 $6.2 0.09% 18 $42.9 0.14% 18 $49.1 0.13%
Chunks
Sugar 33 $6.1 0.09% 60 $23.3 0.07% 52 $29.4 0.08%
Potatoes 34 $6.0 0.09% 33 $30.7 0.10% 32 $36.7 0.10%
Russet (Bulk
& Bag)
Bananas 35 $6.0 0.09% 16 $48.2 0.15% 16 $54.2 0.14%
Frzn Chicken-- 36 $5.6 0.09% 54 $24.6 0.08% 45 $30.2 0.08%
Wht Meat
Ribs [Pork] 37 $5.6 0.08% 70 $20.7 0.07% 61 $26.3 0.07%
Premium [Ice 38 $5.5 0.08% 17 $46.9 0.15% 17 $52.4 0.14%
Cream &
Sherbert]
Isotonic 39 $5.5 0.08% 37 $28.3 0.09% 39 $33.8 0.09%
Drinks Single
Serve
Condensed Soup 40 $5.4 0.08% 29 $32.7 0.10% 31 $38.2 0.10%
Pourable Salad 41 $5.1 0.08% 35 $29.1 0.09% 37 $34.2 0.09%
Dressings
Sft Drnk Sngl 42 $4.9 0.07% 104 $15.3 0.05% 85 $20.2 0.05%
Srv Btl Carb
(Ex)
Choice Beef 43 $4.9 0.07% 77 $19.0 0.06% 71 $23.9 0.06%
Fz Family 44 $4.8 0.07% 80 $18.6 0.06% 74 $23.4 0.06%
Style Entrees
Select Beef 45 $4.8 0.07% 38 $28.3 0.09% 40 $33.0 0.09%
Fz Ss Prem 46 $4.7 0.07% 9 $52.3 0.17% 14 $57.0 0.15%
Nutritional
Meals
Traditional 47 $4.6 0.07% 50 $24.9 0.08% 50 $29.6 0.08%
[Ice Cream &
Sherbert]
Aseptic Pack 48 $4.6 0.07% 74 $19.4 0.06% 69 $24.0 0.06%
Juice And
Drinks
Choice Beef 49 $4.6 0.07% 49 $25.1 0.08% 49 $29.7 0.08%
Fz Bag 50 $4.5 0.07% 48 $25.4 0.08% 48 $29.9 0.08%
Vegetables--P
lain
Mayonnaise & 51 $4.4 0.07% 66 $22.0 0.07% 60 $26.5 0.07%
Whipped
Dressing
Refrigerated 52 $4.4 0.07% 31 $31.1 0.10% 35 $35.5 0.09%
Coffee
Creamers
Strawberries 53 $4.4 0.07% 19 $40.0 0.13% 20 $44.4 0.12%
Adult Cereal 54 $4.2 0.06% 25 $35.0 0.11% 29 $39.2 0.10%
Macaroni & 55 $4.2 0.06% 101 $15.5 0.05% 88 $19.7 0.05%
Cheese Dnrs
Mainstream 56 $4.2 0.06% 72 $19.8 0.06% 70 $24.0 0.06%
[Pasta &
Pizza Sauce]
Hot Dogs--Base 57 $4.2 0.06% 134 $12.6 0.04% 115 $16.8 0.04%
Meat
Choice Beef 58 $4.0 0.06% 114 $14.4 0.05% 99 $18.5 0.05%
Can Pasta 59 $4.0 0.06% 133 $12.8 0.04% 113 $16.8 0.04%
Candy Bags-- 60 $4.0 0.06% 34 $29.9 0.09% 38 $33.8 0.09%
Chocolate
Margarine: 61 $3.9 0.06% 78 $18.9 0.06% 78 $22.8 0.06%
Tubs And
Bowls
Peanut Butter 62 $3.8 0.06% 40 $27.8 0.09% 42 $31.6 0.08%
Butter 63 $3.7 0.06% 23 $35.8 0.11% 27 $39.5 0.10%
Meat: Turkey 64 $3.7 0.06% 20 $37.8 0.12% 23 $41.5 0.11%
Bulk
Mult Pk Bag 65 $3.7 0.06% 132 $12.9 0.04% 118 $16.6 0.04%
Snacks
Frzn French 66 $3.6 0.06% 138 $12.5 0.04% 123 $16.1 0.04%
Fries
Mexican Soft 67 $3.6 0.06% 59 $23.4 0.07% 58 $27.0 0.07%
Tortillas And
Wra
Sw Gds: Donuts 68 $3.6 0.05% 91 $16.7 0.05% 84 $20.2 0.05%
Pizza/Economy 69 $3.5 0.05% 158 $11.4 0.04% 136 $14.9 0.04%
Fruit Snacks 70 $3.5 0.05% 111 $14.5 0.05% 102 $18.0 0.05%
Tuna 71 $3.4 0.05% 73 $19.6 0.06% 77 $23.1 0.06%
Lunchment--Bol 72 $3.4 0.05% 156 $11.5 0.04% 135 $14.9 0.04%
ogna/Sausage
Value Forms/ 73 $3.4 0.05% 139 $12.4 0.04% 128 $15.8 0.04%
18oz And
Larger
[Chicken]
Frzn Breakfast 74 $3.4 0.05% 122 $13.6 0.04% 110 $17.0 0.04%
Sandwiches
Cheese 75 $3.4 0.05% 44 $26.8 0.09% 46 $30.2 0.08%
Crackers
Frzn Meat-- 76 $3.3 0.05% 151 $11.7 0.04% 133 $15.1 0.04%
Beef
Waffles/ 77 $3.3 0.05% 62 $22.5 0.07% 65 $25.9 0.07%
Pancakes/
French Toast
Frzn Chicken-- 78 $3.3 0.05% 470 $3.4 0.01% 308 $6.8 0.02%
Wings
Cream Cheese 79 $3.3 0.05% 45 $26.1 0.08% 51 $29.4 0.08%
Sandwich 80 $3.2 0.05% 83 $17.6 0.06% 83 $20.8 0.05%
Cookies
Pizza/ 81 $3.2 0.05% 100 $15.7 0.05% 94 $18.9 0.05%
Traditional
Fz Skillet 82 $3.2 0.05% 103 $15.4 0.05% 97 $18.6 0.05%
Meals
Sour Creams 83 $3.2 0.05% 69 $21.3 0.07% 68 $24.5 0.06%
Cakes: 84 $3.2 0.05% 160 $11.3 0.04% 143 $14.6 0.04%
Birthday/
Celebration
Sh
Angus [Beef] 85 $3.2 0.05% 61 $22.8 0.07% 64 $25.9 0.07%
Flavored Milk 86 $3.2 0.05% 93 $16.4 0.05% 90 $19.6 0.05%
Chicken Wings 87 $3.2 0.05% 372 $4.7 0.01% 276 $7.8 0.02%
Hamburger Buns 88 $3.0 0.05% 92 $16.6 0.05% 89 $19.6 0.05%
Rts Soup: 89 $3.0 0.05% 65 $22.0 0.07% 66 $25.1 0.07%
Chunky/
Homestyle/Et
Vegetable Oil 90 $3.0 0.05% 269 $6.7 0.02% 221 $9.7 0.03%
Meat: Ham Bulk 91 $3.0 0.05% 43 $27.2 0.09% 44 $30.2 0.08%
String Cheese 92 $3.0 0.05% 51 $24.8 0.08% 55 $27.8 0.07%
Hot Dog Buns 93 $2.9 0.04% 115 $14.4 0.05% 106 $17.3 0.05%
Sweet Goods-- 94 $2.9 0.04% 123 $13.5 0.04% 119 $16.4 0.04%
Full Size
Bagged Cheese 95 $2.9 0.04% 149 $11.9 0.04% 138 $14.8 0.04%
Snacks
Toaster 96 $2.9 0.04% 95 $16.1 0.05% 93 $19.0 0.05%
Pastries
Grapes Red 97 $2.8 0.04% 42 $27.3 0.09% 47 $30.2 0.08%
Candy Bars 98 $2.8 0.04% 159 $11.4 0.04% 148 $14.2 0.04%
(Singles)
(Including)
Salsa & Dips 99 $2.8 0.04% 150 $11.8 0.04% 142 $14.6 0.04%
Ramen Noodles/ 100 $2.8 0.04% 274 $6.5 0.02% 229 $9.3 0.02%
Ramen Cups
------------------------- ----------------------------------------------------------
Top 100 $585.8 8.90% $2,937.8 9.32% $3,523.7 9.25%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-5: Top 100 Subcommodities for SNAP Households by Expenditure: Households Without Children Present
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $50.6 0.77% 1 $322.1 1.02% 1 $372.7 0.98%
White Only
Soft Drinks 12/ 2 $44.1 0.67% 2 $234.1 0.74% 2 $278.3 0.73%
18 & 15pk Can
Car
Lean [Beef] 3 $28.4 0.43% 10 $94.8 0.30% 6 $123.1 0.32%
Shredded 4 $19.9 0.30% 3 $126.8 0.40% 3 $146.7 0.39%
Cheese
Sft Drnk 2 5 $18.7 0.28% 17 $81.5 0.26% 15 $100.2 0.26%
Liter Btl
Carb Incl
Kids Cereal 6 $18.6 0.28% 40 $54.6 0.17% 30 $73.2 0.19%
Primal [Beef] 7 $17.4 0.26% 15 $88.0 0.28% 12 $105.4 0.28%
Potato Chips 8 $17.3 0.26% 8 $98.4 0.31% 8 $115.8 0.30%
Lunchment--Del 9 $14.9 0.23% 12 $91.0 0.29% 11 $105.9 0.28%
i Fresh
Eggs--Large 10 $14.2 0.22% 9 $98.4 0.31% 9 $112.7 0.30%
Chicken Breast 11 $13.3 0.20% 5 $110.1 0.35% 5 $123.5 0.32%
Boneless
Infant Formula 12 $13.1 0.20% 314 $11.2 0.04% 157 $24.3 0.06%
Starter/
Solutio
Fz Ss Prem 13 $12.6 0.19% 19 $77.2 0.24% 18 $89.7 0.24%
Traditional
Meals
Unflavored Can 14 $12.5 0.19% 14 $88.7 0.28% 14 $101.2 0.27%
Coffee
Mainstream 15 $12.5 0.19% 49 $49.8 0.16% 41 $62.3 0.16%
White Bread
Still Water 16 $12.3 0.19% 29 $64.1 0.20% 25 $76.4 0.20%
Drnking/Mnrl
Water
Tortilla/Nacho 17 $12.1 0.18% 22 $73.4 0.23% 19 $85.6 0.22%
Chips
Dairy Case 18 $12.1 0.18% 6 $107.2 0.34% 7 $119.3 0.31%
100% Pure
Juice--O
American 19 $11.6 0.18% 42 $52.8 0.17% 38 $64.4 0.17%
Single Cheese
Bacon--Trad 20 $11.2 0.17% 27 $64.6 0.20% 27 $75.8 0.20%
16oz Or Less
Enhanced [Pork 21 $11.1 0.17% 24 $68.3 0.22% 23 $79.4 0.21%
Boneless Loin/
Rib]
Snacks/ 22 $10.7 0.16% 81 $32.0 0.10% 69 $42.7 0.11%
Appetizers
Snack Cake-- 23 $10.6 0.16% 68 $36.4 0.12% 61 $47.1 0.12%
Multi Pack
Mainstream 24 $10.5 0.16% 25 $66.1 0.21% 24 $76.6 0.20%
Variety
Breads
Fz Ss Economy 25 $10.3 0.16% 94 $28.4 0.09% 75 $38.7 0.10%
Meals All
Natural Cheese 26 $10.2 0.15% 13 $89.8 0.28% 16 $100.0 0.26%
Chunks
Pizza/Premium 27 $10.1 0.15% 39 $55.5 0.18% 36 $65.6 0.17%
Soft Drinks 28 $10.0 0.15% 64 $38.7 0.12% 59 $48.7 0.13%
20pk & 24pk
Can Carb
All Family 29 $10.0 0.15% 16 $85.8 0.27% 17 $95.7 0.25%
Cereal
Sft Drnk Mlt- 30 $10.0 0.15% 21 $74.7 0.24% 20 $84.7 0.22%
Pk Btl Carb
(Excp)
Potatoes 31 $9.9 0.15% 30 $64.0 0.20% 28 $73.9 0.19%
Russet (Bulk
& Bag)
Bananas 32 $9.9 0.15% 7 $100.3 0.32% 10 $110.1 0.29%
Sugar 33 $9.6 0.15% 55 $44.8 0.14% 50 $54.4 0.14%
Ribs [Pork] 34 $9.4 0.14% 60 $42.4 0.13% 53 $51.8 0.14%
Premium [Ice 35 $9.1 0.14% 11 $94.7 0.30% 13 $103.8 0.27%
Cream &
Sherbert]
Condensed Soup 36 $8.7 0.13% 26 $64.7 0.21% 29 $73.4 0.19%
Sandwiches & 37 $8.7 0.13% 128 $23.7 0.08% 94 $32.4 0.08%
Handhelds
Select Beef 38 $8.1 0.12% 33 $59.5 0.19% 33 $67.6 0.18%
Choice Beef 39 $8.1 0.12% 65 $38.3 0.12% 63 $46.4 0.12%
Fz Ss Prem 40 $8.0 0.12% 4 $117.8 0.37% 4 $125.7 0.33%
Nutritional
Meals
Choice Beef 41 $7.9 0.12% 38 $55.7 0.18% 39 $63.6 0.17%
Frzn Chicken-- 42 $7.9 0.12% 70 $36.1 0.11% 66 $44.0 0.12%
Wht Meat
Pourable Salad 43 $7.9 0.12% 36 $56.5 0.18% 37 $64.4 0.17%
Dressings
Isotonic 44 $7.8 0.12% 66 $37.9 0.12% 64 $45.7 0.12%
Drinks Single
Serve
Convenient 45 $7.8 0.12% 186 $18.0 0.06% 139 $25.8 0.07%
Meals--Kids
Meal C
Traditional 46 $7.7 0.12% 44 $51.5 0.16% 43 $59.2 0.16%
[Ice Cream &
Sherbert]
Fz Bag 47 $7.6 0.12% 37 $55.9 0.18% 40 $63.5 0.17%
Vegetables--P
lain
Mayonnaise & 48 $7.5 0.11% 45 $50.9 0.16% 44 $58.4 0.15%
Whipped
Dressing
Refrigerated 49 $7.1 0.11% 34 $58.8 0.19% 35 $65.9 0.17%
Coffee
Creamers
Fz Family 50 $7.0 0.11% 85 $31.3 0.10% 76 $38.3 0.10%
Style Entrees
Adult Cereal 51 $7.0 0.11% 18 $77.2 0.24% 21 $84.2 0.22%
Sft Drnk Sngl 52 $6.9 0.11% 122 $24.2 0.08% 101 $31.1 0.08%
Srv Btl Carb
(Ex)
Margarine: 53 $6.5 0.10% 57 $44.0 0.14% 56 $50.5 0.13%
Tubs And
Bowls
Strawberries 54 $6.5 0.10% 23 $69.9 0.22% 26 $76.4 0.20%
Butter 55 $6.5 0.10% 20 $76.9 0.24% 22 $83.3 0.22%
Hot Dogs--Base 56 $6.3 0.10% 164 $20.4 0.06% 125 $26.7 0.07%
Meat
Choice Beef 57 $6.3 0.10% 93 $28.5 0.09% 86 $34.8 0.09%
Candy Bags-- 58 $6.2 0.09% 28 $64.3 0.20% 31 $70.5 0.19%
Chocolate
Mainstream 59 $5.9 0.09% 96 $28.2 0.09% 88 $34.1 0.09%
[Pasta &
Pizza Sauce]
Lunchment--Bol 60 $5.9 0.09% 117 $24.6 0.08% 102 $30.5 0.08%
ogna/Sausage
Tuna 61 $5.9 0.09% 54 $45.0 0.14% 54 $50.9 0.13%
Macaroni & 62 $5.8 0.09% 175 $19.1 0.06% 148 $24.9 0.07%
Cheese Dnrs
Mexican Soft 63 $5.8 0.09% 63 $39.2 0.12% 65 $45.0 0.12%
Tortillas And
Wra
Chicken Wings 64 $5.8 0.09% 355 $10.0 0.03% 253 $15.8 0.04%
Peanut Butter 65 $5.7 0.09% 47 $50.3 0.16% 46 $55.9 0.15%
Sw Gds: Donuts 66 $5.6 0.09% 83 $31.9 0.10% 77 $37.5 0.10%
Meat: Turkey 67 $5.6 0.08% 31 $62.3 0.20% 32 $67.9 0.18%
Bulk
Aseptic Pack 68 $5.4 0.08% 242 $14.1 0.04% 202 $19.5 0.05%
Juice And
Drinks
Can Pasta 69 $5.4 0.08% 232 $14.8 0.05% 191 $20.2 0.05%
Frzn Chicken-- 70 $5.2 0.08% 547 $5.6 0.02% 372 $10.8 0.03%
Wings
Frzn French 71 $5.2 0.08% 190 $17.8 0.06% 162 $23.0 0.06%
Fries
Rts Soup: 72 $5.2 0.08% 48 $50.1 0.16% 48 $55.3 0.15%
Chunky/
Homestyle/Et
Angus [Beef] 73 $5.1 0.08% 58 $43.9 0.14% 58 $49.0 0.13%
Fz Skillet 74 $5.0 0.08% 80 $32.0 0.10% 79 $37.1 0.10%
Meals
Mult Pk Bag 75 $5.0 0.08% 263 $13.1 0.04% 220 $18.1 0.05%
Snacks
Vegetable Oil 76 $5.0 0.08% 278 $12.5 0.04% 226 $17.5 0.05%
Frzn Breakfast 77 $4.9 0.07% 159 $20.6 0.07% 143 $25.5 0.07%
Sandwiches
Cream Cheese 78 $4.9 0.07% 52 $45.6 0.14% 55 $50.5 0.13%
Sour Creams 79 $4.8 0.07% 67 $37.9 0.12% 70 $42.7 0.11%
Pizza/Economy 80 $4.8 0.07% 256 $13.5 0.04% 217 $18.3 0.05%
Sandwich 81 $4.7 0.07% 105 $26.5 0.08% 100 $31.2 0.08%
Cookies
Frzn Meat-- 82 $4.7 0.07% 209 $16.2 0.05% 184 $20.9 0.05%
Beef
Pizza/ 83 $4.5 0.07% 150 $21.4 0.07% 138 $25.9 0.07%
Traditional
Chix: Frd 8pc/ 84 $4.5 0.07% 73 $35.1 0.11% 73 $39.6 0.10%
Cut Up (Hot)
Meat: Ham Bulk 85 $4.5 0.07% 51 $47.9 0.15% 52 $52.4 0.14%
Hamburger Buns 86 $4.4 0.07% 101 $26.9 0.09% 97 $31.4 0.08%
Grapes Red 87 $4.4 0.07% 50 $48.5 0.15% 51 $52.9 0.14%
Spring Water 88 $4.4 0.07% 71 $36.1 0.11% 71 $40.5 0.11%
Cottage Cheese 89 $4.4 0.07% 46 $50.7 0.16% 49 $55.1 0.14%
Waffles/ 90 $4.4 0.07% 109 $25.8 0.08% 105 $30.2 0.08%
Pancakes/
French Toast
Value Forms/ 91 $4.4 0.07% 271 $12.7 0.04% 232 $17.1 0.04%
18oz And
Larger
[Chicken]
Candy Bars 92 $4.3 0.07% 97 $28.1 0.09% 93 $32.5 0.09%
(Multi Pack)
Cakes: 93 $4.3 0.07% 204 $16.7 0.05% 183 $21.0 0.06%
Birthday/
Celebration
Sh
Hot Dog Buns 94 $4.3 0.07% 137 $22.9 0.07% 120 $27.2 0.07%
Salsa & Dips 95 $4.3 0.07% 163 $20.5 0.06% 151 $24.7 0.06%
Sweet Goods-- 96 $4.3 0.07% 139 $22.9 0.07% 121 $27.2 0.07%
Full Size
Dnr Sausage-- 97 $4.3 0.07% 248 $13.9 0.04% 219 $18.2 0.05%
Links Pork
Ckd/S
Bkfst Sausage-- 98 $4.3 0.06% 113 $25.2 0.08% 111 $29.4 0.08%
Fresh Rolls
Cheese 99 $4.2 0.06% 87 $30.0 0.10% 87 $34.2 0.09%
Crackers
Bagged Cheese 100 $4.2 0.06% 177 $18.8 0.06% 161 $23.1 0.06%
Snacks
------------------------- ----------------------------------------------------------
Top 100 $894.8 13.60% $5,251.7 16.66% $6,146.5 16.13%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-6: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in the Midwest
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $64.3 0.98% 1 $237.1 0.75% 1 $301.4 0.79%
White Only
Soft Drinks 12/ 2 $60.9 0.93% 2 $175.6 0.56% 2 $236.5 0.62%
18 & 15pk Can
Car
Primal [Beef] 3 $34.5 0.52% 4 $101.5 0.32% 3 $136.0 0.36%
Lean [Beef] 4 $32.9 0.50% 28 $43.8 0.14% 12 $76.7 0.20%
Shredded 5 $28.5 0.43% 3 $102.0 0.32% 4 $130.4 0.34%
Cheese
Kids Cereal 6 $26.3 0.40% 18 $51.4 0.16% 11 $77.7 0.20%
Sft Drnk 2 7 $26.3 0.40% 8 $67.4 0.21% 6 $93.7 0.25%
Liter Btl
Carb Incl
Potato Chips 8 $23.0 0.35% 5 $76.5 0.24% 5 $99.5 0.26%
Snacks/ 9 $19.5 0.30% 43 $33.6 0.11% 31 $53.0 0.14%
Appetizers
Infant Formula 10 $18.9 0.29% 180 $12.1 0.04% 68 $31.1 0.08%
Starter/
Solutio
Lunchment--Del 11 $17.9 0.27% 10 $60.7 0.19% 10 $78.6 0.21%
i Fresh
Mainstream 12 $17.4 0.26% 35 $38.8 0.12% 28 $56.1 0.15%
White Bread
Enhanced [Pork 13 $17.2 0.26% 16 $54.2 0.17% 16 $71.4 0.19%
Boneless Loin/
Rib]
American 14 $17.1 0.26% 30 $43.2 0.14% 22 $60.3 0.16%
Single Cheese
Tortilla/Nacho 15 $16.2 0.25% 14 $56.2 0.18% 15 $72.4 0.19%
Chips
Unflavored Can 16 $16.1 0.24% 12 $60.0 0.19% 13 $76.1 0.20%
Coffee
Fz Ss Economy 17 $15.7 0.24% 68 $25.0 0.08% 45 $40.7 0.11%
Meals All
Soft Drinks 18 $15.5 0.24% 38 $36.7 0.12% 34 $52.3 0.14%
20pk & 24pk
Can Carb
Snack Cake-- 19 $15.4 0.23% 42 $33.6 0.11% 38 $49.0 0.13%
Multi Pack
Chicken Breast 20 $15.4 0.23% 7 $68.8 0.22% 7 $84.2 0.22%
Boneless
Fz Ss Prem 21 $15.2 0.23% 22 $46.5 0.15% 21 $61.7 0.16%
Traditional
Meals
Bacon--Trad 22 $14.5 0.22% 32 $42.7 0.14% 25 $57.2 0.15%
16oz Or Less
Eggs--Large 23 $14.2 0.22% 15 $55.8 0.18% 18 $70.0 0.18%
Dairy Case 24 $13.6 0.21% 9 $65.7 0.21% 9 $79.3 0.21%
100% Pure
Juice--O
Still Water 25 $13.5 0.20% 29 $43.5 0.14% 27 $57.0 0.15%
Drnking/Mnrl
Water
Convenient 26 $13.0 0.20% 82 $20.7 0.07% 61 $33.7 0.09%
Meals--Kids
Meal C
Potatoes 27 $13.0 0.20% 31 $42.9 0.14% 29 $55.9 0.15%
Russet (Bulk
& Bag)
Pizza/Premium 28 $12.9 0.20% 37 $37.1 0.12% 36 $50.0 0.13%
All Family 29 $12.6 0.19% 11 $60.1 0.19% 14 $72.7 0.19%
Cereal
Sft Drnk Mlt- 30 $12.5 0.19% 19 $50.1 0.16% 19 $62.6 0.16%
Pk Btl Carb
(Excp)
Sandwiches & 31 $12.4 0.19% 88 $20.2 0.06% 65 $32.6 0.09%
Handhelds
Frzn Chicken-- 32 $12.4 0.19% 48 $31.9 0.10% 41 $44.3 0.12%
Wht Meat
Ribs [Pork] 33 $12.3 0.19% 58 $27.8 0.09% 47 $40.1 0.11%
Mainstream 34 $11.8 0.18% 23 $45.3 0.14% 26 $57.1 0.15%
Variety
Breads
Sugar 35 $11.7 0.18% 56 $27.9 0.09% 49 $39.6 0.10%
Choice Beef 36 $11.3 0.17% 57 $27.9 0.09% 50 $39.2 0.10%
Condensed Soup 37 $11.2 0.17% 21 $46.8 0.15% 23 $58.0 0.15%
Traditional 38 $10.8 0.16% 26 $44.2 0.14% 30 $55.0 0.14%
[Ice Cream &
Sherbert]
Bananas 39 $10.7 0.16% 13 $59.9 0.19% 17 $70.6 0.19%
Pourable Salad 40 $10.6 0.16% 36 $38.6 0.12% 37 $49.2 0.13%
Dressings
Fz Family 41 $9.7 0.15% 74 $22.9 0.07% 66 $32.6 0.09%
Style Entrees
Macaroni & 42 $9.7 0.15% 97 $19.0 0.06% 74 $28.7 0.08%
Cheese Dnrs
Choice Beef 43 $9.6 0.15% 44 $33.0 0.10% 43 $42.5 0.11%
Natural Cheese 44 $9.5 0.14% 20 $48.3 0.15% 24 $57.7 0.15%
Chunks
Mainstream 45 $9.4 0.14% 60 $27.2 0.09% 56 $36.6 0.10%
[Pasta &
Pizza Sauce]
Margarine: 46 $9.1 0.14% 51 $29.9 0.09% 51 $39.0 0.10%
Tubs And
Bowls
Hot Dogs--Base 47 $9.1 0.14% 95 $19.5 0.06% 75 $28.6 0.08%
Meat
Can Pasta 48 $9.0 0.14% 117 $16.1 0.05% 95 $25.1 0.07%
Mayonnaise & 49 $9.0 0.14% 54 $28.7 0.09% 54 $37.7 0.10%
Whipped
Dressing
Fz Ss Prem 50 $8.6 0.13% 6 $72.5 0.23% 8 $81.1 0.21%
Nutritional
Meals
Strawberries 51 $8.6 0.13% 17 $53.1 0.17% 20 $61.7 0.16%
Sft Drnk Sngl 52 $8.3 0.13% 127 $15.4 0.05% 100 $23.7 0.06%
Srv Btl Carb
(Ex)
Meat: Turkey 53 $8.1 0.12% 27 $43.9 0.14% 35 $52.0 0.14%
Bulk
Lunchment--Bol 54 $8.1 0.12% 93 $19.7 0.06% 78 $27.8 0.07%
ogna/Sausage
Aseptic Pack 55 $7.9 0.12% 124 $15.6 0.05% 101 $23.6 0.06%
Juice And
Drinks
Isotonic 56 $7.9 0.12% 59 $27.6 0.09% 58 $35.4 0.09%
Drinks Single
Serve
Fz Bag 57 $7.8 0.12% 45 $32.7 0.10% 46 $40.6 0.11%
Vegetables--P
lain
Select Beef 58 $7.7 0.12% 100 $18.5 0.06% 89 $26.2 0.07%
Frzn French 59 $7.6 0.12% 128 $15.3 0.05% 104 $23.0 0.06%
Fries
Adult Cereal 60 $7.6 0.12% 24 $45.1 0.14% 32 $52.7 0.14%
Pizza/Economy 61 $7.6 0.12% 113 $16.6 0.05% 96 $24.2 0.06%
Sw Gds: Donuts 62 $7.6 0.11% 66 $25.4 0.08% 64 $32.9 0.09%
Frzn Chicken-- 63 $7.5 0.11% 467 $4.2 0.01% 248 $11.7 0.03%
Wings
Flavored Milk 64 $7.5 0.11% 75 $22.7 0.07% 72 $30.3 0.08%
Premium [Ice 65 $7.5 0.11% 25 $45.1 0.14% 33 $52.6 0.14%
Cream &
Sherbert]
Candy Bags-- 66 $7.3 0.11% 34 $39.3 0.12% 40 $46.6 0.12%
Chocolate
Peanut Butter 67 $7.1 0.11% 40 $34.5 0.11% 44 $41.6 0.11%
Sweet Goods-- 68 $7.1 0.11% 81 $20.9 0.07% 77 $28.0 0.07%
Full Size
Meat: Ham Bulk 69 $7.0 0.11% 39 $36.5 0.12% 42 $43.4 0.11%
Refrigerated 70 $7.0 0.11% 49 $31.2 0.10% 53 $38.2 0.10%
Coffee
Creamers
Bkfst Sausage-- 71 $6.6 0.10% 92 $19.7 0.06% 86 $26.4 0.07%
Fresh Rolls
Tuna 72 $6.6 0.10% 62 $26.4 0.08% 63 $33.0 0.09%
Value Forms 73 $6.6 0.10% 157 $13.3 0.04% 126 $19.9 0.05%
Frz Chick/
18oz & Larger
Cakes: 74 $6.5 0.10% 147 $14.1 0.04% 119 $20.6 0.05%
Birthday/
Celebration
Sh
Pizza/ 75 $6.5 0.10% 96 $19.2 0.06% 93 $25.7 0.07%
Traditional
Cream Cheese 76 $6.4 0.10% 47 $32.0 0.10% 52 $38.4 0.10%
Fruit Snacks 77 $6.4 0.10% 167 $13.0 0.04% 129 $19.4 0.05%
Vegetable Oil 78 $6.4 0.10% 265 $8.5 0.03% 189 $14.9 0.04%
Frzn Breakfast 79 $6.4 0.10% 145 $14.3 0.05% 118 $20.7 0.05%
Sandwiches
Frzn Meat-- 80 $6.3 0.10% 164 $13.1 0.04% 130 $19.4 0.05%
Beef
Sandwich 81 $6.2 0.09% 89 $20.1 0.06% 85 $26.4 0.07%
Cookies
Hamburger Buns 82 $6.2 0.09% 76 $22.4 0.07% 76 $28.6 0.08%
Fz Skillet 83 $6.2 0.09% 83 $20.7 0.07% 82 $26.9 0.07%
Meals
Chicken Wings 84 $6.1 0.09% 368 $5.9 0.02% 240 $12.0 0.03%
Sour Creams 85 $6.1 0.09% 71 $24.3 0.08% 71 $30.4 0.08%
Cottage Cheese 86 $6.1 0.09% 41 $33.8 0.11% 48 $39.9 0.10%
Butter 87 $6.0 0.09% 33 $41.9 0.13% 39 $47.9 0.13%
Dnr Sausage-- 88 $6.0 0.09% 103 $17.8 0.06% 99 $23.8 0.06%
Links Fresh
Cheese 89 $5.9 0.09% 65 $25.5 0.08% 67 $31.4 0.08%
Crackers
Rts Soup: 90 $5.8 0.09% 50 $30.3 0.10% 57 $36.1 0.09%
Chunky/
Homestyle/Et
Hot Dog Buns 91 $5.7 0.09% 102 $17.9 0.06% 102 $23.5 0.06%
Waffles/ 92 $5.6 0.09% 85 $20.5 0.07% 90 $26.1 0.07%
Pancakes/
French Toast
Mult Pk Bag 93 $5.6 0.09% 234 $9.9 0.03% 178 $15.5 0.04%
Snacks
Candy Bars 94 $5.6 0.08% 91 $20.0 0.06% 94 $25.6 0.07%
(Multi Pack)
Toaster 95 $5.5 0.08% 121 $15.8 0.05% 113 $21.3 0.06%
Pastries
Salsa & Dips 96 $5.4 0.08% 151 $13.9 0.04% 131 $19.2 0.05%
Angus [Beef] 97 $5.3 0.08% 55 $28.0 0.09% 62 $33.4 0.09%
Dnr Sausage-- 98 $5.3 0.08% 182 $12.0 0.04% 155 $17.4 0.05%
Links Pork
Ckd/S
Tray Pack/Choc 99 $5.2 0.08% 125 $15.6 0.05% 116 $20.8 0.05%
Chip Cookies
Grapes White 100 $5.2 0.08% 80 $21.3 0.07% 84 $26.5 0.07%
------------------------- ----------------------------------------------------------
Top 100 $1,174.1 17.84% $3,685.6 11.70% $4,859.7 12.76%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-7: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in the South
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $66.4 1.01% 1 $305.9 0.97% 1 $372.3 0.98%
White Only
Soft Drinks 12/ 2 $63.3 0.96% 2 $229.6 0.73% 2 $292.8 0.77%
18 & 15pk Can
Car
Lean [Beef] 3 $38.6 0.59% 15 $75.2 0.24% 8 $113.8 0.30%
Kids Cereal 4 $29.8 0.45% 23 $63.5 0.20% 15 $93.3 0.24%
Sft Drnk 2 5 $26.2 0.40% 9 $91.0 0.29% 7 $117.2 0.31%
Liter Btl
Carb Incl
Primal [Beef] 6 $25.7 0.39% 6 $100.9 0.32% 5 $126.6 0.33%
Shredded 7 $25.6 0.39% 3 $121.8 0.39% 3 $147.4 0.39%
Cheese
Potato Chips 8 $23.5 0.36% 12 $87.7 0.28% 10 $111.2 0.29%
Lunchment--Del 9 $22.8 0.35% 7 $95.8 0.30% 6 $118.6 0.31%
i Fresh
Mainstream 10 $21.3 0.32% 24 $62.7 0.20% 21 $84.0 0.22%
White Bread
Still Water 11 $20.1 0.31% 16 $74.1 0.24% 14 $94.2 0.25%
Drnking/Mnrl
Water
Snack Cake-- 12 $19.8 0.30% 37 $48.3 0.15% 32 $68.1 0.18%
Multi Pack
Eggs--Large 13 $18.8 0.29% 11 $88.4 0.28% 12 $107.2 0.28%
American 14 $17.9 0.27% 32 $56.0 0.18% 27 $73.9 0.19%
Single Cheese
Chicken Breast 15 $17.5 0.27% 4 $109.1 0.35% 4 $126.6 0.33%
Boneless
Sugar 16 $17.4 0.26% 41 $46.5 0.15% 35 $63.9 0.17%
Sft Drnk Mlt- 17 $17.2 0.26% 10 $89.0 0.28% 13 $106.2 0.28%
Pk Btl Carb
(Excp)
Fz Ss Prem 18 $16.7 0.25% 27 $59.9 0.19% 24 $76.6 0.20%
Traditional
Meals
Infant Formula 19 $16.5 0.25% 247 $13.1 0.04% 108 $29.5 0.08%
Starter/
Solutio
Tortilla/Nacho 20 $16.1 0.24% 19 $71.5 0.23% 18 $87.6 0.23%
Chips
Dairy Case 21 $15.9 0.24% 8 $92.7 0.29% 11 $108.6 0.29%
100% Pure
Juice--O
Pizza/Premium 22 $15.9 0.24% 29 $59.0 0.19% 26 $74.9 0.20%
Fz Ss Economy 23 $15.2 0.23% 84 $27.9 0.09% 59 $43.1 0.11%
Meals All
Snacks/ 24 $15.2 0.23% 59 $35.7 0.11% 47 $50.9 0.13%
Appetizers
Soft Drinks 25 $15.2 0.23% 58 $36.1 0.11% 46 $51.2 0.13%
20pk & 24pk
Can Carb
Bacon--Trad 26 $14.8 0.23% 30 $58.3 0.18% 29 $73.1 0.19%
16oz Or Less
Mainstream 27 $14.6 0.22% 18 $72.1 0.23% 19 $86.8 0.23%
Variety
Breads
Sandwiches & 28 $14.6 0.22% 87 $27.1 0.09% 63 $41.7 0.11%
Handhelds
Ribs [Pork] 29 $14.1 0.21% 51 $40.4 0.13% 41 $54.5 0.14%
Convenient 30 $14.1 0.21% 80 $28.6 0.09% 60 $42.7 0.11%
Meals--Kids
Meal C
Enhanced [Pork 31 $14.0 0.21% 21 $66.0 0.21% 23 $80.0 0.21%
Boneless Loin/
Rib]
Potatoes 32 $13.8 0.21% 26 $61.4 0.19% 25 $75.3 0.20%
Russet (Bulk
& Bag)
Unflavored Can 33 $13.4 0.20% 17 $73.0 0.23% 20 $86.3 0.23%
Coffee
Chicken Wings 34 $13.4 0.20% 224 $14.2 0.05% 119 $27.6 0.07%
Mult Pk Bag 35 $12.2 0.19% 137 $20.4 0.06% 87 $32.6 0.09%
Snacks
Fz Bag 36 $12.2 0.19% 33 $54.9 0.17% 33 $67.1 0.18%
Vegetables--P
lain
Sft Drnk Sngl 37 $12.2 0.18% 85 $27.5 0.09% 66 $39.7 0.10%
Srv Btl Carb
(Ex)
Premium [Ice 38 $12.1 0.18% 13 $79.1 0.25% 16 $91.3 0.24%
Cream &
Sherbert]
Frzn Chicken-- 39 $12.1 0.18% 338 $9.0 0.03% 173 $21.1 0.06%
Wings
Bananas 40 $11.6 0.18% 14 $78.9 0.25% 17 $90.5 0.24%
All Family 41 $11.3 0.17% 20 $70.1 0.22% 22 $81.4 0.21%
Cereal
Pourable Salad 42 $11.1 0.17% 38 $48.1 0.15% 36 $59.2 0.16%
Dressings
Hot Dogs--Base 43 $11.0 0.17% 106 $23.9 0.08% 80 $34.9 0.09%
Meat
Condensed Soup 44 $10.9 0.17% 31 $56.2 0.18% 34 $67.1 0.18%
Fz Family 45 $10.5 0.16% 69 $32.1 0.10% 61 $42.6 0.11%
Style Entrees
Isotonic 46 $10.2 0.16% 49 $40.6 0.13% 48 $50.8 0.13%
Drinks Single
Serve
Frzn Chicken-- 47 $10.2 0.16% 55 $37.3 0.12% 53 $47.5 0.12%
Wht Meat
Vegetable Oil 48 $10.1 0.15% 204 $15.4 0.05% 132 $25.5 0.07%
Mayonnaise & 49 $10.1 0.15% 46 $43.1 0.14% 43 $53.2 0.14%
