Abstract

This report examines interstate variation in household food security. Using hierarchical

modeling, we identify several contextual dimensions that appear linked to household

food security: the availability and accessibility of Federal nutrition assistance programs,

policies affecting economic well-being of low-income families, and States’ economic

and social characteristics. These dimensions comprise what we refer to as the State food

security infrastructure. We find that a strong food security infrastructure particularly

benefits families that are economically vulnerable yet have incomes above the poverty

line. Almost all of the observed interstate differences in food security can be explained

by cross-State differences in demographic and contextual characteristics.

Acknowledgments

We are grateful for outstanding research assistance from Ruilin Fu and Lingling Wang.

Craig Gundersen, Mark Nord, Ram Chandran, and Steven Carlson provided valuable

feedback on an earlier draft of this report.

United States 

Department 

of Agriculture

www.ers.usda.gov

Electronic Report from the Economic Research Service

October 2005

Contractor

and Cooperator

Report No. 13

State-Level Predictors 

of Food Insecurity and

Hunger Among Households

With Children

By Judi Bartfeld, Department of Consumer 

Science and Institute for Research on Poverty,

University of Wisconsin-Madison, and Rachel

Dunifon, Department of Policy Analysis and

Management, Cornell University

ERS project representative: Ram Chandran, 

chandran@ers.usda.gov, 202-694-5446

Food Assistance 

and Nutrition 

Research Program

This report was prepared by the University of Wisconsin-Madison

and Cornell University under a cooperative agreement with the

Economic Research Service. The views expressed are those of the

authors and not necessarily those of ERS or USDA.
2

CONTENTS 

INTRODUCTION....................................................................................................................................3 

PRIOR RESEARCH ...............................................................................................................................4 

RESEARCH QUESTIONS AND CONCEPTUAL FRAMEWORK..................................................8 

 Federal Nutrition Assistance Programs ...............................................................................................9 

 Policies Affecting Financial Resources.............................................................................................10 

 Economic and Social Attributes of Communities .............................................................................11 

DATA AND METHODS .......................................................................................................................12 

 Data ...................................................................................................................................................12 

 Models...............................................................................................................................................12 

 Measures............................................................................................................................................16 

 Predicting State Impacts on Food Insecurity.....................................................................................21 

RESULTS ...............................................................................................................................................23 

 Food Insecurity: Random Intercept Model........................................................................................23 

 Food Insecurity: Random Slopes Model ...........................................................................................27 

 Can Household and State Characteristics Explain Interstate Differences in Food Insecurity? .........35 

 Hunger Models..................................................................................................................................44 

CONCLUSIONS ....................................................................................................................................52 

REFERENCES.......................................................................................................................................55 

APPENDIX TABLES ............................................................................................................................58 
3

INTRODUCTION 

Household food security – the assured access of all people to enough food for a healthy and 

active life – has received increasing attention from policymakers and researchers over the past decade. 

An emerging body of literature has linked food insecurity to a variety of negative outcomes, particularly 

for children (see, e.g., Dunifon and Kowaleski-Jones, 2003, and Olson, 1999), confirming the importance 

of food security as an indicator of well-being and a legitimate target of public concern. 

Estimates of the prevalence of food insecurity are released annually, revealing substantial 

variability among demographic groups as well as across states. The most recent data show state-level 

food insecurity rates ranging from a low of 6.2 percent in Massachusetts to a high of 15.5 percent in 

Arkansas (Nord, Andrews, and Carlson, 2004). Research on the correlates of food insecurity has largely 

focused on individual and household characteristics, with less attention to the role of the economic, 

policy, and social contexts in which households reside. There are two purposes, then, of the current 

report: First, to further our understanding of the relationships between the characteristics of states and the 

food security of households, and second, to identify the relative role of household and state characteristics 

in explaining the wide variation in food security among states. 
4

PRIOR RESEARCH 

Since 1995, researchers have used a standardized set of 18 questions to measure food security 

among households, and these questions have been incorporated in several national surveys. The questions 

focus on conditions and behaviors that characterize households experiencing difficulty in meeting foodrelated needs due to financial constraints. Depending upon the number of affirmative responses, 

households are classified into one of three categories—food secure, food insecure without hunger, or food 

insecure with hunger. Households must respond affirmatively to three or more questions to be classified 

as food insecure.1

 Hunger is thus conceptualized as a severe form of food insecurity. 

Stimulated by the developments in food security measurement, a growing body of work has 

begun to address the prevalence, causes, and consequences of food insecurity. Recent estimates indicate 

that 11.2 percent of American households are food insecure, including 3.5 percent that experience hunger. 

Among households with children, the corresponding rates are higher: 16.7 percent food insecure, 

including 3.8 percent that experience hunger (Nord, Andrews, and Carlson 2004). 

Children in food-insecure households experience a variety of disruptions in their eating habits. 

Eighty-one percent of food-insecure households reported relying on only a few kinds of low-cost foods to 

feed their children; 52 percent reported that at times they couldn’t afford to feed their children balanced 

meals; and one-quarter reported that at times they couldn’t afford to give the children enough to eat 

(Nord, 2003b). Further, researchers have documented a variety of negative consequences of food 

insecurity, ranging from deficits in nutritional consumption (see, e.g., Kendall, Olson, and Frongillo 

 

1

The food security scale includes questions about behaviors and conditions that range from less severe to 

very severe. At the less severe end of the spectrum, questions include, “We worried about whether our food would 

run out before we got money to buy more. Was that often, sometimes, or never true for you in the past 12 months?” 

and “The food we bought just didn’t last, and we didn’t have money to get more. Was that often, sometimes, or 

never true for you in the past 12 months?” At the mid-range of the spectrum, sample questions include “Did you or 

other adults in your household ever cut the size of your meals or skip meals because there wasn’t enough money for 

food?” and “In the last 12 months, did you ever eat less than you felt you should because there wasn’t enough 

money for food?” At the most severe end of the spectrum, questions include “In the last 12 months, were you ever 

hungry but didn’t eat because you couldn’t afford enough food?” and “In the last 12 months, did you or other adults 

in your household ever not eat for a whole day because there wasn’t enough money for food?” 
5

1996) to poor outcomes on broader measures of well-being. For instance, Dunifon and Kowaleski-Jones 

(2003) find that children living in food-insecure households experience greater health problems and 

increased behavior problems than do children in food-secure households, while Winicki and Jemison 

(2003) find that food-insecure children have lower math scores. 

Research on the determinants of food insecurity has largely focused on the role of 

sociodemographic characteristics of households and individuals. Not surprisingly, poverty and food 

insecurity are closely linked: the prevalence of food insecurity declines from 35.1 percent among poor 

households to 4.9 percent among households with income above 1.85 times the federal poverty line 

(Nord, Andrews, and Carlson, 2004). Nonetheless, poverty and food insecurity are distinct phenomena. 

More than half of poor households are not considered food insecure, and equally important, more than 

half of food-insecure households are not poor (Nord, Andrews, and Carlson, 2004). Food insecurity does 

not reach negligible levels until household income exceeds five times the poverty threshold (Nord and 

Brent, 2002). The factors that moderate the relationship between income and food security are not well 

understood. 

