Christian Gregory

Michelle Ver Ploeg

Margaret Andrews

Alisha Coleman-Jensen

Supplemental Nutrition Assistance 

Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality

Economic

Research

Service

Economic 

Research

Report 

Number 147

April 2013

United States Department of Agriculture
Economic Research Service 

www.ers.usda.gov

Recommended citation format for this publication:

Gregory, Christian, Michelle Ver Ploeg, Margaret Andrews, Alisha ColemanJensen. Supplemental Nutrition Assistance Program (SNAP) Participation 

Leads to Modest Changes in Diet Quality, ERR-147, U.S. Department of 

Agriculture, Economic Research Service, April 2013.

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United States Department of Agriculture
Economic Research Report Number 147 l A Report from the Economic Research Service l April 2013

Abstract

Recent research has shown that the Supplemental Nutrition Assistance Program (SNAP) 

effectively reduces food insecurity. Questions remain, however, about the extent to which 

SNAP affects the quality of adult participants’ diets. These questions have surfaced in 

the context of the increasing public costs of diet-related illnesses, such as diabetes, high 

blood cholesterol, and heart disease, and have led to discussions about restricting the use 

of SNAP benefits to purchase some food items. This report examines Healthy Eating 

Index (HEI) scores for adults in low-income households that do and do not participate in 

SNAP. To disentangle the choice of whether to participate in SNAP from diet choices, 

this model uses a unique data set that matches State-level SNAP policy variables to 

individual-level data from four waves of the National Health and Nutrition Examination 

Survey (NHANES). Two important kinds of results emerge: the effect of SNAP on the 

diet quality of those who choose to enroll, and a total comparison of SNAP participants 

and nonparticipants after SNAP’s effects are taken into account. On the first, this report 

shows that SNAP participation results in a large increase in the likelihood of consuming 

whole fruit and a slightly lower consumption of dark green/orange vegetables. On the 

second, the report finds that SNAP participants have slightly lower HEI scores (both total 

and components) than nonparticipants, meaning that they have slightly lower diet quality. 

They do, however, consume less saturated fat and sodium than nonparticipants.

Keywords: Supplemental Nutrition Assistance Program, food stamp, Healthy Eating 

Index, food insecurity, diet quality, fruit and vegetable consumption, National Health 

and Nutrition Examination Survey, ordinary least squares model, treatment effects 

model, average treatment effect

Acknowledgments

The authors would like to thank Vicki L. Burt and staff at the National Center for 

Health Statistics for providing data and service for this report; USDA’s Economic 

Research Service employees Jay Variyam, Dave Smallwood, Ephraim Leibtag, 

Susmita Pendurthi, and Wynnice Pointer-Napper for their input; and three anonymous reviewers for their comments.

Christian Gregory, cgregory@ers.usda.gov

Michelle Ver Ploeg, Margaret Andrews, 

Alisha Coleman-Jensen

Supplemental Nutrition Assistance 

Program (SNAP) Participation Leads to 

Modest Changes in Diet Quality
ii Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

Contents

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Background and Previous Research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

National Health and Nutrition Examination Survey (NHANES). . . . . . 7

Exogenous Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Sample Means for Supplemental Nutrition Assistance Program

 (SNAP) Participants and Nonparticipants. . . . . . . . . . . . . . . . . . . . . 11

Healthy Eating Index (HEI) Scores by SNAP Participation . . . . . . . . 11

Ordinary Least Squares (OLS) results. . . . . . . . . . . . . . . . . . . . . . . . . 11

Results From Model Accounting for Selection Into SNAP. . . . . . . . . . 15

Determinants of SNAP Participation. . . . . . . . . . . . . . . . . . . . . . . . . . 15

Effect of SNAP on Diet Quality of Participants. . . . . . . . . . . . . . . . . . 15

Expected Differences in Diet Quality Between SNAP 

Participants and Nonparticipants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Robustness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Appendix Table A.1: Parameter Estimates of Outcome Equation. . . 29
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Summary

What Is the Issue?

The Supplemental Nutrition Assistance Program (SNAP, formerly the Food Stamp 

Program) is the largest food assistance program administered by the Food and 

Nutrition Service of the U.S. Department of Agriculture (USDA). Approximately 

47 million people participated in SNAP in 2012, at a cost of almost $75 billion. 

While much of the policy interest in SNAP involves its effectiveness in combating 

food insecurity—defined as uncertain access to enough healthy, nutritious food 

for an active life—another goal of the program is to support low-income families 

in making food choices consistent with dietary guidance. This goal of SNAP has 

recently received public attention, illustrated by suggestions that SNAP participants 

should be prevented from using benefits to purchase certain foods that are perceived 

to contribute to poor dietary health—sugar-sweetened beverages, for example. 

Social scientists have been interested in SNAP’s possible dietary effects since the 

program’s inception, but much of the existing research is inconclusive and limited; 

namely, it cannot address the unobserved characteristics (for example, preferences 

for nutritious food) that could affect SNAP participation and diet quality simultaneously. This report examines Healthy Eating Index (HEI) scores for adults in 

low-income households that do and do not participate in SNAP, taking factors 

into account that could influence both SNAP participation and diet quality. HEI 

measures survey respondents’ adherence to dietary guidance (as detailed in the 

2005 Dietary Guidelines for Americans) and is USDA’s primary tool for monitoring 

the diet quality of the U.S. population. 

What Did the Study Find?

The evidence as to whether SNAP participation is beneficial or adverse regarding diet 

quality is inconclusive. Nevertheless, two sets of observations emerge from the study. 

First, the study shows the effects of SNAP participation on those who choose to 

participate. SNAP increases the likelihood that participants will consume whole 

fruit by 23 percentage points; it also induces participants to decrease their intake of 

dark green/orange vegetables by a modest amount—the equivalent of about 1 ounce 

for a 2,000-calorie diet. 

These effects could be the result of both time constraints associated with SNAP’s 

work requirements and extra income—people participating in SNAP may see whole 

fruit as more affordable with a little extra income, and they may consume more of it 

because it requires no preparation time. At the same time, dark green/orange vegetables could be less attractive to SNAP participants because these foods may require 

more preparation time. Moreover, this could also be due to the substitution of one 

convenient snack food for another—apples or bananas for baby carrots, for example. 

The study also revealed a difference in diet quality between SNAP participants 

and low-income nonparticipants once the effects of SNAP and other unobserved 

characteristics are taken into account. For most components of diet (e.g., fruits, 
iv Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

vegetables, whole grains) measured by HEI scores, SNAP participants are at a small, 

statistically significant disadvantage in terms of diet quality relative to comparable 

nonparticipants. SNAP participants’ total HEI score was about 1.25 points (about 

2.5 percent) lower than that for similar nonparticipants. In terms of dietary components, this difference amounts roughly to a half a cup of fruit, two-thirds of a cup 

of vegetables, or 1-1/3 ounces of whole grain products. At the same time, SNAP 

participants do better on some aspects of diet; for example, they eat less sodium and 

saturated fat than nonparticipants. 

How Was the Study Conducted?

The study used a unique data set that took HEI scores based on responses from 

four waves of the National Health and Nutrition Examination Survey (NHANES) 

and matched them to State-level policies (eligibility/poverty thresholds and vehicle 

exemption allowances) that affect SNAP participation. These State-level variables 

identify the effect of SNAP participation on HEI total and component scores. The 

study used a model that accounts for unobserved, individual-level differences in diet 

quality that affect the likelihood of SNAP participation. 
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Introduction

The Supplemental Nutrition Assistance Program (SNAP, formerly the Food 

Stamp Program) is the largest food assistance program administered by the Food 

and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA). 

Approximately 47 million persons participated in SNAP in 2012, at a cost of almost 

$75 billion. One goal of SNAP is to minimize food insecurity—uncertain access 

to enough healthy, nutritious food for an active life—by providing low-income 

households with the resources to purchase food. Recent research shows that SNAP 

is effective at accomplishing this aim (DePolt et al., 2009; Mykerezi and Mills, 

2010; Nord and Golla, 2009; Nord and Prell, 2011; Yen et al., 2008). Indeed, some 

estimates indicate that SNAP reduces food insecurity by between 33 and 40 percent 

(Ratcliffe et al., 2011; Shaefer and Gutierrez, 2012) at any given time. 

Another goal of SNAP is to improve the quality of low-income families’ diets. 

This aspect of the SNAP program has received public attention recently due to 

heightened awareness of the high prevalence of obesity; diabetes; high blood 

cholesterol; and other chronic, diet-related illnesses for which the public bears a 

sizeable cost. Suggestions that SNAP participants be prevented from using benefits 

to purchase certain foods perceived to contribute to poor dietary health—sugarsweetened beverages, for example—highlight these concerns. Additionally, the 

FNS, aware that food assistance programs need to help address the high prevalence of obesity, is piloting a program that offers financial incentives for the 

purchase of healthy foods—the Healthy Incentives Pilot (HIP).1 The Wholesome 

Wave Double Value Coupon Program, a privately funded program that offers 

SNAP participants incentives to buy from local farmers’ markets, also seeks to 

improve diet quality through SNAP participation.2

Although public attention has only recently focused on the quality of SNAP participants’ diets, this question has been the subject of much social science research over 

the last 30 years (Fox et al., 2004). Much of the literature has found no change in 

diet quality associated with SNAP (or Food Stamp Program) participation.3 A few 

studies have found that the program is associated with improved nutritional intakes 

(Devaney and Moffitt, 1991; Wilde et al., 1999), and a few have found association 

with poorer intakes (Butler and Raymond, 1996; Yen, 2010). 

