Abstract

Although the United States has one of the highest standards of living in the world, nearly 15 percent of all households and 39 percent of near-poor households were food insecure in 2008. The Supplemental Nutrition Assistance 

Program (SNAP, formerly called the Food Stamp Program) is the cornerstone of federal food assistance programs 

and serves as the first line of defense against food-related hardship, such as food insecurity. Using the 1996, 2001, 

and 2004 Survey of Income and Program Participation (SIPP) panels, this paper measures SNAP’s effectiveness in 

reducing food insecurity using a dummy endogenous variable model with instrumental variables to control for selection bias. Recent changes in state SNAP policies and rules provide exogenous variation, which we use to control for 

selection into the program. Results from naïve models that do not control for the endogeneity of SNAP receipt show 

that SNAP receipt is associated with higher food insecurity. However, instrumental variable models that control for 

the endogeneity of SNAP receipt suggest that SNAP receipt reduces the likelihood of being food insecure by roughly 

30 percent and reduces the likelihood of being very food insecure by 20 percent. These findings provide evidence 

that SNAP is meeting its key goal of reducing food-related hardship.

Keywords: Food stamps, SNAP, food insecure, food insufficient, instrumental variables, selection bias

By Caroline Ratcliffe and Signe-Mary McKernan, The Urban Institute

How Much Does Snap Reduce 

Food Insecurity?

Contractor and Cooperator Report No. 60

April 2010

This study was conducted by The Urban Institute under a cooperative research 

contract with USDA’s Economic Research Service (ERS) Food and Nutrition 

Assistance Research Program (FANRP): contract number 59-5000-7-0113. 

(ERS project representative: Laura Tiehen). The views expressed are those of 

the authors and not necessarily those of ERS or USDA.
 

How Much Does SNAP Reduce Food Insecurity? 

Caroline Ratcliffe 

Signe-Mary McKernan

The Urban Institute 

2100 M Street, NW 

Washington, DC 20037 

April 2010 

We thank Gregory Acs, Margaret Andrews, Mark Nord, Laura Tiehen, Douglas Wissoker, and participants at the 

2008 Association for Public Policy Analysis and Management Fall Conference and 2009 Annual Welfare Research 

and Evaluation Conference for their comments. We also thank Katie Vinopal for her excellent research assistance. 

This research was supported by the U.S. Department of Agriculture’s Economic Research Service, Food Assistance 

and Nutrition Research Program. The views expressed are those of the authors and should not be attributed to the 

U.S. Department of Agriculture, the Urban Institute, its trustees, or its funders. 

THE URBAN INSTITUTE 2100 M STREET, N.W. / WASHINGTON D.C. 20037
 1 

Introduction 

Although the United States has one of the highest standards of living in the world, nearly 15 

percent of all households and 39 percent of near-poor households (below 130 percent of the 

poverty threshold) were food insecure in 2008 (Nord, Andrews, and Carlson 2009). These 

numbers are up from 11 percent and 34 percent (respectively) in 2007 and are likely climbing 

with the current economic downturn marked by double-digit unemployment. Food insecurity has 

been connected with an array of negative outcomes, including poor health among children, lower 

academic achievement, and depression (Oberholser and Tuttle 2004). 

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

Program) is the largest food-assistance program in the United States and is the cornerstone of the 

federal food-assistance programs. It serves as the first line of defense against hunger (USDA 

2007) and is designed to reduce food-related hardship, such as food insecurity. Increasingly, a 

key policy question is, how effective is SNAP in reducing food insecurity? Understanding the 

effectiveness of SNAP in meeting its goal is important for SNAP administrators as they make 

changes to their programs, as states have done to a large degree in recent years. 

Identifying the extent to which SNAP reduces food insecurity is complicated by the fact 

that households that do and do not receive SNAP benefits can differ in systematic ways. Persons 

in households that are most needy and food insecure are more likely to be eligible for and to take 

up SNAP benefits, so simple comparisons of food insecurity for those who do and do not receive 

SNAP benefits are likely to find better outcomes for those who do not receive SNAP benefits. 

Selection of more needy households into SNAP makes it difficult to identify a causal relationship 

between SNAP participation and food insecurity. 

This paper measures SNAP’s effectiveness in reducing food insecurity using a dummy 

endogenous variable model with instrumental variables to control for selection bias. Recent 

changes in state SNAP policies and rules provide exogenous variation, which we use to control 

for selection into the program. The federal government began to give states flexibility to change 

SNAP policies in the mid- to late-1990s. These changes culminated in the Farm Security and 

Rural Investment Act of 2002 (the Farm Bill), which provides broader flexibility to states to set 

SNAP policies (and rules). Additional flexibility has been subsequently provided. This variation 

in SNAP policies across states and over time provides the instrumental variables for the analysis. 
 2 

Household level data come from the nationally representative, longitudinal 1996, 2001, and 2004 

Survey of Income and Program Participation (SIPP) panels. State-level SNAP data come 

primarily from the Food Stamp Program State Rules Database. 

We examine two measures of food-related hardship. One measure captures whether 

households are food insecure, while the second captures a higher degree of hardship and 

identifies households that are very food insecure.1

 Results from models that do not control for the 

endogeneity of SNAP receipt show that SNAP receipt is associated with higher food insecurity 

(both measures). This finding is consistent with the self-selection of more needy and foodinsecure households into SNAP. However, instrumental variable models that control for the 

endogeneity of SNAP receipt suggest a different relationship. In these models, the receipt of 

SNAP benefits is found to reduce the likelihood of being food insecure and very food insecure. 

These findings provide evidence that SNAP is meeting its key goal of reducing food-related 

hardship. 

Relevant Literature and Contribution 

There is a growing body of literature examining SNAP participation and food insecurity. This 

literature uses a mix of methods and finds a mix of results. A number of studies have found that 

SNAP participants are more likely than nonparticipants to be food insecure or insufficient 

(Alaimo et al. 1998; Cohen et al. 1999, Jensen 2002; Ribar and Hamrick 2003; Wilde and Nord 

2005). 

Other studies have found that SNAP participation has no statistically significant effect on 

food insecurity or insufficiency (Gibson-Davis and Foster 2006; Gundersen and Oliveira 2001; 

Huffman and Jensen 2008). These studies acknowledge concerns about selection into SNAP and 

several take steps to address this selection. For example, Wilde and Nord (2005) use a panel data 

approach, Gibson-Davis and Foster (2006) use propensity score matching (and caution against 

it), and Gundersen and Oliveira (2001) use an instrumental variables (IV) approach and 

simultaneous probit model for SNAP participation and food insufficiency.2

 Gundersen and 

Oliveira, in one of the early studies that address selection, uses a measure of stigma (shop at 

 

1

 The U.S. Department of Agriculture refers to “very food insecure” households as households with “very low food 

security.” We use the terminology very food insecure so that comparisons of the food insecure and very food 

insecure findings are more straightforward. 