In addition to poverty status, Nord and colleagues (2004) find that single parenthood, race 

(specifically, African ancestry), and Hispanic ethnicity are associated with higher rates of household food 

insecurity. Other research highlights the relationship between food insecurity and low education (Daponte 

and Stephens, 2004; Rose, Gundersen, and Oliveira, 1998), lack of home ownership (Rose, Gundersen, 

and Oliveira, 1998), lack of savings (Olson et al., 1997), recent changes in income (Gundersen and 

Gruber, 2001), unemployment (Daponte and Stephens, 2004), poor health status, and social isolation 

(Tarasuk, 2001). 

Although research initially focused largely on individual and household characteristics, there 

have also been efforts to explore the role of contextual factors in contributing to food security outcomes. 

Such efforts have variously examined the role of food assistance programs, welfare policies, and 
6

economic and social contexts, generally focusing on one or another of these factors rather than using a 

more integrative approach that simultaneously considers multiple factors. 

Efforts to identify the impact of food assistance programs on food security status are complicated 

by self-selection of participants into programs on the basis of unobservable characteristics. For instance, 

Gundersen and Oliveira (2001) find that Food Stamp participants have higher levels of food insufficiency 

(a less detailed measure of households’ difficulty meeting food needs) than nonparticipants, a result they 

attribute to selection. To address this, they use a simultaneous equations model in which exogenous 

variables are used to predict both Food Stamp participation and food insufficiency, and they find no 

difference in the food security levels of Food Stamp participants and nonparticipants. To date, there has 

been little research that examines the impact of participation in other nutrition assistance programs, such 

as WIC, the School Breakfast program, or the National School Lunch program, on food insecurity. A 

recent exception is Nord (2003c), who finds that seasonal differences in food insecurity (higher in the 

summer than the spring) are smaller in states with more widespread participation in the Summer Food 

Service program, providing suggestive evidence that the program helps ameliorate food insecurity among 

households with school-age children. In contrast to the limited research linking food assistance programs 

to reductions in food insecurity, there is a variety of evidence that participation in such programs is linked 

to improved nutritional outcomes (see, e.g., Devaney and Moffitt, 1991; Basiotis, Kramer-LeBlanc, and 

Kennedy, 1998; Bhattacharya and Currie, 2001; Oliveira and Gundersen, 2000). 

Other research has examined potential linkages between welfare policies and food security. Cook 

et al. (2002) find that food insecurity is higher among children in families whose welfare benefits are 

eliminated or reduced, and that participation in the Food Stamp program does not mitigate this 

association. Borjas (2001) uses state variation in the availability of welfare benefits to immigrants to 

predict immigrants’ food security status, finding evidence that a reduction in the availability of a variety 

of welfare benefits leads to an increase in food insecurity. On the other hand, Winship and Jencks (2002) 

find evidence that welfare reform—broadly defined to encompass the range of policy changes in the late 
7

1990s, including expansions of the Earned Income Tax Credit (EITC)—did not increase food insecurity 

among single mothers, and in fact may have improved their food security. Their research does not attempt 

to isolate the role of specific policies. 

Finally, there is some evidence from recent studies suggesting that economic and social contexts 

at the state and sub-state levels may be linked to food insecurity. Tapogna and colleagues (2004) find that 

state differences in residential mobility, peak unemployment, and housing costs are strong predictors of 

state hunger rates. Looking at sub-state factors, Bernell, Weber, and Edwards (2004) find evidence that 

food insecurity in Oregon is more common in the context of high unemployment and low wages, as well 

as among households experiencing greater residential mobility. Finally, Yang and Dunifon (2004), 

examining data from rural communities in New York, find that access to food outlets is not predictive of 

household food security. 

Despite the rapidly increasing literature on food insecurity and its correlates, there has been little 

effort to develop an integrated model of food insecurity that incorporates both household and contextual 

characteristics. In particular, researchers have thus far not articulated a clear conceptual model to 

describe the potential interplay between individual and contextual factors as predictors of household food 

security. Furthermore, there have been only limited efforts to understand the extent to which household 

and contextual characteristics are able to explain cross-state differences in the prevalence of food 

insecurity. This report seeks to fill these gaps. 
8

RESEARCH QUESTIONS AND CONCEPTUAL FRAMEWORK 

We use hierarchical modeling to develop a model of food security that encompasses both 

sociodemographic and contextual characteristics. Our analyses are guided by the following key questions: 

1. What is the association between contextual characteristics, measured here at the state level, 

and household food security? 

2. Do contextual characteristics moderate the detrimental impact of low household income on 

food security? 

3. How much does measurable variation in household characteristics and in contextual 

characteristics contribute to interstate variation in household food security? 

This study is grounded in a conceptual framework that grows out of the emerging interest in the 

roles of the policy, economic, and social environments in contributing to household food security. Our 

underlying model posits that food insecurity is linked to inadequate household resources, but is also 

influenced by the strength of what we term the state food security infrastructure: a set of programs, 

policies, and economic and social attributes that affect the availability, accessibility, and affordability of 

food and the extent to which resources are available to households to meet their food-related needs. We 

expect the food security infrastructure to affect household food security both directly and by moderating 

the detrimental impact of low income. That is, economically vulnerable households will derive particular 

benefits from a strong food security infrastructure, and also may be particularly harmed by a weak one. 

The concept of a food security infrastructure is relevant at a variety of levels, ranging from the 

national to the local. Some potentially relevant characteristics can be meaningfully described at the state 

level, whereas others are only meaningful, or are more meaningful, in a more local context. Here, we 

focus on the following broad components of the food security infrastructure that are relevant to the 

current state-level analysis: the availability and accessibility of federal nutrition assistance programs, 

policies that influence the financial resources available to low-income families, economic attributes of 

communities, and social characteristics of communities. We note, however, that there are at least two 

potentially important components of the food security infrastructure—the emergency food assistance 
9

system, and the food marketing system—that we are not able to adequately capture with state-level 

variables and that we do not consider in this analysis. Furthermore, there is considerable intrastate 

variation, in addition to interstate variation, in many of the components discussed here. 

Federal Nutrition Assistance Programs 

Federal nutrition assistance programs—including the Food Stamp program, the School Breakfast 

and Lunch programs, and summer food programs—represent a major policy commitment to meeting the 

food-related needs of vulnerable segments of the population. Although federal in nature, these programs 

vary in important ways from state to state, and even from community to community, such that households 

in different geographic locations differ in the availability of these programs as sources of support. As a 

result, there is considerable variation in the extent to which programs are utilized by eligible families. 2

 

The Food Stamp program, for instance, is available nationwide to all who meet stated income, 

asset, and other criteria. At the same time, states and localities differ in terms of certain criteria, such as 

vehicle restrictions, frequency with which eligibility recertification is required, number and location of 

application sites, availability of sites that are open during nonstandard hours, extent of outreach about 

program availability, and job search requirements (see, e.g., Gabor et al., 2003). Estimates of state 

participation rates among eligible persons range from under 50 percent to over 70 percent (Schirm and 

Castner, 2003) , and recent research has linked a variety of state-specific program characteristics to 

participation rates (Kornfeld, 2002). 

Other programs exhibit still greater geographic variation. The availability of the School Breakfast 

program among states ranges from 42 percent to 100 percent of schools, and the ratio of low-income 

children participating in the breakfast program relative to those participating in the lunch program during 

 

2

 Because there is very little state variation in the availability of the National School Lunch program 

(NSLP) it is not included in our discussion or analyses. 
10

2001–2 ranged from 24 percent to 57 percent (Food Research and Action Center, 2002b). Similar 

variation is found in the summer meal programs, including the Summer Food Service program and the 

Summer School Lunch program3

, with the ratio of combined summer meal participants to low-income 

lunch participants during the school year ranging from 6.1 to 42.3 percent (Food Research and Action 

Center, 2003). 