The policy question is increasingly relevant, however. Recent research has consistently shown that SNAP participation reduces food insecurity. Changes to the basket 

of foods eligible for SNAP purchase— excluding sugar-sweetened beverages, for 

example—could change the mix of households who select into the program and alter 

its effectiveness at reducing food insecurity. At the same time, there is a legitimate 

question to be asked about whether SNAP does all it can to improve nutritional 

quality and access to calories. For example, the Special Supplemental Nutrition 

Program for Women, Infants, and Children (WIC), which offers vouchers for a 

1Information on the Healthy Incentives Pilot design, as well as plans for evaluation, is at 

http://www.fns.usda.gov/snap/hip/info.htm.

2Information on the Wholesome Wave Double Coupon Program is at http://wholesomewave.org/dvcp/.

3Throughout, we refer to the Supplemental Nutrition Assistance Program when discussing earlier 

studies, although almost all previous research refers to the Food Stamp Program.
2 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

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narrow range of food products, has effected modest improvements in diet outcomes 

for children (Yen, 2010).

Getting an unbiased estimate of the effect of SNAP on diet quality has been difficult. It is reasonable to think that households that choose to participate in SNAP 

are systematically different from similar low-income households that do not. 

Researchers, however, do not observe these differences, and they may be correlated with diet quality. For example, households that participate in SNAP might 

value food and nutrition more than similar households that do not participate in 

the program. In this case, conventional methods for estimating the effect of SNAP 

on diet quality will overestimate the effects of SNAP because SNAP participants’ 

diets are likely already better than similar nonparticipants’ diets anyway. On the 

other hand, households that participate in SNAP may have lower quality diets due 

to unobserved preferences that are correlated with SNAP participation. In this situation, conventional methods would underestimate the effect of SNAP on diet quality. 

This study uses new data to address this problem directly. It examines the effect of 

SNAP participation on adults’ diet quality by using individual-level data on Healthy 

Eating Index (HEI) scores from the National Health and Nutrition Examination 

Survey (NHANES) matched to State-level policy data that capture variation in 

SNAP eligibility criteria. These policies change the eligibility criteria for, and cost 

of, enrolling in SNAP. In particular, we use indicators for whether States have 

adopted two policies: broad-based categorical eligibility, which generally makes any 

family that qualifies for Temporary Assistance to Needy Families (TANF) eligible 

for SNAP; and whether the State exempts one vehicle per household from SNAP 

asset tests. SNAP participation is strongly related to both these variables; moreover, 

it is unlikely that these variables are related to diet quality or HEI except through 

SNAP. The two variables offer the possibility of identifying SNAP participation 

independent of the unobserved household characteristics (unobservables) that also 

affect diet quality. 
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Background and Previous Research 

Studies of the effect of SNAP on diet have examined a wide range of outcomes, 

including food expenditures, nutrient availability, adherence to USDA dietary guidance, food group servings, nutrients (macro and micro), body weight, source of 

food (at-home or away-from-home), and summary measures such as HEI. SNAP 

likely affects all of these outcomes because it increases income available for foodat-home (FAH) purchases by participants. Standard economic theory tells us that 

such increases in income ought to increase consumption of food, if food is a normal 

good and the amount of SNAP benefits does not exceed the household’s regular food 

budget (i.e., what the household would usually spend on food without SNAP).4

Whether we should expect the nutritional quality of SNAP participants’ diets to be 

improved with extra income depends on a host of assumptions about everything 

from the relative prices of “healthy” foods to the effect of SNAP on time spent on 

food acquisition and preparation. For example, if we assume that “healthy” foods 

are more expensive on a per-calorie basis than “unhealthy” ones, and that consumers 

buy and consume food on that basis (i.e., price-per-calorie), and that “healthy” foods 

are a normal good, then, all other things equal, SNAP should have an unequivocally 

positive effect on diet quality (Drewnowski and Specter, 2004; Drewnowski and 

Darmon, 2005). However, it is not clear that people consume food on this basis, nor 

is it clear that it is a meaningful way to characterize food prices (Burns et al., 2010; 

Carlson and Frazao, 2012). Some research has shown that increases in income effect 

very little change in consumption patterns up through the middle of the income 

distribution (Frazao et al., 2007). Moreover, market prices do not capture the time 

component of food price, which will be significant for SNAP households subject to 

work requirements5 and for whom the time-cost of preparing foods consistent with 

dietary guidance is not trivial (Davis and You, 2010).6

A comprehensive review of the literature concerned with the effect of food and 

nutrition programs and SNAP, particularly on diet and health outcomes, has been 

completed by researchers in collaboration with the Economic Research Service 

(ERS) of the USDA (Fox et al., 2004). The authors of this study examined a wide 

range of diet-related outcomes and found that, for studies published between 1973 

and 2002, there was little evidence of a significant association between SNAP and 

individual dietary intake. This was true for all of the outcomes they examined. 

Moreover, none of the studies reviewed addressed the selection problem (i.e., the 

4However, research has consistently found that the marginal propensity to consume (MPCf

) food 

out of Supplemental Nutrition Assistance Program (SNAP) benefits is higher than out of ordinary 

income, so anomalies in spending and consumption patterns may be present. In other words, while 

theory suggests that people ought to spend SNAP benefits as cash, empirical work has found that they 

may spend more on food when extra income comes in the form of SNAP benefits than when it comes 

from a simple cash increase in their budget. For a discussion of the literature on MPCf

, see Meyerhoefer and Yang (2011).

5In general, able-bodied adults between 16 and 60 must register for work, accept suitable employment, or participate in job training in order to qualify for the Supplemental Nutrition Assistance 

Program.

6The Thrifty Food Plan, the least expensive of USDA-produced food plans, specifies types and 

amounts of foods that provide adequate nutrition for a family of four. However, the estimated cost of 

the Thrifty Food Plan does not include the time cost of preparation.
4 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

problem that unobservable characteristics probably influence both SNAP participation and diet choices).7

Two recent studies have tackled the selection problem for food assistance programs. 

Yen (2010) used multi-equation maximum likelihood to examine the effect of 

SNAP and WIC participation on five nutrition outcomes of children and found that 

SNAP had a small negative effect on the fiber intake of children. Deb and Waehrer 

(2012) used instrumental variables to identify the effect of SNAP participation on 

food intakes, particularly the mix of convenience foods and other foods consumed 

away from home, focusing on how SNAP might affect the time available for household food production. The study used data from the American Time Use Survey 

(ATUS) to estimate the amount of time that SNAP participants spent on food preparation and stratified the results by employment status. Additionally, the authors used 

NHANES data to examine intakes of convenience foods (at-home and away-fromhome) and carbonated and sweetened beverages (CSB). Their results suggest that, 

for part-time workers, SNAP increased the amount of time available for household 

food production, but that these increases did not yield better diet quality. The study 

also found that part-time workers who participated in SNAP consumed significantly 

more calories from CSBs and fewer calories from fruits and vegetables but that this 

was not true for full-time workers. 

This study differs from Yen (2010) in that we look at adult diet outcomes and use 

policy variables, rather than the choice of distribution for the unobservables alone, 

to identify the effects of SNAP; it differs from Deb and Waehrer (2012) because 

we look directly at nutrient composition as represented by the HEI score. We adopt 

a strategy that has been used in the recent literature (particularly, Yen et al., 2008; 

Ratcliffe et al., 2011; and Mykerezi and Mills, 2010) on the effect of SNAP on food 

insecurity—namely, using State variation in SNAP policies to better identify and 

isolate the effect of SNAP participation.8 This study adds value by using exogenous 

State variables to examine the nutritional effects of SNAP on adults as measured by 

the HEI. 

7The review does mention a few studies of cash-out experiments (wherein some Supplemental 

Nutrition Assistance Program participants receive cash for their benefits instead of a book of stamps 

or an Electronic Benefit Transfer card); these experiments examine aspects of food spending, rather 

than nutrient intake or other dietary outcomes.

8As in Shaefer and Gutierrez (2012), we also find that the functional form of our model helps to 

identify the effect of the Supplemental Nutrition Assistance Program.
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Methods

In addition to describing the unconditional differences in food intakes for SNAP 

participants and nonparticipants, we estimate two econometric models. First, we 

show the results of a simple econometric model of the effect of SNAP on a given 

diet outcome, which looks like 

, (1) i i i OLS i y X SNAP =+ + β δε

where i indexes an individual, X is a vector of individual- and household-level attributes, y is total HEI or HEI component, and SNAP is an indicator for whether or not 

a sample respondent participates in SNAP.9 This is the ordinary least squares (OLS) 

model, which gives us conditional associations between SNAP receipt and diet 

outcomes. However, in the presence of unobservables correlated with SNAP receipt, 

estimates of the coefficient δOLS will be biased because they will include information not only on the effect of SNAP, but also on the effect of the unobservables 

correlated with both SNAP participation and diet quality. Because we believe that 

diets and SNAP participation are chosen together, we estimate the following model: 

*

,

+ , (2)

i i iS i

i ii i

y X SNAP

SNAP Z X u

β δε

γ θ

=+ +

= +

where Z are exogenous variables that identify SNAP participation, SNAP* is a latent 

index for the probability of enrolling in SNAP (measured by a binary indicator), 

and δs

 is the marginal effect of SNAP, independent of the unobservables. Finally, ε 

and u, the unobservables in each equation, have a bivariate normal distribution with 

covariance matrix 

2 

 1

p

p

σ σ

σ

In this context, ρ is the estimate of the correlation between the unobservables in the 

two equations. We obtain estimates by using maximum likelihood.10

While this model is theoretically identified by the specification of functional form 

that characterizes the joint distribution of ε and u, we use State-level policy variables, Z, to identify variation in SNAP participation. In order for these variables to 

be valid instruments for SNAP participation, they should be correlated with SNAP 

receipt and uncorrelated with HEI scores. The first condition can be tested, and we 

show the results of the instrument tests in this report. The second is not subject to 

9We focus on participation (the extensive margin) rather than the amount of the Supplemental Nutrition Assistance Program (the intensive margin) because the modeling problems are quite distinct. 