Whipped
Dressing
Aseptic Pack 50 $9.9 0.15% 115 $22.7 0.07% 88 $32.5 0.09%
Juice And
Drinks
Frzn Breakfast 51 $9.5 0.14% 83 $27.9 0.09% 70 $37.4 0.10%
Sandwiches
Macaroni & 52 $9.4 0.14% 121 $21.8 0.07% 98 $31.3 0.08%
Cheese Dnrs
Fz Ss Prem 53 $9.2 0.14% 5 $102.2 0.32% 9 $111.5 0.29%
Nutritional
Meals
Frzn French 54 $9.2 0.14% 127 $21.2 0.07% 103 $30.4 0.08%
Fries
Choice Beef 55 $8.9 0.14% 56 $37.2 0.12% 55 $46.1 0.12%
Lunchment--Bol 56 $8.9 0.14% 110 $23.5 0.07% 89 $32.4 0.09%
ogna/Sausage
Natural Cheese 57 $8.9 0.14% 28 $59.2 0.19% 31 $68.1 0.18%
Chunks
Can Pasta 58 $8.8 0.13% 156 $18.7 0.06% 121 $27.5 0.07%
Adult Cereal 59 $8.5 0.13% 22 $64.7 0.21% 28 $73.2 0.19%
Traditional 60 $8.5 0.13% 50 $40.5 0.13% 49 $49.0 0.13%
[Ice Cream &
Sherbert]
Mainstream 61 $8.4 0.13% 81 $28.5 0.09% 74 $36.9 0.10%
[Pasta &
Pizza Sauce]
Dnr Sausage-- 62 $8.3 0.13% 199 $15.7 0.05% 144 $24.1 0.06%
Links Pork
Ckd/S
Chicken Drums 63 $8.3 0.13% 249 $12.9 0.04% 172 $21.2 0.06%
Margarine: 64 $8.1 0.12% 63 $33.4 0.11% 64 $41.5 0.11%
Tubs And
Bowls
Tuna 65 $8.0 0.12% 48 $40.9 0.13% 50 $48.9 0.13%
Pizza/Economy 66 $7.9 0.12% 181 $16.4 0.05% 142 $24.3 0.06%
Strawberries 67 $7.8 0.12% 25 $62.0 0.20% 30 $69.9 0.18%
Angus [Beef] 68 $7.8 0.12% 40 $46.9 0.15% 40 $54.7 0.14%
Shrimp--Raw 69 $7.6 0.12% 70 $31.8 0.10% 68 $39.4 0.10%
Value Forms/ 70 $7.5 0.11% 179 $16.5 0.05% 145 $ 24.0 0.06%
18oz And
Larger
[Chicken]
Select Beef 71 $7.5 0.11% 36 $51.3 0.16% 37 $58.8 0.15%
Fz Skillet 72 $7.4 0.11% 76 $29.7 0.09% 72 $37.1 0.10%
Meals
Cakes: 73 $7.3 0.11% 142 $19.7 0.06% 122 $27.1 0.07%
Birthday/
Celebration
Sh
Bacon--Trad 74 $7.2 0.11% 108 $23.7 0.08% 100 $30.9 0.08%
Greater Than
16oz
Pizza/ 75 $7.2 0.11% 91 $26.2 0.08% 84 $33.4 0.09%
Traditional
Refrigerated 76 $7.1 0.11% 114 $22.8 0.07% 106 $29.9 0.08%
Biscuits
Sw Gds: Donuts 77 $7.0 0.11% 107 $23.8 0.08% 101 $30.8 0.08%
Frzn Meat-- 78 $7.0 0.11% 185 $16.3 0.05% 151 $23.3 0.06%
Beef
Salsa & Dips 79 $7.0 0.11% 122 $21.7 0.07% 114 $28.7 0.08%
Fruit Snacks 80 $7.0 0.11% 194 $16.0 0.05% 154 $23.0 0.06%
Candy Bags-- 81 $6.9 0.11% 42 $46.5 0.15% 42 $53.4 0.14%
Chocolate
Peanut Butter 82 $6.7 0.10% 43 $45.2 0.14% 45 $51.9 0.14%
Sandwich 83 $6.7 0.10% 100 $24.9 0.08% 93 $31.6 0.08%
Cookies
Ramen Noodles/ 84 $6.6 0.10% 327 $9.5 0.03% 243 $16.2 0.04%
Ramen Cups
Waffles/ 85 $6.6 0.10% 82 $27.9 0.09% 81 $34.5 0.09%
Pancakes/
French Toast
Hot Dog Buns 86 $6.3 0.10% 116 $22.5 0.07% 113 $28.9 0.08%
Candy Bars 87 $6.2 0.09% 96 $25.4 0.08% 95 $31.6 0.08%
(Multi Pack)
Bagged Cheese 88 $6.2 0.09% 147 $19.4 0.06% 133 $25.5 0.07%
Snacks
Prepared 89 $6.1 0.09% 125 $21.5 0.07% 118 $27.6 0.07%
Beans--Baked
W/Pork
Loaf Cheese 90 $6.1 0.09% 145 $19.5 0.06% 130 $25.6 0.07%
Meat: Turkey 91 $6.0 0.09% 34 $52.7 0.17% 38 $58.8 0.15%
Bulk
Tray Pack/Choc 92 $6.0 0.09% 141 $19.9 0.06% 129 $26.0 0.07%
Chip Cookies
Hamburger Buns 93 $6.0 0.09% 99 $25.1 0.08% 99 $31.1 0.08%
Green Beans: 94 $6.0 0.09% 102 $24.8 0.08% 102 $30.8 0.08%
Fs/Whl/Cut
Grapes White 95 $6.0 0.09% 75 $29.7 0.09% 79 $35.6 0.09%
Spring Water 96 $6.0 0.09% 64 $32.9 0.10% 69 $38.8 0.10%
Rts Soup: 97 $5.9 0.09% 54 $38.6 0.12% 57 $44.5 0.12%
Chunky/
Homestyle/Et
Butter Spray 98 $5.9 0.09% 88 $26.2 0.08% 91 $32.1 0.08%
Cracker
Instore Cut 99 $5.9 0.09% 57 $36.6 0.12% 62 $42.5 0.11%
Fruit
Toaster 100 $5.8 0.09% 134 $20.5 0.07% 125 $26.4 0.07%
Pastries
------------------------- ----------------------------------------------------------
Top 100 $1,268.9 19.28% $4,783.8 15.18% $6,052.7 15.89%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-8: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in the West
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $60.4 0.92% 1 $310.8 0.99% 1 $371.2 0.97%
White Only
Lean [Beef] 2 $40.9 0.62% 3 $138.9 0.44% 3 $179.8 0.47%
Soft Drinks 12/ 3 $40.5 0.62% 2 $196.0 0.62% 2 $236.5 0.62%
18 & 15pk Can
Car
Kids Cereal 4 $22.0 0.33% 22 $71.5 0.23% 17 $93.5 0.25%
Shredded 5 $20.7 0.31% 4 $118.2 0.38% 4 $138.9 0.36%
Cheese
Eggs--Large 6 $19.1 0.29% 8 $107.4 0.34% 6 $126.5 0.33%
Infant Formula 7 $18.8 0.29% 167 $20.1 0.06% 75 $38.9 0.10%
Starter/
Solutio
Sft Drnk 2 8 $18.4 0.28% 21 $71.8 0.23% 18 $90.2 0.24%
Liter Btl
Carb Incl
Potato Chips 9 $17.9 0.27% 13 $89.0 0.28% 11 $106.9 0.28%
Natural Cheese 10 $16.9 0.26% 7 $108.6 0.34% 7 $125.6 0.33%
Chunks
Chicken Breast 11 $16.7 0.25% 5 $115.0 0.36% 5 $131.7 0.35%
Boneless
Still Water 12 $15.2 0.23% 24 $70.1 0.22% 23 $85.3 0.22%
Drnking/Mnrl
Water
Lunchment--Del 13 $15.2 0.23% 14 $86.0 0.27% 13 $101.2 0.27%
i Fresh
Tortilla/Nacho 14 $15.1 0.23% 17 $81.3 0.26% 16 $96.4 0.25%
Chips
Mexican Soft 15 $15.1 0.23% 23 $71.5 0.23% 21 $86.6 0.23%
Tortillas And
Wra
Dairy Case 16 $14.0 0.21% 6 $110.7 0.35% 8 $124.7 0.33%
100% Pure
Juice--O
Select Beef 17 $12.6 0.19% 19 $73.9 0.23% 22 $86.5 0.23%
Isotonic 18 $12.4 0.19% 39 $51.4 0.16% 38 $63.7 0.17%
Drinks Single
Serve
All Family 19 $12.3 0.19% 15 $84.7 0.27% 15 $97.0 0.25%
Cereal
Mainstream 20 $12.0 0.18% 37 $55.8 0.18% 36 $67.8 0.18%
Variety
Breads
Fz Ss Prem 21 $11.9 0.18% 27 $69.1 0.22% 25 $81.0 0.21%
Traditional
Meals
Bananas 22 $11.9 0.18% 9 $103.9 0.33% 9 $115.8 0.30%
Unflavored Can 23 $11.9 0.18% 29 $65.0 0.21% 27 $76.9 0.20%
Coffee
Premium [Ice 24 $11.6 0.18% 10 $101.7 0.32% 10 $113.3 0.30%
Cream &
Sherbert]
Refrigerated 25 $11.5 0.17% 18 $75.9 0.24% 20 $87.4 0.23%
Coffee
Creamers
Bacon--Trad 26 $11.4 0.17% 36 $56.6 0.18% 34 $68.1 0.18%
16oz Or Less
Pizza/Premium 27 $10.9 0.17% 35 $57.2 0.18% 35 $68.1 0.18%
Enhanced [Pork 28 $10.3 0.16% 49 $47.8 0.15% 42 $58.1 0.15%
Boneless Loin/
Rib]
Fz Ss Economy 29 $10.0 0.15% 104 $27.8 0.09% 83 $37.8 0.10%
Meals All
Snacks/ 30 $10.0 0.15% 85 $31.2 0.10% 70 $41.1 0.11%
Appetizers
Choice Beef 31 $9.9 0.15% 28 $66.5 0.21% 28 $76.5 0.20%
Mainstream 32 $9.3 0.14% 71 $35.3 0.11% 64 $44.6 0.12%
White Bread
American 33 $9.0 0.14% 66 $37.4 0.12% 62 $46.5 0.12%
Single Cheese
Soft Drinks 34 $9.0 0.14% 77 $33.6 0.11% 67 $42.6 0.11%
20pk & 24pk
Can Carb
Potatoes 35 $9.0 0.14% 44 $50.1 0.16% 40 $59.1 0.16%
Russet (Bulk
& Bag)
Adult Cereal 36 $8.8 0.13% 20 $72.8 0.23% 24 $81.6 0.21%
Sandwiches & 37 $8.8 0.13% 113 $26.3 0.08% 92 $35.1 0.09%
Handhelds
Ribs [Pork] 38 $8.6 0.13% 62 $38.5 0.12% 59 $47.1 0.12%
Avocado 39 $8.4 0.13% 26 $69.5 0.22% 26 $77.9 0.20%
Choice Beef 40 $8.2 0.13% 102 $28.4 0.09% 85 $36.6 0.10%
Mayonnaise & 41 $8.2 0.12% 50 $47.2 0.15% 45 $55.4 0.15%
Whipped
Dressing
Sandwiches--(C 42 $8.1 0.12% 54 $44.1 0.14% 51 $52.2 0.14%
old)
Butter 43 $8.0 0.12% 16 $81.6 0.26% 19 $89.6 0.24%
Premium Bread 44 $7.9 0.12% 12 $89.1 0.28% 14 $97.0 0.25%
Sugar 45 $7.8 0.12% 64 $38.3 0.12% 63 $46.1 0.12%
Condensed Soup 46 $7.6 0.12% 42 $50.6 0.16% 41 $58.2 0.15%
Frzn Chicken-- 47 $7.4 0.11% 90 $30.6 0.10% 81 $38.0 0.10%
Wht Meat
Fz Family 48 $7.4 0.11% 100 $28.5 0.09% 87 $35.9 0.09%
Style Entrees
Sft Drnk Sngl 49 $7.3 0.11% 101 $28.5 0.09% 88 $35.8 0.09%
Srv Btl Carb
(Ex)
Candy Bags-- 50 $7.3 0.11% 33 $61.8 0.20% 32 $69.0 0.18%
Chocolate
Pourable Salad 51 $7.3 0.11% 38 $52.8 0.17% 39 $60.1 0.16%
Dressings
Convenient 52 $7.1 0.11% 160 $20.5 0.06% 126 $27.6 0.07%
Meals--Kids
Meal C
Strawberries 53 $7.0 0.11% 31 $63.3 0.20% 31 $70.3 0.18%
Fz Ss Prem 54 $6.9 0.10% 11 $96.9 0.31% 12 $103.7 0.27%
Nutritional
Meals
Sw Gds: Donuts 55 $6.7 0.10% 79 $33.1 0.11% 74 $39.8 0.10%
Peanut Butter 56 $6.6 0.10% 48 $48.1 0.15% 46 $54.7 0.14%
Tuna 57 $6.5 0.10% 59 $42.6 0.14% 57 $49.2 0.13%
Snack Cake-- 58 $6.5 0.10% 168 $19.8 0.06% 141 $26.3 0.07%
Multi Pack
Aseptic Pack 59 $6.4 0.10% 174 $18.8 0.06% 152 $25.3 0.07%
Juice And
Drinks
Traditional 60 $6.3 0.10% 75 $34.1 0.11% 73 $40.4 0.11%
[Ice Cream &
Sherbert]
Margarine: 61 $6.2 0.09% 65 $37.5 0.12% 65 $43.8 0.11%
Tubs And
Bowls
Sour Creams 62 $6.2 0.09% 60 $41.7 0.13% 58 $47.9 0.13%
String Cheese 63 $6.2 0.09% 55 $43.8 0.14% 54 $50.0 0.13%
Candy Bars 64 $6.2 0.09% 103 $28.1 0.09% 95 $34.2 0.09%
(Singles)
(Including)
Bagged Cheese 65 $6.1 0.09% 166 $20.2 0.06% 139 $26.4 0.07%
Snacks
Cream Cheese 66 $6.1 0.09% 46 $48.4 0.15% 47 $54.5 0.14%
Dairy Case 67 $6.0 0.09% 132 $23.5 0.07% 115 $29.5 0.08%
Juice Drnk
Under 10
Rts Soup: 68 $5.9 0.09% 40 $51.0 0.16% 43 $56.9 0.15%
Chunky/
Homestyle/Et
Fz Bag 69 $5.7 0.09% 53 $44.3 0.14% 55 $50.0 0.13%
Vegetables--P
lain
Frzn Meat-- 70 $5.7 0.09% 199 $16.9 0.05% 168 $22.6 0.06%
Beef
Tea Sweetened 71 $5.7 0.09% 89 $30.6 0.10% 86 $36.3 0.10%
Chix: 72 $5.6 0.09% 30 $64.7 0.21% 30 $70.3 0.18%
Rotisserie
(Hot)
Burritos 73 $5.4 0.08% 286 $12.2 0.04% 220 $17.6 0.05%
Spring Water 74 $5.3 0.08% 52 $44.9 0.14% 53 $50.3 0.13%
Ramen Noodles/ 75 $5.3 0.08% 268 $12.8 0.04% 217 $18.1 0.05%
Ramen Cups
Macaroni & 76 $5.2 0.08% 173 $18.8 0.06% 156 $24.0 0.06%
Cheese Dnrs
Natural Cheese 77 $5.2 0.08% 41 $51.0 0.16% 44 $56.2 0.15%
Slices
Fz Skillet 78 $5.2 0.08% 94 $29.0 0.09% 96 $34.1 0.09%
Meals
Waffles/ 79 $5.2 0.08% 95 $28.9 0.09% 97 $34.1 0.09%
Pancakes/
French Toast
Mainstream 80 $5.1 0.08% 117 $25.3 0.08% 113 $30.4 0.08%
[Pasta &
Pizza Sauce]
Meat: Turkey 81 $5.1 0.08% 32 $63.0 0.20% 33 $68.1 0.18%
Bulk
Cheese 82 $5.1 0.08% 78 $33.1 0.11% 79 $38.2 0.10%
Crackers
Grapes Red 83 $5.1 0.08% 51 $46.6 0.15% 52 $51.6 0.14%
Sandwich 84 $5.1 0.08% 110 $26.7 0.08% 107 $31.8 0.08%
Cookies
Shrimp--Cooked 85 $5.1 0.08% 124 $24.5 0.08% 114 $29.5 0.08%
Whole Chicken 86 $5.0 0.08% 107 $27.3 0.09% 104 $32.3 0.08%
(Roasters/
Fryer)
Shrimp--Raw 87 $5.0 0.08% 109 $27.2 0.09% 106 $32.2 0.08%
Hot Dogs--Base 88 $4.9 0.08% 255 $13.5 0.04% 213 $18.4 0.05%
Meat
Cottage Cheese 89 $4.9 0.07% 45 $48.8 0.15% 49 $53.7 0.14%
Oranges Navels 90 $4.9 0.07% 68 $36.8 0.12% 69 $41.6 0.11%
All
Chewing Gum 91 $4.8 0.07% 80 $33.0 0.10% 84 $37.8 0.10%
Lunchment--Bol 92 $4.8 0.07% 190 $17.7 0.06% 170 $22.5 0.06%
ogna/Sausage
Apple Juice & 93 $4.7 0.07% 188 $18.0 0.06% 167 $22.7 0.06%
Cider (Over
50%)
Super Premium 94 $4.7 0.07% 47 $48.3 0.15% 50 $53.1 0.14%
Pints [Ice
Cream &
Sherbert]
Salsa & Dips 95 $4.7 0.07% 152 $21.4 0.07% 143 $26.2 0.07%
Cakes: 96 $4.7 0.07% 206 $16.5 0.05% 184 $21.2 0.06%
Birthday/
Celebration
Sh
Yogurt/Ss 97 $4.7 0.07% 70 $36.3 0.12% 71 $41.0 0.11%
Regular
Value Forms/ 98 $4.6 0.07% 270 $12.8 0.04% 226 $17.3 0.05%
18oz And
Larger
[Chicken]
Energy Drink-- 99 $4.5 0.07% 108 $27.3 0.09% 108 $31.8 0.08%
Single Serve
(N)
Non-Carb Water 100 $4.5 0.07% 88 $30.7 0.09% 90 $35.1 0.09%
Flvr--Drnk/
Mnr
------------------------- ----------------------------------------------------------
Top 100 $971.3 14.76% $5,340.7 16.93% $6,312.0 16.56%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-9: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in Large Metropolitan Counties
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $102.1 1.55% 1 $484.1 1.54% 1 $586.2 1.54%
White Only
Soft Drinks 12/ 2 $84.7 1.29% 2 $346.6 1.10% 2 $431.3 1.13%
18 & 15pk Can
Car
Lean [Beef] 3 $58.3 0.89% 11 $142.4 0.45% 5 $200.7 0.53%
Kids Cereal 4 $44.8 0.68% 18 $110.5 0.35% 14 $155.3 0.41%
Shredded 5 $41.0 0.62% 3 $197.3 0.63% 3 $238.2 0.63%
Cheese
Sft Drnk 2 6 $39.6 0.60% 13 $135.9 0.43% 10 $175.5 0.46%
Liter Btl
Carb Incl
Potato Chips 7 $35.3 0.54% 9 $145.9 0.46% 8 $181.2 0.48%
Lunchment--Del 8 $30.4 0.46% 12 $140.6 0.45% 11 $171.0 0.45%
i Fresh
Eggs--Large 9 $29.6 0.45% 8 $147.8 0.47% 9 $177.3 0.47%
Primal [Beef] 10 $29.6 0.45% 19 $109.9 0.35% 18 $139.5 0.37%
Infant Formula 11 $29.1 0.44% 198 $26.5 0.08% 88 $55.6 0.15%
Starter/
Solution
Still Water 12 $28.9 0.44% 17 $119.0 0.38% 16 $147.9 0.39%
Drnking/Mnrl
Water
Chicken Breast 13 $27.5 0.42% 4 $178.4 0.57% 4 $205.9 0.54%
Boneless
Dairy Case 14 $26.7 0.41% 6 $168.2 0.53% 6 $194.9 0.51%
100% Pure
Juice--O
Tortilla/Nacho 15 $25.7 0.39% 15 $122.3 0.39% 15 $148.0 0.39%
Chips
Fz Ss Prem 16 $25.6 0.39% 23 $108.0 0.34% 20 $133.5 0.35%
Traditional
Meals
Snacks/ 17 $24.7 0.38% 65 $61.0 0.19% 45 $85.7 0.22%
Appetizers
Mainstream 18 $24.3 0.37% 49 $73.5 0.23% 39 $97.8 0.26%
White Bread
American 19 $23.7 0.36% 43 $77.3 0.25% 34 $101.0 0.27%
Single Cheese
Mainstream 20 $23.2 0.35% 25 $102.4 0.32% 22 $125.7 0.33%
Variety
Breads
Fz Ss Economy 21 $22.6 0.34% 91 $46.0 0.15% 70 $68.7 0.18%
Meals All
Bacon--Trad 22 $22.5 0.34% 31 $90.3 0.29% 28 $112.9 0.30%
16oz Or Less
Snack Cake-- 23 $22.3 0.34% 72 $55.8 0.18% 59 $78.1 0.21%
Multi Pack
Pizza/Premium 24 $21.7 0.33% 29 $91.8 0.29% 26 $113.5 0.30%
Unflavored Can 25 $20.3 0.31% 22 $108.3 0.34% 21 $128.7 0.34%
Coffee
Sugar 26 $20.1 0.31% 62 $62.1 0.20% 54 $82.1 0.22%
Bananas 27 $19.9 0.30% 7 $148.3 0.47% 12 $168.2 0.44%
Enhanced [Pork 28 $19.8 0.30% 33 $86.6 0.27% 31 $106.5 0.28%
Boneless Loin/
Rib]
All Family 29 $19.8 0.30% 14 $124.8 0.40% 17 $144.6 0.38%
Cereal
Premium [Ice 30 $19.3 0.29% 10 $144.6 0.46% 13 $163.9 0.43%
Cream &
Sherbert]
Sandwiches & 31 $19.2 0.29% 95 $43.3 0.14% 78 $62.5 0.16%
Handhelds
Ribs [Pork] 32 $19.1 0.29% 64 $61.4 0.19% 56 $80.6 0.21%
Convenient 33 $18.7 0.28% 103 $41.8 0.13% 82 $60.5 0.16%
Meals--Kids
Meal C
Natural Cheese 34 $18.6 0.28% 16 $120.3 0.38% 19 $138.9 0.36%
Chunks
Potatoes 35 $18.5 0.28% 36 $85.2 0.27% 32 $103.7 0.27%
Russet (Bulk
& Bag)
Isotonic 36 $17.7 0.27% 47 $73.7 0.23% 43 $91.4 0.24%
Drinks Single
Serve
Soft Drinks 37 $17.5 0.27% 75 $54.2 0.17% 64 $71.6 0.19%
20pk & 24pk
Can Carb
Frzn Chicken-- 38 $16.9 0.26% 73 $55.6 0.18% 63 $72.5 0.19%
Wht Meat
Sft Drnk Mlt- 39 $16.3 0.25% 30 $90.4 0.29% 29 $106.7 0.28%
Pk Btl Carb
(Excp)
Pourable Salad 40 $16.2 0.25% 39 $82.7 0.26% 37 $98.9 0.26%
Dressings
Choice Beef 41 $16.1 0.24% 40 $81.9 0.26% 38 $98.0 0.26%
Fz Family 42 $15.5 0.24% 82 $49.6 0.16% 74 $65.1 0.17%
Style Entrees
Condensed Soup 43 $15.4 0.23% 38 $84.7 0.27% 35 $100.2 0.26%
Fz Bag 44 $15.1 0.23% 42 $77.6 0.25% 42 $92.7 0.24%
Vegetables--P
lain
Frzn Chicken-- 45 $15.1 0.23% 444 $11.1 0.04% 242 $26.3 0.07%
Wings
Mayonnaise & 46 $14.9 0.23% 55 $68.0 0.22% 50 $82.9 0.22%
Whipped
Dressing
Select Beef 47 $14.9 0.23% 34 $86.5 0.27% 33 $101.4 0.27%
Fz Ss Prem 48 $14.6 0.22% 5 $172.2 0.55% 7 $186.7 0.49%
Nutritional
Meals
Adult Cereal 49 $14.4 0.22% 20 $109.6 0.35% 23 $124.0 0.33%
Sft Drnk Sngl 50 $14.4 0.22% 107 $40.1 0.13% 92 $54.5 0.14%
Srv Btl Carb
(Ex)
Aseptic Pack 51 $14.3 0.22% 122 $36.3 0.12% 100 $50.6 0.13%
Juice And
Drinks
Chicken Wings 52 $14.0 0.21% 282 $18.6 0.06% 190 $32.6 0.09%
Traditional 53 $13.6 0.21% 58 $63.3 0.20% 62 $76.9 0.20%
[Ice Cream &
Sherbert]
Mult Pk Bag 54 $13.5 0.21% 182 $28.3 0.09% 134 $41.8 0.11%
Snacks
Refrigerated 55 $13.5 0.20% 27 $93.2 0.30% 30 $106.6 0.28%
Coffee
Creamers
Mexican Soft 56 $13.4 0.20% 53 $69.5 0.22% 51 $82.9 0.22%
Tortillas And
Wra
Strawberries 57 $13.4 0.20% 21 $109.1 0.35% 24 $122.5 0.32%
Hot Dogs--Base 58 $13.1 0.20% 174 $29.5 0.09% 130 $42.5 0.11%
Meat
Mainstream 59 $13.0 0.20% 86 $48.2 0.15% 80 $61.2 0.16%
[Pasta &
Pizza Sauce]
Macaroni & 60 $12.8 0.19% 136 $34.4 0.11% 109 $47.1 0.12%
Cheese Dnrs
Choice Beef 61 $12.6 0.19% 114 $38.4 0.12% 99 $51.0 0.13%
Margarine: 62 $12.6 0.19% 68 $58.4 0.19% 65 $71.0 0.19%
Tubs And
Bowls
Tuna 63 $12.2 0.19% 56 $68.0 0.22% 57 $80.2 0.21%
Meat: Turkey 64 $12.1 0.18% 24 $105.2 0.33% 25 $117.4 0.31%
Bulk
Vegetable Oil 65 $11.7 0.18% 256 $20.5 0.06% 194 $32.2 0.08%
Frzn French 66 $11.4 0.17% 180 $28.5 0.09% 147 $39.8 0.10%
Fries
Lunchment--Bol 67 $11.3 0.17% 152 $32.9 0.10% 121 $44.2 0.12%
ogna/Sausage
Candy Bags-- 68 $11.3 0.17% 37 $84.8 0.27% 41 $96.0 0.25%
Chocolate
Can Pasta 69 $11.3 0.17% 204 $26.0 0.08% 163 $37.3 0.10%
Fz Skillet 70 $11.2 0.17% 84 $48.9 0.16% 85 $60.1 0.16%
Meals
Sw Gds: Donuts 71 $11.1 0.17% 99 $43.0 0.14% 93 $54.1 0.14%
Butter 72 $11.1 0.17% 26 $102.0 0.32% 27 $113.1 0.30%
Peanut Butter 73 $11.0 0.17% 45 $74.1 0.23% 46 $85.0 0.22%
Frzn Meat-- 74 $10.7 0.16% 196 $26.6 0.08% 162 $37.3 0.10%
Beef
Frzn Breakfast 75 $10.7 0.16% 143 $33.6 0.11% 120 $44.3 0.12%
Sandwiches
Cakes: 76 $10.7 0.16% 169 $30.1 0.10% 140 $40.8 0.11%
Birthday/
Celebration
Sh
Waffles/ 77 $10.4 0.16% 81 $50.0 0.16% 83 $60.5 0.16%
Pancakes/
French Toast
Spring Water 78 $10.4 0.16% 57 $67.7 0.21% 60 $78.0 0.20%
Value Forms/ 79 $10.2 0.16% 212 $24.9 0.08% 177 $35.1 0.09%
18oz And
Larger
[Chicken]
Sandwiches--(C 80 $10.2 0.16% 92 $46.0 0.15% 87 $56.2 0.15%
old)
Dairy Case 81 $10.2 0.15% 158 $31.8 0.10% 131 $42.0 0.11%
Juice Drnk
Under 10
Dnr Sausage-- 82 $10.2 0.15% 232 $23.0 0.07% 186 $33.2 0.09%
Links Pork
Ckd/S
Sandwich 83 $10.1 0.15% 102 $42.0 0.13% 97 $52.1 0.14%
Cookies
Pizza/Economy 84 $10.0 0.15% 234 $22.9 0.07% 188 $32.9 0.09%
Chicken Drums 85 $10.0 0.15% 276 $18.9 0.06% 225 $28.9 0.08%
Rts Soup: 86 $9.9 0.15% 50 $73.4 0.23% 48 $83.4 0.22%
Chunky/
Homestyle/Et
Ramen Noodles/ 87 $9.8 0.15% 302 $17.2 0.05% 237 $27.0 0.07%
Ramen Cups
Cream Cheese 88 $9.8 0.15% 54 $68.9 0.22% 58 $78.7 0.21%
Sour Creams 89 $9.7 0.15% 70 $56.7 0.18% 72 $66.4 0.17%
Bagged Cheese 90 $9.6 0.15% 167 $30.8 0.10% 144 $40.4 0.11%
Snacks
Fruit Snacks 91 $9.6 0.15% 211 $25.1 0.08% 181 $34.6 0.09%
Salsa & Dips 92 $9.5 0.14% 139 $34.0 0.11% 124 $43.5 0.11%
Ground Turkey 93 $9.4 0.14% 74 $55.3 0.18% 75 $64.7 0.17%
Pizza/ 94 $9.3 0.14% 128 $35.3 0.11% 117 $44.7 0.12%
Traditional
Sweet Goods-- 95 $9.3 0.14% 119 $36.5 0.12% 113 $45.7 0.12%
Full Size
Candy Bars 96 $9.2 0.14% 155 $32.3 0.10% 136 $41.5 0.11%
(Singles)
(Including)
Hot Dog Buns 97 $9.2 0.14% 118 $36.7 0.12% 112 $46.0 0.12%
Cheese 98 $9.2 0.14% 71 $55.9 0.18% 73 $65.1 0.17%
Crackers
Shrimp--Raw 99 $9.2 0.14% 104 $41.3 0.13% 101 $50.5 0.13%
Grapes Red 100 $9.2 0.14% 51 $72.9 0.23% 55 $82.1 0.22%
------------------------- ----------------------------------------------------------
Top 100 $1,843.6 28.02% $7,796.5 24.74% $9,640.1 25.31%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-10: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in Smaller Metropolitan Counties
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $62.4 0.95% 1 $264.0 0.84% 1 $326.5 0.86%
White Only
Soft Drinks 12/ 2 $52.7 0.80% 2 $176.7 0.56% 2 $229.4 0.60%
18 & 15pk Can
Car
Lean [Beef] 3 $38.9 0.59% 5 $80.9 0.26% 4 $119.7 0.31%
Kids Cereal 4 $24.8 0.38% 20 $55.9 0.18% 13 $80.7 0.21%
Shredded 5 $24.6 0.37% 3 $104.4 0.33% 3 $129.1 0.34%
Cheese
Primal [Beef] 6 $23.2 0.35% 8 $76.1 0.24% 6 $99.3 0.26%
Sft Drnk 2 7 $23.2 0.35% 12 $70.0 0.22% 8 $93.1 0.24%
Liter Btl
Carb Incl
Potato Chips 8 $20.9 0.32% 7 $76.3 0.24% 7 $97.3 0.26%
Infant Formula 9 $18.7 0.28% 180 $13.8 0.04% 73 $32.5 0.09%
Starter/
Solutio
Lunchment--Del 10 $18.4 0.28% 11 $74.4 0.24% 9 $92.8 0.24%
i Fresh
Eggs--Large 11 $16.4 0.25% 9 $74.8 0.24% 10 $91.2 0.24%
Mainstream 12 $16.1 0.24% 33 $42.8 0.14% 29 $58.9 0.15%
White Bread
Chicken Breast 13 $15.9 0.24% 4 $84.6 0.27% 5 $100.5 0.26%
Boneless
Tortilla/Nacho 14 $15.8 0.24% 16 $63.2 0.20% 16 $79.0 0.21%
Chips
Enhanced [Pork 15 $14.7 0.22% 21 $54.7 0.17% 20 $69.4 0.18%
Boneless Loin/
Rib]
American 16 $14.5 0.22% 35 $41.3 0.13% 33 $55.8 0.15%
Single Cheese
Snacks/ 17 $14.2 0.22% 66 $28.6 0.09% 48 $42.8 0.11%
Appetizers
Unflavored Can 18 $14.2 0.22% 17 $61.8 0.20% 18 $75.9 0.20%
Coffee
Soft Drinks 19 $14.0 0.21% 49 $35.1 0.11% 38 $49.1 0.13%
20pk & 24pk
Can Carb
Still Water 20 $13.9 0.21% 28 $47.9 0.15% 24 $61.8 0.16%
Drnking/Mnrl
Water
Fz Ss Prem 21 $13.6 0.21% 25 $50.3 0.16% 22 $64.0 0.17%
Traditional
Meals
Fz Ss Economy 22 $13.4 0.20% 76 $25.0 0.08% 59 $38.3 0.10%
Meals All
Bacon--Trad 23 $13.2 0.20% 30 $46.6 0.15% 26 $59.8 0.16%
16oz Or Less
Snack Cake-- 24 $13.0 0.20% 61 $30.5 0.10% 46 $43.5 0.11%
Multi Pack
Pizza/Premium 25 $12.8 0.19% 32 $44.8 0.14% 31 $57.6 0.15%
Dairy Case 26 $12.7 0.19% 10 $74.5 0.24% 11 $87.2 0.23%
100% Pure
Juice--O
Potatoes 27 $12.0 0.18% 29 $47.4 0.15% 28 $59.4 0.16%
Russet (Bulk
& Bag)
Sugar 28 $11.9 0.18% 50 $35.0 0.11% 42 $47.0 0.12%
Natural Cheese 29 $11.9 0.18% 14 $68.4 0.22% 14 $80.3 0.21%
Chunks
All Family 30 $11.8 0.18% 15 $66.4 0.21% 17 $78.2 0.21%
Cereal
Sandwiches & 31 $11.7 0.18% 89 $21.8 0.07% 70 $33.5 0.09%
Handhelds
Sft Drnk Mlt- 32 $11.7 0.18% 19 $57.3 0.18% 21 $68.9 0.18%
Pk Btl Carb
(Excp)
Ribs [Pork] 33 $11.4 0.17% 57 $31.2 0.10% 50 $42.6 0.11%
Mainstream 34 $11.2 0.17% 24 $50.9 0.16% 23 $62.2 0.16%
Variety
Breads
Convenient 35 $11.1 0.17% 103 $20.1 0.06% 77 $31.2 0.08%
Meals--Kids
Meal C
Bananas 36 $10.4 0.16% 13 $69.3 0.22% 15 $79.7 0.21%
Condensed Soup 37 $10.0 0.15% 27 $48.8 0.15% 30 $58.7 0.15%
Frzn Chicken-- 38 $9.6 0.15% 58 $31.1 0.10% 51 $40.7 0.11%
Wht Meat
Choice Beef 39 $9.5 0.14% 36 $40.9 0.13% 36 $50.4 0.13%
Pourable Salad 40 $9.3 0.14% 37 $40.9 0.13% 37 $50.2 0.13%
Dressings
Select Beef 41 $9.3 0.14% 34 $41.5 0.13% 35 $50.8 0.13%
Sft Drnk Sngl 42 $9.3 0.14% 87 $22.6 0.07% 76 $31.9 0.08%
Srv Btl Carb
(Ex)
Isotonic 43 $9.2 0.14% 53 $33.6 0.11% 49 $42.7 0.11%
Drinks Single
Serve
Premium [Ice 44 $8.9 0.14% 18 $61.5 0.19% 19 $70.4 0.18%
Cream &
Sherbert]
Fz Family 45 $8.8 0.13% 77 $24.8 0.08% 69 $33.6 0.09%
Style Entrees
Mayonnaise & 46 $8.8 0.13% 45 $36.0 0.11% 45 $44.8 0.12%
Whipped
Dressing
Traditional 47 $8.7 0.13% 41 $39.7 0.13% 39 $48.4 0.13%
[Ice Cream &
Sherbert]
Hot Dogs--Base 48 $8.3 0.13% 121 $18.0 0.06% 92 $26.3 0.07%
Meat
Choice Beef 49 $8.2 0.13% 79 $23.8 0.08% 74 $32.1 0.08%
Macaroni & 50 $8.2 0.12% 118 $18.1 0.06% 93 $26.3 0.07%
Cheese Dnrs
Fz Bag 51 $7.8 0.12% 42 $39.6 0.13% 41 $47.4 0.12%
Vegetables--P
lain
Refrigerated 52 $7.7 0.12% 39 $40.6 0.13% 40 $48.3 0.13%
Coffee
Creamers
Margarine: 53 $7.7 0.12% 63 $30.0 0.10% 61 $37.6 0.10%
Tubs And
Bowls
Adult Cereal 54 $7.7 0.12% 22 $54.1 0.17% 25 $61.7 0.16%
Can Pasta 55 $7.6 0.12% 157 $15.3 0.05% 114 $22.9 0.06%
Mexican Soft 56 $7.6 0.12% 56 $31.9 0.10% 56 $39.5 0.10%
Tortillas And
Wra
Fz Ss Prem 57 $7.6 0.12% 6 $76.8 0.24% 12 $84.4 0.22%
Nutritional
Meals
Aseptic Pack 58 $7.3 0.11% 155 $15.3 0.05% 118 $22.6 0.06%
Juice And
Drinks
Mainstream 59 $7.3 0.11% 80 $23.8 0.08% 78 $31.1 0.08%
[Pasta &
Pizza Sauce]
Candy Bags-- 60 $7.3 0.11% 31 $46.3 0.15% 34 $53.5 0.14%
Chocolate
Strawberries 61 $7.2 0.11% 26 $50.2 0.16% 32 $57.4 0.15%
Lunchment--Bol 62 $7.2 0.11% 115 $18.6 0.06% 97 $25.8 0.07%