We view these programs as important components of the food security infrastructure, and expect 

that differences in their availability and accessibility would be linked to cross-state differences in 

household food security. In addition, we expect these programs to play an important role in moderating 

the association between low household income and food insecurity. That is, we hypothesize that 

economically vulnerable families would be better able to meet their food-related needs when nutrition 

assistance programs are more readily available to provide for at least a portion of such needs. 

Policies Affecting Financial Resources 

Policies that affect financial resources—particularly financial resources of economically 

vulnerable families—are also an important component of the food security infrastructure. When budgets 

are stretched to meet basic needs, expenditures on food are often the easiest to cut back. Conversely, 

increasing the financial resources available to financially stressed families may help ensure that foodrelated needs are adequately met. A variety of state policies may affect family financial resources. In the 

current paper, we focus on tax policy. States vary in their relative reliance on income, property, and sales 

taxes, and different tax regimes have different implications for low-income households. A recent study 

examined the overall tax burden facing low-income families by state, and state estimates ranged from 3.8 

percent to 17.6 percent (Institute for Taxation and Economic Policy, 2003). Although a variety of other 

 

3

 The Summer Food Service Program provides meals to children participating in camps and other 

recreational programs during the summer. The Summer School Lunch Program provides meals to students 

attending summer school. 
11

policies may be relevant, we focus here on tax policy because of its broad relevance to the low income 

population, in contrast to other policies (such as welfare policies) that are relevant to more narrow subsets 

of low-income families. 

Economic and Social Attributes of Communities 

We expect food insecurity to be lower when economic conditions are more favorable. Job 

availability and quality in the community are central, not only because of the direct impact of jobs on 

family income (captured by our income variables), but also because quality job opportunities reduce 

economic uncertainty and increase the overall economic strength of a community. Likewise, the cost of 

living is expected to play an important role. There is substantial geographic variation in costs, particularly 

related to housing, and this has important implications for the availability of money for food. Census data 

reveal that state-level median rent in 2000 ranged from $401 to $779. 

Finally, the social connections between members of a community may play an important role in 

influencing household food security. As described by Coleman (1988) and others, social support 

represents a stock of resources on which households can rely for potential assistance. These resources 

result from connections to friends and family members, and the support they provide can include financial 

or emotional support, as well as access to information. We expect that households may experience less 

food insecurity when there are strong connections among community members, both because of 

possibilities for mutual assistance and because of greater access to information about available resources. 
12

DATA AND METHODS 

Data 

Data are from the 1998 through 2001 Food Security Supplements to the Current Population 

Survey (CPS-FSS).4 The CPS-FSS uses an 18-item scale to classify households as food secure, food 

insecure without hunger, or food insecure with hunger over the past 12 months. Because there were two 

Food Security Supplements administered during 2001 (April and December), we have a total of five 

panels of data. 

We limit our main analysis sample to households with children, because food insecurity is most 

prevalent among these households, and because the determinants of food insecurity may differ among 

different household types. In particular, some of the contextual variables in our analysis describe 

nutrition assistance programs targeting children, such as the school breakfast program and summer food 

program. Our sample includes a total of 70,942 households. 

These data are supplemented with state-level data describing various aspects of the food security 

infrastructure. Those data are described in more detail below. 

Models 

Because our data consist of households clustered within states (or more precisely, within contexts 

that vary by state and year), we use hierarchical modeling for our analyses (Raudenbush and Bryk, 2002). 

Hierarchical modeling is ideally suited to the analysis of data with a nested structure, in which both 

individual and contextual characteristics are thought to affect outcomes of interest (Osborne, 2000). 

With nested data, dependency among observations is potentially problematic, and ordinary least squares 

(OLS) estimates can yield both inefficient parameter estimates and biased standard errors. With 

 

4

For detailed discussion of the CPS sample design, see http://www.bls.census.gov/cps/bmethdoc.htm. 
13

hierarchical (or multilevel) models, some or all of the coefficients are treated as randomly varying by 

context, and these random coefficients can be explicitly modeled as functions of contextual 

characteristics. Such models allow errors to be dependent within contexts, thus implicitly controlling for 

unmeasured contextual characteristics that are correlated with the dependent variable. 

 Hierarchical models are particularly useful in formulating and empirically testing hypotheses 

about how contextual characteristics may affect household-level outcomes, both directly (in the case of 

random intercepts) and by moderating the impact of relevant household attributes (in the case of random 

slopes). Of course, one can also explore contextual effects using fixed-effects models that include 

dummy variables for each unique context; however, such models do not allow one to explore how 

specific contextual characteristics affect the outcome. Alternatively, one can include contextual 

characteristics in an OLS model, while ignoring residual within-group correlation. Hierarchical models, 

in contrast, allow for the estimation of the effects of specific contextual characteristics, while also 

controlling for unmeasured differences across contexts that are correlated with the outcome of interest. 

Furthermore, such models allow the analyst to obtain context-specific parameter estimates by augmenting 

within-context information with evidence from the broader sample (that is, by ‘borrowing strength’ from 

the full sample). 

We present both a random intercept model and a random slopes model. Our random intercept 

model can be written as follows: 

Level 1 Model 

Log[pij/(1-pij)] = β0j + β10X1ij +β20X2ij +...+ βn0Xnij (1) 

Level 2 Model 

β0j = γ00 + γ01W1j + ... + γ0qWqj + μ0j (2), 

Where pij is the probability that household i in state-year j is food insecure; 

Xij is a vector of sociodemographic characteristics of household i in state-year j; 

Wj is a vector of characteristics representing the food security infrastructure in state-year j; 

and u0j is a random normal variable with mean of 0 and variance τ00. 

 
14

Note that the Level 2 unit is the state-year. Conceptually, this reflects the fact that the relevant 

contextual characteristics vary by state, and within states vary over time. Thus, the households in our 

sample can be thought of as nested within a total of 255 different contexts (50 states and the District of 

Columbia, each observed in five different periods). 

In the Level 1 model (presented in Equation 1), the log-odds of household-level food insecurity is 

expressed as a function of various characteristics of an individual household i. The intercept from this 

model, β0j, is a random variable that varies among contexts. The slopes, β10 through βn0 , are assumed to be 

constant. 

In the Level 2 model, the intercept from Level 1 (β0j) is expressed as a function of contextspecific variables Wj (Equation 2). These variables represent various components of the food security 

infrastructure in each state and year. The model implies, then, that there are systematic differences in 

food security across state-years that can be explained in part by characteristics of the state context. 

Substituting Equation 2 into Equation 1 results in a single prediction equation, in which errors are 

dependent within state-years. 5

 The dependence of the errors is a key feature of this type of model, and 

has the effect of controlling for unmeasured contextual characteristics that are correlated with the 

outcome. 

The model parameters do not include each of the state-specific intercepts β0j, but rather, estimates 

of the mean intercept γ00 and the variance of the Level 2 error μ0j. The individual intercepts can, however, 

be predicted, as can the level two errors, μ0j . We discuss this in more detail below. 