2

 For a review of findings by empirical approach see Wilde (2007). 
 3 

store where unknown) as the instrument to identify the food-insecurity equation. In their naïve 

model that does not control for selection, they find that SNAP receipt statistically significantly 

increases food insufficiency. The coefficient on SNAP receipt remains positive and is larger in 

their model designed to control for selection, although the large standard error makes the 

coefficient statistically insignificant at conventional levels. 

While numerous studies find no evidence that SNAP reduces food-related hardship, 

several studies find evidence that SNAP reduces food insecurity or insufficiency (Bartfeld and 

Dunifon 2006; Borjas 2004; DePolt, Moffitt, and Ribar 2008; Nord and Golla 2009; Yen et al. 

2008).3

 These studies examine different populations and use a variety of data and methods. 

Bartfeld and Dunifon (2006), Borjas (2004), and Nord and Golla (2009) use the Current 

Population Survey (CPS) to estimate the relationship between program participation and food 

insecurity, but the three papers focus on different populations. Borjas focuses on immigrants, 

Bartfeld and Dunifon examine households with children, and Nord and Golla focus on 

households shortly before and after beginning to receive SNAP benefits. The analytic approach 

of these three papers also differs. Bartfeld and Dunifon use hierarchal regression and find that 

low-income and near-poor families in states with higher SNAP participation rates are less likely 

to be food insecure. Borjas, on the other hand, uses an IV approach and finds that reductions in 

immigrants’ public assistance participation (cash benefits, SNAP, or Medicaid) leads to increases 

in immigrants’ food insecurity. This IV approach controls for selection into SNAP with an 

instrument for public assistance participation that captures the generosity of states’ immigrant 

eligibility rules after the 1996 federal welfare reform. Nord and Golla use monthly data to 

examine household food insecurity before and after SNAP receipt. They find that food insecurity 

falls by roughly one-third after entry into SNAP. 

Yen et al. (2008) and DePolt et al. (2008) use smaller data sets that are not representative 

of the U.S. population and find results that may be explained in part by their datasets. Yen et al. 

(2008) use data from the 1996–97 National Food Stamp Program Survey, which is a survey of 

roughly 2,200 SNAP participants and income-eligible nonparticipants. DePolt et al. use data 

from the Three-City Study (Boston, Chicago, and San Antonio), which includes roughly 2,500 

 

3

 Studies have also found that SNAP benefit amounts are associated with lower food insecurity among select 

populations including SNAP participants (Rose, Gundersen, and Oliveira 1998) and households that experienced 

hunger in the past year (Kabbani and Kmeid 2005). 
 4 

families with children who had incomes below 200 percent of the federal poverty level at the 

initial interview (in 1999) and includes two follow-up interviews (in 2000–2001 and 2005).4 The 

restriction of these data sets to the low-income population should not be a significant drawback, 

because low-income households are an appropriate study population and the literature tends to 

focus on low-income and SNAP-eligible households. 

Both of these studies use methods to address the endogeneity of SNAP receipt. Unlike 

much of the literature, however, the studies find that SNAP participation is associated with lower 

food hardship in descriptive statistics or models that do not control for the self-selection into 

SNAP.5,6 DePolt et al. (2008) explain that this finding could be due to additional control 

variables in the Three-City Study data that are often not available in other data sets. When 

DePolt et al. use Chamberlain’s quasi-fixed-effect model to control for unobserved family 

characteristics that may affect both SNAP receipt and food hardship, they find a similar, 

although generally stronger, negative relationship. Yen et al. (2008) use IV models to control for 

the endogeneity of SNAP participation and find that SNAP participations lowers the severity of 

food insecurity. 

Our study contributes to the literature on the relationship between SNAP participation 

and food-related hardship by using the nationally representative SIPP data from the late 1990s to 

2005 and taking advantage of recent variation in state SNAP policies to control for selection into 

SNAP. This analysis shows that state SNAP rules and policies are important determinants of 

SNAP participation and are strong instrumental variables for estimating the effects of SNAP 

participation. Our findings provide evidence that SNAP reduces food-related hardship. This 

research is well-timed, as the USDA and others have considered expensive and difficult-toimplement random-assignment research designs to answer this question (Wilde 2007). 

 

4

 The National Food Stamp Program Survey and Three-City Study data may suffer less from SNAP participation 

underreporting than national surveys. 

5

 Yen et al. (2008) do not present findings from naïve models that do not control for the self-selection into SNAP, 

but descriptive statistics show less food hardship among SNAP participants than non-SNAP participants. 

6

 The primary specification in DePolt et al. (2008) measures SNAP participation as a benefit amount, not a binary 

indicator of participation. 
 5 

Conceptual Model 

Determinants of Food Insecurity 

At the micro level, food insecurity is a function of earned income, public and private transfers, 

and household composition—each of which is chosen, to some degree, by the household 

members. Because our primary focus is on the role that SNAP plays in food insecurity, we model 

food insecurity as a function of SNAP participation and the reduced-form determinants of earned 

income, public and private transfers, and household composition. The reduced-form 

determinants and their hypothesized effects are based on human capital theory (Becker 1975) 

and Becker’s (1991) theory of the demand for children. State and year fixed effects and 

economic variables are included in our empirical model to control for macro-level variables. 

Additional children in the household (especially young children) are hypothesized to 

increase food insecurity through their negative effect on wage labor hours and positive effect on 

household size. Additional working-age adults in the household are hypothesized to increase 

household labor supply (and earnings) and decrease food insecurity. However, if these additional 

adults do not work, then food insecurity can increase with the number of adults. Having a 

disabled person in the household is hypothesized to decrease labor supply (and earnings) because 

the individual may be unable (or limited in his or her ability) to work, and because another 

household member’s work hours may be limited by his or her need to care for the disabled 

individual. Increases in human capital are hypothesized to increase income, and thus, decrease 

food insecurity. Being young, a minority, a non-citizen, and/or female is hypothesized to lower 

income through their negative effects on wages, and thus increase food insecurity. Finally, 

improvements in the state of the economy are hypothesized to increase household income 

(through their positive effect on wages and the hours household members can choose to work) 

and reduce food insecurity. These variables provide the reduced-form control variables for our 

empirical model. 

Hypothesized Effect of SNAP Participation on Food Insecurity: SNAP participation can 

have a direct mechanical effect on household food insecurity, as well as an indirect behavioral 

effect. These two effects are hypothesized to go in opposite directions. The direct effect is 

hypothesized to reduce household food insecurity, while the indirect effect is hypothesized to 

increase household food insecurity. 
 6 

SNAP provides direct support to households so that the household can purchase food. 

Because the program transfers resources to households, we hypothesize that the direct 

mechanical effect of SNAP participation is to reduce food insecurity. On the other hand, the 

availability of additional resources to purchase food could lead SNAP-participating households 

to reduce their labor supply, and thus, earnings. For example, household members might choose 

to reduce their labor supply in order to receive a larger benefit or become eligible for the 

program. Ceteris paribus, reduced earnings could lead to reduced food purchases and increased 

food insecurity. Thus, we hypothesize that the indirect behavioral effect of SNAP participation is 

to increase food insecurity. Overall, however, we expect the direct effect to dominate the indirect 

effect, and hypothesize that SNAP participation leads to lower levels of food insecurity. 