ogna/Sausage
Sw Gds: Donuts 63 $7.1 0.11% 70 $27.0 0.09% 66 $34.1 0.09%
Pizza/Economy 64 $7.0 0.11% 151 $15.7 0.05% 117 $22.7 0.06%
Peanut Butter 65 $6.6 0.10% 43 $39.0 0.12% 44 $45.7 0.12%
Frzn French 66 $6.5 0.10% 159 $15.2 0.05% 125 $21.7 0.06%
Fries
Vegetable Oil 67 $6.5 0.10% 246 $10.4 0.03% 184 $16.9 0.04%
Tuna 68 $6.5 0.10% 60 $30.7 0.10% 63 $37.2 0.10%
Chicken Wings 69 $6.4 0.10% 338 $7.6 0.02% 223 $14.1 0.04%
Butter 70 $6.3 0.09% 23 $53.3 0.17% 27 $59.5 0.16%
Frzn Meat-- 71 $6.1 0.09% 177 $14.2 0.04% 142 $20.3 0.05%
Beef
Mult Pk Bag 72 $6.1 0.09% 231 $11.1 0.04% 177 $17.1 0.04%
Snacks
Value Forms/ 73 $6.0 0.09% 197 $12.7 0.04% 158 $18.7 0.05%
18oz And
Larger
[Chicken]
Frzn Breakfast 74 $6.0 0.09% 147 $15.8 0.05% 123 $21.8 0.06%
Sandwiches
Pizza/ 75 $5.9 0.09% 101 $20.2 0.06% 94 $26.1 0.07%
Traditional
Fruit Snacks 76 $5.9 0.09% 189 $13.3 0.04% 154 $19.2 0.05%
Frzn Chicken-- 77 $5.9 0.09% 479 $4.7 0.01% 289 $10.6 0.03%
Wings
Fz Skillet 78 $5.7 0.09% 85 $23.0 0.07% 83 $28.7 0.08%
Meals
Sandwich 79 $5.7 0.09% 93 $21.4 0.07% 86 $27.1 0.07%
Cookies
Sour Creams 80 $5.7 0.09% 69 $28.0 0.09% 68 $33.7 0.09%
Cakes: 81 $5.7 0.09% 178 $14.1 0.04% 147 $19.8 0.05%
Birthday/
Celebration
Sh
Rts Soup: 82 $5.6 0.09% 51 $34.7 0.11% 53 $40.3 0.11%
Chunky/
Homestyle/Et
Chicken Drums 83 $5.5 0.08% 277 $9.0 0.03% 215 $14.6 0.04%
Bagged Cheese 84 $5.4 0.08% 161 $15.0 0.05% 141 $20.5 0.05%
Snacks
Cream Cheese 85 $5.4 0.08% 52 $33.8 0.11% 57 $39.2 0.10%
Salsa & Dips 86 $5.4 0.08% 139 $16.5 0.05% 121 $21.9 0.06%
Flavored Milk 87 $5.4 0.08% 116 $18.5 0.06% 107 $23.9 0.06%
Ramen Noodles/ 88 $5.3 0.08% 312 $8.1 0.03% 233 $13.5 0.04%
Ramen Cups
Cheese 89 $5.3 0.08% 74 $25.8 0.08% 79 $31.0 0.08%
Crackers
Hamburger Buns 90 $5.3 0.08% 92 $21.5 0.07% 90 $26.8 0.07%
Meat: Turkey 91 $5.3 0.08% 38 $40.9 0.13% 43 $46.1 0.12%
Bulk
Waffles/ 92 $5.2 0.08% 99 $20.7 0.07% 96 $25.9 0.07%
Pancakes/
French Toast
Candy Bars 93 $5.2 0.08% 90 $21.7 0.07% 89 $26.9 0.07%
(Multi Pack)
Candy Bars 94 $5.1 0.08% 140 $16.5 0.05% 127 $21.6 0.06%
(Singles)
(Including)
Bkfst Sausage-- 95 $5.1 0.08% 105 $19.4 0.06% 103 $24.5 0.06%
Fresh Rolls
Angus [Beef] 96 $5.0 0.08% 65 $28.7 0.09% 67 $33.7 0.09%
Hot Dog Buns 97 $5.0 0.08% 119 $18.1 0.06% 112 $23.1 0.06%
Cottage Cheese 98 $5.0 0.08% 55 $33.0 0.10% 60 $38.0 0.10%
String Cheese 99 $4.9 0.07% 68 $28.1 0.09% 71 $33.0 0.09%
Sandwiches--(C 100 $4.9 0.07% 145 $16.0 0.05% 135 $20.9 0 .05%
old)
------------------------- ----------------------------------------------------------
Top 100 $1,084.4 16.48% $3,993.9 12.67% $5,078.3 13.33%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-11: Top 100 Subcommodities for SNAP Households by Expenditure: Smaller Micropolitan Counties
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Soft Drinks 12/ 1 $20.5 0.31% 2 $61.5 0.20% 2 $82.0 0.22%
18 & 15pk Can
Car
Fluid Milk/ 2 $20.2 0.31% 1 $82.6 0.26% 1 $102.9 0.27%
White Only
Lean [Beef] 3 $12.0 0.18% 4 $27.1 0.09% 4 $39.0 0.10%
Primal [Beef] 4 $7.5 0.11% 5 $27.0 0.09% 5 $34.5 0.09%
Shredded 5 $7.2 0.11% 3 $31.9 0.10% 3 $39.1 0.10%
Cheese
Kids Cereal 6 $6.8 0.10% 23 $16.0 0.05% 17 $22.8 0.06%
Sft Drnk 2 7 $6.4 0.10% 15 $19.6 0.06% 12 $26.0 0.07%
Liter Btl
Carb Incl
Soft Drinks 8 $6.3 0.10% 33 $13.7 0.04% 24 $20.0 0.05%
20pk & 24pk
Can Carb
Potato Chips 9 $6.3 0.10% 6 $24.3 0.08% 6 $30.5 0.08%
Mainstream 10 $5.6 0.08% 27 $15.7 0.05% 20 $21.3 0.06%
White Bread
Lunchment--Del 11 $5.4 0.08% 10 $21.7 0.07% 8 $27.2 0.07%
i Fresh
Enhanced [Pork 12 $5.4 0.08% 11 $21.1 0.07% 11 $26.5 0.07%
Boneless Loin/
Rib]
Unflavored Can 13 $5.1 0.08% 9 $21.8 0.07% 10 $26.9 0.07%
Coffee
Infant Formula 14 $5.0 0.08% 190 $4.0 0.01% 78 $9.0 0.02%
Starter/
Solutio
Chicken Breast 15 $4.8 0.07% 7 $23.8 0.08% 7 $28.7 0.08%
Boneless
Snack Cake-- 16 $4.8 0.07% 41 $11.9 0.04% 36 $16.6 0.04%
Multi Pack
Eggs--Large 17 $4.7 0.07% 8 $22.4 0.07% 9 $27.1 0.07%
Still Water 18 $4.7 0.07% 21 $16.4 0.05% 21 $21.0 0.06%
Drnking/Mnrl
Water
Tortilla/Nacho 19 $4.6 0.07% 19 $18.4 0.06% 16 $23.0 0.06%
Chips
American 20 $4.5 0.07% 31 $14.0 0.04% 28 $18.5 0.05%
Single Cheese
Sft Drnk Mlt- 21 $4.5 0.07% 14 $20.1 0.06% 13 $24.5 0.06%
Pk Btl Carb
(Excp)
Snacks/ 22 $4.4 0.07% 65 $8.6 0.03% 47 $13.0 0.03%
Appetizers
Potatoes 23 $4.0 0.06% 20 $16.9 0.05% 22 $20.9 0.05%
Russet (Bulk
& Bag)
Pizza/Premium 24 $4.0 0.06% 35 $13.0 0.04% 34 $17.0 0.04%
Fz Ss Economy 25 $3.9 0.06% 71 $7.8 0.02% 56 $11.7 0.03%
Meals All
Sandwiches & 26 $3.8 0.06% 85 $6.8 0.02% 65 $10.7 0.03%
Handhelds
Bacon--Trad 27 $3.8 0.06% 24 $15.9 0.05% 25 $19.7 0.05%
16oz Or Less
Sugar 28 $3.8 0.06% 39 $12.0 0.04% 37 $15.8 0.04%
Fz Ss Prem 29 $3.7 0.06% 32 $13.7 0.04% 31 $17.4 0.05%
Traditional
Meals
Natural Cheese 30 $3.6 0.06% 12 $20.8 0.07% 14 $24.5 0.06%
Chunks
Ribs [Pork] 31 $3.6 0.06% 45 $11.3 0.04% 41 $14.9 0.04%
Convenient 32 $3.5 0.05% 95 $6.3 0.02% 74 $9.8 0.03%
Meals--Kids
Meal C
All Family 33 $3.5 0.05% 17 $18.8 0.06% 19 $22.3 0.06%
Cereal
Condensed Soup 34 $3.3 0.05% 22 $16.1 0.05% 26 $19.4 0.05%
Dairy Case 35 $3.2 0.05% 13 $20.7 0.07% 15 $23.9 0.06%
100% Pure
Juice--O
Select Beef 36 $3.2 0.05% 34 $13.6 0.04% 35 $16.8 0.04%
Sft Drnk Sngl 37 $3.2 0.05% 86 $6.7 0.02% 73 $9.8 0.03%
Srv Btl Carb
(Ex)
Mainstream 38 $3.1 0.05% 28 $15.7 0.05% 27 $18.8 0.05%
Variety
Breads
Bananas 39 $3.0 0.05% 16 $19.6 0.06% 18 $22.6 0.06%
Isotonic 40 $2.9 0.04% 53 $10.0 0.03% 50 $12.9 0.03%
Drinks Single
Serve
Hot Dogs--Base 41 $2.8 0.04% 78 $7.2 0.02% 68 $10.1 0.03%
Meat
Frzn Chicken-- 42 $2.8 0.04% 50 $10.5 0.03% 46 $13.3 0.04%
Wht Meat
Pourable Salad 43 $2.8 0.04% 37 $12.4 0.04% 38 $15.2 0.04%
Dressings
Mayonnaise & 44 $2.7 0.04% 42 $11.8 0.04% 42 $14.5 0.04%
Whipped
Dressing
Macaroni & 45 $2.7 0.04% 107 $5.8 0.02% 88 $8.4 0.02%
Cheese Dnrs
Can Pasta 46 $2.7 0.04% 129 $5.1 0.02% 94 $7.8 0.02%
Fz Family 47 $2.7 0.04% 77 $7.2 0.02% 71 $9.9 0.03%
Style Entrees
Traditional 48 $2.6 0.04% 38 $12.3 0.04% 40 $14.9 0.04%
[Ice Cream &
Sherbert]
Lunchment--Bol 49 $2.5 0.04% 80 $7.2 0.02% 75 $9.7 0.03%
ogna/Sausage
Margarine: 50 $2.5 0.04% 57 $9.8 0.03% 51 $12.2 0.03%
Tubs And
Bowls
Sw Gds: Donuts 51 $2.4 0.04% 59 $9.4 0.03% 55 $11.8 0.03%
Premium [Ice 52 $2.4 0.04% 25 $15.8 0.05% 29 $18.2 0.05%
Cream &
Sherbert]
Angus [Beef] 53 $2.4 0.04% 40 $12.0 0.04% 43 $14.3 0.04%
Choice Beef 54 $2.3 0.03% 72 $7.6 0.02% 72 $9.9 0.03%
Fz Bag 55 $2.3 0.03% 43 $11.8 0.04% 44 $14.0 0.04%
Vegetables--P
lain
Refrigerated 56 $2.3 0.03% 46 $10.7 0.03% 48 $13.0 0.03%
Coffee
Creamers
Pizza/Economy 57 $2.3 0.03% 124 $5.2 0.02% 97 $7.5 0.02%
Choice Beef 58 $2.3 0.03% 48 $10.6 0.03% 49 $12.9 0.03%
Candy Bags-- 59 $2.3 0.03% 36 $12.9 0.04% 39 $15.1 0.04%
Chocolate
Adult Cereal 60 $2.2 0.03% 30 $15.0 0.05% 33 $17.1 0.05%
Strawberries 61 $2.2 0.03% 29 $15.0 0.05% 32 $17.1 0.05%
Peanut Butter 62 $2.2 0.03% 44 $11.6 0.04% 45 $13.7 0.04%
Mexican Soft 63 $2.1 0.03% 64 $8.9 0.03% 59 $11.0 0.03%
Tortillas And
Wra
Mainstream 64 $2.1 0.03% 81 $7.1 0.02% 77 $9.2 0.02%
[Pasta &
Pizza Sauce]
Fz Ss Prem 65 $2.1 0.03% 18 $18.6 0.06% 23 $20.7 0.05%
Nutritional
Meals
Aseptic Pack 66 $2.0 0.03% 163 $4.5 0.01% 121 $6.5 0.02%
Juice And
Drinks
Frzn French 67 $2.0 0.03% 128 $5.1 0.02% 108 $7.2 0.02%
Fries
Flavored Milk 68 $2.0 0.03% 96 $6.2 0.02% 91 $8.2 0.02%
Pizza/ 69 $2.0 0.03% 89 $6.6 0.02% 86 $8.6 0.02%
Traditional
Tuna 70 $2.0 0.03% 62 $8.9 0.03% 64 $10.9 0.03%
Frzn Breakfast 71 $1.9 0.03% 132 $5.1 0.02% 112 $7.0 0.02%
Sandwiches
Hamburger Buns 72 $1.9 0.03% 68 $8.1 0.03% 69 $10.1 0.03%
Value Forms/ 73 $1.9 0.03% 187 $4.0 0.01% 146 $5.9 0.02%
18oz And
Larger
[Chicken]
Vegetable Oil 74 $1.8 0.03% 214 $3.5 0.01% 168 $5.3 0.01%
Pails [Ice 75 $1.8 0.03% 131 $5.1 0.02% 114 $6.9 0.02%
Cream &
Sherbert]
Butter 76 $1.8 0.03% 26 $15.8 0.05% 30 $17.6 0.05%
Candy Bars 77 $1.7 0.03% 83 $6.9 0.02% 83 $8.7 0.02%
(Multi Pack)
Cakes: 78 $1.7 0.03% 154 $4.7 0.01% 126 $6.4 0.02%
Birthday/
Celebration
Sh
Fruit Snacks 79 $1.7 0.03% 198 $3.9 0.01% 159 $5.6 0.01%
Cottage Cheese 80 $1.7 0.03% 52 $10.2 0.03% 54 $11.9 0.03%
Sandwich 81 $1.7 0.03% 91 $6.5 0.02% 90 $8.2 0.02%
Cookies
Salsa & Dips 82 $1.7 0.03% 133 $5.0 0.02% 116 $6.7 0.02%
Frzn Meat-- 83 $1.7 0.03% 174 $4.3 0.01% 144 $6.0 0.02%
Beef
Mult Pk Bag 84 $1.7 0.03% 230 $3.2 0.01% 186 $4.9 0.01%
Snacks
Bkfst Sausage-- 85 $1.7 0.03% 76 $7.3 0.02% 80 $8.9 0.02%
Fresh Rolls
Refrigerated 86 $1.6 0.03% 116 $5.4 0.02% 111 $7.0 0.02%
Biscuits
Sour Creams 87 $1.6 0.02% 66 $8.3 0.03% 70 $10.0 0.03%
Rts Soup: 88 $1.6 0.02% 60 $9.4 0.03% 61 $11.0 0.03%
Chunky/
Homestyle/Et
Bagged Cheese 89 $1.6 0.02% 143 $4.8 0.02% 129 $6.4 0.02%
Snacks
Cream Cheese 90 $1.6 0.02% 54 $10.0 0.03% 57 $11.6 0.03%
Skillet 91 $1.6 0.02% 245 $3.1 0.01% 198 $4.7 0.01%
Dinners
Cheese 92 $1.6 0.02% 84 $6.8 0.02% 89 $8.4 0.02%
Crackers
Chicken Wings 93 $1.5 0.02% 374 $2.0 0.01% 258 $3.5 0.01%
Angus [Beef] 94 $1.5 0.02% 148 $4.8 0.02% 133 $6.3 0.02%
String Cheese 95 $1.5 0.02% 75 $7.3 0.02% 81 $8.9 0.02%
Fz Skillet 96 $1.5 0.02% 99 $6.0 0.02% 98 $7.5 0.02%
Meals
Hot Dog Buns 97 $1.5 0.02% 110 $5.7 0.02% 104 $7.2 0.02%
Sweet Goods-- 98 $1.5 0.02% 135 $5.0 0.02% 123 $6.5 0.02%
Full Size
Candy Bars 99 $1.5 0.02% 153 $4.7 0.01% 135 $6.2 0.02%
(Singles)
(Including)
Toaster 100 $1.5 0.02% 155 $4.7 0.01% 136 $6.2 0.02%
Pastries
------------------------- ----------------------------------------------------------
Top 100 $339.6 $5.16% $1,243.8 $3.95% $1,583.4 $4.16%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 $100% $31,513.8 $100% $38,094.2 $100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-12: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in Noncore Counties
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Soft Drinks 12/ 1 $6.7 0.10% 2 $16.3 0.05% 2 $23.1 0.06%
18 & 15pk Can
Car
Fluid Milk/ 2 $6.4 0.10% 1 $23.0 0.07% 1 $29.4 0.08%
White Only
Lean [Beef] 3 $3.2 0.05% 4 $7.6 0.02% 3 $10.8 0.03%
Primal [Beef] 4 $2.1 0.03% 5 $6.8 0.02% 5 $8.9 0.02%
Shredded 5 $2.0 0.03% 3 $8.4 0.03% 4 $10.3 0.03%
Cheese
Soft Drinks 6 $2.0 0.03% 34 $3.5 0.01% 24 $5.5 0.01%
20pk & 24pk
Can Carb
Mainstream 7 $1.9 0.03% 20 $4.8 0.02% 14 $6.7 0.02%
White Bread
Potato Chips 8 $1.9 0.03% 6 $6.7 0.02% 6 $8.6 0.02%
Kids Cereal 9 $1.8 0.03% 27 $4.0 0.01% 22 $5.8 0.02%
Sft Drnk 2 10 $1.7 0.03% 21 $4.7 0.01% 16 $6.4 0.02%
Liter Btl
Carb Incl
Unflavored Can 11 $1.7 0.03% 9 $6.1 0.02% 8 $7.8 0.02%
Coffee
Sft Drnk Mlt- 12 $1.6 0.02% 11 $5.8 0.02% 10 $7.5 0.02%
Pk Btl Carb
(Excp)
Lunchment--Del 13 $1.6 0.02% 12 $5.8 0.02% 11 $7.4 0.02%
i Fresh
Snack Cake-- 14 $1.6 0.02% 36 $3.5 0.01% 27 $5.0 0.01%
Multi Pack
Enhanced [Pork 15 $1.5 0.02% 14 $5.5 0.02% 13 $7.1 0.02%
Boneless Loin/
Rib]
Eggs--Large 16 $1.4 0.02% 7 $6.6 0.02% 7 $8.0 0.02%
Infant Formula 17 $1.4 0.02% 186 $1.1 0.00% 81 $2.5 0.01%
Starter/
Solutio
American 18 $1.4 0.02% 29 $4.0 0.01% 25 $5.4 0.01%
Single Cheese
Chicken Breast 19 $1.3 0.02% 10 $6.1 0.02% 12 $7.4 0.02%
Boneless
Tortilla/Nacho 20 $1.3 0.02% 16 $5.1 0.02% 18 $6.4 0.02%
Chips
Potatoes 21 $1.3 0.02% 17 $5.0 0.02% 19 $6.2 0.02%
Russet (Bulk
& Bag)
Still Water 22 $1.3 0.02% 23 $4.5 0.01% 23 $5.7 0.02%
Drnking/Mnrl
Water
Snacks/ 23 $1.2 0.02% 67 $2.3 0.01% 49 $3.5 0.01%
Appetizers
Pizza/Premium 24 $1.2 0.02% 32 $3.7 0.01% 30 $4.9 0.01%
Bacon--Trad 25 $1.2 0.02% 19 $4.8 0.02% 20 $6.0 0.02%
16oz Or Less
Natural Cheese 26 $1.1 0.02% 8 $6.5 0.02% 9 $7.7 0.02%
Chunks
Sugar 27 $1.1 0.02% 35 $3.5 0.01% 34 $4.6 0.01%
Sandwiches & 28 $1.0 0.02% 96 $1.7 0.01% 71 $2.8 0.01%
Handhelds
All Family 29 $1.0 0.02% 18 $4.9 0.02% 21 $5.9 0.02%
Cereal
Fz Ss Economy 30 $1.0 0.02% 80 $2.0 0.01% 65 $3.0 0.01%
Meals All
Fz Ss Prem 31 $1.0 0.01% 38 $3.4 0.01% 36 $4.4 0.01%
Traditional
Meals
Convenient 32 $1.0 0.01% 111 $1.6 0.00% 79 $2.5 0.01%
Meals--Kids
Meal C
Sft Drnk Sngl 33 $0.9 0.01% 77 $2.0 0.01% 67 $2.9 0.01%
Srv Btl Carb
(Ex)
Condensed Soup 34 $0.9 0.01% 28 $4.0 0.01% 29 $5.0 0.01%
Bananas 35 $0.9 0.01% 15 $5.5 0.02% 17 $6.4 0.02%
Dairy Case 36 $0.9 0.01% 13 $5.7 0.02% 15 $6.6 0.02%
100% Pure
Juice--O
Mainstream 37 $0.9 0.01% 24 $4.2 0.01% 26 $5.1 0.01%
Variety
Breads
Choice Beef 38 $0.9 0.01% 59 $2.7 0.01% 48 $3.5 0.01%
Hot Dogs--Base 39 $0.8 0.01% 74 $2.1 0.01% 66 $2.9 0.01%
Meat
Ribs [Pork] 40 $0.8 0.01% 48 $2.9 0.01% 43 $3.7 0.01%
Lunchment--Bol 41 $0.8 0.01% 71 $2.2 0.01% 62 $3.0 0.01%
ogna/Sausage
Mayonnaise & 42 $0.8 0.01% 41 $3.3 0.01% 40 $4.1 0.01%
Whipped
Dressing
Sw Gds: Donuts 43 $0.8 0.01% 49 $2.9 0.01% 45 $3.7 0.01%
Traditional 44 $0.8 0.01% 39 $3.4 0.01% 38 $4.2 0.01%
[Ice Cream &
Sherbert]
Pourable Salad 45 $0.8 0.01% 40 $3.4 0.01% 39 $4.1 0.01%
Dressings
Frzn Chicken-- 46 $0.7 0.01% 60 $2.5 0.01% 56 $3.3 0.01%
Wht Meat
Margarine: 47 $0.7 0.01% 58 $2.7 0.01% 51 $3.4 0.01%
Tubs And
Bowls
Can Pasta 48 $0.7 0.01% 159 $1.3 0.00% 108 $2.0 0.01%
Candy Bags-- 49 $0.7 0.01% 33 $3.6 0.01% 37 $4.3 0.01%
Chocolate
Macaroni & 50 $0.7 0.01% 121 $1.5 0.00% 93 $2.2 0.01%
Cheese Dnrs
Isotonic 51 $0.7 0.01% 66 $2.3 0.01% 64 $3.0 0.01%
Drinks Single
Serve
Fz Family 52 $0.7 0.01% 89 $1.8 0.01% 77 $2.5 0.01%
Style Entrees
Peanut Butter 53 $0.7 0.01% 44 $3.1 0.01% 42 $3.8 0.01%
Strawberries 54 $0.7 0.01% 25 $4.2 0.01% 31 $4.8 0.01%
Adult Cereal 55 $0.6 0.01% 31 $4.0 0.01% 33 $4.6 0.01%
Hamburger Buns 56 $0.6 0.01% 64 $2.4 0.01% 63 $3.0 0.01%
Pizza/ 57 $0.6 0.01% 79 $2.0 0.01% 76 $2.6 0.01%
Traditional
Choice Beef 58 $0.6 0.01% 42 $3.2 0.01% 41 $3.9 0.01%
Premium [Ice 59 $0.6 0.01% 26 $4.1 0.01% 32 $4.7 0.01%
Cream &
Sherbert]
Flavored Milk 60 $0.6 0.01% 107 $1.6 0.01% 91 $2.2 0.01%
Refrigerated 61 $0.6 0.01% 56 $2.8 0.01% 53 $3.4 0.01%
Coffee
Creamers
Angus [Beef] 62 $0.6 0.01% 57 $2.7 0.01% 54 $3.3 0.01%
Pails [Ice 63 $0.6 0.01% 110 $1.6 0.00% 95 $2.2 0.01%
Cream &
Sherbert]
Mexican Soft 64 $0.6 0.01% 52 $2.8 0.01% 52 $3.4 0.01%
Tortillas And
Wra
Pizza/Economy 65 $0.6 0.01% 162 $1.3 0.00% 117 $1.9 0.00%
Cottage Cheese 66 $0.6 0.01% 45 $3.1 0.01% 46 $3.6 0.01%
Mainstream 67 $0.6 0.01% 84 $1.9 0.01% 83 $2.4 0.01%
[Pasta &
Pizza Sauce]
Frzn French 68 $0.6 0.01% 123 $1.5 0.00% 107 $2.0 0.01%
Fries
Fz Bag 69 $0.5 0.01% 46 $3.0 0.01% 47 $3.5 0.01%
Vegetables--P
lain
Candy Bars 70 $0.5 0.01% 78 $2.0 0.01% 78 $2.5 0.01%
(Multi Pack)
Cakes: 71 $0.5 0.01% 149 $1.3 0.00% 116 $1.9 0.00%
Birthday/
Celebration
Sh
Aseptic Pack 72 $0.5 0.01% 183 $1.1 0.00% 146 $1.6 0.00%
Juice And
Drinks
Refrigerated 73 $0.5 0.01% 104 $1.6 0.01% 99 $2.1 0.01%
Biscuits
Salsa & Dips 74 $0.5 0.01% 130 $1.4 0.00% 111 $1.9 0.01%
Value Forms/ 75 $0.5 0.01% 192 $1.1 0.00% 158 $1.6 0.00%
18oz And
Larger
[Chicken]
Fz Ss Prem 76 $0.5 0.01% 30 $4.0 0.01% 35 $4.5 0.01%
Nutritional
Meals
Tuna 77 $0.5 0.01% 70 $2.2 0.01% 72 $2.8 0.01%
Sandwich 78 $0.5 0.01% 83 $1.9 0.01% 85 $2.4 0.01%
Cookies
Bkfst Sausage-- 79 $0.5 0.01% 73 $2.1 0.01% 75 $2.6 0.01%
Fresh Rolls
Butter 80 $0.5 0.01% 22 $4.5 0.01% 28 $5.0 0.01%
Frzn Breakfast 81 $0.5 0.01% 172 $1.2 0.00% 139 $1.7 0.00%
Sandwiches
Vegetable Oil 82 $0.5 0.01% 203 $1.0 0.00% 166 $1.5 0.00%
Sweet Goods-- 83 $0.5 0.01% 129 $1.4 0.00% 114 $1.9 0.00%
Full Size
Hot Dog Buns 84 $0.5 0.01% 98 $1.7 0.01% 94 $2.2 0.01%
Candy Bars 85 $0.5 0.01% 119 $1.5 0.00% 110 $2.0 0.01%
(Singles)
(Including)
Bagged Cheese 86 $0.5 0.01% 147 $1.3 0.00% 127 $1.8 0.00%
Snacks
Sandwiches--(C 87 $0.5 0.01% 102 $1.6 0.01% 103 $2.1 0.01%
old)
Cream Cheese 88 $0.5 0.01% 54 $2.8 0.01% 57 $3.3 0.01%
Sour Creams 89 $0.5 0.01% 69 $2.3 0.01% 73 $2.7 0.01%
Select Beef 90 $0.5 0.01% 75 $2.0 0.01% 80 $2.5 0.01%
Frzn Meat-- 91 $0.5 0.01% 166 $1.2 0.00% 136 $1.7 0.00%
Beef
Sticks/Enrobed 92 $0.5 0.01% 124 $1.5 0.00% 113 $1.9 0.01%
[Frozen
Novelties]
String Cheese 93 $0.4 0.01% 76 $2.0 0.01% 82 $2.5 0.01%
Fruit Snacks 94 $0.4 0.01% 222 $0.9 0.00% 185 $1.4 0.00%
Rts Soup: 95 $0.4 0.01% 63 $2.4 0.01% 68 $2.8 0.01%
Chunky/
Homestyle/Et
Angus [Beef] 96 $0.4 0.01% 177 $1.1 0.00% 156 $1.6 0.00%
Cheese 97 $0.4 0.01% 93 $1.8 0.01% 92 $2.2 0.01%
Crackers
Meat: Ham Bulk 98 $0.4 0.01% 62 $2.4 0.01% 69 $2.8 0.01%
Meat: Turkey 99 $0.4 0.01% 51 $2.8 0.01% 58 $3.3 0.01%
Bulk
Tray Pack/Choc 100 $0.4 0.01% 133 $1.4 0.00% 119 $1.8 0.00%
Chip Cookies
------------------------- ----------------------------------------------------------
Top 100 $99.1 1.57% $341.8 1.08% $440.9 1.23%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-13: Top 100 Subcommodities for SNAP Households by Expenditure: Stores with more than $12 Million in
Sales
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $38.9 0.59% 1 $229.9 0.73% 1 $268.8 0.71%
White Only
Soft Drinks 12/ 2 $32.4 0.49% 2 $162.4 0.52% 2 $194.8 0.51%
18 & 15pk Can
Car
Lean [Beef] 3 $22.2 0.34% 8 $74.1 0.24% 5 $96.4 0.25%
Shredded 4 $16.2 0.25% 3 $103.2 0.33% 3 $119.4 0.31%
Cheese
Kids Cereal 5 $15.5 0.23% 23 $52.1 0.17% 17 $67.5 0.18%
Sft Drnk 2 6 $13.3 0.20% 18 $56.1 0.18% 16 $69.4 0.18%
Liter Btl
Carb Incl
Potato Chips 7 $13.0 0.20% 10 $70.8 0.22% 9 $83.8 0.22%
Lunchment--Del 8 $11.6 0.18% 13 $69.9 0.22% 11 $81.5 0.21%
i Fresh
Chicken Breast 9 $11.4 0.17% 4 $89.3 0.28% 4 $100.7 0.26%
Boneless
Infant Formula 10 $11.1 0.17% 259 $10.4 0.03% 119 $21.5 0.06%
Starter/
Solutio
Eggs--Large 11 $10.8 0.16% 9 $73.1 0.23% 8 $83.9 0.22%
Primal [Beef] 12 $10.8 0.16% 24 $49.1 0.16% 23 $59.9 0.16%
Snacks/ 13 $10.4 0.16% 63 $31.6 0.10% 47 $42.1 0.11%
Appetizers
Tortilla/Nacho 14 $9.9 0.15% 15 $62.4 0.20% 15 $72.3 0.19%
Chips
Dairy Case 15 $9.4 0.14% 6 $80.1 0.25% 6 $89.5 0.23%
100% Pure
Juice--O
Fz Ss Prem 16 $9.1 0.14% 26 $47.5 0.15% 25 $56.6 0.15%
Traditional
Meals
Unflavored Can 17 $9.1 0.14% 21 $54.4 0.17% 19 $63.4 0.17%
Coffee
Natural Cheese 18 $9.0 0.14% 12 $70.0 0.22% 12 $79.1 0.21%
Chunks
Still Water 19 $8.8 0.13% 30 $46.5 0.15% 28 $55.3 0.15%
Drnking/Mnrl
Water
Mainstream 20 $8.6 0.13% 56 $33.6 0.11% 46 $42.3 0.11%
White Bread
Enhanced [Pork 21 $8.6 0.13% 28 $47.3 0.15% 26 $55.9 0.15%
Boneless Loin/
Rib]
Bacon--Trad 22 $8.4 0.13% 34 $44.1 0.14% 29 $52.6 0.14%
16oz Or Less
All Family 23 $8.4 0.13% 14 $66.3 0.21% 14 $74.7 0.20%
Cereal
Pizza/Premium 24 $8.4 0.13% 29 $47.0 0.15% 27 $55.4 0.15%
American 25 $8.3 0.13% 51 $35.4 0.11% 44 $43.7 0.11%
Single Cheese
Fz Ss Economy 26 $8.1 0.12% 105 $21.1 0.07% 81 $29.2 0.08%
Meals All
Soft Drinks 27 $7.9 0.12% 67 $30.2 0.10% 58 $38.1 0.10%
20pk & 24pk
Can Carb
Bananas 28 $7.8 0.12% 7 $74.4 0.24% 10 $82.2 0.22%
Snack Cake-- 29 $7.4 0.11% 81 $25.3 0.08% 73 $32.7 0.09%
Multi Pack
Premium [Ice 30 $7.4 0.11% 11 $70.2 0.22% 13 $77.6 0.20%
Cream &
Sherbert]
Mainstream 31 $7.3 0.11% 32 $44.6 0.14% 32 $51.8 0.14%
Variety
Breads
Select Beef 32 $7.2 0.11% 37 $41.6 0.13% 36 $48.8 0.13%
Sandwiches & 33 $7.2 0.11% 107 $20.6 0.07% 89 $27.8 0.07%
Handhelds
Frzn Chicken-- 34 $7.2 0.11% 65 $31.2 0.10% 57 $38.4 0.10%
Wht Meat
Potatoes 35 $7.2 0.11% 35 $42.4 0.13% 35 $49.6 0.13%
Russet (Bulk
& Bag)
Ribs [Pork] 36 $6.8 0.10% 69 $29.4 0.09% 65 $36.2 0.10%
Sugar 37 $6.8 0.10% 64 $31.3 0.10% 59 $38.1 0.10%
Choice Beef 38 $6.7 0.10% 40 $41.1 0.13% 38 $47.8 0.13%
Convenient 39 $6.7 0.10% 114 $19.5 0.06% 98 $26.2 0.07%
Meals--Kids
Meal C
Condensed Soup 40 $6.5 0.10% 33 $44.1 0.14% 34 $50.6 0.13%
Refrigerated 41 $6.4 0.10% 31 $46.0 0.15% 31 $52.3 0.14%
Coffee
Creamers
Isotonic 42 $6.2 0.09% 66 $30.9 0.10% 62 $37.1 0.10%
Drinks Single
Serve
Fz Family 43 $6.1 0.09% 85 $24.7 0.08% 77 $30.8 0.08%
Style Entrees
Pourable Salad 44 $6.0 0.09% 38 $41.5 0.13% 39 $47.6 0.12%
Dressings
Sft Drnk Mlt- 45 $5.9 0.09% 36 $42.2 0.13% 37 $48.1 0.13%
Pk Btl Carb
(Excp)
Fz Ss Prem 46 $5.9 0.09% 5 $82.0 0.26% 7 $87.9 0.23%
Nutritional
Meals
Sft Drnk Sngl 47 $5.8 0.09% 103 $21.3 0.07% 93 $27.1 0.07%
Srv Btl Carb
(Ex)
Mayonnaise & 48 $5.7 0.09% 54 $34.5 0.11% 54 $40.2 0.11%
Whipped
Dressing
Choice Beef 49 $5.7 0.09% 97 $22.6 0.07% 85 $28.3 0.07%
Adult Cereal 50 $5.6 0.08% 20 $55.1 0.17% 22 $60.7 0.16%
Strawberries 51 $5.4 0.08% 19 $55.9 0.18% 21 $61.3 0.16%
Meat: Turkey 52 $5.4 0.08% 17 $57.3 0.18% 20 $62.7 0.16%
Bulk
Mexican Soft 53 $5.4 0.08% 53 $35.2 0.11% 53 $40.6 0.11%
Tortillas And
Wra
Butter 54 $5.4 0.08% 16 $58.3 0.19% 18 $63.7 0.17%
Fz Bag 55 $5.2 0.08% 49 $36.6 0.12% 48 $41.8 0.11%
Vegetables--P
lain
Candy Bags-- 56 $5.0 0.08% 27 $47.4 0.15% 30 $52.4 0.14%
Chocolate
Traditional 57 $5.0 0.08% 68 $29.4 0.09% 69 $34.4 0.09%
[Ice Cream &
Sherbert]
Margarine: 58 $5.0 0.08% 71 $29.2 0.09% 70 $34.2 0.09%
Tubs And
Bowls
Macaroni & 59 $4.9 0.07% 139 $17.4 0.06% 113 $22.3 0.06%
Cheese Dnrs
Peanut Butter 60 $4.8 0.07% 44 $39.1 0.12% 43 $43.9 0.12%
Aseptic Pack 61 $4.7 0.07% 168 $15.3 0.05% 136 $20.0 0.05%
Juice And
Drinks
Tuna 62 $4.7 0.07% 60 $33.0 0.10% 61 $37.6 0.10%
Mainstream 63 $4.6 0.07% 96 $22.9 0.07% 91 $27.5 0.07%
[Pasta &
Pizza Sauce]
Hot Dogs--Base 64 $4.6 0.07% 188 $13.8 0.04% 159 $18.3 0.05%
Meat
Cream Cheese 65 $4.5 0.07% 48 $37.3 0.12% 49 $41.7 0.11%
Sw Gds: Donuts 66 $4.4 0.07% 92 $23.3 0.07% 90 $27.7 0.07%
Sushi--In 67 $4.3 0.07% 42 $40.4 0.13% 40 $44.7 0.12%
Store
Prepared
Premium Bread 68 $4.3 0.06% 22 $53.9 0.17% 24 $58.1 0.15%
Can Pasta 69 $4.3 0.06% 216 $12.4 0.04% 179 $16.7 0.04%
Frzn Meat-- 70 $4.2 0.06% 182 $14.1 0.04% 160 $18.3 0.05%
Beef
Fz Skillet 71 $4.2 0.06% 87 $24.4 0.08% 84 $28.6 0.08%
Meals
Meat: Ham Bulk 72 $4.1 0.06% 43 $40.2 0.13% 41 $44.3 0.12%
Angus [Beef] 73 $4.1 0.06% 62 $31.9 0.10% 66 $35.9 0.09%
Cakes: 74 $4.0 0.06% 170 $15.1 0.05% 151 $19.1 0.05%
Birthday/
Celebration
Sh
Sour Creams 75 $4.0 0.06% 72 $29.2 0.09% 71 $33.2 0.09%
Cheese 76 $4.0 0.06% 73 $29.0 0.09% 72 $33.0 0.09%
Crackers
Value Forms/ 77 $4.0 0.06% 218 $12.3 0.04% 188 $16.3 0.04%
18oz And
Larger
[Chicken]
Frzn French 78 $4.0 0.06% 187 $13.8 0.04% 165 $17.8 0.05%
Fries
Rts Soup: 79 $3.9 0.06% 52 $35.2 0.11% 56 $39.2 0.10%
Chunky/
Homestyle/Et
String Cheese 80 $3.9 0.06% 58 $33.2 0.11% 63 $37.1 0.10%
Sandwiches--(C 81 $3.9 0.06% 98 $22.2 0.07% 99 $26.1 0.07%
old)
Instore Cut 82 $3.9 0.06% 55 $33.8 0.11% 60 $37.6 0.10%
Fruit
Lunchment--Bol 83 $3.9 0.06% 175 $14.6 0.05% 156 $18.5 0.05%
ogna/Sausage
Frzn Chicken-- 84 $3.8 0.06% 585 $3.9 0.01% 395 $7.7 0.02%