The unobservables problem, with respect to the amount of benefits, is more complicated because it 

involves more decisions, including labor force participation and household size.

10All models and estimates of standard errors take into account survey design information using 

Stata survey procedures. All models are weighted using the dietary day one sample weight (WTDRD1).
6 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

empirical verification; however, it seems unlikely that laws that change the cost to 

access SNAP would be correlated with individual HEI scores. 

In the OLS model, δOLS estimates the effect of SNAP on HEI but also captures 

the effect of unobservables correlated with choosing SNAP. The selection model 

isolates the unobservables so that δS shows the marginal effect of SNAP on diet 

outcomes. The total expected difference in HEI scores in this model, also called the 

average treatment effect (ATE), is

( ) , (3) ( )* 1 ( )

i i

i S

ii ii

Z X

ZX ZX

φγ θ μ δ σρ γθ γθ

  +

= +   Φ + −Φ +      

where φ and Φ are the standard normal density function and cumulative distribution function, respectively. The intuition here is that, absent a selection effect—that 

is, if the correlation between unobservables in the two equations (ρ) is zero—the 

marginal effect could be read off of the coefficient δOLS in the OLS model; in much 

of the literature that uses participant/nonparticipant comparisons, this is what 

is estimated. But, if ρ is non-zero, δOLS will estimate the expected difference—

including the selection and treatment effects. Here, in order to address the selection 

effect, we use the coefficient δS plus the expected difference in the value of the error 

terms, conditional on participation status, to estimate the expected differences in 

diet quality over SNAP participation status. The coefficient δS represents the difference that SNAP participation itself makes in diet quality for those who participate. 

(For a fuller treatment of this topic, see Greene (2011).) We calculate the standard 

errors of the ATE (as shown in equation 3) using the delta method.11

11Let α γθ ≡ [ ] , ; the standard error of the marginal effects is vμ= u

V μ

α α

∂ ∂ ′

∂ ∂

, where V is a 

variance-covariance matrix of the treatment equation.
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Data

National Health and Nutrition Examination Survey 

(NHANES) 

We use data from four waves of the National Health and Nutrition Examination 

Survey (NHANES): 2001-02, 2003-04, 2005-06, and 2007-08. NHANES is a 

stratified, multistage probability sample of the civilian, non-institutionalized U.S. 

population. NHANES oversamples Blacks, Mexican-Americans (Hispanics after 

2006), people over 60, and low-income people.12 The survey consists of a series of 

initial interviews, usually conducted at the participants’ homes, and a subsequent 

health examination completed at a Mobile Examination Center (MEC). We use the 

design information from the survey (primary sampling units and sample weights) to 

make unbiased population-level estimates.

Our primary outcome variable is HEI score. The HEI score was developed by 

researchers at the Center for Nutrition Policy and Promotion (CNPP) and the 

National Cancer Institute (NCI) (Guenther et al., 2007). The index measures an 

individual’s adherence to the recommendations in the 2005 Dietary Guidelines for 

Americans, which translate into recommendations by MyPyramid (now MyPlate).13

The HEI is the sum of the scores for 12 dietary elements: total fruit; whole fruit; 

total vegetables; dark green/orange vegetables and legumes; total grains; whole 

grains; milk; meat/beans; oils; saturated fat; sodium; and solid fats, alcohol, and 

added sugar (SoFAAS). 

Scores for all of the food groups (i.e., total fruit, vegetables, etc.) and oils are based 

on intake adequacy on a per-1,000-calorie basis: people with no intakes receive a 

score of zero, while those with intakes that meet or exceed MyPlate recommendations get the maximum score. Intakes between zero and MyPlate recommendations receive scores prorated on a linear basis. For example, if someone eats half 

of the recommended daily amount of whole grains, that person receives half the 

maximum whole grains score—2.5. Scores for saturated fat, sodium, and SoFAAS 

are scaled according to the recommendations for limiting discretionary calories.14

For example, for saturated fat, respondents get a score of 0 if they exceed 15 percent 

of calories from saturated fat, a score of 8 if they get less than 10 percent of their 

calories from saturated fat (which meets the 2005 dietary guidelines), and a score of 

10 if they get less than 7 percent of their calories from saturated fat. The maximum 

score was assigned to those who had intakes below an amount recommended by 

relevant research. Therefore, for the total and all component scores, a high score 

indicates superior nutrient intake.

12Some changes to the sampling frame occurred between 2005-06 and 2007-08. The latter survey 

oversampled the entire Hispanic population, instead of just Mexican-Americans. In addition, the 

over-sample of pregnant women and adolescents during 2001-06 was discontinued. For more information, see http://www.cdc.gov/nchs/nhanes/nhanes2007-2008/generaldoc_e.htm.

13MyPlate is a USDA nutrition guide that displays the amounts and types of food an individual 

should consume per meal. More information on MyPlate is at http://www.choosemyplate.gov/.

14As there is no natural value for a zero score, researchers use the 85th percentile value of intakes 

from the dietary component of the National Health and Nutrition Examination Survey 2001-02.
8 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

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The weighting of each component score for the final score is based on its importance to the overall view of the dietary recommendations. Total fruit, whole fruit, 

total vegetables, dark green/orange vegetables and legumes, total grains, and whole 

grains are all worth 5 points in the final scale; milk, meat/beans, oils, saturated fat, 

and sodium are worth 10 points; SoFAAS is worth 20 points. For more detail on the 

construction of the HEI scores, see Guenther et al. (2007). 

Survey respondents in NHANES received an HEI score based on their 24-hour 

dietary recall data; separate scores were calculated for each of the 2 days. We use 

only the first day of the interview because the second day has a higher rate of nonresponse, and because people consistently report less consumption on the second 

day (which suggests under-reporting or survey fatigue). Each of the foods reported 

in the dietary recall is matched to nutrient and food group equivalents through the 

My Pyramid Equivalents Database (MPED), and HEI scores are constructed from 

that information.15

The independent variable is household SNAP participation. Since we wanted to 

measure the effect of SNAP on diet intake, and because the intake survey pertains 

to a single day’s food consumption, we counted only those currently receiving 

SNAP as SNAP participants.16 However, the SNAP participation variable is coded 

slightly differently across NHANES waves. In the 2001-02, 2003-04, and 2005-06 

waves, the survey asks the respondent if the sample person is currently authorized 

to receive SNAP; we counted anyone who responded in the affirmative as a SNAP 

participant. In the 2007-08 wave, the survey initially asks the sample respondent 

whether anyone in the household has received SNAP benefits in the last 12 months, 

and then asks how long it has been since the household last received SNAP benefits. 

We counted as current participants anyone who responded that they (or someone in 

the household) had received their last benefits within the most recent 30 days. To 

check the robustness of our results, we also estimated models counting persons in 

households that received SNAP benefits in the last 12 months. 

One difficult aspect of modeling the effect of SNAP on diet outcomes is the role 

of body weight and/or body mass index (BMI). It is difficult to argue against the 

idea that diet choices are informed by one’s weight history; however, extant literature generally looks at the effect of SNAP on BMI, rather than BMI on SNAP. (For 

reviews of this literature, see Zagorsky and Smith (2009) and Ver Ploeg and Ralston 

(2008).) We have included self-reported weight lagged 1 year in the treatment and 

outcome equations, assuming that one’s past weight affects current diet choices. 

Using current BMI alone, or with lagged weight, did not change the results.17

15It is an open question as to whether using the second day would introduce measurement error or 

bias into the results. It is possible that people are more likely to misreport their eating on the second 

day because the recall is taken over the phone. That might introduce measurement error, but not necessarily bias. On the other hand, it might be that people misreport in order to seem healthier on the 

second day, or that people actually do eat less on the second day to seem healthier. These would both 

introduce bias into the estimates, although it is not clear whether either is necessarily happening.

16The first day dietary recall survey is completed in the National Health and Nutrition Examination Survey Mobile Examination Center and is administered approximately 10 days after the household interview.

