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 

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PROS AND CONS OF RESTRICTING SNAP PURCHASES 

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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 

( 

Printed for the use of the Committee on Agriculture 

agriculture.house.gov 

COMMITTEE ON AGRICULTURE 

K. MICHAEL CONAWAY, Texas, Chairman 

GLENN THOMPSON, Pennsylvania 

Vice Chairman 

BOB GOODLATTE, Virginia, 

FRANK D. LUCAS, Oklahoma 

STEVE KING, Iowa 

MIKE ROGERS, Alabama 

BOB GIBBS, Ohio 

AUSTIN SCOTT, Georgia 

ERIC A. ‘‘RICK’’ CRAWFORD, Arkansas 

SCOTT DESJARLAIS, Tennessee 

VICKY HARTZLER, Missouri 

JEFF DENHAM, California 

DOUG LAMALFA, California 

RODNEY DAVIS, Illinois 

TED S. YOHO, Florida 

RICK W. ALLEN, Georgia 

MIKE BOST, Illinois 

DAVID ROUZER, North Carolina 

RALPH LEE ABRAHAM, Louisiana 

TRENT KELLY, Mississippi 

JAMES COMER, Kentucky 

ROGER W. MARSHALL, Kansas 

DON BACON, Nebraska 

JOHN J. FASO, New York 

NEAL P. DUNN, Florida 

JODEY C. ARRINGTON, Texas 

COLLIN C. PETERSON, Minnesota, Ranking 

Minority Member 

DAVID SCOTT, Georgia 

JIM COSTA, California 

TIMOTHY J. WALZ, Minnesota 

MARCIA L. FUDGE, Ohio 

JAMES P. MCGOVERN, Massachusetts 

FILEMON VELA, Texas, Vice Ranking 

Minority Member 

MICHELLE LUJAN GRISHAM, New Mexico 

ANN M. KUSTER, New Hampshire 

RICHARD M. NOLAN, Minnesota 

CHERI BUSTOS, Illinois 

SEAN PATRICK MALONEY, New York 

STACEY E. PLASKETT, Virgin Islands 

ALMA S. ADAMS, North Carolina 

DWIGHT EVANS, Pennsylvania 

AL LAWSON, JR., Florida 

TOM O’HALLERAN, Arizona 

JIMMY PANETTA, California 

DARREN SOTO, Florida 

LISA BLUNT ROCHESTER, Delaware 

MATTHEW S. SCHERTZ, Staff Director 

ANNE SIMMONS, Minority Staff Director 

(II) 

(III) 

C O N T E N T S 

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 

(1) 

PROS AND CONS OF RESTRICTING SNAP 

PURCHASES 

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 

2 

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. 

3 

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, DirecVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00009 Fmt 6633 Sfmt 6601 P:\DOCS\115-02\24325.TXT BRIAN
4 

tor 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 

5 

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 

6 

1See Judith Bartfield, et al., eds, SNAP Matters: How Food Stamps Affect Health and WellBeing (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. 4Trudi 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. 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. 

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 foodinsecure 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

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 perVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00012 Fmt 6633 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
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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/. 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. 10White 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. 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. 12 Ibid. 13 Ibid. 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. 

cent 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

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: 

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

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

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 conVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00013 Fmt 6633 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
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17Sara 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. 19Food Stamp Program Act of 1977, https://www.fns.usda.gov/sites/default/files/PL_106- 

580.pdf. 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/. 21See 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. 22See U.S. Department of Agriculture, Food and Nutrition Service, Healthy Incentives Pilot 

Final Evaluation Report, September 2014, https://www.fns.usda.gov/snap/healthy-incentivespilot-final-evaluation-report. 

sumed 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. 

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

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. 

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. 

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. 

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 reVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00014 Fmt 6633 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
9 

23Lisa 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. 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. 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. 

duced 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. 

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. 

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). 

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 

10 

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? 

11 

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 privatesector 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 

12 

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-datashow. 

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 

13 

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. 3Schanzenbach, 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-ratesat-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. 

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. 

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. 

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 


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14 

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-marketsprices/processing-marketing/new-products/. 7Freeland-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. 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. 

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. 

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. 

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

15 

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. 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-assistanceprogram-snap-households. 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. 

Figure 2. Estimated Impact of a $30 Increase in Monthly Per Capita SNAP 

Benefits 

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

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: 

• 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

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 


11502002.eps
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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. 13The 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. 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. 

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. 

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 lowincome 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. 

17 

Figure 3. Price Levels by Food Category, 1980–2016 

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 


11502003.eps
18 

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 

19 

1Food 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. 

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. 

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 

20 

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. 

21 

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 

22 

2Source: 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. 

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. 

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. 

23 

State-by-State Monthly SNAP Benefit Issuance Schedule—Continued 

(Current as of February 13, 2017; Food Marketing Institute Research) 

State Day(s) of SNAP Benefit Distribution 

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. 

24 

State-by-State Monthly SNAP Benefit Issuance Schedule—Continued 

(Current as of February 13, 2017; Food Marketing Institute Research) 

State Day(s) of SNAP Benefit Distribution 

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. 

25 

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) 

State September 2016 October 2016 

Percent Change 

September 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). 

26 

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) 

State October 2015 October 2016 

Percent Change 

October 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). 

27 

Supplemental Nutrition Assistance Program: Five-Year Change in 

Participation 

(Prepared by the Food Research and Action Center (FRAC)) 

(Data as of January 6, 2017) 

State October 2011 October 2016 

Percent Change 

October 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). 

28 

Share of Population Participating in SNAP 

(Prepared by the Food Research and Action Center (FRAC)) 

(Data as of January 6, 2017) 

State Population Estimate 

(2015) 

SNAP Participants, 

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. 

29 

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. 

30 

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:] 

31 

1Foster, 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. 

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. 

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. 

32 

3Waters, 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. 

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. 

Thank you for the opportunity to testify, I look forward to your questions. 

The CHAIRMAN. Thank you very much. 

Dr. Wansink? 

33 

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. 

34 

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 

35 

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. 

36 

The Hierarchy of Health Predisposition 

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 makVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00042 Fmt 6633 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN

11502004.eps
37 

ing 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 evidencebased 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 winwin 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. 

38 

APPENDIX. EXAMPLE OF SCORECARDS THAT ENCOURAGE HEALTHIER CHOICES


11502005.eps
39 

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 MassaVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00045 Fmt 6633 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN

11502006.eps
40 

1There 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). 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 

ƒ on SNAP-eligible food then she has y¥max (0,ƒ¥b) available to buy other goods. Let U (ƒ,n) 

denote the household’s strictly monotone, differentiable, and strictly quasiconcave utility function defined over the dollar amount of SNAP-eligible food consumption ƒ and other consumption 

n. Suppose that there is a solution ƒ* = arg max ƒ U (ƒ,y¥max(0,ƒ¥b)) such that ƒ*>b. The 

first-order necessary condition for this program is a necessary and sufficient condition for a solution to the program max fU (ƒ;y+b¥ƒ) in which the benefits are given in cash. Therefore ƒ* = 

arg max fU(ƒ,y+b¥ƒ). 

chusetts 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, 

justine_hastings@brown.edu; jesse_shapiro_1@brown.edu. 

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

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 

41 

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. 

close to 0.1,4 the textbook treatment says that SNAP benefits should mostly subsidize non-food spending. 

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 eventstudy 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-indifferences 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 doVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00047 Fmt 6633 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
42 

5Fox, 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. 6Wilde, 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. 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. 9Whereas 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. 

main. 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. 

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. 

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 

43 

10Other recent studies analyzing linked unemployment insurance and SNAP data include Anderson, et al., (2012) and Leung and O’Leary (2015). 11This 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. 12Data on earnings are missing from our database for the fourth quarter of 2004 and the second quarter of 2011. 13We 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). 14Past 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 IndividContinued 

Kooreman’s (2000), but avoids the concern that a rejection of fungibility is due to 

misspecification of functional forms (Ketcham, et al., 2016). 

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. 

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. 

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. 

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 eventstudy 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 

44 

uals, 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). 15Federal 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). 

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. 

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). 

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. 

45 

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. 

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. 

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. 

46 

17Grocery 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). 18Store-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). 19Our 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. 

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 SNAPeligible products, 25 percent goes to SNAP-ineligible products, and the remainder 

goes to products that we cannot classify. 

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 storebrand 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. 

3.4 Monthly Spending and Benefits 

For each household in our panel we calculate total monthly spending on SNAPeligible 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

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). 

47 

20The difference in means is statistically significant (t = 2.15, p = 0.032). 21The 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. 

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: aver- age 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

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. 

48 

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 lowincome 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 

49 

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) 

where ξit is a preference shock and Ui () is a utility function strictly increasing in 

ƒ and n. The variables (bit,yit, xit) are random with support Ωi. Assumption 1. For each household i, optimal food spending can be written as 

(2) 

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,ξ) ∈ Ωi the function Ui (ƒ ,y+b¥ƒ;ξ) is smooth and strictly concave in ƒ and has 

a stationary point ƒ* > 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 (ƒit,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 ≥ zit). For each household i, the instruments zit satisfy 

(3) 


11502027.eps 11502028.eps 11502029.eps
50 

Proposition 1. Under assumptions 1 and 2, for each household i 

(4) 

for some function ¶i (). 

Proof. Let Pi denote the CDF of (ξit,vit). Then 

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 βi ∈ (0,1) 

such that: 

(5) 

with βi (y+b)+ξ > b and (1¥βi) (y+b) > ξ at all points in Ωi. Then assumption 1 holds 

with 

(6) 

and, under assumption 2, proposition 1 applies with 

(7) 

for αi ≡ E(ξit). 

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 crosssectional data under various restrictions on αi, βi, and ξit. 

5.2 Testing for Fungibility 

Index a family of perturbations to the model by γ. Let ƒγ

it be food spending under 

perturbation γ, with 

(8) 

for ƒi () the function defined in assumption 1. We may think of γ as the excess sensitivity of food spending to SNAP benefits. The null hypothesis that the model holds 

is equivalent under (8) to γ = 0. 

Let Yit = E(yit + bit≥zit) and Bit = E(bit≥zit) and observe that 

(9) 

where E(eit≥Yit,Bit) = 0. The nuisance terms ¶i () have been ‘‘partialled out’’ of (9) as 

in Robinson (1988). The target γ can be estimated via OLS regression of (ƒγ

it ¥ E 

(ƒγ

it≥Yit)) on (Bit ¥E (Bit≥Yit)). 

Remark 2. It is possible to allow for measurement error in ƒit that depends on 

(yit+bit). Say that for known function m(), unobserved measurement error ηit indeVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00056 Fmt 6633 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN

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51 

pendent of zit, and unknown function λit () we have that measured food spending 

f

ˆ

it follows 

(10) 

Then under perturbations m((ƒγ

it) = m(ƒit) + γbit an analogue of (9) holds, replacing 

ƒγ

it with m(ƒγit). 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 (®it (yit+bit,ηit)). 

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: 

We let ƒγ

it 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(ƒγ

it≥Yit) , E(Bit≥Yit)). In 

the first, we estimate these via first-differenced regression of ƒγ

it and Bˆ it on Yˆ it, pooling across households. In the second, we estimate these via first-differenced regression of ƒγ

it and Bˆ it on Yˆ it, separately by household. In the third, we estimate these 

via first-differenced regression of ƒγ

it and Bˆ it on indicators for the quintiles of Yˆ it, 

separately by household. In the fourth, we estimate these via locally weighted polynomial regression of ƒγ

it and Bˆ it on Yˆ it, separately by household. Thus, the first specification implicitly treats φi as linear and homogeneous across households, the second 

treats φi as linear and heterogeneous across households, and the third and fourth 

allow φi to be nonlinear and heterogeneous across households. 

Table 2 presents the results. Across all three specifications, our estimates of γ are 

0.5 or greater, and in all cases we can reject the null hypothesis that γ = 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 


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52 

22The 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.) 

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

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. 

53 

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 ξ. Let preferences over food consumption ƒ 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) 

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 κ > 0 indexes the importance of sticking to the household’s default plan to spend amount b of SNAP benefits and amount βy of cash income on food. 

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 

(βy + 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 


11502040.eps 11502041.eps
54 

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. 

References 

Abeler, Johannes and Felix Marklein. Forthcoming. Fungibility, labels, and consumption. JOURNAL OF THE EUROPEAN ECONOMIC

ASSOCIATION. 

Aguiar, Mark and Erik Hurst. 2005. Consumption versus expenditure. JOURNAL OF POLITICAL ECONOMY 113(5): 919–948. 

Ahmed, Naeem, Matthew Brzozowski, and Thomas F. Crossley. 2006. Measurement errors in recall food consumption data. Institute for Fiscal Studies Working Paper 06/21. 

Anderson, Theresa, John A. Kirlin, and Michael Wiseman. 2012. Pulling together: Linking unemployment insurance and Supplemental Nutrition Assistance Program administrative data to study effects of the Great Recession. U.S. Department of Agriculture, 

Agricultural Research Service. 

Andreyeva, Tatiana, Joerg Luedicke, Kathryn E. Henderson, and Amanda S. Tripp. 2012. Grocery store beverage choices by participants in Federal food assistance and nutrition programs. AMERICAN JOURNAL OF PREVENTIVE MEDICINE 43 (4): 411–418. 

Banks, James, Richard Blundell, and Sarah Tanner. 1998. Is there a retirement-savings puzzle? AMERICAN ECONOMIC REVIEW

88(4): 769–788. 

Battistin, Erich and Mario Padula. 2016. Survey instruments and the reports of consumption expenditures: Evidence from the Consumer Expenditure Surveys. JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY) 179(2): 559–581. 

Beatty, Timothy K.M. and Charlotte J. Tuttle. 2015. Expenditure response to increases in in-kind transfers: Evidence from the 

Supplemental Nutrition Assistance Program. AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS 97(2): 390–404. 

Benhassine, Najy, Florencia Devoto, Esther Duflo, Pascaline Dupas, and Victor Pouliquen. 2015. Turning a shove into a nudge? A 

‘‘labeled cash transfer’’ for education. AMERICAN ECONOMIC JOURNAL: ECONOMIC POLICY 7(3): 86–125. 

Bitler, Marianne P. 2015. The health and nutrition effects of SNAP: Selection into the program and a review of the literature on 

its effects. In J. Bartfeld, C. Gundersen, T. Smeeding, and J.P. Ziliak (eds.), SNAP MATTERS: HOW FOOD STAMPS AFFECT HEALTH

AND WELL BEING: 134–160. Stanford: Stanford University Press. 

Blundell, Richard W., Martin Browning, and Ian A. Crawford. 2003. Nonparametric Engel curves and revealed preference. 

ECONOMETRICA 71(1): 205–240. 

Blundell, Richard and James L. Powell. 2003. Endogeneity in nonparametric and semiparametric regression models. In M. 

Dewatripont, L.P. Hansen, and S.J. Turnovsky (eds.), ADVANCES IN ECONOMICS AND ECONOMETRICS: THEORY AND APPLICATIONS, 

EIGHTH WORLD CONGRESS 2: 312–357. Cambridge: Cambridge University Press. 

Bronnenberg, Bart J., Jean-Pierre Dube, Matthew Gentzkow, and Jesse M. Shapiro. 2015. Do pharmacists buy Bayer? Informed 

shoppers and the brand premium. QUARTERLY JOURNAL OF ECONOMICS 130(4): 1669–1726. 

Browning, Edgar K. and Mark A. Zupan. 2004. Microeconomics: Theory and Applications. 8th ed. Hoboken: Wiley. 

Browning, Martin, Thomas F. Crossley, and Joachim Winter. 2014. The measurement of household consumption expenditures. ANNUAL REVIEW OF ECONOMICS 6(1): 475–501. 

Bruich, Gregory A. 2014. The effect of SNAP benefits on expenditures: New evidence from scanner data and the November 2013 

benefit cuts. Harvard University. Mimeo. Accessed at http://scholar.harvard.edu/files/bruich/files/bruich_2014b.pdf on May 5, 

2016. 

Castner, Laura and James Mabli. 2010. Low-income household spending patterns and measures of poverty. Washington, D.C.: 

Mathematica Policy Research. 

Collins, Ann M., Ronette Briefel, Jacob Alex Klerman, Anne Wolf, Gretchen Rowe, Chris Logan, Ayesha Enver, Syeda Fatima, 

Anne Gordon, and Julia Lyskawa. 2016. Summer Electronic Benefit Transfer for Children (SEBCT) Demonstration: Summary Report. Accessed at http://www.fns.usda.gov/sites/default/files/ops/sebtcfinalreport.pdf on January 5, 2016. 

Congressional Budget Office. 2012. The Supplemental Nutrition Assistance Program. Accessed at https://www.cbo.gov/sites/default/files/112th-congress-2011-2012/reports/04-19-SNAP.pdf on May 5, 2016. 

____. 2013. Accessed at https://www.cbo.gov/sites/default/files/113th-congress-2013-2014/graphic/43935-means-testedinfographic0.pdf on May 5, 2016. 

Engel, Ernst. 1857. Die Productions- und Consumtionsverha ̈ltnisse des Ko ̈nigreichs Sachsen. ZEITSCHRIFT DES STATISTISCHEN BUREAUS DES KO ̈ NIGLICH SA ̈ CHSISCHEN MINISTERIUM DES INNEREN 8–9: 1–54. 

Fan, Jianqing and Irene Gijbels. 1996. Local Polynomial Modelling and Its Applications. London, UK: Chapman and Hall. 

Farhi, Emmanuel and Xavier Gabaix. 2015. Optimal taxation with behavioral agents. NBER Working Paper No. 21524. 

Feldman, Naomi E. 2010. Mental accounting effects of income tax shifting. THE REVIEW OF ECONOMICS AND STATISTICS 92(1): 70– 

86. 

FNS. 2012. Building a healthy America: A profile of the Supplemental Nutrition Assistance Program. Accessed at http:// 

www.fns.usda.gov/sites/default/files/BuildingHealthyAmerica.pdf on May 5, 2016. 

____. 2014. FNS Handbook 501, Chapter 5. Accessed at http://www.fns.usda.gov/sites/default/files/ 

FNSHANDBOOK_501_Chap5_4_2014_new.pdf on January 5, 2016. 

____. 2016a. Supplemental Nutrition Assistance Program. Accessed at http://www.fns.usda.gov/pd/supplemental-nutrition-assistance-program-snap on May 25, 2016. 

____. 2016b. Supplemental Nutrition Assistance Program (SNAP): Eligibility. Accessed at http://www.fns.usda.gov/snap/eligibility on January 5, 2016. 

____. 2017. Supplemental Nutrition Assistance Program (SNAP): Eligible Food Items. Accessed at http://www.fns.usda.gov/ 

snap/eligible-food-items on January 14, 2017. 

Fox, Mary Kay, William Hamilton, and Biing-Hwan Lin. 2004. Effects of food assistance and nutrition programs on nutrition and 

health: Volume 3, literature review. U.S. Department of Agriculture, Economic Research Service. 

Ganong, Peter and Pascal Noel. 2016. How does unemployment affect consumer spending? Harvard University Working Paper, 

January 2016. 

Garasky, Steven, Kassim Mbwana, Andres Romualdo, Alex Tenaglio, and Manan Roy. 2016. Foods typically purchased by Supplemental Nutrition Assistance Program (SNAP) households. U.S. Department of Agriculture, Food and Nutrition Service. 

Gough, Margaret. 2013. How do unemployment and recessions affect time in food preparation and food expenditures within the 

family? POPULATION ASSOCIATION OF AMERICA ANNUAL MEETING CONFERENCE PAPER. 

Hastings, Justine S. and Jesse M. Shapiro. 2013. Fungibility and consumer choice: Evidence from commodity price shocks. QUARTERLY JOURNAL OF ECONOMICS 128(4): 1449–1498. 

Heath, Chip and Jack B. Soll. 1996. Mental budgeting and consumer decisions. JOURNAL OF CONSUMER RESEARCH 23(1): 40–52. 

Houthakker, Hendrik S. 1957. An international comparison of household expenditure patterns, commemorating the centenary of 

Engel’s law. ECONOMETRICA 25(4): 532–551. 

Hoynes, Hilary W., Leslie McGranahan, and Diane Whitmore Schanzenbach. 2015. SNAP and food consumption. In J. Bartfeld, 

C. Gundersen, T. Smeeding, and J. P. Ziliak (eds.), SNAP MATTERS: HOW FOOD STAMPS AFFECT HEALTH AND WELL BEING: 107– 

133. Stanford: Stanford University Press. 

Hoynes, Hilary W. and Diane Whitmore Schanzenbach. 2009. Consumption responses to in-kind transfers: Evidence from the introduction of the Food Stamp Program. AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS 1(4): 109–139. 

Hoynes, Hilary W. and Diane Whitmore Schanzenbach. 2016. U.S. food and nutrition programs. In R. Moffit (ed.), ECONOMICS OF

MEANS-TESTED TRANSFER PROGRAMS IN THE UNITED STATES, VOLUME I: 219–302. Chicago; London: The University of Chicago 

Press. 

Johnson, Lyndon B. 1964. Remarks upon signing the Food Stamp Act. Accessed at http://www.presidency.ucsb.edu/ws/ 

?pid=26472 on May 5, 2016. 

Ketcham, Jonathan D., Nicolai V. Kuminoff, and Christopher A. Powers. 2016. Choice inconsistencies among the elderly: Evidence 

from plan choice in the Medicare Part D program: Comment. AMERICAN ECONOMIC REVIEW 106(12): 3932–3961. 

55 

References—Continued 

Kilian, Lutz. 2010. Explaining fluctuations in gasoline prices: A joint model of the global crude oil market and the U.S. retailer 

gasoline market. THE ENERGY JOURNAL 31(2): 87–112. 

Kooreman, Peter. 2000. The labeling effect of a child benefit system. AMERICAN ECONOMIC REVIEW 90(3): 571–583. 

Kumcu, Aylin and Phillip Kaufman. 2011. Food spending adjustments during recessionary times. AMBER WAVES September 1, 

2011. Accessed at http://www.ers.usda.gov/amber-waves/2011-september/food-spending.aspx#.V2gqeXoqsYS on June 20, 2016. 

Leung, Pauline and Christopher J. O’Leary. 2015. Should UI eligibility be expanded to low-earning workers? Evidence on employment, transfer receipt and income from administrative data. Upjohn Institute Working Paper 15–236. 

Li, Yiran, Bradford Mills, George C. Davis, and Elton Mykerezi. 2014. Child food insecurity and the food stamp program: What a 

difference monthly data make. SOCIAL SERVICE REVIEW 88(2): 332–348. 

Mabli, James and Rosalie Malsberger. 2013. Recent trends in spending patterns of Supplemental Nutrition Assistance Program 

participants and other low-income Americans. MONTHLY LABOR REVIEW, September 2013. 

Mankiw, Gregory N. 2000. Principles of Microeconomics. Boston: Cengage Learning. 

Milkman, Katherine L. and John Bashears. 2009. Mental accounting and small windfalls: Evidence from an online grocer. JOURNAL OF ECONOMIC BEHAVIOR AND ORGANIZATION 71(2): 384–394. 

Mills, Gregory, Tracy Vericker, Heather Koball, Kye Lippold, Laura Wheaton, and Sam Elkin. 2014. Understanding the rates, 

causes, and costs of churning in the Supplemental Nutrition Assistance Program (SNAP)—Final report. U.S. Department of Agriculture, Food and Nutrition Service. 

Moffitt, Robert. 1989. Estimating the value of an in-kind transfer: The case of food stamps. ECONOMETRICa 57(2): 385–409. 

Nevo, Aviv and Arlene Wong. 2015. The elasticity of substitution between time and market goods: Evidence from the Great Recession. NBER Working Paper No. 21318. 

Nord, Mark and Mark Prell. 2011. Food security improved following the 2009 ARRA increase in SNAP benefits. U.S. Department 

of Agriculture, Economic Research Service. 

Project Bread. 2016. Can I Get SNAP? Accessed at http://www.gettingfoodstamps.org/espanol/canigetsnap.html on January 7, 

2016. 

Rainwater, Lee, Richard P. Coleman, and Gerald Handel. 1959. Workingman’s Wife: Her Personality, World and Life Style. New 

York: Oceana Publications. 

Ratcliffe, Caroline, Signe-Mary McKernan, and Sisi Zhang. 2011. How much does the Supplemental Nutrition Assistance Program 

reduce food insecurity? AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS 93(4): 1082–1098. 

Rhode Island Department of Labor and Training. 2016. 2016 UI and TDI Quick Reference. Accessed at https://web.archive.org/ 

web/20160104022814/http://www.dlt.ri.gov/lmi/news/quickref.htm on December 22, 2016. 

Robinson, Peter M. 1988. Root-N-consistent semiparametric regression. ECONOMETRICA 56(4): 931–954. 

Schanzenbach, Diane Whitmore. 2002. What are food stamps worth? Princeton University Industrial Relations Section Working 

Paper No. 468. 

Thaler, Richard H. 1999. Mental accounting matters. JOURNAL OF BEHAVIORAL DECISION MAKING 12(3): 183–206. 

Thompson, Samuel B. 2011. Simple formulas for standard errors that cluster by both firm and time. JOURNAL OF FINANCIAL ECONOMICS 99(1): 1–10. 

Trippe, Carole and Daisy Ewell. 2007. An analysis of cash food expenditures of food stamp households. Washington, D.C.: 

Mathematica Policy Research. 

U.S. Census Bureau. 2010. County Business Patterns. Accessed at http://www.census.gov/data/datasets/2010/econ/cbp/2010- 

cbp.html on June 15, 2016. 

____. 2016. Accessed at http://www.census.gov/quickfacts/table/PST045215/00 on May 5, 2016. 

Varian, Hal R. 1983. Non-parametric tests of consumer behaviour. REVIEW OF ECONOMIC STUDIES 50(1): 99–110. 

Ver Ploeg, Michele, Lisa Mancino, Jessica E. Todd, Dawn Marie Clay, and Benjamin Scharadin. 2015. Where do Americans usually shop for food and how do they travel to get there? Initial findings from the National Household Food Acquisition and Purchase 

Survey. U.S. Department of Agriculture, Economic Research Service. 

Wilde, Parke E. 2001. The food stamp benefit formula: Implications for empirical research on food demand. JOURNAL OF AGRICULTURAL AND RESOURCE ECONOMICS 26(1): 75–90. 

Wilde, Parke E. and Christine Ranney. 1996. The distinct impact of food stamps on food spending. JOURNAL OF AGRICULTURAL

AND RESOURCE ECONOMICS 21(1): 174–185. 

Wilde, Parke E., Lisa M. Troy, and Beatrice L. Rogers. 2009. Food stamps and food spending: An Engel function approach. AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS 91(2): 416–430. 

Yen, Steven T., Margaret Andrews, Zhuo Chen, and David B. Eastwood. 2008. Food Stamp Program participation and food insecurity: An instrumental variables approach. AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS 90(1): 117–132. 

56 

Table 1: Estimated Marginal Propensities To Consume 

Table 2: Tests of Fungibility 


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Figure 1: Household income and size before and after SNAP adoption 

Panel A: Household Income 

Panel B: Number of Children Under Five Years of Age 

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 Ò2 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. 


11502044.eps 11502045.eps
58 

Figure 2: Distribution of Lengths of SNAP Spells 

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 

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 


11502046.eps 11502047.eps
59 

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 

Panel B: SNAP Benefits 

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. 


11502048.eps 11502049.eps
60 

Figure 5: Monthly Expenditure Before and After SNAP Adoption, By SNAP 

Eligibility of Product 

Panel A: SNAP-Eligible Spending 

Panel B: SNAP-Ineligible Spending 

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 Ò2 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. 


11502050.eps 11502051.eps
61 

Figure 6: Participation, Benefits, and Spending Over the 6 Month SNAP 

Clock 

Panel A: SNAP Use 

Panel B: SNAP Benefits 

Panel C: SNAP-Eligible Spending 


11502052.eps 11502053.eps 11502054.eps
62 

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 Ò2 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 


11502055.eps
63 

Panel B: SNAP-Eligible Spending 

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 


11502056.eps 11502057.eps
64 

Panel B: SNAP-Ineligible Products 

Notes: Each figure plots coefficients from a regression of adjusted storebrand 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 Ò2 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-countweighted 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 


11502058.eps 11502059.eps
65 

Panel B: SNAP-ineligible products 

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 Ò2 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. 


11502060.eps
66 

Appendix Table: Results for Alternative Samples and Specifications 


11502061.eps
67 

Appendix Figure 1: Legislated Changes in SNAP Benefits 

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 


11502062.eps 11502063.eps
68 

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 Ò2 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 observationcount-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, 

69 

Department of Policy Analysis and Management and 

Department of Economics, 

210C Warren Hall, 

Ithaca, NY 14850, 

Cornell University, drj3@cornell.edu; 

Ithaca, NY 14853, 

and NBER, 

JHC38@cornell.edu; 

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 wansink@cornell.edu. 

hanks.46@osu.edu; 

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 energydense 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 

70 

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 researchercreated 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. 

71 

* Editor’s note: There is no footnote no. 1 in this working paper, as submitted. 2Sales 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. 3The prices of unrated items were not altered. 

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). 

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. 

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. 

72 

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. 

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 lessnutritious 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. 

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. 

73 

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) 

The data are aggregated by household (h) and week (w). The difference-in-differences estimator is β0. 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: β0=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): 


11502064.eps
74 

(2) 

The null hypothesis is that the framing of the treatment as either a tax on lessnutritious food, a subsidy of nutritious food, or both, does not alter the treatment 

effect; i.e., that β1=β2=β3. 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 purVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00080 Fmt 6633 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN

11502065.eps
75 

chases 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 eduVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00081 Fmt 6633 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
76 

cational 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 lowincome 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 inVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00082 Fmt 6633 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
77 

formation 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. 

78 

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 

79 

6The 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. 

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. 

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. 

Works Cited 

Akee, Randall, Emilia Simeonova, William Copeland, Adrian Angold, and E. Jane Costello. 2013. ‘‘Young Adult Obesity and 

Household Income: Effects of Unconditional Cash Transfers.’’ American Economic Journal: Applied Economics, 5(2): 1–28. 

Andreyeva, Tatiana, Michael W. Long, and Kelly D. Brownell. 2010. ‘‘The impact of food prices on consumption: a systematic review of research on the price elasticity of demand for food.’’ American Journal of Public Health, 100(2): 216. 

Brownell, K.D., Frieden, T.R., 2009. Ounces of prevention—the public policy case for taxes on sugared beverages. NEW ENGLAND

JOURNAL OF MEDICINE 360, 18. 

British Medical Association. 2015. Food for Thought: Promoting Healthy Diets among Children and Young People. 

Bureau of Labor Statistics. 2015. ‘‘Consumer Expenditures in 2013.’’ Accessed June 17, 2015. http://www.bls.gov/cex/ 

csxann13.pdf. 

Cawley, John. 2015. ‘‘An Economy of Scales: A Selective Review of Obesity’s Economic Causes, Consequences, and Solutions.’’ 

Journal of Health Economics, 43: 244–268. 

Cawley, John and David Frisvold. 2015. ‘‘The Incidence of Taxes on Sugar-Sweetened Beverages: The Case of Berkeley, California.’’ NBER Working Paper #21465. 

Cawley, John, Chad Meyerhoefer, Adam Biener, Mette Hammer and Neil Wintfeld. 2015. ‘‘Savings in Medical Expenditures Associated with Reductions in Body Mass Index Among Adults With Obesity, by Diabetes Status.’’ PharmacoEconomics, 33: 707–722. 

Cawley, John, Matthew J. Sweeney, David R. Just, Harry M. Kaiser, William D. Schultze, Jeffrey Sobal, Elaine Wethington, and 

Brian C. Wansink. 2015. ‘‘The Impact of a Supermarket Nutrition Rating System on Purchases of Nutritious and Less Nutritious 

Foods.’’ Public Health Nutrition, 18(1): 8–14. 

Centers for Disease Control and Prevention. 2015a. ‘‘Heart Disease.’’ http://www.cdc.gov/heartdisease/faqs.htm. 

Centers for Disease Control and Prevention. 2015b. ‘‘Preventing Chronic Diseases: Investing Wisely in Health Preventing Diabetes and Its Complications.’’ http://www.cdc.gov/nccdphp/publications/factsheets/Prevention/pdf/diabetes.pdf. 

Centers for Disease Control and Prevention. 2015c. ‘‘Addressing The Cancer Burden: At A Glance.’’ http://www.cdc.gov/ 

chronicdisease/resources/publications/aag/dcpc.htm. 

Chaloupka, Frank J., Lisa M. Powell, and Jamie F. Chriqui. 2011. ‘‘Sugar-Sweetened Beverages and Obesity: The Potential Impact of Public Policies.’’ Journal of Policy Analysis and Management, 30(3): 645–655. 

Chouinard, Hayley H., David E. Davis, Jeffrey T. LaFrance, and Jeffrey M. Perloff. ‘‘Fat taxes: big money for small change.’’ In 

Forum for Health Economics & Policy, vol. 10, no. 2. 2007. 

80 

Works Cited—Continued 

Chriqui J.F., Eidson S.S., Chaloupka F.J. 2014. State Sales Taxes on Regular Soda (as of January 1, 2014)—BTG Fact Sheet. Chicago, IL: BRIDGING THE GAP PROGRAM, Health Policy Center, Institute for Health Research and Policy, University of Illinois at Chicago. 

Elbel, Brian, Glen B. Taksler, Tod Mijanovich, Courtney B. Abrams, and L.B. Dixon. 2013. ‘‘Promotion of Healthy Eating 

Through Public Policy: A Controlled Experiment.’’ Am. J. Prev. Med. 2013; 45(1): 49–55. 

Epstein, Leonard H., Eric Finkelstein, Hollie Raynor, Chantal Nederkoorn, Kelly D. Fletcher, Noelle Jankowiak, and Rocco A. 

Paluch. ‘‘Experimental analysis of the effect of taxes and subsides on calories purchased in an on-line supermarket.’’ Appetite 95 

(2015): 245–251. 

Epstein, Leonard H., Noelle Jankowiak, Chantal Nederkoorn, Hollie A. Raynor, Simone A. French, and Eric Finkelstein. 2012. 

‘‘Experimental research on the relation between food price changes and food-purchasing patterns: a targeted review.’’ American 

Journal of Clinical Nutrition, 95: 789–809. 

Fischer L.M., Sutherland L.A., Kaley L.A., et al., 2011. ‘‘Development and implementation of the guiding stars nutrition guidance 

program.’’ American Journal of Health Promotion 26: e55–e63. 

Fletcher, Jason M., David Frisvold, and Nathan Tefft. ‘‘Can soft drink taxes reduce population weight?.’’ Contemporary Economic 

Policy 28, no. 1 (2010): 23–35. 

Fletcher, Jason M., David E. Frisvold, and Nathan Tefft. 2011. ‘‘Are Soft Drink Taxes an Effective Mechanism for Reducing Obesity?’’ Journal of Policy Analysis and Management, 30(3): 655–662. 

French S.A., Story M., Jeffery R.W., Snyder P., Eisenberg M., Sidebottom A., Murray D. 1997. ‘‘Pricing strategy to promote fruit 

and vegetable purchase in high school cafeterias.’’ J. Am. Diet. Assoc., 97:1008–10. 

Ga ̈ chter, Simon & Orzen, Henrik & Renner, Elke & Starmer, Chris, 2009. ‘‘Are experimental economists prone to framing effects? 

A natural field experiment,’’ Journal of Economic Behavior & Organization, 70(3): 443–446. 

Grossman, Michael. 1972. ‘‘On the Concept of Health Capital and the Demand for Health.’’ Journal of Political Economy, 80(2): 

223–249. 

Gruber, Jonathan H. and Sendhil Mullainathan. 2005. ‘‘Do Cigarette Taxes Make Smokers Happier,’’ Advances in Economic 

Analysis and Policy, 2005, v5(1), Article 4. 

Harrison, Glenn W., and John A. List. (2004). Field Experiments. JOURNAL OF ECONOMICS LITERATURE, 42(4): 1009–1055. 

Hastings, J. and Washington, E. 2010. ‘‘The first of the month effect: Consumer behavior and store responses.’’ American Economic Journal: Economic Policy, 2(2): 142–162. 

Homonoff, Tatiana A. 2015. ‘‘Can Small Incentives Have Large Effects? The Impact of Taxes versus Bonuses on Disposable Bag 

Use.’’ Working paper, Cornell University. 

Institute of Medicine. 2009. ‘‘Local Government Actions to Prevent Childhood Obesity.’’ Institute of Medicine Report Brief. September 1 2009. url: http://www.iom.edu/Reports/2009/Local-Government-Actions-to-Prevent-Childhood-Obesity.aspx. Accessed Jan. 

24, 2012. 

Jacobson M.F., Brownell K.D. 2000. Small taxes on soft drinks and snack foods to promote health. AMERICAN JOURNAL OF PUBLIC

HEALTH, 90(6): 854–7. 

Jeffery R.W., French S.A., Raether C., Baxter J.E. 1994. ‘‘An environmental intervention to increase fruit and salad purchases in 

a cafeteria.’’ Prev. Med., 23: 788–92. 

Kahneman, Daniel, and Amos Tversky. ‘‘Prospect theory: An analysis of decision under risk.’’ Econometrica: Journal of the Econometric Society (1979): 263–291. 

Kaiser, J. 2007. ‘‘An exact and a Monte Carlo proposal to the Fisher-Pitman permutation tests for paired replicates and for independent samples.’’ Stata Journal 7: 402–412. 

Laibson, D. 1997. ‘‘Golden eggs and hyperbolic discounting.’’ Quarterly Journal of Economics, 112(5), 443–477. 

List, John A. 2009. An Introduction to Field Experiments in Economics. JOURNAL OF ECONOMICS BEHAVIOR AND ORGANIZATION, 

70: 439–442. 

List, John A. 2011. ‘‘Why Economists Should Conduct Field Experiments and 14 Tips for Pulling One Off.’’ Journal of Economic 

Perspectives 25(3): 3–16. 

List, John A., Anya Samek, and Terri Zhu. 2015. ‘‘Incentives to Eat Healthy: Evidence from a Grocery Store Field Experiment.’’ 

CESR—Schaeffer Working Paper No. 2015–025. 

Mani, Anandi, Sendhil Mullainathan, Eldar Shafir, and Jiaying Zhao. 2013. ‘‘Poverty impedes cognitive function.’’ Science 

341(6149): 976–980. 

McCullough, Marjorie L., Diane Feskanich, Meir J. Stampfer, Edward L. Giovannucci, Eric B. Rimm, Frank B. Hu, Donna 

Spiegelman, David J. Hunter, Graham A. Colditz, and Walter C. Willett. 2002. ‘‘Diet quality and major chronic disease risk in men 

and women: moving toward improved dietary guidance.’’ The American Journal of Clinical Nutrition 76(6): 1261–1271. 

Mullainathan, Sendhil, and Eldar Shafir. Scarcity: Why having too little means so much. Macmillan, 2013. 

Ogden C.L., Carroll M.D., Kit B.K., Flegal K.M. 2014. ‘‘Prevalence of Childhood and Adult Obesity in the United States, 2011– 

2012.’’ JAMA, 311(8): 806–814. 

Roe, Brian E., and David R. Just. 2009. ‘‘Internal and external validity in economics research: Tradeoffs between experiments, 

field experiments, natural experiments, and field data.’’ American Journal of Agricultural Economics 91(5): 1266–1271. 

Sassi, F., A. Belloni and C. Capobianco. 2013. ‘‘The Role of Fiscal Policies in Health Promotion’’, OECD Health Working Papers, 

No. 66, OECD Publishing. 

Schmeiser, M.D. 2009. ‘‘Expanding wallets and waistlines: The impact of family income on the BMI of women and men eligible 

for the earned income tax credit.’’ Health Economics, 18: 1277–1294. 

Shapiro, J.M. 2005. ‘‘Is there a daily discount rate? Evidence from the food stamp nutrition cycle.’’ Journal of Public Economics, 

89(2): 303–325. 

Thow, A.M., Quested, C., Juventin, L., Kun, R., Khan, a. N., & Swinburn, B. 2011. ‘‘Taxing soft drinks in the Pacific: Implementation lessons for improving health.’’ Health Promotion International, 26(1): 55–64. 

U.S. Department of Agriculture and U.S. Department of Health and Human Services. Dietary Guidelines for Americans, 2010. 

7th Edition, Washington, D.C.: U.S. Government Printing Office, December 2010. 

U.S. Department of Agriculture. 2013. Healthy Incentives Pilot (HIP) Interim Report, by Susan Bartlett, et al., Project Officer: 

Danielle Berman, Alexandria, VA: July 2013. 

World Health Organization. 2009. Global health risks: mortality and burden of disease attributable to selected major risks. (Geneva, Switzerland: WHO). 

World Health Organization. 2015. Using Price Policies to Promote Healthier Diets. (Geneva, Switzerland: WHO). 

81 

Figure 1: Study Timeline 

Note: Weeks are defined as Monday through Sunday. 

Figure 2: Estimated Coefficients for Overall Price Treatment by Week 

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 

Control 

Group 

Treatment 

Group 1: 

Subsidy 

Treatment 

Group 2: Tax 

Treatment 

Group 3: 

Subsidy 

and Tax 

Discount on all Food Items as a Reward for Participation 10% 5% 15% 10% 

Subsidy on Nutritious Foods — 10% — 5% 

Tax on Less-Nutritious Foods — — 10% 5% 

Reduction in the Relative Price of Nutritious vs LessNutritious Foods None 10% 10% 10% 


11502066.eps 11502067.eps
82 

Table 2: Descriptive Measures of Household Demographic Variables Used 

in Regression 

(standard deviations in parentheses) 

Whole 

Sample Control 

All 

Treatment 

Groups 

Subsidy Tax Tax/ 

Subsidy 

More than high school education 91 .00% 92 .00% 90 .70% 90 .60% 91 .80% 89 .60% 

St. dev. (0 .287) (0 .274) (0 .292) (0 .295) (0 .277) (0 .309) 

N (> HS ed.) 182 46 136 48 45 43 

N (≤ 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) 

Whole 

Sample 

Control 

Group 

All 

Treatment 

Groups 

Subsidy Tax Tax/ 

Subsidy 

% 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 SNAP 4 .3% 5 .8% 3 .9% 3 .6% 3 .9% 4 .1% 

(0 .204) (0 .235) (0 .194) (0 .189) (0 .196) (0 .200) 

% Households Not Receiving Food Assistance 89 .9% 87 .7% 90 .7% 94 .4% 87 .3% 89 .8% 

(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 Under 18 2 .2 1 .8 2 .3 3 .0 1 .9 1 .8 

(3 .852) (0 .936) (4 .412) (7 .295) (1 .051) (0 .889) 

% Household Shopping at Hannaford 83 .58 82 .09 84 .07 83 .15 82 .24 87 .02 

(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) 

Whole 

Sample 

Control 

Group 

All Treatment 

Groups 

Subsidy Tax Tax/ 

Subsidy 

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% 

83 

Table 3: Additional Household Demographic Measures—Continued 

b. Marital Status and Race 

(standard deviations in parentheses) 

Whole 

Sample 

Control 

Group 

All Treatment 

Groups 

Subsidy Tax Tax/ 

Subsidy 

(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 Native 0 .5% 0 .0% 0 .7% 1 .9% 0 .0% 0 .0% 

(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) 

Whole 

Sample 

Control 

Group 

All 

Treatment 

Groups 

Subsidy Tax Tax/ 

Subsidy 

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. 

84 

Table 5: Overall Price Effect on Weekly Household Expenditures and Quantities Purchased 

(standard errors in parentheses) 

Expenditures Quantities 

Nutritious Less Nutritious All Rated Items Unrated All Items Nutritious Less Nutritious All Rated Items Unrated All Items 

All Treatment Groups $1 .11 ¥$1 .55 ¥$0 .44 $0 .81 $0 .37 0 .951 ¥0 .873 0 .078 * 0 .661 0 .739 

(3 .010) (4 .042) (6 .780) (1 .138) (7 .606) (1 .347) (1 .607) (2 .822) (0 .387) (3 .091) 

Weekly Dummy Variables ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ N 6,572 6,572 6,572 6,572 6,572 6,572 6,572 6,572 6,572 6,572 Unconditional mean of dependent 

variable $36 .55 $49 .59 $86 .14 $11 .86 $98 .50 16 .132 18 .853 34 .985 3 .609 38 .744 

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 

Nutritious Less Nutritious All Rated Items Unrated All Items Nutritious Less Nutritious All Rated 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 ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ N 6,572 6,572 6,572 6,572 6,572 6,572 6,572 6,572 6,572 6,572 Unconditional mean of dependent 

variable $36 .55 $49 .59 $86 .14 $11 .86 $98 .50 16 .132 18 .853 34 .985 3 .609 38 .744 

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. 

85 

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 

Nutritious Less Nutritious All Rated Items Unrated All Items Nutritious Less Nutritious All Rated Items Unrated All Items 

All Treatment Groups $7 .03 $7 .11 $14 .14 $2 .47 $16 .61 ¥$1 .27 ¥$4 .02 ¥$5 .29 $0 .24 ¥$5 .05 

(6 .010) (9 .793) (15 .460) (2 .597) (17 .420) (3 .707) (4 .543) (7 .898) (1 .313) (8 .893) 

Weekly Dummy Variables ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ N 1,141 1,141 1,141 1,141 1,141 4,904 4,904 4,904 4,904 4,904 Unconditional mean of dependent 

variable $28 .28 $41 .04 $69 .32 $9 .17 $78 .85 $38 .36 $50 .70 $89 .06 $12 .25 $101 .81 

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 

Nutritious Less Nutritious All Rated Items Unrated All Items Nutritious Less Nutritious All Rated Items Unrated All Items 

All Treatment Groups $2 .36 ¥$4 .02 ¥$1 .65 $6 .18 $4 .52 $0 .52 ¥$2 .17 ¥$1 .65 $0 .46 ¥$1 .19 

(11 .190) (20 .950) (31 .600) (4 .130) (34 .200) (3 .091) (3 .925) (6 .714) (1 .139) (7 .528) 

Weekly Dummy Variables ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ N 567 567 567 567 567 5,759 5,759 5,759 5,759 5,759 Unconditional mean of dependent 

variable $25 .16 $39 .92 $65 .08 $8 .76 $74 .23 $37 .73 $50 .41 $88 .14 $12 .05 $100 .67 

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. 

86 

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 

Nutritious Less Nutritious All Rated Items Unrated All Items Nutritious Less Nutritious All Rated Items Unrated All Items 

Subsidy 11 .58 * a $21 .23 * a, d $32 .81 ** a $5 .78 ** a, d $38 .59 a¥$4 .548 * a d¥$7 .546 a, d¥$12 .09 $0 .414 d¥$11 .68 

(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 .11 a $3 .832 $3 .62 a $7 .451 $0 .588 $8 .039 

(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 ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ N 1,141 1,141 1,141 1,141 1,141 4,904 4,904 4,904 4,904 4,904 Unconditional mean of dependent variable $28 .28 $41 .04 $69 .32 $9 .17 $78 .85 $38 .36 $50 .70 $89 .06 $12 .25 $101 .81 

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 

Nutritious Less Nutritious All Rated Items Unrated All Items Nutritious Less Nutritious All Rated 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 ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ N 567 567 567 567 567 5,759 5,759 5,759 5,759 5,759 

87 

Unconditional mean of dependent 

variable $25 .16 $39 .92 $65 .08 $8 .76 $74 .23 $37 .73 $50 .41 $88 .14 $12 .05 $100 .67 

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. 

88 

Table 11: Overall Price Effect on Shares of Expenditures on Nutritious 

Foods, by Income and Education 

(standard errors in parentheses) 

All 

At or 

Below 

130% FPL 

Above 

130% FPL 

HS Educ. 

or Less 

More than 

HS 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) 

Whole 

Sample 

Control 

Group 

All Treatment 

Groups 

Subsidy Tax Tax/ 

Subsidy 

Interpretation of Treatment 

Penalty for eating unhealthy food 2 .9 2 .6 3 .0 a 2 .4 a 3 .4 3 .2 

(1 .937) (1 .739) (2 .003) (1 .662) (2 .100) (2 .161) 

Reward for eating healthy food 6 .2 6 .1 6 .3 6 .0 6 .0 6 .9 

(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 foods 6 .4 5 .8 * 6 .6 6 .7 6 .2 * 6 .9 

(2 .225) (2 .543) (2 .077) (2 .157) (2 .313) (1 .595) 

Effective in changing what I usually buy 4 .5 4 .2 4 .6 4 .8 4 .2 5 .0 

(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 healthy food 5 .3 4 .8 5 .5 b 4 .9 5 .5 b 6 .2 

(2 .200) (2 .360) (2 .124) (2 .043) (2 .407) (1 .595) 

In general, over the entire program 

Shopped healthier at the beginning than 

at the end 3 .3 3 .1 3 .4 3 .4 3 .1 3 .6 

(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 Interventions P-value 95% Lower Confidence 

Level 

95% Upper 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 

89 

* 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. 

Appendix Table 2: Permutation Tests for Individual Treatments 

Combined Interventions P-value 95% Lower Confidence 

Level 

95% Upper 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 Interventions P-value 95% Lower Confidence 

Level 

95% Upper 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 * 

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 

SPsales@harpercollins.com. 

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 

One: Mindless Eating Solutions 

Your Food Radius 

Nobody Wants Us to Be Fat 

Chinese Buffet Confidential 


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Starting Small to Get Slim 

Sixteen Pounds from Happiness 

Becoming Slim by Design 

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 

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? 

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 

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 

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 

Seven: Slim By Design for Life 

From Can’t to CAN 

From Me to We 


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1The 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/. 2One 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. 4That 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. 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. 6Lots 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 

Continued 

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

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. 

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 

92 

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. 7Denmark 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. 8People—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. 9This 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. 10This 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. 

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. 

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. 

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. 

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. 

93 

11This 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. 12We no longer use the Kleenex Cam but we still call it that. We now use our bottles, hats, 

and iPhones. 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. 15The 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). 

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. 

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

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 


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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. 

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. 

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. 

When you use duct tape at home, you become MacGyver. When it’s used to divide 

your grocery cart, you become healthier.16

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. 

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 . . . 

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 

Do it yourself. Divide your cart with your coat, your purse, or your briefcase. Or bring your own duct tape. 


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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. 20This 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. 

What You Can Do . . . 

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. 

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. 

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 

96 

21Would 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. 22My 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. 23Wansink and Stein, ‘‘Eyes in the Aisle.’’ 

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

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. 

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 highmargin 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. 

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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. 25One source for this is Brian Wansink and Aner Tal, ‘‘Correlates of Purchase Quantities in 

Grocery Stores,’’ under review. 26Of 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. 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. 29Paco Underhill, Why We Buy: The Science of Shopping (New York: Simon & Shuster, 2000). 30There’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. 31Kate Stein and Brian Wansink, ‘‘Eye Height and Purchase Probability,’’ under review. 

Wide Aisles and High Products 24

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. 

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 heightmarker 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

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. 

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

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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Eye-Level Shopping Bull’s-Eye 

* 60 percent of what shoppers buy is within 12′′ of their eye height. 


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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. 33This 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. 

Slim-By-Design Grocery Shopping 

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’’: 


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34One 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. 35This 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 

crackers, chips, cereal, or candy. We want packages we can open and eat 

with our right hand while we drive home with our left. 

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. 

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

101 

healthy foods. John Cawley et al., ‘‘How Nutrition Rating Systems in Supermarkets Impact the 

Purchases of Healthy and Less Healthy Foods,’’ under review. 36This 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. 37There’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. 38Wansink, Mindless Eating, pp. 178–9+. 

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

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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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. 40The 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. 41 A recap of this done by Jane Andrews, Wegmans dietitian, can be found at http://rochester.kidsoutandabout.com/node/1901. 42See more at Wansink and Niman, ‘‘The Half-Plate Rule vs. MyPlate vs. Their Plate.’’ 43Learn more about how Wegmans implemented our idea at http://www.wegmans.com/ 

webapp/wcs/stores/servlet/ProductDisplay?storeId=10052&partNumber=UNIVERSAL_20235. 44Wansink and Hoy, ‘‘Half-plate Versus MyPlate.’’ 

Half-Plate Healthy 

* 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

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

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

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


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45See 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. 

What You Can Do . . . 

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. 

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. 

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46More 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. 

How Your Grocer Can Help You . . . 

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

Which of These Would You Like To See at Your Grocery Store? 

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. 


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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 

106 

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. 

107 

Slim-by-Design Grocery Store Self-Assessment Scorecard—Continued 

Entrance 

b Assign designated parking spots (similar 

to handicapped spots) for pregnant women 

and mothers with infants. 

b Offer preprinted shopping lists of basic 

staples near the entrance. 

b Provide information sheets near the entrance on healthy ways to shop. 

b Offer healthier foods near the entrance to 

prime healthy shopping. 

b Two sizes of shopping carts are available. 

b Handbaskets are available. 

b Divided shopping carts with a ‘‘place fruits 

and vegetables here’’ section are provided. 

b The first area entered by most shoppers is 

the produce section. 

b Free healthy samples are near the entrance. 

b There’s a small ‘‘grab and go’’ area in the 

front of the store with a small selection of 

milk and bread for the in- and-out, or ‘‘fillin’’ shopper. 

b There’s a small ‘‘grab and go’’ area in the 

front of the store with a small selection of 

milk and bread for the in- and-out, or ‘‘fillin’’ shopper. 

Services and Signage 

b Signs promote seasonal combinations of 

fruits and vegetables. 

b Signs provide ‘‘Did you know?’’ facts about 

the health benefits of specific foods. 

b Educational posters are located around the 

stores to educate people about healthy eating (for example, the Half-Plate Rule). 

b Local and seasonal foods are clearly promoted. 

b There is a special section for organic fruits 

and/or vegetables. 

b The organic section is boldly and clearly 

labeled. 

b At least one produce-tasting station is 

near the entrance. 

b A wide range of precut fruits and vegetables are available. 

b There are specific perimeter promotions 

for lean dairy. 

b There are specific promotions for wholegrain products, such as bread and pasta. 

b Calorie information is available in the 

meat section. 

b Healthy food apps such as Fooducate and 

QR codes are promoted. 

b A kiosk with tear-off recipes is available in 

the produce section. 

b Combo packs are available that co-promote 

healthy foods (such as tomatoes and mozzarella). 

b There are separate in-aisle promotions for 

canned fruits. 

b A guidance system such as Guiding Stars 

or a stoplight approach is used. 

b There are separate in-aisle promotions for 

canned vegetables. 

b A dietitian is available and visible in the 

store a couple of days each week. 

b There are separate in-aisle promotions for 

frozen vegetables. 

b Unit pricing ($/oz) is available where relevant. 

b There are specific perimeter promotions 

for lean meat. 

Layout and Atmosphere 

b Relaxing music is played in the produce 

section. 

b Show price per unit along with price per 

weight for healthy food, for ease of calculation. 

b Floor decals are used for way-finding to 

healthy sections. 

b Lighting varies throughout the store, but 

is always brightest on the healthier foods. 

b Healthy tear-off recipe cards are provided 

near the fruits and vegetables. 

b Recipe ingredients for the recipe cards are 

located next to the cards. 

Aisles and Shelves 

b Some fruits are bundled into family-size 

packs. 

b Some vegetables are bundled into familysize packs. 

b Ingredients are organized by preparation 

type (stir-fry versus salad)—for example, 

put mushrooms, eggplants, and peppers in 

a ‘‘stir-fry’’ section. 

108 

Slim-by-Design Grocery Store Self-Assessment Scorecard—Continued 

b A complementary fresh produce display is 

available in the meat section (such as one 

containing broccoli, peas, cauliflower, and 

peppers). 

b A complementary fresh produce display is 

available in the seafood section (such as 

lemons, tomatoes, beans, and asparagus). 

b A complementary fresh produce display is 

available in the frozen food section. 

b Displays of single fruits (such as oranges, 

apples, pears, nectarines, and apricots) are 

next to desserts. 

b Ready-to-eat fruits and vegetables are 

available in variety packs. 

b Expiration dates are visible (at front of 

package or on signs). 

b Aisles with healthy foods are the widest. 

b Less healthy foods are inconveniently 

placed very low or very high on the shelves. 

b Healthier foods are conveniently placed at 

eye level. 

b Aisles with healthy food are brighter than 

aisles with unhealthy food. 

b Hard-to-decide-upon foods (‘‘long-buy’’ 

items), such as soups, dressings, and baby 

foods are located in less busy aisles so people are relaxed enough to comparison shop. 

Prepared Food Area 

b Fruit is available in all foodservice areas. 

b Vegetables are available in all food-service 

areas. 

b A mix of whole fruit options is displayed in 

an attractive bowl or basket. 

b The healthy daily targeted entre ́es have 

creative or descriptive names. 

b Posters displaying healthy foods or a guidance system (such as the Half-Plate Rule) 

are visible in the dining area. 

b The ‘‘pick me up’’ or prepared food section 

has healthy default foods. 

b A daily fruit or vegetable option is bundled 

into all grab-and-go meals. 

b A salad bar is available. 

b All beverage coolers have both water and 

white milk available. 

b The cafeteria tracks the popularity and 

frequency of healthy-option orders to see 

what promotions work most effectively. 

b All promotional signs and posters are rotated, updated, or changed at least monthly. 

b Half portions are available for all entre ́es. 

b Alternative healthy entre ́e options (salad 

bar, yogurt parfaits, and the like) are highlighted on posters or signs within all dining 

areas. 

b Half portions are available for all desserts. 

b Takeout boxes are available for leftovers 

not eaten in the cafeteria. 

b The healthy daily targeted entre ́e is placed 

as the first one seen in all dining areas. 

Shopper Comfort and Service 

b Restrooms are easily accessible in the 

front of the store. 

b Health and nutrition games dominate the 

playroom. 

b A drinking fountain is located in the front 

of the store. 

b There is an area for shoppers to sit and 

relax. 

b There is an area for shoppers to eat. 

b A local fitness club is co-promoted. 

b A small discount to a local fitness club is 

given to loyalty club shoppers. 

b There is a drive-through where you can 

pick up your groceries, if you call ahead. 

b There is a supervised playroom for children. b Home delivery is available (for an extra 

charge). 

Engagement: Employees and Social Media 

b The produce-department manager and 

staff are specifically trained to suggest 

healthy answers to shopper questions. 

b All employees are trained to suggest 

healthy complementary products when 

asked about a particular item. 

b The meat-department manager and staff 

are trained to suggest healthy answers to 

shopper questions. 

b There are plentiful staff in the meat and 

produce sections who are trained to suggest 

healthy upsells or substitutes. 

b The dairy-department manager and staff 

are trained to suggest healthy answers to 

shopper questions. 

b Store or chain has an eng aging website 

that has a health-related blog featuring 

local or seasonal products. 

b The bakery-department manager and staff 

are trained to suggest healthy answers to 

shopper questions. 

b The website has shopper loyalty specials. 

b Tips, features, or videos involving better 

shopping and better living (such as ‘‘Shopping with Kids’’) are available. 

109 

aDyson 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: bcw28@cornell.edu, foodandbrandlab@cornell.edu. 

** 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. 

Slim-by-Design Grocery Store Self-Assessment Scorecard—Continued 

Checkout 

b Loyalty programs specifically reward fruit 

and vegetable consumption. 

b Receipts are itemized in categories or otherwise coded to indicate how healthy you’re 

shopping. 

b The back of receipts feature coupons for 

healthy foods. 

b There is at least one food-free checkout 

aisle. 

b A discount is offered if a certain percentage of purchases are fruits and vegetables. 

b Receipt provides an indication of what percentage of purchases were fruits and vegetables, low-fat meat, and low-fat dairy. 

b A default shopping ‘‘starter’’ list is made 

available to each shopper at the front of the 

store with a number of the major staples 

preprinted on it. 

b The same healthy shopping-tips brochure 

available at the beginning of the shopping 

trip is also available at the checkout register. 

b Individual containers of precut fresh fruit 

are available next to at least one cashier. 

b Healthy snack options are offered next to 

the cashiers. 

b Receipt uses loyalty card information to 

show how much was spent on fruits and 

vegetables compared to past trips. 

b ‘‘Don’t Forget’’ signs are placed at the register to remind customers about certain 

healthy foods. 

b A ‘‘fruits and vegetables only’’ self-checkout station is provided for quick purchases 

of produce. 

Scoring Brackets 

b 70–100—Slim-by-Design Grocery Store—Gold 

b 50–69—Slim-by-Design Grocery Store—Silver 

b 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, * 

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. 

110 

† 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. 

Introduction† 

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-thanhealthy 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. 

111 

Fig. 1. How and Where Retail Interventions Can Influence Shoppers 

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’’ slowthinker; 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. 


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Fig. 2. The Hierarchy of Health Predisposition 

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 


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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 increasVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00119 Fmt 6633 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN

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ing 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 

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. 


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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 to 

purchase 

More attractive to 

purchase More normal to purchase 

Signage • Floor decal arrow stickers 

asking people to follow the 

arrows to eat more nutritiously lead to a nine percent increase in produce 

sales (Payne, et al., 2014) 

• Joint efforts to provide fish 

dinner recipe cards and 

grilling instruction brochures were part of a larger 

campaign that increased 

fish sales by 28% (Karevold, 

Tran, and Wansink 2017) 

• New recipe ideas, co-promotions, and end-of-aisle 

displays increased canned 

fish sales by eighteen percent (Toft, et al., in preparation) 

• Starring items as more 

healthy decreased the purchase of unstarred (less 

healthy foods) by two percent (Cawley, et al., 2015) 

• Signage stating that 

garbonzo beans were the 

most popular beans, increased selection by fourteen percent (Bhana 2017) 

• Shopping cart signs stating 

that the average shopper 

purchased at least five 

fruits and vegetables increased produce sales by ten 

percent (Payne, et al., 2014) 

Structure • A fruit display near cash 

register increased sales 

35%, even when product 

was not discounted (van 

Kleef, Often, and van Tripj 

2012) 

• Items (including produce) 

that was within 12′′ of a 

shopper’s eye-level comprised over 43% of all sales 

(Stein 2018) 

• Fruit samples provided to 

consumers upon entering 

the store increased sales 

fruit sales by seven percent 

(Tal and Wansink 2015) 

• People were sixteen percent 

more likely to purchase a 

product from the first full 

aisle they entered than any 

subsequent aisle (Stein 

2017) 

• Visually diving a shopping 

cart in 1⁄2 and suggesting 

that 1⁄2 should be used for 

fruits and vegetables, increased their sales by fourteen percent (Wansink, 

Payne, and Herbst 2017; 

Wansink, Soman, and 

Herbst 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) 

116 

Table 1. How Sample Findings Fit Into the Retail Intervention Matrix— 

Continued 

More convenient to 

purchase 

More attractive to 

purchase More normal to purchase 

Service • Healthy ‘‘Grab and Go’’ 

lines in store cafeterias led 

to a 82% increase in healthy 

food sales (Hanks, et al., 

2012) 

• Mobil apps that indicated 

what percent of your food is 

healthy and which were 

missing, was rating as 

being most attractive to instore consumers (Mao and 

Atakan 2017) 

• In-store suggestions by staff 

contributed to increased fish 

sales (Karevold, Tran, and 

Wansink 2017) 

• One loyalty program rewarded fruit and vegetable 

purchases by providing a 

scaled discount based on 

how much was purchased a

• ‘‘Half-Plate Healthy’’ on-line 

planner, led to higher 

produce sales and more balanced meals a

• Shopping receipt ‘‘scorecards’’ showed consumers 

how the percentage of fruits 

and vegetables purchased in 

this trip 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 

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, copromoted 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 


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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). 

118 

Fig. 6. Where Research Is Most Needed 

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 profitVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00124 Fmt 6633 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN

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ably 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 

Mix element More convenient to purchase 

More attractive to purchase More normal to purchase 

Signage • Created recipe cards titled 

‘‘Fish in 15’’ (min) 

• Offered a ‘‘Grill Tips’’ flier 

for the grilling salmon 

• Co-promoted the fish with 

vegetables (suck as leeks 

and broccoli) 

• Named select fish and included a map showing the 

part of the world where it 

was caught 

• Created ‘‘Native Norway’’ 

logos to promote fish as 

local 

• Used ‘‘Local Favorite’’ and 

‘‘Managers Special’’ stickers 

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Table 2. A Retail Intervention Matrix of How Scandinavian Retailers 

Doubled the Sales of Frozen Fish—Continued 

Mix element More convenient to purchase 

More attractive to purchase More normal to purchase 

Structure • Utilized vertical display 

cases; moved fish to eye 

level and processed foods 

to the bottom 

• Moved fish displays immediately after vegetables 

• Included a buffer of frozen 

vegetables between the 

fish and the beef so people 

would not make an unfair 

sensory comparison with 

beef 

• Placed the single servings 

of fish and some of the 

lower priced ‘‘sales specials’’ near the highest 

traffic edges of the displays 

Service • Offered frozen freezer 

packages to keep fish frozen until home 

• Offered plastic bags to put 

shrink-wrapped fish in for 

extra separation protection 

from other foods in the 

basket 

• Offered smaller, one-portion servings 

• Put markings on the wrapper to show how much to 

prepare for one, two, three, 

or four persons 

• E-mail promotions were 

send to loyalty card holders, with recipe ideas and 

web-links to downloadable 

coupons 

• Employees were instructed 

to suggest the two best 

selling types of fish and 

the two most common 

items with which they 

were prepared (e.g., rice 

pilaf and broccoli) 

• Employees were 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 midDecember 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 

121 

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 to purchase More attractive to purchase More normal to purchase 

Signage • Use display signs to draw 

attention to and promote 

the store’s selection seasonal fruits and vegetables 

with display signs 

• Provide information sheets 

on healthier ways to shop 

near all entrances 

• Directs traffic entering the 

store such that most shoppers begin in the produce 

section 

• Provide a circular/ad publication featuring and promoting healthier value options at least once per week 

• Use a guidance system, 

such as Guiding Stars or a 

stoplight approach, at the 

shelf edge 

• Use display signs to draw 

attention to and promote 

seasonal fruits and vegetables with display signs 

• Use signs which provide 

‘‘Did You Know?’’ health 

benefit facts, positive messages about specific healthful foods throughout the 

store, or both 

• Bundle recipe ingredients 

for family meals next to recipe cards for a healthy meal 

• Make sure that soda and 

low-nutrient snacks (i.e., 

chips) are not displayed or 

merchandised in the 

produce section 

• Display educational posters 

around the store that encourage healthy eating, 

such as the Half-Plate Rule 

• Co-promote healthier options together in snack 

aisles 

• Highlight healthy alternative entre ́e options such 

as the salad bar on posters 

or signs within all dining 

areas 

• Place posters displaying 

healthier foods or a guidance system such as the 

Half-Plate Rule in visible 

areas in the dining area 

122 

Appendix A. An Abbreviated Scorecard To Help Retailers By Organizing 

Sample Findings Into the Retail Intervention Matrix a–c—Continued 

More convenient to purchase More attractive to purchase More normal to purchase 

Structure • Offer a ‘‘grab and go’’ area 

in the front of the store 

with a small selection of low 

fat milk, eggs, 100% juice, 

low-fat yogurt, and whole 

grain bread for the in-andout shopper 

• Organize ingredients for a 

healthy meal by preparation 

method, such as a stir-fry 

section that includes mushrooms, eggplants, peppers, 

and so forth 

• Place healthier foods conveniently at eye level 

• Make available one percent 

or fat free milk, 100% juice, 

and water in all mini 

fridges in checkout aisles 

• Make sure there is at lest 

one checkout aisle [i]n 

which the only food for sale 

qualifies as healthier (no 

candy aisle) 

• Make sure that all beverage 

coolers have both water and 

low-fat non-flavored milk 

stocked and available 

• Assign designated parking 

spots near at least one entrance for pregnant women 

and mothers with infants 

(similar to handicapped 

spots) d

• Create a fresh produce display in the seafood section 

including items such as 

lemons, tomatoes, beans, 

and asparagus 

• Display whole fruits such as 

oranges, apples, pears, nectarines, and apricots next to 

prepared desserts 

• Make sure that there is at 

least one checkout aisle in 

which the only food for sale 

qualifies as healthier (no 

candy aisle) 

• Offer at least three 

healthier foods for sale at 

all entrances to prime 

healthier shopping 

• Offer pre-printed shopping 

lists of basic staples near all 

entrances 

• Offer healthier food samples 

or demonstrations near at 

least one entrance and at 

least once per week 

• Offer 1⁄2 portions for all 

entre ́es and desserts that 

are served or pre-packaged, 

smaller containers for selfservice entrees and desserts, or both 

• Make sure that takeout 

boxes are available for leftovers not eaten in the dining area 

• Offer divided shopping carts 

with a ‘‘place fruits and 

vegetables here’’ section 

Service • Supply simple five-ingredient recipes as tear-off 

cards next to specific 

produce in-store, on the 

store’s website, mobile 

phone app, or both 

• Make pre-cut vegetables 

available in the meat section 

• Provide an area in the store 

for shoppers to sit and 

relax d

• Provide an area in the store 

for shoppers to eat d

• Offer a salad bar that includes lower calorie 

dressings options such as oil 

and vinegar 

• Promote mobile phone apps 

that encourage healthful 

eating such as Fooducate, 

MyFitnessPal or other 

Barcode/QR code scanners 

• Offer tips, features, or videos involving better shopping and better living on 

the store’s website or social 

media outlets 

• Provide calorie information 

on different types and cuts 

of meat in the form of posters, brochures, or labels 

• Make sure that the store’s 

website, mobile app, or both 

(if they have one) has Shopper Loyalty specials that include deals on healthier 

items 

• Provide a loyalty card program which rewards customers with incentives such 

as bonus points or coupons 

for purchasing fruits and 

vegetables, making 

healthier choices, or both 

• Offer a discount for customers if a certain percentage of purchases are fruits 

and vegetables 

• Offer at least two daily 

healthier grab & go breakfast, lunch, and dinner options 

• Supply useful tips related to 

preparation, storage, and 

food safety in produce section, via mobile phone app, 

or both 

• Use a receipt program 

which can create an 

itemized list indicating 

what percentage of purchases were fruits and vegetables, low-fat meat, and 

low-fat dairy 

• Use a receipt program that 

uses loyalty card information to show how much was 

spent on fruits and vegetables, and compares this 

amount to past trips 

a Reprinted, with permission, ©Slim by Design, Wansink (2014). bFindings 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. dEditor’s note: No footnote in submitted article. 

References 

Ailawadi, Kusum L., Yu Ma and Dhruv Grewal (2016), ‘‘The Impact of Warehouse Club Stores on Our Packaged Food Consumption,’’ working paper. 

Atalay, Selin and Margaret G. Meloy (2011), ‘‘Retail Therapy: A Strategic Effort to Improve Mood,’’ Psychology & Marketing, 28 

(6), 638–59. 

123 

References—Continued 

Atakan, Sinem Stet and Laura Finch (2018), ‘‘ ‘Do You Have a Restroom?’ How Environmental Comfort in Retail Stores Influences Sales,’’ working paper, Cornell Food and Brand Lab. 

Beatty, Sharon E., Alexa M. Givan, George R. Franke and Kristy E. Reynolds (2015), ‘‘Social Store Identification and Adolescent

Females’ Store Attitudes and Behaviors,’’ Journal of Marketing Theory and Practice, 23 (1), 39–56. 

Bhana, Hiershenee (2017), ‘‘Conducting Behavioral Field Research in Food Pantries: Lessons and Tactics for Testing Nutrition 

Interventions,’’ working paper, Columbia University. 

Bhana, Hiershenee and Issabella Contento (2017), ‘‘Social Norm Signage Shifts Pantry Patrons to Healthier Food,’’ working 

paper, Columbia University. 

Biswas, Dipayan, Courtney Szocs and Jeffrey Inman (2016), ‘‘Making Choices for a Sequence of Healthy and Unhealthy Options,’’ 

in Let’s Get Engaged! Crossing the Threshold of Marketing’s Engagement Era Springer International Publishing 167–72. 

Block, Martin P. and Steven Keith Platt (2014), Consumer Location-Based Analytics Deliver Actionable Insights, Platt Retail Institute. 

Bodur, Onur, Noreen M. Klein and Neeraj Arora (2015), ‘‘Online Price Search: Impact of Price Comparison Sites on Offline Price 

Evaluations,’’ Journal of Retailing, 91 (1), 125–39. 

Bommelaer, Columbe and Brian Wansink (2017), ‘‘Healthy Shopping By Design: Redesigning Grocery Stores to Sell More Fruits 

and Vegetables,’’ working paper, Cornell Food and Brand Lab. 

Borstein, David (2015), ‘‘The Art of Getting Opponents to ‘We’ ’’ New York Times, (November) (accessed July 12, 2016), [available 

at http://opinionator.blogs.nytimes.com/2015/11/03/the-art-of-getting-opponents-to-we/]. 

Carroll, Kathryn A., Anya Savikhin Samek and Lydia Zepeda (2016), ‘‘Product Bundling as a Behavioral Nudge: Investigating 

Consumer Fruit and Vegetable Selection using Dual-Self Theory,’’ working paper, Agricultural and Applied Economics Association 

(2016 Annual Meeting, July 31–August 2, 2016, Boston, Massachusetts, No. 236130). 

Cawley, John, Matthew J. Sweeney, Jeffrey Sobal, David R. Just, Harry M. Kaiser, William D. Schulze, Elaine Wethington and 

Brian Wansink (2015), ‘‘The Impact of a Supermarket Nutrition Rating System on Purchases of Nutritious and Less Nutritious 

Foods,’’ Public Health Nutrition, 18, 8–14. 

Cawley, John, Andrew S. Hanks, David R. Just and Brian Wansink (2016), ‘‘Incentivizing Nutritious Diets: A Field Experiment of 

Relative Price Changes and How They are Framed,’’ NBER working paper no. w–21929. 

Chandon, Pierre and Brian Wansink (2012), ‘‘Does Food Marketing Need to Make Us Fat? A Review and Solutions,’’ Nutrition Reviews, 70 (October (10)), 571–93. 

Chen, Charlene Y., Leonard Lee and Andy J. Yap (2011), ‘‘Do People Spend More in a Crowded Store? A Field Experiment on 

Control Deprivation and Compensatory Spending,’’ NA—Advances in Consumer Research, 39 (1), 729–30. 

Cleeran, Kathleen, Kelly Geyskens, Peter C. Verhoef and Joost M.E. Pennings (2016), ‘‘Regular or Low-fat? An Investigation of 

the Long-run Impact of the First Low-fat Purchase on Subsequent Purchase Volumes and Calories,’’ International Journal of Research in Marketing, (in press). 

Desai, Kalpesh and Minakshi Trivedi (2014), ‘‘Do Consumer Perceptions Matter in Measuring Choice Variety and Variety Seeking?,’’ Journal of Business Research, 67 (1), 2786–92. 

de Wijk, Rene ́ A., Anna J. Maaskant, Ilse A. Polet, Nancy T.E. Holthuysen, Ellen van Kleef and Monique H. Vingerhoeds (2016), 

‘‘An In-Store Experiment on the Effect of Accessibility on Sales of Wholegrain and White Bread in Supermarkets,’’ PLoS One, 11 

(3), e0151915. 

Dzhogleva, Hristina, Jeff Inman and Jim Maurer (2013), ‘‘Does Reducing Nutritional Information Complexity Promote Healthier 

Food Choices?,’’ NA—Advances in Consumer Research, 41. 

Gilbride, Timothy J., Jeffrey Inman and Karen Melville Stilley (2015), ‘‘The Role of Within-trip Dynamics in Unplanned Versus 

Planned Purchase Behavior,’’ Journal of Marketing, 79 (3), 57–73. 

Groening, Christopher, Jeffrey Inman and William T. Ross, Jr. (2014), ‘‘Carbon Footprints in the Sand: Marketing in the Age of 

Sustainability,’’ Customer Needs and Solutions, 1 (1), 40–51. 

Grunert, Klaus G., Lisa E. Bolton and Monique M. Raats (2011), ‘‘Processing and Acting upon Nutrition Labeling on Food: The 

State of Knowledge and New Directions for Transformative Consumer Research,’’ in Transformative Consumer Research for Personal and Collective Well-Being, Mick D.G., Pettigrew S., Ozanne J.L. and Pechmann C., eds. New York: Routledge, 333–51. 

Guthrie, Joanne F. (2017), ‘‘Integrating Behavioral Economics into Nutrition Education Research and Practice,’’ Journal of Nutrition Education and Behavior, http://dx.doi.org/10.1016/J.Jneb.2016.09.006 (forthcoming) 

Gustafsson, Anders, Crina Tarasi, Lars Witell and Ruth Bolton (2016), ‘‘How Goals, Emotions and Experiential Attributes Influence Shoppers’ Satisfaction with their Retail Service Experience,’’ working paper. 

Hampson, Daniel P. and Peter J. McGoldrick (2013), ‘‘A Typology of Adaptive Shopping Patterns in Recession,’’ Journal of Business Research, 66 (7), 831–8. 

Haywood, Stephen (2016), ‘‘Farmers Blast Supermarkets over Falling Fruit and Veg Sales—Despite Plunging Prices,’’ Mirror 

(Mirror.co.uk), (May). 

Hsu, Christine (2014), ‘‘Weight Primary Reason for Increased in Tofu Consumption,’’ Counsel & Heal, CounselHeal.com, (July) 

(accessed April 12, 2016), [available at http://www.counselheal.com/articles/10425/20140710/weight-primary-reason-increase-tofuconsumption.htm]. 

Hui, Sam K., Yanliu Huang, Jacob Suher and Jeffrey Inman (2013), ‘‘Deconstructing the First Moment of Truth: Understanding 

Unplanned Consideration and Purchase Conversion Using In-store Video Tracking,’’ Journal of Marketing Research, 50 (4), 445–62. 

Hui, Sam K., Jeffrey Inman, Yanliu Huang and Jacob Suher (2013), ‘‘The Effect of In-store Travel Distance on Unplanned Spending: Applications to Mobile Promotion Strategies,’’ Journal of Marketing, 77 (2), 1–16. 

Huneke, Tabea, Sabine Benoit, Poja Shams and Anders Gustafsson (2015), ‘‘Does Service Employees’ Appearance Affect the 

Healthiness of Food Choice?,’’ Psychology and Marketing, 32 (1), 94–106. 

Hunneman, Auke, Peter C. Verhoef and Laurens M. Sloot (2015), ‘‘The Moderating Role of Shopping Trip Type in Store Satisfaction Formation,’’ working paper, Oslo: BI Norweigan School of Management. 

Inman, Jeffrey (2012), ‘‘The Elephant Not in the Room: The Need for Useful, Actionable Insights in Behavioral Research,’’ NA— 

Advances in Consumer Research, 40. 

Inman, Jeffrey and Hristina Nikolova (2016), ‘‘Shopper-Facing Retail Technology: An Adoption Decision Calculus,’’ (2016). 

Johnson, Eric J., Suzanne B. Shu, Benedict G.C. Dellaert, Craig Fox, Daniel G. Goldstein, Gerald Haeubl, Richard P. Larrick, 

John W. Payne, Ellen Peters, David Schkade, Brian Wansink and Elke U. Weber (2012), ‘‘Beyond Nudges: Tools of a Choice Architecture,’’ Marketing Letters, 23 (June (2)), 487–504. 

Karevold, Knut, Huy Quoc Tran and Brian Wansink (2017), ‘‘Supermarket Interventions to Sell Sustainable Foods: Better to 

Change the Selection or to Change the Store?,’’ Food and Brand Lab working paper, Cornell University. 

Kell, John (2016), ‘‘Wegmans Was Just Named the Best Grocery Chain in America,’’ Fortune, (April) (accessed April 12, 2016) 

[available at http://fortune.com/2016/04/14/best-grocery-store/]. 

Keeling, Kathleen A., Peter J. McGoldrick and Henna Sadhu (2013), ‘‘Staff Word-of-Mouth (SWOM) and Retail Employee Recruitment,’’ Journal of Retailing, 89 (1), 88–104. 

Kovacheva, Aleksandra and Jeffrey Inman (2014), ‘‘Shopper Eye-Cue: Understating the In-Store Decision Process With Field EyeTracking Data,’’ NA—Advances in Consumer Research, 42. 

Laroche, Helena H., Christopher Ford, Kate Anderson, Xueya Cai, David R. Just, Andrew S. Hanks and Brian Wansink (2015), 

‘‘Concession Stand Makeovers: A Pilot Study of Offering Healthy Foods at High School Concession Stands,’’ Journal of Public 

Health, 37 (1), 116–24. 

Laroche, Helena, Chrisne Hradek, Kate Hanson, Andrew S. Hanks, David R. Just and Brian Wansink (2017), ‘‘Healthy Concessions: High School Students’ Responses to Healthy Concession Stand Changes,’’ Journal of School Health, 87 (2), 98–105. http:// 

dx.doi.org/10.1111/josh.12472. 

Lee, Leonard (2015), ‘‘The Emotional Shopper: Assessing the Effectiveness of Retail Therapy,’’ Foundations and Trends in Marketing, 8 (2), 69–145. 

124 

References—Continued 

Lemon, Katherine N. and Peter C. Verhoef (2016), ‘‘Understanding Customer Experience throughout the Customer,’’ Journey 

Journal of Marketing, (in press). 

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), 30–6. 

List, John A., Anya Savikhin Samek and Terri Zhu (2015), ‘‘Incentives to Eat Healthy: Evidence from a Grocery StoreField Experiment,’’ CESR—Schaeffer working paper 2015–025. 

Lowe, Ben, Diogo Souza-Monteiro and Iain Fraser (2013), ‘‘Exploring the Role of Technology in Consumer Processing of Nutritional Information,’’ Journal of Marketing Management, 29 (11–12), 1337–66. http://dx.doi.org/10.1080/0267257X.2013.798673. 

Lund, Donald J. and Detelina Marinova (2014), ‘‘Managing Revenue Across Retail Channels: The Interplay of Service Performance and Direct Marketing,’’ Journal of Marketing, 78 (5), 99–118. 

Mao, Ran and Sinem Atakan (2017), ‘‘What Future Technologies Will Help Grocery Shoppers to Shop Healthier?,’’ working paper, 

Cornell University. 

Marinova, Detelina, Irina V. Kozlenkova, Leona Cuttler and J.B. Silvers (2016), ‘‘To Prescribe or Not to Prescribe? Consumer Access to Life-Enhancing Products,’’ Journal of Consumer Research, http://dx.doi.org/10.1093/jcr/ucw057. 

Mukund, Anupama, Sinem Atakan and Brian Wansink (2018), ‘‘When Does More Time in the Aisle Mean More Food in the 

Cart?,’’ working paper, Cornell Food and Brand Lab. 

Nikolova, Hristina Dzhogleva, Jeffrey Inman, Jim Maurer, Andrew Greiner and Gala Amoroso (2014), The Shopper-Centric Retailer: Three Case Studies on Deriving Shopper Insights from Frequent Shopper Data, Emerald Group Publishing Limited.75–102. 

Nikolova, Hristina Dzhogleva and J. Jeffrey Inman (2015), ‘‘Healthy Choice: The Effect of Simplified Point-of-Sale Nutritional Information on Consumer Food Choice Behavior,’’ Journal of Marketing Research, 52 (6), 817–35. 

Otterbring, Tobias, Erik Wa ̈stlund, Anders Gustafsson and Poja Shams (2014), ‘‘Vision (im)possible? The Effects of In-store Signage on Customers’ Visual Attention,’’ Journal of Retailing and Consumer Sciences, 21 (5), 676–84. 

Payne, C.R., Mihai Niculescu, David R. Just and Michael P. Kelly (2014), ‘‘Shopper Marketing Nutrition Interventions,’’ Physiology & Behavior, 136, 111–20. 

Peters, John C., Jimikaye Beck, Jan Lande, Zhaoxing Pan, Michelle Cardel, Keith Ayoob and James Hill (2016), ‘‘Using Healthy 

Defaults in Walt Disney World Restaurants to Improve Nutritional Choices,’’ Journal of the Association for Consumer Research, 1 

(1) (forthcoming). 

Pope, Lizzy, Andrew S. Hanks, David R. Just and Brian Wansink (2014), ‘‘New Year’s Res-Illusions: Food Shopping in the New 

Year Competes with Healthy Intentions,’’ PLoS One, 9 (12), e110561. 

Produce for Better Health (2015), State of the Plate: 2015 Study on America’s Consumption of Fruit and Vegetables, Washington, 

D.C.: Produce for Better Healthy Foundation. 

Purdy, Chase (2016), ‘‘To Lure People Put Off by the Freakiness of Lab-Made Meat, This is What the Industry Wants to Call It,’’

Quartz, (May) (accessed August 12, 2016) [available at http://qz.com/772987/to-lure-people-put-off-by-the-freakiness-of-lab-mademeat-this-is-what-the-industry-wants-to-call-it/]. 

Robinson, Stacey G., Michael K. Brady, Katherine N. Lemon and Michael Giebelhausen (2016), ‘‘Less of this One? I’ll Take It: 

New Insights on the Influence of Shelf-based Scarcity,’’ International Journal of Research in Marketing, (in press). 

Sciandra, Michael and Jeffrey Inman (2014), ‘‘Smart Phones, Bad Calls? The Impact of In-Store Mobile Technology Use on Purchase Behavior,’’ working paper. 

Shah, Avni, Jim Bettman, Punam Anand Keller and Peter Ubel (2013), ‘‘ ‘Does This Tax Make Me Look Fat?’ Using Stigma-Inducing Labels to Decrease Unhealthy Food Consumption,’’ NA—Advances in Consumer Research, 41. 

Sheehan, D. and Koert van Ittersum (2016), ‘‘In-Store Spending Dynamics,’’ working paper. 

Spence, Charles and Betina Piqueras-Fiszman (2014), The Perfect Meal: The Multisensory Science of Food and Dining, John 

Wiley & Sons. 

Stein, Kate (2017), ‘‘Do Supermarket Aisles Bias Spending?,’’ working paper, Cornell Food and Brand Lab. 

____ (2018), ‘‘Eyes in an Aisle: How Eye Gaze Level Relates to Healthy Grocery Store Purchases,’’ working paper, Cornell University Food and Brand Lab. 

Trivedi, Minakshi, Dinesh K. Gauri and Yu Ma (2016), ‘‘An Empirical Investigation of Inefficiencies for Sales Promotions in Stimulating Category Sales,’’ Management Science, (forthcoming). 

Trivedi, Minakshi, Karthik Sridhar and Ashish Kumar (2016), ‘‘Impact of Healthy Alternatives on Consumer Choice: A Balancing 

Act,’’ Journal of Retailing, 92 (1), 65–82. 

Trudel, Remi, Kyle B. Murray, Soyoung Kim and Shuo Chen (2015), ‘‘The Impact of Traffic Light Color-coding on Food Health 

Perceptions and Choice,’’ Journal of Experimental Psychology: Applied, 21 (3), 255–75. 

Tal, Aner and Brian Wansink (2015), ‘‘An Apple a Day Brings More Apples Your Way: Healthy Samples Prime Healthier 

Choices,’’ Psychology & Marketing, 32 (5), 575–84. 

Toft, Ulla, Lise Lawaetz Winkler, Frank Eriksson, Bent Egberg Mikkelsen and Charlotte Glumer, ‘‘The Effect of 20% Price Discount on Fruit and Vegetables Combined with a Space Management Intervention on Supermarket Purchases During the Three 

Month SoL Project,’’ working paper, University of Copenhagen (in preparation). 

van Herpen, Erica (2016), ‘‘Product Category Layout and Organization: Retail Placement of Food Products,’’ in Reference Module 

in Food Science Elsevier. 

van Herpen, Erica, Evan van den Broek, Hans C.M. van Trijp and Tian Yu (2016), ‘‘Can a Virtual Supermarket Bring Realism 

into the Lab? Comparing Shopping Behavior Using Virtual and Pictorial Store Representations to Behavior in a Physical Store,’’ 

Appetite, 107, 196–207. 

Van der Heide, Martine, Koert van Ittersum and Jenny van Doorn (2016), ‘‘Healthy Shopping Dynamics: The Relative healthiness of Food Purchases Throughout Shopping Trips,’’ In Advances in Consumer Research, Vol. 44, Moreau Page and Puntoni S. eds. 

Duluth, MN: Association for Consumer Research. 

Van Doorn, Jenny and Peter C. Verhoef (2015), ‘‘Drivers of and Barriers to Organic Purchase Behavior,’’ Journal of Retailing, 91 

(3), 436–50. 

van Ittersum, Koert, Brian Wansink, Joost M.E. Pennings and Daniel Sheehan (2013), ‘‘Smart Shopping Carts: How Real-Time 

Feedback Influences Spending,’’ Journal of Marketing, 77 (6), 21–36. 

van Kleef, Ellen, Kai Otten and Hans C.M. van Trijp (2012), ‘‘Healthy Snacks at the Checkout Counter: A Lab and Field Study 

on the Impact of Shelf Arrangement and Assortment Structure on Consumer Choices,’’ BMC Public Health, 12 (1), 1072. 

Vega Zamora, Manuela, Francisco Jose ́ Torres Ruiz, Eva M. Murgado Armenteros and Manuel Parras Rosa (2014), ‘‘Organic as a 

Heuristic Cue: What Spanish Consumers Mean by Organic Foods,’’ Psychology & Marketing, 31 (5), 349–59. 

Verhoef, Peter C. and Jenny van Doorn (2016), ‘‘Segmenting Consumers According to Their Purchase of Products with Organic, 

Fair-Trade, and Health Labels,’’ Journal of Marketing Behavior, 2 (1), 19–37. 

Vermeir, Iris and Patrick Van Kenhove (2005), ‘‘The Influence of Need for Closure and Perceived Time Pressure on Search Effort 

for Price and Promotional Information in a Grocery Shopping Context,’’ Psychology & Marketing, 22 (1), 71–95. 

Wansink, Brian (2014), Slim by Design—Mindless Eating Solutions for Everyday Life, New York, NY: William Morrow. 

_____ (2015), ‘‘Change their Choice! Changing Behavior Using the CAN Approach and Activism Research,’’ Psychology & Marketing, 32 (5), 486–500. 

Wansink, Brian, David R. Just, Collin R. Payne and Matthew Z. Klinger (2012), ‘‘Attractive Names Sustain Increased Vegetable 

Intake in Schools,’’ Preventive Medicine, 55 (4), 330–2. 

Wansink, Brian and Huy Quoc Tran (2017), MyPlate, Half-Plate, or the Whole Plate: How Dietary Guidance Systems Influence 

Eating Behavior, Cornell Food and Brand Lab working paper. 

Wansink, Brian, Collin R. Payne and Kenneth C. Herbst (2017), ‘‘Half-Cart Approach to Increasing Fruit and Vegetable Purchases in Grocery Stores,’’ working paper, Cornell Food and Brand Lab. 

Wansink, Brian, Dilip Soman and Kenneth C. Herbst (2017), ‘‘Larger Partitions Lead to Larger Sales: Divided Grocery Carts 

Alter Purchase Norms and Increase Sales,’’ Journal of Business Research, (forthcoming). 

125 

References—Continued 

Wansink, Brian, Huy Quoc Tran and Knut Ivar Karevold (2017), ‘‘Healthy Eating Interventions Work Best on Mondays and 

Tuesdays, Food and Brand Lab,’’ working paper, Cornell University. 

Werle, C.O.C., G. Ardito, O. Trendal, A. Mallard and P. Nat (2011), ‘‘Unhealthy Food is Not Tastier for Everybody: The Healthy = 

Tasty French Intuition,’’ Actes du Congre ̆s de l’AFM. 

Wilson, Norbert Lance Weston (2016), ‘‘When the Cupboards are Bare: Nudging Food Pantry Clients to Healthier Foods,’’ Journal 

of the Association for Consumer Research, 1 (1) (forthcoming). 

Wilson, Norbert L.W., David R. Just, Jeffery Swiger and Brian Wansink (2016), ‘‘Food Pantry Selection Solutions. A Randomized 

Controlled Trial in Client-Choice Food Pantries to Nudge Clients to Targeted Foods,’’ Journal of Public Health. 

Winkler, Lise L., Ulla Christensen, Charlotte Glu ̈mer, Paul Bloch, Bent E. Mikkelsen, Brian Wansink and Ulla Toft (2017), ‘‘Substituting Candy for Fruit and Healthy Snacks at the Checkout: A Win-Win Solution for Consumers and Food Stores,’’ BMC Public 

Health, (forthcoming). 

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 nutriVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00131 Fmt 6633 Sfmt 6601 P:\DOCS\115-02\24325.TXT BRIAN
126 

tion 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 

127 

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 

128 

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? 

129 

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 

130 

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 

131 

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 

132 

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 

133 

$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 

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grams 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 perVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00140 Fmt 6633 Sfmt 6601 P:\DOCS\115-02\24325.TXT BRIAN
135 

spective 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 

136 

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. 

137 

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 

138 

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 landgrant, 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 

139 

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 

140 

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. 

141 

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 

142 

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? 

143 

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 

144 

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? 

145 

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 randomVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00151 Fmt 6633 Sfmt 6601 P:\DOCS\115-02\24325.TXT BRIAN
146 

ized 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 acVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00152 Fmt 6633 Sfmt 6601 P:\DOCS\115-02\24325.TXT BRIAN
147 

cess 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. 

148 

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, 

149 

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? 

150 

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 

151 

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. 

152 

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:] 

153 

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) 

154 

* 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. 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-annualreport-revised.pdf. 2See, 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). 

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 * 

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 higherincome households are more striking than the differences.2

155 

3Per 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.’’ 4Stores 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). 6SNAP transactions in which SNAP EBT was not the majority tender were not identifiable 

in the data. 7Some 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. 8See http://www.fns.usda.gov/snap/retailers/eligible.htm for more details. 

By using POS data to compare the purchases of SNAP households to those of nonSNAP 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 nonSNAP 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

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

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. 

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 difVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00161 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
156 

9Stores that opened or closed during 2011 were not included in these analyses. 

10On average, SNAP households in the data made 8.5 transactions per month. The average 

total expenditure on SNAP-eligible foods per transaction was $26.99. 11http://www.fns.usda.gov/pd/19SNAPavg$HH.htm. 

ferences 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. 

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 nonSNAP 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 nonSNAP 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. 

157 

• 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 fooditem 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 expenditures on SNAP-eligible foods in dataset $6.7 billion $32.3 billion 

Percentage of all transactions by all households 12% 88% 

Percentage of total annual expenditures by all households 17% 83% 

Top 1,000 subcommodity (of 1,792) expenditures as a percentage 

of all expenditures 

99% 98% 

Top 100 subcommodity expenditures as a percentage of all expenditures 51% 46% 

Top 25 subcommodity expenditures as a percentage of all expenditures 25% 21% 

Top 25 commodity (of 238) expenditures as a percentage of all expenditures 45% 41% 

Top 10 summary categories (of 30) by expenditure Meat/Poultry/Seafood Meat/Poultry/Seafood 

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 238) by expenditure Soft Drinks Fluid Milk Products 

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 Snacks Coffee and Creamers 

Lunchmeat Ice Cream, Ice Milk, and Sherbets 

Top 10 subcommodities (of 1,792) by expenditure Fluid Milk/White Only Fluid Milk/White Only 

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 Solution Bananas 

USDA Food Pattern categories, by expenditure: 

• Dairy 9% 10% 

• Fruits 6% 9% 

• Grains 12% 13% 

• Oils 2% 2% 

• Protein Foods 23% 20% 

• Solid Fats and Added Sugars 13% 12% 

• Vegetables 8% 10% 

• Composite 19% 16% 

158 

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. 13http://www.fns.usda.gov/pd/SNAPsummary.htm. 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. 15Flegal, 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. 16Trust for America’s Health. (2011). F as in fat: How obesity threatens America’s future. 

Available at http://healthyamericans.org/reports/obesity2010/Obesity2010Report.pdf. 

Exhibit 1: SNAP and Non-SNAP Household Food Expenditure Patterns— 

Continued 

Finding SNAP Households Non-SNAP 

Households 

• 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. 

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 lowincome 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. 

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. 

159 

17http://www.cdc.gov/nchs/tutorials/Dietary/SurveyOrientation/intro.htm. 18http://www.ncppanel.com. 19http://www.bls.gov/cex. 20Supplemental 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. 21http://www.fmi.org/facts_figs/?fuseaction=superfact. 22Per 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.’’ 23Stores that opened or closed during 2011 were not included in these analyses. 

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. 

An outstanding question is whether food purchase data collected at the point-ofsale 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. 

• 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. 

• 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 nonVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00165 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
160 

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). 25SNAP transactions in which SNAP EBT was not the majority tender were not identifiable 

in the data. 26Some 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. 27See http://www.fns.usda.gov/snap/retailers/eligible.htm for more details. 28For 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. 

SNAP households over the 12 months were $39.0 billion, or approximately $3.3 billion per month. SNAP households expended approximately $555 million on SNAPeligible food items each month in this dataset, using both SNAP benefits and other 

resources such as cash or credit cards.24

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. 

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 


11502084.eps
161 

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. 30 USDA Center for Nutrition Policy and Promotion Food Patterns (http:// 

www.cnpp.usda.gov/USDAFoodPatterns.htm). 

time. Additionally, the same food item may be sold in multiple forms with different 

brands and labels, each with its own unique UPC.29

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 

Finding SNAP 

Households 

Non-SNAP 

Households 

Total annual expenditures on SNAP-eligible foods in dataset $6.7 billion $32.3 billion 

Percentage of all transactions by all households 12% 88% 

Percentage of total annual expenditures by all households 17% 83% 

Top 1,000 (of 1,792) subcommodity expenditures as a percentage of all expenditures 99% 98% 

Top 100 (of 1,792) subcommodity expenditures as a percentage of all expenditures 51% 46% 

Top 25 (of 1,792) subcommodity expenditures as a percentage of all expenditures 25% 21% 

Top 25 commodity (of 238) expenditures as a percentage of all expenditures 45% 41% 

Total annual expenditures on top 1,000 subcommodities $6.5805 billion $31.5138 billion 

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. 

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. 

162 

31Stores that opened or closed during 2011 were not included in these analyses. 32On average, SNAP households in the data made 8.5 transactions per month. The average total expenditure on SNAP-eligible foods per transaction was $26.99. 33http://www.fns.usda.gov/pd/19SNAPavg$HH.htm. 

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. 

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 


11502085.eps
163 

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 nonSNAP 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 nonSNAP 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. 

164 

Exhibit 5: Summary Categories by Expenditure 

Summary Category 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

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 Products 22 $66.4 1.01% 23 $281.5 0.89% 

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 Sweets 28 $29.1 0.44% 29 $117.5 0.37% 

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 Expenditures (Top 

1,000 subcommodities) $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. 

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 nonSNAP 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 nonSNAP 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 

Commodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

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% 

165 

Exhibit 6: Top 100 Commodities for SNAP Households by Expenditure— 

Continued 

Commodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

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 snacks 50 $41.1 0.62% 45 $226.1 0.72% 

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% 

166 

34See Appendix A for the commodity that corresponds to each subcommodity for the top 1,000 

subcommodities. 

Exhibit 6: Top 100 Commodities for SNAP Households by Expenditure— 

Continued 

Commodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

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 Subcommodities $6,580.5 100% $31,513.8 100% 

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 nonSNAP 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. 

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 nonSNAP households, at 190. 

Exhibit 7: Top 100 Subcommodities for SNAP Households by Expenditure 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

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% 

167 

Exhibit 7: Top 100 Subcommodities for SNAP Households by Expenditure— 

Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

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 Traditional Meals 18 $43.8 0.67% 24 $175.4 0.56% 

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 All 23 $40.9 0.62% 81 $80.7 0.26% 

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 Nutritional Meals 51 $24.7 0.38% 5 $271.6 0.86% 

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 [Chicken] 77 $18.6 0.28% 209 $42.6 0.14% 

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% 

168 

35 USDA Economic Research Service Urban Influence Codes (http://www.ers.usda.gov/dataproducts/urban-influence-codes.aspx). 36Following 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. 

Exhibit 7: Top 100 Subcommodities for SNAP Households by Expenditure— 

Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

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 Subcommodities $3,372.2 51.01% $13,390.0 42.14% 

Expenditures on Top 1,000 subcommodities $6,580.5 100% $31,513.8 100% 

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. 

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 nonSNAP 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 

169 

• 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 

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 nonSNAP 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 

Dairy Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

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% 


11502086.eps
170 

Exhibit 8: Top 25 SNAP Household Dairy Subcommodity Expenditures— 

Continued 

Dairy Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

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 Top 1,000 

Subcommodities $571.2 100% $3,257.4 100% 

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 nonSNAP households. 

Exhibit 9: Top 25 SNAP Household Fruit Subcommodity Expenditures 

Fruit Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

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 Juice) 7 $13.3 3.11% 14 $45.8 1.59% 

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% 

171 

Exhibit 9: Top 25 SNAP Household Fruit Subcommodity Expenditures— 

Continued 

Fruit Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Total Fruit Expenditures Among Top 1,000 

Subcommodities $416.8 100% $2,772.4 100% 

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 nonSNAP households. 

Exhibit 10: Top 25 SNAP Household Grains Subcommodity Expenditures 

Grains Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

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 Top 1,000 

Subcommodities $783.8 100% $4,049.9 100% 

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 nonSNAP 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. 

172 

Exhibit 11: Oils Subcommodity Expenditures 

Oils Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

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 Sauce 9 $1.4 1.14% 10 $7.2 1.26% 

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 the Top 

1,000 Subcommodities $125.9 100% $555.0 100% 

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 

Protein Foods Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

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 Expenditures $815.7 53.62% $3,088.3 48.22% 

173 

Exhibit 12: Top 25 SNAP Household Protein Foods Subcommodity 

Expenditures—Continued 

Protein Foods Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Total Protein Foods Expenditures Among 

Top 1,000 Subcommodities $1,512.2 100% $6,288.8 100% 

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 

Solid Fats and Added Sugars (SoFAS) 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

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 Top 1,000 

Subcommodities $864.1 100% $3,673.1 100% 

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. 

174 

38Fruit 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.’’ 

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, nonSNAP households spent a larger share of their SoFAS expenditures on the butter/ 

cream/solid fats and candy/sweets subcategories. 

Exhibit 14: Solid Fats and Added Sugars (SoFAS) Expenditures by Subcategory 

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 nonSNAP 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 

Vegetables Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

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% 


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Exhibit 15: Top 25 SNAP Household Vegetables Subcommodity 

Expenditures—Continued 

Vegetables Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

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 Expenditures $297.7 56.10% $1,582.6 47.10% 

Total Vegetable Expenditures Among Top 

1,000 Subcommodities $520.5 100% $3,251.8 100% 

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 

Composite Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

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 Traditional Meals 3 $43.8 3.53% 4 $175.4 3.38% 

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 Nutritional Meals 13 $24.7 1.99% 1 $271.6 5.23% 

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 Expenditures $720.5 58.07% $2,637.7 50.83% 

176 

Exhibit 16: Top 25 SNAP Household Composite Subcommodity 

Expenditures—Continued 

Composite Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Total Composite Expenditures Among Top 

1,000 Subcommodities $1,235.4 100% $5,132.0 100% 

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 entre ́e/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 

entre ́e/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 

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. 


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Exhibit 18: Top 25 SNAP Household Other Subcommodity Expenditures 

Other Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

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 Mix) 21 $7.0 1.25% 88 $6.2 0.23% 

Malted Milk/Syrup/Powders/Eggnog 22 $6.9 1.23% 28 $25.3 0.95% 

Still Water Flavored Drink/Mineral Water 23 $6.3 1.11% 17 $38.1 1.43% 

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 Top 1,000 

Subcommodities $550.7 100% $2,533.2 100% 

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 

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 


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39Stores that opened or closed during 2011 were not included in these analyses. 

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. 

Overall, the findings from this study indicate that SNAP households and nonSNAP 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 expenditures on SNAP-eligible foods in dataset $6.7 billion $32.3 billion 

Percentage of all transactions by all households 12% 88% 

Percentage of total annual expenditures by all households 17% 83% 

Top 1,000 (of 1,792) subcommodity expenditures as a percentage 

of all expenditures 

99% 98% 

Top 100 subcommodity expenditures as a percentage of all expenditures 51% 46% 

Top 25 subcommodity expenditures as a percentage of all expenditures 25% 21% 

Top 25 commodity (of 238) expenditures as a percentage of all expenditures 45% 41% 

Top 10 summary categories (of 30) by expenditure Meat, Poultry and Seafood Meat, Poultry and Seafood 

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 238) by expenditure Soft Drinks Fluid Milk Products 

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 Snacks Coffee and Creamers 

Lunchmeat Ice Cream, Ice Milk, and Sherbets 

Top 10 subcommodities (of 1,792) by expenditure Fluid Milk/White Only Fluid Milk/White Only 

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 Solution Bananas 

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. 

179 

Appendix A: Top Purchases by Expenditure for SNAP and Non-SNAP 

Households 

Exhibit A–1: All Commodities 

Commodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

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% 

180 

Exhibit A–1: All Commodities—Continued 

Commodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

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% 

181 

Exhibit A–1: All Commodities—Continued 

Commodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

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 entre ́es 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% 

182 

Exhibit A–1: All Commodities—Continued 

Commodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

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 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Fluid Milk Products Milk/White Only 1 $191.1 2.90% 1 $853.8 2.71% 

Soft Drinks Soft Drinks 12/18 & 

15pk Can Car 

2 $164.6 2.50% 2 $601.2 1.91% 

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 Btl 

Carb Incl 

6 $70.9 1.08% 12 $230.1 0.73% 

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 Fresh 9 $55.8 0.85% 11 $242.6 0.77% 

Infant Formula Infant Formula Starter/ 

Solution 

10 $54.2 0.82% 190 $45.3 0.14% 

Eggs/Muffins/Potatoes Eggs—Large 11 $52.1 0.79% 9 $251.6 0.80% 

Chicken Fresh Chicken Breast Boneless 12 $49.6 0.75% 4 $292.9 0.93% 

Water—(Sparkling & 

Still) 

Still Water Drnking/ 

Mnrl Water 

13 $48.8 0.74% 19 $187.7 0.60% 

Baked Breads Mainstream White Bread 14 $48.0 0.73% 39 $136.8 0.43% 

Bag Snacks Tortilla/Nacho Chips 15 $47.4 0.72% 17 $209.0 0.66% 

Frozen Handhelds & 

Snacks 

Snacks/Appetizers 16 $44.6 0.68% 65 $100.5 0.32% 

Cheese American Single Cheese 17 $44.1 0.67% 41 $136.6 0.43% 

Frzn Ss Premium Meals Fz Ss Prem Traditional 

Meals 

18 $43.8 0.67% 24 $175.4 0.56% 

Refrgratd Juices/Drinks Dairy Case 100% Pure 

Juice—O 

19 $43.5 0.66% 6 $269.0 0.85% 

Baked Sweet Goods Snack Cake—Multi Pack 20 $41.6 0.63% 63 $101.7 0.32% 

Pork Boneless Loin/Rib Enhanced [Pork Boneless 

Loin/Rib] 

21 $41.5 0.63% 27 $168.0 0.53% 

Coffee & Creamers Unflavored Can Coffee 22 $41.3 0.63% 18 $198.0 0.63% 

Frzn Ss Economy Meals Fz Ss Economy Meals All 23 $40.9 0.62% 81 $80.7 0.26% 

Bacon Bacon—Trad 16oz Or 

Less 

24 $40.7 0.62% 29 $157.6 0.50% 

Soft Drinks Soft Drinks 20pk & 24pk 

Can Carb 

25 $39.7 0.60% 60 $106.4 0.34% 

Frozen Pizza Pizza/Premium 26 $39.7 0.60% 32 $153.3 0.49% 

Baked Breads Mainstream Variety 

Breads 

27 $38.4 0.58% 26 $173.2 0.55% 

Sugars & Sweeteners Sugar 28 $36.9 0.56% 55 $112.7 0.36% 

Cold Cereal All Family Cereal 29 $36.2 0.55% 16 $214.9 0.68% 

Frozen Handhelds & 

Snacks 

Sandwiches & 

Handhelds 

30 $35.9 0.54% 91 $73.6 0.23% 

Potatoes Potatoes Russet (Bulk & 

Bag) 

31 $35.8 0.54% 30 $154.5 0.49% 

Cheese Natural Cheese Chunks 32 $35.3 0.54% 15 $216.1 0.69% 

Pork Thin Meats Ribs [Pork] 33 $35.0 0.53% 59 $106.8 0.34% 

Convenient Meals Convenient Meals—Kids 

Meal C 

34 $34.2 0.52% 96 $69.7 0.22% 

Bananas Bananas 35 $34.2 0.52% 10 $242.7 0.77% 

Soft Drinks Sft Drnk Mlt-Pk Btl 

Carb (Excp) 

36 $34.0 0.52% 25 $173.6 0.55% 

Ice Cream Ice Milk & 

Sherbets 

Premium [Ice Cream & 

Sherbert] 

37 $31.2 0.47% 13 $226.0 0.72% 

Isotonic Drinks Isotonic Drinks Single 

Serve 

38 $30.5 0.46% 47 $119.5 0.38% 

Chicken Frozen Frzn Chicken—Wht 

Meat 

39 $30.0 0.46% 66 $99.8 0.32% 

Canned Soups Condensed Soup 40 $29.7 0.45% 31 $153.6 0.49% 

Salad Dresing & Sandwich Spreads Pourable Salad 

Dressings 

41 $29.0 0.44% 37 $139.4 0.44% 

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 Btl 

Carb (Ex) 

44 $27.8 0.42% 94 $71.4 0.23% 

Frzn Multi Serve Fz Family Style Entre ́es 45 $27.6 0.42% 77 $83.5 0.26% 

183 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Salad Dresing & Sandwich Spreads Mayonnaise & Whipped 

Dressing 

46 $27.3 0.41% 48 $119.1 0.38% 

Frozen Vegetable & Veg 

Dish 

Fz Bag Vegetables— 

Plain 

47 $25.7 0.39% 42 $131.9 0.42% 

Ice Cream Ice Milk & 

Sherbets 

Traditional [Ice Cream & 

Sherbert] 

48 $25.6 0.39% 49 $118.7 0.38% 

Hot Dogs Hot Dogs—Base Meat 49 $25.1 0.38% 138 $56.8 0.18% 

Cold Cereal Adult Cereal 50 $24.9 0.38% 21 $182.6 0.58% 

Frzn Ss Premium Meals Fz Ss Prem Nutritional 

Meals 

51 $24.7 0.38% 5 $271.6 0.86% 

Dinner Mixes-Dry Macaroni & Cheese Dnrs 52 $24.3 0.37% 125 $59.7 0.19% 

Aseptic Juice Aseptic Pack Juice And 

Drinks 

53 $24.2 0.37% 134 $57.1 0.18% 

Fluid Milk Products Refrigerated Coffee 

Creamers 

54 $24.1 0.37% 34 $147.2 0.47% 

Beef: Round Choice Beef 55 $24.0 0.37% 92 $72.5 0.23% 

Traditional Mexican 

Foods 

Mexican Soft Tortillas 

And Wraps 

56 $23.7 0.36% 54 $113.1 0.36% 

Berries Strawberries 57 $23.5 0.36% 22 $178.4 0.57% 

Margarines Margarine: Tubs And 

Bowls 

58 $23.4 0.36% 64 $100.9 0.32% 

Pasta & Pizza Sauce Mainstream [Pasta & 

Pizza Sauce] 

59 $23.0 0.35% 80 $81.0 0.26% 

Chicken Fresh Chicken Wings 60 $22.2 0.34% 300 $28.6 0.09% 

Canned Pasta & Mwv 

Fd-Shlf Stbl 

Can Pasta 61 $22.2 0.34% 179 $47.7 0.15% 

Chicken Frozen Frzn Chicken—Wings 62 $22.2 0.34% 452 $17.4 0.06% 

Lunchmeat Lunchmeat—Bologna/ 

Sausage 

63 $21.8 0.33% 121 $60.9 0.19% 

Bag Snacks Mult Pk Bag Snacks 64 $21.6 0.33% 199 $43.4 0.14% 

Candy—Packaged Candy Bags-Chocolate 65 $21.5 0.33% 33 $147.5 0.47% 

Sweet Goods Sw Gds: Donuts 66 $21.3 0.32% 78 $82.3 0.26% 

Can Seafood—Shelf Stable Tuna 67 $21.1 0.32% 57 $109.9 0.35% 

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/Jelly/Jams 

& Honey 

Peanut Butter 70 $20.4 0.31% 43 $127.8 0.41% 

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 Foods Frzn Breakfast Sandwiches 74 $19.1 0.29% 142 $55.7 0.18% 

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 Chicken Value Forms/18oz And 

Larger [Chicken] 

77 $18.6 0.28% 209 $42.6 0.14% 

Cakes Cakes: Birthday/Celebration 78 $18.6 0.28% 164 $50.3 0.16% 

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/Broth Rts Soup: Chunky/Homestyle 82 $17.6 0.27% 46 $119.9 0.38% 

Milk By-Products Sour Creams 83 $17.5 0.27% 70 $95.2 0.30% 

Frozen Breakfast Foods Waffles/Pancakes/French 

Toast 

84 $17.3 0.26% 90 $77.4 0.25% 

Chicken Fresh Chicken Drums 85 $17.3 0.26% 270 $31.5 0.10% 

Bagels & Cream Cheese Cream Cheese 86 $17.2 0.26% 51 $115.5 0.37% 

Beef: Grinds Angus [Beef] 87 $17.1 0.26% 61 $103.8 0.33% 

Bag Snacks Bagged Cheese Snacks 88 $17.1 0.26% 157 $52.0 0.16% 

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 Bouillon Ramen Noodles/Ramen 

Cups 

91 $16.7 0.25% 304 $28.1 0.09% 

Crackers & Misc Baked 

Food 

Cheese Crackers 92 $16.5 0.25% 72 $90.2 0.29% 

Dinner Sausage Dnr Sausage—Links 

Pork Ckd 

93 $16.4 0.25% 233 $37.6 0.12% 

Candy—Checklane Candy Bars (Singles) 94 $16.3 0.25% 146 $54.9 0.17% 

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—(Sparkling & 

Still) 

Spring Water 97 $16.2 0.25% 69 $95.6 0.30% 

Refrgratd Juices/Drinks Dairy Case Juice Drnk 

Under 10 oz 

98 $16.0 0.24% 177 $48.0 0.15% 

Fluid Milk Products Flavored Milk 99 $16.0 0.24% 128 $59.4 0.19% 

Baked Sweet Goods Sweet Goods—Full Size 100 $15.8 0.24% 133 $57.9 0.18% 

Grapes Grapes Red 101 $15.8 0.24% 45 $121.7 0.39% 

Candy—Packaged Candy Bars (Multi Pack) 102 $15.6 0.24% 97 $69.6 0.22% 

184 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Grapes Grapes White 103 $15.5 0.23% 76 $84.9 0.27% 

Cookies Tray Pack/Choc Chip 

Cookies 

104 $15.3 0.23% 153 $53.9 0.17% 

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 Sausage Bkfst Sausage—Fresh 

Rolls 

107 $15.1 0.23% 119 $61.4 0.19% 

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 Products Refrigerated Biscuits 110 $14.7 0.22% 191 $45.2 0.14% 

Crackers & Misc Baked 

Food 

Butter Spray Cracker 111 $14.6 0.22% 101 $68.7 0.22% 

Frozen Novelties—Water 

Ice 

Sticks/Enrobed [Frozen 

Novelties] 

112 $14.2 0.22% 126 $59.7 0.19% 

Spices & Extracts Traditional Spices 113 $14.1 0.21% 120 $61.2 0.19% 

Frozen Novelties—Water 

Ice 

Water Ice [Frozen Novelties] 114 $14.0 0.21% 160 $50.6 0.16% 

Yogurt Yogurt/Kids 115 $14.0 0.21% 212 $42.4 0.13% 

Cnv Breakfast & Wholesome Snks Toaster Pastries 116 $14.0 0.21% 180 $47.6 0.15% 

Dry Bean Veg & Rice Rice Side Dish Mixes 

Dry 

117 $14.0 0.21% 184 $46.7 0.15% 

Ice Cream Ice Milk & 

Sherbets 

Pails [Ice Cream & 

Sherbert] 

118 $13.9 0.21% 250 $35.1 0.11% 

Milk By-Products Cottage Cheese 119 $13.9 0.21% 58 $108.8 0.35% 

Rtd Tea/New Age Juice Tea Sweetened 120 $13.9 0.21% 102 $68.7 0.22% 

Can Beans Prepared Beans—Baked 

W/Pork 

121 $13.4 0.20% 145 $55.3 0.18% 

Cheese Natural Cheese Slices 122 $13.4 0.20% 53 $113.2 0.36% 

Tropical Fruit Avocado 123 $13.4 0.20% 56 $112.6 0.36% 

Meat—Shelf Stable Chili: Canned 124 $13.3 0.20% 206 $42.8 0.14% 

Shelf Stable Juice Apple Juice & Cider 

(Over 50%) 

125 $13.3 0.20% 187 $45.8 0.15% 

Value-Added Fruit Instore Cut Fruit 126 $13.2 0.20% 74 $85.8 0.27% 

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 

Fresh 

131 $13.0 0.20% 132 $58.0 0.18% 

Dinner Mixes-Dry Skillet Dinners 132 $13.0 0.20% 332 $25.8 0.08% 

Dry Noodles & Pasta Long Cut Pasta 133 $13.0 0.20% 122 $60.4 0.19% 

Chicken Fresh Whole Chicken (Roasters/Fryer) 134 $12.9 0.20% 136 $56.9 0.18% 

Frozen Pizza Pizza/Single Serve/Microwave 135 $12.8 0.19% 203 $43.2 0.14% 

Can Vegetables—Shelf 

Stable 

Green Beans: Fs/Whl/ 

Cut 

136 $12.8 0.19% 155 $53.1 0.17% 

Cnv Breakfast & Wholesome Snks Granola Bars 137 $12.8 0.19% 73 $88.9 0.28% 

Candy—Packaged Candy Bags-Non Chocolate 138 $12.6 0.19% 147 $54.9 0.17% 

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/ 

Stuffng 

Potatoes: Dry 141 $12.3 0.19% 262 $32.3 0.10% 

Condiments & Sauces Bbq Sauce 142 $12.3 0.19% 226 $38.6 0.12% 

Chicken Fresh Chicken Thighs 143 $12.2 0.19% 165 $50.0 0.16% 

Dinner Sausage Dnr Sausage—Pork Rope 

Ckd 

144 $12.1 0.18% 227 $38.2 0.12% 

Can Vegetables—Shelf 

Stable 

Corn 145 $12.1 0.18% 197 $44.0 0.14% 

Bacon Bacon—Trad Greater 

Than 16oz 

146 $12.0 0.18% 193 $44.6 0.14% 

Ice Cream Ice Milk & 

Sherbets 

Super Premium Pints 

[Ice Cream & 

Sherbert] 

147 $11.8 0.18% 71 $91.1 0.29% 

Baby Foods Baby Food—Beginner 148 $11.7 0.18% 303 $28.1 0.09% 

Molasses/Syrups/Pancake 

Mixes 

Molasses & Syrups 149 $11.7 0.18% 130 $58.7 0.19% 

Water Non-Carb Water Flvr— 

Drnk/Mnr 

150 $11.6 0.18% 115 $63.4 0.20% 

Vegetables Salad Head Lettuce 151 $11.6 0.18% 143 $55.5 0.18% 

Condiments & Sauces Catsup 152 $11.5 0.17% 216 $41.5 0.13% 

Dry Sce/Gravy/Potatoes/ 

Stuffng 

Sauce Mixes/Gravy 

Mixes Dry 

153 $11.5 0.17% 183 $46.7 0.15% 

Beef: Thin Meats Soup/Stew 154 $11.2 0.17% 195 $44.1 0.14% 

Baby Foods Baby Food Junior/All 

Brands 

155 $11.2 0.17% 311 $27.5 0.09% 

185 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Frzn Prepared Chicken Whole Muscle Breaded/ 

18oz 

156 $11.1 0.17% 285 $29.9 0.09% 

Cakes Cakes: Cupcakes 157 $11.1 0.17% 247 $35.3 0.11% 

Refrgratd Juices/Drinks Dairy Case Citrus Pnch/ 

Oj Subs 

158 $11.0 0.17% 254 $34.4 0.11% 

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 & 

Snacks 

Corn Dogs 161 $10.9 0.17% 401 $20.6 0.07% 

Cnv Breakfast & Wholesome Snks Cereal Bars 162 $10.9 0.17% 86 $78.4 0.25% 

Isotonic Drinks Isotonic Drinks MultiPack 163 $10.8 0.16% 131 $58.1 0.18% 

Cookies Cookies: Regular 164 $10.8 0.16% 127 $59.6 0.19% 

Shelf Stable Juice Fruit Drinks: Canned & 

Glass 

165 $10.6 0.16% 617 $10.9 0.03% 

Single Serve Fruit/Applesauce Fruit Cup 166 $10.6 0.16% 207 $42.7 0.14% 

Can Beans Variety Beans—Kidney/ 

Pinto 

167 $10.5 0.16% 104 $68.0 0.22% 

Frozen Vegetable & Veg 

Dish 

Frzn Steamable Vegetables 168 $10.5 0.16% 79 $81.4 0.26% 

Coffee & Creamers Non Dairy Creamer 169 $10.5 0.16% 244 $35.4 0.11% 

Beef: Thin Meats Cubed Meats [Beef] 170 $10.5 0.16% 286 $29.8 0.09% 

Hot Dogs Hot Dogs—Base Beef 171 $10.3 0.16% 171 $49.4 0.16% 

Yogurt Yogurt/Ss Light 172 $10.2 0.16% 62 $103.1 0.33% 

Traditional Mexican 

Foods 

Mexican Sauces And 

Picante Sauce 

173 $10.2 0.16% 116 $62.3 0.20% 

Frozen Handhelds & 

Snacks 

Burritos 174 $10.2 0.15% 406 $20.0 0.06% 

Eggs/Muffins/Potatoes Eggs—Medium 175 $10.1 0.15% 394 $21.0 0.07% 

Dry Noodles & Pasta Short Cut Pasta 176 $9.9 0.15% 140 $56.2 0.18% 

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 Shoulder] 179 $9.7 0.15% 292 $29.2 0.09% 

Frzn Prepared Chicken Boneless Snack/18oz And 

Larger 

180 $9.6 0.15% 384 $21.5 0.07% 

Rolls Rolls: Dinner 181 $9.5 0.14% 161 $50.5 0.16% 

Chicken & Poultry Chix: Value-Added (Cold) 182 $9.5 0.14% 323 $26.7 0.08% 

Tomato Products-Shelf 

Stable 

Tomatoes Diced 183 $9.5 0.14% 123 $59.9 0.19% 

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 Juice Blended Juice & Combinations 186 $9.3 0.14% 287 $29.6 0.09% 

Sushi Sushi—In Store Prepared 187 $9.2 0.14% 75 $85.4 0.27% 

Tomatoes Tomatoes Hothouse On 

The Vine 

188 $9.2 0.14% 88 $77.7 0.25% 

Candy—Packaged Seasonal Miscellaneous 

[Candy] 

189 $9.2 0.14% 182 $46.9 0.15% 

Frozen Bread/Dough Frzn Garlic Toast 190 $9.1 0.14% 307 $27.8 0.09% 

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 Sausage Bkfst Sausage—Fresh 

Links 

195 $8.9 0.14% 325 $26.3 0.08% 

Crackers & Misc Baked 

Food 

Snack Crackers 196 $8.9 0.14% 68 $98.6 0.31% 

Citrus Clementines 197 $8.8 0.13% 85 $78.6 0.25% 

Frzn Prepared Chicken Bone-In Wings 198 $8.8 0.13% 586 $12.0 0.04% 

Onions Onions Yellow (Bulk & 

Bag) 

199 $8.7 0.13% 225 $39.3 0.12% 

Dry Mix Desserts Pudding & Gelatin Cups/ 

Cans 

200 $8.7 0.13% 310 $27.6 0.09% 

Coffee & Creamers Unflavored Bag Coffee 201 $8.5 0.13% 38 $137.3 0.44% 

Refrgratd Juices/Drinks Dairy Case Tea With 

Sugar 

202 $8.4 0.13% 364 $23.1 0.07% 

Infant Formula Infant Formula Specialty 203 $8.4 0.13% 687 $9.1 0.03% 

Ss/Vending—Salty 

Snacks 

Salty Snacks Vending 204 $8.4 0.13% 480 $15.8 0.05% 

Shortening & Oil Canola Oils 205 $8.3 0.13% 291 $29.3 0.09% 

Infant Formula Infant Formula Starter 

Large 

206 $8.3 0.13% 368 $22.8 0.07% 

Value-Added Fruit Melons Instore Cut 207 $8.2 0.13% 205 $42.8 0.14% 

Vegetables Salad Cucumbers 208 $8.2 0.13% 129 $58.9 0.19% 

Smoked Hams Hams—Half/Port BoneIn 209 $8.2 0.12% 282 $30.0 0.10% 

186 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Crackers & Misc Baked 

Food 

Saltine/Oyster 210 $8.2 0.12% 204 $43.1 0.14% 

Condiments & Sauces Steak & Worchester 

Sauce 

211 $8.2 0.12% 321 $26.7 0.08% 

Cookie/Cracker Multi-Pks Multi-Pack Crackers 212 $8.0 0.12% 217 $41.3 0.13% 

Frozen Novelties—Water 

Ice 

Cones [Frozen Novelties] 213 $7.9 0.12% 273 $31.2 0.10% 

Deli Meat: Bulk Meat: Beef Bulk 214 $7.9 0.12% 154 $53.4 0.17% 

Melons Watermelon Seedless 

Whole 

215 $7.9 0.12% 198 $43.9 0.14% 

Candy—Packaged Seasonal Candy Bags— 

Chocolate 

216 $7.9 0.12% 148 $54.8 0.17% 

Salad & Dips Vegetable Salads— 

Prepack 

217 $7.8 0.12% 238 $36.6 0.12% 

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 Salad 220 $7.8 0.12% 265 $31.9 0.10% 

Energy Drinks Energy Drink—Single 

Serve 

221 $7.7 0.12% 327 $26.3 0.08% 

Smoked Hams Hams—Spiral 222 $7.6 0.12% 240 $36.5 0.12% 

Coffee & Creamers Unflavored Instant Coffee 223 $7.6 0.12% 316 $27.3 0.09% 

Tomatoes Roma Tomatoes (Bulk/ 

Pkg) 

224 $7.5 0.11% 222 $39.6 0.13% 

Cookies Vanilla Wafer/Kids Cookies 225 $7.5 0.11% 236 $36.7 0.12% 

Frozen Novelties—Water 

Ice 

Ice Cream Sandwiches 226 $7.4 0.11% 354 $24.2 0.08% 

Hot Dogs Hot Dogs—Premium 227 $7.4 0.11% 208 $42.7 0.14% 

Yogurt Yogurt/Pro Active Health 228 $7.4 0.11% 113 $63.5 0.20% 

Snack Meat Snack Meat—Pepperoni 229 $7.4 0.11% 263 $32.1 0.10% 

Cakes Cakes: Creme/Pudding 230 $7.4 0.11% 333 $25.8 0.08% 

Meat Frozen Frzn Meat—Breakfast 

Sausage 

231 $7.3 0.11% 602 $11.3 0.04% 

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 & Rice Noodle Side Dish Mixes 234 $7.3 0.11% 390 $21.1 0.07% 

Yogurt Yogurt/Adult MultiPacks 235 $7.2 0.11% 210 $42.5 0.14% 

Dry Bean Veg & Rice Rice—Dry Bag And Box 236 $7.1 0.11% 255 $33.9 0.11% 

Energy Drinks Energy Drink—Single 

Serve 

237 $7.1 0.11% 224 $39.5 0.13% 

Baked Breads Sandwich Buns 238 $7.1 0.11% 137 $56.8 0.18% 

Refrigerated Dairy Case Non-Dairy Milks 239 $7.1 0.11% 105 $67.7 0.21% 

Beef: Round Select Beef 240 $7.1 0.11% 278 $30.4 0.10% 

Powder & Crystal Drink 

Mix 

Unsweetened Envelope 

[Powder Drink Mix] 

241 $7.0 0.11% 802 $6.2 0.02% 

Refrigerated Desserts Refrigerated Pudding 242 $7.0 0.11% 170 $49.5 0.16% 

Carrots Carrots Mini Peeled 243 $7.0 0.11% 118 $61.4 0.19% 

Baking Mixes Layer Cake Mix 244 $7.0 0.11% 251 $35.1 0.11% 

Cocoa Mixes Malted Mlk/Syrup/Pwdrs 

(Eggnog) 

245 $6.9 0.11% 339 $25.3 0.08% 

Stone Fruit Cherries Red 246 $6.9 0.10% 139 $56.7 0.18% 

Frzn Seafood Frz Coated Fish Fillets 247 $6.9 0.10% 389 $21.1 0.07% 

Meat Snacks Jerky/Nuggets/Tenders 248 $6.8 0.10% 334 $25.8 0.08% 

Dry Bean Veg & Rice Rice—Instant & Microwave 249 $6.8 0.10% 231 $38.0 0.12% 

Seafood—Catfish Catfish—Fillet 250 $6.8 0.10% 544 $13.1 0.04% 

Refrgrated Dough Products Refrigerated CookiesBrand 251 $6.8 0.10% 296 $28.8 0.09% 

Fluid Milk Products Specialty/Lactose Free 

Milk 

252 $6.7 0.10% 175 $48.4 0.15% 

Peanut Butter/Jelly/Jams 

& Honey 

Preserves/Jam/Marmalade 253 $6.7 0.10% 141 $56.2 0.18% 

Margarines Margarine Stick 254 $6.7 0.10% 376 $22.3 0.07% 

Rts/Micro Soup/Broth Broth 255 $6.7 0.10% 109 $65.6 0.21% 

Rtd Tea/New Age Juice Juice (Under 10% Juice) 256 $6.7 0.10% 374 $22.4 0.07% 

Apples Apples Gala (Bulk & 

Bag) 

257 $6.6 0.10% 98 $69.3 0.22% 

Chicken Fresh Chicken Legs/Quarters 258 $6.6 0.10% 536 $13.5 0.04% 

Frozen Breakfast Foods Frzn Breakfast Pastry 259 $6.5 0.10% 420 $19.0 0.06% 

Flour & Meals Flour: White & Self Rising 260 $6.4 0.10% 297 $28.8 0.09% 

Seafood—Value-Added Seafood Value-Added 

Breaded Shrimp 

261 $6.4 0.10% 459 $16.9 0.05% 

Sugars & Sweeteners Sweeteners 262 $6.4 0.10% 168 $49.8 0.16% 

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 

Pancake Mixes 265 $6.3 0.10% 379 $21.9 0.07% 

187 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Water—(Sparkling & 

Still) 

Still Water Flvrd Drnk/ 

Mnrl Wtr 

266 $6.3 0.10% 230 $38.1 0.12% 

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 & 

Bag) 

269 $6.2 0.09% 181 $47.4 0.15% 

Shelf Stable Juice Cranapple/Cran Grape 

Juice 

270 $6.1 0.09% 315 $27.3 0.09% 

Frzn Seafood Frz Fishsticks/Tenders/ 

Nuggets 

271 $6.1 0.09% 506 $14.7 0.05% 

Seafood—Crab Crab—Snow 272 $6.1 0.09% 598 $11.4 0.04% 

Bread Bread:Italian/French 273 $6.1 0.09% 172 $49.0 0.16% 

Bulk Service Case 

Cheese 

Bulk Semi-Hard Cheese 274 $6.1 0.09% 196 $44.0 0.14% 

Baking Mixes Muffin & Corn Bread 

Mix 

275 $6.0 0.09% 295 $28.9 0.09% 

Chicken & Poultry Chix: Frd 8pc/Cut Up 

(Cold) 

276 $6.0 0.09% 558 $12.7 0.04% 

Infant Formula Infant Formula Toddler 277 $6.0 0.09% 570 $12.4 0.04% 

Vegetables Cooking Bulk Celery 278 $5.9 0.09% 158 $51.2 0.16% 

Traditional Mexican 

Foods 

Mexican Seasoning 

Mixes 

279 $5.9 0.09% 402 $20.6 0.07% 

Refrigerated Dairy Case Fluid Milk 280 $5.9 0.09% 52 $113.3 0.36% 

Soft Drinks Soft Drinks Can NonCarb 281 $5.9 0.09% 592 $11.5 0.04% 

Condiments & Sauces Hot Sauce 282 $5.8 0.09% 466 $16.4 0.05% 

Apples Apples Red Delicious 

(Bulk & Bag) 

283 $5.8 0.09% 248 $35.2 0.11% 

Single Serve Sweet Goods Snack Cake—Single 

Serve 

284 $5.7 0.09% 470 $16.2 0.05% 

Milk By-Products Refrig Dips 285 $5.7 0.09% 350 $24.7 0.08% 

Tomatoes Tomatoes Vine Ripe 

Bulk 

286 $5.7 0.09% 373 $22.5 0.07% 

Bag Snacks Brand Snacks 287 $5.6 0.09% 176 $48.1 0.15% 

Refrgrated Dough Products Refrigerated Specialty 

Rolls 

288 $5.5 0.08% 312 $27.5 0.09% 

Canned & Dry Milk Canned Milk 289 $5.5 0.08% 305 $27.9 0.09% 

Coffee & Creamers Ready To Drink Coffee 290 $5.5 0.08% 403 $20.5 0.06% 

Salad Mix Garden Plus [Salad Mix] 291 $5.5 0.08% 267 $31.8 0.10% 

Cookies Cookies: Holiday/Special 

Occas 

292 $5.5 0.08% 320 $26.8 0.08% 

Bag Snacks Misc Bag Snacks 293 $5.5 0.08% 591 $11.5 0.04% 

Refrgratd Juices/Drinks 

Dairy Case 

100% Pure Juice Other 294 $5.4 0.08% 261 $32.3 0.10% 

Refrgrated Dough Products Refrigerated Crescent 

Rolls 

295 $5.4 0.08% 274 $31.2 0.10% 

Teas Tea Bags & Bulk Tea 296 $5.4 0.08% 317 $27.2 0.09% 

Aseptic Juice Aseptic Pack Juice And 

Drinks 

297 $5.3 0.08% 449 $17.5 0.06% 

Infant Formula Infant Formula Solutions 

Large 

298 $5.3 0.08% 497 $15.2 0.05% 

Vegetables Cooking Bulk Cabbage 299 $5.3 0.08% 340 $25.1 0.08% 

Melons Cantaloupe Whole 300 $5.3 0.08% 194 $44.4 0.14% 

Dry Sce/Gravy/Potatoes/ 

Stuffng 

Stuffing Mixes 301 $5.3 0.08% 378 $22.1 0.07% 

Frozen Desserts Frozen Fruit Pies & Cobblers 302 $5.3 0.08% 359 $23.7 0.08% 

Frozen Potatoes Frzn Tater Tots/Other 

Extruded 

303 $5.2 0.08% 424 $18.8 0.06% 

Traditional Mexican 

Foods 

Mexican Taco/Tostado/ 

Shells 

304 $5.2 0.08% 417 $19.1 0.06% 

Broccoli/Cauliflower Broccoli Whole & Crowns 305 $5.2 0.08% 156 $52.0 0.16% 

Tomato Products-Shelf 

Stable 

Tomato Sauce 306 $5.1 0.08% 353 $24.2 0.08% 

Candy—Checklane Candy Bars (Singles) 307 $5.1 0.08% 476 $15.9 0.05% 

Lunchmeat Lunchmeat—Chop/Form 

Pltry 

308 $5.1 0.08% 583 $12.1 0.04% 

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 Juice Cranberry Juice (50% 

And Under) 

311 $5.0 0.08% 371 $22.6 0.07% 

Seafood—Salmon-Farm 

Raised 

Salmon Fr—Atlantic 312 $5.0 0.08% 173 $48.8 0.15% 

Tomatoes Tomatoes Hot House 

Bulk 

313 $5.0 0.08% 280 $30.3 0.10% 

Yogurt Yogurt/Specialty Greek 314 $5.0 0.08% 89 $77.4 0.25% 

Frozen Whipped Topping Frzn Whipped Topping 315 $5.0 0.08% 276 $30.9 0.10% 

Can Fruit/Jar Applesauce Pineapple 316 $4.9 0.07% 357 $24.0 0.08% 

Frozen Desserts Frozen Cream Pies 317 $4.9 0.07% 423 $18.9 0.06% 

188 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Infant Formula Infant Formula Concentrate 318 $4.9 0.07% 954 $3.9 0.01% 

Stone Fruit Peaches Yellow Flesh 319 $4.8 0.07% 243 $35.6 0.11% 

Sweet Goods Sw Gds: Sw Rolls/Dan 320 $4.8 0.07% 319 $26.9 0.09% 

Potatoes Potatoes Sweet & Yams 321 $4.8 0.07% 234 $37.1 0.12% 

Seafood—Party Trays Party Tray—Shrimp 322 $4.8 0.07% 347 $24.8 0.08% 

Shelf Stable Juice Blended Juice & Combinations 323 $4.8 0.07% 365 $22.9 0.07% 

Baking Mixes Brownie Mix 324 $4.8 0.07% 313 $27.5 0.09% 

Shelf Stable Juice Grape Juice (Over 50% 

Juice) 

325 $4.8 0.07% 455 $17.1 0.05% 

Frzn Prepared Chicken Fz Meal Kits/Stuffed/ 

Other 

326 $4.8 0.07% 578 $12.2 0.04% 

Peanut Butter/Jelly/Jams 

& Honey 

Jelly 327 $4.7 0.07% 439 $18.1 0.06% 

Smoked Pork Ham Steaks/Cubes/Slices 328 $4.7 0.07% 324 $26.3 0.08% 

Tomatoes Tomatoes Grape 329 $4.7 0.07% 150 $54.6 0.17% 

Traditional Mexican 

Foods 

Mexican Beans/Refried 330 $4.7 0.07% 393 $21.0 0.07% 

Citrus Lemons 331 $4.6 0.07% 257 $33.6 0.11% 

Can Fruit/Jar Applesauce Peaches 332 $4.6 0.07% 387 $21.3 0.07% 

Frozen Potatoes Frzn Hashbrown Potatoes 333 $4.6 0.07% 348 $24.8 0.08% 

Dry Noodles & Pasta Noodles Dry 334 $4.5 0.07% 344 $24.9 0.08% 

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 Foods Frzn Breakfast Entre ́es 338 $4.5 0.07% 473 $16.2 0.05% 

Eggs/Muffins/Potatoes Eggs—X-Large 339 $4.5 0.07% 232 $37.9 0.12% 

Convenient Meals Convenient Meals— 

Adult Meal 

340 $4.5 0.07% 603 $11.2 0.04% 

Bacon Bacon—Poultry 341 $4.5 0.07% 435 $18.4 0.06% 

Smoked Hams Hams—Whole Boneless 342 $4.5 0.07% 510 $14.6 0.05% 

Fluid Milk Products Half & Half 343 $4.4 0.07% 149 $54.6 0.17% 

Deli Meat: Bulk Meat Bulk: Specialty Dry 

Meats 

344 $4.4 0.07% 302 $28.3 0.09% 

Frozen Vegetable & Veg 

Dish 

Fz Box Vegetables— 

Value-Added 

345 $4.4 0.07% 349 $24.7 0.08% 

Apples Apples Granny Smith 

(Bulk & Bag) 

346 $4.4 0.07% 277 $30.9 0.10% 

Baking Needs Bits & Morsels [Baking 

Needs] 

347 $4.4 0.07% 162 $50.3 0.16% 

Meat—Shelf Stable Chunk Meats—Chix/ 

Ham/Etc. 