Wings
Frzn Breakfast 85 $3.8 0.06% 161 $15.8 0.05% 142 $19.6 0.05%
Sandwiches
Waffles/ 86 $3.8 0.06% 91 $23.3 0.07% 92 $27.1 0.07%
Pancakes/
French Toast
Pizza/Economy 87 $3.8 0.06% 226 $11.9 0.04% 200 $15.6 0.04%
Spring Water 88 $3.7 0.06% 77 $27.7 0.09% 75 $31.4 0.08%
Mult Pk Bag 89 $3.7 0.06% 222 $12.0 0.04% 198 $15.7 0.04%
Snacks
Grapes Red 90 $3.6 0.05% 46 $37.7 0.12% 51 $41.3 0.11%
Sandwich 91 $3.6 0.05% 110 $20.3 0.06% 107 $23.9 0.06%
Cookies
Candy Bars 92 $3.6 0.05% 144 $17.1 0.05% 131 $20.6 0.05%
(Singles)
(Including)
Fruit Snacks 93 $3.5 0.05% 209 $12.6 0.04% 189 $16.2 0.04%
Pizza/ 94 $3.5 0.05% 134 $17.9 0.06% 120 $21.4 0.06%
Traditional
Flavored Milk 95 $3.5 0.05% 148 $16.8 0.05% 133 $20.3 0.05%
Sweet Goods-- 96 $3.5 0.05% 162 $15.7 0.05% 150 $19.2 0.05%
Full Size
Vegetable Oil 97 $3.4 0.05% 306 $8.8 0.03% 248 $12.2 0.03%
Natural Cheese 98 $3.4 0.05% 50 $36.0 0.11% 55 $39.4 0.10%
Slices
Salsa & Dips 99 $3.4 0.05% 152 $16.5 0.05% 139 $19.9 0.05%
Avocado 100 $3.4 0.05% 47 $37.5 0.12% 52 $40.9 0.11%
------------------------- ----------------------------------------------------------
Top 100 $699.9 10.64% $4,012.7 12.73% $4,712.5 12.37%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-14: Top 100 Subcommodities for SNAP Households by Expenditure: Stores with $2 to $12 Million in Sales
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $151.9 2.31% 1 $622.5 1.98% 1 $774.4 2.03%
White Only
Soft Drinks 12/ 2 $131.9 2.00% 2 $437.9 1.39% 2 $569.9 1.50%
18 & 15pk Can
Car
Lean [Beef] 3 $90.0 1.37% 7 $183.4 0.58% 4 $273.4 0.72%
Kids Cereal 4 $62.6 0.95% 20 $134.2 0.43% 13 $196.7 0.52%
Shredded 5 $58.4 0.89% 3 $238.3 0.76% 3 $296.8 0.78%
Cheese
Sft Drnk 2 6 $57.5 0.87% 10 $173.7 0.55% 7 $231.2 0.61%
Liter Btl
Carb Incl
Primal [Beef] 7 $51.5 0.78% 12 $169.9 0.54% 9 $221.4 0.58%
Potato Chips 8 $51.3 0.78% 8 $182.1 0.58% 6 $233.4 0.61%
Lunchment--Del 9 $44.1 0.67% 11 $172.4 0.55% 11 $216.5 0.57%
i Fresh
Infant Formula 10 $43.0 0.65% 169 $34.9 0.11% 71 $77.9 0.20%
Starter/
Solutio
Eggs--Large 11 $41.3 0.63% 9 $178.2 0.57% 10 $219.5 0.58%
Still Water 12 $39.9 0.61% 19 $141.1 0.45% 16 $180.9 0.48%
Drnking/Mnrl
Water
Mainstream 13 $39.2 0.60% 32 $102.9 0.33% 27 $142.1 0.37%
White Bread
Chicken Breast 14 $38.1 0.58% 4 $203.4 0.65% 5 $241.5 0.63%
Boneless
Tortilla/Nacho 15 $37.4 0.57% 16 $146.3 0.46% 15 $183.7 0.48%
Chips
American 16 $35.7 0.54% 36 $101.0 0.32% 31 $136.7 0.36%
Single Cheese
Fz Ss Prem 17 $34.7 0.53% 23 $127.8 0.41% 21 $162.5 0.43%
Traditional
Meals
Snack Cake-- 18 $34.1 0.52% 57 $76.2 0.24% 43 $110.4 0.29%
Multi Pack
Dairy Case 19 $34.1 0.52% 6 $188.7 0.60% 8 $222.9 0.58%
100% Pure
Juice--O
Snacks/ 20 $34.1 0.52% 66 $68.7 0.22% 50 $102.8 0.27%
Appetizers
Enhanced [Pork 21 $32.9 0.50% 26 $120.4 0.38% 24 $153.2 0.40%
Boneless Loin/
Rib]
Fz Ss Economy 22 $32.8 0.50% 76 $59.5 0.19% 58 $92.3 0.24%
Meals All
Bacon--Trad 23 $32.2 0.49% 28 $113.2 0.36% 26 $145.4 0.38%
16oz Or Less
Unflavored Can 24 $32.2 0.49% 18 $143.4 0.46% 19 $175.6 0.46%
Coffee
Soft Drinks 25 $31.7 0.48% 58 $76.0 0.24% 46 $107.7 0.28%
20pk & 24pk
Can Carb
Pizza/Premium 26 $31.2 0.47% 31 $106.2 0.34% 30 $137.4 0.36%
Mainstream 27 $31.1 0.47% 22 $128.4 0.41% 22 $159.5 0.42%
Variety
Breads
Sugar 28 $30.1 0.46% 51 $81.2 0.26% 42 $111.3 0.29%
Sandwiches & 29 $28.6 0.43% 88 $52.9 0.17% 67 $81.5 0.21%
Handhelds
Potatoes 30 $28.5 0.43% 29 $111.8 0.35% 28 $140.3 0.37%
Russet (Bulk
& Bag)
Ribs [Pork] 31 $28.2 0.43% 54 $77.3 0.25% 48 $105.4 0.28%
Sft Drnk Mlt- 32 $28.0 0.43% 21 $131.2 0.42% 23 $159.2 0.42%
Pk Btl Carb
(Excp)
All Family 33 $27.7 0.42% 15 $148.4 0.47% 18 $176.1 0.46%
Cereal
Convenient 34 $27.5 0.42% 95 $50.1 0.16% 72 $77.6 0.20%
Meals--Kids
Meal C
Bananas 35 $26.3 0.40% 13 $168.0 0.53% 14 $194.4 0.51%
Natural Cheese 36 $26.2 0.40% 17 $145.8 0.46% 20 $172.0 0.45%
Chunks
Isotonic 37 $24.2 0.37% 45 $88.5 0.28% 41 $112.7 0.30%
Drinks Single
Serve
Premium [Ice 38 $23.9 0.36% 14 $155.6 0.49% 17 $179.5 0.47%
Cream &
Sherbert]
Condensed Soup 39 $23.2 0.35% 30 $109.2 0.35% 32 $132.4 0.35%
Pourable Salad 40 $22.9 0.35% 39 $97.8 0.31% 35 $120.7 0.32%
Dressings
Frzn Chicken-- 41 $22.8 0.35% 67 $68.4 0.22% 59 $91.2 0.24%
Wht Meat
Sft Drnk Sngl 42 $22.0 0.33% 96 $49.9 0.16% 81 $71.9 0.19%
Srv Btl Carb
(Ex)
Choice Beef 43 $21.7 0.33% 40 $95.4 0.30% 37 $117.1 0.31%
Fz Family 44 $21.5 0.33% 79 $58.8 0.19% 69 $80.3 0.21%
Style Entrees
Mayonnaise & 45 $21.5 0.33% 48 $84.4 0.27% 47 $105.9 0.28%
Whipped
Dressing
Select Beef 46 $20.6 0.31% 34 $102.0 0.32% 34 $122.6 0.32%
Traditional 47 $20.6 0.31% 43 $89.1 0.28% 44 $109.7 0.29%
[Ice Cream &
Sherbert]
Fz Bag 48 $20.5 0.31% 41 $95.2 0.30% 40 $115.7 0.30%
Vegetables--P
lain
Hot Dogs--Base 49 $20.5 0.31% 121 $42.9 0.14% 93 $63.3 0.17%
Meat
Aseptic Pack 50 $19.5 0.30% 131 $41.7 0.13% 99 $61.3 0.16%
Juice And
Drinks
Macaroni & 51 $19.4 0.29% 127 $42.2 0.13% 97 $61.6 0.16%
Cheese Dnrs
Adult Cereal 52 $19.3 0.29% 24 $127.3 0.40% 25 $146.7 0.38%
Chicken Wings 53 $18.9 0.29% 274 $22.1 0.07% 176 $41.0 0.11%
Fz Ss Prem 54 $18.8 0.29% 5 $189.5 0.60% 12 $208.2 0.55%
Nutritional
Meals
Margarine: 55 $18.4 0.28% 64 $71.5 0.23% 61 $89.9 0.24%
Tubs And
Bowls
Frzn Chicken-- 56 $18.3 0.28% 425 $13.4 0.04% 240 $31.8 0.08%
Wings
Mainstream 57 $18.3 0.28% 80 $58.0 0.18% 76 $76.3 0.20%
[Pasta &
Pizza Sauce]
Choice Beef 58 $18.3 0.28% 97 $49.7 0.16% 86 $68.0 0.18%
Mexican Soft 59 $18.3 0.28% 53 $77.8 0.25% 53 $96.1 0.25%
Tortillas And
Wra
Strawberries 60 $18.0 0.27% 25 $122.4 0.39% 29 $140.3 0.37%
Mult Pk Bag 61 $17.9 0.27% 194 $31.3 0.10% 143 $49.3 0.13%
Snacks
Can Pasta 62 $17.9 0.27% 165 $35.2 0.11% 120 $53.1 0.14%
Lunchment--Bol 63 $17.9 0.27% 105 $46.2 0.15% 91 $64.1 0.17%
ogna/Sausage
Refrigerated 64 $17.7 0.27% 35 $101.2 0.32% 36 $118.9 0.31%
Coffee
Creamers
Vegetable Oil 65 $17.1 0.26% 237 $26.5 0.08% 167 $43.6 0.11%
Sw Gds: Donuts 66 $16.9 0.26% 78 $58.9 0.19% 78 $75.8 0.20%
Frzn French 67 $16.5 0.25% 157 $36.4 0.12% 121 $52.9 0.14%
Fries
Tuna 68 $16.5 0.25% 56 $76.8 0.24% 56 $93.3 0.24%
Candy Bags-- 69 $16.4 0.25% 37 $100.0 0.32% 38 $116.5 0.31%
Chocolate
Pizza/Economy 70 $16.0 0.24% 180 $33.1 0.11% 144 $49.2 0.13%
Peanut Butter 71 $15.6 0.24% 44 $88.6 0.28% 49 $104.2 0.27%
Frzn Breakfast 72 $15.3 0.23% 139 $39.9 0.13% 112 $55.2 0.14%
Sandwiches
Frzn Meat-- 73 $14.7 0.22% 190 $32.1 0.10% 154 $46.8 0.12%
Beef
Value Forms/ 74 $14.7 0.22% 201 $30.2 0.10% 160 $44.9 0.12%
18oz And
Larger
[Chicken]
Cakes: 75 $14.6 0.22% 167 $35.1 0.11% 139 $49.8 0.13%
Birthday/
Celebration
Sh
Fz Skillet 76 $14.5 0.22% 82 $54.9 0.17% 85 $69.4 0.18%
Meals
Sandwich 77 $14.4 0.22% 92 $51.4 0.16% 88 $65.8 0.17%
Cookies
Chicken Drums 78 $14.3 0.22% 251 $23.7 0.08% 197 $38.1 0.10%
Pizza/ 79 $14.3 0.22% 106 $46.1 0.15% 101 $60.4 0.16%
Traditional
Butter 80 $14.2 0.22% 27 $117.1 0.37% 33 $131.3 0.34%
Fruit Snacks 81 $14.1 0.21% 200 $30.5 0.10% 163 $44.6 0.12%
Meat: Turkey 82 $13.9 0.21% 33 $102.3 0.32% 39 $116.1 0.30%
Bulk
Bagged Cheese 83 $13.8 0.21% 146 $38.3 0.12% 125 $52.1 0.14%
Snacks
Salsa & Dips 84 $13.7 0.21% 136 $40.4 0.13% 118 $54.0 0.14%
Ramen Noodles/ 85 $13.7 0.21% 293 $20.5 0.07% 225 $34.2 0.09%
Ramen Cups
Rts Soup: 86 $13.7 0.21% 47 $84.6 0.27% 52 $98.2 0.26%
Chunky/
Homestyle/Et
Waffles/ 87 $13.5 0.21% 85 $54.0 0.17% 87 $67.5 0.18%
Pancakes/
French Toast
Sour Creams 88 $13.5 0.20% 69 $65.9 0.21% 70 $79.4 0.21%
Dnr Sausage-- 89 $13.3 0.20% 233 $26.7 0.08% 184 $40.0 0.11%
Links Pork
Ckd/S
Angus [Beef] 90 $13.1 0.20% 63 $71.9 0.23% 66 $84.9 0.22%
Hot Dog Buns 91 $13.0 0.20% 111 $45.1 0.14% 105 $58.1 0.15%
Sandwiches--(C 92 $13.0 0.20% 108 $45.4 0.14% 104 $58.4 0.15%
old)
Dairy Case 93 $12.9 0.20% 170 $34.8 0.11% 151 $47.6 0.13%
Juice Drnk
Under 10
Hamburger Buns 94 $12.8 0.20% 94 $50.1 0.16% 94 $63.0 0.17%
Candy Bars 95 $12.8 0.19% 149 $37.8 0.12% 132 $50.6 0.13%
(Singles)
(Including)
Cream Cheese 96 $12.8 0.19% 52 $78.1 0.25% 60 $90.9 0.24%
Candy Bars 97 $12.5 0.19% 93 $50.4 0.16% 95 $62.9 0.17%
(Multi Pack)
Cheese 98 $12.5 0.19% 74 $61.2 0.19% 79 $73.7 0.19%
Crackers
Spring Water 99 $12.5 0.19% 68 $67.9 0.22% 68 $80.3 0.21%
Flavored Milk 100 $12.4 0.19% 124 $42.5 0.13% 114 $54.9 0.14%
------------------------- ----------------------------------------------------------
Top 100 $2,658.3 40.40% $9,463.7 30.03% $12,122.1 31.82%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-15: Top 100 Subcommodities for SNAP Households by Expenditure: Stores with less than $2 Million in
Sales
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $0.4 0.01% 1 $1.4 0.00% 1 $1.8 0.00%
White Only
Soft Drinks 12/ 2 $0.3 0.01% 2 $0.8 0.00% 2 $1.2 0.00%
18 & 15pk Can
Car
Primal [Beef] 3 $0.2 0.00% 3 $0.7 0.00% 3 $0.9 0.00%
Lean [Beef] 4 $0.2 0.00% 6 $0.4 0.00% 5 $0.5 0.00%
Sft Drnk 2 5 $0.1 0.00% 7 $0.3 0.00% 7 $0.5 0.00%
Liter Btl
Carb Incl
Mainstream 6 $0.1 0.00% 11 $0.3 0.00% 9 $0.4 0.00%
White Bread
Soft Drinks 7 $0.1 0.00% 19 $0.2 0.00% 13 $0.3 0.00%
20pk & 24pk
Can Carb
Potato Chips 8 $0.1 0.00% 5 $0.4 0.00% 6 $0.5 0.00%
Shredded 9 $0.1 0.00% 4 $0.4 0.00% 4 $0.5 0.00%
Cheese
Kids Cereal 10 $0.1 0.00% 28 $0.2 0.00% 20 $0.3 0.00%
Lunchment--Del 11 $0.1 0.00% 8 $0.3 0.00% 8 $0.4 0.00%
i Fresh
Snack Cake-- 12 $0.1 0.00% 31 $0.2 0.00% 26 $0.3 0.00%
Multi Pack
American 13 $0.1 0.00% 16 $0.2 0.00% 14 $0.3 0.00%
Single Cheese
Enhanced [Pork 14 $0.1 0.00% 10 $0.3 0.00% 11 $0.3 0.00%
Boneless Loin/
Rib]
Tortilla/Nacho 15 $0.1 0.00% 12 $0.3 0.00% 12 $0.3 0.00%
Chips
Unflavored Can 16 $0.1 0.00% 15 $0.2 0.00% 16 $0.3 0.00%
Coffee
Eggs--Large 17 $0.1 0.00% 9 $0.3 0.00% 10 $0.3 0.00%
Potatoes 18 $0.1 0.00% 18 $0.2 0.00% 17 $0.3 0.00%
Russet (Bulk
& Bag)
Still Water 19 $0.1 0.00% 20 $0.2 0.00% 19 $0.3 0.00%
Drnking/Mnrl
Water
Fz Ss Economy 20 $0.1 0.00% 57 $0.1 0.00% 45 $0.2 0.00%
Meals All
Sugar 21 $0.1 0.00% 32 $0.2 0.00% 31 $0.2 0.00%
Bacon--Trad 22 $0.1 0.00% 21 $0.2 0.00% 21 $0.3 0.00%
16oz Or Less
Convenient 23 $0.1 0.00% 66 $0.1 0.00% 52 $0.2 0.00%
Meals--Kids
Meal C
Mainstream 24 $0.1 0.00% 13 $0.3 0.00% 15 $0.3 0.00%
Variety
Breads
Infant Formula 25 $0.1 0.00% 143 $0.1 0.00% 78 $0.1 0.00%
Starter/
Solutio
Sft Drnk Sngl 26 $0.1 0.00% 51 $0.1 0.00% 44 $0.2 0.00%
Srv Btl Carb
(Ex)
Sft Drnk Mlt- 27 $0.1 0.00% 27 $0.2 0.00% 27 $0.3 0.00%
Pk Btl Carb
(Excp)
Chicken Breast 28 $0.1 0.00% 14 $0.2 0.00% 18 $0.3 0.00%
Boneless
Hot Dogs--Base 29 $0.0 0.00% 46 $0.1 0.00% 36 $0.2 0.00%
Meat
Snacks/ 30 $0.0 0.00% 70 $0.1 0.00% 60 $0.1 0.00%
Appetizers
Traditional 31 $0.0 0.00% 23 $0.2 0.00% 24 $0.3 0.00%
[Ice Cream &
Sherbert]
Pizza/Economy 32 $0.0 0.00% 55 $0.1 0.00% 49 $0.2 0.00%
Pizza/Premium 33 $0.0 0.00% 43 $0.1 0.00% 38 $0.2 0.00%
Condensed Soup 34 $0.0 0.00% 25 $0.2 0.00% 25 $0.3 0.00%
Lunchment--Bol 35 $0.0 0.00% 45 $0.1 0.00% 43 $0.2 0.00%
ogna/Sausage
Flavored Milk 36 $0.0 0.00% 64 $0.1 0.00% 57 $0.1 0.00%
All Family 37 $0.0 0.00% 22 $0.2 0.00% 23 $0.3 0.00%
Cereal
Sandwiches & 38 $0.0 0.00% 75 $0.1 0.00% 66 $0.1 0.00%
Handhelds
Hamburger Buns 39 $0.0 0.00% 38 $0.1 0.00% 34 $0.2 0.00%
Bananas 40 $0.0 0.00% 17 $0.2 0.00% 22 $0.3 0.00%
Pizza/ 41 $0.0 0.00% 47 $0.1 0.00% 46 $0.2 0.00%
Traditional
Pails [Ice 42 $0.0 0.00% 59 $0.1 0.00% 55 $0.2 0.00%
Cream &
Sherbert]
Margarine: 43 $0.0 0.00% 42 $0.1 0.00% 39 $0.2 0.00%
Tubs And
Bowls
Natural Cheese 44 $0.0 0.00% 26 $0.2 0.00% 29 $0.2 0.00%
Chunks
Fz Ss Prem 45 $0.0 0.00% 41 $0.1 0.00% 41 $0.2 0.00%
Traditional
Meals
Macaroni & 46 $0.0 0.00% 88 $0.1 0.00% 74 $0.1 0.00%
Cheese Dnrs
Pourable Salad 47 $0.0 0.00% 35 $0.1 0.00% 35 $0.2 0.00%
Dressings
Choice Beef 48 $0.0 0.00% 53 $0.1 0.00% 54 $0.2 0.00%
Isotonic 49 $0.0 0.00% 44 $0.1 0.00% 48 $0.2 0.00%
Drinks Single
Serve
Strawberries 50 $0.0 0.00% 29 $0.2 0.00% 30 $0.2 0.00%
Can Pasta 51 $0.0 0.00% 118 $0.1 0.00% 97 $0.1 0.00%
Mayonnaise & 52 $0.0 0.00% 48 $0.1 0.00% 50 $0.2 0.00%
Whipped
Dressing
Ribs [Pork] 53 $0.0 0.00% 52 $0.1 0.00% 53 $0.2 0.00%
Candy Bags-- 54 $0.0 0.00% 36 $0.1 0.00% 37 $0.2 0.00%
Chocolate
Cottage Cheese 55 $0.0 0.00% 37 $0.1 0.00% 42 $0.2 0.00%
Dairy Case 56 $0.0 0.00% 24 $0.2 0.00% 28 $0.2 0.00%
100% Pure
Juice--O
Mexican Soft 57 $0.0 0.00% 56 $0.1 0.00% 58 $0.1 0.00%
Tortillas And
Wra
Frzn French 58 $0.0 0.00% 93 $0.1 0.00% 80 $0.1 0.00%
Fries
Candy Bars 59 $0.0 0.00% 71 $0.1 0.00% 70 $0.1 0.00%
(Multi Pack)
Sweet Goods-- 60 $0.0 0.00% 95 $0.1 0.00% 85 $0.1 0.00%
Full Size
Butts [Pork 61 $0.0 0.00% 80 $0.1 0.00% 76 $0.1 0.00%
Shoulder]
Frzn Chicken-- 62 $0.0 0.00% 54 $0.1 0.00% 59 $0.1 0.00%
Wht Meat
Sandwich 63 $0.0 0.00% 63 $0.1 0.00% 63 $0.1 0.00%
Cookies
Mainstream 64 $0.0 0.00% 73 $0.1 0.00% 71 $0.1 0.00%
[Pasta &
Pizza Sauce]
Fz Bag 65 $0.0 0.00% 34 $0.2 0.00% 40 $0.2 0.00%
Vegetables--P
lain
Bagged Cheese 66 $0.0 0.00% 90 $0.1 0.00% 79 $0.1 0.00%
Snacks
Choice Beef 67 $0.0 0.00% 40 $0.1 0.00% 47 $0.2 0.00%
Peanut Butter 68 $0.0 0.00% 50 $0.1 0.00% 56 $0.2 0.00%
Bkfst Sausage-- 69 $0.0 0.00% 61 $0.1 0.00% 62 $0.1 0.00%
Fresh Rolls
Adult Cereal 70 $0.0 0.00% 33 $0.2 0.00% 33 $0.2 0.00%
Loaf Cheese 71 $0.0 0.00% 67 $0.1 0.00% 67 $0.1 0.00%
Refrigerated 72 $0.0 0.00% 86 $0.1 0.00% 82 $0.1 0.00%
Biscuits
Vegetable Oil 73 $0.0 0.00% 131 $0.1 0.00% 108 $0.1 0.00%
Hot Dog Buns 74 $0.0 0.00% 79 $0.1 0.00% 77 $0.1 0.00%
Candy Bars 75 $0.0 0.00% 84 $0.1 0.00% 83 $0.1 0.00%
(Singles)
(Including)
Sour Creams 76 $0.0 0.00% 62 $0.1 0.00% 65 $0.1 0.00%
Sticks/Enrobed 77 $0.0 0.00% 99 $0.1 0.00% 92 $0.1 0.00%
[Frozen
Novelties]
Angus [Beef] 78 $0.0 0.00% 83 $0.1 0.00% 81 $0.1 0.00%
Tray Pack/Choc 79 $0.0 0.00% 85 $0.1 0.00% 84 $0.1 0.00%
Chip Cookies
Salsa & Dips 80 $0.0 0.00% 106 $0.1 0.00% 99 $0.1 0.00%
Skillet 81 $0.0 0.00% 142 $0.1 0.00% 120 $0.1 0.00%
Dinners
Aseptic Pack 82 $0.0 0.00% 154 $0.1 0.00% 126 $0.1 0.00%
Juice And
Drinks
Tuna 83 $0.0 0.00% 72 $0.1 0.00% 75 $0.1 0.00%
Sw Gds: Donuts 84 $0.0 0.00% 89 $0.1 0.00% 89 $0.1 0.00%
Head Lettuce 85 $0.0 0.00% 65 $0.1 0.00% 69 $0.1 0.00%
Fz Family 86 $0.0 0.00% 170 $0.0 0.00% 138 $0.1 0.00%
Style Entrees
Cubed Meats 87 $0.0 0.00% 97 $0.1 0.00% 94 $0.1 0.00%
[Beef]
Select Beef 88 $0.0 0.00% 91 $0.1 0.00% 91 $0.1 0.00%
Value Forms/ 89 $0.0 0.00% 166 $0.0 0.00% 139 $0.1 0.00%
18oz And
Larger
[Chicken]
Fz Ss Prem 90 $0.0 0.00% 30 $0.2 0.00% 32 $0.2 0.00%
Nutritional
Meals
Variety Beans-- 91 $0.0 0.00% 77 $0.1 0.00% 87 $0.1 0.00%
Kidney/Pinto/
E
Cream Cheese 92 $0.0 0.00% 58 $0.1 0.00% 64 $0.1 0.00%
Dnr Sausage-- 93 $0.0 0.00% 129 $0.1 0.00% 122 $0.1 0.00%
Links Pork
Ckd/S
Lunchmeat--Cho 94 $0.0 0.00% 186 $0.0 0.00% 155 $0.1 0.00%
p/Form Pltry
& Ha
Frzn Meat-- 95 $0.0 0.00% 194 $0.0 0.00% 162 $0.1 0.00%
Beef
Toaster 96 $0.0 0.00% 121 $0.1 0.00% 116 $0.1 0.00%
Pastries
Bacon--Trad 97 $0.0 0.00% 76 $0.1 0.00% 88 $0.1 0.00%
Greater Than
16oz
Corn Chips 98 $0.0 0.00% 108 $0.1 0.00% 105 $0.1 0.00%
Water Ice 99 $0.0 0.00% 220 $0.0 0.00% 182 $0.1 0.00%
[Frozen
Novelties]
Eggs--Medium 100 $0.0 0.00% 164 $0.0 0.00% 144 $0.1 0.00%
------------------------- ----------------------------------------------------------
Top 100 $4.9 0.07% $16.8 0.05% $21.7 0.06%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-16: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in Counties with Poverty Rates
Less than 10%
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $12.2 0.18% 1 $105.5 0.33% 1 $117.6 0.31%
White Only
Soft Drinks 12/ 2 $10.3 0.16% 2 $74.1 0.24% 2 $84.4 0.22%
18 & 15pk Can
Car
Lean [Beef] 3 $6.3 0.10% 7 $32.5 0.10% 5 $38.9 0.10%
Shredded 4 $4.8 0.07% 3 $47.5 0.15% 3 $52.3 0.14%
Cheese
Kids Cereal 5 $4.3 0.06% 20 $24.1 0.08% 18 $28.3 0.07%
Sft Drnk 2 6 $3.9 0.06% 18 $25.3 0.08% 17 $29.2 0.08%
Liter Btl
Carb Incl
Potato Chips 7 $3.8 0.06% 9 $31.6 0.10% 7 $35.4 0.09%
Primal [Beef] 8 $3.6 0.05% 16 $27.7 0.09% 14 $31.3 0.08%
Chicken Breast 9 $3.4 0.05% 4 $39.9 0.13% 4 $43.3 0.11%
Boneless
Lunchment--Del 10 $3.3 0.05% 11 $29.7 0.09% 10 $33.0 0.09%
i Fresh
Eggs--Large 11 $3.1 0.05% 8 $31.8 0.10% 9 $34.9 0.09%
Infant Formula 12 $3.1 0.05% 268 $4.3 0.01% 169 $7.4 0.02%
Starter/
Solutio
Snacks/ 13 $3.0 0.05% 54 $14.2 0.05% 48 $17.3 0.05%
Appetizers
Tortilla/Nacho 14 $3.0 0.05% 13 $28.8 0.09% 12 $31.8 0.08%
Chips
Enhanced [Pork 15 $2.8 0.04% 21 $23.6 0.07% 20 $26.4 0.07%
Boneless Loin/
Rib]
Mainstream 16 $2.8 0.04% 40 $17.3 0.05% 36 $20.1 0.05%
White Bread
Unflavored Can 17 $2.8 0.04% 22 $23.4 0.07% 21 $26.1 0.07%
Coffee
Still Water 18 $2.7 0.04% 27 $21.6 0.07% 26 $24.2 0.06%
Drnking/Mnrl
Water
Soft Drinks 19 $2.6 0.04% 59 $13.8 0.04% 53 $16.3 0.04%
20pk & 24pk
Can Carb
Pizza/Premium 20 $2.5 0.04% 28 $21.3 0.07% 27 $23.9 0.06%
Fz Ss Prem 21 $2.5 0.04% 32 $19.3 0.06% 30 $21.8 0.06%
Traditional
Meals
Dairy Case 22 $2.5 0.04% 6 $33.2 0.11% 6 $35.7 0.09%
100% Pure
Juice--O
Natural Cheese 23 $2.4 0.04% 15 $28.1 0.09% 15 $30.6 0.08%
Chunks
American 24 $2.4 0.04% 46 $16.3 0.05% 41 $18.7 0.05%
Single Cheese
All Family 25 $2.3 0.04% 14 $28.2 0.09% 16 $30.5 0.08%
Cereal
Bacon--Trad 26 $2.3 0.03% 35 $19.0 0.06% 35 $21.3 0.06%
16oz Or Less
Snack Cake-- 27 $2.2 0.03% 70 $12.5 0.04% 64 $14.7 0.04%
Multi Pack
Select Beef 28 $2.2 0.03% 34 $19.2 0.06% 33 $21.4 0.06%
Bananas 29 $2.2 0.03% 10 $30.5 0.10% 11 $32.7 0.09%
Potatoes 30 $2.2 0.03% 33 $19.3 0.06% 32 $21.5 0.06%
Russet (Bulk
& Bag)
Sft Drnk Mlt- 31 $2.2 0.03% 25 $22.5 0.07% 24 $24.6 0.06%
Pk Btl Carb
(Excp)
Fz Ss Economy 32 $2.2 0.03% 112 $8.7 0.03% 94 $10.8 0.03%
Meals All
Premium [Ice 33 $2.1 0.03% 12 $29.6 0.09% 13 $31.7 0.08%
Cream &
Sherbert]
Mainstream 34 $2.1 0.03% 26 $21.8 0.07% 28 $23.8 0.06%
Variety
Breads
Sft Drnk Sngl 35 $2.1 0.03% 90 $10.1 0.03% 81 $12.2 0.03%
Srv Btl Carb
(Ex)
Convenient 36 $2.1 0.03% 94 $9.7 0.03% 86 $11.8 0.03%
Meals--Kids
Meal C
Sandwiches & 37 $2.0 0.03% 104 $9.0 0.03% 91 $11.0 0.03%
Handhelds
Sugar 38 $1.9 0.03% 61 $13.6 0.04% 55 $15.5 0.04%
Condensed Soup 39 $1.9 0.03% 31 $19.6 0.06% 31 $21.5 0.06%
Fz Family 40 $1.8 0.03% 83 $11.0 0.03% 77 $12.8 0.03%
Style Entrees
Ribs [Pork] 41 $1.8 0.03% 68 $12.9 0.04% 63 $14.7 0.04%
Isotonic 42 $1.7 0.03% 60 $13.8 0.04% 56 $15.5 0.04%
Drinks Single
Serve
Refrigerated 43 $1.7 0.03% 38 $18.3 0.06% 37 $20.0 0.05%
Coffee
Creamers
Pourable Salad 44 $1.7 0.03% 39 $18.0 0.06% 38 $19.7 0.05%
Dressings
Fz Ss Prem 45 $1.7 0.03% 5 $33.6 0.11% 8 $35.3 0.09%
Nutritional
Meals
Frzn Chicken-- 46 $1.7 0.03% 74 $12.2 0.04% 69 $13.9 0.04%
Wht Meat
Strawberries 47 $1.7 0.03% 17 $26.3 0.08% 19 $27.9 0.07%
Mayonnaise & 48 $1.6 0.02% 52 $14.6 0.05% 54 $16.1 0.04%
Whipped
Dressing
Mexican Soft 49 $1.6 0.02% 51 $14.8 0.05% 51 $16.4 0.04%
Tortillas And
Wra
Candy Bags-- 50 $1.5 0.02% 30 $20.3 0.06% 29 $21.9 0.06%
Chocolate
Adult Cereal 51 $1.5 0.02% 24 $22.8 0.07% 25 $24.3 0.06%
Choice Beef 52 $1.5 0.02% 63 $13.5 0.04% 60 $15.1 0.04%
Sw Gds: Donuts 53 $1.5 0.02% 77 $11.7 0.04% 76 $13.2 0.03%
Traditional 54 $1.5 0.02% 56 $13.9 0.04% 58 $15.3 0.04%
[Ice Cream &
Sherbert]
Meat: Turkey 55 $1.4 0.02% 19 $24.3 0.08% 22 $25.7 0.07%
Bulk
Aseptic Pack 56 $1.4 0.02% 136 $7.8 0.02% 115 $9.2 0.02%
Juice And
Drinks
Fz Bag 57 $1.4 0.02% 47 $16.0 0.05% 46 $17.4 0.05%
Vegetables--P
lain
Butter 58 $1.4 0.02% 23 $23.3 0.07% 23 $24.7 0.06%
Margarine: 59 $1.4 0.02% 75 $12.1 0.04% 72 $13.5 0.04%
Tubs And
Bowls
Hot Dogs--Base 60 $1.4 0.02% 174 $6.5 0.02% 149 $7.9 0.02%
Meat
Can Pasta 61 $1.4 0.02% 193 $6.1 0.02% 166 $7.4 0.02%
Macaroni & 62 $1.4 0.02% 133 $7.9 0.02% 114 $9.2 0.02%
Cheese Dnrs
Choice Beef 63 $1.4 0.02% 107 $8.9 0.03% 100 $10.2 0.03%
Pizza/Economy 64 $1.4 0.02% 191 $6.1 0.02% 164 $7.5 0.02%
Peanut Butter 65 $1.3 0.02% 45 $16.4 0.05% 45 $17.7 0.05%
Mainstream 66 $1.3 0.02% 88 $10.4 0.03% 88 $11.7 0.03%
[Pasta &
Pizza Sauce]
Pizza/ 67 $1.3 0.02% 98 $9.3 0.03% 95 $10.6 0.03%
Traditional
Tuna 68 $1.3 0.02% 64 $13.3 0.04% 65 $14.6 0.04%
Value Forms/ 69 $1.2 0.02% 209 $5.8 0.02% 181 $7.0 0.02%
18oz And
Larger
[Chicken]
Angus 70 $1.2 0.02% 62 $13.6 0.04% 62 $14.8 0.04%
Meat: Ham Bulk 71 $1.2 0.02% 36 $18.4 0.06% 39 $19.6 0.05%
Frzn Breakfast 72 $1.2 0.02% 135 $7.8 0.02% 122 $9.0 0.02%
Sandwiches
Cream Cheese 73 $1.2 0.02% 49 $15.5 0.05% 50 $16.7 0.04%
Cheese 74 $1.2 0.02% 66 $13.1 0.04% 67 $14.2 0.04%
Crackers
Fz Skillet 75 $1.2 0.02% 89 $10.3 0.03% 90 $11.5 0.03%
Meals
String Cheese 76 $1.2 0.02% 53 $14.3 0.05% 57 $15.4 0.04%
Fruit Snacks 77 $1.2 0.02% 170 $6.6 0.02% 152 $7.8 0.02%
Frzn Meat-- 78 $1.1 0.02% 184 $6.3 0.02% 168 $7.4 0.02%
Beef
Frzn French 79 $1.1 0.02% 173 $6.5 0.02% 159 $7.7 0.02%
Fries
Instore Cut 80 $1.1 0.02% 57 $13.8 0.04% 61 $14.9 0.04%
Fruit
Waffles/ 81 $1.1 0.02% 84 $10.9 0.03% 83 $12.0 0.03%
Pancakes/
French Toast
Sandwiches--(C 82 $1.1 0.02% 140 $7.7 0.02% 130 $8.8 0.02%
old)
Sour Creams 83 $1.1 0.02% 73 $12.4 0.04% 73 $13.5 0.04%
Cakes: 84 $1.1 0.02% 164 $6.7 0.02% 150 $7.8 0.02%
Birthday/
Celebration
Sh
Avocado 85 $1.1 0.02% 48 $15.7 0.05% 49 $16.8 0.04%
Rts Soup: 86 $1.1 0.02% 55 $14.2 0.05% 59 $15.3 0.04%
Chunky/
Homestyle/Et
Salsa & Dips 87 $1.1 0.02% 132 $7.9 0.02% 124 $9.0 0.02%
Flavored Milk 88 $1.1 0.02% 145 $7.5 0.02% 137 $8.5 0.02%
Grapes Red 89 $1.1 0.02% 42 $17.0 0.05% 43 $18.0 0.05%
Candy Bars 90 $1.1 0.02% 152 $7.1 0.02% 142 $8.2 0.02%
(Singles)
(Including)
Lunchment--Bol 91 $1.1 0.02% 179 $6.4 0.02% 167 $7.4 0.02%
ogna/Sausage
Sandwich 92 $1.0 0.02% 99 $9.3 0.03% 98 $10.4 0.03%
Cookies
Bkfst Sausage-- 93 $1.0 0.02% 109 $8.8 0.03% 105 $9.9 0.03%
Fresh Rolls
Spring Water 94 $1.0 0.02% 82 $11.0 0.03% 82 $12.0 0.03%
Chix: Frd 8pc/ 95 $1.0 0.02% 85 $10.8 0.03% 84 $11.8 0.03%
Cut Up (Hot)
Bagged Cheese 96 $1.0 0.02% 176 $6.4 0.02% 165 $7.4 0.02%
Snacks
Natural Cheese 97 $1.0 0.02% 50 $15.3 0.05% 52 $16.4 0.04%
Slices
Hamburger Buns 98 $1.0 0.02% 102 $9.1 0.03% 103 $10.1 0.03%
Sweet Goods-- 99 $1.0 0.01% 175 $6.5 0.02% 163 $7.5 0.02%
Full Size
Yogurt/Kids 100 $1.0 0.01% 165 $6.7 0.02% 155 $7.7 0.02%
------------------------- ----------------------------------------------------------
Top 100 $204.3 3.10% $1,763.9 5.60% $1,968.2 5.17%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-17: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in Counties with Poverty Rates