We also present a random slopes model, in which household income coefficients are assumed to 

be random, context-dependent variables. Our model can be expressed as follows: 

 

5

Although our modeling approach explicitly allows for dependence among observations in each state-year 

context, it does not address potential dependence among observations in different years for the same state. Because 

of this, we may be underestimating true standard errors. 
15

Level 1 Model 

Log[pij/(1-pij)] =β0j + β1jX1ij + β2jX2ij + β3jX3ij + β4j X4ij + β50X5ij +β60X6ij +...+ βn0Xnij (3) 

Level 2 Model 

β0j = γ00 + γ01W1j + ... + γ0qWqj + μ0j (2) 

β1j = γ10 + γ11 W1j + ... γ1qWqj + μ1j (4) 

β2j = γ20 + γ21 W1j + ... γ2qWqj + μ2j (5) 

β3j = γ30 + γ31 W1j + ... γ3qWqj + μ3j (6) 

β4j = γ40 + γ41 W1j + ... γ4qWqj + μ4j (7) 

 where u0j, u1j, u2j, u3j, and u4j are random normal variables with means of 0 and variances τ00, τ11, τ22, τ33, 

and τ44. 

 

Here, X1ij through X4ij denote four income categories: poor, near poor (1.0 to 1.3 times the 

poverty line), low income (1.3 to 1.85 times the poverty line), and missing income (where the reference 

category is above 1.85 times poverty line)6

. This model allows us to examine whether aspects of the state 

food security infrastructure moderate the relationship between household income and food security. The 

model reflects our assumption that contextual characteristics are of particular relevance to economically 

vulnerable households’ efforts to maintain food security. In particular, we note that at least some aspects 

of the food security infrastructure—such as the availability and accessibility of nutrition assistance 

programs—are only relevant to lower-income households. 

Our primary focus is on factors linked to food insecurity. However, we also estimate comparable 

models in which food insecurity with hunger—a severe level of food insecurity—is the dependent 

variable. 

 

6

 These classifications are linked to household eligibility for food assistance programs. Children from poor 

or near-poor households may be eligible for free meals and for Food Stamps. Children from low-income households 

may be eligible for reduced-price meals. 
16

Measures 

Level 1 Measures 

We include the following household-level variables in our model (see Table 1 for means and 

standard deviations): Income-to-poverty ratio (and ratio squared), highest education level in household, 

race/ethnicity of household head, home ownership, location (central city, other metropolitan, or 

nonmetropolitan), household structure (single mother, single father, couple, other), number of children, 

presence of employed person(s) in household, presence of elderly person(s) in household, presence of 

disabled person(s) in household, and presence of noncitizens in household. All continuous variables are 

entered as mean-centered variables in our models. 

Level 2 Measures 

We include a variety of Level 2 variables representing components of the food security 

infrastructure. For simplicity, we use the term “state-level variables” to refer to these variables. Note, 

however, that in most cases these variables vary by both state (50 states and Washington, D.C.) and time 

(five different time periods corresponding to the food security reference period of the five CPS-FSS 

panels). Thus, our Level 2 measures describe the context within which households are grouped, where 

that context is both state and time dependent. In a few instances, we have only a single state measure and 

apply it to all years. When the relevant reference year spans two calendar years, we construct the 

measures by prorating the values for each of the years. Variable means and standard deviations are shown 

in Table 1. As with the Level 1 variables, continuous variables are mean-centered in the models. 

Availability and Accessibility of Federal Food Assistance and Nutrition Programs. Unlike research 

seeking to link a household’s program participation to food security or other nutritional outcomes, our 

approach is to treat food assistance programs as components of the food security infrastructure. We are 

interested in the extent to which differences (across states and over time) in the availability and 
17

TABLE 1 

Variable Means and Standard Deviation

Mean SD 

Dependent Variables 

Food insecurity .16 .36 

Food insecurity with hunger .04 .19 

Level 1 Variables 

Income

Income/poverty ratio 2.68 1.40 

Ratio squared 9.32 8.08 

Missing income 0.08 0.27 

Education

High school 0.26 0.44 

Some college 0.33 0.47 

College degree or more 0.34 0.47 

Race

Black 0.11 0.32 

Hispanic 0.11 0.32 

American Indian 0.02 0.12 

Asian 0.04 0.19 

Housing Tenure

Rent 0.29 0.45 

Live without paying 0.02 0.13 

Location

Central City 0.21 0.41 

Nonmetropolitan 0.24 0.43 

Missing 0.00 0.06 

Number of Children

2 0.38 0.48 

3 0.15 0.36 

4 or more 0.06 0.24 

Family Type

Single mother 0.19 0.39 

Single father 0.05 0.21 

Other household with children 0.09 0.28 

Household Characteristics

Any employed in household 0.93 0.26 

Any elderly in household 0.04 0.19 

Any disabled in household 0.05 0.23 

Any noncitizens in household 0.11 0.31 

(table continues) 
18

TABLE 1, continued 

Mean SD 

Level 2 Variables 

Federal Food Programs

Food Stamp recipients per 100 poor persons 59.39 15.05 

Low-income School Breakfast participants per 100 low-income 

School Lunch participants 39.10 8.94 

Low-income Summer Food Service program participants per 100 

low-income School Lunch participants 14.20 9.15 

Low-income Summer School Lunch participants per 100 lowincome School Lunch participants 4.61 6.36 

Economic Policies

Low-income tax burden 10.48 2.25 

Overall tax burden 9.82 1.19 

Economic Attributes

Unemployment rate 4.31 1.04 

Poverty rate 11.60 3.23 

Average wages per job ($1000s) 31.14 5.88 

Median rent ($100s) 5.63 0.99 

Social Attributes

Percentage nonmovers 54.11 5.06 

Survey Year

1999 0.20 0.40 

2000 0.20 0.40 

April 2001 0.20 0.40 

December 2001 0.20 0.40 

Note: The means for the Level 1 variables are based on 70,942 households. The means for the Level 2 

variables are based on 255 state-year contexts. 
19

accessibility of programs are linked to differences in food security outcomes. We include the following 

measures: 

• Food Stamps: To characterize accessibility of the Food Stamp program, we construct a measure 

of average monthly number of food stamp recipients divided by number of poor persons.7

 This 

ranges from 33 to 108 over the 255 state-years included here. Information on number of poor 

persons comes from the Census Bureau’s Small Area Poverty Estimates. 

• School Breakfast program: We characterize availability and accessibility of the School Breakfast 

program by the average number of students eating free or reduced-price breakfast per day for 

each 100 students eating free or reduced-price School Lunch. Participation in the School Lunch 

program is frequently used as a benchmark against which to measure School Breakfast 

participation, because the former is much more uniformly available and more consistently used 

than the latter. This ratio ranges from 19 to 56 in our sample. Differences in this variable reflect 

differences in the availability of the breakfast program, as well as differences in the extent to 

which students participate when the program is offered (see Food Research and Action center, 

2002a, for a discussion of program qualities that may affect the attractiveness of the School 

Breakfast program to students.) 

• Summer Meals: Summer meal programs include the Summer Food Service program and the 

Summer School Lunch program. The former provides meals at a variety of sites that may or may 

not also provide other programming, and participation is not formally linked to attendance in 

summer school programs. The latter provides lunches to low-income students attending school 

programs for the summer. We measure the availability and accessibility of these programs by the 

average daily participation per 100 participants in the free or reduced-price lunch program during 

the school year. In our sample, the Summer Food Service ratio ranges from 1.1 to 53.8, and the 

Summer School Lunch ratio ranges from .5 to 35.4. 