Determinants of SNAP Participation 

Participation in SNAP is affected by demographic and household characteristics and by the rules 

of the program. These program rules determine whether a family is eligible to participate, as well 

as the costs and benefits of program participation. Eligibility is a prerequisite for participation in 

any means-tested program. In some cases, family members can change their behavior to meet 

eligibility requirements (e.g., reduce earnings below the required threshold), while in other cases 

this is not possible (e.g., become a nonimmigrant to avoid eligibility restrictions on immigrants). 

Program rules can also affect the cost (pecuniary and nonpecuniary) of participation. For 

example, biometric technology (typically fingerprint imaging), which is used by some states to 

reduce multiple participation fraud, can increase the costs of participation. Program rules that 

lower the cost of participation are hypothesized to increase program participation, while program 

rules that increase the cost of participation are hypothesized to decrease program participation. 

Instruments for SNAP Participation: Our estimation approach uses state SNAP program 

rules to identify the IV model. Program rules that are strong instruments are those that affect 

SNAP participation, but do not directly lead to different levels of food insecurity across SNAP 

participants. We identify four such variables: use of biometric technology, outreach spending, 

and full and partial immigrant eligibility. 

Biometric technology is often in the form of fingerprint imaging and is used, in part, to 

reduce multiple participation fraud. As discussed above, biometric technology is hypothesized to 

increase the costs of SNAP participation and thus decrease participation. We measure outreach 

spending on a per capita basis, where the target population is those living below 150 percent of 
 7 

the poverty threshold who are not food stamp recipients. Higher outreach spending by states is 

hypothesized to increase participation via an increase in the number of SNAP applicants (due to 

increased knowledge about SNAP). Finally, more lenient immigrant eligibility rules are 

hypothesized to increase SNAP participation among immigrants. States identified as having “full 

immigrant eligibility” are those in which all legal non-elderly adult immigrants who meet other 

program requirements are eligible for federal benefits or state-funded food assistance. States 

identified as having “partial immigrant eligibility” are those in which some, but not all, legal 

non-elderly adult immigrants who meet other program requirements are eligible for federal 

benefits or state-funded food assistance. 

These four variables are set at the state level and are not controlled by any given sample 

member. Still, a potential concern is that these policy changes are endogenous to the processes 

under investigation. Our model design helps alleviate this problem by including state fixed 

effects (which control for time-invariant unobservable heterogeneity within states) and year fixed 

effects (which control for unobservable heterogeneity across years), as well as state-level 

economic controls. The quality of these instruments in our empirical analysis is discussed below. 

Empirical Model 

Our empirical model uses an IV approach to control for the endogeneity of SNAP participation. 

We estimate a bivariate probit model with an endogenous dummy variable, using state SNAP 

policies as instrumental variables. As discussed in the conceptual model, some SNAP policies 

are hypothesized to affect food insecurity only through their effect on SNAP participation. The 

model is identified by these SNAP policies. With this approach, only the effects of SNAP 

participation that are correlated with these SNAP program rules are included in the causal effect 

of participation. 

We measure the total (direct and indirect) effect of SNAP participation on food insecurity 

using a dummy endogenous variable model (Heckman 1978) with instrumental variables. Our 

model consists of two equations: one equation relating food insecurity to SNAP participation and 

a second reduced-form equation describing SNAP participation as a function of state program 

rules. The two equations are as follows: 
 8 

EXSNAP Y μ Y

istts3st2istist

*

ist = β +++++ ετγγ

where Yist =1 if * Yist >0 and Yist =0 otherwise (1) 

EX Z SNAP μ S

st ist st istts

*

ist 1 2 3 +++++= ετδδδ

where ist SNAP =1 if *

ist SNAP >0 and ist SNAP =0 otherwise (2) 

In this model, Yist is an indicator variable measuring whether household i in state s at 

month t is food insecure (or very food insecure). SNAPist is an indicator variable for whether 

household i in state s at month t participates in SNAP. The coefficient on SNAP participation (β) 

captures the total effect of participation, including both the direct effect of participation as well 

as the indirect effect through, for example, changes in labor supply. 

The remaining explanatory variables in the equations are drawn from the conceptual 

framework described above. Xist is a vector of variables controlling for individual-level and 

household-level characteristics (age, race and ethnicity, noncitizen immigrant, educational 

attainment, number of children and adults in household, female- and male-headed household, 

disabled person in household, and metropolitan status).7 The vector Zst represents the instruments 

that identify the model and include four specific state SNAP policies (biometric technology, 

outreach spending per capita, and partial and full immigrant eligibility), as discussed in the 

conceptual model above.8 Est is a vector of time-varying variables controlling for economic 

conditions (monthly state unemployment rate, monthly state employment-population ratio, 

annual state per capita income, and quarterly gross domestic product [GDP]). Finally, μs is the 

state fixed effect, τt is the year fixed effect, and Y

ist ε and Sist ε are the error terms. State and year 

fixed effects are included in all equations to control for state- and year-specific unobservable 

factors that affect SNAP participation or food insecurity. The state fixed effects control for timeinvariant unobservable heterogeneity (differences) across states (e.g., public sentiment toward 

welfare receipt), while the year fixed effects control for unobservable heterogeneity across years. 

 

7

 These characteristics are available monthly except for U.S. citizenship status. U.S. citizenship is available only 

once in the 1996 and 2001 SIPP panels (topical module 2), but is available monthly in the 2004 SIPP panel. 

8

 Biometric technology and partial and full immigrant eligibility rules are available monthly. The outreach spending 

data are reported annually and quarterly, and these dollars are spread equally over the relevant months. That is, fiscal 

year outreach dollars are spread equally over months in the fiscal year and quarterly outreach dollars are spread 

evenly over months in the quarter. To obtain our measure of outreach spending per capita, state outreach spending is 

divided by the state population below 150 percent of the federal poverty threshold (annual measure) minus the 

number of state SNAP recipients (monthly measure), which is a measure of the state target outreach population. 
 9 

To account for potential serial correlation in the error term, we cluster our standard errors by 

state as recommended by Bertrand et al. (2004). 

We estimate a bivariate probit model because the dependent variables in equations 1 and 

2 are binary—food insecure or very food insecure (yes/no) and SNAP participation (yes/no). We 

assume the error terms are draws from a bivariate normal distribution with mean zero and 

variance of one, and estimate the equations simultaneously using a bivariate probit model. The 

correlation coefficient is ρ = Cov( Y

ist ε , Sist ε ). If ρ ≠ 0 , then the error terms are correlated and 

probit estimation of equation 1 ignoring equation 2 will yield inconsistent estimates of the 

parameters due to the endogeneity of SNAP participation.9

Our reduced-form equation for SNAP participation (equation 2) uses the variation across 

states and in the timing of state policy changes to identify the model. As described above, states 

implemented changes to their Supplemental Nutrition Assistance Programs at different times. 