of 10% to 20%
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $147.5 2.24% 1 $651.2 2.07% 1 $798.7 2.10%
White Only
Soft Drinks 12/ 2 $123.8 1.88% 2 $456.0 1.45% 2 $579.8 1.52%
18 & 15pk Can
Car
Lean [Beef] 3 $85.1 1.29% 7 $199.9 0.63% 4 $285.0 0.75%
Kids Cereal 4 $59.3 0.90% 20 $141.9 0.45% 13 $201.2 0.53%
Shredded 5 $57.3 0.87% 3 $255.8 0.81% 3 $313.1 0.82%
Cheese
Sft Drnk 2 6 $54.3 0.83% 13 $175.5 0.56% 9 $229.8 0.60%
Liter Btl
Carb Incl
Potato Chips 7 $49.2 0.75% 8 $192.5 0.61% 6 $241.8 0.63%
Primal [Beef] 8 $44.4 0.68% 17 $156.1 0.50% 15 $200.6 0.53%
Infant Formula 9 $42.1 0.64% 179 $35.8 0.11% 79 $77.9 0.20%
Starter/
Solutio
Lunchment--Del 10 $42.1 0.64% 11 $183.1 0.58% 11 $225.2 0.59%
i Fresh
Eggs--Large 11 $40.0 0.61% 9 $191.2 0.61% 8 $231.2 0.61%
Chicken Breast 12 $38.5 0.58% 4 $221.7 0.70% 5 $260.2 0.68%
Boneless
Still Water 13 $37.9 0.58% 19 $146.8 0.47% 19 $184.8 0.48%
Drnking/Mnrl
Water
Tortilla/Nacho 14 $36.3 0.55% 16 $157.8 0.50% 17 $194.1 0.51%
Chips
Mainstream 15 $35.0 0.53% 42 $100.2 0.32% 35 $135.3 0.36%
White Bread
Snacks/ 16 $34.2 0.52% 67 $75.2 0.24% 49 $109.4 0.29%
Appetizers
Fz Ss Prem 17 $33.7 0.51% 22 $136.9 0.43% 21 $170.6 0.45%
Traditional
Meals
Dairy Case 18 $33.7 0.51% 6 $206.7 0.66% 7 $240.4 0.63%
100% Pure
Juice--O
American 19 $32.8 0.50% 41 $102.4 0.32% 36 $135.2 0.35%
Single Cheese
Unflavored Can 20 $31.4 0.48% 18 $149.8 0.48% 20 $181.2 0.48%
Coffee
Enhanced [Pork 21 $31.1 0.47% 27 $122.2 0.39% 24 $153.3 0.40%
Boneless Loin/
Rib]
Fz Ss Economy 22 $31.1 0.47% 80 $62.1 0.20% 65 $93.1 0.24%
Meals All
Bacon--Trad 23 $31.0 0.47% 29 $119.4 0.38% 26 $150.3 0.39%
16oz Or Less
Pizza/Premium 24 $30.2 0.46% 34 $115.3 0.37% 29 $145.5 0.38%
Snack Cake-- 25 $30.2 0.46% 70 $74.2 0.24% 54 $104.4 0.27%
Multi Pack
Mainstream 26 $29.7 0.45% 25 $130.8 0.42% 22 $160.5 0.42%
Variety
Breads
Soft Drinks 27 $29.0 0.44% 62 $79.6 0.25% 50 $108.6 0.29%
20pk & 24pk
Can Carb
Natural Cheese 28 $28.2 0.43% 14 $167.0 0.53% 16 $195.1 0.51%
Chunks
All Family 29 $28.0 0.43% 15 $163.5 0.52% 18 $191.6 0.50%
Cereal
Sugar 30 $27.3 0.42% 58 $84.4 0.27% 46 $111.8 0.29%
Sandwiches & 31 $27.0 0.41% 93 $56.0 0.18% 73 $83.0 0.22%
Handhelds
Potatoes 32 $26.8 0.41% 32 $116.0 0.37% 30 $142.8 0.37%
Russet (Bulk
& Bag)
Bananas 33 $26.6 0.40% 10 $187.2 0.59% 12 $213.7 0.56%
Ribs [Pork] 34 $25.8 0.39% 60 $80.9 0.26% 53 $106.7 0.28%
Convenient 35 $25.2 0.38% 106 $51.5 0.16% 82 $76.7 0.20%
Meals--Kids
Meal C
Premium [Ice 36 $24.8 0.38% 12 $176.1 0.56% 14 $200.9 0.53%
Cream &
Sherbert]
Isotonic 37 $24.2 0.37% 45 $93.9 0.30% 42 $118.1 0.31%
Drinks Single
Serve
Sft Drnk Mlt- 38 $24.0 0.36% 26 $123.5 0.39% 28 $147.5 0.39%
Pk Btl Carb
(Excp)
Select Beef 39 $23.8 0.36% 30 $117.5 0.37% 31 $141.3 0.37%
Frzn Chicken-- 40 $22.7 0.35% 69 $74.8 0.24% 61 $97.5 0.26%
Wht Meat
Condensed Soup 41 $22.5 0.34% 33 $115.5 0.37% 32 $138.0 0.36%
Pourable Salad 42 $21.7 0.33% 39 $105.6 0.34% 39 $127.3 0.33%
Dressings
Choice Beef 43 $21.3 0.32% 38 $106.3 0.34% 38 $127.6 0.33%
Fz Family 44 $21.2 0.32% 78 $63.1 0.20% 72 $84.3 0.22%
Style Entrees
Sft Drnk Sngl 45 $20.9 0.32% 99 $53.5 0.17% 85 $74.4 0.20%
Srv Btl Carb
(Ex)
Mayonnaise & 46 $20.9 0.32% 49 $90.9 0.29% 45 $111.8 0.29%
Whipped
Dressing
Mexican Soft 47 $19.8 0.30% 50 $90.3 0.29% 48 $110.1 0.29%
Tortillas And
Wra
Refrigerated 48 $19.5 0.30% 31 $116.6 0.37% 33 $136.1 0.36%
Coffee
Creamers
Adult Cereal 49 $19.3 0.29% 21 $139.5 0.44% 23 $158.8 0.42%
Traditional 50 $19.2 0.29% 52 $88.6 0.28% 51 $107.8 0.28%
[Ice Cream &
Sherbert]
Fz Ss Prem 51 $19.1 0.29% 5 $208.6 0.66% 10 $227.7 0.60%
Nutritional
Meals
Fz Bag 52 $19.0 0.29% 43 $98.9 0.31% 43 $117.9 0.31%
Vegetables--P
lain
Aseptic Pack 53 $18.6 0.28% 137 $43.3 0.14% 107 $61.9 0.16%
Juice And
Drinks
Choice Beef 54 $18.4 0.28% 97 $53.6 0.17% 89 $72.1 0.19%
Hot Dogs--Base 55 $18.4 0.28% 145 $42.1 0.13% 111 $60.5 0.16%
Meat
Macaroni & 56 $18.2 0.28% 129 $44.6 0.14% 103 $62.8 0.16%
Cheese Dnrs
Margarine: 57 $18.1 0.27% 63 $77.4 0.25% 64 $95.5 0.25%
Tubs And
Bowls
Strawberries 58 $17.8 0.27% 24 $132.8 0.42% 25 $150.6 0.40%
Mainstream 59 $17.4 0.26% 85 $60.8 0.19% 78 $78.2 0.21%
[Pasta &
Pizza Sauce]
Candy Bags-- 60 $16.7 0.25% 36 $112.6 0.36% 37 $129.3 0.34%
Chocolate
Can Pasta 61 $16.5 0.25% 185 $35.3 0.11% 144 $51.8 0.14%
Frzn Chicken-- 62 $16.4 0.25% 469 $13.0 0.04% 268 $29.4 0.08%
Wings
Tuna 63 $16.4 0.25% 59 $84.4 0.27% 58 $100.8 0.26%
Sw Gds: Donuts 64 $16.2 0.25% 84 $61.0 0.19% 80 $77.2 0.20%
Peanut Butter 65 $15.8 0.24% 44 $96.9 0.31% 44 $112.7 0.30%
Lunchment--Bol 66 $15.7 0.24% 124 $45.6 0.14% 108 $61.3 0.16%
ogna/Sausage
Mult Pk Bag 67 $15.4 0.23% 205 $32.4 0.10% 161 $47.8 0.13%
Snacks
Butter 68 $15.3 0.23% 23 $134.9 0.43% 27 $150.1 0.39%
Meat: Turkey 69 $15.1 0.23% 28 $120.3 0.38% 34 $135.4 0.36%
Bulk
Frzn French 70 $15.1 0.23% 177 $37.0 0.12% 141 $52.1 0.14%
Fries
Vegetable Oil 71 $14.9 0.23% 250 $26.7 0.08% 193 $41.6 0.11%
Pizza/Economy 72 $14.6 0.22% 195 $33.3 0.11% 159 $48.0 0.13%
Frzn Meat-- 73 $14.6 0.22% 188 $35.0 0.11% 154 $49.6 0.13%
Beef
Fz Skillet 74 $14.5 0.22% 87 $60.4 0.19% 84 $74.9 0.20%
Meals
Value Forms/ 75 $14.2 0.22% 214 $31.9 0.10% 168 $46.2 0.12%
18oz And
Larger
[Chicken]
Frzn Breakfast 76 $14.1 0.21% 154 $41.0 0.13% 128 $55.0 0.14%
Sandwiches
Cakes: 77 $14.1 0.21% 172 $37.9 0.12% 143 $52.0 0.14%
Birthday/
Celebration
Sh
Chicken Wings 78 $14.0 0.21% 319 $20.8 0.07% 238 $34.8 0.09%
Sandwiches--(C 79 $13.9 0.21% 94 $56.0 0.18% 92 $69.9 0.18%
old)
Sandwich 80 $13.8 0.21% 95 $54.6 0.17% 94 $68.4 0.18%
Cookies
Sour Creams 81 $13.7 0.21% 71 $73.2 0.23% 71 $86.9 0.23%
Rts Soup: 82 $13.7 0.21% 47 $93.1 0.30% 52 $106.8 0.28%
Chunky/
Homestyle/Et
Pizza/ 83 $13.6 0.21% 118 $47.2 0.15% 109 $60.8 0.16%
Traditional
Cream Cheese 84 $13.5 0.21% 53 $88.0 0.28% 57 $101.6 0.27%
Waffles/ 85 $13.4 0.20% 89 $58.6 0.19% 88 $72.1 0.19%
Pancakes/
French Toast
Fruit Snacks 86 $13.4 0.20% 209 $32.1 0.10% 172 $45.5 0.12%
Bagged Cheese 87 $13.3 0.20% 158 $40.0 0.13% 136 $53.2 0.14%
Snacks
Angus [Beef] 88 $13.1 0.20% 64 $77.0 0.24% 67 $90.2 0.24%
Ramen Noodles/ 89 $12.9 0.20% 298 $22.0 0.07% 237 $34.8 0.09%
Ramen Cups
Salsa & Dips 90 $12.8 0.20% 140 $42.7 0.14% 124 $55.5 0.15%
Cheese 91 $12.8 0.19% 74 $67.9 0.22% 77 $80.7 0.21%
Crackers
Candy Bars 92 $12.8 0.19% 139 $42.9 0.14% 123 $55.7 0.15%
(Singles)
(Including)
Dairy Case 93 $12.6 0.19% 170 $38.2 0.12% 149 $50.7 0.13%
Juice Drnk
Under 10
Spring Water 94 $12.5 0.19% 65 $76.3 0.24% 68 $88.8 0.23%
Chicken Drums 95 $12.4 0.19% 276 $23.9 0.08% 226 $36.3 0.10%
Hot Dog Buns 96 $12.3 0.19% 119 $47.0 0.15% 113 $59.3 0.16%
Sweet Goods-- 97 $12.3 0.19% 128 $44.9 0.14% 118 $57.2 0.15%
Full Size
Hamburger Buns 98 $12.2 0.19% 104 $52.5 0.17% 98 $64.8 0.17%
Grapes Red 99 $12.1 0.18% 48 $91.9 0.29% 55 $104.0 0.27%
Flavored Milk 100 $12.1 0.18% 130 $44.6 0.14% 120 $56.7 0.15%
------------------------- ----------------------------------------------------------
Top 100 $2,551.7 38.78% $10,139.2 32.17% $12,690.9 33.31%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
Exhibit E-18: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in Counties with Poverty Rates
Greater than 20%
----------------------------------------------------------------------------------------------------------------
SNAP Household Expenditures Non-SNAP Household Total Household Expenditures
-------------------------------- Expenditures --------------------------------
Subcommodity ---------------------------------
Rank $ in % of $ in % of Rank $ in % of
millions Expenditures Rank millions Expenditures millions Expenditures
----------------------------------------------------------------------------------------------------------------
Fluid Milk/ 1 $31.5 0.48% 1 $97.0 0.31% 1 $128.5 0.34%
White Only
Soft Drinks 12/ 2 $30.5 0.46% 2 $71.0 0.23% 2 $101.6 0.27%
18 & 15pk Can
Car
Lean [Beef] 3 $21.0 0.32% 13 $25.4 0.08% 5 $46.4 0.12%
Kids Cereal 4 $14.6 0.22% 21 $20.4 0.06% 13 $35.0 0.09%
Primal [Beef] 5 $14.4 0.22% 4 $35.9 0.11% 4 $50.3 0.13%
Shredded 6 $12.7 0.19% 3 $38.6 0.12% 3 $51.3 0.13%
Cheese
Sft Drnk 2 7 $12.6 0.19% 8 $29.3 0.09% 6 $42.0 0.11%
Liter Btl
Carb Incl
Potato Chips 8 $11.3 0.17% 10 $29.1 0.09% 7 $40.4 0.11%
Lunchment--Del 9 $10.5 0.16% 6 $29.8 0.09% 8 $40.2 0.11%
i Fresh
Mainstream 10 $10.1 0.15% 26 $19.3 0.06% 19 $29.4 0.08%
White Bread
Snack Cake-- 11 $9.2 0.14% 38 $15.1 0.05% 30 $24.2 0.06%
Multi Pack
Eggs--Large 12 $9.0 0.14% 11 $28.6 0.09% 10 $37.6 0.10%
Infant Formula 13 $9.0 0.14% 172 $5.2 0.02% 61 $14.2 0.04%
Starter/
Solutio
American 14 $8.8 0.13% 31 $17.9 0.06% 24 $26.8 0.07%
Single Cheese
Still Water 15 $8.1 0.12% 24 $19.3 0.06% 21 $27.5 0.07%
Drnking/Mnrl
Water
Soft Drinks 16 $8.1 0.12% 46 $13.1 0.04% 39 $21.2 0.06%
20pk & 24pk
Can Carb
Tortilla/Nacho 17 $8.1 0.12% 17 $22.4 0.07% 16 $30.5 0.08%
Chips
Sft Drnk Mlt- 18 $7.9 0.12% 12 $27.7 0.09% 12 $35.6 0.09%
Pk Btl Carb
(Excp)
Fz Ss Economy 19 $7.7 0.12% 64 $10.0 0.03% 48 $17.7 0.05%
Meals All
Sugar 20 $7.7 0.12% 41 $14.6 0.05% 36 $22.3 0.06%
Fz Ss Prem 21 $7.7 0.12% 27 $19.3 0.06% 23 $26.9 0.07%
Traditional
Meals
Chicken Breast 22 $7.6 0.12% 5 $31.3 0.10% 9 $39.0 0.10%
Boneless
Chicken Wings 23 $7.6 0.12% 181 $5.1 0.02% 72 $12.7 0.03%
Enhanced [Pork 24 $7.6 0.11% 18 $22.2 0.07% 18 $29.7 0.08%
Boneless Loin/
Rib]
Bacon--Trad 25 $7.5 0.11% 28 $19.2 0.06% 25 $26.7 0.07%
16oz Or Less
Ribs [Pork] 26 $7.4 0.11% 47 $12.9 0.04% 41 $20.3 0.05%
Dairy Case 27 $7.4 0.11% 9 $29.1 0.09% 11 $36.5 0.10%
100% Pure
Juice--O
Snacks/ 28 $7.4 0.11% 60 $11.0 0.03% 44 $18.4 0.05%
Appetizers
Unflavored Can 29 $7.2 0.11% 15 $24.9 0.08% 15 $32.1 0.08%
Coffee
Convenient 30 $7.0 0.11% 86 $8.5 0.03% 53 $15.5 0.04%
Meals--Kids
Meal C
Pizza/Premium 31 $6.9 0.10% 35 $16.7 0.05% 32 $23.6 0.06%
Sandwiches & 32 $6.9 0.10% 82 $8.6 0.03% 56 $15.4 0.04%
Handhelds
Potatoes 33 $6.7 0.10% 29 $19.2 0.06% 26 $25.9 0.07%
Russet (Bulk
& Bag)
Mainstream 34 $6.6 0.10% 20 $20.7 0.07% 22 $27.3 0.07%
Variety
Breads
All Family 35 $5.8 0.09% 16 $23.2 0.07% 20 $29.0 0.08%
Cereal
Frzn Chicken-- 36 $5.6 0.09% 49 $12.8 0.04% 42 $18.4 0.05%
Wht Meat
Choice Beef 37 $5.6 0.09% 34 $16.8 0.05% 34 $22.5 0.06%
Pourable Salad 38 $5.6 0.09% 37 $15.8 0.05% 37 $21.4 0.06%
Dressings
Bananas 39 $5.5 0.08% 14 $24.9 0.08% 17 $30.4 0.08%
Fz Bag 40 $5.3 0.08% 33 $17.0 0.05% 35 $22.3 0.06%
Vegetables--P
lain
Hot Dogs--Base 41 $5.3 0.08% 89 $8.2 0.03% 67 $13.5 0.04%
Meat
Mult Pk Bag 42 $5.3 0.08% 178 $5.1 0.02% 101 $10.4 0.03%
Snacks
Condensed Soup 43 $5.3 0.08% 30 $18.5 0.06% 31 $23.8 0.06%
Frzn Chicken-- 44 $5.2 0.08% 356 $2.6 0.01% 156 $7.8 0.02%
Wings
Lunchment--Bol 45 $5.0 0.08% 79 $8.9 0.03% 65 $14.0 0.04%
ogna/Sausage
Traditional 46 $5.0 0.08% 36 $16.3 0.05% 38 $21.3 0.06%
[Ice Cream &
Sherbert]
Sft Drnk Sngl 47 $4.8 0.07% 99 $7.8 0.02% 73 $12.6 0.03%
Srv Btl Carb
(Ex)
Vegetable Oil 48 $4.8 0.07% 193 $4.9 0.02% 113 $9.7 0.03%
Macaroni & 49 $4.8 0.07% 110 $7.2 0.02% 77 $11.9 0.03%
Cheese Dnrs
Mayonnaise & 50 $4.7 0.07% 43 $13.6 0.04% 43 $18.4 0.05%
Whipped
Dressing
Natural Cheese 51 $4.7 0.07% 19 $21.0 0.07% 27 $25.7 0.07%
Chunks
Fz Family 52 $4.6 0.07% 70 $9.4 0.03% 64 $14.0 0.04%
Style Entrees
Isotonic 53 $4.6 0.07% 56 $11.9 0.04% 49 $16.4 0.04%
Drinks Single
Serve
Can Pasta 54 $4.4 0.07% 135 $6.3 0.02% 96 $10.7 0.03%
Mainstream 55 $4.3 0.07% 67 $9.7 0.03% 63 $14.0 0.04%
[Pasta &
Pizza Sauce]
Premium [Ice 56 $4.3 0.07% 22 $20.3 0.06% 28 $24.6 0.06%
Cream &
Sherbert]
Frzn French 57 $4.3 0.06% 118 $6.8 0.02% 90 $11.0 0.03%
Fries
Choice Beef 58 $4.2 0.06% 65 $10.0 0.03% 62 $14.2 0.04%
Aseptic Pack 59 $4.2 0.06% 144 $6.1 0.02% 102 $10.3 0.03%
Juice And
Drinks
Chicken Drums 60 $4.1 0.06% 231 $4.2 0.01% 140 $8.4 0.02%
Dnr Sausage-- 61 $4.1 0.06% 209 $4.7 0.01% 130 $8.8 0.02%
Links Pork
Ckd/S
Adult Cereal 62 $4.0 0.06% 23 $20.3 0.06% 29 $24.3 0.06%
Strawberries 63 $4.0 0.06% 25 $19.3 0.06% 33 $23.3 0.06%
Margarine: 64 $4.0 0.06% 57 $11.3 0.04% 57 $15.3 0.04%
Tubs And
Bowls
Fz Ss Prem 65 $4.0 0.06% 7 $29.4 0.09% 14 $33.4 0.09%
Nutritional
Meals
Frzn Breakfast 66 $3.9 0.06% 116 $6.9 0.02% 95 $10.8 0.03%
Sandwiches
Pizza/Economy 67 $3.8 0.06% 160 $5.7 0.02% 119 $9.5 0.02%
Sw Gds: Donuts 68 $3.7 0.06% 69 $9.5 0.03% 68 $13.2 0.03%
Tuna 69 $3.5 0.05% 54 $12.2 0.04% 51 $15.7 0.04%
Cakes: 70 $3.4 0.05% 162 $5.6 0.02% 125 $9.1 0.02%
Birthday/
Celebration
Sh
Bacon--Trad 71 $3.4 0.05% 117 $6.8 0.02% 103 $10.3 0.03%
Greater Than
16oz
Peanut Butter 72 $3.3 0.05% 42 $14.5 0.05% 46 $17.8 0.05%
Candy Bags-- 73 $3.3 0.05% 40 $14.6 0.05% 45 $17.9 0.05%
Chocolate
Sandwich 74 $3.2 0.05% 98 $7.9 0.02% 89 $11.0 0.03%
Cookies
Salsa & Dips 75 $3.2 0.05% 130 $6.4 0.02% 115 $9.6 0.03%
Frzn Meat-- 76 $3.2 0.05% 185 $5.0 0.02% 143 $8.2 0.02%
Beef
Bkfst Sausage-- 77 $3.2 0.05% 87 $8.5 0.03% 81 $11.7 0.03%
Fresh Rolls
Value Forms/ 78 $3.2 0.05% 192 $4.9 0.02% 145 $8.1 0.02%
18oz And
Larger
[Chicken]
Fz Skillet 79 $3.1 0.05% 81 $8.6 0.03% 80 $11.7 0.03%
Meals
Refrigerated 80 $3.1 0.05% 121 $6.7 0.02% 109 $9.8 0.03%
Biscuits
Fruit Snacks 81 $3.1 0.05% 218 $4.5 0.01% 162 $7.5 0.02%
Hot Dog Buns 82 $3.0 0.05% 104 $7.5 0.02% 100 $10.5 0.03%
Ramen Noodles/ 83 $3.0 0.05% 330 $2.9 0.01% 213 $5.9 0.02%
Ramen Cups
Hamburger Buns 84 $3.0 0.05% 83 $8.5 0.03% 82 $11.5 0.03%
Tray Pack/Choc 85 $3.0 0.05% 124 $6.6 0.02% 116 $9.6 0.03%
Chip Cookies
Pizza/ 86 $3.0 0.05% 101 $7.6 0.02% 99 $10.6 0.03%
Traditional
Candy Bars 87 $2.9 0.04% 91 $8.1 0.03% 88 $11.1 0.03%
(Multi Pack)
Pails [Ice 88 $2.9 0.04% 194 $4.9 0.02% 153 $7.9 0.02%
Cream &
Sherbert]
Grapes White 89 $2.9 0.04% 72 $9.3 0.03% 76 $12.2 0.03%
Refrigerated 90 $2.9 0.04% 53 $12.3 0.04% 58 $15.2 0.04%
Coffee
Creamers
Butter 91 $2.9 0.04% 32 $17.5 0.06% 40 $20.4 0.05%
Shrimp--Cooked 92 $2.9 0.04% 161 $5.6 0.02% 135 $8.5 0.02%
Rts Soup: 93 $2.9 0.04% 51 $12.6 0.04% 55 $15.5 0.04%
Chunky/
Homestyle/Et
Bagged Cheese 94 $2.8 0.04% 163 $5.6 0.02% 138 $8.4 0.02%
Snacks
Butter Spray 95 $2.8 0.04% 85 $8.5 0.03% 83 $11.4 0.03%
Cracker
Angus [Beef] 96 $2.8 0.04% 45 $13.1 0.04% 50 $15.9 0.04%
Flavored Milk 97 $2.8 0.04% 107 $7.4 0.02% 105 $10.2 0.03%
Waffles/ 98 $2.8 0.04% 97 $7.9 0.03% 97 $10.7 0.03%
Pancakes/
French Toast
Dnr Sausage-- 99 $2.8 0.04% 150 $5.9 0.02% 133 $8.7 0.02%
Pork Rope Ckd/
Sm
Traditional 100 $2.8 0.04% 109 $7.2 0.02% 107 $10.0 0.03%
Spices
------------------------- ----------------------------------------------------------
Top 100 $610.2 9.27% $1,500.2 4.76% $2,110.3 5.54%
Subcommodit
ies
========================= ==========================================================
Total $6,580.5 100% $31,513.8 100% $38,094.2 100%
Expenditu
res
----------------------------------------------------------------------------------------------------------------
Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016.
Note: Columns may not sum to total shown due to rounding.
* Top 100 subcommodities based on SNAP household expenditures.
______
Submitted Policy Brief by Feeding Texas
Policy Brief: Maintaining Choices for SNAP Recipients
Feeding Texas
Our View: SNAP restrictions are an ineffective and costly
strategy to improve recipient health. Our nation would be
better served by educating and empowering recipients to make
better choices, not restricting those choices.
Obesity: A Problem for All, but Improving
Obesity and diet-related disease affect Americans of all income
levels and backgrounds. SNAP consumers face additional barriers to
healthy eating, including limited geographic access to affordable,
healthy food; tight food budgets overall; and inadequate SNAP
allotments. SNAP recipients sometimes manage this shortfall by buying
less-nutritious foods that can adversely affect their health.
Despite these challenges, the most recent USDA report on SNAP
purchases found no major differences in the expenditures of SNAP and
non-SNAP households. Put simply, SNAP consumers shop like Americans do
as a whole.
And Americans as a whole are eating better. Soda consumption, the
behavior most often targeted for SNAP restrictions, is at a thirty-year
low in America. And while dietary quality remains poor, American diets
have steadily improved in recent years.
SNAP Restrictions Can Not Force Dietary Change
A recent, peer-reviewed study (https://www.ncbi.nlm.nih.gov/pubmed/
27653735) in the medical journal JAMA demonstrated how simply
restricting SNAP purchases would not improve recipients' diets.
Participants in this study reported a slight reduction in calories
consumed but no change in overall diet quality.
An associated meta-study (https://www.ncbi.nlm.nih.gov/pubmed/
26647851) concluded that restricting SNAP participants from spending
their benefits on soda only had a ``small to moderate'' impact, because
recipients substituted their own money to purchase soda.
SNAP Restrictions Are Neither Free, nor Freeing
There are significant costs to SNAP purchase restrictions that
would be borne by participants, businesses and the program itself.
Participants
Americans of all income levels view the government restricting food
choices as an intrusion into their autonomy to decide what is best for
their families. Because SNAP restrictions unfairly single out low-
income Americans for a problem that affects all Americans, they
increase the stigma associated with SNAP participation. Increased
stigma could actually reduce health outcomes, as it would lead some
families to forgo nutrition assistance rather than put their dinner
table under Federal scrutiny.
Businesses
Restricting SNAP purchases would constitute an unfunded Federal
mandate on business. SNAP retailers would likely bear the cost of re-
training cashiers, creating signage, reprogramming computers and
implementing rules associated with this broad change.
Because SNAP serves a diverse group of Americans with a wide range
of dietary needs, it would be impossible to restrict SNAP benefits to
an easy-to-control, ``affirmative list'' of approved foods like that
found in the WIC program. More likely, restrictions would be
implemented as a short list of restricted foods, forcing retailers to
evaluate each product on their shelves, as well as thousands of new
products each year against rules made in Washington.
Program Efficiency & Effectiveness
Implementation of EBT technology has made SNAP efficient and cost-
effective for retailers and government. The introduction of purchase
restrictions at checkout would complicate SNAP transactions and
undermine these gains.
Unless SNAP restrictions were accompanied by an increase in overall
benefits, they would also result in a de facto benefit cut by forcing
recipients to purchase alternative foods that cost more. In this way,
restrictions could result in decreased purchasing power for SNAP
recipients, resulting in less food on the family table and a less
effective hunger-fighting program.
There is a Better Way
Our nation would be better served pursuing policies that seek to
educate and empower clients to make better choices, not restrict those
choices.
Congress could achieve these aims in two ways:
1. Make SNAP benefits reflect the actual costs of eating healthy.
The Institute of Medicine has recommended (https://
www.nap.edu/catalog/13485/supplemental-nutrition-
assistance-program-examining-the-evidence-to-define-
benefit) increasing SNAP benefit levels to more accurately
reflect the costs involved in eating a healthy diet. Absent
a broad increase in benefits, research suggests that
funding ``double-dollar incentive'' programs may also
improve participants' consumption patterns
2. Promote well-evaluated, outcomes-driven nutrition education.
Programming directed by Feeding Texas and our local food
banks has demonstrated that health interventions and
nutrition education strategies funded through SNAP-Ed can
effectively promote healthy eating and improve dietary
health. These strategies are especially effective when
paired with the distribution of free produce, which helps
participants to bridge the transition to healthier habits.
We call this combined approach ``Feeding with Impact
(https://www.feedingtexas.org/product/2017/02/Feeding-with-
Impact-Factsheet/).''
______
Submitted Statement by Secretaries' Innovation Group
The Secretaries' Innovation Group (SIG) is a network of state human
services secretaries who have program responsibility for the state SNAP
program, among many others. These SIG member secretaries serve under
Republican governors from states which make up 46% of the country. In
November 2014 the members of the Secretaries' Innovation Group issued a
statement from which these recommendations derive.
The Supplemental Nutrition Assistance Program (SNAP), which is
known as Food Stamps, has quadrupled in cost since 2001. A common sense
approach is needed to allow states the ability to ensure welfare
benefits are being used appropriately. Despite intense opposition,
states have made significant strides in some areas to tackle wasteful
expenditures, fraud and abuse in the system, and with the help of
reform-minded voices in Congress and a new Administration, states will
be able to go much further.
Recommendations
The program which is intended as a nutritional supplement should
restrict the purchase of soda, candy and other unhealthy products.
The Supplemental Nutrition Assistance Program is intended to
subsidize nutrition for needy families. Too many recipients are
utilizing their benefit to purchase items that are \1/3\ of adults and
17% of youth in the United States are obese, according to the Journal
of the American Medical Association.\1\ According to a Health Affairs
study, the medical costs associated with obesity are an estimated $147
billion in 2008.\2\
---------------------------------------------------------------------------
\1\ http://jama.jamanetwork.com/article.aspx?articleid=1832542.
\2\ http://content.healthaffairs.org/content/28/5/
w822.full.pdf+html.