 There are potential biases associated with these variables. We treat higher participation among 

eligible families as a proxy for greater program accessibility. However, it is also likely that nutrition 

assistance programs are more widely used by families with higher levels of need, even after controlling 

for observable characteristics. If this is the case, our estimates of the relationship between greater program 

participation and food insecurity would be biased downward, making such relationships more difficult to 

 

7

This is not intended to be an estimate of the participation rate. Eligibility determination is complex, and 

some families with incomes above the poverty are eligible for food stamps, while some poor families are ineligible. 

We do assume, however, that states with higher ratios also have higher true participation rates among eligible 

families. We further assume that such states have more accessible programs, as per research linking state-specific 

program characteristics to participation rates (Kornfeld, 2002). 
20

detect. Note, though, that unobserved characteristics that contribute to program participation are only a 

problem to the extent that they differ systematically across locations. 

State Policies Affecting the Resources Available to Low-Income Families. As discussed earlier, 

we expect food security to be influenced not only by nutrition assistance programs, but also by other 

kinds of policies that affect resources available to low-income families. As noted above, we focus here on 

tax policy because of its broad relevance to low-income families. 

• Our primary measure is an estimate of the mean percentage of income owed in state and local 

taxes by families in the bottom quintile of the state income distribution. This is available from the 

Institute for Taxation and Economic Policy, and is measured for 2002. Because of limitations in 

data availability, this measure varies by state but not time.8

• We also control for the average percentage of income owed in state and local taxes by all 

families, available from the Tax Foundation. We include this primarily as a control, to insure that 

any apparent impact of the low-income tax burden is not merely proxying for the overall tax 

burden. 

Economic Attributes of Communities. We expect food insecurity to be lower in states with more 

favorable economic conditions and a lower cost of living. We include the following measures: 

• State unemployment rate: State unemployment rate, available from the Bureau of Labor Statistics, 

is used to characterize job availability. 

• Poverty rate: We expect states with higher poverty rates to have fewer collective resources, and 

thus higher rates of food insecurity. 

• Average wages per job: Mean wages per job are available from the Bureau of Economic 

Analysis. We treat mean wages as a proxy for job quality in the state. 

• Median rent: Median rent, available from the 2000 Census, is used as a partial proxy for local 

cost of living. 

Social Context. We expect less food insecurity when there are stronger bonds among community 

members. 

• We use residential stability, measured by Census data on the percentage of households living at 

the same address as five years earlier, to proxy for the strength of bonds among community 

 

8

This measure accounts for state EITC programs. We also experimented with including a separate indicator 

denoting existence of a state EITC program, but it was not substantively or statistically significant. 
21

members. We expect that the greater the mobility of the population, the weaker the social bonds 

and the greater the likelihood of food insecurity. 

Other. Finally, we include dummy variables denoting survey year to control for unmeasured 

factors influencing food security that may differ over time. The year variables also control for year-toyear differences in the way households were screened out of the food security questions. Because of these 

screening differences, the year variables should only be treated as controls, and the coefficients should not 

be given substantive interpretation. 

Predicting State Impacts on Food Insecurity 

We are also interested in the additional risk of food insecurity associated with particular states, 

and in the extent to which these state-specific risks can be explained by observed household and 

contextual characteristics. We explore this question using the results from our random intercept model 

(equations 1-2) together with results from two other models—an empty model, which includes a random 

intercept but no Level 1 or Level 2 variables, and a household-level model, which includes a random 

intercept and also household-level variables, but no Level 2 variables. The context-specific impact is μ0j, 

the Level 2 error. 

Recall that context-specific intercepts (β0j) and residuals (μ0j) are not explicitly estimated as 

model parameters. Rather, the model parameters include the mean intercept (γ00 ) and the variance of the 

Level 2 residual. For each of the three models, we generate empirical Bayes shrinkage estimates of μ0j. 

These shrinkage estimates, μ0j*, are estimates of the OLS residual μ0j

∧

 for a particular context, shrunken 

towards zero, where the shrinkage is proportional to the unreliability of μ0j

∧

.

9

 Compared to μ0j

∧

, μ0j* is 

 

9

 The OLS residual is the difference between the within-context estimate of β0j and the predicted value of 

β0j based on the Level 2 model. In the case of the empty model and household-level model, the Level-2 prediction 

is simply γ00 . In the full model, the Level-2 prediction is based on the characteristics of the particular context. 

Thus, in the empty model, the OLS residual is the difference between the log-odds of the probability of food 

insecurity in a given context and the mean log-odds across contexts; in the household-level model, the OLS residual 
22

biased towards zero but has a smaller mean squared error (Snijders and Bosker, 1999). Note that a 

limitation of this approach is that, the greater the unreliability of μ0j

∧

 , the greater will be the downward 

bias in the estimated residuals – what Raudenbush and Bryk (2002, pp.157-158) refer to as ‘shrinkage as 

a self-fulfilling prophecy’. See Raudenbush and Bryk (2002) for a discussion of μ0j* as an estimator of 

context-specific impacts. 

 

is the difference between the within-context intercept after controlling for household characteristics and the mean 

intercept across contexts (γ00 ); and in the full model, the OLS residual is the difference between the within-context 

intercept and the predicted intercept based on the specific characteristics of that context. The EB estimator weights 

the OLS residual by its reliability λ, where λj = τ

2

 /(τ

2

 + σ2/nj). Thus, the OLS estimate is given increasing weight 

when nj is larger and when the estimated variance of μ0j is greater. 
23

RESULTS 

Food Insecurity: Random Intercept Model 

Table 2 presents coefficients and odds ratios from the random intercept model (Equations 1–2). 

Most of the household variables emerge as significant predictors of food insecurity, largely consistent 

with existing research. Important predictors of food insecurity include lower income10, renting versus 

owning a home, single mother status, more children, lower education, having a disabled person or a 

noncitizen in the household, and race/ethnicity other than nonhispanic white. 

Also in Table 2 are the coefficients and odds ratios for the contextual (Level 2) variables. 

Looking first at variables characterizing availability and accessibility of federal nutrition assistance 

programs, we find that greater state rates of participation in both the Summer Food Service program and 

the Summer School Lunch program are associated with a lower risk of food insecurity. As discussed 

above, these variables represent the extent to which summer meal programs are used by low-income 

children in a particular state, and thus are intended to capture variation in the availability, accessibility, 

and desirability of such programs. They do not indicate anything about the participation status of the 

household. The interpretation, then, is that households in states with more widely utilized summer meal 

programs have lower risk of food insecurity. In contrast, we find no relationship between the extent of 

state-level participation in either the Food Stamp or the School Breakfast program and food security 

outcomes. 