We use this variation across states and time to identify the effects of program rules on 

participation. 

The ability of our bivariate probit model to correct for the endogeneity of SNAP receipt 

depends on the explanatory power of the instruments in the SNAP receipt equation (equation 2) 

and on whether it is appropriate to exclude the instruments from the food-insecurity equation. 

Our set of instruments (biometric technology, outreach spending, and partial and full immigrant 

eligibility) has good predictive power in the SNAP receipt equation, indicating that the 

instruments are strongly correlated with the food insecurity. We also examine the strength of our 

instruments using the Kleibergen-Papp statistic, which provides evidence that the instrumental 

variables identify the model; we reject the null hypothesis that the model is underidentified.10 In 

addition, we find that the instruments do not independently affect household food insecurity. We 

test the exogeneity of our instruments using Hansen’s J-test and find that we do not reject the 

null hypothesis that all of the instruments are exogenous.11 Taken together, these characteristics 

 

9

 As a sensitivity test, we estimate a two-step linear IV model. We find that the marginal effects of SNAP 

participation (the key variable of interest) from the bivariate and linear models have the same sign and are very 

similar in magnitude. As expected, the linear IV model produces less efficient estimates than the bivariate probit 

model. 

10 Our primary specification clusters the standard errors by state, while the Kleibergen-Papp statistics are calculated 

based on models that cluster the standard errors at the household level. The standard errors from these two sets of 

models are quite similar and the levels of statistical significance are virtually unchanged across the two models. 

11 These two tests of our instrumental variables are carried out using the linear IV model, since tests to evaluate the 

quality of instrumental variables are more developed for the linear framework. 
 10 

suggest that the instruments influence food insecurity only through their effect on SNAP 

participation. 

In addition to the bivariate probit model, we estimate a naïve probit model of the effect of 

SNAP receipt on food insecurity (equation 1). If food-insecure households are more likely to 

become SNAP participants, the estimated coefficients from this model will be biased. A 

comparison of the probit and bivariate probit model results highlights the importance of 

correcting for this selection. 

Study Population 

Selection of the study population is an important element of this study, as an inappropriate study 

population could lead to biased estimates. Because the study focuses on SNAP participation and 

food insecurity, one might select the study population to include only households eligible for 

SNAP. Defining the study population this narrowly has a drawback, however. Focusing only on 

the SNAP-eligible population excludes households that can slightly alter their behavior to 

become eligible for benefits (i.e., it excludes households at the margin). Ashenfelter (1983), for 

example, argues that if the elasticity of labor supply does not equal zero, the pool of persons that 

should be examined as eligible for a program is larger than those who would actually qualify for 

the program under current income and asset limits. The concern with limiting the sample to the 

SNAP eligible population is that it results in a sample of households that are disproportionately 

more likely to alter their behavior to become eligible for SNAP benefits. Carrying out our 

analysis on a select group of households may produce biased estimates. As a result, we carry out 

our primary analyses on a more broadly defined study population and then conduct sensitivity 

tests with a more restricted population. 

Our primary study population includes low-income households defined as being below 

150 percent of the poverty threshold and having readily available assets of less than or equal to 

$4,000, or $5,000 if at least one household member is age 60 or older. We also carry out 

robustness checks on a secondary population that more closely mimics the SNAP-eligible 

population—households with incomes below 130 percent of the poverty threshold and readily 
 11 

available assets of less than or equal to $2,000, or $3,000 if at least one household member is age 

60 or older.12 

Data 

Survey of Income and Program Participation (SIPP): Individual-level data for the analysis come 

from the 1996, 2001, and 2004 SIPP panels. Each of these SIPP panels contains a nationally 

representative (noninstitutional) sample of between 36,000 and 46,000 households whose 

members are interviewed at four-month intervals (referred to as waves).13 In addition to 

collecting monthly data on a host of demographic and economic characteristics, the SIPP 

includes “topical modules” that ask periodic questions about a variety of topics including 

material well-being and asset holdings. 

A key strength of the SIPP is its monthly data on SNAP participation, income, and 

household composition. At each interview, data are collected on these and other variables for 

each of the preceding four months. SNAP benefits are received monthly, not annually, so the 

monthly SIPP data allow participation to be examined over the same time period that benefits are 

received. All household-level characteristics identified in the conceptual framework are available 

in the SIPP. 

Variables that measure household food insecurity are created using a series of questions 

available in the adult well-being topical module. These data are available once in each of the 

three SIPP panels. Combining the 1996, 2001, and 2004 SIPP panels provides information on 

whether household are food insecure and very food insecure in three separate years—1998 (April 

to October), 2003 (February to August), and 2005 (February to August). While the SIPP does not 

provide this information on a more regular basis (e.g., annually), the SIPP data do provide these 

food-insecurity measures in years when state SNAP policies were changing and in strong and 

weak economic times. It is this variation that allows us to identify our empirical model. 

 

12 Among other restrictions, households’ monthly gross income must be below 130 percent of the federal poverty 

level to be eligible for SNAP. In addition, households must have no more than $2,000 in countable assets if all 

household members are under age 60 and no more than $3,000 in countable assets if at least one household member 

is age 60 or older. 

13 This analysis is based on individuals who live in the 46 states (including DC) that we are able to identify in the 

SIPP. North Dakota, South Dakota, Wyoming, Maine, and Vermont cannot be individually identified in the SIPP 

and so are excluded from our analysis. 
 12 

Our food-insecurity measures take account of whether households have enough food to 

eat and whether households are able to afford balanced meals. Five questions in the SIPP topical 

module are used to generate our two indicators of household food-related hardship: 

1. The food that you bought just didn't last and you didn't have money to get more. Was that 

[this statement] often, sometimes, or never true for you in the last four months? 

2. You couldn't afford to eat balanced meals. Was that [this statement] often, sometimes, or 

never true for you in the last four months? 

3. In the past four months did you or the other adults in the household ever cut the size of 

your meals or skip meals because there wasn't enough money for food? 

4. In the past four months did you or the other adults in the household ever eat less than you 

felt you should because there wasn't enough money to buy food? 

5. In the past four months did you or the other adults in the household ever not eat for a 

whole day because there wasn't enough money for food? 

These five questions are used in conjunction with the methodology developed by the U.S. 

Department of Agriculture’s Economic Research Service (Nord 2006) to generate our two 

indicators of food-related hardship.14 Our first measure identifies households that have low or

very low food security, while the second is a more severe measure that identifies households that 

have very low food security.15 We refer to these levels of food hardship as “food insecure” and 

“very food insecure,” respectively.16

These two food-insecurity measures capture households’ experiences over the full SIPP 

wave (i.e., a four-month period), while household characteristics such as SNAP participation, 

income, and household structure are available each month of the wave. Household characteristics 

in each month of the wave can influence households’ food insecurity, because the outcomes are 

measured over the four-month period. Thus, households are included in our sample up to four 

 

14 For more information about the questions and the food insecurity measure, go to 

http://www.ers.usda.gov/data/FoodSecurity/SIPP/ (accessed December 1, 2009). 15 Prior to 2006, “low food security” was referred to as “food insecure without hunger” and “very low food security” 

was referred to as “food insecure with hunger.” The new labeling was introduced by USDA based on 

recommendations from the Committee on National Statistics (CNSTAT), but the content of these measures did not 

change (Nord 2006). 