17Parameters for the treatment equation are shown in Appendix table A.1.
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Economic Research Service, USDA to Modest Changes in Diet Quality, ERR-147

To address sample selection meaningfully, we need to include persons in households 

that are not automatically income eligible—that is, that have gross income below 

130 percent of the Federal poverty line—but who might be on the margin of participating in SNAP. Because of the relaxation of eligibility requirements for SNAP 

in many States, and because persons in households with gross incomes above 130 

percent of the Federal poverty line might qualify for SNAP, we include persons in 

households with slightly higher incomes here—up to 200% of the Federal poverty 

line.18 These households help identify the effect of SNAP by representing both 

participating and potentially nonparticipating households in our sample.19

Exogenous Variables

One of the obstacles to getting good estimates of the effect of SNAP on diet 

outcomes is the selection issue. One method for addressing this issue is the treatment effects strategy. To identify the selection effects of interest, we need variables 

that are not correlated with HEI score, but are meaningfully correlated with SNAP 

participation. The exogenous variables that we use capture State-level policy variation in eligibility criteria for SNAP participation, but the policies are unlikely to be 

related to HEI scores except through SNAP participation. These data come from 

the database of SNAP eligibility rules compiled by researchers at ERS and linked 

to geo-coded NHANES data supplied by the National Center for Health Statistics 

(NCHS). We use indicator variables for whether or not the State used broad-based 

categorical eligibility rules to determine SNAP eligibility and whether or not the 

State exempted one vehicle from asset tests to determine SNAP eligibility. These 

two variables are positively associated with SNAP participation—in other words, if 

an individual lives in a State with broad-based categorical eligibility rules, or if the 

State exempts one vehicle from the SNAP asset tests, then there is a greater likelihood that this individual will participate in SNAP. At the same time, these policies 

are not likely to be related to individual food choices, except through their effect on 

SNAP participation.

There is considerable cross-State and cross-time variation in these policies. In 

2001, Delaware, Maine, Maryland, Massachusetts, Michigan, North Dakota, 

Oregon, South Carolina, and Texas had broad-based categorical eligibility policies 

in place. Washington and Wisconsin added them in 2004; Minnesota and Arizona 

added them in 2006 and 2007, respectively; and Georgia and West Virginia added 

them in 2008. In 2001, Alaska, Arkansas, Colorado, Connecticut, Montana, 

Nevada, New York, Pennsylvania, and South Dakota had vehicle exemption 

policies in place. Virginia added this policy in 2002, Iowa in 2004. This study 

observed 34 unique States, 12 of which had one or the other of these policies 

18We are aware that, between 2002 and 2008, 11 States raised gross income limits above the 

130-percent cutoff (AZ, DE, MD, MA, NC, WA, WI-200 percent; ME, OR-185 percent; MN, TX-165 

percent), using options under the broad-based eligibility rules. Nonetheless, there are few households 

over 200 percent of the Federal poverty line that report Supplemental Nutrition Assistance Program 

(SNAP) receipt in our sample; the majority of households are still subject to the 130-percent constraint in applying for SNAP. In addition to the income restriction, we restrict the sample to those at 

least 19 years old.

19It is important to include those who have annual incomes above the nominal income cutoff 

because there is considerable intra-year income volatility for low-income households. This volatility 

could render them eligible for part of a year. See Newman (2006) and Newman and Joliffe (2009).
10 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

during the time of the study.20 We use the variation across States to identify SNAP 

participation, and we use time-varying State-level characteristics—unemployment 

rate, gross State product, State per capita income, and State spending on nutrition 

education—to adjust for time trends in State-level factors that may be correlated 

with SNAP participation.21

20Because of the confidential nature of the data, we cannot determine the actual State in which 

the respondent resides. We do, however, have masked State identifiers that allow us to count the number of States in the sample.

21In our sample, even though participants are matched to States by month and year, we do not observe 

the month or year that respondents are in the sample, so we cannot directly control for time effects. We 

have estimated all of the models using wave dummies to control for time variation; those estimates are 

identical to those we show, which control for State-level, time-varying economic characteristics.
Supplemental Nutrition Assistance Program (SNAP) Participation Leads 11

Economic Research Service, USDA to Modest Changes in Diet Quality, ERR-147

Results

Sample Means for Supplemental Nutrition Assistance 

Program (SNAP) Participants and Nonparticipants

In our sample, SNAP households are more likely to be non-Hispanic Black (table 

1). By many measures, SNAP households are significantly more disadvantaged than 

nonparticipant households. They have lower incomes, are more likely to be high 

school dropouts, and are less likely to have a college degree. Respondents from 

SNAP households are less likely to have been employed in the previous week and 

less likely to be married; they are younger, on average, and have higher self-reported 

weight a year before their interview. The States that SNAP participants live in are, 

on average, poorer, although they show no difference in unemployment rates. SNAP 

participants are more likely to live in States that have broad-based categorical eligibility policies.

Healthy Eating Index (HEI) Scores by SNAP Participation 

The only HEI component scores on which SNAP participants do better are saturated 

fat and sodium, although only the latter is statistically significant (table 2). People 

in SNAP households have lower scores for total fruit; whole fruit; total vegetables; 

dark green/orange vegetables; total grain; whole grain; milk; and solid fats, alcohol, 

and added sugar (SoFAAS). Most of the differences are small—less than 1 point—

although the difference in SoFAAS scores is 1.5 points, which is unsurprising as it 

is the largest component score of all.

Ordinary Least Squares (OLS) results

The coefficients (which represent δOLS from equation (1)) in table 3 are estimates of 

the association of HEI scores for respondents in SNAP households relative to those 

not in SNAP households, with all other observed determinants accounted for.22 This 

coefficient captures both the effect of SNAP and the selection effect on diet quality. 

The model’s determinants are ethnicity (Hispanic, non-Hispanic), race (White, 

Black, other), educational achievement (high school dropout, high school graduate, 

some college, college graduate), marital status (married/unmarried), age, annual 

household income, self-reported weight 1 year prior, employment status (employed/

unemployed), household size, gross State product, State per capita income, State 

spending on nutrition education per poor person, current unemployment rate, 1-year 

lagged unemployment rate, and State fixed effects.23

22Although our main results pertain to adults in the Supplemental Nutrition Assistance Program 

households, we use individual-level (instead of household-level) weights (WTDRD1, the dietary day 

one sample weight) in all of the models, since the individual-level Healthy Eating Index score is the 

outcome of interest.

23We used the race/ethnicity recode variable in the National Health and Nutrition Examination 

Survey to assign racial/ethnic status. We have run all of the models with gender dummies and found 

no difference in the results, so we omit that from our empirical specification. 
12 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

Table 1

Sample means for Supplemental Nutrition Assistance Program (SNAP) 

participants and nonparticipants

Non-SNAP SNAP

White 0.601 0.494

0.020 0.054

Hispanic 0.204 0.199

0.020 0.032

Black 0.126 0.293***

0.012 0.028

Other race 0.046 0.030**

0.006 0.005

High school dropout 0.330 0.434***

0.014 0.018

Some college 0.280 0.245

0.013 0.018

College graduate 0.097 0.036***

0.008 0.005

Annual household income ($000’s) 19.929 14.478***

0.322 0.388

Age 46.837 39.728***

0.521 0.576

Married 0.536 0.412***

0.014 0.025

Employed 0.507 0.383***

0.012 0.019

Body weight, lagged 172.531 179.369***

1.010 1.999

Household size 3.115 3.587***

0.057 0.081

Gross State product ($Billions) 519.201 440.895***

36.435 27.509

State per capita income ($000's) 33.966 34.199

0.393 0.572

Nutrition education, $ per poor person 6.062 5.429*

0.436 0.338

Current unemployment rate 5.258 5.291

0.113 0.124

Lagged unemployment rate 5.068 5.080

0.092 0.117

Broad-based categorical eligibility 0.172 0.202*

0.032 0.037

One vehicle per SNAP unit exempt 0.103 0.133

0.028 0.030

N 5,053 1,615

Note: 200 percent of Federal poverty line, estimation sample. *p<.10, **p<.05, ***p<.01 denote 

significance of differences in the sample means between SNAP participants and nonparticipants. 

Standard errors, adjusted for sample design, in parenthesis. These are means for the regression 

estimation sample, for which there are no missing values. Estimates weighted by dietary day one sample 

weight (WTDRD1). N denotes sample size.
Supplemental Nutrition Assistance Program (SNAP) Participation Leads 13

Economic Research Service, USDA to Modest Changes in Diet Quality, ERR-147

Table 2

Healthy Eating Index (HEI) scores by Supplemental Nutrition Assistance 

Program (SNAP) participation

Non-SNAP SNAP Difference

HEI total

51.297 47.220 -4.077***

(0.444) (0.530) (0.799)

Total fruit

2.116 1.685 -0.431***

(0.062) (0.078) (0.114)

Whole fruit

1.931 1.311 -0.620***

(0.061) (0.061) (0.087)

Total veg

2.973 2.696 -0.277***

(0.037) (0.071) (0.088)

Dark green/orange veg 

(DKGOrVeg)

1.190 0.925 -0.265***

(0.047) (0.059) (0.088)

Total grain

4.262 4.080 -0.183***

(0.027) (0.045) (0.048)

Whole grain

0.938 0.647 -0.291***

(0.035) (0.032) (0.051)

Milk

4.778 4.201 -0.577***

(0.096) (0.135) (0.166)

Meat/Beans

8.214 7.991 -0.224*

(0.062) (0.099) (0.122)

Oils

5.360 5.267 -0.092

(0.070) (0.132) (0.161)

Saturated fat

5.926 6.021 0.095

(0.078) (0.129) (0.141)

Sodium

4.197 4.494 0.297**

(0.070) (0.101) (0.127)

Solid fats, alcohol, and added 

sugar (SoFAAS)

9.412 7.902 -1.510***

(0.199) (0.227) (0.366)

N 6,668

Note: *** p<0.01, ** p<0.05, * p<0.1 denote significance of differences in the sample means between 

SNAP participants and nonparticipants. Standard errors, adjusted for complex survey design, in 

parenthesis. Estimates weighted by dietary day one sample weight (WTDRD1). N denotes sample size.
14 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

SNAP participants have lower HEI scores (on total and most component scores) 

than nonparticipants, as seen from the coefficients from an OLS regression of HEI 

score or component scores on determinants of HEI score. The total score—as well 

as the scores for whole fruit, total grains, milk, and meat/beans—are all significantly lower for participants than for nonparticipants. The differences in the other 

component scores are insignificant. 