348 $4.4 0.07% 338 $25.3 0.08% 

Yogurt Yogurt/Large Size (16oz 

Or Larger) 

349 $4.4 0.07% 219 $40.4 0.13% 

Energy Drinks Energy Drink—MultiPack 350 $4.3 0.07% 421 $19.0 0.06% 

Frozen Fruits Frozen Fruit 351 $4.3 0.07% 174 $48.6 0.15% 

Turkey Frozen Whole Toms (Over 16lbs) 

[Turkey] 

352 $4.3 0.06% 407 $20.0 0.06% 

Lunchmeat Lunchmeat—Whole Muscle Pltry 353 $4.2 0.06% 413 $19.7 0.06% 

Dry Bean Veg & Rice Dry Beans/Peas/Barley: 

Bag & Bulk 

354 $4.2 0.06% 425 $18.8 0.06% 

Frozen Novelties—Water 

Ice 

Adult Premium [Frozen 

Novelties] 

355 $4.2 0.06% 151 $54.5 0.17% 

Traditional Mexican 

Foods 

Mexican Dinners And 

Foods 

356 $4.2 0.06% 597 $11.4 0.04% 

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/Jelly/Jams 

& Honey 

Honey 359 $4.1 0.06% 294 $28.9 0.09% 

Pickle/Relish/Pckld Veg 

& Olives 

Ripe Olives 360 $4.1 0.06% 337 $25.3 0.08% 

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 & 

Bag) 

363 $4.1 0.06% 264 $32.0 0.10% 

Croutons/Bread Stick & 

Salad Top 

Salad Toppers 364 $4.1 0.06% 500 $15.1 0.05% 

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 Can 

Carb 

367 $4.1 0.06% 308 $27.8 0.09% 

Single Serve Fruit/Applesauce Applesauce Cup 368 $4.1 0.06% 370 $22.6 0.07% 

Dry Sce/Gravy/Potatoes/ 

Stuffng 

Gravy Can/Glass 369 $4.0 0.06% 485 $15.7 0.05% 

189 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Cookies Graham Crackers 370 $4.0 0.06% 342 $24.9 0.08% 

Candy—Packaged Miscellaneous Candy 371 $4.0 0.06% 418 $19.0 0.06% 

Frozen Vegetable & Veg 

Dish 

Frzn Corn On The Cob 372 $4.0 0.06% 708 $8.4 0.03% 

Cookies Chocolate Covered Cookies 373 $4.0 0.06% 432 $18.5 0.06% 

Value-Added Vegetables Vegetable Party Tray 374 $4.0 0.06% 341 $25.1 0.08% 

Value-Added Vegetables Cut Vegetables All Other 375 $4.0 0.06% 213 $42.2 0.13% 

Deli Meat: Bulk Bologna/Loaves/Franks 376 $4.0 0.06% 415 $19.2 0.06% 

Condiments & Sauces Marinades 377 $3.9 0.06% 434 $18.4 0.06% 

Nuts Pistachios 378 $3.9 0.06% 293 $29.1 0.09% 

Service Case Meat Seasoned Poultry 379 $3.9 0.06% 463 $16.5 0.05% 

Salad & Dips Protein Salads—Bulk 380 $3.9 0.06% 326 $26.3 0.08% 

Hot Cereal Standard Oatmeal 381 $3.9 0.06% 284 $29.9 0.09% 

Cheese Miscellaneous Cheese 382 $3.8 0.06% 214 $42.1 0.13% 

Salad & Dips Vegetable Salads—Bulk 383 $3.8 0.06% 275 $31.0 0.10% 

Shelf Stable Juice Veg Juice (Except Tomato) 384 $3.8 0.06% 279 $30.4 0.10% 

Juices Super Premium Juices Superfoods/Enhanced 385 $3.8 0.06% 367 $22.8 0.07% 

Breakfast Sausage Bkfst Sausage—Fresh 

Patties 

386 $3.8 0.06% 651 $9.8 0.03% 

Vegetables Cooking Bulk Asparagus 387 $3.8 0.06% 159 $50.7 0.16% 

Baby Foods Baby Food Cereals 388 $3.8 0.06% 756 $7.1 0.02% 

Baked Breads English Muffins/Waffles 389 $3.8 0.06% 169 $49.5 0.16% 

Baked Breads Main Meal Bread 390 $3.8 0.06% 252 $34.9 0.11% 

Juice Non-Carb Jce (Over 50% 

Juice) 

391 $3.8 0.06% 268 $31.7 0.10% 

Deli Meat: Bulk Meat: Chicken Bulk 392 $3.7 0.06% 253 $34.6 0.11% 

Breakfast Sausage Bkfst Sausage— 

Precooked 

393 $3.7 0.06% 385 $21.4 0.07% 

Dietary Aid Prdct/Med 

Liq Nutr 

Diet Cntrl Liqs Nutritional 394 $3.7 0.06% 281 $30.3 0.10% 

Refrgratd Juices/Drinks 

Dairy Case 

Fruit Drinks 395 $3.7 0.06% 1,041 $2.8 0.01% 

Dinner Sausage Dnr Sausage—Beef Rope 

Ckd 

396 $3.7 0.06% 577 $12.2 0.04% 

Canned Pasta & Mwv 

Fd-Shlf Stbl 

Microwavable Cups 397 $3.7 0.06% 690 $9.0 0.03% 

Turkey Frozen Whole Hens (Under 

16lbs) [Turkey] 

398 $3.6 0.06% 419 $19.0 0.06% 

Cakes Cakes: Cheesecake 399 $3.6 0.06% 507 $14.7 0.05% 

Enhancements (Pickles/ 

Spreads) 

Enhancements—Pickles/ 

Kraut 

400 $3.6 0.06% 410 $19.8 0.06% 

Tomatoes Tomatoes Vine Ripe Pkg 401 $3.6 0.06% 743 $7.3 0.02% 

Peppers Peppers Red Bell 402 $3.6 0.05% 211 $42.5 0.13% 

Dinner Sausage Dnr Sausage—Other 

Forms 

403 $3.6 0.05% 381 $21.6 0.07% 

Pork Offal External Fresh 404 $3.5 0.05% 937 $4.2 0.01% 

Pasta & Pizza Sauce Value [Pasta & Pizza 

Sauce] 

405 $3.5 0.05% 657 $9.7 0.03% 

Aseptic Juice Aseptic Pack Juice And 

Drinks 

406 $3.5 0.05% 934 $4.2 0.01% 

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 And 

Cheese 

409 $3.5 0.05% 383 $21.5 0.07% 

Can Vegetables—Shelf 

Stable 

Peas/Green 410 $3.5 0.05% 504 $14.7 0.05% 

Dry/Ramen Bouillon Dry Soup 411 $3.5 0.05% 362 $23.3 0.07% 

Can Vegetables—Shelf 

Stable 

Spinach & Greens 412 $3.5 0.05% 765 $7.0 0.02% 

Frzn Multi Serve Fz Meatballs 413 $3.5 0.05% 447 $17.7 0.06% 

Milk By-Products Aerosol Toppings [Milk 

By-Products] 

414 $3.5 0.05% 351 $24.5 0.08% 

Baked Breads Dinner Rolls 415 $3.5 0.05% 513 $14.5 0.05% 

Cocoa Mixes Hot Chocolate/Cocoa Mix 416 $3.5 0.05% 445 $17.8 0.06% 

Infant Formula Infant Formula Ready 

To Use 

417 $3.5 0.05% 768 $6.9 0.02% 

Powder & Crystal Drink 

Mix 

Sugar Free Canister 

[Powder Drink Mix] 

418 $3.5 0.05% 391 $21.1 0.07% 

Cnv Breakfast & Wholesome Snks Treats [Breakfast] 419 $3.5 0.05% 605 $11.2 0.04% 

Smoked Hams Hams—Half/Port 

Boneless 

420 $3.4 0.05% 392 $21.0 0.07% 

Fitness & Diet Fitness & Diet—Bars W/ 

Flour 

421 $3.4 0.05% 124 $59.8 0.19% 

Refrgrated Dough Products Refrigerated Cookie 

Dough 

422 $3.4 0.05% 551 $12.9 0.04% 

190 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Grapes Grapes Black/Blue 423 $3.4 0.05% 380 $21.8 0.07% 

Bulk Service Case 

Cheese 

Bulk Processed [Cheese] 424 $3.4 0.05% 411 $19.8 0.06% 

Candy—Packaged Seasonal Candy Box— 

Chocolate 

425 $3.4 0.05% 462 $16.6 0.05% 

Coffee & Creamers Coffee Pods/Singles/Filter Pack 426 $3.4 0.05% 167 $49.8 0.16% 

Can Fruit/Jar Applesauce Fruit Cocktail/Fruit 

Salad 

427 $3.4 0.05% 569 $12.5 0.04% 

Peppers Peppers Other Bell 428 $3.4 0.05% 301 $28.4 0.09% 

Mushrooms Mushrooms White Sliced 

Pkg 

429 $3.3 0.05% 306 $27.8 0.09% 

Lunchmeat Lunchmeat—Chip Meat 430 $3.3 0.05% 653 $9.7 0.03% 

Soft Drinks Sft Drnk 1 Liter Btl 

Carb 

431 $3.3 0.05% 716 $8.2 0.03% 

Cakes Cakes: Fancy/Service 

Case 

432 $3.3 0.05% 451 $17.4 0.06% 

Salad Mix Shredded Lettuce 433 $3.3 0.05% 616 $10.9 0.03% 

Powder & Crystal Drink 

Mix 

Sugar Free Sticks [Powder Drink Mix] 434 $3.3 0.05% 426 $18.8 0.06% 

Dinner Mixes-Dry Package Dinners/Pasta 

Salads 

435 $3.3 0.05% 664 $9.5 0.03% 

Cakes Cakes: Layers/Sheets 

Novelties 

436 $3.3 0.05% 565 $12.5 0.04% 

Flour & Meals Breadings/Coatings/ 

Crumbs 

437 $3.2 0.05% 474 $16.0 0.05% 

Pies Pies: Pumpkin/Custard 438 $3.2 0.05% 545 $13.1 0.04% 

Refrigerated Dairy Case Yogurt 439 $3.2 0.05% 107 $67.0 0.21% 

Apples Mixed Fruit Bags 440 $3.2 0.05% 829 $5.7 0.02% 

Shelf Stable Juice Fruit Drinks: Canned & 

Glass 

441 $3.2 0.05% 870 $5.0 0.02% 

Dry Mix Desserts Puddings Dry 442 $3.2 0.05% 400 $20.8 0.07% 

Can Seafood—Shelf Stable Salmon 443 $3.2 0.05% 534 $13.6 0.04% 

Shortening & Oil Cooking Sprays 444 $3.2 0.05% 396 $21.0 0.07% 

Meat—Shelf Stable Sandwich Sauce 

(Manwich) 

445 $3.2 0.05% 733 $7.7 0.02% 

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/Snacking Jarred Fruit Single 

Serve 

449 $3.1 0.05% 511 $14.6 0.05% 

Powder & Crystal Drink 

Mix 

Soft Drink Canisters 450 $3.1 0.05% 723 $7.9 0.03% 

Frozen Breakfast Foods Frzn Breakfast Sausage 451 $3.1 0.05% 647 $9.8 0.03% 

Ss/Vending—Cookie/ 

Cracker 

Vendor Size/Single Serve 

Cookie 

452 $3.1 0.05% 770 $6.8 0.02% 

Water—(Sparkling & 

Still) 

Sparkling Water—Flvrd 

Sweet 

453 $3.1 0.05% 355 $24.1 0.08% 

Service Case Meat Stuffed/Mixed Beef 454 $3.1 0.05% 416 $19.2 0.06% 

Meat—Shelf Stable Vienna Sausage 455 $3.1 0.05% 867 $5.1 0.02% 

Mushrooms Mushrooms White Whole 

Pkg 

456 $3.1 0.05% 288 $29.6 0.09% 

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 Case 

Cheese 

Bulk Semi-Soft 459 $3.0 0.05% 363 $23.3 0.07% 

Bag Snacks Bagged Popped Popcorn 460 $3.0 0.05% 566 $12.5 0.04% 

Condiments & Sauces Yellow Mustard 461 $3.0 0.05% 571 $12.4 0.04% 

Vegetables Salad Green Onions 462 $3.0 0.05% 361 $23.5 0.07% 

Frozen Bread/Dough Frzn Dinner Rolls 463 $3.0 0.05% 398 $20.9 0.07% 

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 Products Whipping Cream 466 $3.0 0.04% 249 $35.2 0.11% 

Dried Fruit Raisins 467 $2.9 0.04% 330 $26.0 0.08% 

Dinner Sausage Dnr Sausage—Links 

Beef Ckd 

468 $2.9 0.04% 722 $8.0 0.03% 

Rolls Rolls: Croissants/ 

Breadsticks 

469 $2.9 0.04% 464 $16.5 0.05% 

Lunchmeat Lunchmeat—Brauns/ 

Liver/Loave 

470 $2.9 0.04% 632 $10.3 0.03% 

Cookie/Cracker Multi-Pks Multi-Pack Cookies 471 $2.9 0.04% 596 $11.4 0.04% 

Snack Meat Snack Meat—Salami/ 

Smr Sausage 

472 $2.9 0.04% 481 $15.8 0.05% 

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 & Veg 

Dish 

Fz Bag Vegetables— 

Value-Added 

477 $2.8 0.04% 505 $14.7 0.05% 

191 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Traditional Asian Foods Asian Other Sauces/ 

Marinade 

478 $2.8 0.04% 422 $18.9 0.06% 

Frozen Novelties—Water 

Ice 

Cups/Push Ups/Other 

[Frozen Novelties] 

479 $2.8 0.04% 661 $9.6 0.03% 

Refrigerated Hispanic 

Grocery 

Refrigerated Tortillas 480 $2.8 0.04% 669 $9.4 0.03% 

Frzn Prepared Chicken Whole Muscle 

Unbreaded Chicken 

481 $2.8 0.04% 555 $12.8 0.04% 

Meat—Shelf Stable Luncheon Meat (Spam) 482 $2.8 0.04% 693 $8.9 0.03% 

Frzn Prepared Chicken Boneless Snack/Value/ 

Small 

483 $2.8 0.04% 836 $5.5 0.02% 

Croutons/Bread Stick & 

Salad Top 

Croutons 484 $2.8 0.04% 526 $14.0 0.04% 

Apples Apples Other (Bulk & 

Bag) 

485 $2.8 0.04% 314 $27.4 0.09% 

Apples Apples Fuji (Bulk & Bag) 486 $2.8 0.04% 242 $36.2 0.11% 

Apples Apples Gold Delicious 

(Bulk & Bag) 

487 $2.8 0.04% 443 $17.9 0.06% 

Salad & Dips Sal: Hommus 488 $2.8 0.04% 189 $45.4 0.14% 

Dinner Sausage Dnr Sausage—Cocktails 489 $2.7 0.04% 562 $12.7 0.04% 

Can Vegetables—Shelf 

Stable 

Mushrooms Cnd & Glass 490 $2.7 0.04% 521 $14.3 0.05% 

Frozen Desserts Frzn Pie Shells/Pastry 

Shell 

491 $2.7 0.04% 475 $16.0 0.05% 

Lunchmeat Lunchmeat—Variety 

Pack 

492 $2.7 0.04% 677 $9.3 0.03% 

Frozen Desserts Frozen Cakes/Desserts 493 $2.7 0.04% 611 $11.0 0.03% 

Pickle/Relish/Pckld Veg 

& Olives 

Peppers 494 $2.7 0.04% 537 $13.5 0.04% 

Cakes Cakes: Angel Fds/Cke 

Rolls 

495 $2.7 0.04% 440 $18.1 0.06% 

Berries Blackberries 496 $2.7 0.04% 283 $29.9 0.09% 

Frozen Bread/Dough Frzn Garlic Bread 497 $2.7 0.04% 608 $11.1 0.04% 

Traditional Mexican 

Foods 

Mexican Enchilada 

Sauce 

498 $2.7 0.04% 532 $13.7 0.04% 

Fluid Milk Products Egg Nog/Boiled Custard 499 $2.7 0.04% 539 $13.3 0.04% 

Hot Dogs Hot Dogs—Base Poultry 500 $2.7 0.04% 667 $9.4 0.03% 

Beef: Thin Meats Brisket [Beef] 501 $2.7 0.04% 446 $17.8 0.06% 

Cookies Wellness/Portion Control 

[Cookies] 

502 $2.7 0.04% 358 $23.8 0.08% 

Baking Needs Pie Filling/Mincemeat/ 

Glazes 

503 $2.7 0.04% 345 $24.8 0.08% 

Soft Drinks Tea Can With Sweetener/Sugar 504 $2.7 0.04% 807 $6.1 0.02% 

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 

Foods 

Mexican Taco Sauce 507 $2.6 0.04% 761 $7.0 0.02% 

Soft Drinks Soft Drink Bottle NonCarb 508 $2.6 0.04% 887 $4.7 0.02% 

Seafood—Salmon-Wild 

Caught 

Salmon Wc—Pink 509 $2.6 0.04% 612 $11.0 0.03% 

Frozen Bread/Dough Frzn Biscuits 510 $2.6 0.04% 550 $12.9 0.04% 

Frzn Pasta Frozen Pasta 511 $2.6 0.04% 458 $16.9 0.05% 

Chicken Frozen Frzn Chicken—Drk Meat 512 $2.6 0.04% 818 $5.9 0.02% 

Syrups Toppings & Cones Ice Cream Toppings 513 $2.6 0.04% 524 $14.1 0.04% 

Candy—Packaged Seasonal Candy Bags 

Non-Chocolate 

514 $2.6 0.04% 502 $14.9 0.05% 

Salad & Dips Pasta/Grain Salads— 

Prepack 

515 $2.6 0.04% 631 $10.3 0.03% 

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/Pckld Veg 

& Olives 

Green Olives 519 $2.6 0.04% 483 $15.8 0.05% 

Candy—Packaged Candy Bars Multi Pack 

W/Flour 

520 $2.6 0.04% 695 $8.8 0.03% 

Stone Fruit Nectarines Yellow Flesh 521 $2.5 0.04% 430 $18.6 0.06% 

Onions Onions Red (Bulk & 

Bag) 

522 $2.5 0.04% 397 $20.9 0.07% 

Flour & Meals Cornmeal 523 $2.5 0.04% 746 $7.3 0.02% 

Tropical Fruit Pineapple Whole & Peel/ 

Cored 

524 $2.5 0.04% 377 $22.1 0.07% 

Bagels & Cream Cheese Refrigerated Bagels 525 $2.5 0.04% 731 $7.7 0.02% 

Onions Onions White (Bulk & 

Bag) 

526 $2.5 0.04% 482 $15.8 0.05% 

Meat Frozen Frzn Meat—Turkey 527 $2.5 0.04% 652 $9.7 0.03% 

Pickle/Relish/Pckld Veg 

& Olives 

Relishes 528 $2.5 0.04% 590 $11.6 0.04% 

192 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Candy—Packaged Candy Bags—Chocolate 

W/Flour 

529 $2.5 0.04% 496 $15.2 0.05% 

Nuts Cashews 530 $2.5 0.04% 437 $18.3 0.06% 

Cakes Cakes:Birthday/Celebration Lay 531 $2.5 0.04% 684 $9.1 0.03% 

Smoked Pork Smoked Offal [Pork] 532 $2.4 0.04% 940 $4.1 0.01% 

Apples Apples Honeycrisp 533 $2.4 0.04% 235 $36.9 0.12% 

Sweet Goods & Snacks Sw Gds: Swt/Flvrd 

Loaves 

534 $2.4 0.04% 528 $13.9 0.04% 

Fluid Milk Products Buttermilk 535 $2.4 0.04% 478 $15.9 0.05% 

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 & Creamers Flavored Bag Coffee 539 $2.4 0.04% 328 $26.2 0.08% 

Stone Fruit Plums 540 $2.4 0.04% 543 $13.1 0.04% 

Refrigerated Italian Refrigerated Pasta 541 $2.4 0.04% 290 $29.3 0.09% 

Spices & Extracts Gourmet Spices 542 $2.4 0.04% 259 $33.2 0.11% 

Baked Breads Diet/Light Bread 543 $2.4 0.04% 356 $24.0 0.08% 

Bacon Bacon—Trad Center Cut 544 $2.3 0.04% 395 $21.0 0.07% 

Salad & Dips Pasta/Grain Salads— 

Bulk 

545 $2.3 0.04% 460 $16.9 0.05% 

Rice Cakes Mini-Cakes 546 $2.3 0.04% 454 $17.2 0.05% 

Authentic Hispanic Fds 

& Product 

Authentic Sauces/Salsa/ 

Picante 

547 $2.3 0.03% 678 $9.2 0.03% 

Ice Cream Ice Milk & 

Sherbets 

Premium Pints [Ice 

Cream & Sherbert] 

548 $2.3 0.03% 787 $6.5 0.02% 

Can Fruit/Jar Applesauce Mandarin Oranges/Citrus Sect 549 $2.3 0.03% 564 $12.6 0.04% 

Baby Foods Baby Juices 550 $2.3 0.03% 1013 $3.1 0.01% 

Salad Mix Salad Mix Blends Organic 551 $2.3 0.03% 239 $36.5 0.12% 

Salad & Dips Salad: Lettuce 552 $2.2 0.03% 576 $12.2 0.04% 

Baked Breads Fruit/Breakfast Bread 553 $2.2 0.03% 427 $18.7 0.06% 

Seafood—Salad/Dip/Sce/ 

Cond 

Breading [Seafood] 554 $2.2 0.03% 966 $3.7 0.01% 

Seafood—Finfish Other Finfish—Other 555 $2.2 0.03% 826 $5.8 0.02% 

Frozen Bread/Dough Frzn Breadsticks 556 $2.2 0.03% 871 $5.0 0.02% 

Bag Snacks Pork Skins/Cracklins 557 $2.2 0.03% 804 $6.2 0.02% 

Frozen Juice And 

Smoothies 

Frzn Conc Allieds Over 

50% Juice 

558 $2.2 0.03% 638 $10.1 0.03% 

Broccoli/Cauliflower Cauliflower Whole 559 $2.2 0.03% 352 $24.5 0.08% 

Mushrooms Mushrooms Portabella 560 $2.2 0.03% 372 $22.6 0.07% 

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 Applesauce Apple Sauce (Excludes 

Cup) 

563 $2.2 0.03% 530 $13.8 0.04% 

Traditional Mexican 

Foods 

Mexican Peppers Chilies 564 $2.2 0.03% 487 $15.7 0.05% 

Candy—Checklane Mints/Candy & Breath 565 $2.1 0.03% 582 $12.1 0.04% 

Citrus Tangerines & Tangelos 566 $2.1 0.03% 600 $11.3 0.04% 

Juices Super Premium Juices Smoothies/Blended 567 $2.1 0.03% 613 $11.0 0.03% 

Can Vegetables—Shelf 

Stable 

Fried Onions 568 $2.1 0.03% 574 $12.3 0.04% 

Carrots Carrots Bagged 569 $2.0 0.03% 453 $17.2 0.05% 

Eggs/Muffins/Potatoes Eggs—Jumbo 570 $2.0 0.03% 548 $13.0 0.04% 

Potatoes Potatoes Gourmet 571 $2.0 0.03% 405 $20.3 0.06% 

Can Vegetables—Shelf 

Stable 

Sweet Potatoes 572 $2.0 0.03% 777 $6.7 0.02% 

Seafood—Value-Added 

Seafood 

Value-Added Shrimp 573 $2.0 0.03% 840 $5.4 0.02% 

Baked Breads Rye Breads 574 $2.0 0.03% 375 $22.3 0.07% 

Salad Dresing & Sandwich Spreads Dry Salad Dressing & 

Dip Mixes 

575 $2.0 0.03% 498 $15.1 0.05% 

Condiments & Sauces Mustard—All Other 576 $2.0 0.03% 436 $18.3 0.06% 

Fluid Milk Products Organic Milk 577 $2.0 0.03% 245 $35.4 0.11% 

Dry Mix Desserts Gelatin 578 $2.0 0.03% 517 $14.3 0.05% 

Nuts Sunflower/Other Seeds 579 $1.9 0.03% 656 $9.7 0.03% 

Vinegar & Cooking Wines Vinegar/White & Cider 580 $1.9 0.03% 515 $14.4 0.05% 

Dinner Sausage Dnr Sausage—Poultry 

Rope Ckd 

581 $1.9 0.03% 618 $10.9 0.03% 

Corn Corn Is Packaged 582 $1.9 0.03% 556 $12.8 0.04% 

Candy—Packaged Miscellaneous Candy 583 $1.9 0.03% 607 $11.2 0.04% 

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 

Smoothies 

Frzn Oj&Oj Substitutes 

(Over 50%) 

586 $1.9 0.03% 472 $16.2 0.05% 

Sweet Goods & Snacks Sw Gds: Brownie/Bar 

Cookie 

587 $1.9 0.03% 606 $11.2 0.04% 

193 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Rolls Rolls: Bagels 588 $1.9 0.03% 494 $15.4 0.05% 

Melons Watermelon Personal 589 $1.9 0.03% 477 $15.9 0.05% 

Nuts Pecans Shelled 590 $1.9 0.03% 448 $17.6 0.06% 

Infant Formula Baby Isotonic Drinks 591 $1.9 0.03% 878 $4.9 0.02% 

Mixers Cocktail Mixes-Fluid: 

Add Liq 

592 $1.9 0.03% 468 $16.4 0.05% 

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 Applesauce Pears 596 $1.9 0.03% 646 $10.0 0.03% 

Baking Mixes Biscuit Flour & Mixes 597 $1.9 0.03% 529 $13.8 0.04% 

Chicken Specialty/Natural Chicken Breast Boneless 598 $1.9 0.03% 343 $24.9 0.08% 

Sweet Goods Sw Gds: Coffee Cakes 599 $1.8 0.03% 588 $11.9 0.04% 

Refrigerated Dairy Case Eggs 600 $1.8 0.03% 289 $29.5 0.09% 

Condiments & Sauces Wing Sauce 601 $1.8 0.03% 872 $5.0 0.02% 

Seafood—Salmon-Wild 

Caught 

Salmon Wc—Sockeye 602 $1.8 0.03% 335 $25.7 0.08% 

Baking Needs Pie Crust Mixes & Shells 603 $1.8 0.03% 676 $9.3 0.03% 

Salad Mix Salad Spinach 604 $1.8 0.03% 442 $17.9 0.06% 

Eggs/Muffins/Potatoes Eggs Substitute 605 $1.8 0.03% 329 $26.2 0.08% 

Crackers & Misc Baked 

Food 

Aerosol Cheese 606 $1.8 0.03% 857 $5.2 0.02% 

Poultry Other Cornish Hen 607 $1.8 0.03% 773 $6.7 0.02% 

Tomato Products-Shelf 

Stable 

Tomato Paste 608 $1.8 0.03% 633 $10.2 0.03% 

Turkey Frozen Turkey Breast Bone In 609 $1.8 0.03% 553 $12.8 0.04% 

Sweet Goods & Snacks Sw Gds: Puff Pastry 610 $1.8 0.03% 573 $12.3 0.04% 

Seafood—Catfish Catfish—Whole 611 $1.8 0.03% 1,055 $2.6 0.01% 

Cake De ́cor Cake De ́cors & Icing 612 $1.8 0.03% 645 $10.0 0.03% 

Convenience/Snacking Convenience/Snacking 

Fruit 

613 $1.8 0.03% 670 $9.4 0.03% 

Salad & Dips Sal: Salsa/Dips Bulk 614 $1.8 0.03% 730 $7.7 0.02% 

Pork Bone In Loin/Rib Dry [Pork Bone In Loin/ 

Rib] 

615 $1.8 0.03% 734 $7.6 0.02% 

Authentic Hispanic Fds 

& Product 

Authentic Pasta/Rice/ 

Beans 

616 $1.7 0.03% 884 $4.8 0.02% 

Spices & Extracts Pure Extracts 617 $1.7 0.03% 493 $15.4 0.05% 

Powder & Crystal Drink 

Mix 

Enhanced Stick [Powder 

Drink Mix] 

618 $1.7 0.03% 621 $10.7 0.03% 

Bread Bread: Artisan 619 $1.7 0.03% 237 $36.7 0.12% 

Infant Formula Infant Formula Soy Base 620 $1.7 0.03% 1,270 $1.1 0.00% 

Juices Super Premium Juices Proteins 621 $1.7 0.03% 640 $10.1 0.03% 

Salad & Dips Sal: Dip Prepack [Salad 

& Dips] 

622 $1.7 0.03% 584 $12.1 0.04% 

Dietary Aid Prdct/Med 

Liq Nutr 

Diet Energy Drinks 623 $1.7 0.03% 554 $12.8 0.04% 

Nuts Peanuts All 624 $1.7 0.03% 594 $11.5 0.04% 

Rts/Micro Soup/Broth Microwavable Soups 625 $1.7 0.03% 495 $15.3 0.05% 

Service Case Meat Marinated Pork 626 $1.7 0.03% 519 $14.3 0.05% 

Chicken & Poultry Chix: Baked 8pc Cut Up 

(Cold) 

627 $1.7 0.03% 837 $5.5 0.02% 

Vegetables Cooking Bulk Beans 628 $1.7 0.03% 457 $16.9 0.05% 

Baby Foods Baby Spring Waters 629 $1.7 0.03% 1,128 $2.0 0.01% 

Shelf Stable Juice Tomato Juice (Over 50% 

Jce) 

630 $1.7 0.03% 662 $9.6 0.03% 

Authentic Hispanic Fds 

& Product 

Authentic Vegetables 

And Foods 

631 $1.7 0.03% 998 $3.2 0.01% 

Meat Snacks Meat Sticks/Bites 632 $1.7 0.03% 972 $3.6 0.01% 

Refrigerated Hispanic 

Grocery 

Hispanic Cheese 633 $1.7 0.03% 769 $6.9 0.02% 

Can Fruit/Jar Applesauce Cranberry Sauce 634 $1.7 0.03% 642 $10.0 0.03% 

Fitness & Diet Fitness & Diet—Bars W/ 

O Flour 

635 $1.7 0.03% 298 $28.7 0.09% 

Pies Pies: Cream/Meringue 636 $1.6 0.02% 728 $7.8 0.02% 

Berries Strawberries Organic 637 $1.6 0.02% 386 $21.4 0.07% 

Candy—Packaged Novelty Candy 638 $1.6 0.02% 827 $5.7 0.02% 

Party Tray Deli Tray: Sandwiches 639 $1.6 0.02% 636 $10.2 0.03% 

Value-Added Fruit Cut Fruit All Other 

Prepack 

640 $1.6 0.02% 704 $8.5 0.03% 

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/Specialty/ 

Blend 

643 $1.6 0.02% 533 $13.6 0.04% 

Frozen Ethnic Frozen Internaional 

[Ethnic Foods] 

644 $1.6 0.02% 771 $6.7 0.02% 

Deli Meat: Presliced Deli Meat: Specialty Dry 

Meats 

645 $1.6 0.02% 336 $25.5 0.08% 

Dressings/Dips Dressing Creamy 646 $1.6 0.02% 512 $14.5 0.05% 

194 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Spices & Extracts Table Salt/Popcorn Salt 647 $1.6 0.02% 698 $8.6 0.03% 

Meat—Shelf Stable Hash: Canned [Meat] 648 $1.6 0.02% 863 $5.1 0.02% 

Water—(Sparkling & 

Still) 

Distilled Water 649 $1.6 0.02% 579 $12.2 0.04% 

Frozen Desserts Frzn Pastry & Cookies 650 $1.6 0.02% 694 $8.8 0.03% 

Potatoes Potatoes Gold (Bulk & 

Bag) 

651 $1.6 0.02% 503 $14.8 0.05% 

Herbs/Garlic Garlic Whole Cloves 652 $1.6 0.02% 557 $12.7 0.04% 

Salad Mix Coleslaw 653 $1.6 0.02% 589 $11.9 0.04% 

Apples Caramel/Candy Apples 654 $1.6 0.02% 985 $3.4 0.01% 

Nuts Almonds Shelled 655 $1.5 0.02% 412 $19.8 0.06% 

Service Case Meat Marinated Poultry 656 $1.5 0.02% 702 $8.5 0.03% 

Carrots Carrots Bagged Organic 657 $1.5 0.02% 429 $18.6 0.06% 

Frozen Desserts Single Serv/Portion Control 658 $1.5 0.02% 898 $4.6 0.01% 

Seasonal Pumpkins 659 $1.5 0.02% 626 $10.3 0.03% 

Chicken Offal Internal [Chicken Offal] 660 $1.5 0.02% 929 $4.3 0.01% 

Specialty Cheese Pre 

Pack 

Specialty Ppk Cheese 

Hard/Grated 

661 $1.5 0.02% 299 $28.7 0.09% 

Pears Pears Bartlett 662 $1.5 0.02% 486 $15.7 0.05% 

Meat—Shelf Stable Beef Stew 663 $1.5 0.02% 897 $4.6 0.01% 

Bread Bread: Pita/Pocket/ 

Flatbrd 

664 $1.5 0.02% 523 $14.1 0.04% 

Chicken & Poultry Chix: Rotisserie Cold 665 $1.5 0.02% 848 $5.4 0.02% 

Dry/Ramen Bouillon Bouillon 666 $1.5 0.02% 663 $9.6 0.03% 

Nuts Trail Mix 667 $1.5 0.02% 610 $11.0 0.03% 

Enhancements (Pickles/ 

Spreads) 

Enhancements—Salads/ 

Spreads 

668 $1.5 0.02% 858 $5.2 0.02% 

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 Products Refrigerated Cookies— 

Seasonal 

671 $1.5 0.02% 834 $5.5 0.02% 

Traditional Asian Foods Asian Soy Sauce 672 $1.5 0.02% 630 $10.3 0.03% 

Salad Dresing & Sandwich Spreads Sand/Horseradish & Tartar Sauce 673 $1.4 0.02% 749 $7.2 0.02% 

Refrgrated Dough Products Refrigerated Pie Crust 674 $1.4 0.02% 538 $13.5 0.04% 

Frozen Juice And 

Smoothies 

Frzn Fruit Drinks 

(Under 10% Juice) 

675 $1.4 0.02% 685 $9.1 0.03% 

Sweet Goods & Snacks Sw Gds: Specialty Desserts 676 $1.4 0.02% 784 $6.6 0.02% 

Dinner Mixes-Dry Pizza Mix Dry 677 $1.4 0.02% 845 $5.4 0.02% 

Authentic Central American Fds Central American Foods 678 $1.4 0.02% 838 $5.5 0.02% 

Cereal Bars Breakfast Bars/Tarts/ 

Scones 

679 $1.4 0.02% 360 $23.6 0.07% 

Service Case Meat Seasoned Beef 680 $1.4 0.02% 724 $7.9 0.03% 

Herbs/Garlic Herbs Cilanto 681 $1.4 0.02% 637 $10.1 0.03% 

Value-Added Fruit Fruit Party Tray 

Prepack 

682 $1.4 0.02% 785 $6.5 0.02% 

Dried Fruit Dried Fruit—Other 683 $1.4 0.02% 491 $15.6 0.05% 

Non-Dairy/Dairy Aseptic Aseptic Milk 684 $1.4 0.02% 535 $13.6 0.04% 

Eggs/Muffins/Potatoes Misc Dairy Refigerated 685 $1.4 0.02% 686 $9.1 0.03% 

Shelf Stable Juice Pineapple Juice (Over 

50% Juice) 

686 $1.4 0.02% 788 $6.4 0.02% 

Frozen Entre ́es Meatless/Vegetarian 687 $1.4 0.02% 382 $21.5 0.07% 

Powder & Crystal Drink 

Mix 

Sugar Sweetened Sticks 688 $1.4 0.02% 1,071 $2.5 0.01% 

Lunchmeat Lunchmeat—Other 689 $1.4 0.02% 951 $3.9 0.01% 

Dietary Aid Prdct/Med 

Liq Nutr 

Diet Cntrl Bars Nutritional 690 $1.4 0.02% 409 $19.9 0.06% 

Popcorn Popcorn—Other 691 $1.4 0.02% 641 $10.0 0.03% 

Salad & Dips Sal: Desserts-Prepack 692 $1.4 0.02% 906 $4.5 0.01% 

Dry Cheese Misc Dry Cheese 693 $1.4 0.02% 739 $7.3 0.02% 

Shelf Stable Juice Cranberry Juice (Over 

50% Juice) 

694 $1.4 0.02% 706 $8.4 0.03% 

Baking Mixes Cookies Mix 695 $1.4 0.02% 699 $8.6 0.03% 

Frozen Potatoes Frzn Baked/Stuffed/ 

Mashed 

696 $1.3 0.02% 689 $9.0 0.03% 

Turkey Fresh Whole Hen (Under 16lbs) 

[Turkey] 

697 $1.3 0.02% 658 $9.7 0.03% 

Vegetables Cooking Packaged Broccoli/Cauliflower 

Processed 

698 $1.3 0.02% 567 $12.5 0.04% 

Dressings/Dips Dips Caramel/Fruit 

Glazes 

699 $1.3 0.02% 819 $5.9 0.02% 

Dressings/Dips Dips Guacamole/Salsa/ 

Queso 

700 $1.3 0.02% 563 $12.6 0.04% 

Meat—Shelf Stable Hot Dog Chili Sauce 701 $1.3 0.02% 1,063 $2.6 0.01% 

Breakfast Sausage Bkfst Sausage—Bkfast 

Side 

702 $1.3 0.02% 986 $3.4 0.01% 

195 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Traditional Asian Foods Asian Noodles/Rice 703 $1.3 0.02% 623 $10.5 0.03% 

Deli Meat: Presliced Deli Meat: Semi-Dry 

Sausage 

704 $1.3 0.02% 674 $9.3 0.03% 

Breakfast Sausage Bkfst Sausage—Other 

Forms 

705 $1.3 0.02% 916 $4.4 0.01% 

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 Asian Foods And Meals 709 $1.3 0.02% 793 $6.3 0.02% 

Can Vegetables—Shelf 

Stable 

Mixed Vegetables 710 $1.3 0.02% 905 $4.5 0.01% 

Authentic Hispanic Fds 

& Product 

Authentic Peppers 711 $1.3 0.02% 910 $4.5 0.01% 

Dinner Sausage Dnr Sausage—Links 

Poultry Ck 

712 $1.3 0.02% 766 $7.0 0.02% 

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 Products Soy Milk 715 $1.3 0.02% 753 $7.1 0.02% 

Bread Bread: Sweet/Breakfast 716 $1.3 0.02% 707 $8.4 0.03% 

Bulk Food Trail Mix/Nuts Bulk 717 $1.3 0.02% 441 $18.0 0.06% 

Service Case Meat Seasoned Pork 718 $1.3 0.02% 744 $7.3 0.02% 

Refrigerated Vegetarian Vegetarian Meats 719 $1.3 0.02% 625 $10.4 0.03% 

Candy—Packaged Seasonal Miscellaneous 

W/Flour [Candy] 

720 $1.2 0.02% 754 $7.1 0.02% 

Teas Tea Bags/Green 721 $1.2 0.02% 604 $11.2 0.04% 

Chicken Specialty/Natural Chicken Wings 722 $1.2 0.02% 1,111 $2.1 0.01% 

Refrgrated Dough Products Refrigerated Breads 723 $1.2 0.02% 634 $10.2 0.03% 

Shelf Stable Juice Lemon Juice & Lime 

Juice 

724 $1.2 0.02% 727 $7.8 0.02% 

Specialty Cheese Pre 

Pack 

Specialty Ppk Cheese 

Spreads 

725 $1.2 0.02% 469 $16.2 0.05% 

Baking Flours/Grains/Sugar 726 $1.2 0.02% 509 $14.6 0.05% 

Smoked Hams Hams—Dry Cured/Country 727 $1.2 0.02% 917 $4.4 0.01% 

Coffee & Creamers Specialty Instant Coffee 

W/Swe 

728 $1.2 0.02% 732 $7.7 0.02% 

Cookies Fruit Filled Cookies 729 $1.2 0.02% 601 $11.3 0.04% 

Traditional Mexican 

Foods 

Mexican Con Queso 730 $1.2 0.02% 1,009 $3.1 0.01% 

Nuts Dry Roast Peanuts 731 $1.2 0.02% 479 $15.9 0.05% 

Can Seafood—Shelf Stable Sardines 732 $1.2 0.02% 822 $5.8 0.02% 

Service Case Meat Stuffed/Mixed Poultry 733 $1.2 0.02% 717 $8.2 0.03% 

Citrus Oranges Non Navel All 734 $1.2 0.02% 868 $5.0 0.02% 

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 Hispanic 

Grocery 

Misc Hispanic Grocery 738 $1.2 0.02% 635 $10.2 0.03% 

Prepared/Pdgd Foods Boxed Prepared/Entre ́e/ 

Dry Prep 

739 $1.2 0.02% 489 $15.6 0.05% 

Shelf Stable Juice Prune Juice (Over 50% 

Juice) 

740 $1.2 0.02% 711 $8.3 0.03% 

Specialty Cheese Pre 

Pack 

Specialty Ppk Cheese 

Feta 

741 $1.2 0.02% 433 $18.5 0.06% 

Teas Instant Tea & Tea Mix 

(W/Sugar) 

742 $1.1 0.02% 914 $4.4 0.01% 

Pre-Slice Service Case 

Cheese 

Pre-Sliced Semi-Soft 

Cheese 

743 $1.1 0.02% 514 $14.4 0.05% 

Shortening & Oil Cooking Oil: Peanut/Safflower 744 $1.1 0.02% 775 $6.7 0.02% 

Authentic Hispanic Fds 

& Product 

Hispanic Cookies Crackers 745 $1.1 0.02% 1,152 $1.8 0.01% 

Can Vegetables—Shelf 

Stable 

Carrots 746 $1.1 0.02% 900 $4.5 0.01% 

Juice Drinks—Carb Juice (Over 50% juice) 747 $1.1 0.02% 659 $9.7 0.03% 

Juices Super Premium Juice Single Blend 748 $1.1 0.02% 673 $9.4 0.03% 

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 Bell 752 $1.1 0.02% 599 $11.4 0.04% 

Baking Needs Baking Powder & Soda 753 $1.1 0.02% 715 $8.2 0.03% 

Frzn Meatless Meatless Burgers 754 $1.1 0.02% 639 $10.1 0.03% 

Candy—Checklane Misc Checklane Candy 755 $1.1 0.02% 1,052 $2.6 0.01% 

Pears Pears Anjou 756 $1.1 0.02% 649 $9.8 0.03% 

Powder & Crystal Drink 

Mix 

Fluid Pouch [Powder 

Drink Mix] 

757 $1.1 0.02% 781 $6.6 0.02% 

196 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Pasta & Pizza Sauce Pizza Sauce 758 $1.1 0.02% 810 $6.1 0.02% 

Spices/Jarred Garlic Garlic Jar 759 $1.1 0.02% 729 $7.7 0.02% 

Sweet Goods & Snacks Sweet Goods: Candy 760 $1.1 0.02% 920 $4.4 0.01% 

Soft Drinks Tea Bottles With Sweetener/Sugar 761 $1.1 0.02% 1,148 $1.9 0.01% 

Random Weight Meat 

Products 

Lunch Meats 762 $1.1 0.02% 947 $4.0 0.01% 

Authentic Hispanic Fds 

& Product 

Hispanic Carbonated 

Beverages 

763 $1.1 0.02% 979 $3.5 0.01% 

Isotonic Drinks Isotonic Drinks MultiServe 764 $1.1 0.02% 889 $4.7 0.01% 

Juices Super Premium Juices Antioxidant/ 

Wellness 

765 $1.0 0.02% 719 $8.1 0.03% 

Spices/Jarred Garlic Spices & Seasonings 766 $1.0 0.02% 892 $4.6 0.01% 

Trail Mix & Snacks Trail Mixes/Snack 767 $1.0 0.02% 650 $9.8 0.03% 

Lunchmeat Lunchmeat—Natural/Organic 768 $1.0 0.02% 559 $12.7 0.04% 

Lunchmeat Lunchmeat—Peggable 

Deli Fresh 

769 $1.0 0.02% 877 $4.9 0.02% 

Bread Bread: Tortillas/Wraps 770 $1.0 0.02% 648 $9.8 0.03% 

Ice Cream Ice Milk & 

Sherbets 

Quarts [Ice Cream & 

Sherbert] 

771 $1.0 0.02% 924 $4.3 0.01% 

Infant Formula Infant Formula Up Age 772 $1.0 0.02% 1,015 $3.0 0.01% 

Tropical Fruit Kiwi Fruit 773 $1.0 0.02% 764 $7.0 0.02% 

Peppers Peppers Jalapen ̃ o 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 & Snacks Candy W/O Flour 776 $1.0 0.02% 844 $5.4 0.02% 

Condiments Oils/Vinegar 777 $1.0 0.02% 643 $10.0 0.03% 

Value-Added Vegetables Instore Cut Vegetables 778 $1.0 0.02% 654 $9.7 0.03% 

Candy—Packaged Candy Boxed Chocolates 

W/Flour 

779 $1.0 0.02% 852 $5.3 0.02% 

Dried Fruit Dried Plums 780 $1.0 0.02% 609 $11.0 0.03% 

Shelf Stable Juice Apple Juice & Cider 

(50% And Under) 

781 $1.0 0.02% 1,024 $3.0 0.01% 

Pre-Slice Service Case 

Cheese 

Pre-Sliced Semi-Hard 

[Cheese] 

782 $1.0 0.02% 520 $14.3 0.05% 

Tomato Products-Shelf 

Stable 

Tomato Stewed 783 $1.0 0.02% 790 $6.4 0.02% 

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 Package 

Mixes 

787 $1.0 0.02% 752 $7.2 0.02% 

Mediterranean Bar Sal: Olives/Pickles—Bulk 788 $1.0 0.02% 492 $15.5 0.05% 

Dry Sce/Gravy/Potatoes/ 

Stuffng 

Cooking Bags With 

Spices/Season 

789 $1.0 0.01% 1,078 $2.4 0.01% 

Stone Fruit Cherries Ranier 790 $1.0 0.01% 691 $9.0 0.03% 

Energy Drinks Energy Drink—MultiPack 791 $1.0 0.01% 671 $9.4 0.03% 

Meat—Shelf Stable Beef/Pork—Dried Sliced 792 $1.0 0.01% 990 $3.3 0.01% 

Cookies Cookies/Sweet Goods 793 $1.0 0.01% 542 $13.1 0.04% 

Turkey Fresh Whole Tom (Over 16lbs) 

[Turkey] 

794 $1.0 0.01% 747 $7.3 0.02% 

Ss/Vending—Cookie/ 

Cracker 

Vending Size/Sngl Serve 

Cracker 

795 $1.0 0.01% 1,090 $2.3 0.01% 

Can Vegetables—Shelf 

Stable 

White Potatoes 796 $1.0 0.01% 927 $4.3 0.01% 

Can Seafood—Shelf Stable Oysters 797 $0.9 0.01% 1,025 $3.0 0.01% 

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 Chocolates 800 $0.9 0.01% 772 $6.7 0.02% 

Chicken Grinds Ground Chicken 801 $0.9 0.01% 767 $6.9 0.02% 

Candy—Packaged Seasonal Candy Box 

Non-Chocola 

802 $0.9 0.01% 949 $4.0 0.01% 

Frozen Meat Alternatives Soy/Tofu 803 $0.9 0.01% 688 $9.0 0.03% 

Can Vegetables—Shelf 

Stable 

Kraut & Cabbage 804 $0.9 0.01% 814 $6.0 0.02% 

Cereals Granola 805 $0.9 0.01% 501 $15.1 0.05% 

Baking Needs Cooking Chocolate (Ex 

Smi-Swt) 

806 $0.9 0.01% 627 $10.3 0.03% 

Candy—Packaged Candy Box Non-Chocolate 807 $0.9 0.01% 953 $3.9 0.01% 

Dinner Sausage Dnr Sausage—Natural/ 

Organic 

808 $0.9 0.01% 585 $12.1 0.04% 

Dressings/Dips Dressing Blue Cheese 809 $0.9 0.01% 666 $9.5 0.03% 

Herbs/Garlic Herbs Fresh Other Organic 810 $0.9 0.01% 518 $14.3 0.05% 

Shelf Stable Juice Tomato Juice (50% And 

Under) 

811 $0.9 0.01% 975 $3.5 0.01% 

197 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Popcorn Caramel Coated Snacks 812 $0.9 0.01% 1,006 $3.1 0.01% 

Deli Meat: Presliced Deli Meat: Turkey 813 $0.9 0.01% 516 $14.3 0.05% 

Cake De ́cor Cake De ́cors—Candies 814 $0.9 0.01% 841 $5.4 0.02% 

Specialty Cheese Pre 

Pack 

Specialty Ppk Cheese 

Mozzarell 

815 $0.9 0.01% 471 $16.2 0.05% 

Shelf Stable Juice Cranapple/Cran Grape 

Juice 

816 $0.9 0.01% 797 $6.3 0.02% 

Rtd Tea/New Age Juice Juice (Over 50% Juice) 817 $0.9 0.01% 1,047 $2.7 0.01% 

Crackers & Misc Baked 

Food 

Specialty Crackers 818 $0.9 0.01% 444 $17.8 0.06% 

Salad & Dips Salad Bar 819 $0.9 0.01% 644 $10.0 0.03% 

Service Case Meat Marinated Beef 820 $0.9 0.01% 782 $6.6 0.02% 

Juice Non-Carb Jce (Under 

50% Juice) 

821 $0.9 0.01% 880 $4.8 0.02% 

Organics Fruit & Vegetables Organic Salad Mix 822 $0.9 0.01% 499 $15.1 0.05% 

Chilled Ready Meals Store Brand 823 $0.9 0.01% 932 $4.2 0.01% 

Frzn Meatless Meatless Breakfast 824 $0.9 0.01% 697 $8.6 0.03% 

Dry Tea/Coffee/Coco 

Mixes 

Tea Bags (Supplement) 825 $0.9 0.01% 681 $9.2 0.03% 

Melons Watermelon W/Seeds 

Whole 

826 $0.9 0.01% 1,019 $3.0 0.01% 

Dry Mix Desserts Misc: Cheesecake/Mousse 

Mixes 

827 $0.9 0.01% 1,087 $2.3 0.01% 

Value-Added Fruit Parfait Cups Instore 828 $0.8 0.01% 1,032 $2.9 0.01% 

Vinegar & Cooking Wines Specialty Vinegar 829 $0.8 0.01% 552 $12.9 0.04% 

Pork Shoulder Fresh Hams 830 $0.8 0.01% 1,030 $2.9 0.01% 

Specialty Cheese Pre 

Pack 

Specialty Ppk Cheese 

Processed 

831 $0.8 0.01% 815 $6.0 0.02% 

Turkey Smoked Turkey Wings 832 $0.8 0.01% 1,228 $1.3 0.00% 

Frzn Seafood Frz Non-Coated Fish Fillets 833 $0.8 0.01% 860 $5.2 0.02% 

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 Asian Foods Traditional Thai Foods 836 $0.8 0.01% 710 $8.3 0.03% 

Yogurt Yogurt/Adult Drinks 837 $0.8 0.01% 958 $3.8 0.01% 

Specialty Cheese Pre 

Pack 

Specialty Ppk Cheese 

Cheddar 

838 $0.8 0.01% 527 $13.9 0.04% 

Peppers Peppers All Other 839 $0.8 0.01% 864 $5.1 0.02% 

Pickle/Relish/Pckld Veg 

& Olives 

Pickld Veg/Peppers/Etc. 840 $0.8 0.01% 820 $5.9 0.02% 

Candy—Packaged Candy Bags-Non Chocolate W/Flour 841 $0.8 0.01% 965 $3.7 0.01% 

Frozen Juice And 

Smoothies 

Frzn Conc Under 50% 

Juice 

842 $0.8 0.01% 983 $3.4 0.01% 

Pickle/Relish/Pckld Veg 

& Olives 

Specialty Olives 843 $0.8 0.01% 614 $11.0 0.03% 

Salad & Dips Sal: Desserts—Bulk 844 $0.8 0.01% 890 $4.7 0.01% 

Authentic Asian Foods Authentic Japanese 

Foods 

845 $0.8 0.01% 755 $7.1 0.02% 

Crackers Crackers 846 $0.8 0.01% 508 $14.6 0.05% 

Smoked Pork Smoked Picnics [Pork] 847 $0.8 0.01% 1,105 $2.2 0.01% 

Condiments Nut Butters/Peanut Butter 848 $0.8 0.01% 549 $12.9 0.04% 

Tomato Products-Shelf 

Stable 

Tomatoes/Whole 849 $0.8 0.01% 865 $5.1 0.02% 

Party Tray Deli Tray: Appetizers & Hors 

D’oe 

850 $0.8 0.01% 957 $3.9 0.01% 

Soup Cans Soup/Chili 851 $0.8 0.01% 561 $12.7 0.04% 

Service Case Meat Kabobs Beef 852 $0.8 0.01% 843 $5.4 0.02% 

Vegetables Salad Variety Lettuce Organic 853 $0.8 0.01% 568 $12.5 0.04% 

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 & Sauces Chili Sauce/Cocktail 

Sauce 

856 $0.7 0.01% 813 $6.0 0.02% 

Tropical Fruit Pomegranates 857 $0.7 0.01% 926 $4.3 0.01% 

Organics Fruit & Vegetables Organic Value-Added 

Vegetables 

858 $0.7 0.01% 762 $7.0 0.02% 

Grapes Grapes Other 859 $0.7 0.01% 960 $3.8 0.01% 

Chicken Fresh Mixed Packs [Chicken] 860 $0.7 0.01% 923 $4.3 0.01% 

Nuts Nuts Inshell 861 $0.7 0.01% 894 $4.6 0.01% 

Authentic Hispanic Fds 

& Product 

Hispanic Juice Under 

50% Juice 

862 $0.7 0.01% 1,123 $2.0 0.01% 

Coffee & Creamers Flavored Can Coffee 863 $0.7 0.01% 823 $5.8 0.02% 

Prepared/Pdgd Foods Vegetables/Dry Beans 864 $0.7 0.01% 575 $12.2 0.04% 

Bread Bread: Rye/Cocktail 865 $0.7 0.01% 720 $8.1 0.03% 

Baking Needs Maraschino Cherries 866 $0.7 0.01% 944 $4.1 0.01% 

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% 

198 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Smoked Hams Hams—Whole Bone-In 869 $0.7 0.01% 1,092 $2.3 0.01% 

Apples Apples Braeburn (Bulk 

& Bag) 

870 $0.7 0.01% 668 $9.4 0.03% 

Shelf Stable Juice Grapefruit Juice (Over 

50% Juice) 

871 $0.7 0.01% 939 $4.1 0.01% 

Water Fortified/Water 872 $0.7 0.01% 913 $4.4 0.01% 

Meat—Shelf Stable Potted Meats And 

Spreads 

873 $0.7 0.01% 1,103 $2.2 0.01% 

Water—(Sparkling & 

Still) 

Sparkling Water— 

Unflavored 

874 $0.7 0.01% 581 $12.1 0.04% 

Seafood—Trout Steelhead Fr [Trout] 875 $0.7 0.01% 812 $6.0 0.02% 

Can Vegetables—Shelf 

Stable 

Beets 876 $0.7 0.01% 825 $5.8 0.02% 

Frozen Juice And 

Smoothies 

Smoothies-Frozen 877 $0.7 0.01% 950 $4.0 0.01% 

Frozen Breakfast Foods Frzn Bagels 878 $0.7 0.01% 1,035 $2.9 0.01% 

Party Tray Deli Tray: Fruit And Vegetable 879 $0.7 0.01% 758 $7.1 0.02% 

Chicken Specialty/Natural Whole Chicken (Roasters/Fryer) 880 $0.7 0.01% 902 $4.5 0.01% 

Bread Bread: Wheat/Whl Grain 881 $0.7 0.01% 629 $10.3 0.03% 

Non-Dairy/Dairy Aseptic Soy Beverage 882 $0.7 0.01% 849 $5.3 0.02% 

Fitness & Diet Fitness & Diet-Powder 

Ntrtnl 

883 $0.7 0.01% 741 $7.3 0.02% 

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 

Poultry 

886 $0.7 0.01% 918 $4.4 0.01% 

Spices & Extracts Imitation Extracts 887 $0.7 0.01% 973 $3.5 0.01% 

Beverages Can/Btl Carb Beve 50% 

And Under 

888 $0.7 0.01% 736 $7.6 0.02% 

Vegetables Cooking Packaged Vegetables Cooking 

Packaged 

889 $0.7 0.01% 821 $5.9 0.02% 

Frozen Vegetable & Veg 

Dish 

Fz Box Vegetables— 

Plain 

890 $0.7 0.01% 824 $5.8 0.02% 

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—Shelf 

Stable 

Peas Fresh Pack/ 

Crowder 

893 $0.7 0.01% 978 $3.5 0.01% 

Snacks Snacks: Salty 894 $0.7 0.01% 703 $8.5 0.03% 

Salad & Dips Protein Salads—Prepack 895 $0.6 0.01% 946 $4.0 0.01% 

Turkey Smoked Turkey Drums 896 $0.6 0.01% 1,250 $1.2 0.00% 

Apples Apples Gala (Bulk & 

Bag) Organic 

897 $0.6 0.01% 672 $9.4 0.03% 

Stone Fruit Peaches White Flesh 898 $0.6 0.01% 833 $5.5 0.02% 

Tomatoes Tomatoes—Other 899 $0.6 0.01% 1,003 $3.2 0.01% 

Service Case Meat Kabobs Poultry 900 $0.6 0.01% 879 $4.9 0.02% 

Frzn Meatless Meatless Miscellaneous 901 $0.6 0.01% 869 $5.0 0.02% 

Seafood—Scallops Scallops—Sea 902 $0.6 0.01% 791 $6.4 0.02% 

Convenience/Snacking Jarred Fruit Multi Serve 903 $0.6 0.01% 901 $4.5 0.01% 

Traditional Asian Foods Asian Vegetables 904 $0.6 0.01% 847 $5.4 0.02% 

Shelf Stable Juice Cranapple/Cran Grape 

Juice 

905 $0.6 0.01% 760 $7.0 0.02% 

Frozen Juice And 

Smoothies 

Cocktail Mixes-Frz 906 $0.6 0.01% 1,107 $2.2 0.01% 

Shelf Stable Juice Grapefruit Juice (50% 

And Under) 

907 $0.6 0.01% 1,007 $3.1 0.01% 

Tomato Products-Shelf 

Stable 

Tomato Crushed 908 $0.6 0.01% 780 $6.6 0.02% 

Condiments & Sauces Misc Meat Sauces 909 $0.6 0.01% 962 $3.7 0.01% 

Shelf Stable Juice Blended Juice & Combinations 910 $0.6 0.01% 1,022 $3.0 0.01% 

Coffee & Creamers Bulk Coffee 911 $0.6 0.01% 701 $8.6 0.03% 

Specialty Cheese Pre 

Pack 

Specialty Ppk Cheese 

Semi Soft 

912 $0.6 0.01% 595 $11.4 0.04% 

Non-Dairy/Dairy Aseptic Nut Milk 913 $0.6 0.01% 763 $7.0 0.02% 

Specialty Cheese Pre 

Pack 

Specialty Ppk Cheese 

Soft & Ripe 

914 $0.6 0.01% 620 $10.8 0.03% 

Authentic Hispanic Fds 

& Product 

Authentic Soups/Bouillons 915 $0.6 0.01% 1,200 $1.5 0.00% 

Authentic Asian Foods Authentic Chinese Foods 916 $0.6 0.01% 931 $4.2 0.01% 

Baby Food Baby Food 917 $0.6 0.01% 835 $5.5 0.02% 

Deli Meat: Presliced Deli Meat: Ham 918 $0.6 0.01% 665 $9.5 0.03% 

Bacon Bacon—Natural/Organic 919 $0.6 0.01% 759 $7.1 0.02% 

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 (Retail 

Pk) 

Dl Spec: Dry/Refrig Pastas 922 $0.6 0.01% 850 $5.3 0.02% 

Seafood—Crab Crab—Other 923 $0.6 0.01% 1,213 $1.4 0.00% 

199 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Specialty Cheese Pre 

Pack 

Specialty Ppk Cheese 

Blue/Gorg 

924 $0.6 0.01% 619 $10.8 0.03% 

Tomatoes Tomatoes Others Organic 925 $0.6 0.01% 808 $6.1 0.02% 

Teas Instant Tea & Tea Mix 926 $0.6 0.01% 1,038 $2.9 0.01% 

Refrigerated Vegetarian Vegetarian Misc 927 $0.6 0.01% 963 $3.7 0.01% 

Canned & Dry Milk Non Fat Dry Milk 928 $0.6 0.01% 859 $5.2 0.02% 

Refrigerated Dairy Case Kefir 929 $0.6 0.01% 751 $7.2 0.02% 

Coffee & Creamers Specialty Instant Coffee 930 $0.6 0.01% 1,043 $2.8 0.01% 

Can Vegetables—Shelf 

Stable 

Artichokes 931 $0.6 0.01% 682 $9.1 0.03% 

Soft Drinks Mixers (Tonic Water/ 

Gngr Ale) 

932 $0.5 0.01% 540 $13.2 0.04% 

Refrigerated Italian Refrigerated Pasta Sauce 933 $0.5 0.01% 742 $7.3 0.02% 

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 Milk 

Base 

936 $0.5 0.01% 1,455 $0.3 0.00% 

Seafood—Salad/Dip/Sce/ 

Cond 

Dips/Spreads 937 $0.5 0.01% 1,069 $2.5 0.01% 

Authentic Hispanic Fds 

& Product 

Hispanic Baking Needs 938 $0.5 0.01% 1,233 $1.3 0.00% 

Baking Needs Marshmallow Cre`me 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 Salmon 943 $0.5 0.01% 709 $8.4 0.03% 

Processed Packaged Dry Mixes 944 $0.5 0.01% 1,039 $2.9 0.01% 

Frozen Meat Alternatives Micro Protein [Meats] 945 $0.5 0.01% 899 $4.6 0.01% 

Refrgrated Dough Products Misc Refrig Dough Products 946 $0.5 0.01% 1,162 $1.7 0.01% 

Deli Meat: Presliced Deli Meat: Beef 947 $0.5 0.01% 862 $5.2 0.02% 

Vegetables Cooking Bulk Celery Organic 948 $0.5 0.01% 779 $6.6 0.02% 

Cakes Cakes: Creme/Pudding 

Novelties 

949 $0.5 0.01% 1,171 $1.7 0.01% 

Lamb Loin [Lamb] 950 $0.5 0.01% 882 $4.8 0.02% 

Refrgratd Juices/Drinks Dairy Case Tea No 

Sugar Or Sweetner 

951 $0.5 0.01% 1,002 $3.2 0.01% 

Baking Needs Coconut [Baking Needs] 952 $0.5 0.01% 873 $4.9 0.02% 

Salad Mix Salad Spinach Organic 953 $0.5 0.01% 696 $8.7 0.03% 

Pork Grinds Ground Pork 954 $0.5 0.01% 928 $4.3 0.01% 

Processed Squeeze Lemons/Limes 955 $0.5 0.01% 988 $3.3 0.01% 

Lamb Chuck/Shoulder [Lamb] 956 $0.5 0.01% 1,083 $2.4 0.01% 

Berries Raspberries Organic 957 $0.5 0.01% 683 $9.1 0.03% 

Rolls Rolls: Biscuits/Eng Muffins 958 $0.5 0.01% 886 $4.7 0.02% 

Snacks Snacks: Tortilla Chips 959 $0.5 0.01% 874 $4.9 0.02% 

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 Foods Italian Vegetables 962 $0.5 0.01% 738 $7.4 0.02% 

Dressings/Dips Dips Fruit And Chocolate 963 $0.5 0.01% 1,149 $1.9 0.01% 

Potatoes Potatoes Other Organic 964 $0.5 0.01% 789 $6.4 0.02% 

Juices Super Premium Juices (50% And Under 

Juice) 

965 $0.5 0.01% 1,141 $1.9 0.01% 

Specialty Cheese Pre 

Pack 

Specialty Ppk Cheese 

Hispanic 

966 $0.5 0.01% 1,192 $1.5 0.00% 

Seafood—Value-Added Seafood Value-Added 

Crab 

967 $0.5 0.01% 997 $3.2 0.01% 

Service Case Meat Stuffed/Mixed Pork 968 $0.5 0.01% 1,051 $2.7 0.01% 

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 Stable Corn Beef 971 $0.5 0.01% 1,169 $1.7 0.01% 

Refrigerated Vegetarian Non-Dairy Cheese 972 $0.5 0.01% 893 $4.6 0.01% 

Isotonic Drinks Sports Drink N/ 

Supplmnt Milk 

973 $0.5 0.01% 1,017 $3.0 0.01% 

Soft Drinks Seltzer Unflavored 974 $0.5 0.01% 757 $7.1 0.02% 

Refrigerated Vegetarian Tofu 975 $0.5 0.01% 809 $6.1 0.02% 

Berries Blueberries Organic 976 $0.5 0.01% 660 $9.6 0.03% 

Trail Mix & Snacks Candy W/Flour 977 $0.5 0.01% 1,027 $2.9 0.01% 

Cakes Cakes: Cheesecake Novelties 978 $0.5 0.01% 1,115 $2.1 0.01% 

Water—(Sparkling & 

Still) 

Sparkling Water—Flvrd 

Unsweetened 

979 $0.5 0.01% 675 $9.3 0.03% 

Powder & Crystal Drink 

Mix 

Breakfast Crystals 980 $0.5 0.01% 1,209 $1.4 0.00% 

Non-Dairy/Dairy Aseptic Rice Beverage 981 $0.5 0.01% 891 $4.6 0.01% 

Pies Pies: Tarts/Minis/Crstdas 982 $0.5 0.01% 1,045 $2.7 0.01% 

Specialty Cheese Pre 

Pack 

Specialty Ppk Cheese 

Gouda & Eda 

983 $0.5 0.01% 721 $8.0 0.03% 

200 

Exhibit A–2: Top 1,000 Subcommodities by Expenditures of SNAP 

Households—Continued 

Commodity Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Rank $ in 

Millions 

% of 

Expenditures Rank $ in 

Millions 

% of 

Expenditures 

Enhancements (Pickles/ 

Spreads) 

Enhancements—Spices/ 

Sauces 

984 $0.5 0.01% 1,082 $2.4 0.01% 

Snacks Snacks: Crackers/Cookies 985 $0.5 0.01% 705 $8.4 0.03% 

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 Foods Pasta/Ramen 988 $0.5 0.01% 801 $6.2 0.02% 

Specialty Cheese Pre 

Pack 

Specialty Ppk Cheese 

Goat Milk 

989 $0.5 0.01% 624 $10.4 0.03% 

Herbs/Garlic Herbs Basil Organic 990 $0.4 0.01% 692 $9.0 0.03% 

Bakery Party Trays Party Trays: Cakes 991 $0.4 0.01% 1,147 $1.9 0.01% 

Mushrooms Mushrooms White Bulk 992 $0.4 0.01% 830 $5.7 0.02% 

Candy Candy/Chocolate 993 $0.4 0.01% 786 $6.5 0.02% 

Candy—Packaged Seasonal Candy BagsChocolate 994 $0.4 0.01% 999 $3.2 0.01% 

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 

Caribbean Foods 997 $0.4 0.01% 1,273 $1.1 0.00% 

Dry Bean Veg & Rice Misc Grain Mixes 998 $0.4 0.01% 735 $7.6 0.02% 

Can Vegetables—Shelf 

Stable 

Peas & Onions/Peas & 

Carrot 

999 $0.4 0.01% 1,136 $1.9 0.01% 

Seafood—Shellfish Other Shellfish—Other 1,000 $0.4 0.01% 1,225 $1.3 0.00% 

Top 1,000 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. 

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/Pork Beans 

Can Beans Variety Beans—Kidney/Pinto/E Beans 

Dry Bean Veg & Rice Dry Beans/Peas/Barley: Bag & B Beans 

Frozen Meat Alternatives Soy/Tofu Beans 

Salad & Dips Sal: Hommus Beans 

Traditional Mexican Foods Mexican Beans/Refried Beans 

Vegetables Cooking Bulk Beans Beans 

Frozen Ice Ice—Crushed/Cubed Bottled water 

Water Fortified/Water Bottled water 

Water—(Sparkling & Still) Still Water Drnking/Mnrl Water Bottled water 

Water—(Sparkling & Still) Spring Water Bottled water 

Water—(Sparkling & Still) Distilled Water Bottled water 

Water—(Sparkling & Still) Sparkling Water—Unflavored Bottled water 

Water—(Sparkling & Still) Sparkling Water—Flvrd Unswee Bottled water 

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 

201 

EAppendix B. Crosswalk of Top 1,000 Subcommodities to Summary 

Categories—Continued 

Commodity Subcommodity Summary Category 

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/Flatbrd Bread and Crackers 

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-Pks Multi-Pack Crackers Bread and Crackers 

Crackers Crackers Bread and Crackers 

Crackers & Misc Baked Food Cheese Crackers Bread and Crackers 

Crackers & Misc Baked Food Butter Spray Cracker Bread and Crackers 

Crackers & Misc Baked Food Snack Crackers Bread and Crackers 

Crackers & Misc Baked Food Saltine/Oyster Bread and Crackers 

Crackers & Misc Baked Food Specialty Crackers Bread and Crackers 

Croutons/Bread Stick&Salad Top Croutons Bread and Crackers 

Dry Sce/Gravy/Potatoes/Stuffng Stuffing Mixes Bread and Crackers 

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 Products Refrigerated Biscuits Bread and Crackers 

Refrgrated Dough Products Refrigerated Specialty Rolls Bread and Crackers 

Refrgrated Dough Products Refrigerated Crescent Rolls Bread and Crackers 

Refrgrated Dough Products Refrigerated Breads Bread and Crackers 

Refrgrated Dough Products Misc Refrig Dough Products Bread and Crackers 

Refrigerated Hispanic Grocery Refrigerated Tortillas Bread and Crackers 

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/Breadsticks Bread and Crackers 

Rolls Rolls: Bagels Bread and Crackers 

Rolls Rolls: Biscuits/Eng Muffins Bread and Crackers 

Ss/Vending—Cookie/Cracker Vending Size/Sngl Serve Cracke Bread and Crackers 

Traditional Mexican Foods Mexican Soft Tortillas And Wra Bread and Crackers 

Traditional Mexican Foods Mexican Taco/Tostado/Shells Bread and Crackers 

Apples Caramel/Candy Apples Candy 

Candy Candy/Chocolate Candy 

Candy—Checklane Candy Bars (Singles) (Including) Candy 

Candy—Checklane Chewing Gum Candy 

Candy—Checklane Candy Bars (Singles) (Including) Candy 

Candy—Checklane Mints/Candy & Breath (Not Life) Candy 

Candy—Checklane Misc Checklane Candy Candy 

Candy—Checklane Mints/Candy & Breath (Not Life) Candy 

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-Chocolate Candy 

Candy—Packaged Gum (Packaged) Candy 

Candy—Packaged Miscellaneous Candy (Including) Candy 

Candy—Packaged Seasonal Candy Box-Chocolate Candy 

Candy—Packaged Seasonal Candy Bags Non-Chocol Candy 

Candy—Packaged Candy Bars Multi Pack W/Flour Candy 

Candy—Packaged Candy Bags-Chocolate W/Flour Candy 

Candy—Packaged Miscellaneous Candy (Including) Candy 

Candy—Packaged Novelty Candy Candy 

Candy—Packaged Seasonal Miscellaneous W/Flour Candy 

Candy—Packaged Candy Boxed Chocolates W/Flour Candy 

Candy—Packaged Candy Boxed Chocolates Candy 

Candy—Packaged Seasonal Candy Box Non-Chocola Candy 

Candy—Packaged Candy Box Non-Chocolate Candy 

Candy—Packaged Candy Bags-Non Chocolate W/Flo Candy 

Candy—Packaged Bulk Candy Candy 

Candy—Packaged Seasonal Candy Bags-Chocolate Candy 

Candy—Packaged Seasonal Candy Bags Non-Chocol Candy 

Candy—Packaged Seasonal Candy Box Non-Chocola Candy 

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/Scones Cereal 

Cereals Cereal—Cold Cereal 

Cereals Granola Cereal 

Cnv Breakfast & Wholesome Snks Granola Bars Cereal 

Cnv Breakfast & Wholesome Snks Cereal Bars Cereal 

Cold Cereal Kids Cereal Cereal 

202 

EAppendix B. Crosswalk of Top 1,000 Subcommodities to Summary 

Categories—Continued 

Commodity Subcommodity Summary Category 

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/Filter Pac Coffee and tea 

Coffee & Creamers Flavored Bag Coffee Coffee and tea 

Coffee & Creamers Specialty Instant Coffee W/Swe Coffee and tea 

Coffee & Creamers Flavored Can Coffee Coffee and tea 

Coffee & Creamers Bulk Coffee Coffee and tea 

Coffee & Creamers Specialty Instant Coffee W/O S Coffee and tea 

Dry Tea/Coffee/Coco Mixes Tea Bags (Supplement) Coffee and tea 

Refrgratd Juices/Drinks Dairy Case Tea No Sugar Or Swe Coffee and tea 

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 & Product Authentic Sauces/Salsa/Picante Condiments and seasoning 

Bag Snacks Salsa & Dips Condiments and seasoning 

Can Vegetables—Shelf Stable Fried Onions Condiments and 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 Sauce Condiments and seasoning 

Condiments & Sauces Misc Meat Sauces Condiments and seasoning 

Croutons/Bread Stick & Salad Top Salad Toppers Condiments and seasoning 

Dressings/Dips Dips Guacamole/Salsa/Queso Condiments and seasoning 

Dressings/Dips Dips Veggie Condiments and seasoning 

Dressings/Dips Dips Fruit And Chocolate Condiments and seasoning 

Dry Sce/Gravy/Potatoes/Stuffng Sauce Mixes/Gravy Mixes Dry Condiments and seasoning 

Dry Sce/Gravy/Potatoes/Stuffng Gravy Can/Glass Condiments and seasoning 

Dry Sce/Gravy/Potatoes/Stuffng Cooking Bags With Spices/Seaso Condiments and seasoning 

Enhancements Enhancements—Pickles/Kraut Condiments and seasoning 

Enhancements Enhancements—Salads/Spreads Condiments and seasoning 

Enhancements Enhancements—Spices/Sauces Condiments and 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 Veg & Olives Ripe Olives Condiments and seasoning 

Pickle/Relish/Pckld Veg & Olives Peppers Condiments and seasoning 

Pickle/Relish/Pckld Veg & Olives Green Olives Condiments and seasoning 

Pickle/Relish/Pckld Veg & Olives Relishes Condiments and seasoning 

Pickle/Relish/Pckld Veg & Olives Pickld Veg/Peppers/Etc. Condiments and seasoning 

Pickle/Relish/Pckld Veg & Olives Specialty Olives Condiments and 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 & Sandwich Spreads Dry Salad Dressing & Dip Mixes Condiments and seasoning 

Seafood—Salad/Dip/Sce/Cond Dips/Spreads Condiments and 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/Ice Cr Condiments and seasoning 

Spices & Extracts Imitation Extracts Condiments and seasoning 

Spices/Jarred Garlic Spices & Seasonings Condiments and seasoning 

Traditional Asian Foods Asian Other Sauces/Marinad Condiments and seasoning 

Traditional Asian Foods Asian Soy Sauce Condiments and seasoning 

Traditional Mexican Foods Mexican Sauces And Picante Sau Condiments and seasoning 

Traditional Mexican Foods Mexican Seasoning Mixes Condiments and seasoning 

Traditional Mexican Foods Mexican Taco Sauce Condiments and seasoning 

Vinegar & Cooking Wines Vinegar/White & Cider Condiments and seasoning 

Vinegar & Cooking Wines Specialty Vinegar Condiments and 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 Case Eggs Eggs 

203 

EAppendix B. Crosswalk of Top 1,000 Subcommodities to Summary 

Categories—Continued 

Commodity Subcommodity Summary Category 

Dressings/Dips Dressing Creamy Fats and oils 

Dressings/Dips Dressing Blue Cheese Fats and oils 

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 & Sandwich Spreads Pourable Salad Dressings Fats and oils 

Salad Dresing & Sandwich Spreads Mayonnaise & Whipped Dressing Fats and oils 

Salad Dresing & Sandwich Spreads Sand/Horseradish & Tartar Sauce Fats and oils 

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/Safflower/ Fats and oils 

Baking Flours/Grains/Sugar Flour and prepared flour mixes 

Flour & Meals Flour: White & Self Rising Flour and prepared flour mixes 

Flour & Meals Breadings/Coatings/Crumbs Flour and prepared flour mixes 

Flour & Meals Flour: Misc/Specialty/Blend Et Flour and prepared flour mixes 

Molasses/Syrups/Pancake Mixes Pancake Mixes Flour and prepared flour mixes 

Frozen Breakfast Foods Frzn Breakfast Sandwiches Frozen prepared foods 

Frozen Breakfast Foods Waffles/Pancakes/French Toast Frozen prepared foods 

Frozen Breakfast Foods Frzn Breakfast Entrees Frozen prepared foods 

Frozen Entre ́es Meatless/Vegetarian Frozen prepared foods 

Frozen Ethnic Frozen International [Ethnic Food] Frozen prepared foods 

Frozen Handhelds & Snacks Snacks/Appetizers Frozen prepared foods 

Frozen Handhelds & Snacks Sandwiches & Handhelds Frozen prepared foods 

Frozen Handhelds & Snacks Corn Dogs Frozen prepared foods 

Frozen Handhelds & Snacks Burritos Frozen prepared foods 

Frozen Meat Alternatives Micro Protein [Meat] Frozen prepared foods 

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/Microwave Frozen prepared foods 

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 Entre ́es 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 And Frozen prepared foods 

Frzn Prepared Chicken Boneless Snack/18oz And Larger Frozen prepared foods 

Frzn Prepared Chicken Bone-In Wings Frozen prepared foods 

Frzn Prepared Chicken Fz Meal Kits/Stuffed/Other Frozen prepared foods 

Frzn Prepared Chicken Whole Muscle Unbreaded Frozen prepared foods 

Frzn Prepared Chicken Boneless Snack/Value/Small Frozen prepared foods 

Frzn Seafood Frz Coated Fish Fillets Frozen prepared foods 

Frzn Seafood Frz Fishsticks/Tenders/Nuggets Frozen prepared foods 

Frzn Seafood Frz Non-Coated Fish Fillets Frozen prepared foods 

Frzn Ss Economy Meals Fz Ss Economy Meals All Frozen prepared foods 

Frzn Ss Premium Meals Fz Ss Prem Traditional Meals Frozen prepared foods 

Frzn Ss Premium Meals Fz Ss Prem Nutritional Meals Frozen prepared foods 

Apples Apples Gala (Bulk & Bag) Fruits 

Apples Apples Red Delicious (Bulk & Bag) Fruits 

Apples Apples Granny Smith (Bulk & Bag) Fruits 

Apples Mixed Fruit Bags Fruits 

Apples Apples Other (Bulk & Bag) Fruits 

Apples Apples Fuji (Bulk & Bag) Fruits 

Apples Apples Gold Delicious (Bulk & Bag) Fruits 

Apples Apples Honeycrisp Fruits 

Apples Apples Braeburn (Bulk & Bag) Fruits 

Apples Apples Gala (Bulk & Bag) Organic Fruits 

Apples Apples Red Delicious (Bulk & Bag) Fruits 

Apples Apples Granny Smith (Bulk & Bag) Fruits 

Apples Apples Gold Delicious (Bulk & Bag) Fruits 

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 Applesauce Pineapple Fruits 

Can Fruit/Jar Applesauce Peaches Fruits 

Can Fruit/Jar Applesauce Fruit Cocktail/Fruit Salad Fruits 

Can Fruit/Jar Applesauce Mandarin Oranges/Citrus Sect Fruits 

Can Fruit/Jar Applesauce Apple Sauce (Excludes Cup) Fruits 

Can Fruit/Jar Applesauce Pears Fruits 

Can Fruit/Jar Applesauce Cranberry Sauce Fruits 

204 

EAppendix B. Crosswalk of Top 1,000 Subcommodities to Summary 

Categories—Continued 

Commodity Subcommodity Summary Category 

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 Fruit Pro Fruits 

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/Applesauce Fruit Cup Fruits 

Single Serve Fruit/Applesauce Applesauce Cup Fruits 

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/Cored Fruits 

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 Prepack Fruits 

Value-Added Fruit Fruit Party Tray Prepack Fruits 

Bagels & Cream Cheese Cream Cheese High fat dairy/cheese 

Bulk Service Case Cheese Bulk Semi-Hard [Cheese] High fat dairy/cheese 

Bulk Service Case Cheese Bulk Processed [Cheese] High fat dairy/cheese 

Bulk Service Case Cheese Bulk Semi-Soft [Cheese] High fat dairy/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 Food Aerosol Cheese High fat dairy/cheese 

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 Creamers High fat dairy/cheese 

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 & Sherbets Premium [Ice Cream & Sherbert] High fat dairy/cheese 

Ice Cream Ice Milk & Sherbets Traditional [Ice Cream & Sherbert] High fat dairy/cheese 

Ice Cream Ice Milk & Sherbets Pails [Ice Cream & Sherbert] High fat dairy/cheese 

Ice Cream Ice Milk & Sherbets Super Premium Pints [Ice Cream & 

Sherbert] 

High fat dairy/cheese 

Ice Cream Ice Milk & Sherbets Premium Pints [Ice Cream & Sherbert] High fat dairy/cheese 

Ice Cream Ice Milk & Sherbets Quarts [Ice Cream & Sherbert] High fat dairy/cheese 

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-Products] High fat dairy/cheese 

Milk By-Products Ricotta Cheese High fat dairy/cheese 

Pre-Slice Service Case Cheese Pre-Sliced Semi-Soft [Cheese] High fat dairy/cheese 

Pre-Slice Service Case Cheese Pre-Sliced Semi-Hard [Cheese] High fat dairy/cheese 

Refrigerated Hispanic Grocery Hispanic Cheese High fat dairy/cheese 

Specialty Cheese Pre Pack Specialty Ppk Cheese Hard/Grat High fat dairy/cheese 

Specialty Cheese Pre Pack Specialty Ppk Cheese Spreads High fat dairy/cheese 

Specialty Cheese Pre Pack Specialty Ppk Cheese Feta High fat dairy/cheese 

Specialty Cheese Pre Pack Specialty Ppk Cheese Mozzarell High fat dairy/cheese 

Specialty Cheese Pre Pack Specialty Ppk Cheese Processed High fat dairy/cheese 

205 

EAppendix B. Crosswalk of Top 1,000 Subcommodities to Summary 

Categories—Continued 

Commodity Subcommodity Summary Category 

Specialty Cheese Pre Pack Specialty Ppk Cheese Cheddar & C High fat dairy/cheese 

Specialty Cheese Pre Pack Specialty Ppk Cheese Semi Soft High fat dairy/cheese 

Specialty Cheese Pre Pack Specialty Ppk Cheese Soft & Ripe High fat dairy/cheese 

Specialty Cheese Pre Pack Specialty Ppk Cheese Blue/Gorg High fat dairy/cheese 

Specialty Cheese Pre Pack Specialty Ppk Cheese Hispanic High fat dairy/cheese 

Specialty Cheese Pre Pack Specialty Ppk Cheese Gouda & Eda High fat dairy/cheese 

Specialty Cheese Pre Pack Specialty Ppk Cheese Goat Milk High fat dairy/cheese 

Traditional Mexican Foods Mexican Con Queso High fat dairy/cheese 

Fruit Snacks Fruit Snacks Jams, jellies, preserves and other sweets 

Peanut Butter/Jelly/Jams & Honey Preserves/Jam/Marmalade Jams, jellies, preserves and other sweets 

Peanut Butter/Jelly/Jams & Honey Jelly Jams, jellies, preserves and other sweets 

Aseptic Juice Aseptic Pack Juice And Drinks Juices 

Frozen Juice And Smoothies Frzn Conc Allieds Over 50% Jui Juices 

Frozen Juice And Smoothies Frzn Oj & Oj Substitutes (Over 5 Juices 

Juice Non-Carb Jce(Over 50% Jce) Juices 

Juice Drinks—Carb Juice (Over 50%) Juices 

Juices Super Premium Juices Superfoods/Enhanced Juices 

Juices Super Premium Juices Proteins Juices 

Juices Super Premium Juice Single Blend Juices 

Processed Squeeze Lemons/Limes Juices 

Refrgratd Juices/Drinks Dairy Case 100% Pure Juice—O Juices 

Refrgratd Juices/Drinks Dairy Case 100% Pure Juice Oth Juices 

Rtd Tea/New Age Juice Juice (Over 50% Juice) Juices 

Shelf Stable Juice Apple Juice & Cider (Over 50%) Juices 

Shelf Stable Juice Blended Juice & Combinations (Ov) Juices 

Shelf Stable Juice Grape Juice (Over 50% Juice) Juices 

Shelf Stable Juice Veg Juice (Except Tomato) (Ove) Juices 

Shelf Stable Juice Tomato Juice (Over 50% Jce) Juices 

Shelf Stable Juice Pineapple Juice (Over 50% Juic) Juices 

Shelf Stable Juice Cranberry Juice (Over 50% Jce) Juices 

Shelf Stable Juice Lemon Juice & Lime Juice (Over) Juices 

Shelf Stable Juice Prune Juice (Over 50% Juice) Juices 

Shelf Stable Juice Cranapple/Cran Grape Juice (Ov) Juices 

Shelf Stable Juice Grapefruit Juice (Over 50% Jui) Juices 

Shelf Stable Juice Cranapple/Cran Grape Juice (Un) Juices 

Shelf Stable Juice Grapefruit Juice (50% And Unde) Juices 

Bacon Bacon—Trad 16oz Or Less Meat/Poultry/Seafood 

Bacon Bacon—Trad Greater Than 16oz Meat/Poultry/Seafood 

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 Rolls Meat/Poultry/Seafood 

Breakfast Sausage Bkfst Sausage—Fresh Links Meat/Poultry/Seafood 

Breakfast Sausage Bkfst Sausage—Fresh Patties Meat/Poultry/Seafood 

Breakfast Sausage Bkfst Sausage—Precooked Meat/Poultry/Seafood 

Breakfast Sausage Bkfst Sausage—Bkfast Side Di Meat/Poultry/Seafood 

Breakfast Sausage Bkfst Sausage—Other Forms Meat/Poultry/Seafood 

Buffalo Grinds [Buffalo] Meat/Poultry/Seafood 

Can Seafood—Shelf Stable Tuna Meat/Poultry/Seafood 

Can Seafood—Shelf Stable Salmon Meat/Poultry/Seafood 

Can Seafood—Shelf Stable Sardines Meat/Poultry/Seafood 

Can Seafood—Shelf Stable Oysters Meat/Poultry/Seafood 

Chicken & Poultry Chix: Value-Added (Cold) Meat/Poultry/Seafood 

Chicken & Poultry Chix: Frd 8pc/Cut Up (Cold) Meat/Poultry/Seafood 

Chicken & Poultry Chix: Baked 8pc Cut Up (Cold) Meat/Poultry/Seafood 

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/Fryer) Meat/Poultry/Seafood 

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 

206 

EAppendix B. Crosswalk of Top 1,000 Subcommodities to Summary 

Categories—Continued 

Commodity Subcommodity Summary Category 

Chicken Grinds Ground Chicken Meat/Poultry/Seafood 

Chicken Offal Internal Chicken Offal Meat/Poultry/Seafood 

Chicken Specialty/Natural Chicken Breast Boneless Meat/Poultry/Seafood 

Chicken Specialty/Natural Chicken Wings Meat/Poultry/Seafood 

Chicken Specialty/Natural Whole Chicken (Roasters/Fryer) Meat/Poultry/Seafood 

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 Meats Meat/Poultry/Seafood 

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 Meats Meat/Poultry/Seafood 

Deli Meat: Presliced Deli Meat: Semi-Dry Sausage Meat/Poultry/Seafood 

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 Ckd/S Meat/Poultry/Seafood 

Dinner Sausage Dnr Sausage—Links Fresh Meat/Poultry/Seafood 

Dinner Sausage Dnr Sausage—Pork Rope Ckd/Sm Meat/Poultry/Seafood 

Dinner Sausage Dnr Sausage—Beef Rope Ckd/Sm Meat/Poultry/Seafood 

Dinner Sausage Dnr Sausage—Other Forms Meat/Poultry/Seafood 

Dinner Sausage Dnr Sausage—Links Beef Ckd/S Meat/Poultry/Seafood 

Dinner Sausage Dnr Sausage—Poultry Rope Ckd Meat/Poultry/Seafood 

Dinner Sausage Dnr Sausage—Links Poultry Ck Meat/Poultry/Seafood 

Dinner Sausage Dnr Sausage—Natural/Organic Meat/Poultry/Seafood 

Dinner Sausage Dnr Sausage—Fresh Poultry Meat/Poultry/Seafood 

Frozen Breakfast Foods Frzn Breakfast Sausage Meat/Poultry/Seafood 

Frzn Multi Serve Frzn Burgers Meat/Poultry/Seafood 

Frzn Prepared Chicken Value Forms/18oz And Larger [Chicken] Meat/Poultry/Seafood 

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/Sausage Meat/Poultry/Seafood 

Lunchmeat Lunchmeat—Chop/Form Pltry & Ha Meat/Poultry/Seafood 

Lunchmeat Lunchmeat—Whole Muscle Pltry Meat/Poultry/Seafood 

Lunchmeat Lunchmeat—Chip Meat Meat/Poultry/Seafood 

Lunchmeat Lunchmeat—Brauns/Liver/Loave Meat/Poultry/Seafood 

Lunchmeat Lunchmeat—Variety Pack Meat/Poultry/Seafood 

Lunchmeat Lunchmeat—Other Meat/Poultry/Seafood 

Lunchmeat Lunchment—Natural/Organic Meat/Poultry/Seafood 

Lunchmeat Lunchmeat—Peggable Deli Fres Meat/Poultry/Seafood 

Meat Frozen Frzn Meat—Beef Meat/Poultry/Seafood 

Meat Frozen Frzn Meat—Breakfast Sausage Meat/Poultry/Seafood 

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/Rib] Meat/Poultry/Seafood 

Pork Boneless Loin/Rib Enhanced [Pork Boneless Loin/Rib] Meat/Poultry/Seafood 

Pork Grinds Ground Pork Meat/Poultry/Seafood 

Pork Offal External Fresh [Pork Offal] Meat/Poultry/Seafood 

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 Products Lunch Meats Meat/Poultry/Seafood 

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 Raised Salmon Fr—Altantic Meat/Poultry/Seafood 

Seafood—Salmon-Wild Caught Salmon Wc—Pink Meat/Poultry/Seafood 

Seafood—Salmon-Wild Caught Salmon Wc—Sockeye Meat/Poultry/Seafood 

Seafood—Scallops Scallops—Sea Meat/Poultry/Seafood 

Seafood—Shrimp Shrimp—Raw Meat/Poultry/Seafood 

Seafood—Shrimp Shrimp—Cooked Meat/Poultry/Seafood 

Seafood—Smoked Seafood Smoked Salmon Meat/Poultry/Seafood 

Seafood—Tilapia Tilapia—Fillet Meat/Poultry/Seafood 

Seafood—Trout Steelhead Fr [Trout] Meat/Poultry/Seafood 

Seafood—Value-Added Seafood Value-Added Breaded Shrimp Meat/Poultry/Seafood 

207 

EAppendix B. Crosswalk of Top 1,000 Subcommodities to Summary 

Categories—Continued 

Commodity Subcommodity Summary Category 

Seafood—Value-Added Seafood Value-Added Shrimp Meat/Poultry/Seafood 

Seafood—Value-Added Seafood Value-Added Crab Meat/Poultry/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 Sausag Meat/Poultry/Seafood 

Turkey Fresh Whole Hen (Under 16lbs) [Turkey] Meat/Poultry/Seafood 

Turkey Fresh Whole Tom (Over 16lbs) [Turkey] Meat/Poultry/Seafood 

Turkey Frozen Whole Toms (Over 16lbs) [Turkey] Meat/Poultry/Seafood 

Turkey Frozen Whole Hens (Under 16lbs) [Turkey] Meat/Poultry/Seafood 

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 Aseptic Milk Milk 

Non-Dairy/Dairy Aseptic Soy Beverage Milk 

Non-Dairy/Dairy Aseptic Nut Milk Milk 

Non-Dairy/Dairy Aseptic Rice Beverage Milk 

Refrigerated Dairy Case Non-Dairy Milks Milk 

Refrigerated Dairy Case Fluid Milk Milk 

Authentic Asian Foods Authentic Japanese Foods Miscellaneous 

Authentic Asian Foods Authentic Chinese Foods Miscellaneous 

Authentic Central American Fds Central American Foods Miscellaneous 

Authentic Hispanic Fds & Product Hispanic Baking Needs Miscellaneous 

Baking Needs Baking Powder & Soda Miscellaneous 

Baking Needs Yeast: Dry Miscellaneous 

Baking Needs Corn Starch Miscellaneous 

Dietary Aid Prdct/Med Liq Nutr Diet Cntrl Liqs Nutritional Miscellaneous 

Dietary Aid Prdct/Med Liq Nutr Diet Energy Drinks Miscellaneous 

Dietary Aid Prdct/Med Liq Nutr Diet Cntrl Bars Nutritional Miscellaneous 

Fitness & Diet Fitness & Diet—Bars W/Flour Miscellaneous 

Fitness & Diet Fitness & Diet—Bars W/O Flour Miscellaneous 

Fitness & Diet Fitness & Diet—Powder Ntrtnl Miscellaneous 

Refrigerated Hispanic Grocery Misc Hispanic Grocery Miscellaneous 

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/Jams & Honey Peanut Butter Nuts and seeds 

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 Case Yogurt Other dairy products 

Refrigerated Dairy Case Kefir Other dairy products 

Yogurt Yogurt/Kids Other dairy products 

208 

EAppendix B. Crosswalk of Top 1,000 Subcommodities to Summary 

Categories—Continued 

Commodity Subcommodity Summary Category 

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 Or Lar) Other dairy products 

Yogurt Yogurt/Adult Drinks Other dairy products 

Deli Specialties (Retail Pk) Dl Spec: Dry/Refrig Pastas Pasta, cornmeal, 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—Prepack Pasta, cornmeal, other cereal products 

Salad & Dips Pasta/Grain Salads—Bulk Pasta, cornmeal, other cereal products 

Seafood—Salad/Dip/Sce/Cond Breading Pasta, cornmeal, other cereal products 

Traditional Asian Foods Asian Noodles/Rice Pasta, cornmeal, other cereal products 

Authentic Hispanic Fds & Product Hispanic Cookies/Crackers Prepared Desserts 

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 Mixes Prepared Desserts 

Baking Needs Bits & Morsels [Baking Needs] Prepared Desserts 

Baking Needs Marshmallows Prepared Desserts 

Baking Needs Pie Filling/Mincemeat/Glazes Prepared Desserts 

Baking Needs Pie Crust Mixes & Shells Prepared Desserts 

Baking Needs Cooking Chocolate (Ex: Smi-Swt) Prepared Desserts 

Baking Needs Maraschino Cherries Prepared Desserts 

Baking Needs Baking Cocoa Prepared Desserts 

Baking Needs Marshmallow Cre`me Prepared Desserts 

Baking Needs Coconut [Baking Needs] Prepared Desserts 

Cake De ́cor Cake De ́cors & Icing Prepared Desserts 

Cake De ́cor Cake De ́cors—Candies Prepared Desserts 

Cakes Cakes: Birthday/Celebration Sh Prepared Desserts 

Cakes Cakes: Cupcakes Prepared Desserts 

Cakes Cakes: Layers Prepared Desserts 

Cakes Cakes: Cre`me/Pudding Prepared Desserts 

Cakes Cakes: Cheesecake Prepared Desserts 

Cakes Cakes: Fancy/Service Case Prepared Desserts 

Cakes Cakes: Layers/Sheets Novelties Prepared Desserts 

Cakes Cakes: Angel Fds/Cke Rolls Prepared Desserts 

Cakes Cakes: Ice Cream Prepared Desserts 

Cakes Cakes: Birthday/Celebration Lay Prepared Desserts 

Cakes Cakes: Sheet Prepared Desserts 

Cakes Cakes: Cre`me/Pudding Novelties Prepared Desserts 

Cakes Cakes: Cheesecake Novelties Prepared Desserts 

Cnv Breakfast & Wholesome Snks Toaster Pastries Prepared Desserts 

Cnv Breakfast & Wholesome Snks Treats Prepared Desserts 

Cookie/Cracker Multi-Pks Multi-Pack Cookies Prepared Desserts 

Cookies Sandwich Cookies Prepared Desserts 

Cookies Tray Pack/Choc Chip Cookies Prepared Desserts 

Cookies Cookies: Regular Prepared Desserts 

Cookies Vanilla Wafer/Kids Cookies Prepared Desserts 

Cookies Cookies: Holiday/Special Occas Prepared Desserts 

Cookies Premium Cookies (Ex: Pepperidg) Prepared Desserts 

Cookies Graham Crackers Prepared Desserts 

Cookies Chocolate Covered Cookies Prepared Desserts 

Cookies Wellness/Portion Control [Cookies] Prepared Desserts 

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/Cans Prepared Desserts 

Dry Mix Desserts Puddings Dry Prepared Desserts 

Dry Mix Desserts Gelatin Prepared Desserts 

Dry Mix Desserts Misc: Cheesecake/Mousse Mixes Prepared Desserts 

Frozen Breakfast Foods Frzn Breakfast Pastry Prepared Desserts 

Frozen Desserts Frozen Fruit Pies & Cobblers Prepared Desserts 

Frozen Desserts Frozen Cream Pies Prepared Desserts 

Frozen Desserts Frzn Pie Shells/Pastry Shell/F Prepared Desserts 

Frozen Desserts Frozen Cakes/Desserts Prepared Desserts 

Frozen Desserts Frzn Pastry & Cookies Prepared Desserts 

Frozen Desserts Single Serv/Portion Control Prepared Desserts 

Frozen Novelties—Water Ice Sticks/Enrobed [Frozen Novelties] Prepared Desserts 

Frozen Novelties—Water Ice Water Ice [Frozen Novelties] Prepared Desserts 

209 

EAppendix B. Crosswalk of Top 1,000 Subcommodities to Summary 

Categories—Continued 

Commodity Subcommodity Summary Category 

Frozen Novelties—Water Ice Cones [Frozen Novelties] Prepared Desserts 

Frozen Novelties—Water Ice Ice Cream Sandwiches Prepared Desserts 

Frozen Novelties—Water Ice Adult Premium [Frozen Novelties] Prepared Desserts 

Frozen Novelties—Water Ice Cups/Push Ups/Other [Frozen Novelties] Prepared Desserts 

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 Products Refrigerated Cookies—Break N B Prepared Desserts 

Refrgrated Dough Products Refrigerated Cookie Dough Prepared Desserts 

Refrgrated Dough Products Refrigerated Cookies—Seasonal Prepared Desserts 

Refrgrated Dough Products Refrigerated Pie Crust Prepared Desserts 

Refrigerated Desserts Refrigerated Pudding Prepared Desserts 

Salad & Dips Sal: Desserts—Prepack Prepared Desserts 

Salad & Dips Sal: Desserts—Bulk Prepared Desserts 

Single Serve Sweet Goods Snack Cake—Single Serve Prepared Desserts 

Ss/Vending—Cookie/Cracker Vendor Size/Single Serve Cooki Prepared Desserts 

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 Cookie Prepared Desserts 

Sweet Goods & Snacks Sw Gds: Puff Pastry Prepared Desserts 

Sweet Goods & Snacks Sw Gds: Specialty Desserts Prepared Desserts 

Syrups Toppings & Cones Ice Cream Toppings Prepared Desserts 

Value-Added Fruit Parfait Cups Instore Prepared Desserts 

Canned Pasta & Mwv Fd-Shlf Stbl Can Pasta Prepared Foods 

Canned Pasta & Mwv Fd-Shlf Stbl Microwavable Cups [Pasta] Prepared Foods 

Chilled Ready Meals Store Brand Prepared Foods 

Convenient Meals Convenient Meals—Kids Meal C Prepared Foods 

Convenient Meals Convenient Meals—Adult Meal Prepared Foods 

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 Salads Prepared Foods 

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/Etc. Prepared Foods 

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/Gra Prepared Foods 

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: Appetizers&Hors D’oe Prepared Foods 

Prepared/Pdgd Foods Boxed Prepared/Entre ́e/Dry Prep Prepared Foods 

Prepared/Pdgd Foods Vegetables/Dry Beans Prepared Foods 

Refrigerated Vegetarian Vegetarian Meats Prepared Foods 

Refrigerated Vegetarian Vegetarian Misc Prepared Foods 

Refrigerated Vegetarian Non-Dairy Cheese Prepared Foods 

Refrigerated Vegetarian Tofu Prepared Foods 

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 Foods Asian Foods And Meals Prepared Foods 

Traditional Asian Foods Traditional Thai Foods Prepared Foods 

Traditional Mexican Foods Mexican Dinners And Foods Prepared Foods 

Traditional Mexican Foods Mexican Enchilada Sauce Prepared Foods 

Authentic Hispanic Fds & Product Authentic Pasta/Rice/Beans Rice 

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 

210 

EAppendix B. Crosswalk of Top 1,000 Subcommodities to Summary 

Categories—Continued 

Commodity Subcommodity Summary Category 

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 Snacks Salty Snacks Vending Salty snacks 

Warehouse Snacks Canister Snacks Salty snacks 

Warehouse Snacks Snack Mix Salty snacks 

Warehouse Snacks Misc Snacks Salty snacks 

Authentic Hispanic Fds & Product Authentic Soups/Bouillons Soup 

Canned Soups Condensed Soup Soup 

Dry/Ramen Bouillon Dry Soup Soup 

Dry/Ramen Bouillon Bouillon Soup 

Rts/Micro Soup/Broth Rts Soup: Chunky/Homestyle/Et Soup 

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/Pancake Mixes Molasses & Syrups Sugars 

Peanut Butter/Jelly/Jams & Honey Honey Sugars 

Sugars & Sweeteners Sugar Sugars 

Sugars & Sweeteners Sweeteners Sugars 

Aseptic Juice Aseptic Pack Juice And Drinks Sweetened Beverages 

Aseptic Juice Aseptic Pack Juice And Drinks Sweetened Beverages 

Authentic Hispanic Fds & Product Hispanic Carbonated Beverages Sweetened Beverages 

Authentic Hispanic Fds & Product Hispanic Juice Under 50% Juice Sweetened Beverages 

Beverages Can/Btl Carb Beve 50% And Unde Sweetened Beverages 

Cocoa Mixes Malted Mlk/Syrup/Pwdrs (Eggnog) Sweetened Beverages 

Cocoa Mixes Hot Chocolate/Cocoa Mix Sweetened Beverages 

Energy Drinks Energy Drink—Single Serve Sweetened Beverages 

Energy Drinks Energy Drink—Single Serve (N) Sweetened Beverages 

Energy Drinks Energy Drink—Multi-Pack Sweetened Beverages 

Energy Drinks Energy Drink—Multi-Pack (Non) Sweetened Beverages 

Frozen Juice And Smoothies Frzn Fruit Drinks (Under 10% J) Sweetened Beverages 

Frozen Juice And Smoothies Frzn Conc Under 50% Juice Sweetened Beverages 

Frozen Juice And Smoothies Smoothies-Frz Sweetened Beverages 

Frozen Juice And Smoothies Cocktail Mixes-Frz Sweetened Beverages 

Isotonic Drinks Isotonic Drinks Single Serve Sweetened Beverages 

Isotonic Drinks Isotonic Drinks Multi-Pack Sweetened Beverages 

Isotonic Drinks Isotonic Drinks Multi-Serve Sweetened Beverages 

Isotonic Drinks Sports Drink N/Supplmnt Milk/M Sweetened Beverages 

Juice Non-Carb Jce (Under 50% Jce) Sweetened Beverages 

Juices Super Premium Juices Smoothies/Blended Sweetened Beverages 

Juices Super Premium Juices Antioxidant/Wellness Sweetened Beverages 

Juices Super Premium Juices (50% And Under Juice) Sweetened Beverages 

Mixers Cocktail Mixes—Fluid: Add Liq Sweetened Beverages 

Powder & Crystal Drink Mix Unsweetened Envelope [Powder Drink 

Mix] 

Sweetened Beverages 

Powder & Crystal Drink Mix Sugar Free Canister [Powder Drink Mix] Sweetened Beverages 

Powder & Crystal Drink Mix Sugar Free Sticks [Powder Drink Mix] Sweetened Beverages 

Powder & Crystal Drink Mix Soft Drink Canisters Sweetened Beverages 

Powder & Crystal Drink Mix Enhanced Stick [Powder Drink Mix] Sweetened Beverages 

Powder & Crystal Drink Mix Sugar Sweetened Sticks Sweetened Beverages 

Powder & Crystal Drink Mix Fluid Pouch [Powder Drink Mix] Sweetened Beverages 

Powder & Crystal Drink Mix Breakfast Crystals Sweetened Beverages 

Processed Packaged Dry Mixes Sweetened Beverages 

Refrgratd Juices/Drinks Dairy Case Juice Drnk Under 10 Sweetened Beverages 

Refrgratd Juices/Drinks Dairy Case Citrus Pnch/Oj Subs Sweetened Beverages 

Refrgratd Juices/Drinks Dairy Case Tea With Sugar Or S Sweetened Beverages 

Refrgratd Juices/Drinks Dairy Case Fruit Drinks (No Ju) Sweetened Beverages 

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 & Glass Sweetened Beverages 

Shelf Stable Juice Cranapple/Cran Grape Juice (50) Sweetened Beverages 

Shelf Stable Juice Cranberry Juice (50% And Under) Sweetened Beverages 

Shelf Stable Juice Blended Juice & Combinations (50) Sweetened Beverages 

Shelf Stable Juice Fruit Drinks: Canned & Glass Sweetened Beverages 

Shelf Stable Juice Apple Juice & Cider (50% And U) Sweetened Beverages 

Shelf Stable Juice Tomato Juice (50% And Under) Sweetened Beverages 

Shelf Stable Juice Blended Juice & Combinations (Un) Sweetened Beverages 

Shelf Stable Juice Fruit Drinks: Canned & Glass Sweetened Beverages 

Soft Drinks Soft Drinks 12/18 & 15pk Can Car Sweetened Beverages 

Soft Drinks Sft Drnk 2 Liter Btl Carb Incl Sweetened Beverages 

Soft Drinks Soft Drinks 20pk & 24pk Can Carb Sweetened Beverages 

Soft Drinks Sft Drnk Mlt-Pk Btl Carb (Excp) Sweetened Beverages 

Soft Drinks Sft Drnk Sngl Srv Btl Carb (Ex) Sweetened Beverages 

Soft Drinks Soft Drinks Can Non-Carb (Exce) Sweetened Beverages 

Soft Drinks Soft Drinks 6pk Can Carb (Exp) Sweetened Beverages 

Soft Drinks Sft Drnk 1 Liter Btl Carb (Exc) Sweetened Beverages 

Soft Drinks Tea Can With Sweetener/Sugar Sweetened Beverages 

211 

EAppendix B. Crosswalk of Top 1,000 Subcommodities to Summary 

Categories—Continued 

Commodity Subcommodity Summary Category 

Soft Drinks Soft Drink Bottle Non-Carb (Ex) Sweetened Beverages 

Soft Drinks Tea Bottles With Sweetener/Sug Sweetened Beverages 

Soft Drinks Mixers (Tonic Water/Gngr Ale) Sweetened Beverages 

Soft Drinks Seltzer Unflavored Sweetened Beverages 

Teas Instant Tea & Tea Mix (W/Sugar) Sweetened Beverages 

Water Non-Carb Water Flvr—Drnk/Mnr Sweetened Beverages 

Water—(Sparkling & Still) Still Water Flvrd Drnk/Mnrl Wt Sweetened Beverages 

Water—(Sparkling & Still) Sparkling Water—Flvrd Sweet Sweetened Beverages 

Authentic Hispanic Fds & Product Authentic Vegetables And Foods Vegetables 

Authentic Hispanic Fds & Product Authentic Peppers Vegetables 

Authentic Italian Foods Italian Vegetables Vegetables 

Broccoli/Cauliflower Broccoli Whole & Crowns Vegetables 

Broccoli/Cauliflower Cauliflower Whole Vegetables 

Can Vegetables—Shelf Stable Green Beans: Fs/Whl/Cut Vegetables 

Can Vegetables—Shelf Stable Corn Vegetables 

Can Vegetables—Shelf Stable Peas/Green Vegetables 

Can Vegetables—Shelf Stable Spinach & Greens Vegetables 

Can Vegetables—Shelf Stable Mushrooms Cnd & Glass Vegetables 

Can Vegetables—Shelf Stable Sweet Potatoes Vegetables 

Can Vegetables—Shelf Stable Mixed Vegetables Vegetables 

Can Vegetables—Shelf Stable Carrots Vegetables 

Can Vegetables—Shelf Stable White Potatoes Vegetables 

Can Vegetables—Shelf Stable Kraut & Cabbage Vegetables 

Can Vegetables—Shelf Stable Beets Vegetables 

Can Vegetables—Shelf Stable Peas Fresh Pack/Crowder Vegetables 

Can Vegetables—Shelf Stable Artichokes Vegetables 

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/Stuffng Potatoes: Dry Vegetables 

Frozen Potatoes Frzn French Fries Vegetables 

Frozen Potatoes Frzn Tater Tots/Other Extruded Vegetables 

Frozen Potatoes Frzn Hashbrown Potatoes Vegetables 

Frozen Potatoes Frzn Baked/Stuffed/Mashed & Spec Vegetables 

Frozen Potatoes Frzn Onion Rings Vegetables 

Frozen Vegetable & Veg Dish Fz Bag Vegetables—Plain Vegetables 

Frozen Vegetable & Veg Dish Frzn Steamable Vegetables Vegetables 

Frozen Vegetable & Veg Dish Fz Box Vegetables—Value-Added Vegetables 

Frozen Vegetable & Veg Dish Frzn Corn On The Cob Vegetables 

Frozen Vegetable & Veg Dish Fz Bag Vegetables—Value-Added Vegetables 

Frozen Vegetable & Veg Dish Fz Box Vegetables—Plain Vegetables 

Herbs/Garlic Garlic Whole Cloves Vegetables 

Herbs/Garlic Sprouts Vegetables 

Mushrooms Mushrooms White Sliced Pkg Vegetables 

Mushrooms Mushrooms White Whole Pkg Vegetables 

Mushrooms Mushrooms Portabella Vegetables 

Mushrooms Mushrooms White Bulk Vegetables 

Onions Onions Yellow (Bulk & Bag) Vegetables 

Onions Onions Sweet (Bulk & Bag) Vegetables 

Onions Onions Red (Bulk & Bag) Vegetables 

Onions Onions White (Bulk & Bag) Vegetables 

Organics Fruit & Vegetables Organic Salad Mix Vegetables 

Organics Fruit & Vegetables Organic Value-Added Vegetables Vegetables 

Party Tray Deli Tray: Fruit And Vegetable Vegetables 

Pasta & Pizza Sauce Mainstream [Pasta & Pizza Sauce] Vegetables 

Pasta & Pizza Sauce Value [Pasta & Pizza Sauce] Vegetables 

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 Jalapen ̃ o Vegetables 

Peppers Peppers All Other Vegetables 

Potatoes Potatoes Russet (Bulk & Bag) Vegetables 

Potatoes Potatoes Sweet & Yams Vegetables 

Potatoes Potatoes Red (Bulk & Bag) Vegetables 

Potatoes Potatoes Gourmet Vegetables 

Potatoes Potatoes Gold (Bulk & Bag) Vegetables 

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 

212 

EAppendix B. Crosswalk of Top 1,000 Subcommodities to Summary 

Categories—Continued 

Commodity Subcommodity Summary Category 

Salad Mix Coleslaw Vegetables 

Salad Mix Salad Spinach Organic Vegetables 

Seasonal Pumpkins Vegetables 

Spices/Jarred Garlic Garlic Jar Vegetables 

Tomato Products—Shelf Stable Tomatoes Diced Vegetables 

Tomato Products—Shelf Stable Tomato Sauce Vegetables 

Tomato Products—Shelf Stable Tomato Paste Vegetables 

Tomato Products—Shelf Stable Tomato Stewed Vegetables 

Tomato Products—Shelf Stable Tomatoes/Whole Vegetables 

Tomato Products—Shelf Stable Tomato Crushed Vegetables 

Tomatoes Tomatoes Hothouse On The Vine Vegetables 

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 Foods Asian Vegetables Vegetables 

Traditional Mexican Foods Mexican Peppers Chilies Vegetables 

Value-Added Vegetables Vegetable Party Tray Vegetables 

Value-Added Vegetables Cut Vegetables All Other Vegetables 

Value-Added Vegetables Instore Cut Vegetables Vegetables 

Vegetables Cooking Bulk Celery Vegetables 

Vegetables Cooking Bulk Cabbage Vegetables 

Vegetables Cooking Bulk Asparagus Vegetables 

Vegetables Cooking Bulk Celery Organic Vegetables 

Vegetables Cooking Packaged Broccoli/Cauliflower Processed Vegetables 

Vegetables Cooking Packaged Vegetables Cooking Packaged Vegetables 

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 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS 

Subcategories 

Composite 

Subcategories Other Subcategories 

Aseptic Juice Kids Milk Drinks—Aseptic Dairy 

Bag Snacks Bagged Cheese Snacks Dairy 

Bulk Service Case Cheese Bulk Processed [Cheese] Dairy 

Bulk Service Case Cheese Bulk Semi-Hard [Cheese] Dairy 

Bulk Service Case Cheese Bulk Semi-Soft [Cheese] Dairy 

Bulk Service Case Cheese Cheese: Cheeseballs/Spreads Dairy 

Bulk Service Case Cheese Cheese: Specialty Bulk Dairy 

Bulk Service Case Cheese Cheese: Specialty Prepack Dairy 

Bulk Service Case Cheese Service Case Natural [Cheese] Dairy 

Bulk Service Case Cheese Service Case Natural Prepackage 

[Cheese] 

Dairy 

Bulk Service Case Cheese Service Case Processed Prepack 

[Cheese] 

Dairy 

Canned & Dry Milk Aseptic Milk & Milk Drinks Dairy 

Canned & Dry Milk Canned Milk Dairy 

Canned & Dry Milk Non Fat Dry Milk Dairy 

Cheese American Single Cheese Dairy 

Cheese Miscellaneous Cheese Dairy 

Cheese Natural Cheese Chunks Dairy 

Cheese Natural Cheese Random Wt Dairy 

Cheese Natural Cheese Slices Dairy 

Cheese Shredded Cheese Dairy 

Cheese String Cheese Dairy 

Crackers & Misc Baked Food Aerosol Cheese Dairy 

Cubes/Prepackage Cheese Cubes Cheese Dairy 

Cubes/Prepackage Cheese Prepackage Cheese Dairy 

Dry Cheese Grated Cheese Dairy 

Dry Cheese Loaf Cheese Dairy 

Dry Cheese Misc Dry Cheese Dairy 

Fluid Milk Products Buttermilk Dairy 

Fluid Milk Products Egg Nog/Boiled Custard Dairy 

Fluid Milk Products Flavored Milk Dairy 

Fluid Milk Products Fluid Milk/White Only Dairy 

Fluid Milk Products Half & Half Dairy 

Fluid Milk Products Organic Milk Dairy 

Fluid Milk Products Soy Milk Dairy 

Fluid Milk Products Specialty/Lactose Free Milk Dairy 

Milk By-Products Cottage Cheese Dairy 

Milk By-Products Ricotta Cheese Dairy 

Non-Dairy/Dairy Aseptic Aseptic Milk Dairy 

Non-Dairy/Dairy Aseptic Soy Beverage Dairy 

Pre-Slice Service Case Cheese Pre-Sliced Processed [Cheese] Dairy 

Pre-Slice Service Case Cheese Pre-Sliced Semi-Hard [Cheese] Dairy 

Pre-Slice Service Case Cheese Pre-Sliced Semi-Soft [Cheese] Dairy 

213 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Refrigerated Dairy Case Cheese Spreads Dairy 

Refrigerated Dairy Case Dairy Cheese Dairy 

Refrigerated Dairy Case Fluid Milk Dairy 

Refrigerated Dairy Case Kefir Dairy 

Refrigerated Dairy Case Yogurt Dairy 

Refrigerated Hispanic Grocery Hispanic Cheese Dairy 

Service Beverage Sv Bev: Milk/Milk Products Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Blue Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Cheddar Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Cheeseba Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Feta Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Fresh Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Gift Pac Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Goat Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Gouda & Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Hard Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Hispanic Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Mozzarel Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Semi-Sof Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Smallwar Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Soft Rip Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Spreads Dairy 

Specialty Bulk Cheese Specialty Bulk Cheese Swiss Dairy 

Specialty Cheese Pre Pack Ppk Cheese Shoppe Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Blue/Gorg Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Cheddar & 

C 

Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Feta Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Fresh Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Gift Pack Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Goat Milk Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Gouda & 

Eda 

Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Hard/Grat Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Hispanic Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Mozzarell Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Processed Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Semi Soft Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Soft & Ripe Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Spreads Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese Swiss Dairy 

Specialty Cheese Pre Pack Specialty Ppk Cheese: Smallwar Dairy 

Traditional Mexican Foods Mexican Con Queso Dairy 

Yogurt Yogurt/Adult Drinks Dairy 

Yogurt Yogurt/Adult Multi-Packs Dairy 

Yogurt Yogurt/Kids Dairy 

Yogurt Yogurt/Large Size (16oz Or Lar) Dairy 

Yogurt Yogurt/Pro Active Health Dairy 

Yogurt Yogurt/Specialty Greek Dairy 

Yogurt Yogurt/Ss Light Dairy 

Yogurt Yogurt/Ss Regular Dairy 

Apples Apples Braeburn (Bulk & Bag) Fruit 

Apples Apples Braeburn (Bulk & Bag) Org Fruit 

Apples Apples Fuji (Bulk & Bag) Fruit 

Apples Apples Fuji (Bulk & Bag) Organic Fruit 

Apples Apples Gala (Bulk & Bag) Fruit 

Apples Apples Gala (Bulk & Bag) Organic Fruit 

Apples Apples Gold Delicious (Bulk & 

Bag) 

Fruit 

Apples Apples Gold Delicious (Bulk & 

Bag) 

Fruit 

Apples Apples Granny Smith (Bulk & 

Bag) 

Fruit 

Apples Apples Granny Smith (Bulk & 

Bag) 

Fruit 

Apples Apples Honeycrisp Fruit 

Apples Apples Honeycrisp Organic Fruit 

Apples Apples Other (Bulk & Bag) Fruit 

Apples Apples Other (Bulk & Bag) Organic Fruit 

Apples Apples Red Delicious (Bulk & Bag) Fruit 

Apples Apples Red Delicious (Bulk & Bag) Fruit 

Apples Caramel/Candy Apples Fruit 

Apples Mixed Fruit Bags Fruit 

Authentic Hispanic Foods & Products Hispanic Juices Over 50% Juice Fruit 

Baking Needs Maraschino Cherries Fruit 

Bananas Bananas Fruit 

Bananas Bananas Organic Fruit 

Bananas Bananas: Variety Fruit 

Berries Berries Other Fruit 

Berries Berries Other Organic Fruit 

Berries Blackberries Fruit 

Berries Blackberries Organic Fruit 

Berries Blueberries Fruit 

Berries Blueberries Organic Fruit 

Berries Cranberries Fruit 

Berries Cranberries Organic Fruit 

Berries Raspberries Fruit 

Berries Raspberries Organic Fruit 

Berries Strawberries Fruit 

Berries Strawberries Organic Fruit 

Beverages Can/Btl Beverage Over 50% Juice Fruit 

Bulk Food Fruit Bulk Fruit 

Bulk Food Fruit W/Sweetener Fruit 

Can Fruit/Jar Applesauce Apple Sauce (Excludes Cup) Fruit 

Can Fruit/Jar Applesauce Apples/Crabapples Fruit 

Can Fruit/Jar Applesauce Berries/Cnd (Blu/Blk/Rasp) Fruit 

Can Fruit/Jar Applesauce Cherries (Except Maraschino) Fruit 

Can Fruit/Jar Applesauce Cranberry Sauce Fruit 

214 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Can Fruit/Jar Applesauce Fruit Cocktail/Fruit Salad Fruit 

Can Fruit/Jar Applesauce Mandarin Oranges/Citrus Sect Fruit 

Can Fruit/Jar Applesauce Misc. Cnd Fruit (Grapes/Figs) Fruit 

Can Fruit/Jar Applesauce Peaches Fruit 

Can Fruit/Jar Applesauce Pears Fruit 

Can Fruit/Jar Applesauce Pineapple Fruit 

Citrus Citrus—Other Fruit 

Citrus Citrus Other Organic Fruit 

Citrus Clementines Fruit 

Citrus Clementines Organic Fruit 

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 Organic Fruit 

Citrus Oranges Non Navel All Fruit 

Citrus Oranges Non Navel All Organic Fruit 

Citrus Tangerines & Tangelos Fruit 

Citrus Tangerines & Tangelos Organic Fruit 

Coffee Shop Sv Bev: Bev/Juice 50–100% Jce Fruit 

Coffee Shop Sv Bev: Bev/Juice 50–100% Jce Fruit 

Convenience/Snacking Convenience/Snacking Fruit Pro Fruit 

Convenience/Snacking Jarred Fruit Multi Serve Fruit 

Convenience/Snacking Jarred Fruit Single Serve Fruit 

Convenience/Snacking Squeeze Fruits Fruit 

Dried Fruit Dates Fruit 

Dried Fruit Dried Fruit—Other Fruit 

Dried Fruit Dried Fruit Cranberries Fruit 

Dried Fruit Dried Fruit Other Organic Fruit 

Dried Fruit Dried Fruit W/Sweetener Fruit 

Dried Fruit Dried Plums Fruit 

Dried Fruit Glace Fruit Fruit 

Dried Fruit Raisins Fruit 

Frozen Breakfast Juice Over 50% Juice Fruit 

Frozen Fruits Frozen Fruit Fruit 

Frozen Juice And Smoothies Frzn Conc Allieds Over 50% Juice Fruit 

Frozen Juice And Smoothies Frozen Oj & Oj Substitutes Fruit 

Fruit Snacks Fruit Snacks Fruit 

Gift & Fruit Baskets Fruit Baskets Fruit 

Gift & Fruit Baskets In Store Made Fruit Baskets Fruit 

Gift & Fruit Baskets Ready To Sell Fruit Baskets Fruit 

Grapes Grapes Black/Blue Fruit 

Grapes Grapes Black/Blue Organic Fruit 

Grapes Grapes Other Fruit 

Grapes Grapes Other Organic Fruit 

Grapes Grapes Red Fruit 

Grapes Grapes Red Globe Fruit 

Grapes Grapes Red Globe Organic Fruit 

Grapes Grapes Red Organic Fruit 

Grapes Grapes White Fruit 

Grapes Grapes White Organic Fruit 

Grapes Grapes Wine Fruit 

Juice Drinks—Carb Juice (Over 50% 

Juice) 

Fruit 

Juice Non-Carb Jce(Over 50% Juice) Fruit 

Juices Super Premium Cider Fruit 

Juices Super Premium Juice Single Blend Fruit 

Juices Super Premium Juices Organic (Over 50% Juice) Fruit 

Melons Cantaloupe Whole Fruit 

Melons Cantaloupe Whole Organic Fruit 

Melons Honeydew Whole Fruit 

Melons Honeydew Whole Organic Fruit 

Melons Melons Whole Other Fruit 

Melons Melons Whole Other Organic Fruit 

Melons Watermelon Personal Fruit 

Melons Watermelon Personal Organic Fruit 

Melons Watermelon Seedless Whole Fruit 

Melons Watermelon Seedless Whole Organic Fruit 

Melons Watermelon W/Seeds Whole Fruit 

Packaged Natural Snacks Dried Fruit Fruit 

Packaged Natural Snacks Dried Fruit W/Sweetener Fruit 

Peanut Butter/Jelly/Jams & Honey Apple Butter/Fruit Butter Fruit 

Pears Pears Anjou Fruit 

Pears Pears Anjou Organic Fruit 

Pears Pears Asian Fruit 

Pears Pears Asian Organic Fruit 

Pears Pears Bartlett Fruit 

Pears Pears Bartlett Organic Fruit 

Pears Pears Bosc Fruit 

Pears Pears Bosc Organic Fruit 

Pears Pears Other Fruit 

Pears Pears Other Organic Fruit 

Pears Pears Red Fruit 

Prepared/Pdgd Foods Apple Sauce/Pudding Fruit 

Prepared/Pdgd Foods Canned Fruit Fruit 

Processed Jarred Fruit Fruit 

Processed Juice Fruit 

Processed Squeeze Lemons/Limes Fruit 

Refrgratd Juices/Drinks Dairy Case 100% Pure Juice—Orange Fruit 

Refrgratd Juices/Drinks Dairy Case 100% Pure Juice Other Fruit 

Refrigerated Dairy Case Nut Refrig Juice Over 50% Fruit 

Rtd Tea/New Age Juice Juice (100% Juice) Fruit 

Rtd Tea/New Age Juice Juice (Over 50% Juice) Fruit 

Salad Bar Salad Bar Fresh Fruit Fruit 

Seasonal Fruit Baskets Fruit 

Service Beverage Sv Bev: Bev/Juice 50–100% Juice Fruit 

215 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Shelf Stable Juice Apple Juice & Cider (Over 50% 