16 If respondents do not answer the food insecurity questions, the U.S. Census Bureau imputes values for these 

households. Our main analysis excludes households with imputed food insecurity or SNAP receipt data. However, 

we conduct sensitivity tests on the full sample of households (those with and without imputed data) and the model 

results are very similar (discussed below). 
 13 

times—once for each month in the wave that food insecurity and food insufficiency are 

measured.17 

Potential weaknesses of the SIPP involve concerns about the underreporting of SNAP 

receipt and attrition bias. Estimates suggest that the SIPP underreports SNAP receipt by 7 

percent to 19 percent (Bitler, Currie, and Scholz 2002; Cody and Tuttle 2002). These studies also 

find that SNAP underreporting is smaller in the SIPP than in the CPS. A recent article by 

Gundersen and Kreider (2008) suggests that misreported SNAP receipt could explain why earlier 

studies found no effect of SNAP participation on food-related hardship. While misreporting is an 

issue in the SIPP, and therefore our analysis, it does not lead us to conclude that SNAP does not 

affect food insecurity. Adjusting the SIPP data to account for the underreporting might be 

possible if the root causes of the underreporting were known. However, the source of the 

underreporting is likely complex and choosing the wrong adjustment strategy could lead to 

greater biases (Cody and Tuttle 2002, p. 25). Research to investigate the degree of attrition bias 

measurable in the SIPP suggests that poorer persons are more likely to leave the sample prior to 

the end of the panel. This analysis uses the SIPP weights to help account for attrition, 

nonresponse, and a complex sample design. 

Among our sample of low-income households, 24.4 percent are food insecure and 10.3 

percent are very food insecure (Table 1). Comparisons of these outcomes for households that do 

and do not participate in SNAP show higher rates of food insecurity among SNAP-recipient 

households. While 35.6 percent of SNAP-recipient households are food insecure, 19.9 percent of 

nonparticipating households are food insecure. Similarly, the percent of households that are very 

food insecure is higher among SNAP-participating than SNAP-nonparticipating households—

15.4 percent and 8.3 percent, respectively. The higher rates of food-related hardship among 

SNAP participants suggest that these households are more needy and are more likely to selfselect into SNAP. 

SNAP receipt is quite prevalent among low-income households, with roughly one-quarter 

(28.6 percent) of our sample receiving SNAP benefits. As compared with non-SNAP recipients, 

SNAP-recipient households tend to be younger, minority, less educated, and female headed, and 

 

17 We conduct sensitivity tests to examine whether the results are sensitive to the inclusion of households in multiple 

months. In models that include households only once per panel, we continue to find that SNAP participation 

statistically significantly reduces food insecurity (discussed below). 
 14 

to have more children and include a disabled member. Food-insecure households have these 

same tendencies, as compared with households that are food secure. Appendix Table A-1 

presents the means of all variables included in the analysis. 

Economic Variables: To control for changes in the economy, the SIPP data are 

supplemented with (1) monthly state unemployment rates, (2) annual state per capita income, (3) 

the monthly state employment-population ratio, and (4) quarterly gross domestic product from 

the Bureau of Economic Analysis (2008). 

SNAP Rules: Measures of state specific SNAP rules, which are the instrumental variables 

in the analysis, come largely from USDA’s Food Stamp Program State Rules Database.18 The 

SNAP rules used in this analysis—biometric technology, outreach spending per capita, and 

immigrant eligibility—are available in this database. This database only contains data through 

December 2004, so we used additional documents provided to us by the USDA to update these 

variables through 2005. 

Five states used biometric technology at some point over the study period. Two of the 

states made changes to their policies—California began to use biometric technology and 

Massachusetts ceased using biometric technology in the early 2000s. Twenty-five states changed 

their level of outreach spending over the analysis period, with 19 of these states making multiple 

changes in their level of spending. Average state per capita outreach spending has varied over the 

study period from a low of less than one cent per person to a high of 4.5 cents per person. 

Finally, 35 states began or ceased providing benefits for some, but not all, legal non-elderly adult 

immigrants (partial immigrant eligibility) and five states made changes to whether they provided 

benefits to all legal non-elderly adult immigrants (full immigrant eligibility). Over the analysis 

period, for example, two states moved from not providing to providing full immigrant eligibility 

(California and Wisconsin), two states shifted from providing to not providing full immigrant 

eligibility (Massachusetts and Rhode Island), and one state had changes in both directions 

(Minnesota).19

 

18 For more information about the Food Stamp Program State Rules Database go to: 

http://www.ers.usda.gov/Briefing/FoodNutritionAssistance/data/#fsdatabase (accessed December 1, 2009). 19 These descriptive statistics are based on the 46 states (including DC) that can be individually identified in the 

SIPP (excludes North Dakota, South Dakota, Wyoming, Maine, and Vermont). 
 15 

Results 

Food Insecure: The naïve probit model results, which do not control for the endogeneity of 

SNAP receipt, show that SNAP receipt is associated with higher food insecurity. The estimated 

coefficient on SNAP participation suggests that participating in SNAP is associated with an 8.6 

percentage point increase in the probability of being food insecure (Table 2, model 1). This result 

is consistent with the self-selection of more needy and food-insecure households into SNAP. 

Results from the bivariate probit model, which controls for the endogeneity of SNAP 

receipt, suggest a different relationship. The receipt of SNAP benefits is found to reduce the 

likelihood of food insecurity by 16.2 percentage points (Table 2, model 2).20 Nearly one-quarter 

of our low-income sample is food insecure, so these results suggest that SNAP has a substantial 

effect on households’ food insecurity and is achieving exactly what the program was designed to 

do—reduce food-related hardship. To further put this number in context, we use the marginal 

effect along with the SNAP recipients’ level of food insecurity to estimate the percent (versus 

percentage point) decline in food insecurity implied by our model. Our summary statistics show 

that 35.6 percent of SNAP recipients are food insecure. The bivariate probit model estimates 

suggest that SNAP recipients’ food insecurity would be 16.2 percentage points higher (51.8 

percent), if SNAP benefits were not available. The decrease in the likelihood of food insecurity 

from 0.518 without the SNAP program to 0.356 with the program suggests that SNAP receipt 

reduces food insecurity by 31.2 percent. The magnitude of this decline is consistent with Nord 

and Golla (2009), who find that the likelihood of being very food insecure falls by roughly onethird when households begin receiving SNAP benefits. 