The results, however, do not address selection into SNAP; that is, they do not isolate 

the effect of those unobserved variables that correlate with both SNAP and HEI. We 

address this shortcoming in the results of our preferred models (shown in tables 4-10).

Table 3

Association of Supplemental Nutrition Assistance Program (SNAP) with Healthy Eating Index 

(HEI) scores: Ordinary Least Squares regression results

HEI Total fruit Whole fruit Total veg

Dark green/

orange veg Total grain Whole grain

SNAP

-1.280* -0.111 -0.242** -0.088 -0.096 -0.137* -0.089

(0.767) (0.111) (0.121) (0.101) (0.083) (0.079) (0.062)

Milk Meat/Beans Oils Saturated fat Sodium SoFAAS N

SNAP

-0.323* -0.402*** -0.042 0.256 0.264 -0.271

6,668 (0.172) (0.145) (0.198) (0.232) (0.164) (0.326)

SoFAAS = Solid fats, alcohol, and added sugar

Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors, adjusted for complex survey design, in parenthesis. Dependent 

variable at the top of each column. Other regressors include race/ethnicity, education, annual income, age, marital 

status, lagged body weight, employment status, household size, gross State product, State per capita income, State 

unemployment rate, State unemployment rate lagged 1 year, State spending on nutrition education per poor person, and 

State fixed effects. Estimates weighted by dietary day one sample weight (WTDRD1). N denotes sample size.
Supplemental Nutrition Assistance Program (SNAP) Participation Leads 15

Economic Research Service, USDA to Modest Changes in Diet Quality, ERR-147

Results From Model Accounting for Selection Into SNAP

Determinants of SNAP Participation

Table 4 shows coefficients from the second equation of model (2) outlined above, 

which estimates the probability of SNAP participation and measures the effect of 

person, household, and State characteristics on this probability. Because this is a probit 

equation24, the coefficients let us know the direction of the effect on the probability of 

SNAP receipt, but the magnitude and significance cannot be read directly off of them 

or their standard errors. The results show that Blacks are more likely to be enrolled in 

SNAP than Whites (the reference group). Those with less education are more likely 

to participate in SNAP, respondents who were employed in the last week are less 

likely to be in SNAP households, and married persons are less likely to be enrolled in 

SNAP. Of the State-level variables, only lagged unemployment rate has a significant 

effect on the probability of SNAP enrollment. Having a higher self-reported weight 1 

year prior to the interview increases the likelihood of SNAP participation by a small, 

but statistically significant, amount. 

The policy variables are strongly correlated with observed participation: persons in 

States that have adopted expanded categorical eligibility and vehicle exemption policies are more likely to participate in SNAP. However, the correlation is not as strong 

as we might have assumed. We discuss this below.

Effect of SNAP on Diet Quality of Participants

For all but a few outcomes, SNAP has no discernible effect on consumption as 

measured by HEI scores, as shown in the estimates of δS displayed in table 5. This 

coefficient measures the effect of SNAP on HEI total and component scores for 

people who choose to participate in the program. The effect of SNAP on whole 

fruit consumption, however, is large, significant, and positive, and the effect on dark 

green/orange vegetable consumption is small, significant, and negative. 

The values of the correlation parameter, ρ, (in table 5) estimate the association 

between the unobservables in the treatment and outcome equations. For most HEI 

components, this parameter is small, positive, and insignificant—that is, there is 

little correlation between selection into SNAP and diet quality “before”25 adults in 

this low-income sample enroll in SNAP. This parameter is significant for the models 

that examine whole fruit and dark green/orange vegetables. It is large and negative 

for the whole fruit model, suggesting that adults who are likely to enroll in SNAP 

are less likely to eat whole fruit to begin with. The correlation coefficient is smaller 

24A probit model is used to isolate the effect of factors on a variable that is a 0/1 outcome (such 

as Supplemental Nutrition Assistance Program participation). The magnitudes and, to a lesser extent, 

significances of the associations of the determinants of participation cannot be read off these coefficients because it is a non-linear model.

25Our model is not able to determine what Supplemental Nutrition Assistance Program (SNAP) 

participants consumed before they participated in SNAP. However, it does tell us something about the 

correlation between the unobservables—e.g., people most likely to participate in SNAP are less likely 

to consume a lot of whole fruit.
16 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

Table 4

Determinants of Supplemental Nutrition Assistance Program (SNAP) participation, main model 

HEI Total fruit Whole fruit Total veg DkGOrVeg Total grain Whole grain

Hispanic

-0.087 -0.091 -0.117 -0.072 -0.085 -0.087 -0.087

(0.111) (0.109) (0.092) (0.112) (0.108) (0.109) (0.110)

Black

0.501*** 0.498*** 0.445*** 0.513*** 0.507*** 0.501*** 0.503***

(0.088) (0.086) (0.081) (0.089) (0.085) (0.087) (0.087)

Other race

-0.195 -0.202 -0.233* -0.166 -0.152 -0.195 -0.207

(0.142) (0.144) (0.138) (0.147) (0.145) (0.140) (0.145)

High school 

dropout

0.221*** 0.223*** 0.180*** 0.220*** 0.219*** 0.221*** 0.229***

(0.064) (0.064) (0.061) (0.063) (0.063) (0.064) (0.067)

Some college

-0.027 -0.018 0.021 -0.033 -0.030 -0.026 -0.021

(0.091) (0.093) (0.086) (0.091) (0.090) (0.090) (0.091)

College graduate

-0.446*** -0.455*** -0.450*** -0.447*** -0.461*** -0.444*** -0.432***

(0.144) (0.140) (0.122) (0.144) (0.148) (0.142) (0.143)

Annual household 

income ($000’s)

-0.000*** -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** -0.000***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Age

-0.015*** -0.015*** -0.014*** -0.015*** -0.015*** -0.015*** -0.015***

(0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Married

-0.396*** -0.398*** -0.359*** -0.396*** -0.399*** -0.399*** -0.395***

(0.069) (0.070) (0.061) (0.070) (0.070) (0.069) (0.069)

Body weight, 

lagged

0.002*** 0.002*** 0.002*** 0.002*** 0.002*** 0.002*** 0.002***

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Employed 

-0.493*** -0.489*** -0.420*** -0.502*** -0.506*** -0.494*** -0.495***

(0.062) (0.062) (0.055) (0.061) (0.062) (0.062) (0.062)

Household size

0.124*** 0.124*** 0.120*** 0.124*** 0.124*** 0.124*** 0.122***

(0.025) (0.025) (0.024) (0.025) (0.025) (0.025) (0.025)

Gross State 

product ($Billions)

0.000* 0.000** 0.000 0.000* 0.000** 0.000* 0.000**

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

State per capita 

income ($000's)

0.060*** 0.061*** 0.057*** 0.063*** 0.060*** 0.060*** 0.061***

(0.020) (0.019) (0.020) (0.020) (0.020) (0.019) (0.019)

Nutrition education, $ per poor 

person

0.012 0.012 0.011 0.013 0.012 0.013 0.011

(0.027) (0.027) (0.025) (0.028) (0.027) (0.028) (0.027)

Current unemployment rate

0.014 0.020 0.059 0.013 0.012 0.015 0.009

(0.074) (0.071) (0.056) (0.069) (0.069) (0.070) (0.069)

Lagged unemployment rate

0.069 0.068 -0.005 0.078 0.071 0.068 0.075

(0.051) (0.051) (0.048) (0.052) (0.049) (0.050) (0.050)

Broad-based 

categorical 

eligibility

0.294** 0.287* 0.289** 0.290** 0.286** 0.284** 0.276**

(0.149) (0.151) (0.126) (0.136) (0.146) (0.144) (0.141)

Vehicle exempt

0.340 0.319 0.281 0.364 0.360 0.317 0.393

(0.291) (0.294) (0.209) (0.296) (0.276) (0.275) (0.299)

N 6,667

—continued
Supplemental Nutrition Assistance Program (SNAP) Participation Leads 17

Economic Research Service, USDA to Modest Changes in Diet Quality, ERR-147

Table 4

Determinants of Supplemental Nutrition Assistance Program (SNAP) participation, 

main model—Continued

Milk Meat/Beans Oils SatFat Sodium SoFAAS

Hispanic

-0.086 -0.088 -0.084 -0.085 -0.086 -0.085

(0.109) (0.109) (0.109) (0.111) (0.109) (0.110)