---------------------------------------------------------------------------
One option to balance SNAP purchases toward healthier choices is to
allow SNAP purchases to mirror allowable purchases in the Women,
Infants and Children (WIC) program. A second alternative is to restrict
the purchase of products with zero nutritional value such as candy,
energy drinks and other sugar-sweetened drinks. A third alternative is
to establish a pilot project with up to ten states for a one-time
waiver that would allow for some nutrition controls on SNAP purchases.
These pilot waivers would require an evaluation of measurable outcomes.
Make key SNAP purchase data available to states.
Micro-level transaction data which shows how SNAP benefits are
being spent is not available to the states. However this data would
provide an objective, measurable picture of where reforms are needed to
ensure the program is effective in providing essential nutrition for
those in need. SIG recommends FNS and SNAP-EBT vendors (i.e., Xerox) to
capture all SNAP transaction-level data and make it available to
states. Transparency is important to inform program officials,
legislators and the public on what changes are needed in the program to
ensure its effectiveness as a supplementary nutrition program.
Convenience stores need more stringent requirements to participate
in SNAP.
The ``convenience store'' category of EBT retailers is of
particular concern (e.g., gas stations, food marts, party stores). The
majority of EBT trafficking occurs in these venues. These
establishments typically do not stock the type of eligible food
products that satisfy the original intent of the SNAP program. EBT
redemptions often exceed eligible food inventory at these locations. We
recommend increasing the minimum eligible food inventory requirements
of the four major food groups to be stocked for sale at convenience
store category retailers. An alternative option is to require EBT
retailers to submit food inventory records on a frequency basis
(quarterly or semi-annually) in order to reconcile with EBT redemptions
which could serve as a deterrent to trafficking redemptions.
______
Submitted Letter by David B. Allison, Ph.D., Distinguished Quetelet
Endowed Professor; Associate Dean for Research & Science; Director,
Office of Energetics; Director, Nutrition & Obesity Research Center,
Department of Nutrition Sciences, School of Health Professions,
University of Alabama at Birmingham
Tuesday, February 14, 2017
Hon. K. Michael Conaway,
Chairman,
House Committee on Agriculture,
Washington, D.C.
Thank you for inviting me to testify before the House Committee on
Agriculture for your February 16, 2017 meeting.
I regret that I will be unable to join you at that meeting, but
instead wanted to offer you some thoughts, information, and materials
that may be helpful to you and the Committee in your deliberations. I
reference several articles below and include them, as well as my
current CV,* as enclosed attachments to this e-mail.
---------------------------------------------------------------------------
* The document referred to is retained in Committee file.
---------------------------------------------------------------------------
Before proceeding further, I wish to emphasize that the opinions
below are my own and I am not speaking on behalf of my university or
any other organization.
I. The Challenge in Predicting Intervention Effects
Some individuals may assert that if society implements a particular
policy, scientists can predict that it will have a particular effect on
obesity levels. In the vast majority of cases, at present, such
statements are unwarranted. This is so for two reasons.
First, human physiology and even more so human behavior are complex
and insufficiently understood to permit confident conclusions about how
even the average person will respond to some intervention, let alone to
predict with confidence how any one individual will respond, without
performing an experiment to actually observe the effects. That is why
scientists do randomized controlled trials (RCTs) to test the effects
of things. If you look at this website (http://
www.obesityandenergetics.org/) under the category ``Contrary or Null
Findings,'' in each weekly entry, you will see many examples of this
unpredictability of intervention effects. That does not mean that
scientists have no ability to predict effects, but rather that our
ability is rather limited.
Second, some will posit that if it is known that an intervention
affects energy (calorie) intake or expenditure by a particular amount,
then one can calculate the expected weight or obesity change that will
result from such an intervention using validated mathematical models
(for such a claim, see: http://www.ajpmonline.org/article/S0749-
3797(13)00269-9/abstract). The problem with such reasoning is that
these calculations assume that people take no compensatory action,
i.e., that they do not change their food intake, physical patterns, or
any other factors that influence weight in response to the proposed
intervention. However, much evidence indicates that people do take such
compensatory actions (see: https://www.ncbi.nlm.nih.gov/pmc/articles/
PMC4516704/). As a result, interventions generally have far lesser
impact on body weight than one might initially predict.
II. Myths & Presumptions in Nutrition and Obesity.
Many academics or nutrition or obesity experts may assert that a
particular thing is known to be true about nutrition or obesity. In
some cases, they will be correct. However, experience shows that in
many cases, propositions asserted to be true by such experts turn out
to be either false or unsupported presumptions. Therefore, when any
assertions are made, the complete scientific evidence supporting those
assertions should be requested. Two papers which discuss the commonness
of mistaken beliefs about nutrition or obesity are these:
Casazza, K., Fontaine, K.R., Astrup, A., Birch, L., Brown,
A.W., Bohan Brown, M.M., Durant, N., Dutton, G., Foster, E.M.,
Heymsfield, S.B., McIver, K., Mehta, T., Menachemi, N., Newby,
P.K., Pate, R., Rolls, B. J., Sen, B., Smith, D. L., Thomas,
D., & Allison, D. B. (2013). Myths, Presumptions, and Facts
about Obesity. New England Journal of Medicine, Jan. 31;
368(5): 446-54. doi: 10.1056/NEJMsa1208051. https://
www.ncbi.nlm.nih.gov/pubmed/23363498.
Allison D.B., Assaganya-Riera J., Burlingame B., Brown A.,
Le Coutre J., Dickson S.L., Van Eden W., Garssen J.,
Hontecillas R., Khoo C.S., Knorr D., Kussmann M., Magiestretti
P.J., Mehta T., Meule Adrian, Rychlik M., & Vogele C. (2015).
Goals in Nutrition Science 2015-2020. Frontiers in Nutrition,
Sep 2015 2:26. doi: 10.3389/fnut.2015.00026. http://
journal.frontiersin.org/article/10.3389/fnut.2015.00026/
abstract.
III. Separating the Moral, Social, and Legal Issues from the Scientific
Issues
It is important not to conflate the moral, social, and legal issues
with the scientific issues in policy questions around nutrition and
weight. The scientific information can inform the policy decision, but
generally cannot determine the best policy decision, because moral,
social, and legal factors are also involved. In some cases, moral,
social, or legal factors may be overwhelming and may appropriately
drive a decision largely independently of data.
You have asked me about the wisdom of restriction on purchases of
certain food items with SNAP benefits.
Some persons might offer reasonable arguments for such restriction
which rely minimally on data. Here the values of beneficence (wanting
to help people) and responsible stewardship of taxpayer dollars
predominate. Such persons could argue that certain foods (e.g.,
confections, pastries, sugar-sweetened beverages) are luxuries which
are unnecessary for life or health and without which most persons'
health would be no worse and possibly better. Given that, it can be
argued that: (a) It is in the best interests of SNAP participants
(i.e., beneficence) to not consume these items; and (b) It is
questionable for the government to spend tax-payer money on items which
are at best unnecessary and at worst harmful. By these arguments, one
could, with little need to rely on specific data, argue for such
exclusions.
Alternatively, other persons might offer reasonable arguments
against such restriction which rely minimally on data. Here the values
of autonomy (allowing people to make their own choices about their
lives) and equity (not disadvantaging lower-income persons further and
unduly hampering their access to goods others can partake of)
predominate. Some might argue that these are important values and
people should have a right to decide how to spend their resources on
food and which food choices to make, however nutritionally sound or
unsound those choices are.
The choice between the two perspectives above is largely not one
that hinges on data, but rather on the differential value one places on
beneficence and responsible stewardship of taxpayer dollars vs autonomy
and equity. These are, of course, not the only values or factors that
can be brought to bear on these questions. See:
Brown, A. & Allison, D.B. (2013). Unintended consequences of
obesity-targeted health policy. Virtual Mentor. 2013 Apr. 1;
15(4):339-46. doi: 10.1001/virtualmentor.2013.15.4.pfor2-1304.
http://journalofethics.ama-assn.org/2013/04/pfor2-1304.html.
IV. Standards of Evidence for Scientific Conclusions vs. Policy
Decisions
A frequent question is ``what is the standard of evidence for
effectiveness of a policy needed to justify a decision to enact a
policy?'' The answer is that there is no single standard that applies
in all contexts and this is a matter of social and legal judgment, not
scientific judgement. In contrast, there are standards (albeit with
some judgement still involved) for drawing scientific conclusions about
the effects of interventions or policies. I raise this important
distinction because this distinction is sometimes blurred by those who
feel strongly that it is reasonable to move forward with a decision to
take some action. Such individuals sometimes seem to feel compelled to
dispute a data-based conclusion that evidence is insufficient to show
the proposed action will have its desired effects. However,
definitiveness in a decision to act despite uncertainty about drawing a
conclusion, poses no contradiction. These ideas are discussed more
fully in these two papers.
Allison, D.B. (2011). Evidence, Discourse, and Values in
Obesity-Oriented Policy: Menu-Labeling as a Conversation
Starter. International Journal of Obesity, Apr.; 35(4): 464-71.
http://www.nature.com/ijo/journal/v35/n4/full/ijo201128a.html.
Richardson, M.B. Williams, M.S., Fontaine, K.R., & Allison,
D.B. (in press). The development of scientific evidence for
health policies for obesity: why and how. International Journal
of Obesity.
V. Information on Sugar Sweetened Beverages and Weight
You have specifically asked me about the effects of sugar-sweetened
beverages (SSBs) on weight. Two papers I have written on this topic
are:
Kaiser, K.A., Shikany, J.M., Keating, K.D. & Allison, D.B.
(2013). Will reducing sugar-sweetened beverage consumption
reduce obesity? Evidence supporting conjecture is strong, but
evidence when testing effect is weak. Obesity Reviews, Aug.;
14(8): 620-33. doi: 10.1111/obr.12048. https://
www.ncbi.nlm.nih.gov/pmc/articles/PMC3929296/.
Allison, D.B. (2014). Liquid calories, energy compensation,
and weight: what we know and what we still need to learn.
Invited Commentary. British Journal of Nutrition, Feb.;
111(3):384-6. doi: 10.1017/S0007114513003309. https://
www.ncbi.nlm.nih.gov/pmc/articles/PMC4973863/.
VI. Biases and Emotion
The topics you are addressing are ones where many strong interests
are at play. These interests include selfless interests in benefitting
members of our country, economic interests, and personal interests.
Consideration of this fact is important for at least two reasons:
A. Some will try to discredit the statements of individuals who
have some connection to commerce involving food or agriculture,\1\
based on claims that they are biased. In considering this, persons
interested in reason and rationality should:
---------------------------------------------------------------------------
\1\ For the record, I disclose that I have received funds from
multiple for-profit, not-for-profit, and government organizations with
interests in nutrition and obesity, including commodity groups and
food, beverage, and restaurant companies.
1. First and foremost note that in Science, three things matter: (a)
The data; (b) The methods by which the data were collected
which give them their probative value; and (c) The logic by
which the data are connected to conclusions. Everything
---------------------------------------------------------------------------
else is a distraction.
2. The claim that research produced by those with financial
connections to food and agricultural commerce are biased
has not been demonstrated. See:
http://jamanetwork.com/journals/jamainternalmedicine/
articleabstract/
2517951.
https://www.theatlantic.com/health/archive/2017/01/the-
limits-of-
sugarguidelines/512045/.
http://journals.sagepub.com/doi/abs/10.1177/
0162243912456271.
3. Trying to overturn arguments or discredit individuals based on
their personal characteristics is argumentum ad hominem. It
is logically invalid, uncivil, and unethical. See:
http://www.nature.com/ijo/journal/v38/n5/full/
ijo201432a.html.
http://www.prnewswire.com/news-releases/the-obesity-
society-encourages-
science-industry-collaborations-to-support-obesity-
science-public-health-
252453321.html.
http://utminers.utep.edu/omwilliamson/ENGL1311/
fallacies.htm.
B. Interests other than financial connections to for-profit groups
can create biases. Therefore,the scientific bases of everyone's
statements need to be scrutinized. See the articles below.
Cope, M., Allison, D.B. (2010). White Hat Bias: A Threat to
the Integrity of Scientific Reporting. Acta Paediatrica, Nov.;
99(11): 1615-7. https://www.ncbi.nlm.nih.gov/pubmed/21039822
Cope, M. B. & Allison, D. B. (2010). White Hat Bias:
Examples of its Presence in Obesity Research and a Call for
Renewed Commitment to Faithfulness in Research Reporting.
International Journal of Obesity, 34(1): 84-8. https://
www.ncbi.nlm.nih.gov/pubmed/19949416.
VII. Things You Can Do to Enhance The Science
Finally, there are things your Committee can do to enhance what
society knows on questions about the effects of interventions. For
questions such as ``What is the effect of some intervention on health
or weight,'' the best way to answer that question, if feasible, is with
randomized controlled trials (RCTs).
When such trials exist, your Committee could request the raw
data from all investigators who have conducted these RCTs and
commission a statistician to analyze all the data together in
an open and transparent manner and issue a public report to
you.
When such trials do not exist or are insufficient to
generate confident conclusions, your Committee could take steps
to have a large, statistically powerful, well-designed RCT
commissioned and executed.
In doing so, you would add substantially to our objective knowledge
about outcomes.
I hope this information is helpful to you in your deliberations.
Sincerely,
David B. Allison, Ph.D.
attachment 1
The Caloric Calculator: Average Caloric Impact of Childhood Obesity
Interventions
August 2013
Y. Claire Wang, M.D., Sc.D., Amber Hsiao, M.P.H., C. Tracy Orleans,
Ph.D., Steven L. Gortmaker, Ph.D.*
---------------------------------------------------------------------------
* From the Department of Health Policy & Management (Wang, Hsiao),
Mailman School of Public Health, Columbia University, New York, New
York; the Robert Wood Johnson Foundation (Orleans), Princeton, New
Jersey; and Department of Society, Health, and Human Development
(Gortmaker), Harvard School of Public Health, Cambridge, Massachusetts
Address correspondence to: Y. Claire Wang, M.D., Sc.D., Department
of Health Policy and Management, Mailman School of Public Health,
Columbia University, 600 W 168th St, Rm 602 New York NY 10032. E-mail:
[email protected].
0749-3797/$36.00
http://dx.doi.org/10.1016/j.amepre.2013.03.012
---------------------------------------------------------------------------
This activity is available for CME credit. See page A4 for information.
Background: The childhood obesity epidemic reflects the daily
accumulation of an ``energy gap''--excess calories consumed
over calories expended. Population-level interventions to
reverse the epidemic can be assessed by the degree to which
they increase energy expenditure and/or reduce caloric intake.
However, no common metric exists for such comparative
assessment.
Purpose: To develop a common metric, the Average Caloric
Impact (ACI), for estimating and comparing population-level
effect sizes of a range of childhood obesity interventions.
Methods: An iterative, collaborative process was used to
review literature from 1996 to 2012 and select illustrative
interventions showing effects on youth diet and/or activity
levels, energy balance, and weight. The ACIs of physical
activity interventions were estimated based on program reach,
frequency, duration, and intensity and mean body weight of the
targeted age and gender group from the 2009-2010 National
Health and Nutrition Examination Survey. ACIs of dietary
interventions were based on reach and changes in foods and/or
beverages consumed.
Results: Fifteen interventions informed by 29 studies were
included, ranging from individual behavioral to population-
level policies. A web tool, the Caloric Calculator, was
developed to allow researchers and policymakers to estimate the
ACIs of interventions on target populations with reference to
energy gap reductions required to reach the nation's Healthy
People childhood obesity goals.
Conclusions: The Caloric Calculator and ACIs provide
researchers and policymakers with a common metric for
estimating the potential effect sizes of various interventions
for reducing childhood obesity, providing a platform for
evidence-based dialogues on new program or policy approaches as
data emerge.
(Am. J. Prev. Med. 2013; 45(2): e3-e13)
2013 American Journal of Preventive
Medicine.
Background
The obesity epidemic costs the U.S. $147-$210 billion in annual
healthcare costs.\1\ Although the trends have shown some signs of
leveling, more than \1/3\ of U.S. adults and nearly 17% of children and
adolescents are obese.\2\ As a result, it was predicted that one in
three children born in 2000 would be diagnosed with type 2 diabetes in
his or her lifetime.\3\
The rise in childhood obesity since the early 1970s reflects the
accumulation of the small daily ``energy gap''--the excess of calories
consumed over calories expended.4-5 Previous analyses
estimated that an average surplus of 110-165 kcal/day in energy
accounted for the excess weight gain seen in U.S. children and youth
over a 10 year period.\4\ Thus, effective interventions would have to
bring about a net reduction in this energy gap to reverse the epidemic.
A recent study estimated that among U.S. children aged 2-19 years, a
net reduction of 64 kcal/day per capita in energy surplus would be
needed to achieve the Healthy People 2020 childhood obesity goals, with
a range from 22 kcal/day for those aged 2-5 years, to 77 kcal/day for
those aged 6-11 years, 98 kcal/day for those aged 12-19 years, and much
higher levels among those who are already overweight or obese.\5\
The evidence base for population-level interventions to reduce
childhood obesity levels has grown rapidly, ranging from strategies to
change individual behaviors to those that seek to alter policies,
environments, and social norms. In most cases, however, these policies
or programs are evaluated independently. No common metric exists to
allow comparative assessments of effects across interventions with
varied configurations for a target population.6-7
In the current paper, the Average Caloric Impact (ACI) is proposed
as a metric to gauge the population-level average effect on daily
calories expended/consumed. This metric was applied to an illustrative
set of interventions evaluated in the literature. Greater emphasis was
placed on population-, school-, or state-level programs than on medical
treatments of overweight/obese youth. The results are presented using a
user-friendly web tool, the Caloric Calculator.
Methods
Selection of Interventions
Using recently published reviews, a set of obesity prevention
interventions targeting U.S. children and adolescents aged 2-5 years
(preschool); 6-11 years (primary school); 12-14 years (middle school);
and/or 15-18 years (high school) was selected. Target populations were
defined by grade level based on the divisions within the typical K-12
system. Mean height and weight for each age group (by gender) were
based on the nationally representative 2009-2010 National Health and
Nutrition Examination Survey (NHANES).
From an initial list of 67 studies published between 2000 and 2009,
as reviewed by Brennan, et al.,\8\ only seven physical activity
interventions were included that lasted >6 months and reported outcome
measures that were sufficient to have an influence on calories. For
example, several studies of school lunch programs or wellness policies
were excluded because they reported consumption of only specific
nutrients (e.g., % fat), and/or servings of fruits and vegetables,
rather than changes in total calories consumed or body weight.
Similarly, many evaluations of physical activity programs did not use
objective measures of activity levels (e.g., accelerometers) and thus
were unable to inform changes in energy expenditure.
An iterative and collaborative process was used to identify an
additional 22 studies published between 1996 and 2012; of these, 12
were empirical studies that met the research design and measurement
standards used in the Brennan, et al., review. The remaining studies
provided inputs for the model-based estimates. For dietary
interventions selected, the studies assessed changes in daily caloric
intake before and after the intervention (e.g., California schools'
competitive foods standards).\9\ For studies reporting changes in
consumption of particular foods and/or beverages, published estimates
on the average caloric contribution of these foods and beverages in the
indicated setting (e.g., removing sugar-sweetened beverages from
schools) \10\ were used. Strategies were categorized by implementation
level (individual, school, state/national). Because empirical data were
lacking for some strategies (e.g., promoting walking to schools),
analytic models were used to incorporate available evidence to estimate
the likely caloric effect of these strategies, if broadly implemented.
Caloric Impact Calculations
Physical activity interventions. The physical activity
interventions were placed into one of the following categories: (1)
varied school physical education (PE) classes; (2) school PE
interventions designed to increase moderate-to-vigorous physical
activity (MVPA) levels to achieve more active PE; (3) afterschool
physical activity programs; and (4) active commuting (e.g., walking) to
school. When multiple high-quality studies were available within a
category, the study with the largest effect size was typically used to
represent the best-possible outcome and population-level
implementation.
The effect of the intervention on daily caloric impact was
estimated based on the calculated basal metabolic rate (BMR, which is a
function of age, gender, and body weight), as well as the frequency
(e.g., twice a week); duration (e.g., 30 minutes); and the intensity of
the physical activity (e.g., moderate/vigorous). BMR for an average-
weight child is calculated based on published equations.\11\
Intervention intensity was estimated in METs, representing the amount
of energy expended from carrying out a specific activity relative to
sitting quietly (MET value of 1.0) for a defined period of time. For
instance, walking at a pace of 3 miles per hour represents an average
intensity of 3.3 METs, which burns 3.3 times as many calories than
sitting quietly for the same individual.\12\
Pre-intervention activity levels were based on published baseline
measures of study participants and/or national averages. When MET
values were not reported, activity-specific MET values from the
Ainsworth Compendium for adults \12\ were combined with calculated
youth-specific BMR estimates, following recommendations by Ridley, et
al.\13\ Table 1 provides examples of how various inputs affect the
number of calories expended by different physical activity
interventions.
Dietary interventions. Dietary interventions were similarly
reviewed and categorized. For example, a number of interventions only
measured changes in fruit and vegetable intake, and were excluded
because net impact on caloric intake could not be estimated. One study
that empirically measured the caloric impact of competitive food
policies in high schools was included.\9\ The other five dietary
interventions (e.g., reducing intake of calories from chips) were
estimated based on the authors' calculations.
For policy interventions with limited direct, empirical data (e.g.,
removing sugar-sweetened beverages [SSBs] from schools, and a portion-
size cap on sugary drinks sold in New York City),\14\ dietary data from
NHANES were used to inform the baseline consumption level among those
who would be hypothetically affected by the policy. For example, NHANES
1999-2004 showed that SSBs contributed an average of 224 kcal/day to
the overall caloric intake of U.S. children and adolescents, and 7-15%
of SSBs were consumed in schools.\10\ The estimated caloric impact of
replacing all SSB intake from schools (in session 180 days a year) with
water was averaged across the whole calendar year.
Combined physical activity/dietary interventions. Sonneville and
Gortmaker \15\ have estimated that every 1 hour increase in TV watching
is associated with a 105.5-kcal increase in net total energy intake, or
a 92-kcal increase in energy intake for video- or computer-game
playing. Their findings are consistent with a previously published
randomized trial, which found that reducing TV watching among children
led to lower caloric intake.16 It was hypothesized that children who
spend more time watching TV or playing video games may be more exposed
and/or influenced by food advertising through characters present in
commercials and interactive games that can shape food preferences and
intake.17-19
Table 1. Daily Caloric Effects of Physical Activity for Select Groups Using Schofield Equations
----------------------------------------------------------------------------------------------------------------
Inputs
------------------------------------------------
Average Schofield equation Caloric
Population weight (BMR=) a Intervention Duration School- effect
(kg) D METs (minutes/ based? b (kcal/day)
day) c
----------------------------------------------------------------------------------------------------------------
Boys, age in
years:
2-5 18 22.706 kg + 504.3 Add 30 minutes/ 2.3 30 No 44
day of walking
6-11 34 22.706 kg + 504.3 Add 30 minutes/ 7 30 No 186
day of jogging
12-14 59 17.686 kg + 658.2 Add 15 minutes/ 2.6 15 Yes 23
day of PE
15-18 77 17.686 kg + 658.2 Implement SPARK 3.5 30 Yes 73
Girls, age in
years:
2-5 17 20.315 kg + 485.9 Add afterschool 3.5 10.5 Yes 11
program
6-11 35 13.384 kg + 485.9 Make PE more Varies d 60 Yes 9
active
12-14 57 13.384 kg + 692.6 Add 30 minutes/ 2.6 30 Yes 39
day of PE
15-18 65 13.384 kg + 692.6 Add 10 minutes/ 7 10 No 76
day of jogging
----------------------------------------------------------------------------------------------------------------
a The Schofield equations are grouped by gender and age groups (broken down as 0-3 years, 3-10 years, and 10-18
years). Because of this, some age groups have the same equations.
b If the intervention is applied over a full school year, it multiples the caloric impact by 180 days. This is
then averaged over 365 days to account for no change in activity on holidays, weekends, and summer vacation.
c Daily caloric impact = (BMR D METs duration in minutes) 1,440 minutes/day.
d The MET value for ``Make PE more active'' is a composite of MET values from five different activities, based
on the Ainsworth Compendium: \12\ lying down, sitting, standing, walking, and running. The change in METs from
the intervention depends on user input of baseline versus target % MVPA. BMR, basal metabolism rate; MVPA,
moderate-to-vigorous physical activity; PE, physical education; SPARK, Sports, Play, and Active Recreation for
Kids.
Online ``Caloric Calculator'' Tool
Accompanying the current paper is a web-based tool
(www.caloriccalculator.org) designed to help users visualize and query
the estimated caloric effects of defined interventions within a defined
target population. Programmed in PHP script for HTML, the tool allows
users to choose one or more interventions and customize their
configurations. For example, the user can select as the target ``Boys''
and ``Middle School (12-14)'' from the dropdown menu, and ``implement''
an intervention to increase PE intensity (e.g., moderate/vigorous) for
a duration of time by specifying the baseline MVPA (default is 37%) and
desired post-intervention level (e.g., 50% as recommended).\20\
The resulting caloric effect is benchmarked against two ``energy
gap'' goals: to return the prevalence of obesity to (1) the early 1970s
and/or (2) the Year-2000 levels. The former more ambitious goal
corresponds to the original goals set in Healthy People 2010; \21\ the
latter provides a rough estimate of the current, more modest Healthy
People 2020 goals.\22\ The methodology underlying the calculations of
these targets for various population subgroups has been described
previously.\5\ All interventions listed assume that no compensatory
changes affecting daily energy balance occur, beyond any effects
observed in the empirical studies cited. For example, the ACI of
increasing MVPA from 37% to 50% during PE classes assumes that students
will not consume additional calories to compensate for additional
physical activity, or that removing a food item from one's diet does
not result in increased consumption of other foods or beverages.
Results
The estimated caloric effect of the 15 interventions in the tool,
by gender and age group, are summarized in Tables 2 and 3. For
instance, for high school boys and girls, adding 15 minutes of PE time
per day for a full school year was estimated to increase mean energy
expenditure by 25 kcal/day; replacing SSBs with water in schools for
the same group would reduce mean energy intake by 15 kcal/day. For this
group, however, an average per capita reduction of 82 kcal/day in
energy surplus would be needed to meet the Healthy People 2020 obesity
prevalence goal of reducing obesity rates from 20.8% to 14.8%.
Returning to the early 1970s level of obesity prevalence--the target
set by the more ambitious Healthy People 2010 goal--would require an
average per capita reduction in energy gap of 217 kcal/day. These
estimates suggest that although any single intervention may not be
sufficient to achieve the Healthy People goals, substantial progress
could be made through a combination of feasible, sustained policy and
environmental interventions.
Table 2. Caloric Impact of Physical Activity Interventions for Average
Student, By Age Group
------------------------------------------------------------------------
Inputs for caloric calculations
----------------------------------
Population Avg.
Intervention (age caloric Assumptions
group, Target Avg.weight impact
years) METs a (lbs) b (kcal/
day)
------------------------------------------------------------------------
Modeled estimates
------------------------------------------------------------------------
Add walking Both (2-5) 3.3 39 21 Same
at a 3-mph Both (6- 3.3 76 30 baseline
pace, 15 11) 3.3 127 38 (1.0,
minutes/day Both (12- sitting
14) quietly)
and target
METs for
all ages,
based on
Ainsworth,
et
al.,\12\
and
Ridley, et
al.\13\
Both (15- 3.3 157 43
18)
Add jogging Both (2-5) 8.0 39 64 Same
at a 5-mph Both (6- 8.0 76 90 baseline
pace, 15 11) 8.0 127 115 (1.0,
minutes/day Both (12- 8.0 157 130 sitting
14) quietly)
Both (15- and target
18) METs for
all ages,
based on
Ainsworth,
et
al.,\12\
and
Ridley, et
al.\13\
Walking to Both (2-5) 3.3 39 9 Interventio
and from Both (6- 3.3 76 12 n model
school 11) estimates
(roundtrip) based on
METs from
Ainsworth,
et
al.,\12\
and
Ridley, et
al.,\13\
Both (12- 3.3 127 15 and
14) 3.3 157 17 published
Both (15- data on
18) average
distances
from
schools
and
students
living
within 1
mile of
school.23
24
Caloric
impact
estimate
uses METs
of 1.0 as
baseline
(i.e.,
sitting in
car).
Implemented
for a full
academic
year.b
------------------------------------------------------------------------
Empirical estimates
------------------------------------------------------------------------
Add school PE Both (2-5) 3.4 39 11 McKenzie,
time, 15 Both (6- 3.4 76 15 et
minutes/day 11) al.,\25\
estimate
3.4 METs
for
elementary
school PE.
Same value
used for
pre-
Both (12- 3.6 127 21 school.
14) 3.7 157 25 Nader, et
Both (15- al.,\26\
18) estimate
3.6 METs
for middle
school PE.
Smith, et
al.,\27\
estimate
3.7 METs
for high
school PE.
Implemente
d for a
full
academic
year.b
Make current Both (2-5) 4.5 39 3 MET values
PE more Both (6- 4.5 76 4 used at
active, 30 11) 4.5 127 6 baseline
minutes/day Both (12- and target
14) is a
composite
of
estimated
MET
values,
based on
Wu, et
al.,\7\
and
Ainsworth,
et
al.,\12\
(4.5 METs)
Both (15- 4.5 157 6 for MVPA,
18) 1.8 METs
for non-
MVPA).
Because of
high
variance
in METs,
baseline
activity
levels,
and
population
characteri
stics
between
CATCH,20,
26, 28 29
MSPAN,\25\
and TAAG
30 35
interventi
ons, same
averaged
MVPA% used
for all
age
groups.
Changing
the
intensity
of current
PE time
(not
adding
additional
PE time).
Base case
increases
MVPA from
37% to
50%, based
on DHHS
national
recommenda
tion.\20\
Implemented
for a full
academic
year.b
Implement Both (2-5) 7.2 39 34 7.2 METs
SPARK using Both (6- 7.2 76 48 for PE
only PE 11) 7.2 127 58 specialist
specialists Both (12- 7.2 157 64 s for
to teach PE, 14) SPARK
30 minutes/ Both (15- interventi
day 18) on from
McKenzie,
et
al.,\36\
and
Sallis, et
al.,\37\
used in
calculatio
n to
demonstrat
e maximum
potential
of
interventi
on
(compared
to 5.8
METs for
trained
classroom
teachers).
Adding PE
time to
existing
PE time.
Baseline
METs
assumed to
be 3.4 for
preschool
and
elementary
,\25\ 3.6
for
middle,\26
\ and 3.7
for high
school.\27
\
Add Both (2-5) 4.5 39 11 Gortmaker,
afterschool Both (6- 4.5 76 16 et
physical 11) 4.5 127 20 al.,\38\
activity Both (12- estimate
program 14) %4.0 METs
in
interventi
on. 4.5
METs is
used here
as a
conservati
ve
composite
target
based on
Wu, et
al.\7\
Both (15- 4.5 157 22 Same
18) baseline
(1.0,
sitting
quietly)
and target
METs for
all ages,
based on
Ainsworth,
et
al.,\12\
and
Ridley, et
al.\13\
Implemented
for a full
academic
year.b
------------------------------------------------------------------------
a METs expresses how much energy is needed for physical activities.
Caloric impacts expressed in this table are calculated assuming the
physical activity is above a baseline of 1.0 METs (except where noted,
as with implementing SPARK), which is the baseline resting metabolic
rate when sitting quietly.
b Intervention is applied over a full school year (on average, 180
days). The total caloric impact is averaged over 365 days to account
for no change in activity on holidays, weekends, and summer vacation.
CATCH, The Child and Adolescent Trial for Cardiovascular Health;
MSPAN, The Middle-School Physical Activity and Nutrition intervention;
MVPA, moderate-to-vigorous physical activity; PE, physical education;
SPARK, Sports, Play, and Active Recreation for Kids; TAAG, The Trial
of Activity for Adolescent Girls.
Table 3. Caloric Impact of Dietary and Other Interventions for Average Student By Group
----------------------------------------------------------------------------------------------------------------
Inputs for caloric calculations
------------------------------------------
Population (age Avg.
Intervention group, years) Affected caloric Assumptions
Amount a pop., % b impact (kcal/
day)
----------------------------------------------------------------------------------------------------------------
Modeled estimates
----------------------------------------------------------------------------------------------------------------
Reduce unhealthy food All 1-oz bag of 100 154 Intervention models
intake All chips per estimates based on
day 100 55 published caloric
1 cookie per values of average bag
day of regular potato
chips and single Oreo
cookie.
Reduce SSB intake All 12-oz can 100 136 Intervention models
All per day 100 240 estimates based on
20-oz bottle published caloric
per day values of average can
or bottle of regular
caffeinated cola.
Replace SSBs with water Both (2-5) 124 5.5 3 Affected population
in schools Both (6-11) 184 6.5 6 and amounts based on
Both (12-14) 301 10.3 15 published analysis
Both (15-18) 301 10.3 15 from Wang, et al.\10\
Implemented for a
full academic year.c
Switch from 1 cup of Both (2-5) 0.64 cups 48.4 7 Averaged grams/cup and
sugary cereals to Boys (2-5) 0.64 cups 47.3 7 standardized serving
cereals scored highest Girls (2-5) 0.64 cups 49.6 7 sizes of top ten \39\
in nutritional quality Both (6-11) 0.93 cups 39.5 6 and bottom ten \40\
Boys (6-11) 0.93 cups 40.2 6 cereals by nutrition
Girls (6-11) 0.94 cups 38.8 6 score, as determined
Both (12-14) 1.16 cups 34.5 5 by CerealFACTS.
Boys (12-14) 1.32 cups 35.5 5 org.\41\
Girls (12-14) 1.0 cups 33.5 5 Affected population
Both (15-18) 1.15 cups 26.6 4 and average grams/
Boys (15-18) 1.25 cups 26.1 4 serving consumed
Girls (15-18) 1.06 cups 27.0 4 based on analysis of
NHANES 2007-2010 data
on 24-hour dietary
recall.
Proportion of cups
consumed in Amount
column based on
standardized 39.2
grams/cup (as
described above), and
grams/serving from
NHANES.
Pass NYC's proposed Both (2-5) 24.2 0.6 0 Amount is average
sugary drink size Boys (2-5) 21.1 0.9 0 kilocalorie reduction
limit Girls (2-5) 32.3 0.4 0 per day if limited
Both (6-11) 67.9 5.1 3 consumption to 16 oz/
Boys (6-11) 70.0 6.1 4 day as in Elbel, et
Girls (6-11) 64.9 4.2 3 al.,\42\ and Wang, et
Both (12-14) 93.6 9.4 9 al.\14\
Boys (12-14) 109.3 10.1 11 Affected population
Girls (12-14) 77.7 8.7 7 and average
Both (15-18) 111.8 13.3 15 kilocalorie reduction
Boys (15-18) 120.3 15.3 18 based on analysis of
Girls (15-18) 100.1 11.2 11 NHANES 2007-2010 data
on 24-hour dietary
recall.
Those consuming >16 oz
limit consumption to
maximum of 16 oz/day
No ``upsizing'' occurs
(i.e., individuals
purchase more than
one 16-oz beverage to
compensate for size
limit).
SSB definition
includes sodas,
sports drinks, fruit
drinks and punches,
low-calorie drinks,
sweetened tea, and
other sweetened
beverages consumed in
food service
establishments.
Implemented
nationally.
----------------------------------------------------------------------------------------------------------------
Empirical estimates
----------------------------------------------------------------------------------------------------------------
Pass California's Both (15-18) 157.8 100 78 Taber, et al.,\9\
competitive food estimate 157.9 kcal
nutrition standards in per weekday fewer
high schools calories consumed in
nationally California high
schools, compared to
14 other states with
weaker competitive
food laws states.
The intervention only
applies to high
school students.
Implemented for a full
academic year.c
Reduce TV viewing, 60 All 106 100 106 Sonneville and
minutes/day Gortmaker \38\
estimate TV watching
and video/computer
game playing
associated with 105.5-
kcal/hour and 91.8-
kcal/hour increase in
total energy intake
in boys aged 13-15
years and girls aged
12-14 years. Epstein,
et al.,\16\ and
Miller, et al.,\18\
report similar
changes in energy
intake.
Reduce video- or All 92 100 92 Same calorie change
computer-game playing for other age groups
time, 60 minutes/day
----------------------------------------------------------------------------------------------------------------
a The amount designates the current pre-intervention consumption level of the item by the selected population;
amounts are kilocalories unless otherwise specified.
b The impact designates the percentage of the selected eligible population that is affected by the intervention.
c Intervention is applied over a full school year (on average, 180 days). The total caloric impact is averaged
over 365 days to account for no change in activity on holidays, weekends, and summervacation.
NHANES, National Health and Nutrition Examination Survey; NYC, New York City; SSB, sugar-sweetened beverage.
Many of the ACI estimates built into the Caloric Calculator require
stipulated assumptions, which are shown in detail in Tables 2 and 3, as
well as within the web tool. For example, the calculations of energy
expended through increased MVPA during PE involved the following
assumptions: a national baseline of 37% MVPA during PE time,\28\ a
target level of 50% recommended by the CDC,\20\ and 180 school days a
year for school-based interventions. The assumed MET level for non-MVPA
PE time was estimated as 1.8 METs, using an average of lying down,
sitting, and standing.\12\
The time spent on MVPA was estimated to be 4.5 METs based on the
average of moderate physical activity (3 METs) and vigorous physical
activity (6 METs).\7\ For example, for a typical high school adolescent
(average weight: 157 lbs), increasing MVPA from 37% to 50% during a
daily 30 minute PE class for a school year was estimated to produce an
average increase in energy expenditure of 6 kcal/day--clearly
insufficient on its own to reverse the childhood obesity epidemic.