A comparison of the SNAP receipt coefficients from the probit and bivariate probit 

models suggests that controlling for selection into SNAP is important for disentangling the effect 

of SNAP receipt on food insecurity. The model that does not control for the endogeneity of 

SNAP receipt shows that SNAP participation is associated with increased food insecurity, while 

the model that does control for the endogeneity shows that SNAP participation reduces food 

insecurity. Further, the correlation coefficient from the bivariate probit model indicates a positive 

 

20 The marginal effects are calculated as the average difference in the predicted probability of being food insecure 

for those with and without SNAP receipt. The calculations are based on the estimated parameters from the bivariate 

probit food insecurity equation (equation 1), which have been corrected for the endogeneity of SNAP receipt. 

Estimation of the univariate probablities is appropriate because our goal is to understand how SNAP receipt affects 

food insecurity, not the joint probabilities. We estimate the marginal effects using the Stata marginal effects 

command, mfx, with the option predict(pmarg1). 
 16 

and statistically significant correlation between unobservables that affect SNAP receipt and food 

insecurity (ρ=0.509 with a standard error of 0.054). Households that are more likely to be food 

insecure are also more likely to participate in SNAP. 

As discussed above, the validity of our IV model depends on the quality of the 

instruments. Because our model is overidentified, we are able to test the exogeneity of the 

instruments. Using Hansen’s J-test, we conclude that the instruments are indeed exogenous (we 

do not reject the null hypothesis that all of the instruments are exogenous, p=0.89). Using the 

Kleibergen-Papp statistic, we test whether the instrumental variables identify the model and 

reject the null hypothesis that the model is underidentified (p=0.0002). Each of the four 

instruments has the anticipated sign and statistically significantly (p<0.1) affects SNAP receipt 

(Table 3), and a joint test for significance of the four instruments indicates that they are jointly 

statistically significant at the one percent level (χ2(4)=15.8, p=0.003). The coefficients and 

standard errors of the instruments from the reduced-form SNAP participation equation are 

presented in Table 3 (column 1). 

Many household demographic characteristics are important determinants of food 

insecurity (Table 3, column 2). Households headed by younger persons are more likely to be 

food insecure, as are households headed by minorities and persons with limited education. 

Households with more children are also more likely to be food insecure. Female-headed and 

male-headed households are more likely than two-adults-headed households to be food insecure. 

Finally, having a disabled person in the household is associated with a higher likelihood of food 

insecurity. The state unemployment rate and employment-population ratio do not affect food 

insecurity, although a stronger economy as measured by quarterly GDP is found to reduce food 

insecurity. 

Very Food Insecure: Findings from our analysis of the relationship between SNAP 

participation and the likelihood of being very food insecure show a similar pattern. The model 

that does not control for selection into SNAP finds a positive, statistically significant relationship 

between SNAP receipt and being very food insecure, while the model that does control for 

selection finds that SNAP receipt statistically significantly reduces the likelihood of being very 

food insecure (Table 2, models 3 and 4). Fewer households are very food insecure than food 

insecure (10.3 percent versus 24.4 percent, respectively), and the magnitudes of the estimated 

coefficients are consistent with this lower prevalence. The bivariate probit model results suggest 
 17 

that SNAP benefit receipt reduces the likelihood of being very food secure by 3.9 percentage 

points. Translating this percentage point decline into a percent decline (as done above for food 

insecurity), we find that SNAP reduces the likelihood of being very food insecure by 20.2 

percent. This is lower than the roughly 30 percent decline found for food insecurity, although is 

still substantial. 

Like our analysis of food insecurity, the correlation coefficient indicates a positive and 

statistically significant relationship between unobservables that affect SNAP receipt and being 

very food insecure (ρ=0.284 with a standard error of 0.035). Also, we again find that Hansen’s Jtest suggests that all of the instruments are exogenous (p=0.53) and the Kleibergen-Papp statistic 

leads us to reject the null hypothesis that the model is underidentified (p=0.0002). Each of the 

four instruments has the anticipated sign and statistically significantly (p<0.1) affects SNAP 

receipt, and a joint test for significance of the four instruments indicates that they are jointly 

statistically significant at the 1 percent level (χ2(4)=14.9, p=0.005). Appendix Table A-2 presents 

the full set of coefficients from this model. 

Additional Specifications: As discussed above, we estimate our model on somewhat 

different samples to test the sensitivity of our results. First, we examine households with incomes 

below 130 percent of the poverty threshold, which more closely mimics the SNAP eligibility 

criteria. Analyses based on this more disadvantaged population show very similar results. The 

naïve models that do not control for selection into SNAP find a positive relationship between 

SNAP receipt and food insecurity, while models that control for selection find that SNAP receipt 

reduces food insecurity (Table 4). The bivariate probit results suggest that SNAP receipt reduces 

the likelihood of being food insecure by 12.8 percentage points and reduces the likelihood of 

being very food insecure by 3.4 percentage points. These percentage point declines translate into 

declines of 27.8 percent and 18.2 percent, respectively. These declines are very similar to what 

was found for the broader population of households with incomes below 150 percent of the 

poverty threshold. 

We also examine whether our findings are sensitive to having each household in the 

study sample for up to four months, by estimating the model on a sample that includes each 

household only once. Results from the bivariate probit model (estimated with this smaller 

sample) suggest that SNAP receipt statistically significantly (at the 1 percent level) reduces the 

likelihood of being food insecure by 16.0 percentage points and the likelihood of being very food 
 18 

insecure by 3.9 percentage points (not shown). These estimated effects are nearly identical to 

those presented in Table 3 (16.2 and 3.9 percentage points, respectively). We also estimate 

models on a sample that includes households that have imputed food-insecurity or SNAP-receipt 

data, and again find very similar results. In this case, the bivariate probit model results suggest 

that SNAP receipt statistically significantly (at the 1 percent level) reduces the likelihood of 

being food insecure by 16.0 percentage points and being very food insecure by 4.1 percentage 

points (not shown). 

In addition to testing sensitivity to different study populations, we examine another 

measure of food-related hardship—food insufficiency—which has also been examined in the 

literature. Food insufficiency captures a relatively severe level of food hardship and is more 

similar to our very-food-insecure than to our food-insecure measure. Food-insufficient 

households are those that report sometimes or often not having enough to eat. Food insufficiency 

is not our primary outcome, because USDA official statistics report on food insecurity, not food 

insufficiency. Among our sample of households with incomes below 150 percent of the poverty 

threshold, 6.9 percent are food insufficient (compared with 10.3 percent that are very food 

insecure). Results from the bivariate probit model suggest that SNAP participation statistically 

significantly (at the 1 percent level) reduces food insufficiency by 2.7 percentage points, or by 

19.4 percent. This is nearly identical to our finding that SNAP reduces the likelihood of being 

very food insecure by 20.2 percent. 