 

10 The income-to-poverty coefficient, as well as the coefficient on the squared income-to-poverty ratio, are 

both negative, suggesting the risk of food insecurity declines at an increasing rate as needs-adjusted income 

increases. We explored this using an alternative specification involving dummy variables for narrowly defined 

income-to-poverty ranges. These coefficients indicate that the risk of food insecurity is largely constant at income 

levels below the poverty line, decreases rapidly until a ratio of 4.5, after which the decline tapers off. The remaining 

coefficients are robust across a variety of income specifications. 
24

TABLE 2 

Coefficients and Odds Ratios from Random Intercept Logistic Regression Analysis of Household Food Insecurity 

Fixed Effect Coefficient SE Odds R

Intercept -2.218*** 0.066 -33.556 Level 1 Variables Income

Income/poverty ratio -0.482*** 0.042 0.618 Ratio squared -0.045*** 0.009 0.956 Missing income -0.011 0.049 0.989 

Education

High school -0.120*** 0.039 0.887 Some college -0.174*** 0.040 0.840 College degree or more -0.705*** 0.049 0.494 

Race

Black 0.183*** 0.036 1.201 Hispanic 0.129*** 0.041 1.138 American Indian 0.332*** 0.077 1.394 Asian -0.052 0.075 0.949 

Housing Tenure

Rent 0.418*** 0.027 1.518 Live without paying -0.019 0.083 1.019 

Location

Central city 0.101*** 0.030 1.106 Nonmetropolitan -0.091** 0.031 0.913 Missing -0.234 0.257 0.791 

Number of Children

2 0.029 0.028 1.667 3 0.176*** 0.034 1.192 4 or more 0.257*** 0.044 1.293 

Family Type

Single mother 0.511*** 0.030 1.667 Single father 0.053 0.053 1.054 Other household with children 0.105** 0.042 1.111 

Household Characteristics

Any employed in household -0.180*** 0.038 0.835 Any elderly in household -0.306*** 0.062 0.737 Any disabled in household 0.687*** 0.041 1.987 Any noncitizens in household 0.100** 0.042 1.105 

(table continues) 
25

TABLE 2, continued 

Fixed Effect Coefficient SE Odds Ratio Level 2 Variables 

Federal Food Programs

Food Stamp recipients per 100 poor persons -0.002 0.002 0.998 Low-income School Breakfast participants per 100 low-income School Lunch participants -0.001 0.002 0.999 Low-income Summer Food Service program participants per 100 lowincome School Lunch participants -0.004* 0.002 0.996 Low-income Summer School Lunch participants per 100 low-income School Lunch participants -0.010*** 0.002 0.990 

Economic Policies

Low-income tax burden 0.012* 0.007 1.012 Overall tax burden 0.025 0.015 1.025 

Economic Attributes

Unemployment rate 0.053** 0.023 1.054 Poverty rate 0.003 0.009 1.003 Average wages per job ($1000s) -0.035*** 0.005 0.966 Median rent ($100s) 0.181*** 0.030 1.198 

Social Attributes

Percentage nonmovers -0.012*** 0.004 0.988 

Survey Year

1999 -0.189*** 0.043 0.828 2000 0.139** 0.050 1.149 April 2001 0.107** 0.049 1.113 December 2001 0.102** 0.049 1.107 

Random Effect 

Variance 

Component DF Chi-Squ

Intercept .009*** 239 299.2*p<.1 **p<.05 ***p<.01 Note: The following variables have been centered around their grand mean: Food Stamp recipients per 100 poor persons, ratio of SchooSchool Lunch participants, ratio of Summer Food Service program recipients to Summer School Lunch participants, low-income tax bu

unemployment rate, poverty rate, average wages per job, median rent, percentage nonmovers. 

aReference categories are less than high school, white, homeowner, metropolitan county (not central city), one child, married.
26

We also find some evidence that tax policy is associated with household food security. Our results 

suggest that the greater the state tax burden on low-income families, the greater the risk of food 

insecurity, although this is only marginally significant (p<.1). Specifically, each percentage-point increase 

in the low-income tax burden is associated with a 1.2 percent increase in the odds of food insecurity. The 

overall tax burden, in contrast, is not significantly related to food insecurity. Despite its lack of 

significance, we note that the coefficient is larger than that on the low-income tax burden, and the p-value 

only slightly higher (.11 vs .07). We interpret these results as offering suggestive evidence that the tax 

burden matters in regard to food insecurity, and more persuasive evidence that the burden on low-income 

households plays a role. 

Economic characteristics of the state are strongly linked to food insecurity in this model. Median 

rent is among the strongest predictors of food insecurity: the results imply that a $100 increase in the 

median rent in a state is associated with a 20 percent increase in the odds of food insecurity. Focusing on 

job availability and quality, we find that higher average wages per job are linked to lower food insecurity, 

whereas higher unemployment rates are linked to higher food insecurity. Because the model controls for 

employment and income at the household level, we interpret these variables as indicators of the 

availability and quality of job opportunities households have faced over the past 12 months, as well of the 

overall economic strength of the state, rather than as proxies for current household circumstances. The 

poverty rate has no evident link to household food security, although needs-adjusted income at the 

household level is a strong predictor. Finally, we find that greater residential stability in the state (a higher 

percentage of the population living in the same house in 1995 and 2000) is linked to lower odds of food 

insecurity. As noted above, we interpret residential stability as a proxy for social connectedness among 

community members.11

 

11 Tapogna and Suter examine the relationship between residential mobility and food insecurity, and find 

that intra-state moves have a stronger relationship to food insecurity than do inter-state moves. They interpret this as 
27

As a sensitivity test, we estimate the same model on a sample of households without children 

(Table 3). In general, we expect to find similar predictors of food insecurity to those in our primary 

sample. However, we do not expect the variables denoting meals for children—including extent of 

participation in School Breakfast, and extent of participation in the Summer Food Service and Summer 

School Lunch programs—to be linked to household food insecurity for childless households. Results are 

mixed. As expected, we find that most predictors of food insecurity are similar to those in our sample of 

households with children. The state-level School Breakfast participation variable is not significant, as in 

our primary sample. Results for summer meals are ambiguous. State-level participation in the Summer 

Food Service program is no longer significant, consistent with expectations. On the other hand, state-level 

participation in the Summer School Lunch program continues to be significantly linked to household food 

security, although the coefficient is smaller than for our primary sample of households with children 

(-.007 versus -.01). Because it is not plausible that Summer School Lunch programs for children would 

have any impact on food security among childless households, these findings imply that the Summer 

School Lunch variable is correlated with unobserved differences among states, and that those differences 

may account for at least a portion of the apparent benefits of the Summer School Lunch program. Thus, 

our results regarding the apparent benefits of that program should be interpreted cautiously. 

Food Insecurity: Random Slopes Model 

Table 4 presents the results from the random slopes model. Here the relationship between income 

and food insecurity is allowed to vary by context, and state-level characteristics interact with householdlevel income status to predict food insecurity. Thus, the model tests whether state characteristics moderate 

the relationship between income and food insecurity. In order to better interpret the results of these 

 

suggestive evidence that the relationship between residential instability and food insecurity may reflect financial 

disruptions. 
28

TABLE 3 

Coefficients and Odds Ratios from Random Intercept Logistic Regression Analysis of Household Food Insecurity for Househ

(N= 128,917)

Fixed Effect Coefficient SE Odds Ra

Intercept -2.800*** 0.055 0.061 Level 1 Variables Income

Income/poverty ratio -0.775*** 0.027 0.462 Ratio squared 0.037*** 0.004 1.038 Missing income 0.038 0.045 1.039 

Education

High school -0.311*** 0.033 0.733 Some college -0.308*** 0.036 0.735 College degree or more -0.740*** 0.043 0.477 

Race

Black 0.500*** 0.034 1.649 Hispanic 0.292*** 0.048 1.339 American Indian 0.567*** 0.087 1.763 Asian -0.263*** 0.083 0.769 

Housing Tenure

Rent 0.653*** 0.258 1.921 Live without paying 0.217*** 0.076 1.242 

Location

Central City 0.115*** 0.030 1.122 Nonmetropolitan -0.111*** 0.032 0.895 Missing 0.206 0.184 1.229 