Further, even this small effect could potentially be diminished if
compensation occurred for this additional caloric expenditure with
increased food or beverage consumption.
It is important to note that all estimates used in creating the
Caloric Calculator were population-based. In addition, for
interventions designed to remove a particular food or beverage from the
diet, caloric benefits were accrued only from the population affected
(e.g., the population affected by the NYC sugary drink portion-size cap
was presumed to include those consuming sugary beverages of >16 ounces
per serving, estimated to include only 12% of adolescents aged 12-19
years).\14\
Discussion
Reversing the nation's current childhood obesity epidemic will
require multiple individual, behavioral, policy, environmental, and
normative changes--through public health and clinical strategies--to
reverse the daily accumulation of a positive ``energy gap'' that
brought us to this point. New evidence from New York City,\43\
Philadelphia,\44\ California,9, 45 and Mississippi \46\
demonstrates that broad approaches involving multifaceted policies and
environmental strategies have the power to halt and reverse the
trend.\47\ However, what has been missing is a metric for estimating
the individual and combined effects of specific interventions to
increase children's activity levels and reduce their intake of energy-
dense, low-nutrient foods and beverages.
This paper expands on the previously published ``energy gap''
framework--which estimated the magnitude of energy surplus underlying
the obesity epidemic among U.S. youth 4-5--to examine the
effects of various interventions, alone or in combination, to favorably
tip the energy balance. The lack of a common metric for comparing the
effectiveness of strategies with differing behavioral targets (i.e.,
reducing excess caloric intake and/or increasing physical activity) has
stymied past efforts to apply analytic tools to rank existing
strategies on their contribution to reversing the childhood obesity
trend. The development and application of the Average Caloric Impact
(ACI) metric and the Caloric Calculator tool offer an opportunity to
fill this gap.
Although the Caloric Calculator begins to address these issues,
there are nuances in the obesity reduction equation that will require
further research and discussion. The evidence used to estimate ACIs is
still in many ways limited and dependent on the rigor of existing
intervention studies and on the availability and reliability of
intervention outcome measures (e.g., the use of objectively measured,
versus self-reported, outcomes or ecologic associations that can be
examined across studies). In addition, many studies focus narrowly on
specific populations, such as middle school girls30 or a specific age
range.29, 36, 37
Most challenging at this stage in childhood obesity prevention
research is the lack of high-quality studies with a sufficiently long
follow-up. A 2011 Cochrane review of obesity prevention efforts found
that only 14 of the 55 included studies had interventions lasting more
than 12 months, most of which focused only on children aged 6-12 years.
There is virtually no evidence from studies aimed at younger children
to determine whether intervention benefits can be sustained into later
adolescence or adulthood.\6\ Therefore, it would be inaccurate to make
predictions of weight change from fixed caloric changes using these
estimates, particularly given the multitude of factors that drive
weight change over time \48\ and the large changes seen from childhood
to adolescence.\49\
Study populations also have varied widely with respect to racial/
ethnic composition, SES, and prevalence of obesity at baseline,
limiting the generalizability and comparability of intervention
effects. Thus, the tool represents the authors' best effort to assess
the average impact if these programs were broadly implemented. Local
contexts and subpopulation characteristics are likely to modify the
actual outcomes. The estimates will continue to be refined and updated
as new data emerge from periodic scans of newly published data and
feedback from collaborators in the field of childhood obesity
prevention. Going forward, the Calculator will be further developed to
address specific subsets of the population or allow more user inputs to
facilitate broader dissemination and policy discussions. For example, a
principal of a disproportionately low-income school could use the tool
based on the school's demographics, or parents could use the tool by
entering their child's age, gender, and body weight.
Despite these limitations, there is value in the Caloric
Calculator's ability to translate evidence into practice by generating
caloric impact estimates and projecting the potential cumulative
effects of multicomponent interventions addressing one or both sides of
the energy balance equation. The ACI is a summary measure of several
dimensions of the program or policy evaluated: reach, effectiveness/
efficacy, adoption, implementation, and maintenance.\50\ These
dimensions also convey why the net caloric impact of the same program
will vary from population to population when implemented in the real
world. As such, the tool is expected to offer a starting point to
support policymakers and practitioners in using existing evidence to
drive decision making in a more straightforward manner.
The development of a common metric can lay the groundwork for more
evidence-based resource allocation decisions, both in program
implementation and in further evidence gathering. Future expansion of
this framework may include finer granularity in the population
targeted, such as overweight status, race/ethnicity, and urban/rural
locations as well as concerns for equity, cost effectiveness, and other
long-term outcomes.\47\ Further, the current review underscored the
need to encourage the evaluations of programs and policies to use and
report objective and comparable outcome measures, such as changes in
activity levels (e.g., MET values); duration (e.g., minutes of MVPA
added); net changes in calories consumed in addition to key nutrients
or diet quality; and measured BMI whenever possible.
Because the Caloric Calculator uses national data with the aim of
estimating mean population-level effect sizes, the effect of an
intervention is averaged across those who received and benefited from
the program and those who did not. Therefore, an intervention that has
a large effect but reaches only a small number of children may appear
to have less of an impact at the population level. For example, an
active transport program may target children who live within 1 mile of
their school, which will reach at most 31% of children in Grades K-
8.\23\ The daily caloric impact, when averaged across all children, is
therefore a fraction of the net caloric impact for those who
participate in walking to school. Although not evaluated in the current
study, the same consideration applies to interventions specifically
targeted at overweight adolescents (who have an average energy gap of
700-1,000 kcal/day).\4\
It is important to note that although the analyses presented in
this paper focus on intervention effects on daily energy gaps and
obesity levels in youth, there are important health and nonhealth
benefits gained from improving physical activity and diet that are not
captured by the ACI measure. For instance, there is growing evidence
that physical activity has beneficial effects on mental health outcomes
and academic performance.\51\ Similarly, an intervention to improve the
nutritional quality of a la carte foods and beverages improves the
overall nutritional profile of foods consumed at school despite having
no significant effect on the total number of calories
sold.52-53
Some investments in childhood obesity prevention have been
projected to be cost effective.\54\ But without knowing what types of
interventions to invest in, efforts may fail to produce the expected
results. There have been many controversial, yet noteworthy, recent
policy recommendations that will be scaled up to the national level
(e.g., menu labeling). Without experimental evidence, however, it can
be difficult to convince the public and policymakers of the
implications and demonstrate the possible impact of implementation. The
Caloric Calculator provides a novel tool for appraising these policies
and interventions based on their potential efficacy, alone or combined,
providing an evidence-based platform to inform practice and policy.
The authors acknowledge the contribution of Dr. Laura K.
Brennan, Ph.D., M.P.H., President and CEO of Transtria LLC (St.
Louis, MO), and her team in the evidence-review process. The
authors thank Shawn Nowicki, M.P.H., and Andrew Wang, M.P.H.,
as graduate student assistants in literature review and the
early development of the tool. The authors also thank Michael
Slaven, MA, who designed and implemented the web tool,
www.caloriccalculator.org, as well as Kevin Hall, Ph.D., and
Carson Chow, Ph.D. (NIH/National Institute of Diabetes and
Digestive and Kidney Diseases), for their methodologic advice
on the analysis.
This work was supported by the Robert Wood Johnson Foundation
(grant no. 68162). This work is solely the responsibility of
the authors and does not represent the official views of the
Robert Wood Johnson Foundation.
No financial disclosures were reported by the authors of this
paper.
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attachment 2
Predicting Adult Weight Change in the Real World: A Systematic Review
and Meta-Analysis Accounting for Compensatory Changes in Energy
Intake or Expenditure *
---------------------------------------------------------------------------
* Received 6 May 2014; revised 19 August 2014; accepted 8 September
2014; accepted article preview online 17 October 2014; advance online
publication, 23 December 2014.
---------------------------------------------------------------------------
Review
E.J. Dhurandhar,[1-3, 7] K.A. Kaiser,[1, 3-4, 7]
J.A. Dawson,[3] A.S. Alcorn,[3] K.D. Keating
[5-6] and D.B. Allison [1, 3-4
---------------------------------------------------------------------------
\[1]\ Nutrition Obesity Research Center, University of Alabama at
Birmingham, Birmingham, AL, USA; [2] Department of Health
Behavior, University of Alabama at Birmingham, Birmingham, AL, USA;
[3] Office of Energetics, University of Alabama at
Birmingham, Birmingham, AL, USA; [4] School of Public
Health, Dean's Office, University of Alabama at Birmingham, Birmingham,
AL, USA; [5] Department of Biostatistics, School of Public
Health, University of Alabama at Birmingham, Birmingham, AL, USA and
[6] Department of Statistics, Kansas State University,
Manhattan, KS, USA. Correspondence: Dr. Professor D.B. Allison, School
of Public Health, Dean's Office, University of Alabama at Birmingham,
1665 University Boulevard, RPHB 140J, Birmingham, AL 35294, USA.
E-mail: [email protected].
[7] These authors contributed equally to this work.
Background: Public health and clinical interventions for
obesity in free-living adults may be diminished by individual
compensation for the intervention. Approaches to predict weight
outcomes do not account for all mechanisms of compensation, so
they are not well suited to predict outcomes in free-living
adults. Our objective was to quantify the range of compensation
in energy intake or expenditure observed in human randomized
controlled trials (RCTs).
Methods: We searched multiple databases (PubMed, CINAHL,
SCOPUS, Cochrane, ProQuest, PsycInfo) up to 1 August 2012 for
RCTs evaluating the effect of dietary and/or physical activity
interventions on body weight/composition. Inclusion criteria:
subjects per treatment arm %5; %1 week intervention; a reported
outcome of body weight/body composition; the intervention was
either a prescribed amount of over- or underfeeding and/or
supervised or monitored physical activity was prescribed; %80%
compliance; and an objective method was used to verify
compliance with the intervention (for example, observation and
electronic monitoring). Data were independently extracted and
analyzed by multiple reviewers with consensus reached by
discussion. We compared observed weight change with predicted
weight change using two models that predict weight change
accounting only for metabolic compensation.
Findings: Twenty-eight studies met inclusion criteria.
Overfeeding studies indicate 96% less weight gain than expected
if no compensation occurred. Dietary restriction and exercise
studies may result in up to 12-44% and 55-64% less weight loss
than expected, respectively, under an assumption of no
behavioral compensation.
Interpretation: Compensation is substantial even in high-
compliance conditions, resulting in far less weight change than
would be expected. The simple algorithm we report allows for
more realistic predictions of intervention effects in free-
living populations by accounting for the significant
compensation that occurs.
International Journal of Obesity (2015) 39, 1181-1187;
doi:10.1038/ijo.2014.184.
Introduction
Obesity is a serious and prevalent public health concern.\1\ New
public health and clinical interventions to reduce obesity are
frequently advocated or implemented based on hypothetical estimates of
an outcome that may have little empirical support (for example, the
3,500 kcal rule). For example, imagine an initiative from a large
company that replaces its 250 kcal candy bars in its vending machines
with 50 kcal protein bars to reduce energy intake (EI) from snacking
among its employees. This initiative can be expected to produce (in
those who consume at least 250 kcal per day from such snacks), on
average, 5.7 kg of weight loss after 1 year (for example, for a 35 year
old man who is 183 cm tall and weighs 100 kg at baseline, body mass
index = 30). This estimate is based on one of the mathematically
validated prediction models \2\ sometimes used to justify such
interventions.\3\ But is this estimate realistic?
On the basis of the evidence, this estimate is likely optimistic
because current models for predicting weight change are not well suited
for use in free-living subjects. A common rule of thumb used for
decades to predict weight change outcomes is that losing or gaining 1
pound of fat requires a deficit of 3,500 kcals of energy.\4\ This rule
does not consider that human energy balance is a dynamic and adaptable
system or that lean and fat mass is lost during negative energy
balance, and this leads to an underestimation of the change in EI or
energy expenditure (EE) needed to produce weight change.5-8
Recently, more sophisticated models have been developed to predict
weight changes, which consider the metabolic adaptations that occur
during weight change.9-12 To accurately predict weight
change in free-living individuals, however, both metabolic and
behavioral compensatory mechanisms must be accounted for.
Specifically, we define the modes of possible compensation as
follows:
Metabolic Compensation
It is a compensation for an energy balance intervention through
physiological changes in metabolism. For example, current mathematical
models account for changes in resting metabolic rate, fluid balance,
the thermic effect of food and spontaneous physical activity resulting
from an energy balance intervention.11-13
Behavioral Compensation
It is a compensation for an energy balance intervention through
behavior changes. For example, when a dietary or physical activity
intervention attempts to create negative energy balance, an individual
may respond by reducing voluntary EE and/or increasing EI if these
avenues are not strictly controlled. Similarly, during an energy
balance intervention of added energy, voluntary EE may increase and/or
EI may decrease from other sources.
Others have shown that behavioral compensation occurs for physical
activity interventions.\14\ Behavioral compensation may also occur for
interventions that reduce caloric intake or add calorie-containing
foods to the diet.15-16 Current prediction models are
intended for use where interventions are implemented with high fidelity
(that is, intended intervention exposure was achieved) in isolation,
and when metabolic compensation is the only route of compensation for
the intervention possible. During interventions in free-living
subjects, however, compensation can occur through metabolic
compensation and through behavioral compensation. Behavioral
compensation may diminish the effects of an intervention, making it
important to quantify and account for when predicting outcomes in free-
living populations. It is imperative that more realistic models be used
for predicting outcomes for the reasons stated recently:
``. . . to establish a less controversial legacy for this
important field, we should avoid past traps and be explicit
about reasonable expectations. Implausible results that are
`too good to be true' still threaten nutritional research on
many fronts, including survey measurements, observational
associations, treatment effects in randomized trials, and
estimates of the impact on populations.'' \17\
We therefore set out to build an empirically based model to predict
weight change outcomes in free-living subjects, and to quantify the
extent to which observed weight change in free-living subjects differs
from that predicted under the assumption of no behavioral compensation.
The approach we took was to use systematic review techniques to collect
study data and conduct meta-regression on studies meeting a priori
inclusion criteria. These criteria guided identification of high-
fidelity interventions implemented in free-living adults. The subjects
had some ability to behaviorally compensate for the intervention, yet
the reported information about the intervention and compliance
verification allowed for a high degree of confidence in treatment
fidelity. For our main analysis, we compared the predictions from
models that assume no active compensation 2, 18 with the
observed outcomes as an estimate of the effects of behavioral
compensation.
Materials and Methods
Systematic Review of the Literature and Study Selection
Articles, abstracts and doctoral dissertations were retrieved using
searches performed on the following electronic databases: PubMed,
Cochrane Library, SCOPUS, PsycInfo, Cumulative Index to Nursing and
Allied Health Literature (CINAHL) and Dissertation Abstracts. We
searched PubMed without MeSH headings to identify publications for
inclusion, using the following limits: dated 1 August 2012 back to
earliest records of human studies. Detailed search methods are provided
on the PROSPERO registry website (Registry #CRD42013002912). No ethics
committee approval was required as the data used are published summary
statistics.
All studies were evaluated according to the following inclusion
criteria: (1) the data were from adult human randomized controlled
trials in free-living subjects, (2) the intervention was either a
prescribed amount of over- or underfeeding given and reported (or could
be converted) in kcal and/or supervised or monitored physical activity
was prescribed and verified, (3) an objective verification method was
used to verify the intervention at %80% (for example, observation,
electronic monitoring and provision of food with returned unused
portions), (4) the study had a total sample size of at least five
participants at enrollment, (5) the study protocol included an
intervention period of at least 7 days, (6) the publication was
available in the English language and (7) the study was published and
listed in the above databases on or before 1 August 2012.
Our exclusion criteria are detailed in the online Supplementary
Material. Briefly, we excluded studies on samples that were completely
or predominantly made up of individuals younger than 18 years old or
older than 60 years or having any health conditions that may affect
weight. The filtering process of the initial search results is detailed
in Figure 1 and also described in more detail in the online supplement.
Statistical Analysis
Quantifying the effect of behavioral compensation-comparison with
metabolic compensation models. We entered sample demographic and
intervention data into each of the metabolic compensation model
calculators to most closely represent each intervention as described in
the published papers to estimate weight changes that would occur if
only metabolic compensation occurred. As we included data that had
samples of both men and women where separate baseline data and results
were not reported (only combined summaries), we entered the data for
both genders and mathematically adjusted the outputs for the relative
proportions of men and women. For the NIDDK simulator,\2\ we assumed a
baseline value (when not otherwise reported) of sedentary activity
level (1.4 metabolic equivalents). The difference between the observed
weight change for each study and the weight change predicted by these
models is indicative of the degree of behavioral compensation that is
observed for the interventions in free-living adults included in our
review and meta-analysis.
All model data were analyzed with R routines \19\ and descriptive
summaries were generated with Microsoft Excel version 2010. Further
details of statistical approaches used for the predictive model
building are on the online supplement. Risk of bias was assessed by two
authors (EJD and KAK) independently and discrepancies were discussed
until consensus was reached.
Role of Funding Source
The funding agency (International Life Science Institute--North
America) had no role in the design, conduct, analysis, manuscript
preparation or decision to publish the results of this study.
Results
Results of Publication Search
We retrieved citations dated back to 1935, but more than \2/3\ of
the initial publications retrieved were published after 2001. The final
data set for building the predictive model consisted of 28 studies
published between 1987 and 2012, including 15 exercise studies, nine
studies with added energy, three dietary restriction studies and two
studies that included both dietary restriction and exercise in the
intervention (see Table 1 for a complete listing of included studies
with selected summary data and intervention descriptions). The primary
reasons for exclusion after full text review were studies not being
truly randomized or not having a control group, followed by reliance
only on self-report for EI or physical activity without any objective
verification of compliance. Studies were all published journal
articles, except for two dissertations.20-21 Eleven studies
had samples that were either 100% men or 100% women. Three other
studies reported results by gender separately if both males and females
were included in the sample. Only six studies (21%) reported the racial
makeup of the samples; therefore, this factor was excluded from further
analysis. Mean ages of the samples ranged from 20.6 to 60 years. Mean
baseline body mass index of the samples ranged from 22.6 to 35.1
kgm^\2\.
Figure 1
PRISMA diagram-literature search and study selection process.
Table 1. Master List and Summary of Included Studies Grouped By Treatment Type and Sorted in Ascending Year of Publication
--------------------------------------------------------------------------------------------------------------------------------------------------------
Sample studied
(mean age-years, Adjusted daily Study N randomized, Method of
Reference(s) Intervention pct female, dose(s) (kcal: duration Intervention completed, missing data Overall mean
baseline BMI treatment- control) (weeks) notes analyzed handling compliance
kgm^\2\)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Johnstone, et Diet 38, 0%, 35.1 ^167.2 4 High protein, 20, 17, 17 Completers 100
al.\22\ ketogenic diet
Das, et al.\23\ Diet 35, 76.3%, 27.6 ^285.6 26 Caloric 46, 39, 39 Completers 100
restriction
Zachwieja, et Diet and 24, 45.8%, 24.1 ^675 2 Caloric 24, 24, 24 No drops 90
al.\24\ exercise restriction and
daily treadmill
exercise
Moreira, et Diet and 49, 68%, 30 ^556.0, ^753.3 11 25% caloric 36, 35, 36 ITT 99
al.\25\ exercise restriction
(separate (controlled
treatments) feeding) versus
aerobic
exercise
(individualized
and supervised
sessions 3 per
week)
Leon, et al.\26\ Exercise 32.6, 0%, 26 ^245.6 12 Walking and 22, 16, 16 Completers 86
stair climbing
Van Etten, et Exercise 33.7, 0%, 23.7 ^31.6 18 Weight training 26, 26, 26 Completers 95
al.\27\
Murphy, et Exercise 44.4, 100%, 25.76 ^81.6, ^84.5 10 Long versus 47, 34, 34 Completers 86.5
al.\28\ short bouts of
walking
Crandall \21\ Exercise 51.75, 44%, 30.8 ^76.7 12 Recumbent cycle 13, 13, 13 No drops 100
ergometer
Shaw and Shaw Exercise 41, 92%, 32.6 ^13.7 8 Resistance 28, 28, 28 Completers 91.1
\29\ training
Kirk, et al.\30\ Exercise 20.6, 0%, 28.2 ^104.7 24 High-intensity 25, 19, 19 Completers 96
resistance
training
Whybrow, et Exercise 27.2, 50%, 23.6 ^455.6, ^513.6, 2 Progressive 12, 12, 12 No drops 100
al.\31\ ^907.1 exercise on
cycle ergometer
or treadmill
Guadalupe-Grau, Exercise 23.7, 65.2%, 23.03 ^51.7 9 Strength 88, 72, 66 Completers 85
et al.\32\ training and
plyometric
jumps
Alves, et Exercise 38.2, 100%, 30 ^106.1 26 Group exercises 156, 146, 156 ITT, BOCF 96
al.\33\
Turner, et Exercise 54, 0%, 28 ^187.3 24 Structured 54, 41, 29 Completers 94
al.\34\ exercise
Bell, et al.\35\ Exercise 49, 100%, 34.7 ^399.0, ^395.1 24 Pedometer-based 211, 128, 128 Completers 84.77
walking program
Vispute, et Exercise 23.66, 41.7%, 24.6 ^41.9 6 Abdominal 24, 24, 24 No drops 95.71
al.\36\ exercises
Hornbuckle, et Exercise 28.5, 0%, 25.42 ^57.7 12 Resistance 44, 32, 44 ITT 96
al.\37\ training
Heydari, et Exercise 37.7, 56.3%, 27.8 ^186.4 12 High-intensity 46, 38, 38 Completers 100
al.\38\ intermittent
exercise
Thompson, et Exercise 49.7, 72.8%, 31.8 ^174.8 16 Supervised 162, 137, 162 ITT 91
al.,\39\ and aerobic
Church, et exercise
al.\40\
Addington \20\ Feeding 38.74, 63.8%, 2.9 (aspartame 4 Artificially 150, 111, 111 Completers 100
32.09 group), 142.9 (SSB sweetened
group) beverage
(aspartame)
versus SSB
Lammert, et Feeding 22.4, 0%, 22.61 191 3 Overfeeding 20, 20, 20 No drops 100
al.\41\ carbohydrate or
fat
Martin, et Feeding 37.7, 56.3%, 27.8 597.1 2 Low- versus high- 10, 10, 10 No drops 100
al.\42\ calorie
breakfast
Sabate, et Feeding 42.6, 45.2%, 23.7 219 26 Walnuts 90, 90, 90 No drops 95
al.\43\
Whybrow, et Feeding 60, 26.7%, 27.7 122.8, 227.5 8 Added fruits and 90, 62, 62 Completers 92.6
al.\44\ vegetables
Whybrow, et Feeding 35.05, 50%, 25.35 343.9, 687.9 2 Added snacks 100, 87, 72 Completers 96
al.\45\
Sheridan, et Feeding 24.9, 0%, 28.7 314.8 4 Pistachio nuts 15, 15, 15 No drops 99
al.\46\
Casas-Agustench, Feeding 54.4, 56.3%, 26.5 176.9 12 Mixed nuts 52, 50, 50 Completers 94
et al.\47\
Maersk, et Feeding 28, 0%, 22.2 3.1, 365.2, 385.5 26 1 l per day of 60, 47, 47 Completers 85
al.\48\ diet soda, SSB
or milk versus
water
--------------------------------------------------------------------------------------------------------------------------------------------------------
Abbreviations: BOCF, baseline observation carried forward; ITT, intention-to-treat analysis reported; pct, percentage; SSB, sugar-sweetened beverage.
Building a Predictive Model
We expected to find enough studies to build a robust regression
model, incorporating mean participant characteristics and evaluating
any significant interactions. However, the relatively low number and
sparsely distributed data prevented reliable estimates from our final
model. Details of the model and its estimations can be found in the
online supplement, Supplementary Figure S1 and Supplementary Tables S1a
and S1b.
Comparison with Metabolic Compensation Models--Estimating Behavioral
Compensation
To address our main research question (What is the effect of
behavioral compensation that occurs in free-living subjects who receive
an energy balance intervention on weight outcomes?), we generated
output for each study using the NIDDK and Pennington weight change
prediction calculators 2, 18 to estimate weight changes that
would occur if only metabolic compensation occurred. The difference
between the observed weight loss for each study and the weight change
predicted by these models is indicative of behavioral compensation
occurring during the intervention. The NIDDK and Pennington models are
highly correlated (Pearson's r = 0.98, P<0.0001) in predicted weight
change (Supplementary Figure S2). In general, the Pennington calculator
is slightly more conservative than the predictions made by the NIDDK
calculator.
The overall degree of behavioral compensation estimated by the gap
between the observed and metabolic compensation--only predicted values
is illustrated in Supplementary Figure S3, panels A and B. Both slopes
being less than 1 (that is, 0.344 and 0.399 for the NIDDK and
Pennington Models, respectively) indicate that the observed weight
change is less than predicted after accounting for metabolic
compensation. This quantifies the degree of behavioral compensation
that is occurring (that is, the compensation that is in addition to the
metabolic compensation, resulting in less weight change than expected).
The degree of behavioral compensation appears to differ depending
on intervention type. As shown in Supplementary Figure S3, panels A and
B, all types of interventions demonstrated less weight change than
either the Pennington or NIDDK calculators predicted. The plot of
overfeeding trials has a slope (95% confidence interval) of 0.06
(^0.04, 0.16) and 0.07 (^0.05, 0.18), plotted against the NIDDK and
Pennington calculators, respectively (Figure 2, panels a and b). A
slope of 1 would indicate that, on average, the interventions produced
exactly as much weight change as expected from the mathematical models,
which assume no behavioral compensation. As such, this suggests that
behavioral compensation may result in as much as 96% less weight gain
than predicted by metabolic calculators when adding energy to the diet.
The slopes of the plots for dietary restriction and exercise studies
are more similar to each other. Specifically, slopes (95% confidence
interval) of 0.56 (0.17, 0.96) and 0.88 (0.36, 1.40) were plotted
against the NIDDK and Pennington calculators, respectively, for dietary
restriction studies (Figure 2). For exercise intervention studies,
slopes (confidence interval) of 0.38 (0.16, 0.60) and 0.46 (0.19, 0.72)
were plotted against the NIDDK and Pennington calculators, respectively
(Figure 3). Thus, behavioral compensation may result in up to 12-44%
less weight loss than predicted for dietary restriction studies and 55-
64% less weight loss than predicted for exercise intervention studies.
Risk of Bias Assessment for Included Studies
See online supplement for risk of bias summary and detailed rating
figure (Supplementary Figure S4) for each included study. The greatest
proportions of study aspects with high risk of bias were judged to be
due to the lack of analysis for incomplete data (attrition bias--for
example, use of intention-to-treat analysis) and lack of attention
placebo for control groups. Four studies reported results using
intention-to-treat analysis.
Figure 2
NIDDK and Pennington calculator predictions for caloric
restriction (D, squares) and overfeeding (F, triangles)
interventions. NIDDK (a) and Pennington (b) model predictions
(x axis) versus actual observed weight changes for all studies
(y axis). Each individual point represents a control versus
treatment comparison; the solid lines are lines of best fit for
slope and black dashed lines are 95% confidence intervals. Gray
dashes lines are axes and lines of identity. Overall,
predictions are an overestimate of observed weight change.
Figure 3
NIDDK and Pennington calculator predictions for exercise
interventions (E). NIDDK (a) and Pennington (b) model
predictions (x axis) versus actual observed weight changes for
all studies (y axis). Each individual point represents a
treatment versus control comparison; the solid lines are lines
of best fit for slope and black dashed lines are 95% confidence
intervals. Gray dashes lines are axes and lines of identity.
Overall, predictions are an overestimate of observed weight
change.
Discussion
We generated simple adjustment factors to predict weight change
resulting from energy balance interventions in free-living adult
populations, with the ability to compensate both behaviorally and
metabolically, using 73 treatment and control arm group outcomes from
28 studies. One of the notable findings was the small number of studies
meeting our inclusion criteria (that is, where compliance was
objectively measured), making it difficult to study the role of
behavioral compensation in a free-living context beyond a very basic
level. Although our estimates are the only ones for this purpose to
date based on the currently available literature, this highlights a gap
in the literature of studies designed to determine the impact of energy
balance perturbations in humans in the context of a full range of
compensation that prevents a more precise estimate. As these studies
are crucial to understanding the effect of public health interventions,
their limited quantity underscores a need for future research in this
area.
Perhaps, the most robust finding from our study most relevant to
public health is that currently available predictions consistently
overestimate weight change, which is evidence of significantly
diminished weight change resulting from behavioral compensation. This
is in spite of some instances where explicit instructions were given to
make no other changes in routine habits, a form of compliance that is
less commonly tracked or verified. In particular, the treatment effect
of added calories was only, on average, 5% of the weight gain
predicted from models assuming no behavioral compensation. Several
included studies reported a mean weight loss effect from added energy.
This indicates that even if a new food is introduced to the diet, for
example, adding a daily snack or beverage, EI and/or EE can be adjusted
reasonably well, resulting in very little weight gain relative to how
much would be expected if this behavioral compensation did not occur.
Behavioral compensation for negative energy balance interventions such
as exercise or dietary restriction is also evident from our analysis,
and results in 37-45% and 56-88% of the weight loss predicted from
metabolic-only compensation models. In our initial example of reducing
EI via snacks by 200 kcals per day for the hypothetical man, the
adjusted estimate of weight change after 1 year would be closer to 3.2
kg. This is lower than the 5.7 kg estimate given by the body weight
simulator that predicts metabolic compensation only.
Therefore, our results suggest that current public health
interventions or clinical interventions that alter one aspect of energy
balance, without holding other aspects constant, may result in more
modest weight changes than predicted or desired. A similar approach has
been reported in pediatric studies,\3\ but it did not attempt to
account for both behavioral and metabolic compensation components. It
is important to take all modes of compensation into consideration when
planning an intervention with targeted amounts of weight change and
when anticipating its outcomes. It is likely that increased doses of
energy perturbations are required. Increased control over compliance
and compensation is necessary to achieve target outcomes. Estimates of
what is required to achieve a specific weight change may be made more
accurate for the purposes of public health recommendations if the
present estimations are considered.
Our results suggest that there might be a differential effect of
treatment type on the degree of behavioral compensation. However, an
aspect of our data set needs to be considered in interpreting this
result. Dietary restriction interventions are associated with greater
treatment effects, and less behavioral compensation, than either
exercise or overfeeding interventions. However, this finding may be
because the dietary restriction interventions included in our analysis
only allowed for behavioral compensation through EE changes, whereas
all exercise and overfeeding interventions allowed for behavioral
compensation through both dietary intake and EE changes.
Our approach has strengths and limitations. First, our inclusion
criteria were rigorous. All included studies have at least 80%
compliance with the prescribed intervention, with compliance verified
objectively (no reliance solely on self-report). In addition, the dose
was corrected in our calculations for the level of compliance reported
in the study. Further, included studies were randomized controlled
trials, and our outcome for generating the predictive model and for
comparing with metabolic compensation models was the control group-
adjusted weight change. Therefore, our models are built to assess true
treatment effect, and are corrected for any weight change due to
factors such as regression to the mean, maturation, historical factors
and behaviors that result from simply participating in a study, rather
than from the treatment itself.
Several limitations should also be considered when interpreting our
analysis. Weight was not always the primary outcome in studies that met
our inclusion criteria. This is particularly true for those with added
EI in the form of nuts. Differences in stated outcomes of interest,
time with researchers and other factors may affect weight outcomes for
individual studies. In addition, body composition may be an important
outcome that we were not able to adequately analyze because of the
limited number of studies including body composition measurements such
as changes in fat mass and fat-free mass. Because of our rigorous
inclusion criteria, our data set is small (28 studies). The types of
studies we selected are necessary for making definitive conclusions
about the impact of perturbations in one aspect of energy balance on
body weight. Studies also tended to be shorter in duration, thus it is
difficult to make conclusions about long-term effects. This is a large
gap in the literature, and a more systematic approach to large, well-
controlled studies to answer these questions is warranted. In addition,
16 of the 28 studies reported data only for those participants who
completed the intervention period, and across all studies there was a
17.8% dropout rate (Table 1), which may have biased our estimates of
weight change toward overestimation. We used the intention to treat
data when reported (four studies). Eight studies reported no dropouts.
Future research is needed to understand potential differences in
compensation between dietary interventions (added or reduced energy),
different food forms and macronutrient compositions. Also, certain
factors should be considered as potential confounders when quantifying
the compensatory response to a specific intervention. For example,
bioavailability of energy in food, efficiencies in physical activity
and food utilization, seasonal effects and durations of interventions
may all influence both the metabolic and behavioral compensatory
response to an intervention. It is also unclear whether compensation
would remain constant over time. Moreover, evaluating the influence of
participant characteristics related to eating behavior (cognitive
restraint, disinhibition and hunger) and compensation during
interventions is needed as this may hold promise for optimizing
treatment effectiveness.
To conclude, we have presented the first empirically based,
quantitative estimation for the range of behavioral compensation that
may be observed for energy balance interventions. This information may
assist in the estimation of weight outcomes of clinical health
interventions. It may also inform public health projections for obesity
interventions or public health initiatives.
Conflict of Interest
DBA has received consulting fees and his university has received
gifts, grants and donations from multiple nonprofit and for-profit
organizations with interests in obesity including publishers,
litigators and food and pharmaceutical companies. KAK has received a
speaker honorarium from Coca-Cola Iberia. The remaining authors declare
no conflict of interest.
Acknowledgements
This project was sponsored by the International Life Sciences
Institute--North America (EJD and KAK, co-PIs). We thank the following
experts for their helpful comments on earlier versions of this
manuscript: Steve Blair, Steve Heymsfield, Rick Mattes, Robert
Matthews, Diana Thomas and Kevin Fontaine. Registry Information:
PROSPERO (http://www.crd.york.ac.uk/prospero/search.asp)
CRD42013002912.
Author Contributions
EJD, KAK and DBA conceived the study and developed the design and
selection criteria. KAK performed the literature searches. KAK and EJD
reviewed the literature, selected studies, extracted data, evaluated
risk of bias and wrote significant portions of the manuscript. ASA
assisted with literature selection, data extraction and summary
calculations. JAD and KDK performed the statistical analysis and wrote
some portions of the manuscript. DBA directed the statistical analysis
and wrote some portions of the manuscript.
Supplementary Information accompanies this paper on
International Journal of Obesity website (http://www.nature.com/
ijo).
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attachment 3
Myths, Presumptions, and Facts About Obesity *
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* This article was updated on June 6, 2013, at NEJM.org.
N. Engl. J. Med. 2013;368:446-54.
DOI: 10.1056/NEJMsa1208051.
Copyright 13 Massachusetts Medical Society.
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Krista Casazza, Ph.D., R.D., Kevin R. Fontaine, Ph.D., Arne Astrup,
M.D., Ph.D., Leann L. Birch, Ph.D., Andrew W. Brown, Ph.D., Michelle M.
Bohan Brown, Ph.D., Nefertiti Durant, M.D., M.P.H., Gareth Dutton,
Ph.D., E. Michael Foster, Ph.D., Steven B. Heymsfield, M.D., Kerry
McIver, M.S., Tapan Mehta, M.S., Nir Menachemi, Ph.D., P.K. Newby,
Sc.D., M.P.H., Russell Pate, Ph.D., Barbara J. Rolls, Ph.D., Bisakha
Sen, Ph.D., Daniel L. Smith, Jr., Ph.D., Diana M. Thomas, Ph.D., and
David B. Allison, Ph.D.**
---------------------------------------------------------------------------
** From the Departments of Nutrition Sciences (K.C., M.M.B.B.,
D.L.S., D.B.A.), Health Behavior (K.R.F.), Pediatrics (N.D.), Medicine
(G.D.), Health Care Organization and Policy (E.M.F., N.M., B.S.), and
Biostatistics (T.M., D.B.A.) and the School of Public Health, Office of
Energetics, Nutrition Obesity Research Center (A.W.B., D.B.A.),
University of Alabama at Birmingham, Birmingham; the OPUS Center and
the Department of Nutrition, Exercise, and Sports, University of
Copenhagen, Copenhagen (A.A.); the Departments of Development and
Family Studies (L.L.B.) and Nutritional Sciences (B.J.R.), Pennsylvania
State University, University Park; Pennington Biomedical Research
Center, Baton Rouge, LA (S.B.H.); Children's Physical Activity Research
Group, Department of Exercise Science, Arnold School of Public Health,
University of South Carolina, Columbia (K.M., R.P.); the Departments of
Pediatrics and Epidemiology, Program in Graduate Medical Nutrition
Sciences, and Program in Gastronomy, Culinary Arts, and Wine Studies,
Boston University, Boston (P.K.N.); and the Center for Quantitative
Obesity Research, Montclair State University, Montclair, NJ (D.M.T.).
Address reprint requests to Dr. Allison at the University of Alabama at
Birmingham, Department of Biostatistics, Birmingham, AL 35294, or at
[email protected].