Discussion and Conclusion 

Using nationally representative data from the late 1990s and early- to mid-2000s and 

instrumental variables models, this study provides evidence that SNAP reduces households’ 

food-related hardships. We find that SNAP participation reduces the likelihood of being food 

insecure, very food insecure, and food insufficient. How much does SNAP reduce food-related 

hardship? The results suggest that the effect of the program is sizable. Results from our primary 

specification suggest that participation in SNAP reduces the likelihood of being food insecure by 

16.2 percentage points (31.2 percent) and reduces the likelihood of being very food insecure by 

3.9 percentage points (20.2 percent). Results from our specification tests show similar declines. 

Further, we find that SNAP receipt reduces food insufficiency by about 20 percent. These 

estimated effects are substantial and provide evidence that SNAP is meeting its key goal of 

reducing food-related hardship. 
 19 

Given inherent difficulties in identifying causal effects where individuals self-select into 

programs (such as SNAP), the conclusions of any single analysis cannot be taken as definitive. 

This study contributes recent, nationally representative findings from models designed to control 

for self-selection to a growing body of literature that finds SNAP reduces food insecurity. For 

example, this study provides results consistent with Nord and Golla’s recent study (2009) using 

CPS data, which finds that food insecurity falls by roughly one-third after entry into SNAP. Our 

results are also consistent with analyses by DePolt et al. (2008) and Yen et al. (2008) that find 

SNAP reduces food hardship using data from the National Food Stamp Program Survey and the 

Three-City Study, respectively. 

In the current economic downturn, it is important for policymakers and program 

administrators to understand the effectiveness of their programs so they can better serve lowincome households and those experiencing food-related hardship. The results of this study 

suggest that program administrators can improve the well-being of households by increasing 

their enrollment in SNAP. Prior research suggests that this can be accomplished by making 

SNAP program rules more lenient, for example, and by expanding outreach (e.g., Bartlett, 

Burstein, and Hamilton 2004; Ratcliffe, McKernan, and Finegold 2008; Yen et al. 2008; Ziliak, 

Gundersen, and Figlio 2003). In addition, easing SNAP rules is a cost-efficient way for states to 

increase SNAP participation and improve the well-being of residents, as the federal government 

pays roughly half of the programs’ administrative costs and the full cost of benefits. States, 

however, should weigh concerns about program fraud and abuse and federal resources in 

deciding whether and which SNAP policies to ease. 
 20 

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Variable All Households

SNAP 

Participants

SNAP 

Nonparticipants

Food Insecure 24.4% 35.6% 19.9%

Very Food Insecure 10.3% 15.4% 8.3%

SNAP Receipt 28.6% 100% 0.0%

Number of Observations 65,269 20,197 45,072

Note: All percentages are weighted.

1

 Sample includes households with income below 150% of the poverty threshold who have liquid assets below $4,000, or below 

$5,000 if one member of the household is age 60 or older.

Table 1: Low-Income Households' Food-Related Hardship and Supplemental Nutrition 

Assistance Program (SNAP) Participation,

Households with Income below 150% of Poverty Threshold1

23
Explanatory Variable

SNAP Receipt 0.275 *** 0.086 -0.582 *** -0.162 0.208 *** 0.034 -0.268 *** 

(0.028) (0.091) (0.033) (0.062)

Rho 0.509 *** 0.284 ***

(0.054) (0.035)

Number of 

Observations 65,269 65,269 65,269 65,269

1 Sample includes householdes with income below 150% of the poverty threshold who have liquid assets below $4,000, or below $5,000 if one member 

household is age 60 or older.

Notes: The unit of observation is a household-month. Robust standard errors are presented within parentheses. Standard errors are adjusted for clustering models include controls for age, age squared, noncitizen immigrant, black, Hispanic, other non-white race, no high school degree, high school degree onchildren in household, number of adults in household, female-headed household, male-headed household, disabled person in household, and metropolitaunemployment rate, state employment-population ratio, state per capita income, and gross domestic product; and state and year fixed effects. Instrumentare biometric technology, outreach spending per capita, and immigrant eligibility rules (i.e., all legal immigrants eligible interacted with noncitizen and simmigrants eligible interacted with noncitizen). *** p<0.01, ** p<0.05, * p<0.1

Coeff/SE

Marginal 

Effect Coeff/SE

Marginal 

Effect Coeff/SE

Marginal 

Effect Coeff/SE

Table 2: Estimates of the Effects of SNAP Participation on the Likelihood of Being Food Insecure and Very Food Insecu

Households with Income below 150% of Poverty Threshold1

Bivariate P

(4

Food Insecure

Probit

(1)

Bivariate Probit (IV)

(2)

Probit

(3)

Very Food Insecure

24
SNAP participation -0.582***

(0.091)

Instruments-State Food Stamp Rules 2

Biometric technology -0.269***

(0.095)

Outreach spending per capita 0.402*

(0.228)

All legal immigrants eligible X noncitizen immigrant 0.370**

(0.201)

Some legal immigrants eligible X noncitizen immigrant 0.312*

(0.180)

Demographic Characteristics

Age -0.016*** 0.021***

(0.004) (0.005)

Age squared 0.000** -0.000***

(0.000) (0.000)

Noncitizen immigrant -0.414** -0.015

(0.173) (0.055)

Race/Ethnicity (Omitted: White, non-Hispanic)

Black, non-Hispanic 0.383*** 0.290***

(0.028) (0.033)

Hispanic 0.214* 0.220***

(0.120) (0.074)

Other, non-Hispanic 0.294*** 0.144**

(0.078) (0.072)

Educational Attainment (Omitted: More than high school)

Less than high school 0.462*** 0.282***

(0.037) (0.030)

High school only 0.223*** 0.115***

(0.031) (0.025)

Number of children in household 0.264*** 0.088***

(0.015) (0.014)

Number of adults in household -0.045** 0.008

(0.015) (0.021)

Continued on next page

Table 3: Bivariate Probit (IV) Estimates of the Effects of SNAP Participation 

on the Likelihood of Being Food Insecure, 

Households with Income below 150% of Poverty Threshold1

Explanatory Variable

SNAP

Participation 

Food

Insecure

25
Variable

SNAP 

Participation

Food

Insecure

Household Structure (Omitted: Two adult-headed household)

Female-headed household 0.666*** 0.410***

(0.029) (0.046)

Male-headed household 0.240*** 0.296***

(0.042) (0.033)

Disabled person in household 0.793*** 0.614***

(0.036) (0.032)

Metropolitan area -0.093** 0.029

(0.037) (0.037)

Economic Characteristics

State monthly unemployment -0.078 -0.012

(0.219) (0.207)

State monthly employment-population ratio -6.398 -10.76

(22.07) (20.15)

State annual per capita income (in $100s) -0.001 0.003

(0.002) (0.002)

Quarterly GDP (in trillions) -0.069 -0.236**

(0.095) (0.096)

Year

1998 -0.460** -0.338*

(0.181) (0.186)

2003 -0.249*** -0.332***

(0.080) (0.096)

Constant 6.584 10.54

(22.339) (20.12)

Rho 0.509***

(0.054)

Number of Observations 65,269

1

 Sample includes householdes with income below 150% of the poverty threshold who have liquid assets below $4,000, or below 

$5,000 if one member of the household is age 60 or older.