Household Characteristics

Any employed in household -0.028 0.031 0.972 Any elderly in household -0.974*** 0.032 0.378 Any disabled in household 0.901*** 0.031 2.462 Any noncitizens in household -0.156*** 0.052 0.856 

(table continues) 
29

TABLE 3, continued 

Fixed Effect Coefficient SE Odds Ra

Level 2 Variables Federal Food Programs

Food Stamp recipients per 100 poor persons -0.0001 0.001 1.000 Low-income School Breakfast participants per 100 low-income School Lunch participants -0.001 0.002 0.999 Low-income Summer Food Service program participants per 100 low-income School Lunch participants -0.002 0.002 0.998 Low-income Summer School Lunch participants per 100 low-income School Lunch participants -0.007*** 0.002 0.993 

Economic Policies

Low-income tax burden 0.010 0.007 1.010 Overall tax burden 0.021 0.015 1.021 

Economic Attributes

Unemployment rate 0.095*** 0.023 1.100 Poverty rate -0.007*** 0.001 0.993 Average wages per job ($1000s) -0.026*** 0.005 0.974 Median rent ($100s) 0.135*** 0.030 1.145 

Social Attributes

Percentage nonmovers -0.013*** 0.003 0.987 

Survey Year

1999 -0.103** 0.044 0.902 2000 0.027 0.047 1.027 April 2001 0.101** 0.049 1.106 December 2001 0.074 0.050 1.077 

Random Effect 

Variance 

Component DF Chi-Squ

Intercept 0.010 239 320.11 *p<.1 **p<.05 ***p<.01 Note: The following variables have been centered around their grand mean: Food Stamp recipients per 100 poor persons, ratio of Schparticipants to School Lunch participants, ratio of low-income Summer Food Service program recipients to Summer School Lunch patax burden, overall tax burden, unemployment rate, poverty rate, average wages per job, median rent, percentage nonmovers. 

aReference categories are less than high school, white, homeowner, metropolitan county (not central city). 
30

TABLE 4 

Coefficients and Odds Ratios from Random Slope Logistic Regression Analysis of Household Food Insecurity (N

Fixed Effect Coefficient SE Odds R

Intercept -2.445*** 0.076 0.08Level 1 Variables Income

Poverty 1.406*** 0.081 4.08Near poverty (1–1.3 of poverty line) 1.184*** 0.101 3.26Low income (1.3–1.85 of poverty line) 0.935*** 0.091 2.54Missing poverty 0.280** 0.132 1.32

Education

High school -0.105*** 0.039 0.90Some college -0.199*** 0.041 0.81College degree or more -0.968*** 0.050 0.38

Race

Black 0.223*** 0.036 1.25Hispanic 0.178*** 0.041 1.19American Indian 0.350*** 0.078 1.41Asian -0.015 0.075 0.98

Housing Tenure

Rent 0.529*** 0.027 1.69Live without paying 0.140* 0.083 1.15

Location

Central City 0.116*** 0.031 1.12Nonmetropolitan -0.027 0.032 0.97Missing -0.175 0.257 0.83

Number of Children

2 0.094*** 0.028 1.093 0.300*** 0.035 1.344 or more 0.423*** 0.045 1.52

Family Type

Single mother 0.598*** 0.030 1.81Single father 0.090* 0.053 1.09Other household with children 0.176*** 0.042 1.19

Household Characteristics

Any employed in household -0.213*** 0.038 0.80Any elderly in household -0.243*** 0.063 0.78Any disabled in household 0.746*** 0.042 2.10Any noncitizens in household 0.146*** 0.042 1.15

(table continues) 
31

TABLE 4, continued 

Fixed Effect Coefficient SE Odds R

Level 2 Variables Federal Food Programs

Food Stamp recipients per 100 poor persons 0.001 0.002 1.00Low-income School Breakfast participants per 100 low-income School Lunch participants -0.003 0.004 0.99Low-income Summer Food Service program participants per 100 low-income School Lunch participants -0.005 0.003 0.99Low-income Summer School Lunch participants per 100 low-income School Lunch participants -0.013*** 0.003 0.98

Economic Policies

Low-income tax burden 0.006 0.010 1.00Overall tax burden 0.030 0.023 1.03

Economic Attributes

Unemployment rate 0.019 0.035 1.01Poverty rate 0.023 0.014 1.02Average wages per job ($1000s) -0.032*** 0.007 0.96Median rent ($100s) 0.157*** 0.045 1.17

Social Attributes

Percentage nonmovers -0.020*** 0.006 0.98

Other

1999 -0.202 0.068 0.812000 0.118* 0.070 1.12April 2001 0.129* 0.075 1.13December 2001 0.126 0.076 1.13

Cross-Level Interactions 

Poverty* Food Stamp recipients per 100 poor persons -0.001 0.003 0.99Poverty* Low-income School Breakfast participants per 100 low-income School Lunch participants -0.002 0.005 0.99Poverty* Low-income Summer Food Service program participants per 100 lowincome School Lunch participants 0.002 0.005 1.00Poverty*Low-income Summer School Lunch participants per 100 low-income School Lunch participants 0.008 0.005 1.00Poverty* Low-income tax burden 0.003 0.016 1.00Poverty* Overall tax burden 0.017 0.036 1.01Poverty* Unemployment rate 0.002 0.054 1.00Poverty* Poverty rate -0.008 0.021 0.99

(table continues) 
32

TABLE 4, continued 

Fixed Effect Coefficient SE Odds R

Poverty* Average wages per job ($1000s) -0.012 0.011 0.98Poverty* Median rent ($100s) 0.017 0.071 1.01Poverty* Percentage nonmovers 0.010 0.009 1.01Poverty*1999 -0.006 0.102 0.99Poverty*2000 0.026 0.108 1.02

Poverty*April 2001 -0.109 0.115 0.89Poverty*December 2001 -0.052 0.114 0.95Near poverty* Food Stamp recipients per 100 poor persons -0.012** 0.005 0.98Near poverty* Low-income School Breakfast participants per 100 low-income School Lunch participants 0.009 0.007 1.00Near poverty* Low-income Summer Food Service program participants per 100 low-income School Lunch participants 0.005 0.007 1.00Near poverty*Low-income Summer School Lunch participants per 100 lowincome School Lunch participants -0.001 0.007 0.86Near poverty* Low-income tax burden 0.018 0.022 1.01Near poverty* Overall tax burden -0.035 0.050 0.96Near poverty* Unemployment rate 0.116 0.075 1.12Near poverty* Poverty rate -0.060** 0.029 0.94Near poverty* Average wages per job ($1000s) -0.025 0.015 0.97Near poverty* Median rent ($100s) 0.113 0.097 1.11Near poverty* Percentage nonmovers 0.025* 0.013 1.02Near poverty*1999 -0.019 0.132 0.98Near poverty*2000 0.004 0.143 1.00Near poverty*April 2001 -0.016 0.153 0.98Near poverty*December 2001 -0.064 0.155 0.93Low income* Food Stamp recipients per 100 poor persons -0.010** 0.004 0.99Low income* Low-income School Breakfast participants per 100 low-income School Lunch participants 0.005 0.006 1.00Low income* Low-income Summer Food Service program participants per 100 low-income School Lunch participants 0.003 0.006 1.00Low income*Low-income Summer School Lunch participants per 100 lowincome School Lunch participants 0.004 0.006 1.00Low income* Low-income tax burden 0.039** 0.018 1.04Low income* Overall tax burden -0.008 0.041 0.99