Black

0.500*** 0.501*** 0.501*** 0.501*** 0.501*** 0.503***

(0.087) (0.087) (0.087) (0.086) (0.087) (0.087)

Other race

-0.191 -0.196 -0.187 -0.200 -0.198 -0.187

(0.142) (0.141) (0.146) (0.139) (0.138) (0.143)

High school 

dropout

0.222*** 0.220*** 0.222*** 0.221*** 0.221*** 0.221***

(0.064) (0.064) (0.065) (0.064) (0.064) (0.064)

Some college

-0.028 -0.027 -0.025 -0.029 -0.026 -0.029

(0.090) (0.090) (0.090) (0.090) (0.090) (0.090)

College graduate

-0.443*** -0.442*** -0.442*** -0.431*** -0.447*** -0.443***

(0.144) (0.143) (0.141) (0.156) (0.142) (0.145)

Annual household 

income ($000’s)

-0.000*** -0.000*** -0.000*** -0.000*** -0.000*** -0.000***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Age

-0.015*** -0.015*** -0.015*** -0.015*** -0.015*** -0.015***

(0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Married

-0.397*** -0.395*** -0.394*** -0.396*** -0.397*** -0.395***

(0.069) (0.069) (0.069) (0.069) (0.069) (0.069)

Body weight, 

lagged

0.002*** 0.002*** 0.002*** 0.002*** 0.002*** 0.002***

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Employed 

-0.493*** -0.493*** -0.496*** -0.495*** -0.493*** -0.494***

(0.062) (0.062) (0.062) (0.062) (0.063) (0.062)

Household size

0.124*** 0.124*** 0.124*** 0.124*** 0.125*** 0.123***

(0.025) (0.025) (0.025) (0.025) (0.026) (0.025)

Gross State 

product ($Billions)

0.000* 0.000* 0.000** 0.000** 0.000* 0.000*

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

State per capita 

income ($000's)

0.060*** 0.060*** 0.060*** 0.060*** 0.060*** 0.060***

(0.019) (0.019) (0.020) (0.019) (0.019) (0.020)

Nutrition 

education, $ per 

poor person

0.012 0.012 0.013 0.012 0.013 0.011

(0.027) (0.027) (0.027) (0.027) (0.027) (0.027)

Current 

unemployment 

rate

0.014 0.014 0.018 0.012 0.015 0.012

(0.069) (0.070) (0.070) (0.070) (0.070) (0.071)

—continued
18 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

and positive for the dark green/orange vegetables model, suggesting that people who 

are more likely to enroll in SNAP tend to eat more dark green/orange vegetables. 

The results for the whole fruit model are particularly curious because they suggest 

that SNAP induces a very large increase in whole fruit consumption for SNAP 

participants. Although this seems possible, especially for those consuming small 

amounts of whole fruit, it might also be an artifact of the distributional assumpTable 4

Determinants of Supplemental Nutrition Assistance Program (SNAP) participation, 

main model—Continued

Milk Meat/Beans Oils SatFat Sodium SoFAAS

Lagged 

unemployment 

rate

0.069 0.069 0.067 0.070 0.068 0.070

(0.050) (0.050) (0.050) (0.050) (0.050) (0.050)

Broad-based 

categorical 

eligibility

0.299** 0.297** 0.313** 0.293** 0.287* 0.299**

(0.144) (0.143) (0.157) (0.142) (0.147) (0.139)

Vehicle exempt 0.334 0.341 0.326 0.326 0.330 0.347

(0.278) (0.282) (0.279) (0.281) (0.279) (0.288)

N 6,668

HEI = Healthy Eating Index; DkGOrVeg = Dark green/orange vegetables.; SoFAAS = Solid fats, alcohol, and added sugar

Note: *p<.10, **p<.05, ***p<.01. Standard errors, adjusted for complex survey design, in parenthesis. State fixed effects not 

shown. Estimates weighted by dietary day one sample weight (WTDRD1). N denotes sample size.

Table 5

Effect of Supplemental Nutrition Assistance Program (SNAP) on Healthy Eating Index (HEI) 

scores

HEI Total fruit Whole fruit Total veg DkGOrVeg Total grain Whole grain

δS

-1.441 0.270 2.795*** -0.660 -0.735*** -0.039 -0.383

(4.503) (0.710) (0.209) (0.592) (0.187) (0.147) (0.233)

ρ

0.007 -0.112 -0.979*** 0.199 0.216*** -0.049 0.125

(0.195) (0.201) (0.084) (0.203) (0.052) (0.051) (0.092)

F (IV) 2.955 2.293 3.468 3.291 2.946 2.639 3.172

Milk Meat/Beans Oils Saturated fat Sodium SoFAAS N

δS

-0.054 -0.248 0.372 -0.193 -0.046 -0.820

6,668

(0.590) (0.310) (0.741) (0.987) (0.740) (1.423)

ρ

-0.046 -0.033 -0.068 0.073 0.057 0.050

(0.093) (0.067) (0.115) (0.145) (0.136) (0.121)

F (IV) 3.042 2.975 2.878 2.915 2.628 3.178

DkGOrVeg = Dark green/orange vegetables; SoFAAS = Solid fats, alcohol, and added sugar

Note: *p<.10, **p<.05, ***p<.01. Marginal effect of SNAP from two-equation maximum likelihood model, as described in 

text. Selection and outcome models include race/ethnicity, education, annual income, age, marital status, lagged body 

weight, employment status, household size, gross State product, State per capita income, State unemployment rate, State 

unemployment rate lagged 1 year, State spending on nutrition education per poor person, and State fixed effects. Selection 

equation includes indicators for State SNAP policies: broad-based categorical eligibility and one vehicle per SNAP unit 

exemption. Standard errors, adjusted for complex survey design, in parenthesis. Estimates weighted by dietary day one 

sample weight (WTDRD1). N denotes sample size.
Supplemental Nutrition Assistance Program (SNAP) Participation Leads 19

Economic Research Service, USDA to Modest Changes in Diet Quality, ERR-147

tions of the model. Table 6 confirms this suspicion. It shows the frequencies with 

which respondents in the sample have whole fruit scores that are zero, more than 

zero but less than five, and five (a perfect score for this component). Sixty percent 

of SNAP participants have scores that are zero, and more than 80 percent have 

scores that are zero or five. These distributions suggest not only that the joint 

distribution is not bivariate normal, but that SNAP might not affect HEI score 

linearly. It seems more likely that SNAP might induce a change in the “type” 

of whole fruit consumer when he or she enrolls; that is, it induces those who 

consume no whole fruit to consume some, or those who consume some fruit to 

consume the recommended amount.

To examine this possibility, we estimated bivariate probit models where the two 

outcomes were SNAP participation and the whole fruit component score above 

zero.26 As suspected, the marginal effect of SNAP participation on consuming some 

whole fruit is positive, and the parameter value for SNAP participation is significant 

(table 7). SNAP participation increases the probability that participants will eat 

some whole fruit by about 23 percentage points. 

26To check the robustness of all the results, we also estimated Instrumental Variables (2 Stage 

Least Squares) and Generalized Method of Moments models for all outcomes, with the idea that these 

specifications would relax the bivariate normality assumption. These models yielded similar results 

in terms of the significance reported, but with even larger and more implausible marginal effects in 

some cases.

Table 6

Frequencies of whole fruit score

No SNAP SNAP

0 2,622 623

0<score<5 1,261 199

5 1,737 226

N 6,668

N denotes sample size.

Table 7

Effect of Supplemental Nutrition Assistance Program (SNAP) on probability 

of eating more fruit 

Parameter

Effect on probability 

of eating some fruit

SNAP

0.548* 0.238

(0.32)

N 6,668

Note: *p<.10, **p<.05, ***p<.01. Parameters and marginal effects from bivariate probit model in which outcome variables 

are SNAP participation and having above a zero score on HEI whole fruit score. Both equations include race/ethnicity, 

education, annual income, age, marital status, lagged body weight, employment status, household size, gross State 

product, State per capita income, State unemployment rate, State unemployment rate lagged 1 year, State spending on 

nutrition education per poor person, and State fixed effects. SNAP equation includes indicators for State SNAP policies: 

broad-based categorical eligibility and one vehicle exemption per SNAP unit. Standard errors, adjusted for complex survey 

design, in parenthesis. Estimates weighted by dietary day one sample weight (WTDRD1). N denotes sample size.
20 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

Expected Differences in Diet Quality Between SNAP 

Participants and Nonparticipants

After program effects, observed characteristics, and unobservables are accounted 

for, SNAP participants do marginally worse on total HEI than comparable nonparticipants: about 1.25 points lower, or about 2.5 percent of the mean for this group 

(figure 1 and table 8). Scores for all of the HEI components (except saturated fat and 

sodium) are lower for SNAP participants than nonparticipants, although most by 

small fractions of a point. Those components that are higher are also higher by only 

a fraction of a point. 

The results also show that one of the benefits of using maximum likelihood to estimate the parameters of this model—even though it is more complex than OLS—is 

that it is more efficient, which is reflected in the more precise estimates of the differences in total HEI and HEI components for participants and nonparticipants. 

Robustness

The F-test of the correlation of the exogenous variables with SNAP participation is 

in table 5. While these policies are positively correlated with SNAP participation, 

they fail to meet the rule-of-thumb value for strong instruments (10) that has been 

established in the literature (Bound et al., 1995).27 This is something of a concern, 

27The work of Bound et al. (1995) remains a touchstone for non-linear models despite the fact that 

it was developed for linear models—primarily 2 Stage Least Squares. For more on this, see Greene 

(2011).