Notes: The unit of observation is a household-month. Robust standard errors are presented within parentheses. Standard errors 

are adjusted for clustering by state. *** p<0.01, ** p<0.05, * p<0.1

2

 A joint test for significance of the four instruments indicates that they are jointly statistically significant at the one percent level 

(χ

2

(4)=15.8, p=0.003).

Table 3: Bivariate Probit (IV) Estimates of the Effects of SNAP Participation 

on the Likelihood of Being Food Insecure, 

Households with Income below 150% of Poverty Threshold1

, continued

26
Explanatory Variable

SNAP Receipt 0.251 *** 0.081 -0.424 *** -0.128 0.157 *** 0.027 -0.211 *** -0.034

(0.026) (0.109) (0.033) (0.073)

Rho 0.403 *** 0.221 ***

(0.065) (0.040)

Number of Observations 52,029 52,029 52,029 52,029

1 Sample includes householdes with income below 130% of the poverty threshold who have liquid assets below $2,000, or below $3,000 if one member of the 

household is age 60 or older.

Notes: The unit of observation is a household-month. Robust standard errors are presented within parentheses. Standard errors are adjusted for clustering by staAll models include controls for age, age squared, noncitizen immigrant, black, Hispanic, other non-white race, no high school degree, high school degree only, number of children in household, number of adults in household, female-headed household, male-headed household, disabled person in household, and metropolitan area; state unemployment rate, state employment-population ratio, state per capita income, and gross domestic product; state and year fixed effectInstrumental variables are biometric technology, outreach spending per capita, and immigrant eligibility rules (i.e., all legal immigrants eligible interacted with noncitizen and some legal immigrants eligible interacted with noncitizen). *** p<0.01, ** p<0.05, * p<0.1

Table 4: Estimates of the Effects of SNAP Participation on the Likelihood of Being Food Insecure and Very Food Insecure, 

Households with Income below 130% of Poverty Threshold1

Coeff/SE

Marginal 

Effect Coeff/SE

Marginal 

Effect Coeff/SE

Marginal 

Effect Coeff/SE

Margin

Effect

Bivariate Probit (IV)

(4)

Food Insecure

Probit

(1)

Bivariate Probit (IV)

(2)

Probit

(3)

Very Food Insecure

27
Variable Mean

Standard 

Deviation

Program Participation and Food-Related Hardship 

SNAP Receipt 0.286

Food Insecure 0.244

Very Food Insecure 0.103

Age 47.982 18.547

Noncitizen immigrant 0.112

White, non-Hispanic (omitted) 0.537

Black, non-Hispanic 0.231

Hispanic 0.189

Other, non-Hispanic 0.043

Education less than high school 0.271

Education high school only 0.354

Education more than high school (omitted) 0.375

Number of children in household 1.010 1.385

Number of adults in household 1.544 0.747

Female-headed household 0.518

Male-headed household 0.198

Two adult-headed household (omitted) 0.284

Disabled person in household 0.294

Metropolitan area 0.747

State monthly unemployment 5.290 1.040

State monthly employment-population ratio 0.947 0.010

State annual per capita income 29,361 4,058

Quarterly GDP (in billions) 10,275 730

Biometric technology 0.257

Outreach spending per capita 0.024 0.083

All legal immigrants eligible X noncitizen immigrant 0.026

Some legal immigrants eligible X noncitizen immigrant 0.078

Number of Observations 65,269

Appendix Table A-1: Descriptive Statistics, 

Households with Income below 150% of Poverty Threshold1

1

 Sample includes householdes with income below 150% of the poverty threshold who have liquid assets below 

$4,000, or below $5,000 if one member of the household is age 60 or older.

Demographic Characteristics

Economic Variables

Instruments

28
Explanatory Variable

SNAP

Participation

Very Food 

Insecure

SNAP participation -0.268***

(0.062)

Instruments-State Food Stamp Rules 2

Biometric technology -0.261**

(0.108)

Outreach spending per capita 0.384*

(0.223)

All legal immigrants eligible X noncitizen immigrant 0.418**

(0.179)

Some legal immigrants eligible X noncitizen immigrant 0.365**

(0.174)

Demographic Characteristics

Age -0.015*** 0.031***

(0.004) (0.005)

Age squared 0.000** -0.000***

(0.000) (0.000)

Noncitizen immigrant -0.462*** -0.009

(0.168) (0.055)

Race/Ethnicity (Omitted: White, non-Hispanic)

Black, non-Hispanic 0.380*** 0.132***

(0.028) (0.043)

Hispanic 0.209* 0.045

(0.121) (0.053)

Other, non-Hispanic 0.292*** 0.198*

(0.080) (0.118)

Educational Attainment (Omitted: More than high school)

Less than high school 0.461*** 0.109**

(0.038) (0.045)

High school only 0.221*** 0.015

(0.031) (0.035)

Number of children in household 0.266*** 0.038**

(0.015) (0.017)

Continued on next page

Appendix Table A-2: 

Bivariate Probit (IV) Estimates of the Effects of SNAP Participation 

on the Likelihood of Being Very Food Insecure,

 Households with Income below 150% of Poverty Threshold1

29
Variable

SNAP

Participation

Very Food 

Insecure

Number of adults in household -0.045*** -0.044*

(0.015) (0.027)

Household Structure (Omitted: Two adult-headed household)

Female-headed household 0.665*** 0.367***

(0.030) (0.047)

Male-headed household 0.237*** 0.276***

(0.043) (0.051)

Disabled person in household 0.791*** 0.528***

(0.036) (0.038)

Metropolitan area -0.092** 0.032

(0.037) (0.043)

Economic Characteristics

State monthly unemployment -0.074 0.112

(0.216) (0.253)

State employment-population ratio -5.883 1.395

(21.78) (25.40)

State per capita income (in $100s) -0.001 0.001

(0.002) (0.003)

Quarterly GDP (in trillions) -0.073 -0.015

(0.093) (0.171)

Year Fixed Effects

1998 -0.470*** -0.028

(0.178) (0.308)

2003 -0.255*** -0.206

(0.078) (0.143)

Constant 6.197 -3.859

(20.02) (26.17)

Rho 0.284***

(0.035)

Number of Observations 65,269

Notes: The unit of observation is a household-month. Robust standard errors are presented within parentheses. Standard errors 

are adjusted for clustering by state. *** p<0.01, ** p<0.05, * p<0.1

2

 A joint test for significance of the four instruments indicates that they are jointly statistically significant at the one percent 

level (χ2

(4)=14.9, p=0.005).

Bivariate Probit (IV) Estimates of the Effects of SNAP Participation 

on the Likelihood of Being Very Food Insecure,

 Households with Income below 150% of Poverty Threshold1

, continued

Appendix Table A-2

1

 Sample includes householdes with income below 150% of poverty threshold who have liquid assets below $4,000, or below 

$5,000 if one member of the household is age 60 or older.

30