Juice) 

Fruit 

Shelf Stable Juice Blended Juice & Combinations Fruit 

Shelf Stable Juice Cranapple/Cran Grape Juice Fruit 

Shelf Stable Juice Cranapple/Cran Grape Juice Fruit 

Shelf Stable Juice Cranberry Juice (Over 50% Juice) Fruit 

Shelf Stable Juice Grape Juice (Over 50% Juice) Fruit 

Shelf Stable Juice Grapefruit Juice (Over 50% Juice) Fruit 

Shelf Stable Juice Lemon Juice & Lime Juice (Over 

50% Juice) 

Fruit 

Shelf Stable Juice Nectars (Over 50% Juice) Fruit 

Shelf Stable Juice Orange Juice (Over 50% Juice) Fruit 

Shelf Stable Juice Other Citrus Juices (50% And 

Under Juice) 

Fruit 

Shelf Stable Juice Other Citrus Juices (Over 50% 

Juice) 

Fruit 

Shelf Stable Juice Pineapple Juice (Over 50% Juice) Fruit 

Shelf Stable Juice Prune Juice (Over 50% Juice) Fruit 

Single Serve Fruit/Applesauce Applesauce Cup Fruit 

Single Serve Fruit/Applesauce Applesauce Pouch Fruit 

Single Serve Fruit/Applesauce Fruit Cup Fruit 

Stone Fruit Apricots Fruit 

Stone Fruit Cherries Ranier Fruit 

Stone Fruit Cherries Red Fruit 

Stone Fruit Cherries Red Organic Fruit 

Stone Fruit Nectarines White Flesh Fruit 

Stone Fruit Nectarines Yellow Flesh Fruit 

Stone Fruit Nectarines Yellow Flesh Organic Fruit 

Stone Fruit Peaches White Flesh Fruit 

Stone Fruit Peaches White Flesh Organic Fruit 

Stone Fruit Peaches Yellow Flesh Fruit 

Stone Fruit Peaches Yellow Flesh Organic Fruit 

Stone Fruit Plums Fruit 

Stone Fruit Plums Organic Fruit 

Stone Fruit Pluots Fruit 

Stone Fruit Stone Fruit Other Organic Fruit 

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 & Peel/Cored Fruit 

Tropical Fruit Pineapple Whole & Peel/Cored Organic Fruit 

Tropical Fruit Pomegranates Fruit 

Tropical Fruit Pomegranates Organic Fruit 

Tropical Fruit Tropical Fruit—Other Fruit 

Tropical Fruit Tropical Fruit Other Organic Fruit 

Unknown Frozen Fruit Fruit 

Value-Added Fruit Cut Fruit All Other Prepack Fruit 

Value-Added Fruit Fruit Party Tray Prepack Fruit 

Value-Added Fruit Instore Cut Fruit Fruit 

Value-Added Fruit Melon Halves/Quarters Prepack Fruit 

Value-Added Fruit Melons Instore Cut Fruit 

Value-Added Fruit Pineapple Wedge/Sliced/Chunks Fruit 

Value-Added Fruit Value-Added Fruit Organic Fruit 

Authentic Hispanic Fds & Product Hispanic Tostados & Tortillas Grains 

Bag Snacks Bagged Popped Popcorn Grains 

Bag Snacks Bagged Popped Popcorn W/Sweetener Grains 

Bag Snacks Corn Chips Grains 

Bag Snacks Pretzel W/Sweetner Grains 

Bag Snacks Pretzels Grains 

Bag Snacks Tortilla/Nacho Chips Grains 

Bagels & Cream Cheese Refrigerated Bagels Grains 

Baked Breads Bagels Grains 

Baked Breads Diet/Light Bread Grains 

Baked Breads Dinner Rolls Grains 

Baked Breads English Muffins/Waffles Grains 

Baked Breads Fruit/Breakfast Bread Grains 

Baked Breads Hamburger Buns Grains 

Baked Breads Hot Dog Buns Grains 

Baked Breads Main Meal Bread Grains 

Baked Breads Mainstream Variety Breads Grains 

Baked Breads Mainstream White Bread Grains 

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: Rolls Grains 

Baking Mixes Biscuit Flour & Mixes Grains 

Baking Mixes Muffin & Corn Bread Mix Grains 

Baking Needs Corn Starch Grains 

Bread All Other Bread Grains 

Bread Bread—Ingredients Grains 

Bread Bread Snacks Grains 

Bread Bread: Diet/Organic Grains 

Bread Bread: Kosher Grains 

Bread Bread: Artisan Grains 

Bread Bread: Italian/French Grains 

Bread Bread: Pita/Pocket/Flatbrd Grains 

Bread Bread: Retail Seasonings Grains 

Bread Bread: Rye/Cocktail Grains 

Bread Bread: Sourdough Grains 

Bread Bread: Specialty Grains 

Bread Bread: Sweet/Breakfast Grains 

Bread Bread: Brand Grains 

Bread Bread: Tortillas/Wraps Grains 

Bread Bread: Wheat/Whl Grain Grains 

Bread Bread: White Loaf Grains 

Bread Gluten Free Grains 

216 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Bread Whole Grain Bread Grains 

Bulk Food Cereal Bulk Grains 

Cereal Bars Breakfast Bars/Tarts/Scones Grains 

Cereals Cereal—Cold Grains 

Cereals Cereal—Hot Grains 

Cereals Grains Grains 

Cereals Granola Grains 

Cnv Breakfast & Wholesome Snks Cereal Bars Grains 

Cnv Breakfast & Wholesome Snks Granola Bars Grains 

Cnv Breakfast & Wholesome Snks Toaster Pastries Grains 

Coffee Shop Sweet Goods & Rtl Coffee Shop: Bagged Snacks Grains 

Cold Cereal Adult Cereal Grains 

Cold Cereal All Family Cereal Grains 

Cold Cereal Kids Cereal Grains 

Cold Cereal Misc. Cereal Grains 

Cookie/Cracker Multi-Pks Multi-Pack Crackers Grains 

Cookies Graham Crackers Grains 

Crackers Crackers Grains 

Crackers & Misc Baked Food Butter Spray Cracker Grains 

Crackers & Misc Baked Food Cheese Crackers Grains 

Crackers & Misc Baked Food Saltine/Oyster Grains 

Crackers & Misc Baked Food Snack Crackers Grains 

Crackers & Misc Baked Food Specialty Crackers Grains 

Croutons/Bread Stick & Salad Toppings Bread Sticks Grains 

Croutons/Bread Stick & Salad Toppings Croutons Grains 

Croutons/Bread Stick & Salad Toppings Salad Toppers Grains 

Deli Specialties (Retail Pk) Dl Spec: Dry/Refrig Pastas Grains 

Dietary Aid Prdct/Med Liq Nutr Diabetic Dry Cereal Grains 

Dinner Mixes—Dry Pizza Mix Dry Grains 

Dry Bean Veg & Rice Misc Grain Mixes Grains 

Dry Bean Veg & Rice Noodle Side Dish Mixes Grains 

Dry Bean Veg & Rice Rice—Dry Bag And Box Grains 

Dry Bean Veg & Rice Rice—Instant & Microwave Grains 

Dry Bean Veg & Rice Rice Side Dish Mixes Dry Grains 

Dry Noodles & Pasta Long Cut Pasta Grains 

Dry Noodles & Pasta Noodles Dry Grains 

Dry Noodles & Pasta Short Cut Pasta Grains 

Dry Noodles & Pasta Specialty Pasta Grains 

Dry Sauce/Gravy/Potatoes/Stuffing Stuffing Mixes Grains 

Dry/Ramen Bouillon Ramen Noodles/Ramen Cups Grains 

Eggs/Muffins/Potatoes Refrigerated English Muffins Grains 

Flour & Meals Breadings/Coatings/Crumbs Grains 

Flour & Meals Cornmeal Grains 

Flour & Meals Flour: Misc/Specialty/Blend Et Grains 

Flour & Meals Flour: White & Self Rising Grains 

Frozen Bread And Desserts Allergen Free [Frozen Bread] Grains 

Frozen Bread And Desserts Breads Grains 

Frozen Bread And Desserts Muffins/Bagels Grains 

Frozen Bread And Desserts Rolls Grains 

Frozen Bread And Desserts Sprouted Breads Grains 

Frozen Bread/Dough Frzn Biscuits Grains 

Frozen Bread/Dough Frzn Bread Dough Grains 

Frozen Bread/Dough Frzn Breadsticks Grains 

Frozen Bread/Dough Frzn Dinner Rolls Grains 

Frozen Bread/Dough Frzn Garlic Bread Grains 

Frozen Bread/Dough Frzn Garlic Toast Grains 

Frozen Bread/Dough Frzn Sweet Rolls & Muffins Grains 

Frozen Breakfast Pancakes/French Toast Grains 

Frozen Breakfast Waffles Grains 

Frozen Breakfast Foods Frzn Bagels Grains 

Frozen Breakfast Foods Frzn Breakfast Pastry Grains 

Frozen Breakfast Foods Waffles/Pancakes/French Toast Grains 

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 And Products Kosher Matzas Grains 

Kosher Foods And Products Kosher Noodles And Rice Grains 

Meat—Shelf Stable Tamales Grains 

Molasses/Syrups/Pancake Mixes Pancake Mixes Grains 

Multicultural Products Rice Bulk/Bag Grains 

Non-Dairy/Dairy Aseptic Rice Beverage Grains 

Pies Pies: Sugar Free Grains 

Popcorn Caramel Coated Snacks Grains 

Popcorn Popcorn—Microwave Grains 

Popcorn Popcorn—Other Grains 

Prepared/Pdgd Foods Grains Grains 

Prepared/Pdgd Foods Pasta/Ramen Grains 

Refrgrated Dough Products Misc Refrig Dough Products Grains 

Refrgrated Dough Products Refrigerated Biscuits Grains 

Refrgrated Dough Products Refrigerated Breads Grains 

Refrgrated Dough Products Refrigerated Crescent Rolls Grains 

Refrgrated Dough Products Refrigerated Specialty Rolls Grains 

Refrigerated Hispanic Grocery Refrigerated Tortillas Grains 

Refrigerated Italian Refrigerated Pasta Grains 

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—Less Than 6 Grains 

Rolls Rolls: Biscuits/Eng Muffins Grains 

Rolls Rolls: Croissants/Breadsticks Grains 

Rolls Rolls: Diet/Organic Grains 

Rolls Rolls: Dinner Grains 

217 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Rolls Rolls: Kosher Grains 

Rolls Rolls: Sandwich Grains 

Salad & Dips Pasta/Grain Salads—Bulk Grains 

Salad & Dips Pasta/Grain Salads—Prepack Grains 

Salad Toppings Croutons Organic Grains 

Seafood—Salad/Dip/Sce/Cond Breading Grains 

Snack Popcorn Grains 

Snack Popcorn W/Sweetener Grains 

Snack Tortilla Chips Grains 

Snacks Snacks: Crackers/Cookies Grains 

Snacks Snacks: Bagel Chips Grains 

Snacks Snacks: Pita Chips Grains 

Snacks Snacks: Tortilla Chips Grains 

Specialty Cheese Pre Pack Gourmet Crackers Grains 

Ss/Vending—Cookie/Cracker Vending Size/Sngl Serve Cracke Grains 

Syrups Toppings & Cones Cones [Frozen Novelties] Grains 

Traditional Asian Foods Asian Noodles/Rice Grains 

Traditional Mexican Foods Mexican Soft Tortillas And Wra Grains 

Traditional Mexican Foods Mexican Taco/Tostado/Shells Grains 

Unknown Frozen Bread Grains 

Unknown Frozen Convenience/Pockets Grains 

Bacon Bacon—Natural/Organic Protein Foods 

Bacon Bacon—Other Protein Foods 

Bacon Bacon—Poultry Protein Foods 

Bacon Bacon—Pre-Cooked Protein Foods 

Bacon Bacon—Trad 16oz Or Less Protein Foods 

Bacon Bacon—Trad Center Cut Protein Foods 

Bacon Bacon—Trad Greater Than 16oz Protein Foods 

Baking Needs Baking Nuts Protein Foods 

Beef: Chuck/Shoulder Choice Beef Protein Foods 

Beef: Chuck/Shoulder Natural Beef Protein Foods 

Beef: Chuck/Shoulder Organic Beef Protein Foods 

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 Offal] Protein Foods 

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 Meats Brisket Protein Foods 

Beef: Thin Meats Corned Beef Protein Foods 

Beef: Thin Meats Cubed Meats [Beef] Protein Foods 

Beef: Thin Meats Flank [Beef] Protein Foods 

Beef: Thin Meats Lifter Meat [Beef] Protein Foods 

Beef: Thin Meats Skirt [Beef] Protein Foods 

Beef: Thin Meats Soup/Stew Protein Foods 

Breakfast Sausage Bkfst Sausage—Bkfast Side Di Protein Foods 

Breakfast Sausage Bkfst Sausage—Fresh Links Protein Foods 

Breakfast Sausage Bkfst Sausage—Fresh Patties Protein Foods 

Breakfast Sausage Bkfst Sausage—Fresh Rolls Protein Foods 

Breakfast Sausage Bkfst Sausage—Other Forms Protein Foods 

Breakfast Sausage Bkfst Sausage—Precooked Protein Foods 

Buffalo Chuck/Shoulder [Buffalo] Protein Foods 

Buffalo Grinds [Buffalo] Protein Foods 

Buffalo Loin [Buffalo] Protein Foods 

Buffalo Natural [Buffalo] Protein Foods 

Buffalo Rib [Buffalo] Protein Foods 

Buffalo Round/Leg [Buffalo] Protein Foods 

Buffalo Thin Meats [Buffalo] Protein Foods 

Bulk Food Nuts Bulk W/Sweetener Protein Foods 

Bulk Food Trail Mix/Nuts Bulk Protein Foods 

Can Beans Prepared Beans—Baked W/Pork Protein Foods 

Can Beans Variety Beans—Kidney/Pinto/E Protein Foods 

Can Seafood—Shelf Stable Anchovies Protein Foods 

Can Seafood—Shelf Stable Caviar Protein Foods 

Can Seafood—Shelf Stable Clam Juice Protein Foods 

Can Seafood—Shelf Stable Clams Protein Foods 

Can Seafood—Shelf Stable Crabmeat Protein Foods 

Can Seafood—Shelf Stable Kipper Snack Protein Foods 

Can Seafood—Shelf Stable Mackerel Protein Foods 

Can Seafood—Shelf Stable Misc. Cnd Seafoods (Crab/Etc.) Protein Foods 

Can Seafood—Shelf Stable Oysters Protein Foods 

Can Seafood—Shelf Stable Salmon Protein Foods 

Can Seafood—Shelf Stable Sardines Protein Foods 

Can Seafood—Shelf Stable Shrimp Protein Foods 

Can Seafood—Shelf Stable Tuna Protein Foods 

Chicken & Poultry Chix/Poultry Ingredients Protein Foods 

Chicken & Poultry Chix: Baked 8pc Cut Up (Cold) Protein Foods 

Chicken & Poultry Chix: Chicken Dinners/Snacks C Protein Foods 

Chicken & Poultry Chix: Chicken Dinners/Snacks H Protein Foods 

Chicken & Poultry Chix: Kosher (Cold) Protein Foods 

Chicken & Poultry Chix: Rotisserie Cold Protein Foods 

Chicken & Poultry Chix: Frd 8pc/Cut Up (Cold) Protein Foods 

Chicken & Poultry Chix: Value-Added (Cold) Protein Foods 

Chicken Fresh Chicken Breast Boneless Protein Foods 

Chicken Fresh Chicken Drums Protein Foods 

Chicken Fresh Chicken Legs/Quarters Protein Foods 

Chicken Fresh Chicken Thighs Protein Foods 

Chicken Fresh Chicken Wings Protein Foods 

Chicken Fresh Mixed Packs Protein Foods 

218 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Chicken Fresh Whole Chicken (Roasters/Fryer) Protein Foods 

Chicken Frozen Chicken—Frz Iqf—Raw Protein Foods 

Chicken Frozen Frzn Chicken—Drk Meat Protein Foods 

Chicken Frozen Frzn Chicken—Wht Meat Protein Foods 

Chicken Frozen Frzn Chicken—Wings Protein Foods 

Chicken Frozen Whole/Cutup [Chicken] Protein Foods 

Chicken Frozen (Rw) Chicken Breast Bone In Protein Foods 

Chicken Frozen (Rw) Chicken Breast Boneless Protein Foods 

Chicken Frozen (Rw) Chicken Drums Protein Foods 

Chicken Frozen (Rw) Chicken Legs/Quarters Protein Foods 

Chicken Frozen (Rw) Chicken Thighs Protein Foods 

Chicken Frozen (Rw) Chicken Wings Protein Foods 

Chicken Frozen (Rw) Whole Chicken (Roasters/Fryer) Protein Foods 

Chicken Grinds Ground Chicken Protein Foods 

Chicken Offal External [Chicken Offal] Protein Foods 

Chicken Offal Internal [Chicken Offal] Protein Foods 

Chicken Organic Chicken Breast Bone In Protein Foods 

Chicken Smoked Chicken Breast Bone In Protein Foods 

Chicken Specialty/Natural Chicken Breast Bone In Protein Foods 

Chicken Specialty/Natural Chicken Breast Boneless Protein Foods 

Chicken Specialty/Natural Chicken Drums Protein Foods 

Chicken Specialty/Natural Chicken Legs/Quarters Protein Foods 

Chicken Specialty/Natural Chicken Thighs Protein Foods 

Chicken Specialty/Natural Chicken Wings Protein Foods 

Chicken Specialty/Natural Mixed Packs [Chicken] Protein Foods 

Chicken Specialty/Natural Whole Chicken (Roasters/Fryer) Protein Foods 

Condiments Nut Butters/Peanut Butter Protein Foods 

Deli Meat: Bulk Bologna/Loaves/Franks Protein Foods 

Deli Meat: Bulk Meat Bulk: Specialty Dry Meats Protein Foods 

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/Prslc Protein Foods 

Deli Meat: Bulk Meat: Pates/Mousse Protein Foods 

Deli Meat: Bulk Meat: Saus Dry Ppk/Prslc Protein Foods 

Deli Meat: Bulk Meat: Turkey Bulk Protein Foods 

Deli Meat: Bulk Meat:Ham Bulk Protein Foods 

Deli Meat: Bulk Meat: Lnchmt Ppk/Prslc Protein Foods 

Deli Meat: Other Deli Meat: Bacon Protein Foods 

Deli Meat: Other Deli Meat: Kosher Protein Foods 

Deli Meat: Other Deli Meat: Pates/Mousse Protein Foods 

Deli Meat: Other Deli Meat: Shippers/Gift Packs Protein Foods 

Deli Meat: Presliced Deli Meat: Beef Protein Foods 

Deli Meat: Presliced Deli Meat: Bologna/Loaves/Fran Protein Foods 

Deli Meat: Presliced Deli Meat: Chicken Protein Foods 

Deli Meat: Presliced Deli Meat: Ham Protein Foods 

Deli Meat: Presliced Deli Meat: Semi-Dry Sausage Protein Foods 

Deli Meat: Presliced Deli Meat: Specialty Dry Meats Protein Foods 

Deli Meat: Presliced Deli Meat: Turkey Protein Foods 

Dinner Sausage Dnr Sausage—Beef Rope Ckd/Sm Protein Foods 

Dinner Sausage Dnr Sausage—Cocktails Protein Foods 

Dinner Sausage Dnr Sausage—Fresh Poultry Protein Foods 

Dinner Sausage Dnr Sausage—Links Beef Ckd Protein Foods 

Dinner Sausage Dnr Sausage—Links Fresh Protein Foods 

Dinner Sausage Dnr Sausage—Links Pork Ckd Protein Foods 

Dinner Sausage Dnr Sausage—Links Poultry Ck Protein Foods 

Dinner Sausage Dnr Sausage—Natural/Organic Protein Foods 

Dinner Sausage Dnr Sausage—Other Forms Protein Foods 

Dinner Sausage Dnr Sausage—Pork Rope Ckd/Sm Protein Foods 

Dinner Sausage Dnr Sausage—Poultry Rope Ckd Protein Foods 

Dinner Sausage Dnr Saus-Rope/Link-Smkd/Preckd Protein Foods 

Eggs/Muffins/Potatoes Eggs—Jumbo Protein Foods 

Eggs/Muffins/Potatoes Eggs—Large Protein Foods 

Eggs/Muffins/Potatoes Eggs—Medium Protein Foods 

Eggs/Muffins/Potatoes Eggs—Small Protein Foods 

Eggs/Muffins/Potatoes Eggs—X-Large Protein Foods 

Eggs/Muffins/Potatoes Eggs Substitute Protein Foods 

Eggs/Muffins/Potatoes Specialty Eggs Protein Foods 

Exotic Goat Protein Food 

Exotic Rabbit Protein Foods 

Frozen Breakfast Foods Frzn Breakfast Sausage Protein Foods 

Frozen Breakfast Foods Frzn Egg Substitutes Protein Foods 

Frozen Entre ́es Meat Protein Protein Foods 

Frozen Meat Frozen Meat Protein Foods 

Frozen Meat Alternatives Meat Protein Foods 

Frozen Meat Alternatives Soy/Tofu Protein Foods 

Frzn Multi Serve Frzn Burgers Protein Foods 

Frzn Multi Serve Fz Bbq Protein Foods 

Frzn Multi Serve Fz Meatballs Protein Foods 

Frzn Prepared Chicken Bone-In Wings Protein Foods 

Frzn Prepared Chicken Boneless Snack/18oz And Larger Protein Foods 

Frzn Prepared Chicken Boneless Snack/Value/Small Protein Foods 

Frzn Prepared Chicken Value Forms/18oz And Larger 

[Chicken] 

Protein Foods 

Frzn Prepared Chicken Whole Muscle Breaded/18oz And Protein Foods 

Frzn Prepared Chicken Whole Muscle Unbreaded Protein Foods 

Frzn Seafood Frz Coated Fish Fillets Protein Foods 

Frzn Seafood Frz Fishsticks/Tenders/Nuggets Protein Foods 

Frzn Seafood Frz Non-Coated Fish Fillets Protein Foods 

Frzn Seafood Frz Seafood Entre ́es Protein Foods 

Frzn Seafood Frzn Misc Seafood Protein Foods 

Hot Dogs Hot Dogs—Base Beef Protein Foods 

Hot Dogs Hot Dogs—Base Meat Protein Foods 

Hot Dogs Hot Dogs—Base Poultry Protein Foods 

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 

219 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Kosher Veal Protein Foods 

Kosher Foods And Products Kosher Seafood Protein Foods 

Lamb Chuck/Shoulder [Lamb] Protein Foods 

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/Liver/Loave Protein Foods 

Lunchmeat Lunchmeat—Chip Meat Protein Foods 

Lunchmeat Lunchmeat—Chop/Form Pltry & 

Ha 

Protein Foods 

Lunchmeat Lunchmeat—Other Protein Foods 

Lunchmeat Lunchmeat—Peggable Deli Fres Protein Foods 

Lunchmeat Lunchmeat—Variety Pack Protein Foods 

Lunchmeat Lunchmeat—Whole Muscle Pltry Protein Foods 

Lunchmeat Lunchmeat—Rw-All Protein Foods 

Lunchmeat Lunchmeat—Bologna/Sausage Protein Foods 

Lunchmeat Lunchmeat—Deli Fresh Protein Foods 

Lunchmeat Lunchmeat—Natural/Organic Protein Foods 

Meat—Shelf Stable Beef Stew Protein Foods 

Meat—Shelf Stable Beef/Pork—Dried Sliced W/Gra Protein Foods 

Meat—Shelf Stable Chicken & Dumplings Protein Foods 

Meat—Shelf Stable Chili: Canned Protein Foods 

Meat—Shelf Stable Chunk Meats—Chix/Ham/Etc. Protein Foods 

Meat—Shelf Stable Corn Beef Protein Foods 

Meat—Shelf Stable Hash: Canned Protein Foods 

Meat—Shelf Stable Hot Dog Chili Sauce Protein Foods 

Meat—Shelf Stable Luncheon Meat (Spam) Protein Foods 

Meat—Shelf Stable Misc Cnd Meats Protein Foods 

Meat—Shelf Stable Potted Meats And Spreads Protein Foods 

Meat—Shelf Stable Sandwich Sauce (Manwich) Protein Foods 

Meat—Shelf Stable Vienna Sausage Protein Foods 

Meat Frozen Frzn Meat—Beef Protein Foods 

Meat Frozen Frzn Meat—Breakfast Sausage Protein Foods 

Meat Frozen Frzn Meat—Exotic Protein Foods 

Meat Frozen Frzn Meat—Natural/Organic Protein Foods 

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/Tenders Protein Foods 

Meat Snacks Meat Sticks/Bites Protein Foods 

Nat Foods—Refrigerated Meat Ntrn Refrig Meat: Breakfast Me Protein Foods 

Nat Foods—Refrigerated Meat Ntrn Refrig Meat: Hot Dogs/Sau Protein Foods 

Nat Foods—Refrigerated Meat Ntrn Refrig Meat: Lunchmeat Protein Foods 

Non-Dairy/Dairy Aseptic Nut Milk Protein Foods 

Nuts Almonds Protein Foods 

Nuts Almonds Shelled Protein Foods 

Nuts Almonds W/Sweetener Protein Foods 

Nuts Cashews Protein Foods 

Nuts Cashews W/Sweetener Protein Foods 

Nuts Dry Roast Peanuts Protein Foods 

Nuts Dry Roast Peanuts W/Sweetener Protein Foods 

Nuts Misc Snack Nuts Protein Foods 

Nuts Misc Snacks Nuts W/Sweetener Protein Foods 

Nuts Mixed Nuts Protein Foods 

Nuts Mixed Nuts W/Sweetener Protein Foods 

Nuts Nuts Inshell Protein Foods 

Nuts Nuts Other Protein Foods 

Nuts Nuts Other Organic Protein Foods 

Nuts Nuts Sugar Coated All Protein Foods 

Nuts Oil Roast Peanuts Protein Foods 

Nuts Oil Roast Peanuts W/Sweetener Protein Foods 

Nuts Peanuts All Protein Foods 

Nuts Pecans Shelled Protein Foods 

Nuts Pecans W/Sweetener Protein Foods 

Nuts Pistachios Protein Foods 

Nuts Sunflower/Other Seeds Protein Foods 

Nuts Sunflower/Other Seeds W/Sweete Protein Foods 

Nuts Trail Mix Protein Foods 

Nuts Walnuts Shelled Protein Foods 

Packaged Natural Snacks Nuts Protein Foods 

Packaged Natural Snacks Nuts W/Sweetener Protein Foods 

Peanut Butter/Jelly/Jams & Honey Peanut Butter Protein Foods 

Pkgd Meat Corp Use Only Pkgd Meat Corp Use Only Protein Foods 

Pork Bone In Loin/Rib Dry [Pork Bone In Loin/Rib] Protein Foods 

Pork Boneless Loin/Rib Enhanced [Pork Boneless Loin/Rib] Protein Foods 

Pork Boneless Loin/Rib Natural [Pork Boneless Loin/Rib] Protein Foods 

Pork Grinds Ground Pork Protein Foods 

Pork Offal External Fresh [Pork Offal] Protein Foods 

Pork Offal Internal Fresh [Pork Offal] Protein Foods 

Pork Shoulder Butts [Pork Shoulder] Protein Foods 

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/Fajitas [Pork] Protein Foods 

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 Foods Meat—Can/Pouch Protein Foods 

Processed Beans Dried Protein Foods 

Random Weight Meat Products Lunch Meats Protein Foods 

Refrigerated Dairy Case Eggs Protein Foods 

Refrigerated Vegetarian Non-Dairy Cheese Protein Foods 

Refrigerated Vegetarian Tofu Protein Foods 

Refrigerated Vegetarian Vegetarian Meats Protein Foods 

220 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Restricted Diet Pnut Btr/Jelly Protein Foods 

Salad & Dips Protein Salads—Bulk Protein Foods 

Salad & Dips Protein Salads—Prepack Protein Foods 

Seafood—Catfish Catfish—Fillet Protein Foods 

Seafood—Catfish Catfish—Nuggets Protein Foods 

Seafood—Catfish Catfish—Other Form Protein Foods 

Seafood—Catfish Catfish—Whole Protein Foods 

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 Snapper Protein Foods 

Seafood—Exotic Exotic—Shark Protein Foods 

Seafood—Exotic Exotic—Swordfish Protein Foods 

Seafood—Exotic Exotic—Tuna Protein Foods 

Seafood—Finfish Other Finfish—Halibut Protein Foods 

Seafood—Finfish Other Finfish—Other Protein Foods 

Seafood—Finfish Other Finfish—Other Protein Foods 

Seafood—Finfish Other Finfish—Rockfish Protein Foods 

Seafood—Finfish Other Finfish—Sole/Flounder Protein Foods 

Seafood—Finfish Other Finfish—Sole/Flounder Protein Foods 

Seafood—Imitation Seafood Imitation Crab Protein Foods 

Seafood—Imitation Seafood Imitation Other Protein Foods 

Seafood—Imitation Seafood Imitation Shrimp Protein Foods 

Seafood—Lobster Lobster—Live Protein Foods 

Seafood—Lobster Lobster—Meat Protein Foods 

Seafood—Lobster Lobster—Other Form Protein Foods 

Seafood—Lobster Lobster—Tails Protein Foods 

Seafood—Oyster Oyster—Bulk Protein Foods 

Seafood—Oyster Oyster—Cup (Packaged) Protein Foods 

Seafood—Oyster Oyster—Cup (Packaged) Protein Foods 

Seafood—Party Trays Party Tray—Shrimp Protein Foods 

Seafood—Salmon-Farm Raised Salmon Fr—Altantic Protein Foods 

Seafood—Salmon-Farm Raised Salmon Fr—Other Form Protein Foods 

Seafood—Salmon-Farm Raised Salmon Fr—Atlantic Protein Foods 

Seafood—Salmon-Farm Raised Salmon Fr—Coho Protein Foods 

Seafood—Salmon-Farm Raised Salmon Fr—King Protein Foods 

Seafood—Salmon-Farm Raised Seafood—Fre-Catfish Protein Foods 

Seafood—Salmon-Farm Raised Seafood—Fre-Misc Protein Foods 

Seafood—Salmon-Farm Raised Seafood—Fre-Raw Finfish—Other Protein Foods 

Seafood—Salmon-Wild Caught Salmon Wc—Other Form Protein Foods 

Seafood—Salmon-Wild Caught Salmon Wc—Coho Protein Foods 

Seafood—Salmon-Wild Caught Salmon Wc—King Protein Foods 

Seafood—Salmon-Wild Caught Salmon Wc—Pink Protein Foods 

Seafood—Salmon-Wild Caught Salmon Wc—Silverbrite Protein Foods 

Seafood—Salmon-Wild Caught Salmon Wc—Silverbrite Protein Foods 

Seafood—Salmon-Wild Caught Salmon Wc—Sockeye Protein Foods 

Seafood—Scallops Scallops—Bay Protein Foods 

Seafood—Scallops Scallops—Sea Protein Foods 

Seafood—Shellfish Other Shellfish—Clams Protein Foods 

Seafood—Shellfish Other Shellfish—Clams Protein Foods 

Seafood—Shellfish Other Shellfish—Mussles Protein Foods 

Seafood—Shellfish Other Shellfish—Other Protein Foods 

Seafood—Shrimp Shrimp—Cooked Protein Foods 

Seafood—Shrimp Shrimp—Natural/Organic Protein Foods 

Seafood—Shrimp Shrimp—Raw Protein Foods 

Seafood—Smoked Seafood Smoked Other Protein Foods 

Seafood—Smoked Seafood Smoked Salmon Protein Foods 

Seafood—Tilapia Tilapia—Fillet Protein Foods 

Seafood—Tilapia Tilapia—Other Form Protein Foods 

Seafood—Tilapia Tilapia—Whole Protein Foods 

Seafood—Trout Steelhead Fr Protein Foods 

Seafood—Trout Trout—Fillet Protein Foods 

Seafood—Trout Trout—Whole Protein Foods 

Seafood—Value-Added Seafood Value-Added Catfish Protein Foods 

Seafood—Value-Added Seafood Value-Added In-Store Cooked Ho Protein Foods 

Seafood—Value-Added Seafood Value-Added Breaded Shrimp Protein Foods 

Seafood—Value-Added Seafood Value-Added Crab Protein Foods 

Seafood—Value-Added Seafood Value-Added Finfish Protein Foods 

Seafood—Value-Added Seafood Value-Added In-Store Cooked Co Protein Foods 

Seafood—Value-Added Seafood Value-Added Kabobs Protein Foods 

Seafood—Value-Added Seafood Value-Added Other Protein Foods 

Seafood—Value-Added Seafood Value-Added Salmon Protein Foods 

Seafood—Value-Added Seafood Value-Added Shrimp Protein Foods 

Seafood—Value-Added Seafood Value-Added Tilapia Protein Foods 

Seafood—Salad/Dips/Sce/Cond Herring Protein Foods 

Service Case Meat Cooked Protein Foods 

Service Case Meat Ingredients Protein Foods 

Service Case Meat Kabobs Beef Protein Foods 

Service Case Meat Kabobs Pork Protein Foods 

Service Case Meat Kabobs Poultry Protein Foods 

Service Case Meat Marinated Beef Protein Foods 

Service Case Meat Marinated Pork Protein Foods 

Service Case Meat Marinated Poultry Protein Foods 

Service Case Meat Seasoned Protein Foods 

Service Case Meat Seasoned Beef Protein Foods 

Service Case Meat Seasoned Pork Protein Foods 

Service Case Meat Seasoned Poultry Protein Foods 

Service Case Meat Stuffed/Mixed Beef Protein Foods 

Service Case Meat Stuffed/Mixed Pork Protein Foods 

Service Case Meat Stuffed/Mixed Poultry Protein Foods 

Smoked Hams Hams—Canned Protein Foods 

Smoked Hams Hams—Dry Cured/Country Protein Foods 

Smoked Hams Hams—Half/Port Bone-In Protein Foods 

Smoked Hams Hams—Half/Port Boneless Protein Foods 

Smoked Hams Hams—Spiral Protein Foods 

221 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Smoked Hams Hams—Whole Bone-In Protein Foods 

Smoked Hams Hams—Whole Boneless Protein Foods 

Smoked Pork Bacon—Belly/Jowl Protein Foods 

Smoked Pork Ham Steaks/Cubes/Slices Protein Foods 

Smoked Pork Smoked Chops Bone-In [Pork] Protein Foods 

Smoked Pork Smoked Chops Boneless [Pork] Protein Foods 

Smoked Pork Smoked Offal [Pork] Protein Foods 

Smoked Pork Smoked Picnics [Pork] Protein Foods 

Snack Meat Grnd/Patty—Chuck Protein Foods 

Snack Meat Snack Meat—Other Protein Foods 

Snack Meat Snack Meat—Pepperoni Protein Foods 

Snack Meat Snack Meat—Salami/Smr Sausag Protein Foods 

Snacks Snacks: Deli Nuts Protein Foods 

Ss/Vending—Salty Snacks Tube Nuts Protein Foods 

Ss/Vending—Salty Snacks Tube Nuts W/Sweetener Protein Foods 

Turkey Fresh Turkey Legs Protein Foods 

Turkey Fresh Whole Hen (Under 16lbs) [Turkey] Protein Foods 

Turkey Fresh Whole Tom (Over 16lbs) [Turkey] Protein Foods 

Turkey Frozen Turkey Breast Bone In Protein Foods 

Turkey Frozen Turkey Breast Boneless Protein Foods 

Turkey Frozen Turkey Halves/Quarters Protein Foods 

Turkey Frozen Turkey Thighs Protein Foods 

Turkey Frozen Whole Hens (Under 16lbs) [Turkey] Protein Foods 

Turkey Frozen Whole Toms (Over 16lbs) [Turkey] Protein Foods 

Turkey Grinds Ground Turkey Protein Foods 

Turkey Offal External [Turkey Offal] Protein Foods 

Turkey Offal Internal [Turkey Offal] Protein Foods 

Turkey Organic Whole Hens (Under 15lbs) [Turkey] Protein Foods 

Turkey Organic Whole Toms (Over 15lbs) [Turkey] Protein Foods 

Turkey Smoked Turkey Drums Protein Foods 

Turkey Smoked Turkey Wings Protein Foods 

Turkey Specialty Natural Whole Hens (Under 15lbs) [Turkey] Protein Foods 

Turkey Specialty Natural Whole Toms (Over 15lbs) [Turkey] Protein Foods 

Unknown Beef—Boneless-Choice Protein Foods 

Unknown Beef—Grinds Protein Foods 

Unknown Breast—Bone-In (Frz) Protein Foods 

Unknown Frozen Burgers Protein Foods 

Unknown Frozen Meat Protein Foods 

Unknown Frozen Meat (Vegetarian) Protein Foods 

Unknown Ham—Bone-In Whole Protein Foods 

Unknown Ham—Boneless Half/Port Protein Foods 

Unknown Marinated Protein Foods 

Unknown Meal Sol—Precooked Meats Protein Foods 

Unknown Meal Sol—Raw Frthr Preprd Mt Protein Foods 

Unknown Meat Frz—Misc Protein Foods 

Unknown Seafood—Frz—Rw—All Protein Foods 

Unknown Smkd Ham Country—All Protein Foods 

Unknown Turkey—Grinds Protein Foods 

Unknown Turkey—Other Parts/Pieces—Fre Protein Foods 

Unknown Whole—Tom (16 Lbs & Over Frz Protein Foods 

Veal Whole/Half [Veal] Protein Foods 

Nuts Pecans Protein Foods 

Authentic Hispanic Fds & Product Authentic Peppers Vegetables 

Authentic Hispanic Fds & Product Authentic Sauces/Salsa/Picante Vegetables 

Authentic Hispanic Fds & Product Authentic Vegetables And Foods Vegetables 

Authentic Italian Foods Italian Vegetables Vegetables 

Broccoli/Cauliflower Brocco—Flower Vegetables 

Broccoli/Cauliflower Broccoli Whole & Crowns Organi Vegetables 

Broccoli/Cauliflower Broccoli Whole&Crowns Vegetables 

Broccoli/Cauliflower Cauliflower Whole Vegetables 

Broccoli/Cauliflower Cauliflower Whole Organic Vegetables 

Can Vegetables—Shelf Stable Artichokes Vegetables 

Can Vegetables—Shelf Stable Beans/Wax/Shellies Vegetables 

Can Vegetables—Shelf Stable Beets Vegetables 

Can Vegetables—Shelf Stable Carrots Vegetables 

Can Vegetables—Shelf Stable Corn Vegetables 

Can Vegetables—Shelf Stable Fried Onions Vegetables 

Can Vegetables—Shelf Stable Green Beans: Fs/Whl/Cut Vegetables 

Can Vegetables—Shelf Stable Hominy Vegetables 

Can Vegetables—Shelf Stable Kraut & Cabbage Vegetables 

Can Vegetables—Shelf Stable Lima Beans Vegetables 

Can Vegetables—Shelf Stable Miscellaneous Vegetables Vegetables 

Can Vegetables—Shelf Stable Mixed Vegetables Vegetables 

Can Vegetables—Shelf Stable Mushrooms Cnd & Glass Vegetables 

Can Vegetables—Shelf Stable Peas & Onions/Peas & Carrot Vegetables 

Can Vegetables—Shelf Stable Peas Fresh Pack/Crowder Vegetables 

Can Vegetables—Shelf Stable Peas/Green Vegetables 

Can Vegetables—Shelf Stable Pimentos Vegetables 

Can Vegetables—Shelf Stable Salads Cnd (Bean/Potato) Vegetables 

Can Vegetables—Shelf Stable Spinach & Greens Vegetables 

Can Vegetables—Shelf Stable Squash Vegetables 

Can Vegetables—Shelf Stable Sweet Potatoes Vegetables 

Can Vegetables—Shelf Stable White Potatoes Vegetables 

Carrots Carrots—Bulk Vegetables 

Carrots Carrots Bagged Vegetables 

Carrots Carrots Bagged Organic Vegetables 

Carrots Carrots Bulk Organic Vegetables 

Carrots Carrots Mini Peeled Vegetables 

Carrots Carrots Mini Peeled Organic Vegetables 

Condiments Salsa/Dips Vegetables 

Convenience/Snacking Convenience/Snacking Organic Vegetables 

Convenience/Snacking Convenience/Snacking Vegetable Vegetables 

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/Stuffing Potatoes: Dry Vegetables 

222 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Frozen Potatoes Frzn Baked/Stuffed/Mashed & 

Spec 

Vegetables 

Frozen Potatoes Frzn French Fries Vegetables 

Frozen Potatoes Frzn Hashbrown Potatoes Vegetables 

Frozen Potatoes Frzn Onion Rings Vegetables 

Frozen Potatoes Frzn Tater Tots/Other Extruded Vegetables 

Frozen Vegetable & Veg Dish Frzn Breaded Vegetables Vegetables 

Frozen Vegetable & Veg Dish Frzn Corn On The Cob Vegetables 

Frozen Vegetable & Veg Dish Frzn Organic Vegetables Vegetables 

Frozen Vegetable & Veg Dish Frzn Steamable Vegetables Vegetables 

Frozen Vegetable & Veg Dish Fz Bag Vegetables—Plain Vegetables 

Frozen Vegetable & Veg Dish Fz Bag Vegetables—Value-Added Vegetables 

Frozen Vegetable & Veg Dish Fz Box Vegetables—Plain Vegetables 

Frozen Vegetable & Veg Dish Fz Box Vegetables—Value-Added Vegetables 

Frozen Vegetables And Potatoes Bag Vegetables Vegetables 

Frozen Vegetables And Potatoes Box Vegetables Vegetables 

Frozen Vegetables And Potatoes Edamame Vegetables 

Frozen Vegetables And Potatoes Potatoes Vegetables 

Fruit & Veg Plants (Outdoor) Herbs (Outdoor) Vegetables 

Fruit & Veg Plants (Outdoor) Vegetable Vegetables 

Herbs/Garlic Garlic Whole Cloves Vegetables 

Herbs/Garlic Garlic Whole Cloves Organic Vegetables 

Herbs/Garlic Herbs Basil Vegetables 

Herbs/Garlic Herbs Basil Organic Vegetables 

Herbs/Garlic Herbs Cilanto Vegetables 

Herbs/Garlic Herbs Cilantro Organic Vegetables 

Herbs/Garlic Herbs Fresh Other Vegetables 

Herbs/Garlic Herbs Fresh Other Organic Vegetables 

Herbs/Garlic Herbs Parsley Vegetables 

Herbs/Garlic Herbs Parsley Organic Vegetables 

Herbs/Garlic Sprouts Vegetables 

Kosher Foods And Products Kosher Potato Vegetable Vegetables 

Mushrooms Mushrooms Dried Vegetables 

Mushrooms Mushrooms Other Vegetables 

Mushrooms Mushrooms Others Organic Vegetables 

Mushrooms Mushrooms Portabella Vegetables 

Mushrooms Mushrooms White Bulk Vegetables 

Mushrooms Mushrooms White Sliced Pkg Vegetables 

Mushrooms Mushrooms White Whole Pkg Vegetables 

Mushrooms Mushrooms White Whole Pkg Organic Vegetables 

Onions Onions Gourmet Vegetables 

Onions Onions Other Vegetables 

Onions Onions Other Organic Vegetables 

Onions Onions Red (Bulk & Bag) Vegetables 

Onions Onions Sweet (Bulk & Bag) Vegetables 

Onions Onions White (Bulk & Bag) Vegetables 

Onions Onions Yellow (Bulk & Bag) Vegetables 

Organics Fruit & Vegetables Organic Broccoli/Cauliflower Vegetables 

Organics Fruit & Vegetables Organic Fruit/Veg Instore Proc Vegetables 

Organics Fruit & Vegetables Organic Other Vegetables 

Organics Fruit & Vegetables Organic Processed Vegetables 

Organics Fruit & Vegetables Organic Salad Mix Vegetables 

Organics Fruit & Vegetables Organic Value-Added Vegetables Vegetables 

Organics Fruit & Vegetables Organic Vegetables Salad Vegetables 

Pasta & Pizza Sauce Mainstream [Pasta & Pizza Sauce] Vegetables 

Pasta & Pizza Sauce Pizza Sauce Vegetables 

Pasta & Pizza Sauce Specialty Italian Sauce Vegetables 

Pasta & Pizza Sauce Value [Pasta & Pizza Sauce] Vegetables 

Peppers Peppers All Other Vegetables 

Peppers Peppers All Others Organic Vegetables 

Peppers Peppers Green Bell Vegetables 

Peppers Peppers Green Bell Organic Vegetables 

Peppers Peppers Jalapen ̃ o Vegetables 

Peppers Peppers Mini Sweet Packaged Vegetables 

Peppers Peppers Other Bell Vegetables 

Peppers Peppers Other Bell Organic Vegetables 

Peppers Peppers Red Bell Vegetables 

Peppers Peppers Red Bell Organic Vegetables 

Peppers Peppers Serrano Vegetables 

Peppers Peppers Yellow Bell Vegetables 

Peppers Peppers Yellow Bell Organic Vegetables 

Potatoes Potatoes Gold (Bulk & Bag) Vegetables 

Potatoes Potatoes Gourmet Vegetables 

Potatoes Potatoes Other Vegetables 

Potatoes Potatoes Other Organic Vegetables 

Potatoes Potatoes Red (Bulk & Bag) Vegetables 

Potatoes Potatoes Russet (Bulk & Bag) Vegetables 

Potatoes Potatoes Sweet&Yams Vegetables 

Potatoes Potatoes White (Bulk & Bag) Vegetables 

Prepared/Pdgd Foods Vegetables/Dry Beans Vegetables 

Processed Jarred Vegetables Vegetables 

Refrigerated Italian Refrigerated Pasta Sauce Vegetables 

Salad & Dips Sal: Hommus Vegetables 

Salad & Dips Sal: Salsa/Dips Bulk Vegetables 

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—Bulk Vegetables 

Salad & Dips Vegetable Salads—Prepack Vegetables 

Salad Bar Processed Salad Vegetables 

Salad Mix Blends [Salad Mix] Vegetables 

Salad Mix Coleslaw Vegetables 

Salad Mix Garden Plus [Salad Mix] Vegetables 

Salad Mix Kits [Salad Mix] Vegetables 

Salad Mix Regular Garden [Salad Mix] Vegetables 

Salad Mix Salad Bowls Vegetables 

Salad Mix Salad Mix Blends Organic Vegetables 

Salad Mix Salad Mix Kits Organic Vegetables 

Salad Mix Salad Mix Other Vegetables 

223 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Salad Mix Salad Spinach Vegetables 

Salad Mix Salad Spinach Organic Vegetables 

Salad Mix Shredded Lettuce Vegetables 

Seasonal Pumpkins Vegetables 

Shelf Stable Juice Tomato Juice (50% And Under) Vegetables 

Shelf Stable Juice Tomato Juice (Over 50% Juice) Vegetables 

Shelf Stable Juice Veg Juice (Except Tomato) (50% 

And Under) 

Vegetables 

Shelf Stable Juice Veg Juice (Except Tomato) (Over 

50% Juice) 

Vegetables 

Snack Salsa Vegetables 

Spices/Jarred Garlic Garlic Jar Vegetables 

Spices/Jarred Garlic Garlic Jar Organic Vegetables 

Spices/Jarred Garlic Herbs Dried Vegetables 

Spices/Jarred Garlic Herbs Squeeze Tube Organic Vegetables 

Spices/Jarred Garlic Peppers Dried Vegetables 

Tomato Products—Shelf Tomato Stewed Vegetables 

Tomato Products—Shelf Stable Tomato Paste Vegetables 

Tomato Products—Shelf Stable Tomatoes Diced Vegetables 

Tomato Products—Shelf Stable Tomato Crushed Vegetables 

Tomato Products—Shelf Stable Tomato Puree Vegetables 

Tomato Products—Shelf Stable Tomato Sauce Vegetables 

Tomato Products—Shelf Stable Tomato Sun Dried Vegetables 

Tomato Products—Shelf Stable Tomatoes/Whole Vegetables 

Tomatoes Roma Tomatoes (Bulk/Pkg) Vegetables 

Tomatoes Tomatoes Cherry Vegetables 

Tomatoes Tomatoes Cherry Organic Vegetables 

Tomatoes Tomatoes Cocktail Vegetables 

Tomatoes Tomatoes Grape Vegetables 

Tomatoes Tomatoes Grape Organic Vegetables 

Tomatoes Tomatoes Hot House Bulk Vegetables 

Tomatoes Tomatoes Hothouse On The Vine Vegetables 

Tomatoes Tomatoes Hothouse Pkg Vegetables 

Tomatoes Tomatoes Others Organic Vegetables 

Tomatoes Tomatoes Snacking Colored Vegetables 

Tomatoes Tomatoes Vine Ripe Bulk Vegetables 

Tomatoes Tomatoes Vine Ripe Pkg Vegetables 

Tomatoes Tomatoes—Other Vegetables 

Traditional Asian Foods Asian Vegetables Vegetables 

Traditional Mexican Foods Mexican Beans/Refried Vegetables 

Traditional Mexican Foods Mexican Enchilada Sauce Vegetables 

Traditional Mexican Foods Mexican Peppers Chilies Vegetables 

Traditional Mexican Foods Mexican Sauces And Picante Sau Vegetables 

Tropical Fruit Avocado Vegetables 

Tropical Fruit Avocado Organic Vegetables 

Unknown Frozen Vegetables Vegetables 

Value-Added Vegetables Celery Chopped/Sticks Vegetables 

Value-Added Vegetables Cut Vegetables All Other Vegetables 

Value-Added Vegetables Instore Cut Vegetables Vegetables 

Value-Added Vegetables Onions Processed Vegetables 

Value-Added Vegetables Vegetable Party Tray Vegetables 

Vegetables Cooking Bulk Asparagus Vegetables 

Vegetables Cooking Bulk Beans Vegetables 

Vegetables Cooking Bulk Beans Organic Vegetables 

Vegetables Cooking Bulk Cabbage Vegetables 

Vegetables Cooking Bulk Cabbage Organic Vegetables 

Vegetables Cooking Bulk Celery Vegetables 

Vegetables Cooking Bulk Celery Organic Vegetables 

Vegetables Cooking Bulk Greens Bulk Vegetables 

Vegetables Cooking Bulk Greens Bulk Organic Vegetables 

Vegetables Cooking Bulk Hard Squash Vegetables 

Vegetables Cooking Bulk Organic Vegetables All Others Vegetables 

Vegetables Cooking Bulk Squash Other Vegetables 

Vegetables Cooking Bulk Squash Other Organic Vegetables 

Vegetables Cooking Bulk Vegetables All Other Vegetables 

Vegetables Cooking Packaged Vegetables Cooking Packaged Organic Vegetables 

Vegetables Cooking Packaged Broccoli/Cauliflower Processed Vegetables 

Vegetables Cooking Packaged Potatoes/Onions Processed Vegetables 

Vegetables Cooking Packaged Vegetables Cooking Packaged Vegetables 

Vegetables Salad Cucumbers Vegetables 

Vegetables Salad Cucumbers Organic Vegetables 

Vegetables Salad Green Onions Vegetables 

Vegetables Salad Green Onions Organic Vegetables 

Vegetables Salad Head Lettuce Vegetables 

Vegetables Salad Head Lettuce Organic Vegetables 

Vegetables Salad Radish Vegetables 

Vegetables Salad Radishes Organic Vegetables 

Vegetables Salad Spinach Bulk Vegetables 

Vegetables Salad Spring Mix Bulk Vegetables 

Vegetables Salad Variety Lettuce Vegetables 

Vegetables Salad Variety Lettuce Organic Vegetables 

Authentic Italian Foods Italian Oils And Dressings Oils 

Deli Specialties (Retail Pk) Dl Spec: Sauces/Sld Dressings Oils 

Dressings/Dips Dressing Blue Cheese Oils 

Dressings/Dips Dressing Cole Slaw Oils 

Dressings/Dips Dressing Creamy Oils 

Dressings/Dips Dressing Ginger Oils 

Dressings/Dips Dressing Organics Oils 

Dressings/Dips Dressing Vinegarette Oils 

Dressings/Dips Dressing Yogurt Based Oils 

Margarines Margarine: Squeeze Oils 

Margarines Margarine: Tubs And Bowls Oils 

Processed Dressings Oils 

Salad Dresing & Sandwich 

Spreads 

Mayonnaise & Whipped Dressing Oils 

Salad Dresing & Sandwich 

Spreads 

Pourable Salad Dressings Oils 

Salad Dresing & Sandwich 

Spreads 

Sand/Horseradish & Tartar Sauce Oils 

Shortening & Oil Canola Oils Oils 

224 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern SoFAS Subcategories 

Composite 

Subcategories Other Subcategories 

Shortening & Oil Cooking Oil: Peanut/Safflower Oils 

Shortening & Oil Cooking Sprays Oils 

Shortening & Oil Corn Oil Oils 

Shortening & Oil Misc Oils Oils 

Shortening & Oil Olive Oil Oils 

Shortening & Oil Vegetable Oil Oils 

Aseptic Juice Aseptic Pack Juice And Drinks Solid Fats & Added 

Sugar 

sweetened beverage 

Aseptic Juice Aseptic Pack Juice And Drinks Solid Fats & Added 

Sugar 

sweetened beverage 

Aseptic Juice Aseptic Pack Juice And Drinks Solid Fats & Added 

Sugar 

sweetened beverage 

Authentic Central American Fds Central American Candy W/O 

Flour 

Solid Fats & Added 

Sugar 

candy/sweet 

Authentic Central American Fds Central American Carbonated Bev Solid Fats & Added 

Sugar 

sweetened beverage 

Authentic Hispanic Fds & Product Hispanic Carbonated Beverages Solid Fats & Added 

Sugar 

sweetened beverage 

Authentic Hispanic Fds & Product Authentic Dry Beverages W/Sweetener Solid Fats & Added 

Sugar 

sweetened beverage 

Authentic Hispanic Fds & Product Hispanic Juice Under 50% Juice Solid Fats & Added 

Sugar 

sweetened beverage 

Authentic South American Fds South American Candy W/O Flour Solid Fats & Added 

Sugar 

candy/sweet 

Bag Snacks Pork Skins/Cracklins Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Bagels & Cream Cheese Cream Cheese Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Baking Chocolate Chips & Bars (Sweete) Solid Fats & Added 

Sugar 

candy/sweet 

Baking Mixes Frosting Solid Fats & Added 

Sugar 

candy/sweet 

Baking Needs Coconut [Baking Needs] Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Baking Needs Marshmallow Cre`me Solid Fats & Added 

Sugar 

candy/sweet 

Baking Needs Marshmallows Solid Fats & Added 

Sugar 

candy/sweet 

Beverages Can/Btl Carb Beve 50% And 

Under 

Solid Fats & Added 

Sugar 

sweetened beverage 

Beverages Can/Btl N/Carb Beve 50% And 

Under 

Solid Fats & Added 

Sugar 

sweetened beverage 

Beverages Tea (Canned/Bottled) W/Sweetener Solid Fats & Added 

Sugar 

sweetened beverage 

Bulk Food Candy Bulk Solid Fats & Added 

Sugar 

candy/sweet 

Bulk Food Candy Bulk W/Flour Solid Fats & Added 

Sugar 

candy/sweet 

Cake De ́cor Cake De ́cors—Candies Solid Fats & Added 

Sugar 

candy/sweet 

Cake De ́cor Cake De ́cors & Icing Solid Fats & Added 

Sugar 

candy/sweet 

Candy Candy W/Flour Solid Fats & Added 

Sugar 

candy/sweet 

Candy Candy/Chocolate Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Checklane Candy Bars (Singles) (Including) Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Checklane Candy Bars (Singles) (Including) Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Checklane Chewing Gum Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Checklane Mints/Candy & Breath (Not Lifesavers) Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Checklane Mints/Candy & Breath (Not Lifesavers) Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Checklane Misc Checklane Candy Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Bulk Candy Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Bulk Candy W/Flour Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Candy & Breath Mints (Pkgd) Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Candy & Breath Mints (Pkgd) Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Candy Bags—Chocolate Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Candy Bags—Chocolate W/Flour Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Candy Bags—Non Chocolate Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Candy Bags—Non Chocolate W/ 

Flour 

Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Candy Bars (Multi Pack) Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Candy Bars Multi Pack W/Flour Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Candy Box Non-Chocolate Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Candy Box Non-Chocolate W/Flour Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Candy Boxed Chocolates Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Candy Boxed Chocolates W/Flour Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Candy Refrigerated Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Gum (Packaged) Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Hispanic Candy Solid Fats & Added 

Sugar 

candy/sweet 

225 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Candy—Packaged Miscellaneous Candy Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Miscellaneous Candy Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Novelty Candy Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Novelty Candy W/Flour Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Novelty Candy—Taxable Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Seasonal Candy Bags Non-Chocolate Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Seasonal Candy Bags Non-Chocolate Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Seasonal Candy Bags—Chocolate Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Seasonal Candy Bags—Chocolate Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Seasonal Candy Box Non-Chocolate Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Seasonal Candy Box Non-Chocolate Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Seasonal Candy Box—Chocolate Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Seasonal Candy Box—Chocolate 

W/Flour 

Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Seasonal Miscellaneous [Candy] Solid Fats & Added 

Sugar 

candy/sweet 

Candy—Packaged Seasonal Miscellaneous W/Flour Solid Fats & Added 

Sugar 

candy/sweet 

Cocoa Mixes Hot Chocolate/Cocoa Mix Solid Fats & Added 

Sugar 

sweetened beverage 

Cocoa Mixes Malted Mlk/Syrup/Pwdrs (Eggnog) Solid Fats & Added 

Sugar 

sweetened beverage 

Coffee & Creamers Coffee Sweeteners Solid Fats & Added 

Sugar 

sweetened beverage 

Coffee & Creamers Non Dairy Creamer Solid Fats & Added 

Sugar 

sweetened beverage 

Coffee Shop Sweet Goods & Rtl Coffee Shop: Candy Solid Fats & Added 

Sugar 

candy/sweet 

Condiments Honey/Syrup Solid Fats & Added 

Sugar 

candy/sweet 

Condiments Jellies/Preserves/Apple Butter Solid Fats & Added 

Sugar 

candy/sweet 

Deli Specialties (Retail Pk) Dl Spec: Jellies/Toppings Solid Fats & Added Sugar candy/sweet 

Dressings/Dips Dips Caramel/Fruit Glazes Solid Fats & Added 

Sugar 

candy/sweet 

Dressings/Dips Dips Fruit And Chocolate Solid Fats & Added 

Sugar 

candy/sweet 

Dry Mix Desserts Topping Mixes/Whip Topping Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Dry Tea/Coffee/Coco Mixes Coco Mix Solid Fats & Added 

Sugar 

sweetened beverage 

Dry Tea/Coffee/Coco Mixes Tea Concentrate W/Sweetener/Su Solid Fats & Added 

Sugar 

sweetened beverage 

Dry Tea/Coffee/Coco Mixes Tea Rtd With Sweetener/Sugar Solid Fats & Added 

Sugar 

sweetened beverage 

Energy Drinks Energy Drink—Multi-Pack Solid Fats & Added 

Sugar 

sweetened beverage 

Energy Drinks Energy Drink—Multi-Pack (Non) Solid Fats & Added 

Sugar 

sweetened beverage 

Energy Drinks Energy Drink—Single Serve Solid Fats & Added 

Sugar 

sweetened beverage 

Energy Drinks Energy Drink—Single Serve Solid Fats & Added 

Sugar 

sweetened beverage 

European Foods British Carbonated Beverages Solid Fats & Added 

Sugar 

sweetened beverage 

European Foods European Carbonated Beverages Solid Fats & Added 

Sugar 

sweetened beverage 

Fluid Milk Products Refrigerated Coffee Creamers Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Fluid Milk Products Whipping Cream Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Frozen Breakfast Foods Frzn Non-Dairy Creamers Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Frozen Juice And Smoothies Cocktail Mixes-Frz Solid Fats & Added 

Sugar 

sweetened beverage 

Frozen Juice And Smoothies Frzn Conc Under 50% Juice Solid Fats & Added 

Sugar 

sweetened beverage 

Frozen Juice And Smoothies Frzn Fruit Drinks (Under 10% J) Solid Fats & Added 

Sugar 

sweetened beverage 

Frozen Whipped Topping Frzn Whipped Topping Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Gift & Fruit Baskets Candy Arrangements Food Only Solid Fats & Added 

Sugar 

candy/sweet 

Juice Drinks—Carb Juice (Under 50%) Solid Fats & Added 

Sugar 

sweetened beverage 

Juice Non-Carb Jce (Under 50% Juice) Solid Fats & Added 

Sugar 

sweetened beverage 

Juices Super Premium Juices (50% And Under Juice) Solid Fats & Added 

Sugar 

sweetened beverage 

Juices Super Premium Juices Organic (50% And Under) Solid Fats & Added 

Sugar 

sweetened beverage 

Juices Super Premium Juices Smoothies/Blended Solid Fats & Added 

Sugar 

sweetened beverages 

Juices Super Premium Juices Superfoods/Enhanced Solid Fats & Added 

Sugar 

sweetened beverages 

Juices Super Premium Juices/Smoothies Instore Produ Solid Fats & Added 

Sugar 

sweetened beverages 

Kosher Foods And Products Kosher Beverage Solid Fats & Added 

Sugar 

sweetened beverages 

226 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern SoFAS Subcategories 

Composite 

Subcategories Other Subcategories 

Kosher Foods And Products Kosher Candy Solid Fats & Added 

Sugar 

candy/sweet 

Kosher Foods And Products Kosher Carbonated Soft Drinks Solid Fats & Added 

Sugar 

sweetened beverage 

Margarines Butter Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Margarines Margarine Stick Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Milk By-Products Aerosol Toppings [Milk By-Products] Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Milk By-Products Refrig Dips Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Milk By-Products Sour Creams Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Mixers Cocktail Mixes—Dry Solid Fats & Added 

Sugar 

sweetened beverage 

Mixers Cocktail Mixes—Fluid: Add Liq Solid Fats & Added 

Sugar 

sweetened beverage 

Molasses/Syrups/Pancake Mixes Molasses & Syrups Solid Fats & Added 

Sugar 

candy/sweet 

Packaged Natural Snacks Candy Solid Fats & Added 

Sugar 

candy/sweet 

Peanut Butter/Jelly/Jams & Honey Preserves/Jam/Marmalade Solid Fats & Added 

Sugar 

candy/sweet 

Peanut Butter/Jelly/Jams & Honey Honey Solid Fats & Added 

Sugar 

candy/sweet 

Peanut Butter/Jelly/Jams & Honey Jelly Solid Fats & Added 

Sugar 

candy/sweet 

Powder & Crystal Drink Mix Breakfast Crystals Solid Fats & Added 

Sugar 

sweetened beverage 

Powder & Crystal Drink Mix Enhanced Stick [Powder Drink 

Mix] 

Solid Fats & Added 

Sugar 

sweetened beverage 

Powder & Crystal Drink Mix Fluid Pouch [Powder Drink Mix] Solid Fats & Added 

Sugar 

sweetened beverage 

Powder & Crystal Drink Mix Soft Drink Canisters [Powder 

Drink Mix] 

Solid Fats & Added 

Sugar 

sweetened beverage 

Powder & Crystal Drink Mix Sugar Sweetened Envelopes Solid Fats & Added 

Sugar 

candy/sweet 

Powder & Crystal Drink Mix Sugar Sweetened Sticks Solid Fats & Added 

Sugar 

candy/sweet 

Processed Dips Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Processed Packaged Dry Smoothie Mix Solid Fats & Added 

Sugar 

sweetened beverages 

Refrgratd Juices/Drinks Dairy Case Citrus Pnch/Oj Subs Solid Fats & Added 

Sugar 

sweetened beverage 

Refrgratd Juices/Drinks Dairy Case Fruit Drinks (No Ju) Solid Fats & Added 

Sugar 

sweetened beverage 

Refrgratd Juices/Drinks Dairy Case Juice Drnk Under 10 Solid Fats & Added 

Sugar 

sweetened beverage 

Refrgratd Juices/Drinks Dairy Case Tea With Sugar Or S Solid Fats & Added 

Sugar 

sweetened beverage 

Refrigerated Dairy Case Ntrn Refrig Juice Under 50% Solid Fats & Added 

Sugar 

sweetened beverage 

Refrigerated Dairy Case Sour Cream/Cottage Cheese Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Refrigerated Dairy Case Tea With Sweetener/Sugar Solid Fats & Added 

Sugar 

sweetened beverage 

Rtd Tea/New Age Juice Juice (Under 10% Juice) Solid Fats & Added 

Sugar 

sweetened beverage 

Rtd Tea/New Age Juice Juice (Under 50% Juice) Solid Fats & Added 

Sugar 

sweetened beverage 

Rtd Tea/New Age Juice Tea Sweetened Solid Fats & Added 

Sugar 

sweetened beverage 

Service Beverage Sv Bev: Bev/Juic 10–50% Juice Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Apple Juice & Cider (50% And 

Under Juice) 

Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Apple Juice & Cider (Under 10% 

Juice) 

Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Blended Juice & Combinations Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Blended Juice & Combinations Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Cranapple/Cran Grape Juice Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Cranberry Juice (50% And Under 

Juice) 

Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Fruit Drinks: Canned & Glass Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Fruit Drinks: Canned & Glass Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Fruit Drinks: Canned & Glass Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Fruit Drinks: Canned & Glass Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Grape Juice (50% And Under 

Juice) 

Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Grapefruit Juice (50% And Under 

Juice) 

Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Lemon Juice & Lime Juice (50% 

And Under Juice) 

Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Lemon Juice & Lime Juice Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Nectars (50% And Under Juice) Solid Fats & Added 

Sugar 

sweetened beverage 

Shelf Stable Juice Prune Juice (50% And Under 

Juice) 

Solid Fats & Added 

Sugar 

sweetened beverage 

Shortening & Oil Solid Shortening Solid Fats & Added 

Sugar 

butter/cream/solid 

fat 

Soft Drinks Mixers (Tonic Water/Gngr Ale) Solid Fats & Added 

Sugar 

sweetened beverage 

227 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Soft Drinks Mixers (Tonic Wtr/Gngr Ale) Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Sft Drnk 1 Liter Btl Carb Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Sft Drnk 2 Liter Btl Carb Incl Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Sft Drnk 3 Liter Btl Carb Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Sft Drnk Misc Btl (Any Btl) Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Sft Drnk Misc Can (Ex: 4/8/18pk) Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Sft Drnk Mlt-Pk Btl Carb Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Sft Drnk Sngl Srv Btl Carb Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Soft Drink Bottle Non-Carb Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Soft Drinks 12/18 & 15pk Can Car Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Soft Drinks 20pk & 24pk Can 

Carb 

Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Soft Drinks 6pk Can Carb Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Soft Drinks Bottle Returnable Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Soft Drinks Can Non-Carb Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Soft Drinks Single Cans Carb Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Tea Bottles With Sweetener/Sugar Solid Fats & Added 

Sugar 

sweetened beverage 

Soft Drinks Tea Can With Sweetener/Sugar Solid Fats & Added 

Sugar 

sweetened beverage 

Sugars & Sweeteners Sugar Solid Fats & Added 

Sugar 

candy/sweet 

Sugars & Sweeteners Sweeteners Solid Fats & Added 

Sugar 

candy/sweet 

Sweet Goods & Snacks Sweet Goods: Candy Solid Fats & Added 

Sugar 

candy/sweet 

Sweet Goods & Snacks Sweet Goods: Candy W/Flour Solid Fats & Added 

Sugar 

candy/sweet 

Syrups Toppings & Cones Ice Cream Toppings Solid Fats & Added 

Sugar 

candy/sweet 

Teas Instant Tea & Tea Mix (W/Sugar) Solid Fats & Added 

Sugar 

sweetened beverage 

Traditional Mexican Foods Mexican Candy Solid Fats & Added 

Sugar 

candy/sweet 

Trail Mix & Snacks Candy W/Flour Solid Fats & Added 

Sugar 

candy/sweet 

Trail Mix & Snacks Candy W/O Flour Solid Fats & Added 

Sugar 

candy/sweet 

Trail Mix & Snacks Candy W/O Flour Organic Solid Fats & Added 

Sugar 

candy/sweet 

Water Carb Water—Flvrd Sweetened Solid Fats & Added 

Sugar 

sweetened beverage 

Water Energy Drinks Solid Fats & Added 

Sugar 

sweetened beverage 

Authentic Hispanic Fds & Product Authentic Pasta/Rice/Beans Composite entre ́e/meal 

Authentic Hispanic Fds & Product Authentic Soups/Bouillons Composite soup 

Authentic Hispanic Fds & Product Hispanic Cookies/Crackers Composite desserts 

Authentic Italian Foods Italian Pasta And Pasta Sauce Composite entre ́e/meal 

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 Goods Snack Cake—Multi Pack Composite desserts 

Baked Sweet Goods Sweet Goods—Full Size Composite desserts 

Bakery Party Trays Composite desserts 

Bakery Party Trays Party Trays: Breakfast Sweets Composite desserts 

Bakery Party Trays Party Trays: Cakes Composite desserts 

Bakery Party Trays Party Trays: Cookies—Rolls Composite desserts 

Baking Mixes Brownie Mix Composite desserts 

Baking Mixes Cookies Mix Composite desserts 

Baking Mixes Layer Cake Mix Composite desserts 

Baking Mixes Microwavable Cake Mix Composite desserts 

Baking Needs Pie Crust Mixes & Shells Composite desserts 

Baking Needs Pie Filling/Mincemeat/Glazes Composite desserts 

Bulk Food Grain/Beans Bulk Composite entre ́e/meal 

Bulk Food Misc Bulk Snacks Sweetened Composite snacks 

Bulk Food Snacks Bulk Composite snacks 

Cakes Cakes Ingredients Composite desserts 

Cakes Cakes: Angel Fds/Cke Rolls Composite desserts 

Cakes Cakes: Angl Fd/Roll Novelties Composite desserts 

Cakes Cakes: Birthday/Celebration Sheet Composite desserts 

Cakes Cakes: Cheesecake Composite desserts 

Cakes Cakes: Cheesecake Novelties Composite desserts 

Cakes Cakes: Cndles/Retl Accss Composite desserts 

Cakes Cakes: Cre`me/Pudding Composite desserts 

Cakes Cakes: Cre`me/Pudding Novelties Composite desserts 

Cakes Cakes: Cupcakes Composite desserts 

Cakes Cakes: Fancy/Service Case Composite desserts 

Cakes Cakes: Ice Cream Composite desserts 

Cakes Cakes: Kosher Composite desserts 

Cakes Cakes: Layers Composite desserts 

Cakes Cakes: Layers/Sheets Novelties Composite desserts 

Cakes Cakes: Novelties Composite desserts 

Cakes Cakes: Pound Composite desserts 

Cakes Cakes: Pound Cake Novelties Composite desserts 

Cakes Cakes: Sheet Composite desserts 

228 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Cakes Cakes: Birthday/Celebration Layer Composite desserts 

Cakes Cakes: Wedding/Designer Series Composite desserts 

Canned Pasta & Mwv Fd—Shlf 

Stbl 

Can Pasta Composite entre ́e/meal 

Canned Pasta & Mwv Fd—Shlf 

Stbl 

Microwavable Cups [Canned 

Pasta] 

Composite entre ́e/meal 

Canned Pasta & Mwv Fd—Shlf 

Stbl 

Microwavable Trays [Canned 

Pasta] 

Composite entre ́e/meal 

Canned Soups Condensed Soup Composite soup 

Chilled Ready Meals Store Brand Composite entre ́e/meal 

Chilled Ready Meals Fresh Meals Composite entre ́e/meal 

Chilled Ready Meals Fresh Side Dishes Composite entre ́e/meal 

Cnv Breakfast & Wholesome Snks Treats Composite snacks 

Convenient Meals Convenient Meals—Adult Meal Composite entre ́e/meal 

Convenient Meals Convenient Meals—Kids Meal Composite entre ́e/meal 

Cookie/Cracker Multi-Pks Multi-Pack Cookies Composite desserts 

Cookies Chocolate Covered Cookies Composite desserts 

Cookies Cookies/Sweet Goods Composite desserts 

Cookies Cookies: Gourmet Composite desserts 

Cookies Cookies: Holiday/Special Occas Composite desserts 

Cookies Cookies: Kosher Composite desserts 

Cookies Cookies: Less Than 6 Composite desserts 

Cookies Cookies: Message Composite desserts 

Cookies Cookies: Party Composite desserts 

Cookies Cookies: Regular Composite desserts 

Cookies Fruit Filled Cookies Composite desserts 

Cookies Premium Cookies (Ex: Pepperidge) Composite desserts 

Cookies Sandwich Cookies Composite desserts 

Cookies Specialty Cookies Composite desserts 

Cookies Tray Pack/Choc Chip Cookies Composite desserts 

Cookies Vanilla Wafer/Kids Cookies Composite desserts 

Cookies Wellness/Portion Control [Cookies] Composite desserts 

Dinner Mixes—Dry Macaroni & Cheese Dnrs Composite entre ́e/meal 

Dinner Mixes—Dry Microwave Dinners Composite entre ́e/meal 

Dinner Mixes—Dry Package Dinners Meat Included Composite entre ́e/meal 

Dinner Mixes—Dry Package Dinners W/O Meat Composite entre ́e/meal 

Dinner Mixes—Dry Package Dinners/Pasta Salads Composite entre ́e/meal 

Dinner Mixes—Dry Skillet Dinners Composite entre ́e/meal 

Dressings/Dips Dips Guacamole/Salsa/Queso Composite snacks 

Dressings/Dips Dips Organic Composite snacks 

Dressings/Dips Dips Veggie Composite snacks 

Dry Bean Veg & Rice Dry Beans/Peas/Barley: Bag & B Composite entre ́e/meal 

Dry Mix Desserts Freeze Mixes/Pwdrs/Liquids Composite desserts 

Dry Mix Desserts Misc: Cheesecake/Mousse Mixes Composite desserts 

Dry Mix Desserts Pudding & Gelatin Cups/Cans Composite desserts 

Dry Mix Desserts Puddings Dry Composite desserts 

Dry/Ramen Bouillon 12 Pack Soup/Case Soup/Etc. Composite soup 

Dry/Ramen Bouillon Bouillon Composite soup 

Dry/Ramen Bouillon Dry Soup Composite soup 

Fitness & Diet Fitness & Diet—Bars (Supplement) Composite snacks 

Fitness & Diet Fitness & Diet—Bars W/Flour Composite snacks 

Fitness & Diet Fitness & Diet—Bars W/O Flour Composite snacks 

Frozen Bread And Desserts Desserts Composite desserts 

Frozen Breakfast Donuts Composite desserts 

Frozen Breakfast Meals/Sandwichs Composite entre ́e/meal 

Frozen Breakfast Foods Frzn Breakfast Entre ́es Composite entre ́e/meal 

Frozen Breakfast Foods Frzn Breakfast Sandwiches Composite entre ́e/meal 

Frozen Desserts Frozen Cakes/Desserts Composite desserts 

Frozen Desserts Frozen Cream Pies Composite desserts 

Frozen Desserts Frozen Fruit Pies & Cobblers Composite desserts 

Frozen Desserts Frzn Pastry&Cookies Composite desserts 

Frozen Desserts Frzn Pie Shells/Pastry Shell/F Composite desserts 

Frozen Desserts Single Serv/Portion Control Composite desserts 

Frozen Entre ́es Bowls Composite entre ́e/meal 

Frozen Entre ́es Meatless/Vegetarian Composite entre ́e/meal 

Frozen Entre ́es Pasta/Skillet Meals Composite entre ́e/meal 

Frozen Entre ́es Soup Composite soup 

Frozen Handhelds & Snacks Burritos Composite entre ́e/meal 

Frozen Handhelds & Snacks Corn Dogs Composite snacks 

Frozen Handhelds & Snacks Sandwiches & Handhelds Composite entre ́e/meal 

Frozen Handhelds & Snacks Snacks/Appetizers Composite snacks 

Frozen Ice Cream & Novelties Almond Composite desserts 

Frozen Ice Cream & Novelties Ice Cream Composite desserts 

Frozen Ice Cream & Novelties Novelties—Dairy Composite desserts 

Frozen Ice Cream & Novelties Novelties—Non Dairy Composite desserts 

Frozen Ice Cream & Novelties Novelties—Water Base Composite desserts 

Frozen Ice Cream & Novelties Rice Composite desserts 

Frozen Ice Cream & Novelties Soy Composite desserts 

Frozen Ice Cream & Novelties Yogurt/Sorbet And Kefir Composite desserts 

Frozen Juice And Smoothies Smoothies—Frz Composite desserts 

Frozen Novelties—Water Ice Adult Premium [Frozen Novelties] Composite desserts 

Frozen Novelties—Water Ice Cones [Frozen Novelties] Composite desserts 

Frozen Novelties—Water Ice Cups/Push Ups/Other [Frozen 

Novelties] 

Composite desserts 

Frozen Novelties—Water Ice Ice Cream Sandwiches Composite desserts 

Frozen Novelties—Water Ice Sticks/Enrobed [Frozen Novelties] Composite desserts 

Frozen Novelties—Water Ice Water Ice [Frozen Novelties] Composite desserts 

Frozen Pizza Meatless/Vegetarian Composite entre ́e/meal 

Frozen Pizza Pizza/Economy Composite entre ́e/meal 

Frozen Pizza Pizza/Premium Composite entre ́e/meal 

Frozen Pizza Pizza/Single Serve/Microwave Composite entre ́e/meal 

Frozen Pizza Pizza/Traditional Composite entre ́e/meal 

Frozen Pizza Pizza/Value Composite entre ́e/meal 

Frozen Pizza Single Serve Composite entre ́e/meal 

Frozen Snacks And Burritos—Meatless/Vegetarian Composite entre ́e/meal 

Frozen Snacks And Handhelds Appetizers Composite snacks 

Frozen Snacks And Handhelds Burritos—Meat Protein Composite entre ́e/meal 

Frozen Snacks And Handhelds Wraps/Handhelds—Meat Composite entre ́e/meal 

Frozen Snacks And Handhelds Wraps/Handhelds—Meatless Composite entre ́e/meal 

Frozen Vegetables And Potatoes Meals Composite entre ́e/meal 

229 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Frzn Meatless Meatless Breakfast Composite entre ́e/meal 

Frzn Meatless Meatless Burgers Composite entre ́e/meal 

Frzn Meatless Meatless Entrees Composite entre ́e/meal 

Frzn Meatless Meatless Meal Starters Composite entre ́e/meal 

Frzn Meatless Meatless Miscellaneous Composite entre ́e/meal 

Frzn Meatless Meatless Poultry Composite entre ́e/meal 

Frzn Meatless Meatless Snacks Composite snacks 

Frzn Multi Serve Fz Crockpots/Soups Composite soup 

Frzn Multi Serve Fz Family Style Entre ́es Composite entre ́e/meal 

Frzn Multi Serve Fz Skillet Meals Composite entre ́e/meal 

Frzn Prepared Chicken Fz Meal Kits/Stuffed/Other Composite entre ́e/meal 

Frzn Ss Economy Meals Fz Ss Economy Meals All Composite entre ́e/meal 

Frzn Ss Premium Meals Fz Regional/Other Composite entre ́e/meal 

Frzn Ss Premium Meals Fz Ss Prem Nutritional Meals Composite entre ́e/meal 

Frzn Ss Premium Meals Fz Ss Prem Traditional Meals Composite entre ́e/meal 

Gift & Fruit Baskets Snack Packs W/Soda Composite snacks 

Ice Cream Ice Milk & Sherbets Pails [Ice Cream & Sherbert] Composite desserts 

Ice Cream Ice Milk & Sherbets Premium [Ice Cream & Sherbert] Composite desserts 

Ice Cream Ice Milk & Sherbets Premium Pints [Ice Cream & 

Sherbert] 

Composite desserts 

Ice Cream Ice Milk & Sherbets Quarts [Ice Cream & Sherbert] Composite desserts 

Ice Cream Ice Milk & Sherbets Super Premium Pints [Ice Cream 

& Sherbert] 

Composite desserts 

Ice Cream Ice Milk & Sherbets Traditional [Ice Cream & 

Sherbert] 

Composite desserts 

Kosher Foods And Products Kosher Snacks Composite snacks 

Kosher Foods And Products Kosher Soups Composite soup 

Packaged Natural Snacks Trail Mixes Composite snacks 

Party Tray Deli Tray—Includes Non-Foods Composite entre ́e/meal 

Party Tray Deli Tray: Appetizers & Hors D’oe Composite entre ́e/meal 

Party Tray Deli Tray: Chicken Composite entre ́e/meal 

Party Tray Deli Tray: Fruit And Vegetable Composite entre ́e/meal 

Party Tray Deli Tray: Meat And Cheese Composite entre ́e/meal 

Party Tray Deli Tray: Sandwiches Composite entre ́e/meal 

Party Tray Deli Trays: Hot Composite entre ́e/meal 

Pies Pie Ingredients Composite desserts 

Pies Pies: Cream/Meringue Composite desserts 

Pies Pies: Fruit/Nut Composite desserts 

Pies Pies: Kosher Composite desserts 

Pies Pies: Pumpkin/Custard Composite desserts 

Pies Pies: Tarts/Minis/Crstdas Composite desserts 

Prepared/Pdgd Foods Boxed Prepared/Entre ́e/Dry Prep Composite entre ́e/meal 

Refrgrated Dough Products Refrigerated Cookie Dough Composite desserts 

Refrgrated Dough Products Refrigerated Cookies—Brand Composite desserts 

Refrgrated Dough Products Refrigerated Cookies—Seasonal Composite desserts 

Refrgrated Dough Products Refrigerated Pie Crust Composite desserts 

Refrigerated Desserts Refrigerated Pudding Composite desserts 

Restricted Diet Cookies Composite desserts 

Rts/Micro Soup/Broth Broth Composite soup 

Rts/Micro Soup/Broth Microwavable Soups Composite soup 

Rts/Micro Soup/Broth Rts Soup: Chunky/Homestyle/Et Composite soup 

Salad & Dips Sal: Desserts—Bulk Composite desserts 

Salad & Dips Sal: Desserts—Prepack Composite desserts 

Salad Bar Soups Composite soup 

Sandwiches Sandwich Ingredients Composite entre ́e/meals 

Sandwiches Sandwiches—(Cold) Composite entre ́e/meals 

Sandwiches Sandwiches: Kosher (Cold) Composite entre ́e/meals 

Seafood—Party Trays Party Tray Other Composite entre ́e/meal 

Seafood—Party Trays Party Tray Other Composite entre ́e/meal 

Seafood—Salad/Dips/Sce/Cond Salads Composite entre ́e/meal 

Service Case Meat Side Dishes Composite entre ́e/meal 

Service Case Meat Stuffed/Mixed Composite entre ́e/meal 

Single Serve Items Single Serve Desserts Composite desserts 

Single Serve Items Single Serve Snacks Composite snacks 

Single Serve Sweet Goods Snack Cake—Single Serve Composite desserts 

Snack Nuts/Trail Mix/Dried Fruit Composite snacks 

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—Cookie/Cracker Vendor Size/Single Serve Cookie Composite desserts 

Ss/Vending—Salty Snacks Salty Snacks Vending Composite snacks 

Ss/Vending—Salty Snacks Salty Snacks W/Sweetener Vending Composite snacks 

Sushi Sushi—In Store Prepared Composite entre ́e/meal 

Sushi Sushi—Kosher Composite entre ́e/meal 

Sushi Sushi—Prepackaged Composite entre ́e/meal 

Sushi Sushi: In Store Prepared (Hot) Composite entre ́e/meal 

Sushi Sushi: Ingredients Composite entre ́e/meal 

Sushi Sushi: In-Store Prepared (Dine) Composite entre ́e/meal 

Sushi Sushi: Smallwares Composite entre ́e/meal 

Sweet Goods Sw Gds: Kosher Breakfast Composite desserts 

Sweet Goods Sw Gds: Muffins Composite desserts 

Sweet Goods Sw Gds: Sw Rolls/Dan Composite desserts 

Sweet Goods Sw Gds: Coffee Cakes Composite desserts 

Sweet Goods Sw Gds: Donuts Composite desserts 

Sweet Goods Sw Gds: Donuts—Less Than 6 Composite desserts 

Sweet Goods Sw Gds: Muffins—Lss Thn 6 Composite desserts 

Sweet Goods Swt Gds Ingredients Composite desserts 

Sweet Goods & Snacks Sw Gds: Brownie/Bar Cookie Composite desserts 

Sweet Goods & Snacks Sw Gds: Kosher Composite desserts 

Sweet Goods & Snacks Sw Gds: Puff Pastry Composite desserts 

Sweet Goods & Snacks Sw Gds: Specialty Desserts Composite desserts 

Sweet Goods & Snacks Sw Gds: Swt/Flvrd Loaves Composite desserts 

Traditional Asian Foods Asian Foods And Meals Composite entre ́e/meal 

230 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Traditional Mexican Foods Mexican Dinners And Foods Composite entre ́e/meal 

Trail Mix & Snacks Trail Mixes/Snack Composite snacks 

Trail Mix & Snacks Trail Mixes/Snacks Organic Composite snacks 

Unknown Frozen Breakfast Composite entre ́e/meal 

Unknown Frozen Dessert (Ice Cream Cake) Composite desserts 

Unknown Frozen Entrees Composite entre ́e/meal 

Unknown Frozen Ice Cream Composite desserts 

Unknown Frozen Side Dish Composite entre ́e/meal 

Value-Added Fruit Parfait Cups Instore Composite desserts 

Warehouse Snacks Canister Snacks Composite snacks 

Warehouse Snacks Misc Snacks Composite snacks 

Warehouse Snacks Misc Snacks W/Sweetener Composite snacks 

Warehouse Snacks Snack Mix Composite snacks 

Authentic Asian Foods Authentic Chinese Foods Other misc 

Authentic Asian Foods Authentic Japanese Foods Other misc 

Authentic Asian Foods Authentic Thai Foods Other misc 

Authentic Asian Foods Other Authentic Asian Foods Other misc 

Authentic Caribbean Foods Caribbean Foods Other misc 

Authentic Central American Fds Central American Foods Other misc 

Authentic Hispanic Fds & Product Hispanic Baking Needs Other seasoning/baking need 

Authentic Hispanic Fds & Product Authentic Dry Beverages W/O 

Sweetener 

Other unsweetened beverage 

Authentic Hispanic Fds & Product Hispanic Condiments Other condiments 

Authentic Hispanic Fds & Product Hispanic Spices And Seasonings Other seasoning/baking need 

Authentic Italian Foods Other Italian Foods Other misc 

Authentic South American Fds South American Foods Other misc 

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—Beginner Other infant formula/baby food 

Baby Foods Baby Food Cereals Other infant formula/baby food 

Baby Foods Baby Food Junior/All Brands Other infant formula/baby food 

Baby Foods Baby Juices Other infant formula/baby food 

Baby Foods Baby Spring Waters Other infant formula/baby food 

Baking Flours/Grains/Sugar Other seasoning/baking need 

Baking Mixes Other seasoning/baking need 

Baking Spices Other seasoning/baking need 

Baking Mixes Microwave Mixes: All Other Other seasoning/baking need 

Baking Mixes Miscellaneous Package Mixes Other seasoning/baking need 

Baking Needs Baking Cocoa Other seasoning/baking need 

Baking Needs Baking Powder & Soda Other seasoning/baking need 

Baking Needs Bits & Morsels [Baking Needs] Other seasoning/baking need 

Baking Needs Cooking Chocolate (Ex.: Smi-Swt) Other seasoning/baking need 

Baking Needs Cooking Chocolate Unsweetened Other seasoning/baking need 

Baking Needs Yeast: Dry Other seasoning/baking need 

Beverages Tea Unsweetened (Can/Bottle) Other unsweetened beverage 

Bulk Food Bulk Spices Other seasoning/baking need 

Bulk Food Coffee & Tea Bulk Other unsweetened beverage 

Bulk Food Misc Bulk Other misc 

Coffee & Creamers Bulk Coffee Other unsweetened beverage 

Coffee & Creamers Coffee Pods/Singles/Filter Pac Other unsweetened beverage 

Coffee & Creamers Flavored Bag Coffee Other unsweetened beverage 

Coffee & Creamers Flavored Can Coffee Other unsweetened beverage 

Coffee & Creamers Flavored Instant Coffee Other unsweetened beverage 

Coffee & Creamers Ready To Drink Coffee Other unsweetened beverage 

Coffee & Creamers Ready To Drink Coffee Suppleme Other unsweetened beverage 

Coffee & Creamers Specialty Instant Coffee W/O S Other unsweetened beverage 

Coffee & Creamers Specialty Instant Coffee W/Swe Other unsweetened beverage 

Coffee & Creamers Unflavored Bag Coffee Other unsweetened beverage 

Coffee & Creamers Unflavored Can Coffee Other unsweetened beverage 

Coffee & Creamers Unflavored Instant Coffee Other unsweetened beverage 

Coffee Shop Sv Bev: Inged/Portion Pk Other unsweetened beverage 

Coffee Shop Sv Bev: Carb Wat-Flv/Unflv Other unsweetened beverage 

Coffee Shop Sweet Goods & Rtl Coff Shop: Instant Retail Pack Other unsweetened beverage 

Coffee Shop Sweet Goods & Rtl Coff Shop: Retail Pack Beverag Other unsweetened beverage 

Coffee Shop Sweet Goods & Rtl Coff Shop: Whole Bean Retail P Other unsweetened beverage 

Condiments Ketchup/Mustard/Bbq Sce/Marina Other condiments 

Condiments Oils/Vinegar Other condiments 

Condiments Pickles/Olives/Kraut Other condiments 

Condiments & Sauces Bbq Sauce Other condiments 

Condiments & Sauces Catsup Other condiments 

Condiments & Sauces Chili Sauce/Cocktail Sauce Other condiments 

Condiments & Sauces Hot Sauce Other condiments 

Condiments & Sauces Marinades Other condiments 

Condiments & Sauces Misc Meat Sauces Other condiments 

Condiments & Sauces Mustard—All Other Other condiments 

Condiments & Sauces Steak & Worchester Sauce Other condiments 

Condiments & Sauces Wing Sauce Other condiments 

Condiments & Sauces Yellow Mustard Other condiments 

Deli Specialties (Retail Pk) Dl Spec: Beverages Other unsweetened beverage 

Deli Specialties (Retail Pk) Dl Spec: Must/Oils/Vinegars Other condiments 

Deli/Bakery Discontnued Items Deli/Bakery Discontinued Items Other misc 

Dietary Aid Prdct/Med Liq Nutr Diet Cntrl Liqs Supplement Other supplements/meal replacements/energy drinks 

Dietary Aid Prdct/Med Liq Nutr Diet Cntrl Powders Nutritional Other supplements/meal replacements/energy drinks 

Dietary Aid Prdct/Med Liq Nutr Diet Control Water Other supplements/meal replacements/energy drinks 

Dietary Aid Prdct/Med Liq Nutr Diet Cntrl Bars (Supplement) Other supplements/meal replacements/energy drinks 

Dietary Aid Prdct/Med Liq Nutr Diet Cntrl Bars Nutritional Other supplements/meal replacements/energy drinks 

Dietary Aid Prdct/Med Liq Nutr Diet Cntrl Bars Nutritional W/ Other supplements/meal replacements/energy drinks 

Dietary Aid Prdct/Med Liq Nutr Diet Cntrl Liqs Nutritional Other supplements/meal replacements/energy drinks 

Dietary Aid Prdct/Med Liq Nutr Diet Energy Drinks Other supplements/meal replacements/energy drinks 

Dietary Aid Prdct/Med Liq Nutr Powder Nutrition Products Other supplements/meal replacements/energy drinks 

231 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS Subcategories Composite 

Subcategories Other Subcategories 

Dry Mix Desserts Gelatin Other seasoning/baking need 

Dry Tea/Coffee/Coco Mixes Coffee Ground Other unsweetened beverage 

Dry Tea/Coffee/Coco Mixes Coffee Whole Bean Other unsweetened beverage 

Dry Tea/Coffee/Coco Mixes Tea Bags (Supplement) Other unsweetened beverage 

Dry Tea/Coffee/Coco Mixes Tea Dry Other unsweetened beverage 

Dry Sauce/Gravy/Potatoes/Stuffing Cooking Bags With Spices/Seaso Other seasoning/baking need 

Dry Sauce/Gravy/Potatoes/Stuffing Gravy Can/Glass Other seasoning/baking need 

Dry Sauce/Gravy/Potatoes/Stuffing Sauce Mixes/Gravy Mixes Dry Other seasoning/baking need 

Eggs/Muffins/Potatoes Misc Dairy Refigerated Other misc 

Enhancements Enhancements—Other Other supplements/meal replacements/energy drinks 

Enhancements Enhancements—Pickled Items Other condiments 

Enhancements Enhancements—Pickles/Kraut Other condiments 

Enhancements Enhancements—Salads/Spreads Other condiments 

Enhancements Enhancements—Spices/Sauces Other seasoning/baking need 

European Foods British Foods Other misc 

European Foods French Foods Other misc 

European Foods German Foods Other misc 

European Foods Mediterranean/Greek Foods Other misc 

European Foods Other Ethnic Foods Other misc 

European Foods Polish Foods Other misc 

European Foods Scandinavian Foods Other misc 

Fitness & Diet Fitness & Diet Energy Drinks F/S Other supplements/meal replacements/energy drinks 

Fitness & Diet Fitness & Diet Energy Drinks Non Other supplements/meal replacements/energy drinks 

Fitness & Diet Fitness & Diet Isotonic Drinks Other supplements/meal replacements/energy drinks 

Fitness & Diet Fitness & Diet Isotonic Drinks Other supplements/meal replacements/energy drinks 

Fitness & Diet Fitness & Diet—Liq (Supplement) Other supplements/meal replacements/energy drinks 

Fitness & Diet Fitness & Diet—Liq Ntrtnl Other supplements/meal replacements/energy drinks 

Fitness & Diet Fitness & Diet—Powder (Supplement) Other supplements/meal replacements/energy drinks 

Fitness & Diet Fitness & Diet—Powder Ntrtnl Other supplements/meal replacements/energy drinks 

Fitness & Diet Fitness/Diet—Meal Replacement Other supplements/meal replacements/energy drinks 

Frozen Ethnic Frozen Internaional Other misc 

Frozen Ethnic Frozen Kosher Other misc 

Frozen Meat Alternatives Micro Protein Other supplements/meal replacements/energy drinks 

Frzn Multi Serve Frozen Other Other misc 

Gift & Fruit Baskets Gift Baskets W/Food Other misc 

Gift & Fruit Baskets Snack Packs W/Food Other misc 

Indian Foods Authentic Indian Foods Other misc 

Infant Formula Baby Isotonic Drinks Other infant formula/baby food 

Infant Formula Infant Formula Concentrate Other infant formula/baby food 

Infant Formula Infant Formula Milk Base Other infant formula/baby food 

Infant Formula Infant Formula Ready To Use Other infant formula/baby food 

Infant Formula Infant Formula Solutions Large Other infant formula/baby food 

Infant Formula Infant Formula Soy Base Other infant formula/baby food 

Infant Formula Infant Formula Specialty Other infant formula/baby food 

Infant Formula Infant Formula Starter Large Pk Other infant formula/baby food 

Infant Formula Infant Formula Starter/Solution Other infant formula/baby food 

Infant Formula Infant Formula Toddler Other infant formula/baby food 

Infant Formula Infant Formula Up Age Other infant formula/baby food 

Isotonic Drinks Isotonic Drinks Multi-Pack Other supplements/meal replacements/energy drinks 

Isotonic Drinks Isotonic Drinks Multi-Serve Other supplements/meal replacements/energy drinks 

Isotonic Drinks Isotonic Drinks Powdered Other supplements/meal replacements/energy drinks 

Isotonic Drinks Isotonic Drinks Single Serve Other supplements/meal replacements/energy drinks 

Isotonic Drinks Sports Bars Other supplements/meal replacements/energy drinks 

Isotonic Drinks Sports Drink N/Supplmnt Milk Other supplements/meal replacements/energy drinks 

Isotonic Drinks Sports Drink Supplement Other supplements/meal replacements/energy drinks 

Juices Super Premium Juices Antioxidant/Wellness Other supplements/meal replacements/energy drinks 

Juices Super Premium Juices Proteins Other supplements/meal replacements/energy drinks 

Kosher Exotic [Kosher Foods] Other misc 

Kosher Further Prepared Other misc 

Kosher Foods And Products Kosher Baking Needs Other seasoning/baking need 

Kosher Foods And Products Kosher Condiments Other condiments 

Kosher Foods And Products Passover Products Other misc 

Mediterranean Bar Sal: Olives/Pickles—Bulk Other condiments 

Mediterranean Bar Sal: Olives/Pickles—Bulk Other condiments 

Mediterranean Bar Sal: Olives/Pickls—Prpck Other condiments 

Mediterranean Bar Sal: Olives/Pickls—Prpck Other condiments 

Mixers Margarita Salt/Sugar/Misc Other condiments 

Multicultural Products Asian Processed Other misc 

Multicultural Products Hispanic Processed Produce Other misc 

Non-Dairy/Dairy Aseptic Soy/Rice Powder Other misc 

Pickle/Relish/Pckld Veg & Olives Green Olives Other condiments 

Pickle/Relish/Pckld Veg & Olives Peppers Other condiments 

Pickle/Relish/Pckld Veg & Olives Pickld Veg/Peppers/Etc. Other condiments 

Pickle/Relish/Pckld Veg & Olives Relishes Other condiments 

Pickle/Relish/Pckld Veg & Olives Ripe Olives Other condiments 

Pickle/Relish/Pckld Veg & Olives Specialty Olives Other condiments 

Powder & Crystal Drink Mix Sugar Free Canister [Powder 

Drink Mix] 

Other unsweetened beverage 

Powder & Crystal Drink Mix Sugar Free Sticks [Powder Drink 

Mix] 

Other unsweetened beverage 

Powder & Crystal Drink Mix Tea Other unsweetened beverage 

232 

Appendix C. Crosswalk of Subcommodities to USDA Food Pattern 

Categories—Continued 

Commodity Subcommodity USDA Food 

Pattern 

SoFAS 

Subcategories 

Composite 

Subcategories Other Subcategories 

Powder & Crystal Drink Mix Unsweetened Envelope [Powder 

Drink Mix] 

Other unsweetened beverage 

Prepared/Pdgd Foods Prepared/Pkgd Food Misc Other misc 

Processed Packaged Dry Mixes Other misc 

Processed Processed Other Other misc 

Refrgratd Juices/Drinks Dairy Case Tea No Sugar Or Swe Other unsweetened beverage 

Refrigerated Dairy Case Non-Dairy Milks Other misc 

Refrigerated Dairy Case Tea W/O Sweetener/Sugar Other unsweetened beverage 

Refrigerated Grocery Misc: Herring/Pickles/Horserad Other condiments 

Refrigerated Grocery Refrigerated Kosher Products Other misc 

Refrigerated Hispanic Grocery Hispanic Cultured Products Other misc 

Refrigerated Hispanic Grocery Misc Hispanic Grocery Other misc 

Refrigerated Hispanic Grocery Refrigerated Hispanic Drinks Other misc 

Refrigerated Vegetarian Vegetarian Misc Other misc 

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 Liquid Meals Other supplements/meal replacements/energy drinks 

Restricted Diet Misc Diet Other supplements/meal replacements/energy drinks 

Rtd Tea/New Age Juice Sparkling Tea Other unsweetened beverage 

Rtd Tea/New Age Juice Tea Unsweetened Other unsweetened beverage 

Salad & Dips Sal: Kosher Other misc 

Salad & Dips Sal:Dip Prepack Other condiments 

Salad Bar Condiments/Supplies Other condiments 

Salad Bar Salad Bar Other Other misc 

Salad Dresing & Sandwich 

Spreads 

Dry Salad Dressing & Dip Mixes Other condiments 

Seafood—Salad/Dip/Sce/Cond Dips/Spreads Other condiments 

Seafood—Salad/Dip/Sce/Cond Sauces Other condiments 

Seafood—Salad/Dips/Sce/Cond Other Pkgd Dip/Sauce/Condiment Other condiments 

Seafood—Salad/Dips/Sce/Cond Sauces Other condiments 

Seafood—Salad/Dips/Sce/Cond Spices/Marinades Other condiments 

Service Beverage Sv Bev: Coffee Other unsweetened beverage 

Service Beverage Sv Bev: Flav Tea Products Other unsweetened beverage 

Service Beverage Sv Bev: N/Carb Flv Frk/Minwtr Other unsweetened beverage 

Service Beverage Sv Bev: Spring Water Other unsweetened beverage 

Shelf Stable Juice Tea Bottles Other unsweetened beverage 

Soft Drinks Club Soda Other unsweetened beverage 

Soft Drinks Misc Items For Soft Drinks Other unsweetened beverage 

Soft Drinks Seltzer Unflavored Other unsweetened beverage 

Soft Drinks Unswntd Flavored Seltzer Water Other unsweetened beverage 

Spices & Extracts Food Colorings Other seasoning/baking need 

Spices & Extracts Gourmet Spices Other seasoning/baking need 

Spices & Extracts Imitation Extracts Other seasoning/baking need 

Spices & Extracts Pure Extracts Other seasoning/baking need 

Spices & Extracts Salt Substitutes Other seasoning/baking need 

Spices & Extracts Spices & Seasonings Other seasoning/baking need 

Spices & Extracts Table Salt/Popcorn Salt/Ice Cr Other seasoning/baking need 

Spices & Extracts Traditional Spices Other seasoning/baking need 

Spices/Jarred Garlic Spices & Seasonings Other seasoning/baking need 

Spices/Jarred Garlic Spices & Seasonings Organic Other seasoning/baking need 

Teas Bulk Tea Other unsweetened beverage 

Teas Instant Tea & Tea Mix Other unsweetened beverage 

Teas Supplemental Tea Other unsweetened beverage 

Teas Tea Bags & Bulk Tea Other unsweetened beverage 

Teas Tea Bags/Chai Other unsweetened beverage 

Teas Tea Bags/Green Other unsweetened beverage 

Teas Tea Bags/Herbal Other unsweetened beverage 

Traditional Asian Foods Asian Other Sauces/Marinad Other seasoning/baking need 

Traditional Asian Foods Asian Soy Sauce Other seasoning/baking need 

Traditional Asian Foods Traditional Thai Foods Other misc 

Traditional Mexican Foods Mexican Seasoning Mixes Other seasoning/baking need 

Traditional Mexican Foods Mexican Taco Sauce Other condiments 

Unknown Frozen Misc Other misc 

Vinegar & Cooking Wines Cooking Wines Other seasoning/baking need 

Vinegar & Cooking Wines Specialty Vinegar Other seasoning/baking need 

Vinegar & Cooking Wines Vinegar/White & Cider Other seasoning/baking need 

Water Carb Water Unflvrd Other unsweetened beverage 

Water Carb Water—Flvrd Unsweetened Other unsweetened beverage 

Water Fortified/Water Other unsweetened beverage 

Water Non-Carb Water Flvr—Drnk/Mnr Other unsweetened beverage 

Water Non-Carb Water Flvr—Unsweetened Other unsweetened beverage 

Water—(Sparkling & Still) Distilled Water Other unsweetened beverage 

Water—(Sparkling & Still) Sparkling Water—Flvrd Sweet Other unsweetened beverage 

Water—(Sparkling & Still) Sparkling Water—Flvrd Unsweetened Other unsweetened beverage 

Water—(Sparkling & Still) Sparkling Water—Unflavored Other unsweetened beverage 

Water—(Sparkling & Still) Spring Water Other unsweetened beverage 

Water—(Sparkling & Still) Still Water Drnking/Mnrl Water Other unsweetened beverage 

Water—(Sparkling & Still) Still Water Flvrd Drnk/Mnrl Wt Other unsweetened beverage 

Water—(Sparkling & Still) Still Water Flvrd Unsweetened Other unsweetened beverage 

Water—(Sparkling & Still) Water—Supplies Other unsweetened beverage 

Source: Foods Typically Purchased by SNAP Households, IMPAQ International, LLC, 2016. 

233 

Appendix D. Top 100 Subcommodities for SNAP Households By Expenditure for Each USDA Food Pattern Category 

Exhibit D–1: Dairy 

Dairy 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $191.1 33.25% 1 $853.8 25.69% 1 $1,044.9 26.80% 

Shredded Cheese 2 $74.7 13.00% 2 $342.0 10.29% 2 $416.7 10.69% 

American Single Cheese 3 $44.1 7.67% 4 $136.6 4.11% 4 $180.7 4.63% 

Natural Cheese Chunks 4 $35.3 6.14% 3 $216.1 6.50% 3 $251.4 6.45% 

Bagged Cheese Snacks 5 $17.1 2.98% 16 $52.0 1.56% 15 $69.1 1.77% 

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 Slices 10 $13.4 2.33% 6 $113.2 3.41% 5 $126.6 3.25% 

Yogurt/Ss Regular 11 $11.0 1.91% 11 $69.0 2.07% 11 $79.9 2.05% 

Loaf Cheese 12 $10.9 1.90% 23 $38.1 1.15% 21 $49.1 1.26% 

Yogurt/Ss Light 13 $10.2 1.78% 8 $103.1 3.10% 9 $113.3 2.91% 

Yogurt/Pro Active Health 14 $7.4 1.29% 13 $63.5 1.91% 13 $70.9 1.82% 

Yogurt/Adult Multi-Packs 15 $7.2 1.25% 19 $42.5 1.28% 20 $49.7 1.28% 

Specialty/Lactose Free Milk 16 $6.7 1.17% 17 $48.4 1.46% 18 $55.1 1.41% 

Grated Cheese 17 $6.2 1.08% 25 $33.6 1.01% 24 $39.9 1.02% 

Bulk Semi-Hard [Cheese] 18 $6.1 1.05% 18 $44.0 1.32% 19 $50.1 1.28% 

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/Specialty Greek 21 $5.0 0.86% 10 $77.4 2.33% 10 $82.4 2.11% 

Half & Half 22 $4.4 0.77% 15 $54.6 1.64% 16 $59.1 1.52% 

Yogurt/Large Size (16oz Or 

Lar) 

23 $4.4 0.76% 22 $40.4 1.22% 23 $44.8 1.15% 

Miscellaneous Cheese 24 $3.8 0.67% 21 $42.1 1.27% 22 $45.9 1.18% 

Bulk Processed [Cheese] 25 $3.4 0.59% 29 $19.8 0.60% 29 $23.2 0.59% 

Yogurt 26 $3.2 0.56% 12 $67.0 2.02% 14 $70.2 1.80% 

Bulk Semi-Soft [Cheese] 27 $3.0 0.53% 28 $23.3 0.70% 28 $26.3 0.68% 

Egg Nog/Boiled Custard 28 $2.7 0.47% 39 $13.3 0.40% 35 $16.0 0.41% 

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 Cheese 33 $1.7 0.29% 50 $6.9 0.21% 45 $8.6 0.22% 

Specialty Ppk Cheese Hard/ 

Grat 

34 $1.5 0.27% 26 $28.7 0.86% 27 $30.2 0.78% 

Aseptic Milk 35 $1.4 0.24% 38 $13.6 0.41% 38 $15.0 0.38% 

Misc Dry Cheese 36 $1.4 0.24% 46 $7.3 0.22% 44 $8.7 0.22% 

Soy Milk 37 $1.3 0.22% 49 $7.1 0.22% 47 $8.4 0.22% 

Specialty Ppk Cheese 

Spreads 

38 $1.2 0.21% 31 $16.2 0.49% 33 $17.5 0.45% 

Mexican Con Queso 39 $1.2 0.21% 63 $3.1 0.09% 61 $4.3 0.11% 

Specialty Ppk Cheese Feta 40 $1.2 0.20% 30 $18.5 0.56% 30 $19.6 0.50% 

Pre-Sliced Semi-Soft 

[Cheese] 

41 $1.1 0.20% 35 $14.4 0.43% 36 $15.5 0.40% 

Pre-Sliced Semi-Hard 

[Cheese] 

42 $1.0 0.18% 36 $14.3 0.43% 37 $15.3 0.39% 

Specialty Ppk Cheese 

Mozzarell 

43 $0.9 0.15% 32 $16.2 0.49% 34 $17.1 0.44% 

Specialty Ppk Cheese Processed 44 $0.8 0.15% 52 $6.0 0.18% 52 $6.8 0.17% 

Yogurt/Adult Drinks 45 $0.8 0.14% 60 $3.8 0.12% 60 $4.7 0.12% 

Specialty Ppk Cheese Cheddar & C 46 $0.8 0.14% 37 $13.9 0.42% 39 $14.7 0.38% 

Soy Beverage 47 $0.7 0.12% 53 $5.3 0.16% 54 $6.0 0.15% 

Specialty Ppk Cheese Semi 

Soft 

48 $0.6 0.10% 40 $11.4 0.34% 40 $12.0 0.31% 

Specialty Ppk Cheese Soft & 

Ripe 

49 $0.6 0.10% 42 $10.8 0.32% 41 $11.4 0.29% 

Specialty Ppk Cheese Blue/ 

Gorg 

50 $0.6 0.10% 41 $10.8 0.33% 42 $11.4 0.29% 

Non Fat Dry Milk 51 $0.6 0.10% 55 $5.2 0.16% 55 $5.7 0.15% 

Kefir 52 $0.6 0.10% 48 $7.2 0.22% 48 $7.8 0.20% 

Specialty Ppk Cheese Hispanic 53 $0.5 0.09% 68 $1.5 0.05% 68 $2.0 0.05% 

Specialty Ppk Cheese Gouda 

& Eda 

54 $0.5 0.08% 44 $8.0 0.24% 46 $8.5 0.22% 

Specialty Ppk Cheese Goat 

Milk 

55 $0.5 0.08% 43 $10.4 0.31% 43 $10.9 0.28% 

Total Dairy Expenditures * Among Top 

1,000 Subcommodities 

$571.2 99.37% $3,989.3 98.04% $4,767.6 98.22% 

234 

Exhibit D–1: Dairy—Continued 

Dairy 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Total Dairy Expenditures Among 1,792 

Subcommodities 

$574.9 100% $3,323.6 100% $3,898.5 100% 

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 

Fruit 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Dairy Case 100% Pure 

Juice—O 

1 $43.5 10.18% 1 $269.0 9.35% 1 $312.6 9.46% 

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 & Cider (Over 

50%) 

7 $13.3 3.11% 14 $45.8 1.59% 13 $59.0 1.79% 

Instore Cut Fruit 8 $13.2 3.09% 5 $85.8 2.98% 6 $99.0 3.00% 

Oranges Navels All 9 $12.6 2.94% 8 $79.3 2.75% 7 $91.8 2.78% 

Fruit Cup 10 $10.6 2.47% 19 $42.7 1.49% 14 $53.3 1.61% 

Blended Juice & Combinations (Ov) 11 $9.3 2.17% 29 $29.6 1.03% 24 $38.9 1.18% 

Clementines 12 $8.8 2.06% 9 $78.6 2.73% 8 $87.5 2.65% 

Melons Instore Cut 13 $8.2 1.93% 18 $42.8 1.49% 17 $51.1 1.55% 

Watermelon Seedless Whole 14 $7.9 1.84% 16 $43.9 1.53% 16 $51.8 1.57% 

Cherries Red 15 $6.9 1.61% 11 $56.7 1.97% 11 $63.6 1.93% 

Apples Gala (Bulk & Bag) 16 $6.6 1.54% 10 $69.3 2.41% 10 $75.9 2.30% 

Cranapple/Cran Grape Juice 

(50) 

17 $6.1 1.43% 31 $27.3 0.95% 29 $33.4 1.01% 

Apples Red Delicious (Bulk 

& Bag) 

18 $5.8 1.35% 23 $35.2 1.22% 20 $41.0 1.24% 

Dairy Case 100% Pure Juice 

Oth 

19 $5.4 1.26% 25 $32.3 1.12% 26 $37.7 1.14% 

Cantaloupe Whole 20 $5.3 1.24% 15 $44.4 1.54% 18 $49.7 1.50% 

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 Flesh 23 $4.8 1.13% 22 $35.6 1.24% 21 $40.5 1.22% 

Grape Juice (Over 50% 

Juice) 

24 $4.8 1.12% 44 $17.1 0.60% 41 $21.9 0.66% 

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 Smith (Bulk 

& Bag) 

27 $4.4 1.03% 27 $30.9 1.07% 28 $35.3 1.07% 

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 (Over 50% Jce) 30 $3.8 0.88% 26 $31.7 1.10% 27 $35.4 1.07% 

Raspberries 31 $3.5 0.83% 13 $45.8 1.59% 19 $49.3 1.49% 

Grapes Black/Blue 32 $3.4 0.80% 37 $21.8 0.76% 37 $25.2 0.76% 

Fruit Cocktail/Fruit Salad 33 $3.4 0.79% 54 $12.5 0.43% 52 $15.8 0.48% 

Mixed Fruit Bags 34 $3.2 0.75% 79 $5.7 0.20% 73 $8.9 0.27% 

Jarred Fruit Single Serve 35 $3.1 0.73% 49 $14.6 0.51% 47 $17.7 0.54% 

Raisins 36 $2.9 0.69% 32 $26.0 0.90% 32 $28.9 0.87% 

Apples Other (Bulk & Bag) 37 $2.8 0.66% 30 $27.4 0.95% 31 $30.2 0.91% 

Apples Fuji (Bulk & Bag) 38 $2.8 0.65% 21 $36.2 1.26% 23 $39.0 1.18% 

Apples Gold Delicious (Bulk 

& Bag) 

39 $2.8 0.65% 43 $17.9 0.62% 43 $20.7 0.62% 

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 Yellow Flesh 42 $2.5 0.60% 42 $18.6 0.64% 42 $21.1 0.64% 

Pineapple Whole & Peel/ 

Cored 

43 $2.5 0.59% 36 $22.1 0.77% 38 $24.6 0.75% 

Apples Honeycrisp 44 $2.4 0.57% 20 $36.9 1.28% 22 $39.4 1.19% 

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 Oranges/Citrus 

Sect 

47 $2.3 0.53% 53 $12.6 0.44% 54 $14.8 0.45% 

Frzn Conc Allieds Over 50% 

Jui 

48 $2.2 0.52% 57 $10.1 0.35% 56 $12.3 0.37% 

Mango 49 $2.2 0.52% 50 $14.1 0.49% 50 $16.3 0.49% 

Apple Sauce (Excludes Cup) 50 $2.2 0.51% 51 $13.8 0.48% 51 $16.0 0.48% 

Tangerines & Tangelos 51 $2.1 0.49% 55 $11.3 0.39% 55 $13.4 0.41% 

235 

Exhibit D–2: Fruit—Continued 

Fruit 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Frzn Oj & Oj Substitutes 

(Over 5) 

52 $1.9 0.44% 45 $16.2 0.56% 45 $18.1 0.55% 

Watermelon Personal 53 $1.9 0.44% 46 $15.9 0.55% 46 $17.8 0.54% 

Bananas Organic 54 $1.9 0.44% 41 $18.7 0.65% 44 $20.6 0.62% 

Pears 55 $1.9 0.43% 59 $10.0 0.35% 58 $11.8 0.36% 

Convenience/Snacking Fruit 

Pro 

56 $1.8 0.41% 64 $9.4 0.33% 60 $11.2 0.34% 

Cranberry Sauce 57 $1.7 0.39% 58 $10.0 0.35% 59 $11.6 0.35% 

Strawberries Organic 58 $1.6 0.38% 38 $21.4 0.74% 40 $23.0 0.70% 

Cut Fruit All Other Prepack 59 $1.6 0.38% 69 $8.5 0.29% 65 $10.1 0.31% 

Caramel/Candy Apples 60 $1.6 0.36% 94 $3.4 0.12% 84 $4.9 0.15% 

Pears Bartlett 61 $1.5 0.35% 47 $15.7 0.55% 48 $17.2 0.52% 

Fruit Party Tray Prepack 62 $1.4 0.33% 74 $6.5 0.23% 75 $7.9 0.24% 

Dried Fruit—Other 63 $1.4 0.33% 48 $15.6 0.54% 49 $17.0 0.51% 

Pineapple Juice (Over 50% 

Juic) 

64 $1.4 0.33% 75 $6.4 0.22% 76 $7.8 0.24% 

Cranberry Juice (Over 50% 

Jce) 

65 $1.4 0.32% 70 $8.4 0.29% 69 $9.8 0.30% 

Lemon Juice & Lime Juice 

(Over) 

66 $1.2 0.29% 72 $7.8 0.27% 72 $9.0 0.27% 

Oranges Non Navel All 67 $1.2 0.28% 81 $5.0 0.18% 80 $6.2 0.19% 

Prune Juice (Over 50% 

Juice) 

68 $1.2 0.27% 71 $8.3 0.29% 71 $9.5 0.29% 

Drinks—Carb Juice (Over 

50%) 

69 $1.1 0.26% 61 $9.7 0.34% 62 $10.8 0.33% 

Juice Single Blend 70 $1.1 0.26% 66 $9.4 0.33% 63 $10.5 0.32% 

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 Ranier 74 $1.0 0.23% 68 $9.0 0.31% 68 $10.0 0.30% 

Cranapple/Cran Grape Juice 

(Ov) 

75 $0.9 0.21% 77 $6.3 0.22% 77 $7.2 0.22% 

Juice (Over 50% Juice) 76 $0.9 0.21% 100 $2.7 0.09% 98 $3.6 0.11% 

Watermelon W/Seeds Whole 77 $0.9 0.20% 98 $3.0 0.11% 93 $3.9 0.12% 

Honeydew Whole 78 $0.8 0.18% 78 $5.9 0.21% 79 $6.7 0.20% 

Grapes Red Globe 79 $0.8 0.18% 92 $3.5 0.12% 91 $4.2 0.13% 

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 Cherries 82 $0.7 0.17% 88 $4.1 0.14% 87 $4.8 0.14% 

Apples Braeburn (Bulk & 

Bag) 

83 $0.7 0.17% 63 $9.4 0.33% 64 $10.1 0.31% 

Grapefruit Juice (Over 50% 

Jui) 

84 $0.7 0.17% 86 $4.1 0.14% 85 $4.8 0.15% 

Apples Gala (Bulk & Bag) 

Organic 

85 $0.6 0.15% 65 $9.4 0.33% 67 $10.0 0.30% 

Peaches White Flesh 86 $0.6 0.15% 80 $5.5 0.19% 81 $6.2 0.19% 

Jarred Fruit Multi Serve 87 $0.6 0.14% 82 $4.5 0.16% 82 $5.1 0.16% 

Squeeze Lemons/Limes 88 $0.5 0.12% 95 $3.3 0.12% 94 $3.9 0.12% 

Raspberries Organic 89 $0.5 0.12% 67 $9.1 0.32% 70 $9.6 0.29% 

Pears Bosc 90 $0.5 0.11% 84 $4.3 0.15% 86 $4.8 0.14% 

Blueberries Organic 91 $0.5 0.11% 62 $9.6 0.33% 66 $10.1 0.30% 

Pears Asian 92 $0.4 0.10% 90 $3.8 0.13% 92 $4.2 0.13% 

Total Fruit Expenditures * Among Top 

1,000 subcommodities 

$416.8 97.49% $2,772.4 96.36% $3,189.2 96.54% 

Total Fruit Expenditures Among 1,792 

Subcommodities 

$427.6 100% $2,877.2 100% $3,304.8 100% 

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 

Grain 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Kids Cereal 1 $78.1 9.88% 3 $186.4 4.51% 1 $264.5 5.37% 

Mainstream White Bread 2 $48.0 6.07% 7 $136.8 3.31% 6 $184.7 3.75% 

Tortilla/Nacho Chips 3 $47.4 5.99% 2 $209.0 5.05% 2 $256.4 5.21% 

Mainstream Variety Breads 4 $38.4 4.86% 5 $173.2 4.19% 4 $211.7 4.30% 

All Family Cereal 5 $36.2 4.58% 1 $214.9 5.20% 3 $251.1 5.10% 

Adult Cereal 6 $24.9 3.15% 4 $182.6 4.42% 5 $207.5 4.21% 

236 

Exhibit D–3: Grains—Continued 

Grain 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Mexican Soft Tortillas And 

Wra 

7 $23.7 3.00% 8 $113.1 2.74% 8 $136.8 2.78% 

Waffles/Pancakes/French 

Toast 

8 $17.3 2.19% 13 $77.4 1.87% 12 $94.7 1.92% 

Ramen Noodles/Ramen Cups 9 $16.7 2.12% 43 $28.1 0.68% 34 $44.8 0.91% 

Cheese Crackers 10 $16.5 2.08% 10 $90.2 2.18% 10 $106.7 2.17% 

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 Biscuits 13 $14.7 1.86% 30 $45.2 1.09% 26 $59.9 1.22% 

Butter Spray Cracker 14 $14.6 1.85% 15 $68.7 1.66% 15 $83.3 1.69% 

Toaster Pastries 15 $14.0 1.77% 27 $47.6 1.15% 23 $61.6 1.25% 

Rice Side Dish Mixes Dry 16 $14.0 1.76% 28 $46.7 1.13% 24 $60.6 1.23% 

Popcorn—Microwave 17 $13.1 1.65% 17 $63.4 1.53% 17 $76.5 1.55% 

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 Pasta 22 $9.9 1.25% 21 $56.2 1.36% 20 $66.1 1.34% 

Rolls: Dinner 23 $9.5 1.21% 23 $50.5 1.22% 25 $60.1 1.22% 

Frzn Garlic Toast 24 $9.1 1.16% 44 $27.8 0.67% 39 $36.9 0.75% 

Corn Chips 25 $9.1 1.15% 29 $45.6 1.10% 28 $54.7 1.11% 

Instant Oatmeal 26 $8.9 1.13% 33 $41.1 0.99% 32 $50.0 1.02% 

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 Crackers 29 $8.0 1.01% 32 $41.3 1.00% 33 $49.3 1.00% 

Bagels 30 $7.8 0.99% 16 $66.9 1.62% 18 $74.7 1.52% 

Noodle Side Dish Mixes 31 $7.3 0.92% 53 $21.1 0.51% 49 $28.4 0.58% 

Rice—Dry Bag And Box 32 $7.1 0.90% 37 $33.9 0.82% 36 $41.1 0.83% 

Sandwich Buns 33 $7.1 0.90% 20 $56.8 1.37% 21 $63.9 1.30% 

Rice—Instant & Microwave 34 $6.8 0.86% 34 $38.0 0.92% 35 $44.8 0.91% 

Frzn Breakfast Pastry 35 $6.5 0.82% 57 $19.0 0.46% 52 $25.4 0.52% 

Flour: White & Self Rising 36 $6.4 0.81% 42 $28.8 0.70% 41 $35.2 0.71% 

Pretzels 37 $6.2 0.79% 22 $55.4 1.34% 22 $61.6 1.25% 

Bread: Italian/French 38 $6.1 0.77% 25 $49.0 1.19% 27 $55.1 1.12% 

Muffin & Corn Bread Mix 39 $6.0 0.76% 41 $28.9 0.70% 42 $34.9 0.71% 

Refrigerated Specialty Rolls 40 $5.5 0.70% 45 $27.5 0.66% 44 $33.0 0.67% 

Refrigerated Crescent Rolls 41 $5.4 0.68% 38 $31.2 0.76% 40 $36.6 0.74% 

Mexican Taco/Tostado/Shells 42 $5.2 0.66% 56 $19.1 0.46% 55 $24.3 0.49% 

Noodles Dry 43 $4.5 0.58% 48 $24.9 0.60% 47 $29.4 0.60% 

Rolls: Sandwich 44 $4.1 0.52% 46 $26.7 0.65% 46 $30.9 0.63% 

Salad Toppers 45 $4.1 0.52% 68 $15.1 0.37% 64 $19.2 0.39% 

Graham Crackers 46 $4.0 0.51% 47 $24.9 0.60% 48 $29.0 0.59% 

Standard Oatmeal 47 $3.9 0.49% 39 $29.9 0.72% 43 $33.8 0.69% 

English Muffins/Waffles 48 $3.8 0.48% 24 $49.5 1.20% 29 $53.3 1.08% 

Main Meal Bread 49 $3.8 0.48% 36 $34.9 0.84% 37 $38.7 0.79% 

Dinner Rolls 50 $3.5 0.44% 71 $14.5 0.35% 67 $18.0 0.36% 

Breadings/Coatings/Crumbs 51 $3.2 0.41% 65 $16.0 0.39% 62 $19.3 0.39% 

Bread: Specialty 52 $3.2 0.40% 51 $22.9 0.55% 51 $26.0 0.53% 

Bagged Popped Popcorn 53 $3.0 0.38% 77 $12.5 0.30% 75 $15.5 0.32% 

Frzn Dinner Rolls 54 $3.0 0.38% 54 $20.9 0.50% 56 $23.9 0.48% 

Rolls: Croissants/Breadsticks 55 $2.9 0.37% 64 $16.5 0.40% 61 $19.4 0.39% 

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 Tortillas 58 $2.8 0.36% 86 $9.4 0.23% 80 $12.3 0.25% 

Croutons 59 $2.8 0.36% 73 $14.0 0.34% 69 $16.8 0.34% 

Frzn Garlic Bread 60 $2.7 0.34% 78 $11.1 0.27% 78 $13.8 0.28% 

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 Salads— 

Prepack 

63 $2.6 0.33% 82 $10.3 0.25% 79 $12.9 0.26% 

Cornmeal 64 $2.5 0.32% 95 $7.3 0.18% 90 $9.8 0.20% 

Refrigerated Bagels 65 $2.5 0.32% 93 $7.7 0.19% 87 $10.2 0.21% 

Refrigerated Pasta 66 $2.4 0.30% 40 $29.3 0.71% 45 $31.7 0.64% 

Diet/Light Bread 67 $2.4 0.30% 49 $24.0 0.58% 50 $26.3 0.53% 

Pasta/Grain Salads—Bulk 68 $2.3 0.30% 63 $16.9 0.41% 63 $19.3 0.39% 

Mini-Cakes 69 $2.3 0.30% 60 $17.2 0.42% 60 $19.5 0.40% 

Fruit/Breakfast Bread 70 $2.2 0.28% 58 $18.7 0.45% 58 $21.0 0.43% 

Breading 71 $2.2 0.28% 114 $3.7 0.09% 104 $5.9 0.12% 

Frzn Breadsticks 72 $2.2 0.28% 106 $5.0 0.12% 97 $7.2 0.15% 

Rye Breads 73 $2.0 0.25% 52 $22.3 0.54% 54 $24.3 0.49% 

Other Hot Cereal 74 $1.9 0.24% 80 $10.3 0.25% 81 $12.2 0.25% 

Rolls: Bagels 75 $1.9 0.24% 67 $15.4 0.37% 68 $17.3 0.35% 

Biscuit Flour & Mixes 76 $1.9 0.23% 74 $13.8 0.33% 72 $15.7 0.32% 

Bread: Artisan 77 $1.7 0.22% 35 $36.7 0.89% 38 $38.4 0.78% 

Flour: Misc/Specialty/Blend 

Et 

78 $1.6 0.20% 75 $13.6 0.33% 77 $15.2 0.31% 

Bread: Pita/Pocket/Flatbrd 79 $1.5 0.19% 72 $14.1 0.34% 73 $15.6 0.32% 

Pizza Mix Dry 80 $1.4 0.18% 102 $5.4 0.13% 98 $6.8 0.14% 

Breakfast Bars/Tarts/Scones 81 $1.4 0.18% 50 $23.6 0.57% 53 $25.0 0.51% 

Popcorn—Other 82 $1.4 0.17% 84 $10.0 0.24% 84 $11.4 0.23% 

Asian Noodles/Rice 83 $1.3 0.17% 79 $10.5 0.25% 82 $11.8 0.24% 

237 

Exhibit D–3: Grains—Continued 

Grain 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Instant Breakfast 84 $1.3 0.16% 91 $8.1 0.20% 93 $9.4 0.19% 

Tortilla Chips 85 $1.3 0.16% 55 $19.9 0.48% 57 $21.2 0.43% 

Bread: Sweet/Breakfast 86 $1.3 0.16% 90 $8.4 0.20% 91 $9.7 0.20% 

Refrigerated Breads 87 $1.2 0.16% 83 $10.2 0.25% 83 $11.5 0.23% 

Bread: Sourdough 88 $1.2 0.15% 61 $17.1 0.41% 66 $18.3 0.37% 

Bread: Tortillas/Wraps 89 $1.0 0.13% 85 $9.8 0.24% 86 $10.8 0.22% 

Vending Size/Sngl Serve 

Cracke 

90 $1.0 0.12% 124 $2.3 0.06% 120 $3.3 0.07% 

Snacks: Pita Chips 91 $0.9 0.12% 66 $15.7 0.38% 70 $16.7 0.34% 

Granola 92 $0.9 0.12% 69 $15.1 0.37% 71 $16.0 0.33% 

Caramel Coated Snacks 93 $0.9 0.11% 118 $3.1 0.08% 115 $4.0 0.08% 

Specialty Crackers 94 $0.9 0.11% 59 $17.8 0.43% 65 $18.7 0.38% 

Crackers 95 $0.8 0.10% 70 $14.6 0.35% 76 $15.4 0.31% 

Bread: Rye/Cocktail 96 $0.7 0.09% 92 $8.1 0.20% 95 $8.8 0.18% 

Whole Grain Bread 97 $0.7 0.09% 88 $9.2 0.22% 88 $9.9 0.20% 

Frzn Bagels 98 $0.7 0.09% 120 $2.9 0.07% 119 $3.6 0.07% 

Bread: Wheat/Whl Grain 99 $0.7 0.09% 81 $10.3 0.25% 85 $11.0 0.22% 

Pies: Sugar Free 100 $0.7 0.09% 111 $4.5 0.11% 111 $5.2 0.11% 

Top 100 Grain Expenditures * $778.3 98.43% $3,989.3 96.47% $4,767.6 96.79% 

Total Grain Expenditures Among Top 

1,000 Subcommodities 

$783.8 99.13% $4,049.9 96.28% $4,833.8 98.63% 

Total Grain Expenditures Among 1,792 

Subcommodities 

$790.7 100% $4,135.0 100% $4,925.7 100% 

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 

Oil 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Pourable Salad Dressings 1 $29.0 22.71% 1 $139.4 24.28% 1 $168.4 23.99% 

Mayonnaise & Whipped 

Dressing 

2 $27.3 21.34% 2 $119.1 20.73% 2 $146.3 20.84% 

Margarine: Tubs And Bowls 3 $23.4 18.37% 3 $100.9 17.56% 3 $124.3 17.71% 

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 Creamy 8 $1.6 1.23% 8 $14.5 2.53% 8 $16.1 2.30% 

Sand/Horseradish & Tartar 

Sauce 

9 $1.4 1.14% 10 $7.2 1.26% 10 $8.7 1.23% 

Corn Oil 10 $1.3 1.01% 14 $4.1 0.71% 12 $5.4 0.77% 

Cooking Oil: Peanut/ 

Safflower/ 

11 $1.1 0.89% 11 $6.7 1.17% 11 $7.8 1.12% 

Dressing Blue Cheese 12 $0.9 0.71% 9 $9.5 1.65% 9 $10.4 1.48% 

Margarine: Squeeze 13 $0.6 0.44% 13 $4.2 0.74% 14 $4.8 0.68% 

Total Oil Expenditures * 

Among Top 1,000 Subcommodities 

$125.9 98.58% $555.0 96.65% $680.9 96.99% 

Total Oil Expenditures Among 1,792 

Subcommodities 

$127.0 100% $574.4 100% $702.1 100% 

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. 