Non-SNAP SNAP

50.50 49.25

Supplemental Nutrition Assistance Program (SNAP) participants’ 

Healthy Eating Index (HEI) scores are comparable to nonparticipants’ scores

Source: Calculations based on National Health and Nutrition Examination Survey (NHANES) data 

and the treatment effects models described in this report.

HEI Score: Maximum-100

Figure 1
Supplemental Nutrition Assistance Program (SNAP) Participation Leads 21

Economic Research Service, USDA to Modest Changes in Diet Quality, ERR-147

because it indicates that we may not have identified SNAP participation independent 

of individual-level unobserved characteristics as well as we had assumed. However, 

while we count only those enrolled in SNAP in the current month as participating in 

SNAP in the applicable models for the tests in table 5, it is reasonable to think that 

SNAP policies do not affect whether one enrolls in a particular month, but whether 

one enrolls in SNAP at all. To address this, and to check the robustness of these 

results in general, we re-estimated the main model above but with SNAP participation redefined: we count as SNAP participants anyone who reports that they have 

participated in SNAP in the last 12 months.28

There are several noteworthy aspects of these results (table 9). First, the State policy 

variables have F-test values that are generally above the threshold value for strong 

instruments (10). Second, these results mirror those of table 5 in suggesting large 

increases in whole fruit consumption and small decreases in dark green/orange 

vegetable consumption among SNAP participants. Third, these results indicate small 

negative effects of SNAP on whole grain consumption and large negative effects on 

the solid fats, alcohol, and added sugar (SoFAAS) score. Since larger scores are better 

for all components, this indicates that SNAP participants—defined as those who have 

been on SNAP at any time in the last 12 months—eat too much solid fat and added 

sugar, and not enough whole grains. Finally, we note that total HEI scores are about 

10 percent lower for persons in SNAP households. As in the main sample results, the 

parameter ρ is mostly insignificant; here, as in table 5, it is significant and negative in 

28In the 2003-04 and 2005-06 surveys, two questions pertain to household Supplemental Nutrition Assistance Program (SNAP) receipt: the number of persons in the household 

authorized to receive SNAP, and whether the household received SNAP. Both variables pertain to the previous 12 months. The 2007-08 survey asks only if SNAP receipt occurred in 

the previous 12 months. We tested to make sure that, using either the number of persons or 

the indicator in the earlier iterations, we had the same number of (unweighted) food stamp 

households. There are small differences in the number of cases using these variables, but 

our results are not sensitive to these differences. 

Table 8

Expected differences in Healthy Eating Index (HEI) scores by participation in the 

Supplemental Nutrition Assistance Program (SNAP)

HEI Total fruit Whole fruit Total veg DkGOrVeg Total grain Whole grain

ATESNAP

-1.249*** -0.186*** -0.678*** 0.023 0.030 -0.157*** -0.031***

(0.006) (0.013) (0.105) (0.021) (0.023) (0.003) (0.011)

Milk Meat/Beans Oils Saturated fat Sodium SoFAAS N

ATESNAP

-0.376*** -0.431*** -0.123*** 0.344*** 0.325*** -0.163***

6,668 (0.010) (0.005) (0.015) (0.016) (0.011) (0.020)

DkGOrVeg = Dark green/orange vegetables; SoFAAS = Solid fats, alcohol, and added sugar

Note: *p<.10, **p<.05, ***p<.01. Total effects of SNAP on HEI and HEI component scores from two-equation maximum 

likelihood model, calculated as described in text. Selection and outcome models include race/ethnicity, education, annual 

income, age, marital status, lagged body weight, employment status, household size, gross State product, State per capita 

income, State unemployment rate, State unemployment rate lagged 1 year, State spending on nutrition education per poor 

person, and State fixed effects. Selection equation includes indicators for State SNAP policies: broad-based categorical 

eligibility and one vehicle exemption per SNAP unit. Standard errors, calculated by the delta method and adjusted for 

complex survey design, in parenthesis. Estimates weighted by dietary day one sample weight (WTDRD1). N denotes 

sample size.
22 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

the whole fruit model, and significant and positive in the dark green/orange vegetable 

model. It is also significant and positive in the SoFAAS model. 

The expected differences in diet quality for SNAP participants and nonparticipants for these models are shown in table 10. As in the main results, SNAP 

participants do worse than their counterparts in terms of total HEI and many 

component scores. However, SNAP participants do slightly better on the score 

for saturated fat. The magnitudes of all of the effects are quite small: the largest 

effect, 1.28 points for total HEI, amounts to about 2.5 percent of the unconditional 

average for this income group.

Table 9

Effects of Supplemental Nutrition Assistance Program (SNAP) on Healthy Eating Index 

(HEI) scores, SNAP participation any time in last 12 months

HEI Total fruit Whole fruit Total veg DkGOrVeg Total grain Whole grain

δS

-5.138* -0.212 2.100*** -0.620 -0.718*** -0.058 -0.460*

(3.004) (0.511) (0.595) (0.561) (0.153) (0.139) (0.251)

ρ

0.157 0.010 -0.717*** 0.196 0.214*** -0.018 0.160

(0.133) (0.151) (0.200) (0.198) (0.048) (0.049) (0.102)

F (IV) 11.178 11.000 7.296 9.934 10.962 10.734 11.934

Milk Meat/Beans Oils Saturated fat Sodium SoFAAS N

δS

0.112 -0.382 -0.205 -0.431 1.275 -2.970***

6,668

(0.748) (0.608) (0.639) (1.139) (0.814) (1.086)

ρ

-0.045 0.020 0.025 0.079 -0.210 0.219**

(0.115) (0.129) (0.099) (0.172) (0.150) (0.098)

F (IV) 12.213 11.502 11.520 11.323 12.598 11.749

DkGOrVeg = Dark green/orange vegetables; SoFAAS = Solid fats, alcohol, and added sugar

Note: *p<.10, **p<.05, ***p<.01. Marginal effect of SNAP from two-equation maximum likelihood model, 

as described in text. Selection and outcome models include race/ethnicity, education, annual income, age, 

marital status, lagged body weight, employment status, household size, gross State product, State per capita 

income, State unemployment rate, State unemployment rate lagged 1 year, State spending on nutrition 

education per poor person, and State fixed effects. Selection equation includes indicators for State SNAP 

policies: broad-based categorical eligibility and one vehicle per SNAP unit exemption. Standard errors, 

adjusted for complex survey design, in parenthesis. Estimates weighted by dietary day one sample weight 

(WTDRD1). N denotes sample size.
Supplemental Nutrition Assistance Program (SNAP) Participation Leads 23

Economic Research Service, USDA to Modest Changes in Diet Quality, ERR-147

Table 10

Expected differences in Healthy Eating Index (HEI) scores by Supplemental Nutrition 

Assistance Program (SNAP) participation any time in last 12 months

HEI Total fruit Whole fruit Total veg DkGOrVeg Total grain Whole grain

ATESNAP

-1.280*** -0.176*** -0.511*** -0.002 -0.021 -0.097*** -0.047***

(0.108) (0.001) (0.072) (0.017) (0.020) (0.001) (0.012)

Milk Meat/Beans Oils Saturated fat Sodium SoFAAS N

ATESNAP

-0.178*** -0.277*** -0.036*** 0.103*** 0.029 -0.327***

6,668 (0.008) (0.003) (0.005) (0.015) (0.036) (0.074)

DkGOrVeg = Dark green/orange vegetables; SoFAAS = Solid fats, alcohol, and added sugar

Note: *p<.10, **p<.05, ***p<.01. Total effects of SNAP on HEI and HEI component scores from two-equation maximum 

likelihood model, calculated as described in text. Selection and outcome models include race/ethnicity, education, annual 

income, age, marital status, lagged body weight, employment status, household size, gross State product, State per capita 

income, State unemployment rate, State unemployment rate lagged 1 year, State spending on nutrition education per poor 

person, and State fixed effects. Selection equation includes indicators for State SNAP policies: broad-based categorical 

eligibility and one vehicle exemption per SNAP unit. Standard errors, calculated by the delta method and adjusted for 

complex survey design, in parenthesis. Estimates weighted by dietary day one sample weight (WTDRD1). N denotes 

sample size.
24 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

Conclusion

For most components of diet measured by the HEI score, SNAP participants are at a 

small, statistically significant disadvantage in terms of diet quality relative to comparable nonparticipants. At the same time, there are aspects of diet quality on which 

SNAP participants do better; in particular, they consume less sodium and saturated 

fat than their counterparts. Additionally, we find that SNAP induces participants 

to increase their whole fruit consumption, possibly by relatively large amounts; at 

the very least, it increases the likelihood that SNAP participants will change from 

eating no whole fruit to eating some. We also find that SNAP induces participants 

to decrease their intake of dark green/orange vegetables by a modest amount. This 

effect could be the result of both time constraints associated with SNAP work 

requirements and extra income: people on SNAP may see whole fruit as more 

affordable with a little extra income, and consume more of it because it requires 

no preparation time. At the same time, dark green/orange vegetables may be less 

attractive to SNAP participants because they may require more preparation time. 