---------------------------------------------------------------------------
Abstract
Background
Many beliefs about obesity persist in the absence of
supporting scientific evidence (presumptions); some persist
despite contradicting evidence (myths). The promulgation of
unsupported beliefs may yield poorly informed policy decisions,
inaccurate clinical and public health recommendations, and an
unproductive allocation of research resources and may divert
attention away from useful, evidence-based information.
Methods
Using Internet searches of popular media and scientific
literature, we identified, reviewed, and classified obesity-
related myths and presumptions. We also examined facts that are
well supported by evidence, with an emphasis on those that have
practical implications for public health, policy, or clinical
recommendations.
Results
We identified seven obesity-related myths concerning the
effects of small sustained increases in energy intake or
expenditure, establishment of realistic goals for weight loss,
rapid weight loss, weight-loss readiness, physical-education
classes, breast-feeding, and energy expended during sexual
activity. We also identified six presumptions about the
purported effects of regularly eating breakfast, early
childhood experiences, eating fruits and vegetables, weight
cycling, snacking, and the built (i.e., human-made)
environment. Finally, we identified nine evidence-supported
facts that are relevant for the formulation of sound public
health, policy, or clinical recommendations.
Conclusions
False and scientifically unsupported beliefs about obesity
are pervasive in both scientific literature and the popular
press. (Funded by the National Institutes of Health.)
Passionate interests, the human tendency to seek explanations for
observed phenomena, and everyday experience appear to contribute to
strong convictions about obesity, despite the absence of supporting
data. When the public, mass media, government agencies, and even
academic scientists espouse unsupported beliefs, the result may be
ineffective policy, unhelpful or unsafe clinical and public health
recommendations, and an unproductive allocation of resources. In this
article, we review some common beliefs about obesity that are not
supported by scientific evidence and also provide some useful evidence-
based concepts. We define myths as beliefs held to be true despite
substantial refuting evidence, presumptions as beliefs held to be true
for which convincing evidence does not yet confirm or disprove their
truth, and facts as propositions backed by sufficient evidence to
consider them empirically proved for practical purposes.
When standards for evidence are considered, it is critical to
distinguish between drawing conclusions from scientific evidence and
making decisions about prudent actions. Stakeholders must sometimes
take action in the absence of strong scientific evidence. Yet this
principle of action should not be mistaken as justification for drawing
conclusions. Regardless of the urgency of public health issues,
scientific principles remain unchanged. We find the language of the
Federal Trade Commission to be apt: its standard for making claims is
``competent and reliable scientific evidence,'' defined as ``tests,
analyses, research, studies, or other evidence . . . conducted and
evaluated in an objective manner . . . using procedures generally
accepted . . . to yield accurate and reliable results.'' \1\
The scientific community recognizes that randomized experiments
offer the strongest evidence for drawing causal inferences.
Nevertheless, at least since the 1960s, when Sir Austin Bradford Hill
spearheaded the scientific activities that led to the acceptance of the
claim that smoking causes lung cancer and to his classic writing on
association and causation,\2\ the scientific community has acknowledged
that under some circumstances (i.e., when it is unethical or unfeasible
to conduct a randomized study and when observed associations are not
plausibly due to confounding), inferring causality in the absence of
data from randomized, controlled trials is necessary and appropriate.
However, the fact that the appropriateness of inferring causality holds
only under certain circumstances is sometimes discounted by those who
are eager to garner support for a proposal in the absence of strong
data from randomized studies.
Notably, the circumstances that justify drawing a conclusion of
causation from nonexperimental data are rarely met in clinical and
public proposals regarding obesity. It is possible to conduct
randomized studies of even the most sensitive and invasive obesity
procedures, as exemplified by recent articles in the Journal. Moreover,
observational associations germane to the causes, treatment, and
prevention of obesity are subject to substantial confounding, fraught
with measurement problems, and typically small and inconsistent.\3\
Such observational associations are often found to differ from those
later obtained by more rigorously designed studies.\4\ Hence, in the
present discussion, we generally conclude that a proposition has been
shown to be true only when it has been supported by confirmatory
randomized studies. References to published studies are used sparingly
herein, with a more comprehensive listing provided in the Supplementary
Appendix, available with the full text of this article at NEJM.org.
Myths
We review seven myths about obesity, along with the refuting
evidence. Table 1 provides anecdotal support that the beliefs are
widely held or stated, in addition to reasons that support conjecture.
Table 1. Seven Myths about Obesity *
------------------------------------------------------------------------
Myth Basis of Conjecture
------------------------------------------------------------------------
Small sustained changes in energy National health guidelines and
intake or expenditure will reputable websites advertise that
produce large, long-term weight large changes in weight accumulate
changes indefinitely after small sustained
daily lifestyle modifications (e.g.,
walking for 20 minutes or eating two
additional potato chips)
Setting realistic goals in According to goal-setting theory,
obesity treatment is important unattainable goals impair
because otherwise patients will performance and discourage goal-
become frustrated and lose less attaining behavior; in obesity
weight treatment, incongruence between
desired and actual weight loss is
thought to undermine the patient's
perceived ability to attain goals,
which may lead to the
discontinuation of behaviors
necessary for weight loss
Large, rapid weight loss is This notion probably emerged in
associated with poorer long-term reaction to the adverse effects of
weight outcomes than is slow, nutritionally insufficient very-low-
gradual weight loss calorie diets (<800 kcal per day) in
the 1960s; the belief has persisted,
has been repeated in textbooks and
recommendations from health
authorities, and has been offered as
a rule by dietitians
Assessing the stage of change or Many believe that patients who feel
diet readiness is important in ready to lose weight are more likely
helping patients who seek weight- to make the required lifestyle
loss treatment changes
Physical-education classes in The health benefits of physical
their current format play an activity of sufficient duration,
important role in preventing or frequency, and intensity are well
reducing childhood obesity established and include reductions
in adiposity
Breast-feeding is protective The belief that breast-fed children
against obesity are less likely to become obese has
persisted for more than a century
and is passionately defended
A bout of sexual activity burns Many sources state that substantial
100 to 300 kcal for each person energy is expended in typical sexual
involved activity between two adults
------------------------------------------------------------------------
* We define myths as beliefs held true despite substantial evidence
refuting them. A list of articles in which these myths are espoused is
provided in the Supplementary Appendix.
Small Sustained Changes in Energy Intake Or Expenditure
Myth number 1: Small sustained changes in energy intake or
expenditure will produce large, long-term weight changes.
Predictions suggesting that large changes in weight will accumulate
indefinitely in response to small sustained lifestyle modifications
rely on the half-century-old 3,500-kcal rule, which equates a weight
alteration of 1 lb (0.45 kg) to a 3,500-kcal cumulative deficit or
increment.5-6 However, applying the 3,500-kcal rule to cases
in which small modifications are made for long periods violates the
assumptions of the original model, which were derived from short-term
experiments predominantly performed in men on very-low-energy diets
(<800 kcal per day).5, 7 Recent studies have shown that
individual variability affects changes in body composition in response
to changes in energy intake and expenditure,\7\ with analyses
predicting substantially smaller changes in weight (often by an order
of magnitude across extended periods) than the 3,500-kcal rule
does.5, 7 For example, whereas the 3,500-kcal rule predicts
that a person who increases daily energy expenditure by 100 kcal by
walking 1 mile (1.6 km) per day will lose more than 50 lb (22.7 kg)
over a period of 5 years, the true weight loss is only about 10 lb (4.5
kg),\6\ assuming no compensatory increase in caloric intake, because
changes in mass concomitantly alter the energy requirements of the
body.
Setting Realistic Weight-Loss Goals
Myth number 2: Setting realistic goals for weight loss is
important, because otherwise patients will become frustrated and lose
less weight.
Although this is a reasonable hypothesis, empirical data indicate
no consistent negative association between ambitious goals and program
completion or weight loss.\8\ Indeed, several studies have shown that
more ambitious goals are sometimes associated with better weight-loss
outcomes (see the Supplementary Appendix).\8\ Furthermore, two studies
showed that interventions designed to improve weight-loss outcomes by
altering unrealistic goals resulted in more realistic weight-loss
expectations but did not improve outcomes.
Rate of Weight Loss
Myth number 3: Large, rapid weight loss is associated with poorer
long-term weight-loss outcomes, as compared with slow, gradual weight
loss.
Within weight-loss trials, more rapid and greater initial weight
loss has been associated with lower body weight at the end of long-term
follow-up.9-10 A meta-analysis of randomized, controlled
trials that compared rapid weight loss (achieved with very-low-energy
diets) with slower weight loss (achieved with low-energy diets--i.e.,
800 to 1200 kcal per day) at the end of short-term follow-up (<1 yr)
and long-term follow-up (%1 year) showed that, despite the association
of very-low-energy diets with significantly greater weight loss at the
end of short-term follow-up (16.1% of body weight lost, vs. 9.7% with
low-energy diets), there was no significant difference between the
very-low-energy diets and low-energy diets with respect to weight loss
at the end of long-term follow-up.\10\ Although it is not clear why
some obese persons have a greater initial weight loss than others do, a
recommendation to lose weight more slowly might interfere with the
ultimate success of weight-loss efforts.
Diet Readiness
Myth number 4: It is important to assess the stage of change or
diet readiness in order to help patients who request weight-loss
treatment.
Readiness does not predict the magnitude of weight loss or
treatment adherence among persons who sign up for behavioral programs
or who undergo obesity surgery.\11\ Five trials (involving 3,910
participants; median study period, 9 months) specifically evaluated
stages of change (not exclusively readiness) and showed an average
weight loss of less than 1 kg and no conclusive evidence of sustained
weight loss (see the Supplementary Appendix). The explanation may be
simple--people voluntarily choosing to enter weight-loss programs are,
by definition, at least minimally ready to engage in the behaviors
required to lose weight.
Importance of Physical Education
Myth number 5: Physical-education classes, in their current form,
play an important role in reducing or preventing childhood obesity.
Physical education, as typically provided, has not been shown to
reduce or prevent obesity. Findings in three studies that focused on
expanded time in physical education \12\ indicated that even though
there was an increase in the number of days children attended physical-
education classes, the effects on body-mass index (BMI) were
inconsistent across sexes and age groups. Two meta-analyses showed that
even specialized school-based programs that promoted physical activity
were ineffective in reducing BMI or the incidence or prevalence of
obesity.\13\ There is almost certainly a level of physical activity (a
specific combination of frequency, intensity, and duration) that would
be effective in reducing or preventing obesity. Whether that level is
plausibly achievable in conventional school settings is unknown,
although the dose-response relationship between physical activity and
weight warrants investigation in clinical trials.
Breast-Feeding and Obesity
Myth number 6: Breast-feeding is protective against obesity.
A World Health Organization (WHO) report states that persons who
were breast-fed as infants are less likely to be obese later in life
and that the association is ``not likely to be due to publication bias
or confounding.'' \14\ Yet the WHO, using Egger's test and funnel
plots, found clear evidence of publication bias in the published
literature it synthesized.\15\ Moreover, studies with better control
for confounding (e.g., studies including within-family sibling
analyses) and a randomized, controlled trial involving more than 13,000
children who were followed for more than 6 years \16\ provided no
compelling evidence of an effect of breast-feeding on obesity. On the
basis of these findings, one long-term proponent of breast-feeding for
the prevention of obesity wrote that breast-feeding status ``no longer
appears to be a major determinant'' of obesity risk; \17\ however, he
speculated that breast-feeding may yet be shown to be modestly
protective, current evidence to the contrary. Although existing data
indicate that breast-feeding does not have important antiobesity
effects in children, it has other important potential benefits for the
infant and mother and should therefore be encouraged.
Sexual Activity and Energy Expenditure
Myth number 7: A bout of sexual activity burns 100 to 300 kcal for
each participant.
The energy expenditure of sexual intercourse can be estimated by
taking the product of activity intensity in metabolic equivalents
(METs),\18\ the body weight in kilograms, and time spent. For example,
a man weighing 154 lb (70 kg) would, at 3 METs, expend approximately
3.5 kcal per minute (210 kcal per hour) during a stimulation and orgasm
session. This level of expenditure is similar to that achieved by
walking at a moderate pace (approximately 2.5 miles [4 km] per hour).
Given that the average bout of sexual activity lasts about 6
minutes,\19\ a man in his early-to-mid-30s might expend approximately
21 kcal during sexual intercourse. Of course, he would have spent
roughly \1/3\ that amount of energy just watching television, so the
incremental benefit of one bout of sexual activity with respect to
energy expended is plausibly on the order of 14 kcal.
Presumptions
Just as it is important to recognize that some widely held beliefs
are myths so that we may move beyond them, it is important to recognize
presumptions, which are widely accepted beliefs that have neither been
proved nor disproved, so that we may move forward to collect solid data
to support or refute them. Instead of attempting to comprehensively
describe all the data peripherally related to each of the six
presumptions shown in Table 2, we describe the best evidence.
Table 2. Presumptions about Obesity *
------------------------------------------------------------------------
Presumption Basis of Conjecture
------------------------------------------------------------------------
Regularly eating (vs. skipping) Skipping breakfast purportedly
breakfast is protective against leads to overeating later in the
obesity day
Early childhood is the period during Weight-for-height indexes, eating
which we learn exercise and eating behaviors, and preferences that
habits that influence our weight are present in early childhood
throughout life are correlated with those later
in life
Eating more fruits and vegetables By eating more fruits and
will result in weight loss or less vegetables, a person presumably
weight gain, regardless of whether spontaneously eats less of other
one intentionally makes any other foods, and the resulting
behavioral or environmental changes reduction in calories is greater
than the increase in calories
from the fruit and vegetables
Weight cycling (i.e., yo-yo dieting) In observational studies,
is associated with increased mortality rates have been lower
mortality among persons with stable weight
than among those with unstable
weight
Snacking contributes to weight gain Snack foods are presumed to be
and obesity incompletely compensated for at
subsequent meals, leading to
weight gain
The built environment, in terms of Neighborhood-environment features
sidewalk and park availability, may promote or inhibit physical
influences obesity activity, thereby affecting
obesity
------------------------------------------------------------------------
* We define presumptions as unproved yet commonly espoused propositions.
A list of articles in which these presumptions are implied is provided
in the Supplementary Appendix.
Value of Breakfast
Presumption number 1: Regularly eating (versus skipping) breakfast
is protective against obesity.
Two randomized, controlled trials that studied the outcome of
eating versus skipping breakfast showed no effect on weight in the
total sample.\20\ However, the findings in one study suggested that the
effect on weight loss of being assigned to eat or skip breakfast was
dependent on baseline breakfast habits.\20\
Early Childhood Habits and Weight
Presumption number 2: Early childhood is the period in which we
learn exercise and eating habits that influence our weight throughout
life.
Although a person's BMI typically tracks over time (i.e., tends to
be in a similar percentile range as the person ages), longitudinal
genetic studies suggest that such tracking may be primarily a function
of genotype rather than a persistent effect of early learning.\21\ No
randomized, controlled clinical trials provide evidence to the
contrary.
Value of Fruits and Vegetables
Presumption number 3: Eating more fruits and vegetables will result
in weight loss or less weight gain, regardless of whether any other
changes to one's behavior or environment are made.
It is true that the consumption of fruits and vegetables has health
benefits. However, when no other behavioral changes accompany increased
consumption of fruits and vegetables, weight gain may occur or there
may be no change in weight.\22\
Weight Cycling and Mortality
Presumption number 4: Weight cycling (i.e., yo-yo dieting) is
associated with increased mortality.
Although observational epidemiologic studies show that weight
instability or cycling is associated with increased mortality, such
findings are probably due to confounding by health status. Studies of
animal models do not support this epidemiologic association.\23\
Snacking and Weight Gain
Presumption number 5: Snacking contributes to weight gain and
obesity.
Randomized, controlled trials do not support this presumption.\24\
Even observational studies have not shown a consistent association
between snacking and obesity or increased BMI.
Built Environment and Obesity
Presumption number 6: The built environment, in terms of sidewalk
and park availability, influences the incidence or prevalence of
obesity.
According to a systematic review, virtually all studies showing
associations between the risk of obesity and components of the built
environment (e.g., parks, roads, and architecture) have been
observational.\25\ Furthermore, these observational studies have not
shown consistent associations, so no conclusions can be drawn.
Facts
Our proposal that myths and presumptions be seen for what they are
should not be mistaken as a call for nihilism. There are things we do
know with reasonable confidence. Table 3 lists nine such facts and
their practical implications for public health, policy, or clinical
recommendations. The first two facts help establish a framework in
which intervention and preventive techniques may work. The next four
facts are more prescriptive, offering tools that can be conveyed to the
public as well established. The last three facts are suited to clinical
settings.
Table 3. Facts about Obesity *
------------------------------------------------------------------------
Fact Implication
------------------------------------------------------------------------
Although genetic factors play a If we can identify key environmental
large role, heritability is not factors and successfully influence
destiny; calculations show that them, we can achieve clinically
moderate environmental changes significant reductions in obesity
can promote as much weight loss
as the most efficacious
pharmaceutical agents available
\26\
Diets (i.e., reduced energy This seemingly obvious distinction is
intake) very effectively reduce often missed, leading to erroneous
weight, but trying to go on a conceptions regarding possible
diet or recommending that treatments for obesity; recognizing
someone go on a diet generally this distinction helps our
does not work well in the long- understanding that energy reduction
term \27\ is the ultimate dietary intervention
required and approaches such as
eating more vegetables or eating
breakfast daily are likely to help
only if they are accompanied by an
overall reduction in energy intake
Regardless of body weight or Exercise offers a way to mitigate the
weight loss, an increased level health-damaging effects of obesity,
of exercise increases health even without weight loss
\28\
Physical activity or exercise in Physical-activity programs are
a sufficient dose aids in long- important, especially for children,
term weight maintenance 28 29 but for physical activity to affect
weight, there must be a substantial
quantity of movement, not mere
participation
Continuation of conditions that Obesity is best conceptualized as a
promote weight loss promotes chronic condition, requiring ongoing
maintenance of lower weight \30\ management to maintain long-term
weight loss
For overweight children, programs Programs provided only in schools or
that involve the parents and the other out-of-home structured
home setting promote greater settings may be convenient or
weight loss or maintenance \31\ politically expedient, but programs
including interventions that involve
the parents and are provided at home
are likely to yield better outcomes
Provision of meals and use of More structure regarding meals is
meal-replacement products associated with greater weight loss,
promote greater weight loss \32\ as compared with seemingly holistic
programs that are based on concepts
of balance, variety, and moderation
Some pharmaceutical agents can While we learn how to alter the
help patients achieve clinically environment and individual behaviors
meaningful weight loss and to prevent obesity, we can offer
maintain the reduction as long moderately effective treatmentto
as the agents continue to be obese persons
used \33\
In appropriate patients, For severely obese persons, bariatric
bariatric surgery results in surgery can offer a life-changing,
long-term weight loss and and in some cases lifesaving,
reductions in the rate of treatment
incident diabetes and mortality
\34\
------------------------------------------------------------------------
* We classify the listed propositions as facts because there is
sufficient evidence to consider them empirically proved.
Implications
Myths and presumptions about obesity are common. Several
presumptions appear to be testable, and some of them (e.g., effects of
eating breakfast daily, eating more fruits and vegetables, and
snacking) can be tested with standard study designs. Despite enormous
efforts promoting these ideas, research often seems mired in the
accrual of observational data. Many of the trials that have been
completed or are in progress do not isolate the effect of the presumed
influence and the findings are therefore not definitive.
Many of the myths and presumptions about obesity reflect a failure
to consider the diverse aspects of energy balance,\35\ especially
physiological compensation for changes in intake or expenditure.\36\
Some myths and presumptions involve an implicit assumption that there
is no physiological compensation whatsoever (i.e., the 3,500-kcal rule)
or only minimal compensation (e.g., a reduction in snacking as a means
of reducing weight). In other cases, there is an implicit assumption of
overcompensation (e.g., eating breakfast daily or increasing the intake
of fruits and vegetables as a means of reducing weight). Proponents of
other unsupported ideas fail to consider that people burn some amount
of energy even without engaging in the activity in question (e.g.,
increased sexual activity). In addition, interested parties do not
regularly request the results from randomized, long-term studies that
measure weight or adiposity as an outcome. Therefore, the presented
data are rife with circumstantial evidence, and people are not informed
that the existing evidence is not compelling (e.g., breakfast
consumption). Furthermore, some suggested treatment or prevention
strategies may work well (e.g., increasing the consumption of fruits
and vegetables) but only as part of a multifaceted program for weight
reduction. Yet such a strategy is often presented as though it will
have effects in isolation and even among persons not participating in
weight-loss programs. We must recognize that evidence that a technique
is beneficial for the treatment of obesity is not necessarily evidence
that it will be helpful in population-based approaches to the
prevention of obesity, and vice versa.
Knowing and Not Knowing
Why do we think or claim we know things that we actually do not
know? Numerous cognitive biases lead to an unintentional retention of
erroneous beliefs.37-38 When media coverage about obesity is
extensive, many people appear to believe some myths (e.g., rapid weight
loss facilitates weight regain) simply because of repeated exposure to
the claims.\39\ Cognitive dissonance may prevent us from abandoning
ideas that are important to us, despite contradictory evidence (e.g.,
the idea that breast-feeding prevents obesity in children). Similarly,
confirmation bias may prevent us from seeking data that might refute
propositions we have already intuitively accepted as true because they
seem obvious (e.g., the value of realistic weight-loss goals).
Moreover, we may be swayed by persuasive yet fallacious arguments
(Whately provides a classic catalogue) \40\ unless we are prepared to
identify them as spurious.
Fortunately, the scientific method and logical thinking offer ways
to detect erroneous statements, acknowledge our uncertainty, and
increase our knowledge. When presented with an alleged truth, we can
pause to ask simple questions, such as, ``How could someone actually
know that?'' Such a simple question allows one to easily recognize some
beliefs as spurious (e.g., 300 kcal is burned during sexual
intercourse). Moreover, we often settle for data generated with the use
of inadequate methods in situations in which inferentially stronger
study designs, including quasi-experiments and true randomized
experiments, are possible, as recently illustrated (see the
Supplementary Appendix). In addition, eliminating the distortions of
scientific information that sometimes occur with public health advocacy
would reduce the propagation of misinformation.
The myths and presumptions about obesity that we have discussed are
just a sampling of the numerous unsupported beliefs held by many
people, including academics, regulators, and journalists, as well as
the general public. Yet there are facts about obesity of which we may
be reasonably certain--facts that are useful today. While we work to
generate additional useful knowledge, we may in some cases justifiably
move forward with hypothesized, but not proved, strategies. However, as
a scientific community, we must always be open and honest with the
public about the state of our knowledge and should rigorously evaluate
unproved strategies.
The views expressed in this article are those of the authors and do
not necessarily represent the official views of the National Institutes
of Health.
Supported in part by a grant (P30DK056336) from the National
Institutes of Health.
Dr. Astrup reports receiving payment for board membership from the
Global Dairy Platform, Kraft Foods, Knowledge Institute for Beer,
McDonald's Global Advisory Council, Arena Pharmaceuticals, Basic
Research, Novo Nordisk, Pathway Genomics, Jenny Craig, and Vivus;
receiving lecture fees from the Global Dairy Platform, Novo Nordisk,
Danish Brewers Association, GlaxoSmithKline, Danish Dairy Association,
International Dairy Foundation, European Dairy Foundation, and
AstraZeneca; owning stock in Mobile Fitness; holding patents regarding
the use of flaxseed mucilage or its active component for suppression of
hunger and reduction of prospective consumption (patents EP1744772,
WO2009033483-A1, EP2190303-A1, US2010261661-A1, and priority
applications DK001319, DK001320, S971798P, and US971827P); holding
patents regarding the use of an alginate for the preparation of an
aqueous dietary product for the treatment or prevention of overweight
and obesity (patent WO2011063809-A1 and priority application DK070227);
and holding a patent regarding a method for regulating energy balance
for body-weight management (patent WO2007062663-A1 and priority
application DK001710). Drs. Brown and Bohan Brown report receiving
grant support from the Coca-Cola Foundation through their institution.
Dr. Mehta reports receiving grant support from Kraft Foods. Dr. Newby
reports receiving grant support from General Mills Bell Institute of
Health and Nutrition. Dr. Pate reports receiving consulting fees from
Kraft Foods. Dr. Rolls reports having a licensing agreement for the
Volumetrics trademark with Jenny Craig. Dr. Thomas reports receiving
consulting fees from Jenny Craig. Dr. Allison reports serving as an
unpaid board member for the International Life Sciences Institute of
North America; receiving payment for board membership from Kraft Foods;
receiving consulting fees from Vivus, Ulmer and Berne, Paul, Weiss,
Rifkind, Wharton, Garrison, Chandler Chicco, Arena Pharmaceuticals,
Pfizer, National Cattlemen's Association, Mead Johnson Nutrition,
Frontiers Foundation, Orexigen Therapeutics, and Jason Pharmaceuticals;
receiving lecture fees from Porter Novelli and the Almond Board of
California; receiving payment for manuscript preparation from Vivus;
receiving travel reimbursement from International Life Sciences
Institute of North America; receiving other support from the United
Soybean Board and the Northarvest Bean Growers Association; receiving
grant support through his institution from Wrigley, Kraft Foods, Coca-
Cola, Vivus, Jason Pharmaceuticals, Aetna Foundation, and McNeil
Nutritionals; and receiving other funding through his institution from
the Coca-Cola Foundation, Coca-Cola, PepsiCo, Red Bull, World Sugar
Research Organisation, Archer Daniels Midland, Mars, Eli Lilly and
Company, and Merck. No other potential conflict of interest relevant to
this article was reported.
Disclosure forms provided by the authors are available with the full
text of this article at NEJM.org.
We thank Drs. Kyle Grimes and S. Louis Bridges for their suggestions
on an earlier version of the manuscript.
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Copyright 13 Massachusetts Medical Society.
attachment 4
Goals in Nutrition Science 2015-2020 *
---------------------------------------------------------------------------
* Edited by: Steven H. Zeisel, University of North Carolina at
Chapel Hill, USA; Reviewed by: Naima Moustaid-Moussa, Texas Tech
University, USA Patrick John Stover, Cornell University, USA; Received:
26 May 2015; Accepted: 14 August 2015; Published: 08 September 2015.
Citation: Allison D.B., Bassaganya-Riera J., Burlingame B., Brown
A.W., le Coutre J., Dickson S.L., van Eden W., Garssen J., Hontecillas
R., Khoo C.S.H., Knorr D., Kussmann M., Magistretti P.J., Mehta T.,
Meule A., Rychlik M., and Vogele C. (2015) Goals in nutrition science
2015-2020. Front. Nutr. 2:26. doi: 10.3389/fnut.2015.00026
---------------------------------------------------------------------------
David B. Allison,[1-4] Josep Bassaganya-Riera,[5]
Barbara Burlingame,[6-7] Andrew W. Brown,[1]
Johannes le Coutre,[8-10, *] Suzanne L.
Dickson,[11] Willem van Eden,[12] Johan
Garssen,[13] Raquel Hontecillas,[5] Chor San H.
Khoo,[14] Dietrich Knorr,[15] Martin
Kussmann,[10, 16] Pierre J. Magistretti,[17-18]
Tapan Mehta,[19] Adrian Meule,[20] Michael
Rychlik,[21] and Claus Vogele [22]
---------------------------------------------------------------------------
\[1]\ Office of Energetics and Nutrition Obesity Research Center,
School of Public Health, University of Alabama at Birmingham,
Birmingham, AL, USA, [2] Section on Statistical Genetics,
University of Alabama at Birmingham, Birmingham, AL, USA,
[3] Department of Nutrition Sciences, University of Alabama
at Birmingham, Birmingham, AL, USA, [4] Department of
Biostatistics, University of Alabama at Birmingham, Birmingham, AL,
USA, [5] Nutritional Immunology and Molecular Medicine
Laboratory, Virginia Bioinformatics Institute, Virginia Tech,
Blacksburg, VA, USA, [6] Deakin University, Melbourne, VIC,
Australia, [7] American University of Rome, Rome, Italy,
[8] Nestle Research Center, Lausanne, Switzerland,
[9] Organization for Interdisciplinary Research Projects,
The University of Tokyo, Tokyo, Japan, [10] Ecole
Polytechnique Federale de Lausanne, Lausanne, Switzerland,
[11] Institute of Neuroscience and Physiology, The
Sahlgrenska Academy at the University of Gothenburg, Gothenburg,
Sweden, [12] Department of Infectious Diseases and
Immunology, Faculty of Veterinary Medicine, Utrecht University,
Utrecht, Netherlands, [13] Faculty of Science, Utrecht
Institute for Pharmaceutical Sciences, Utrecht University, Utrecht,
Netherlands, [14] North American Branch of International
Life Sciences Institute, Washington, D.C., USA, 15 Technische
Universitat Berlin, Berlin, Germany, [16] Nestle Institute
of Health Sciences SA, Lausanne, Switzerland, [17] Division
of Biological and Environmental Sciences and Engineering, King Abdullah
University of Science and Technology, Thuwal, Saudi Arabia,
[18] Laboratory of Neuroenergetics and Cellular Dynamics,
Brain Mind Institute, Ecole Polytechnique Federale de Lausanne,
Lausanne, Switzerland, [19] Department of Health Services
Administration, Nutrition Obesity Research Center, University of
Alabama at Birmingham, Birmingham, AL, USA, [20] Department
of Psychology, University of Salzburg, Salzburg, Austria,
[21] Analytical Food Chemistry, Technische Universitat
Munchen, Freising, Germany, [22] Research Unit INSIDE,
Institute for Health and Behaviour, University of Luxembourg,
Luxembourg, Luxembourg.
* Correspondence: Johannes le Coutre
[email protected], [email protected].
---------------------------------------------------------------------------
With the definition of goals in Nutrition Science, we are
taking a brave step and a leap of faith with regard to
predicting the scope and direction of nutrition science over
the next 5 years. The content of this editorial has been
discussed, refined, and evaluated with great care by the
Frontiers in Nutrition editorial board. We feel the topics
described represent the key opportunities, but also the biggest
challenges in our field. We took a clean-slate, bottom-up
approach to identify and address these topics and present them
in eight categories. For each category, the authors listed take
responsibility, and deliberately therefore this document is a
collection of thoughts from active minds, rather than a
complete integration or consensus.
At Frontiers in Nutrition, we are excited to develop and
share a platform for this discussion. Healthy Nutrition for
all--an ambition too important to be handled by
detachedinterest groups.
Johannes le Coutre, Field Chief Editor, Frontiers in Nutrition.
Sustainable Development Goals for Food and Nutrition
(Barbara Burlingame, Chor San H. Khoo, and Dietrich Knorr)
To deliver successfully, nutrition research needs a bold dose of
innovation. Moving forward from the Millennium Development Goals to the
post-2015 sustainable development goals (SDG), global nutrition appears
to require an improved model. Under current practices, feeding the
exploding world population necessitates to close a gap of nearly 70%
between the amount of food available today and the projected
availability by 2050.(1) Today, globally, an estimated 805
million people are undernourished or food-insecure,(2) yet 1
out of 4 calories from food goes uneaten. Meanwhile, overweight and
obesity affect approximately two billion people, including 42 million
children under the age of 5. Human health notwithstanding environmental
health is also at stake. Agriculture alone accounts for about 70% of
our global water usage and 24% of our greenhouse gas emissions. As a
result, our strategies to overcome issues of food sustainability, food
waste, and food loss must be multifarious and include, at the very
least: (i) Improving the global consumption of food. (ii) Increasing
production efficiencies on existing agricultural land. (iii) Developing
sustainable approaches that reduce the environmental impact of food
production, and in particular greenhouse gas emissions. Certainly, the
impact of agriculture on climate, ecosystems, and water will have to be
reduced, while at the same time, we will need to ensure that it
supports inclusive economic and social development.(1)
Systems science, the interdisciplinary field that explores the
nature of complex systems, is perhaps the best research model we have
for addressing the urgent needs of a precariously unhealthy planet. For
better or for worse, nutrition imparts a quintessential challenge,
straddling many sectors and disciplines.
In the past, at times, the agenda for mainstream nutrition has been
pushing sectoral lines of reasoning by implementing policies that leave
long-standing problems unresolved, while disrupting other sectors in
the process. Of course, nutrition is not alone in this, but the history
of unintended consequence is long and discouraging.(3-4)
Agriculture and health have been the mainstay sectors at the United
Nations level, in government ministries, and in academic departments.
Increasingly, nutrition is being recognized as an important pillar for
the environmental sector, with biodiversity for food and nutrition
acknowledged by the Convention on Biological Diversity,(5)
and the Commission on Genetic Resources for Food and Agriculture
accepting whole diets, food, and nutrients for human nutrition as
ecosystem services.(6)
For all their embracing of nutrition, these sectors often work at
cross-purposes, providing many useful illustrations of policies and
programs that undermine each other's development efforts. We have
policies and interventions in agriculture that contribute to diet-
related chronic disease, environmental degradation, and food
insecurity; (4, 7) conversely, in the health sector we have
policies and interventions that compromise agricultural
development;(8) and in the environmental sector that lead to
micronutrient malnutrition.(9) Agriculture in particular,
while solving some of its own sector problems, has been associated with
many of the environmental and human health crises we now face, which
directly impact upon nutrition, including chemical contamination of
food supplies, loss of agrobiodiversity, and severe environmental
degradation.(10)
In spite of the clear need to develop innovation for the future,
``systematic attempts to explore existing methods and to develop new
technologies of more sustainable food production systems have so far
been scarce''.(11) Although this quote is from over 30 years
ago, it still quite accurately describes the current situation
regarding activities related to sustainable diets and sustainable food
systems. A sustainable development lens with a systems science approach
offers not only a new analytical model for nutrition, but also an
ethical and inclusive framework. Within this framework, nutrition
encompasses more than its traditional domains and takes on issues of
climate change,(12) biodiversity and
ecosystems,(13) water use/waste,(14) food losses
and waste,(15-16) sustainable forests and
seas,(17) chemical contamination of food and water
supplies,(18) environmental regulatory issues and food law,
risk and risk/benefit assessments,(19) and monitoring
adherence to and compliance with a range of relevant treaties and
signed declarations/commitments.(13)
With this mindset of sensitive, cross-sectoral resolve, tangible
and specific solutions will envisage a holistic food chain integration
taking into account a total life cycle assessment. Food and nutrition
security must be an intrinsic component of any solution for food
sustainability. Forthcoming strategies will also have to explore the
potential and utilization of new raw materials.
Improvements of food safety, storage, packaging, and
transportation--including the use of sensor technologies--can reduce
food losses and waste. Innovation will have to equally encompass the
re-evaluation of existing food processing, storage, and home
preparation operations employing existing modern toolboxes. Moreover,
low energy, waste-free or waste-reduced processing, and preparation
operations need to be implemented to a larger extent, including
alternative energy sources. In the same context, water decontamination,
recycling, and preservation tools need to be applied.
Unintended consequences must be considered with any sustainability
program and global solutions are not necessarily applicable in local
contexts. For example, reducing livestock production and consumption in
one setting may benefit both human and environmental health, while in
another setting it may reduce further already marginal intakes of high-
quality protein and micronutrients and marginalize grazing lands that
are self-renewing, sustainable repositories of biodiversity. Finally,
young engineers and scientists need to be encouraged, trained, and
involved to tackle the challenges of the future.
We have a planet in crisis on so many fronts. Regardless of how the
SDGs evolve, this multi-sectoral vision of nutrition research and
action has the potential to make meaningful, and sustainable,
contributions.
Identifying and Mitigating Errors in Nutritional Science
(David B. Allison, Andrew W. Brown, and Tapan Mehta)
``Science,'' as Adam Smith famously said, ``is the great antidote
to the poison of enthusiasm and superstition''.(20)
Complementarily, Stephen Hawking has called scientists, ``the bearers
of the torch of discovery in our quest for knowledge''.(21)
Thus, science can be seen as having two key complementary roles--
dispelling false beliefs, and creating new knowledge. For science to
fulfill this joint mission, its practice must be true to its principles
and precepts, including objectivity, methodological rigor,
transparency, and reproducibility. Yet, there are concerns that
departures from these precepts are too common.(22-28) Some
have speculated that deviations from good scientific practices have
increased in recent years due to a number of social, institutional, and
economic factors in science.(25, 29) Others have speculated
that the problem may be especially severe in the related domains of
nutrition research and obesity research, perhaps because of emotional,
economic, and other factors involved in those topics or because the
everyday familiarity with aspects of those topics is mistaken for
expertise.(23, 26-28) It is difficult to quantify whether
the situation is better or worse today than in the past, or whether
this is especially true in nutrition and obesity research compared to
other fields. Nevertheless, it is clear that the problem exists.
Table 1: Common Errors Noted in the Published Literature a
------------------------------------------------------------------------
Error Example(s) of error
------------------------------------------------------------------------
Errors involving or resulting Self-reported energy intake
from poor measurement (33, 118, 119)b (34) c (32) d
Self-reported weights (120)
b (121, 122) d
Errors involving inappropriate Cluster randomized trials
choice of or incorrect study with no degrees of freedom (123) c
design Lack of control for non-
specific factors, i.e., failure to
isolate the independent variable of
interest (124) c
Non-random assignment in
self-described RCTs (125) b
Errors involving replication Not validating prediction
models in fresh samples (126) d