238 

Exhibit D–5: Protein Foods 

Protein Foods 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

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—Deli Fresh 3 $55.8 3.67% 4 $242.6 3.79% 4 $298.4 3.76% 

Eggs—Large 4 $52.1 3.43% 3 $251.6 3.93% 3 $303.7 3.83% 

Chicken Breast Boneless 5 $49.6 3.26% 1 $292.9 4.57% 2 $342.5 4.32% 

Enhanced [Pork Boneless 

Loin/Rib] 

6 $41.5 2.73% 6 $168.0 2.62% 6 $209.5 2.64% 

Bacon—Trad 16oz Or Less 7 $40.7 2.68% 8 $157.6 2.46% 7 $198.3 2.50% 

Ribs [Pork] 8 $35.0 2.30% 15 $106.8 1.67% 13 $141.8 1.79% 

Frzn Chicken—Wht Meat 9 $30.0 1.97% 17 $99.8 1.56% 16 $129.8 1.64% 

Choice Beef (Loins) 10 $28.4 1.87% 11 $136.6 2.13% 10 $165.1 2.08% 

Select Beef 11 $27.9 1.83% 9 $143.7 2.24% 9 $171.5 2.16% 

Hot Dogs—Base Meat 12 $25.1 1.65% 27 $56.8 0.89% 23 $81.9 1.03% 

Choice Beef (Rounds) 13 $24.0 1.58% 20 $72.5 1.13% 19 $96.5 1.22% 

Chicken Wings 14 $22.2 1.46% 58 $28.6 0.45% 40 $50.9 0.64% 

Frzn Chicken—Wings 15 $22.2 1.46% 97 $17.4 0.27% 52 $39.5 0.50% 

Lunchment—Bologna/Sausage 16 $21.8 1.43% 24 $60.9 0.95% 22 $82.7 1.04% 

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 Bulk 19 $19.3 1.27% 7 $159.6 2.49% 8 $178.9 2.26% 

Frzn Meat—Beef 20 $19.0 1.25% 34 $46.3 0.72% 30 $65.2 0.82% 

Value Forms/18oz And Larger [Chicken] 21 $18.6 1.22% 41 $42.6 0.67% 33 $61.2 0.77% 

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—Links Pork 

Ckd/S 

24 $16.4 1.08% 45 $37.6 0.59% 38 $54.1 0.68% 

Meat: Ham Bulk 25 $15.3 1.00% 13 $115.9 1.81% 14 $131.2 1.65% 

Bkfst Sausage—Fresh Rolls 26 $15.1 0.99% 23 $61.4 0.96% 25 $76.5 0.96% 

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 Beans—Baked W/ 

Pork 

29 $13.4 0.88% 28 $55.3 0.86% 29 $68.7 0.87% 

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—Links Fresh 32 $13.0 0.86% 25 $58.0 0.91% 26 $71.1 0.90% 

Whole Chicken (Roasters/ 

Fryer) 

33 $12.9 0.85% 26 $56.9 0.89% 27 $69.8 0.88% 

Chicken Thighs 34 $12.2 0.80% 31 $50.0 0.78% 31 $62.2 0.78% 

Dnr Sausage—Pork Rope 

Ckd/Sm 

35 $12.1 0.80% 43 $38.2 0.60% 42 $50.4 0.64% 

Bacon—Trad Greater Than 

16oz 

36 $12.0 0.79% 35 $44.6 0.70% 35 $56.6 0.71% 

Soup/Stew 37 $11.2 0.74% 36 $44.1 0.69% 37 $55.3 0.70% 

Whole Muscle Breaded/18oz 

And 

38 $11.1 0.73% 53 $29.9 0.47% 49 $41.0 0.52% 

Variety Beans—Kidney/ 

Pinto/E 

39 $10.5 0.69% 22 $68.0 1.06% 24 $78.5 0.99% 

Cubed Meats [Beef] 40 $10.5 0.69% 54 $29.8 0.46% 51 $40.3 0.51% 

Hot Dogs—Base Beef 41 $10.3 0.68% 32 $49.4 0.77% 34 $59.8 0.75% 

Eggs—Medium 42 $10.1 0.66% 81 $21.0 0.33% 64 $31.1 0.39% 

Butts [Pork Shoulder] 43 $9.7 0.63% 56 $29.2 0.46% 54 $38.8 0.49% 

Boneless Snack/18oz And 

Larger 

44 $9.6 0.63% 77 $21.5 0.33% 65 $31.1 0.39% 

Chix: Value-Added (Cold) 45 $9.5 0.63% 62 $26.7 0.42% 58 $36.2 0.46% 

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—Fresh Links 48 $8.9 0.59% 64 $26.3 0.41% 59 $35.3 0.44% 

Bone-In Wings 49 $8.8 0.58% 123 $12.0 0.19% 94 $20.8 0.26% 

Hams—Half/Port Bone-In 50 $8.2 0.54% 52 $30.0 0.47% 56 $38.2 0.48% 

Meat: Beef Bulk 51 $7.9 0.52% 30 $53.4 0.83% 32 $61.3 0.77% 

Hams—Spiral 52 $7.6 0.50% 46 $36.5 0.57% 47 $44.1 0.56% 

Hot Dogs—Premium 53 $7.4 0.49% 40 $42.7 0.67% 43 $50.1 0.63% 

Snack Meat—Pepperoni 54 $7.4 0.48% 48 $32.1 0.50% 53 $39.5 0.50% 

Frzn Meat—Breakfast Sausage 55 $7.3 0.48% 128 $11.3 0.18% 109 $18.6 0.23% 

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 Fish Fillets 58 $6.9 0.45% 79 $21.1 0.33% 74 $28.0 0.35% 

Jerky/Nuggets/Tenders 59 $6.8 0.45% 67 $25.8 0.40% 62 $32.6 0.41% 

Catfish—Fillet 60 $6.8 0.45% 110 $13.1 0.20% 102 $19.9 0.25% 

Chicken Legs/Quarters 61 $6.6 0.43% 109 $13.5 0.21% 101 $20.1 0.25% 

Value-Added Breaded 

Shrimp 

62 $6.4 0.42% 98 $16.9 0.26% 86 $23.3 0.29% 

Pancake Mixes 63 $6.3 0.41% 65 $21.9 0.34% 68 $28.1 0.35% 

Frz Fishsticks/Tenders/Nuggets 64 $6.1 0.40% 104 $14.7 0.23% 95 $20.8 0.26% 

Crab—Snow 65 $6.1 0.40% 127 $11.4 0.18% 110 $17.5 0.22% 

Chix: Frd 8pc/Cut Up (Cold) 66 $6.0 0.39% 117 $12.7 0.20% 107 $18.7 0.24% 

Lunchmeat—Chop/Form 

Pltry & Ha 

67 $5.1 0.34% 121 $12.1 0.19% 111 $17.2 0.22% 

239 

Exhibit D–5: Protein Foods—Continued 

Protein Foods 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Salmon Fr—Altantic 68 $5.0 0.33% 33 $48.8 0.76% 39 $53.8 0.68% 

Party Tray—Shrimp 69 $4.8 0.32% 73 $24.8 0.39% 71 $29.6 0.37% 

Ham Steaks/Cubes/Slices 70 $4.7 0.31% 63 $26.3 0.41% 66 $31.0 0.39% 

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 Boneless 73 $4.5 0.29% 105 $14.6 0.23% 106 $19.1 0.24% 

Meat Bulk: Specialty Dry 

Meats 

74 $4.4 0.29% 59 $28.3 0.44% 61 $32.8 0.41% 

Chunk Meats—Chix/Ham/ 

Etc. 

75 $4.4 0.29% 70 $25.3 0.40% 70 $29.7 0.37% 

Whole Toms (Over 16lbs) 

[Turkey] 

76 $4.3 0.28% 84 $20.0 0.31% 83 $24.2 0.31% 

Lunchmeat—Whole Muscle 

Pltry 

77 $4.2 0.28% 86 $19.7 0.31% 84 $24.0 0.30% 

Bacon—Pre-Cooked 78 $4.1 0.27% 72 $24.8 0.39% 72 $28.9 0.36% 

Baking Nuts 79 $4.1 0.27% 38 $43.2 0.67% 46 $47.3 0.60% 

Bologna/Loaves/Franks 80 $4.0 0.26% 87 $19.2 0.30% 87 $23.1 0.29% 

Pistachios 81 $3.9 0.26% 57 $29.1 0.45% 60 $33.0 0.42% 

Seasoned Poultry 82 $3.9 0.26% 100 $16.5 0.26% 99 $20.4 0.26% 

Protein Salads—Bulk 83 $3.9 0.26% 65 $26.3 0.41% 69 $30.2 0.38% 

Bkfst Sausage—Fresh Patties 84 $3.8 0.25% 136 $9.8 0.15% 126 $13.6 0.17% 

Meat: Chicken Bulk 85 $3.7 0.25% 47 $34.6 0.54% 55 $38.4 0.48% 

Bkfst Sausage—Precooked 86 $3.7 0.25% 78 $21.4 0.33% 80 $25.2 0.32% 

Dnr Sausage—Beef Rope 

Ckd/Sm 

87 $3.7 0.24% 120 $12.2 0.19% 115 $15.9 0.20% 

Whole Hens (Under 16lbs) 

[Turkey] 

88 $3.6 0.24% 89 $19.0 0.30% 89 $22.6 0.29% 

Dnr Sausage—Other Forms 89 $3.6 0.24% 76 $21.6 0.34% 81 $25.2 0.32% 

External Fresh [Pork Offal] 90 $3.5 0.23% 204 $4.2 0.06% 169 $7.7 0.10% 

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/Port Boneless 93 $3.4 0.23% 80 $21.0 0.33% 82 $24.5 0.31% 

Lunchmeat—Chip Meat 94 $3.3 0.22% 138 $9.7 0.15% 130 $13.1 0.16% 

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—Fillet 97 $3.2 0.21% 101 $16.4 0.26% 103 $19.6 0.25% 

Frozen Burgers 98 $3.2 0.21% 217 $3.1 0.05% 185 $6.3 0.08% 

Frozen Breakfast Sausage 99 $3.1 0.20% 135 $9.8 0.15% 132 $12.9 0.16% 

Stuffed/Mixed Beef 100 $3.1 0.20% 88 $19.2 0.30% 90 $22.3 0.28% 

Top 100 Protein Foods 

Expenditures * 

$1,342.3 87.82% $5,249.5 81.66% $6,591.7 82.84% 

Total Protein Foods Expenditures Among 

Top 1,000 Subcommodities 

$1,512.2 98.95% $6,288.8 97.83% $7,801.0 98.04% 

Total Protein Foods 

Expenditures 

Among 1,792 Subcommodities 

$1,528.3 100% $6,428.5 100% $7,956.9 100% 

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) 

(SoFAS) 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Soft Drinks 12/18 & 15pk 

Can Car 

1 $164.6 18.86% 1 $601.2 16.11% 1 $765.8 16.63% 

Sft Drnk 2 Liter Btl Carb 

Incl 

2 $70.9 8.12% 2 $230.1 6.17% 2 $301.0 6.54% 

Soft Drinks 20pk & 24pk 

Can Carb 

3 $39.7 4.55% 9 $106.4 2.85% 8 $146.1 3.17% 

Sugar 4 $36.9 4.23% 8 $112.7 3.02% 7 $149.6 3.25% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

5 $34.0 3.90% 4 $173.6 4.65% 3 $207.6 4.51% 

Sft Drnk Sngl Srv Btl Carb 

(Ex) 

6 $27.8 3.18% 11 $71.4 1.91% 11 $99.2 2.15% 

Aseptic Pack Juice And 

Drinks 

7 $24.2 2.78% 16 $57.1 1.53% 15 $81.4 1.77% 

240 

Exhibit D–6: Saturated Fats and Added Sugars (SoFAS)—Continued 

(SoFAS) 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Refrigerated Coffee Creamers 8 $24.1 2.76% 6 $147.2 3.95% 5 $171.3 3.72% 

Candy Bags—Chocolate 9 $21.5 2.46% 5 $147.5 3.95% 6 $169.1 3.67% 

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 (Singles) (Including) 13 $16.3 1.87% 18 $54.9 1.47% 16 $71.3 1.55% 

Dairy Case Juice Drnk 

Under 10 

14 $16.0 1.83% 22 $48.0 1.29% 19 $64.0 1.39% 

Candy Bars (Multi Pack) 15 $15.6 1.79% 12 $69.6 1.86% 12 $85.2 1.85% 

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—Non Chocolate 18 $12.6 1.44% 19 $54.9 1.47% 18 $67.5 1.46% 

Molasses & Syrups 19 $11.7 1.34% 15 $58.7 1.57% 17 $70.4 1.53% 

Dairy Case Citrus Pnch/Oj 

Subs 

20 $11.0 1.26% 27 $34.4 0.92% 26 $45.4 0.99% 

Fruit Drinks: Canned & 

Glass 

21 $10.6 1.21% 60 $10.9 0.29% 46 $21.5 0.47% 

Non Dairy Creamer 22 $10.5 1.20% 25 $35.4 0.95% 25 $45.9 1.00% 

Seasonal Miscellaneous 

[Candy] 

23 $9.2 1.05% 23 $46.9 1.26% 23 $56.0 1.22% 

Dairy Case Tea With Sugar 

Or S 

24 $8.4 0.96% 36 $23.1 0.62% 33 $31.5 0.68% 

Seasonal Candy Bags—Chocolate 25 $7.9 0.90% 20 $54.8 1.47% 21 $62.7 1.36% 

Energy Drink—Single Serve 26 $7.7 0.88% 32 $26.3 0.70% 29 $33.9 0.74% 

Energy Drink—Single Serve 

(N) 

27 $7.1 0.82% 24 $39.5 1.06% 24 $46.7 1.01% 

Preserves/Jam/Marmalade 28 $6.7 0.77% 17 $56.2 1.51% 20 $63.0 1.37% 

Margarine Stick 29 $6.7 0.77% 41 $22.3 0.60% 37 $29.0 0.63% 

Juice (Under 10% Juice) 30 $6.7 0.76% 40 $22.4 0.60% 36 $29.1 0.63% 

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 Can Non-Carb 

(Exce) 

33 $5.9 0.67% 57 $11.5 0.31% 54 $17.4 0.38% 

Refrig Dips 34 $5.7 0.66% 34 $24.7 0.66% 34 $30.4 0.66% 

Aseptic Pack Juice And 

Drinks 

35 $5.3 0.61% 46 $17.5 0.47% 44 $22.9 0.50% 

Candy Bars (Singles) (Including) 36 $5.1 0.59% 50 $15.9 0.43% 48 $21.1 0.46% 

Cranberry Juice (50% And 

Under) 

37 $5.0 0.58% 39 $22.6 0.61% 40 $27.6 0.60% 

Frzn Whipped Topping 38 $5.0 0.57% 28 $30.9 0.83% 28 $35.9 0.78% 

Blended Juice & Combinations (50) 39 $4.8 0.55% 37 $22.9 0.61% 39 $27.7 0.60% 

Jelly 40 $4.7 0.54% 44 $18.1 0.48% 45 $22.8 0.50% 

Energy Drink—Multi-Pack 41 $4.3 0.49% 43 $19.0 0.51% 42 $23.3 0.51% 

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 6pk Can Carb 

(Exp) 

44 $4.1 0.47% 30 $27.8 0.74% 32 $31.9 0.69% 

Miscellaneous Candy (Including) 45 $4.0 0.46% 42 $19.0 0.51% 43 $23.0 0.50% 

Juices Superfoods/Enhanced 46 $3.8 0.44% 38 $22.8 0.61% 41 $26.6 0.58% 

Dairy Case Fruit Drinks (No 

Ju) 

47 $3.7 0.42% 102 $2.8 0.08% 80 $6.5 0.14% 

Aseptic Pack Juice And 

Drinks 

48 $3.5 0.41% 87 $4.2 0.11% 72 $7.7 0.17% 

Aerosol Toppings [Milk ByProducts] 49 $3.5 0.40% 35 $24.5 0.66% 38 $28.0 0.61% 

Hot Chocolate/Cocoa Mix 50 $3.5 0.40% 45 $17.8 0.48% 47 $21.2 0.46% 

Seasonal Candy Box—Chocolate 51 $3.4 0.39% 47 $16.6 0.45% 49 $20.0 0.43% 

Sft Drnk 1 Liter Btl Carb 

(Exc) 

52 $3.3 0.38% 65 $8.2 0.22% 63 $11.5 0.25% 

Fruit Drinks: Canned & 

Glass 

53 $3.2 0.37% 80 $5.0 0.13% 71 $8.2 0.18% 

Soft Drink Canisters 54 $3.1 0.36% 66 $7.9 0.21% 65 $11.1 0.24% 

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 Shortening 57 $2.9 0.33% 54 $14.0 0.38% 55 $16.9 0.37% 

Tea Can With Sweetener/ 

Sugar 

58 $2.7 0.31% 74 $6.1 0.16% 67 $8.8 0.19% 

Soft Drink Bottle Non-Carb 

(Ex) 

59 $2.6 0.30% 83 $4.7 0.13% 76 $7.4 0.16% 

Ice Cream Toppings 60 $2.6 0.30% 53 $14.1 0.38% 56 $16.7 0.36% 

Seasonal Candy Bags NonChocol 61 $2.6 0.30% 52 $14.9 0.40% 53 $17.5 0.38% 

Candy Bars Multi Pack W/ 

Flour 

62 $2.6 0.29% 64 $8.8 0.23% 64 $11.3 0.25% 

241 

Exhibit D–6: Saturated Fats and Added Sugars (SoFAS)—Continued 

(SoFAS) 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Candy Bags—Chocolate W/ 

Flour 

63 $2.5 0.29% 51 $15.2 0.41% 52 $17.7 0.38% 

Pork Skins/Cracklins 64 $2.2 0.26% 73 $6.2 0.17% 68 $8.4 0.18% 

Mints/Candy & Breath (Not 

Life) 

65 $2.1 0.25% 56 $12.1 0.32% 57 $14.3 0.31% 

Juices Smoothies/Blended 66 $2.1 0.24% 59 $11.0 0.29% 60 $13.1 0.28% 

Miscellaneous Candy (Including) 67 $1.9 0.22% 58 $11.2 0.30% 59 $13.1 0.28% 

Cocktail Mixes—Fluid: Add 

Liq 

68 $1.9 0.22% 49 $16.4 0.44% 51 $18.3 0.40% 

Cake De ́cors & Icing 69 $1.8 0.20% 62 $10.0 0.27% 62 $11.7 0.25% 

Enhanced Stick [Powder 

Drink Mix] 

70 $1.7 0.20% 61 $10.7 0.29% 61 $12.5 0.27% 

Novelty Candy 71 $1.6 0.19% 76 $5.7 0.15% 77 $7.4 0.16% 

Sugar Sweetened Sticks 72 $1.4 0.16% 104 $2.5 0.07% 96 $3.9 0.08% 

Dips Caramel/Fruit Glazes 73 $1.3 0.15% 75 $5.9 0.16% 78 $7.2 0.16% 

Seasonal Miscellaneous W/ 

Flour 

74 $1.2 0.14% 68 $7.1 0.19% 69 $8.4 0.18% 

Instant Tea & Tea Mix (W/ 

Sugar) 

75 $1.1 0.13% 84 $4.4 0.12% 85 $5.6 0.12% 

Misc Checklane Candy 76 $1.1 0.13% 103 $2.6 0.07% 97 $3.7 0.08% 

Fluid Pouch [Powder Drink 

Mix] 

77 $1.1 0.13% 71 $6.6 0.18% 73 $7.7 0.17% 

Sweet Goods: Candy 78 $1.1 0.12% 85 $4.4 0.12% 87 $5.4 0.12% 

Tea Bottles With Sweetener/ 

Sug 

79 $1.1 0.12% 114 $1.9 0.05% 105 $3.0 0.06% 

Hispanic Carbonated Beverages 80 $1.1 0.12% 93 $3.5 0.09% 92 $4.6 0.10% 

Candy W/O Flour 81 $1.0 0.12% 78 $5.4 0.15% 81 $6.5 0.14% 

Candy Boxed Chocolates W/ 

Flour 

82 $1.0 0.12% 79 $5.3 0.14% 83 $6.3 0.14% 

Apple Juice & Cider (50% 

And U) 

83 $1.0 0.12% 98 $3.0 0.08% 95 $4.0 0.09% 

Energy Drink—Multi-Pack 

(Non) 

84 $1.0 0.11% 63 $9.4 0.25% 66 $10.4 0.22% 

Candy Boxed Chocolates 85 $0.9 0.11% 70 $6.7 0.18% 74 $7.7 0.17% 

Seasonal Candy Box NonChocola 86 $0.9 0.11% 89 $4.0 0.11% 88 $4.9 0.11% 

Candy Box Non—Chocolate 87 $0.9 0.11% 90 $3.9 0.10% 89 $4.8 0.10% 

Cake De ́cors—Candies 88 $0.9 0.10% 77 $5.4 0.15% 82 $6.3 0.14% 

Non-Carb Jce (Under 50% 

Jce) 

89 $0.9 0.10% 82 $4.8 0.13% 84 $5.7 0.12% 

Candy Bags—Non Chocolate 

W/Flo 

90 $0.8 0.09% 91 $3.7 0.10% 93 $4.5 0.10% 

Hispanic Juice Under 50% 

Juice 

91 $0.7 0.08% 113 $2.0 0.07% 109 $2.7 0.05% 

Can/Btl Carb Beve 50% And 

Unde 

92 $0.7 0.08% 67 $7.6 0.20% 70 $8.3 0.18% 

Cranapple/Cran Grape Juice 

(Un) 

93 $0.6 0.07% 69 $7.0 0.19% 75 $7.6 0.17% 

Grapefruit Juice (50% And 

Unde) 

94 $0.6 0.07% 96 $3.1 0.08% 98 $3.7 0.08% 

Blended Juice & Combinations (Un) 95 $0.6 0.07% 97 $3.0 0.08% 100 $3.6 0.08% 

Mixers (Tonic Water/Gngr 

Ale) Un 

96 $0.5 0.06% 55 $13.2 0.35% 58 $13.7 0.30% 

Marshmallow Cre`me 97 $0.5 0.06% 92 $3.5 0.09% 94 $4.1 0.09% 

Coconut [Baking Needs] 98 $0.5 0.06% 81 $4.9 0.13% 86 $5.5 0.12% 

Honey/Syrup 99 $0.5 0.06% 86 $4.3 0.11% 90 $4.8 0.10% 

Dips Fruit And Chocolate 100 $0.5 0.06% 106 $1.9 0.05% 112 $2.4 0.04% 

Top 100 SoFAS Expenditures * $862.5 98.70% $3,660.7 97.93% $4,523.2 98.05% 

Total SoFAS Expenditures Among Top 

1,000 Subcommodities 

$864.1 98.96% $3,673.1 98.42% $4,537.3 98.53% 

Total SoFAS Expenditures Among 1,792 

Subcommodities 

$873.2 100% $3,731.9 100% $4,605.0 100% 

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. 

242 

Exhibit D–7: Vegetables 

Vegetable 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Potatoes Russet (Bulk & 

Bag) 

1 $35.8 6.74% 1 $154.5 4.60% 1 $190.2 4.89% 

Fz Bag Vegetables—Plain 2 $25.7 4.85% 2 $131.9 3.93% 2 $157.7 4.05% 

Mainstream [Pasta & Pizza 

Sauce] 

3 $23.0 4.33% 6 $81.0 2.41% 5 $103.9 2.67% 

Frzn French Fries 4 $20.5 3.86% 19 $50.3 1.50% 9 $70.8 1.82% 

Avocado 5 $13.4 2.52% 4 $112.6 3.35% 4 $126.0 3.24% 

Blends [Salad Mix] 6 $13.1 2.47% 3 $124.0 3.69% 3 $137.1 3.52% 

Green Beans: Fs/Whl/Cut 7 $12.8 2.41% 15 $53.1 1.58% 15 $65.9 1.69% 

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 Vegetables 11 $10.5 1.98% 5 $81.4 2.42% 6 $91.9 2.36% 

Mexican Sauces And Picante 

Sau 

12 $10.2 1.93% 9 $62.3 1.85% 8 $72.5 1.86% 

Tomatoes Diced 13 $9.5 1.79% 11 $59.9 1.78% 11 $69.4 1.79% 

Tomatoes Hothouse On The 

Vine 

14 $9.2 1.74% 7 $77.7 2.31% 7 $86.9 2.23% 

Onions Yellow (Bulk & Bag) 15 $8.7 1.65% 27 $39.3 1.17% 24 $48.1 1.24% 

Cucumbers 16 $8.2 1.55% 12 $58.9 1.75% 13 $67.1 1.73% 

Vegetable Salads—Prepack 17 $7.8 1.48% 29 $36.6 1.09% 29 $44.4 1.14% 

Peppers Green Bell 18 $7.8 1.47% 25 $41.5 1.24% 22 $49.3 1.27% 

Regular Garden 19 $7.8 1.46% 35 $31.9 0.95% 31 $39.6 1.02% 

Roma Tomatoes (Bulk/Pkg) 20 $7.5 1.41% 26 $39.6 1.18% 25 $47.1 1.21% 

Carrots Mini Peeled 21 $7.0 1.32% 10 $61.4 1.83% 12 $68.5 1.76% 

Onions Sweet (Bulk & Bag) 22 $6.2 1.16% 20 $47.4 1.41% 21 $53.6 1.38% 

Celery 23 $5.9 1.11% 17 $51.2 1.52% 18 $57.1 1.47% 

Tomatoes Vine Ripe Bulk 24 $5.7 1.07% 51 $22.5 0.67% 48 $28.2 0.72% 

Garden Plus [Salad Mix] 25 $5.5 1.03% 36 $31.8 0.95% 34 $37.2 0.96% 

Cabbage 26 $5.3 1.00% 43 $25.1 0.75% 43 $30.5 0.78% 

Frzn Tater Tots/Other Extruded 27 $5.2 0.99% 55 $18.8 0.56% 53 $24.1 0.62% 

Broccoli Whole & Crowns 28 $5.2 0.97% 16 $52.0 1.55% 17 $57.1 1.47% 

Tomato Sauce 29 $5.1 0.96% 48 $24.2 0.72% 45 $29.3 0.75% 

Variety Lettuce 30 $5.1 0.96% 8 $65.2 1.94% 10 $70.3 1.81% 

Tomatoes Hot House Bulk 31 $5.0 0.94% 39 $30.3 0.90% 37 $35.3 0.91% 

Potatoes Sweet & Yams 32 $4.8 0.91% 28 $37.1 1.11% 30 $41.9 1.08% 

Tomatoes Grape 33 $4.7 0.88% 14 $54.6 1.63% 16 $59.3 1.52% 

Mexican Beans/Refried 34 $4.7 0.88% 52 $21.0 0.63% 51 $25.6 0.66% 

Frzn Hashbrown Potatoes 35 $4.6 0.86% 45 $24.8 0.74% 44 $29.3 0.75% 

Corn Bulk 36 $4.5 0.85% 32 $32.5 0.97% 35 $37.1 0.95% 

Fz Box Vegetables—ValueAdded 37 $4.4 0.83% 46 $24.7 0.73% 47 $29.1 0.75% 

Kits [Salad Mix] 38 $4.2 0.79% 31 $33.5 1.00% 33 $37.6 0.97% 

Potatoes Red (Bulk & Bag) 39 $4.1 0.78% 34 $32.0 0.95% 36 $36.1 0.93% 

Frzn Corn On The Cob 40 $4.0 0.75% 94 $8.4 0.25% 83 $12.4 0.32% 

Vegetable Party Tray 41 $4.0 0.75% 44 $25.1 0.75% 46 $29.1 0.75% 

Cut Vegetables All Other 42 $4.0 0.75% 24 $42.2 1.26% 26 $46.2 1.19% 

Vegetable Salads—Bulk 43 $3.8 0.72% 37 $31.0 0.92% 38 $34.8 0.89% 

Veg Juice (Except Tomato) 

(Ove) 

44 $3.8 0.72% 38 $30.4 0.91% 39 $34.2 0.88% 

Asparagus 45 $3.8 0.72% 18 $50.7 1.51% 20 $54.5 1.40% 

Tomatoes Vine Ripe Pkg 46 $3.6 0.68% 101 $7.3 0.22% 89 $10.9 0.28% 

Peppers Red Bell 47 $3.6 0.68% 23 $42.5 1.27% 27 $46.1 1.19% 

Value (Pasta Tomato Sauce) 48 $3.5 0.67% 87 $9.7 0.29% 78 $13.2 0.34% 

Peas/Green 49 $3.5 0.66% 64 $14.7 0.44% 61 $18.2 0.47% 

Spinach & Greens 50 $3.5 0.66% 103 $7.0 0.21% 92 $10.5 0.27% 

Peppers Other Bell 51 $3.4 0.63% 41 $28.4 0.85% 41 $31.8 0.82% 

Mushrooms White Sliced 

Pkg 

52 $3.3 0.63% 42 $27.8 0.83% 42 $31.2 0.80% 

Shredded Lettuce 53 $3.3 0.62% 81 $10.9 0.32% 75 $14.2 0.36% 

Mushrooms White Whole 

Pkg 

54 $3.1 0.58% 40 $29.6 0.88% 40 $32.7 0.84% 

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 Vegetables—ValueAdded 57 $2.8 0.54% 65 $14.7 0.44% 63 $17.6 0.45% 

Sal: Hommus 58 $2.8 0.52% 21 $45.4 1.35% 23 $48.2 1.24% 

Mushrooms Cnd & Glass 59 $2.7 0.52% 67 $14.3 0.42% 64 $17.0 0.44% 

Mexican Enchilada Sauce 60 $2.7 0.51% 69 $13.7 0.41% 66 $16.4 0.42% 

Onions Red (Bulk & Bag) 61 $2.5 0.48% 53 $20.9 0.62% 54 $23.5 0.60% 

Onions White (Bulk & Bag) 62 $2.5 0.47% 60 $15.8 0.47% 60 $18.3 0.47% 

Authentic Sauces/Salsa/ 

Picante 

63 $2.3 0.43% 89 $9.2 0.27% 87 $11.5 0.30% 

Salad Mix Blends Organic 64 $2.3 0.43% 30 $36.5 1.09% 32 $38.8 1.00% 

Salad: Lettuce 65 $2.2 0.42% 77 $12.2 0.36% 72 $14.5 0.37% 

Cauliflower Whole 66 $2.2 0.42% 47 $24.5 0.73% 49 $26.8 0.69% 

Mushrooms Portabella 67 $2.2 0.42% 50 $22.6 0.67% 52 $24.8 0.64% 

Mexican Peppers Chilies 68 $2.2 0.41% 61 $15.7 0.47% 62 $17.9 0.46% 

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% 

243 

Exhibit D–7: Vegetables—Continued 

Vegetable 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Potatoes Gourmet 71 $2.0 0.38% 54 $20.3 0.60% 55 $22.3 0.57% 

Sweet Potatoes 72 $2.0 0.38% 104 $6.7 0.20% 101 $8.7 0.22% 

Corn Is Packaged 73 $1.9 0.36% 70 $12.8 0.38% 71 $14.7 0.38% 

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/Dips Bulk 76 $1.8 0.33% 98 $7.7 0.23% 95 $9.5 0.24% 

Beans 77 $1.7 0.32% 59 $16.9 0.50% 59 $18.6 0.48% 

Tomato Juice (Over 50% Jce) 78 $1.7 0.32% 88 $9.6 0.28% 88 $11.2 0.29% 

Authentic Vegetables And 

Foods 

79 $1.7 0.32% 136 $3.2 0.10% 128 $4.9 0.13% 

Potatoes Gold (Bulk & Bag) 80 $1.6 0.29% 63 $14.8 0.44% 65 $16.4 0.42% 

Garlic Whole Cloves 81 $1.6 0.29% 71 $12.7 0.38% 74 $14.3 0.37% 

Coleslaw 82 $1.6 0.29% 79 $11.9 0.35% 77 $13.5 0.35% 

Carrots Bagged Organic 83 $1.5 0.29% 56 $18.6 0.55% 56 $20.2 0.52% 

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/Stuffed/Mashed 

& Spec 

86 $1.3 0.25% 91 $9.0 0.27% 93 $10.4 0.27% 

Broccoli/Cauliflower Processed 87 $1.3 0.25% 72 $12.5 0.37% 76 $13.8 0.36% 

Mixed Vegetables 88 $1.3 0.24% 124 $4.5 0.13% 119 $5.8 0.15% 

Authentic Peppers 89 $1.3 0.24% 125 $4.5 0.13% 120 $5.7 0.15% 

Sal: Salsa Prepack 90 $1.3 0.24% 68 $13.7 0.41% 70 $15.0 0.38% 

Carrots 91 $1.1 0.21% 123 $4.5 0.14% 121 $5.7 0.15% 

Peppers Yellow Bell 92 $1.1 0.21% 80 $11.4 0.34% 82 $12.5 0.32% 

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 Jalapen ̃ o 95 $1.0 0.19% 126 $4.4 0.13% 125 $5.5 0.14% 

Tomatoes Cherry 96 $1.0 0.19% 78 $12.1 0.36% 80 $13.1 0.34% 

Instore Cut Vegetables 97 $1.0 0.19% 86 $9.7 0.29% 91 $10.7 0.28% 

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 Cabbage 100 $0.9 0.17% 111 $6.0 0.18% 109 $6.9 0.18% 

Top 100 Vegetable Expenditures * $500.7 94.36% $3,035.6 90.37% $3,536.4 90.91% 

Total Vegetable Expenditures Among 

Top 1,000 Subcommodities 

$520.5 98.08% $3,251.8 96.80% $3,772.3 96.97% 

Total Vegetable Expenditures Among 

1,792 Subcommodities 

$530.7 100% $3,359.3 100% $3,890.0 100% 

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 

Composite 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Potato Chips 1 $64.4 5.19% 2 $253.2 4.88% 1 $317.6 4.94% 

Snacks/Appetizers 2 $44.6 3.59% 10 $100.5 1.94% 7 $145.0 2.26% 

Fz Ss Prem Traditional 

Meals 

3 $43.8 3.53% 4 $175.4 3.38% 4 $219.3 3.41% 

Snack Cake—Multi Pack 4 $41.6 3.36% 9 $101.7 1.96% 9 $143.3 2.23% 

Fz Ss Economy Meals All 5 $40.9 3.30% 15 $80.7 1.56% 11 $121.6 1.89% 

Pizza/Premium 6 $39.7 3.20% 6 $153.3 2.95% 5 $193.0 3.00% 

Sandwiches & Handhelds 7 $35.9 2.89% 17 $73.6 1.42% 13 $109.4 1.70% 

Convenient Meals—Kids 

Meal C 

8 $34.2 2.76% 19 $69.7 1.34% 14 $104.0 1.62% 

Premium [Ice Cream & 

Sherbert] 

9 $31.2 2.52% 3 $226.0 4.35% 3 $257.2 4.00% 

Condensed Soup 10 $29.7 2.39% 5 $153.6 2.96% 6 $183.2 2.85% 

Fz Family Style Entre ́es 11 $27.6 2.23% 13 $83.5 1.61% 12 $111.1 1.73% 

Traditional [Ice Cream & 

Sherbert] 

12 $25.6 2.07% 8 $118.7 2.29% 8 $144.4 2.25% 

Fz Ss Prem Nutritional 

Meals 

13 $24.7 1.99% 1 $271.6 5.23% 2 $296.3 4.61% 

Macaroni & Cheese Dnrs 14 $24.3 1.96% 24 $59.7 1.15% 21 $84.0 1.31% 

Can Pasta 15 $22.2 1.79% 36 $47.7 0.92% 29 $69.9 1.09% 

244 

Exhibit D–8: Composite Foods—Continued 

Composite 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Mult Pk Bag Snacks 16 $21.6 1.74% 38 $43.4 0.84% 32 $65.0 1.01% 

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 Sandwiches 19 $19.1 1.54% 29 $55.7 1.07% 23 $74.8 1.16% 

Fz Skillet Meals 20 $18.8 1.51% 16 $79.3 1.53% 17 $98.1 1.53% 

Cakes: Birthday/Celebration 

Sh 

21 $18.6 1.50% 33 $50.3 0.97% 31 $68.9 1.07% 

Sandwich Cookies 22 $18.0 1.45% 18 $71.8 1.38% 19 $89.8 1.40% 

Pizza/Traditional 23 $17.9 1.44% 22 $64.1 1.24% 22 $82.0 1.27% 

Rts Soup: Chunky/ 

Homestyle/Et 

24 $17.6 1.42% 7 $119.9 2.31% 10 $137.5 2.14% 

Salsa & Dips 25 $17.1 1.38% 28 $57.0 1.10% 24 $74.1 1.15% 

Sandwiches—(Cold) 26 $16.9 1.36% 20 $67.7 1.30% 20 $84.6 1.32% 

Sweet Goods—Full Size 27 $15.8 1.28% 27 $57.9 1.12% 26 $73.8 1.15% 

Tray Pack/Choc Chip Cookies 28 $15.3 1.23% 31 $53.9 1.04% 30 $69.2 1.08% 

Sticks/Enrobed [Frozen Novelties] 29 $14.2 1.14% 25 $59.7 1.15% 25 $73.9 1.15% 

Water Ice [Frozen Novelties] 30 $14.0 1.13% 32 $50.6 0.97% 34 $64.6 1.00% 

Pails [Ice Cream & Sherbert] 31 $13.9 1.12% 46 $35.1 0.68% 41 $49.1 0.76% 

Skillet Dinners 32 $13.0 1.05% 57 $25.8 0.50% 49 $38.9 0.60% 

Pizza/Single Serve/Microwave 33 $12.8 1.03% 39 $43.2 0.83% 38 $56.0 0.87% 

Super Premium Pints [Ice 

Cream & Sherbert] 

34 $11.8 0.95% 11 $91.1 1.76% 16 $103.0 1.60% 

Cakes: Cupcakes 35 $11.1 0.89% 45 $35.3 0.68% 43 $46.3 0.72% 

Corn Dogs 36 $10.9 0.88% 68 $20.6 0.40% 59 $31.5 0.49% 

Cookies: Regular 37 $10.8 0.87% 26 $59.6 1.15% 28 $70.4 1.09% 

Burritos 38 $10.2 0.82% 69 $20.0 0.39% 61 $30.1 0.47% 

Microwave Dinners 39 $9.8 0.79% 40 $39.9 0.77% 40 $49.8 0.77% 

Cakes: Layers 40 $9.8 0.79% 42 $38.2 0.74% 42 $48.1 0.75% 

Sushi—In Store Prepared 41 $9.2 0.74% 12 $85.4 1.64% 18 $94.6 1.47% 

Canister Snacks 42 $9.1 0.73% 44 $36.4 0.70% 45 $45.5 0.71% 

Pudding & Gelatin Cups/ 

Cans 

43 $8.7 0.70% 53 $27.6 0.53% 51 $36.3 0.56% 

Salty Snacks Vending 44 $8.4 0.67% 80 $15.8 0.31% 67 $24.2 0.38% 

Cones [Frozen Novelties] 45 $7.9 0.64% 50 $31.2 0.60% 48 $39.2 0.61% 

Vanilla Wafer/Kids Cookies 46 $7.5 0.60% 43 $36.7 0.71% 46 $44.2 0.69% 

Ice Cream Sandwiches 47 $7.4 0.60% 60 $24.2 0.47% 58 $31.6 0.49% 

Cakes: Cre`me/Pudding 48 $7.4 0.59% 58 $25.8 0.50% 54 $33.2 0.52% 

Refrigerated Pudding 49 $7.0 0.57% 34 $49.5 0.95% 37 $56.5 0.88% 

Layer Cake Mix 50 $7.0 0.56% 47 $35.1 0.68% 47 $42.1 0.65% 

Refrigerated Cookies—Brand 51 $6.8 0.55% 51 $28.8 0.56% 53 $35.6 0.55% 

Broth 52 $6.7 0.54% 21 $65.6 1.26% 27 $72.3 1.12% 

Pies: Fruit/Nut 53 $6.3 0.51% 41 $39.6 0.76% 44 $45.9 0.71% 

Snack Cake—Single Serve 54 $5.7 0.46% 77 $16.2 0.31% 74 $22.0 0.34% 

Better For You Snacks 55 $5.6 0.45% 35 $48.1 0.93% 39 $53.7 0.84% 

Cookies: Holiday/Special 

Occas 

56 $5.5 0.44% 56 $26.8 0.52% 56 $32.2 0.50% 

Misc Bag Snacks 57 $5.5 0.44% 98 $11.5 0.22% 83 $17.0 0.26% 

Frozen Fruit Pies & Cobblers 58 $5.3 0.43% 62 $23.7 0.46% 62 $28.9 0.45% 

Frozen Cream Pies 59 $4.9 0.39% 71 $18.9 0.36% 69 $23.8 0.37% 

Sw Gds: Sw Rolls/Dan 60 $4.8 0.39% 55 $26.9 0.52% 57 $31.7 0.49% 

Brownie Mix 61 $4.8 0.39% 54 $27.5 0.53% 55 $32.3 0.50% 

Fz Meal Kits/Stuffed/Other 62 $4.8 0.38% 96 $12.2 0.23% 84 $16.9 0.26% 

Sw Gds: Muffins 63 $4.5 0.36% 48 $31.8 0.61% 50 $36.3 0.57% 

Frzn Breakfast Entre ́es 64 $4.5 0.36% 78 $16.2 0.31% 78 $20.7 0.32% 

Convenient Meals—Adult 

Meal 

65 $4.5 0.36% 102 $11.2 0.22% 92 $15.7 0.24% 

Dry Beans/Peas/Barley: Bag 

& B 

66 $4.2 0.34% 72 $18.8 0.36% 71 $23.1 0.36% 

Adult Premium [Frozen Novelties] 67 $4.2 0.34% 30 $54.5 1.05% 36 $58.7 0.91% 

Mexican Dinners And Foods 68 $4.2 0.34% 100 $11.4 0.22% 93 $15.6 0.24% 

Premium Cookies (Ex: 

Pepperidg) 

69 $4.2 0.33% 49 $31.5 0.61% 52 $35.7 0.55% 

Chocolate Covered Cookies 70 $4.0 0.32% 73 $18.5 0.36% 73 $22.5 0.35% 

Microwavable Cups 71 $3.7 0.29% 116 $9.0 0.17% 106 $12.7 0.20% 

Cakes: Cheesecake 72 $3.6 0.29% 84 $14.7 0.28% 81 $18.3 0.28% 

Deli Tray: Meat And Cheese 73 $3.5 0.28% 65 $21.5 0.41% 66 $25.0 0.39% 

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 & Diet—Bars W/ 

Flour 

76 $3.4 0.28% 23 $59.8 1.15% 35 $63.2 0.98% 

Refrigerated Cookie Dough 77 $3.4 0.28% 90 $12.9 0.25% 89 $16.3 0.25% 

Cakes: Fancy/Service Case 78 $3.3 0.27% 76 $17.4 0.34% 77 $20.7 0.32% 

Package Dinners/Pasta Salads 79 $3.3 0.26% 112 $9.5 0.18% 105 $12.7 0.20% 

Cakes: Layers/Sheets Novelties 80 $3.3 0.26% 94 $12.5 0.24% 91 $15.8 0.25% 

245 

Exhibit D–8: Composite Foods—Continued 

Composite 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Pies: Pumpkin/Custard 81 $3.2 0.26% 89 $13.1 0.25% 87 $16.3 0.25% 

Puddings Dry 82 $3.2 0.26% 67 $20.8 0.40% 68 $23.9 0.37% 

Vendor Size/Single Serve 

Cooki 

83 $3.1 0.25% 126 $6.8 0.13% 120 $9.9 0.15% 

Snack Mix 84 $3.0 0.24% 75 $17.5 0.34% 79 $20.5 0.32% 

Multi-Pack Cookies 85 $2.9 0.23% 99 $11.4 0.22% 96 $14.3 0.22% 

Cups/Push Ups/Other 86 $2.8 0.23% 110 $9.6 0.18% 108 $12.4 0.19% 

Frzn Pie Shells/Pastry Shell/ 

F 

87 $2.7 0.22% 79 $16.0 0.31% 80 $18.7 0.29% 

Frozen Cakes/Desserts 88 $2.7 0.22% 105 $11.0 0.21% 101 $13.7 0.21% 

Cakes: Angel Fds/Cke Rolls 89 $2.7 0.22% 74 $18.1 0.35% 76 $20.8 0.32% 

Wellness/Portion Control 90 $2.7 0.22% 61 $23.8 0.46% 65 $26.5 0.41% 

Pie Filling/Mincemeat/Glazes 91 $2.7 0.22% 59 $24.8 0.48% 63 $27.5 0.43% 

Misc Snacks 92 $2.6 0.21% 87 $13.2 0.25% 90 $15.8 0.25% 

Cakes: Ice Cream 93 $2.6 0.21% 120 $8.6 0.17% 113 $11.2 0.17% 

Sushi—Prepackaged 94 $2.6 0.21% 70 $19.2 0.37% 75 $21.8 0.34% 

Cakes: Birthday/Celebration 

Lay 

95 $2.5 0.20% 114 $9.1 0.18% 110 $11.6 0.18% 

Sw Gds: Swt/Flvrd Loaves 96 $2.4 0.20% 85 $13.9 0.27% 88 $16.3 0.25% 

Cakes: Sheet 97 $2.4 0.19% 124 $7.2 0.14% 121 $9.6 0.15% 

Cookies: Gourmet 98 $2.4 0.19% 66 $20.8 0.40% 70 $23.2 0.36% 

Premium Pints [Ice Cream & 

Sherbert] 

99 $2.3 0.18% 128 $6.5 0.13% 125 $8.8 0.14% 

Sw Gds: Brownie/Bar Cookie 100 $1.9 0.15% 104 $11.2 0.22% 104 $13.1 0.20% 

Top 100 Composite Expenditures * $1,179.3 95.05% $4,717.8 90.90% $5,897.1 91.70% 

Total Composite Expenditures Among 

Top 1,000 Subcommodities 

$1,235.4 99.57% $5,132.0 98.88% $6,367.4 99.01% 

Total Composite Expenditures Among 

1,792 Subcommodities 

$1,240.7 100% $5,190.0 100% $6,430.7 100% 

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 

Other 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Infant Formula Starter/Solution 1 $54.2 9.60% 14 $45.3 1.70% 6 $99.5 3.07% 

Still Water Drnking/Mnrl 

Water 

2 $48.8 8.64% 2 $187.7 7.03% 2 $236.5 7.31% 

Unflavored Can Coffee 3 $41.3 7.32% 1 $198.0 7.41% 1 $239.3 7.39% 

Isotonic Drinks Single Serve 4 $30.5 5.40% 4 $119.5 4.47% 3 $150.0 4.63% 

Spring Water 5 $16.2 2.87% 5 $95.6 3.58% 5 $111.8 3.45% 

Traditional Spices 6 $14.1 2.49% 8 $61.2 2.29% 7 $75.2 2.32% 

Bbq Sauce 7 $12.3 2.17% 16 $38.6 1.45% 16 $50.9 1.57% 

Baby Food—Beginner 8 $11.7 2.07% 21 $28.1 1.05% 18 $39.8 1.23% 

Non-Carb Water Flvr—Drnk/ 

Mnr 

9 $11.6 2.05% 7 $63.4 2.37% 8 $74.9 2.32% 

Catsup 10 $11.5 2.03% 15 $41.5 1.55% 15 $53.0 1.64% 

Sauce Mixes/Gravy Mixes 

Dry 

11 $11.5 2.03% 13 $46.7 1.75% 12 $58.2 1.80% 

Baby Food Junior/All Brands 12 $11.2 1.98% 22 $27.5 1.03% 19 $38.7 1.20% 

Isotonic Drinks Multi-Pack 13 $10.8 1.92% 9 $58.1 2.17% 10 $68.9 2.13% 

Ice—Crushed/Cubed 14 $9.3 1.65% 11 $49.9 1.87% 11 $59.2 1.83% 

Unflavored Bag Coffee 15 $8.5 1.50% 3 $137.3 5.14% 4 $145.8 4.50% 

Infant Formula Specialty 16 $8.4 1.49% 71 $9.1 0.34% 47 $17.5 0.54% 

Infant Formula Starter 

Large P 

17 $8.3 1.46% 30 $22.8 0.85% 27 $31.0 0.96% 

Steak & Worchester Sauce 18 $8.2 1.44% 25 $26.7 1.00% 21 $34.9 1.08% 

Unflavored Instant Coffee 19 $7.6 1.34% 23 $27.3 1.02% 22 $34.8 1.08% 

Non-Dairy Milks 20 $7.1 1.25% 6 $67.7 2.53% 9 $74.8 2.31% 

Unsweetened Envelope 

[Powder Drink Mix] 

21 $7.0 1.25% 88 $6.2 0.23% 61 $13.3 0.41% 

Malted Mlk/Syrup/Pwdrs 

(Eggnog) 

22 $6.9 1.23% 28 $25.3 0.95% 26 $32.2 1.00% 

246 

Exhibit D–9: Other Subcommodities—Continued 

Other 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Still Water Flvrd Drnk/Mnrl 

Wt 

23 $6.3 1.11% 17 $38.1 1.43% 17 $44.4 1.37% 

Infant Formula Toddler 24 $6.0 1.06% 55 $12.4 0.46% 45 $18.4 0.57% 

Mexican Seasoning Mixes 25 $5.9 1.05% 33 $20.6 0.77% 32 $26.5 0.82% 

Hot Sauce 26 $5.8 1.03% 42 $16.4 0.61% 38 $22.2 0.69% 

Ready To Drink Coffee 27 $5.5 0.98% 34 $20.5 0.77% 33 $26.0 0.80% 

Tea Bags & Bulk Tea 28 $5.4 0.95% 24 $27.2 1.02% 25 $32.5 1.01% 

Infant Formula Solutions 

Large 

29 $5.3 0.95% 47 $15.2 0.57% 42 $20.5 0.63% 

Stuffing Mixes 30 $5.3 0.94% 31 $22.1 0.83% 30 $27.4 0.85% 

Infant Formula Concentrate 31 $4.9 0.86% 111 $3.9 0.15% 82 $8.8 0.27% 

Salad Bar Other 32 $4.5 0.81% 41 $18.2 0.68% 36 $22.8 0.70% 

Bits & Morsels [Baking 

Needs] 

33 $4.4 0.77% 10 $50.3 1.88% 13 $54.7 1.69% 

Ripe Olives 34 $4.1 0.73% 27 $25.3 0.95% 28 $29.5 0.91% 

Gravy Can/Glass 35 $4.0 0.72% 44 $15.7 0.59% 44 $19.8 0.61% 

Marinades 36 $3.9 0.70% 39 $18.4 0.69% 37 $22.4 0.69% 

Baby Food Cereals 37 $3.8 0.67% 82 $7.1 0.27% 70 $10.9 0.34% 

Diet Cntrl Liqs Nutritional 38 $3.7 0.66% 20 $30.3 1.13% 24 $34.0 1.05% 

Enhancements—Pickles/ 

Kraut 

39 $3.6 0.64% 36 $19.8 0.74% 35 $23.4 0.72% 

Infant Formula Ready To 

Use 

40 $3.5 0.61% 85 $6.9 0.26% 72 $10.4 0.32% 

Sugar Free Canister [Powder 

Drink Mix] 

41 $3.5 0.61% 32 $21.1 0.79% 34 $24.5 0.76% 

Coffee Pods/Singles/Filter 

Pac 

42 $3.4 0.60% 12 $49.8 1.87% 14 $53.2 1.65% 

Sugar Free Sticks [Powder 

Drink Mix] 

43 $3.3 0.58% 38 $18.8 0.70% 39 $22.1 0.68% 

Sparkling Water—Flvrd 

Sweet 

44 $3.1 0.55% 29 $24.1 0.90% 31 $27.2 0.84% 

Tea Bags/Herbal 45 $3.1 0.54% 19 $31.2 1.17% 23 $34.3 1.06% 

Yellow Mustard 46 $3.0 0.53% 56 $12.4 0.46% 55 $15.4 0.48% 

Asian Other Sauces/Marinad 47 $2.8 0.50% 37 $18.9 0.71% 40 $21.8 0.67% 

Peppers 48 $2.7 0.48% 52 $13.5 0.50% 53 $16.2 0.50% 

Mexican Taco Sauce 49 $2.6 0.47% 84 $7.0 0.26% 76 $9.7 0.30% 

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 Coffee 52 $2.4 0.42% 26 $26.2 0.98% 29 $28.6 0.88% 

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 Dressing & Dip 

Mixes 

55 $2.0 0.35% 48 $15.1 0.57% 49 $17.1 0.53% 

Mustard—All Other 56 $2.0 0.35% 40 $18.3 0.69% 43 $20.3 0.63% 

Gelatin 57 $2.0 0.35% 51 $14.3 0.54% 52 $16.3 0.50% 

Vinegar/White & Cider 58 $1.9 0.34% 50 $14.4 0.54% 51 $16.3 0.50% 

Baby Isotonic Drinks 59 $1.9 0.33% 101 $4.9 0.18% 92 $6.8 0.21% 

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 Soy Base 62 $1.7 0.31% 161 $1.1 0.04% 135 $2.8 0.09% 

Juices Proteins 63 $1.7 0.30% 66 $10.1 0.38% 64 $11.8 0.36% 

Sal: Dip Prepack 64 $1.7 0.30% 59 $12.1 0.45% 58 $13.8 0.43% 

Diet Energy Drinks 65 $1.7 0.30% 54 $12.8 0.48% 56 $14.5 0.45% 

Baby Spring Waters 66 $1.7 0.30% 138 $2.0 0.07% 119 $3.7 0.11% 

Frozen Internaional 67 $1.6 0.28% 86 $6.7 0.25% 83 $8.3 0.26% 

Table Salt/Popcorn Salt/Ice 

Cr 

68 $1.6 0.28% 72 $8.6 0.32% 73 $10.2 0.31% 

Distilled Water 69 $1.6 0.28% 57 $12.2 0.46% 59 $13.7 0.42% 

Enhancements—Salads/ 

Spreads 

70 $1.5 0.26% 99 $5.2 0.19% 95 $6.6 0.21% 

Asian Soy Sauce 71 $1.5 0.26% 64 $10.3 0.39% 66 $11.7 0.36% 

Central American Foods 72 $1.4 0.25% 94 $5.5 0.21% 90 $6.9 0.21% 

Misc Dairy Refigerated 73 $1.4 0.25% 70 $9.1 0.34% 71 $10.5 0.32% 

Diet Cntrl Bars Nutritional 74 $1.4 0.24% 35 $19.9 0.74% 41 $21.3 0.66% 

Tea Bags/Green 75 $1.2 0.22% 61 $11.2 0.42% 63 $12.5 0.38% 

Flours/Grains/Sugar 76 $1.2 0.22% 49 $14.6 0.55% 54 $15.9 0.49% 

Specialty Instant Coffee W/ 

Swe 

77 $1.2 0.22% 77 $7.7 0.29% 81 $8.9 0.27% 

Misc Hispanic Grocery 78 $1.2 0.21% 65 $10.2 0.38% 67 $11.4 0.35% 

Baking Powder & Soda 79 $1.1 0.20% 75 $8.2 0.31% 77 $9.4 0.29% 

Isotonic Drinks Multi-Serve 80 $1.1 0.19% 103 $4.7 0.18% 103 $5.7 0.18% 

Juices Antioxidant/Wellness 81 $1.0 0.19% 76 $8.1 0.30% 78 $9.2 0.28% 

Spices & Seasonings 82 $1.0 0.19% 104 $4.6 0.17% 104 $5.7 0.17% 

Infant Formula Up Age 83 $1.0 0.18% 119 $3.0 0.11% 117 $4.1 0.13% 

Oils/Vinegar 84 $1.0 0.18% 67 $10.0 0.37% 69 $11.0 0.34% 

Miscellaneous Package 

Mixes 

85 $1.0 0.18% 80 $7.2 0.27% 84 $8.2 0.25% 

Sal: Olives/Pickles—Bulk 86 $1.0 0.18% 45 $15.5 0.58% 50 $16.5 0.51% 

Cooking Bags With Spices/ 

Seaso 

87 $1.0 0.17% 132 $2.4 0.09% 124 $3.4 0.10% 

Cooking Chocolate (Ex: SmiSwt) 88 $0.9 0.16% 63 $10.3 0.39% 68 $11.2 0.35% 

247 

Exhibit D–9: Other Subcommodities—Continued 

Other 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Tea Bags (Supplement) 89 $0.9 0.15% 69 $9.2 0.34% 74 $10.0 0.31% 

Specialty Vinegar 90 $0.8 0.15% 53 $12.9 0.48% 60 $13.7 0.42% 

Traditional Thai Foods 91 $0.8 0.14% 74 $8.3 0.31% 80 $9.1 0.28% 

Pickld Veg/Peppers/Etc. 92 $0.8 0.14% 91 $5.9 0.22% 94 $6.7 0.21% 

Specialty Olives 93 $0.8 0.14% 62 $11.0 0.41% 65 $11.7 0.36% 

Authentic Japanese Foods 94 $0.8 0.14% 81 $7.1 0.27% 86 $7.9 0.24% 

Chili Sauce/Cocktail Sauce 95 $0.7 0.13% 89 $6.0 0.22% 93 $6.7 0.21% 

Flavored Can Coffee 96 $0.7 0.13% 92 $5.8 0.22% 96 $6.5 0.20% 

Fortified/Water 97 $0.7 0.13% 108 $4.4 0.17% 107 $5.1 0.16% 

Sparkling Water— 

Unflavored 

98 $0.7 0.12% 58 $12.1 0.45% 62 $12.8 0.40% 

Fitness & Diet—Powder 

Ntrtnl 

99 $0.7 0.12% 78 $7.3 0.27% 85 $8.0 0.25% 

Imitation Extracts 100 $0.7 0.12% 115 $3.5 0.13% 116 $4.2 0.13% 

Top 100 Other Expenditures * $540.1 95.68% $2,453.1 91.80% $2,993.1 92.48% 

Total Other Expenditures Among Top 

1,000 Subcommodities 

$550.7 97.56% $2,533.2 94.80% $3,083.9 95.28% 

Total Other Expenditures Among 1,792 

Subcommodities 

$564.5 100% $2,672.1 100% $3,236.6 100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $30.7 0.47% 1 $143.7 0.46% 1 $174.3 0.46% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $25.1 0.38% 2 $95.9 0.30% 2 $121.0 0.32% 

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 Cheese 5 $13.0 0.20% 3 $67.1 0.21% 3 $80.1 0.21% 

Sft Drnk 2 Liter Btl Carb 

Incl 

6 $11.3 0.17% 13 $39.1 0.12% 8 $50.5 0.13% 

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 Starter/ 

Solutio 

9 $9.8 0.15% 150 $9.2 0.03% 73 $19.0 0.05% 

Lunchment—Deli Fresh 10 $8.9 0.14% 6 $43.0 0.14% 7 $51.9 0.14% 

Chicken Breast Boneless 11 $8.4 0.13% 4 $54.2 0.17% 4 $62.5 0.16% 

Tortilla/Nacho Chips 12 $8.2 0.13% 10 $41.1 0.13% 10 $49.3 0.13% 

Eggs—Large 13 $7.8 0.12% 12 $39.4 0.13% 12 $47.2 0.12% 

Snacks/Appetizers 14 $7.7 0.12% 45 $20.6 0.07% 32 $28.3 0.07% 

Still Water Drnking/Mnrl 

Water 

15 $7.6 0.12% 20 $30.5 0.10% 18 $38.1 0.10% 

Mainstream White Bread 16 $7.4 0.11% 31 $23.4 0.07% 25 $30.8 0.08% 

American Single Cheese 17 $7.0 0.11% 34 $22.8 0.07% 26 $29.8 0.08% 

Dairy Case 100% Pure 

Juice—O 

18 $6.8 0.10% 9 $41.4 0.13% 11 $48.2 0.13% 

Enhanced [Pork Boneless 

Loin/Rib] 

19 $6.6 0.10% 24 $27.1 0.09% 23 $33.6 0.09% 

Pizza/Premium 20 $6.5 0.10% 22 $28.2 0.09% 20 $34.8 0.09% 

Snack Cake—Multi Pack 21 $6.5 0.10% 57 $18.9 0.06% 40 $25.5 0.07% 

Fz Ss Economy Meals All 22 $6.3 0.10% 90 $13.6 0.04% 72 $19.9 0.05% 

Convenient Meals—Kids 

Meal C 

23 $6.2 0.09% 48 $20.3 0.06% 38 $26.6 0.07% 

All Family Cereal 24 $6.2 0.09% 14 $37.6 0.12% 15 $43.8 0.11% 

Fz Ss Prem Traditional 

Meals 

25 $6.1 0.09% 52 $19.7 0.06% 39 $25.8 0.07% 

Sandwiches & Handhelds 26 $6.0 0.09% 77 $14.9 0.05% 64 $20.9 0.05% 

Soft Drinks 20pk & 24pk 

Can Carb 

27 $6.0 0.09% 61 $17.9 0.06% 48 $23.9 0.06% 

248 

Exhibit E–1: Top 100 Subcommodities for SNAP Households by 

Expenditure: Household Head Age 19–44 Year Olds—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Bacon—Trad 16oz Or Less 28 $6.0 0.09% 30 $23.5 0.07% 29 $29.4 0.08% 

Mainstream Variety 

Breads 

29 $5.8 0.09% 23 $28.0 0.09% 22 $33.8 0.09% 

Sugar 30 $5.6 0.09% 62 $17.9 0.06% 50 $23.5 0.06% 

Natural Cheese Chunks 31 $5.6 0.08% 17 $34.5 0.11% 17 $40.1 0.11% 

Unflavored Can Coffee 32 $5.5 0.08% 32 $23.3 0.07% 30 $28.8 0.08% 

Frzn Chicken—Wht Meat 33 $5.4 0.08% 51 $19.9 0.06% 42 $25.2 0.07% 

Potatoes Russet (Bulk & 

Bag) 

34 $5.3 0.08% 37 $22.4 0.07% 35 $27.7 0.07% 

Bananas 35 $5.2 0.08% 15 $37.0 0.12% 16 $42.2 0.11% 

Isotonic Drinks Single 

Serve 

36 $5.1 0.08% 33 $22.9 0.07% 34 $28.0 0.07% 

Ribs [Pork] 37 $5.1 0.08% 78 $14.8 0.05% 71 $19.9 0.05% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

38 $5.0 0.08% 35 $22.6 0.07% 36 $27.6 0.07% 

Premium [Ice Cream & 

Sherbert] 

39 $4.7 0.07% 18 $32.9 0.10% 19 $37.6 0.10% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

40 $4.7 0.07% 89 $13.7 0.04% 77 $18.4 0.05% 

Pourable Salad Dressings 41 $4.7 0.07% 36 $22.4 0.07% 37 $27.1 0.07% 

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 Style Entre ́es 44 $4.5 0.07% 82 $14.3 0.05% 74 $18.8 0.05% 

Aseptic Pack Juice And 

Drinks 

45 $4.4 0.07% 66 $16.9 0.05% 61 $21.3 0.06% 

Select Beef 46 $4.3 0.06% 46 $20.5 0.07% 45 $24.8 0.07% 

Macaroni & Cheese Dnrs 47 $4.2 0.06% 92 $13.5 0.04% 82 $17.7 0.05% 

Choice Beef 48 $4.1 0.06% 63 $17.8 0.06% 56 $21.9 0.06% 

Mainstream [Pasta & 

Pizza Sauce] 

49 $4.0 0.06% 70 $16.1 0.05% 67 $20.1 0.05% 

Mayonnaise & Whipped 

Dressing 

50 $4.0 0.06% 67 $16.8 0.05% 65 $20.8 0.05% 

Fz Ss Prem Nutritional 

Meals 

51 $4.0 0.06% 7 $42.9 0.14% 13 $46.9 0.12% 

Refrigerated Coffee 

Creamers 

52 $4.0 0.06% 26 $25.8 0.08% 27 $29.7 0.08% 

Fz Bag Vegetables—Plain 53 $3.9 0.06% 54 $19.4 0.06% 51 $23.3 0.06% 

Hot Dogs—Base Meat 54 $3.9 0.06% 137 $9.8 0.03% 113 $13.6 0.04% 

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 Tortillas And 

Wra 

58 $3.8 0.06% 39 $21.7 0.07% 41 $25.4 0.07% 

Traditional [Ice Cream & 

Sherbert] 

59 $3.8 0.06% 69 $16.2 0.05% 70 $19.9 0.05% 

Choice Beef 60 $3.7 0.06% 124 $10.6 0.03% 104 $14.3 0.04% 

Mult Pk Bag Snacks 61 $3.6 0.05% 132 $10.0 0.03% 114 $13.6 0.04% 

Pizza/Economy 62 $3.5 0.05% 128 $10.3 0.03% 111 $13.7 0.04% 

Margarine: Tubs And 

Bowls 

63 $3.5 0.05% 88 $13.8 0.04% 84 $17.3 0.05% 

Frzn Chicken—Wings 64 $3.4 0.05% 441 $3.0 0.01% 269 $6.4 0.02% 

Frzn French Fries 65 $3.4 0.05% 143 $9.6 0.03% 119 $13.0 0.03% 

Peanut Butter 66 $3.4 0.05% 40 $21.4 0.07% 44 $24.8 0.07% 

Candy Bags—Chocolate 67 $3.4 0.05% 42 $20.8 0.07% 47 $24.2 0.06% 

Value Forms/18oz And 

Larger [Chicken] 

68 $3.3 0.05% 120 $10.7 0.03% 108 $13.9 0.04% 

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 Bulk 71 $3.2 0.05% 21 $28.5 0.09% 24 $31.8 0.08% 

Frzn Meat—Beef 72 $3.2 0.05% 161 $8.8 0.03% 139 $12.0 0.03% 

Chicken Wings 73 $3.1 0.05% 350 $4.0 0.01% 247 $7.2 0.02% 

Frzn Breakfast Sandwiches 74 $3.1 0.05% 125 $10.5 0.03% 115 $13.6 0.04% 

Tuna 75 $3.1 0.05% 74 $15.6 0.05% 75 $18.8 0.05% 

Waffles/Pancakes/French 

Toast 

76 $3.1 0.05% 59 $18.2 0.06% 62 $21.3 0.06% 

Cakes: Birthday/Celebration Sh 77 $3.1 0.05% 152 $9.2 0.03% 136 $12.2 0.03% 

Sour Creams 78 $3.0 0.05% 64 $17.5 0.06% 66 $20.5 0.05% 

Cheese Crackers 79 $3.0 0.05% 44 $20.7 0.07% 49 $23.7 0.06% 

Fz Skillet Meals 80 $3.0 0.05% 97 $12.6 0.04% 93 $15.6 0.04% 

Vegetable Oil 81 $3.0 0.05% 253 $5.7 0.02% 196 $8.7 0.02% 

Lunchment—Bologna/Sausage 82 $3.0 0.05% 177 $8.1 0.03% 149 $11.1 0.03% 

Pizza/Traditional 83 $3.0 0.05% 101 $12.3 0.04% 97 $15.3 0.04% 

Cream Cheese 84 $3.0 0.04% 49 $20.3 0.06% 53 $23.2 0.06% 

Sandwich Cookies 85 $2.9 0.04% 100 $12.4 0.04% 95 $15.4 0.04% 

Butter 86 $2.9 0.04% 27 $25.1 0.08% 33 $28.0 0.07% 

Ramen Noodles/Ramen 

Cups 

87 $2.9 0.04% 258 $5.6 0.02% 208 $8.5 0.02% 

249 

Exhibit E–1: Top 100 Subcommodities for SNAP Households by 

Expenditure: Household Head Age 19–44 Year Olds—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

String Cheese 88 $2.8 0.04% 38 $22.0 0.07% 46 $24.7 0.06% 

Bagged Cheese Snacks 89 $2.7 0.04% 153 $9.0 0.03% 142 $11.7 0.03% 

Salsa & Dips 90 $2.7 0.04% 136 $9.8 0.03% 129 $12.5 0.03% 

Toaster Pastries 91 $2.7 0.04% 107 $11.8 0.04% 103 $14.5 0.04% 

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: Chunky/ 

Homestyle/Et 

94 $2.7 0.04% 65 $17.4 0.06% 68 $20.0 0.05% 

Flavored Milk 95 $2.6 0.04% 118 $10.8 0.03% 116 $13.4 0.04% 

Candy Bars (Singles) (Including) 96 $2.6 0.04% 158 $8.9 0.03% 146 $11.5 0.03% 

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—Full Size 100 $2.5 0.04% 145 $9.5 0.03% 137 $12.0 0.03% 

Top 100 Subcommodities $537.8 8.17% $2,251.0 7.14% $2,788.8 7.32% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $41.3 0.63% 1 $258.9 0.82% 1 $300.1 0.79% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $36.6 0.56% 2 $197.3 0.63% 2 $233.9 0.61% 

Lean [Beef] 3 $22.4 0.34% 8 $77.3 0.25% 5 $99.7 0.26% 

Shredded Cheese 4 $16.7 0.25% 3 $112.7 0.36% 6 $129.4 0.34% 

Sft Drnk 2 Liter Btl Carb 

Incl 

5 $15.6 0.24% 14 $70.8 0.22% 3 $86.4 0.23% 

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—Deli Fresh 9 $12.2 0.19% 9 $76.8 0.24% 73 $89.1 0.23% 

Eggs—Large 10 $11.3 0.17% 10 $75.4 0.24% 7 $86.7 0.23% 

Chicken Breast Boneless 11 $11.1 0.17% 4 $95.0 0.30% 4 $106.1 0.28% 

Unflavored Can Coffee 12 $10.2 0.16% 18 $64.2 0.20% 10 $74.4 0.20% 

Mainstream White Bread 13 $10.2 0.15% 44 $40.6 0.13% 12 $50.8 0.13% 

Fz Ss Prem Traditional 

Meals 

14 $10.1 0.15% 26 $53.5 0.17% 32 $63.7 0.17% 

Tortilla/Nacho Chips 15 $10.0 0.15% 17 $67.1 0.21% 18 $77.1 0.20% 

Still Water Drnking/Mnrl 

Water 

16 $9.9 0.15% 22 $56.0 0.18% 25 $65.9 0.17% 

Infant Formula Starter/ 

Solutio 

17 $9.8 0.15% 363 $7.7 0.02% 26 $17.4 0.05% 

Dairy Case 100% Pure 

Juice—O 

18 $9.7 0.15% 7 $80.7 0.26% 11 $90.4 0.24% 

American Single Cheese 19 $9.4 0.14% 42 $41.5 0.13% 23 $50.9 0.13% 

Bacon—Trad 16oz Or Less 20 $9.1 0.14% 30 $50.1 0.16% 20 $59.2 0.16% 

Enhanced [Pork Boneless 

Loin/Rib] 

21 $9.0 0.14% 24 $54.8 0.17% 40 $63.9 0.17% 

Snacks/Appetizers 22 $8.9 0.14% 64 $32.2 0.10% 72 $41.1 0.11% 

Snack Cake—Multi Pack 23 $8.8 0.13% 66 $31.8 0.10% 38 $40.5 0.11% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

24 $8.6 0.13% 19 $61.3 0.19% 15 $69.9 0.18% 

Mainstream Variety 

Breads 

25 $8.4 0.13% 29 $50.8 0.16% 39 $59.2 0.16% 

Fz Ss Economy Meals All 26 $8.3 0.13% 104 $22.2 0.07% 64 $30.6 0.08% 

Pizza/Premium 27 $8.3 0.13% 34 $48.7 0.15% 48 $57.0 0.15% 

Natural Cheese Chunks 28 $8.3 0.13% 15 $69.9 0.22% 29 $78.2 0.21% 

All Family Cereal 29 $8.1 0.12% 16 $68.0 0.22% 22 $76.1 0.20% 

Soft Drinks 20pk & 24pk 

Can Carb 

30 $8.1 0.12% 62 $33.3 0.11% 50 $41.5 0.11% 

Potatoes Russet (Bulk & 

Bag) 

31 $8.1 0.12% 31 $49.4 0.16% 17 $57.5 0.15% 

Bananas 32 $7.9 0.12% 12 $74.3 0.24% 30 $82.3 0.22% 

250 

Exhibit E–2: Top 100 Subcommodities for SNAP Households by 

Expenditure: Household Head Age 45–64 Year Olds—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

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 Cream & 

Sherbert] 

35 $7.4 0.11% 13 $73.2 0.23% 16 $80.6 0.21% 

Condensed Soup 36 $7.2 0.11% 33 $49.0 0.16% 34 $56.2 0.15% 

Sandwiches & Handhelds 37 $7.1 0.11% 100 $22.5 0.07% 71 $29.5 0.08% 

Fz Ss Prem Nutritional 

Meals 

38 $6.7 0.10% 5 $91.3 0.29% 36 $98.0 0.26% 

Convenient Meals—Kids 

Meal C 

39 $6.6 0.10% 143 $18.0 0.06% 19 $24.6 0.06% 

Isotonic Drinks Single 

Serve 

40 $6.6 0.10% 54 $36.0 0.11% 77 $42.6 0.11% 

Select Beef 41 $6.6 0.10% 32 $49.3 0.16% 37 $55.9 0.15% 

Frzn Chicken—Wht Meat 42 $6.5 0.10% 65 $32.0 0.10% 31 $38.5 0.10% 

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 Dressings 45 $6.5 0.10% 37 $46.3 0.15% 61 $52.7 0.14% 

Traditional [Ice Cream & 

Sherbert] 

46 $6.2 0.09% 52 $37.1 0.12% 45 $43.3 0.11% 

Fz Bag Vegetables—Plain 47 $6.2 0.09% 40 $42.0 0.13% 82 $48.2 0.13% 

Mayonnaise & Whipped 

Dressing 

48 $6.0 0.09% 49 $38.0 0.12% 56 $44.0 0.12% 

Refrigerated Coffee 

Creamers 

49 $5.9 0.09% 35 $48.1 0.15% 67 $54.0 0.14% 

Fz Family Style Entre ́es 50 $5.8 0.09% 80 $26.5 0.08% 65 $32.3 0.08% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

51 $5.7 0.09% 111 $21.2 0.07% 13 $26.9 0.07% 

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—Chocolate 55 $5.2 0.08% 28 $50.9 0.16% 21 $56.1 0.15% 

Hot Dogs—Base Meat 56 $5.1 0.08% 161 $16.6 0.05% 28 $21.7 0.06% 

Margarine: Tubs And 

Bowls 

57 $5.1 0.08% 71 $30.5 0.10% 102 $35.6 0.09% 

Choice Beef 58 $5.1 0.08% 99 $22.6 0.07% 41 $27.7 0.07% 

Mainstream [Pasta & 

Pizza Sauce] 

59 $4.9 0.07% 87 $25.0 0.08% 70 $29.9 0.08% 

Tuna 60 $4.8 0.07% 58 $35.1 0.11% 104 $39.9 0.10% 

Lunchment—Bologna/Sausage 61 $4.7 0.07% 138 $18.5 0.06% 114 $23.2 0.06% 

Meat: Turkey Bulk 62 $4.7 0.07% 23 $55.8 0.18% 111 $60.5 0.16% 

Macaroni & Cheese Dnrs 63 $4.7 0.07% 154 $17.1 0.05% 84 $21.8 0.06% 

Peanut Butter 64 $4.7 0.07% 45 $40.5 0.13% 269 $45.1 0.12% 

Aseptic Pack Juice And 

Drinks 

65 $4.5 0.07% 194 $14.2 0.04% 119 $18.7 0.05% 

Chicken Wings 66 $4.5 0.07% 346 $8.1 0.03% 44 $12.6 0.03% 

Mexican Soft Tortillas And 

Wra 

67 $4.5 0.07% 63 $33.0 0.10% 47 $37.5 0.10% 

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 Fries 70 $4.3 0.07% 166 $16.2 0.05% 92 $20.5 0.05% 

Angus [Beef] 71 $4.3 0.07% 53 $36.2 0.11% 24 $40.5 0.11% 

Rts Soup: Chunky/ 

Homestyle/Et 

72 $4.2 0.06% 48 $38.2 0.12% 139 $42.4 0.11% 

Fz Skillet Meals 73 $4.1 0.06% 85 $25.2 0.08% 247 $29.4 0.08% 

Cream Cheese 74 $4.1 0.06% 51 $37.6 0.12% 115 $41.7 0.11% 

Frzn Chicken—Wings 75 $4.1 0.06% 514 $4.8 0.02% 75 $8.9 0.02% 

Mult Pk Bag Snacks 76 $4.1 0.06% 208 $13.4 0.04% 62 $17.5 0.05% 

Frzn Breakfast Sandwiches 77 $4.0 0.06% 147 $17.7 0.06% 136 $21.6 0.06% 

Sandwich Cookies 78 $3.9 0.06% 94 $23.3 0.07% 66 $27.2 0.07% 

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—Beef 81 $3.9 0.06% 180 $15.2 0.05% 196 $19.1 0.05% 

Meat: Ham Bulk 82 $3.9 0.06% 46 $40.3 0.13% 149 $44.1 0.12% 

Pizza/Traditional 83 $3.8 0.06% 125 $19.6 0.06% 97 $23.4 0.06% 

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 Crackers 87 $3.7 0.06% 74 $29.0 0.09% 46 $32.7 0.09% 

Candy Bars (Multi Pack) 88 $3.6 0.05% 96 $22.9 0.07% 142 $26.5 0.07% 

Value Forms/18oz And 

Larger [Chicken] 

89 $3.6 0.05% 240 $11.6 0.04% 129 $15.2 0.04% 

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/Pancakes/French 

Toast 

92 $3.6 0.05% 105 $22.1 0.07% 100 $25.6 0.07% 

Spring Water 93 $3.6 0.05% 73 $29.4 0.09% 68 $32.9 0.09% 

Sweet Goods—Full Size 94 $3.6 0.05% 144 $18.0 0.06% 116 $21.5 0.06% 

251 

Exhibit E–2: Top 100 Subcommodities for SNAP Households by 

Expenditure: Household Head Age 45–64 Year Olds—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Cottage Cheese 95 $3.5 0.05% 56 $35.4 0.11% 146 $38.9 0.10% 

Cakes: Birthday/Celebration Sh 96 $3.5 0.05% 190 $14.6 0.05% 85 $18.2 0.05% 

Bkfst Sausage—Fresh 

Rolls 

97 $3.5 0.05% 117 $20.2 0.06% 80 $23.7 0.06% 

Dnr Sausage—Links Pork 

Ckd/S 

98 $3.5 0.05% 242 $11.5 0.04% 241 $15.0 0.04% 

Candy Bars (Singles) (Including) 99 $3.5 0.05% 155 $17.1 0.05% 137 $20.5 0.05% 

Fruit Snacks 100 $3.5 0.05% 224 $12.2 0.04% 203 $15.6 0.04% 

Top 100 Subcommodities $731.2 11.09% $4,237.7 13.52% $5,004.7 13.17% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $12.6 0.19% 1 $109.6 0.35% 1 $122.2 0.32% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $10.9 0.17% 2 $69.4 0.22% 2 $80.3 0.21% 

Lean [Beef] 3 $6.3 0.10% 18 $26.1 0.08% 12 $32.4 0.09% 

Sft Drnk 2 Liter Btl Carb 

Incl 

4 $4.2 0.06% 29 $21.4 0.07% 24 $25.6 0.07% 

Primal [Beef] 5 $4.2 0.06% 15 $27.5 0.09% 13 $31.7 0.08% 

Shredded Cheese 6 $4.2 0.06% 10 $29.8 0.09% 10 $34.0 0.09% 

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 Coffee 10 $3.5 0.05% 6 $35.6 0.11% 5 $39.1 0.10% 

Fz Ss Prem Traditional 

Meals 

11 $3.4 0.05% 9 $31.9 0.10% 9 $35.3 0.09% 

Lunchment—Deli Fresh 12 $3.4 0.05% 19 $24.6 0.08% 19 $28.0 0.07% 

Mainstream White Bread 13 $3.2 0.05% 40 $16.9 0.05% 36 $20.1 0.05% 

Dairy Case 100% Pure 

Juice—O 

14 $3.1 0.05% 3 $38.6 0.12% 3 $41.7 0.11% 

Bacon—Trad 16oz Or Less 15 $2.9 0.04% 24 $23.1 0.07% 23 $26.0 0.07% 

Chicken Breast Boneless 16 $2.8 0.04% 17 $26.2 0.08% 18 $29.0 0.08% 

Bananas 17 $2.7 0.04% 4 $37.1 0.12% 4 $39.8 0.10% 

American Single Cheese 18 $2.7 0.04% 38 $17.4 0.06% 35 $20.1 0.05% 

Enhanced [Pork Boneless 

Loin/Rib] 

19 $2.7 0.04% 26 $22.9 0.07% 25 $25.6 0.07% 

Mainstream Variety 

Breads 

20 $2.7 0.04% 27 $22.6 0.07% 28 $25.3 0.07% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

21 $2.6 0.04% 20 $24.5 0.08% 20 $27.1 0.07% 

Still Water Drnking/Mnrl 

Water 

22 $2.6 0.04% 49 $15.2 0.05% 43 $17.8 0.05% 

Potatoes Russet (Bulk & 

Bag) 

23 $2.6 0.04% 25 $22.9 0.07% 27 $25.5 0.07% 

Snack Cake—Multi Pack 24 $2.6 0.04% 68 $11.4 0.04% 62 $14.0 0.04% 

Natural Cheese Chunks 25 $2.5 0.04% 14 $28.3 0.09% 16 $30.8 0.08% 

All Family Cereal 26 $2.5 0.04% 12 $29.0 0.09% 14 $31.5 0.08% 

Fz Ss Economy Meals All 27 $2.5 0.04% 87 $9.3 0.03% 73 $11.8 0.03% 

Premium [Ice Cream & 

Sherbert] 

28 $2.5 0.04% 7 $35.5 0.11% 6 $38.0 0.10% 

Tortilla/Nacho Chips 29 $2.5 0.04% 48 $15.6 0.05% 41 $18.0 0.05% 

Condensed Soup 30 $2.4 0.04% 22 $24.5 0.08% 21 $26.8 0.07% 

Soft Drinks 20pk & 24pk 

Can Carb 

31 $2.3 0.04% 82 $9.7 0.03% 70 $12.1 0.03% 

Sugar 32 $2.3 0.04% 51 $15.1 0.05% 47 $17.5 0.05% 

Traditional [Ice Cream & 

Sherbert] 

33 $2.3 0.03% 23 $23.2 0.07% 26 $25.5 0.07% 

Ribs [Pork] 34 $2.3 0.03% 57 $13.4 0.04% 53 $15.6 0.04% 

Snacks/Appetizers 35 $2.2 0.03% 144 $6.6 0.02% 112 $8.9 0.02% 

Infant Formula Starter/ 

Solutio 

36 $2.2 0.03% 583 $1.4 0.00% 336 $3.6 0.01% 

252 

Exhibit E–3: Top 100 Subcommodities for SNAP Households by 

Expenditure: Household Head Age 65 Years or Older—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

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 Nutritional 

Meals 

39 $2.0 0.03% 5 $35.8 0.11% 7 $37.8 0.10% 

Fz Bag Vegetables—Plain 40 $2.0 0.03% 32 $19.9 0.06% 31 $21.9 0.06% 

Choice Beef 41 $1.9 0.03% 60 $12.7 0.04% 58 $14.6 0.04% 

Mayonnaise & Whipped 

Dressing 

42 $1.9 0.03% 34 $18.2 0.06% 34 $20.1 0.05% 

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: Tubs And 

Bowls 

46 $1.8 0.03% 33 $18.5 0.06% 33 $20.4 0.05% 

Pourable Salad Dressings 47 $1.8 0.03% 39 $16.9 0.05% 39 $18.7 0.05% 

Sandwiches & Handhelds 48 $1.8 0.03% 219 $4.9 0.02% 165 $6.7 0.02% 

Strawberries 49 $1.7 0.03% 21 $24.5 0.08% 22 $26.1 0.07% 

Candy Bags—Chocolate 50 $1.6 0.02% 28 $22.4 0.07% 29 $24.1 0.06% 

Convenient Meals—Kids 

Meal C 

51 $1.6 0.02% 324 $3.4 0.01% 240 $5.0 0.01% 

Refrigerated Coffee 

Creamers 

52 $1.6 0.02% 45 $16.1 0.05% 44 $17.7 0.05% 

Frzn Chicken—Wht Meat 53 $1.6 0.02% 96 $8.8 0.03% 86 $10.4 0.03% 

Lunchment—Bologna/Sausage 54 $1.6 0.02% 84 $9.5 0.03% 78 $11.1 0.03% 

Fz Family Style Entre ́es 55 $1.6 0.02% 90 $9.0 0.03% 85 $10.6 0.03% 

Isotonic Drinks Single 

Serve 

56 $1.6 0.02% 123 $7.4 0.02% 108 $9.0 0.02% 

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 Meat 59 $1.5 0.02% 146 $6.6 0.02% 125 $8.1 0.02% 

Peanut Butter 60 $1.4 0.02% 44 $16.1 0.05% 45 $17.6 0.05% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

61 $1.4 0.02% 237 $4.5 0.01% 195 $6.0 0.02% 

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: Chunky/ 

Homestyle/Et 

65 $1.3 0.02% 41 $16.6 0.05% 42 $17.9 0.05% 

Chicken Wings 66 $1.3 0.02% 405 $2.6 0.01% 310 $3.9 0.01% 

Meat: Turkey Bulk 67 $1.2 0.02% 47 $15.9 0.05% 48 $17.1 0.04% 

Mainstream [Pasta & 

Pizza Sauce] 

68 $1.2 0.02% 142 $6.9 0.02% 126 $8.1 0.02% 

Grapes Red 69 $1.2 0.02% 37 $17.4 0.06% 40 $18.6 0.05% 

Macaroni & Cheese Dnrs 70 $1.2 0.02% 277 $4.0 0.01% 231 $5.2 0.01% 

Mexican Soft Tortillas And 

Wra 

71 $1.2 0.02% 115 $7.9 0.03% 106 $9.1 0.02% 

Frzn Breakfast Sandwiches 72 $1.2 0.02% 165 $6.1 0.02% 150 $7.2 0.02% 

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—Full Size 75 $1.1 0.02% 93 $8.9 0.03% 90 $10.1 0.03% 

Meat: Ham Bulk 76 $1.1 0.02% 46 $15.9 0.05% 49 $17.0 0.04% 

Bkfst Sausage—Fresh 

Rolls 

77 $1.1 0.02% 105 $8.3 0.03% 97 $9.5 0.02% 

Fz Skillet Meals 78 $1.1 0.02% 83 $9.6 0.03% 82 $10.8 0.03% 

Vegetable Oil 79 $1.1 0.02% 305 $3.6 0.01% 258 $4.7 0.01% 

Frzn French Fries 80 $1.1 0.02% 234 $4.6 0.01% 212 $5.7 0.01% 

Sandwich Cookies 81 $1.1 0.02% 102 $8.4 0.03% 96 $9.5 0.03% 

Candy Bars (Multi Pack) 82 $1.1 0.02% 78 $9.9 0.03% 79 $11.0 0.03% 

Butter Spray Cracker 83 $1.1 0.02% 69 $10.9 0.03% 71 $12.0 0.03% 

Premium Bread 84 $1.1 0.02% 30 $21.2 0.07% 30 $22.3 0.06% 

Aseptic Pack Juice And 

Drinks 

85 $1.1 0.02% 420 $2.5 0.01% 343 $3.6 0.01% 

Sticks/Enrobed [Frozen 

Novelties] 

86 $1.1 0.02% 76 $10.2 0.03% 77 $11.3 0.03% 

Sour Creams 87 $1.1 0.02% 71 $10.7 0.03% 72 $11.8 0.03% 

Waffles/Pancakes/French 

Toast 

88 $1.1 0.02% 111 $8.1 0.03% 102 $9.2 0.02% 

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 Snacks 91 $1.0 0.02% 408 $2.6 0.01% 341 $3.6 0.01% 

Frzn Chicken—Wings 92 $1.0 0.02% 654 $1.2 0.00% 479 $2.2 0.01% 

Flavored Milk 93 $1.0 0.02% 178 $5.8 0.02% 161 $6.8 0.02% 

Refrigerated Biscuits 94 $1.0 0.02% 164 $6.2 0.02% 151 $7.2 0.02% 

Grapes White 95 $1.0 0.02% 70 $10.8 0.03% 74 $11.8 0.03% 

Dnr Sausage—Links Pork 

Ckd/S 

96 $1.0 0.02% 284 $3.9 0.01% 249 $4.9 0.01% 

Pizza/Economy 97 $1.0 0.02% 357 $3.0 0.01% 305 $4.0 0.01% 

Frzn Meat—Beef 98 $1.0 0.01% 279 $3.9 0.01% 248 $4.9 0.01% 

253 

Exhibit E–3: Top 100 Subcommodities for SNAP Households by 

Expenditure: Household Head Age 65 Years or Older—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Pizza/Traditional 99 $1.0 0.01% 211 $5.1 0.02% 184 $6.1 0.02% 

Candy Bars (Singles) 100 $1.0 0.01% 167 $6.0 0.02% 154 $7.0 0.02% 

Top 100 Subcommodities $213.1 3.29% $1,664.6 5.23% $1,877.6 4.94% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $33.9 0.52% 1 $190.0 0.60% 1 $223.9 0.59% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $28.4 0.43% 2 $128.5 0.41% 2 $156.9 0.41% 

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 Cheese 5 $13.9 0.21% 3 $82.7 0.26% 3 $96.7 0.25% 

Sft Drnk 2 Liter Btl Carb 

Incl 

6 $12.4 0.19% 12 $49.7 0.16% 9 $62.2 0.16% 

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—Deli Fresh 9 $9.6 0.15% 8 $53.4 0.17% 8 $63.0 0.17% 

Chicken Breast Boneless 10 $8.9 0.14% 4 $65.2 0.21% 4 $74.1 0.19% 

Infant Formula Starter/ 

Solutio 

11 $8.7 0.13% 258 $7.1 0.02% 127 $15.8 0.04% 

Tortilla/Nacho Chips 12 $8.5 0.13% 11 $50.4 0.16% 12 $58.9 0.15% 

Eggs—Large 13 $8.5 0.13% 14 $49.1 0.16% 13 $57.6 0.15% 

Mainstream White Bread 14 $8.3 0.13% 32 $31.1 0.10% 28 $39.4 0.10% 

Snacks/Appetizers 15 $8.2 0.12% 41 $27.4 0.09% 34 $35.6 0.09% 

Still Water Drnking/Mnrl 

Water 

16 $7.8 0.12% 21 $37.7 0.12% 19 $45.5 0.12% 

American Single Cheese 17 $7.5 0.11% 36 $28.9 0.09% 33 $36.4 0.10% 

Dairy Case 100% Pure 

Juice—O 

18 $7.5 0.11% 6 $53.5 0.17% 11 $61.0 0.16% 

Snack Cake—Multi Pack 19 $7.2 0.11% 47 $25.7 0.08% 41 $32.9 0.09% 

Enhanced [Pork Boneless 

Loin/Rib] 

20 $7.2 0.11% 22 $36.4 0.12% 21 $43.6 0.11% 

Fz Ss Prem Traditional 

Meals 

21 $7.1 0.11% 39 $27.9 0.09% 36 $35.0 0.09% 

Pizza/Premium 22 $6.9 0.11% 27 $34.2 0.11% 24 $41.1 0.11% 

Fz Ss Economy Meals All 23 $6.9 0.10% 90 $16.7 0.05% 73 $23.5 0.06% 

All Family Cereal 24 $6.8 0.10% 15 $48.9 0.16% 15 $55.7 0.15% 

Unflavored Can Coffee 25 $6.8 0.10% 26 $34.3 0.11% 25 $41.0 0.11% 

Bacon—Trad 16oz Or Less 26 $6.8 0.10% 30 $32.1 0.10% 30 $38.8 0.10% 

Convenient Meals—Kids 

Meal C 

27 $6.7 0.10% 58 $23.7 0.08% 43 $30.3 0.08% 

Soft Drinks 20pk & 24pk 

Can Carb 

28 $6.5 0.10% 63 $22.3 0.07% 53 $28.7 0.08% 

Mainstream Variety 

Breads 

29 $6.3 0.10% 24 $35.3 0.11% 22 $41.6 0.11% 

Sandwiches & Handhelds 30 $6.2 0.09% 79 $18.6 0.06% 67 $24.8 0.07% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

31 $6.2 0.09% 28 $33.7 0.11% 26 $39.9 0.10% 

Natural Cheese Chunks 32 $6.2 0.09% 18 $42.9 0.14% 18 $49.1 0.13% 

Sugar 33 $6.1 0.09% 60 $23.3 0.07% 52 $29.4 0.08% 

Potatoes Russet (Bulk & 

Bag) 

34 $6.0 0.09% 33 $30.7 0.10% 32 $36.7 0.10% 

Bananas 35 $6.0 0.09% 16 $48.2 0.15% 16 $54.2 0.14% 

Frzn Chicken—Wht Meat 36 $5.6 0.09% 54 $24.6 0.08% 45 $30.2 0.08% 

Ribs [Pork] 37 $5.6 0.08% 70 $20.7 0.07% 61 $26.3 0.07% 

Premium [Ice Cream & 

Sherbert] 

38 $5.5 0.08% 17 $46.9 0.15% 17 $52.4 0.14% 

Isotonic Drinks Single 

Serve 

39 $5.5 0.08% 37 $28.3 0.09% 39 $33.8 0.09% 

Condensed Soup 40 $5.4 0.08% 29 $32.7 0.10% 31 $38.2 0.10% 

Pourable Salad Dressings 41 $5.1 0.08% 35 $29.1 0.09% 37 $34.2 0.09% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

42 $4.9 0.07% 104 $15.3 0.05% 85 $20.2 0.05% 

254 

Exhibit E–4: Top 100 Subcommodities for SNAP Households by 

Expenditure: Households with Children Present—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Choice Beef 43 $4.9 0.07% 77 $19.0 0.06% 71 $23.9 0.06% 

Fz Family Style Entre ́es 44 $4.8 0.07% 80 $18.6 0.06% 74 $23.4 0.06% 

Select Beef 45 $4.8 0.07% 38 $28.3 0.09% 40 $33.0 0.09% 

Fz Ss Prem Nutritional 

Meals 

46 $4.7 0.07% 9 $52.3 0.17% 14 $57.0 0.15% 

Traditional [Ice Cream & 

Sherbert] 

47 $4.6 0.07% 50 $24.9 0.08% 50 $29.6 0.08% 

Aseptic Pack Juice And 

Drinks 

48 $4.6 0.07% 74 $19.4 0.06% 69 $24.0 0.06% 

Choice Beef 49 $4.6 0.07% 49 $25.1 0.08% 49 $29.7 0.08% 

Fz Bag Vegetables—Plain 50 $4.5 0.07% 48 $25.4 0.08% 48 $29.9 0.08% 

Mayonnaise & Whipped 

Dressing 

51 $4.4 0.07% 66 $22.0 0.07% 60 $26.5 0.07% 

Refrigerated Coffee 

Creamers 

52 $4.4 0.07% 31 $31.1 0.10% 35 $35.5 0.09% 

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 & Cheese Dnrs 55 $4.2 0.06% 101 $15.5 0.05% 88 $19.7 0.05% 

Mainstream [Pasta & 

Pizza Sauce] 

56 $4.2 0.06% 72 $19.8 0.06% 70 $24.0 0.06% 

Hot Dogs—Base Meat 57 $4.2 0.06% 134 $12.6 0.04% 115 $16.8 0.04% 

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—Chocolate 60 $4.0 0.06% 34 $29.9 0.09% 38 $33.8 0.09% 

Margarine: Tubs And 

Bowls 

61 $3.9 0.06% 78 $18.9 0.06% 78 $22.8 0.06% 

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 Bulk 64 $3.7 0.06% 20 $37.8 0.12% 23 $41.5 0.11% 

Mult Pk Bag Snacks 65 $3.7 0.06% 132 $12.9 0.04% 118 $16.6 0.04% 

Frzn French Fries 66 $3.6 0.06% 138 $12.5 0.04% 123 $16.1 0.04% 

Mexican Soft Tortillas And 

Wra 

67 $3.6 0.06% 59 $23.4 0.07% 58 $27.0 0.07% 

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—Bologna/Sausage 72 $3.4 0.05% 156 $11.5 0.04% 135 $14.9 0.04% 

Value Forms/18oz And 

Larger [Chicken] 

73 $3.4 0.05% 139 $12.4 0.04% 128 $15.8 0.04% 

Frzn Breakfast Sandwiches 74 $3.4 0.05% 122 $13.6 0.04% 110 $17.0 0.04% 

Cheese Crackers 75 $3.4 0.05% 44 $26.8 0.09% 46 $30.2 0.08% 

Frzn Meat—Beef 76 $3.3 0.05% 151 $11.7 0.04% 133 $15.1 0.04% 

Waffles/Pancakes/French 

Toast 

77 $3.3 0.05% 62 $22.5 0.07% 65 $25.9 0.07% 

Frzn Chicken—Wings 78 $3.3 0.05% 470 $3.4 0.01% 308 $6.8 0.02% 

Cream Cheese 79 $3.3 0.05% 45 $26.1 0.08% 51 $29.4 0.08% 

Sandwich Cookies 80 $3.2 0.05% 83 $17.6 0.06% 83 $20.8 0.05% 

Pizza/Traditional 81 $3.2 0.05% 100 $15.7 0.05% 94 $18.9 0.05% 

Fz Skillet Meals 82 $3.2 0.05% 103 $15.4 0.05% 97 $18.6 0.05% 

Sour Creams 83 $3.2 0.05% 69 $21.3 0.07% 68 $24.5 0.06% 

Cakes: Birthday/Celebration Sh 84 $3.2 0.05% 160 $11.3 0.04% 143 $14.6 0.04% 

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: Chunky/ 

Homestyle/Et 

89 $3.0 0.05% 65 $22.0 0.07% 66 $25.1 0.07% 

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—Full Size 94 $2.9 0.04% 123 $13.5 0.04% 119 $16.4 0.04% 

Bagged Cheese Snacks 95 $2.9 0.04% 149 $11.9 0.04% 138 $14.8 0.04% 

Toaster Pastries 96 $2.9 0.04% 95 $16.1 0.05% 93 $19.0 0.05% 

Grapes Red 97 $2.8 0.04% 42 $27.3 0.09% 47 $30.2 0.08% 

Candy Bars (Singles) (Including) 98 $2.8 0.04% 159 $11.4 0.04% 148 $14.2 0.04% 

Salsa & Dips 99 $2.8 0.04% 150 $11.8 0.04% 142 $14.6 0.04% 

Ramen Noodles/Ramen 

Cups 

100 $2.8 0.04% 274 $6.5 0.02% 229 $9.3 0.02% 

Top 100 Subcommodities $585.8 8.90% $2,937.8 9.32% $3,523.7 9.25% 

255 

Exhibit E–4: Top 100 Subcommodities for SNAP Households by 

Expenditure: Households with Children Present—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $50.6 0.77% 1 $322.1 1.02% 1 $372.7 0.98% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $44.1 0.67% 2 $234.1 0.74% 2 $278.3 0.73% 

Lean [Beef] 3 $28.4 0.43% 10 $94.8 0.30% 6 $123.1 0.32% 

Shredded Cheese 4 $19.9 0.30% 3 $126.8 0.40% 3 $146.7 0.39% 

Sft Drnk 2 Liter Btl Carb 

Incl 

5 $18.7 0.28% 17 $81.5 0.26% 15 $100.2 0.26% 

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—Deli Fresh 9 $14.9 0.23% 12 $91.0 0.29% 11 $105.9 0.28% 

Eggs—Large 10 $14.2 0.22% 9 $98.4 0.31% 9 $112.7 0.30% 

Chicken Breast Boneless 11 $13.3 0.20% 5 $110.1 0.35% 5 $123.5 0.32% 

Infant Formula Starter/ 

Solutio 

12 $13.1 0.20% 314 $11.2 0.04% 157 $24.3 0.06% 

Fz Ss Prem Traditional 

Meals 

13 $12.6 0.19% 19 $77.2 0.24% 18 $89.7 0.24% 

Unflavored Can Coffee 14 $12.5 0.19% 14 $88.7 0.28% 14 $101.2 0.27% 

Mainstream White Bread 15 $12.5 0.19% 49 $49.8 0.16% 41 $62.3 0.16% 

Still Water Drnking/Mnrl 

Water 

16 $12.3 0.19% 29 $64.1 0.20% 25 $76.4 0.20% 

Tortilla/Nacho Chips 17 $12.1 0.18% 22 $73.4 0.23% 19 $85.6 0.22% 

Dairy Case 100% Pure 

Juice—O 

18 $12.1 0.18% 6 $107.2 0.34% 7 $119.3 0.31% 

American Single Cheese 19 $11.6 0.18% 42 $52.8 0.17% 38 $64.4 0.17% 

Bacon—Trad 16oz Or Less 20 $11.2 0.17% 27 $64.6 0.20% 27 $75.8 0.20% 

Enhanced [Pork Boneless 

Loin/Rib] 

21 $11.1 0.17% 24 $68.3 0.22% 23 $79.4 0.21% 

Snacks/Appetizers 22 $10.7 0.16% 81 $32.0 0.10% 69 $42.7 0.11% 

Snack Cake—Multi Pack 23 $10.6 0.16% 68 $36.4 0.12% 61 $47.1 0.12% 

Mainstream Variety 

Breads 

24 $10.5 0.16% 25 $66.1 0.21% 24 $76.6 0.20% 

Fz Ss Economy Meals All 25 $10.3 0.16% 94 $28.4 0.09% 75 $38.7 0.10% 

Natural Cheese Chunks 26 $10.2 0.15% 13 $89.8 0.28% 16 $100.0 0.26% 

Pizza/Premium 27 $10.1 0.15% 39 $55.5 0.18% 36 $65.6 0.17% 

Soft Drinks 20pk & 24pk 

Can Carb 

28 $10.0 0.15% 64 $38.7 0.12% 59 $48.7 0.13% 

All Family Cereal 29 $10.0 0.15% 16 $85.8 0.27% 17 $95.7 0.25% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

30 $10.0 0.15% 21 $74.7 0.24% 20 $84.7 0.22% 

Potatoes Russet (Bulk & 

Bag) 

31 $9.9 0.15% 30 $64.0 0.20% 28 $73.9 0.19% 

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 Cream & 

Sherbert] 

35 $9.1 0.14% 11 $94.7 0.30% 13 $103.8 0.27% 

Condensed Soup 36 $8.7 0.13% 26 $64.7 0.21% 29 $73.4 0.19% 

Sandwiches & Handhelds 37 $8.7 0.13% 128 $23.7 0.08% 94 $32.4 0.08% 

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 Nutritional 

Meals 

40 $8.0 0.12% 4 $117.8 0.37% 4 $125.7 0.33% 

Choice Beef 41 $7.9 0.12% 38 $55.7 0.18% 39 $63.6 0.17% 

Frzn Chicken—Wht Meat 42 $7.9 0.12% 70 $36.1 0.11% 66 $44.0 0.12% 

Pourable Salad Dressings 43 $7.9 0.12% 36 $56.5 0.18% 37 $64.4 0.17% 

Isotonic Drinks Single 

Serve 

44 $7.8 0.12% 66 $37.9 0.12% 64 $45.7 0.12% 

Convenient Meals—Kids 

Meal C 

45 $7.8 0.12% 186 $18.0 0.06% 139 $25.8 0.07% 

Traditional [Ice Cream & 

Sherbert] 

46 $7.7 0.12% 44 $51.5 0.16% 43 $59.2 0.16% 

256 

Exhibit E–5: Top 100 Subcommodities for SNAP Households by 

Expenditure: Households Without Children Present—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fz Bag Vegetables—Plain 47 $7.6 0.12% 37 $55.9 0.18% 40 $63.5 0.17% 

Mayonnaise & Whipped 

Dressing 

48 $7.5 0.11% 45 $50.9 0.16% 44 $58.4 0.15% 

Refrigerated Coffee 

Creamers 

49 $7.1 0.11% 34 $58.8 0.19% 35 $65.9 0.17% 

Fz Family Style Entre ́es 50 $7.0 0.11% 85 $31.3 0.10% 76 $38.3 0.10% 

Adult Cereal 51 $7.0 0.11% 18 $77.2 0.24% 21 $84.2 0.22% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

52 $6.9 0.11% 122 $24.2 0.08% 101 $31.1 0.08% 

Margarine: Tubs And 

Bowls 

53 $6.5 0.10% 57 $44.0 0.14% 56 $50.5 0.13% 

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 Meat 56 $6.3 0.10% 164 $20.4 0.06% 125 $26.7 0.07% 

Choice Beef 57 $6.3 0.10% 93 $28.5 0.09% 86 $34.8 0.09% 

Candy Bags—Chocolate 58 $6.2 0.09% 28 $64.3 0.20% 31 $70.5 0.19% 

Mainstream [Pasta & 

Pizza Sauce] 

59 $5.9 0.09% 96 $28.2 0.09% 88 $34.1 0.09% 

Lunchment—Bologna/Sausage 60 $5.9 0.09% 117 $24.6 0.08% 102 $30.5 0.08% 

Tuna 61 $5.9 0.09% 54 $45.0 0.14% 54 $50.9 0.13% 

Macaroni & Cheese Dnrs 62 $5.8 0.09% 175 $19.1 0.06% 148 $24.9 0.07% 

Mexican Soft Tortillas And 

Wra 

63 $5.8 0.09% 63 $39.2 0.12% 65 $45.0 0.12% 

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 Bulk 67 $5.6 0.08% 31 $62.3 0.20% 32 $67.9 0.18% 

Aseptic Pack Juice And 

Drinks 

68 $5.4 0.08% 242 $14.1 0.04% 202 $19.5 0.05% 

Can Pasta 69 $5.4 0.08% 232 $14.8 0.05% 191 $20.2 0.05% 

Frzn Chicken—Wings 70 $5.2 0.08% 547 $5.6 0.02% 372 $10.8 0.03% 

Frzn French Fries 71 $5.2 0.08% 190 $17.8 0.06% 162 $23.0 0.06% 

Rts Soup: Chunky/ 

Homestyle/Et 

72 $5.2 0.08% 48 $50.1 0.16% 48 $55.3 0.15% 

Angus [Beef] 73 $5.1 0.08% 58 $43.9 0.14% 58 $49.0 0.13% 

Fz Skillet Meals 74 $5.0 0.08% 80 $32.0 0.10% 79 $37.1 0.10% 

Mult Pk Bag Snacks 75 $5.0 0.08% 263 $13.1 0.04% 220 $18.1 0.05% 

Vegetable Oil 76 $5.0 0.08% 278 $12.5 0.04% 226 $17.5 0.05% 

Frzn Breakfast Sandwiches 77 $4.9 0.07% 159 $20.6 0.07% 143 $25.5 0.07% 

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 Cookies 81 $4.7 0.07% 105 $26.5 0.08% 100 $31.2 0.08% 

Frzn Meat—Beef 82 $4.7 0.07% 209 $16.2 0.05% 184 $20.9 0.05% 

Pizza/Traditional 83 $4.5 0.07% 150 $21.4 0.07% 138 $25.9 0.07% 

Chix: Frd 8pc/Cut Up 

(Hot) 

84 $4.5 0.07% 73 $35.1 0.11% 73 $39.6 0.10% 

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/Pancakes/French 

Toast 

90 $4.4 0.07% 109 $25.8 0.08% 105 $30.2 0.08% 

Value Forms/18oz And 

Larger [Chicken] 

91 $4.4 0.07% 271 $12.7 0.04% 232 $17.1 0.04% 

Candy Bars (Multi Pack) 92 $4.3 0.07% 97 $28.1 0.09% 93 $32.5 0.09% 

Cakes: Birthday/Celebration Sh 93 $4.3 0.07% 204 $16.7 0.05% 183 $21.0 0.06% 

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—Full Size 96 $4.3 0.07% 139 $22.9 0.07% 121 $27.2 0.07% 

Dnr Sausage—Links Pork 

Ckd/S 

97 $4.3 0.07% 248 $13.9 0.04% 219 $18.2 0.05% 

Bkfst Sausage—Fresh 

Rolls 

98 $4.3 0.06% 113 $25.2 0.08% 111 $29.4 0.08% 

Cheese Crackers 99 $4.2 0.06% 87 $30.0 0.10% 87 $34.2 0.09% 

Bagged Cheese Snacks 100 $4.2 0.06% 177 $18.8 0.06% 161 $23.1 0.06% 

Top 100 Subcommodities $894.8 13.60% $5,251.7 16.66% $6,146.5 16.13% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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. 

257 

Exhibit E–6: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores in the Midwest 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $64.3 0.98% 1 $237.1 0.75% 1 $301.4 0.79% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $60.9 0.93% 2 $175.6 0.56% 2 $236.5 0.62% 

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 Cheese 5 $28.5 0.43% 3 $102.0 0.32% 4 $130.4 0.34% 

Kids Cereal 6 $26.3 0.40% 18 $51.4 0.16% 11 $77.7 0.20% 

Sft Drnk 2 Liter Btl Carb 

Incl 

7 $26.3 0.40% 8 $67.4 0.21% 6 $93.7 0.25% 

Potato Chips 8 $23.0 0.35% 5 $76.5 0.24% 5 $99.5 0.26% 

Snacks/Appetizers 9 $19.5 0.30% 43 $33.6 0.11% 31 $53.0 0.14% 

Infant Formula Starter/ 

Solutio 

10 $18.9 0.29% 180 $12.1 0.04% 68 $31.1 0.08% 

Lunchment—Deli Fresh 11 $17.9 0.27% 10 $60.7 0.19% 10 $78.6 0.21% 

Mainstream White Bread 12 $17.4 0.26% 35 $38.8 0.12% 28 $56.1 0.15% 

Enhanced [Pork Boneless 

Loin/Rib] 

13 $17.2 0.26% 16 $54.2 0.17% 16 $71.4 0.19% 

American Single Cheese 14 $17.1 0.26% 30 $43.2 0.14% 22 $60.3 0.16% 

Tortilla/Nacho Chips 15 $16.2 0.25% 14 $56.2 0.18% 15 $72.4 0.19% 

Unflavored Can Coffee 16 $16.1 0.24% 12 $60.0 0.19% 13 $76.1 0.20% 

Fz Ss Economy Meals All 17 $15.7 0.24% 68 $25.0 0.08% 45 $40.7 0.11% 

Soft Drinks 20pk & 24pk 

Can Carb 

18 $15.5 0.24% 38 $36.7 0.12% 34 $52.3 0.14% 

Snack Cake—Multi Pack 19 $15.4 0.23% 42 $33.6 0.11% 38 $49.0 0.13% 

Chicken Breast Boneless 20 $15.4 0.23% 7 $68.8 0.22% 7 $84.2 0.22% 

Fz Ss Prem Traditional 

Meals 

21 $15.2 0.23% 22 $46.5 0.15% 21 $61.7 0.16% 

Bacon—Trad 16oz Or Less 22 $14.5 0.22% 32 $42.7 0.14% 25 $57.2 0.15% 

Eggs—Large 23 $14.2 0.22% 15 $55.8 0.18% 18 $70.0 0.18% 

Dairy Case 100% Pure 

Juice—O 

24 $13.6 0.21% 9 $65.7 0.21% 9 $79.3 0.21% 

Still Water Drnking/Mnrl 

Water 

25 $13.5 0.20% 29 $43.5 0.14% 27 $57.0 0.15% 

Convenient Meals—Kids 

Meal C 

26 $13.0 0.20% 82 $20.7 0.07% 61 $33.7 0.09% 

Potatoes Russet (Bulk & 

Bag) 

27 $13.0 0.20% 31 $42.9 0.14% 29 $55.9 0.15% 

Pizza/Premium 28 $12.9 0.20% 37 $37.1 0.12% 36 $50.0 0.13% 

All Family Cereal 29 $12.6 0.19% 11 $60.1 0.19% 14 $72.7 0.19% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

30 $12.5 0.19% 19 $50.1 0.16% 19 $62.6 0.16% 

Sandwiches & Handhelds 31 $12.4 0.19% 88 $20.2 0.06% 65 $32.6 0.09% 

Frzn Chicken—Wht Meat 32 $12.4 0.19% 48 $31.9 0.10% 41 $44.3 0.12% 

Ribs [Pork] 33 $12.3 0.19% 58 $27.8 0.09% 47 $40.1 0.11% 

Mainstream Variety 

Breads 

34 $11.8 0.18% 23 $45.3 0.14% 26 $57.1 0.15% 

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 [Ice Cream & 

Sherbert] 

38 $10.8 0.16% 26 $44.2 0.14% 30 $55.0 0.14% 

Bananas 39 $10.7 0.16% 13 $59.9 0.19% 17 $70.6 0.19% 

Pourable Salad Dressings 40 $10.6 0.16% 36 $38.6 0.12% 37 $49.2 0.13% 

Fz Family Style Entre ́es 41 $9.7 0.15% 74 $22.9 0.07% 66 $32.6 0.09% 

Macaroni & Cheese Dnrs 42 $9.7 0.15% 97 $19.0 0.06% 74 $28.7 0.08% 

Choice Beef 43 $9.6 0.15% 44 $33.0 0.10% 43 $42.5 0.11% 

Natural Cheese Chunks 44 $9.5 0.14% 20 $48.3 0.15% 24 $57.7 0.15% 

Mainstream [Pasta & 

Pizza Sauce] 

45 $9.4 0.14% 60 $27.2 0.09% 56 $36.6 0.10% 

Margarine: Tubs And 

Bowls 

46 $9.1 0.14% 51 $29.9 0.09% 51 $39.0 0.10% 

Hot Dogs—Base Meat 47 $9.1 0.14% 95 $19.5 0.06% 75 $28.6 0.08% 

Can Pasta 48 $9.0 0.14% 117 $16.1 0.05% 95 $25.1 0.07% 

Mayonnaise & Whipped 

Dressing 

49 $9.0 0.14% 54 $28.7 0.09% 54 $37.7 0.10% 

Fz Ss Prem Nutritional 

Meals 

50 $8.6 0.13% 6 $72.5 0.23% 8 $81.1 0.21% 

Strawberries 51 $8.6 0.13% 17 $53.1 0.17% 20 $61.7 0.16% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

52 $8.3 0.13% 127 $15.4 0.05% 100 $23.7 0.06% 

Meat: Turkey Bulk 53 $8.1 0.12% 27 $43.9 0.14% 35 $52.0 0.14% 

Lunchment—Bologna/Sausage 54 $8.1 0.12% 93 $19.7 0.06% 78 $27.8 0.07% 

Aseptic Pack Juice And 

Drinks 

55 $7.9 0.12% 124 $15.6 0.05% 101 $23.6 0.06% 

Isotonic Drinks Single 

Serve 

56 $7.9 0.12% 59 $27.6 0.09% 58 $35.4 0.09% 

Fz Bag Vegetables—Plain 57 $7.8 0.12% 45 $32.7 0.10% 46 $40.6 0.11% 

Select Beef 58 $7.7 0.12% 100 $18.5 0.06% 89 $26.2 0.07% 

258 

Exhibit E–6: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores in the Midwest—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Frzn French Fries 59 $7.6 0.12% 128 $15.3 0.05% 104 $23.0 0.06% 

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—Wings 63 $7.5 0.11% 467 $4.2 0.01% 248 $11.7 0.03% 

Flavored Milk 64 $7.5 0.11% 75 $22.7 0.07% 72 $30.3 0.08% 

Premium [Ice Cream & 

Sherbert] 

65 $7.5 0.11% 25 $45.1 0.14% 33 $52.6 0.14% 

Candy Bags—Chocolate 66 $7.3 0.11% 34 $39.3 0.12% 40 $46.6 0.12% 

Peanut Butter 67 $7.1 0.11% 40 $34.5 0.11% 44 $41.6 0.11% 

Sweet Goods—Full Size 68 $7.1 0.11% 81 $20.9 0.07% 77 $28.0 0.07% 

Meat: Ham Bulk 69 $7.0 0.11% 39 $36.5 0.12% 42 $43.4 0.11% 

Refrigerated Coffee 

Creamers 

70 $7.0 0.11% 49 $31.2 0.10% 53 $38.2 0.10% 

Bkfst Sausage—Fresh 

Rolls 

71 $6.6 0.10% 92 $19.7 0.06% 86 $26.4 0.07% 

Tuna 72 $6.6 0.10% 62 $26.4 0.08% 63 $33.0 0.09% 

Value Forms Frz Chick/ 

18oz & Larger 

73 $6.6 0.10% 157 $13.3 0.04% 126 $19.9 0.05% 

Cakes: Birthday/Celebration Sh 74 $6.5 0.10% 147 $14.1 0.04% 119 $20.6 0.05% 

Pizza/Traditional 75 $6.5 0.10% 96 $19.2 0.06% 93 $25.7 0.07% 

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 Sandwiches 79 $6.4 0.10% 145 $14.3 0.05% 118 $20.7 0.05% 

Frzn Meat—Beef 80 $6.3 0.10% 164 $13.1 0.04% 130 $19.4 0.05% 

Sandwich Cookies 81 $6.2 0.09% 89 $20.1 0.06% 85 $26.4 0.07% 

Hamburger Buns 82 $6.2 0.09% 76 $22.4 0.07% 76 $28.6 0.08% 

Fz Skillet Meals 83 $6.2 0.09% 83 $20.7 0.07% 82 $26.9 0.07% 

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—Links 

Fresh 

88 $6.0 0.09% 103 $17.8 0.06% 99 $23.8 0.06% 

Cheese Crackers 89 $5.9 0.09% 65 $25.5 0.08% 67 $31.4 0.08% 

Rts Soup: Chunky/ 

Homestyle/Et 

90 $5.8 0.09% 50 $30.3 0.10% 57 $36.1 0.09% 

Hot Dog Buns 91 $5.7 0.09% 102 $17.9 0.06% 102 $23.5 0.06% 

Waffles/Pancakes/French 

Toast 

92 $5.6 0.09% 85 $20.5 0.07% 90 $26.1 0.07% 

Mult Pk Bag Snacks 93 $5.6 0.09% 234 $9.9 0.03% 178 $15.5 0.04% 

Candy Bars (Multi Pack) 94 $5.6 0.08% 91 $20.0 0.06% 94 $25.6 0.07% 

Toaster Pastries 95 $5.5 0.08% 121 $15.8 0.05% 113 $21.3 0.06% 

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—Links Pork 

Ckd/S 

98 $5.3 0.08% 182 $12.0 0.04% 155 $17.4 0.05% 

Tray Pack/Choc Chip 

Cookies 

99 $5.2 0.08% 125 $15.6 0.05% 116 $20.8 0.05% 

Grapes White 100 $5.2 0.08% 80 $21.3 0.07% 84 $26.5 0.07% 

Top 100 Subcommodities $1,174.1 17.84% $3,685.6 11.70% $4,859.7 12.76% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $66.4 1.01% 1 $305.9 0.97% 1 $372.3 0.98% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $63.3 0.96% 2 $229.6 0.73% 2 $292.8 0.77% 

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% 

259 

Exhibit E–7: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores in the South—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Sft Drnk 2 Liter Btl Carb 

Incl 

5 $26.2 0.40% 9 $91.0 0.29% 7 $117.2 0.31% 

Primal [Beef] 6 $25.7 0.39% 6 $100.9 0.32% 5 $126.6 0.33% 

Shredded Cheese 7 $25.6 0.39% 3 $121.8 0.39% 3 $147.4 0.39% 

Potato Chips 8 $23.5 0.36% 12 $87.7 0.28% 10 $111.2 0.29% 

Lunchment—Deli Fresh 9 $22.8 0.35% 7 $95.8 0.30% 6 $118.6 0.31% 

Mainstream White Bread 10 $21.3 0.32% 24 $62.7 0.20% 21 $84.0 0.22% 

Still Water Drnking/Mnrl 

Water 

11 $20.1 0.31% 16 $74.1 0.24% 14 $94.2 0.25% 

Snack Cake—Multi Pack 12 $19.8 0.30% 37 $48.3 0.15% 32 $68.1 0.18% 

Eggs—Large 13 $18.8 0.29% 11 $88.4 0.28% 12 $107.2 0.28% 

American Single Cheese 14 $17.9 0.27% 32 $56.0 0.18% 27 $73.9 0.19% 

Chicken Breast Boneless 15 $17.5 0.27% 4 $109.1 0.35% 4 $126.6 0.33% 

Sugar 16 $17.4 0.26% 41 $46.5 0.15% 35 $63.9 0.17% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

17 $17.2 0.26% 10 $89.0 0.28% 13 $106.2 0.28% 

Fz Ss Prem Traditional 

Meals 

18 $16.7 0.25% 27 $59.9 0.19% 24 $76.6 0.20% 

Infant Formula Starter/ 

Solutio 

19 $16.5 0.25% 247 $13.1 0.04% 108 $29.5 0.08% 

Tortilla/Nacho Chips 20 $16.1 0.24% 19 $71.5 0.23% 18 $87.6 0.23% 

Dairy Case 100% Pure 

Juice—O 

21 $15.9 0.24% 8 $92.7 0.29% 11 $108.6 0.29% 

Pizza/Premium 22 $15.9 0.24% 29 $59.0 0.19% 26 $74.9 0.20% 

Fz Ss Economy Meals All 23 $15.2 0.23% 84 $27.9 0.09% 59 $43.1 0.11% 

Snacks/Appetizers 24 $15.2 0.23% 59 $35.7 0.11% 47 $50.9 0.13% 

Soft Drinks 20pk & 24pk 

Can Carb 

25 $15.2 0.23% 58 $36.1 0.11% 46 $51.2 0.13% 

Bacon—Trad 16oz Or Less 26 $14.8 0.23% 30 $58.3 0.18% 29 $73.1 0.19% 

Mainstream Variety 

Breads 

27 $14.6 0.22% 18 $72.1 0.23% 19 $86.8 0.23% 

Sandwiches & Handhelds 28 $14.6 0.22% 87 $27.1 0.09% 63 $41.7 0.11% 

Ribs [Pork] 29 $14.1 0.21% 51 $40.4 0.13% 41 $54.5 0.14% 

Convenient Meals—Kids 

Meal C 

30 $14.1 0.21% 80 $28.6 0.09% 60 $42.7 0.11% 

Enhanced [Pork Boneless 

Loin/Rib] 

31 $14.0 0.21% 21 $66.0 0.21% 23 $80.0 0.21% 

Potatoes Russet (Bulk & 

Bag) 

32 $13.8 0.21% 26 $61.4 0.19% 25 $75.3 0.20% 

Unflavored Can Coffee 33 $13.4 0.20% 17 $73.0 0.23% 20 $86.3 0.23% 

Chicken Wings 34 $13.4 0.20% 224 $14.2 0.05% 119 $27.6 0.07% 

Mult Pk Bag Snacks 35 $12.2 0.19% 137 $20.4 0.06% 87 $32.6 0.09% 

Fz Bag Vegetables—Plain 36 $12.2 0.19% 33 $54.9 0.17% 33 $67.1 0.18% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

37 $12.2 0.18% 85 $27.5 0.09% 66 $39.7 0.10% 

Premium [Ice Cream & 

Sherbert] 

38 $12.1 0.18% 13 $79.1 0.25% 16 $91.3 0.24% 

Frzn Chicken—Wings 39 $12.1 0.18% 338 $9.0 0.03% 173 $21.1 0.06% 

Bananas 40 $11.6 0.18% 14 $78.9 0.25% 17 $90.5 0.24% 

All Family Cereal 41 $11.3 0.17% 20 $70.1 0.22% 22 $81.4 0.21% 

Pourable Salad Dressings 42 $11.1 0.17% 38 $48.1 0.15% 36 $59.2 0.16% 

Hot Dogs—Base Meat 43 $11.0 0.17% 106 $23.9 0.08% 80 $34.9 0.09% 

Condensed Soup 44 $10.9 0.17% 31 $56.2 0.18% 34 $67.1 0.18% 

Fz Family Style Entre ́es 45 $10.5 0.16% 69 $32.1 0.10% 61 $42.6 0.11% 

Isotonic Drinks Single 

Serve 

46 $10.2 0.16% 49 $40.6 0.13% 48 $50.8 0.13% 

Frzn Chicken—Wht Meat 47 $10.2 0.16% 55 $37.3 0.12% 53 $47.5 0.12% 

Vegetable Oil 48 $10.1 0.15% 204 $15.4 0.05% 132 $25.5 0.07% 

Mayonnaise & Whipped 

Dressing 

49 $10.1 0.15% 46 $43.1 0.14% 43 $53.2 0.14% 

Aseptic Pack Juice And 

Drinks 

50 $9.9 0.15% 115 $22.7 0.07% 88 $32.5 0.09% 

Frzn Breakfast Sandwiches 51 $9.5 0.14% 83 $27.9 0.09% 70 $37.4 0.10% 

Macaroni & Cheese Dnrs 52 $9.4 0.14% 121 $21.8 0.07% 98 $31.3 0.08% 

Fz Ss Prem Nutritional 

Meals 

53 $9.2 0.14% 5 $102.2 0.32% 9 $111.5 0.29% 

Frzn French Fries 54 $9.2 0.14% 127 $21.2 0.07% 103 $30.4 0.08% 

Choice Beef 55 $8.9 0.14% 56 $37.2 0.12% 55 $46.1 0.12% 

Lunchment—Bologna/Sausage 56 $8.9 0.14% 110 $23.5 0.07% 89 $32.4 0.09% 

Natural Cheese Chunks 57 $8.9 0.14% 28 $59.2 0.19% 31 $68.1 0.18% 

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 [Ice Cream & 

Sherbert] 

60 $8.5 0.13% 50 $40.5 0.13% 49 $49.0 0.13% 

Mainstream [Pasta & 

Pizza Sauce] 

61 $8.4 0.13% 81 $28.5 0.09% 74 $36.9 0.10% 

Dnr Sausage—Links Pork 

Ckd/S 

62 $8.3 0.13% 199 $15.7 0.05% 144 $24.1 0.06% 

260 

Exhibit E–7: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores in the South—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Chicken Drums 63 $8.3 0.13% 249 $12.9 0.04% 172 $21.2 0.06% 

Margarine: Tubs And 

Bowls 

64 $8.1 0.12% 63 $33.4 0.11% 64 $41.5 0.11% 

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/18oz And 

Larger [Chicken] 

70 $7.5 0.11% 179 $16.5 0.05% 145 $ 24.0 0.06% 

Select Beef 71 $7.5 0.11% 36 $51.3 0.16% 37 $58.8 0.15% 

Fz Skillet Meals 72 $7.4 0.11% 76 $29.7 0.09% 72 $37.1 0.10% 

Cakes: Birthday/Celebration Sh 73 $7.3 0.11% 142 $19.7 0.06% 122 $27.1 0.07% 

Bacon—Trad Greater 

Than 16oz 

74 $7.2 0.11% 108 $23.7 0.08% 100 $30.9 0.08% 

Pizza/Traditional 75 $7.2 0.11% 91 $26.2 0.08% 84 $33.4 0.09% 

Refrigerated Biscuits 76 $7.1 0.11% 114 $22.8 0.07% 106 $29.9 0.08% 

Sw Gds: Donuts 77 $7.0 0.11% 107 $23.8 0.08% 101 $30.8 0.08% 

Frzn Meat—Beef 78 $7.0 0.11% 185 $16.3 0.05% 151 $23.3 0.06% 

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—Chocolate 81 $6.9 0.11% 42 $46.5 0.15% 42 $53.4 0.14% 

Peanut Butter 82 $6.7 0.10% 43 $45.2 0.14% 45 $51.9 0.14% 

Sandwich Cookies 83 $6.7 0.10% 100 $24.9 0.08% 93 $31.6 0.08% 

Ramen Noodles/Ramen 

Cups 

84 $6.6 0.10% 327 $9.5 0.03% 243 $16.2 0.04% 

Waffles/Pancakes/French 

Toast 

85 $6.6 0.10% 82 $27.9 0.09% 81 $34.5 0.09% 

Hot Dog Buns 86 $6.3 0.10% 116 $22.5 0.07% 113 $28.9 0.08% 

Candy Bars (Multi Pack) 87 $6.2 0.09% 96 $25.4 0.08% 95 $31.6 0.08% 

Bagged Cheese Snacks 88 $6.2 0.09% 147 $19.4 0.06% 133 $25.5 0.07% 

Prepared Beans—Baked 

W/Pork 

89 $6.1 0.09% 125 $21.5 0.07% 118 $27.6 0.07% 

Loaf Cheese 90 $6.1 0.09% 145 $19.5 0.06% 130 $25.6 0.07% 

Meat: Turkey Bulk 91 $6.0 0.09% 34 $52.7 0.17% 38 $58.8 0.15% 

Tray Pack/Choc Chip 

Cookies 

92 $6.0 0.09% 141 $19.9 0.06% 129 $26.0 0.07% 

Hamburger Buns 93 $6.0 0.09% 99 $25.1 0.08% 99 $31.1 0.08% 

Green Beans: Fs/Whl/Cut 94 $6.0 0.09% 102 $24.8 0.08% 102 $30.8 0.08% 

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: Chunky/ 

Homestyle/Et 

97 $5.9 0.09% 54 $38.6 0.12% 57 $44.5 0.12% 

Butter Spray Cracker 98 $5.9 0.09% 88 $26.2 0.08% 91 $32.1 0.08% 

Instore Cut Fruit 99 $5.9 0.09% 57 $36.6 0.12% 62 $42.5 0.11% 

Toaster Pastries 100 $5.8 0.09% 134 $20.5 0.07% 125 $26.4 0.07% 

Top 100 Subcommodities $1,268.9 19.28% $4,783.8 15.18% $6,052.7 15.89% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $60.4 0.92% 1 $310.8 0.99% 1 $371.2 0.97% 

Lean [Beef] 2 $40.9 0.62% 3 $138.9 0.44% 3 $179.8 0.47% 

Soft Drinks 12/18 & 15pk 

Can Car 

3 $40.5 0.62% 2 $196.0 0.62% 2 $236.5 0.62% 

Kids Cereal 4 $22.0 0.33% 22 $71.5 0.23% 17 $93.5 0.25% 

Shredded Cheese 5 $20.7 0.31% 4 $118.2 0.38% 4 $138.9 0.36% 

Eggs—Large 6 $19.1 0.29% 8 $107.4 0.34% 6 $126.5 0.33% 

Infant Formula Starter/ 

Solutio 

7 $18.8 0.29% 167 $20.1 0.06% 75 $38.9 0.10% 

Sft Drnk 2 Liter Btl Carb 

Incl 

8 $18.4 0.28% 21 $71.8 0.23% 18 $90.2 0.24% 

261 

Exhibit E–8: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores in the West—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Potato Chips 9 $17.9 0.27% 13 $89.0 0.28% 11 $106.9 0.28% 

Natural Cheese Chunks 10 $16.9 0.26% 7 $108.6 0.34% 7 $125.6 0.33% 

Chicken Breast Boneless 11 $16.7 0.25% 5 $115.0 0.36% 5 $131.7 0.35% 

Still Water Drnking/Mnrl 

Water 

12 $15.2 0.23% 24 $70.1 0.22% 23 $85.3 0.22% 

Lunchment—Deli Fresh 13 $15.2 0.23% 14 $86.0 0.27% 13 $101.2 0.27% 

Tortilla/Nacho Chips 14 $15.1 0.23% 17 $81.3 0.26% 16 $96.4 0.25% 

Mexican Soft Tortillas And 

Wra 

15 $15.1 0.23% 23 $71.5 0.23% 21 $86.6 0.23% 

Dairy Case 100% Pure 

Juice—O 

16 $14.0 0.21% 6 $110.7 0.35% 8 $124.7 0.33% 

Select Beef 17 $12.6 0.19% 19 $73.9 0.23% 22 $86.5 0.23% 

Isotonic Drinks Single 

Serve 

18 $12.4 0.19% 39 $51.4 0.16% 38 $63.7 0.17% 

All Family Cereal 19 $12.3 0.19% 15 $84.7 0.27% 15 $97.0 0.25% 

Mainstream Variety 

Breads 

20 $12.0 0.18% 37 $55.8 0.18% 36 $67.8 0.18% 

Fz Ss Prem Traditional 

Meals 

21 $11.9 0.18% 27 $69.1 0.22% 25 $81.0 0.21% 

Bananas 22 $11.9 0.18% 9 $103.9 0.33% 9 $115.8 0.30% 

Unflavored Can Coffee 23 $11.9 0.18% 29 $65.0 0.21% 27 $76.9 0.20% 

Premium [Ice Cream & 

Sherbert] 

24 $11.6 0.18% 10 $101.7 0.32% 10 $113.3 0.30% 

Refrigerated Coffee 

Creamers 

25 $11.5 0.17% 18 $75.9 0.24% 20 $87.4 0.23% 

Bacon—Trad 16oz Or Less 26 $11.4 0.17% 36 $56.6 0.18% 34 $68.1 0.18% 

Pizza/Premium 27 $10.9 0.17% 35 $57.2 0.18% 35 $68.1 0.18% 

Enhanced [Pork Boneless 

Loin/Rib] 