This could also be due to the substitution of one convenient snack food for another—

apples or bananas for baby carrots, for example. The correlation coefficient in many 

of our models is small, positive, and not often significant, which means that we find 

inconclusive evidence as to whether selection into SNAP is beneficial or adverse.

Our exogenous variables do not meet the values determined in the literature to 

measure strong correlation for the purposes of identification. However, we think that 

this is due to two possible data challenges—the choice of whether to use a current 

or 12-month SNAP participation variable, and the fact that NHANES surveys are 

conducted in only a subset of States each year. When we examine models that use 

any SNAP receipt in the last 12 months, the instruments are strong for nearly all of 

our models. Moreover, the two main results from our preferred models—regarding 

whole fruit and dark green/orange vegetable intake—are mirrored in these latter 

models. We think this suggests that the evidence for our preferred results is strong, 

although identification of the effects of SNAP is partly from our exogenous variables and partly from the distributional assumptions that we make about the unobservables. Moreover, we only have observations from a subset of States in each 

NHANES wave, and the strength of the instruments may depend on how different 

policies change in the subset over successive NHANES waves. This could explain 

why instruments used in previous studies concerning food security are not as strong 

in our data. 

Our results give mixed evidence concerning policy proposals to limit foods that 

SNAP participants can purchase. Taking selection effects into account, SNAP 

participants have slightly lower HEI scores than comparable nonparticipants. 

However, SNAP itself induces a large improvement in the likelihood of consuming 

whole fruit, but a slight decline in the consumption of dark green/orange vegetables. 

While the program could do more to improve participants’ diets in that context, the 

question is how to alter the program without reducing its effectiveness in improving 

other diet outcomes and combating food insecurity. For example, it may be possible 

to reduce food insecurity while incentivizing the purchase of healthier foods, rather 

than restricting purchases of unhealthy foods. This approach is also supported by 

empirical evidence that subsidies for healthy foods (rather than taxes of unhealthy 
Supplemental Nutrition Assistance Program (SNAP) Participation Leads 25

Economic Research Service, USDA to Modest Changes in Diet Quality, ERR-147

ones) would help reduce the costs of cardiovascular disease (Rahkovsky and 

Gregory, 2013). Addressing this question—how to strike a programmatic balance 

between reducing food insecurity and mandating healthy food purchases— is an 

avenue for further research.
26 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

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Appendix table A.1

Parameter Estimates of Outcome Equation

HEI Total fruit Whole fruit Total veg DkGOrVeg Total grain Whole grain

SNAP

-1.441 0.270 2.795*** -0.660 -0.735*** -0.039 -0.383

(4.503) (0.710) (0.209) (0.592) (0.187) (0.147) (0.233)

Hispanic

5.631*** 0.744*** 0.661*** 0.222** 0.335*** 0.263*** -0.166**

(0.979) (0.118) (0.127) (0.100) (0.103) (0.060) (0.076)

Black

-1.290 0.182 -0.551*** -0.145 0.113 -0.081 -0.076

(0.843) (0.122) (0.141) (0.119) (0.093) (0.066) (0.069)

Other race

3.331*** 0.430** 0.526*** 0.407*** 0.252 0.312*** -0.141

(1.156) (0.182) (0.180) (0.137) (0.171) (0.089) (0.144)

High school 

dropout

-1.097* -0.175** -0.263*** -0.145* 0.145** 0.032 -0.107*

(0.571) (0.080) (0.100) (0.078) (0.071) (0.062) (0.060)

Some college

0.646 0.093 0.175 -0.104 0.076 0.041 0.188***

(0.582) (0.076) (0.121) (0.077) (0.068) (0.061) (0.064)

College graduate

5.712*** 0.689*** 0.961*** 0.324** 0.564*** 0.121 0.339***

(1.314) (0.148) (0.171) (0.144) (0.170) (0.078) (0.112)

Annual 

household 

income ($000’s)

-0.000 0.000 0.000*** -0.000 -0.000* 0.000 -0.000

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Age

0.139*** 0.024*** 0.037*** 0.011*** 0.010*** 0.003*** 0.011***

(0.020) (0.003) (0.003) (0.003) (0.002) (0.001) (0.002)

Married

1.194* 0.056 0.140 0.056 0.058 0.024 -0.020

(0.616) (0.104) (0.108) (0.086) (0.070) (0.043) (0.053)

Body weight, 

lagged

-0.009* -0.002*** -0.003*** -0.000 -0.000 -0.000 -0.000

(0.006) (0.001) (0.001) (0.001) (0.001) (0.000) (0.000)

Employed

-1.201* -0.092 0.192** -0.059 0.034 0.008 -0.248***

(0.632) (0.100) (0.083) (0.106) (0.058) (0.065) (0.057)

Household size

-0.274 -0.043 -0.120*** 0.038 0.041 0.002 -0.058***

(0.255) (0.035) (0.032) (0.030) (0.026) (0.017) (0.019)

Gross State 

product 

($Billions)

-0.000 0.000 -0.000 0.000** 0.000 -0.000 0.000

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

State per capita 

income ($000's)

0.420** 0.024 -0.012 -0.008 0.019 0.010 0.025*

(0.186) (0.034) (0.036) (0.016) (0.014) (0.010) (0.014)

Nutrition education, $ per poor 

person

-0.171 -0.024 -0.027 -0.013 -0.021 -0.003 0.012

(0.166) (0.021) (0.032) (0.022) (0.017) (0.011) (0.018)

Current unemployment rate

-0.563 -0.049 -0.081 -0.150*** -0.074 -0.061** 0.034

(0.495) (0.079) (0.098) (0.054) (0.069) (0.030) (0.058)

Lagged unemployment rate

0.467 0.101 0.102 0.101** 0.007 0.021 0.072

(0.446) (0.075) (0.084) (0.047) (0.057) (0.034) (0.052)

—continued
30 Supplemental Nutrition Assistance Program (SNAP) Participation Leads 

to Modest Changes in Diet Quality, ERR-147 Economic Research Service, USDA

Appendix table A.1

Parameter Estimates of Outcome Equation—Continued

Milk Meat/Beans Oils Saturated fat Sodium SoFAAS

SNAP

-0.054 -0.248 0.372 -0.193 -0.046 -0.820

(0.590) (0.310) (0.741) (0.987) (0.740) (1.423)

Hispanic

-0.370** 0.401*** -0.584*** 1.257*** 0.430** 2.458***

(0.185) (0.151) (0.152) (0.161) (0.182) (0.486)

Black

-1.658*** 0.444*** 0.001 0.450** 0.220 -0.395

(0.172) (0.135) (0.211) (0.198) (0.166) (0.439)

Other race

-1.435*** 0.419 -0.526* 1.084*** -0.763** 2.823***

(0.347) (0.267) (0.311) (0.324) (0.371) (0.661)

High school dropout

-0.116 0.177 -0.662*** 0.235 -0.037 -0.268

(0.164) (0.147) (0.165) (0.167) (0.144) (0.268)

Some college

0.385*** -0.106 0.073 0.073 -0.026 -0.213

(0.146) (0.176) (0.156) (0.170) (0.147) (0.300)

College graduate

0.544** 0.239 0.034 0.369 -0.431** 2.060***

(0.252) (0.206) (0.281) (0.302) (0.202) (0.521)

Annual household 

income ($000’s)

0.000* 0.000 -0.000 -0.000 -0.000 -0.000

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Age

-0.007 0.007** 0.006 -0.005 -0.010* 0.058***

(0.004) (0.003) (0.005) (0.005) (0.006) (0.008)

Married

-0.149 0.259** 0.246 0.214 -0.103 0.539*

(0.172) (0.131) (0.151) (0.212) (0.136) (0.276)

Body weight, lagged

-0.002* 0.006*** -0.000 -0.003** -0.004*** -0.001

(0.001) (0.001) (0.001) (0.001) (0.001) (0.002)

Employed

-0.282** 0.087 0.083 -0.014 -0.032 -0.695**

(0.143) (0.117) (0.152) (0.163) (0.159) (0.279)

Household size

-0.100** 0.028 -0.009 -0.036 0.027 -0.093

(0.047) (0.027) (0.054) (0.048) (0.040) (0.111)

Gross State product 

($Billions)

0.000 -0.000*** 0.000 0.000 0.000 -0.000

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

State per capita 

income ($000's)

0.091*** 0.096*** 0.011 -0.045 -0.060** 0.238***

(0.035) (0.028) (0.031) (0.033) (0.030) (0.067)

Nutrition education, $ 

per poor person

-0.107*** 0.010 0.021 0.004 0.019 -0.048

(0.041) (0.024) (0.031) (0.047) (0.033) (0.065)

Current unemployment rate

0.051 -0.131* -0.230** 0.103 0.380*** -0.367*

(0.104) (0.068) (0.109) (0.107) (0.101) (0.206)

Lagged unemployment rate

0.155 0.081 0.165* -0.400*** -0.073 0.127

(0.116) (0.094) (0.100) (0.075) (0.113) (0.196)

N 6,668

SNAP = Supplemental Nutrition Assistance Program; HEI = Healthy Eating Index; DkGOrVeg = Dark green/orange vegetables; SoFAAS = Solid 

fats, alcohol, and added sugar

Note: *p<.10, **p<.05, ***p<.01. State fixed effects not shown. N denotes sample size.