28 $10.3 0.16% 49 $47.8 0.15% 42 $58.1 0.15% 

Fz Ss Economy Meals All 29 $10.0 0.15% 104 $27.8 0.09% 83 $37.8 0.10% 

Snacks/Appetizers 30 $10.0 0.15% 85 $31.2 0.10% 70 $41.1 0.11% 

Choice Beef 31 $9.9 0.15% 28 $66.5 0.21% 28 $76.5 0.20% 

Mainstream White Bread 32 $9.3 0.14% 71 $35.3 0.11% 64 $44.6 0.12% 

American Single Cheese 33 $9.0 0.14% 66 $37.4 0.12% 62 $46.5 0.12% 

Soft Drinks 20pk & 24pk 

Can Carb 

34 $9.0 0.14% 77 $33.6 0.11% 67 $42.6 0.11% 

Potatoes Russet (Bulk & 

Bag) 

35 $9.0 0.14% 44 $50.1 0.16% 40 $59.1 0.16% 

Adult Cereal 36 $8.8 0.13% 20 $72.8 0.23% 24 $81.6 0.21% 

Sandwiches & Handhelds 37 $8.8 0.13% 113 $26.3 0.08% 92 $35.1 0.09% 

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 & Whipped 

Dressing 

41 $8.2 0.12% 50 $47.2 0.15% 45 $55.4 0.15% 

Sandwiches—(Cold) 42 $8.1 0.12% 54 $44.1 0.14% 51 $52.2 0.14% 

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—Wht Meat 47 $7.4 0.11% 90 $30.6 0.10% 81 $38.0 0.10% 

Fz Family Style Entre ́es 48 $7.4 0.11% 100 $28.5 0.09% 87 $35.9 0.09% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

49 $7.3 0.11% 101 $28.5 0.09% 88 $35.8 0.09% 

Candy Bags—Chocolate 50 $7.3 0.11% 33 $61.8 0.20% 32 $69.0 0.18% 

Pourable Salad Dressings 51 $7.3 0.11% 38 $52.8 0.17% 39 $60.1 0.16% 

Convenient Meals—Kids 

Meal C 

52 $7.1 0.11% 160 $20.5 0.06% 126 $27.6 0.07% 

Strawberries 53 $7.0 0.11% 31 $63.3 0.20% 31 $70.3 0.18% 

Fz Ss Prem Nutritional 

Meals 

54 $6.9 0.10% 11 $96.9 0.31% 12 $103.7 0.27% 

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—Multi Pack 58 $6.5 0.10% 168 $19.8 0.06% 141 $26.3 0.07% 

Aseptic Pack Juice And 

Drinks 

59 $6.4 0.10% 174 $18.8 0.06% 152 $25.3 0.07% 

Traditional [Ice Cream & 

Sherbert] 

60 $6.3 0.10% 75 $34.1 0.11% 73 $40.4 0.11% 

Margarine: Tubs And 

Bowls 

61 $6.2 0.09% 65 $37.5 0.12% 65 $43.8 0.11% 

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 (Singles) (Including) 64 $6.2 0.09% 103 $28.1 0.09% 95 $34.2 0.09% 

Bagged Cheese Snacks 65 $6.1 0.09% 166 $20.2 0.06% 139 $26.4 0.07% 

Cream Cheese 66 $6.1 0.09% 46 $48.4 0.15% 47 $54.5 0.14% 

Dairy Case Juice Drnk 

Under 10 

67 $6.0 0.09% 132 $23.5 0.07% 115 $29.5 0.08% 

262 

Exhibit E–8: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores in the West—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Rts Soup: Chunky/ 

Homestyle/Et 

68 $5.9 0.09% 40 $51.0 0.16% 43 $56.9 0.15% 

Fz Bag Vegetables—Plain 69 $5.7 0.09% 53 $44.3 0.14% 55 $50.0 0.13% 

Frzn Meat—Beef 70 $5.7 0.09% 199 $16.9 0.05% 168 $22.6 0.06% 

Tea Sweetened 71 $5.7 0.09% 89 $30.6 0.10% 86 $36.3 0.10% 

Chix: Rotisserie (Hot) 72 $5.6 0.09% 30 $64.7 0.21% 30 $70.3 0.18% 

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/Ramen 

Cups 

75 $5.3 0.08% 268 $12.8 0.04% 217 $18.1 0.05% 

Macaroni & Cheese Dnrs 76 $5.2 0.08% 173 $18.8 0.06% 156 $24.0 0.06% 

Natural Cheese Slices 77 $5.2 0.08% 41 $51.0 0.16% 44 $56.2 0.15% 

Fz Skillet Meals 78 $5.2 0.08% 94 $29.0 0.09% 96 $34.1 0.09% 

Waffles/Pancakes/French 

Toast 

79 $5.2 0.08% 95 $28.9 0.09% 97 $34.1 0.09% 

Mainstream [Pasta & 

Pizza Sauce] 

80 $5.1 0.08% 117 $25.3 0.08% 113 $30.4 0.08% 

Meat: Turkey Bulk 81 $5.1 0.08% 32 $63.0 0.20% 33 $68.1 0.18% 

Cheese Crackers 82 $5.1 0.08% 78 $33.1 0.11% 79 $38.2 0.10% 

Grapes Red 83 $5.1 0.08% 51 $46.6 0.15% 52 $51.6 0.14% 

Sandwich Cookies 84 $5.1 0.08% 110 $26.7 0.08% 107 $31.8 0.08% 

Shrimp—Cooked 85 $5.1 0.08% 124 $24.5 0.08% 114 $29.5 0.08% 

Whole Chicken (Roasters/ 

Fryer) 

86 $5.0 0.08% 107 $27.3 0.09% 104 $32.3 0.08% 

Shrimp—Raw 87 $5.0 0.08% 109 $27.2 0.09% 106 $32.2 0.08% 

Hot Dogs—Base Meat 88 $4.9 0.08% 255 $13.5 0.04% 213 $18.4 0.05% 

Cottage Cheese 89 $4.9 0.07% 45 $48.8 0.15% 49 $53.7 0.14% 

Oranges Navels All 90 $4.9 0.07% 68 $36.8 0.12% 69 $41.6 0.11% 

Chewing Gum 91 $4.8 0.07% 80 $33.0 0.10% 84 $37.8 0.10% 

Lunchment—Bologna/Sausage 92 $4.8 0.07% 190 $17.7 0.06% 170 $22.5 0.06% 

Apple Juice & Cider (Over 

50%) 

93 $4.7 0.07% 188 $18.0 0.06% 167 $22.7 0.06% 

Super Premium Pints [Ice 

Cream & Sherbert] 

94 $4.7 0.07% 47 $48.3 0.15% 50 $53.1 0.14% 

Salsa & Dips 95 $4.7 0.07% 152 $21.4 0.07% 143 $26.2 0.07% 

Cakes: Birthday/Celebration Sh 96 $4.7 0.07% 206 $16.5 0.05% 184 $21.2 0.06% 

Yogurt/Ss Regular 97 $4.7 0.07% 70 $36.3 0.12% 71 $41.0 0.11% 

Value Forms/18oz And 

Larger [Chicken] 

98 $4.6 0.07% 270 $12.8 0.04% 226 $17.3 0.05% 

Energy Drink—Single 

Serve (N) 

99 $4.5 0.07% 108 $27.3 0.09% 108 $31.8 0.08% 

Non-Carb Water Flvr— 

Drnk/Mnr 

100 $4.5 0.07% 88 $30.7 0.09% 90 $35.1 0.09% 

Top 100 Subcommodities $971.3 14.76% $5,340.7 16.93% $6,312.0 16.56% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $102.1 1.55% 1 $484.1 1.54% 1 $586.2 1.54% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $84.7 1.29% 2 $346.6 1.10% 2 $431.3 1.13% 

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 Cheese 5 $41.0 0.62% 3 $197.3 0.63% 3 $238.2 0.63% 

Sft Drnk 2 Liter Btl Carb 

Incl 

6 $39.6 0.60% 13 $135.9 0.43% 10 $175.5 0.46% 

Potato Chips 7 $35.3 0.54% 9 $145.9 0.46% 8 $181.2 0.48% 

Lunchment—Deli Fresh 8 $30.4 0.46% 12 $140.6 0.45% 11 $171.0 0.45% 

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 Starter/Solution 11 $29.1 0.44% 198 $26.5 0.08% 88 $55.6 0.15% 

263 

Exhibit E–9: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores in Large Metropolitan Counties—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Still Water Drnking/Mnrl 

Water 

12 $28.9 0.44% 17 $119.0 0.38% 16 $147.9 0.39% 

Chicken Breast Boneless 13 $27.5 0.42% 4 $178.4 0.57% 4 $205.9 0.54% 

Dairy Case 100% Pure 

Juice—O 

14 $26.7 0.41% 6 $168.2 0.53% 6 $194.9 0.51% 

Tortilla/Nacho Chips 15 $25.7 0.39% 15 $122.3 0.39% 15 $148.0 0.39% 

Fz Ss Prem Traditional 

Meals 

16 $25.6 0.39% 23 $108.0 0.34% 20 $133.5 0.35% 

Snacks/Appetizers 17 $24.7 0.38% 65 $61.0 0.19% 45 $85.7 0.22% 

Mainstream White Bread 18 $24.3 0.37% 49 $73.5 0.23% 39 $97.8 0.26% 

American Single Cheese 19 $23.7 0.36% 43 $77.3 0.25% 34 $101.0 0.27% 

Mainstream Variety 

Breads 

20 $23.2 0.35% 25 $102.4 0.32% 22 $125.7 0.33% 

Fz Ss Economy Meals All 21 $22.6 0.34% 91 $46.0 0.15% 70 $68.7 0.18% 

Bacon—Trad 16oz Or Less 22 $22.5 0.34% 31 $90.3 0.29% 28 $112.9 0.30% 

Snack Cake—Multi Pack 23 $22.3 0.34% 72 $55.8 0.18% 59 $78.1 0.21% 

Pizza/Premium 24 $21.7 0.33% 29 $91.8 0.29% 26 $113.5 0.30% 

Unflavored Can Coffee 25 $20.3 0.31% 22 $108.3 0.34% 21 $128.7 0.34% 

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 Boneless 

Loin/Rib] 

28 $19.8 0.30% 33 $86.6 0.27% 31 $106.5 0.28% 

All Family Cereal 29 $19.8 0.30% 14 $124.8 0.40% 17 $144.6 0.38% 

Premium [Ice Cream & 

Sherbert] 

30 $19.3 0.29% 10 $144.6 0.46% 13 $163.9 0.43% 

Sandwiches & Handhelds 31 $19.2 0.29% 95 $43.3 0.14% 78 $62.5 0.16% 

Ribs [Pork] 32 $19.1 0.29% 64 $61.4 0.19% 56 $80.6 0.21% 

Convenient Meals—Kids 

Meal C 

33 $18.7 0.28% 103 $41.8 0.13% 82 $60.5 0.16% 

Natural Cheese Chunks 34 $18.6 0.28% 16 $120.3 0.38% 19 $138.9 0.36% 

Potatoes Russet (Bulk & 

Bag) 

35 $18.5 0.28% 36 $85.2 0.27% 32 $103.7 0.27% 

Isotonic Drinks Single 

Serve 

36 $17.7 0.27% 47 $73.7 0.23% 43 $91.4 0.24% 

Soft Drinks 20pk & 24pk 

Can Carb 

37 $17.5 0.27% 75 $54.2 0.17% 64 $71.6 0.19% 

Frzn Chicken—Wht Meat 38 $16.9 0.26% 73 $55.6 0.18% 63 $72.5 0.19% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

39 $16.3 0.25% 30 $90.4 0.29% 29 $106.7 0.28% 

Pourable Salad Dressings 40 $16.2 0.25% 39 $82.7 0.26% 37 $98.9 0.26% 

Choice Beef 41 $16.1 0.24% 40 $81.9 0.26% 38 $98.0 0.26% 

Fz Family Style Entre ́es 42 $15.5 0.24% 82 $49.6 0.16% 74 $65.1 0.17% 

Condensed Soup 43 $15.4 0.23% 38 $84.7 0.27% 35 $100.2 0.26% 

Fz Bag Vegetables—Plain 44 $15.1 0.23% 42 $77.6 0.25% 42 $92.7 0.24% 

Frzn Chicken—Wings 45 $15.1 0.23% 444 $11.1 0.04% 242 $26.3 0.07% 

Mayonnaise & Whipped 

Dressing 

46 $14.9 0.23% 55 $68.0 0.22% 50 $82.9 0.22% 

Select Beef 47 $14.9 0.23% 34 $86.5 0.27% 33 $101.4 0.27% 

Fz Ss Prem Nutritional 

Meals 

48 $14.6 0.22% 5 $172.2 0.55% 7 $186.7 0.49% 

Adult Cereal 49 $14.4 0.22% 20 $109.6 0.35% 23 $124.0 0.33% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

50 $14.4 0.22% 107 $40.1 0.13% 92 $54.5 0.14% 

Aseptic Pack Juice And 

Drinks 

51 $14.3 0.22% 122 $36.3 0.12% 100 $50.6 0.13% 

Chicken Wings 52 $14.0 0.21% 282 $18.6 0.06% 190 $32.6 0.09% 

Traditional [Ice Cream & 

Sherbert] 

53 $13.6 0.21% 58 $63.3 0.20% 62 $76.9 0.20% 

Mult Pk Bag Snacks 54 $13.5 0.21% 182 $28.3 0.09% 134 $41.8 0.11% 

Refrigerated Coffee 

Creamers 

55 $13.5 0.20% 27 $93.2 0.30% 30 $106.6 0.28% 

Mexican Soft Tortillas And 

Wra 

56 $13.4 0.20% 53 $69.5 0.22% 51 $82.9 0.22% 

Strawberries 57 $13.4 0.20% 21 $109.1 0.35% 24 $122.5 0.32% 

Hot Dogs—Base Meat 58 $13.1 0.20% 174 $29.5 0.09% 130 $42.5 0.11% 

Mainstream [Pasta & 

Pizza Sauce] 

59 $13.0 0.20% 86 $48.2 0.15% 80 $61.2 0.16% 

Macaroni & Cheese Dnrs 60 $12.8 0.19% 136 $34.4 0.11% 109 $47.1 0.12% 

Choice Beef 61 $12.6 0.19% 114 $38.4 0.12% 99 $51.0 0.13% 

Margarine: Tubs And 

Bowls 

62 $12.6 0.19% 68 $58.4 0.19% 65 $71.0 0.19% 

Tuna 63 $12.2 0.19% 56 $68.0 0.22% 57 $80.2 0.21% 

Meat: Turkey Bulk 64 $12.1 0.18% 24 $105.2 0.33% 25 $117.4 0.31% 

Vegetable Oil 65 $11.7 0.18% 256 $20.5 0.06% 194 $32.2 0.08% 

Frzn French Fries 66 $11.4 0.17% 180 $28.5 0.09% 147 $39.8 0.10% 

Lunchment—Bologna/Sausage 67 $11.3 0.17% 152 $32.9 0.10% 121 $44.2 0.12% 

Candy Bags—Chocolate 68 $11.3 0.17% 37 $84.8 0.27% 41 $96.0 0.25% 

Can Pasta 69 $11.3 0.17% 204 $26.0 0.08% 163 $37.3 0.10% 

264 

Exhibit E–9: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores in Large Metropolitan Counties—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fz Skillet Meals 70 $11.2 0.17% 84 $48.9 0.16% 85 $60.1 0.16% 

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—Beef 74 $10.7 0.16% 196 $26.6 0.08% 162 $37.3 0.10% 

Frzn Breakfast Sandwiches 75 $10.7 0.16% 143 $33.6 0.11% 120 $44.3 0.12% 

Cakes: Birthday/Celebration Sh 76 $10.7 0.16% 169 $30.1 0.10% 140 $40.8 0.11% 

Waffles/Pancakes/French 

Toast 

77 $10.4 0.16% 81 $50.0 0.16% 83 $60.5 0.16% 

Spring Water 78 $10.4 0.16% 57 $67.7 0.21% 60 $78.0 0.20% 

Value Forms/18oz And 

Larger [Chicken] 

79 $10.2 0.16% 212 $24.9 0.08% 177 $35.1 0.09% 

Sandwiches—(Cold) 80 $10.2 0.16% 92 $46.0 0.15% 87 $56.2 0.15% 

Dairy Case Juice Drnk 

Under 10 

81 $10.2 0.15% 158 $31.8 0.10% 131 $42.0 0.11% 

Dnr Sausage—Links Pork 

Ckd/S 

82 $10.2 0.15% 232 $23.0 0.07% 186 $33.2 0.09% 

Sandwich Cookies 83 $10.1 0.15% 102 $42.0 0.13% 97 $52.1 0.14% 

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: Chunky/ 

Homestyle/Et 

86 $9.9 0.15% 50 $73.4 0.23% 48 $83.4 0.22% 

Ramen Noodles/Ramen 

Cups 

87 $9.8 0.15% 302 $17.2 0.05% 237 $27.0 0.07% 

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 Snacks 90 $9.6 0.15% 167 $30.8 0.10% 144 $40.4 0.11% 

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/Traditional 94 $9.3 0.14% 128 $35.3 0.11% 117 $44.7 0.12% 

Sweet Goods—Full Size 95 $9.3 0.14% 119 $36.5 0.12% 113 $45.7 0.12% 

Candy Bars (Singles) (Including) 96 $9.2 0.14% 155 $32.3 0.10% 136 $41.5 0.11% 

Hot Dog Buns 97 $9.2 0.14% 118 $36.7 0.12% 112 $46.0 0.12% 

Cheese Crackers 98 $9.2 0.14% 71 $55.9 0.18% 73 $65.1 0.17% 

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 Subcommodities $1,843.6 28.02% $7,796.5 24.74% $9,640.1 25.31% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $62.4 0.95% 1 $264.0 0.84% 1 $326.5 0.86% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $52.7 0.80% 2 $176.7 0.56% 2 $229.4 0.60% 

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 Cheese 5 $24.6 0.37% 3 $104.4 0.33% 3 $129.1 0.34% 

Primal [Beef] 6 $23.2 0.35% 8 $76.1 0.24% 6 $99.3 0.26% 

Sft Drnk 2 Liter Btl Carb 

Incl 

7 $23.2 0.35% 12 $70.0 0.22% 8 $93.1 0.24% 

Potato Chips 8 $20.9 0.32% 7 $76.3 0.24% 7 $97.3 0.26% 

Infant Formula Starter/ 

Solutio 

9 $18.7 0.28% 180 $13.8 0.04% 73 $32.5 0.09% 

Lunchment—Deli Fresh 10 $18.4 0.28% 11 $74.4 0.24% 9 $92.8 0.24% 

Eggs—Large 11 $16.4 0.25% 9 $74.8 0.24% 10 $91.2 0.24% 

Mainstream White Bread 12 $16.1 0.24% 33 $42.8 0.14% 29 $58.9 0.15% 

Chicken Breast Boneless 13 $15.9 0.24% 4 $84.6 0.27% 5 $100.5 0.26% 

Tortilla/Nacho Chips 14 $15.8 0.24% 16 $63.2 0.20% 16 $79.0 0.21% 

Enhanced [Pork Boneless 

Loin/Rib] 

15 $14.7 0.22% 21 $54.7 0.17% 20 $69.4 0.18% 

265 

Exhibit E–10: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores in Smaller Metropolitan Counties—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

American Single Cheese 16 $14.5 0.22% 35 $41.3 0.13% 33 $55.8 0.15% 

Snacks/Appetizers 17 $14.2 0.22% 66 $28.6 0.09% 48 $42.8 0.11% 

Unflavored Can Coffee 18 $14.2 0.22% 17 $61.8 0.20% 18 $75.9 0.20% 

Soft Drinks 20pk & 24pk 

Can Carb 

19 $14.0 0.21% 49 $35.1 0.11% 38 $49.1 0.13% 

Still Water Drnking/Mnrl 

Water 

20 $13.9 0.21% 28 $47.9 0.15% 24 $61.8 0.16% 

Fz Ss Prem Traditional 

Meals 

21 $13.6 0.21% 25 $50.3 0.16% 22 $64.0 0.17% 

Fz Ss Economy Meals All 22 $13.4 0.20% 76 $25.0 0.08% 59 $38.3 0.10% 

Bacon—Trad 16oz Or Less 23 $13.2 0.20% 30 $46.6 0.15% 26 $59.8 0.16% 

Snack Cake—Multi Pack 24 $13.0 0.20% 61 $30.5 0.10% 46 $43.5 0.11% 

Pizza/Premium 25 $12.8 0.19% 32 $44.8 0.14% 31 $57.6 0.15% 

Dairy Case 100% Pure 

Juice—O 

26 $12.7 0.19% 10 $74.5 0.24% 11 $87.2 0.23% 

Potatoes Russet (Bulk & 

Bag) 

27 $12.0 0.18% 29 $47.4 0.15% 28 $59.4 0.16% 

Sugar 28 $11.9 0.18% 50 $35.0 0.11% 42 $47.0 0.12% 

Natural Cheese Chunks 29 $11.9 0.18% 14 $68.4 0.22% 14 $80.3 0.21% 

All Family Cereal 30 $11.8 0.18% 15 $66.4 0.21% 17 $78.2 0.21% 

Sandwiches & Handhelds 31 $11.7 0.18% 89 $21.8 0.07% 70 $33.5 0.09% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

32 $11.7 0.18% 19 $57.3 0.18% 21 $68.9 0.18% 

Ribs [Pork] 33 $11.4 0.17% 57 $31.2 0.10% 50 $42.6 0.11% 

Mainstream Variety 

Breads 

34 $11.2 0.17% 24 $50.9 0.16% 23 $62.2 0.16% 

Convenient Meals—Kids 

Meal C 

35 $11.1 0.17% 103 $20.1 0.06% 77 $31.2 0.08% 

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—Wht Meat 38 $9.6 0.15% 58 $31.1 0.10% 51 $40.7 0.11% 

Choice Beef 39 $9.5 0.14% 36 $40.9 0.13% 36 $50.4 0.13% 

Pourable Salad Dressings 40 $9.3 0.14% 37 $40.9 0.13% 37 $50.2 0.13% 

Select Beef 41 $9.3 0.14% 34 $41.5 0.13% 35 $50.8 0.13% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

42 $9.3 0.14% 87 $22.6 0.07% 76 $31.9 0.08% 

Isotonic Drinks Single 

Serve 

43 $9.2 0.14% 53 $33.6 0.11% 49 $42.7 0.11% 

Premium [Ice Cream & 

Sherbert] 

44 $8.9 0.14% 18 $61.5 0.19% 19 $70.4 0.18% 

Fz Family Style Entre ́es 45 $8.8 0.13% 77 $24.8 0.08% 69 $33.6 0.09% 

Mayonnaise & Whipped 

Dressing 

46 $8.8 0.13% 45 $36.0 0.11% 45 $44.8 0.12% 

Traditional [Ice Cream & 

Sherbert] 

47 $8.7 0.13% 41 $39.7 0.13% 39 $48.4 0.13% 

Hot Dogs—Base Meat 48 $8.3 0.13% 121 $18.0 0.06% 92 $26.3 0.07% 

Choice Beef 49 $8.2 0.13% 79 $23.8 0.08% 74 $32.1 0.08% 

Macaroni & Cheese Dnrs 50 $8.2 0.12% 118 $18.1 0.06% 93 $26.3 0.07% 

Fz Bag Vegetables—Plain 51 $7.8 0.12% 42 $39.6 0.13% 41 $47.4 0.12% 

Refrigerated Coffee 

Creamers 

52 $7.7 0.12% 39 $40.6 0.13% 40 $48.3 0.13% 

Margarine: Tubs And 

Bowls 

53 $7.7 0.12% 63 $30.0 0.10% 61 $37.6 0.10% 

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 Tortillas And 

Wra 

56 $7.6 0.12% 56 $31.9 0.10% 56 $39.5 0.10% 

Fz Ss Prem Nutritional 

Meals 

57 $7.6 0.12% 6 $76.8 0.24% 12 $84.4 0.22% 

Aseptic Pack Juice And 

Drinks 

58 $7.3 0.11% 155 $15.3 0.05% 118 $22.6 0.06% 

Mainstream [Pasta & 

Pizza Sauce] 

59 $7.3 0.11% 80 $23.8 0.08% 78 $31.1 0.08% 

Candy Bags—Chocolate 60 $7.3 0.11% 31 $46.3 0.15% 34 $53.5 0.14% 

Strawberries 61 $7.2 0.11% 26 $50.2 0.16% 32 $57.4 0.15% 

Lunchment—Bologna/Sausage 62 $7.2 0.11% 115 $18.6 0.06% 97 $25.8 0.07% 

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 Fries 66 $6.5 0.10% 159 $15.2 0.05% 125 $21.7 0.06% 

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—Beef 71 $6.1 0.09% 177 $14.2 0.04% 142 $20.3 0.05% 

Mult Pk Bag Snacks 72 $6.1 0.09% 231 $11.1 0.04% 177 $17.1 0.04% 

Value Forms/18oz And 

Larger [Chicken] 

73 $6.0 0.09% 197 $12.7 0.04% 158 $18.7 0.05% 

266 

Exhibit E–10: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores in Smaller Metropolitan Counties—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Frzn Breakfast Sandwiches 74 $6.0 0.09% 147 $15.8 0.05% 123 $21.8 0.06% 

Pizza/Traditional 75 $5.9 0.09% 101 $20.2 0.06% 94 $26.1 0.07% 

Fruit Snacks 76 $5.9 0.09% 189 $13.3 0.04% 154 $19.2 0.05% 

Frzn Chicken—Wings 77 $5.9 0.09% 479 $4.7 0.01% 289 $10.6 0.03% 

Fz Skillet Meals 78 $5.7 0.09% 85 $23.0 0.07% 83 $28.7 0.08% 

Sandwich Cookies 79 $5.7 0.09% 93 $21.4 0.07% 86 $27.1 0.07% 

Sour Creams 80 $5.7 0.09% 69 $28.0 0.09% 68 $33.7 0.09% 

Cakes: Birthday/Celebration Sh 81 $5.7 0.09% 178 $14.1 0.04% 147 $19.8 0.05% 

Rts Soup: Chunky/ 

Homestyle/Et 

82 $5.6 0.09% 51 $34.7 0.11% 53 $40.3 0.11% 

Chicken Drums 83 $5.5 0.08% 277 $9.0 0.03% 215 $14.6 0.04% 

Bagged Cheese Snacks 84 $5.4 0.08% 161 $15.0 0.05% 141 $20.5 0.05% 

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/Ramen 

Cups 

88 $5.3 0.08% 312 $8.1 0.03% 233 $13.5 0.04% 

Cheese Crackers 89 $5.3 0.08% 74 $25.8 0.08% 79 $31.0 0.08% 

Hamburger Buns 90 $5.3 0.08% 92 $21.5 0.07% 90 $26.8 0.07% 

Meat: Turkey Bulk 91 $5.3 0.08% 38 $40.9 0.13% 43 $46.1 0.12% 

Waffles/Pancakes/French 

Toast 

92 $5.2 0.08% 99 $20.7 0.07% 96 $25.9 0.07% 

Candy Bars (Multi Pack) 93 $5.2 0.08% 90 $21.7 0.07% 89 $26.9 0.07% 

Candy Bars (Singles) (Including) 94 $5.1 0.08% 140 $16.5 0.05% 127 $21.6 0.06% 

Bkfst Sausage—Fresh 

Rolls 

95 $5.1 0.08% 105 $19.4 0.06% 103 $24.5 0.06% 

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—(Cold) 100 $4.9 0.07% 145 $16.0 0.05% 135 $20.9 0 .05% 

Top 100 Subcommodities $1,084.4 16.48% $3,993.9 12.67% $5,078.3 13.33% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Soft Drinks 12/18 & 15pk 

Can Car 

1 $20.5 0.31% 2 $61.5 0.20% 2 $82.0 0.22% 

Fluid Milk/White Only 2 $20.2 0.31% 1 $82.6 0.26% 1 $102.9 0.27% 

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 Cheese 5 $7.2 0.11% 3 $31.9 0.10% 3 $39.1 0.10% 

Kids Cereal 6 $6.8 0.10% 23 $16.0 0.05% 17 $22.8 0.06% 

Sft Drnk 2 Liter Btl Carb 

Incl 

7 $6.4 0.10% 15 $19.6 0.06% 12 $26.0 0.07% 

Soft Drinks 20pk & 24pk 

Can Carb 

8 $6.3 0.10% 33 $13.7 0.04% 24 $20.0 0.05% 

Potato Chips 9 $6.3 0.10% 6 $24.3 0.08% 6 $30.5 0.08% 

Mainstream White Bread 10 $5.6 0.08% 27 $15.7 0.05% 20 $21.3 0.06% 

Lunchment—Deli Fresh 11 $5.4 0.08% 10 $21.7 0.07% 8 $27.2 0.07% 

Enhanced [Pork Boneless 

Loin/Rib] 

12 $5.4 0.08% 11 $21.1 0.07% 11 $26.5 0.07% 

Unflavored Can Coffee 13 $5.1 0.08% 9 $21.8 0.07% 10 $26.9 0.07% 

Infant Formula Starter/ 

Solutio 

14 $5.0 0.08% 190 $4.0 0.01% 78 $9.0 0.02% 

Chicken Breast Boneless 15 $4.8 0.07% 7 $23.8 0.08% 7 $28.7 0.08% 

Snack Cake—Multi Pack 16 $4.8 0.07% 41 $11.9 0.04% 36 $16.6 0.04% 

Eggs—Large 17 $4.7 0.07% 8 $22.4 0.07% 9 $27.1 0.07% 

Still Water Drnking/Mnrl 

Water 

18 $4.7 0.07% 21 $16.4 0.05% 21 $21.0 0.06% 

267 

Exhibit E–11: Top 100 Subcommodities for SNAP Households by 

Expenditure: Smaller Micropolitan Counties—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Tortilla/Nacho Chips 19 $4.6 0.07% 19 $18.4 0.06% 16 $23.0 0.06% 

American Single Cheese 20 $4.5 0.07% 31 $14.0 0.04% 28 $18.5 0.05% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

21 $4.5 0.07% 14 $20.1 0.06% 13 $24.5 0.06% 

Snacks/Appetizers 22 $4.4 0.07% 65 $8.6 0.03% 47 $13.0 0.03% 

Potatoes Russet (Bulk & 

Bag) 

23 $4.0 0.06% 20 $16.9 0.05% 22 $20.9 0.05% 

Pizza/Premium 24 $4.0 0.06% 35 $13.0 0.04% 34 $17.0 0.04% 

Fz Ss Economy Meals All 25 $3.9 0.06% 71 $7.8 0.02% 56 $11.7 0.03% 

Sandwiches & Handhelds 26 $3.8 0.06% 85 $6.8 0.02% 65 $10.7 0.03% 

Bacon—Trad 16oz Or Less 27 $3.8 0.06% 24 $15.9 0.05% 25 $19.7 0.05% 

Sugar 28 $3.8 0.06% 39 $12.0 0.04% 37 $15.8 0.04% 

Fz Ss Prem Traditional 

Meals 

29 $3.7 0.06% 32 $13.7 0.04% 31 $17.4 0.05% 

Natural Cheese Chunks 30 $3.6 0.06% 12 $20.8 0.07% 14 $24.5 0.06% 

Ribs [Pork] 31 $3.6 0.06% 45 $11.3 0.04% 41 $14.9 0.04% 

Convenient Meals—Kids 

Meal C 

32 $3.5 0.05% 95 $6.3 0.02% 74 $9.8 0.03% 

All Family Cereal 33 $3.5 0.05% 17 $18.8 0.06% 19 $22.3 0.06% 

Condensed Soup 34 $3.3 0.05% 22 $16.1 0.05% 26 $19.4 0.05% 

Dairy Case 100% Pure 

Juice—O 

35 $3.2 0.05% 13 $20.7 0.07% 15 $23.9 0.06% 

Select Beef 36 $3.2 0.05% 34 $13.6 0.04% 35 $16.8 0.04% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

37 $3.2 0.05% 86 $6.7 0.02% 73 $9.8 0.03% 

Mainstream Variety 

Breads 

38 $3.1 0.05% 28 $15.7 0.05% 27 $18.8 0.05% 

Bananas 39 $3.0 0.05% 16 $19.6 0.06% 18 $22.6 0.06% 

Isotonic Drinks Single 

Serve 

40 $2.9 0.04% 53 $10.0 0.03% 50 $12.9 0.03% 

Hot Dogs—Base Meat 41 $2.8 0.04% 78 $7.2 0.02% 68 $10.1 0.03% 

Frzn Chicken—Wht Meat 42 $2.8 0.04% 50 $10.5 0.03% 46 $13.3 0.04% 

Pourable Salad Dressings 43 $2.8 0.04% 37 $12.4 0.04% 38 $15.2 0.04% 

Mayonnaise & Whipped 

Dressing 

44 $2.7 0.04% 42 $11.8 0.04% 42 $14.5 0.04% 

Macaroni & Cheese Dnrs 45 $2.7 0.04% 107 $5.8 0.02% 88 $8.4 0.02% 

Can Pasta 46 $2.7 0.04% 129 $5.1 0.02% 94 $7.8 0.02% 

Fz Family Style Entre ́es 47 $2.7 0.04% 77 $7.2 0.02% 71 $9.9 0.03% 

Traditional [Ice Cream & 

Sherbert] 

48 $2.6 0.04% 38 $12.3 0.04% 40 $14.9 0.04% 

Lunchment—Bologna/Sausage 49 $2.5 0.04% 80 $7.2 0.02% 75 $9.7 0.03% 

Margarine: Tubs And 

Bowls 

50 $2.5 0.04% 57 $9.8 0.03% 51 $12.2 0.03% 

Sw Gds: Donuts 51 $2.4 0.04% 59 $9.4 0.03% 55 $11.8 0.03% 

Premium [Ice Cream & 

Sherbert] 

52 $2.4 0.04% 25 $15.8 0.05% 29 $18.2 0.05% 

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 Vegetables—Plain 55 $2.3 0.03% 43 $11.8 0.04% 44 $14.0 0.04% 

Refrigerated Coffee 

Creamers 

56 $2.3 0.03% 46 $10.7 0.03% 48 $13.0 0.03% 

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—Chocolate 59 $2.3 0.03% 36 $12.9 0.04% 39 $15.1 0.04% 

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 Tortillas And 

Wra 

63 $2.1 0.03% 64 $8.9 0.03% 59 $11.0 0.03% 

Mainstream [Pasta & 

Pizza Sauce] 

64 $2.1 0.03% 81 $7.1 0.02% 77 $9.2 0.02% 

Fz Ss Prem Nutritional 

Meals 

65 $2.1 0.03% 18 $18.6 0.06% 23 $20.7 0.05% 

Aseptic Pack Juice And 

Drinks 

66 $2.0 0.03% 163 $4.5 0.01% 121 $6.5 0.02% 

Frzn French Fries 67 $2.0 0.03% 128 $5.1 0.02% 108 $7.2 0.02% 

Flavored Milk 68 $2.0 0.03% 96 $6.2 0.02% 91 $8.2 0.02% 

Pizza/Traditional 69 $2.0 0.03% 89 $6.6 0.02% 86 $8.6 0.02% 

Tuna 70 $2.0 0.03% 62 $8.9 0.03% 64 $10.9 0.03% 

Frzn Breakfast Sandwiches 71 $1.9 0.03% 132 $5.1 0.02% 112 $7.0 0.02% 

Hamburger Buns 72 $1.9 0.03% 68 $8.1 0.03% 69 $10.1 0.03% 

Value Forms/18oz And 

Larger [Chicken] 

73 $1.9 0.03% 187 $4.0 0.01% 146 $5.9 0.02% 

Vegetable Oil 74 $1.8 0.03% 214 $3.5 0.01% 168 $5.3 0.01% 

Pails [Ice Cream & 

Sherbert] 

75 $1.8 0.03% 131 $5.1 0.02% 114 $6.9 0.02% 

Butter 76 $1.8 0.03% 26 $15.8 0.05% 30 $17.6 0.05% 

268 

Exhibit E–11: Top 100 Subcommodities for SNAP Households by 

Expenditure: Smaller Micropolitan Counties—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Candy Bars (Multi Pack) 77 $1.7 0.03% 83 $6.9 0.02% 83 $8.7 0.02% 

Cakes: Birthday/Celebration Sh 78 $1.7 0.03% 154 $4.7 0.01% 126 $6.4 0.02% 

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 Cookies 81 $1.7 0.03% 91 $6.5 0.02% 90 $8.2 0.02% 

Salsa & Dips 82 $1.7 0.03% 133 $5.0 0.02% 116 $6.7 0.02% 

Frzn Meat—Beef 83 $1.7 0.03% 174 $4.3 0.01% 144 $6.0 0.02% 

Mult Pk Bag Snacks 84 $1.7 0.03% 230 $3.2 0.01% 186 $4.9 0.01% 

Bkfst Sausage—Fresh 

Rolls 

85 $1.7 0.03% 76 $7.3 0.02% 80 $8.9 0.02% 

Refrigerated Biscuits 86 $1.6 0.03% 116 $5.4 0.02% 111 $7.0 0.02% 

Sour Creams 87 $1.6 0.02% 66 $8.3 0.03% 70 $10.0 0.03% 

Rts Soup: Chunky/ 

Homestyle/Et 

88 $1.6 0.02% 60 $9.4 0.03% 61 $11.0 0.03% 

Bagged Cheese Snacks 89 $1.6 0.02% 143 $4.8 0.02% 129 $6.4 0.02% 

Cream Cheese 90 $1.6 0.02% 54 $10.0 0.03% 57 $11.6 0.03% 

Skillet Dinners 91 $1.6 0.02% 245 $3.1 0.01% 198 $4.7 0.01% 

Cheese Crackers 92 $1.6 0.02% 84 $6.8 0.02% 89 $8.4 0.02% 

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 Meals 96 $1.5 0.02% 99 $6.0 0.02% 98 $7.5 0.02% 

Hot Dog Buns 97 $1.5 0.02% 110 $5.7 0.02% 104 $7.2 0.02% 

Sweet Goods—Full Size 98 $1.5 0.02% 135 $5.0 0.02% 123 $6.5 0.02% 

Candy Bars (Singles) (Including) 99 $1.5 0.02% 153 $4.7 0.01% 135 $6.2 0.02% 

Toaster Pastries 100 $1.5 0.02% 155 $4.7 0.01% 136 $6.2 0.02% 

Top 100 Subcommodities $339.6 $5.16% $1,243.8 $3.95% $1,583.4 $4.16% 

Total Expenditures $6,580.5 $100% $31,513.8 $100% $38,094.2 $100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Soft Drinks 12/18 & 15pk 

Can Car 

1 $6.7 0.10% 2 $16.3 0.05% 2 $23.1 0.06% 

Fluid Milk/White Only 2 $6.4 0.10% 1 $23.0 0.07% 1 $29.4 0.08% 

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 Cheese 5 $2.0 0.03% 3 $8.4 0.03% 4 $10.3 0.03% 

Soft Drinks 20pk & 24pk 

Can Carb 

6 $2.0 0.03% 34 $3.5 0.01% 24 $5.5 0.01% 

Mainstream White Bread 7 $1.9 0.03% 20 $4.8 0.02% 14 $6.7 0.02% 

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 Liter Btl Carb 

Incl 

10 $1.7 0.03% 21 $4.7 0.01% 16 $6.4 0.02% 

Unflavored Can Coffee 11 $1.7 0.03% 9 $6.1 0.02% 8 $7.8 0.02% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

12 $1.6 0.02% 11 $5.8 0.02% 10 $7.5 0.02% 

Lunchment—Deli Fresh 13 $1.6 0.02% 12 $5.8 0.02% 11 $7.4 0.02% 

Snack Cake—Multi Pack 14 $1.6 0.02% 36 $3.5 0.01% 27 $5.0 0.01% 

Enhanced [Pork Boneless 

Loin/Rib] 

15 $1.5 0.02% 14 $5.5 0.02% 13 $7.1 0.02% 

Eggs—Large 16 $1.4 0.02% 7 $6.6 0.02% 7 $8.0 0.02% 

Infant Formula Starter/ 

Solutio 

17 $1.4 0.02% 186 $1.1 0.00% 81 $2.5 0.01% 

American Single Cheese 18 $1.4 0.02% 29 $4.0 0.01% 25 $5.4 0.01% 

Chicken Breast Boneless 19 $1.3 0.02% 10 $6.1 0.02% 12 $7.4 0.02% 

Tortilla/Nacho Chips 20 $1.3 0.02% 16 $5.1 0.02% 18 $6.4 0.02% 

Potatoes Russet (Bulk & 

Bag) 

21 $1.3 0.02% 17 $5.0 0.02% 19 $6.2 0.02% 

Still Water Drnking/Mnrl 

Water 

22 $1.3 0.02% 23 $4.5 0.01% 23 $5.7 0.02% 

269 

Exhibit E–12: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores in Noncore Counties—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Snacks/Appetizers 23 $1.2 0.02% 67 $2.3 0.01% 49 $3.5 0.01% 

Pizza/Premium 24 $1.2 0.02% 32 $3.7 0.01% 30 $4.9 0.01% 

Bacon—Trad 16oz Or Less 25 $1.2 0.02% 19 $4.8 0.02% 20 $6.0 0.02% 

Natural Cheese Chunks 26 $1.1 0.02% 8 $6.5 0.02% 9 $7.7 0.02% 

Sugar 27 $1.1 0.02% 35 $3.5 0.01% 34 $4.6 0.01% 

Sandwiches & Handhelds 28 $1.0 0.02% 96 $1.7 0.01% 71 $2.8 0.01% 

All Family Cereal 29 $1.0 0.02% 18 $4.9 0.02% 21 $5.9 0.02% 

Fz Ss Economy Meals All 30 $1.0 0.02% 80 $2.0 0.01% 65 $3.0 0.01% 

Fz Ss Prem Traditional 

Meals 

31 $1.0 0.01% 38 $3.4 0.01% 36 $4.4 0.01% 

Convenient Meals—Kids 

Meal C 

32 $1.0 0.01% 111 $1.6 0.00% 79 $2.5 0.01% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

33 $0.9 0.01% 77 $2.0 0.01% 67 $2.9 0.01% 

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 100% Pure 

Juice—O 

36 $0.9 0.01% 13 $5.7 0.02% 15 $6.6 0.02% 

Mainstream Variety 

Breads 

37 $0.9 0.01% 24 $4.2 0.01% 26 $5.1 0.01% 

Choice Beef 38 $0.9 0.01% 59 $2.7 0.01% 48 $3.5 0.01% 

Hot Dogs—Base Meat 39 $0.8 0.01% 74 $2.1 0.01% 66 $2.9 0.01% 

Ribs [Pork] 40 $0.8 0.01% 48 $2.9 0.01% 43 $3.7 0.01% 

Lunchment—Bologna/Sausage 41 $0.8 0.01% 71 $2.2 0.01% 62 $3.0 0.01% 

Mayonnaise & Whipped 

Dressing 

42 $0.8 0.01% 41 $3.3 0.01% 40 $4.1 0.01% 

Sw Gds: Donuts 43 $0.8 0.01% 49 $2.9 0.01% 45 $3.7 0.01% 

Traditional [Ice Cream & 

Sherbert] 

44 $0.8 0.01% 39 $3.4 0.01% 38 $4.2 0.01% 

Pourable Salad Dressings 45 $0.8 0.01% 40 $3.4 0.01% 39 $4.1 0.01% 

Frzn Chicken—Wht Meat 46 $0.7 0.01% 60 $2.5 0.01% 56 $3.3 0.01% 

Margarine: Tubs And 

Bowls 

47 $0.7 0.01% 58 $2.7 0.01% 51 $3.4 0.01% 

Can Pasta 48 $0.7 0.01% 159 $1.3 0.00% 108 $2.0 0.01% 

Candy Bags—Chocolate 49 $0.7 0.01% 33 $3.6 0.01% 37 $4.3 0.01% 

Macaroni & Cheese Dnrs 50 $0.7 0.01% 121 $1.5 0.00% 93 $2.2 0.01% 

Isotonic Drinks Single 

Serve 

51 $0.7 0.01% 66 $2.3 0.01% 64 $3.0 0.01% 

Fz Family Style Entre ́es 52 $0.7 0.01% 89 $1.8 0.01% 77 $2.5 0.01% 

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/Traditional 57 $0.6 0.01% 79 $2.0 0.01% 76 $2.6 0.01% 

Choice Beef 58 $0.6 0.01% 42 $3.2 0.01% 41 $3.9 0.01% 

Premium [Ice Cream & 

Sherbert] 

59 $0.6 0.01% 26 $4.1 0.01% 32 $4.7 0.01% 

Flavored Milk 60 $0.6 0.01% 107 $1.6 0.01% 91 $2.2 0.01% 

Refrigerated Coffee 

Creamers 

61 $0.6 0.01% 56 $2.8 0.01% 53 $3.4 0.01% 

Angus [Beef] 62 $0.6 0.01% 57 $2.7 0.01% 54 $3.3 0.01% 

Pails [Ice Cream & 

Sherbert] 

63 $0.6 0.01% 110 $1.6 0.00% 95 $2.2 0.01% 

Mexican Soft Tortillas And 

Wra 

64 $0.6 0.01% 52 $2.8 0.01% 52 $3.4 0.01% 

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 [Pasta & 

Pizza Sauce] 

67 $0.6 0.01% 84 $1.9 0.01% 83 $2.4 0.01% 

Frzn French Fries 68 $0.6 0.01% 123 $1.5 0.00% 107 $2.0 0.01% 

Fz Bag Vegetables—Plain 69 $0.5 0.01% 46 $3.0 0.01% 47 $3.5 0.01% 

Candy Bars (Multi Pack) 70 $0.5 0.01% 78 $2.0 0.01% 78 $2.5 0.01% 

Cakes: Birthday/Celebration Sh 71 $0.5 0.01% 149 $1.3 0.00% 116 $1.9 0.00% 

Aseptic Pack Juice And 

Drinks 

72 $0.5 0.01% 183 $1.1 0.00% 146 $1.6 0.00% 

Refrigerated Biscuits 73 $0.5 0.01% 104 $1.6 0.01% 99 $2.1 0.01% 

Salsa & Dips 74 $0.5 0.01% 130 $1.4 0.00% 111 $1.9 0.01% 

Value Forms/18oz And 

Larger [Chicken] 

75 $0.5 0.01% 192 $1.1 0.00% 158 $1.6 0.00% 

Fz Ss Prem Nutritional 

Meals 

76 $0.5 0.01% 30 $4.0 0.01% 35 $4.5 0.01% 

Tuna 77 $0.5 0.01% 70 $2.2 0.01% 72 $2.8 0.01% 

Sandwich Cookies 78 $0.5 0.01% 83 $1.9 0.01% 85 $2.4 0.01% 

Bkfst Sausage—Fresh 

Rolls 

79 $0.5 0.01% 73 $2.1 0.01% 75 $2.6 0.01% 

Butter 80 $0.5 0.01% 22 $4.5 0.01% 28 $5.0 0.01% 

Frzn Breakfast Sandwiches 81 $0.5 0.01% 172 $1.2 0.00% 139 $1.7 0.00% 

270 

Exhibit E–12: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores in Noncore Counties—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Vegetable Oil 82 $0.5 0.01% 203 $1.0 0.00% 166 $1.5 0.00% 

Sweet Goods—Full Size 83 $0.5 0.01% 129 $1.4 0.00% 114 $1.9 0.00% 

Hot Dog Buns 84 $0.5 0.01% 98 $1.7 0.01% 94 $2.2 0.01% 

Candy Bars (Singles) (Including) 85 $0.5 0.01% 119 $1.5 0.00% 110 $2.0 0.01% 

Bagged Cheese Snacks 86 $0.5 0.01% 147 $1.3 0.00% 127 $1.8 0.00% 

Sandwiches—(Cold) 87 $0.5 0.01% 102 $1.6 0.01% 103 $2.1 0.01% 

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—Beef 91 $0.5 0.01% 166 $1.2 0.00% 136 $1.7 0.00% 

Sticks/Enrobed [Frozen 

Novelties] 

92 $0.5 0.01% 124 $1.5 0.00% 113 $1.9 0.01% 

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: Chunky/ 

Homestyle/Et 

95 $0.4 0.01% 63 $2.4 0.01% 68 $2.8 0.01% 

Angus [Beef] 96 $0.4 0.01% 177 $1.1 0.00% 156 $1.6 0.00% 

Cheese Crackers 97 $0.4 0.01% 93 $1.8 0.01% 92 $2.2 0.01% 

Meat: Ham Bulk 98 $0.4 0.01% 62 $2.4 0.01% 69 $2.8 0.01% 

Meat: Turkey Bulk 99 $0.4 0.01% 51 $2.8 0.01% 58 $3.3 0.01% 

Tray Pack/Choc Chip 

Cookies 

100 $0.4 0.01% 133 $1.4 0.00% 119 $1.8 0.00% 

Top 100 Subcommodities $99.1 1.57% $341.8 1.08% $440.9 1.23% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $38.9 0.59% 1 $229.9 0.73% 1 $268.8 0.71% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $32.4 0.49% 2 $162.4 0.52% 2 $194.8 0.51% 

Lean [Beef] 3 $22.2 0.34% 8 $74.1 0.24% 5 $96.4 0.25% 

Shredded Cheese 4 $16.2 0.25% 3 $103.2 0.33% 3 $119.4 0.31% 

Kids Cereal 5 $15.5 0.23% 23 $52.1 0.17% 17 $67.5 0.18% 

Sft Drnk 2 Liter Btl Carb 

Incl 

6 $13.3 0.20% 18 $56.1 0.18% 16 $69.4 0.18% 

Potato Chips 7 $13.0 0.20% 10 $70.8 0.22% 9 $83.8 0.22% 

Lunchment—Deli Fresh 8 $11.6 0.18% 13 $69.9 0.22% 11 $81.5 0.21% 

Chicken Breast Boneless 9 $11.4 0.17% 4 $89.3 0.28% 4 $100.7 0.26% 

Infant Formula Starter/ 

Solutio 

10 $11.1 0.17% 259 $10.4 0.03% 119 $21.5 0.06% 

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/Appetizers 13 $10.4 0.16% 63 $31.6 0.10% 47 $42.1 0.11% 

Tortilla/Nacho Chips 14 $9.9 0.15% 15 $62.4 0.20% 15 $72.3 0.19% 

Dairy Case 100% Pure 

Juice—O 

15 $9.4 0.14% 6 $80.1 0.25% 6 $89.5 0.23% 

Fz Ss Prem Traditional 

Meals 

16 $9.1 0.14% 26 $47.5 0.15% 25 $56.6 0.15% 

Unflavored Can Coffee 17 $9.1 0.14% 21 $54.4 0.17% 19 $63.4 0.17% 

Natural Cheese Chunks 18 $9.0 0.14% 12 $70.0 0.22% 12 $79.1 0.21% 

Still Water Drnking/Mnrl 

Water 

19 $8.8 0.13% 30 $46.5 0.15% 28 $55.3 0.15% 

Mainstream White Bread 20 $8.6 0.13% 56 $33.6 0.11% 46 $42.3 0.11% 

Enhanced [Pork Boneless 

Loin/Rib] 

21 $8.6 0.13% 28 $47.3 0.15% 26 $55.9 0.15% 

Bacon—Trad 16oz Or Less 22 $8.4 0.13% 34 $44.1 0.14% 29 $52.6 0.14% 

All Family Cereal 23 $8.4 0.13% 14 $66.3 0.21% 14 $74.7 0.20% 

Pizza/Premium 24 $8.4 0.13% 29 $47.0 0.15% 27 $55.4 0.15% 

American Single Cheese 25 $8.3 0.13% 51 $35.4 0.11% 44 $43.7 0.11% 

Fz Ss Economy Meals All 26 $8.1 0.12% 105 $21.1 0.07% 81 $29.2 0.08% 

Soft Drinks 20pk & 24pk 

Can Carb 

27 $7.9 0.12% 67 $30.2 0.10% 58 $38.1 0.10% 

271 

Exhibit E–13: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores with more than $12 Million in Sales—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Bananas 28 $7.8 0.12% 7 $74.4 0.24% 10 $82.2 0.22% 

Snack Cake—Multi Pack 29 $7.4 0.11% 81 $25.3 0.08% 73 $32.7 0.09% 

Premium [Ice Cream & 

Sherbert] 

30 $7.4 0.11% 11 $70.2 0.22% 13 $77.6 0.20% 

Mainstream Variety 

Breads 

31 $7.3 0.11% 32 $44.6 0.14% 32 $51.8 0.14% 

Select Beef 32 $7.2 0.11% 37 $41.6 0.13% 36 $48.8 0.13% 

Sandwiches & Handhelds 33 $7.2 0.11% 107 $20.6 0.07% 89 $27.8 0.07% 

Frzn Chicken—Wht Meat 34 $7.2 0.11% 65 $31.2 0.10% 57 $38.4 0.10% 

Potatoes Russet (Bulk & 

Bag) 

35 $7.2 0.11% 35 $42.4 0.13% 35 $49.6 0.13% 

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 Meals—Kids 

Meal C 

39 $6.7 0.10% 114 $19.5 0.06% 98 $26.2 0.07% 

Condensed Soup 40 $6.5 0.10% 33 $44.1 0.14% 34 $50.6 0.13% 

Refrigerated Coffee 

Creamers 

41 $6.4 0.10% 31 $46.0 0.15% 31 $52.3 0.14% 

Isotonic Drinks Single 

Serve 

42 $6.2 0.09% 66 $30.9 0.10% 62 $37.1 0.10% 

Fz Family Style Entre ́es 43 $6.1 0.09% 85 $24.7 0.08% 77 $30.8 0.08% 

Pourable Salad Dressings 44 $6.0 0.09% 38 $41.5 0.13% 39 $47.6 0.12% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

45 $5.9 0.09% 36 $42.2 0.13% 37 $48.1 0.13% 

Fz Ss Prem Nutritional 

Meals 

46 $5.9 0.09% 5 $82.0 0.26% 7 $87.9 0.23% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

47 $5.8 0.09% 103 $21.3 0.07% 93 $27.1 0.07% 

Mayonnaise & Whipped 

Dressing 

48 $5.7 0.09% 54 $34.5 0.11% 54 $40.2 0.11% 

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 Bulk 52 $5.4 0.08% 17 $57.3 0.18% 20 $62.7 0.16% 

Mexican Soft Tortillas And 

Wra 

53 $5.4 0.08% 53 $35.2 0.11% 53 $40.6 0.11% 

Butter 54 $5.4 0.08% 16 $58.3 0.19% 18 $63.7 0.17% 

Fz Bag Vegetables—Plain 55 $5.2 0.08% 49 $36.6 0.12% 48 $41.8 0.11% 

Candy Bags—Chocolate 56 $5.0 0.08% 27 $47.4 0.15% 30 $52.4 0.14% 

Traditional [Ice Cream & 

Sherbert] 

57 $5.0 0.08% 68 $29.4 0.09% 69 $34.4 0.09% 

Margarine: Tubs And 

Bowls 

58 $5.0 0.08% 71 $29.2 0.09% 70 $34.2 0.09% 

Macaroni & Cheese Dnrs 59 $4.9 0.07% 139 $17.4 0.06% 113 $22.3 0.06% 

Peanut Butter 60 $4.8 0.07% 44 $39.1 0.12% 43 $43.9 0.12% 

Aseptic Pack Juice And 

Drinks 

61 $4.7 0.07% 168 $15.3 0.05% 136 $20.0 0.05% 

Tuna 62 $4.7 0.07% 60 $33.0 0.10% 61 $37.6 0.10% 

Mainstream [Pasta & 

Pizza Sauce] 

63 $4.6 0.07% 96 $22.9 0.07% 91 $27.5 0.07% 

Hot Dogs—Base Meat 64 $4.6 0.07% 188 $13.8 0.04% 159 $18.3 0.05% 

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 Store Prepared 67 $4.3 0.07% 42 $40.4 0.13% 40 $44.7 0.12% 

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—Beef 70 $4.2 0.06% 182 $14.1 0.04% 160 $18.3 0.05% 

Fz Skillet Meals 71 $4.2 0.06% 87 $24.4 0.08% 84 $28.6 0.08% 

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: Birthday/Celebration Sh 74 $4.0 0.06% 170 $15.1 0.05% 151 $19.1 0.05% 

Sour Creams 75 $4.0 0.06% 72 $29.2 0.09% 71 $33.2 0.09% 

Cheese Crackers 76 $4.0 0.06% 73 $29.0 0.09% 72 $33.0 0.09% 

Value Forms/18oz And 

Larger [Chicken] 

77 $4.0 0.06% 218 $12.3 0.04% 188 $16.3 0.04% 

Frzn French Fries 78 $4.0 0.06% 187 $13.8 0.04% 165 $17.8 0.05% 

Rts Soup: Chunky/ 

Homestyle/Et 

79 $3.9 0.06% 52 $35.2 0.11% 56 $39.2 0.10% 

String Cheese 80 $3.9 0.06% 58 $33.2 0.11% 63 $37.1 0.10% 

Sandwiches—(Cold) 81 $3.9 0.06% 98 $22.2 0.07% 99 $26.1 0.07% 

Instore Cut Fruit 82 $3.9 0.06% 55 $33.8 0.11% 60 $37.6 0.10% 

Lunchment—Bologna/Sausage 83 $3.9 0.06% 175 $14.6 0.05% 156 $18.5 0.05% 

Frzn Chicken—Wings 84 $3.8 0.06% 585 $3.9 0.01% 395 $7.7 0.02% 

Frzn Breakfast Sandwiches 85 $3.8 0.06% 161 $15.8 0.05% 142 $19.6 0.05% 

Waffles/Pancakes/French 

Toast 

86 $3.8 0.06% 91 $23.3 0.07% 92 $27.1 0.07% 

272 

Exhibit E–13: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores with more than $12 Million in Sales—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

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 Snacks 89 $3.7 0.06% 222 $12.0 0.04% 198 $15.7 0.04% 

Grapes Red 90 $3.6 0.05% 46 $37.7 0.12% 51 $41.3 0.11% 

Sandwich Cookies 91 $3.6 0.05% 110 $20.3 0.06% 107 $23.9 0.06% 

Candy Bars (Singles) (Including) 92 $3.6 0.05% 144 $17.1 0.05% 131 $20.6 0.05% 

Fruit Snacks 93 $3.5 0.05% 209 $12.6 0.04% 189 $16.2 0.04% 

Pizza/Traditional 94 $3.5 0.05% 134 $17.9 0.06% 120 $21.4 0.06% 

Flavored Milk 95 $3.5 0.05% 148 $16.8 0.05% 133 $20.3 0.05% 

Sweet Goods—Full Size 96 $3.5 0.05% 162 $15.7 0.05% 150 $19.2 0.05% 

Vegetable Oil 97 $3.4 0.05% 306 $8.8 0.03% 248 $12.2 0.03% 

Natural Cheese Slices 98 $3.4 0.05% 50 $36.0 0.11% 55 $39.4 0.10% 

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 Subcommodities $699.9 10.64% $4,012.7 12.73% $4,712.5 12.37% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $151.9 2.31% 1 $622.5 1.98% 1 $774.4 2.03% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $131.9 2.00% 2 $437.9 1.39% 2 $569.9 1.50% 

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 Cheese 5 $58.4 0.89% 3 $238.3 0.76% 3 $296.8 0.78% 

Sft Drnk 2 Liter Btl Carb 

Incl 

6 $57.5 0.87% 10 $173.7 0.55% 7 $231.2 0.61% 

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—Deli Fresh 9 $44.1 0.67% 11 $172.4 0.55% 11 $216.5 0.57% 

Infant Formula Starter/ 

Solutio 

10 $43.0 0.65% 169 $34.9 0.11% 71 $77.9 0.20% 

Eggs—Large 11 $41.3 0.63% 9 $178.2 0.57% 10 $219.5 0.58% 

Still Water Drnking/Mnrl 

Water 

12 $39.9 0.61% 19 $141.1 0.45% 16 $180.9 0.48% 

Mainstream White Bread 13 $39.2 0.60% 32 $102.9 0.33% 27 $142.1 0.37% 

Chicken Breast Boneless 14 $38.1 0.58% 4 $203.4 0.65% 5 $241.5 0.63% 

Tortilla/Nacho Chips 15 $37.4 0.57% 16 $146.3 0.46% 15 $183.7 0.48% 

American Single Cheese 16 $35.7 0.54% 36 $101.0 0.32% 31 $136.7 0.36% 

Fz Ss Prem Traditional 

Meals 

17 $34.7 0.53% 23 $127.8 0.41% 21 $162.5 0.43% 

Snack Cake—Multi Pack 18 $34.1 0.52% 57 $76.2 0.24% 43 $110.4 0.29% 

Dairy Case 100% Pure 

Juice—O 

19 $34.1 0.52% 6 $188.7 0.60% 8 $222.9 0.58% 

Snacks/Appetizers 20 $34.1 0.52% 66 $68.7 0.22% 50 $102.8 0.27% 

Enhanced [Pork Boneless 

Loin/Rib] 

21 $32.9 0.50% 26 $120.4 0.38% 24 $153.2 0.40% 

Fz Ss Economy Meals All 22 $32.8 0.50% 76 $59.5 0.19% 58 $92.3 0.24% 

Bacon—Trad 16oz Or Less 23 $32.2 0.49% 28 $113.2 0.36% 26 $145.4 0.38% 

Unflavored Can Coffee 24 $32.2 0.49% 18 $143.4 0.46% 19 $175.6 0.46% 

Soft Drinks 20pk & 24pk 

Can Carb 

25 $31.7 0.48% 58 $76.0 0.24% 46 $107.7 0.28% 

Pizza/Premium 26 $31.2 0.47% 31 $106.2 0.34% 30 $137.4 0.36% 

Mainstream Variety 

Breads 

27 $31.1 0.47% 22 $128.4 0.41% 22 $159.5 0.42% 

Sugar 28 $30.1 0.46% 51 $81.2 0.26% 42 $111.3 0.29% 

Sandwiches & Handhelds 29 $28.6 0.43% 88 $52.9 0.17% 67 $81.5 0.21% 

Potatoes Russet (Bulk & 

Bag) 

30 $28.5 0.43% 29 $111.8 0.35% 28 $140.3 0.37% 

Ribs [Pork] 31 $28.2 0.43% 54 $77.3 0.25% 48 $105.4 0.28% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

32 $28.0 0.43% 21 $131.2 0.42% 23 $159.2 0.42% 

273 

Exhibit E–14: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores with $2 to $12 Million in Sales—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

All Family Cereal 33 $27.7 0.42% 15 $148.4 0.47% 18 $176.1 0.46% 

Convenient Meals—Kids 

Meal C 

34 $27.5 0.42% 95 $50.1 0.16% 72 $77.6 0.20% 

Bananas 35 $26.3 0.40% 13 $168.0 0.53% 14 $194.4 0.51% 

Natural Cheese Chunks 36 $26.2 0.40% 17 $145.8 0.46% 20 $172.0 0.45% 

Isotonic Drinks Single 

Serve 

37 $24.2 0.37% 45 $88.5 0.28% 41 $112.7 0.30% 

Premium [Ice Cream & 

Sherbert] 

38 $23.9 0.36% 14 $155.6 0.49% 17 $179.5 0.47% 

Condensed Soup 39 $23.2 0.35% 30 $109.2 0.35% 32 $132.4 0.35% 

Pourable Salad Dressings 40 $22.9 0.35% 39 $97.8 0.31% 35 $120.7 0.32% 

Frzn Chicken—Wht Meat 41 $22.8 0.35% 67 $68.4 0.22% 59 $91.2 0.24% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

42 $22.0 0.33% 96 $49.9 0.16% 81 $71.9 0.19% 

Choice Beef 43 $21.7 0.33% 40 $95.4 0.30% 37 $117.1 0.31% 

Fz Family Style Entre ́es 44 $21.5 0.33% 79 $58.8 0.19% 69 $80.3 0.21% 

Mayonnaise & Whipped 

Dressing 

45 $21.5 0.33% 48 $84.4 0.27% 47 $105.9 0.28% 

Select Beef 46 $20.6 0.31% 34 $102.0 0.32% 34 $122.6 0.32% 

Traditional [Ice Cream & 

Sherbert] 

47 $20.6 0.31% 43 $89.1 0.28% 44 $109.7 0.29% 

Fz Bag Vegetables—Plain 48 $20.5 0.31% 41 $95.2 0.30% 40 $115.7 0.30% 

Hot Dogs—Base Meat 49 $20.5 0.31% 121 $42.9 0.14% 93 $63.3 0.17% 

Aseptic Pack Juice And 

Drinks 

50 $19.5 0.30% 131 $41.7 0.13% 99 $61.3 0.16% 

Macaroni & Cheese Dnrs 51 $19.4 0.29% 127 $42.2 0.13% 97 $61.6 0.16% 

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 Nutritional 

Meals 

54 $18.8 0.29% 5 $189.5 0.60% 12 $208.2 0.55% 

Margarine: Tubs And 

Bowls 

55 $18.4 0.28% 64 $71.5 0.23% 61 $89.9 0.24% 

Frzn Chicken—Wings 56 $18.3 0.28% 425 $13.4 0.04% 240 $31.8 0.08% 

Mainstream [Pasta & 

Pizza Sauce] 

57 $18.3 0.28% 80 $58.0 0.18% 76 $76.3 0.20% 

Choice Beef 58 $18.3 0.28% 97 $49.7 0.16% 86 $68.0 0.18% 

Mexican Soft Tortillas And 

Wra 

59 $18.3 0.28% 53 $77.8 0.25% 53 $96.1 0.25% 

Strawberries 60 $18.0 0.27% 25 $122.4 0.39% 29 $140.3 0.37% 

Mult Pk Bag Snacks 61 $17.9 0.27% 194 $31.3 0.10% 143 $49.3 0.13% 

Can Pasta 62 $17.9 0.27% 165 $35.2 0.11% 120 $53.1 0.14% 

Lunchment—Bologna/Sausage 63 $17.9 0.27% 105 $46.2 0.15% 91 $64.1 0.17% 

Refrigerated Coffee 

Creamers 

64 $17.7 0.27% 35 $101.2 0.32% 36 $118.9 0.31% 

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 Fries 67 $16.5 0.25% 157 $36.4 0.12% 121 $52.9 0.14% 

Tuna 68 $16.5 0.25% 56 $76.8 0.24% 56 $93.3 0.24% 

Candy Bags—Chocolate 69 $16.4 0.25% 37 $100.0 0.32% 38 $116.5 0.31% 

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 Sandwiches 72 $15.3 0.23% 139 $39.9 0.13% 112 $55.2 0.14% 

Frzn Meat—Beef 73 $14.7 0.22% 190 $32.1 0.10% 154 $46.8 0.12% 

Value Forms/18oz And 

Larger [Chicken] 

74 $14.7 0.22% 201 $30.2 0.10% 160 $44.9 0.12% 

Cakes: Birthday/Celebration Sh 75 $14.6 0.22% 167 $35.1 0.11% 139 $49.8 0.13% 

Fz Skillet Meals 76 $14.5 0.22% 82 $54.9 0.17% 85 $69.4 0.18% 

Sandwich Cookies 77 $14.4 0.22% 92 $51.4 0.16% 88 $65.8 0.17% 

Chicken Drums 78 $14.3 0.22% 251 $23.7 0.08% 197 $38.1 0.10% 

Pizza/Traditional 79 $14.3 0.22% 106 $46.1 0.15% 101 $60.4 0.16% 

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 Bulk 82 $13.9 0.21% 33 $102.3 0.32% 39 $116.1 0.30% 

Bagged Cheese Snacks 83 $13.8 0.21% 146 $38.3 0.12% 125 $52.1 0.14% 

Salsa & Dips 84 $13.7 0.21% 136 $40.4 0.13% 118 $54.0 0.14% 

Ramen Noodles/Ramen 

Cups 

85 $13.7 0.21% 293 $20.5 0.07% 225 $34.2 0.09% 

Rts Soup: Chunky/ 

Homestyle/Et 

86 $13.7 0.21% 47 $84.6 0.27% 52 $98.2 0.26% 

Waffles/Pancakes/French 

Toast 

87 $13.5 0.21% 85 $54.0 0.17% 87 $67.5 0.18% 

Sour Creams 88 $13.5 0.20% 69 $65.9 0.21% 70 $79.4 0.21% 

Dnr Sausage—Links Pork 

Ckd/S 

89 $13.3 0.20% 233 $26.7 0.08% 184 $40.0 0.11% 

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% 

274 

Exhibit E–14: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores with $2 to $12 Million in Sales—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Sandwiches—(Cold) 92 $13.0 0.20% 108 $45.4 0.14% 104 $58.4 0.15% 

Dairy Case Juice Drnk 

Under 10 

93 $12.9 0.20% 170 $34.8 0.11% 151 $47.6 0.13% 

Hamburger Buns 94 $12.8 0.20% 94 $50.1 0.16% 94 $63.0 0.17% 

Candy Bars (Singles) (Including) 95 $12.8 0.19% 149 $37.8 0.12% 132 $50.6 0.13% 

Cream Cheese 96 $12.8 0.19% 52 $78.1 0.25% 60 $90.9 0.24% 

Candy Bars (Multi Pack) 97 $12.5 0.19% 93 $50.4 0.16% 95 $62.9 0.17% 

Cheese Crackers 98 $12.5 0.19% 74 $61.2 0.19% 79 $73.7 0.19% 

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 Subcommodities $2,658.3 40.40% $9,463.7 30.03% $12,122.1 31.82% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $0.4 0.01% 1 $1.4 0.00% 1 $1.8 0.00% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $0.3 0.01% 2 $0.8 0.00% 2 $1.2 0.00% 

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 Liter Btl Carb 

Incl 

5 $0.1 0.00% 7 $0.3 0.00% 7 $0.5 0.00% 

Mainstream White Bread 6 $0.1 0.00% 11 $0.3 0.00% 9 $0.4 0.00% 

Soft Drinks 20pk & 24pk 

Can Carb 

7 $0.1 0.00% 19 $0.2 0.00% 13 $0.3 0.00% 

Potato Chips 8 $0.1 0.00% 5 $0.4 0.00% 6 $0.5 0.00% 

Shredded Cheese 9 $0.1 0.00% 4 $0.4 0.00% 4 $0.5 0.00% 

Kids Cereal 10 $0.1 0.00% 28 $0.2 0.00% 20 $0.3 0.00% 

Lunchment—Deli Fresh 11 $0.1 0.00% 8 $0.3 0.00% 8 $0.4 0.00% 

Snack Cake—Multi Pack 12 $0.1 0.00% 31 $0.2 0.00% 26 $0.3 0.00% 

American Single Cheese 13 $0.1 0.00% 16 $0.2 0.00% 14 $0.3 0.00% 

Enhanced [Pork Boneless 

Loin/Rib] 

14 $0.1 0.00% 10 $0.3 0.00% 11 $0.3 0.00% 

Tortilla/Nacho Chips 15 $0.1 0.00% 12 $0.3 0.00% 12 $0.3 0.00% 

Unflavored Can Coffee 16 $0.1 0.00% 15 $0.2 0.00% 16 $0.3 0.00% 

Eggs—Large 17 $0.1 0.00% 9 $0.3 0.00% 10 $0.3 0.00% 

Potatoes Russet (Bulk & 

Bag) 

18 $0.1 0.00% 18 $0.2 0.00% 17 $0.3 0.00% 

Still Water Drnking/Mnrl 

Water 

19 $0.1 0.00% 20 $0.2 0.00% 19 $0.3 0.00% 

Fz Ss Economy Meals All 20 $0.1 0.00% 57 $0.1 0.00% 45 $0.2 0.00% 

Sugar 21 $0.1 0.00% 32 $0.2 0.00% 31 $0.2 0.00% 

Bacon—Trad 16oz Or Less 22 $0.1 0.00% 21 $0.2 0.00% 21 $0.3 0.00% 

Convenient Meals—Kids 

Meal C 

23 $0.1 0.00% 66 $0.1 0.00% 52 $0.2 0.00% 

Mainstream Variety 

Breads 

24 $0.1 0.00% 13 $0.3 0.00% 15 $0.3 0.00% 

Infant Formula Starter/ 

Solutio 

25 $0.1 0.00% 143 $0.1 0.00% 78 $0.1 0.00% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

26 $0.1 0.00% 51 $0.1 0.00% 44 $0.2 0.00% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

27 $0.1 0.00% 27 $0.2 0.00% 27 $0.3 0.00% 

Chicken Breast Boneless 28 $0.1 0.00% 14 $0.2 0.00% 18 $0.3 0.00% 

Hot Dogs—Base Meat 29 $0.0 0.00% 46 $0.1 0.00% 36 $0.2 0.00% 

Snacks/Appetizers 30 $0.0 0.00% 70 $0.1 0.00% 60 $0.1 0.00% 

Traditional [Ice Cream & 

Sherbert] 

31 $0.0 0.00% 23 $0.2 0.00% 24 $0.3 0.00% 

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—Bologna/Sausage 35 $0.0 0.00% 45 $0.1 0.00% 43 $0.2 0.00% 

275 

Exhibit E–15: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores with less than $2 Million in Sales—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Flavored Milk 36 $0.0 0.00% 64 $0.1 0.00% 57 $0.1 0.00% 

All Family Cereal 37 $0.0 0.00% 22 $0.2 0.00% 23 $0.3 0.00% 

Sandwiches & Handhelds 38 $0.0 0.00% 75 $0.1 0.00% 66 $0.1 0.00% 

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/Traditional 41 $0.0 0.00% 47 $0.1 0.00% 46 $0.2 0.00% 

Pails [Ice Cream & 

Sherbert] 

42 $0.0 0.00% 59 $0.1 0.00% 55 $0.2 0.00% 

Margarine: Tubs And 

Bowls 

43 $0.0 0.00% 42 $0.1 0.00% 39 $0.2 0.00% 

Natural Cheese Chunks 44 $0.0 0.00% 26 $0.2 0.00% 29 $0.2 0.00% 

Fz Ss Prem Traditional 

Meals 

45 $0.0 0.00% 41 $0.1 0.00% 41 $0.2 0.00% 

Macaroni & Cheese Dnrs 46 $0.0 0.00% 88 $0.1 0.00% 74 $0.1 0.00% 

Pourable Salad Dressings 47 $0.0 0.00% 35 $0.1 0.00% 35 $0.2 0.00% 

Choice Beef 48 $0.0 0.00% 53 $0.1 0.00% 54 $0.2 0.00% 

Isotonic Drinks Single 

Serve 

49 $0.0 0.00% 44 $0.1 0.00% 48 $0.2 0.00% 

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 & Whipped 

Dressing 

52 $0.0 0.00% 48 $0.1 0.00% 50 $0.2 0.00% 

Ribs [Pork] 53 $0.0 0.00% 52 $0.1 0.00% 53 $0.2 0.00% 

Candy Bags—Chocolate 54 $0.0 0.00% 36 $0.1 0.00% 37 $0.2 0.00% 

Cottage Cheese 55 $0.0 0.00% 37 $0.1 0.00% 42 $0.2 0.00% 

Dairy Case 100% Pure 

Juice—O 

56 $0.0 0.00% 24 $0.2 0.00% 28 $0.2 0.00% 

Mexican Soft Tortillas And 

Wra 

57 $0.0 0.00% 56 $0.1 0.00% 58 $0.1 0.00% 

Frzn French Fries 58 $0.0 0.00% 93 $0.1 0.00% 80 $0.1 0.00% 

Candy Bars (Multi Pack) 59 $0.0 0.00% 71 $0.1 0.00% 70 $0.1 0.00% 

Sweet Goods—Full Size 60 $0.0 0.00% 95 $0.1 0.00% 85 $0.1 0.00% 

Butts [Pork Shoulder] 61 $0.0 0.00% 80 $0.1 0.00% 76 $0.1 0.00% 

Frzn Chicken—Wht Meat 62 $0.0 0.00% 54 $0.1 0.00% 59 $0.1 0.00% 

Sandwich Cookies 63 $0.0 0.00% 63 $0.1 0.00% 63 $0.1 0.00% 

Mainstream [Pasta & 

Pizza Sauce] 

64 $0.0 0.00% 73 $0.1 0.00% 71 $0.1 0.00% 

Fz Bag Vegetables—Plain 65 $0.0 0.00% 34 $0.2 0.00% 40 $0.2 0.00% 

Bagged Cheese Snacks 66 $0.0 0.00% 90 $0.1 0.00% 79 $0.1 0.00% 

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—Fresh 

Rolls 

69 $0.0 0.00% 61 $0.1 0.00% 62 $0.1 0.00% 

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 Biscuits 72 $0.0 0.00% 86 $0.1 0.00% 82 $0.1 0.00% 

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 (Singles) (Including) 75 $0.0 0.00% 84 $0.1 0.00% 83 $0.1 0.00% 

Sour Creams 76 $0.0 0.00% 62 $0.1 0.00% 65 $0.1 0.00% 

Sticks/Enrobed [Frozen 

Novelties] 

77 $0.0 0.00% 99 $0.1 0.00% 92 $0.1 0.00% 

Angus [Beef] 78 $0.0 0.00% 83 $0.1 0.00% 81 $0.1 0.00% 

Tray Pack/Choc Chip 

Cookies 

79 $0.0 0.00% 85 $0.1 0.00% 84 $0.1 0.00% 

Salsa & Dips 80 $0.0 0.00% 106 $0.1 0.00% 99 $0.1 0.00% 

Skillet Dinners 81 $0.0 0.00% 142 $0.1 0.00% 120 $0.1 0.00% 

Aseptic Pack Juice And 

Drinks 

82 $0.0 0.00% 154 $0.1 0.00% 126 $0.1 0.00% 

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 Style Entre ́es 86 $0.0 0.00% 170 $0.0 0.00% 138 $0.1 0.00% 

Cubed Meats [Beef] 87 $0.0 0.00% 97 $0.1 0.00% 94 $0.1 0.00% 

Select Beef 88 $0.0 0.00% 91 $0.1 0.00% 91 $0.1 0.00% 

Value Forms/18oz And 

Larger [Chicken] 

89 $0.0 0.00% 166 $0.0 0.00% 139 $0.1 0.00% 

Fz Ss Prem Nutritional 

Meals 

90 $0.0 0.00% 30 $0.2 0.00% 32 $0.2 0.00% 

Variety Beans—Kidney/ 

Pinto/E 

91 $0.0 0.00% 77 $0.1 0.00% 87 $0.1 0.00% 

Cream Cheese 92 $0.0 0.00% 58 $0.1 0.00% 64 $0.1 0.00% 

Dnr Sausage—Links Pork 

Ckd/S 

93 $0.0 0.00% 129 $0.1 0.00% 122 $0.1 0.00% 

Lunchmeat—Chop/Form 

Pltry & Ha 

94 $0.0 0.00% 186 $0.0 0.00% 155 $0.1 0.00% 

Frzn Meat—Beef 95 $0.0 0.00% 194 $0.0 0.00% 162 $0.1 0.00% 

Toaster Pastries 96 $0.0 0.00% 121 $0.1 0.00% 116 $0.1 0.00% 

276 

Exhibit E–15: Top 100 Subcommodities for SNAP Households by 

Expenditure: Stores with less than $2 Million in Sales—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Bacon—Trad Greater 

Than 16oz 

97 $0.0 0.00% 76 $0.1 0.00% 88 $0.1 0.00% 

Corn Chips 98 $0.0 0.00% 108 $0.1 0.00% 105 $0.1 0.00% 

Water Ice [Frozen Novelties] 99 $0.0 0.00% 220 $0.0 0.00% 182 $0.1 0.00% 

Eggs—Medium 100 $0.0 0.00% 164 $0.0 0.00% 144 $0.1 0.00% 

Top 100 Subcommodities $4.9 0.07% $16.8 0.05% $21.7 0.06% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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% 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $12.2 0.18% 1 $105.5 0.33% 1 $117.6 0.31% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $10.3 0.16% 2 $74.1 0.24% 2 $84.4 0.22% 

Lean [Beef] 3 $6.3 0.10% 7 $32.5 0.10% 5 $38.9 0.10% 

Shredded Cheese 4 $4.8 0.07% 3 $47.5 0.15% 3 $52.3 0.14% 

Kids Cereal 5 $4.3 0.06% 20 $24.1 0.08% 18 $28.3 0.07% 

Sft Drnk 2 Liter Btl Carb 

Incl 

6 $3.9 0.06% 18 $25.3 0.08% 17 $29.2 0.08% 

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 Boneless 9 $3.4 0.05% 4 $39.9 0.13% 4 $43.3 0.11% 

Lunchment—Deli Fresh 10 $3.3 0.05% 11 $29.7 0.09% 10 $33.0 0.09% 

Eggs—Large 11 $3.1 0.05% 8 $31.8 0.10% 9 $34.9 0.09% 

Infant Formula Starter/ 

Solutio 

12 $3.1 0.05% 268 $4.3 0.01% 169 $7.4 0.02% 

Snacks/Appetizers 13 $3.0 0.05% 54 $14.2 0.05% 48 $17.3 0.05% 

Tortilla/Nacho Chips 14 $3.0 0.05% 13 $28.8 0.09% 12 $31.8 0.08% 

Enhanced [Pork Boneless 

Loin/Rib] 

15 $2.8 0.04% 21 $23.6 0.07% 20 $26.4 0.07% 

Mainstream White Bread 16 $2.8 0.04% 40 $17.3 0.05% 36 $20.1 0.05% 

Unflavored Can Coffee 17 $2.8 0.04% 22 $23.4 0.07% 21 $26.1 0.07% 

Still Water Drnking/Mnrl 

Water 

18 $2.7 0.04% 27 $21.6 0.07% 26 $24.2 0.06% 

Soft Drinks 20pk & 24pk 

Can Carb 

19 $2.6 0.04% 59 $13.8 0.04% 53 $16.3 0.04% 

Pizza/Premium 20 $2.5 0.04% 28 $21.3 0.07% 27 $23.9 0.06% 

Fz Ss Prem Traditional 

Meals 

21 $2.5 0.04% 32 $19.3 0.06% 30 $21.8 0.06% 

Dairy Case 100% Pure 

Juice—O 

22 $2.5 0.04% 6 $33.2 0.11% 6 $35.7 0.09% 

Natural Cheese Chunks 23 $2.4 0.04% 15 $28.1 0.09% 15 $30.6 0.08% 

American Single Cheese 24 $2.4 0.04% 46 $16.3 0.05% 41 $18.7 0.05% 

All Family Cereal 25 $2.3 0.04% 14 $28.2 0.09% 16 $30.5 0.08% 

Bacon—Trad 16oz Or Less 26 $2.3 0.03% 35 $19.0 0.06% 35 $21.3 0.06% 

Snack Cake—Multi Pack 27 $2.2 0.03% 70 $12.5 0.04% 64 $14.7 0.04% 

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 Russet (Bulk & 

Bag) 

30 $2.2 0.03% 33 $19.3 0.06% 32 $21.5 0.06% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

31 $2.2 0.03% 25 $22.5 0.07% 24 $24.6 0.06% 

Fz Ss Economy Meals All 32 $2.2 0.03% 112 $8.7 0.03% 94 $10.8 0.03% 

Premium [Ice Cream & 

Sherbert] 

33 $2.1 0.03% 12 $29.6 0.09% 13 $31.7 0.08% 

Mainstream Variety 

Breads 

34 $2.1 0.03% 26 $21.8 0.07% 28 $23.8 0.06% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

35 $2.1 0.03% 90 $10.1 0.03% 81 $12.2 0.03% 

Convenient Meals—Kids 

Meal C 

36 $2.1 0.03% 94 $9.7 0.03% 86 $11.8 0.03% 

Sandwiches & Handhelds 37 $2.0 0.03% 104 $9.0 0.03% 91 $11.0 0.03% 

Sugar 38 $1.9 0.03% 61 $13.6 0.04% 55 $15.5 0.04% 

277 

Exhibit E–16: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in Counties with Poverty Rates Less than 10%—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Condensed Soup 39 $1.9 0.03% 31 $19.6 0.06% 31 $21.5 0.06% 

Fz Family Style Entre ́es 40 $1.8 0.03% 83 $11.0 0.03% 77 $12.8 0.03% 

Ribs [Pork] 41 $1.8 0.03% 68 $12.9 0.04% 63 $14.7 0.04% 

Isotonic Drinks Single 

Serve 

42 $1.7 0.03% 60 $13.8 0.04% 56 $15.5 0.04% 

Refrigerated Coffee 

Creamers 

43 $1.7 0.03% 38 $18.3 0.06% 37 $20.0 0.05% 

Pourable Salad Dressings 44 $1.7 0.03% 39 $18.0 0.06% 38 $19.7 0.05% 

Fz Ss Prem Nutritional 

Meals 

45 $1.7 0.03% 5 $33.6 0.11% 8 $35.3 0.09% 

Frzn Chicken—Wht Meat 46 $1.7 0.03% 74 $12.2 0.04% 69 $13.9 0.04% 

Strawberries 47 $1.7 0.03% 17 $26.3 0.08% 19 $27.9 0.07% 

Mayonnaise & Whipped 

Dressing 

48 $1.6 0.02% 52 $14.6 0.05% 54 $16.1 0.04% 

Mexican Soft Tortillas And 

Wra 

49 $1.6 0.02% 51 $14.8 0.05% 51 $16.4 0.04% 

Candy Bags—Chocolate 50 $1.5 0.02% 30 $20.3 0.06% 29 $21.9 0.06% 

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 [Ice Cream & 

Sherbert] 

54 $1.5 0.02% 56 $13.9 0.04% 58 $15.3 0.04% 

Meat: Turkey Bulk 55 $1.4 0.02% 19 $24.3 0.08% 22 $25.7 0.07% 

Aseptic Pack Juice And 

Drinks 

56 $1.4 0.02% 136 $7.8 0.02% 115 $9.2 0.02% 

Fz Bag Vegetables—Plain 57 $1.4 0.02% 47 $16.0 0.05% 46 $17.4 0.05% 

Butter 58 $1.4 0.02% 23 $23.3 0.07% 23 $24.7 0.06% 

Margarine: Tubs And 

Bowls 

59 $1.4 0.02% 75 $12.1 0.04% 72 $13.5 0.04% 

Hot Dogs—Base Meat 60 $1.4 0.02% 174 $6.5 0.02% 149 $7.9 0.02% 

Can Pasta 61 $1.4 0.02% 193 $6.1 0.02% 166 $7.4 0.02% 

Macaroni & Cheese Dnrs 62 $1.4 0.02% 133 $7.9 0.02% 114 $9.2 0.02% 

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 [Pasta & 

Pizza Sauce] 

66 $1.3 0.02% 88 $10.4 0.03% 88 $11.7 0.03% 

Pizza/Traditional 67 $1.3 0.02% 98 $9.3 0.03% 95 $10.6 0.03% 

Tuna 68 $1.3 0.02% 64 $13.3 0.04% 65 $14.6 0.04% 

Value Forms/18oz And 

Larger [Chicken] 

69 $1.2 0.02% 209 $5.8 0.02% 181 $7.0 0.02% 

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 Sandwiches 72 $1.2 0.02% 135 $7.8 0.02% 122 $9.0 0.02% 

Cream Cheese 73 $1.2 0.02% 49 $15.5 0.05% 50 $16.7 0.04% 

Cheese Crackers 74 $1.2 0.02% 66 $13.1 0.04% 67 $14.2 0.04% 

Fz Skillet Meals 75 $1.2 0.02% 89 $10.3 0.03% 90 $11.5 0.03% 

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—Beef 78 $1.1 0.02% 184 $6.3 0.02% 168 $7.4 0.02% 

Frzn French Fries 79 $1.1 0.02% 173 $6.5 0.02% 159 $7.7 0.02% 

Instore Cut Fruit 80 $1.1 0.02% 57 $13.8 0.04% 61 $14.9 0.04% 

Waffles/Pancakes/French 

Toast 

81 $1.1 0.02% 84 $10.9 0.03% 83 $12.0 0.03% 

Sandwiches—(Cold) 82 $1.1 0.02% 140 $7.7 0.02% 130 $8.8 0.02% 

Sour Creams 83 $1.1 0.02% 73 $12.4 0.04% 73 $13.5 0.04% 

Cakes: Birthday/Celebration Sh 84 $1.1 0.02% 164 $6.7 0.02% 150 $7.8 0.02% 

Avocado 85 $1.1 0.02% 48 $15.7 0.05% 49 $16.8 0.04% 

Rts Soup: Chunky/ 

Homestyle/Et 

86 $1.1 0.02% 55 $14.2 0.05% 59 $15.3 0.04% 

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 (Singles) (Including) 90 $1.1 0.02% 152 $7.1 0.02% 142 $8.2 0.02% 

Lunchment—Bologna/Sausage 91 $1.1 0.02% 179 $6.4 0.02% 167 $7.4 0.02% 

Sandwich Cookies 92 $1.0 0.02% 99 $9.3 0.03% 98 $10.4 0.03% 

Bkfst Sausage—Fresh 

Rolls 

93 $1.0 0.02% 109 $8.8 0.03% 105 $9.9 0.03% 

Spring Water 94 $1.0 0.02% 82 $11.0 0.03% 82 $12.0 0.03% 

Chix: Frd 8pc/Cut Up 

(Hot) 

95 $1.0 0.02% 85 $10.8 0.03% 84 $11.8 0.03% 

Bagged Cheese Snacks 96 $1.0 0.02% 176 $6.4 0.02% 165 $7.4 0.02% 

Natural Cheese Slices 97 $1.0 0.02% 50 $15.3 0.05% 52 $16.4 0.04% 

Hamburger Buns 98 $1.0 0.02% 102 $9.1 0.03% 103 $10.1 0.03% 

Sweet Goods—Full Size 99 $1.0 0.01% 175 $6.5 0.02% 163 $7.5 0.02% 

278 

Exhibit E–16: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in Counties with Poverty Rates Less than 10%—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Yogurt/Kids 100 $1.0 0.01% 165 $6.7 0.02% 155 $7.7 0.02% 

Top 100 Subcommodities $204.3 3.10% $1,763.9 5.60% $1,968.2 5.17% 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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% 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $147.5 2.24% 1 $651.2 2.07% 1 $798.7 2.10% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $123.8 1.88% 2 $456.0 1.45% 2 $579.8 1.52% 

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 Cheese 5 $57.3 0.87% 3 $255.8 0.81% 3 $313.1 0.82% 

Sft Drnk 2 Liter Btl Carb 

Incl 

6 $54.3 0.83% 13 $175.5 0.56% 9 $229.8 0.60% 

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 Starter/ 

Solutio 

9 $42.1 0.64% 179 $35.8 0.11% 79 $77.9 0.20% 

Lunchment—Deli Fresh 10 $42.1 0.64% 11 $183.1 0.58% 11 $225.2 0.59% 

Eggs—Large 11 $40.0 0.61% 9 $191.2 0.61% 8 $231.2 0.61% 

Chicken Breast Boneless 12 $38.5 0.58% 4 $221.7 0.70% 5 $260.2 0.68% 

Still Water Drnking/Mnrl 

Water 

13 $37.9 0.58% 19 $146.8 0.47% 19 $184.8 0.48% 

Tortilla/Nacho Chips 14 $36.3 0.55% 16 $157.8 0.50% 17 $194.1 0.51% 

Mainstream White Bread 15 $35.0 0.53% 42 $100.2 0.32% 35 $135.3 0.36% 

Snacks/Appetizers 16 $34.2 0.52% 67 $75.2 0.24% 49 $109.4 0.29% 

Fz Ss Prem Traditional 

Meals 

17 $33.7 0.51% 22 $136.9 0.43% 21 $170.6 0.45% 

Dairy Case 100% Pure 

Juice—O 

18 $33.7 0.51% 6 $206.7 0.66% 7 $240.4 0.63% 

American Single Cheese 19 $32.8 0.50% 41 $102.4 0.32% 36 $135.2 0.35% 

Unflavored Can Coffee 20 $31.4 0.48% 18 $149.8 0.48% 20 $181.2 0.48% 

Enhanced [Pork Boneless 

Loin/Rib] 

21 $31.1 0.47% 27 $122.2 0.39% 24 $153.3 0.40% 

Fz Ss Economy Meals All 22 $31.1 0.47% 80 $62.1 0.20% 65 $93.1 0.24% 

Bacon—Trad 16oz Or Less 23 $31.0 0.47% 29 $119.4 0.38% 26 $150.3 0.39% 

Pizza/Premium 24 $30.2 0.46% 34 $115.3 0.37% 29 $145.5 0.38% 

Snack Cake—Multi Pack 25 $30.2 0.46% 70 $74.2 0.24% 54 $104.4 0.27% 

Mainstream Variety 

Breads 

26 $29.7 0.45% 25 $130.8 0.42% 22 $160.5 0.42% 

Soft Drinks 20pk & 24pk 

Can Carb 

27 $29.0 0.44% 62 $79.6 0.25% 50 $108.6 0.29% 

Natural Cheese Chunks 28 $28.2 0.43% 14 $167.0 0.53% 16 $195.1 0.51% 

All Family Cereal 29 $28.0 0.43% 15 $163.5 0.52% 18 $191.6 0.50% 

Sugar 30 $27.3 0.42% 58 $84.4 0.27% 46 $111.8 0.29% 

Sandwiches & Handhelds 31 $27.0 0.41% 93 $56.0 0.18% 73 $83.0 0.22% 

Potatoes Russet (Bulk & 

Bag) 

32 $26.8 0.41% 32 $116.0 0.37% 30 $142.8 0.37% 

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 Meals—Kids 

Meal C 

35 $25.2 0.38% 106 $51.5 0.16% 82 $76.7 0.20% 

Premium [Ice Cream & 

Sherbert] 

36 $24.8 0.38% 12 $176.1 0.56% 14 $200.9 0.53% 

Isotonic Drinks Single 

Serve 

37 $24.2 0.37% 45 $93.9 0.30% 42 $118.1 0.31% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

38 $24.0 0.36% 26 $123.5 0.39% 28 $147.5 0.39% 

Select Beef 39 $23.8 0.36% 30 $117.5 0.37% 31 $141.3 0.37% 

Frzn Chicken—Wht Meat 40 $22.7 0.35% 69 $74.8 0.24% 61 $97.5 0.26% 

Condensed Soup 41 $22.5 0.34% 33 $115.5 0.37% 32 $138.0 0.36% 

Pourable Salad Dressings 42 $21.7 0.33% 39 $105.6 0.34% 39 $127.3 0.33% 

Choice Beef 43 $21.3 0.32% 38 $106.3 0.34% 38 $127.6 0.33% 

279 

Exhibit E–17: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in Counties with Poverty Rates of 10% to 20%—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fz Family Style Entre ́es 44 $21.2 0.32% 78 $63.1 0.20% 72 $84.3 0.22% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

45 $20.9 0.32% 99 $53.5 0.17% 85 $74.4 0.20% 

Mayonnaise & Whipped 

Dressing 

46 $20.9 0.32% 49 $90.9 0.29% 45 $111.8 0.29% 

Mexican Soft Tortillas And 

Wra 

47 $19.8 0.30% 50 $90.3 0.29% 48 $110.1 0.29% 

Refrigerated Coffee 

Creamers 

48 $19.5 0.30% 31 $116.6 0.37% 33 $136.1 0.36% 

Adult Cereal 49 $19.3 0.29% 21 $139.5 0.44% 23 $158.8 0.42% 

Traditional [Ice Cream & 

Sherbert] 

50 $19.2 0.29% 52 $88.6 0.28% 51 $107.8 0.28% 

Fz Ss Prem Nutritional 

Meals 

51 $19.1 0.29% 5 $208.6 0.66% 10 $227.7 0.60% 

Fz Bag Vegetables—Plain 52 $19.0 0.29% 43 $98.9 0.31% 43 $117.9 0.31% 

Aseptic Pack Juice And 

Drinks 

53 $18.6 0.28% 137 $43.3 0.14% 107 $61.9 0.16% 

Choice Beef 54 $18.4 0.28% 97 $53.6 0.17% 89 $72.1 0.19% 

Hot Dogs—Base Meat 55 $18.4 0.28% 145 $42.1 0.13% 111 $60.5 0.16% 

Macaroni & Cheese Dnrs 56 $18.2 0.28% 129 $44.6 0.14% 103 $62.8 0.16% 

Margarine: Tubs And 

Bowls 

57 $18.1 0.27% 63 $77.4 0.25% 64 $95.5 0.25% 

Strawberries 58 $17.8 0.27% 24 $132.8 0.42% 25 $150.6 0.40% 

Mainstream [Pasta & 

Pizza Sauce] 

59 $17.4 0.26% 85 $60.8 0.19% 78 $78.2 0.21% 

Candy Bags—Chocolate 60 $16.7 0.25% 36 $112.6 0.36% 37 $129.3 0.34% 

Can Pasta 61 $16.5 0.25% 185 $35.3 0.11% 144 $51.8 0.14% 

Frzn Chicken—Wings 62 $16.4 0.25% 469 $13.0 0.04% 268 $29.4 0.08% 

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—Bologna/Sausage 66 $15.7 0.24% 124 $45.6 0.14% 108 $61.3 0.16% 

Mult Pk Bag Snacks 67 $15.4 0.23% 205 $32.4 0.10% 161 $47.8 0.13% 

Butter 68 $15.3 0.23% 23 $134.9 0.43% 27 $150.1 0.39% 

Meat: Turkey Bulk 69 $15.1 0.23% 28 $120.3 0.38% 34 $135.4 0.36% 

Frzn French Fries 70 $15.1 0.23% 177 $37.0 0.12% 141 $52.1 0.14% 

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—Beef 73 $14.6 0.22% 188 $35.0 0.11% 154 $49.6 0.13% 

Fz Skillet Meals 74 $14.5 0.22% 87 $60.4 0.19% 84 $74.9 0.20% 

Value Forms/18oz And 

Larger [Chicken] 

75 $14.2 0.22% 214 $31.9 0.10% 168 $46.2 0.12% 

Frzn Breakfast Sandwiches 76 $14.1 0.21% 154 $41.0 0.13% 128 $55.0 0.14% 

Cakes: Birthday/Celebration Sh 77 $14.1 0.21% 172 $37.9 0.12% 143 $52.0 0.14% 

Chicken Wings 78 $14.0 0.21% 319 $20.8 0.07% 238 $34.8 0.09% 

Sandwiches—(Cold) 79 $13.9 0.21% 94 $56.0 0.18% 92 $69.9 0.18% 

Sandwich Cookies 80 $13.8 0.21% 95 $54.6 0.17% 94 $68.4 0.18% 

Sour Creams 81 $13.7 0.21% 71 $73.2 0.23% 71 $86.9 0.23% 

Rts Soup: Chunky/ 

Homestyle/Et 

82 $13.7 0.21% 47 $93.1 0.30% 52 $106.8 0.28% 

Pizza/Traditional 83 $13.6 0.21% 118 $47.2 0.15% 109 $60.8 0.16% 

Cream Cheese 84 $13.5 0.21% 53 $88.0 0.28% 57 $101.6 0.27% 

Waffles/Pancakes/French 

Toast 

85 $13.4 0.20% 89 $58.6 0.19% 88 $72.1 0.19% 

Fruit Snacks 86 $13.4 0.20% 209 $32.1 0.10% 172 $45.5 0.12% 

Bagged Cheese Snacks 87 $13.3 0.20% 158 $40.0 0.13% 136 $53.2 0.14% 

Angus [Beef] 88 $13.1 0.20% 64 $77.0 0.24% 67 $90.2 0.24% 

Ramen Noodles/Ramen 

Cups 

89 $12.9 0.20% 298 $22.0 0.07% 237 $34.8 0.09% 

Salsa & Dips 90 $12.8 0.20% 140 $42.7 0.14% 124 $55.5 0.15% 

Cheese Crackers 91 $12.8 0.19% 74 $67.9 0.22% 77 $80.7 0.21% 

Candy Bars (Singles) (Including) 92 $12.8 0.19% 139 $42.9 0.14% 123 $55.7 0.15% 

Dairy Case Juice Drnk 

Under 10 

93 $12.6 0.19% 170 $38.2 0.12% 149 $50.7 0.13% 

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—Full Size 97 $12.3 0.19% 128 $44.9 0.14% 118 $57.2 0.15% 

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 Subcommodities $2,551.7 38.78% $10,139.2 32.17% $12,690.9 33.31% 

280 

Exhibit E–17: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in Counties with Poverty Rates of 10% to 20%—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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% 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Fluid Milk/White Only 1 $31.5 0.48% 1 $97.0 0.31% 1 $128.5 0.34% 

Soft Drinks 12/18 & 15pk 

Can Car 

2 $30.5 0.46% 2 $71.0 0.23% 2 $101.6 0.27% 

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 Cheese 6 $12.7 0.19% 3 $38.6 0.12% 3 $51.3 0.13% 

Sft Drnk 2 Liter Btl Carb 

Incl 

7 $12.6 0.19% 8 $29.3 0.09% 6 $42.0 0.11% 

Potato Chips 8 $11.3 0.17% 10 $29.1 0.09% 7 $40.4 0.11% 

Lunchment—Deli Fresh 9 $10.5 0.16% 6 $29.8 0.09% 8 $40.2 0.11% 

Mainstream White Bread 10 $10.1 0.15% 26 $19.3 0.06% 19 $29.4 0.08% 

Snack Cake—Multi Pack 11 $9.2 0.14% 38 $15.1 0.05% 30 $24.2 0.06% 

Eggs—Large 12 $9.0 0.14% 11 $28.6 0.09% 10 $37.6 0.10% 

Infant Formula Starter/ 

Solutio 

13 $9.0 0.14% 172 $5.2 0.02% 61 $14.2 0.04% 

American Single Cheese 14 $8.8 0.13% 31 $17.9 0.06% 24 $26.8 0.07% 

Still Water Drnking/Mnrl 

Water 

15 $8.1 0.12% 24 $19.3 0.06% 21 $27.5 0.07% 

Soft Drinks 20pk & 24pk 

Can Carb 

16 $8.1 0.12% 46 $13.1 0.04% 39 $21.2 0.06% 

Tortilla/Nacho Chips 17 $8.1 0.12% 17 $22.4 0.07% 16 $30.5 0.08% 

Sft Drnk Mlt-Pk Btl Carb 

(Excp) 

18 $7.9 0.12% 12 $27.7 0.09% 12 $35.6 0.09% 

Fz Ss Economy Meals All 19 $7.7 0.12% 64 $10.0 0.03% 48 $17.7 0.05% 

Sugar 20 $7.7 0.12% 41 $14.6 0.05% 36 $22.3 0.06% 

Fz Ss Prem Traditional 

Meals 

21 $7.7 0.12% 27 $19.3 0.06% 23 $26.9 0.07% 

Chicken Breast Boneless 22 $7.6 0.12% 5 $31.3 0.10% 9 $39.0 0.10% 

Chicken Wings 23 $7.6 0.12% 181 $5.1 0.02% 72 $12.7 0.03% 

Enhanced [Pork Boneless 

Loin/Rib] 

24 $7.6 0.11% 18 $22.2 0.07% 18 $29.7 0.08% 

Bacon—Trad 16oz Or Less 25 $7.5 0.11% 28 $19.2 0.06% 25 $26.7 0.07% 

Ribs [Pork] 26 $7.4 0.11% 47 $12.9 0.04% 41 $20.3 0.05% 

Dairy Case 100% Pure 

Juice—O 

27 $7.4 0.11% 9 $29.1 0.09% 11 $36.5 0.10% 

Snacks/Appetizers 28 $7.4 0.11% 60 $11.0 0.03% 44 $18.4 0.05% 

Unflavored Can Coffee 29 $7.2 0.11% 15 $24.9 0.08% 15 $32.1 0.08% 

Convenient Meals—Kids 

Meal C 

30 $7.0 0.11% 86 $8.5 0.03% 53 $15.5 0.04% 

Pizza/Premium 31 $6.9 0.10% 35 $16.7 0.05% 32 $23.6 0.06% 

Sandwiches & Handhelds 32 $6.9 0.10% 82 $8.6 0.03% 56 $15.4 0.04% 

Potatoes Russet (Bulk & 

Bag) 

33 $6.7 0.10% 29 $19.2 0.06% 26 $25.9 0.07% 

Mainstream Variety 

Breads 

34 $6.6 0.10% 20 $20.7 0.07% 22 $27.3 0.07% 

All Family Cereal 35 $5.8 0.09% 16 $23.2 0.07% 20 $29.0 0.08% 

Frzn Chicken—Wht Meat 36 $5.6 0.09% 49 $12.8 0.04% 42 $18.4 0.05% 

Choice Beef 37 $5.6 0.09% 34 $16.8 0.05% 34 $22.5 0.06% 

Pourable Salad Dressings 38 $5.6 0.09% 37 $15.8 0.05% 37 $21.4 0.06% 

Bananas 39 $5.5 0.08% 14 $24.9 0.08% 17 $30.4 0.08% 

Fz Bag Vegetables—Plain 40 $5.3 0.08% 33 $17.0 0.05% 35 $22.3 0.06% 

Hot Dogs—Base Meat 41 $5.3 0.08% 89 $8.2 0.03% 67 $13.5 0.04% 

Mult Pk Bag Snacks 42 $5.3 0.08% 178 $5.1 0.02% 101 $10.4 0.03% 

Condensed Soup 43 $5.3 0.08% 30 $18.5 0.06% 31 $23.8 0.06% 

Frzn Chicken—Wings 44 $5.2 0.08% 356 $2.6 0.01% 156 $7.8 0.02% 

Lunchment—Bologna/Sausage 45 $5.0 0.08% 79 $8.9 0.03% 65 $14.0 0.04% 

Traditional [Ice Cream & 

Sherbert] 

46 $5.0 0.08% 36 $16.3 0.05% 38 $21.3 0.06% 

Sft Drnk Sngl Srv Btl 

Carb (Ex) 

47 $4.8 0.07% 99 $7.8 0.02% 73 $12.6 0.03% 

281 

Exhibit E–18: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in Counties with Poverty Rates Greater than 20%—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Vegetable Oil 48 $4.8 0.07% 193 $4.9 0.02% 113 $9.7 0.03% 

Macaroni & Cheese Dnrs 49 $4.8 0.07% 110 $7.2 0.02% 77 $11.9 0.03% 

Mayonnaise & Whipped 

Dressing 

50 $4.7 0.07% 43 $13.6 0.04% 43 $18.4 0.05% 

Natural Cheese Chunks 51 $4.7 0.07% 19 $21.0 0.07% 27 $25.7 0.07% 

Fz Family Style Entre ́es 52 $4.6 0.07% 70 $9.4 0.03% 64 $14.0 0.04% 

Isotonic Drinks Single 

Serve 

53 $4.6 0.07% 56 $11.9 0.04% 49 $16.4 0.04% 

Can Pasta 54 $4.4 0.07% 135 $6.3 0.02% 96 $10.7 0.03% 

Mainstream [Pasta & 

Pizza Sauce] 

55 $4.3 0.07% 67 $9.7 0.03% 63 $14.0 0.04% 

Premium [Ice Cream & 

Sherbert] 

56 $4.3 0.07% 22 $20.3 0.06% 28 $24.6 0.06% 

Frzn French Fries 57 $4.3 0.06% 118 $6.8 0.02% 90 $11.0 0.03% 

Choice Beef 58 $4.2 0.06% 65 $10.0 0.03% 62 $14.2 0.04% 

Aseptic Pack Juice And 

Drinks 

59 $4.2 0.06% 144 $6.1 0.02% 102 $10.3 0.03% 

Chicken Drums 60 $4.1 0.06% 231 $4.2 0.01% 140 $8.4 0.02% 

Dnr Sausage—Links Pork 

Ckd/S 

61 $4.1 0.06% 209 $4.7 0.01% 130 $8.8 0.02% 

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: Tubs And 

Bowls 

64 $4.0 0.06% 57 $11.3 0.04% 57 $15.3 0.04% 

Fz Ss Prem Nutritional 

Meals 

65 $4.0 0.06% 7 $29.4 0.09% 14 $33.4 0.09% 

Frzn Breakfast Sandwiches 66 $3.9 0.06% 116 $6.9 0.02% 95 $10.8 0.03% 

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: Birthday/Celebration Sh 70 $3.4 0.05% 162 $5.6 0.02% 125 $9.1 0.02% 

Bacon—Trad Greater 

Than 16oz 

71 $3.4 0.05% 117 $6.8 0.02% 103 $10.3 0.03% 

Peanut Butter 72 $3.3 0.05% 42 $14.5 0.05% 46 $17.8 0.05% 

Candy Bags—Chocolate 73 $3.3 0.05% 40 $14.6 0.05% 45 $17.9 0.05% 

Sandwich Cookies 74 $3.2 0.05% 98 $7.9 0.02% 89 $11.0 0.03% 

Salsa & Dips 75 $3.2 0.05% 130 $6.4 0.02% 115 $9.6 0.03% 

Frzn Meat—Beef 76 $3.2 0.05% 185 $5.0 0.02% 143 $8.2 0.02% 

Bkfst Sausage—Fresh 

Rolls 

77 $3.2 0.05% 87 $8.5 0.03% 81 $11.7 0.03% 

Value Forms/18oz And 

Larger [Chicken] 

78 $3.2 0.05% 192 $4.9 0.02% 145 $8.1 0.02% 

Fz Skillet Meals 79 $3.1 0.05% 81 $8.6 0.03% 80 $11.7 0.03% 

Refrigerated Biscuits 80 $3.1 0.05% 121 $6.7 0.02% 109 $9.8 0.03% 

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/Ramen 

Cups 

83 $3.0 0.05% 330 $2.9 0.01% 213 $5.9 0.02% 

Hamburger Buns 84 $3.0 0.05% 83 $8.5 0.03% 82 $11.5 0.03% 

Tray Pack/Choc Chip 

Cookies 

85 $3.0 0.05% 124 $6.6 0.02% 116 $9.6 0.03% 

Pizza/Traditional 86 $3.0 0.05% 101 $7.6 0.02% 99 $10.6 0.03% 

Candy Bars (Multi Pack) 87 $2.9 0.04% 91 $8.1 0.03% 88 $11.1 0.03% 

Pails [Ice Cream & 

Sherbert] 

88 $2.9 0.04% 194 $4.9 0.02% 153 $7.9 0.02% 

Grapes White 89 $2.9 0.04% 72 $9.3 0.03% 76 $12.2 0.03% 

Refrigerated Coffee 

Creamers 

90 $2.9 0.04% 53 $12.3 0.04% 58 $15.2 0.04% 

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: Chunky/ 

Homestyle/Et 

93 $2.9 0.04% 51 $12.6 0.04% 55 $15.5 0.04% 

Bagged Cheese Snacks 94 $2.8 0.04% 163 $5.6 0.02% 138 $8.4 0.02% 

Butter Spray Cracker 95 $2.8 0.04% 85 $8.5 0.03% 83 $11.4 0.03% 

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/Pancakes/French 

Toast 

98 $2.8 0.04% 97 $7.9 0.03% 97 $10.7 0.03% 

Dnr Sausage—Pork Rope 

Ckd/Sm 

99 $2.8 0.04% 150 $5.9 0.02% 133 $8.7 0.02% 

Traditional Spices 100 $2.8 0.04% 109 $7.2 0.02% 107 $10.0 0.03% 

Top 100 Subcommodities $610.2 9.27% $1,500.2 4.76% $2,110.3 5.54% 

282 

Exhibit E–18: Top 100 Subcommodities for SNAP Households by Expenditure: Stores in Counties with Poverty Rates Greater than 20%—Continued 

Subcommodity 

SNAP Household 

Expenditures 

Non-SNAP Household 

Expenditures 

Total Household 

Expenditures 

Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures Rank $ in 

millions 

% of 

Expenditures 

Total Expenditures $6,580.5 100% $31,513.8 100% $38,094.2 100% 

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 

283 

1http://jama.jamanetwork.com/article.aspx?articleid=1832542. 2http://content.healthaffairs.org/content/28/5/w822.full.pdf+html. 

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 reformminded 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

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 

284 

* The document referred to is retained in Committee file. 

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 semiannually) 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. 

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/ 

285 

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., & Vo ̈gele 

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. 

286 

1For 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. 

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. 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-ofsugarguidelines/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. 

287 

* 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: Ycw2102@columbia.edu. 

0749–3797/$36.00 

http://dx.doi.org/10.1016/j.amepre.2013.03.012 

• http://www.prnewswire.com/news-releases/the-obesity-society-encouragesscience-industry-collaborations-to-support-obesity-science-public-health252453321.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.* 

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. 


11502068.eps
288 

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 

289 

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 populationlevel 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 sugarsweetened 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

290 

Table 1. Daily Caloric Effects of Physical Activity for Select Groups Using 

Schofield Equations 

Population 

Average 

weight 

(kg) 

Schofield 

equation 

(BMR=) a

Intervention 

Inputs 

Δ METs 

Duration 

(minutes/ 

day) 

Schoolbased? b

Caloric 

effect 

(kcal/ 

day) c

Boys, age in years: 

2–5 18 22.706 kg + 504.3 Add 30 minutes/day of 

walking 

2.3 30 No 44 

6–11 34 22.706 kg + 504.3 Add 30 minutes/day of 

jogging 

7 30 No 186 

12–14 59 17.686 kg + 658.2 Add 15 minutes/day of 

PE 

2.6 15 Yes 23 

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 program 3.5 10.5 Yes 11 

6–11 35 13.384 kg + 485.9 Make PE more active Varies d 60 Yes 9 

12–14 57 13.384 kg + 692.6 Add 30 minutes/day of 

PE 

2.6 30 Yes 39 

15–18 65 13.384 kg + 692.6 Add 10 minutes/day of 

jogging 

7 10 No 76 

aThe 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. cDaily caloric impact = (BMR × Δ METs × duration in minutes) ÷ 1,440 minutes/day. dThe 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. 

291 

Table 2. Caloric Impact of Physical Activity Interventions for Average 

Student, By Age Group 

Intervention Population (age 

group, years) 

Inputs for caloric calculations 

Assumptions Target 

METs a

Avg.weight 

(lbs) b

Avg. 

caloric 

impact 

(kcal/ 

day) 

Modeled estimates 

Add walking at a 3- 

mph pace, 15 minutes/day 

Both (2–5) 

Both (6–11) 

Both (12–14) 

3.3 

3.3 

3.3 

39 

76 

127 

21 

30 

38 

Same baseline (1.0, sitting quietly) and target 

METs for all ages, based on Ainsworth, et 

al.,12 and Ridley, et al.13

Both (15–18) 3.3 157 43 

Add jogging at a 5- 

mph pace, 15 minutes/day 

Both (2–5) 

Both (6–11) 

Both (12–14) 

Both (15–18) 

8.0 

8.0 

8.0 

8.0 

39 

76 

127 

157 

64 

90 

115 

130 

Same baseline (1.0, sitting quietly) and target 

METs for all ages, based on Ainsworth, et 

al.,12 and Ridley, et al.13

Walking to and from 

school (roundtrip) 

Both (2–5) 

Both (6–11) 

3.3 

3.3 

39 

76 

9 

12 

Intervention model estimates based on METs 

from Ainsworth, et al.,12 and Ridley, et al.,13

Both (12–14) 

Both (15–18) 

3.3 

3.3 

127 

157 

15 

17 

and published data on 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 time, 

15 minutes/day 

Both (2–5) 

Both (6–11) 

3.4 

3.4 

39 

76 

11 

15 

McKenzie, et al.,25 estimate 3.4 METs for elementary school PE. Same value used for preBoth (12–14) 

Both (15–18) 

3.6 

3.7 

127 

157 

21 

25 

school. Nader, et al.,26 estimate 3.6 METs for 

middle school PE. Smith, et al.,27 estimate 3.7 

METs for high school PE. Implemented for a 

full academic year.b

Make current PE 

more active, 30 

minutes/day 

Both (2–5) 

Both (6–11) 

Both (12–14) 

4.5 

4.5 

4.5 

39 

76 

127 

3 

4 

6 

MET values used at baseline and target is a 

composite of estimated MET values, based on 

Wu, et al.,7 and Ainsworth, et al.,12 (4.5 

METs) 

Both (15–18) 4.5 157 6 for MVPA, 1.8 METs for non-MVPA). 

Because of high variance in METs, baseline activity levels, and population characteristics between CATCH,20, 26, 28–29 MSPAN,25 and 

TAAG 30–35 interventions, 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 recommendation.20

Implemented for a full academic year.b

Implement SPARK 

using only PE specialists to teach 

PE, 30 minutes/day 

Both (2–5) 

Both (6–11) 

Both (12–14) 

Both (15–18) 

7.2 

7.2 

7.2 

7.2 

39 

76 

127 

157 

34 

48 

58 

64 

7.2 METs for PE specialists for SPARK intervention from McKenzie, et al.,36 and Sallis, et 

al.,37 used in calculation to demonstrate maximum potential of intervention (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 afterschool physical activity program Both (2–5) 

Both (6–11) 

Both (12–14) 

4.5 

4.5 

4.5 

39 

76 

127 

11 

16 

20 

Gortmaker, et al.,38 estimate ≥4.0 METs in 

intervention. 4.5 METs is used here as a conservative composite target based on Wu, et al.7

Both (15–18) 4.5 157 22 Same 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

aMETs 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. 

292 

Table 3. Caloric Impact of Dietary and Other Interventions for Average 

Student By Group 

Intervention 

Population 

(age group, 

years) 

Inputs for caloric calculations 

Assumptions 

Amount a Affected 

pop., % b

Avg. 

caloric 

impact 

(kcal/day) 

Modeled estimates 

Reduce unhealthy 

food intake 

All 

All 

1-oz bag of 

chips per 

day 

1 cookie per 

day 

100 

100 

154 

55 

Intervention models estimates based on published caloric values of average bag of regular potato chips and single Oreo cookie. 

Reduce SSB intake All 

All 

12-oz can per 

day 

20-oz bottle 

per day 

100 

100 

136 

240 

Intervention models estimates based on published caloric values of average can or bottle of regular caffeinated cola. 

Replace SSBs with 

water in schools 

Both (2–5) 

Both (6–11) 

Both (12–14) 

Both (15–18) 

124 

184 

301 

301 

5.5 

6.5 

10.3 

10.3 

3 

6 

15 

15 

Affected population and amounts based on 

published analysis from Wang, et al.10 Implemented for a full academic year.c

Switch from 1 cup 

of sugary cereals 

to cereals scored 

highest in nutritional quality 

Both (2–5) 

Boys (2–5) 

Girls (2–5) 

Both (6–11) 

Boys (6–11) 

Girls (6–11) 

Both (12–14) 

Boys (12–14) 

Girls (12–14) 

Both (15–18) 

Boys (15–18) 

Girls (15–18) 

0.64 cups 

0.64 cups 

0.64 cups 

0.93 cups 

0.93 cups 

0.94 cups 

1.16 cups 

1.32 cups 

1.0 cups 

1.15 cups 

1.25 cups 

1.06 cups 

48.4 

47.3 

49.6 

39.5 

40.2 

38.8 

34.5 

35.5 

33.5 

26.6 

26.1 

27.0 

7 

7 

7 

6 

6 

6 

5 

5 

5 

4 

4 

4 

Averaged grams/cup and standardized serving sizes of top ten 39 and bottom ten 40 cereals by nutrition score, as determined by 

CerealFACTS. org.41

Affected population and average grams/serving consumed 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 sugary 

drink size limit 

Both (2–5) 

Boys (2–5) 

Girls (2–5) 

Both (6–11) 

Boys (6–11) 

Girls (6–11) 

Both (12–14) 

Boys (12–14) 

Girls (12–14) 

Both (15–18) 

Boys (15–18) 

Girls (15–18) 

24.2 

21.1 

32.3 

67.9 

70.0 

64.9 

93.6 

109.3 

77.7 

111.8 

120.3 

100.1 

0.6 

0.9 

0.4 

5.1 

6.1 

4.2 

9.4 

10.1 

8.7 

13.3 

15.3 

11.2 

0 

0 

0 

3 

4 

3 

9 

11 

7 

15 

18 

11 

Amount is average kilocalorie reduction per 

day if limited consumption to 16 oz/day as 

in Elbel, et al.,42 and Wang, et al.14

Affected population and average kilocalorie 

reduction based on analysis of 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 

competitive food 

nutrition standards in high 

schools nationally 

Both (15–18) 157.8 100 78 Taber, et al.,9 estimate 157.9 kcal per weekday fewer calories consumed in 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 minutes/day 

All 106 100 106 Sonneville and 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 

computer-game 

playing time, 60 

minutes/day 

All 92 100 92 Same calorie change for other age groups 

aThe amount designates the current pre-intervention consumption level of the item by the selected population; 

amounts are kilocalories unless otherwise specified. bThe 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 

293 

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 portionsize 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 selfreported, 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 disVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00299 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
294 

proportionately 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 evidencebased 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. 

295 

References 

1. Trust for America’s Health and the Robert Wood Johnson Foundation. F as in fat: how obesity threatens America’s future 2012. 

healthyamericans.org/report/100/2012. 

2. Ogden C.L., Carroll M.D., Kit B.K., Flegal K.M. Prevalence of obesity and trends in body mass index among U.S. children and 

adolescents, 1999–2010. JAMA 2012; 307(5): 483–90. 

3. Narayan K.M., Boyle J.P., Thompson T.J., Sorensen S.W., Williamson D.F. Lifetime risk for diabetes mellitus in the U.S. JAMA 

2003; 290(14): 1884–90. 

4. Wang Y.C., Gortmaker S.L., Sobol A.M., Kuntz K.M. Estimating the energy gap among U.S. children: a counterfactual approach. PEDIATRICS 2006; 118(6): e1721–e1733. 

5. Wang Y.C., Orleans C.T., Gortmaker S.L. Reaching the healthy people goals for reducing childhood obesity: closing the energy 

gap. AM. J. PREV. MED. 2012; 42(5): 437–44. 

6. Waters E., de Silva-Sanigorski A., Hall B.J., et al. Interventions for preventing obesity in children. COCHRANE DATABASE SYST. 

REV. (Online) 2011(12): CD001871. 

7. Wu S., Cohen D., Shi Y., Pearson M., Sturm R.. Economic analysis of physical activity interventions. AM. J. PREV. MED. 2011; 

40(2): 149–58. 

8. Brennan L., Castro S., Brownson R.C., Claus J., Orleans C.T. Accelerating evidence reviews and broadening evidence standards 

to identify effective, promising, and emerging policy and environmental strategies for prevention of childhood obesity. ANNU. REV. 

PUBLIC HEALTH 2011; 32: 199–223. 

9. Taber D.R., Chriqui J.F., Chaloupka F.J.. Differences in nutrient intake associated with state laws regarding fat, sugar, and caloric content of competitive foods. ARCH. PEDIATR. ADOLESC. MED. 2012; 166(5): 452–8. 

10. Wang Y.C., Bleich S.N., Gortmaker S.L. Increasing caloric contribution from sugar-sweetened beverages and 100% fruit juices 

among U.S. children and adolescents, 1988–2004. PEDIATRICS 2008; 121(6): e1604–e1614. 

11. Schofield W.N. Predicting basal metabolic rate, new standards and review of previous work. HUM. NUTR. CLIN. NUTR. 1985; 

39(S 1): 5–41. 

12. Ainsworth B.E., Haskell W.L., Whitt M.C., et al. Compendium of physical activities: an update of activity codes and MET intensities. Med. Sci. Sports Exerc. 2000; 32(9S): S498–S504. 

13. Ridley K., Olds T.S. Assigning energy costs to activities in children: a review and synthesis. MED. SCI. SPORTS EXERC. 2008; 

40(8): 1439–46. 

14. Wang Y., Vine S. Caloric impact of a 16-ounce portion size cap on sugar-sweetened beverages served in restaurants. AM. J. 

CLIN. NUTR. 2013 [In Press]. 

15. Sonneville K.R., Gortmaker S.L. Total energy intake, adolescent discretionary behaviors and the energy gap. INT. J. OBES. 

2008; 32(S6): S19–S27. 

16. Epstein L.H., Roemmich J.N., Robinson J.L., et al. A randomized trial of the effects of reducing television viewing and computer use on body mass index in young children. ARCH. PEDIATR. ADOLESC. MED. 2008; 162(3): 239–45. 

17. Guran T., Bereket A. International epidemic of childhood obesity and television viewing. MINERVA PEDIATR. 2011; 63(6): 483– 

90. 

18. Miller S.A., Taveras E.M., Rifas-Shiman S.L., Gillman M.W. Association between television viewing and poor diet quality in 

young children. INT. J. PEDIATR. OBES. 2008; 3(3): 168–76. 

19. Healthy Eating Research. Food and beverage marketing to children and adolescents: an environment at odds with good health. 

Robert Wood Johnson Foundation, 2011. 

20. DHHS. Strategies to improve the quality of physical education. Washington D.C.: CDC, National Center for Chronic Disease 

Prevention and Healthy Promotion, Division of Adolescent and School Health, 2010. 

21. DHHS, Office of Disease Prevention and Health Promotion. Healthy People 2010: nutrition and overweight. 

hp2010.nhlbihin.net/2010Objs/19Nutrition.html. 

22. DHHS, Office of Disease Prevention and Health Promotion. Healthy People 2020: nutrition and weight status. 2012. 

www.healthypeople.gov/2020/topicsobjectives2020/objectiveslist.aspx?topicId=29. 

23. McDonald N.C., Brown A.L., Marchetti L.M., Pedroso M.S. U.S. school travel, 2009 an assessment of trends. AM. J. PREV. 

MED. 2011; 41(2): 146–51. 

24. National Center for Safe Routes to School. How children get to school: school travel patterns from 1969 to 2009. November 

2011. 

25. McKenzie T.L., Sallis J.F., Prochaska J.J., Conway T.L., Marshall S.J., Rosengard P. Evaluation of a two-year middle-school 

physical education intervention: M–SPAN. MED. SCI. SPORTS EXERC. 2004; 36(8): 1382–8. 

26. Nader P.R. Frequency and intensity of activity of third-grade children in physical education. ARCH. PEDIATR. ADOLESC. MED. 

2003; 157(2): 185–90. 

27. Smith N.J., Lounsbery M.A., McKenzie T.L. Physical activity in high school physical education: impact of lesson context and 

class gender composition. J. PHYS. ACT. HEALTH 2013: In Press. 

28. McKenzie T.L., Nader P.R., Strikmiller P.K., et al. School physical education: effect of the Child and Adolescent Trial for Cardiovascular Health. PREV. MED. 1996; 25(4): 423–31. 

29. Kelder S., Hoelscher D.M., Barroso C.S., Walker J.L., Cribb P., Hu S. The CATCH Kids Club: a pilot after-school study for improving elementary students’ nutrition and physical activity. PUBLIC HEALTH NUTR. 2005; 8(2): 133–40. 

30. Stevens J., Murray D.M., Catellier D.J, et al. Design of the Trial of Activity in Adolescent Girls (TAAG). CONTEMP. CLIN. 

TRIALS 2005; 26(2): 223–33. 

31. Baggett C.D., Stevens J., Catellier D.J., et al. Compensation or displacement of physical activity in middle-school girls: the 

Trial of Activity for Adolescent Girls. INT. J. OBES. 2010; 34(7): 1193–9. 

32. Elder J.P., Shuler L., Moe S.G., et al. Recruiting a diverse group of middle school girls into the trial of activity for adolescent 

girls. J. SCHOOL HEALTH 2008; 78(10): 523–31. 

33. Gittelsohn J., Steckler A., Johnson C.C., et al. Formative research in school and community-based health programs and studies: ‘‘state of the art’’ and the TAAG approach. HEALTH. EDUC. BEHAV. 2006; 33(1): 25–39. 

34. Webber L.S., Catellier D.J., Lytle L.A., et al. Promoting physical activity in middle school girls: Trial of Activity for Adolescent 

Girls. AM. J. PREV. MED. 2008; 34(3): 173–84. 

35. Young D.R., Johnson C.C., Steckler A., et al. Data to action: using formative research to develop intervention programs to increase physical activity in adolescent girls. HEALTH EDUC. BEHAV. 2006; 33(1): 97–111. 

36. McKenzie T.L., Sallis J.F., Kolody B., Faucette F.N. Long-term effects of a physical education curriculum and staff development program: SPARK. RES. Q. EXERC. SPORT 1997; 68(4): 280–91. 

37. Sallis J.F., McKenzie T.L., Alcaraz J.E., Kolody B., Faucette N., Hovell M.F. The effects of a 2-year physical education program (SPARK) on physical activity and fitness in elementary school students. Sports, Play and Active Recreation for Kids. AM. J. 

PUBLIC HEALTH 1997; 87(8): 1328–34. 

38. Gortmaker S.L., Lee R.M., Mozaffarian R.S., et al. Effect of an after-school intervention on increases in children’s physical activity. MED. SCI. SPORTS EXERC. 2012; 44(3): 450–7. 

39. Cereal FACTS. Top 10 cereals by nutrition score. n.d.; cerealfacts.org/cereal_nutrition_advanced_earch.aspx?l=t. 

40. Cereal FACTS. Bottom 10 cereals by nutrition score. n.d.; cerealfacts.org/cereal_nutrition_advanced_search.aspx?l=b. 

41. Yale Rudd Center for Food Policy & Obesity. Limited progress in the nutrition quality and marketing of children’s cereals, 

2012. 

42. Elbel B., Cantor J., Mijanovich T. Potential effect of the New York City policy regarding sugared beverages. N. ENGL. J. MED. 

2012; 367(7): 680–1. 

43. CDC. Obesity in K–8 students—New York City, 2006–07 to 2010–11 school years. MMWR MORBID MORTAL WKLY. REP. 2011; 

60(49): 1673–8. 

44. Robbins J.M., Mallya G., Polansky M., Schwarz D.F. Prevalence, disparities, and trends in obesity and severe obesity among 

students in the Philadelphia, Pennsylvania, school district, 2006–2010. PREV. CHRONIC DIS. 2012; 9: E145. 

296 

* 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. [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: dallison@uab.edu. [7]These authors contributed equally to this work. 

References—Continued 

45. Babey S.H., Wolstein J., Diamant A.L., Bloom A., Goldstein H.A.. Patchwork of progress: changes in overweight and obesity 

among California’s 5th, 7th, and 9th graders, 2005–2010. 2011. 

46. Molaison E.F., Kolbo J.R., Zhang L., et al. Prevalence and trends in obesity among Mississippi public school students, 2005– 

2009. J. MISS. STATE. MED. ASSOC. 2010; 51(3): 67–72. 

47. Gortmaker S.L., Swinburn B.A., Levy D., et al. Changing the future of obesity: science, policy, and action. LANCET 2011; 

378(9793): 838–47. 

48. Hall K.D., Sacks G., Chandramohan D., et al. Quantification of the effect of energy imbalance on bodyweight. LANCET 2011; 

378(9793): 826–37. 

49. Van Cleave J., Gortmaker S.L., Perrin J.M.. Dynamics of obesity and chronic health conditions among children and youth. 

JAMA 2010; 303(7): 623–30. 

50. Glasgow R.E., Vogt T.M., Boles S.M.. Evaluating the public health impact of health promotion interventions: the RE–AIM 

framework. AM. J. PUBLIC HEALTH 1999; 89(9): 1322–7. 

51. Singh A., Uijtdewilligen L., Twisk J.W., van Mechelen W., Chinapaw M.J. Physical activity and performance at school: a systematic review of the literature including a methodological quality assessment. ARCH. PEDIATR. ADOLESC. MED. 2012; 166(1): 49–55. 

52. Cullen K.W., Hartstein J., Reynolds K.D., et al. Improving the school food environment: results from a pilot study in middle 

schools. J. AM. DIET. ASSOC. 2007; 107(3): 484–9. 

53. Hartstein J., Cullen K.W., Reynolds K.D., Harrell J., Resnicow K., Kennel P. Impact of portion-size control for school a ̆ la 

carte items: changes in kilocalories and macronutrients purchased by middle school students. J. AM. DIET. ASSOC. 2008; 108(1): 

140–4. 

54. Trasande L. How much should we invest in preventing childhood obesity? HEALTH AFF. 2010; 29(3): 372–8. 

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 * 

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

Background: Public health and clinical interventions for obesity in freeliving 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 intervenVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00302 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
297 

tion 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 

298 

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 metaanalysis. 

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 intervenVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00304 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
299 

tion (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. 


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300 

Table 1. Master List and Summary of Included Studies Grouped By Treatment Type and Sorted in Ascending Year of 

Publication 

Reference(s) Intervention 

Sample studied 

(mean ageyears, pct 

female, 

baseline BMI 

kgm¥2) 

Adjusted daily 

dose(s) (kcal: 

treatmentcontrol) 

Study 

duration 

(weeks) 

Intervention notes 

N 

randomized, 

completed, 

analyzed 

Method of missing 

data handling 

Overall 

mean 

compliance 

Johnstone, et al.22 Diet 38, 0%, 35.1 ¥167.2 4 High protein, ketogenic diet 20, 17, 17 Completers 100 Das, et al.23 Diet 35, 76.3%, 27.6 ¥285.6 26 Caloric restriction 46, 39, 39 Completers 100 Zachwieja, et al.24 Diet and exercise 24, 45.8%, 24.1 ¥675 2 Caloric restriction and daily treadmill exercise 24, 24, 24 No drops 90 Moreira, et al.25 Diet and exercise (separate treatments) 49, 68%, 30 ¥556.0, ¥753.3 11 25% caloric restriction (controlled feeding) versus aerobic exercise (individualized and supervised sessions 3× per 

week) 

36, 35, 36 ITT 99 

Leon, et al.26 Exercise 32.6, 0%, 26 ¥245.6 12 Walking and stair climbing 22, 16, 16 Completers 86 Van Etten, et al.27 Exercise 33.7, 0%, 23.7 ¥31.6 18 Weight training 26, 26, 26 Completers 95 Murphy, et al.28 Exercise 44.4, 100%, 25.76 ¥81.6, ¥84.5 10 Long versus short bouts of walking 47, 34, 34 Completers 86.5 Crandall 21 Exercise 51.75, 44%, 30.8 ¥76.7 12 Recumbent cycle ergometer 13, 13, 13 No drops 100 Shaw and Shaw 29 Exercise 41, 92%, 32.6 ¥13.7 8 Resistance training 28, 28, 28 Completers 91.1 Kirk, et al.30 Exercise 20.6, 0%, 28.2 ¥104.7 24 High-intensity resistance training 25, 19, 19 Completers 96 Whybrow, et al.31 Exercise 27.2, 50%, 23.6 ¥455.6, ¥513.6, 

¥907.1 

2 Progressive exercise on cycle ergometer or treadmill 12, 12, 12 No drops 100 

Guadalupe-Grau, et 

al.32

Exercise 23.7, 65.2%, 23.03 ¥51.7 9 Strength training and plyometric jumps 88, 72, 66 Completers 85 

Alves, et al.33 Exercise 38.2, 100%, 30 ¥106.1 26 Group exercises 156, 146, 156 ITT, BOCF 96 Turner, et al.34 Exercise 54, 0%, 28 ¥187.3 24 Structured exercise 54, 41, 29 Completers 94 Bell, et al.35 Exercise 49, 100%, 34.7 ¥399.0, ¥395.1 24 Pedometer-based walking program 211, 128, 128 Completers 84.77 Vispute, et al.36 Exercise 23.66, 41.7%, 24.6 ¥41.9 6 Abdominal exercises 24, 24, 24 No drops 95.71 Hornbuckle, et al.37 Exercise 28.5, 0%, 25.42 ¥57.7 12 Resistance training 44, 32, 44 ITT 96 Heydari, et al.38 Exercise 37.7, 56.3%, 27.8 ¥186.4 12 High-intensity intermittent exercise 46, 38, 38 Completers 100 Thompson, et al.,39 and 

Church, et al.40

Exercise 49.7, 72.8%, 31.8 ¥174.8 16 Supervised aerobic exercise 162, 137, 162 ITT 91 

Addington 20 Feeding 38.74, 63.8%, 

32.09 

2.9 (aspartame 

group), 142.9 (SSB 

group) 

4 Artificially sweetened beverage (aspartame) versus SSB 150, 111, 111 Completers 100 

Lammert, et al.41 Feeding 22.4, 0%, 22.61 191 3 Overfeeding carbohydrate or fat 20, 20, 20 No drops 100 Martin, et al.42 Feeding 37.7, 56.3%, 27.8 597.1 2 Low- versus high-calorie breakfast 10, 10, 10 No drops 100 Sabate, et al.43 Feeding 42.6, 45.2%, 23.7 219 26 Walnuts 90, 90, 90 No drops 95 Whybrow, et al.44 Feeding 60, 26.7%, 27.7 122.8, 227.5 8 Added fruits and vegetables 90, 62, 62 Completers 92.6 Whybrow, et al.45 Feeding 35.05, 50%, 25.35 343.9, 687.9 2 Added snacks 100, 87, 72 Completers 96 Sheridan, et al.46 Feeding 24.9, 0%, 28.7 314.8 4 Pistachio nuts 15, 15, 15 No drops 99 Casas-Agustench, et 

al.47

Feeding 54.4, 56.3%, 26.5 176.9 12 Mixed nuts 52, 50, 50 Completers 94 

Maersk, et al.48 Feeding 28, 0%, 22.2 3.1, 365.2, 385.5 26 1 l per day of diet soda, SSB or milk versus water 60, 47, 47 Completers 85 

Abbreviations: BOCF, baseline observation carried forward; ITT, intention-to-treat analysis reported; pct, percentage; SSB, sugar-sweetened beverage. 

301 

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. 

302 

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. 


11502070.eps
303 

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 


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304 

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. 

305 

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). 

References 

1. Flegal K.M., Carroll M.D., Kit B.K., Ogden C.L. Prevalence of obesity and trends in the distribution of body mass index among 

U.S. adults, 1999–2010. JAMA 2012; 307: 491–497. 

2. National Institute of Diabetes and Digestive and Kidney Diseases Body weight simulator. 2013. Available at http:// 

bwsimulator.niddk.nih.gov/ (accessed on 28 September 2013). 

3. Wang Y.C., Hsiao A., Tracy Orleans C., Gortmaker S.L. The caloric calculator: average caloric impact of childhood obesity 

interventions. AM. J. PREV. MED. 2013; 45: e3–e13. 

4. Wishnofsky M. Caloric equivalents of gained or lost weight. AM. J. CLIN. NUTR. 1958; 6: 542–546. 

5. Hall K.D. What is the required energy deficit per unit weight loss? INT. J. OBES. (Lond) 2008; 32: 573–576. 

6. Hall K.D., Chow C.C. Why is the 3,500 kcal per pound weight loss rule wrong? INT. J. OBES. (Lond) 2013; 37: 1614. 

7. Thomas D.M., Martin C.K., Lettieri S., Bredlau C., Kaiser K., Church T., et al. Response to ‘Why is the 3500 kcal per pound 

weight loss rule wrong?’. INT. J. OBES. (Lond) 2013; 37: 1614–1615. 

8. Thomas D.M., Martin C.K., Lettieri S., Bredlau C., Kaiser K., Church T., et al. Can a weight loss of one pound a week be 

achieved with a 3,500-kcal deficit? Commentary on a commonly accepted rule. INT. J. OBES. (Lond) 2013; 37: 1611–1613. 

9. Hall K.D., Butte B.A., Swinburn B.A., Chow C.C. Dynamics of childhood growth and obesity: development and validation of a 

quantitative mathematical model. LANCET DIABETES ENDOCRINOL. 2013; 1: 97–105. 

10. Hall K.D., Chow C.C. Estimating changes in free-living energy intake and its confidence interval. AM. J. CLIN. NUTR. 2011; 94: 

66–74. 

11. Thomas D.M., Ciesla A., Levine J.A., Stevens J.G., Martin C.K. A mathematical model of weight change with adaptation. 

MATH BIOSCI. ENG. 2009; 6: 873–887. 

12. Thomas D.M., Schoeller D.A., Redman L.A., Martin C.K., Levine J.A., Heymsfield S.B. A computational model to determine 

energy intake during weight loss. AM. J. CLIN. NUTR. 2010; 92: 1326–1331. 

13. Hall K.D. Predicting metabolic adaptation, body weight change, and energy intake in humans. AM. J. PHYSIOL. ENDOCRINOL. 

METAB. 2010; 298: E449–E466. 

14. Thomas D.M., Bouchard C., Church T., Slentz C., Kraus W.E., Redman L.M., et al. Why do individuals not lose more weight 

from an exercise intervention at a defined dose? An energy balance analysis. OBES. REV. 2012; 13: 835–847. 

15. Martin C.K., Heilbronn L.K., de Jonge L., DeLany J.P., Volaufova J., Anton S.D., et al. Effect of calorie restriction on resting 

metabolic rate and spontaneous physical activity. OBESITY (Silver Spring) 2007; 15: 2964–2973. 

16. Alper C.M., Mattes R.D. Effects of chronic peanut consumption on energy balance and hedonics. INT. J. OBES. RELAT. METAB. 

DISORD. 2002; 26: 1129–1137. 

17. Ioannidis J.P. Implausible results in human nutrition research. BMJ 2013; 347: f6698. 

18. Multisubject weight change predictor. Pennington Biomedical Research Center. Available at: http://www.pbrc.edu/researchand-faculty/calculators/mswcp/ (accessed on 28 September 2013). 

19. Team RDC. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria, 2009. 

20. Addington E. Aspartame- or Sugar-Sweetened Beverages. Effects on Food Appetites and Mood in Young Adults (Doctoral Dissertation). Kansas State University: Manhattan, KS, USA, 1998. 

21. Crandall K.J. The Effects of Exercise Intensity on Energy-Derived Macronutrient Intake, Caloric Intake, Body Composition, 

and Body Weight in the Overweight (Doctoral Dissertation). University of Northern Colorado: Greeley, CO, USA, 1999. 

22. Johnstone A.M., Horgan G.W., Murison S.D., Bremner D.M., Lobley G.E. Effects of a high-protein ketogenic diet on hunger, 

appetite, and weight loss in obese men feeding ad libitum. AM. J. CLIN. NUTR. 2008; 87: 44–55. 

23. Das S.K, Saltzman E., Gilhooly C.H., Delany J.P., Golden J.K., Pittas A.G., et al. Low or moderate dietary energy restriction 

for long-term weight loss: what works best. OBESITY 2009; 17: 2019–2024. 

24. Zachwieja J.J., Ezell D.M., Cline A.D., Ricketts J.C., Vicknair P.C., Schorle S.M., et al. Short-term dietary energy restriction 

reduces lean body mass but not performance in physically active men and women. INT. J. SPORTS MED. 2001; 22: 310–316. 

25. Moreira E.A., Most M., Howard J., Ravussin E. Dietary adherence to long-term controlled feeding in a calorie-restriction study 

in overweight men and women. NUTR. CLIN. PRACT. 2011; 26: 309–315. 

26. Leon A.S., Casal D., Jacobs D., Jr. Effects of 2,000 kcal per week of walking and stair climbing on physical fitness and risk 

factors for coronary heart disease. J. CARDIOPULM. REHABIL. 1996; 16: 183–192. 

27. Van Etten L.M., Westerterp K.R., Verstappen F.T., Boon B.J., Saris W.H. Effect of an 18-wk weight-training program on energy expenditure and physical activity. J. APPL. PHYSIOL/ 1997; 82: 298–304. 

28. Murphy M.H., Hardman A.E. Training effects of short and long bouts of brisk walking in sedentary women. MED. SCI. SPORTS

EXERC. 1998; 30: 152–157. 

29. Shaw I., Shaw B.S. Consequence of resistance training on body composition and coronary artery disease risk. CARDIOVASC. J. 

S. AFR. 2006; 17: 111–116. 

30. Kirk E.P., Washburn R.A., Bailey B.W., LeCheminant J.D., Donnelly J.E. Six months of supervised high-intensity low-volume 

resistance training improves strength independent of changes in muscle mass in young overweight men. J. STRENGTH COND. RES. 

2007; 21: 151–156. 

306 

* 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. 

** 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 dallison@uab.edu. 

References—Continued 

31. Whybrow S., Hughes D.A., Ritz P., Johnstone A.M., Horgan G.W., King N., et al. The effect of an incremental increase in exercise on appetite, eating behaviour and energy balance in lean men and women feeding. BR. J. NUTR. 2008; 100: 1109–1115. 

32. Guadalupe-Grau A., Perez-Gomez J., Olmedillas H., Chavarren J., Dorado C., Santana A., et al. Strength training combined 

with plyometric jumps in adults: sex differences in fat-bone axis adaptations. J. APPL. PHYSIOL. 2009; 106: 1100–1111. 

33. Alves J.G., Gale C.R., Mutrie N., Correia J.B., Batty G.D. A 6-month exercise intervention among inactive and overweight 

Favela-residing women in Brazil: the caranguejo exercise trial. AM. J. PUBLIC HEALTH 2009; 99: 76–80. 

34. Turner J.E., Markovitch D., Betts J.A., Thompson D. Nonprescribed physical activity energy expenditure is maintained with 

structured exercise and implicates a compensatory increase in energy intake. AM. J. CLIN. NUTR. 2010; 92: 1009–1016. 

35. Bell G.J., Harber V., Murray T., Courneya K.S., Rodgers W. A comparison of fitness training to a pedometer-based walking 

program matched for total energy cost. J. PHYS. ACT. HEALTH 2010; 7: 203–213. 

36. Vispute S.S., Smith J.D., Lecheminant J.D., Hurley K.S. The effect of abdominal exercise on abdominal fat. J. STRENGTH

COND. RES. 2011; 25: 2559–2564. 

37. Hornbuckle L.M., Liu P.Y., Ilich J.Z., Kim J.S., Arjmandi B.H., Panton L.B. Effects of resistance training and walking on cardiovascular disease risk in African-American Women. MED. SCI. SPORTS EXERC. 2012; 44: 525–533. 

38. Heydari M., Freund J., Boutcher S.H. The effect of high-intensity intermittent exercise on body composition of overweight 

young males. J. OBES. 2012; 2012: 480467. 

39. Thompson A.M., Mikus C.R., Rodarte R.Q., Distefano B., Priest E.L., Sinclair E., et al. Inflammation and exercise (INFLAME): Study rationale, design, and methods. CONTEMP. CLIN. TRIALS 2008; 29: 418–427. 

40. Church T.S., Earnest C.P., Thompson A.M., Priest E.L., Rodarte R.Q., Saunders T., et al. Exercise without weight loss does not 

reduce C-reactive protein: the INFLAME study. MED. SCI. SPORTS EXERC. 2010; 42: 708–716. 

41. Lammert O., Grunnet N., Faber P., Bjornsbo K.S., Dich J., Larsen L.O., et al. Effects of isoenergetic overfeeding of either carbohydrate or fat in young men. BR. J. NUTR. 2000; 84: 233–245. 

42. Martin A., Normand S., Sothier M., Peyrat J., Louche-Pelissier C., Laville M. Is advice for breakfast consumption justified? 

Results from a short-term dietary and metabolic experiment in young healthy men. BR. J. NUTR. 2000; 84: 337–344. 

43. Sabate J., Cordero-Macintyre Z., Siapco G., Torabian S., Haddad E. Does regular walnut consumption lead to weight gain? BR. 

J. NUTR. 2005; 94: 859–864. 

44. Whybrow S., Harrison C.L., Mayer C., James Stubbs R. Effects of added fruits and vegetables on dietary intakes and body 

weight in Scottish adults. BR. J. NUTR. 2006; 95: 496–503. 

45. Whybrow S., Mayer C., Kirk T.R., Mazlan N., Stubbs R.J. Effects of two weeks’ mandatory snack consumption on energy intake 

and energy balance. Obesity 2007; 15: 673–685. 

46. Sheridan M.J., Cooper J.N., Erario M., Cheifetz C.E. Pistachio nut consumption and serum lipid levels. J. AM. COLL. NUTR. 

2007; 26: 141–148. 

47. Casas-Agustench P., Lopez-Uriarte P., Bullo M., Ros E., Cabre-Vila J.J., Salas-Salvado J. Effects of one serving of mixed nuts 

on serum lipids, insulin resistance and inflammatory markers in patients with the metabolic syndrome. NUTR. METAB. CARDIOVASC. 

DIS. 2011; 21: 126–135. 

48. Maersk M., Belza A., Stodkilde-Jorgensen H., Ringgaard S., Chabanova E., Thomsen H., et al. Sucrose-sweetened beverages increase fat storage in the liver, muscle, and visceral fat depot: a 6-mo randomized intervention study. AM. J. CLIN. NUTR. 2012; 95: 

283–289. 

ATTACHMENT 3 

Myths, Presumptions, and Facts About Obesity * 

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.** 

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. 

307 

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, physicaleducation 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 comVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00313 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
308 

prehensive 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 intake 

or expenditure will produce large, longterm weight changes 

National health guidelines and reputable websites advertise that 

large changes in weight accumulate indefinitely after small sustained daily lifestyle modifications (e.g., walking for 20 minutes 

or eating two additional potato chips) 

Setting realistic goals in obesity treatment 

is important because otherwise patients 

will become frustrated and lose less 

weight 

According to goal-setting theory, unattainable goals impair performance and discourage goal-attaining behavior; in obesity 

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 associated with 

poorer long-term weight outcomes than is 

slow, gradual weight loss 

This notion probably emerged in reaction to the adverse effects of 

nutritionally insufficient very-low-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 diet readiness is important in helping patients who 

seek weight-loss treatment 

Many believe that patients who feel ready to lose weight are more 

likely to make the required lifestyle changes 

Physical-education classes in their current 

format play an important role in preventing or reducing childhood obesity 

The health benefits of physical activity of sufficient duration, frequency, and intensity are well established and include reductions in adiposity 

Breast-feeding is protective against obesity The belief that breast-fed children are less likely to become obese 

has persisted for more than a century and is passionately defended 

A bout of sexual activity burns 100 to 300 

kcal for each person involved 

Many sources state that substantial energy is expended in typical 

sexual 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. 

309 

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 breastfed 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 stimVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00315 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
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ulation 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-tomid-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) breakfast is 

protective against obesity 

Skipping breakfast purportedly leads to overeating later in the 

day 

Early childhood is the period during which 

we learn exercise and eating habits that 

influence our weight throughout life 

Weight-for-height indexes, eating behaviors, and preferences that 

are present in early childhood are correlated with those later in 

life 

Eating more fruits and vegetables will result in weight loss or less weight gain, regardless of whether one intentionally 

makes any other behavioral or environmental changes 

By eating more fruits and vegetables, a person presumably spontaneously eats less of other foods, and the resulting reduction 

in calories is greater than the increase in calories from the fruit 

and vegetables 

Weight cycling (i.e., yo-yo dieting) is associated with increased mortality In observational studies, mortality rates have been lower among 

persons with stable weight than among those with unstable 

weight 

Snacking contributes to weight gain and 

obesity 

Snack foods are presumed to be incompletely compensated for at 

subsequent meals, leading to weight gain 

The built environment, in terms of sidewalk 

and park availability, influences obesity 

Neighborhood-environment features may promote or inhibit physical 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 conVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00316 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
311 

founding 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 large role, 

heritability is not destiny; calculations 

show that moderate environmental 

changes can promote as much weight loss 

as the most efficacious pharmaceutical 

agents available 26

If we can identify key environmental factors and successfully influence them, we can achieve clinically significant reductions in 

obesity 

Diets (i.e., reduced energy intake) very effectively reduce weight, but trying to go 

on a diet or recommending that someone 

go on a diet generally does not work well 

in the long-term 27

This seemingly obvious distinction is often missed, leading to erroneous conceptions regarding possible treatments for obesity; 

recognizing this distinction helps our understanding that energy reduction 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 weight loss, 

an increased level of exercise increases 

health 28

Exercise offers a way to mitigate the health-damaging effects of 

obesity, even without weight loss 

Physical activity or exercise in a sufficient 

dose aids in long-term weight maintenance 28–29

Physical-activity programs are important, especially for children, 

but for physical activity to affect weight, there must be a substantial quantity of movement, not mere participation 

Continuation of conditions that promote 

weight loss promotes maintenance of 

lower weight 30

Obesity is best conceptualized as a chronic condition, requiring 

ongoing management to maintain long-term weight loss 

For overweight children, programs that involve the parents and the home setting 

promote greater weight loss or maintenance 31

Programs provided only in schools or other out-of-home structured settings may be convenient or 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 meal-replacement products promote greater weight 

loss 32

More structure regarding meals is associated with greater weight 

loss, as compared with seemingly holistic programs that are 

based on concepts of balance, variety, and moderation 

Some pharmaceutical agents can help patients achieve clinically meaningful 

weight loss and maintain the reduction 

as long as the agents continue to be 

used 33

While we learn how to alter the environment and individual behaviors to prevent obesity, we can offer moderately effective 

treatmentto obese persons 

In appropriate patients, bariatric surgery 

results in long-term weight loss and reductions in the rate of incident diabetes 

and mortality 34

For severely obese persons, bariatric surgery can offer a lifechanging, and in some cases lifesaving, treatment 

* We classify the listed propositions as facts because there is sufficient evidence to consider 

them empirically proved. 

312 

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 quasiexperiments 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. 

313 

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. 

References 

1. Federal Trade Commission. Dietary supplements: an advertising guide for industry. April 2001 (http://business.ftc.gov/documents/bus09-dietary-supplementsadvertising-guide-industry#endnotes). 

2. Hill A.B. The environment and disease: association or causation? PROC. R. SOC. MED. 1965; 58: 295–300. 

3. Taubes G. Epidemiology faces its limits. SCIENCE 1995; 269: 164–9. 

4. Fairman K.A. Why hypotheses informed by observation are often wrong: results of randomized controlled trials challenge chronic disease management strategies based on epidemiological evidence. J. MANAG. CARE. PHARM. 2011; 17: 224–31. 

5. Hall K.D. Predicting metabolic adaptation, body weight change, and energy intake in humans. AM. J. PHYSIOL. ENDOCRINOL. 

METAB. 2010; 298: E449–66. 

6. Thomas D.M., Martin C.K., Heymsfield S., Redmon L.M., Schoeller D.A., Levine J.A. A simple model predicting individual 

weight change in humans. J. BIOL. DYN. 2011; 5: 579–99. 

7. Thomas D.M., Schoeller D.A., Redman L.A., Martin C.K., Levine J.A., Heymsfield S.B. A computational model to determine energy intake during weight loss. AM. J. CLIN. NUTR. 2010; 92: 1326–31. 

8. Linde J.A., Jeffery R.W., Levy R.L., Pronk N.P., Boyle R.G. Weight loss goals and treatment outcomes among overweight men 

and women enrolled in a weight loss trial. INT. J. OBES. (Lond) 2005; 29: 1002–5. 

9. Astrup A., Rossner S. Lessons from obesity management programmes: greater initial weight loss improves long-term maintenance. OBES. REV. 2000; 1: 17–9. 

10. Nackers L.M., Ross K.M., Perri M.G. The association between rate of initial weight loss and long-term success in obesity treatment: does slow and steady win the race? INT. J. BEHAV. MED. 2010; 17: 161–7. 

11. Fontaine K.R., Wiersema L. Dieting readiness test fails to predict enrollment in a weight loss program. J. AM. DIET. ASSOC. 

1999; 99 :664. 

12. Kriemler S., Zahner L., Schindler C., et al. Effect of school based physical activity programme (KISS) on fitness and adiposity 

in primary schoolchildren: cluster randomised controlled trial. BMJ 2010; 340: c785. 

13. Dobbins M., De Corby K., Robeson P., Husson H., Tirilis D. School-based physical activity programs for promoting physical activity and fitness in children and adolescents aged 6–18. COCHRANE DATABASE SYST. REV. 2009; 1: CD007651. 

14. Horta B.L., Bahl R., Martine ́s J.C., Victora C.G. Evidence of the long-term effects of breastfeeding: systematic reviews and 

meta-analyses. Geneva: World Health Organization, 2007. 

15. Casazza K., Fernandez J.R., Allison D.B. Modest protective effects of breast-feeding on obesity: is the evidence truly supportive? 

NUTR. TODAY 2012; 47: 33–8. 

16. Kramer M.S., Matush L., Vanilovich I., et al. Effects of prolonged and exclusive breastfeeding on child height, weight, adiposity, and blood pressure at age 6.5 y: evidence from a large randomized trial. AM. J. CLIN. NUTR. 2007; 86: 1717–21. 

17. Gillman M.W. Breastfeeding and obesity—the 2011 scorecard. INT. J. EPIDEMIOL. 2011; 40: 681–4. 

18. Jette ́ M., Sidney K., Blu ̈mchen G. Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of 

functional capacity. CLIN. CARDIOL. 1990; 13: 555–65. 

19. Bohlen J.G., Held J.P., Sanderson M.O., Patterson R.P. Heart rate, rate-pressure product, and oxygen uptake during four sexual activities. ARCH. INTERN. MED. 1984; 144: 1745–8. 

20. Schlundt D.G., Hill J.O., Sbrocco T., Pope-Cordle J., Sharp T. The role of breakfast in the treatment of obesity: a randomized 

clinical trial. AM. J. CLIN. NUTR. 1992; 55: 645–51. 

21. Brisbois T.D., Farmer A.P., McCargar L.J. Early markers of adult obesity: a review. OBES. REV. 2012; 13: 347–67. 

22. Rolls B.J., Ello-Martin J.A., Tohill B.C. What can intervention studies tell us about the relationship between fruit and vegetable consumption and weight management? NUTR. REV. 2004; 62: 1–17. 

23. Vasselli J.R., Weindruch R., Heymsfield S.B., et al. Intentional weight loss reduces mortality rate in a rodent model of dietary 

obesity. OBES. RES. 2005; 13: 693–702. 

24. Whybrow S., Mayer C., Kirk T.R., Mazlan N., Stubbs R.J. Effects of two weeks’ mandatory snack consumption on energy intake 

and energy balance. OBESITY (Silver Spring) 2007; 15: 673–85. 

25. Ferdinand A., Sen B., Rahurkar S., Engler S., Menachemi N. The relationship between built environments and physical activity: a systematic review. AM. J. PUBLIC HEALTH 2012; 102(10): e7–e13. 

26. Hewitt J.K. The genetics of obesity: what have genetic studies told us about the environment. BEHAV. GENET. 1997; 27: 353–8. 

27. Heymsfield S.B. Energy intake: reduced as prescribed? AM. J. CLIN. NUTR. 2011; 94: 3–4. 

28. Carroll S., Dudfield M. What is the relationship between exercise and metabolic abnormalities? A review of the metabolic syndrome. SPORTS MED. 2004; 34: 371–418. 

29. Wu T., Gao X., Chen M, van Dam R.M. Long-term effectiveness of diet-plus-exercise interventions vs. diet-only interventions for 

weight loss: a meta-analysis. OBES. REV. 2009; 10: 313–23. 

30. Middleton K.M., Patidar S.M., Perri M.G. The impact of extended care on the long-term maintenance of weight loss: a systematic review and meta-analysis. OBES. REV. 2012; 13: 509–17. 

31. McLean N., Griffin S., Toney K., Hardeman W. Family involvement in weight control, weight maintenance and weightloss 

interventions: a systematic review of randomised trials. INT. J. OBES. RELAT. METAB. DISORD. 2003; 27: 987–1005. 

32. Wing R.R., Jeffery R.W. Food provision as a strategy to promote weight loss. OBES. RES. 2001; 9: Suppl. 4: 271S–275S. 

33. Wright S.M., Aronne L.J. Obesity in 2010: the future of obesity medicine: where do we go from here? NAT. REV. ENDOCRINOL. 

2011; 7: 69–70. 

34. Sjo ̈stro ̈m L., Lindroos A.-K., Peltonen M., et al. Lifestyle, diabetes, and cardiovascular risk factors 10 years after bariatric surgery. N. ENGL. J. MED. 2004; 351: 2683–93. 

314 

* 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 Vo ̈gele C. (2015) Goals in nutrition science 

2015–2020. FRONT. NUTR. 2:26. doi: 10.3389/fnut.2015.00026 [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] E ́ cole 

Polytechnique Fe ́de ́rale 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 Universita ̈t 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, E ́ cole Polytechnique Fe ́de ́rale 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 Universita ̈t

Mu ̈nchen, Freising, Germany, [22] Research Unit INSIDE, Institute for Health and Behaviour, 

University of Luxembourg, Luxembourg, Luxembourg. * Correspondence: Johannes le Coutre johannes.lecoutre@frontiersin.org, johannes.lecoutre@rdls.nestle.com. 

References—Continued 

35. Hill J.O., Wyatt H.R., Peters J.C. Energy balance and obesity. Circulation 2012; 126: 126–32. 

36. Hall K.D., Heymsfield S.B., Kemnitz J.W., Klein S., Schoeller D.A., Speakman J.R. Energy balance and its components: implications for body weight regulation. AM. J. CLIN. NUTR. 2012; 95: 989–94. 

37. Lilienfeld S.O., Ammirti R., Landfield K. Giving debiasing away: can psychological research on correcting cognitive errors promote human welfare? PERSPECT. PSYCHOL. SCI. 2007; 4: 390–8. 

38. Shermer M. Why people believe weird things: pseudoscience, superstition, and other confusions of our time. 3rd ed. London: 

Souvenir Press, 2007. 

39. Kahneman D. Thinking fast and slow. New York: Farrar, Straus and Giroux, 2011. 

40. Whately R. Elements of logic. 9th ed. London: Longman, Greens, 1875. 

Copyright © 13 Massachusetts Medical Society.

ATTACHMENT 4 

Goals in Nutrition Science 2015–2020 * 

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 Vo ̈gele [22]

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 develVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00320 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
315 

opment 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. 

316 

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 from poor 

measurement 

• Self-reported energy intake (33, 118, 119)b (34) c (32) d

• Self-reported weights (120) b (121, 122) d

Errors involving inappropriate choice of or 

incorrect study design 

• Cluster randomized trials with no degrees of freedom (123) c

• 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

• Gratuitous replication (35) d

Errors in statistical analyses • Inappropriate baseline testing in parallel groups RCTs (127) c

(128) d

• Failure to appropriately manage missing data (129) c (130, 131) d

• Not accounting for clustering in cluster randomized trials (132,

133) b (134, 135) c (136) d

Errors involving insufficient transparency 

in choices made about how to analyze and 

present the data 

• Changing endpoints in a study (137) b (138) d

• Excessive or unacknowledged multiple testing [called p-hacking,(139) d investigator degrees of freedom,(140) d or p-value fiddling,(141) d among other names](142) c (143) b

Errors of misleadingly describing past literature • Selectively citing only the part of a study that supports a hypothesis (35) d

• Perpetuating citations from previous researchwithout confirming the original source (144) b

Errors that distort the scientific record by 

publishing studies as a function of study 

outcomes 

• Publication bias (145) b (23, 146) d

Errors of interpretation or communication • Inappropriate use of causal language (24, 35) d

• Exaggerating or mis-describing results (35) d

• Highlighting benefits of treatment when the effects were nonsignificant (i.e., spin)(147) d

• Issuing misleading press-releases (148) d

Errors of logic and mathematics • Unreasonable linear extrapolations (e.g., 3,500 kcal 

rule)(149–150) b

a Errors, examples, and references were identified in a manner neither systematic nor comprehensive. bDenotes references correcting or commenting on specific errors. 

c references in which the error in question occurred. 

317 

dDenotes references that provide tutorials on avoiding or overviews of the errors. 

Several initiatives are going to be important in the coming years to improve nutrition as a science. First is classifying errors that exist in the nutrition literature. 

Just as Mendeleev’s Periodic Table of the Elements led to increased understanding 

of chemistry and Linnaeus’ taxonomy of life led to a framework for the study of biology, if we can develop a ‘‘pathology’’ or classification of these errors, we may be better able to quantify the situation, identify patterns, develop an understanding of origins, and ultimately reduce the occurrence and severity of these errors. In our nonsystematic study of these issues, we see a number of categories of common errors 

(Table 1). We refer to them as errors without making any inference that they are 

intentional or unintentional errors. 

Second, there is a general movement in science for ‘‘Transparency and Openness 

Promotion,’’ formalized in ‘‘The TOP Guidelines’’.(30) The guidelines recognize eight 

standards: citation, data transparency, analytic methods (code) transparency, research materials transparency, design and analysis transparency, preregistration of 

studies, preregistration of analysis plans, and replication. These standards aim to 

improve the communication of science, allowing improved understanding and 

replicability of results. Because the TOP Guidelines are being adopted across fields 

of science, the field of nutrition will not have to act in isolation to improve its scientific practices. Instead, we can build on and work with the minds and resources 

coming from a spectrum of scientific inquiry. Indeed, Frontiers in Nutrition was one 

of the initial signatories. 

Third, there is a need to develop sound methodology for evaluating nutrition and 

diet in free-living research participants. Issues are continually documented with 

self-report diet methodology,(31–33) and yet dietary recommendations depend heavily 

on dietary recall data.(34) Similarly, although existing nutrition-related health 

hypotheses can be investigated using randomized controlled trials (pragmatic or explanatory), the field often relies on ordinary association tests using observational 

data to quantify evidence (35–36) that policy-makers may then use to create policies 

or guidelines. The needs here are twofold: to develop and implement study designs 

that lie in the causality spectrum between ordinary association tests and randomized controlled trials (37–38) and to develop objective, reliable data on dietary patterns 

and nutrient status.(31–33)

We believe that by recognizing and acknowledging these problems, we also recognize and acknowledge that our field can do better. This will pave the way toward 

constructive efforts to reduce such problems and to ultimately improve the scientific 

foundations of nutrition science. 

Building the Foundation: Procurement of Relevant Measures and Big Data 

Analysis 

(Martin Kussmann, Josep Bassaganya-Riera, Raquel Hontecillas, Tapan Mehta, and 

Chor San H. Khoo) 

Diet is considered a key environmental factor for maintaining health and preventing disease. As such, we need to better understand the interactions of nutrition 

and lifestyle with an individual’s genetic makeup in order to delay or prevent metabolic and cognitive decline. Nutrition science is therefore undergoing a paradigm 

shift to better leverage the potential of nutrigenomics, a discipline that is already 

transforming the field.(39) To achieve this, the field will need to transform its current 

approach to research and implementation actions, and to take advantage of emerging advances in other disciplines—research designs, methods, new technologies, big 

data analysis, and bioinformation sharing. 

The conceptual basis of gene—environmental interactions require not only research and technology, but also the cross-fertilization of disciplines: genomics will 

encompass other-omics, and nutrition research will need to take on a holistic or system biology approach rather than just nutrients, ingredients, or genes. Nutrition 

science now encompasses more than the classic reductionist and descriptive approaches to more quantitative and systems-level approaches.(40) Translational research to maintain health and prevent or delay disease onset requires a 

transdisciplinary approach that embraces the complexity of human individuality in 

a rapidly changing environment. Nutrigenomics fuels this research by investigating 

how genomic and epigenomic individuality predisposes dietary, health, and disease 

responses. It also influences how an individual’s genome expresses itself at different 

omic levels (proteomics, metabolomics, lipidomics) in response to environmental factors, including nutrition. Molecular phenotyping of humans over time and across 

healthy and safe exposures and challenges have thus been proposed.(41)

Both the ongoing prevalence of malnutrition and the increasing incidence of 

nutrition- and lifestyle-related chronic diseases require comprehensive characterizaVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00323 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
318 

tion of the complex interactions between environment and genetic makeup. Systems 

thinking in human nutrition, environment, and health requires improvement and 

translational thinking in three areas: 

(a) In vitro and in vivo models: a systems approach to human health implies 

rethinking of in vitro and in vivo models with regard to their translatability 

into human phenotypes: natural human cell models and panels of rodent 

strains should complement cancer cell lines and single rodent strains. 

(b) Human intervention study designs: classical case/control designs of human 

clinical/nutritional intervention studies should be complemented by longitudinal crossover studies, in which every subject is one’s own case and control. 

Human clinical study subjects should not only be assessed at homeostasis, but 

also during a challenge to, and restoration of, homeostasis. 

(c) Tools for molecular phenotyping and capturing of human diet and lifestyle: 

nutrigenomic studies have typically been technology-driven rather than technology-rooted. Normative science methods and approaches need to be complemented by more comprehensive systems biology-based investigations deploying a multitude of omic platforms in an integrated fashion.(41) While comprehensive and quantitative omics are rapidly progressing in terms of data 

generation, quantitative capture and monitoring of diet and lifestyle have 

lagged behind. Non-invasive technologies are now providing more attractive 

and precise image- and webbased or body-wearable consumer/research interfaces.(42) The bottleneck in knowledge generation has moved from (omics and 

clinical) data acquisition to processing, visualization, and interpretation. Innovative tools and methods for statistical treatment and biological network 

analysis are now at the forefront of nutritional and biomedical sciences.(43)

To achieve this transformation and advancement of nutritional science, it is critical to connect researchers from all disciplines conducting direct or indirect research 

in the areas, e.g., (gen) omics, clinicals, dietetics, food science and technology, physiology, epidemiology, bioengineering, analytics, biomathematics. A transdisciplinary 

approach needs to be considered—enabling a spectrum of communicating and sharing from fundamental laboratory research, patient- and consumer-relevant outputs 

from personalized dietary/nutritional counseling to monitoring/diagnostics. Progress 

in advancing nutrigenomic interventions for consumers and patients can only be accelerated if nutrition research is broadened to include quantitative, holistic, and molecular sciences.(44)

‘‘Let the food be your medicine, and medicine be your food,’’ a statement attributed to Hippocrates, the father of Western Medicine, delineates the impact of nutrition in human health and disease. Indeed, several decades of research at the interface of nutrition and immunology demonstrate that infectious, immune-mediated 

and metabolic diseases are safely and effectively preventable through dietary interventions. Nonetheless, there is a major disconnect between the description of nutrition-based protection from disease and an insufficient mechanistic understanding at 

the systems-level of the complex network interactions by which nutrition mediates 

clinical protection. As a result, a comprehensive understanding of the mechanisms 

of action underlying the actions of nutritional interventions and the combinatorial 

effects of nutrients (i.e., synergistic, antagonistic, or additive) at the systems-level 

remains largely unknown. As about 70% of the immune system is located in the gastrointestinal tract since the gut mucosa houses the largest repertoire of immune 

cells and commensal microbiota that symbiotically coexist to elicit protective immunity, studying nutritional immunology of the gut mucosa is incredibly important.(45)

Coupling host-nutrient-microbiota actions, enabled through computational modeling 

of the gastrointestinal tract(46–50) with systems immunology frameworks has the potential to predict combinatorial outcomes of nutrient-microbiota-immune system 

interactions and advance toward a comprehensive systems-level mechanistic understanding of how nutrition and foods prevent disease. Computational models of nutritional immunology that funnel omics and cellular data judiciously, coupled with systems biology models of the underlying disease/organ, will bridge the connection between traditional methods of nutritional immunology research and their effect on 

the whole organism, which will enhance mechanistic insights and translational 

value. Over 163 nutrition themed systems biology markup language models (SBML) 

are already available in the Biomodels database.(51) In summary, applying the 

iterative systems biology cycle of model building, calibration, refinement, and validation in nutritional immunology research has the potential to accelerate the discovery 

of novel network biomarkers and systems-level mechanistic understanding of the action of dietary components on immuneresponses. 

319 

There has been an explosion in data collection and aggregation, some of which can 

be used for public health purposes, including obesity and nutrition-related research. 

Consequently, ample opportunities emerge to utilize ‘‘big data’’ in the pursuit of interesting outcomes and effectiveness studies related to nutrition and obesity using 

techniques such as quasi-experimental approaches. These approaches, when assumptions are satisfied, are intermediate between ordinary association tests and 

randomized controlled trials (37) in terms of presenting evidence for causality. In this 

article, the term ‘‘big data,’’ which is often used subjectively, refers to very large 

amounts of data: structured and unstructured that may also increase over time rapidly.(52) These types of data are collected by both the public and private sectors and 

increasingly require a distributed architecture to manage them efficiently. Big data 

analysis has generally referred to the confluence of statistical, machine learning and 

computational approaches to synthesize and analyze these large amounts of data. 

Administrative data, such as micro-level data aggregated by governments as well 

as private companies, can be used to evaluate the effectiveness of pharmacological 

and surgical interventions. In fact, private companies have started collecting unprecedented amounts of data with some companies specializing in data linkages. For 

example, companies such as Optum not only aggregate claims data from private insurance companies but are able to provide linked clinical data from the corresponding electronic health records (EHR). Data linkages are an extremely powerful tool since they allow researchers to answer questions that are otherwise not accessible using a single data source. For example, claims data do not provide information about the height and weight of an individual, but the linked clinical data 

do. Similarly, the increasing availability of EHR data and the initiatives to link 

these EHR data with genomic data can enable us to pursue a variety of studies, 

including pharmacogenetic and precision medicine studies. One of the challenges in 

accessing and leveraging ‘‘big data’’ is the resources, including the associated cost 

of purchasing the data, especially from private companies. Collaborations between 

industry and academic researchers are essential to fully exploit the data and to 

overcome logistical challenges.(53–54)

So far, big data analysis has primarily focused on high-dimensional prediction 

models. The data mining and statistical toolkit for such approaches includes, but is 

not limited to, techniques such as boosting, random forests, classification and regression trees, and lasso-like penalized regression models.(53) While randomized control trials are considered gold standards, there are a variety of methods and designs 

that may allow us to generate evidence that may lie in the spectrum between purely 

association and definitively causal. Coupled with ‘‘big data’’ is an opportunity to estimate a degree of causality using techniques such as high-dimensional propensity 

score and differential comparison approaches to provide evidence that is indicative 

of causality.(55–56) There is also a potential to use instrument variable approaches, 

used commonly in health policy studies, by identifying appropriate instruments from 

‘‘big data.’’ Recent attempts to develop methods that enable to provide a degree of 

causal evidence are very encouraging and can allow us to maximize the potential 

of ‘‘big data’’.(57–58)

Authenticity and Safety of Foods 

(Michael Rychlik) 

The authenticity of food is generally related to one or more of the following attributes: geographic origin, type of agricultural production, species and kind of raw 

materials, or certain process qualities such as sustainability or ecological foot print. 

Regularly uncovered crises of food adulteration underline the sensitivity of consumers to this issue. Apart from meat, foods that are often adulterated are olive 

oil, fish, organic foods, spices, tea, cocoa, coffee, and nuts. 

In recent years, there has been tremendous progress in high-resolution methods 

to elucidate the molecular fingerprint of food. On the genetic scale, apart from classical polymerase chain reaction, new developments of isothermal amplifications or 

next generation sequencing will enable more accurate identification of species. 

On the protein level, specific biomarker peptides can be used. For a fingerprint 

of metabolites, the new methods of non-targeted and targeted metabolomics already 

allow a specific authentication. In this field, the methods currently showing the best 

resolution are Fourier transform ion cyclotron mass spectrometry (FT/ICR–MS) or 

nuclear magnetic resonance (NMR) spectroscopy.(59) These new methodologies generate ‘‘big data,’’ from which the relevant information is only accessible when applying novel bioinformatics approaches. 

Regarding food safety, microbiological decay and foodborne infections still play an 

important role. However, contaminants also endanger the safety of all links in the 

whole food chain. The recent discoveries of process contaminants encompass simple 

molecules, such as acrylamide, furan, benzene, styrene, as well as more complex 

320 

compounds such as 3-monochloropropane-1,2-diol (MCPD) esters. An end of new discoveries cannot be foreseen yet and we may assume that the sum of all these contaminants has a significant impact on life-style diseases such as cancer. Further 

new contaminants arise from packaging materials such as mineral oil saturated hydrocarbons (MOSH) or mineral oil aromatic hydrocarbons (MOAH), and pollutants 

from the environment such as the polyfluorinated alkyl substances (PFAS). Moreover, the historic toxin arsenic is more relevant than ever as rice and rice products 

are often contaminated and the mechanisms of arsenic carcinogenicity are still 

under controversial discussion. 

Generally, risk assessment of food contaminants or residues is predominantly performed on single compounds. However, almost completely missing is an assessment 

of the combined effects of toxins, be it within one group of compounds or spanning 

various structural groups. The current concept for assessing combinatorial effects is 

that of cumulative assessment groups (CAGs), which, e.g., assess the cumulative potency corrected dose of acute reference doses (ARfD) for pesticides showing the same 

mode of toxic action.(60) However, this approach is still preliminary and lacks comprehensive confirmation. 

The Science Behind Food-Related Behavior in Humans 

(Adrian Meule, Chor San H. Khoo, and Claus Vo ̈gele) 

Numerous environmental, social, and individual factors influence human food 

choice and intake.(61) In Western and Westernized societies, household expenditures 

and dietary energy availability decreased for unprocessed or minimally processed 

foods in the last decades while they increased for convenience foods and processed 

products.(62–63) An environment where there is easy and frequent accessibility to 

food, and where cues signaling food are ubiquitous, requires constant self-monitoring and -regulation in order to prevent or manage weight gain.(61) This, however, 

can be a highly effortful endeavor, leading many people to struggle with long-term 

weight maintenance. As evident from data from the last century, these self-regulatory efforts are made more difficult by increased consumption of energy-dense palatable foods and ingredients (e.g., sugar, fat, and salt).(64) As a result, some have 

argued that these foods might have an addictive potential and that a subset of individuals who have difficulties in controlling consumption of these foods may be addicted to them.(65–68)

In the scientific literature, the association between food and addiction and the actual use of the term food addiction has a long history, dating back to the 1950s and 

even earlier times.(69–70) Not until recently, however, have researchers tried to more 

precisely define what is meant by food addiction and to systematically investigate 

its validity, as a consequence of which the number of publications, including the 

term food addiction, increased substantially over the past 5–6 years.(65, 71) In humans, research on food addiction has been promoted by the Yale Food Addiction 

Scale (YFAS), a self-report questionnaire developed in 2009, which measures symptoms of addiction-like eating based on the diagnostic criteria for substance dependence as outlined in the fourth version of the Diagnostic and Statistical Manual of 

Mental Disorders (DSM–IV).(72) Since 2013, these diagnostic criteria have been revised in the fifth version of the DSM and a new version of the YFAS, which has 

been adapted accordingly, is currently under way.(73)

Although research on food addiction is growing, it remains a controversial and debated topic with many researchers questioning the validity of the food addiction concept based on conceptual considerations or physiological mechanisms.(74–78) To address these issues, more and better human studies are needed to resolve questions 

related to, for example, whether animal models of food addiction are transferable 

to human eating behavior.(79–80) These controversies, in particular, lead us to argue 

that food addiction research in humans is still in its infancy, that it would be premature to conclude that some foods are addictive, and that research efforts to clarify 

this will further increase in the years to come. 

There are numerous avenues for future directions, which may include, but are not 

limited to: how do we define and harmonize definitions of food addiction? What are 

the implications of changes in the diagnostic criteria for substance dependence in 

the DSM–5 for food addiction? (73) Are all addiction criteria (as described in the 

DSM–5) equally applicable to human eating behavior? If not, does this obliterate the 

concept of food addiction? (81) How can food addiction be measured in humans other 

than using the YFAS and which methodological improvements need to be made to 

better design human behavior studies, including randomized controlled trials? (72)

How relevant is the concept of food addiction for the treatment of obesity or binge 

eating and in public policy making? If it is relevant, how can it best be implemented? (70, 82) What are the disadvantages (if any) of the concept of food addiction? (83–85) How can animal models of addiction-like eating be improved to more speVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00326 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
321 

cifically reflect relevant processes in humans? (86) Which foods are possibly addictive? (87) Can addiction-like eating actually be reduced to the addictive effects of substances or should the discussion about ‘‘food addiction’’ rather be replaced by a discussion on ‘‘eating addiction’’? (88)

The Molecular and Physiological Science Underlying Nutrition and Brain 

Health 

(Pierre Magistretti, Johannes le Coutre, and Suzanne L. Dickson) 

Cognitive decline, dementia, Alzheimer’s disease, and other age-related neurological diseases are on a rise in high income countries as well as in low and middle 

income countries.(89) Achieving and maintaining brain health is a lifelong endeavor 

with identifiable targets that are specific for each period in a lifetime. Thus, targeting cognitive development in the early phases of life and preventing cognitive decline during aging are priorities for any preventive or interventional approach. 

While pharmacological approaches can only be envisioned for brief periods of time 

and, for the most part, have been unsuccessful, nutritional approaches are 

implementable for extended periods of time. Initiatives on brain health should incorporate a nutrition-based approach that can be implemented throughout the different 

phases of life. 

In order to identify valid nutritional approaches for brain health, it is important 

to better understand the mechanisms that are at the basis of brain energy metabolism regulation. Key advances have been made in recent years in the identification 

of the molecular and cellular mechanisms that regulate the delivery of energy to active neurons. In particular, an active metabolic exchange has been characterized between neurons and astrocytes with specific molecular steps that can become targets 

for nutritional interventions. 

For the identification of the efficacy of such nutritional interventions, means for 

appropriate monitoring of markers need to be defined. This can be achieved by monitoring with brain imaging techniques, structural markers with morphometric approaches and myelination with MR as well as functional activation with fMRI, PET, 

EEG, and MRS, coupled with neuropsychological tests monitoring cognitive performance, motivation, and attention. The utility of these technologies goes beyond brain 

health and many of these approaches are being used to validate, in humans, the 

neuroscience of nutrition that, so far, has only been conducted in rodent models.(90–91)

There is no doubt that targeting the molecular steps of brain metabolism with nutritional interventions and monitoring their structural and functional outcomes in 

vivo in humans, in particular regarding cognitive performance, represents a promising approach for developing nutritional interventions for achieving brain health 

that can be maintained on the long term. Meaningful nutrient intake and nutritional intervention likely has an impact on the development of cognitive and behavioral performance measures, thereby determining our health span throughout life. 

Brain imaging studies on infants demonstrate how breast milk promotes healthy 

neural growth and early white matter development.(92)

Nutrients also engage brain pathways linked to metabolic control, appetite, and 

food-linked behaviors. There has been a general expectation that it must be possible 

to use food formulation/composition to control how much and what we eat by altering the satiating and/or reward value of food combinations.(93–94) Currently, we lack 

a sufficient scientific evidence base that certain ‘‘unhealthy’’ foods fall short of 

‘‘healthy’’ foods in their ability to induce satiation, limit hunger, or reduce hedonic 

over-eating. Moreover, it has not yet been demonstrated that any food or combination of foods has beneficial effects on appetite and energy intake of sufficient duration or magnitude to impact on body weight or metabolic health.(95) This is a new 

and emerging field for which major advances are likely to progress through a better 

understanding of how nutrients communicate with the appetite-regulatory brain 

networks. Nutrient-brain communication could be direct but likely engages intrinsic 

physiological control systems. For example, when we eat, sensing mechanisms in the 

gut signal information about the amount and content of the food to the brain by 

nervous and endocrine afferent signals. Indeed, gut-derived hormones such as 

ghrelin and glucagon-like peptide 1 communicate with hypothalamic and brainstem 

areas linked to energy balance but also to brain areas processing the reward value 

of food and even brain areas linked to emotion and cognition.(96–97) Thus, while it 

seems clear that appetite-regulating hormones have a capacity to redirect behaviors 

important for governing how much and what we eat, the extent to which nutrients 

can control these behaviors through engaging intrinsic endocrine signals remains to 

be elucidated. 

A related question is whether specific nutrients or food combinations can act on 

the brain to reinforce their own intake, leading to addictive-like over-consumption. 

322 

As reviewed recently (88) and as mentioned already in the previous section, it is very 

difficult to demonstrate in humans or rodents that foods act on the brain in a manner similar to addictive drugs, causing individuals to become addicted to them. It 

was suggested therefore that the term ‘‘eating addiction’’ rather than ‘‘food addiction’’ should be used to better describe addiction-like behavioral over-eating disorders. If it becomes possible to diagnose this patient group, e.g., through combining 

questionnaires about addictive-like behavior for food with brain imaging,(98–99) there 

will be a large public health impact on treatment and prevention strategies. Additionally, industrial stakeholders and politicians will need to find solutions to circumvent or treat eating addiction.(88)

The Science of the Human Microbiome 

(Dietrich Knorr and Chor San H. Khoo) 

The human body harbors over eight million microbial genes, over 10,000 species, 

and plays host to over a trillion microbes. Microbial cells outnumber human cells 

by a factor of 10.(100) As a result, there is considerable interest to better define and 

understand the microbial role in host physiology, health, and disease etiology. In the 

last decade, there has been a tremendous surge in microbiome research funded by 

programs such as the Human Microbiome Project (HMP) and the MetHIT Program. 

Advancing new and multiple technological approaches—whole genome sequencing, 

metagenomics, high-throughput-analysis, proteomics, transcriptomics, cultivation, 

metabolomics, and bioinformatics—has led to new insights into microbial variety 

and abundance in 15–18 body sites, including the oral cavity, skin, airway, gut, and 

vagina, from 242 healthy participants in the largest cohort study to date. Findings 

from this research were published in two seminal papers in 2012 by the Human 

Microbiome Consortium.(100–101) The HMP study has the largest collection of data on 

abundance and variety of the human microbiome, with 5,177 unique microbial taxonomic profiles from 16S ribosomal RNA genes, more than 3.5 terabases of 

metagenomic sequence, and 800 reference strains isolated and sequenced.(100) Noteworthy observations from the HMP study are outlined in Table 2.(102)

Table 2: Variation in Microbial Ecology Among Individuals (102)

Each person’s microbiome is unique and no two individuals have the same microbiome (102) However, in spite 

of individual microbial differences, different individuals can still be considered healthy 

Microbial communities across varying body regions may predict some characteristics such as breast fed history and educational level 

Microbial communities from different body regions from an individual were predictive for others. For example, 

the oral community can be used to predict the gut community 

Overall, low relative numbers of pathogens have been observed 

Strong site specialization but considerable variation in diversity and abundance of each habitat’s signature 

microbes among subjects 

Strong functional stability. This means that while the microbial compositions were widely different, the 

functionality is similar. This suggests flexibility to develop microbial communities that can provide similar 

performance 

Wide variation in patterns of alpha and beta diversity (alpha-diversity within a site; beta diversity among 

subjects) 

Correlations between ethnicity and microbiome composition across all body habitats 

A positive correlation of vaginal pH to microbial diversity (higher pH having higher diversity) 

An association of age with skin microbiome-associated metabolic pathways and oral microbiome composition 

Translating learnings from emerging microbiome and health research presents exciting opportunities for future food and nutrition development. The use of microbes 

in food product development is not new. Fermented products are widespread and 

common in the marketplace. Food biotechnology has been in existence for more than 

8,000 years.(103) The potential health impact of gut microbiota has been postulated 

by Metchnikoff(104) and since then, numerous related research results have been 

provided.(105–107) Probiotics are supplied in starter cultures and thus need to be preserved for transportation and use. As the highest possible cell density is required, 

losses that occur during processing, transportation, and storage, including in products, are detrimental. Consequently, approaches to increase and retain physiological 

fitness have been explored.(108–109)

Emerging capabilities to characterize microbial communities and their functions 

in the oral cavity present insights into the role microbes may play in taste and olfaction, and present new opportunities to further personalize and refine food products 

to better suit individual taste and palatability preferences. Oral pre- and probiotics 

may be an opportunity for innovation. 

These emerging advances in human microbiome structure, diversity, and function 

present exciting new opportunities for new food products, ingredients, or dietary approaches that can be used for supporting daily health, direct or adjunct intervention 

323 

for risk reduction, or for new therapeutics for symptom reliefs (IBS). However, to 

advance these undertakings, several key questions need to be addressed. How easy 

is it to translate microbiome research to food and dietary applications? Limited welldesigned studies have been performed that explore the impact of food and diet on 

microbial ecology and function. What biomarkers are available or need to be developed to understand how food and diet impact on the microbiome (gut, gut-brain, gutkidney, etc.)? What microbial combination will be best suited for achieving specific 

outcomes? Of challenge is the ability to identify and separate the ‘‘good’’ from the 

‘‘bad’’ microbes that can present foodborne illness or exacerbate disease risks. Gene 

sequencing and whole genome sequencing technologies have been used to diagnose 

and trace food contamination, and are now also applied in medicine. How can current microbiome research be easily translated for food and product applications? 

How easy is it to transfer available technologies and tools already developed for use 

in food and nutrition applications? 

In addition, there remains room for improvement when translating to innovative 

or tailor-made products. Needs and opportunities include process generated structures, which impact on food properties (process-structure-function relationship) as 

outlined in the European Technology Platform Strategic Research Agenda (ETP 

SRA) (2007; 2012; 2014) for designing tailor-made foods for the targeted release of 

essential food constituents at points of need to support human microbiota growth 

and metabolic fitness. This needs to include the entire human digestion system encompassing the chewing apparatus, mouth microbiota, and enzymes. Moreover, food 

can contain viable microbial cultures as well as active enzymes. Understanding their 

role in and during digestion as well as their impact on gut, mouth, and skin 

microbiota may lead to the development of new food design concepts with targeted 

nutritional benefits. 

Finally, emerging technologies are being introduced to the food processing area, 

including high hydrostatic pressure, pulsed electric fields, and atmospheric plasma. 

Little is known about their impact and function with regard to the human 

microbiota. These technologies could open new avenues for process-function-structure relationships as well as provide foods with metabolic properties not achieved 

via traditional processing.(36)

Nourishing the Immune System and Preventing Disease 

(Johan Garssen, Willem van Eden, and Josep Bassaganya-Riera) 

Whereas the disciplines of pharmaceutical and nutritional sciences have evolved 

separately in the Western world, for Asia these two research areas have been connected for centuries. However, today, with the ever-growing burden of chronic diseases in modern societies, the high relevance of specialized nutrition in both prevention and therapeutic approaches receives increased attention and recognition. The 

gap between food and pharma is narrowing.(110) One reason might be that, scientifically, the evidence for the so-called multi-target or polypharmacology approaches 

aimed at disease management is growing. Medical nutrition is beginning to be recognized as a unique and potentially powerful area in Western societies at the interface between food and pharma. 

Medical nutrition targets innovative nutritional therapies, offering healthcare professionals solutions to effectively manage disease-related malnutrition and specific 

disease states. Medical nutrition is and will be increasingly understood as a useful 

and sometimes even essential component in the management of patient health. 

Many medical conditions can be managed better when patients are receiving a specialized diet adapted to their unique circumstances. Sometimes, the constraints to 

appetite may be physical, as in the case of stroke patients who may find it difficult 

or impossible to swallow, or of young children with neurological disabilities. Sometimes, the problem may simply be insufficient intake, caused by the loss of appetite. 

It is well known that many chronic diseases are associated with malnutrition, a phenomenon that is not solely based on body mass index or body weight. Many obese 

patients suffer from specific malnutrition. Examples of disease areas that might be 

associated with specific malnutrition are cancer, stroke, and COPD. However, frail 

or elderly people are treated and fed with this type of medical nutrition as well. 

Medical nutrition might bring solutions and support to these cases across a broad 

range of care settings—in the hospital, in the care home, or in the community. It 

contains unique compositions of specific nutrients that would be impossible or impractical to achieve through normal food intake alone. In most cases, it is administered via the gastrointestinal tract orally or with a feeding tube, utilizing the natural route for nutrient digestion and absorption. These cases are underpinned by 

a unique scientific rationale, preclinical and clinical research, and health economic 

evaluation making it very similar to the traditional pharma approach. By making 

medical nutrition an integral part of care, patient outcomes are significantly imVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00329 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
324 

proved. Lower healthcare costs by shortening hospital stays and keeping patients 

independent for longer are key outcomes for medical nutrition intervention. The food 

for special medical purposes (FSMP) is the regulatory directive involved with the 

quality/safety and efficacy of medical foods. 

Another and unique medical area for which medical nutrition is aimed is diseasespecific (the so-called disease targeted) medical nutrition. This type of medical nutrition is a unique, effective, therapeutic nutritional intervention for patients with, e.g., 

a clinical need to avoid certain nutrients due to specific diseases or conditions where 

normal food intake is harmful. Examples are inborn errors of metabolism such as 

phenylketonuria (PKU) or severe cow’s milk allergy and childhood epilepsy. 

Ketogenic therapy during refractory epilepsy can reduce seizures significantly. 

Other examples for disease-specific medical nutrition are science-driven concepts 

containing different and uniquely selected nutrients that can act in an orchestra 

leading to a delay in disease progression. Validated examples have been described 

for Alzheimer’s, HIV, diabetes, and cancer.(111–114)

Disease-targeted medical nutrition can be aimed at conditions such as chronic inflammation. These inflammatory conditions are on the rise. This is caused by 

changes in life-style, food consumption patterns, and aging. Inflammation-associated 

conditions, such as atherosclerosis, type 1 and type 2 diabetes, obesity, Alzheimer’s 

disease, and many others, are a growing burden to health budgets. Inflammatory 

conditions are thought to result from failing mechanisms of immunological tolerance. Of these mechanisms, deficient suppressive activities of a specialized subset 

of T cells, called regulatory T cells (Tregs), are being recognized as a major factor 

in the failure of immunological tolerance. A start has been made with the definition 

of antigen-specific Tregs with a broad anti-inflammatory effect, such as, for example, 

those that recognize inflammation-associated stress-proteins.(115) Herewith, the restoration of this regulation will be a widely sought goal, also for the field of nutrition. 

A telling example of what may be possible is the following. Wieten, et al., have 

shown that the up-regulation of stress-proteins, such as heat shock protein 70 

(HSP70), in the cells lining the gut, leads to the local induction of Tregs.(116) Working with a model of chronic and relapsing arthritis, it was found that HSP70 was 

also induced in Peyer’s patches and the induced HSP70-specific Tregs were having 

a systemic effect seen to fully control arthritis. This up-regulation was achieved by 

the oral administration in mice of carvacrol, an essential oil of Oregano species. It 

showed that our diet may contain effective coinducers of stress-proteins and that 

these co-induced proteins can elicit anti-inflammatory activity in the immune system. Similar activities have now been described for other food components.(117)

Therefore, especially for the diets of the aging individual, substances with anti-inflammatory activities will be an attractive component. In the field of veterinary 

medicine and food animal production, restrictions are now being imposed on the use 

of antibiotics, certainly on the use of antibiotics as growth-enhancers. Also here, 

feed additives are searched with the purpose of controlling inflammation and thereby enhancing weight gain. 

In combination with drugs, medical devices and lifestyle modification, medical nutrition, and immune system targeted nutraceuticals can play an essential role in 

health care and precision medicine. Expectedly, it will lead to lower costs of care: 

fewer complications, shorter hospital stays and reduced mortality, and the reduction 

of disease manifestations. 

Over the coming years, Medical Nutrition and Nutraceuticals have the opportunity to be accepted as a bridge between food and traditional pharma approaches— 

not as isolated therapy but as part of integrated systems-wide health care. Additionally, pharma often is focusing on a monotherapeutic approach (one molecule one target) and medical nutrition will be recognized as the multi-target approach for disease management. Regulation and acceptance depends on national and international 

guidelines. Changes in regulation for medical nutrition are to be expected since 

medical nutrition is a relatively new therapeutic area that falls between different 

regulations and guidelines. For instance, in the USA, under section 5(b) of the Orphan Drug Act [21 U.S.C. 360ee(b)(3)], a medical food is formulated to be consumed 

or administered enterally under the supervision of a physician and which is intended for the specific dietary management of a disease or condition for which distinctive nutritional requirements, based on recognized scientific principles, are established by medical evaluation. Thus, from a regulatory perspective, medical foods 

are different than dietary supplements in that claims for medical foods can allude 

to disease management whereas dietary supplement claims cannot. Medical foods 

are exempted from the labeling requirements for health claims and nutrient content 

claims under the Nutrition Labeling and Education Act of 1990. In order to be a 

medical food, a product must meet the following criteria: to be a food for oral or tube 

feeding, the product must be labeled for the dietary management of a specific medVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00330 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
325 

ical disorder, disease, or condition for which there are distinctive nutritional requirements, and the product must be intended to be used under medical supervision. Essentially, medical food comes into play when dietary management cannot 

be achieved by the modification of the normal diet alone. For instance, medical foods 

could be used to replete key metabolic components that might be depleted in diabetes or inflammation. Only translational research and randomized, placebo controlled 

double-blind clinical trials can validate these new concepts. 

References 

1. Creating a Sustainable Food Future. Available from: http://www.wri.org/sites/default/files/wri13_report_4c_wrr_online.pdf. 

2. FAO, IFAD, and WFP. The State of Food Insecurity in the World 2014. Strengthening the Enabling Environment for Food Security and Nutrition. Rome (2014). Available from: http://www.fao.org/3/a-i4030e.pdf. 

3. Garnett T., Appleby M.C., Balmford A., Bateman I.J., Benton T.G., Bloomer P., et al. Agriculture. Sustainable intensification in 

agriculture: premises and policies. SCIENCE (2013) 341: 33–4. doi:10.1126/science.1234485. 

4. Tillotson J.E. America’s obesity: conflicting public policies, industrial economic development, and unintended human consequences. ANNU. REV. NUTR. (2004) 24: 617–43. doi:10.1146/annurev.nutr.24.012003.132434. 

5. Agricultural Biodiversity A. Cross-Cutting Initiative on Biodiversity for Food and Nutrition (2006). Available from: https:// 

www.cbd.int/decision/cop/?id=11037. 

6. Fourteenth Regular Session of the Commission on Genetic Resources for Food and Agriculture. Rome (2013). Available from: 

http://www.fao.org/docrep/meeting/028/mg538e.pdf. 

7. Zhang R. Food security: food trade regime and food aid regime. J. INT. ECON. LAW (2004) 7: 565–84. doi:10.1093/jiel/7.3.565. 

8. Latham M. The great vitamin A fiasco. WORLD. NUTR. (2010) 1: 12–45. 

9. Golden C.D., Fernald L.C.H., Brashares J.S., Rasolofoniaina B.J.R., Kremen C. Benefits of wildlife consumption to child nutrition in a biodiversity hotspot. PROC. NATL. ACAD. SCI. USA (2011) 108:19653–6. doi:10.1073/pnas.1112586108. 

10. Foley J.A., Defries R., Asner G.P., Barford C., Bonan G., Carpenter S.R., et al.Global consequences of land use. SCIENCE (2005) 

309:570–4. doi:10.1126/science.1111772. 

11. Knorr D, editor. Sustainable Food Systems. Westport, CT: AVI Publishing (1983). 

12. Wheeler T., von Braun J. Climate change impacts on global food security. SCIENCE (2013) 341:508–13. doi:10.1126/ 

science.1239402. 

13. Toledo A., Burlingame B. Biodiversity and nutrition: a common path toward global food security and sustainable development. 

J. FOOD. COMPOST. ANAL. (2006) 19: 477–83. doi:10.1016/j.jfca.2006.05.001. 

14. Waldron S., Flowers H., Arlaud C., Bryant C., McFarlane S. The significance of organic carbon and nutrient export from 

peatland-dominated landscapes subject to disturbance, a stoichiometric perspective. BIOGEOSCIENCES (2009) 6: 363–74. doi:10.5194/ 

bg-6-363-2009. 

15. Gustavsson J., Cederberg C., Sonesson U., Meybeck A., van Otterdijk R.. Global food losses and food waste. In: Interpack 2011 

(Du ̈ sseldorf) (2011). Available from: http://www.fao.org/docrep/014/mb060e/mb060e.pdf. 

16. Godfray H.C.J., Beddington J.R., Crute I.R., Haddad L., Lawrence D., Muir J.F., et al. Food security: the challenge of feeding 9 

billion people. SCIENCE (2010) 327: 812–8. doi:10.1126/science.1185383. 

17. Vinceti B., Termote C., Ickowitz A., Powell B., Kehlenbeck K., Hunter D. The contribution of forests and trees to sustainable 

diets. SUSTAINABILITY (2013) 5: 4797–824. doi:10.3390/su5114797. 

18. Meffe R., de Bustamante I. Emerging organic contaminants in surface water and groundwater: a first overview of the situation 

in Italy. SCI. TOTAL ENVIRON. (2014) 481: 280–95. doi:10.1016/j.scitotenv.2014.02.053. 

19. Myers G.J., Davidson P.W., Strain J.J. Nutrient and methyl mercury exposure from consuming fish. J. NUTR. (2007) 137: 

2805–8. 

20. Smith A. An Inquiry into the Nature and Causes of the Wealth of Nations. Vol. 3, 4th ed. London: W. Strahan and T. Cadell 

(1786). 206 p. 

21. Hawking S., Mlodinow L. The Grand Design. New York: Random House Publishing Group (2010) p. 5. 

22. Collins F.S., Tabak L.A. Policy: NIH plans to enhance reproducibility. NATURE (2014) 505: 612–3. doi:10.1038/505612a. 

23. Schoenfeld J.D., Ioannidis J.P.A. Is everything we eat associated with cancer? A systematic cookbook review. AM. J. CLIN. 

NUTR. (2013) 97: 127–34. doi:10.3945/ajcn.112.047142. 

24. Cofield S.S., Corona R.V., Allison D.B. Use of causal language in observational studies of obesity and nutrition. OBES. FACTS

(2010) 3: 353–6. doi:10.1159/000322940. 

25. Kaiser K.A., Cofield S.S., Fontaine K.R., Glasser S.P., Thabane L., Chu R., et al. Is funding source related to study reporting 

quality in obesity or nutrition randomized control trials in top-tier medical journals? INT. J. OBES. (Lond) (2012) 36: 977–81. 

doi:10.1038/ijo.2011.207. 

26. Casazza K., Allison D.B. Stagnation in the clinical, community and public health domain of obesity: the need for probative research. CLIN. OBES. (2012) 2: 83–5. doi:10.1111/j.1758-8111.2012.00052.x. 

27. Casazza K., Fontaine K.R., Astrup A., Birch L.L., Brown A.W., Bohan Brown M.M., et al. Myths, presumptions, and facts 

about obesity. N. ENGL. J. MED. (2013) 368: 446–54. doi:10.1056/NEJMsa1208051. 

28. Bohan Brown M.M., Brown A.W., Allison D.B. Nutritional epidemiology in practice: learning from data or promulgating beliefs? AM. J. CLIN. NUTR. (2013) 97: 5–6. doi:10.3945/ajcn.112.052472. 

29. PLoS Medicine Editors. Making sense of non-financial competing interests. PLOS MED. (2008) 5: e199. doi:10.1371/journal.pmed.0050199. 

30. Nosek B.A., Alter G., Banks G.C., Borsboom D., Bowman S.D., Breckler S.J., et al. Promoting an open research culture. 

SCIENCE (2015) 348: 1422–5. doi:10.1126/science.aab2374. 

31. Archer E., Pavela G., Lavie C.J. The inadmissibility of what we eat in America and NHANES dietary data in nutrition and 

obesity research and the scientific formulation of national dietary guidelines. MAYO CLINIC PROCEEDINGS (2015) 90(7): 911–26. 

doi:10.1016/j.mayocp.2015.04.009. 

32. Dhurandhar N.V., Schoeller D., Brown A.W., Heymsfield S.B., Thomas D., Sorensen T.I.A., et al. Energy balance measurement: when something is not better than nothing. INT. J. OBES. (Lond) (2014) 39(7): 1109–13. doi:10.1038/ijo.2014.199. 

33. Schoeller D.A., Thomas D., Archer E., Heymsfield S.B., Blair S.N., Goran M.I., et al. Self-report-based estimates of energy intake offer an inadequate basis for scientific conclusions. AM. J. CLIN. NUTR. (2013) 97: 1413–5. doi:10.3945/ajcn.113.062125. 

34. Dietary Guidelines Advisory Committee. Scientific Report of the 2015 Dietary Guidelines Advisory Committee (2015). Available from: http://www.health.gov/dietaryguidelines/2015-scientific-report/PDFs/Scientific-Report-of-the-2015-Dietary-GuidelinesAdvisory-Committee.pdf. 

35. Brown A.W., Bohan Brown M.M., Allison D.B. Belief beyond the evidence: using the proposed effect of breakfast on obesity to 

show 2 practices that distort scientific evidence. AM. J. CLIN. NUTR. (2013) 98: 1298–308. doi:10.3945/ajcn.113.064410. 

36. Brown J., de Vos W.M., DiStefano P.S., Dore ́ J., Huttenhower C., Knight R., et al. Translating the human microbiome. NAT. 

BIOTECHNOL. (2013) 31: 304–8. doi:10.1038/nbt.2543. 

37. Mehta T., Allison D.B. From measurement to analysis reporting: grand challenges in nutritional methodology. FRONT. NUTR. 

(2014) 1. doi:10.3389/fnut.2014.00006. 

38. Pavela G., Wiener H., Fontaine K.R., Fields D.A., Voss J.D., Allison D.B. Packet randomized experiments for eliminating classes of confounders. EUR. J. CLIN. INVEST. (2015) 45(1): 45–55. doi:10.1111/eci.12378. 

39. Kussmann M., Van Bladeren P.J. The extended nutrigenomics—understanding the interplay between the genomes of food, gut 

microbes, and human host. FRONT. GENET. (2011) 2: 21. doi:10.3389/fgene.2011.00021. 

40. van Ommen B., El-Sohemy A., Hesketh J., Kaput J., Fenech M., Evelo C.T., et al. The Micronutrient Genomics Project: a community-driven knowledge base for micronutrient research. GENES. NUTR. (2010) 5: 285–96. doi:10.1007/s12263-010-0192-8. 

326 

References—Continued 

41. Kussmann M., Morine M.J., Hager J., Sonderegger B., Kaput J.. Perspective: a systems approach to diabetes research. FRONT. 

GENET. (2013) 4: 205. doi:10.3389/fgene.2013.00205. 

42. Stumbo P.J., Weiss R., Newman J.W., Pennington J.A., Tucker K.L., Wiesenfeld P.L., et al. Web-enabled and improved software tools and data are needed to measure nutrient intakes and physical activity for personalized health research. J. NUTR. (2010) 

140: 2104–15. doi:10.3945/jn.110.128371. 

43. Nguyen T.-P., Scotti M., Morine M.J., Priami C. Model-based clustering reveals vitamin D dependent multi-centrality hubs in 

a network of vitamin-related proteins. BMC SYST. BIOL. (2011) 5: 195. doi:10.1186/1752-0509-5-195. 

44. Kussmann M., Fay L.B. Nutrigenomics and personalized nutrition: science and concept. PER. MED. (2008) 5: 447–55. 

doi:10.2217/17410541.5.5.447. 

45. Viladomiu M., Hontecillas R., Yuan L., Lu P., Bassaganya-Riera J. Nutritional protective mechanisms against gut inflammation. J. NUTR. BIOCHEM. (2013) 24: 929–39. doi:10.1016/j.jnutbio.2013.01.006. 

46. Carbo A., Bassaganya-Riera J., Pedragosa M., Viladomiu M., Marathe M., Eubank S., et al. Predictive computational modeling of the mucosal immune responses during Helicobacter pylori infection. PLOS ONE (2013) 8: e73365. doi:10.1371/journal.pone.0073365. 

47. Carbo A., Hontecillas R., Kronsteiner B., Viladomiu M., Pedragosa M., Lu P., et al. Systems modeling of molecular mechanisms controlling cytokine-driven CD4+ T cell differentiation and phenotype plasticity. PLOS COMPUT. BIOL. (2013) 9: e1003027. 

doi:10.1371/journal.pcbi.1003027. 

48. Carbo A., Olivares-Villago ́mez D., Hontecillas R., Bassaganya-Riera J., Chaturvedi R., Piazuelo M.B., et al. Systems modeling 

of the role of interleukin-21 in the maintenance of effector CD4+ T cell responses during chronic Helicobacter pylori infection. MBIO. 

(2014) 5:e1243–1214. doi:10.1128/mBio.01243-14. 

49. Mei Y., Abedi V., Carbo A., Zhang X., Lu P., Philipson C., et al. Multiscale modeling of mucosal immune responses. BMC 

BIOINFORMATICS (2015) 16(Suppl. 12). 

50. Leber A., Viladomiu M., Hontecillas R., Abedi V., Philipson C., Hoops S., et al. Systems modeling of interactions between 

mucosal immunity and the gut microbiome during clostridium difficile infection. PLOS ONE (2015) 10: e0134849. doi:10.1371/journal.pone.0134849. 

51. Mc Auley M.T., Proctor C.J., Corfe B.M., Cuskelly G.C, Mooney K.M. Nutrition research and the impact of computational systems biology. J. COMPUT. SCI. SYST. BIOL. (2013) 6: 271–85. doi:10.4172/jcsb.1000122. 

52. Laney D. 3D Data Management: Controlling Data Volume, Velocity, and Variety. Application Delivery Strategies. Stamford, 

CT: META Group Inc. (2001). 

53. Einav L., Levin J. Economics in the age of big data. SCIENCE (2014) 346: 1243089. doi:10.1126/science.1243089. 

54. Wallace P.J., Shah N.D., Dennen T., Bleicher P.A., Bleicher P.D., Crown W.H. Optum labs: building a novel node in the learning health care system. HEALTH AFF. (Millwood) (2014) 33: 1187–94. doi:10.1377/hlthaff.2014.0038. 

55. Schneeweiss S., Rassen J.A., Glynn R.J., Avorn J., Mogun H., Brookhart M.A. High-dimensional propensity score adjustment 

in studies of treatment effects using health care claims data. EPIDEMIOLOGY (2009) 20: 512–22. doi:10.1097/EDE.0b013e3181a663cc. 

56. Rosenbaum P.R. Using Differential Comparisons in Observational Studies (2013). Available from: http:// 

amstat.tandfonline.com/doi/abs/10.1080/09332480.2013.845002 (accessed May 23, 2015). 

57. Peters J., Bu ̈hlmann P., Meinshausen N. Causal Inference Using Invariant Prediction: Identification and Confidence Intervals 

(2015). Available from: http://arxiv.org/abs/1501.01332 (accessed May 23, 2015). 

58. Bu ̈hlmann P., Peters J., Ernest J. CAM: causal additive models, high-dimensional order search and penalized regression. 

ANN. STAT. (2014) 42: 2526–56. doi:10.1214/14-AOS1260. 

59. Roullier-Gall C., Witting M., Gougeon R.D., Schmitt-Kopplin P. High precision mass measurements for wine metabolomics. 

FRONT. CHEM. (2014) 2: 102. doi:10.3389/fchem.2014.00102. 

60. Leeman W.R., Krul L., Houben G.F. Complex mixtures: relevance of combined exposure to substances at low dose levels. FOOD

CHEM. TOXICOL. (2013) 58: 141–8. doi:10.1016/j.fct.2013.03.050. 

61. Meule A., Vo ̈gele C. The psychology of eating. FRONT. PSYCHOL. (2013) 4:215. doi:10.3389/fpsyg.2013.00215. 

62. Moubarac J.-C., Batal M., Martins A.P.B., Claro R., Levy R.B., Cannon G., et al. Processed and ultra-processed food products: 

consumption trends in Canada from 1938 to 2011. CAN. J. DIET. PRACT. RES. (2014) 75: 15–21. doi:10.3148/75.1.2014.15. 

63. Poti J.M., Mendez M.A., Ng S.W., Popkin B.M. Is the degree of food processing and convenience linked with the nutritional 

quality of foods purchased by U.S. households? AM. J. CLIN. NUTR. (2015) 101: 1251–62. doi:10.3945/ajcn.114.100925. 

64. Davis C. Evolutionary and neuropsychological perspectives on addictive behaviors and addictive substances: relevance to the 

‘‘food addiction’’ construct. SUBST. ABUSE. REHABIL. (2014) 5: 129–37. doi:10.2147/SAR.S56835. 

65. Gearhardt A.N., Davis C., Kuschner R., Brownell K.D. The addiction potential of hyperpalatable foods. CURR. DRUG ABUSE

REV. (2011) 4: 140–5. doi:10.2174/1874473711104030140. 

66. Davis C. From passive overeating to ‘‘food addiction’’: a spectrum of compulsion and severity. ISRN OBES. (2013) 2013: 435027. 

doi:10.1155/2013/435027. 

67. Avena N.M., Gold M.S. Food and addiction—sugars, fats and hedonic overeating. ADDICTION (2011) 106: 1214–5. doi:10.1111/ 

j.1360-0443.2011.03373.x. 

68. Ahmed S.H., Guillem K., Vandaele Y.. Sugar addiction: pushing the drug-sugar analogy to the limit. CURR. OPIN. CLIN. NUTR. 

METAB. CARE. (2013) 16: 434–9. doi:10.1097/MCO.0b013e328361c8b8. 

69. Davis C., Carter J.C. Compulsive overeating as an addiction disorder. A review of theory and evidence. APPETITE (2009) 53: 1– 

8. doi:10.1016/j.appet.2009.05.018. 

70. Davis C., Carter J.C. If certain foods are addictive, how might this change the treatment of compulsive overeating and obesity? 

CURR. ADDICT. REP. (2014) 1: 89–95. doi:10.1007/s40429-014-0013-z. 

71. Krashes M.J., Kravitz A.V. Optogenetic and chemogenetic insights into the food addiction hypothesis. FRONT. BEHAV. 

NEUROSCI. (2014) 8: 57. doi:10.3389/fnbeh.2014.00057. 

72. Meule A., Gearhardt A.N. Five years of the Yale Food Addiction Scale: taking stock and moving forward. CURR. ADDICT. REP. 

(2014) 1: 193–205. doi:10.1007/s40429-014-0021-z. 

73. Meule A., Gearhardt A.N. Food addiction in the light of DSM–5. NUTRIENTS (2014) 6:3 653–71. doi:10.3390/nu6093653. 

74. Rogers P.J. Obesity—is food addiction to blame? ADDICTION (2011) 106: 1213–4. doi:10.1111/j.1360-0443.2011.03371.x. 

75. Benton D. The plausibility of sugar addiction and its role in obesity and eating disorders. CLIN. NUTR. (2010) 29: 288–303. 

doi:10.1016/j.clnu.2009.12.001. 

76. Wilson G.T. Eating disorders, obesity and addiction. EUR. EAT. DISORD. REV. (2010) 18: 341–51. doi:10.1002/erv.1048. 

77. Ziauddeen H., Farooqi I.S., Fletcher P.C. Obesity and the brain: how convincing is the addiction model? NAT. REV. NEUROSCI. 

(2012) 13: 279–86. doi:10.1038/nrn3212. 

78. Ziauddeen H., Fletcher P.C. Is food addiction a valid and useful concept? OBES. REV. (2013) 14: 19–28. doi:10.1111/j.1467- 

789X.2012.01046.x. 

79. Hill J.O., Berridge K., Avena N.M., Ziauddeen H., Alonso-Alonso M., Allison D.B., et al. Neurocognition: the food-brain connection. ADV. NUTR. (2014) 5:5 44–6. doi:10.3945/an.114.006437. 

80. Salamone J.D., Correa M. Dopamine and food addiction: lexicon badly needed. BIOL. PSYCHIATRY (2013) 73: e15–24. 

doi:10.1016/j.biopsych.2012.09.027. 

81. Gearhardt A.N., Corbin W.R., Brownell K.D. Food addiction: an examination of the diagnostic criteria for dependence. J. ADDICT. MED. (2009) 3: 1–7. doi:10.1097/ADM.0b013e318193c993. 

82. Gearhardt A.N., Brownell K.D. Can food and addiction change the game? BIOL. PSYCHIATRY (2013) 73: 802–3. doi:10.1016/ 

j.biopsych.2012.07.024. 

83. DePierre J.A., Puhl R.M., Luedicke J. A new stigmatized identity? Comparisons of a ‘‘food addict’’ label with other stigmatized 

health conditions. BASIC APPL. SOC. PSYCH. (2013) 35: 10–21. doi:10.1080/01973533.2012.746148. 

84. Latner J.D., Puhl R.M., Murakami J.M., O’Brien K.S. Food addiction as a causal model of obesity. Effects on stigma, blame, 

and perceived psychopathology. APPETITE (2014) 77: 77–82. doi:10.1016/j.appet.2014.03.004. 

327 

References—Continued 

85. Lee N.M., Hall W.D., Lucke J., Forlini C., Carter A. Food addiction and its impact on weight-based stigma and the treatment 

of obese individuals in the U.S. and Australia. NUTRIENTS (2014) 6: 5312–26. doi:10.3390/nu6115312. 

86. Avena N.M. The study of food addiction using animal models of binge eating. APPETITE (2010) 55: 734–7. doi:10.1016/ 

j.appet.2010.09.010. 

87. Schulte E.M., Avena N.M., Gearhardt A.N. Which foods may be addictive? The roles of processing, fat content, and glycemic 

load. PLOS ONE (2015) 10: e0117959. doi:10.1371/journal.pone.0117959. 

88. Hebebrand J., Albayrak O ̈ ., Adan R., Antel J., Dieguez C., de Jong J., et al. ‘‘Eating addiction’’, rather than ‘‘food addiction’’, 

better captures addictive-like eating behavior. NEUROSCI. BIOBEHAV. REV. (2014) 47: 295–306. doi:10.1016/j.neubiorev.2014.08.016. 

89. Wimo A., Prince, M. World Alzheimer Report 2010 (2010). Available from: http://www.alz.co.uk/research/files/ 

WorldAlzheimerReport.pdf. 

90. Ohla K., Toepel U., le Coutre J., Hudry J. Visual-gustatory interaction: orbitofrontal and insular cortices mediate the effect of 

high-calorie visual food cues on taste pleasantness. PLOS ONE (2012) 7: e32434. doi:10.1371/journal.pone.0032434. 

91. Crouzet S.M., Busch N.A., Ohla K. Taste quality decoding parallels taste sensations. CURR. BIOL. (2015) 25: 890–6. 

doi:10.1016/j.cub.2015.01.057. 

92. Deoni S.C.L., Dean D.C., Piryatinsky I., O’Muircheartaigh J., Waskiewicz N., Lehman K., et al. Breastfeeding and early white 

matter development: a cross-sectional study. NEUROIMAGE (2013) 82: 77–86. doi:10.1016/j.neuroimage.2013.05.090. 

93. Blundell J. Making claims: functional foods for managing appetite and weight. NAT. REV. ENDOCRINOL. (2010) 6: 53–6. 

doi:10.1038/nrendo.2009.224. 

94. Mercer J.G., Johnstone A.M., Halford J.C.G. Approaches to influencing food choice across the age groups: from children to the 

elderly. PROC. NUTR. SOC. (2015) 74(2): 1–9. doi:10.1017/S0029665114001712. 

95. Halford J.C.G., Harrold J.A. Satiety-enhancing products for appetite control: science and regulation of functional foods for 

weight management. PROC. NUTR. SOC. (2012) 71: 350–62. doi:10.1017/S0029665112000134. 

96. Skibicka K.P., Dickson S.L. Enteroendocrine hormones—central effects on behavior. CURR. OPIN. PHARMACOL. (2013) 13: 977– 

82. doi:10.1016/j.coph.2013.09.004. 

97. Egecioglu E., Skibicka K.P., Hansson C., Alvarez-Crespo M., Friberg P.A., Jerlhag E., et al. Hedonic and incentive signals for 

body weight control. REV. ENDOCR. METAB. DISORD. (2011) 12: 141–51. doi:10.1007/s11154-011-9166-4. 

98. Gearhardt A.N., Corbin W.R., Brownell K.D. Preliminary validation of the Yale Food Addiction Scale. APPETITE (2009) 52: 

430–6. doi:10.1016/j.appet.2008.12.003. 

99. Iozzo P., Guiducci L., Guzzardi M.A., Pagotto U. Brain PET imaging in obesity and food addiction: current evidence and hypothesis. OBES. FACTS (2012) 5: 155–64. doi:10.1159/000338328. 

100. Human Microbiome Project Consortium. A framework for human microbiome research. NATURE (2012) 486: 215–21. 

doi:10.1038/nature11209. 

101. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. NATURE (2012) 

486: 207–14. doi:10.1038/nature11234. 

102. Ding T., Schloss P.D. Dynamics and associations of microbial community types across the human body. NATURE (2014) 509: 

357–60. doi:10.1038/nature13178. 

103. Knorr D., Sinskey A.J. Biotechnology in food production and processing. SCIENCE (1985) 229: 1224–9. doi:10.1126/ 

science.229.4719.1224. 

104. Metchnikoff E. The Prolongation of Life: Optimistic Studies. New York, London: G.P. Putnam’s Sons (1908). 

105. Chung D. It’s a jungle in there. NEW SCI. (2004) 182: 42–5. 

106. Claesson M.J., Jeffery I.B., Conde S., Power S.E., O’Connor E.M., Cusack S., et al. Gut microbiota composition correlates 

with diet and health in the elderly. NATURE (2012) 488: 178–84. doi:10.1038/nature11319. 

107. Salonen A., Lahti L., Saloja ̈ rvi J., Holtrop G., Korpela K., Duncan S.H., et al. Impact of diet and individual variation on intestinal microbiota composition and ermentation products in obese men. ISME J. (2014) 8: 2218–30. doi:10.1038/ismej.2014.63. 

108. Ananta E., Birkeland S.-E., Corcoran B., Fitzgerald G., Hinz S., Klijn A., et al. Processing effects on the nutritional advancement of probiotics and prebiotics. MICROB. ECOL. HEALTH DIS. (2004) 16: 114–24. doi:10.3402/mehd.v16i2-3.7933. 

109. Volkert M., Ananta E., Luscher C., Knorr D. Effect of air freezing, spray freezing, and pressure shift freezing on membrane 

integrity and viability of Lactobacillus rhamnosus GG. J. FOOD ENG. (2008) 87: 532–40. doi:10.1016/j.jfoodeng.2008.01.008. 

110. Georgiou N.A., Garssen J., Witkamp R.F. Pharma-nutrition interface: the gap is narrowing. EUR. J. PHARMACOL. (2011) 651: 

1–8. doi:10.1016/j.ejphar.2010.11.007. 

111. Gori A., Rizzardini G., Van’t Land B., Amor K.B., van Schaik J., Torti C., et al. Specific prebiotics modulate gut microbiota 

and immune activation in HAART-naı ̈ve HIV-infected adults: results of the ‘‘COPA’’ pilot randomized trial. MUCOSAL. IMMUNOL. 

(2011) 4: 554–63. doi:10.1038/mi.2011.15. 

112. Cahn P., Ruxrungtham K., Gazzard B., Diaz R.S., Gori A., Kotler D.P., et al. The immunomodulatory nutritional intervention 

NR100157 reduced CD4+ T-cell decline and immune activation: a 1-year multicenter randomized controlled double-blind trial in 

HIV-infected persons not receiving antiretroviral therapy (The BITE Study). CLIN. INFECT. DIS. (2013) 57: 139–46. doi:10.1093/cid/ 

cit171. 

113. Scheltens P., Twisk J.W.R., Blesa R., Scarpini E., von Arnim C.A.F., Bongers A., et al. Efficacy of Souvenaid in mild Alzheimer’s disease: results from a randomized, controlled trial. J. ALZHEIMERS DIS. (2012) 31: 225–36. doi:10.3233/JAD-2012-121189. 

114. Faber J., Berkhout M., Fiedler U., Avlar M., Witteman B.J., Vos A.P., et al. Rapid EPA and DHA incorporation and reduced 

PGE2 levels after one week intervention with a medical food in cancer patients receiving radiotherapy, a randomized trial. CLIN. 

NUTR. (2013) 32: 338–45. doi:10.1016/j.clnu.2012.09.009. 

115. van Herwijnen M.J.C., Wieten L., van der Zee R., van Kooten P.J., Wagenaar-Hilbers J.P., Hoek A., et al. Regulatory T cells 

that recognize a ubiquitous stress-inducible self-antigen are long-lived suppressors of autoimmune arthritis. PROC. NATL. ACAD. SCI. 

USA (2012) 109: 14134–9. doi:10.1073/pnas.1206803109. 

116. Wieten L., van der Zee R., Spiering R., Wagenaar-Hilbers J., van Kooten P., Broere F., et al. A novel heat-shock protein coinducer boosts stress protein Hsp70 to activate T cell regulation of inflammation in autoimmune arthritis. ARTHRITIS RHEUM. (2010) 

62: 1026–35. doi:10.1002/art.27344. 

117. Wieten L., van der Zee R., Goedemans R., Sijtsma J., Serafini M., Lubsen N.H., et al. Hsp70 expression and induction as a 

readout for detection of immune modulatory components in food. CELL STRESS CHAPERONES (2010) 15: 25–37. doi:10.1007/s12192- 

009-0119-8. 

118. Levitsky D.A., Brown A.W., Hansen B.C., Atkinson R.L., Byrne N., Cheskin L.J., et al. An unjustified conclusion from self-report-based estimates of energy intake. AM. J. MED. (2014) 127:e33. doi:10.1016/j.amjmed.2014.08.029. 

119. Schoeller D., Archer E., Dawson J.A., Heymsfield S. Implausible results from the use of invalid methods. J. NUTR. (2015) 145: 

150. doi:10.3945/jn.114.199521. 

120. Keith S.W., Stommel M., Allison D.B., Schoenborn C.A. Self-report corrections for BMI: comment on Keith, et al. INT. J. 

OBES. (Lond) (2012) 36: 1591. doi:10.1038/ijo.2011.277. 

121. Le A., Judd S.E., Allison D.B., Oza-Frank R., Affuso O., Safford M.M., et al. The geographic distribution of obesity in the U.S. 

and the potential regional differences in misreporting of obesity. OBESITY (Silver Spring) (2014) 22: 300–6. doi:10.1002/oby.20451. 

122. Keith S.W., Fontaine K.R., Pajewski N.M., Mehta T., Allison D.B. Use of self-reported height and weight biases the body 

mass index-mortality association. INT. J. OBES. (Lond) (2011) 35: 401–8. doi:10.1038/ijo.2010.148. 

123. Tarro L., Llaurado ́ E., Albaladejo R., Morin ̃ a D., Arija V., Sola ̆ R., et al. A primary-school-based study to reduce the prevalence of childhood obesity—the EdAl (Educacio ́ en Alimentacio ́) study: a randomized controlled trial. TRIALS (2014) 15: 58. 

doi:10.1186/1745-6215-15-58. 

124. Barr S.B., Wright J.C. Postprandial energy expenditure in whole-food and processed-food meals: implications for daily energy 

expenditure. FOOD NUTR. RES. (2010) 54: 5144. doi:10.3402/fnr.v54i0.5144. 

125. Williams L.K., Abbott G., Thornton L.E., Worsley A., Ball K., Crawford D. Improving perceptions of healthy food affordability: results from a pilot intervention. INT. J. BEHAV. NUTR. PHYS. ACT. (2014) 11: 33. doi:10.1186/1479-5868-11-33. 

328 

References—Continued 

126. Siontis G.C.M., Tzoulaki I., Castaldi P.J., Ioannidis J.P.A. External validation of new risk prediction models is infrequent 

and reveals worse prognostic discrimination. J. CLIN. EPIDEMIOL. (2015) 68: 25–34. doi:10.1016/j.jclinepi.2014.09.007. 

127. Cassani R.S.L., Fassini P.G., Silvah J.H., Lima C.M., Marchini J.S. Impact of weight loss diet associated with flaxseed on inflammatory markers in men with cardiovascular risk factors: a clinical study. NUTR. J. (2015) 14: 5. doi:10.1186/1475-2891-14-5. 

128. Bland J.M., Altman D.G. Comparisons against baseline within randomised groups are often used and can be highly misleading. TRIALS (2011) 12: 264. doi:10.1186/1745-6215-12-264. 

129. Weerts S.E., Amoran A. Pass the fruits and vegetables! A community—university—industry partnership promotes weight loss 

in African American women. HEALTH PROMOT. PRACT. (2009) 12: 252–60. doi:10.1177/1524839908330810. 

130. Kaiser K.A., Affuso O., Beasley T.M., Allison D.B. Getting carried away: a note showing baseline observation carried forward 

(BOCF) results can be calculated from published complete-cases results. INT. J. OBES. (2011) 36: 886–9. doi:10.1038/ijo.2011.25. 

131. Elobeid M.A., Padilla M.A., McVie T., Thomas O., Brock D.W., Musser B., et al. Missing data in randomized clinical trials 

for weight loss: scope of the problem, state of the field, and performance of statistical methods. PLOS ONE (2009) 4: e6624. 

doi:10.1371/journal.pone.0006624. 

132. Li P., Brown A., Oakes J.M., Allison D. Comment on ‘‘Intervention effects of a school-based health promotion programme on 

obesity related behavioural outcomes’’. J. OBES. (2015). doi:10.1155/2015/708181. 

133. Li P., Brown A., Oakes J.M., Allison D. School-based obesity prevention Intervention in Chilean children: effective in controlling, but not reducing obesity. J. OBES. (2015). doi:10.1155/2015/183528. 

134. Grydeland M., Bjelland M., Anderssen S.A., Klepp K.-I., Bergh I.H., Andersen L.F., et al. Effects of a 20-month cluster 

randomised controlled school-based intervention trial on BMI of school-aged boys and girls: the HEIA study. BR. J. SPORTS MED. 

(2014) 48: 768–73. doi:10.1136/bjsports-2013-092284. 

135. Bere E., Klepp K.-I., Overby N. Free school fruit: can an extra piece of fruit every school day contribute to the prevention of 

future weight gain? A cluster randomized trial. FOOD NUTR. RES. (2014) 58: 23194. doi:10.3402/fnr.v58.23194. 

136. Brown A.W., Li P.B., Bohan Brown M.M., Kaiser K.A., Keith S.W., Oakes J.M., et al. Best (but oft forgotten) practices: designing, analyzing, and reporting cluster randomized controlled trials. AM. J. CLIN. NUTR. (2015) 102: 1–8. doi:10.3945/ 

ajcn.114.105072. 

137. Lewis D.W., Fields D.A., Allison D.B. Inconsistencies and inaccuracies in reporting on choice of endpoints and of statistical 

results in RCT of maternal diet. PEDIATR. OBES. (2015). doi:10.1111/ijpo.12030. 

138. Su C.-X., Han M., Ren J., Li W.-Y., Yue S.-J., Hao Y.-F., et al. Empirical evidence for outcome reporting bias in randomized 

clinical trials of acupuncture: comparison of registered records and subsequent publications. TRIALS (2015) 16:28. doi:10.1186/ 

s13063-014-0545-5. 

139. Simonsohn U., Nelson L.D., Simmons J.P. P-curve: a key to the file-drawer. J. EXP. PSYCHOL. GEN. (2014) 143: 534–47. 

doi:10.1037/a0033242. 

140. Simmons J.P., Nelson L.D., Simonsohn U. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. PSYCHOL. SCI. (2011) 22: 1359–66. doi:10.1177/0956797611417632. 

141. Gadbury G.L., Allison D.B. Inappropriate fiddling with statistical analyses to obtain a desirable p-value: tests to detect its 

presence in published literature. PLOS ONE (2012) 7: e46363. doi:10.1371/journal.pone.0046363. 

142. Herna ́ndez-Cordero S., Barquera S., Rodrı ́guez-Ramı ́rez S., Villanueva-Borbolla M.A., Gonza ́lez de Cossio T., Dommarco J.R., 

et al. Substituting water for sugar-sweetened beverages reduces circulating triglycerides and the prevalence of metabolic syndrome in 

obese but not in overweight Mexican women in a randomized controlled trial. J. NUTR. (2014) 144: 1742–52. doi:10.3945/ 

jn.114.193490. 

143. Brown A.W., Sievenpiper J.L., Kyle T.A., Kaiser K.A. Communication of randomized controlled trial results must match the 

study focus. J. NUTR. (2015) 145: 1027–9. doi:10.3945/jn.114.207282. 

144. Katz T.J. Propagation of errors in review articles. SCIENCE (2006) 313: 1236. doi:10.1126/science.313.5791.1236a. 

145. Cope M.B., Allison D.B. Critical review of the World Health Organization’s (WHO) 2007 report on ‘‘evidence of the long-term 

effects of breastfeeding: systematic reviews and meta-analysis’’ with respect to obesity. OBES. REV. (2008) 9: 594–605. doi:10.1111/ 

j.1467-789X.2008.00504.x. 

146. Allison D.B., Faith M.S., Gorman B.S. Publication bias in obesity treatment trials? INT. J. OBES. RELAT. METAB. DISORD. 

(1996) 20: 931–7. 

147. Boutron I., Dutton S., Ravaud P., Altman D.G. Reporting and interpretation of randomized controlled trials with statistically 

nonsignificant results for primary outcomes. JAMA (2010) 303: 2058–64. doi:10.1001/jama.2010.651. 

148. Yavchitz A., Boutron I., Bafeta A., Marroun I., Charles P., Mantz J., et al. Misrepresentation of randomized controlled trials 

in press releases and news coverage: a cohort study. PLOS MED. (2012) 9:e1001308. doi:10.1371/journal.pmed.1001308. 

149. Brown A.W., Hall K.D., Thomas D., Dhurandhar N.V., Heymsfield S.B., Allison D.B. Order of magnitude misestimation of 

weight effects of children’s meal policy proposals. CHILD. OBES. (2014) 10: 542–4. doi:10.1089/chi.2014.0081. 

150. Bohan Brown M.M., Brown A.W., Allison D.B. Linear extrapolation results in erroneous overestimation of plausible stressorrelated yearly weight changes. BIOL. PSYCHIATRY (2014) 78(4): e10–1. doi:10.1016/j.biopsych.2014.10.028. 

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 

Copyright ©2015 Allison, Bassaganya-Riera, Burlingame, Brown, le Coutre, Dickson, van Eden, Garssen, Hontecillas, Khoo, 

Knorr, Kussmann, Magistretti, Mehta, Meule, Rychlik and Vo ̈gele. This is an open-access article distributed under the terms of the 

Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the 

original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. 

ATTACHMENT 5 

Unintended Consequences of Obesity-Targeted Health Policy 

Virtual Mentor 

American Medical Association Journal of Ethics 

April 2013, Volume 15, Number 4: 339–346. 

Policy Forum 

Andrew W. Brown, Ph.D., and David B. Allison, Ph.D. 

L’enfer est plein de bonnes volonte ́s et de ́sirs. [Hell is full of good wishes 

and desires.] 

Saint Bernard of Clairvaux.[1]

The conflict between individual freedom of choice and a government’s obligation 

to protect its citizenry from threats to public health is often at the center of health 

policy debates. This has played out in New York City, for instance, with freedom 

of choice being the rallying cry of those opposed to a citywide ban on large containers of beverages,[2] while saving lives through health-motivated policies is ofVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00334 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
329 

fered as the justification for the regulations.[3] However, several other ethical concerns exist related to the creation or implementation of public policy. Herein, we 

will discuss a catalog of ethical concerns identified by M. ten Have, et al.[4] related 

to policies intended to prevent or treat obesity. 

We discuss these ethical concerns in light of two key issues: (1) Under which circumstances does obesity merit being considered a public, as opposed to simply a 

common, health concern? Whether or not obesity is considered a public health concern is important in deciding whether impinging on individuals’ rights may be warranted. (2) How plausible is it that a given policy or program will have negative unintended consequences? These consequences are important to consider when deciding if a policy should be implemented. We then suggest strategies for minimizing 

ethical and other unintended adverse consequences of obesity-targeted health policies. 

Ethical Concerns in Obesity-Targeted Health Policies 

In ‘‘Ethics and Prevention of Overweight and Obesity: An Inventory,’’ Marieke ten 

Have and colleagues identify ethical concerns posed by 60 actual or proposed public 

policies, corporate initiatives, and behavior recommendations intended to prevent or 

treat obesity.[4] One group of ethical concerns comprises direct negative consequences of a program, including physical and psychosocial harm, dissemination of 

inadequate information, and creation or exacerbation of inequalities. The other 

group of ethical concerns encompasses disrespect for individuals and their rights 

and values, including transgressing personal and cultural values of eating, invading 

privacy, assigning fault for obesity, and abridging freedom of choice. Typically, more 

than one of these concerns exist with varying degrees of severity for any proposed 

policy or recommendation, but often the debate is dichotomized as a desire to promote health versus a desire to preserve individual liberty. 

The complexity of ethical considerations in obesity policymaking can be demonstrated by a policy that would allow the government to remove an obese child 

from his or her home (see Table 1). Note that the pros and cons listed in the table 

are not necessarily weighted by importance because importance is dependent on individual perspectives and specific situations. Here, the assumed benefit of the policy 

is that removing the child from the home will improve his or her weight and therefore health, though that assumption is itself contentious.[5] As the table shows, the 

ethical considerations are far more complex than health vs. freedom of choice. To 

add to the complexity, a given individual may consider one specific ethical concern 

more important than all others: for health advocates the physical health implications may outweigh all other concerns, while for the parents the sanctity of the parent-child relationship may be paramount.[6]

Table 1. Ethical Concerns of an Example Policy in Which the Government 

Is Allowed To Remove Obese Children From Homes. The Ethical Concerns Are Not Necessarily Equally Prevalent and Do Not Necessarily 

Carry Equal Weight 

Ethical concern [4] Pro-policy view Anti-policy view 

Physical health Improved health if professionals can affect weight. There may not be the resources or knowledge to improve the health of the removed child in the long term. 

Psychosocial wellbeing Obesity is associated with 

psychological disorders. 

Removing children from parents may be 

more traumatic than the obesity. 

Equality All children have the right to 

a healthy childhood and 

life. 

Obesity affects the poor and minorities to a 

greater extent, so this policy will disproportionately target these groups. 

Informed choice Parents are no longer able to make decisions for their child. 

Social/cultural 

values 

The social value placed on 

fitness and health is 

upheld. 

The social value placed on parent-child relationships is violated. 

Privacy The family’s and child’s privacy may be 

compromised. 

Attribution of responsibility Responsibility for the child’s 

obesity is shared among 

society and medical professionals. 

The parents are directly or indirectly 

blamed for the obesity and stigmatized. 

330 

Table 1. Ethical Concerns of an Example Policy in Which the Government 

Is Allowed To Remove Obese Children From Homes. The Ethical Concerns Are Not Necessarily Equally Prevalent and Do Not Necessarily 

Carry Equal Weight—Continued 

Ethical concern [4] Pro-policy view Anti-policy view 

Liberty The parent’s and child’s liberties are violated. 

Under Which Circumstances Should Obesity Be Considered a Public Health 

Concern? 

The example in Table 1 has ramifications for specific individuals in specific circumstances and particularly focuses on minors, who are broadly considered not fully 

responsible for their own actions. The justifications and ramifications of broad 

health-targeted policies affecting ordinary adults are quite different. 

Before proceeding, we must distinguish between two distinct uses of the phrase 

‘‘public health’’ as a prefix to terms such as ‘‘problem,’’ ‘‘concern,’’ or ‘‘issue.’’ The 

phrase is often used merely to convey that the problem affects a large number of 

people. The term ‘‘population health’’ is emerging to express this idea.[7] But in debates about policies that may impinge on individual rights and values, the phrase 

is used more specifically to denote health problems in which individuals’ actions 

may not be sufficient to protect them from ill health and collective action may offer 

such protection. Examples of the latter definition include certain infectious diseases 

from which protection can be afforded by mass vaccination and toxins in public 

drinking water supplies, which can be minimized by a variety of government policies. 

Using the more specific definition, it is not clear that obesity qualifies as a public 

health concern in all circumstances.[8] When considering some putative contributors 

to obesity, such as adenovirus 36 or environmental endocrine disruptors,[9] the definition does seem to apply: individuals generally cannot fully detect and protect 

themselves from exposure to these factors by their own action, and collective action 

at a societal level mandated by government policies might do so. However, when 

considering some other putative contributors to obesity such as ingesting excess energy or being insufficiently active, there generally are not external unavoidable constraints, as opposed to influences, on individuals. Thus, collective action to protect 

individuals from undetectable or unavoidable contributing factors is not required in 

such cases. 

At this point, we should address a related argument. This is perhaps the most 

commonly used argument to justify policies about obesity: obesity is costly to society, 

largely through the healthcare system, and this justifies collectively infringing upon 

individual liberty to decrease obesity. We do not agree with this argument. Regardless of the cost of obesity, that cost itself does not necessarily justify society’s imposing such policies. The fact that one party (society in this case) voluntarily takes on 

an obligation to cover some costly benefit to a second party (individual citizens in 

this case) does not necessarily give the first party the right to dictate the behaviors 

of the second party. There are several alternatives which include society’s not volunteering to take on the obligation, society’s taking on the obligation but distributing 

the costs equitably to its members (e.g., charging obese persons more for health coverage), or society’s voluntarily accepting the obligation and then simply agreeing to 

be ‘‘magnanimous’’ and bear the additional expense of costly behaviors in the interests of preserving individual liberty. 

This is not to say that obesity is not a problem. Obesity is associated with many 

chronic diseases, decreased productivity, and psychosocial difficulties. But if a 

health policy targeting a putative cause of obesity does not address an issue in 

which individuals’ actions are insufficient to protect themselves from obesity, then 

the policy may be unwarranted regardless of cost. 

Good Intentions, Unintended Consequences 

Various policy advocates insist that obesity needs to be addressed by public policy, 

either because they reject the definition of public health provided above or because 

they believe action must be taken despite obesity’s not specifically being a public 

health concern. Innumerable policy recommendations have been proposed or enacted 

in an effort to reduce obesity, from ‘‘sin’’ taxes [10] and ‘‘psychic’’ taxes [11] to information campaigns [12] and alterations to the built environment.[13] In some cases, the 

scientific evidence demonstrates fairly clearly that the recommendation will not substantially reduce obesity, which means these policies not only raise ethical concerns 

331 

but may have no beneficial outcome; other recommendations are simply equivocal— 

the potential exists for benefits and harms—and the balance between ethical consequences and health benefits is thus uncertain.[14]

When the outcomes of a particular proposal are uncertain, especially for interventions grounded in ‘‘common sense,’’ one could ask, ‘‘How could it hurt to try?’’ Some 

ways various policies could hurt, despite good intentions, were previously highlighted.[15] Such negative consequences include direct negative effects and encroachment on individual freedom like the list from ten Have, et al., but also include direct 

costs of resources, damage to scientific and political credibility, and distraction from 

more promising efforts and policies. In fact, direct, unintended negative consequences of some policy proposals have been demonstrated (Table 2). 

Table 2. Unintended Consequences of Actions Intended To Affect Obesity 

Action Good intention Documented unintended 

consequence 

Tax sugar-sweetened beverages 

(SSBs). 

Decrease energy intake to decrease 

weight. 

Increased consumption of beer beyond the decrease in sugarsweetened beverages.[17]

Alert patients to their heavy 

weight status. 

Make the patient 

aware of a problem 

as a first step in addressing it. 

Patients may feel stigmatized, become depressed and eat more, 

and avoid future appointments.[16]

Labeling calories on vending machine beverages. Awareness of calories 

will result in decreased consumption. 

Purchases of SSBs increased in 

some settings.[18]

Label ‘‘unhealthful’’ foods with 

messages that encourage consuming fruits and vegetables. 

Increase ‘‘healthful’’ 

behaviors and decrease ‘‘unhealthful’’ 

behaviors. 

Increased selection of an 

‘‘unhealthful’’ snack.[19]

Describe certain restaurants and 

foods as more ‘‘healthful’’ and 

‘‘low-calorie.’’ 

Decrease caloric consumption and shift 

consumption toward 

‘‘healthful’’ foods. 

Consumers consumed more calories in side dishes and beverages, and underestimated 

total meal calories when choosing ‘‘healthy’’ restaurants or 

main dishes.[20]

Labeling calories and removing 

value pricing on menu items. 

Awareness of calories 

and eliminating 

value pricing will 

decrease energy consumption. 

Men ate more calories.[21]

Discourage chocolate consumption. Decrease caloric consumption. Chocolate consumption increased 

for some women in some circumstances.[22]

Encourage children to consume 

fruits by incorporating them 

into games. 

Children prompted to 

eat fruits will increase consumption 

of ‘‘healthful’’ foods 

and decrease caloric 

consumption overall. 

Children ate as many calories 

when prompted by fruit games 

as when prompted by energydense-snack games, did not increase fruit consumption, and 

ate more overall than when not 

prompted by food.[23]

For instance, the ‘‘common sense’’ impetus behind informing patients that they 

are obese may be the old maxim, ‘‘the first step in solving a problem is admitting 

you have one.’’ Yet, there is evidence that clinically relevant words to describe a patient’s weight (e.g., morbidly obese and obese) are considered stigmatizing, which patients state may make them avoid future appointments.[16]

It is important to note that the good intentions and unintended consequences in 

the table represent hand-picked examples and these interventions may not be negative in all circumstances. For instance, there is some evidence that the effects of 

menu labeling on consumer choice can be inconsistent or even positive if delivered 

in specific ways, including whether or not educational information is included and 

whether the participants are male or female.[21, 24–25] Thus, the selected examples 

in Table 2 bring up yet another ethical concern: if a policy intervention benefits one 

subset of the population but harms another, what action should be taken? One could 

argue against implementing a policy so as to do no harm to one group, while anVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00337 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
332 

other could argue that failing to act is tantamount to harming the group that stands 

to benefit.[26–27]

Minimizing Negative Ethical Consequences in Reversing Obesity 

Marieke ten Have and colleagues raise an important complementary point to ethical concerns over policy recommendations: ‘‘The fact that objections are raised does 

not automatically imply that a programme should not be implemented’’.[4] When 

considering an obesity-targeted public health policy, we propose six recommendations: 

1. Evaluate whether the proposed policy addresses an exposure that can truly 

be considered a public health concern.[8]

2. Be honest about the quality and quantity of evidence about the policy.[14]

3. Generate sufficient, high-quality evidence before implementing the policy and 

have plans in place to generate quality evidence about the effectiveness of the 

policy once instated.[28]

4. Do not assume there is negligible or no harm from the policy (see Table 2). 

5. Do not assume that achieving a health benefit overrides respect for other values and ethical principles.[4, 29]

6. Given a choice between two or more plausible policies, choose the policy that 

least compromises ethical values.[29]

These guidelines should help prevent us from paving the roads to health with 

good wishes but unintended consequences. 

References 

1. Shapiro F.R., ed. The Yale Book of Quotations. New Haven, CT: Yale University Press; 2006: 319. 

2. Grynbaum M.M.. New York plans to ban sale of big sizes of sugary drinks.NEW YORK TIMES. May 30, 2012. http:// 

www.nytimes.com/2012/05/31/nyregion/bloomberg-plans-a-ban-onlarge-sugared-drinks.html?pagewanted=all&_r=0. Accessed January 3, 2013. 

3. Levine S. Supporters of Mayor Bloomberg’s anti-obesity initiative [news release]. New York: City of New York; September 13, 

2012. http://www.nyc.gov/html/om/html/2012b/support_for_soda_limits.html. Accessed January 8, 2013. 

4. ten Have M., de Beaufort I.D., Teixeira P.J., Mackenbach J.P., van der Heide A. Ethics and prevention of overweight and obesity: an inventory. OBES. REV. 2011; 12(9): 669–679. 

5. Summar P. Anamarie 4 years later: weight gain, size of child, 7, remain unexplained. ALBUQUERQUE JOURNAL. March 13, 2005. 

http://www.abqjournal.com/news/metro/320825metro03-13-05.htm. Accessed January 4, 2013. 

6. Parents visit overweight child. ABC News. August 31, 2000. http://abcnews.go.com/US/story?id=95940&page=1. Accessed 

January 4, 2013. 

7. Kindig D., Stoddart G. What is population health? AM. J. PUBLIC HEALTH. 2003; 93(3): 380–383. 

8. Anomaly J. Is obesity a public health problem? PUBLIC HEALTH ETHICS. 2012; 5(3): 216–221. 

9. McAllister E.J., Dhurandhar N.V., Keith S.W., et al. Ten putative contributors to the obesity epidemic. CRIT. REV. FOOD. SCI. 

NUTR. 2009; 49(10): 868–913. 

10. Chaufan C., Hong G.H., Fox P. Taxing ‘‘sin foods’’—obesity prevention and public health policy. N. ENGL. J. MED. 2009; 

361(24): e113. 

11. Lucas, Jr. G. Paternalism and psychic taxes: the government’s use of negative emotions to save us from ourselves. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2150402. Accessed March 20, 2013. 

12. Puhl R., Peterson J.L., Luedicke J. Fighting obesity or obese persons? Public perceptions of obesity-related health messages. 

INT. J. OBES. (Lond). 2012. [Epub ahead of print] 

13. White House Task Force on Childhood Obesity. Solving the Problem of Obesity Within a Generation: White House Task Force 

on Childhood Obesity Report to the President. 2010: 78–82. http://www.letsmove.gov/sites/letsmove.gov/files/ 

TaskForce_on_Childhood_Obesity_May2010_FullReport.pdf. Accessed March 20, 2013. 

14. Casazza K., Fontaine K.R., Astrup A., et al. Myths, presumptions, and facts about obesity. N. ENGL. J. MED. 2013; 368(5): 

446–454. 

15. Allison D.B. Evidence, discourse and values in obesity-oriented policy: menu labeling as a conversation starter. INT. J. OBES. 

(Lond). 2011; 35(4): 464–471. 

16. Puhl R., Peterson J.L., Luedicke J. Motivating or stigmatizing? Public perceptions of weight-related language used by health 

providers. INT. J. OBES. (Lond). 2012. [Epub ahead of print] 

17. Wansink B., Just D.R., Cawley J., et al. From Coke to Coors: a field study of a sugar-sweetened beverage tax and its unintended consequences. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2079840. Accessed March 20, 2013. 

18. Jue J.J., Press M.J., McDonald D., et al. The impact of price discounts and calorie messaging on beverage consumption: A 

multi-site field study. PREV. MED. 2012; 55(6): 629–633. 

19. Werle C.O.C., Cuny C. The boomerang effect of mandatory sanitary messages to prevent obesity. MARKETING LETTERS. 2012; 

23(3): 883–891. 

20. Chandon P., Wansink B. The biasing health halos of fast-food restaurant health claims: lower calorie estimates and higher 

side-dish consumption intentions. J. CONSUMER RES. 2007; 34(3): 301–314. 

21. Harnack L.J., French S.A., Oakes J.M., Story M.T., Jeffery R.W., Rydell S.A. Effects of calorie labeling and value size pricing 

on fast food meal choices: results from an experimental trial. INT. J. BEHAV. NUTR. PHYS. ACT. 2008; 5: 63. 

22. Durkin K., Hendry A., Stritzke W.G. Mixed selection. Effects of body images, dietary restraint, and persuasive messages on females’ orientations towards chocolate. APPETITE. 2013; 60(1): 95–102. 

23. Folkvord F., Anschutz D.J., Buijzen M., Valkenburg P.M. The effect of playing advergames that promote energy-dense snacks 

or fruit on actual food intake among children. AM. J. CLIN. NUTR. 2012; 97(2): 239–245. 

24. Girz L., Polivy J., Herman C.P., Lee H. The effects of calorie information on food selection and intake. INT. J. OBES. (Lond). 

2012; 36(10): 1340–1345. 

25. Roberto C.A., Larsen P.D., Agnew H., Baik J., Brownell K.D. Evaluating the impact of menu labeling on food choices and intake. AM. J. PUBLIC HEALTH. 2010; 100(2): 312–318. 

26. Vartanian L.R., Smyth J.M. Primum non nocere: obesity stigma and public health. J. BIOETH. INQ. 2013; 10(1): 49–57. 

27. Gill T.P. Key issues in the prevention of obesity. BR. MED. BULL. 1997; 53(2): 359–388. 

28. Landefeld C.S., Shojania K.G., Auerbach A.D. Should we use large scale healthcare interventions without clear evidence that 

benefits outweigh costs and harms? No. BMJ. 2008; 336(7656): 1277. 

29. Have M.T., van der Heide A., Mackenbach J.P., de Beaufort I.D. An ethical framework for the prevention of overweight and 

obesity: a tool for thinking through a programme’s ethical aspects. EUR. J. PUBLIC HEALTH. 2012. [Epub ahead of print] 

333 

* Correspondence: Dr. D.B. Allison, Department of Biostatistics, Ryals Public Health Building, 

1665 University Boulevard, University of Alabama at Birmingham, UAB Station, Birmingham, 

AL 35294–0022, USA. 

E-mail: DAllison@uab.edu. 

Andrew W. Brown, Ph.D., is a postdoctoral trainee in the Office of Energetics and Nutrition Obesity Research Center at the University of Alabama at Birmingham. Trained in nutritional biochemistry and statistics, Dr. Brown’s research focuses on establishing 

an evidence base for common assumptions about nutrition and health on such topics as organic agriculture, artificial sweeteners, 

and dietary supplements. His recent work relates to research reporting fidelity and its implications for science and policy. 

David B. Allison, PhD, is distinguished professor, Quetelet Endowed Professor of Public Health, associate dean for science, director of the Office of Energetics, and director of the NIH-funded Nutrition Obesity Research Center at the University of Alabama at 

Birmingham. He has authored more than 450 scientific publications, received many awards for his research and mentoring, is an 

elected fellow of many academic societies, and in 2012 was elected to the Institute of Medicine of the National Academies. 

Disclosure 

Dr. Brown receives grant support from the Coca-Cola Foundation through his institution. 

Dr. Allison has served as an unpaid board member for the International Life Sciences Institute of North America. He has received: payment for board membership from Kraft Foods; 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; lecture fees from Porter Novelli and the Almond Board of 

California; payment for manuscript preparation from Vivus; travel reimbursement from International Life Sciences Institute of 

North America; other support from the United Soybean Board and the Northarvest Bean Growers Association; grant support 

through his institution from Wrigley, Kraft Foods, Coca-Cola, Vivus, Jason Pharmaceuticals, Aetna Foundation, and McNeil 

Nutritionals; and 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. Dr. Allison has no financial interests in 

any of these companies. 

Acknowledgments 

We would like to thank Michelle M. Bohan Brown, Ph.D. (University of Alabama at Birmingham), for her valuable input. 

The viewpoints expressed on this site are those of the authors and do not necessarily reflect the views and policies of the AMA. 

Copyright 2013 American Medical Association. All rights reserved. 

ATTACHMENT 6 

Evidence, Discourse, and Values in Obesity-Oriented Policy: Menu-Labeling 

as a Conversation Starter 

Commentary 

D.B. Allison.* 

Departments of Biostatistics and Nutrition Sciences, Nutrition Obesity Research 

Center, University of Alabama at Birmingham, Birmingham, AL, USA. 

International Journal of Obesity (2011) 35, 464–471; doi:10.1038/ijo.2011.28; published online 15 March 2011. 

The new study by Dr. Elbel and colleagues provides an opportunity to reflect on 

where we are with respect to menu labeling as a tool in our antiobesity arsenal and 

more generally to consider some issues surrounding policy-level proposals for stemming the obesity epidemic. By ‘menu labeling’, I refer to listing the calories of menu 

items on the menu. In this commentary, I address two aspects: evidence on menu 

labeling per se; and, at least as important, several scientific, social/political and epistemological issues that generally apply when considering obesity-related policies. 

Elbel, et al.,1 deserve praise for this interesting paper. Dr Elbel previously offered 

that menu labeling is a good idea and should be implemented, stating ‘I see particular value in it when the options are this versus nothing at all . . . Given that 

the problem is so intense, I think we have to try things.’ 2 Yet, despite his previous 

favorable view, he does not mince words upon completing his latest study: ‘Our evaluation of New York City’s labeling law suggests that . . . this public policy intervention had no significant effect on purchasing behavior within our study period for 

a low-income, racially and ethnically diverse population of parents and adolescents.’ 

The authors’ qualifying clauses are appropriate as their study cannot rule out any 

effect, in any period, for any population. Yet, it certainly is one more bit of evidence 

tipping the scales toward the conclusion that menu labeling does not have substantial or consistent beneficial effects on energy intake. 

Strengths and Limitations of the Study by Elbel, et al. 

The study had multiple strengths, including using Newark as a comparator city, 

acquisition of actual receipts, the real-world setting and a population of interest. 

There are also key limitations. First, it is an observational (non-experimental) study 

that, similar to all observational studies, is subjected to potential confounding and 

cannot alone justify conclusions about causation. Second, because no body mass 

index measurements were taken, we cannot discern whether a thinner or more 

obese clientele was buying food at these restaurants, which might affect our interpretation of the value of menu labeling or whether some body mass index categories 

increased and others decreased their calories purchased. Third, the statistical analyses did not account for potential clustering (potential non-independence) of observations within restaurants and families as it should have, although it seems unlikely 

334 

that this would make the nonsignificant results significant. Finally, and importantly, the study only looks at calories purchased at a single eating occasion. It does 

not tell us about calories actually consumed at that occasion (except perhaps the 

upper limit), about whether the knowledge of the calories one purchased on that occasion or simply being ‘attuned to calories’ might have affected energy intake 

throughout the day, nor about how menu labeling affected body weight or body fat 

over an extended time period, the ultimate goal of menu labeling. Although the fact 

that calories purchased did not differ before and after menu labeling makes a beneficial effect less plausible, one cannot rule out that, for example, patrons who realized how many calories were in their purchases chose to eat less of the total food 

they purchased or chose to indulge at the restaurant, but eat less later. 

If we are to understand the value of any macro-environmental manipulation intended to reduce obesity levels, we must eventually measure body weight, fat or obesity levels because we know that people often compensate for perturbations in energy intake or expenditure (c.f. 3). For example, Anderson and Matsa4 showed that 

‘On average, when a given individual eats out, he consumes 238 more calories per 

meal than when he eats at home. . . . (However,) eating out increases intake over 

the entire day by only 35 calories . . . although individuals tend to eat more at restaurants, they compensate to a substantial degree by eating less throughout the rest 

of the day. Meal-level estimates therefore overestimate the net effect of restaurants 

on total caloric intake.’ 

Empirical Issues 

What does the empirical literature show on the effects of menu labeling? 

Although it is impossible to comprehensively summarize this literature herein (for 

an extensive tabulation through mid 2009, see ref. 5), the study by Elbel, et al., ac- cords with most literature in showing no clear and consistent benefit. Furthermore, 

to my knowledge, no study has assessed effects on weight, total energy balance or 

total energy intake for periods beyond 24 h. Thus, no studies are truly probative 

on the actual question of interest: whether menu labeling reduces obesity levels. 

Of the extant studies, many are observational 6–7 and those that are experimental 

are typically, if not exclusively, in laboratory analog settings (for example, see ref. 

8). Some studies show that menu labeling is associated with or affects reduced calories purchased, although among men the association appears less than among 

women, or is absent entirely.9–10 Other studies show no association or effect,7 and 

some even show statistically significant increases (adverse effects) in calories purchased with menu labeling among young men.8, 11 One study suggests menu labeling may decrease calories that parents purchase for children, but not calories that 

they purchase for themselves.12 Another study suggests that, when ingested calories 

(measured by self-report) after the eating occasion on the same day in which menu 

labeling is used are considered, there is a reduction in total energy intake if the 

menu labeling is accompanied by a statement that ‘The recommended daily caloric 

intake for an average adult is 2,000 calories.’ 13 As treatment-induced biases in selfreport measures are well documented (for example, see ref. 14), it is unclear whether this statement affected actual intake or merely reporting. Finally, yet another 

study shows that calorie labeling may either increase or decrease energy consumed, 

depending on the food item labeled.15 Thus, although under some circumstances 

there are hints of short-term (that is, within one eating occasion or day) benefits 

on energy purchased or consumed, overall the results do not offer compelling evidence for effectiveness. When we contrast these recent results with earlier literature 

offering statements such as ‘. . . we estimated that menu labeling would avert 

40.6% of the 6.75 million pound average annual weight gain in the county population aged 5 years and older’,16 it seems that some initial expectations were overly 

optimistic. 

Important Biases To Consider 

In reviewing this literature, it is also important to note biases that may be 

present. One type is what Cope and Allison 17 called ‘white hat bias,’ the tendency 

to distort research information in the service of seemingly righteous ends. Cope and 

Allison 18 cited an example of the Food and Drug Administration in its proposed ruling on menu labeling, citing a study as supporting a favorable conclusion that its 

data did not actually support. As another example, consider an article that reported 

in the abstract ‘Results were similar in most analyses conducted stratified by factors 

such as age, race and education level’.8 Although not explicitly inaccurate, this 

seems to be a misleading statement given that in the article’s body the authors report that ‘Average energy intake was higher among males in the . . . (menu labeling) conditions compared with those who selected their meal from the control menu’ 

with a P-value of 0.01 and no such effect was observed among females. In a similar 

335 

vein, a recent New York Times 19 article based on private e-mails from within the 

NY City Health Department shows that in their zeal to make consumers ‘fear’ soda 

as adiposity promoting, they were willing to knowingly ‘oversimplify’ messages and 

knowingly ignore some science. 

Another form of bias is publication bias, whereby the probability that a paper is 

published or perhaps where it is published, and the resulting attention it receives, 

depends on its outcome, which is often a bias toward studies showing positive effects.20 Notably, opinion pieces suggesting that policies, such as menu labeling, 

front-of-pack labeling and taxation of sugar-sweetened beverages, will be beneficial 

have appeared in the most prominent journals such as JAMA 21–22 or New England 

Journal of Medicine.23–24 Yet, subsequent empirical reports stating that menu labeling, programs designed to reduce consumption of sugar-sweetened beverages and 

front-of-pack labeling do not seem to be very effective have been published in respectable but far less prominent journals and may receive less attention. Moreover, 

several dissertations and theses that have found no beneficial effects do not appear 

to be published as yet (see Table below). If these studies are not published and considered, the published literature may offer a misleadingly favorable view. 

Reference Design Results 

15 Randomized experiment ‘ . . . provision of calorie information does not alter food choice but 

does influence the amount people eat. Although the salad and 

pasta contained the same number of calories, calorie information 

decreased consumption of pasta, but increased consumption of 

salad.’ 25 Randomized experiment ‘There are no differences in total calorie . . . chosen across the three 

groups: price only, nutrition info only, and price + info groups.’ 26 Randomized experiment ‘No significant differences were found in the foods ordered among 

the various menu conditions.’. 

Reporting quality? 

The published research record should have the greatest precision possible and be 

reported in a way that helps readers understand the nature of the research, including its limitations, especially when the research has the potential to affect clinical 

care, public health practices or legislative policies. In part because of this, companybased clinical trials are held to rigorous scrutiny; hence, perhaps consequently, the 

reporting quality of industry-sponsored obesity trials seems to be at least as good 

as, and perhaps better than, non-industry-sponsored trials.27 These same rigorous 

standards of reporting quality should be applied to all articles in peer-reviewed journals, especially those that may influence policy. In this light, consider that, although 

the paper by Elbel, et al.,1 is subtitled ‘A Natural Experiment’, the design is not 

an experiment as defined in the scientific literature (for example, see ref. 28, p. 1) 

but a type of observational study sometimes referred to as a quasi-experiment.29

Such quasi-experiments do not permit the strong inferences to causation that true 

experiments do. Hence, when the conclusion by Elbel, et al.,1 states ‘This study examines the effects (emphasis added) of menu labeling . . .’, causal language is used 

without justification. Although this is a common slip,30 it may nevertheless confuse 

readers, including mass media reporters or policy makers into thinking an effect (or 

lack thereof) has been shown when, in fact, only a lack of an association has been 

shown. Further, just as association does not necessarily imply causation, lack of association does not necessarily imply lack of causation. Similarly, Elbel, et al.,1 discuss ‘calories consumed’, but the actual outcome of the study was calories purchased. This is an important distinction because the menu labeling could conceivably cause consumers to consume less of a perceived high-calorie item even if it is 

purchased. I state these points about precision, when I have made similar errors 

of imprecise language in my own papers, to point out that we should hold papers 

in the public health policy arena to every bit as rigorous a standard of reporting 

as we do big pharmaceutical randomized controlled trials (RCTs). Allowing ourselves 

to slip into imprecise language potentially creates misunderstandings among readers that can lead to erroneous public discourse about proposed policies. 

Social and Philosophical Issues 

Does it matter if it matters? 

At a presentation on menu labeling at the 2009 Obesity Society meeting, Professor 

K.D. Brownell asked ‘Does it matter if it matters?’ That is, does it matter whether 

data show that menu labeling is beneficial in deciding whether to endorse it? He 

used the instructive analogy of tags that describe a garment’s composition. We do 

not demand randomized experiments showing that such tags produce a benefit; we 

336 

simply take as given that people have a ‘right’ to know the composition of the fabrics they purchase. Can we extend this argument by analogy to menu labeling? Perhaps, but trying invites the question, who is ‘we’ in the preceding sentence? Is it 

society at large? Or is it we members of the scientific community acting in our role 

as scientists per se rather than as member of the general public who just happen 

to be scientists by profession? This is an important distinction, because if we are 

not predicating our endorsement of a policy based on empirical evidence or even expert scientific opinion on the policy’s effects, then this does not seem to be a scientific issue at all but rather one of politics, morality or taste, and it is not obvious 

that scientists qua scientists have anything special to add here. And if not, is this 

even a relevant topic of discussion for our scientific conferences and journals? In 

fact, because there are potential costs and harms of all interventions, balancing 

risks and benefits is important. By analogy, consider what Food and Drug Administration wrote in its final ruling banning ephedra as a weight loss supplement and 

in determining whether there was a ‘significant or unreasonable risk’: ‘There is no 

requirement that there be evidence proving . . . harm to specific individuals, only 

that scientific evidence supports the existence of risk. . . . ‘Unreasonable risk,’ thus, 

represents a relative weighing of . . . known and reasonably likely risks against its 

known and reasonably likely benefits.’ 31 In this light, it does matter if it matters. 

Choice-Limiting Versus Choice-Promoting Strategies 

Obesity-related legislation is mushrooming. Between 2003 and 2005, in the 

United States, at least 717 bills and 134 legislative resolutions were proposed, of 

which 17% of bills and 53% of resolutions were adopted.32 Proposals for policy-based 

approaches almost invariably meet resistance when the policies offend the moral or 

political sensibilities of some persons. This especially occurs when the proposed policy is seen as treading on the rights or autonomy of individuals in the interests of 

public health paternalism. Therefore, if we wish to minimize such resistance, we 

should aim to advance proposals that are freedom and choice promoting, rather than 

restricting. Consequently, many advocates of policy level approaches recognize the 

merit of ‘opt-out’ versus ‘opt-in’ approaches 33 that can be effective in promoting desired behavioral changes without compromising individual liberty. Unfortunately, 

not all public health advocates are sensitive to this issue. For example, proposals 

for taxing certain foods, such as sugar-sweetened beverages, limit individual freedom to negotiate a price for a desired product with the purveyor. By way of contrast, 

consider the experiments by experiments by Sharpe, et al.,34 showing that consumers’ extremity avoidance behavior (for example, not wanting to choose the smallest or largest item in a series) led to a potentially useful effect. Specifically, merely 

offering a smaller size drink in addition to (not instead of) the existing sizes led to 

an overall reduction in the amount of drink purchased. On the basis of these results, 

one could imagine a policy in which restaurants that offer multiple sizes of drinks 

would be required to offer an additional drink smaller than their current smallest 

size and, if effective, the principle might be extended to other foods that are sold 

in multiple distinct serving sizes. Such a policy would limit the freedom of corporations, but not of individuals, and hence presumably meet far less resistance from 

libertarians. 

How might libertarian concerns relate to menu labeling? One benefit of menu labeling is that it provides more information for consumers to make more informed 

choices. As someone with libertarian leanings, I generally favor more and accurate 

information being made available to citizens 35 and, as an individual consumer, I 

enjoy learning about the foods I may choose to eat; therefore, in many settings such 

as fast-food restaurants, I would enjoy menus labeled with nutritional information. 

However, that is just a statement of my personal preferences and tastes, not a scientific statement, and other individuals may have other preferences and tastes. For 

example, in 2008, ‘After students and parents raised concerns about displayed calorie counts leading to or worsening eating disorders, Harvard University Dining 

Services removed the index cards detailing nutritional information from dining 

halls’.36 Regardless of the empirical basis for the Harvard parents’ concerns, their 

feelings and preferences are real, and if we seek policies that allow choice, we will 

respect such feelings. 

How might advocates of menu labeling minimize the resistance they receive if 

they try to move menu labeling into all restaurants, not just into fast-food chains? 

In this light, consider that this is not the first time in history that restaurant menu 

content has been subject to scrutiny and change. Years ago, it was common for restaurants to have ‘blind menus’ without prices listed so patrons could take out their 

guests without the mood of the dinner being affected by the guest seeing the 

prices.37 Although such practice is now uncommon, many higher-end restaurants 

still provide such menus to patrons upon request. In this way, consumer choice is 

337 

enhanced. The price information is there for all who want it and hidden for those 

who do not (in an ‘opt-out’ manner). There is an analogy with respect to calorie information. In some circumstances, for example, when someone takes their special 

someone out for that romantic dinner to propose marriage or celebrate an anniversary, even the most health conscious among us may not wish to consider the calories 

in our food. This potential preference could be respected and choice enhanced if consumers had the option of viewing a menu with or without the calorie contents (in 

a ‘opt-out’ manner). In a full-service restaurant with printed menus, this can be easily accomplished, and if menu labeling advocates propose such a choice-promoting 

approach, they are likely to meet applause instead of resistance from libertarians. 

Can we lead by example? 

Mohandas Gandhi said, ‘We must be the change we wish to see in the world. 

Change can only come from the roots upwards, never from the treetop down’.38 Our 

more modern guru, Michael Jackson, sang ‘I’m Starting With The Man In The Mirror. . . . If You Wanna Make The World A Better Place, Take A Look At Yourself 

And Then Make That Change!’ In this light, an irony to the menu-labeling advocacy 

has occurred. According to Friedman,39 ‘Fast food is eaten disproportionately by lowincome people, who are more likely to be overweight.’ Further, meals eaten in fullservice restaurants tend to be high in calories and fat as well,40 maybe even higher 

than those in fast-food restaurants, especially in the case of children’s and adolescents’ meals.41–42 Further, higher income people, who are more likely to patronize 

such restaurants, are not exempt from obesity. Yet, our early public health salvo 

at restaurants has not been aimed at those establishments likely frequented by the 

well-established senior academicians and high-ranking public health officials who 

propose the policies, but at those more frequented by people of lesser means. Other 

proposals that seem targeted more at persons of lesser means and that may seem 

restrictive or punitive have been made, such as restricting the provision of toys in 

children’s fast-food meals, disallowing purchase of sugar-sweetened beverages with 

Supplemental Nutrition Assistance Program funds (food stamps), and restricting the 

location of fast-food outlets. 

I do not doubt that the intentions of most policy advocates are sincerely beneficent, nor that the greater uniformity of chain restaurants offers a rationale for 

starting there within the category of restaurants. Nevertheless, incarnations of public health paternalism aimed more at changing unhealthy behaviors among members of less-powerful social classes than the equally unhealthy behaviors of the social classes proposing the policies cannot be seen as Gandhian and has likely provoked some of the pushback that has occurred. This sentiment is well illustrated 

by the feminist writer, Anna Kirkland. 

‘. . . this environmental approach to obesity has been sold as a progressive, 

structurally focused alternative to stigmatization, but it actually embeds and reproduces a persistent tension in feminist approaches to social problems: wellmeant efforts to improve poor women’s living conditions at a collective level 

often end up as intrusive, moralizing, and punitive direction of their lives . . . 

good choice dominated by elite norms of consumption and movement’.43

As our field moves forward with the consideration of other public health policies 

that may have paternalistic aspects, perhaps we should start with the man in the 

mirror. Given the plausible weight-reducing effects of less heating and air conditioning and more sleep,44 perhaps advocates of paternalistic public health policies 

should first demand that the use of heating and air conditioning be reduced in the 

buildings in which they and we work (which would also have an environmental and 

economic benefit) and take a pledge not to work on grant proposals, manuscripts 

or e-mail correspondence after 2200 hours. That would be leadership that walks the 

walk. 

Majority rules? 

An argument sometimes made in support of a proposed policy approach to obesity 

is that the general population desires the policy. Regarding menu labeling, a Robert 

Wood Johnson Foundation briefing states that survey and focus group research indicates that ‘males and females of diverse educational backgrounds reacted favorably 

to the idea of labeling menu items with just calorie information or identifying 

healthier options with a uniform, commonly defined symbol to help them make better choices’.5 Of course, public opinion should count. In the extreme case of a unanimous population opinion, the decision is easy. 

However, in cases in which there is a majority view favoring a new policy, but 

not unanimity, should majority rule? There are reasons to question ‘majority rules’ 

as a justification for a new policy, and again we should be open in acknowledging 

338 

that subjective values in addition to scientific evidence come into play. First, one 

should view opinion poll results about proposed policies with healthy scientific skepticism. Research on survey methodology has repeatedly shown that seemingly minor 

variations in question wording can have major influences on responses.45 For example, very different responses are obtained when people are asked whether the government should ‘forbid’ something as opposed to whether the government should 

‘allow’ something, even though one question is just the complement of the other. 

Thus, we should perhaps only be persuaded by results of surveys purportedly showing popular support when such results are replicated with multiple differently worded questionnaires prepared by different parties. 

Second, sometimes the public may desire a policy based on an erroneous view of 

its likely effects. Those advocates who accept paternalistic public health approaches 

could argue that one should act in accordance with the public’s interests, not necessarily on the basis of their expressed views. 

Finally, and most importantly from a libertarian view in which individual freedom 

is paramount, the desire of the majority is insufficient justification to tread upon 

the rights of the individual. Consider, for example, our response to a proposal that 

we have required prayer of a particular religious doctrine because the majority of 

the population thinks it is a desirable thing. The United States and many other 

democratic countries have already affirmed that, regardless of popular view, such 

a proposal is unacceptable. Clearly, most proposed obesity-related policies, including 

mandatory menu labeling, are not so extreme, and yet we should remember that 

multiple children in multiple countries (including the United States) have been removed from their parents’ and homes by governmental actions on the basis of protecting them from their obesity-promoting environment,46–47 and that in 2008 three 

Mississippi legislators proposed ‘An Act To Prohibit Certain Food Establishments 

From Serving Food To Any Person Who Is Obese, Based On Criteria Prescribed By 

The State Department Of Health’ (http://billstatus.ls.state.ms.us/documents/2008/ 

pdf/HB/0200-0299/HB0282IN.pdf). Not surprisingly, the legislation was never enacted. Thus, we should be skeptical of majority public opinion as a justification for 

restrictive policies. 

Epistemological Issues 

What constitutes an adequate evidence base? 

Consider that, after analyzing 38 policy documents from five European countries, 

authors found that ‘Only 22% of the obesity statements were evidence based’.48 We 

seem to have a dearth of evidence in the obesity policy domain. There is little (although not zero) debate that randomized experiments offer the highest quality evidence we can obtain about the effects of interventions, including policies. There is 

also little debate that results obtained from well-done randomized experiments, especially on long-term outcomes on variables such as body weight or fat, will not always be available when we wish to make a statement, conclusion or decision about 

a proposed policy. In such situations, what should the standard of evidence be and 

who gets to decide that standard? At present, there is no obvious consensus. Importantly, answering the question of what constitutes adequate evidence depends on 

the context in which the question is called and will not be the academic community’s 

alone (or in some cases at all) to decide. For example, in deciding whether a particular piece of litigation is justifiable, the level of scientific evidence required will 

be determined by applicable law as interpreted by the judiciary, not by academics. 

Second, in considering what constitutes adequate evidence, it is essential to distinguish between conclusion making and decision making. Scientists and public 

health advocates sometimes clash because they conflate this distinction. The advocate, who may be someone who is also sometimes a scientist, maligns the scientist 

qua scientist as trying to hold back progress and upholding an unreasonable standard, whereas the scientist maligns the advocate as playing ‘fast and loose with’ or 

even ignoring scientific evidence. The problem is rarely that the two parties disagree 

on what evidence exists or what it shows, but rather they are answering two different types of questions and fail to realize or acknowledge this. The scientist is concerned with questions about the truth of propositions and addresses questions such 

as ‘By generally accepted scientific standards, can we reasonably conclude today to 

a reasonable degree of certainty that A causes B?’ In contrast, the well-meaning 

public health advocate is concerned with questions about what we should do, such 

as ‘Given what we know today, however limited, is it prudent to implement A in 

the hopes that B will happen in response?’ If we recognize this, there is no contradiction between the scientist saying that the evidence for the benefit of a proposed policy is weak, limited, inconclusive, or even non-existent and the advocate 

saying that, despite the fact that there is insufficient evidence to conclude that the 

policy will be effective, we should give it a try to determine whether it might plauVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00344 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
339 

sibly work and whether the likely benefits outweigh the likely costs. Recognizing 

this, the honest advocate need not and should not try to inflate the evidence in support of a policy they wish to advance, as seems to be done now,18–19 but can honestly 

acknowledge the degree of uncertainty, respect scientists for contributing discussions of evidence to promote an informed decision-making process, and then advocate for prudential decision making. 

Are randomized studies needed and possible? 

Some advocates of policy approaches are dismissive of the role of randomized 

trials. In arguing this position, several valid points are commonly noted. These are 

listed in the first column of Table below. Unfortunately, gaining acceptance of these 

points is often followed by a rhetorical sleight of hand, whereby they are replaced 

with the far stronger and fundamentally different points listed in the second column 

of the table below. Writings that cogently show the fallacies of these points are listed in the third column. 

Valid points about which there is 

little if any debate 

Invalid points often conflated with the 

valid ones by advocates of proposed 

actions 

References 

disputing the 

invalid point 

It is difficult to conduct randomized experiments to test the effects of public policies. It is impossible and perhaps irrelevant to conduct randomized experiments to test the effects of public policies. 

49

It is sometimes necessary to move forward 

and take actions even in the absence of 

the highest quality evidence that is practically obtainable. 

Once we decide to move forward, there is no 

need to do so in a manner in which the highest quality evidence that is practically obtainable is indeed obtained. 

50

In some situations (e.g., when considering 

smoking as a cause of lung cancer), the 

scientific community has judged that it is 

appropriate to draw strong conclusions 

about causation in the absence of experimental evidence. 

In general, it is appropriate to draw strong conclusions about causation in the absence of experimental evidence. 

51–52

In conversation, a colleague who is a strong advocate of public policy interventions 

such as menu labeling and taxation of selected foods said, ‘You know, David, we are 

never going to know for sure whether these policies work before we implement 

them.’ I replied, ‘You may be right, but the way we are going about it, we will never 

know afterward either.’ It is unfortunate that, to date, all the evidence we have on 

menu labeling is either from short-term laboratory analog experiments or from nonexperimental observational studies. Although practical complexities undoubtedly 

exist, it is certainly possible that as the Federal Government proceeds with national 

menu labeling 53 they could randomly introduce it in 1⁄2 of the states or counties in 

the United States and not in the other 1⁄2 for a year in a valid cluster-randomized 

trial. At year’s end, they might plausibly have unequivocal evidence on the effects 

of menu labeling on food purchases. Such data could offer guidance about whether 

to then implement the program in the remaining locations or discontinue the requirement. Similarly, many major national fast-food restaurant chains, were they 

inclined to conduct such a study, would easily have the financial resources and computer recording infrastructure of purchases to conduct such a trial by randomly assigning labeled menus to some of restaurants and unlabeled menus to others and 

then comparing the sales figures. If such studies were conducted at the state or 

county level, one could even solicit participation from and enroll specific subjects 

who are known to be high fast-food consumers at baseline and go beyond merely 

examining the effects on purchases at the cluster level; instead, one could study the 

actual end point of interest, body weight or fat in individual persons. Remarkably, 

for all the passion that public policy advocates bring to the table to push policies 

forward, they have not used that passion to demand that such studies be carried 

out. If they did so, perhaps they would help us learn how effective the policies they 

advocate are. 

Could it hurt? 

An argument sometimes made by advocates of policies that seem intuitively sound 

to them but are not supported by strong evidence is that, even if ineffective, such 

policies will be harmless. This is fallacious reasoning. There are at least five types 

of harm, or more generically cost, that may accrue. The first is direct negative effects of the policies on collateral outcomes (concerns about economic impacts and 

stigmatization are sometimes raised) or on the outcomes that are themselves the 

targets of policy, such as the increase in calories purchased by males seen in some 

menu-labeling studies.11, 35 The second is encroachment on individual freedom that 

340 

occurs with some policies as discussed earlier. The third is that if the scientific community advances a policy as very likely to be beneficial, which is then found not 

to be beneficial, our credibility may be damaged and, like the boy who cried wolf, 

our voices will carry less weight when we genuinely have important messages to 

convey. The fourth cost is distraction. When we focus our efforts on advocating and 

implementing methods that turn out to be ineffective, we are not spending those efforts on other approaches that might be better. Finally, there are direct resource 

costs. Every dollar our society spends implementing one policy is $1 less that we 

have available to support education, the arts, or any number of other things our citizens may find as equally deserving causes. This is not to say that these harms or 

costs will come to fruition in implementing any one policy, but they are plausible, 

should enter into society’s decision analysis, and justify asking about evidence of 

benefit when considering proposed policy approaches to obesity. 

Conclusion 

The timely study by Elbel, et al.,1 adds to the growing body of evidence suggesting, but not demonstrating, that menu labeling has no important effect on reducing calories purchased at a single dining occasion. Moreover, it highlights the 

frustrating truth that, as such policies are implemented, they are not implemented 

in a manner that allows the most rigorous assessments of their effects to be conducted. Like all other studies to date, the study by Elbel, et al.,1 does not offer 

strong evidence about causation (or lack thereof), information about long-term effects, or effects on the variable that menu labeling is intended to affect, namely, obesity levels. As we move forward to consider this and other policy-level proposals for 

addressing obesity, as scientists we should hold high standards of discourse and of 

evidence and we should maintain a sense of humility about the accuracy of our predictions about the effects of our proposed policies. Society will sometimes be justified 

in moving forward even in the absence of strong evidence for the benefits of a proposed policy; yet, as scientists we should offer our most unbiased assessment of the 

current evidential base and ask that, as any new policies are implemented, rigorous 

evaluations of their effects should be conducted. 

Conflict of Interest 

Dr. Allison has received grants, honoraria, donations and consulting fees from numerous food, beverage, pharmaceutical companies and other commercial, government and nonprofit entities with interests in obesity and nutrition; receives royalties from obesity-related books; and in the past has received funds from litigators representing the restaurant industry in menu-labeling litigation. 

Acknowledgements 

I am grateful to Drs. Kevin Fontaine, Alexis Wood and Kyle Grimes for their helpful comments. 

Disclaimer 

The opinions expressed are the author’s and not necessarily those of any organization with which he is affiliated. 

References 

1. Elbel B., Gyafami J., Kersh R. Child and adolescent fast food choice and the influence of calorie labeling: a natural experiment. 

INT. J. OBESITY 2011; 35: 493–500. 

2. Winerman L. With menu labeling law, diners will soon know calorie counts, 2010, http://www.pbs.org/newshour/rundown/ 

2010/03/with-menu-labeling-law-diners-will-soon-know-caloriecounts.html#. Accessed 28 December 2010. 

3. Faith M.S., Fontaine K.R., Baskin M.L., Allison D.B. Toward the reduction of population obesity: macro-level environmental approaches to the problems of food, eating, and obesity. PSYCHOL. BULL. 2007; 133: 205–226. 

4. Anderson M.L., Matsa D.A. Are restaurants really supersizing America? AMER. ECON. J: APPL. ECON. (in press). http://papers.ssrn.com/sol3/papers.cfm?abstract_id%1079584. Accessed December 28, 2010. 

5. Robert Wood Johnson Foundation. Menu labeling: does providing nutrition information at the point of purchase affect consumer 

behavior?, 2009, http://www.rwjf.org/files/research/20090630hermenulabeling.pdf. Accessed 26 December 2010. 

6. Pulos E., Leng K. Evaluation of a voluntary menu-labeling program in full-service restaurants. AM. J. PUBLIC HEALTH 2010; 

100: 1035–1039. 

7. Elbel B., Kersh R., Brescoll V.L., Dixon L.B. Calorie labeling and food choices: a first look at the effects on low-income people in 

New York City. HEALTH AFF. (Millwood) 2009; 28: w1110–w1121. 

8. Harnack L.J., French S.A., Oakes J.M., Story M.T., Jeffery R.W., Rydell S.A. Effects of calorie labeling and value size pricing 

on fast food meal choices: Results from an experimental trial. INT. J. BEHAV. NUTR. PHYS. ACT. 2009; 5: 63. 

9. Bollinger B., Leslie P., Sorensen A. Calorie Posting in Chain Restaurants. Unpublished manuscript. http:// 

www.gsb.stanford.edu/news/StarbucksCaloriePostingStudy.pdf. Accessed 29 December2010. 

10. Gerend M.A. Does calorie information promote lower calorie fast food choices among college students? J. ADOLESC. HEALTH

2009; 44: 84–86. 

11. Aaron J., Evans R., Mela D. Paradoxical effect of a nutrition labeling 33. Scheme in a student cafeteria. NUTR. RES. 1995; 15: 

1251–1261. 

12. Tandon P.S., Wright J., Zhou C., Rogers C.B., Christakis D.A. Nutrition menu labeling may lead to lower-calorie restaurant 

meal choices for children. PEDIATRICS 2010; 125: 244–248. 

13. Roberto C.A., Larsen P.D., Agnew H., Baik J., Brownell K.D. Evaluating the impact of menu labeling on food choices and intake. AM. J. PUBLIC HEALTH 2010; 100: 312–318. 

14. Taber D.R., Stevens J., Murray D.M., Elder J.P., Webber L.S., Jobe J.B., et al. The effect of a physical activity intervention on 

bias in self-reported activity. ANN. EPIDEMIOL. 2009; 19: 316–322. 

15. Girz L.P.. The Effects of Calorie Information on Food Selection and Intake (Master’s Thesis). University of Toronto: Toronto, 

Canada, 2009. 

16. Kuo T., Jarosz C.J., Simon P., Fielding J.E. Menu labeling as a potential strategy for combating the obesity epidemic: a health 

impact assessment. AM. J. PUBLIC HEALTH 2009; 99: 1680–1686. 

341 

[1]Department of Population Health Sciences, Virginia Polytechnic Institute and State University. [2] Nutrition Obesity Research Center, University of Alabama at Birmingham (UAB). [3]School of Nursing, Auburn University. 

* Corresponding Author: School of Public Health, University of Alabama at Birmingham, 

Ryals Building, Room 140J, 1665 University Boulevard, Birmingham, Alabama 35294, Phone: 

(205) 975–9167, dallison@uab.ed. 

References—Continued 

17. Cope M.B., Allison D.B. White hat bias: examples of its presence in obesity research and a call for renewed commitment to 

faithfulness in research reporting. INT. J. OBES. 2010; 34: 84–88. 

18. Cope M., Allison D.B. White hat bias: a threat to the integrity of scientific reporting. ACTA. PAEDIATR. 2010; 99: 1615–1617. 

19. Hartocollis A. E-mails reveal dispute over city’s ad against sodas. NEW YORK TIMES 2010, e-pub ahead of print 29 October 

2010. 

20. Emerson G.B., Warme W.J., Wolf F.M., Heckman J.D., Brand R.A., Leopold S.S. Testing for the presence of positive-outcome 

bias in peer review: a randomized controlled trial. ARCH. INTERN. MED. 2010; 170: 1934–1939. 

21. Ludwig D.S., Brownell K.D. Public health action amid scientific uncertainty: the case of restaurant calorie labeling regulations. 

JAMA 2009; 302: 434–435. 

22. Nestle M., Ludwig D.S. Front-of-package food labels: public health or propaganda? JAMA 2010; 303: 771–772. 

23. Brownell K.D., Farley T., Willett W.C., Popkin B.M., Chaloupka F.J., Thompson J.W., et al. The public health and economic 

benefits of taxing sugar-sweetened beverages. N. ENGL. J. MED. 2009; 361: 1599–1605. 

24. Nestle M. Health care reform in action-calorie labeling goes national. N. ENGL. J. MED. 2010; 362: 2343–2345. 

25. Read J.L. The Effect of Point-of-Purchase Nutrition Information on Consumer Choice in Fast Food Restaurants (Master’s Thesis). University of Guelph: Ontario, Canada, 2008. 

26. Platkin C.S. Think Before You Eat: Calories and Exercise Equivalents Presented on Menus at Point-of-Choice (Doctoral Dissertation). Florida International University: Miami, FL, 2009. 

27. Thomas O., Thabane L., Douketis J., Chu R., Allison D.B. Industry funding and the reporting quality of large long-term 

weight loss trials. INT. J. OBES. 2008; 32: 1531–1536. 

28. Cox D.R., Reid N. The Theory of the Design of Experiments. Chapman & Hall/CRC Press: Boca Raton, FL, 2000. 

29. Campbell D.T., Stanley J.C. Experimental and Quasi-Experimental Designs for Research. Rand McNally: Chicago, 1963. 

30. Cofield S.C., Corona R.V., Allison D.B. Use of causal language in observational studies of obesity and nutrition. OBES. FACTS

2010; 3: 353–356. 

31. Food and Drug Administration. FEDERAL REGISTER Final Rule, FFR 696787 Final Rule Declaring Dietary Supplements Containing Ephedrine Alkaloids Adulterated Because They Present an Unreasonable Risk, 2004, http://www.fda.gov/Food/ 

DietarySupplements/GuidanceComplianceRegulatoryInformation/RegulationsLaws/ucm079733.htm. Accessed 8 January 2011. 

32. Boehmer T.K., Brownson R.C., Haire-Joshu D., Dreisinger M.L. Patterns of childhood obesity prevention legislation in the 

United States. PREV. CHRONIC DIS. 2007; 4: A56. 

33. Yudin H. Opt out, not opt in. CAN. MED. ASSOC. J. 2003; 169: 279. 

34. Sharpe K.M., Staelin R., Huber J. Using extremeness aversion to fight obesity: policy implications of context dependent demand. J. CONSUMER RES. 2008; 35: 406–422. 

35. Allison D.B., Heshka S., Sepulveda D., Heymsfield S.B. Counting calories—caveat emptor. JAMA 1993; 270: 1454–1456. 

36. Hu M.Y. Calorie cards removed from dining halls. HARVARD CRIMSON 2008, http://www.thecrimson.com/article/2008/9/24/ 

calorie-cards-removed-from-dining-halls/. 

37. Bruni F. Disappearing Ink. NEW YORK TIMES 2006, http://dinersjournal.blogs.nytimes.com/2006/08/15/disappearing-ink/ 

?apage%3. Accessed 28 December 2010. 

38. Quoted in Gandhi A. In: Einhorn L., Gandhi A. (eds). Forgiveness and Child Abuse: Would YOU Forgive?, Chapter 17. Robert 

Reed Publishers: Bandon, OR, 2006. p 71. 

39. Friedman R.R. Scientific Studies Related to Menu Labeling. http://www.cspinet.org/new/pdf/ 

yale_rudd_ctr_menu_labeling_grouped_studies.pdf. Accessed 29 December 2010. 

40. Guthrie J.F., Lin B.H., Frazao E. Role of food prepared away from home in the American diet, 1977–78 versus 1994–96: 

changes and consequences. J. NUTR. EDUC. BEHAV. 2002; 34: 140–150. 

41. Serrano E.L., Jedda V.B. Comparison of fast-food and non-fast-food children’s menu items. J. NUTR. EDUC. BEHAV. 2009; 41: 

132–137. 

42. Yamamoto J.A., Yamamoto J.B., Yamamoto B.E., Yamamoto L.G. Adolescent calorie/fat menu ordering at fast food restaurants compared to other restaurants. HAWAII MED. J. 2006; 65: 231–236. 

43. Kirkland A. The environmental account of obesity: a case for feminist skepticism. J. WOMEN CULTURE SOCIETY 2011; 36: 463– 

485. 

44. McAllister E.J., Dhurandhar N.V., Keith S.W., Aronne L.J., Barger J., Baskin M., et al. Ten putative contributors to the obesity epidemic. CRIT. REV. FOOD SCI. NUTR. 2009; 49: 868–913. 

45. Schwartz N., Groves R.N., Schuman H. Survey Methods. In: Gilbert D., Fiske S.T., Lindzey T. (eds). The Handbook of Social 

Psychology. vol. 1, 4th edn. Oxford University Press: New York, NY, 1998, pp. 143–179. 

46. Faure G. Should parents of obese kids lose custody? TIME MAGAZINE 2009, http://www.time.com/time/health/article/ 

0,8599,1930772,00.html. Accessed 26 December 2010. 

47. Murtagh L. Judicial interventions for morbidly obese children. J. LAW MED. ETHICS 2007; 35: 497. 

48. Martin-Bautista E., Campoy C., Decsi T., Bokor S., von Rosen-von Hoewel J., Laitinen K., et al. Obesity related programming 

statements in infant feeding policies in five European countries. ADV. EXP. MED. BIOL. 2009; 646: 169–173. 

49. Rosen L., Manor O., Engelhard D., Zucker D. In defense of the randomized controlled trial for health promotion research. AM. 

J. PUBLIC HEALTH 2006; 96: 1181–1186. 

50. Ayres I. Government By Chance. In: Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart, Chapter 3. 

Bantam Books, 2007. 

51. Green M.D., Freedman D.M., Gordis L. Reference Guide on Epidemiology. In: Reference Manual on Scientific Evidence, 2nd 

edn. Federal Judicial Center, Washington, D.C., http://www.fjc.gov/public/pdf.nsf/lookup/sciman06.pdf/$file/sciman06.pdf. 

Accessed 27 December 2010. 

52. Hill A.B. The environment and disease: association or causation? PROC. R. SOC. MED. 1965; 58: 295–300. 

53. Food and Drug Administration. Guidance for industry: Questions and Answers Regarding the Effect of Section 4205 of the Patient Protection and Affordable Care Act of 2010 on State and Local Menu and Vending Machine Labeling Laws. FED. REG. 2010; 

75: 164 http://edocket.access.gpo.gov/2010/pdf/2010-21067.pdf. 

ATTACHMENT 7 

The Development of Scientific Evidence for Health Policies for Obesity: Why 

and How 

Molly B. Richardson,[1–2] Michelle S. Williams,[3] Kevin R. Fontaine,[2]–[3] David B. 

Allison.[2], *

342 

Running title: Scientific process to develop obesity policy 

Acknowledgements: Supported in part by the NIH (T32HL105349) and the Nutrition Obesity Research Center (P30DK0563360). The opinions expressed are those of 

the authors and do not necessarily represent those of the NIH or any other organization. We gratefully acknowledge Gregory Pavela, Alexandra S. Allison, Ted Kyle, 

Jennifer Holmes, Olivia Affuso, Paula Chandler-Laney, and Daniel L. Smith, Jr. for 

input on this manuscript. 

Conflict of Interest: DBA has received financial support from numerous nonprofit 

and for profit organizations including government, litigators, food and beverage, 

pharmaceutical, and publishing companies. 

Abstract 

Potential obesity-related policy approaches have recently been receiving more attention. While some have been implemented and others only proposed, few have 

been formally evaluated. We discuss the relevance, and in some cases irrelevance, 

of some of the types of evidence that are often brought to bear in considering obesity-related policy decisions. We discuss major methods used to generate such evidence, emphasizing study design and the varying quality of the evidence obtained. 

Third, we consider what the standards of evidence should be in various contexts, 

who ought to set those standards, as well as the inherent subjectivity involved in 

making policy decisions. Finally, we suggest greater transparency from both academics and policymakers in the acknowledgment of subjectivities so they can distinguish and communicate the roles played by empirical evidence and subjective values 

in the formulation of policy. 

Introduction 

Proposals to use policy measures such as taxing persons with obesity as ways to 

raise revenue and discourage poor health behaviors, including high levels of consumption, existed at least as early as 1904.1 However, it was largely in the mid1990s that the academic and professional dialogue around obesity shifted from one 

dominated by basic science and clinical research to involve a third branch, namely, 

public health approaches. Inspired in part by the successful efforts to curtail cigarette smoking, potential obesity-related policy approaches began receiving more attention. A selection of such policies include, but are not limited to, providing information (e.g., labeling restaurant menus with nutritional facts), marketing ideas to 

inspire behavior change (e.g., placing public health posters in subway systems to 

discourage or encourage certain food or activity behaviors), mandating the measurement and reporting of the body mass index (BMI) of schoolchildren to parents, enacting worksite economic contingencies, changing food offerings for schoolchildren, 

zoning of allowable restaurants, banning the sale of certain portion sizes, taxing or 

subsidizing certain foods, and providing economic incentives and disincentives 

through insurance charges. Some of these have been implemented and some have 

only been proposed. Few have been rigorously evaluated and fewer still have unequivocal evidence demonstrating efficacy in stabilizing or reducing body weight. 

Because the implementation of such policies typically involves at least some of the 

following: money, limitations on the freedom of businesses to engage in certain types 

of commerce, limitations on personal freedom, and opportunity cost with regard to 

time and attention; it is not surprising that obesity-related policy proposals often 

provoke heated debate. Moreover, the debate frequently focuses on moral issues, 

sometimes involving the balance between autonomy and beneficence or individual 

fairness and societal benefits. Because these issues revolve around morals and values, they are difficult to reconcile. As such, they are repeatedly deferred while the 

dialogue jumps to questions of judging the quality of evidence. Yet even here, disagreements abound as to the strength of evidence and whether it supports a particular position on a proposed policy. Equally important and sometimes debated, but 

often simply glossed over, are questions such as, (1) What type of evidence is needed 

and appropriate for a particular situation? (2) How can such evidence be generated? 

and (3) Is evidence even needed at all to justify the implementation or rejection of 

a particular proposed policy? 

In this article, we address three macro-level questions. First, concerning evidence, 

we raise questions about the relevance of some types of evidence that are often 

brought to bear in policy dialogues. Second, we discuss the major methods used to 

generate such evidence, with particular focus on the fact that there are a range of 

study designs (i.e., ordinary association tests to pure randomized controlled trials 

[RCTs]) that yield evidence of varying quality and varying ability to support causality. Third, we consider what the standards of evidence should be in various contexts, as well as who ought to set those standards, and emphasize the inherent 

subjectivities involved in making policy decisions. We conclude by noting that it 

343 

would be beneficial if both academics and policymakers were transparent in recognizing and conveying those subjectivities while taking care to both understand and 

distinguish the roles of empirical evidence and subjective values. 

What Do We Want Evidence About? 

Evidence Regarding Plausibility 

When considering a potential policy, the first evidence-oriented question we might 

ask is, ‘‘Is there evidence that the policy will plausibly be effective?’’ That is, is there 

reason to speculate that the policy will work? Of course, beyond simply saying we 

cannot prove the contrary, the plausibility of a proposition is subjective, but one’s 

reasons for declaring something plausible or implausible can be specified. At the 

most superficial level, many obesity policies can be deemed plausible on the basis 

of the simple concept of energy imbalance as a cause of obesity. Any policy directed 

at either increasing energy expenditure or decreasing energy intake might thus be 

assessed as plausible by some. In some cases, this general plausibility is all that 

is needed to initiate a policy. For example, when considering calorie-labeling of restaurant menus, U.S. District Judge Richard J. Holwell ruled that: 

‘‘The Court agrees with Dr. Allison that one cannot conclude with scientific 

certainty from the available evidence that a regulation of this type will ultimately be successful in combating obesity. But even if there are no data demonstrating conclusively that Regulation 81.50 will be effective, conclusive proof 

is not required to establish a reasonable relationship between Regulation 81.50 

and the City’s interest in reducing obesity. Based on the evidence presented by 

the City, as well as common sense, it seems reasonable to expect that some consumers will use the information disclosed pursuant to Regulation 81.50 to select 

lower calorie meals when eating at covered restaurants and that these choices 

will lead to a lower incidence of obesity.’’ 2

In contrast, empiricists (or Bayesians) might state that the existing evidence indicates that no proposed public health approach to obesity has been convincingly 

shown to work or, at best, that no approach has more than very modest effects when 

it has been applied or tested.3–4 Therefore, the a priori expectation is that the next 

proposed policy will have little to no effect. By analogy, this rationale is similar to 

the statistically minded high school guidance counselor who advises the basketball 

star to study academics because, while the counselor cannot rule out that this player will be the one to get drafted to the NBA or WNBA, it is unlikely. 

Plausibility may also be low in some people’s minds for policies that aim to affect 

one component of energy balance in one context while leaving other components of 

energy balance untouched. Such policies, even if effective in altering the one component of energy balance in the desired direction, will only be effective if this alteration is not compensated (or is at least incompletely compensated) for by alterations 

in other components of energy balance. Empirical, experimental evidence indicates 

that such compensation does indeed occur, although the compensation is usually incomplete.5 This suggests that the plausible effects of policies that work through proposed alterations in one component of energy balance should not be based on models 

that assume no compensation (c.f., The Caloric Calculator, which estimates average 

caloric impact, which predicts effect sizes for childhood obesity interventions),6 as such models will likely markedly overestimate plausible effects. 

The plausible benefit of many proposed policy approaches also rests of the assumption of additivity—a small effect coupled with several other slight effects will 

collectively produce a larger response in the outcome. This is particularly applicable 

to the category of ‘‘nudge,’’ a term introduced by Thaler and Sunstein to describe 

multiple, minor, likely unnoticeable changes to alter one’s behavior.7 Rozin, et al., 

showed that multiple modest changes, or nudges, affecting food accessibility (location of ingredients at a salad bar and size and type of serving utensils) in a cafeteria 

setting reduced the calories purchased during single meals without removing 

choices.8 They predicted that the reduced purchasing would translate to a cumulative benefit of weight loss over 1 year. Again, this type of study relies on several 

assumptions: that fewer calories purchased translates to fewer calories consumed; 

that ‘‘all else is equal,’’ i.e., that no compensation occurs; that short-term effects persist in the long term; that multiple interventions have additive effects; and that effects of interventions work equally well when subjects are fully aware of the interventions (as in ordinary commerce) as when the interventions are not disclosed (as 

in many studies). Such a study also brings up questions of whether patrons would 

purchase fewer calories in an ordinary setting such as a store and that would result 

in weight loss. For example, Wansink. et al., found that increasing the cost of soda 

resulted in reduced soda purchased but was associated with increased sales of beer.9

These nudges also may elicit a different response when persons are made aware of 

344 

the interventions or with repeated long-term exposure (i.e., daily or weekly grocery 

shopping).10 The nudge approach has also been criticized on several other 

grounds,11–12 and such criticism highlights that what seems plausible to one person 

may not seem so to another. 

Evidence Regarding Postulated Intermediaries 

Evidence of the effectiveness of obesity policy may also rest on evidence regarding 

presumed mediating variables. An example is a proposed policy for an action intended to increase fruit and vegetable consumption, with the main assumption 

being that increased intake of fruits and vegetables (the mediating factor) will decrease adiposity or promote less weight gain. Empirical support for the policy may 

include a demonstration that the proposed action does indeed lead to increased fruit 

and vegetable consumption. However, such support can only be suggestive because 

it does not necessarily follow that increases in fruit and vegetable intake will actually decrease or prevent adiposity or lessen weight gain, and the same argument 

applies for other postulated intermediaries.13–14

Evidence from Analogue Studies 

Analogue studies attempt to represent key aspects of ordinary life while controlling or limiting external factors, which increases internal validity and can yield key 

insights 15 yet potentially decreases external validity.16 An example of an analogue 

study was conducted by Epstein, et al., to compare the effects of taxation versus subsidization on food purchases.17 They found that using taxes on foods with low nutrient density but also high caloric content was successful at reducing caloric intake, 

whereas subsidizing low-calorie foods increased caloric intake. This type of evidence 

supports the plausibility, but not necessarily the effectiveness, of a policy for decreasing obesity. One area of opportunity is the use of pragmatic RCTs, which emphasize rigorous methods in real-world contexts.18

Direct Evidence Regarding Effectiveness 

Of course, the key evidence desired is evidence of a policy’s effectiveness on the 

ultimate outcome: decreased levels of obesity. Although optimal, such evidence is 

often difficult to obtain. Ultimately, an ideal study would bear direct evidence of effectiveness, under actual conditions of use, during extended periods of time, and 

would be of a nature to allow strong inference of cause and effect. These would be 

randomized studies of actual policy or of extremely close proximity. There is no 

question that these studies would be difficult, expensive, time-consuming, and in 

some cases potentially unethical. We do not advocate a lack of action without this 

type of evidence; however, there should be a clear understanding that without such 

evidence, statements about the effects of a policy remain speculative. 

Evidence Regarding Unintended Consequences 

It is important to keep in mind that implementation of any policy often brings 

with it unintended and undesirable consequences. Many of these consequences have 

been previously highlighted.19–20 Such consequences can include, but are not limited 

to, inequitable distribution of the costs to implement the policy, encroaching on individual freedoms, over-consumption or increased purchasing of certain foods, stigmatization, depression, and avoidance of doctor appointments.19–20 One author contends that the emphasis on body weight has led to weight-based bullying, increased 

disordered eating, body dissatisfaction, extreme dieting, and complications from obesity surgery, among others.21 While some evidence exists on potential unintended 

and undesirable consequences, it is fairly limited as this field has not been fully investigated. Again, fear of unintended negative consequences should not paralyze us 

into inaction, but should lead us to practice humility about the potential value of 

our proposals, to think things through carefully, and to vigilantly monitor implemented policies for any potential unintended consequences. 

Evidence Regarding Public Opinion 

Reports of the results of public opinion surveys on the desirability of particular 

obesity-related policies have proliferated in recent years.22 By implication, this suggests that if a large portion of the population supports a proposed policy, then implementing the proposal is merited. Is such a conclusion reasonable? Should evidence of public opinion about the desirability of policies be considered? 

Suh, et al., suggest that public opinion should be solicited to ‘‘better understand 

the public mindset about relevant policy strategies, and to identify attitudes among 

different subsets of the population towards specific legal measures that can increase 

protections for individuals affected by obesity.’’ 23 Pollard, et al., also contend that 

it is important to survey public opinion or community perception, especially when 

the policy in question involves what may be thought of as government ‘‘interference’’ 

345 

in issues concerning food (labeling, advertising, and supply of environmentally 

friendly food).24 But are such opinions always important? When assessing public 

opinion is warranted, which methodologic issues are involved? And, are there actually circumstances when assessing public opinion would be quite inappropriate? Because this article is primarily about evidence for effectiveness, we consider these 

questions only briefly here. 

Are scientific assessments of public opinions about policies always important? 

Throughout the history of the United States, political leaders have wrestled with 

the pursuit of what seems morally right based on fundamental principles and doing 

what is popular. One such example is the famous Lincoln-Douglas debates about 

slavery. In one of the debates, Lincoln famously said, ‘‘In this and like communities, 

public sentiment is everything. With public sentiment, nothing can fail; without it 

nothing can succeed. Consequently he who moulds public sentiment, goes deeper 

than he who enacts statutes or pronounces decisions. He makes statutes and decisions possible or impossible to be executed.’’ 25 It is noteworthy that Lincoln, like 

some modern day authors interested in obesity policy,26 is talking about ‘‘moulding’’ 

public opinion to enable what one has already determined is right and just, and not 

assessing public opinion to determine what is right and just. 

If Lincoln had conducted a public opinion poll and found that most pre-Civil War 

Americans favored retaining slavery in the United States, would he have judged 

that pertinent evidence as to whether the practice should be abolished? Would we? 

The answer is evidently no. When something is judged to be morally wrong, it is 

wrong and should be ‘‘off the table’’ for discussion regardless of its popularity. Consider the recent posting from Ted Kyle on a ‘‘UK Proposal for Explicit Weight Discrimination in Healthcare.’’ 27 Kyle argues that a proposed policy was a grossly unjust form of discrimination against persons with obesity in terms of health care access. Or, consider proposed policies that entail institutionalized ‘‘fat shaming’’ 28 or a failed/withdrawn Mississippi bill to limit access of persons with obesity to restaurants.29 Many, including the current authors, would consider such proposals 

morally indefensible, and if one adopts such a position, then no public opinion polls 

are needed. If moral opinion has superior authority relative to public opinion, this 

invites important questions of who or how many determine the moral authority and 

on what basis. 

When public opinion assessment is warranted, which methodologic issues are involved? The above notwithstanding, situations certainly exist where public opinion 

is important, such as to determine whether a policy which is neither morally indefensible nor a moral imperative is desired by the citizenry. In such situations, it will 

be important to rely on good principles of designing and interpreting opinion surveys and to keep in mind that who is surveyed 30 and how questions are worded 31

can both be used to manipulate the answers one receives. Extensive discussions on 

these and other methodologic points are covered in standard textbooks on survey 

and sampling methods. 

Are there actually circumstances when assessing public opinion would be quite inappropriate? Finally, we suggest that in some circumstances, assessing public opinion is not only unnecessary, but inappropriate. Specifically, in situations where a 

proposed approach is morally indefensible, to admit the value of public opinion surveys on determining whether a policy should be enacted invites a ‘‘tyranny of the 

majority.’’ 32–33 An interesting corollary of this is that empirical evidence on the 

harm or lack of benefit for some morally indefensible practice might also be seen 

as not only unnecessary, but counterproductive, because the very act of considering 

the empirical evidence implies that the practice under consideration might be worthy of adoption if the evidence came out a particular way. For example, consider 

this headline from an Internet posting: ‘‘Science Says Fat Shaming Backfires—So 

Can We Finally Stop It?’’ 34 The article seems to be referencing an observational 

study 35 that is interpreted to show that perceived weight discrimination leads to 

greater future obesity in the person experiencing the discrimination. The answer to 

the headline’s rhetorical query, So Can We Finally Stop It?, in our opinion is that 

we unequivocally should stop fat shaming, but not because of this (or any other) 

study but rather because it is wrong. Even if one accepts our view that fat shaming 

is wrong a priori, might one ask where the harm is of buttressing the position with 

some empirical support. The harm is that the empirical support, like all empirical 

support, is subject to differential interpretation, criticism, and being overturned. In 

the observational study in this example, it would be easy to point out many limits, 

most notably that the study cannot show cause and effect. This may lead others to 

conclude, ‘‘Well, if the wrongness of fat shaming depended in part on the empirical 

evidence and the empirical evidence has holes in it, I guess fat-shaming may not 

be wrong after all.’’ If this example is not stark enough, we can ask ourselves would 

we take seriously the need for studies to show deleterious effects of policies that inVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00351 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
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stitutionalized racial or religious discrimination as justifications for eliminating 

such heinous policies? 

How Might Evidence for Obesity Policies Be Generated? 

We now turn from the question of what evidence we want to the question of how 

such evidence can be generated. In doing so, we emphasize that we are focusing in 

this section on questions regarding the effects of potential policies on outcomes and 

do not consider questions about assessing other things such as public opinion about 

policies. In considering the generation of evidence regarding the effects of potential 

policies, we are considering questions of cause and effect and readers may find the 

videos available from an annual short course on this topic of interest (see: 

http://www.norc.uab.edu/courses/shortcourse). 

Here, we divide the types of research to be considered into three categories: (1) 

research that can be determinative of the causal effects of policies; (2) research that 

can contribute to an overall assessment of the causal effects of policies, but cannot 

on its own determine causation; and (3) research that formally synthesizes multiple 

sources of information to estimate the causal effects of policies. 

Research That Can Be Determinative of the Causal Effects of Policies 

Role of Randomized Controlled Trials 

Empirical evidence derived from RCTs aimed at identifying factors that increase 

or decrease the risk or magnitude of obesity can provide the strongest evidence to 

guide the development of obesity policies.36 RCTs are regarded as the gold standard 

in the hierarchy of research designs because they are the most reliable method for 

determining causality.37 Evidence generated from RCTs has been used to guide the 

development of several types of obesity policies such as dietary recommendations, 

sugar-sweetened beverage taxes, and food pricing.38–40 Despite the acknowledgement 

that RCTs offer the strongest inferences about cause and effect, several arguments 

are commonly offered against reliance on RCTs for causal inference in policy research. We very briefly review these arguments here. 

1. RCTs are imperfect. Some authors note that RCTs are imperfect. They can be 

designed and executed with flaws. Like all empirical studies, they are subject 

to stochastic variation. Finally, they often entail subject selection criteria and/ 

or study conditions that limit generalizability of the results owing to the 

broader population and more ‘‘real-life’’ circumstances. These are all legitimate criticisms, but two things are noteworthy. First, these weaknesses are 

all surmountable. RCTs can be designed and executed well and can be executed in large enough samples and tested with small enough nominal type 1 

error levels to minimize stochastic errors. Finally, pragmatic controlled trials 

offer investigators the ability to conduct a study that examines the effectiveness and efficacy of an intervention in the real world by allowing for the inclusion of a diverse sample of the population and by enabling the intervention 

to be adapted to local settings.41 For example, the Moving to Opportunity 

study found that certain social programs involving housing vouchers providing the ‘‘opportunity to move from a neighborhood with a high level of poverty to one with a lower level of poverty was associated with [caused] modest 

but potentially important reductions in the prevalence of extreme obesity and 

diabetes.’’ 42

2. RCTs are sometimes impractical or impossible. We agree that RCTs are sometimes impractical or impossible, but this has no bearing on the extent to 

which RCTs and other designs can or cannot provide strong inferences or causation. The argument that (a) RCTs are sometimes impractical or impossible, 

(b) such that if we relied on only them for strong causal inferences we would 

not be able to make strong causal inferences in some situations in which we 

wished to make strong causal inferences, and (c) therefore we should not 

make strong causal inferences solely from RCTs is simply a special case of 

Argumentum ad Consequentiam.43

3. There are no RCTs showing that parachutes work. It is sometimes noted that 

we accept many propositions as true on the basis of some evidence and intuitive obviousness such as that smoking causes lung cancer or that parachutes 

save lives among skydivers.44 This is an example of argument by analogy.45

Arguments by analogy can be useful foils to provoke thought, but in and of 

themselves prove or disprove nothing. 

4. We cannot wait for perfect data. It is sometimes argued that we cannot (or 

more aptly should not) wait for perfect data to take certain actions, such as 

enact certain policies. We agree with this proposition. However, the statement 

‘‘we cannot (or more aptly should not) wait for perfect data to take certain acVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00352 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
347 

tions’’ is not equivalent to ‘‘we cannot (or more aptly should not) wait for perfect data to draw strong conclusions about causation.’’ Taking actions and 

drawing causal conclusions are distinct processes and the need and justification to take prudent action in the face of uncertainty is not a justification for 

denying that the uncertainty exists.19, 46–47

5. Inadvertently Promoting a False Dichotomy. Majumdar and Soumerai 48 have 

cogently noted that ‘‘some contend that only randomized controlled trials 

produce trustworthy evidence. Unfortunately, such a position discounts valid 

nonrandomized or quasi-experimental study designs, even though health policy randomized controlled trials are rarely feasible. Such a constrained view 

inappropriately lumps together valid evidence from strong nonrandomized designs (that is, before-after studies with concurrent controls or the interrupted 

time series study in which a policy causes a sudden, visible change in trend) 

with evidence from weak designs that permit little causal inference (that is, 

the commonly conducted cross-sectional analysis that looks at outcomes only 

after a policy has been implemented).’’ We agree that there is a continuum 

of non-RCT designs that vary in the strength of causal inferences they justify. 

We also agree that the stronger designs are underutilized as we discuss later 

in this article. However, these recognitions do not affect the validity of propositions that randomization is key to valid causal inference.49 If we accepted 

otherwise, we would again be engaging in Argumentum ad Consequentiam.43

Research That Can Contribute to an Overall Assessment of the Causal Effects of Policies 

Having emphasized the critical role of RCTs in humans on the policy under question and for the outcomes under question in drawing strong causal inference, we 

also note that with such information often unavailable and sometimes unattainable, 

it is frequently necessary to make decisions about actions without drawing firm conclusions about causation. In doing so, we must commonly integrate multiple sources 

of information, none of which alone is unequivocally dispositive about a conclusion 

of causation, to make informed decisions about what might reasonably be expected 

to work. Several sources of evidence can contribute to such decisions. 

Role of Model Organism Evidence 

Model organisms are used to generate information regarding casual relationships 

that cannot be derived through human studies. For example, exposure to environmental obesogens, such as endocrine-disrupting chemicals, has been identified as a 

possible factor that increases the risk of obesity.50–51 Such studies are vital in policy 

decisions, for example, to approve or disapprove use of a food additive, but cannot 

offer unequivocal conclusions about causation in humans because of the possible 

heterogeneity of effects across species.52

Role of Observational Evidence: of Ordinary Association Tests (OATs) and Extended 

Assoc[i]ation Tests (EATs) 

Observational evidence generally plays a vital role in assessing the likely value 

of proposed policies. Observational studies are useful in generating hypotheses that 

can inform the conduct of more rigorous studies (i.e., randomized trials) to begin to 

establish causality. With regard to policies developed to address the obesity epidemic, observational studies have been used to investigate associations between the 

initiations of policies and relevant outcomes. That said, not all observational evidence is of equal value. Here we distinguish between two broad classes of observational evidence which we will call Ordinary Association Tests (OATs) and Extended 

Association Tests (EATs). 

Ordinary Association Tests. We define ordinary association tests (OATs) to be observational studies on samples of individuals in which the sole or primary means 

of controlling for potential confounding factors is inclusion of measures of some potential confounding factors as covariates in statistical models (or stratifying by 

measures of such factors). OATs are heavily relied upon in thinking about plausible 

effects of polices, but have also been heavily criticized in general 53–54 and in the obesity and nutrition domains in particular 55–57 for multiple reasons. We refer the 

reader to those references for details. 

Extended Association Tests. Most dialogue and research in obesity does not consider the evidence continuum between OATs, which do not offer strong assessments 

of causal effects, and RCTs, which do offer strong inferences, but cannot be done 

in all circumstances. In contrast to this polarized view, there are techniques that 

we refer to as extended association tests (EATs) that lie intermediary between ordinary association tests and RCTs, including but not limited to quasi-experimental 

studies and natural experiments. Such designs are increasingly used, especially in 

348 

the disciplines of economics and genetics, but are rarely used in obesity research. 

However, the ability to draw causal inferences in obesity research could be strengthened by increased judicious use of such approaches. In-depth understanding and appropriate use of the full continuum of these methods requires input from disciplines 

including statistics, economics, psychology, epidemiology, mathematics, philosophy, 

and in some cases behavioral or statistical genetics. The application of these techniques, however, does not involve routine well-known ‘‘cookbook’’ approaches but requires understanding of underlying principles so the investigator can tailor approaches to specific and varying situations. 

Some of the key methods in use for situations where standard RCTs may not be 

available include natural experiments, quasi-experiments, and experiments in which 

true randomization is used but subjects are not randomized directly to levels of the 

independent variable, as described with examples in Table 1. 

Natural experiments are a useful type of observational study that can be used to 

investigate the impact of environmental changes on obesity, that is, changes that 

the investigator did not manipulate. In this case, investigators merely measure outcomes before and after the implementation of a new policy, regulation, or other factor that has changed. Within the context of efforts at obesity modification, natural 

experiments have been used to assess the effectiveness of new policies (e.g., inclusion of calorie information on menus, implementation of environmental elements 

thought to promote physical activity [i.e., parks, bike lanes, walking trails], use of 

school-based obesity screening and BMI report cards).61, 68–70

A prime example comes from the U.S. Food and Drug Administration, which implemented regulations requiring franchise restaurant chains with 20 or more locations to provide calorie information on their menus and menu boards. In a natural 

experiment conducted in New York City, receipts were collected from patrons of fast 

food restaurants before and after menu labeling was implemented. The investigators 

found that adding calorie information to the menus did not appear to influence the 

food choices of parents or adolescents.61 Natural experiments such as this are a costeffective way to evaluate the effects of obesity policies, as well as provide information that might inform modifications to existing policies. 

The existence of EATs seems to be less well known to many investigators in public health, medicine, psychology, and related fields. We believe that many questions 

about behavioral, psychological, and economic influences on obesity-related variables 

and many applied questions about the effects of extant or proposed interventions 

can be addressed more informatively and more rigorously if more investigators 

availed themselves of these evolving methodologies related to causal inference from 

a basis of a sound understanding of fundamental principles. 

Research That Formally Synthesizes Multiple Sources of Information To 

Estimate The Causal Effects of Policies 

Apart from the need to embrace and use the range of potential design strategies 

available, it is also essential to ‘‘step back’’ and synthesize the multiple and varied 

sources of information to evaluate what they can tell us about the causal effects of 

policies. 

Role of Systematic Reviews and Meta-Analysis 

As a result of the growing rates of obesity around the world, the volume of evidence from obesity research has burgeoned. However, owing to variations in the 

quality and type of study design, implementation, and the outcomes measured, determining effects from various studies can be challenging. Debates on obesity policies are often fueled by the contradictory findings of empirical studies, such as those 

regarding the influence of sugar-sweetened beverage consumption on childhood obesity.40 As such, high-quality systematic reviews and meta-analyses can be useful 

when attempting to evaluate the state of the evidence related to a particular intervention or policy with objective approaches to identifying and integrating evidence.71

That said, as Ingram Olkin once wrote, ‘‘Doing a meta-analysis is easy. Doing one 

well is hard,’’ 72 and we have found that errors in obesity-related meta-analyses 

abound.73 Hence, while meta-analyses are vital, our field needs to improve their 

execution, and meta-analyses should be as critically reviewed as are any other studies. 

Role of Modelling 

One drawback of RCTs, noted above, is that they often are not large enough to 

capture the entire spectrum of effects (both desired and undesired) that a policy 

may have.74 Mathematical and computational models of health policies are tools 

that can be used to predict the outcomes of an obesity policy and to identify implementation barriers before the policy is adopted.75 Moreover, the modelling of obesity 

policy enables policymakers to estimate the costs of implementing policies and to deVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00354 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
349 

termine the resource allocation required to implement a given policy.76–78 For example, a dynamic weight loss model was used to estimate the effects of a tax on sugarsweetened beverages on the prevalence of obesity in New York City.79 The model 

suggested that there would be decreases in obesity prevalence over a 10 year period.79 The model also estimated the magnitude of the projected reductions in obesity prevalence, allowing readers to better judge the potential public health impact 

of such a policy.79

Models are also valuable for monitoring the effects of policies over time. Evidence 

has shown that the effects of health policies can increase or diminish with the passage of time.78 Therefore, new data concerning the effects of a policy should be continually generated to estimate its effects in order to allow policymakers to revise or 

even discontinue implementation of the policy if it is shown to be ineffective.74

Despite the benefits of using models in the development and refinement of health 

policies, some challenges and limitations must be recognized. For example, health 

policy modellers are not often integrated into the health policymaking process. 

Therefore, models are seen as ‘‘one-offs’’ rather than as tools that should be used 

during the lifecycle of the policy to ensure that it retains its value. Perhaps most 

importantly, models offer projections of effects, not demonstrations of effects. Such 

projections can be heavily dependent on the input parameters (i.e., assumptions) of 

the model, and some published modelling activities (e.g.,80) are so heavy on assumptions of efficacy of the policies considered that the modelling can be seen as an instance of petitio principii.81

Standards for Evidence and Related Factors Influencing Policy Decisions 

According to Donaldson and colleagues, most obesity prevention bills enacted between 2010 and 2013 were based on initiating strategies (e.g., ‘‘initiated farmer[s’] 

markets, increased access to walking trails, local menu labeling’’) that had little to 

no evidence of benefit.82 But is this wrong? A vital consideration, often not made 

explicit a priori, concerns the standards for evidence that will used to both generate 

a policy decision and to evaluate its effect once implemented. In general, the standards of evidence for a scientific conclusion are thought to be far more rigorous, because they are based on long-established methodologies that are considered to be 

objective, repeatable, and relatively immune to biases of the individuals conducting 

the study. In contrast, the evidence (if any) needed to reach a policy decision (which 

is distinct from reaching a scientific conclusion) depends on many factors and is not 

constant across circumstances. Opinions can also vary. For example, the Society for 

Prevention Research states, ‘‘To be ready for broad dissemination, a program must 

not only be of proven effectiveness, but it must also meet other criteria . . .’’ (emphasis added).83 This stands in marked contrast to the statement of District Judge 

Richard J. Holwell quoted above that ‘‘even if there are no data demonstrating conclusively that Regulation 81.50 will be effective, conclusive proof is not required . . .’’ and in the context of the legal proceedings, his interpretation of law 

is what determined the evidence standard. There are yet other standards in different contexts and so no universal rule about how much evidence is or is not needed for policymaking can be given. This stands in contrast to occasional statements 

from academics that seem to state from no formal basis of authority that a particular amount of evidence is or is not needed to enact a policy. 

The four quotations listed (see Box 1) are from discussions and presentations involving policies directed at curbing sugar intake in the public. They reflect the varying perspectives of differing standards of evidence among researchers. The first 

two 84–85 put rigor of evidence aside and instead emphasize that the decision to develop policy is the priority based on a decision that seems to have already been committed to based upon some combination of suggestive evidence or intuition. In contrast, the third and fourth statements progress from needing ‘‘a strong sense that 

it will be effective’’ 86 to confidently requiring ‘‘strong evidence’’ prior to any public 

policy decision.87 Thus, disagreement on the amount and rigor or evidence needed 

to enact a policy exists even among researchers discussing a particular target 

(sugar) of public policy. They illustrate the subjectivity of the standards of evidence 

for decision making. 

In Summation 

In closing, our field will benefit from a greater emphasis on probative research. 

Probative research would meaningfully move us forward in our ability to state that 

a given treatment or prevention strategy does or does not have a particular effect.88

This is in contrast to studies that merely continue to draw attention to the plausibility of some treatment having some effect but do not increase our knowledge that 

such an effect actually exists.88 Finally, the quest for rigorous evidence and scrupulous truthfulness in reporting is fully compatible with the quest for beneficence 

350 

and passionate pursuit of action for the betterment of others. Recognizing these 

comparabilities (see Box 2) may pave the way for public health dialogue in obesity 

that is both more honest and more collegial. 

References 

1. Taxation by weight. BR. MED. J. 1904; I 1904: 1094. 

2. Holwell R.J. Case 1:08–cv–01000–RJH. [Memorandum Opinion and Order]. Doc 42. 04/16/2008. 2008: 1–27. 

3. Cawley J. Does anything work to reduce obesity? (Yes, modestly). J. HEALTH POLIT. POLICY LAW. 2016; (Feb): 26. 

4. Essington M., Hertelendy A.J. Legislating weight loss: are antiobesity public health policies making an impact? J. HEALTH

POLIT. POLICY LAW. 2016; (Feb): 26. 

5. Dhurandhar E.J., Kaiser K.A., Dawson J.A., Alcorn A.S., Keating K.D., Allison D.B. Predicting adult weight change in the real 

world: a systematic review and meta-analysis accounting for compensatory changes in energy intake or expenditure. INT. J. OBES. 

2015; 39: 1181–1187. 

6. Wang Y.C., Hsiao A., Orleans C.T., Gortmaker S.L. The caloric calculator: average caloric impact of childhood obesity interventions. AM. J. PREV. MED. 2013; 45: e3–e13. 

7. Thaler R.H., Sunstein C.R. Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press: New 

Haven, CT, 2008. 

8. Rozin R., Scott S., Dingley M., Urbanek J.K., Jiang H., Kaltenbach M. Nudge to nobesity I: minor changes in the accessibility 

decrease food intake. Judgement and Decision Making. 2011; 6: 323–332. 

9. Hanks A., Wansink B., Just D., et al. From Coke to Coors: a field study of a fat tax and its unintended consequences. J. NUTR. 

EDUC. BEHAV. 2013; 45: S40. 

10. Matjasko J.L., Cawley J.H., Baker-Goering M.M., Yokum D.V. Applying Behavioral Economics to Public Health Policy: Illustrative Examples and Promising Directions. AM. J. PREV. MED. 2016; 50 (5S1): S13–S19. 

11. Howlett M. Matching Policy Tools & Their Targets: Beyond Nudges and Utility Maximation in Policy Design. Presented at: 

European Consortium for Political Research; April 24–28, 2016, 2016; Pisa, Italy. 

12. Marlow M.L. Market test or government guess? Are government efforts to ‘‘nudge’’ us to lose weight really based on science? 

REGULATION. 2014; Winter 2014–2015: 48–54. 

13. Kaiser K.A., Brown A.W., Bohan Brown M.M., Shikany J.M., Mattes R.D., Allison D.B. Increased fruit and vegetable intake 

has no discernible effect on weight loss: a systematic review and meta-analysis. AM. J. CLIN. NUTR. 2014; 100: 567–576. 

14. Ledoux T.A., Hingle M.D., Baranowski T. Relationship of fruit and vegetable intake with adiposity: a systematic review. OBES. 

REV. 2011; 12: e143–150. 

15. Mook D.G. In defense of external invalidity. AM. PSYCHOL. 1983; 38: 379–387. 

16. Reynolds S., Streiner D. Why we do not abstract analogue studies of treatment outcome and scale development. EVID. BASED. 

MENT. HEALTH. 1998; 1: 101–102. 

17. Epstein L.H., Dearing K.K., Roba L.G., Finkelstein E. The influence of taxes and subsidies on energy purchased in an experimental purchasing study. PSYCHOL. SCI. 2010; 21: 406–414. 

18. Patsopoulos N.A. A pragmatic view on pragmatic trials. DIALOGUES CLIN. NEUROSCI. 2011; 13: 217–224. 

19. Allison D.B. Evidence, discourse and values in obesity-oriented policy: menu labeling as a conversation starter. INT. J. OBES. 

2011; 35: 464–471. 

20. Brown A.W., Allison D.B. Unintended consequences of obesity-targeted health policy. VIRTUAL MENTOR. 2013; 15: 339–346. 

21. Ramos Salas X. The ineffectiveness and unintended consequences of the public health war on obesity. CAN. J. PUBLIC HEALTH. 

2015; 106: e79–81. 

22. Gendall P., Hoek J., Taylor R., Mann J., Krebs J., Parry-Strong A. Should support for obesity interventions or perceptions of 

their perceived effectiveness shape policy? AUST N.Z.J. PUBLIC HEALTH. 2015; 39: 172–176. 

23. Suh Y., Puhl R., Liu S., Milici F.F. Support for laws to prohibit weight discrimination in the United States: public attitudes 

from 2011 to 2013. OBESITY (Silver Spring). 2014; 22: 1872–1879. 

24. Pollard C.M., Daly A., Moore M., Binns C.W. Public say food regulatory policies to improve health in Western Australia are 

important: population survey results. AUST. N.Z.J. PUBLIC HEALTH. 2013; 37: 475–482. 

25. Zarefsky D. ‘‘Public Sentiment Is Everything’’: Lincoln’s View of Political Persuasion. JOURNAL OF THE ABRAHAM LINCOLN ASSOCIATION. 1994; 15: 23–40. 

26. Robbins R.N.J., Lundell H., Meyerson J. Views of city, county, and state policy makers about childhood obesity in New York 

State, 2010–2011. PREV. CHRONIC. DIS. 2013; 10: E195. 

27. Kyle T. UK Proposal for Explicit Weight Discrimination in Healthcare. http://conscienhealth.org/2016/09/uk-proposal-explicit-weight-discrimination-healthcare/. Accessed 5 Sept. 2016. 

28. Abrams L. A Case for Shaming Obese People, Tastefully. THE ATLANTIC. Published January 23, 2013. http:// 

www.theatlantic.com/health/archive/2013/01/a-case-for-shaming-obese-people-tastefully/267446/. Accessed 8 Sept. 2016. 

29. Obesity Action Coalition. Obesity Action Coalition Calls on the State of Mississippi House of Representatives to Withdraw 

House Bill 282 Discriminating Against Those Affected by Obesity. Published February 1, 2008. http://www.prnewswire.com/newsreleases/obesity-action-coalition-calls-on-the-state-of-mississippi-house-of-representatives-to-withdraw-house-bill-282-discriminatingagainst-those-affected-by-obesity-56763767.html. Accessed 8 Sept. 2016. 

30. Gelman A. Tracking public opinion with biased polls. THE WASHINGTON POST. Published April 8, 2014. https:// 

www.washingtonpost.com/news/monkey-cage/wp/2014/04/09/tracking-public-opinion-with-biased-polls/. Accessed 8 Sept. 2016. 

31. Newport F. The Scientific Approach to Question Wording Differences. GALLUP. Published September 5, 2003. http:// 

www.gallup.com/poll/9193/scientific-approach-question-wording-differences.aspx. Accessed 8 Sept. 2016. 

32. Adams J., American Imprint Collection (Library of Congress). A Defence of the Constitutions of Government of the United 

States of America. London printed, Boston re-printed and sold by Edmund Freeman, 1788. 

33. Mill J.S. On Liberty. Vol. 25. 1909 ed. THE INTERNET WIRETAP: HARVARD CLASSICS, P.F. Collier & Son. 

34. M.K. Science Says Fat Shaming Backfires—So Can We Finally Stop It? SELF. Published September 15, 2014. http:// 

www.yourtango.com/2014225975/love-your-body-science-says-fat-shaming-backfires-so-can-we-finally-stop-it. Accessed 8 Sept. 2016. 

35. Jackson S.E., Steptoe A., Beeken R.J., Croker H., Wardle J. Perceived weight discrimination in England: a population-based 

study of adults aged 50 years. INT. J. OBES. 2015; 39: 858–864. 

36. Swinburn B.A., Caterson I., Seidell J.C., James W.P. Diet, nutrition and the prevention of excess weight gain and obesity. PUBLIC HEALTH NUTR. 2004; 7: 123–146. 

37. Byar D.P., Simon R.M., Friedewald W.T., et al. Randomized clinical trials. Perspectives on some recent ideas. N. ENGL. J. 

MED. 1976; 295: 74–80. 

38. Mozaffarian D., Ludwig D.S. Why is the Federal Government afraid of fat? NEW YORK TIMES. Published July 9, 2015. http:// 

www.nytimes.com/2015/07/09/opinion/why-is-the-federal-government-afraid-of-fat.html?_r=0. Accessed 8 Sept. 2016. 

39. Mozaffarian D., Ludwig D.S. The 2015 U.S. Dietary Guidelines: lifting the ban on total dietary fat. JAMA. 2015; 313: 2421– 

2422. 

40. Keller A., Bucher Della Torre S. Sugar-sweetened beverages and obesity among children and adolescents: a review of systematic literature reviews. CHILD. OBES. 2015; 11: 338–346. 

41. Peikes D., Geonnotti K., Wang W. Using Pragmatic Clinical Trials to Test the Effectiveness of Patient-Centered Medical Home 

Model in Real-World Setting. Rockville, MD: Agency for Healthcare Research and Quality; 2013. 

42. Ludwig J., Sanbonmatsu L., Gennetian L., et al. Neighborhoods, obesity, and diabetes—a randomized social experiment. N. 

ENGL. J. MED. 2011; 365: 1509–1519. 

43. Van Eemeren F.H., Grootendorst R. Fallacies in pragma-dialectical perspective. ARGUMENTATION. 1987; 1: 283–301. 

44. Smith G.C., Pell J.P. Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of 

randomised controlled trials. BMJ. 2003; 327: 1459–1461. 

351 

References—Continued 

45. Cummings L. Argument from Analogy. Reasoning and Public Health: New Ways of Coping with Uncertainty. Springer International Publishing: Cham, Switzerland, 2015: 93–120. 

46. Hill A.B. The environment and disease: association or causation? PROC. R. SOC. MED. 1965; 58: 295–300. 

47. Grutters J.P., van Asselt M.B., Chalkidou K., Joore M.A. Healthy decisions: towards uncertainty tolerance in healthcare policy. 

PHARMACOECONOMICS. 2015; 33: 1–4. 

48. Majumdar S.R., Soumerai S.B. The unhealthy state of health policy research. HEALTH AFF. (Millwood). 2009; 28: w900–w908. 

49. Rubin D.B. Practical implications of modes of statistical inference for causal effects and the critical role of the assignment 

mechanism. BIOMETRICS. 1991; 47: 1213–1234. 

50. Ouadah-Boussouf N., Babin P.J. Pharmacological evaluation of the mechanisms involved in increased adiposity in zebrafish 

triggered by the environmental contaminant tributyltin. TOXICOL. APPL. PHARMACOL. 2016; 294: 32–42. 

51. Grun F., Blumberg B. Environmental obesogens: organotins and endocrine disruption via nuclear receptor signaling. ENDOCRINOLOGY. 2006; 147 (suppl.): S50–S55. 

52. Reference Manual on Scientific Evidence. 3rd ed. The National Academies Press:Washington, D.C., 2011. 

53. Albert R.K. ‘‘Lies, damned lies . . .’’ and observational studies in comparative effectiveness research. AM. J. RESPIR. CRIT. 

CARE MED. 2013; 187: 1173–1177. 

54. Taubes G. Epidemiology faces its limits. SCIENCE. 1995; 269: 164–169. 

55. Schoenfeld J.D.I.J., Ioannidis J.P. Is everything we eat associated with cancer? A systematic cookbook review. AM. J. CLIN. 

NUTR. 2013; 97: 127–134. 

56. Bohan Brown M.M., Brown A.W., Allison D.B. Nutritional epidemiology in practice: learning from data or promulgating beliefs? AM. J. CLIN. NUTR. 2013; 97: 5–6. 

57. Young S.S., Karr A. Deming, data and observational studies: A process out of control and needing fixing. SIGNIFICANCE. 2011; 

8: 116–120. 

58. Metzger M.W., McDade T.W. Breastfeeding as obesity prevention in the United States: a sibling difference model. AM. J. HUM. 

BIOL. 2010; 22: 291–296. 

59. Nelson M.C., Gordon-Larsen P., Adair L.S. Are adolescents who were breast-fed less likely to be overweight? Analyses of sibling 

pairs to reduce confounding. EPIDEMIOLOGY. 2005; 16: 247–253. 

60. Fontaine K.R., Robertson H.T., Holst C., et al. Is socioeconomic status of the rearing environment causally related to obesity in 

the offspring? PLOS ONE. 2011; 6: e27692. 

61. Elbel B., Gyamfi J., Kersh R. Child and adolescent fast-food choice and the influence of calorie labeling: a natural experiment. 

INT. J. OBES. 2011; 35: 493–500. 

62. Gibson J., McKenzie D., Stillman S. What happens to diet and child health when migration splits households? Evidence from 

a migration lottery program. FOOD POLICY. 2011; 36: 7–15. 

63. Yakusheva O., Kapinos K., Weiss M. Peer effects and the freshman 15: evidence from a natural experiment. ECON. HUM. BIOL. 

2011; 9: 119–132. 

64. Braakmann N. The causal relationship between education, health and health related behaviour: evidence from a natural experiment in England. J. HEALTH ECON. 2011; 30: 753–763. 

65. Jones-Smith J.C., Dow W.H., Chichlowska K. Association between casino opening or expansion and risk of childhood overweight and obesity. JAMA. 2014; 311: 929–936. 

66. Voss J., Allison D.A., Webber B.J., Otto J.L., Clark L.L. Lower obesity rate during residence at high altitude among a military 

population with frequent migration: a quasi experimental model for investigating spatial causation. PLOS ONE. 2014; 9: e93493. 

67. Allison D.B., Paultre F., Heymsfield S.B., Pi-Sunyer F.X. Is the intra-uterine period really a critical period for the development 

of adiposity? INT. J. OBES. RELAT. METAB. DISORD. 1995; 19: 397–402. 

68. Naylor P.J., Olstad D.L., Therrien S. An intervention to enhance the food environment in public recreation and sport settings: 

a natural experiment in British Columbia, Canada. CHILD. OBES. 2015; e-pub ahead of print 19 May 2015. 

69. Benjamin Neelon S.E., Namenek Brouwer R.J., Ostbye T., et al. A community-based intervention increases physical activity 

and reduces obesity in school-age children in North Carolina. CHILD. OBES. 2015; 11: 297–303. 

70. Madsen K.A. School-based body mass index screening and parent notification: a statewide natural experiment. ARCH. PEDIATR. 

ADOLESC. MED. 2011; 165: 987–992. 

71. Crombie I., Davies H. What is Meta-Analysis? HAYWORD MEDICAL COMMUNICATIONS: LONDON, 2009. 

72. Shuster J.J., Gieser P.W. Meta-analysis and prospective meta-analysis in childhood leukemia clinical research. ANN. ONCOL. 

1996; 7: 1009–1014. 

73. George B.J., Beasley T.M., Brown A.W., et al. Common scientific and statistical errors in obesity research. OBESITY (Silver 

Spring). 2016; 24: 781–790. 

74. Metcalf C.J., Edmunds W.J., Lessler J. Six challenges in modelling for public health policy. EPIDEMICS. 2015; 10: 93–96. 

75. Hawkes C., Smith T.G., Jewell J., et al. Smart food policies for obesity prevention. LANCET. 2015; 385: 2410–2421. 

76. Backholer K., Beauchamp A., Ball K., et al. A framework for evaluating the impact of obesity prevention strategies on socioeconomic inequalities in weight. AM. J. PUBLIC HEALTH. 2014; b: e43–e50. 

77. Dietz W.H., Bland M.G., Gortmaker S.L., Molloy M., Schmid T.L. Policy tools for the childhood obesity epidemic. J. LAW MED. 

ETHICS. 2002; 30 (suppl.): 83–87. 

78. Levy D.T., Mabry P.L., Wang Y.C., et al. Simulation models of obesity: a review of the literature and implications for research 

and policy. OBES. REV. 2011; 12: 378–394. 

79. Ruff R.R., Zhen C. Estimating the effects of a calorie-based sugar-sweetened beverage tax on weight and obesity in New York 

City adults using dynamic loss models. ANN EPIDEMIOL. 2015; 25: 350–357. 

80. Gortmaker S.L., Wang Y.C., Long M.W., et al. Three interventions that reduce childhood obesity are projected to save more 

than they cost to implement. HEALTH AFF. (Millwood). 2015; 34: 1932–1939. 

81. Cummings L. Informal fallacies as cognitive heuristics in public health reasoning. INFORMAL LOG. 2014; 34: 1–37. 

82. Donaldson E.A., Cohen J.E., Villanti A.C., Kanarek N.F., Barry C.L., Rutkow L. Patterns and predictors of state adult obesity 

prevention legislation enactment in U.S. states: 2010–2013. PREV. MED. 2015; 74: 117–122. 

83. Society for Prevention Research. Standards of Evidence. Criteria for Efficacy, Effectiveness, and Dissemination. http:// 

www.preventionresearch.org/StandardsofEvidencebook.pdf. Accessed 8 Sept. 2016. 

84. National Public Radio (Producer). (2016, Dec. 19). How Much Is Too Much? New Study Casts Doubts On Sugar Guidelines. 

Retrieved from: http://www.npr.org/templates/transcript/transcript.php?storyId=505867535. 

85. National Academy of Medicine (Panel). (2016, Oct 24). 2016 Annual Meeting Session 3 Question and Answer: Evidence of Preventing Obesity and Diabetes at the Population Level and Promising New Directions. Retrieved from: https://www.youtube.com/ 

watch?v=I8RyxGfQyWg&list=PLqRL5HO_hA8faH-DrOXZV7sAHuGMdktom&index=26. 

86. Nancy Kass (Presenter). (2016, Nov. 8). Obesity Prevention policies in the United States: which approaches make the most ethical sense? UT Southwestern Health Sciences Digital Library and Learning Center. Retrieved from: https://repositories.tdl.org/ 

utswmed-ir/handle/2152.5/3892. 

87. Bradford Hill, Austin (Presenter). (1965, Jan. 14) President’s Address Section of Occupational Medicine: The Environment and 

Disease: Association or Causation? 295–300. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1898525/pdf/ 

procrsmed00196-0010.pdf. 

88. Casazza K., Allison D.B. Stagnation in the clinical, community and public health domain of obesity: the need for probative research. CLIN. OBES. 2012; 2: 83–85. 

89. Mehta T., Allison D.B. From measurement to analysis reporting: grand challenges in nutritional methodology. FRONT. NUTR. 

2014; 1:6. 

90. Allison D.B., Bassaganya-Riera J., Burlingame B., et al. Goals in nutrition science 2015–2020. FRONT. NUTR. 2015; 2: 26. 

91. Allison D.B., Brown A.W., George B.J., Kaiser K.A. Reproducibility: a tragedy of errors. NATURE. 2016; 530: 27–29. 

92. Dietz, T. Bringing values and deliberation to science communication. PNAS. 2013; 110: 14081–14087. 

352 

Table 1. Examples of Extended Association Tests (EATs) in Obesity 

Research 

Topic Under Study Design Finding Ref. 

Effects of Breastfeeding on Offspring Obesity Co-Sib Control Mixed 58–59

Effects of Socioeconomic Status of Rearing Parents on Child BMI Adoption Study Consistent with a causal effect, but only 1⁄2 of 

ordinary association 

60

Effects of Menu Labeling on Calories Purchased Quasi-Experiment No support 61

Effects of Migrating from Tonga to New Zealand Natural Experiment Some evidence for BMI increase in some ages 62

Effects of Roommate Characteristics on Freshman Weight Gain Packet Randomized Experiment Association suggesting that being assigned to 

higher BMI roommate leads to less weight 

gain 

63

Effect of Education on Food Choice Quasi-Experiment No support 64

Effect of Casinos (as Economic Boosters) on 

Child Obesity 

Quasi-Experiment Association suggestive of beneficial effect 65

Effects of Altitude of Residence on Obesity Quasi-Packet-Randomized Experiment Association suggestive of beneficial effect 66

Effects of Environmental Factors Influencing 

Birthweight on Adult BMI 

Co-Twin Control No support 67

Box 1: Contrasting Ideas on the Amount and Rigor of Evidence Regarding 

Policies Targeted at Sugar Consumption 

[emphases added below]. 

‘‘It’s a classic example. It’s industry-funded authors saying that the dietary guidelines 

recommendations about sugar aren’t based on science. I’m laughing because what kind 

of evidence do you need? Sugar is calories and no nutrients and everybody would be 

healthier eating less of it.’’—Marion Nestel 84

‘‘I would be very surprised if any one pot policy had any effect on obesity. And in some 

ways I think it’s a trap to expect it to do that because so many things are driving the problem. So many things are affecting the food supply, levels of physical activity, and [ . . . ] 

structural things like poverty, education, and access to healthcare. To expect any one 

policy to turn that around I think is wishful thinking. But it is certainly important that it be studied as much as it can so that you know at the end of the day, so 

that you know [ . . . ] where you get the biggest impact per dollar of policy 

change’’ —Kelly Brownell 85

‘‘In public health, when someone is going to act, particularly for something that is a public policy my goodness, we have to have some pretty strong sense that it’s going to be 

effective. Public policy cannot be enacted simply based on a good idea. There has to be reason that it’s going to make a difference and a difference relative to public interest.’’—Nancy 

E. Kass 86

‘‘But we should need very strong evidence before we made people burn a fuel in their 

homes that they do not like or stop smoking the cigarettes and eating the fats and sugar 

that they do like.’’—Sir Austin Bradford Hill 87

Box 2: Clarification Within Two Domains: Evidence for scientific 

conclusions and for Advocacy Of Policy Decisions 

Evidence for Scientific 

Conclusions 

• Greater candor in scientific presentations 89–92

• Acceptance by empirically minded scientists that action can sometimes legitimately precede without strong evidence 

• Articulating distinctions between our values and our assessments 

of empirical evidence 91

Evidence for Advocacy 

of Policy Decisions 

• Eschewing fallacious rhetorical arguments 

• Acceptance by advocates that advocacy neither requires nor justifies making evidence seem stronger than it is 

ATTACHMENT 8 

Will Reducing Sugar-Sweetened Beverage Consumption Reduce Obesity? Evidence Supporting Conjecture Is Strong, But Evidence When Testing Effect Is Weak 

Pro v. Con Debate: Role of Sugar Sweetened Beverages in Obesity 

353 

[1]Office of Energetics, Dean’s Office, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, USA. [2]Division of Preventive Medicine, School of Medicine, University of Alabama at Birmingham, 

Birmingham, Alabama, USA. 

Address for correspondence: Dr. D.B. Allison, Ryals Public Health Building, Rm 140J, University of Alabama at Birmingham, 1665 University Boulevard, Birmingham, AL 35294, USA. Email: Dallison@uab.edu. 

K.A. Kaiser,[1] J.M. Shikany,[2] K.D. Keating,[1] and D.B. Allison [1]

Received 12 February 2013; revised 25 April 2013; accepted 26 April 2013. 

Obesity Reviews (2013) 14, 620–633. ©2013 The Authors 

Summary 

We provide arguments to the debate question and update a previous meta-analysis with recently published studies on effects of sugar-sweetened beverages (SSBs) 

on body weight/composition indices (BWIs). We abstracted data from randomized 

controlled trials examining effects of consumption of SSBs on BWIs. Six new studies 

met these criteria: (i) human trials, (ii) ≥3 weeks duration, (iii) random assignment 

to conditions differing only in consumption of SSBs and (iv) including a BWI outcome. Updated meta-analysis of a total of seven studies that added SSBs to persons’ 

diets showed dose-dependent increases in weight. Updated meta-analysis of eight 

studies attempting to reduce SSB consumption showed an equivocal effect on BWIs 

in all randomized subjects. When limited to subjects overweight at baseline, metaanalysis showed a significant effect of roughly 0.25 standard deviations (more 

weight loss/less weight gain) relative to controls. Evidence to date is equivocal in 

showing that decreasing SSB consumption will reduce the prevalence of obesity. Although new evidence suggests that an effect may yet be demonstrable in some populations, the integrated effect size estimate remains very small and of equivocal statistical significance. Problems in this research area and suggestions for future research are highlighted. 

Introduction 

The proposition we have been asked to address and for which we evaluate the 

available evidence is as follows: 

‘There is sufficient scientific evidence that decreasing sugar-sweetened beverage (SSB) consumption will reduce the prevalence of obesity and obesity-related diseases.’ 

What We Are Debating 

In examining the proposition, it is useful to carefully consider several of its components as follows: 

Sufficient Evidence 

The word sufficient invites the question, sufficient for what? As the remainder of 

the proposition indicates, the answer is for drawing a conclusion that decreasing 

SSB consumption will reduce the prevalence of obesity and obesity-related diseases. 

This must be distinguished from the question of sufficiency for taking public health 

action or guiding public health policy. What constitutes sufficiency for actions (as 

opposed to drawing conclusions) is not a purely scientific question that can be answered objectively. Such decisions depend only in part on scientific evidence of the 

likely effects of those actions and also depend on other inputs including but not limited to legal authority, moral values and personal tastes, none of which are determined by empirical evidence. The question ‘Is there sufficient evidence for action?’ 

is inherently subjective and depends on which action, in which regulatory context 

and according to whose tastes and moral values. As Sir Austin Bradford Hill wrote, 

‘The evidence is there to be judged on its merits and the judgment . . . should be 

utterly independent of what hangs upon it—or who hangs because of it’.(1)

Scientific Evidence 

We are not asked for conjecture, but rather whether empirical evidence exists 

showing that decreasing SSBs has the effects stated. We therefore examine the 

highest quality evidence available in the form of randomized controlled trials 

(RCTs). Because such trials are ethically possible and have been performed, we assert that this type of scientific evidence supersedes correlation or cohort studies.(2)

When RCTs are not possible, other evidence must be amassed to attempt to inform 

causation. However, RCTs are possible to address this question and data are available. Hence, we rely on these results in the present case as they are probative (by 

probative, we mean studies which can generate evidence which settles questions by 

354 

proving or disproving propositions, as opposed to simply influencing the strength of 

speculation) with respect to causation.(3)

Decreasing 

We cannot assume that the effects of decreasing consumption are the opposite (direction and magnitude) of the effects of increasing consumption. Therefore, we provide examinations of available experimental reports that evaluate both interventions so as to quantify the observed effects in each case. 

Reduce the Prevalence of Obesity and Obesity-Related Diseases 

As to ‘obesity-related diseases,’ one must first demonstrate an effect on obesity to 

suggest an effect on obesity-related diseases. Else in what way can the diseases be 

said to be obesity-related? We therefore focus our present meta-analysis on studies 

of the effect on body weight or body composition. 

What We Are Not Debating 

Just as we have clarified the proposition being debated, it is equally important 

to not be distracted by questions that we have not been asked to address. For example, we have not been asked to address whether obesity is a crisis, if fructose is 

toxic, are some sugars worse than others, are food company marketing budgets too 

large, have portion sizes increased to absurd levels, do SSBs affect dental caries, are 

pictures of an average American’s sugar consumption dramatic, is liberty better 

than paternalism (or vice versa), is food marketing like tobacco marketing, or do we 

sometimes need to take public health actions in the absence of strong evidence. Although these are provocative questions, they are not germane to the necessary evaluation of evidence regarding the question we have been asked to debate. Yet we 

mention them because they and similar questions are often introduced into such discussions and serve as emotion-raising distractions to an evaluation of the pertinent 

evidence. 

There Is Evidence To Support Conjecture 

We freely concede that there is evidence to support the conjecture that reducing 

SSB consumption might reduce obesity and obesity-related diseases. However, many 

of these data are not probative in terms of causation. Specifically, there are three 

forms of human evidence supporting this conjecture. 

First, we address ecological correlation. SSB consumption has risen just as obesity 

rates have risen.(4) This is the weakest form of evidence available. Other beverage 

consumption patterns (e.g., bottled water (5) depicted in Fig. 1) have also demonstrated a strong correlation with the obesity epidemic in the United States.(6–7)

Second, we note an association in some observational studies.(8–10) Whereas there 

is an ever-growing body of epidemiologic studies, some of which demonstrate statistically significant associations, it is well known that association does not establish 

causation. Moreover, the association is weak,(11) inconsistent(12–13) and biased,(14) as 

we will discuss later. Again, as Dr. Hu (our debate opponent) wrote, ‘Although the 

overall results were not entirely consistent, the weight of epidemiologic and experimental evidence indicates greater consumption of sugar-sweetened beverages is associated with weight gain and obesity in children and adults. However, the existing 

studies suffer from many methodological limitations, including cross-sectional design, small sample size, short follow-up, inadequate dietary assessment, and a lack 

of repeated measures of diet and lifestyle. . . . any single dietary factor is unlikely 

to have a large effect on body weight’.(13)

For the third and final point which supports conjecture, we acknowledge that lesser compensation with liquid versus solid calories has been found in some in shortterm feeding studies.(15–17) By compensation, we refer to the definition provided by 

Mattes (18) whereby later energy intake may be reduced to compensate for preloads 

or added calories from some other intervention. It must also be acknowledged that 

compensation for added intake may also take the form of altered energy expenditure, which can offset the intake component of energy balance. Few feeding studies 

examine this component. Additionally, short-term feeding effects are by no means 

equivalent to long-term weight effects.(19) Moreover, the short-term effects are inconsistent, with some studies showing near perfect compensation for liquid calories (11,

20–21) and others showing imperfect but equivalent (between forms) compensation to 

solid calories.(22) Finally, there is far more than zero compensation as implied by 

common and exaggerated public statements such as, ‘When we drink sugary beverages, we simply do not compensate by eating less food’ (23) or ‘Liquid calories don’t 

register with our appetite controls’.(24)

355 

Figure 1 

Rise in obesity rates (6) (round markers) and bottled water consumption (5)

(square markers), United States. BMI, body mass index, kg m¥2. 

We agree with Dr. Pan and Dr. Hu’s statement in 2011 that ‘. . . the isolated 

tests in the laboratory may not be directly reproduced in real life because the effect 

of any food or food component on satiety could be influenced by other dietary factors. 

Thus, results from short-term, well-controlled interventions may not be representative of a real-life setting, and long-term clinical trials on different physical forms 

of carbohydrates on energy intake and weight management are still lacking’.(25)

Later in this article, we provide even more compelling evidence from longer-term 

trials on weight that some compensation for added liquid calories indeed occurs. 

Evaluation of Evidence To Draw Scientifically Supported Conclusions 

When randomized trials can be performed ethically and safely (which they have 

been), these study results are the strongest level of evidence of independent effects. 

Many scientists who have gone on record on the question we now debate have acknowledged the limitations of association studies and the need for well-designed 

randomized trials.(13, 26–28) If these same scientists are calling for well-designed 

trials, it is curious that strong statements are then made about weaker forms of evidence. Use of Hill’s guidelines (1) is irrelevant in the instance of the effects of SSBs 

on weight because randomized trials can be done (and have been done). In such situations, the ‘totality’ of the evidence, including evidence that is not probative, 

should not be relied upon for drawing conclusions of causation in favour of the probative studies. More recent trials have taken steps to reduce the level of bias (29–30)

and future studies may advance this effort further. 

Specific Questions We Address By Use of the Best Available Evidence 

1. Does an increase in SSB intake increase body weight or body mass index 

(BMI) in humans? 

2. Does reduction of SSB intake reduce body weight or BMI in humans? 

We now evaluate and summarize the currently available evidence that could potentially be probative with respect to drawing conclusions about the effects of SSB 

reduction on weight or obesity. 

Methods 

See supporting information for details of the updated literature review, study selection and data extraction methods. As the present paper was in review, an additional study meeting our criteria became public as a conference abstract.(31) This 

trial tested the effects of home water delivery and an educational programme to reduce SSB consumption in overweight, adult, Mexican women as compared to the 

education-only control group. Based on the available information in the abstract, we 


11502073.eps
356 

were unable to formally include this study result in our meta-analysis, but we discuss the possible effects on our conclusions using estimates from data reported in 

the abstract in the next section on results. 

Results 

The Extent of the Data Available: Studies Included and Excluded 

Table 1 contains a brief listing and description of the six new studies (29–30, 32–36)

added for meta-analysis. We provide more details of each study in the supporting 

information online. Supporting Information Figure S1 contains a flow chart of the 

screening and selection of recently published studies. 

In the three new studies in which SSBs were added [90 to 500 kcal day¥1 to the 

diets of adults (30,34); 158 kcal day¥1 in children (36)], statistically significant 

weight gain was observed in both adult trials, ranging from 0.39 to 1.14 kg (Supporting Information Table S1). No significant difference in weight gain was observed 

in the study in children between the treatment and control participants.(36) When 

we compared observed weight gain to theoretical weight gain from added SSBs in 

all RCTs published to date (Fig. 2), compensation appeared to occur in longer-term 

studies. 

In the one new study of adults (35) and the two new studies of children (29, 32–33)

in which participants who drank some amount of SSBs at baseline were asked to 

eliminate or reduce their SSB consumption, standardized mean differences (SMDs) 

in percentage weight loss or BMI reduction ranged from 0.13 to 0.33 (Supporting 

Information Table S2). The overall results for added SSBs (small but statistically 

significant weight gain; Fig. 3) or for reduced SSBs in subjects of all weight ranges 

(small and not statistically significant weight loss; Fig. 4) did not differ greatly from 

our earlier analysis.(37)

In new studies in which all participants were overweight or obese at baseline, 

SMDs ranged from 0.13 to 0.73 (Supporting Information Table S3). In combination 

with earlier studies or subgroup analysis of the effects of reducing SSBs on overweight subjects (Fig. 5), the overall SMD was 0.25 (95% confidence interval [CI]: 

0.13 to 0.38 standard deviations, P <0.0001). 

In the newly published study by Herna ́ndez-Cordero, et al.,(31) the authors reported no significant effect with a P-value of 0.50. Assuming this is a two-tailed Pvalue, the reported sample size yields an effect size of either ¥0.086 or +0.086. The 

means were not reported so we cannot determine the direction. If the sample effect 

size were +0.086, then the summary statistic would not change at all from the summary estimate and CI shown in Fig. 4. Alternatively, if the sample effect size was 

¥0.086, the summary estimate would be reduced towards zero (from 0.06 to 0.05) 

and remain statistically non-significant. Similarly, for the analysis shown in Fig. 5 

for subjects overweight at baseline, the addition of this study would shift the overall 

estimate from 0.25 to 0.21, or as low as 0.17 depending on direction of observed effect. 

Assessment of Study-Level Risk of Bias 

Supporting Information Figure S2 summarizes our cumulative assessment of potential areas of bias of the pertinent studies to date. The most important areas for 

risk of bias overall come from lack of participant blinding and selective reporting. 

Some study designs failed to adequately isolate treatment effects from the attention 

researchers paid to some groups. Additionally, only two studies’ protocols (29, 34) had 

an objective measure of participant compliance (returned containers, urinary 

sucralose measures), making cross comparisons and estimates of true effects difficult. Failure to mention whether assessors were blinded was common (ten out of 

15 studies), further clouding assessment of potential sources of bias. 

Assessment of Publication Bias 

Supporting Information Figures S3–S5 are funnel plots (38) for the assessment of 

potential publication bias from only the published studies and analyses for each of 

the three groups of designs or populations we analysed (excluding some analyses we 

performed on data not published but received upon request). We also evaluated potential publication bias by using the rank correlation test.(39) We found no present 

evidence of publication bias for studies on the effects of adding SSBs; (30, 34, 36, 40–42)

P = 0.805), for studies on the effects of reducing SSBs in all weight categories; (29,

33, 35, 43–46) P = 0.976), or for studies on the effects of reducing SSBs in subjects who 

were overweight at baseline; (33, 35, 43, 44, 46) P = 0.858). 

Sensitivity Analysis 

Age differences. There was unequal representation of age groups among the types 

of trials. The added SSB studies were all on adults except one,(36) and the reducing 

357 

studies were predominantly in children with two exceptions.(35, 47) Therefore, we 

evaluated the overall summary effects by excluding the studies referenced above. 

The overall SMD for the added SSB studies (adults only) increased by 0.06 (to 0.34; 

95% CI: 0.15 to 0.54). The overall SMD for the reduction of SSBs in children of all 

weight categories was reduced by 0.01 (to 0.07; 95% CI: ¥0.01 to 0.15). The overall 

SMD for the reduction studies in children only who were overweight or obese at 

baseline increased by 0.05 (to 0.30; 95% CI: 0.13 to 0.46). These results are not 

largely different from the combined analysis reported in Figs 3–5. Per the convention put forth by Cohen,(48) these standardized effects would all be categorized as 

‘small.’ 

Table 1 Studies Published Since January 2009 Meeting the Original 

Inclusion Criteria (37)

Reference 

Question that can be 

addressed regarding 

the effects of SSBs 

on weight 

How meta-analysed 

Primary outcome(s) and analysis 

as stated in trials registry and 

paper 

Njike, et al., 2011 (30) Added two servings per day 

of sugar-free cocoa, sugared cocoa, or placebo 

cocoa in obese adults in a 

crossover trial, 6 weeks 

each phase. 

Meta-analysed all response 

data for all phases (author 

provided raw data on request)—combined both caloric groups (sugared cocoa 

and placebo cocoa) and 

subtracted sugar-free 

group. 

Trial registry: NCT00538083 

Primary—Endothelial function 

Secondary—Blood pressure, lipid profile, lowdensity lipoprotein (LDL) oxidation, lipid 

hydroperoxide, C-reactive protein (CRP), glucose, body weight, waist circumference, 

endothelin 

Paper: 

Primary—Endothelial function 

Secondary—Blood pressure, lipid profile and 

fasting glucose, food intake, endothelin, CRP, 

oxidized LDL, lipid hydroperoxide, anthropometric measures (body weight, body mass 

index (BMI), waist circumference) 

Missing data handling: Intention to treat 

analysis 

Vaz, et al., 2011 (36) Added choco-malt beverage 

mix to water and gave one 

serving per day to children 

in a parallel trial. 

Meta-analysed untreated 

control group versus 

unfortified group.* 

Trial registry: NCT00876018 

Primary—Physical fitness and performance 

Secondary—Nutritional status, muscle strength 

and endurance 

Paper: 

Primary—Within participant change in physical 

performance: whole-body endurance, aerobic 

capacity, speed and visual reaction time 

Secondary—Nutritional status, muscle strength, 

endurance in forearm flexor muscle group 

Missing data handling: Complete case analysis 

Maersk, et al., 

2012 (34)

Added 1 litre per day of 

milk, regular cola, diet 

cola or water in overweight/obese adults in a 

parallel trial for 6 months. 

Meta-analysed regular cola 

group versus diet group. 

Trial registry: NCT00777647 

Primary—Body weight, magnetic resonance 

spectroscopy, magnetic resonance imaging, 

dual-energy x-ray absorptiometry scan 

Secondary—Circulating metabolic parameters, 

blood pressure 

Paper: 

Primary—Intrahaepatic fat and 

intramyocellular fat 

Secondary—Fat mass, fat distribution, metabolic 

risk factors 

Missing data handling: Complete case analysis except for two cases who dropped out at 5 

months, for whom last observation was carried forward 

Ebbeling, et al., 

2012 (33)

Multicomponent programme 

to reduce/replace SSBs 

with non-caloric beverages 

in adolescents. 

Meta-analysed weight 

change at end of 1 year 

intervention period. 

Trial registry: NCT00381160 

Primary—BMI change at 2 years 

Secondary—none stated 

Paper: 

Primary—Change in mean BMI at 2 years (1 

year post-intervention) 

Secondary—Differences between ethnicities, 

change in body fat as a percentage of total 

weight 

Missing data handling: Imputed—baseline 

and last observation carried forward in separate analyses 

358 

Table 1 Studies Published Since January 2009 Meeting the Original 

Inclusion Criteria (37)—Continued 

Reference 

Question that can be 

addressed regarding 

the effects of SSBs 

on weight 

How meta-analysed 

Primary outcome(s) and analysis 

as stated in trials registry and 

paper 

de Ruyter, et al., 

2012 (29)

Provided school children 

identically labelled SSB or 

non-caloric equivalent to 

consume one can day¥1. 

Considered an SSB reduction 

study as inclusion criteria 

was current SSB consumers. 

Trial Registry: NCT00893529 

Primary—BMI Z-score at 6, 12 and 18 months 

Secondary—Body composition using skinfolds, 

bioelectrical impedance analysis (BIA), waistto-height ratio, dental health, sensory evaluation (satiety and liking of study drink) 

Paper: 

Primary—Z-score of BMI for age at 18 months. 

Secondary—(all pre-specified) waist-to-height 

ratio, sum of the four skinfold thickness measurements and fat mass (BIA). Additional outcomes were weight, height, z score for height, 

waist circumference and weight change adjusted for height change 

Missing data handling: Multiple imputation 

and complete case analysis 

Tate, et al., 2012 (35) Substituted SSBs with artificially sweetened equivalent or water in obese 

adults who drink two or 

more servings per day at 

baseline. 

Meta-analysed water and artificially sweetened groups 

together versus SSB group. 

Trial registry: NCT01017783 

Primary—Weight change at 3 and 6 months 

Secondary—Urine specific gravity, fasting glucose 

Paper: 

Primary—Weight change at 6 months. 

Secondary—Compare the non-caloric beverage 

groups with the control group on criterion 

measures of weight loss, waist circumference, 

blood pressure, glucose, and urine osmolality 

from 0 to 3 and 0 to 6 months 

Missing data handling: Multiple imputation 

for continuous variables, complete cases for 

5% weight loss criterion analysis 

* We originally excluded any types of beverages that had growth promotion as a function, but the 

unfortified beverage met our original inclusion criteria and is included in this analysis. SSB, 

Sugar-sweetened Beverage. 

Figure 2 

Observed (30, 34, 40–42, 62) versus theoretical (63) weight gain effect of mandatory sugar-sweetened beverage (SSB) consumption. 

Notes: For observed values on the Y axis, weight change was determined 

by the change of those drinking more SSBs minus those drinking less. The 

X axis was determined by multiplying the added kcal per day times the duration of the study divided by 1,000. Fit lines were generated by setting the 


11502074.eps
359 

origin to zero and by using the linear regression (least squares) options in 

Microsoft® Excel. The theoretical values (round markers) were generated by 

entering mean baseline values for each study sample into the NIDDK body 

weight simulator (63) and adding the same number of calories per day for 

the same number of days as reported in the studies.(30, 34, 40–42, 62) Activity 

settings in the simulator were at the lowest level of sedentary and no activity or dietary changes over the study duration were entered into the simulator. Observed data represent an average energy compensation rate of 85% 

(range = 57–110% compensation). 

Figure 3 

Forest plot comparing studies of added sugar-sweetened beverage (SSB) 

consumption. 

Note: R square values were calculated from the overall standardized 

mean difference estimate (d) per the method found in.(64)

Study heterogeneity in reduction studies. Because the heterogeneity statistic was 

significant (Fig. 4) in the reduction studies in both weight groups, we evaluated 

which study exerted the most influence for its effects on the overall SMD.(46) Exclusion of this study resulted in a non-significant heterogeneity statistic (χ2 (6) = 10.15, 

P = 0.12, I2 = 41%) and an increased overall SMD of 0.13 (95% CI: 0.04 to 0.22). 

These analyses shifted the overall statistics by relatively small amounts when considering the observed shifts in body weight among the analysis groups. 

Interpreting the magnitude of effects. At this juncture, it may be helpful to express 

the estimated effect sizes for SSB reduction on BMI in some additional metrics 

which may ease interpretation. One such metric is the probability that a randomly 

selected person from a hypothetical population in which SSB reduction was implemented will be better off (with respect to BMI) than a randomly selected person 

from a hypothetical population that is the same in all ways except that SSB reduction has not been implemented. Without intervention, the probability is 0.50 that 

a person from one population weighs more than a person from the other population. 

After the interventions included in our analysis, these probabilities would change 

slightly. The probability that a randomly selected person from the reduced SSB population will have lower BMI than a person randomly selected from the control population would be 0.52. The probability that a randomly selected overweight person 

from the reduced SSB population will have a lower BMI than an overweight person 

randomly selected from the control population would be 0.57. 

Figure 4 

Forest plot comparing studies of reduced sugar-sweetened beverage (SSB) 

consumption; subjects in all weight categories included. 

Note: R square values were calculated from the overall standardized 

mean difference estimate (d) per the method found in.(64)


11502075.eps 11502076.eps
360 

Figure 5 

Forest plot comparing studies of reduced sugar sweetened beverage (SSB) 

consumption; only subjects overweight/obese at baseline included. 

R square values were calculated from the overall standardized mean difference estimate (d) per the method found in.(64)

Another way to place the effect sizes in perspective is to consider the γ2 metric 

shown in Figs 3–5. Increasing consumption of SSBs explains 1.92% of the variance 

in body weight or BMI change. Reducing consumption of SSBs in persons of all 

weight categories explains 0.09% of the variance in body weight or BMI change. 

Among persons who are overweight or obese at baseline, reducing the consumption 

of SSBs explains 1.54% of the variance in body weight or BMI change. It is possible 

to apply other methods such as risk analysis for evaluating potential effects on population levels of obesity,(49) but that is beyond the scope of the present analysis. 

Additional Considerations 

Having demonstrated that, although the conjecture that decreasing SSB consumption will decrease obesity and obesity-related diseases is reasonable, the pertinent 

data testing the hypothesis are equivocal (i.e., the pooled results are nearly but not 

quite statistically significant), we now address several related questions. 

If the data are as weak as we have shown, why do some members of the public and 

the scientific community seem to perceive that the proposition has been proven? 

We suggest three major reasons for this confusion. 

Emotion-Raising Language 

Emotion-raising language has often been used in discussions of SSBs and obesity. 

Some authors have used words like ‘plague’,(50) ‘toxic’,(51–52) ‘hazardous’ (4, 53) and 

‘deadly’ (4, 54) when describing SSBs or the sugars they contain and have tried to promote perceived connections between SSB marketers and the worst behaviour of tobacco marketers.(55) Although such words may help to advance an agenda,(56) they 

do not educate or inform the public. Moreover, they likely raise emotions and impair 

logical reasoning.(57) As Kersh and Morone (56) wrote, ‘Scientific findings never carry 

the same political weight as does a villain threatening American youth. If critics 

successfully cast portions of the industry in this way, far-reaching political interventions are possible, even likely. When an industry becomes demonized, plausible 

counter-arguments (privacy, civil liberties, property rights, and the observation that 

‘‘everyone does it’’) begin to totter.’ 


11502077.eps
361 

Figure 6 

Comparison of weight gain attributed to consumption of sugar-sweetened 

beverages for 1 year from various sources. 

Note: For the Haub study, the weight change shown above is adjusted by 

subtracting the control group weight change. 

* Body mass index of 27.8 kg m¥2 (NHANES 2010 50th percentile for 

both men and women in the United States (65) entered into NIDDK body 

weight simulator.(63)

+ (66) # (67) $ (68). 

Distortion of Scientific Information 

A second factor that has likely contributed to misperceptions in this area is the 

distortion of scientific information by some authors and commentators. Table 2 lists 

some of the types of distortion that have occurred with quantitative or anecdotal 

documentation. Figure 6 depicts disparities in projected versus actual outcomes of 

the effects of added SSBs over 1 year. Clearly, such practices mislead and have likely contributed to misperceptions in the scientific and lay communities about the 

strength of the evidence regarding the proposition debated here. 

The Mere Exposure Effect 

The final factor that we believe has led to the erroneous perception that the evidence showing that the proposition of this debate has been unequivocally proven is 

the ‘mere exposure effect.’ The mere exposure effect is the label psychologists use 

for the phenomenon that the more a person is exposed to an idea, the more they 

come to like and accept it. As the Nobel Prize-winning economist Daniel Kahneman 

described, ‘A reliable way to make people believe in falsehood is frequent repetition, 

because familiarity is not easily distinguished from truth. Authoritarian institutions 

and marketers have always known this fact. But it was psychologists who discovered that you do not have to repeat the entire statement of a fact or idea to make 

it appear true’.(58)

The number of articles on SSBs and obesity and the number of statements that 

SSBs are especially problematic in obesity are extraordinary, especially in comparison to the modest amount of probative data.(3) Thus, opinions about SSBs may have 

been offered so often that these opinions have become accepted as fact by many in 

the scientific community, media and lay public. 


11502078.eps
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Table 2 Some types of Distortion of Information that Have Occurred Regarding Sugar-Sweetened Beverages (SSBs) and 

Obesity 

Type of distortion Where it occurs Documentation of occurrence Comments 

Papers citing original randomized controlled trials (RCTs) that 

investigated the effect of SSB reduction on weight exaggerated the extent of the evidence supporting a beneficial effect 

In the scientific peer-reviewed 

literature 

Cope and Allison (14) documented in a quantitative analysis of the literature that this exaggerated reporting was the norm rather than an exception. This is not a criticism of the original RCTs, but rather the 

manner in which subsequent authors cite them. 

Association studies are described by using language that indicates a cause and effect relationship has been found In the scientific peer-reviewed literature [e.g.,(69)], in government-sponsored newsletters 

[e.g.,(70)], and in mass media 

articles (71–73)

‘A new study . . . suggests a key way to reduce childhood obesity could be to limit your child’s 

salt intake. The study looked at 4,000 children in Australia and found kids who ate more salt also had more cravings for sugary-sweetened drinks like soda and juice.’ (72)

‘Cravings’ were not mentioned 

in the published study,(74)

which did not overstate the findings, but were exaggerated in media coverage. This misleading practice is common in the obesity field overall.(75)

Public statements that contradict available evidence Communications from public 

health agencies 

In 2010, [T]he New York Times ran an article in which, through e-mails obtained under the 

Freedom of Information Act, they showed that the New York City (NYC) Department of Health was knowingly making exaggerated statements about the amount of weight gain expected from drinking SSBs. Even after this expose ́ (September 2012), the NYC Department 

of Health made even more exaggerated and evidence-contradicted statements about the amount of weight gain expected from drinking SSBs. 

See Figure 6 for details and 

specific references. 

Changing what is considered the primary endpoint or analysis 

in an RCT 

In the scientific peer-reviewed 

literature 

An example (34) of this occurred in an RCT published in the American Journal of Clinical Nutrition (AJCN). In the paper, the authors state ‘Our main aim was to test the hypothesis 

that sucrose-sweetened cola increases ectopic fat including VAT4, total body fataccumulation, and metabolic risk factors . . . ,’ whereas the registration in ClinicalTrials.gov states ‘Primary Outcome Measures: Body Weight; MR spectroscopy; MRI; DEXA scan.’ Similarly, in ClinicalTrials.gov, the title of the trial is ‘Effect of Carbonated Soft Drinks on the Body Weight,’ whereas in the article the title is ‘Sucrose-sweetened beverages increase fat storage in the liver, muscle, and visceral fat depot: a 6-mo randomized intervention study.’ The fact that there was no significant effect on weight was not mentioned in the abstract of the paper. 

This does not conform to the 

CONSORT guidelines for publishing RCTs to which authors publishing in AJCN are expected to adhere. 

Conclusion statements from paper do not match the results In peer-reviewed papers, press 

releases, and mass media interviews 

An example from the peer-reviewed literature occurred in a paper in AJCN (35) in which the 

results section of the abstract stated ‘Mean (ÒSEM) weight losses at 6 months were ¥2.5 Ò0.45% in the DB group, ¥2.03 Ò0.40% in the Water group, and ¥1.76 Ò0.35% in the AC 

group; there were no significant differences between groups.’ Yet, the conclusion section of the abstract stated ‘Replacement of caloric beverages with non-caloric beverages as a weight-loss strategy resulted in average weight losses of 2% to 2.5%.’ Given the non-significant result, it does not seem justifiable to state there is any weight loss as a result of the treatment. Even if point estimates were being provided in a merely descriptive manner, the unbiased estimates of treatment effects in an RCT are the control-subtracted means, not the raw means in the treatment group. Examples from press releases and media interviews can be found in (14, 76) and in these sources.(77,78)

Although a trained scientist 

carefully reading the original papers will understand the results, journalists, regulators, clinicians and scientists who only rapidly read an abstract are likely to be misled. 

363 

Publication bias In the scientific peer-reviewed 

literature 

Cope and Allison (14) showed that in observational epidemiologic studies of the association of 

SSB consumption and obesity, a standard test of publication bias was significant, suggesting that investigators are more likely to publish positive statistically significant findings than to publish null findings. 

This is why we wrote earlier in 

this paper that the observed magnitude of association is likely biased upwards. Interestingly, Cope and Allison found that this publication bias seemed to occur among non-industry-funded authors and not among industryfunded authors. 

Table 3 Quotations Illustrating that Others Do Not Believe the Benefits of Interventions Aimed at SSB Reduction on Weight 

Have Been Established 

Person(s) or body offering statement Statement Reference 

United States Department of Agriculture Dietary Guidelines Advisory 

Committee 

‘Thus, there are mixed results on this topic. RCTs report that added sugars are not different from other calories in increasing energy intake 

or body weight. Prospective studies report some relationship with SSB and weight gain, but it is not possible to determine if these relationships are merely linked to additional calories, as opposed to added sugars per se. The systematic reviews in this area are also inconsistent, probably based on different measures used to determine added sugars intake or intake of SSB.’ [We should] ‘Conduct well-controlled and powered research studies testing interventions that are likely to improve energy balance in children at increased risk of childhood obesity, including dietary approaches that reduce . . . sugar-sweetened beverages’ [because] ‘very few solid data are available on interventions in children.’ 

(12)

European Food Safety Authority ‘The Panel concludes that a cause and effect relationship has not been established between total sugar intake and body weight gain, and that 

a cause and effect relationship has not been established between the consumption of foods and beverages in which sugars have been replaced by intense sweeteners and contribution to the maintenance or achievement of a normal body weight.’ 

(79)

Lisa Te Morenga, Simonette Mallard, Jim Mann ‘Trials in children, which involved recommendations to reduce intake of sugar sweetened foods and beverages, had low participant compliance to dietary advice; these trials showed no overall change in body weight.’ 

(80)

German Nutrition Society ‘From two of the four available meta-analyses the conclusion is drawn that increased consumption of sugar-sweetened beverages in children 

and adolescents is associated with a higher risk of obesity. In contrast, another meta-analysis judges the effect as almost zero. The cohort studies published since then verify this risk-increasing effect only in part. The most recent meta-analysis concludes that the risk-increasing effect is limited to individuals with initially already increased BMI or existing overweight, respectively.’ 

(81)

Thomas Baranowski ‘Another concern is the behavior or behaviors targeted for change. Many obesity prevention interventions have targeted increasing fruit and 

vegetable intake and decreasing sweetened beverage intake. Systematic reviews, however, showed no consistent evidence that increased fruit and vegetable intake protected against obesity or that sweetened beverage intake contributed to it.’ 

(82)

Joint statement from American Heart Association and the American 

Diabetes Association 

‘At this time, there are insufficient data to determine conclusively whether the use of NNS [non-nutritive sweeteners] to displace caloric sweeteners in beverages and foods reduces added sugars or carbohydrate intakes, or benefits appetite, energy balance, body weight, or 

cardiometabolic risk factors.’ 

(83)

364 

Are we alone in the view that a beneficial effect of SSB reduction on obesity has not 

been demonstrated? 

In a word, no. As the quotations in Table 3 reveal, our views are concordant with 

those of other individual scientists and authoritative expert panels. 

What would it take to shift the balance of evidence? 

In a possibly apocryphal interchange, a devotee of Karl Popper’s philosophy of 

science once challenged the great mathematical geneticist J.B.S. Haldane to specify 

what it would take to change his views about the validity of evolutionary theory. 

Haldane reportedly retorted ‘Fossil rabbits in the Precambrian!’ Although a poetic 

retort, Haldane was effectively specifying objective empirical evidence that would be 

sufficient for him to change his view, something any scientist addressing empirical 

questions should be prepared to do. 

In the debate at The Obesity Society Meeting (September 20, 2012), the senior 

author [DBA] stated: 

‘The day that multiple RCTs are published that 

• are well designed, executed, and analyzed; 

• show statistically significant outcomes in preplanned analyses of the total 

randomized sample on measures of total body weight, BMI, or total body fat 

and clearly support the value of reducing SSBs; and 

• are sufficient in inferential weight to outweigh the existing RCT data; 

I will be delighted to modify my opinion.’ 

The day after the debate (September 21, 2012), two new RCTs were published.(29,

33) These two publications together met some (but not all) of the criteria specified 

above as we discussed earlier. Most notably, their collective evidential weight moved 

the integrated meta-analytic estimate for the effects of SSB reduction very close to 

the border of the conventional 0.05 level of statistical significance. For this reason, 

we believe that these two new studies can be described as ‘tilting the needle’ in the 

direction of demonstrating the obesity-reducing benefit of SSB reduction, but that 

the data remain equivocal. Nevertheless, we remain open-minded that future RCTs 

(and according to ClinicalTrials.gov some will be forthcoming) may fulfil the criteria 

above and offer unequivocal support for the proposition. 

We also suggest that the following approaches can increase the transparency of, 

and confidence in, RCTs in this area: (i) registering all RCTs in advance in 

ClinicalTrials.gov; (ii) making the raw data from all RCTs publicly available for 

common and open analyses, regardless of the source of funding; (iii) providing documentation via ClinicalTrials.gov as to which analyses are (were) pre-planned; and 

(iv) publishing all results regardless of outcome. These are laudable practices in all 

situations, but especially important in an area that has become so contentious. 

How does the strength of evidence for conclusions relate to support for actions? 

As we mentioned earlier, we are not addressing whether any particular policy or 

programme should or should not be implemented. Rather, our sole purpose has been 

to present a synthesis of the currently available literature that provides an estimate 

of the degree of evidence for the debate proposition. Moreover, it is important to 

note that our paper assessed the evidence for effect of reducing SSB consumption, 

which should not be conflated with the effects of particular policies (e.g., taxes, bans, 

advertising campaigns, etc.) intended to reduce SSB consumption. The effects of any 

such policies represent a different question and not one for which we have evaluated 

the evidence. 

The question of whether the available evidence is sufficiently strong to justify a 

particular action is a subjective one subject to societal perceptions, values, goals and 

the plausibility of unintended consequences.(59–60) This is illustrated by quotations 

from two authoritative sources on this point as food for thought: 

‘Since taking office, the President has emphasized the need to use evidence 

and rigorous evaluation in budget, management, and policy decisions to make 

government work effectively. . . . Where evidence is strong, we should act on 

it. Where evidence is suggestive, we should consider it. Where evidence is weak, 

we should build the knowledge to support better decisions in the future.’ (61)

‘On fair evidence we might take action on what appears to be an occupational 

hazard, e.g., we might change from probably carcinogenic oil to a non-carcinogenic oil in a limited environment and without too much injustice if we are 

wrong. But we should need very strong evidence before we made people burn 

a fuel in their homes that they do not like or stop smoking the cigarettes and 

eating the fats and sugar that they do like.’ (1)

365 

Conclusions 

Our updated meta-analysis shows that the currently available randomized evidence for the effects of reducing SSB intake on obesity is equivocal. Even if statistical significance is ignored, the point estimates of effects on BMI reduction are 

small, accounting for only 1.5% of the variance observed in those who were overweight at baseline. Therefore, we conclude that the debate proposition cannot be 

supported at this time. Of course, absence of evidence is not evidence of absence. 

The lower limit of the confidence interval around the estimated effect of SSB reduction is very close to the border of statistical significance. It is certainly possible that 

additional, larger or otherwise stronger studies will in the future provide clear and 

convincing evidence that lowering SSB consumption will reduce obesity and obesityrelated disease prevalence. We are certainly not arguing against the common-sense 

recommendation that for individuals who wish to lose weight and who presently 

drink large amounts of SSBs, reducing intake of these and other sources of energy 

seems wise. 

We greatly respect our debate opponent, Dr. Hu, for addressing these issues in 

a manner that is both thoroughly scientific and equally collegial. We are hopeful 

that this debate may be seen not only as a careful consideration of the evidence regarding SSBs and obesity, but also as an exemplar of and call to a more informed, 

unexaggerated, open-minded, rational and civil dialogue on the many public health 

issues around obesity that, like SSB-related issues, have become so contentious. 

Author Contributions 

KAK performed an updated systematic review, reviewed papers for inclusion criteria, extracted data from papers, wrote summaries of new studies included in appendix, checked meta-analysis calculations, assessed risk of bias for newly included studies, 

generated tables, generated figures and wrote a significant portion of the text. KDK extracted data from papers, analysed supplemental data received from authors, generated new meta-analysis statistics and verified prior data reported. JMS reviewed papers

for inclusion criteria, assessed risk of bias for newly included studies, wrote summaries of new studies included in appendix and reviewed and edited text. DBA conceived of the project scope, developed debate arguments, directed meta-analysis methods, reviewed 

papers for inclusion criteria, edited and wrote a significant portion of the text. 

Conflict of Interest Statement 

In the last 36 months, Dr. Allison has received consulting fees from Kraft Foods. The University of Alabama at Birmingham has 

received gifts and grants from multiple organizations including but not limited to The Coca-Cola Company, PepsiCo, Red Bull and

Kraft Foods. Drs. Kaiser, Keating and Shikany have no competing interests to report. 

Ethical Approval 

Not required. 

Acknowledgements 

Supported in part by NIH grant P30DK056336. The opinions expressed are those of the authors and not necessarily those of the 

NIH or any other organization with which the authors are affiliated. This paper is based on a debate held at The Obesity Society 

2012 Annual Meeting. The authors thank Sigrid Gibson; Drs. Michelle Bohan Brown, Richard Forshee, Richard Mattes and Douglas Weed for their suggestions on drafts of this manuscript. The authors also thank Dr. Marc Reitman for the use of Fig. 1. The authors are also grateful to those who kindly responded to our request for additional data about their studies: Dr. Valentine Njike, 

Dr. David Katz, Dr. Mario Vaz, Dr. Tinku Thompson, Ms. Janne de Ruyter, Dr. Sonia Herna ́ndez-Cordero and Dr. Martijn Katan. 

Supporting Information 

Additional Supporting Information may be found in the online version of this article, http://dx.doi.org/10.1111/obr.12048. 

Figure S1. Study screening and selection process of new studies added since the original meta-analysis (21)

Figure S2. Methodological quality summary; review authors’ judgments about each methodological quality item for each included 

study (1–6, 20, 25–32)

Figure S3. Funnel plot of published studies of added sugar-sweetened beverage (SSB) consumption (3–4, 6, 27, 30, 32)

Figure S4. Funnel plot of published studies on reduced sugar-sweetened beverage (SSB) consumption in subjects of all weight categories (1–2, 5, 20, 26, 28, 31)

Figure S5. Funnel plot of published studies of reduced sugar-sweetened beverage (SSB) consumption in subjects overweight/obese 

at baseline (2, 5, 20, 26, 31)

Table S1. Unstandardized effect sizes of new studies assessing the effects of adding mandatory SSB consumption to persons diets 

Table S2. Standardized effect sizes from new studies assessing the effect of attempting to get people to reduce or eliminate SSB 

consumption on body composition/adiposity indicators 

Table S3. Standardized effect sizes from new studies assessing the effect of attempting to get people to reduce or eliminate SSB 

consumption on body mass index (BMI) only for subjects overweight/obese at baseline or above the top of tertile of baseline BMI

Appendix S1. Updated literature review, selection and data extraction methods 

References 

1. Hill A.B. The environment and disease: association or causation? PROC. R. SOC. MED. 1965; 58: 295–300. 

2. U.S. Preventive Services Task Force. U.S. Preventive Services Task Force Procedure Manual, 2008. 

3. Casazza K., Allison D.B. Stagnation in the clinical, community, and public health domain of obesity: the need for probative research. CLIN. OBES. 2012; 2: 83–85. 

4. Bray G.A. Fructose: pure, white, and deadly? Fructose, by any other name, is a health hazard. J. DIAB. SCI. TECHNOL. 2010; 4: 

1003–1007. 

5. Earth Policy Institute. Bottled Water Consumption Per Person in the United States, 1976–2007. Posted: 12–7–2007. [WWW document]. URL http://www.earth-policy.org/index.php?/data_center/C21/ (accessed December 2012). 

6. Ogden C.L., Carroll M.D. Prevalence of Overweight, Obesity, and Extreme Obesity Among Adults: United States, Trends 1960– 

1962 Through 2007–2008. Posted: 2010. [WWW document]. URL http://www.cdc.gov/NCHS/data/hestat/obesity_adult_07_08/ 

obesity_adult_07_08.pdf (accessed December 2012). 

7. Ogden C.L., Carroll M.D., Kit B.K., Flegal K.M. Prevalence of obesity and trends in body mass index among U.S. children and 

adolescents, 1999–2010. JAMA 2012; 307: 483–490. 

8. Malik V.S., Willett W.C., Hu F.B. Sugar-sweetened beverages and BMI in children and adolescents: reanalyses of a meta-analysis. AM. J. CLIN. NUTR. 2009; 89: 438–439. 

366 

References—Continued 

9. Malik V.S., Popkin B.M., Bray G.A., Despres J.P., Willett W.C., Hu F.B. Sugar-sweetened beverages and risk of metabolic syndrome and type 2 diabetes: a meta-analysis. DIABETES CARE 2010; 33: 2477–2483. 

10. Mozaffarian D., Hao T., Rimm E.B., Willett W.C., Hu F.B. Changes in diet and lifestyle and long-term weight gain in women 

and men. N. ENGL. J. MED. 2011; 364: 2392–2404. 

11. Drewnowski A., Bellisle F. Liquid calories, sugar, and body weight. AM. J. CLIN. NUTR. 2007; 85: 651–661. 

12. Dietary Guidelines Advisory Committee. Report of the DGAC on the Dietary Guidelines for Americans, 2010: Part D—Section 

5: Carbohydrates. Posted: 2010. [WWW document]. URL http://www.cnpp.usda.gov/Publications/DietaryGuidelines/2010/DGAC/ 

Report/D-5-Carbohydrates.pdf (accessed December 2012). 

13. Hu F.B. Obesity Epidemiology. Oxford University Press: New York, NY, 2008. 

14. Cope M.B., Allison D.B. White hat bias: examples of its presence in obesity research and a call for renewed commitment to 

faithfulness in research reporting. INT. J. OBES. (Lond) 2010; 34: 84–88. 

15. DiMeglio D.P., Mattes R.D. Liquid versus solid carbohydrate: effects on food intake and body weight. INT. J. OBES. RELAT. 

METAB. DISORD. 2000; 24: 794–800. 

16. Tieken S.M., Leidy H.J., Stull A.J., Mattes R.D., Schuster R.A., Campbell W.W. Effects of solid versus liquid meal-replacement products of similar energy content on hunger, satiety, and appetite-regulating hormones in older adults. HORM. METAB. RES. 

2007; 39: 389–394. 

17. Cassady B.A., Considine R.V., Mattes R.D. Beverage consumption, appetite, and energy intake: what did you expect? AM. J. 

CLIN. NUTR. 2012; 95: 587–593. 

18. Mattes R.D. Dietary compensation by humans for supplemental energy provided as ethanol or carbohydrate in fluids. PHYSIOL. 

BEHAV. 1996; 59: 179–187. 

19. McKiernan F., Hollis J.H., Mattes R.D. Short-term dietary compensation in free-living adults. PHYSIOL. BEHAV. 2008; 93: 975– 

983. 

20. Almiron-Roig E., Chen Y., Drewnowski A. Liquid calories and the failure of satiety: how good is the evidence? OBES. REV. 

2003; 4: 201–212. 

21. Almiron-Roig E., Flores S.Y., Drewnowski A. No difference in satiety or in subsequent energy intakes between a beverage and a 

solid food. PHYSIOL. BEHAV. 2004; 82: 671–677. 

22. Houchins J.A., Burgess J.R., Campbell W.W., et al. Beverage vs. solid fruits and vegetables: effects on energy intake and body 

weight. OBESITY (Silver Spring) 2012; 20: 1844–1850. 

23. Kulze A.G. Eat Right for Life: Chapter 5—Drink the Right Beverages. Posted: 2013. [WWW document]. URL http:// 

www.welcoa.org/store/resources/documents/er-sample-chapter.pdf (accessed December 2012). 

24. Liebman B. Nutrition Action Healthletter: Pour Better or Pour Worse. Posted: 6–1–2006. [WWW document]. URL http:// 

www.cspinet.org/nah/06_06/beverage.pdf (accessed December 2012). 

25. Pan A., Hu F.B. Effects of carbohydrates on satiety: differences between liquid and solid food. CURR. OPIN. CLIN. NUTR. 

METAB. CARE. 2011; 14: 385–390. 

26. Bray G.A., Nielsen S.J., Popkin B.M. Consumption of high fructose corn syrup in beverages may play a role in the epidemic of 

obesity. AM. J. CLIN. NUTR. 2004; 79: 537–543. 

27. Malik V.S., Schulze M.B., Hu F.B. Intake of sugar-sweetened beverages and weight gain: a systematic review. AM. J. CLIN. 

NUTR. 2006; 84: 274–288. 

28. Vartanian L.R., Schwartz M.B., Brownell K.D. Effects of soft drink consumption on nutrition and health: a systematic review 

and meta-analysis. AM. J. PUBLIC HEALTH 2007; 97: 667–675. 

29. de Ruyter J.C., Olthof M.R., Seidell J.C., Katan M.B. A trial of sugar-free or sugar-sweetened beverages and body weight in 

children. N. ENGL. J. MED. 2012; 367: 1397–1406. 

30. Njike V.Y., Faridi Z., Shuval K., et al. Effects of sugar-sweetened and sugar-free cocoa on endothelial function in overweight 

adults. INT. J. CARDIOL. 2011; 149: 83–88. 

31. Herna ́ndez-Cordero S., Barquera S., Rodriguez-Ramirez S., et al. Water intake and metabolic syndrome risk: a randomized 

clinical trial. Experimental Biology Conference, Boston. Abstract presented 4–22–2013. 

32. de Ruyter J.C., Olthof M.R., Kuijper L.D., Katan M.B. Effect of sugar-sweetened beverages on body weight in children: design 

and baseline characteristics of the Double-blind, Randomized INtervention study in Kids. CONTEMP. CLIN. TRIALS 2012; 33: 247–257. 

33. Ebbeling C.B., Feldman H.A., Chomitz V.R., et al. A randomized trial of sugar-sweetened beverages and adolescent body 

weight. N. ENGL. J. MED. 2012; 367: 1407–1416. 

34. Maersk M., Belza A., Stodkilde-Jorgensen H., et al. Sucrose-sweetened beverages increase fat storage in the liver, muscle, and 

visceral fat depot: a 6-mo randomized intervention study. AM. J. CLIN. NUTR. 2012; 95: 283–289. 

35. Tate D.F., Turner-McGrievy G., Lyons E., et al. Replacing caloric beverages with water or diet beverages for weight loss in 

adults: main results of the Choose Healthy Options Consciously Everyday (CHOICE) randomized clinical trial. AM. J. CLIN. NUTR. 

2012; 95: 555–563. 

36. Vaz M., Pauline M., Unni U.S., et al. Micronutrient supplementation improves physical performance measures in Asian Indian 

school-age children. J. NUTR. 2011; 141: 2017–2023. 

37. Mattes R.D., Shikany J.M., Kaiser K.A., Allison D.B. Nutritively sweetened beverage consumption and body weight: a systematic review and meta-analysis of randomized experiments. OBES. REV. 2011; 12: 346–365. 

38. Review Manager (RevMan) [Computer Program] Version 5.1.6. Copenhagen: The Nordic Cochrane Centre. The Cochrane Collaboration, 2011. 

39. Begg C.B., Mazumdar M. Operating characteristics of a rank correlation test for publication bias. BIOMETRICS 1994; 50: 1088– 

1101. 

40. Haub M.D., Simons T.R., Cook C.M., Remig V.M., Al-Tamimi E.K., Holcomb C.A. Calcium-fortified beverage supplementation 

on body composition in postmenopausal women. NUTR. J. 2005; 4: 21. e-pub 1–6 (DOI:10.1186/1475–2891–4–21). 

41. Reid M., Hammersley R., Hill A.J., Skidmore P. Long-term dietary compensation for added sugar: effects of supplementary sucrose drinks over a 4-week period. BR. J. NUTR. 2007; 97: 193–203. 

42. Tordoff M.G., Alleva A.M. Effect of drinking soda sweetened with aspartame or high-fructose corn syrup on food intake and 

body weight. AM. J. CLIN. NUTR. 1990; 51: 963–969. 

43. Albala C., Ebbeling C.B., Cifuentes M., Lera L., Bustos N., Ludwig D.S. Effects of replacing the habitual consumption of 

sugar-sweetened beverages with milk in Chilean children. AM. J. CLIN. NUTR. 2008; 88: 605–611. 

44. Ebbeling C.B., Feldman H.A., Osganian S.K., Chomitz V.R., Ellenbogen S.J., Ludwig D.S. Effects of decreasing sugar-sweetened beverage consumption on body weight in adolescents: a randomized, controlled pilot study. PEDIATRICS 2006; 117: 673–680. 

45. James J., Thomas P., Cavan D., Kerr D. Preventing childhood obesity by reducing consumption of carbonated drinks: cluster 

randomised controlled trial. BMJ 2004; 328: 1237. e-pub 1–6 (DOI: 10.1136/bmj.38077.458438.EE). 

46. Sichieri R., Paula T.A., de Souza R.A., Veiga G.V. School randomised trial on prevention of excessive weight gain by discouraging students from drinking sodas. PUBLIC HEALTH NUTR. 2009; 12: 197–202. 

47. Munoz D. The efficacy of two brief interventions to reduce soda consumption in a college population (Doctoral dissertation). Albany, New York State University of New York. 2006. 

48. Cohen J. Statistical Power Analysis for the Behavioral Sciences. Academic Press: New York, NY, 1988. 

49. Forshee R.A., Storey M.L., Ginevan M.E. A risk analysis model of the relationship between beverage consumption from school 

vending machines and risk of adolescent overweight. RISK ANAL. 2005; 25: 1121–1135. 

50. Popkin B.M. Sugary beverages represent a threat to global health. TRENDS ENDOCRINOL. METAB. 2012; 23: 591–593. 

51. Lustig RH, Gupta S. Is sugar toxic? [WWW document]. URL http://www.cbsnews.com/8301-18560_162-57407294/issugartoxic/ (accessed December 2012). 

52. Lustig R.H., Schmidt L.A., Brindis C.D. Public health: the toxic truth about sugar. NATURE 2012; 482: 27–29. 

53. Bray G.A. Soft drink consumption and obesity: it is all about fructose. CURR. OPIN. LIPIDOL. 2010; 21: 51–57. 

54. Yudkin J.S. Pure, White and Deadly. Penguin Books: London, 1986. 

367 

* Office of Energetics, Nutrition Obesity Research Center, University of Alabama at Birmingham, Birmingham, AL 35294–0022, USA, e-mail dallison@uab.edu. 

doi:10.1017/S0007114513003309. 

References—Continued 

55. Brownell K.D., Warner K.E. The perils of ignoring history: Big Tobacco played dirty and millions died. How similar is Big 

Food? MILBANK Q. 2009; 87: 259–294. 

56. Kersh R., Morone J. The politics of obesity: seven steps to government action. HEALTH AFF. (Millwood) 2002; 21: 142–153. 

57. Schmeichel B.J., Vohs K.D., Baumeister R.F. Intellectual performance and ego depletion: role of the self in logical reasoning 

and other information processing. J. PERS. SOC. PSYCHOL. 2003; 85: 33–46. 

58. Kahneman D. Thinking, Fast and Slow. Farrar, Straus & Giroux: New York, NY, 2011. 

59. Sharpe K.M., Staelin R., Huber J. Using extremeness aversion to fight obesity: policy implications of context dependent demand. J. CONSUM. RES. 2008; 35: 406–422. 

60. Wansink B., Hanks D., Just D.R., et al. From Coke to Coors: A Field Study of a Sugar-Sweetened Beverage Tax and its Unintended Consequences. Posted: 5–26–2012. [WWW document]. URL http://dx.doi.org/10.2139/ssrn.2079840 (accessed December 

2012). 

61. Zients J.D. Memorandum to the Heads of Executive Departments and Agencies. Posted: 2012. [WWW document]. URL http:// 

www.whitehouse.gov/sites/default/files/omb/memoranda/2012/m-12-14.pdf (accessed December 2012). 

62. Addington E.A. Aspartame- or sugar-sweetened beverages. Effects on food appetites and mood in young adults (Doctoral dissertation). Manhattan, Kansas: Kansas State University. 1998. 

63. Hall K.D. Body Weight Simulator. Posted: 4–4–2012. [WWW document]. URL http://bwsimulator.niddk.nih.gov/ (accessed 

December 2012). 

64. Hedges L.V., Olkin I. Statistical Methods for Meta-Analysis. Academic Press: Orlando, FL, 1985. 

65. Flegal K.M., Carroll M.D., Kit B.K., Ogden C.L. Prevalence of obesity and trends in the distribution of body mass index among 

U.S. adults, 1999–2010. JAMA 2012; 307: 491–497. 

66. Hartocollis A. E-Mails Reveal Dispute Over City’s Ad Against Sodas. Posted: 10–28–2010. [WWW document]. URL http:// 

www.nytimes.com/2010/10/29/nyregion/29fat.html?pagewanted=all (accessed December 2012). 

67. New York City Department of Health and Mental Hygiene. Man Drinking Fat. NYC Health Anti-Soda Ad. Are You Pouring 

on the Pounds? Posted: 12–14–2009. [WWW document]. URL http://www.youtube.com/watch?v=-F4t8zL6F0c (accessed December 

2012). 

68. Kansangra S. Maximum Size For Sugary Drinks: Proposed Amendment of Article 81—Response to Comments. Posted: 9–13– 

2012. [WWW document]. URL http://www.nyc.gov/html/doh/downloads/pdf/boh/article81-response-to-comments-ppt.pdf 

(accessed December 2012). 

69. Davis J.N., Whaley S.E., Goran M.I. Effects of breastfeeding and low sugar-sweetened beverage intake on obesity prevalence in 

Hispanic toddlers. AM. J. CLIN. NUTR. 2012; 95: 3–8. 

70. Norton A. Could Kids’ Salt Intake Affect Their Weight? Posted: 2012. [WWW document]. URL http://consumer.healthday.com/Article.asp?AID=671410 (accessed December 2012). 

71. Azuz C. Lots of salt means lots of soda. Posted: 12–10–2012. [WWW document]. URL http://www.cnn.com/video/ 

?hpt=he_mid#/video/health/2012/12/10/hm-salt-and-soda.cnn (accessed December 2012). 

72. Mackey L. Lowering salt intake may be key to lowering childhood obesity rates. Posted: 12–10–2012. [WWW document]. URL 

http://wtvr.com/2012/12/10/lowering-salt-intake-may-be-key-tolowering-childhood-obesity-rates/ (accessed December 2012). 

73. McKenzie A. Study says salt may be a catalyst for childhood obesity. Posted: 12–13–2012. [WWW document]. URL http:// 

triad.news14.com/content/top_stories/676361/study-says-salt-may-be-a-catalyst-for-childhood-obesity (accessed December 2012). 

74. Grimes C.A., Riddell L.J., Campbell K.J., Nowson C.A. Dietary Salt Intake, Sugar-Sweetened Beverage Consumption, and Obesity Risk. PEDIATRICS 2012; 13: 14–21. e-pub (DOI: 10.1542/peds.2012–1628). 

75. Cofield S.S., Corona R.V., Allison D.B. Use of causal language in observational studies of obesity and nutrition. OBES. FACTS

2010; 3: 353–356. 

76. Cope M.B., Allison D.B. White hat bias: a threat to the integrity of scientific reporting. ACTA. PAEDIATR. 2010; 99: 1615–1617. 

77. James G. Are Diet Drinks The Key To Weight Loss? Posted: 2–15–2012. [WWW document]. URL http:// 

www.huffingtonpost.co.uk/2012/02/15/diet-drinks-water-key-to-weightloss_n_1279488.html (accessed December 2012). 

78. Lane P. Press Release: Weighing the difference: switching to water, diet beverages can tip the scales. Posted: 2012. [WWW document]. URL http://uncnews.unc.edu/content/view/5088/71 (accessed December 2012). 

79. European Food Safety Authority. Scientific Opinion on the substantiation of health claims related to intense sweeteners and 

contribution to the maintenance or achievement of a normal body weight. Posted: 2012. [WWW document]. URL http:// 

www.efsa.europa.eu/en/efsajournal/doc/2229.pdf (accessed December 2012). 

80. Te Morenga L., Mallard S., Mann J. Dietary sugars and body weight: systematic review and meta-analyses of randomised controlled trials and cohort studies. BMJ 2013; 346: e7492. 

81. Hauner H., Bechthold A., Boeing H., et al. Evidence-based guideline of the German Nutrition Society: carbohydrate intake and 

prevention of nutrition-related diseases. ANN. NUTR. METAB. 2012; 60 (Suppl. 1): 1–58. 

82. Baranowski T. School-based obesity-prevention interventions in low- and middle-income countries: do they really work? AM. J. 

CLIN. NUTR. 2012; 96: 227–228. 

83. Gardner C., Wylie-Rosett J., Gidding S.S., et al. Nonnutritive sweeteners: current use and health perspectives: a scientific statement from the American Heart Association and the American Diabetes Association. CIRCULATION 2012; 126: 509–519. 

ATTACHMENT 9 

Liquid Calories, Energy Compensation, and Weight: What We Know and 

What We Still Need To Learn 

Invited Commentary 

David B. Allison * 

British Journal of Nutrition (2014), 111, 384–386 ® The Author 2013 

(First published online 28 October 2013) 

Roughly 10,000 years ago, sugar was first domesticated in New Guinea. Roughly 

8,000 years ago, it was transplanted to India. Sometime around the seventh century, cultivation and some industrial production began in southern Europe, and the 

crusades subsequently acquainted more Europeans with sugar imported from Arab 

lands. Until the sixteenth century, sugar was often viewed by Europeans as having 

medicinal properties. Colonisation of the New World led to mass production and distribution of sugar as a major foodstuff.(1–5) By 1713, a writer in a scholarly journal 

was extolling the health virtues of high levels of sugar consumption, including in 

beverages.(6) In 1893, Harley (7) conducted self-experiments and concluded that conVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00373 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
368 

sumption of 250 g (approximately 4184 kJ or approximately 1000 kcal) of sugar 

greatly increased muscular work capacity. In 1899, a controlled trial involving soldiers reported that those given a ration of sugar were in better health, felt more 

vigorous and gained more weight (presumably judged to be a good thing at the 

time).(8) As the century turned, Gardner (9) described sugar as a nutritional necessity 

that increased the health and vigour of populations. Yet, the positive health halo 

of sugar could not last. A generation later, authors of scientific papers did write 

about ‘The social problem growing out of the overconsumption of sugar’ and described school-based programmes to teach children to consume less sugar.(10)

Sugar consumed in liquid form has come to be seen by some as especially deserving of scrutiny. In 1990, Tordoff & Alleva (11) published seminal trial results showing 

that persons required to consume additional sugar in the form of a beverage gained 

more weight than did a control group given a non-energetic beverage. After 13 

years, suspicion was increasing that metabolisable energy, perhaps especially sugar, 

consumed as liquids promoted less satiety, less energy compensation and more 

weight gain than did the same energy consumed in solid form.(12) The topic has become controversial to say the least,(13) and there is substantial evidence that the 

strength of the supporting data has often been exaggerated and distorted.(14,15)

Newspaper articles offer statements such as ‘People who drink sugary soft drinks 

do not appear to compensate by reducing calories somewhere else in their diets, so 

they tend to pack on extra pounds’ (16) and ‘Study after study has shown that like 

experimental animals, people do not compensate for extra liquid calories by eating 

less food’.(17) This concept that people do not adjust their energy intake (or expenditure) to compensate for energy consumed as liquids is at the heart of the matter. 

Yet, is it true? Although opinions on matters of energy compensation in response 

to various forms of sugar intake and/or liquid energy have been offered for over 70 

years,(18,19) convincing data on these issues have been scarce. 

In this issue of the British Journal of Nutrition, Reid, et al.,(20) offer a new and 

valuable piece of evidence on this question. In a study of obese adult women, those 

consuming sugar in liquid form at a level of 1,800 kJ (approximately 430 kcal) per 

d gained far less weight than expected and no more weight than did women in a 

control group drinking zero-energy beverages. The study has several strengths. It 

was a controlled trial that was run for long enough to observe weight changes and 

that was at least partially conducted in a blinded fashion. It also has several limitations, including a modest sample size, incomplete blinding and the fact that it was 

not strictly randomised. I will not belabour those points here as Reid and colleagues 

discuss them in their article. It should also be noted that the study concerns only 

adult women and cannot necessarily tell us about the effects in men or children. 

What does the study show? 

The study’s essential finding concerns the question of compensation for liquid energy. The sucrose group gained no appreciable weight. This shows that over an extended period, at least in conditions similar to those of this study, women do compensate for additional energy consumed in the form of a sugar-sweetened beverage 

(SSB). Moreover, that the weight gained in the sucrose group was significantly less 

than that predicted by an established mathematical model based on the amount of 

energy consumed in the form of SSB further indicates that the vast majority of the 

energy consumed was compensated for. Reid, et al., state that ‘Obese women who 

received 1,800 kJ sucrose per day in soft drinks for 4 weeks gained a mean of 1.72 

kg less than predicted by the model.’ Interestingly, the model predicted a total 

weight gain for a woman with the average characteristics listed in Reid, et al.,’s 

Table 1 of only about 1.8 kg. 

Are the findings consistent with those of other studies? 

Yes. Kaiser, et al.,(15) meta-analysed other studies in which adults were required 

to consume additional energy in SSB in randomised controlled trials (RCT), and 

found that, on average, such required SSB consumption did indeed cause weight 

gain, but that the amount of weight gained was far less than 1⁄2 the amount one 

would have predicted to be gained by use of the same mathematical model used by 

Reid, et al. (see Kaiser, et al.,’s Fig. 2). This indicates that, as Reid, et al., found, 

over extended periods of time, the majority of the energy consumed as SSB is indeed 

compensated for. 

Do the findings inform us about the effects of reducing sugar-sweetened 

beverage consumption among adult women? 

No. Though tempting, we cannot necessarily infer the effects of reducing SSB consumption from studies of the effects of increasing SSB consumption. That said, as 

Kaiser, et al.,(15) reported, no RCT of adults reported to date has found a statistically significant effect of reducing SSB consumption on weight. 

369 

Do the findings inform us about the differential effects (if any) of consuming liquid v. solid energy on weight? 

No. The results of Reid, et al., only show what happens with SSB. From these 

data alone, we have no way of knowing whether the same results would have been 

obtained if the women were required to consume 1,800 kJ of food in some solid form. 

Returning to the literature at large, there is evidence from a recent meta-analysis 

that in short-term (typically single-day) studies with food intake as the outcome, liquid energy is less well compensated for than is solid energy.(21) Yet, we cannot assume that individuals will not adapt to dietary changes over time. Long-term effects 

on weight cannot be reliably inferred from short-term effects on food intake. Indeed, 

to my knowledge, there are only two human RCT comparing the effects of liquid v. 

solid foods on weight over an extended period of time, and neither found a statistically significant difference between the liquid and solid conditions when the entire 

samples were analysed.(22–23)

In conclusion, what we know from the overall literature is that when adults are 

required to consume additional energy in the form of SSB, on average, they gain 

some weight. What we also know from the overall literature and this new study is 

that, on average, adults gain far less weight than they would be expected to gain 

if they did not compensate. Thus, people clearly do compensate for liquid energy, 

although they do so incompletely. What we do not know, despite all the drama and 

vituperation surrounding SSB, is whether, over extended periods of time, people 

compensate any differently for liquid v. solid energy. It is high time we learned. 

Acknowledgements 

The present work was supported in part by National Institutes of Health (NIH) grant P30DK056336. The opinions expressed are 

those of the author and not necessarily those of the NIH or any other organisation. 

The author received grants and gifts to his university and consulting fees from multiple for-profit and not-for-profit organisations 

with interests in obesity, sugar and SSB. 

References 

1. Anonymous (1853) History of sugar. THE ILLUSTRATED MAGAZINE OF ART, vol. 2, no. 2, p. 147. http://www.jstor.org/stable/ 

20538093 (accessed August 2013). 

2. Baru S. (1987) Sugar in history: sweetness and power. Review of Sweetness and Power: The Place of Sugar in Modern History 

by Sidney W. Mintz. ECONOMIC AND POLITICAL WEEKLY vol. 22, no. 33 (15 August 1987), pp. 1391–1393. http://www.jstor.org/stable/4377370 (accessed August 2013). 

3. Levi L. (1864) On the Sugar Trade and Sugar Duties. A Lecture Delivered at King’s College, London, February 29, 1864. London: Effingham Wilson, Royal Exchange. http://www.jstor.org/stable/60101270 (accessed August 2013). 

4. U.S. Department of Agriculture, Economic Research Service (1971) A History of Sugar Marketing. Agriculture Economic Report 

No. 197. Washington, D.C.: U.S. Government Printing Office. 

5. Galloway J.H. (1977) The Mediterranean sugar industry. GEOGRAPHIC REV. 67, 177–194. http://www.jstor.org/stable/214019 

(accessed August 2013). 

6. Slare F. (1713) Part of a letter from Dr. Fred Slare to Dr. Hans Sloane; concerning a person who had a new set of teeth after 

80 years of age; with some observations upon the virtues and properties of sugar. Phil. Trans. R. Soc. (1683–1775) 28, 273–274. 

7. Harley V. (1893) Sugar as a food in the production of muscular work. PROC. R. SOC. LOND. 54, 480–487. 

8. Anonymous (1899) Sugar as a ration. BR. MED. J. 1, 105. 

9. Gardner H.W. (1901) The dietetic value of sugar. BR. MED. J. 1, 1010–1013. 

10. Sollins I.V. (1930) Sugar in diet: an experiment in instruction in candy consumption. J. EDUC. SOCIOL. 3, 546–555. 

11. Tordoff M.G. & Alleva A.M. (1990) Effect of drinking soda sweetened with aspartame or high-fructose corn syrup on food intake and body weight. AM. J. CLIN. NUTR. 51, 963–969. 

12. Almiron-Roig E., Chen Y. & Drewnowski A. (2003) Liquid calories and the failure of satiety: how good is the evidence? OBES. 

REV. 4, 201–212. 

13. Slavin J. (2012) Beverages and body weight: challenges in the evidence-based review process of the Carbohydrate Subcommittee 

from the 2010 Dietary Guidelines Advisory Committee. NUTR. REV. 70, Suppl. 2, S111–S120. 

14. 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. INT. J. OBES. (Lond) 34, 84–88. 

15. Kaiser K.A., Shikany J.M., Keating K.D., et al. (2013) Will reducing sugar-sweetened beverage consumption reduce obesity? 

Evidence supporting conjecture is strong, but evidence when testing effect is weak. OBES. REV. 14, 620–633. 

16. Hellmich N. (2007) Soda drinkers consume more calories. USA TODAY. http://usatoday30.usatoday.com/news/health/2007- 

03-25-soda-drinkers_N.htm (accessed August 2013). 

17. Brody J.E. (2010) A tax to combat America’s sugary diet. NEW YORK TIMES. http://www.nytimes.com/2010/04/06/health/ 

06brod.html?_r%0 (accessed August 2013). 

18. Anonymous (1942) Sugar rationing called a ‘‘Godsend’’ to national health. SCIENCE NEWS-LETTER vol. 41, no. 11 (14 March 

1942), p. 164. http://www.jstor.org/stable/3918542 (accessed August 2013). 

19. Anonymous (1944) Advice given to go easy on use of chocolate milk. SCIENCE NEWS-LETTER vol. 45, no. 25 (17 June 1944), p. 

398. 

20. Reid M., Hammersley R., Duffy M., et al. (2014) Effects on obese women of the sugar sucrose added to the diet over 28 days, a 

quasi-randomised, single-blind, controlled trial. BR. J. NUTR. 111, 563–570. 

21. Almiron-Roig E., Palla L., Guest K., et al. (2013) Factors that determine energy compensation: a systematic review of preload 

studies. NUTR. REV. 71, 458–473. 

22. DiMeglio D.P. & Mattes R.D. (2000) Liquid versus solid carbohydrate: effects on food intake and body weight. INT. J. OBES. 

RELAT. METAB. DISORD. 24, 794–800. 

23. Houchins J.A., Burgess J.R., Campbell W.W., et al. (2012) Beverage vs. solid fruits and vegetables: effects on energy intake and 

body weight. OBESITY (Silver Spring) 20, 1844–1850. 

ATTACHMENT 10 

Corporate Funding of Nutrition Research and Unjustified Conclusions 

JAMA Internal Medicine, May 2016, Volume 176, Number 5 

370 

To the Editor In her Viewpoint about corporate funding of food and nutrition 

research, Dr Nestle criticizes the food industry and scientists who associate with it.1

Dr. Nestle claims that scientists who receive industry-derived research grants 

‘‘often fail to realize that food-industry funding may affect their work.’’.1 (p. 13) She 

cites newspaper articles that ‘‘illustrate the concerns about biases introduced by industry funding.’’ 1 (p. 13) She also cites reports 2–3 showing that there are relatively 

few studies funded by industry whose results are contrary to the funders’ interest 

and discusses her work on the subject including reference to her Food Politics blog.4

The study by Massougbodji, et al.,2 also determined that the quality of the methods of the studies reviewed did not explain the orientation of the authors’ conclusions, nor was there any relationship between the source of funding and the overall 

quality of the studies examined. The study by Lesser, et al.,3 did not examine any 

aspect of the studies that were reviewed other than funding source. The newspaper 

stories did not describe any flaw in the research of the scientists profiled. In addition, the authors of a great number of the presumably tainted industry-sponsored 

studies discussed on the blog written by Dr. Nestle 4 explicitly stated that the funding source was not involved in the design, conduct, data analysis and interpretation, 

or manuscript preparation. Although Dr. Nestle also states that the quality of dietary advice is adversely affected by the source of research funds, many believe that 

the real problem is the overall poor quality of nutrition research.5 Before guilt by 

association is established, criticisms by Dr. Nestle deserve much more analysis. 

It would certainly be helpful, if not essential, for Dr. Nestle or others to show that 

industry-funded studies have more design flaws, inappropriate analyses, or unjustified conclusions relative to similar studies funded by other sources. Furthermore, in 

addition to financial conflicts of interest, there are nonfinancial conflicts resulting 

from career self-interest or unbounded intellectual passion that can be just as worrisome. Conflicts of interest in science can affect anyone, and are relevant to proponents of any point of view. 

RICHARD KAHN, Ph.D. 

Author Affiliation: Department of Medicine, University of North Carolina, School of Medicine, Chapel Hill, North Carolina. 

Corresponding Author: Richard Kahn, PhD, Department of Medicine, University of North Carolina, School of Medicine, Chapel 

Hill, NC 27599–7005 (rak6200@gmail.com). 

Conflict of Interest Disclosures: None reported. 

Additional Information: Dr. Kahn served as the Chief Scientific and Medical Officer of the American Diabetes Association. 

[References] 

1. Nestle M. Corporate funding of food and nutrition research: science or marketing. JAMA INTERN. MED. 2016; 176(1): 13–14. 

2. Massougbodji J., Le Bodo Y., Fratu R., DeWals P. Reviews examining sugar-sweetened beverages and body weight: correlates of 

their quality and conclusions. AM. J. CLIN. NUTR. 2014; 99(5): 1096–1104. 

3. Lesser L.I., Ebbeling C.B., Goozner M., Wypij D., Ludwig D.S. Relationshipbetween funding source and conclusion among nutrition-related scientific articles. PLOS MED. 2007; 4(1): e5. 

4. Nestle M. Food Politics Blog. http://www.foodpolitics.com/. Accessed March 2, 2016. 

5. Ioannidis J.P. Implausible results in human nutrition research. BMJ. 2013; 347: f6698. 

In Reply: Dr. Kahn requests evidence that nutrition research funded by food 

companies is of lesser quality than studies funded by independent agencies or performed by investigators with nonfinancial conflicts of interest. Concerns about such 

issues are relatively recent; few published studies address them directly. Instead, 

concerns about industry sponsorship of nutrition research derive from comparisons 

with the results of studies of funding by tobacco, chemical, drug, or medical device 

companies. This research typically finds industry-sponsored studies to report results 

more favorable to the products of the sponsor than studies not funded by industry. 

It identifies subtle rather than substantive differences in the quality of this research; industry-funded studies are more likely to underreport unfavorable results 

and interpret neutral results more positively.1 When results are negative, they are 

less likely to be published.2

Between March 2015 and March 2016, I identified 166 industry-funded nutrition 

research studies and posted and discussed them on my blog.3 Of these, 154 reported 

results favorable to the interest of the sponsor; only 12 reported contrary results. 

The few studies systematically examining the influence of industry funding on nutrition research tend to confirm results obtained from other industries. For example, 

a systematic review comparing industry-funded and nonindustry-funded trials of 

probiotics in infant formula reported no association of funding source with research 

quality. Industry-funded studies, however, seemed more likely to report favorable 

conclusions unsupported by the data.4

Dr. Kahn states that sponsored studies often specify that the funder had no role 

in the study. Only recently have some journals required such statements, and I am 

unaware of research on the extent of this practice or authors’ adherence to it. 

371 

Among the 166 industry-funded studies that I reviewed, few disclosed involvement 

of a sponsor. 

Dr. Kahn asks whether industry funding is any more biasing than career self-interest or intellectual passion. Unlike industry funding, self-interest and passions are 

intrinsic to every scientist who conducts research, are a matter of public record, cannot be eliminated, and have not been shown to consistently bias research results 

in the same ways as industry funding.5 Fortunately, nutrition societies and research 

institutions are developing policies to manage financial relationships with industry.6

Such policies hold promise for preventing financial conflicts of research in nutrition 

research. 

MARION NESTLE, Ph.D., M.P.H. 

Author Affiliation: New York University, Nutrition, Food Studies, and Public Health, New York, New York. 

Corresponding Author: Marion Nestle, PhD, MPH, New York University, Nutrition, Food Studies, and Public Health, 411 Lafayette, 5th Floor, New York, NY 10003–7035 (marion.nestle@nyu.edu). 

Conflict of Interest Disclosures: Dr. Nestle’s salary from New York University supports her research, manuscript preparation, 

website, and blog at http://www.foodpolitics.com. She also earns royalties from books and honoraria from lectures to university 

and health professional groups about matters relevant to this letter and the Viewpoint to which it refers. 

[References] 

1. Lundh A., Sismondo S., Lexchin J., Busuioc O.A., Bero L. Industry sponsorship and research outcome. COCHRANE DATABASE

SYST. REV. 2012; 12: MR000033. 

2. Rising K., Bacchetti P., Bero L. Reporting bias in drug trials submitted to the Food and Drug Administration: review of publication and presentation. PLOS MED. 2008; 5(11): e217. 

3. Nestle M. Food Politics Blog. http://www.foodpolitics.com/. Accessed March 2, 2016. 

4. Mugambi M.N., Musekiwa A., Lombard M., Young T., Blaauw R. Association between funding source, methodological quality 

and research outcomes in randomized controlled trials of synbiotics, probiotics and prebiotics added to infant formula: a systematic 

review. BMC MED. RES. METHODOL. 2013; 13: 137. 

5. Bero L. What is in a name? Nonfinancial influences on the outcomes of systematic reviews and guidelines. J. CLIN. EPIDEMIOL. 

2014; 67(11): 1239–1241. 

6. Charles Perkins Centre. Engagement with Industry Guidelines 2015. University of Sydney, 2015. https:// 

intranet.sydney.edu.au/perkins/research-support/engaging-with-industry.html. Accessed March 2, 2016. 

ATTACHMENT 11 

The Limits of Sugar Guidelines 

Is there a danger in governments offering too-specific advice on sugar consumption? 

The Atlantic 

Nina Teicholz 

Jan. 17, 2017 

Sugary drinks on display in New York City in 2012, at a news conference 

about a proposed ban on all soft drinks over 16 ounces in the city’s restaurants and stores Andrew Burton/Reuters. 

A firestorm recently erupted over a paper in the Annals of Internal Medicine 

(https://www.ncbi.nlm.nih.gov/pubmed/27992898) that found official advice limiting sugar in diets to be based on ‘‘low’’ or ‘‘very low’’ quality evidence. Because a 

food-industry group had funded the study, a slew of critics accused the authors of 

distorting the science to undermine nutrition guidelines and make sugar seem less 

harmful than it actually is. One prominent nutrition professor called the paper 

(http://www.npr.org/sections/thesalt/2016/12/19/505867535/how-much-is-tooVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00377 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN

11502079.eps
372 

much-new-study-casts-doubts-on-sugar-guidelines) ‘‘shameful.’’ ‘‘It was really an attempt to undermine the scientific process,’’ said another (http://www.npr.org/sections/thesalt/2016/12/19/505867535/how-much-is-too-much-new-study-castsdoubts-on-sugar-guidelines). 

Lost in this torrent of criticism was any significant discussion of the science itself. 

Regardless of its funding source, was the paper correct in saying that there is insufficient evidence to recommend limiting sugar? And do official guidelines even matter, since we pretty much know that sugar is bad for us? 

The Annals paper examined a dozen guidelines on sugar passed by governments 

around the world since 2002, including the Dietary Guidelines for Americans, which 

last year recommended limiting sugar intake to ten percent of calories. One would 

assume that such advice is based on an ample body of rigorous research. But the 

Annals study, which included all the papers listed in the various guidelines’ bibliographies themselves, claimed that reviews to date had overstated the evidence. 

In the most rigorous review on sugar and weight (http://www.bmj.com/content/ 

bmj/346/bmj.e7492.full.pdf), for instance, only five trials lasting 6 months or longer 

could be found, on a total of just 1,245 people. According to the Annals authors, this 

review portrayed the data as more consistent than it actually was and failed to adequately account for evidence indicating that studies in which sugar was shown to 

have no detrimental effect may have been suppressed from publication. 

Moreover, less rigorous data from observational studies was widely found to be 

‘‘inconsistent.’’ Sometimes sugar was associated with health problems—weight gain, 

Type 2 diabetes, and tooth decay—but sometimes it wasn’t. 

‘‘Overall, I would say the guidelines are not trustworthy,’’ Bradley Johnston 

(http://ihpme.utoronto.ca/faculty/bradley-c-johnston/), the study’s lead author and 

an assistant professor of clinical epidemiology and biostatistics at McMasters University told me. 

The study’s finding should come as a surprise to anyone who has been avoiding 

sugar for years already. Sugar is a potent source of glucose, which, over time, does 

appear to wreak havoc on one’s metabolism and pave a direct path to obesity and 

diabetes. A large body of trial evidence has shown (http://www.nytimes.com/2016/ 

09/11/opinion/sunday/before-you-spend-26000-on-weight-loss-surgery-dothis.html?_r=0) that when carbohydrate consumption is reduced, these diseases 

start to reverse themselves. Also, given all the recent headlines about sugar’s ill effects, from Katie Couric’s movie Fed Up to the passage of soda taxes in several cities, one could be forgiven for assuming that the evidence condemning sugar must 

be a done deal. 

Clinical trials on sugar are possible; it’s just that very few have been done. 

Yet here were the Annals authors saying it’s not. Reaction to the paper from nutrition experts and advocacy groups was swift, with criticism focused on the paper’s 

Achilles heel: It had been paid for by the International Life Sciences Institute, 

which receives 60 percent of its funding from 400 industry members, including 

some, like Coca-Cola, PepsiCo, and Mars, that very much stand to benefit from a 

study questioning caps on sugar. 

‘‘This is a classic example of industry-funded research aimed at one purpose and 

one purpose only: to cast doubt on the science linking diets high in sugars to poor 

health,’’ Marion Nestle, a prominent professor of nutrition at New York University, 

told National Public Radio (http://www.npr.org/sections/thesalt/2016/12/19/ 

505867535/how-much-is-too-much-new-study-casts-doubts-on-sugar-guidelines). 

Dean Schillinger, the chief of the division of general internal medicine at San Francisco General Hospital, told the New York Times (http://www.nytimes.com/2016/ 

12/19/well/eat/a-food-industry-study-tries-to-discredit-advice-aboutsugar.html?_r=0): ‘‘They’re hijacking the scientific process in a disingenuous way to 

sow doubt and jeopardize public health.’’ 

Schillinger, with his colleague Cristin Kearns, also penned an editorial in Annals 

(https://www.ncbi.nlm.nih.gov/pubmed/27992900), which likened the sugar-review 

authors to lackeys hired by the tobacco industry to be ‘‘merchants of doubt’’ about 

the health hazards of smoking. 

Industry manipulation of the science is obviously an ongoing, serious concern. It 

was, in part, why the editor-in-chief of Annals, Christine Laine, invited this editorial. ‘‘I wanted to show both sides of the issue,’’ she told me, although she said 

that she considered the editorial to be unusually ‘‘strident and hostile’’ for an academic journal. Indeed, Schillinger and Kearns both part-time advocates against 

sugar; they write articles (http://www.sugarscience.org/sugar-papers-reveal-industry-role-in-shifting-focus.html#.WGdB-M6PTyA) and do other work for Sugar Science 

(http://www.sugarscience.org/), a group devoted to educating the public about sugar’s health dangers. ‘‘It’s shown me that conflicts of interest are not only financial 

but also intellectual,’’ said Laine, who added disclosures about the authors’ Sugar 

373 

Science affiliations to the editorial after a reader brought them to her attention, she 

says. 

Ironically, undercutting a scientific paper by focusing on its funding source has 

mainly been used in the past to shoot down sugar skeptics. For instance, when the 

British nutrition professor John Yudkin suggested sugar as a dietary culprit in the 

early 1970s, the University of Minnesota researcher Ancel Keys, a key defender of 

the competing hypothesis, that dietary fat was responsible for chronic health issues, 

accused Yudkin of issuing ‘‘propaganda,’’ linked to ‘‘commercial backers [who] are 

not deterred by the facts.’’ 

Now that the nation’s top nutrition authority, the U.S. Dietary Guidelines, has 

backed off caps on total fat and begun to condemn sugar instead, the public debate 

is also increasingly focusing on the sugar industry—indeed, so much so that other 

industry actors are escaping scrutiny. One has to ask, for instance, why there was 

no similar outrage over another recent paper (https://www.ncbi.nlm.nih.gov/ 

pubmed/27881409), in The BMJ, with favorable findings for vegetable oils, nearly 1⁄2 of whose authors were actual employees of the giant vegetable-oil manufacturer 

Unilever. This would be like workers at Mars, Inc. publishing a study on the health 

benefits of sugar. Yet this sizable conflict of interest largely got a pass by the many 

journalists covering the story (https://pubpeer.com/publications/DF70B2D23429 

0DF834A8F183BB6F8C#fb114267). 

To be clear, industry funding absolutely can deter good science; tobacco promotion 

will always be the epitome of that. But the influence of funding isn’t invariable: 

While one meta-analysis found that funding sources do influence the conclusions of 

nutrition papers (https://www.ncbi.nlm.nih.gov/pubmed/17214504), another, by a 

fierce critic of industry (http://www.smh.com.au/technology/sci-tech/cocacolas-secret-plan-to-monitor-sydney-university-academic-lisa-bero-20161020-gs6m4a.html) 

funding, paradoxically did not (https://www.ncbi.nlm.nih.gov/pubmed/27802480). 

A healthy dose of skepticism over funding from all sources—including governments 

and other institutions, which may have their own pet hypotheses—is warranted, so 

long as it doesn’t sideline the science or shut down legitimate debate. 

Schillinger and Kearns were right to raise doubts. Sugar defenders have, since the 

early part of the 20th century, worked diligently to promote their product, such that 

President Franklin Roosevelt, in the mid-1930s, was quoted as saying the sugar 

lobby was ‘‘the most powerful pressure group that had descended on the national 

capitol’’ during his lifetime. The extent of industry manipulation, through ad campaigns and efforts to twist the science are described by the journalist Gary Taubes 

in his new book, The Case Against Sugar. 

Yet Taubes believes that any industry with a PR budget has attempted pretty 

much the same. And he is up-front about the lack of rigorous evidence against 

sugar, stating in the introduction of his book, ‘‘I’m going to concede in advance a 

key point that those who defend the role of sugar in our diet will invariably 

make. . . . [I]t cannot be established definitively, with the science as it now stands, 

that sugar is uniquely harmful.’’ 

Clinical trials on sugar are possible; it’s just that very few have been done. Emerging evidence suggests (https://www.ncbi.nlm.nih.gov/pubmed/25756179) that the 

sugar industry may have stifled those inquiries (https://therussells.crossfit.com/ 

2016/09/15/did-big-soda-derail-the-governments-cancer-research/), but Taubes believes more evidence supports the explanation that for decades, a monolith of nutrition scientists has just genuinely and obsessively had a preoccupation with fat and 

cholesterol which simply blotted out everything else. The National Institutes of 

Health (NIH) spent billions of dollars on large clinical trials, all trying to pin chronic disease on dietary fat and cholesterol. In fact, sugar was such a non-suspect for 

so many years that the major, NIH-funded observational studies took few pains 

even to measure it. 

While the evidence to date shows zero benefit from sugar and a clear signal of 

harm, there hasn’t been enough time to fund and conduct definitive trials. Meanwhile, governments naturally feel they can’t wait. Facing panic over the continued, 

relentless climb in obesity and diabetes rates with no solution in sight, they’ve gone 

ahead and passed sugar guidelines pinned to exact thresholds, of ten percent or five 

percent of calories. This advice is clearly well-intentioned. Yet if, as the Annals 

paper concludes, experts are skirting scientific norms by passing guidelines based 

on weak evidence, the whole process of guideline-making is effectively watered 

down. And the need for reliable guidance is no abstract question; indeed, everything 

from our waistlines to whether we might eat eggs for breakfast depends upon it. 

As Americans well know, there have been many reversals in our guidelines in recent years—on dietary cholesterol, on total fat, on whether to eat breakfast to maintain a healthy weight. These were all official guidelines based on weak evidence 

that, when actually tested in clinical trials, were found to be unjustified. It turned 

374 

[1]Department of Urban & Environmental Policy & Planning, Tufts University, Medford, MA, 

USA 

Corresponding Author: 

Sheldon Krimsky, Department of Urban & Environmental Policy & Planning, Tufts University, Medford, MA 02155, USA. E-mail: sheldon.krimsky@tufts.edu. 

out that people had been avoiding egg yolks, lobster, and fat, generally, to no avail, 

and that skipping breakfast altogether might actually be the best option (https:// 

www.washingtonpost.com/news/wonk/wp/2015/08/10/the-science-of-skippingbreakfast-how-government-nutritionists-may-have-gotten-it-wrong/?utm_term=.ca4 

bb87d6c30) for weight loss. 

It’s worth at least considering criticism of the potentially ‘‘low’’ quality evidence 

behind existing nutrition advice. 

Instances of flip-flopping on nutritional advice not only erode the public trust, but 

make people think that the basic science itself is flawed—which, for the most part, 

it’s not. Instead, the central problem has been that experts and policy makers have 

passed judgment before that good science was done. And once a judgment is codified 

as policy, it’s hard to repeal. This was the case, for instance, with the low-fat diet, 

which although adopted as a U.S. guideline in 1980, wasn’t actually studied in trials 

for another decade-plus. This kind of mistake, at its very worst, is potentially deadly: Indeed, the low-fat advice, by shifting consumption to carbohydrates such as 

grains and sugar, is now regarded as a probable cause of the obesity and diabetes 

epidemics. 

When the Senate first passed the government’s warnings against fat and cholesterol in the late 1970s, officials argued that the urgency of responding to publichealth crises overrode any concerns about insufficient scientific evidence. ‘‘Undoubtedly there will be people who have said we have not proven our point,’’ said Harvard’s Mark Hegsted (https://naldc.nal.usda.gov/naldc/download.xhtml?id=1759 

572&content=PDF), an advisor to the report, at the time of its release. Yet, citing 

the epidemics of heart disease, cancer, diabetes, and hypertension, he stated, ‘‘We 

cannot afford to temporize. We have an obligation to assist the public in making 

correct food choices. . . . To do less is to avoid our responsibility.’’ 

These are the same arguments being made today, on sugar. It makes sense to 

have a strong hunch that sugar is bad. Sugar has no nutritional value. It’s a direct 

shot of glucose to the blood stream and fructose to the liver. The historical evidence 

against it presented by Taubes in his book is compelling. Personally, I try hard to 

avoid it. But I also tend to avoid refined carbohydrates, such as white bread and 

cereals. Based on the existing data, I suspect that too much fructose from today’s 

highly sweetened fruit crops is bad, and that the 40+ percent increase in our consumption of grains since 1970 (https://www.ers.usda.gov/publications/pub-details/ 

?pubid=44220) have simply overloaded us in carbohydrates altogether (http:// 

www.nutritionjrnl.com/article/S0899-9007%2815%2900077-5/abstract). 

The NIH should fund rigorous trials to sort out these issues. Meanwhile, in the 

absence of more definitive science, it’s worth at least considering criticism of the potentially ‘‘low’’ quality evidence behind existing nutrition advice. Maybe the government should be issuing a strong cautionary note, based on the existing, emerging 

evidence, rather than a specific formal ‘‘Guideline’’—since basing guidelines on 

hunches that are probably right unavoidably opens up the possibility for guidelines 

based on hunches that are wrong. 

An educated guess is not enough, warned the late Senator Charles Percy, in objecting to the government’s original dietary advice, 35 years ago. He thought it 

paved over limitations in the data with excessive confidence. ‘‘The best way to [provide dietary guidance] is to fully inform the public not only about what is known 

but also what remains controversial,’’ he said. 

He was talking about fat and cholesterol; today’s Annals paper is talking about 

sucrose, glucose, fructose. We’ve been down this road before, with experts, pressed 

into urgency on behalf of the public health, convincing themselves that insufficient 

evidence could suffice. Therefore, in the matter of national guidelines, it’s worth 

being cautious—and not immediately dismissing those who send up cautionary 

flags. 

ATTACHMENT 12 

Do Financial Conflicts of Interest Bias Research? An Inquiry into the ‘‘Funding Effect’’ Hypothesis 

Sheldon Krimsky [1]

Science, Technology, & Human Values, 38(4) 566–587 ©The Author(s) 2012 

375 

Reprints and permission: sagepub.com/journalsPermissions.nav 

DOI: 10.1177/0162243912456271 

sthv.sagepub.com 

Abstract 

In the mid-1980s, social scientists compared outcome measures of related drug 

studies, some funded by private companies and others by nonprofit organizations or 

government agencies. The concept of a ‘‘funding effect’’ was coined when it was discovered that study outcomes could be statistically correlated with funding sources, 

largely in drug safety and efficacy studies. Also identified in tobacco research and 

chemical toxicity studies, the ‘‘funding effect’’ is often attributed, implicitly or explicitly, to research bias. This article discusses the meaning of scientific bias in research, examines the strongest evidence for the ‘‘funding effect,’’ and explores the 

question of whether the ‘‘funding effect’’ is an indicator of biased research that is 

driven by the financial interests of the for-profit sponsor. This article argues that 

the ‘‘funding effect’’ is merely a symptom of the factors that could be responsible for 

outcome disparities in product assessment. Social scientists should not suspend 

their skepticism and choose as a default hypothesis that bias is always or typically 

the cause. 

Introduction 

The philosopher Charles Sanders Peirce claimed that of all ways of fixing our beliefs, science is the most dependable. He wrote in 1877, ‘‘Scientific investigation has 

had the most wonderful triumphs in the way of settling opinion’’ (Peirce, 1877). Not 

only have we come to believe in the ‘‘dependability’’ of scientific claims, we have 

come to depend upon them for making important life decisions. It is generally understood that the production of scientific knowledge is accompanied by quality controls that are designed to filter out errors and bias. By errors I shall mean those 

assertions or calculations in a study that are factually incorrect and which would 

be recognized as such by anyone trained in the discipline. These can include errors 

in statistical analysis, citations, recording of data, or the application of measuring 

devices. Bias, on the other, is a more complex term. 

As distinguished from error, bias is not as simple as an oversight or a mistake. 

Bias can be conscious or unconscious. It can be structural (by the choice of method) 

or nonstructural (by the interpretation of data). By ‘‘structural bias,’’ I mean the 

adoption of certain norms or methods that would distort (over- or underreport) the 

effects being studied. This term has been used in media studies where a structural 

bias is said to be the result of a preference of journalists for some type of story or 

frame that leads them to pay more attention to some events over others (van Dalen, 

2011). 

Bias could involve proper or improper (scientific misconduct) behavior. In his book 

The Bias of Science, Brian Martin considers ‘‘biased’’ research as synonymous with 

‘‘value-laden’’ research ‘‘conditioned by social and political forces and dependent on 

judgments and human choices’’ (Martin, 1979, 7). Under this definition, science, according to Martin, might never be unbiased or value-free. Resnik (1998, 85) argues 

that a bias is an invalid assumption: ‘‘The person who conducts biased research is 

more like the person who defends a hypothesis that is later proven wrong than a 

person who makes a mistake or attempts to deceive his audience.’’ 

I am using ‘‘bias’’ in a different sense. By research bias, I shall mean the use of 

a method, data collection, data analysis, or interpretation of results that, in the consensus view of scientists of a discipline, tends to yield results that distort the truth 

of a hypothesis under consideration, diminishing or negating the reliability of the 

knowledge claim. Bias must be viewed in terms of the current operating norms of 

science. Since ‘‘bias’’ distorts the truth, scientists must be aware of its presence and 

where possible prevent or diminish it. I leave open the question of whether research 

considered unbiased in one time period could be viewed as biased by scientists during another time period. 

The function of our system of peer review is to identify error or bias before scientific studies are accepted for publication. After a study is published, it may still 

be criticized or corrected. Moreover, if an empirical finding cannot be replicated, the 

article may be withdrawn by the journal editors. Unlike other sources of establishing belief, science is considered to be a self-correcting enterprise where truth 

claims are kept open to new evidence. No one doubts, however, that bias can enter 

into published scientific work. While bias can be built into scientific methodology 

(structural), sometimes its subtlety can elude even the most careful reviewer and 

journal editor. 

Only recently have government and journals turned their attention to Conflict of 

Interest (COI) as a source of bias. The first Federal guidelines on scientific COI, 

376 

issued simultaneously by the Department of Health and Human Services’ (DHHS) 

Public Health Service (PHS) and the National Science Foundation were titled ‘‘Objectivity in Research.’’ The stated purpose of the regulation was ‘‘to ensure that the 

design, conduct, or reporting of research funded under PHS grants, cooperative 

agreements or contracts will not be biased by any conflicting financial interest of 

those investigators responsible for the research’’ (DHHS, 1995). And while the 

DHHS focused on financial COIs (FCOIs), it is generally recognized that interests 

other than direct financial interests can also play a potentially biasing role in 

science (Levinsky, 2002). Writing in the journal Cell Stem Cell about the ethics of 

stem cells, Jeremy Sugarman (2008, 532) noted: ‘‘Both nonfinancial and financial 

conflicts of interest may adversely affect good judgment regarding stem cell research.’’ But Sugarman also wrote that ‘‘financial conflicts of interest in research 

may be easier to identify, simply because financial interests can be measured and 

more easily described than those associated with nonfinancial interests, such as the 

advancement of scientific and professional concerns’’ (Sugarman 2008, 532). 

Following the maxim ‘‘study what you can measure,’’ social scientists began investigating the relationship between FCOIs and bias in the mid-1980s, when author 

disclosures of author FCOIs were still in their infancy. Most of the studies investigating a link between author FCOIs and private funding of science were carried 

out in the field of medicine, specifically medical pharmacology. The concept of a 

‘‘funding effect’’ was coined after a body of research revealed that study outcomes 

were significantly different in privately funded versus publicly funded drug studies 

(Krimsky, 2006 2010). The funding effect was also identified in tobacco, 

pharmacoeconomic, and chemical toxicity research (Als-Nielsen, et al., 2003). This 

article examines the strongest evidence for the ‘‘funding effect,’’ and explores the 

question of whether the ‘‘funding effect’’ is an indicator of scientific research bias, 

based on a previously stated criterion of ‘‘bias.’’ To begin, I shall discuss sources of 

evidence behind the ‘‘funding effect.’’ I shall argue that the ‘‘funding effect’’ is a 

symptom of the factors that are responsible for outcome disparities in product assessments and that social scientists should not, without further investigation and 

the elimination of other explanations, chose bias as the default hypothesis. 

Evidence of the ‘‘Funding Effect’’ in Science 

Beginning in the mid-1980s, scientists began testing the hypothesis that the 

source of funding from for-profit companies compared to nonprofit institutions and 

government can be correlated with the outcome of research, such as safety and efficacy in drug studies. This has been called ‘‘the funding effect’’ in science (Krimsky, 

2005). The assumption has been that where there is a ‘‘funding effect’’ there must 

be bias. I shall begin with the evidence for the ‘‘funding effect,’’ largely from a group 

of studies in drug trials, and then discuss the possible causes of the effect. 

Badil Als-Nielsen, et al. (2003) tested the hypothesis that industry-sponsored drug 

trials tend to draw pro-industry conclusions. The authors selected a random sample 

of 167 Cochrane reviews and found 25 with meta-analyses that met their criteria. 

From the meta-analyses, they studied 370 drug trials. After coding and numerically 

scoring the trials’ conclusions and applying a logistic regression analysis, the authors found that ‘‘conclusions were significantly more likely to recommend the experimental drug as treatment of choice in trials funded by for-profit organizations 

alone compared with trials funded by nonprofit organizations’’ (Als-Nielsen, et al., 

2003, 925). The authors ruled out as an explanation of industry favored outcomes 

both the magnitude of the treatment effect and the occurrence of adverse events reported. They also noted that the clinical trial methods between for-profit and nonprofit organizations were not of the same quality. ‘‘Trials funded by for-profit organizations had better methodological quality than trials funded by nonprofit organizations regarding allocation concealment and double blinding’’ (Als-Nielsen, et al., 

2003, 925). The authors do not report on the sponsor involvement and influence on 

the conduct and reporting of a trial. Such information could help us understand 

whether the external funder influences the scientist running the trial. The effects 

they observed between funding and outcome occurred whether the sponsor’s contribution was minimal (provided the drug) or maximal (funded the study). 

The authors distinguish between potential biases in the empirical trial results 

(collection of data) and in the interpretation of those results, particularly in the recommendations they make about the experimental drug. As previously noted, bias 

can enter into any or all the stages of a study: the methodology, execution of the 

study, interpretation of results and recommendations (whether the experimental 

drug is better than the existing drug). 

It is also possible that industry-funded studies, having been identified as being 

of higher quality, have gone through more internal (company-sponsored) study and 

analyses, than one would expect of a nonprofit organization. This study found statisVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00382 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
377 

tically significant outcome differences in a class of studies, but not necessarily 

bias—although systemic bias is one hypothesis. 

John Yaphe, et al. (2001) selected for their study randomized controlled trials 

(RCTs) published between 1992 and 1994 of drugs or food products with therapeutic 

properties appearing in five journals: Annals of Internal Medicine, BMJ, JAMA, 

Lancet, and NEJM. A total of 314 articles met their inclusion criteria. Of the 209 

industry-funded studies, 181 (87 percent) and 28 (13 percent) had positive and negative findings, respectively, while of 96 nonindustry-funded studies, 62 (65 percent) 

and 34 (35 percent) had positive and negative findings, respectively. What can account for this disparity in the outcomes of industry and nonindustry trials? Clearly, 

the bias of an investigator internalizing the financial interests of the sponsor is one 

potential hypothesis. 

Paula Rochon, et al., investigated the relationship between reported drug performance and manufacturer association. They adopted a broad definition of ‘‘manufacturer association,’’ which included supplying the drug or sponsoring a journal supplement where the publication of the study appeared. The authors selected as their 

study sample randomized drug trials (identified in MEDLINE between 1997 and 

1990) of nonsteroidal anti-inflammatory drugs used in the treatment of arthritis 

(Rochon, et al., 1994). The authors found 1,008 articles published within that period 

but only 61 articles representing 69 individuals met their inclusion criteria. All the 

trials in their study had a ‘‘manufacturer association,’’ because they reported there 

was a scarcity of nonmanufacturer-associated trials. Therefore, they could not compare trials funded/supported by private companies with those funded/supported by 

nonprofit organizations. The authors also used several rating systems to estimate 

drug efficacy. The critical outcome measure was whether the drug being tested was 

superior, the same, or inferior to a comparison drug. 

The results of the study showed the ‘‘the manufacturer-associated drug is always 

reported as being either superior to or comparable with the comparison drug’’ and 

that ‘‘these claims of superiority, especially with regard to side-effect profiles, are 

often not supported by trial data’’ (Rochon, et al., 1994, 158). It is logically possible 

that head-to-head testing of new versus old drugs always shows the new drug superior. After all, that is the impetus for developing new drugs. But in this case, the 

framing of the tests can bias the outcome. Marcia Angell explains the process with 

an illustration from statins—drugs that lower blood cholesterol levels. ‘‘There is little reason to think one is any better than another at comparable doses. But to get 

a toehold in the market, me-too statins were sometimes tested for slightly different 

outcomes in slightly different kinds of patients, and then promoted as especially effective for those uses’’ (Angell, 2004, 81). 

In a study by Benjamin Djulbegovic, et al. (2000), the investigators explored 

whether the reports of pharmaceutical-industry sponsored randomized trials result 

in biased findings. They selected 113 articles published from 1996 to 1998 that described 136 randomized trials on multiple myeloma (Djulbegovic, et al., 2000, 637). 

The authors compared the new therapy versus the standard therapy in the trials 

and then analyzed the outcome according to whether the sponsors were nonprofit 

or for-profit organizations. Nonprofit organizations showed a 53 percent versus 47 

percent support for new therapies, but when the trials were sponsored by for-profit 

organizations the ratio was 74 percent to 26 percent, a statistically significant difference. 

Friedman and Richer (2004) investigated whether sources of funding could be correlated to reported findings. The authors analyzed original contributions in NEJM 

and JAMA published in 2001. They classified the presentation of results as positive 

(statistically significant clinical benefit from a treatment or absence of suspected 

side effects), mixed (clinical benefits but adverse side effects), negative (absence of 

clinical benefits), or other (unclear significance). They located 193 original articles 

in NEJM, 76 (39.4 percent) with a COI and 205 articles in JAMA, 76 (37.1 percent) 

with COI. The authors found 119 studies that investigated drug treatments and 174 

studies for all treatments. They observed a ‘‘strong association between positive results and COI among all treatment studies’’ with an odds ratio of 2.35 and for drug 

studies alone an odds ratio of 2.64. The odds ratio is the ratio of probability of an 

event occurring in one group to the probability of it occurring in another group. An 

odds ratio of 2.35 for the drug studies is the probability of a positive result in a 

drug treatment study conducted by individuals with a FCOI divided by the probability of a positive result from a similar drug treatment conducted by individuals 

without a financial conflict of interest. In other words, an odds ratio of 2.35 means 

that investigators with an FCOI are more than twice as likely to produce positive 

results in a drug treatment study. 

Another interesting finding is that the probability of reporting negative results in 

cases where an author had a FCOI was very low. One negative study of the 60 drug 

378 

studies with FCOIs versus 21 negative studies of the 59 drug studies without FCOIs 

were reported. The authors conclude that ‘‘the odds are extremely small that negative results would be published by authors with COI’’ (Friedman and Richter, 2004, 

53). 

The authors cannot provide an explanation for their observed association between 

FCOI and reported findings in medical treatments. They can only theorize about the 

cause. ‘‘One could surmise that drug companies are selective and only want to invest 

in treatments proven to produce positive results and that early clinical trials filter 

out the most promising treatments, which could explain the small number of studies 

funded by private corporations presenting negative findings’’ (Friedman and Richter, 

2004, 55). But they also consider the possibility of bias and ‘‘spin.’’ The question 

arises as to whether an investigator with a conflict of interest may be more inclined 

to present findings in order to gain favor with the sponsor or achieve any other extraneous objective—for example, to ‘‘spin’’ (Friedman and Richter, 2004, 55). Notwithstanding the fact that the cause of the association is not apparent in their data, 

they state that: 

The observation that negative findings are less commonly reported among 

studies funded by private corporations raises troublesome ethical questions. Researchers appear to be failing to promote both the benefits and negative side 

effects of commercial products they review or simply failing to submit negative 

studies for publication because they are viewed as uninteresting. (Friedman and 

Richter, 2004, 55) 

For social scientists studying the funding effect, the issue in this case is less a 

question of bias in the reported studies than it is an issue of bias in a failure of 

reporting negative studies, that is, in subverting the complete scientific record. 

Not all studies testing a hypothesis that there is an association between trial outcome or study quality and funding source reached positive findings. Tammy Clifford, 

Barrowman, and Moher (2002) selected a convenience sample of RCTs published between 1999 and 2000 by hand-searching five high impact general medical journals— 

Annals of Internal Medicine, BMJ, JAMA, The Lancet, and NEJM. The quality of 

the trial report was evaluated according to the Jadad scale, which included randomization, allocation concealment, and withdrawals. The authors classified the trials 

according to funding source in four categories: entirely industry, entirely no[t]-forprofit, mixed, and not reported. Sixty-six of the hundred trials reviewed were funded 

in whole or in part by industry; six did not disclose their source of funding. Of the 

100 trials, 67 favored the new therapy, six favored conventional treatments, 19 reported neutral findings, and for eight the outcome was unclear. Of the 67 trials that 

favored the new treatment, 30 came from ‘‘industry only,’’ 15 came from ‘‘not-forprofit only,’’ and 16 came from mixed sources; of the six trials that favored the conventional treatment, four came from ‘‘industry only,’’ one came from ‘‘not-for-profit 

only,’’ and one came from mixed sources. 

The numbers for ‘‘favored conventional’’ were so low that statistical findings were 

not relevant. Also, this study only focused on funding and not on the financial ties 

of individual faculty associated with the trials. The authors noted limitations of 

their results. ‘‘Our failure to detect any significant association may result from a 

type 2 error that indicates inadequate statistical power. Although our results do not 

even hint at a trend . . . the potential for type 2 error is real’’ (Clifford, Barrowman, 

and Moher, 2002, 21). Perhaps one conclusion can be drawn: of the 100 trials, 66 

percent were funded in whole or in part by industry and 67 percent favored the new 

therapy. Thus, it appears that industry trials are dominant and driving the advocacy of new drugs over old treatments even without adding author FCOI. 

Finally, I shall summarize the first meta-analysis that explored the ‘‘funding effect.’’ Bekelman, et al., culled 1,664 original research articles and ended up with 37 

studies that met their criteria. They concluded: ‘‘Although only 37 articles met [our] 

inclusion criteria, evidence suggests that the financial ties that intertwine industry, 

investigators, and academic institutions can influence the research process. Strong 

and consistent evidence shows that industry sponsored-research tends to draw 

proindustry conclusions’’ (Bekelman, Li, and Gross, 2003, 463). Bekelman, et al., 

were convinced that the ‘‘funding effect’’ is real. 

I shall now turn to the relationship between FCOI and pharmacoeconomics, defined as the discipline that evaluates the clinical, economic, and humanistic aspects 

of pharmaceutical products, services, and programs. 

Pharmacoeconomic Studies 

A few studies have examined whether the results of economic analyses of drugs 

are correlated with the funding source. Because there is greater discretion in developing the methodology for economic studies of drugs, any inferences of bias must 

379 

be addressed through the modeling, the stakeholder interests, and the specific parameters used in cost-benefit analysis rather than the omission or manipulation of 

clinical data. Johnson and Coons (1995, 165) note that ‘‘Many different guidelines 

have been proposed for conducting pharmacoeconomic studies. The differences 

among the various versions reflect the diverse and sometimes conflicting views of 

those who specialize in economic evaluations.’’ 

Mark Friedberg, et al. (2010) searched the Medline and Health Star databases for 

articles published between 1985 and 1998 on cost or cost-effectiveness analyses of 

six oncology drugs. The found forty-four eligible articles whose texts were analyzed 

for qualitative and quantitative conclusions and the funding source, based on predetermined criteria. Of the forty-four articles, twenty-four were funded by nonprofit 

organizations and twenty were funded by drug manufacturers. The authors found 

a statistically significant relationship between funding source and qualitative conclusions. Unfavorable conclusions were found in 38 percent (9⁄24) of the nonprofitsponsored studies and five percent (1⁄20) of company-sponsored studies. 

Studies funded by pharmaceutical companies were almost eight times less likely 

to reach unfavorable qualitative conclusions than nonprofit-funded studies and 1.4 

times more likely to reach favorable qualitative conclusions. 

C.M., Bell, et al. (2006) undertook a systematic review of published papers on 

cost-utility analyses. The authors found that industry-funded studies were more 

than twice as likely to report a cost-utility ratio below $20,000 per quality adjusted 

life year (QALY) as compared to studies sponsored by nonindustry sources. A similar study reported in the International Journal of Technology Assessment in Health 

Care assessed the relation between industry funding and findings of 

pharmacoeconomic analyses (Garattini, Rolova, and Casasdei, 2010). The authors 

searched Pub Med for articles on cost-effectiveness and cost utility, performed during 2004–2009 on single drug treatments. They found 200 articles that met their 

criteria. They divided the articles into two groups based on whether or not the authors had financial support from the pharmaceutical industry. ‘‘Studies co-signed by 

at least one author affiliated to a pharmaceutical company and/or studies that declared any type of company funding were considered sponsored’’ (Garattini, Rolova, 

and Casasdei, 2010, 331). The authors also classified the main conclusions as favorable, doubtful, or unfavorable toward the drug. Of the 200 articles, 138 (69 percent) 

were sponsored by a pharmaceutical company. Sponsored articles reported a favorable conclusion 95 percent of the time as against 50 percent of the time for nonsponsored articles. They claimed that ‘‘the presence of a pharmaceutical sponsorship is 

highly predictive of a positive conclusion’’ (Garattini, Rolova, and Casasdei, 2010, 

331). According to Krimsky, 1999, 1475): 

The differences observed between [pharmacoeconomic] studies funded by industry and nonprofit organizations may be the result of methods chosen, 

prescreening, or bias due to the source of funding. By following the traditions 

of professional societies, such as those of engineering and psychiatry in setting 

guidelines of practice, pharmacoeconomists can attain a special role in the 

health care policy community in developing independent studies that are based 

on accepted canons that meet the highest standards of the profession. Canada 

and the United Kingdom have developed national guidelines for cost effectiveness studies. 

K.S. Knox, et al. (2000) reported on data collected in Friedberg, et al., in comparing practices of pharmaceutical-sponsored and nonprofit-sponsored 

pharmacoeconomic studies. They found that nonprofit studies more likely make an 

explicit statement of the significance of the findings (38 percent vs. 20 percent), provide a source of cost data (67 percent vs. 45 percent), and make a clear statement 

about the reproducibility of the findings in other settings (58 percent vs. 35 percent). 

As in Friedberg, et al., Knox, et al., considered only one type of economic relationship between industry and researchers, namely, direct funding of a study and omitted many other types of financial relationships. Had they broadened their criteria, 

some of the 42 pharmacoeconomic analyses they studied might be reclassified as 

‘‘pharmaceutical associated’’ thus changing the statistical results. 

Some of the authors who found a ‘‘funding effect’’ were cautious about inferring 

a bias from the data, although it was included in the list of hypotheses they considered. The next section explores alternative explanations. 

Explanations of the ‘‘Funding Effect’’ Other than Bias 

In Yaphe, et al., the authors note that ‘‘the higher frequency of good outcomes in 

industry supported trials may stem from a decision to fund the testing of drugs at 

a more advanced stage of development’’ (Yaphe, et al., 2001, 567). In other words, 

industry has already done a lot of internal studies weeding out ineffective drugs. 

380 

Thus, by the time a private company funds a trial, it would likely do better than 

a drug has not gone through its internal review. To fully understand this process, 

we need to know the extent to which companies test and reject drugs internally before funding a study by an academic group and whether the outcome results of ‘‘new 

drugs are always better’’ would be found in trials of the same drugs but funded by 

nonprofit organizations. 

The methodologies of industry-funded as compared to nonprofit-funded trials may 

differ. For example, comparison of new drugs with a placebo may be more prevalent 

among industry-financed studies compared to nonindustry-financed studies. ‘‘Comparison with placebo may produce more positive results than comparison with alternative active treatment’’ (Yaphe, et al., 2001, 567). Unless we have a profit organization and nonprofit organization using the same or very similar methods to test the 

same drugs, drawing an inference about bias can yield false conclusions. The appearance of low negative outcomes from private sponsors could be the result of company screening for low probability drugs before they sponsor the trial or the ‘‘reticence of investigators to submit negative findings for publication, fearing discontinuation of future funding’’ (Yaphe, et al., 2001, 567). These caveats speak against a 

conclusion that bias can be inferred from the data that show outcome differences. 

Some tests use different doses of the new drugs and compare them to lower doses 

of the old drugs. This is corroborated by Rochon, et al., in their study. ‘‘When we 

evaluated the relative range of dosing of the manufacturer-associated drug and the 

comparison agents in the trials on the basis of the recommended dosage suggested 

in standard tests, there was a considerable mismatch. In the majority of cases 

where the doses were not equivalent, the drug given at the higher dose was that 

of the supporting manufacturer’’ (Rochon, et al., 1994, 161). 

The authors surmise that higher doses ‘‘bias the study results on efficacy in favor 

of the manufacturer-associated drug’’ (Rochon, et al., 1994, 161). This illustrates 

that bias may enter into the ‘‘funding effect’’ in subtle and complex ways that deal 

with how the trial is organized. 

Some authors try to explain the ‘‘funding effect’’ by maintaining that most industry studies use a placebo and as a result are more likely to show a positive outcome. 

Also, the method of drug delivery used by companies may have been different than 

that used in nonprofit sponsor trials. 

Others have questioned whether industry trials are of lower quality and thus are 

likely to produce more favorable results. Djulbegovic, et al., rated the trial quality 

and concluded that ‘‘trials funded solely or in part by commercial organizations had 

a trend toward higher quality . . . than those supported by the governmental or 

other nonprofit organizations’’ (Djulbegovic, et al., 2000, 637). Thus, the outcome effect found in the industry-funded work of this group was not related to poor quality 

trials. 

In Frieberg’s pharmacoeconomic study, the authors offer several possible explanations for the ‘‘funding effect.’’ First, for-profit companies are more likely than nonprofit companies to get ‘‘early looks’’ at the drugs, preliminary trial results, and economic data, weeding out those that would fail a cost-effectiveness standard. Companies might censor unfavorable studies by not funding them. Second, they surmise 

that funded studies with unfavorable results are less likely to be submitted for peer 

review and published. A third explanation for the disproportionate favorable results 

could arise from ‘‘unconscious bias that could influence study conclusions’’ from scientists who have a financial conflict of interest—such as being paid by the company 

or holding an equity interest in the drug. As previously noted, the economists engaged in the study may internalize the values of the study sponsor, which could 

translate into a methodology that is more likely to yield a positive economic analysis. 

And the final explanation suggested by the authors is that ‘‘the pharmaceutical 

companies can collaborate directly with investigators in devising protocols for economic analyses and indirectly shape the economic evaluation criteria’’ (Friedberg, et 

al., 2010, 1475). The assessment of bias requires a standard or norm for 

pharmacoeconomic analysis against which one can compare different outcomes 

(Krimsky, 1999). Several studies have addressed the quality of pharmacoeconomic 

analysis of drugs (Sacristan, Soto, and Galende, 1993; Jefferson, et al., 1988). Currently, no standardization or best practice for pharmacoeconomic analyses exists. 

Because the choice of method can have a significant effect on outcome, a method 

that systematically yields outcomes consistent with the private sponsor’s 

financialinterest may be biased. 

Single Product Assessment: Tobacco 

The studies of funding effects in pharmaceutical products include many types of 

drugs in order to develop aggregate statistics. Companies may do in-house studies 

381 

before sponsoring extramural studies. The type of drug studied is generally considered not relevant to the findings of a funding effect. However, investigators may 

have different histories with the products they are testing. Nonprofit investigators 

may have seen the product for the first time. By eliminating product variability, investigators of the funding effect can more precisely judge the possible linkage between the source of funding and outcome findings such as product quality, safety, 

or economic efficiency. Two product studies for a funding effect meet these criteria: 

tobacco and the chemical bisphenol A (BPA). I shall begin with a discussion of tobacco research. 

Turner and Spilich (1997) investigated whether there was a relationship between 

tobacco industry support of basic research and the conclusions reached by authors 

of the study. They utilized a comprehensive review of the literature on tobacco and 

cognitive development and used that to obtain their reference studies. Beginning 

with 171 citations, the authors selected 91 studies fulfilling their selection criteria 

that investigated the effects of tobacco and nicotine upon cognitive performance. 

They coded the conclusions of the papers as positive, negative, or neutral on the 

question of whether tobacco enhances performance and segmented the papers into 

those that acknowledged corporate sponsorship and those that did not. When one 

or more of the authors was an employee of a tobacco company, the article was coded 

as industry-supported. All other articles were coded as ‘‘noncorporate sponsorship,’’ 

even in cases where one or more of the authors had previously received industry 

support. 

For those papers reporting a negative relationship between tobacco and cognitive 

performance, sixteen were coded ‘‘nonindustry supported,’’ and one was coded ‘‘industry-supported.’’ For those reporting a positive relationship, twenty-nine came 

from nonindustry supported papers and twenty-seven from tobacco industry-supported papers. Among those papers reporting a neutral effect, eleven were from nonindustry studies and seven from industry-supported studies. In this study, the industry/nonindustry demarcation in the papers shows a disparity in negative results 

compared to positive results. Why did so few studies funded by the tobacco industry 

report negative effects on performance from tobacco use? Because the study methodologies were different, we cannot say that investigator bias played a role. It may 

just be that the industry-funded studies used a method that yielded fewer negative 

outcomes compared with an alternative method(s) used by the nonindustry-funded 

studies. There is a phenomenon known as ‘‘bias in the study design,’’ but that was 

not examined in the study. As previously mentioned, systematic bias in a study design seeking to test the toxicity of a chemical would be introduced by animal models 

that are inherently insensitive to the chemical in question (Bailar, 2006). 

Deborah Barnes and Lisa Bero (1998) investigated whether review articles on the 

health effects of passive smoking reached conclusions that are correlated with the 

authors’ affiliations with the tobacco companies. Since tobacco is a relatively homogenous product, differences in outcome cannot be attributed to product variability or 

company pre-testing. Just as in pharmacoeconomic studies, there is no canonical 

method in undertaking a review article. Authors make a selection of articles that 

become part of the review. Some reviewers make their selection algorithm transparent. Others may not. Any two studies may use a different selection algorithm 

and they may weigh studies differently. ‘‘Ultimately, the conclusion of any review 

article must be based on the judgment and interpretation of the author’’ (Barnes 

and Bero, 1998, 1570). 

For this study, the authors adopted a search strategy use by the Cochrane Collaboration to select review articles from 1980 to 1995 on the health effects of passive 

smoking from the databases MEDLINE and EMBASE. They located additional review articles from a database of symposium articles on passive smoking. Articles 

were evaluated on quality and were classified as concluding that passive smoking 

was either harmful or not harmful. The authors found that 94 percent (29⁄31) of reviews by tobacco-industry affiliated authors concluded that passive smoking is not 

harmful compared with 13 percent (10⁄75) of reviews without tobacco industry affiliations. The influence of tobacco-industry affiliation on the finding of ‘‘safety of passive smoking’’ was very strong. ‘‘The odds that a review article with tobacco with 

tobacco industry-affiliated authors would conclude that passive smoking is not 

harmful were 88.4 times higher than the odds for a review article with nontobacco 

affiliated authors, when controlling for article quality, peer review status, article 

topic, and year of publication’’ (Barnes and Bero, 1998, 1569). The authors reported 

that the ‘‘only factor that predicted a review article’s conclusion was whether its author was affiliated with the tobacco industry’’ (Barnes and Bero, 1998, 1570). In this 

study, the authors had no alternative hypotheses other than the inherent bias of 

authors with industry affiliation. Because there is a great deal of discrepancy among 

authors in how a review is carried out, including the selection and weighting of artiVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00387 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
382 

cles that form the basis of the review, there are a number of ways that the conclusion can be made to favor the funder’s interests, not the least of which is to set a 

high bar for establishing evidence of causality. The authors impute conscious 

intentionality of bias to the funders in their statement that ‘‘the tobacco industry 

may be attempting to influence scientific opinion by flooding the scientific literature 

with large numbers of review articles supporting its position [which they paid for] 

that passive smoking is not harmful to health’’ (Barnes and Bero, 1998, 1569). From 

tobacco, I shall now turn to an industrial chemical used in many products— 

bisphenol A. 

Single Product Assessment: BPA 

While there are different variants of tobacco that depend on where the tobacco 

plant is grown, and even greater variation in cigarettes because of chemicals added 

to the tobacco and the paper, there is still greater homogeneity in studying tobacco 

than in studying different types of drugs. BPA, on the other hand, is a synthetic 

chemical that has a precise chemical structure. It was first reported synthesized in 

1905 by a German chemist. In 1953, scientists in Germany and the United States 

developed new manufacturing processes for a plastic material, polycarbonate, using 

BPA as the starting material. In the 1990s, scientists began studying the toxicological effects of BPA leaching from plastic food and water containers. Despite the 

fact that some scientists claimed there was extensive evidence that BPA can disrupt 

mouse, rat, and human cell function at low part per trillion doses and that disruption at the same low doses is also found in snails [and] has profound implications 

for human health (vom Saal, et al., 2005, 249), other scientists disagreed. Vom Saal 

and Welshons (2006, 61) divided the studies into those funded by industry and those 

funded by nonprofit organizations. Of the 119 studies funded by the Federal Government, 109 showed harmful toxicological outcomes while ten had outcomes which 

showed no harm. Of the studies funded by the chemical companies, there were zero 

with outcomes showing harm and 11 with outcomes of no harm. 

The authors write: ‘‘Evidence of bias in industry-funded research on BPA.’’ Is it 

systematic bias and if so what form does it take? Is industry using a different methodology than most of the federally-supported studies? If so, is their methodology 

sound or is it designed to get a ‘‘no harm’’ outcome? 

vom Saal and Welshons argue that industry-funded studies have a built in bias 

[what I have referred to as structural bias] against finding positive effects of BPA. 

They maintain that ‘‘To interpret whether there is a positive or negative effect of 

a test chemical, such as BPA, appropriate negative and positive controls also have 

to be examined’’ (vom Saal and Welshons, 2006, 62). Vom Saal argues that the industry-supported tests omitted a positive control and without positive control findings, one cannot interpret a reason for purely negative results. The authors also 

noted that some industry-funded BPA studies used test animals that had very low 

sensitivity to exogenous estradiol and thus would not be expected to exhibit effects 

from BPA. Other industry-funded investigators used a type of animal feed, which 

because of its estrogenic activity, would give a false result. ‘‘Inclusion of an appropriate positive control . . . would have allowed a determination of whether the failure to find effects of BPA was due to the lack of activity of BPA or to a lack of sensitivity of the animal model and/or estrogenic contamination of the feed that was 

used’’ (vom Saal and Welshons, 2006, 63). 

In his classic work, The Logic of Scientific Discovery, Karl Popper (1968) developed the philosophical foundations of scientific methodology. Science, Popper argued, 

is not an inductivist enterprise, where truth is built up from data that are consistent with a hypothesis. Scientists must seek to falsify a hypothesis, and only 

when a hypothesis is recalcitrant against a rigorous attempt at falsification can it 

be accepted as truth. The critical point is that deduction and not induction is the 

logical grounding of empirical science. In the latter case, scientists would be given: 

A1 is B, A2 is B . . . An is B therefore All A is B. In the former case, scientists seek 

to falsify ‘‘All A is B’’ by trying to find a disconfirming instance (Ax is not B). 

For example, one can reach the conclusion that ‘‘all crows are black’’ by observing 

crops in certain parts of Africa. Or you could imagine a geographical location that 

would most likely nurture a nonblack crow—such as the North or South Pole. If 

after all the seeking for a falsifying instance none appears, then, under the 

Popperian program, you can claim that the hypothesis ‘‘all crows are black’’ is confirmed. vom Saal and Welshons illustrate this point in the toxicology of BPA. 

. . . it is a common event in toxicological studies conducted by the chemical 

industry for purposes of reporting about chemical safety to regulatory agencies 

to provide only negative results from a study in which no positive control was 

included but from which positive conclusions of safety of the test chemical are 

drawn. (von Saal and Welshons 2006, 63) 

383 

As Peirce noted, ‘‘We are, doubtless, in the main logical animals, but are not perfectly so’’ (Peirce, 1877). Both he and Popper understood that knowledge claims 

drawn inductively can be easily distorted by the social context of scientists. This is 

most notably the case in the field of toxicology, which is composed of academic scientists and contract toxicologists working on behalf of for-profit companies. These 

scientists are usually paid by chemical companies to fulfill the information needs 

of their regulatory requirements. The standards for doing toxicological research may 

vary, especially in new subfields like low-dose, endocrine toxicology. Thus, until the 

norms of good scientific practices are adopted across the subfield and by the government regulators, contract toxicologists may perform studies that have structural biases because they are more likely than not to produce false negatives. This is the 

take-home message from the criticism by vom Saal and Welshons of private-company-sponsored studies. They are looking to confirm the null (no effect) hypothesis 

rather than trying to falsify the null hypothesis, which would provide more confidence in the claim that the chemical is not harmful. 

Conclusion 

This analytical review of studies of studies that investigate an association between funding source and study conclusions has revealed several important results. 

First, there is sufficient evidence in drug efficacy and safety studies to conclude that 

the funding effect is real. Industry-sponsored trials are more likely than trials sponsored by nonprofit organizations, including government agencies, to yield results 

that are consistent with the sponsor’s commercial interests. Second, there is some 

circumstantial evidence that this effect arises from two possible causes. Either the 

drugs sponsored by industry have gone through more internal testing and less-effective drugs are screened out, or the methods used in industry-sponsored drug testing 

have a structural bias that is more likely to yield positive outcomes. 

Third, a small number of pharmacoeconomic studies also show evidence of a funding effect. Without standardization of economic studies or the use of third-party 

‘‘economic auditors’’ who have no economic ties to a company, it is difficult to account for the factors that explain this effect. 

A person who files his income tax is likely to use whatever discretionary decisions 

at his disposal to reduce his tax obligation. Similarly, a company that performs its 

own economic analysis of a new drug is likely to choose a model and use inputs that 

are advantageous to it. When a company hires an independent agent to undertake 

the economic analysis, little is known about what influence the company has in 

shaping the study. Also, little is known about drugs that are kept out of the testing 

pool by companies because they have already done the economic analysis. 

When we turn to studies of the funding effect on individualized commodities, the 

results are less ambiguous. There is an extensive body of research on tobacco, both 

primary (smokers) and secondary (secondhand smoke) exposures. This research 

shows a clear demarcation between studies funded by the cigarette industry and 

studies funded by nonprofit and governmental organizations. From this body of research, it is reasonable to conclude that the tobacco industry hired scientists to play 

a similar role as their contracted lawyers, namely, to develop a brief, in this case 

a scientific argument, that provides the best case or their interest. If that interpretation of tobacco-funded research is correct, it could explain the funding effect in tobacco studies. 

The second homogenous product discussed in this article is BPA. However, with 

only one study of this compound found that addresses the funding effect, a generalization cannot be drawn. But the scientists who published the study help the reader 

understand why a funding effect is a probable outcome. They show the systemic bias 

involved in the industry-funded studies that ordinarily do not appear in studies 

funded by nonprofit organizations. 

What I have argued in this article is that the ‘‘funding effect,’’ namely the correlation between research outcome and funding source, is not definitive evidence of bias, 

but is prima facie evidence that bias may exist. Additional analyses of the methodology of the studies, interpretation of the data, interviews with investigators, and 

comparison of the products studied can resolve whether the existence of a funding 

effect is driven by scientific bias. Social scientists should follow Robert Merton’s 

norm of ‘‘organized skepticism’’ when they frame an initial hypothesis about the 

cause behind the ‘‘funding effect’’ phenomenon (Merton, 1968, 608). The notion of 

bias based on possessing a financial conflict of interest is certainly one viable hypothesis. But there are others. Social scientists must be equipped to compare the 

methods used across a cluster of studies funded by for-profit and not-for-profit companies to determine whether a particular method biases the results toward ‘‘no detectable outcome’’ while other more sensitive methods yield positive results. Certain 

chemical effects may show up in animal fetuses and not on the adult animals. 

384 

In addition, social scientists must gain an understanding of the entities being 

tested across a series of studies to determine whether the differences in the entities 

can account for the ‘‘funding effect.’’ Calcium channel blockers represent a class of 

drugs. It is important to understand whether the partition of studies between forprofit and not-for-profit funders coincides with a random distribution of the entities 

being studied. Drugs that have passed a prescreening test are more likely to show 

more favorable outcomes than similar drugs that have not. This potential confounder can be eliminated when the entities are relatively homogenous, like tobacco 

or a chemical like BPA. 

In some cases, ethnographic studies can determine whether for-profit companies 

have made internal decisions about drugs before they send them out to academic 

laboratories for study and how that compares with drug studies funded by not-forprofit organizations. Ethnography can also help social scientists ascertain when investigators reach beyond the data when they interpret results and whether the frequency of such overinterpretation (claiming benefits not found in the data) is more 

likely in studies funded by for-profit funders. Interviews with academic investigators, who are funded by private for-profit companies, and company executives, can 

reveal whether and how the funding organization helps frame the study, contributes 

to the interpretation of the data, and plays a role in deciding whether the results 

get sent for publication. The ‘‘funding effect’’ is merely a symptom of the factors that 

could be driving outcome disparities. Social scientists should not suspend skepticism 

and choose as the default hypothesis that ‘‘bias’’ is always the cause. 

Acknowledgment 

This research was supported in part by funding from the International Center forAlcohol Policies. 

Declaration of Conflicting Interests 

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. 

Funding 

The author received funding for an earlier version of this paper from the International Center for Alcohol Policies. 

References 

Als-Nielsen, B., W. Chen, C. Gluud, and L. L. Kjaergard. 2003. ‘‘Association of Funding and Conclusions in Randomized Drug 

Trials: A Reflection of Treatment Effect or Adverse Events?’’ The Journal of the American Medical Association 290 (4): 921–28. 

Angell, Marcia. 2004. The Truth about the Drug Companies. New York, NY: Random House. 

Bailar, J.C. 2006. ‘‘How to Distort the Scientific Record without Actually Lying: Truth and the Arts of Science.’’ European Journal of Oncology 11 (4): 217–24. 

Barnes, D.E., and L.A. Bero. 1998. ‘‘Why Review Articles on the Health Effects of Passive Smoking Reach Different Conclusions.’’ 

The Journal of American Medical Association 279 (19): 1566–570. 

Bekelman, J.E., Yan Li, and C.P. Gross. 2003. ‘‘Scope and Impact of Financial Conflicts of Interest in Biomedical Research: A 

Systematic Review.’’ The Journal of American Medical Association 289: 454–65. 

Bell, C.M., D.R. Urbach, J.G. Ray, A. Bayoumi, A.B. Rosen, D. Greenberg, and P.J. Neumann . 2006. ‘‘Bias in Published Cost Effectiveness Studies: Systematic Review.’’ British Medical Journal 332 (7543): 699–703. 

Clifford, T. J., N.J. Barrowman, and D. Moher. 2002. ‘‘Funding Source, Trial Outcome and Reporting Quality: Are they Related? 

Results of a Pilot Study.’’ BMC Health Services Research 2 (18): 1–6. 

DHHS (Department of Health and Human Services, Public Health Service). 1995. ‘‘Objectivity in Research.’’ Federal Register s60: 

35810–823. 

Djulbegovic, B., M. Lacevic, A. Cantor, K.K. Fields, C.L. Bennett, J.R. Adams, N.M. Kuderer, and G.H. Lyman. 2000. ‘‘The Uncertainty Principle and Industry-sponsored Research.’’ Lancet 356 (9230): 635–8. 

Friedberg, M., B. Saffran, T.J. Stinson, W. Nelson and C.L. Bennett. 1999. ‘‘Evaluation of Conflict of Interest in Economic Analyses of New Drugs used in Oncology’’. Journal of the American Medical Association 282 (15): 1453–7. 

Friedman, L.S., and E.D. Richter. 2004. ‘‘Relationship between Conflict of Interest and Research Results.’’ Journal of General Internal Medicine 19 (1): 51–56. 

Garattini, L., D. Rolova, and G. Casasdei. 2010. ‘‘Modeling in Pharamacoeconomic Studies: Funding Sources and Outcomes.’’ 

International Journal of Technology Assessment Health Care 26 (3): 330–33. 

Jefferson, T., R. Smith, Y. Yee, M. Drummond, M. Pratt, and R. Gale 1998. ‘‘Evaluating the BMJ Guidelines for Economic Submissions.’’ The Journal of the American Medical Association 280 (3): 275–77. 

Johnson, J.A., and S.J. Coons. 1995. Evaluation in Published Pharmacoeconomic Studies. JOURNAL OF PHARMACY PRACTICE 8 (8): 

156–66. 

Knox, K.S., J.R. Adams, B. Djulbegovic, T.J. Stinson, C. Tomor, and C.L. Bennett. 2000. ‘‘Reporting and Dissemination of Industry versus Non-profit Sponsored Economic Analyses of Six Novel Drugs used in Oncology.’’ Annals of Oncology 11 (12): 1591–95. 

Krimsky, Sheldon. 1999. ‘‘Conflict of Interest and Cost Effectiveness Analysis.’’ The Journal of American Medical Association 282 

(10): 1474–75. 

____. 2005. ‘‘The Funding Effect in Science and its Implications for the Judiciary.’’ Journal of Law and Policy 23 (1): 43–68. 

____. 2006. ‘‘Publication Bias, Data Ownership, and the Funding Effect in Science: Threats to the Integrity of Biomedical Research.’’ In Rescuing Science from Politics: Regulation and the Distortion of Scientific Research, edited by W. Wagner and R. 

Steinzor, 61–85. New York: Cambridge University Press. 

____. 2010. ‘‘Combating the Funding Effect in Science: What’s Beyond Transparency?’’ Stanford Law & Policy Review 21: 101–23. 

Levinsky, N.G. 2002. ‘‘Nonfinancial Conflicts of Interest in Research.’’ The New England Journal of Medicine 347: 759–61. 

Martin, Brian. 1979. The Bias of Science. Canberra, Australia: Society for Social Responsibility in Science. 

Merton, Robert K. 1968. Social Theory and Social Structure. New York, NY: The Free Press. 

Peirce, Charles S. 1877. ‘‘The Fixation of Belief.’’ Popular Science Monthly 12 (11): 1–15. Accessed October 1, 2010. http:// 

www.peirce.org/writings/p.107.html. 

Popper, Karl. 1968. The Logic of Scientific Discovery. New York, NY: Harper &Row. 

Resnik, David B. 1998. The Ethics of Science. London, England: Routledge. 

Rochon, P.A., J.H. Gurwitz, R.W. Simms, P.R. Fortin, D.T. Felson, K.L. Minaker, and T.C. Chalmers. 1994. ‘‘A Study of Manufacture-supported Trials of Nonsteroidal Anti-inflammatory Drugs in the Treatment of Arthritis.’’ Archives of Internal Medicine 154 

(2): 157–63. 

385 

1Kesselheim A.S., Robertson C.T., Myers J.A., Rose S.L., Gillet V., Ross K.M., et al. A randomized study of how physicians interpret research funding disclosures. N. ENGL. J. MED. 2012; 

367: 1119–1127. 2Fanelli D. Negative results are disappearing from most disciplines and countries. 

SCIENTOMETRICS 2012; 90: 891–904. 

References—Continued 

Sacristan, J.A., J. Soto, and I. Galende. 1988. ‘‘Evaluation of Pharmacoeconomic Studies: Utilization of a Checklist.’’ The Annals 

of Pharmacotherapy 27: 1126–33. 

Sugarman, Jeremy. 2008. ‘‘Human Stem Cell Ethics: Beyond the Embryo.’’ Cell Stem Cell 2 (7): 529–33. 

Turner, Christina, and George J. Spilich. 1997. ‘‘Research into Smoking or Nicotine and Human Cognitive Performance: Does the 

Source of Funding make a Difference?’’ Addiction 92 (11): 1423–426. 

Van Dalen, Arjen. 2012. ‘‘Structural Bias in Cross-National Perspective: How Political Systems and Journalism Cultures Influence Government Dominance in the News.’’ The International Journal of Press/Politics 17 (1): 32–55. 

vom Saal, F.S., S.C. Nagel, B.G. Timms, and W.V. Welshons. 2005. ‘‘Implications for Human Health of the Extensive Bisphenol A 

Literature Showing Adverse Effects at Low Doses: A Response to Attempts to Mislead the Public.’’ Toxicology 212 (2–3): 244–252. 

vom Saal, F.S., and W.V. Welshons. 2006. ‘‘Large Effects from Small Exposures.’’ Environmental Research 100 (1): 50–76. 

Yaphe, J., R. Edman, B. Knishkowy, and J. Herman. 2001. ‘‘The Association Between Funding by Commercial Interests and 

Study Outcome in Randomized Controlled Drug Trials.’’ Family Practice 18 (12): 565–68. 

Author Biography 

Sheldon Krimsky is the Lenore Stern Professor of Humanities and Social Sciences at Tufts University and the Carol Zicklin Visiting Professor at Brooklyn College. He is author of Science in the Private Interest. 

ATTACHMENT 13 

Judge the Science, Not the Funding Source 

Editorial 

International Journal of Obesity (2014) 38, 625; doi:10.1038/ijo.2014.32; published 

online 18 March 2014 

©2014 Macmillan Publishers Limited All rights reserved 0307–0565/14 

www.nature.com/ijo 

Medical research has long been engaged in debate over the influences of corporate 

sponsorship on research findings. These discussions are a necessary element of our 

scientific process. However, recently the issue of ‘funding source’ has taken on a life 

of its own, particularly in the realm of obesity research. Discussions about the merit 

and objectivity of the underlying science frequently take a back seat to ad hominem 

attacks on researchers or accusations of malicious corporate intent in the absence 

of any objective scientific appraisal of the research. 

These discussions are based on the faulty logic that somehow direct corporate 

funding is inherently bias-producing in otherwise ethical researchers and that, by 

implication, public (for example, NIH, USDA) and/or philanthropic (for example, 

RWJ, AHA) funding, by way of the intervening agency, ensures objectivity. A recent 

controlled study of over 500 board-certified internists found that the participant’s 

perception of methodological quality was lower if they believed a trial was corporate 

sponsored even when no actual methodological difference was present.1 This suggests a bias against scientifically valid studies based solely on funding source, which 

could in fact negatively impact public health through out-of-hand dismissal of relevant, high-quality research. 

Conversely, the implied bias favoring noncorporate trial objectivity is equally concerning as it may lead us to overlook very real potential bias from publicly funded 

trials. For example, it could be argued that a scientist who is funded by USDA is 

at similar risk for being biased in favor of sugar, corn and other agriculture-based 

products vs. artificial sweeteners. Moreover, an often overlooked threat to objectivity 

is the pressure to find statistically significant results. This has been found in both 

corporate- and noncorporate-sponsored research. A recent examination of over 4,600 

scientific papers from all disciplines published internationally between 1990 and 

2007 found that the frequency of positive support for hypotheses has increased 22%. 

The author notes that this bias toward publishing positive results may influence the 

objectivity of the research literature both directly and also more subtly by discouraging more innovative (higher-risk) projects.2

Assuming that the well-intentioned yet misguided goal of those leveling these ad 

hominem attacks is to keep scientists in check and to help us avoid moral and scientific pitfalls, there is a far better model available. When the model works, the 

public is protected, scientific discovery and rigor supported, and advances in our 

knowledge achieved. At the core of the model is a safe environment that allows for 

transparency. This includes full disclosure of all potential conflicts of interest without fear of judgment or reprisal and peer-reviewed publication of findings with appropriate methodological detail to allow for objective analysis and scientific scrutiny. 

Beyond these commonly and universally held practices, more consistent application 

of additional tools may be useful. For example, a requirement for all research to be 

386 

[1]Department of Nutritional Sciences, Texas Tech University, Lubbock, TX, USA. [2] Binks Behavioral Health, PLLC, Hillsborough, NC, USA, E-mail: m.binks@ttu.edu. 

preregistered in Clinicaltrials.gov or a similar database. Reviewers and journal editors can be encouraged to ensure that final manuscripts are consistent with the 

stated a priori objectives before final acceptance of manuscripts, which could further 

add to our protection of scientific integrity. Finally, we should all aspire to solve the 

intellectual property and other barriers that limit our ability to review and replicate 

studies on the basis of lack of access to primary data sources in some corporate 

trials. Although these barriers are complex and beyond the scope of this paper, we 

need to begin to find solutions that will enhance the ways scientific process can be 

used to be the judge of all science. 

In summary, what are the best practices for ensuring a strong, unbiased body of 

obesity research? Certainly not refusing funding from those who wish to collaborate 

with scientists in becoming part of the solution; absolutely not by launching unsubstantiated attacks on reputable scientists with longstanding records of ethical conduct and meaningful scientific contribution; rather, it is first by giving funding 

source its proper position among many possible and equally important threats to objectivity and implementing safeguards to protect against such bias (and worse yet 

malfeasance). Second, we need to redouble our efforts to adhere to the basic principles of good science like reproducibility, replicability and other core evaluative procedures that ensure objective and reliable scientific reporting. Finally, we need to 

work toward open access to data regardless of its source. This will require the cooperation of those in the scientific community and among potential sources of funding. Ultimately, this type of transparency regardless of funding source will deliver 

a more robust and complete body of evidence. In short, scientists need to practice 

good science, sponsors must commit to transparency and noninfluence, media needs 

to practice responsible scientific journalism, and we all need to base our evaluations 

on scientific data and not on predetermined opinions rooted in our own emotionladen bias for or against specific funding sources. 

Conflict of Interest 

Dr. Martin Binks [1–2] reports the following potential conflicts of interest: Dr. Binks is sole proprietor of Binks Behavioral Health 

PLLC; he has also received financial compensation from: The Obesity Society (Communications & Social Media Consultant), 2011– 

present; Guidepoint Global Consulting, 2007–present; AbbVie Men’s Health Initiative, 2012; Everyday Health Inc., 2006–2011; Evolution Health Systems, 2006–2011. Dr Binks also currently serves in the following volunteer roles: The Obesity Society, Secretary 

Treasurer & Development Chair. No funding from any source is directly associated with the development of this manuscript. 

ATTACHMENT 14 

The Obesity Society Encourages Science-Industry Collaborations to Support 

Obesity Science, Public Health 

New Position Statement Condemns Ad Hominem Attacks on Researchers 

Mar. 26, 2014, 11:00 ET from The Obesity Society 

The Obesity Society Logo. (PRNewsFoto/The Obesity Society) 

Silver Spring, Md., March 26, 2014—PRNewswire-USNewswire—Collaborations 

between scientists and industries, including food and pharmaceuticals, have a 

strong history of aiding in new scientific discoveries and supporting public health. 

For example, earlier this year, food industry corporations announced success in cutting 1.5 trillion calories from food products, which The Obesity Society referred to 

(http://www.obesity.org/news-center/cutting-trillions-of-calories-from-food-productscan-have-a-significant-impact-on-the-nations-health.htm) as an effort that could 

‘‘make a significant difference in our nation’s weight and health, helping to reverse 

the obesity epidemic.’’ 

‘‘It’s clear, efforts to combat obesity cannot succeed without the engagement of the 

many industries that have the power to positively impact the health of billions of 

people,’’ said TOS President Steven Smith, M.D. 

However, in recent years, nutrition and obesity researchers have frequently endured ad hominem attacks, or inappropriate criticisms of character and ethics on 

the sole basis of collaborative relationships and/or funding from Industry. Today, 

The Obesity Society (TOS) issued a position statement supporting and encouraging 


11502080.eps
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[1]Solae LLC, St Louis, MO, USA. [2] Biostatistics and Nutrition & Obesity Research Center, University of Alabama at Birmingham, Birmingham, AL, USA. 

Correspondence: David B. Allison, Biostatistics and Nutrition & Obesity Research Center, 

University of Alabama at Birmingham, Birmingham, AL, USA. Tel: 205–975–9169, Fax: 205– 

975–2540, E-mail: dallison@uab.edu 

collaborative relationships between scientists and Industry in the interest of scientific discovery and public health. The position goes further to condemn these character attacks against credible and ethical professionals providing transparency and 

full disclosure about these collaborations. 

‘‘Many of our members are the obesity and nutrition scientists that offer valuable 

insight and spark meaningful dialogue with Industry leaders, and they deserve to 

be treated and recognized as the credible and ethical professionals that they are,’’ 

said Dr. Smith. ‘‘Discrediting the scientific opinions of these professionals based on 

their working relationships has no place in the scientific process.’’ 

The position statement, ‘‘Acceptance of Financial Support from Industry for Research, Education and Consulting,’’ authored by members of TOS leadership, including Advocacy Chair Emily Dhurandhar, Ph.D., President-elect Nikhil Dhurandhar, 

Ph.D., Secretary-Treasurer Martin Binks, Ph.D., and Advocacy Advisor Ted Kyle, 

RPh, discourages the practice of ‘‘dismissing the contributions of individual scientists and attempting to discredit individuals based on funding source.’’ 

‘‘We have seen too many scientists with long-standing records of scientific excellence and ethical conduct dragged into the spotlight of public criticism based solely 

on a funding source, and despite full disclosure and transparency,’’ said Dr. 

Dhurandhar, who led the development of the statement. ‘‘Scientists serve a clear 

role in these relationships and must operate with the ability to do their work to advance public health by engaging in free and open dialogue, offering expert opinion, 

and conducting meaningful research to support obesity treatment and prevention, 

and advance public health.’’ 

In the new position statement, TOS recognizes that individual motivations can 

sometimes create a risk of bias, which can come in many forms outside of funding 

source. However, from advisory panels to scientific publications, policies are in place 

to ensure transparency and disclosure of all potential sources of bias, which is common practice. 

‘‘Scientists are very familiar with the importance of making relevant disclosures 

and ensuring funding sources do not influence the design, analysis, interpretation, 

and publication of the scientific process,’’ said Dr. Dhurandhar. 

The Obesity Society has a long-standing commitment to ensuring ethical and 

transparent relationships between science and Industry, and the organization hopes 

to advance the science behind obesity research, treatment and prevention through 

ongoing dialogue on this issue. 

Read the full position statement here (http://www.obesity.org/publications/acceptance-of-financial-support-from-industry-for-research-education-a-consulting.htm). 

About The Obesity Society (http://www.obesityweek.com/) 

The Obesity Society (TOS) is the leading professional society dedicated to better 

understanding, preventing and treating obesity. Through research, education and 

advocacy, TOS is committed to improving the lives of those affected by the disease. 

For more information visit: www.Obesity.org. 

ATTACHMENT 15 

White Hat Bias: A Threat to the Integrity of Scientific Reporting 

A Different View 

Acta Pædiatrica ISSN 0803–5253 

Mark B. Cope,[1] David B. Allison (dallison@uab.edu)[2]

Received: 26 August 2010; accepted 2 September 2010. 


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1The term sugar-sweetened beverages (SSBs) is used for consistency with common use in the 

literature to denote any beverage, which has been sweetened by the addition of a substance containing a nontrivial amount of metabolizable energy. Thus, SSBs do not include beverages 

sweetened with high-intensity ‘noncaloric’ sweeteners such as sucralose or aspartame, but may 

include beverages sweetened with substances not conventionally termed sugar. 

DOI:10.1111/j.1651–2227.2010.02006.x 

Articles in the series A Different View are edited by Alan Leviton 

(alan.leviton@childrens.harvard.edu) We encourage you to offer your 

own different view either in response to A Different View you do not fully 

agree with, or on an unrelated topic. 

Background 

Like other people, scientific researchers have their own motivations. Such motivations include, but are not limited to, direct financial gain, interests in recruiting financial resources to their institutions, fame, social dominance, being perceived as 

righteous and upstanding, and a genuine interest in beneficence and improving the 

human condition. Pursuing these motivations may at times suggest behaviours on 

the part of scientists that accord with the behaviours that are generally accepted 

as sound and honest scientific practice. Yet in other situations, such motives may 

conflict with the precepts of scientific research. 

Although the potential for financial conflicts of interests (COIs) to bias research 

and research reporting is widely recognized,(1) far less attention has been devoted 

to other factors that may contribute to bias in research. Some people within the research and lay communities appear to think that direct financial COIs resulting 

from industry connections are the only factors of significant concern. For example, 

Lesser, et al., wrote ‘We agree that financial conflict is not the only cause of 

bias. . . . long-standing scientific viewpoints, career considerations, and even political opinions might color study design or interpretation. However, these types of individual bias tend to cancel themselves out among large groups of scientists over 

the long term. While one investigator’s career may rise on a cherished theory, another’s may rise by debunking that theory. We contend that financial conflict of interest is qualitatively different, producing selective bias that acts consistently in one 

direction over time’.(2)

Another report, evaluating the possible financial competing interest among researchers who had published clinical studies in the British Medical Journal, con- cluded that ‘authors’ conclusions were positively associated with financial competing 

interests. Other competing interests such as personal or academic were not significantly associated withauthors’ conclusions’.(3) This study had low power—only 19 

trial reports listed ‘other competing interests—for instance, personal, academic, or 

political.’ Also, the reporting of nonfinancial interests is by no means standard or 

covered by most guidelines, so most nonfinancial interests probably go unreported. 

Recently, we published a paper describing ‘White hat bias (WHB),’ which we defined as bias leading to distortion of information in the service of what may be perceived to be righteous ends.(4) Using quantitative evidence, we showed, at least in 

some areas of investigation, that WHBs that do not stem from financial connections 

to industry, clearly do not ‘cancel out’ over the long run as Lesser, et al.,(2) hypothesized they would. Rather, WHB seemed to be consistently pushing conclusions in 

a single direction and systematically distorting the research record. 

In the remainder of this paper, we summarize the results of our previously reported investigation into WHB, offer a few additional examples of apparent WHB, 

often anecdotal, and finally close with some suggestions to reduce the influence of 

biases, including WHBs, in research. 

Summary of Our Previous Findings Related to Breastfeeding and Obesity 

Some reports that do not agree with main stream opinion (e.g., a report that 

sugar-sweetened beverages (SSBs) 1 are not associated with obesity in children or 

a report that breastfeeding is not protective against childhood weight gain) may 

never be published. Other reports contain secondary references to support a current 

position, but incompletely or inaccurately describe the overall results from the secondary reference cited (4) [also labelled as ‘unbalanced citations’ by Atkinson and 

Macdonald].(5)

One area in which we documented WHB involved the question of the beneficial 

effect (or lack thereof) of breastfeeding on the development of obesity.(4) In a review 

commissioned and published by the World Health Organization (WHO) on the 

health benefits related to breastfeeding, specifically for obesity in the breastfed offspring, the authors presented evidence about whether breastfeeding protects against 

obesity and whether there is evidence of publication bias (PB).(6) PB occurs when 

389 

the probability of publication depends on the results of the study.(7–8) For example, 

positive (statistically significant) results are much more likely to be reported than 

statistically insignificant (null) findings.(9)

Within the WHO-commissioned report, the authors presented a graph showing 

clear evidence consistent with PB, suggesting that the probability a study was published was positively related to the degree it showed a statistically significant protective association of breastfeeding on obesity. To evaluate the impact of industry 

funding on such PB, we retrieved and reviewed the papers summarized in this 

graph. None of the papers reported any industry funding or were written by employees of the infant formula industry. Thus, the strong PB in the literature pertaining 

to breastfeeding and its relationship to obesity seems because of the behaviour of 

nonindustry funded scientists and does not appear to be fuelled by industry interests. 

Research can also be reported in a misleading manner when authors choose to include incorrect or questionable material and exclude otherwise pertinent information in their reviews or meta-analyses. In our review of the WHO report on 

breastfeeding,(4) we cited several examples where careful study of some of the original papers revealed that WHO authors selectively included some values from certain primary papers and thereby generated stronger associations of breastfeeding 

with reduced obesity risk and excluded less impressive values from the same papers 

without explanation. Misleading reports (or unexplained exclusion of data) can also 

occur in other areas of research. 

Some Additional Examples and Evidence 

‘Spin,’ defined as distorted presentation of data, was identified in reports of randomized clinical trials.(10) In each, the primary outcome was statistically nonsignificant, yet somewhere in the report (title, abstract, etc.), the experimental treatment 

was ‘spun’ in a way to mislead the reader. In an evaluation of reports of 102 randomized clinical trials (122 published journal articles), 62% of them added a new 

outcome, had at least one of the primary outcomes changed, or simply omitted the 

findings.(11)

Recently, the Food and Drug Administration of the U.S. Government issued a 

docket describing how menu labelling in restaurants would give people the opportunity to make healthier diet choices when eating out.(12) The docket stated that ‘the 

availability of nutritional information through menu labeling would provide Americans the opportunity to exercise personal responsibility and make informed choices 

about their diets. Studies show that providing nutrition information at restaurants 

can help people make healthier choices [(13)

, Journal of Consumer Research 2009 

36(3): 494–503]’ (14) This cited study actually reported that parents will choose 

slightly lower calorie food options for their children in a restaurant setting; however, 

the parents did not choose lower calorie foods for themselves (13). This is an example of an unbalanced citation. Rather than stating ‘Studies show that providing nutrition information at restaurants can help people make healthier choices,’ an accurate statement from that study would have been ‘A single study (but not all studies) 

showed that providing nutrition information at restaurants can help people make 

healthier choices when they choose food for other people, but not when they choose 

food for themselves.’ 

What Can We Do? 

Faithful reporting, acknowledging study limitations and evaluating bodies of evidence without selectively excluding information on the basis of its desirability are 

a few examples of how paediatricians can become committed to scientific truthfulness. They are also encouraged to be sensitive to the possibility of WHB. 

Publication bias and exclusion of pertinent data for no apparent reason are examples of WHB and according to Atkinson and Macdonald (5) ‘all scientists should 

strive to have the ‘spin’ stop with them’. ‘Science itself is the antidote to the poison 

of bias in research’.(15) We need to encourage, and perhaps require the publication 

of reports that minimize publication bias, and to require that trials and their protocols be registered to enable identification of omissions or distortions of any key procedures, which would affect interpretation of results. These and other ways of shoring up the integrity of the scientific process are not easy steps, will not solve all 

problems, will create some new challenges and cannot be implemented overnight. 

Yet we should not make the perfect the enemy of the good and should make the 

development and implementation of such procedures a priority. 

Conclusion 

To reduce the influence of bias in research and in general media reporting of scientific findings, there will need to be a concerted effort at all levels from scientistauthors to editors and journalists. We do not yet know how much of WHB is inadVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00395 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
390 

[1]Department of Pharmacology and Toxicology, School of Medicine, University of Alabama at 

Birmingham, Birmingham, AL, USA. [2]Section of Statistical Genetics, Department of Biostatistics, School of Public Health, and 

Clinical Nutrition Research Center, University of Alabama at Birmingham, Birmingham, AL, 

USA. 

Correspondence: Professor D.B. Allison, Section of Statistical Genetics, Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Ryals Public Health 

Building, 1530 3rd Avenue S, RPHB 327, Birmingham, AL 35294–0022, USA. E-mail: 

Dallison@uab.edu. 

vertent and how much it results from an anti-industry sentiment, feelings of righteous indignation, a passionate interest in justifying public health actions, or yet 

other factors. Yet regardless of the root of the WHB, medical professionals, reporters, government policy makers and the public should be aware of such biases and 

view scientific literature with a critical eye. 

Drs. Allison and Cope have received grants, book royalties, honoraria, donations 

and consulting fees from numerous food, beverage, dietary supplement, pharmaceutical companies, litigators and other commercial, government and nonprofit entities with interests in obesity and nutrition, including interests in breastfeeding and 

SSBs. Dr. Cope recently accepted a position with Solae LLC (St Louis, MO, USA). 

References 

1. Friedman L.S., Richter E.D. Relationship between conflicts of interest and research results. J. GEN. INTERN. MED. 2004; 19: 51– 

6. 

2. Lesser L.I., Ebbeling C.B., Goozner M., Wypij D., Ludwig D.S. Relationship between funding source and conclusion among nutrition-related scientific articles. PLOS MED. 2007; 4: e5. 

3. Kjaergard L.L., Als-Nielsen B. Association between competing interests and authors’ conclusions: epidemiological study of 

randomised clinical trials published in the BMJ. BMJ 2002; 325: 249. 

4. Cope M.B., Allison D.B. White hat bias: examples of its presence in obesity research and a call for renewed commitment to faithfulness in research reporting. INT. J. OBES. (Lond) 2010; 34: 84–8; discussion 3. 

5. Atkinson R.L., Macdonald I. White hat bias: the need for authors to have the spin stop with them. INT. J. OBES. (Lond) 2010; 34: 

83. 

6. Horta B., Bahl R., Martines J., Victora C. Evidence of the Long-Term Effects of Breastfeeding: Systematic Reviews and MetaAnalysis. WHO, Geneva, Switzerland, 2007. 

7. Riechelmann R.P., Wang L., O’Carroll A., Krzyzanowska M.K. Disclosure of conflicts of interest by authors of clinical trials and 

editorials in oncology. J. CLIN. ONCOL. 2007; 25: 4642–7. 

8. Friedman C.P., Wyatt J.C. Publication bias in medical informatics. J. AM. MED. INFORM. ASSOC. 2001; 8: 189–91. 

9. Rosenthal R.H. The ‘‘File Drawer Problem’’ and Tolerance for Null Results. PSYCHOL. BULL. 1979; 86: 638–41. 

10. Boutron I., Dutton S., Ravaud P., Altman D.G. Reporting and interpretation of randomized controlled trials with statistically 

nonsignificant results for primary outcomes. JAMA 2010; 303: 2058–64. 

11. Chan A.W., Hrobjartsson A., Haahr M.T., Gotzsche P.C., Altman D.G. Empirical evidence for selective reporting of outcomes in 

randomized trials: comparison of protocols to published articles. JAMA 2004; 291: 2457–65. 

12. Registry F. FDA Docket. 2010; 75: 39026–8. 

13. Tandon P.S., Wright J., Zhou C., Rogers C.B., Christakis D.A. Nutrition menu labeling may lead to lower-calorie restaurant 

meal choices for children. PEDIATRICS 2010; 125: 244–8. 

14. Howlett E.A., Burton S., Bates K. and Huggins K. Coming to a Restaurant Near You? Potential Consumer Responses to Nutrition Information Disclosure on Menus. JOURNAL OF CONSUMER RESEARCH 2009; 36: 494–503. 

15. Allison D.B. The antidote to bias in research. SCIENCE 2009; 326: 522–3. 

ATTACHMENT 16 

White Hat Bias: Examples of Its Presence in Obesity Research and a Call for 

Renewed Commitment to Faithfulness in Research Reporting 

Commentary 

International Journal of Obesity (2010) 34, 84–88; doi:10.1038/ijo.2009.239; published online 1 December 2009 

M.B. Cope [1] and D.B. Allison [2]

‘White hat bias’ (WHB) (bias leading to distortion of information in the 

service of what may be perceived to be righteous ends) is documented 

through quantitative data and anecdotal evidence from the research record 

regarding the postulated predisposing and protective effects of nutritively 

sweetened beverages and breastfeeding, respectively, on obesity. Evidence 

of an apparent WHB is found in a degree sufficient to mislead readers. 

WHB bias may be conjectured to be fuelled by feelings of righteous zeal, 

indignation toward certain aspects of industry or other factors. Readers 

should beware of WHB, and our field should seek methods to minimize it. 

Introduction 

Scientific dialogue is dependent on fair and open presentation of data and evidence, yet concerns have been raised in recent years about bias in research practice. 

We present data and examples pertinent to a particular bias, a ‘white hat bias’ 

(WHB), which we define to be bias leading to distortion of research-based information in the service of what may be perceived as righteous ends. We evaluate WHB 

in the context of two illustrative obesity topics, nutritively sweetened beverage 

391 

(NSB) consumption as a postulated risk factor 1and breastfeeding as a postulated 

protective factor.2

Example 1—Data on citation bias 

If secondary reportings of original research misleadingly cite papers with statements that inaccurately describe available evidence, then inaccurate beliefs may inappropriately influence clinical practice, public policy or future research. Previously,3 we observed that two papers 4–5 had both statistically and non-statistically 

significant results on body weight, body mass index (BMI) or overweight/obesity status, which allowed future writers to potentially choose which results to cite, and 

were also widely cited, permitting a quantitative analysis of citations. 

Cited Versus Citing Papers 

A Web of Science search (through to October 2008) yielded 195 and 45 papers citing James, et al.,4 and Ebbeling, et al.,5 respectively. We analyzed those in English 

(165 and 41, respectively). 

James, et al.,4 studied an intervention to decrease NSB consumption and adiposity among children. Dichotomized (overweight or obese versus neither overweight 

nor obese) and continuous (change in BMI) data were analyzed for statistical significance. The authors wrote: 

‘After 12 months there was no significant change in the difference in body 

mass index (mean difference 0.13, ¥0.08–0.34) or z score (0.04, ¥0.04–0.12). At 

12 months the mean percentage of overweight and obese children increased in 

the control clusters by 7.5%, compared with a decrease in the intervention 

group of 0.2% (mean difference 7.7%, 2.2–13.1%).’ 

Ebbeling, et al.,5 described a randomized controlled trial of a 25 week NSB reduction program in adolescents and wrote: 

‘The net difference (in BMI), 0.14Ò0.21 kg/m2, was not significant overall.’ 

They then report a subgroup finding: 

‘Among the subjects in the upper baseline—BMI tertile, BMI change differed 

significantly between the intervention . . . and control . . . groups, a net effect 

of 0.75Ò0.34 kg/m2.’ 

Ebbeling, et al. (p. 676) label the analysis in the total sample as the ‘primary 

analysis.’ 

Table 1 Categorization of 165 Papers Citing James, et al.2

Score A B C D E F G H 

No. of references in each category 14 74 2 21 2 1 1 50 

Proportion (exact CIs) a 0.127 0.644 0.017 0.183 0.017 0.009 0.009 

(0.071–0.199) (0.548–0.729) (0.003–0.068) (0.119–0.268) (0.003–0.068) (0.001–0.055) (0.001–0.055) 

Abbreviations: BMI, body mass index; CI, confidence interval. 

aProportions and CIs are calculated with only categories A through to G in the denominator. Scoring key: (A) Accurate—described the non-significant result on continuous outcome (change in BMI) and described the significant 

result on the dichotomous outcome (overweight versus non-overweight). (B) Mildly misleading (positively)—Described the result of the intervention study as showing efficacy, benefit or statistical significance for the dichotomous 

outcome of overweight status, without mentioning the non-significant result on the continuous outcome. (C) Moderately misleading (positively)—Described the result of the intervention study as showing efficacy, benefit or statistical significance on some weight-related outcome without explicitly stating that it was on the proportion overweight 

per se. (D) Explicitly misleading (positively)—Described, with a factually incorrect statement, that the result of the 

intervention for a continuous weight-related outcome was significant or showed effectiveness. (E) Mildly misleading 

(negatively)—Described the result of the intervention study as not showing efficacy, benefit or statistical significance on the continuous measure of BMI, without mentioning the significant result on the dichotomous outcome. (F) 

Moderately misleading (negatively)—Described the result of the intervention study as not showing efficacy, benefit 

or statistical significance on some weight-related outcome without explicitly stating that it was on the continuous 

measure of BMI. (G) Explicitly misleading (negatively)—Described, with a factually incorrect statement, that the result for the dichotomous outcome was not significant or that a lack of effectiveness was shown for the dichotomous 

outcome. (H) Unscorable—Did not make explicit statements about the effects of the study, made statements that 

were too ambiguous to code or made statements that were self-contradictory. 

Data Coding and Analysis 

Each paper citing either James, et al.,4 or Ebbeling, et al.,5 was categorized (see 

Tables 1 and 2) on the basis of how authors cited results related to body weight, 

BMI or overweight/obesity outcomes from these two papers in their report. Papers 

citing James, et al., were independently coded by the authors of this paper (DBA 

or MBC). Any discrepancies were resolved by discussion. Papers citing Ebbeling, et 

al., were scored by DBA and cross-checked by MBC. Proportions (with confidence 

intervals) were calculated (Tables 1 and 2). Exact binomial calculation tested the 

null hypothesis that the proportion citing papers in a misleading manner that exaggerated the strength of evidence was equal to the proportion citing papers in a misVerDate Aug 31 2005 09:46 May 17, 2017 Jkt 041481 PO 00000 Frm 00397 Fmt 6621 Sfmt 6621 P:\DOCS\115-02\24325.TXT BRIAN
392 

leading manner that diminished the strength of evidence; as such an equal proportion would suggest a lack of bias in the overall literature, even if not in any one 

paper. 

Citation Analysis Results 

Results were quite consistent across papers citing either James, et al.,4 or 

Ebbeling, et al.,5 The majority, 84.3% for James, et al.,4 and 66.7% for Ebbeling, et 

al.,5 described results in a misleadingly positive manner to varying degrees (that is, 

exaggerating the strength of the evidence that NSB reduction showed beneficial effects on obesity outcomes). Some were blatantly factually incorrect in their misleading statements, describing the result as showing an effect for a continuous obesity outcome, when no statistically significant effect for continuous obesity outcomes 

was observed. In contrast, only four papers (3.5%) were negatively misleading (that 

is, underplayed the strength of evidence) for James, et al.,4 and none were negatively misleading for Ebbeling, et al.5 Only 12.7 and 33% of papers accurately described complete overall findings related to obesity outcomes from James, et al.,4

and Ebbeling, et al.,5 respectively. 

To test whether the proportion of misleading reporting in the positive direction 

was equal to the proportion in the negative direction, we calculated the confidence 

interval on the proportion of misleading reportings in either direction that was positively misleading. This yields a proportion of 0.96 (95% CI: 0.903–0.985) for those 

citing James, et al.,4 and 1.00 (95% CI: .832–1.000) for those citing Ebbeling, et al.,5

and is significantly different from c for each (P<0.0001), indicating a clear bias and 

potential for readers of the secondary literature to be deceived. 

Example 2—Data on publication bias 

NSB Consumption 

A meta-analysis on NSB consumption and obesity 6 found that estimated adverse 

associations were significantly smaller (that is, less adverse) among industry-funded 

than among non-industry-funded studies. One troubling conceivable explanation for 

this is that industry does something to bias results to make NSBs seem less harmful, but this is not the only conceivable explanation. 

To examine this further, we requested, and Dr. Vartanian 6 graciously provided, 

his meta-analysis data file. Focusing on cross-sectional studies, because a large 

number had adiposity indicators as outcomes, we conducted publication bias (PB) 

detection analyses.7 PB causes the sample of studies published to not constitute a 

representative sample of the relevant studies that hypothetically could have been 

published. With PB, the probability of a study being published depends on its outcome. Typically, PB involves statistically significant studies having a higher likelihood of being published than non-statistically significant ones. Our analysis (Figure 

1) shows a clear inverse association between study precision and association magnitude. This PB hallmark suggests that studies with statistically significant NSB 

findings are more likely to be published than are nonstatistically significant ones. 

Interestingly, this bias seems to be present only for non-industry-funded research, 

suggesting that non-industry-funded scientists tend not to publish their non-significant associations in this area. Contrarily, all industry-funded studies seem to exceed 

a minimal level of precision. Thus, much of the reason for the smaller associations 

detected by Vartanian, et al.,6 for industry-funded research seems to be because of 

PB in non-industry-funded research. However, even after accounting for precision, 

the mean difference between the association magnitudes of industry and non-industry-funded studies is reduced by 33%, but not eliminated, suggesting that there may 

be competing biases operating in industry-funded research. 

Table 2 Categorization of 41 Papers Citing Ebbeling, et al.3

Score A B C D E F G 

No. of references in each category 10 9 11 0 0 7 4 

Proportion (exact CIs) a 0.333 0.300 0.367 0.000 0.000 

(0.173–0.528) (0.147–0.494) (0.199–0.561) (0.000–0.116) (0.000–0.116) 

Abbreviations: BMI, body mass index; CI, confidence interval. 

aProportions and CIs are calculated with only categories A through to E in the denominator. Scoring key: (A) Accurate—Described both the non-significant result in the total sample and the significant result in the heaviest subgroup. (B) Patently misleading overpositive—Described as positive on weight without mentioning anything about 

the result only being in heaviest children. (C) Mildly misleading overpositive—Described as positive among the 

heaviest children without explicitly mentioning that there was no significant result in the total sample. (D) Mildly 

misleading over-negative—Described the null result in the total sample without explicitly mentioning the significant result in the heaviest subgroup. (E) Patently misleading over-negative—Described as negative in a way that explicitly indicated that there were no significant effects even in sub-groups. (F) Not directly relevant—Did not make 

clear and explicit statements about the effects of the study. (G) Ambiguous as to whether category A or E applies. 

393 

Figure 1 

Plot of sample effect sizes from cross-sectional studies of the association 

between sugar-sweetened beverage consumption and obesity indexes indicating publication bias among non-industry-funded studies (Blue diamonds—industry funded; Red diamonds—non-industry funded). 

Breastfeeding 

The World Health Organization (WHO; 8) published a meta-analysis on whether 

breastfeeding protects against obesity and also found evidence of PB. Figure 2 indicates this strikingly. We retrieved all papers from which data were obtained for Figure 2 to evaluate the impact of industry funding on this PB. None of the papers 

reported any industry funding or were obviously authored by authors employed by 

the infant formula industry. Thus, as with the NSB literature, there seems to be 

a strong PB that is not apparently fueled by industry interests. 

Figure 2 

Plot of the relationship between association magnitude and study precision indicating publication bias in studies of breastfeeding and obesity 

(from Horta, et al.8). 

Example 3—Anecdotal Examples of Miscommunications in Press Releases 

Evidence suggests that ‘Press releases from academic medical centers often promote research that has uncertain relevance to human health and do not provide key 

facts or acknowledge important limitations’.9 This is also occurring in the obesity 

field. For example, the paper by Ebbeling, et al.,5 states, ‘change in body mass index 

(BMI) was the primary end point. The net difference, 0.14Ò0.21 kg/m2, was not significant overall,’ and then reports the subgroup finding, ‘Among the subjects in the 

upper baseline-BMI tertile, BMI change differed significantly between the intervention . . . and control . . . groups.’ Contrast this modest finding in a sample subset 

and the circumspect presentation in the original paper with the presentation in the 

press release issued by the authors’ institution (http://www.childrenshospital.org/ 

newsroom/Site1339/mainpageS1339P1sublevel192.html (accessed on 31 October 

2008)), which states ‘In randomized trial, a simple beverage-focused intervention led 

to weight loss’ and never states that the primary analysis was not statistically significant. 

When the paper by James, et al.,4 was released, the press release issued on the 

BMJ website (http://www.bmj.com/content/vol328/issue7446/press_release.shtml 

(accessed on 20 September 2009)) stated ‘Discouraging children from drinking fizzy 

drinks can prevent excessive weight gain, according to new research available on 

bmj.com,’ despite the facts that no analysis of weight change per se was reported 


11502082.eps 11502083.eps
394 

and that there was no significant effect on BMI change. Neither of these facts was 

mentioned in the press release. 

Finally, in 2009, describing an observational epidemiological study, UCLA issued 

a press release (http://www.healthpolicy.ucla.edu/NewsReleaseDetails.aspx?id%35 

(accessed on 20 September 2009)) stating ‘. . . research released today provides the 

first scientific evidence of the potent role soda and other sugar-sweetened beverages 

play in fueling California’s expanding girth’ One of the study authors was quoted 

in a subsequent news story stating ‘For the first time, we have strong scientific evidence that soda is one of the—if not the largest—contributors to the obesity epidemic’ (http://www.drcutler.com/poor-diet/study-soda-making-californians-fat19373657/ (accessed on 25 September 2009)). These statements are inaccurate and 

also unfair to all authors of observational studies who published such research years 

before. The press release further stated ‘The science is clear and conclusive [emphasis added],’ despite the fact that this was a correlational research, and offered no 

statement to the reader to interpret the results as indicative of correlation and not 

necessarily causation. 

Example 4—Inappropriate Or Questionable Inclusion of Information 

Research may also be misleadingly presented by inclusion of incorrect or questionable material in reviews. In our critical review of the WHO report on breastfeeding, 

we noted several examples (see, Cope and Allison,2 p. 597) in which an inspection 

of the original papers reviewed revealed that the authors of the WHO report selectively included some values from certain primary papers that led to stronger associations of breastfeeding with reduced obesity risk and excluded less impressive values from the same papers without explanation. 

Similarly, Mattes, et al.,3 noted that several reviews of NSB consumption and obesity inappropriately included a study 10 that was actually neither a test of nutritive 

sweetener-containing solid food versus beverage nor of NSB consumption versus nonNSB consumption. Sweeteners were presented in both solid and beverage food 

forms. The original authors 10 wrote, ‘. . . subjects who were given supplemental 

drinks and foods [emphasis added] containing sucrose for 10 wk experienced increases in . . . body weight’, and thus the study should never have been considered 

as evaluative of NSB effects. Mattes, et al.,3 provide other examples of papers being 

inappropriately included in past reviews of NSB consumption and obesity. 

Conclusion 

Finding effective methods to reduce obesity is an important goal, and appropriate 

evaluations of the strength of the evidence supporting the procedures under consideration are vital. Sound evaluations critically depend on evidence being presented 

in non-misleading ways. Alarms have been sounded about dramatic rises in obesity 

levels, not without justification. And yet, these alarms may also have aroused passions. Certain postulated causes have come to be demonized (for example, fast food, 

NSBs, formula feeding of infants) and certain postulated palliatives (for example, 

consumption of fruits and vegetables, building of sidewalks and walking trails) seem 

to have been sanctified. Such demonization and sanctification may come at a cost. 

Such casting may ignite feelings of righteous zeal. 

Some authors compare NSBs, fast foods and other food and restaurant industry 

offerings to the tobacco industry (for example, see Browne 11 and Warner 11), suggesting, for example, comparisons between ‘Joe Camel’ and ‘Ronald McDonald’ 

(http://www.time.com/time/magazine/article/0,9171,1187241,00.html). To the extent that such comparisons inform us about important causes of obesity and how 

to reduce them, this is all to the good. But to the extent that such comparisons and 

other appeals to passions inflame rather than inform, they may cloud judgment and 

decrease inhibitions against breaching ordinary rules of conduct. Historians indicate 

that during times of war, propagandists demonize (that is, dehumanize) the enemy 

to inflame spirits and this facilitates some breaches of codes of conduct such as massacres.12 Although inflaming the passions of scientists interested in public health 

is unlikely to provoke bloodshed, we scientists have, as a discipline, our own code 

of conduct. Central to it is a commitment to faithful reporting, to acknowledging our 

study limitations, to evaluating bodies of evidence without selectively excluding information on the basis of its desirability—in short, a commitment to truthfulness. 

The demonization of some aspects and sanctification of others, although perhaps 

helpful in spurring social action, may be more harmful to us in the long run by giving unconscious permission to breach that code, thereby eroding the foundation of 

scientific discipline. 

Evidence presented herein suggests that at least one aspect has been demonized 

(NSB consumption) and another sanctified (breastfeeding), leading to bias in the 

presentation of research literature to other scientists and to the public at large, a 

395 

* There was no response from the witness by the time this hearing was published. 

bias sufficient to misguide readers. Interestingly, although many papers point out 

what seem to be biases resulting from industry funding, we have identified here, 

perhaps for the first time, clear evidence that WHBs can also exist in opposition to 

industry interests. 

Whether WHB is intentional or unintentional, and whether it stems from a bias 

toward anti-industry results, significant findings, feelings of righteous indignation, 

results that may justify public health actions, or yet other factors, is unclear. Future 

research should study approaches to minimize such distortions in the research 

record. We suggest that authors be more attentive to reporting primary results from 

earlier studies rather than selectively including only a part of the results, to avoiding PB, as well as to ensuring that their institutional press releases are commensurate with the studies described. Journal editors and peer reviewers should also be 

vigilant and seek to minimize WHB. Clinicians, media, public health policy makers 

and the public should also be cognizant of such biases and view the literature on 

NSBs, breastfeeding and other obesity-related topics more critically. 

Conflict of Interest 

Drs. Allison and Cope have received grants, honoraria, donations and consulting fees from numerous food, beverage, dietary supplement, pharmaceutical companies, litigators and other commercial, government and nonprofit entities with interests in obesity

and nutrition, including interests in breastfeeding and NSBs. Dr Cope has recently accepted a position with The Solae Company 

(St. Louis, MO, USA). 

Acknowledgements 

We gratefully acknowledge Dr. Alfred A. Bartolucci for his comments on our data analysis and Dr. Lenny Vartanian for sharing 

his data file. Supported in part by the NIH grant P30DK056336. The opinions expressed are those of the authors and not necessarily those of the NIH or any other organization with which the authors are affiliated. 

References 

1. Allison D.B., Mattes R.D. Nutritively sweetened beverage consumption and obesity: the need for solid evidence on a fluid issue. 

JAMA 2009; 301: 318–320. 

2. Cope M.B., Allison D.B. Critical review of the World Health Organization’s (WHO) 2007 report on ‘evidence of the long-term effects of breastfeeding: systematic reviews and meta-analysis’ with respect to obesity. OBES. REV. 2008; 9: 594–605. 

3. Mattes R.D., Shikany J.M., Allison B.D. What is the demonstrated value of moderating nutritively sweetened beverage consumption in reducing weight gain or promoting weight loss? An evidence-based review and meta-analysis of randomized studies. (Submitted for publication). 

4. James J., Thomas J.T., Cavan D., Kerr D. Preventing childhood obesity by reducing consumption of carbonated drinks: cluster 

randomised controlled trial. BMJ 2004; 328: 123743. 

5. Ebbeling C.B., Feldman H.A., Osganian S.K., Chomitz V.R., Ellenbogen S.J., Ludwig D.S. Effects of decreasing sugar-sweetened 

beverage consumption on body weight in adolescents: a randomized, controlled pilot study. PEDIATRICS 2006; 117: 673–680. 

6. Vartanian L.R., Schwartz M.B., Brownell K.D. Effects of soft drink consumption on nutrition and health: a systematic review 

and meta-analysis. AM. J. PUBLIC HEALTH 2007; 97: 667–675. 

7. Sterne J.A.C., Egger M. Regression methods to detect publication and other bias in meta-analysis. In: Rothstein H.R., Sutton 

A.J., Borenstein M. (eds). Publication Bias in Meta-Analysis. John Wiley & Sons Ltd: West Sussex, UK, 2005. 

8. Horta B., Bahl R., Martines J., Victora C. Evidence of the Long-Term Effects of Breastfeeding: Systematic Reviews and MetaAnalysis. World Health Organization Publication: Geneva, Switzerland, 2007. 

9. Woloshin S., Schwartz L.M., Casella S.L., Kennedy A.T., Larson R.J. Press releases by academic medical centers: not so academic? ANN. INTERN. MED. 2009; 150: 613–618. 

10. Raben A., Vasilaras T.H., Moller A.C., Astrup A. Sucrose compared with artificial sweeteners: different effects on ad libitum 

food intake and body weight after 10 wk of supplementation in overweight subjects. AM. J. CLIN. NUTR. 2002; 76: 721–729. 

11. Brownell K.D., Warner K.E. The perils of ignoring history: big tobacco played dirty and millions died. How similar is Big 

Food? MILBANK Q. 2009; 87: 259–294. 

12. Levene M., Roberts P. (eds). The Massacre in History (Studies on War and Genocide). Berghahn Books: Oxford, UK, 1999. 

SUBMITTED QUESTION

Response from Diane Whitmore Schanzenbach, Ph.D., Director and Senior 

Fellow, Economic Studies, Brookings Institution; Professor of Social 

Policy, and of Economics, The Hamilton Project, Northwestern University * 

Question Submitted by Hon. David Scott, a Representative in Congress from Georgia 

Question. I have a question for you about the potential effects of adopting WIC 

restrictions as SNAP restrictions. As you know, WIC is meant to ensure low-income 

women have access to foods that meet the nutrient needs of pregnancy, infancy and 

developing young children. The purpose of SNAP, however, is meant to ensure that 

our low-income people are not hungry. WIC food packages are extremely restrictive, 

even when it comes to healthy foods, and vary widely by state. For example, currently only $10 is allowed per month on fruits and vegetables. In some states, frozen 

and canned fruits and vegetables are not allowed to be purchased with WIC benefits. Many states don’t even offer yogurt, and when they do, they offer only the large 

size, certain brands and certain flavors. Many of us, no matter the income level, 

have dealt with a picky eater in our family, and we’ve had to find little solutions 

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to getting them to eat healthy foods. Maybe they hate strawberry yogurt, but they’ll 

eat blueberry! Maybe they won’t eat raw broccoli, but they’ll eat steamed frozen 

broccoli with a little cheese on top. It begs the question, are we trying to make it 

harder or easier for Americans to feed our families? 

Some states are considering asking for a waiver from USDA to restrict SNAP purchases to the preexisting and restrictive list of foods under WIC. Could you please 

describe what implementation would look like, health outcomes and any unintended 

negative consequences of states restricting SNAP benefits to those foods offered 

through the WIC program? Would it further the program’s goal of reducing hunger? 

Answer. 

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