(table continues) 
33

TABLE 4, continued 

Fixed Effect Coefficient SE Odds R

Low income* Unemployment rate 0.117* 0.062 1.12Low income* Poverty rate -0.061** 0.025 0.94Low income* Average wages per job ($1000s) -0.005 0.012 0.99Low income* Median rent ($100s) -0.001 0.001 0.99Low income* Percentage nonmovers 0.013 0.010 1.01Low income*1999 0.096 0.119 1.10Low income*2000 0.148 0.124 1.15Low income*April 2001 -0.058 0.133 0.94Low income*December 2001 -0.040 0.132 0.96Random Effect

Variance 

Component DF Chi-Squar

Intercept variance 0.005 236 232.99“Poverty” variance 0.028 236 272.53“Near poverty” variance 0.022 236 229.92“Low income” variance 0.016 236 218.84“Missing poverty” variance 0.161 236 211.85*p<.1 **p<.05 ***p<.01 Note: The following variables have been centered around their grand mean: Food Stamp recipients per 100 poor persons, ratio of SchooSchool Lunch participants, ratio of low-income Summer Food Service program recipients to School Lunch participants, low-income tax 

burden, unemployment rate, poverty rate, average wages per job, median rent, percentage nonmovers. 

aReference categories are less than high school, white, homeowner, metropolitan county (not central city), one child, married. 
34

interactions, household income is represented as a series of dummy variables rather than the continuous 

income-to-needs ratio used in the random intercept model. Four dummy variables are used, denoting 

poverty (less than 100 percent of the poverty line), near poverty (100–130 percent of the poverty line), 

low income (131–185 percent of the poverty line), and missing information. The omitted category is 

households above 185 percent of the poverty line. 

The results for the Level 1 (household) variables are largely consistent with the random intercept 

model, with only minor exceptions. We therefore focus our discussion on the role of state characteristics, 

where these characteristics may influence overall food insecurity and may also moderate the relationship 

between income and food insecurity. Looking first at nutrition assistance programs, we again find no 

direct impact of the Food Stamp participation rate. However, we do find that a higher Food Stamp 

participation rate—which we treat as a proxy for a more accessible program—reduces the negative 

relationship between both near-poverty and low-income status and food security outcomes (see crosslevel interactions in Table 4). That is, economically vulnerable families—but not poor families or higherincome families—have a lower risk of food insecurity in contexts characterized by a more accessible 

Food Stamp program. This makes sense in that eligible families who are closer to the eligibility cutoff, 

and therefore qualify for lower benefits than the poorest families, may be most responsive to differences 

in program accessibility, whereas higher income families who are ineligible for any assistance should not 

be influenced by the Food Stamp program. Higher participation in the Summer School Lunch program 

continues to be linked to lower food insecurity rates, whereas participation in the Summer Food Service 

program is no longer significant; neither of the summer food programs appear to moderate the impacts of 

poverty or low income, counter to our expectation. As in the random intercept model, the School 

Breakfast program does not emerge as a significant predictor of food insecurity. 

We look next at policies affecting economic resources. The low-income tax burden is no longer a 

significant predictor of overall food insecurity; it does, however, appear to strengthen the relationship 

between low-income status and food insecurity—low-income households have a greater risk of food 
35

insecurity when the low-income tax burden is higher. This is consistent with our expectations. As before, 

we find no significant relationship between the overall tax burden and the risk of food insecurity. 

Turning to economic characteristics, we again find that higher average wages in a state are linked 

to lower odds of food insecurity. The unemployment rate is not linked to the overall food insecurity rate 

in this model; however, we find some evidence that higher unemployment may exacerbate the detrimental 

impact of low-income status on food insecurity (p<.1). Median rent continues to have a strong impact on 

food insecurity, but there do not seem to be any differential impacts for particular income groups. We also 

find that higher poverty rates are linked to lower food insecurity among near-poor and low-income 

households. While this may seem counterintuitive, it could indicate that the cost of living is lower in highpoverty areas, a situation that could contribute to greater food security for economically vulnerable 

families (Nord, 2003a). Finally, looking at social context, we find that greater residential stability in the 

state continues to be linked to lower food insecurity, although less so for near-poor households. 

Notable in these results is the finding that none of the state characteristics in our model moderate 

the detrimental impact of poverty on food insecurity. In contrast, several characteristics—including the 

extent of Food Stamp participation, the tax burden on low-income households, the unemployment rate, 

and high poverty rates (which may proxy for lower cost of living)—serve to moderate the relationship 

between either near-poverty or low-income status and food insecurity. It appears, then, that the state 

context may be particularly important to families who are economically vulnerable even though they have 

incomes above the poverty line. 

Can Household and State Characteristics Explain Interstate Differences in Food Insecurity? 

An important focus of our inquiry is to ascertain the extent to which the identified household and 

state-level characteristics are able to explain the wide variation in state food insecurity rates. As 

described earlier, we explore this using results from three models—an empty model (shown in Appendix 

Table 1), which includes a random intercept but has no Level 1 or Level 2 variables; a household-level 

model (shown in Appendix Table 2), which includes a random intercept and also household-level 
36

variables, but no Level 2 variables; and our previously-shown random intercept model (Table 2) which 

includes independent variables both at Level 1 and Level 2. The random components of the three models 

are summarized in Table 5. The variance of μ0j, the Level 2 error, decreases from .071 in the empty 

model, to .040 in the household-level model, to .009 in the full model. Thus, almost all of the betweencontext variance in food insecurity can be explained by the household and contextual variables included 

in the model. 

We examine this further by generating empirical Bayes estimates of the residual μ0j for each 

unique context j (where j represents a particular state in a particular year). The odds ratios of the 

estimated residuals from the three models can be interpreted as the additional risk of food insecurity 

associated with a particular context, under three scenarios—without controlling for household or 

contextual differences, controlling for household differences, and controlling for both household and 

contextual differences. 

Figure 1 illustrates the mean estimated impact of each state on the odds of food insecurity, based 

on the empty model that controls for neither household nor state characteristics. The 51 estimated state 

impacts are each calculated from the mean of the five different residuals estimated for each state (one for 

each of the different time periods), thus allowing us to present 51 rather than 255 unique impacts 

(separate estimates for each of the state-years are available on request). As is evident from this figure, and 

as we know from raw differences in state food insecurity rates, states differ widely in their odds of food 

insecurity. In the states with the greatest food insecurity problems, the odds of food insecurity are up to 36 

percent greater than average, and in states with the least problems, the odds of food insecurity are up to 29 

percent lower than average. 

Figure 2 illustrates the mean impact of each state on the odds of food insecurity, this time based 

on the household-level model. Here, the estimated impacts reflect the state-specific additional risk of food 

insecurity, after controlling for differences in the sociodemographic characteristics of the population. A 

positive state residual implies that a state has a food insecurity rate higher than expected based on the 
37

Table 5. Variance components from random intercept models (n=70,942). 

Fixed Effect Random Effect 

Model Intercept Coefficient SE Variance Component Empty model -1.728 .020 .071 Level 1 variables -2.197 .061 .040 Level 1 and Level 2 variables -2.218 .066 .009 
38

FIGURE 1

Estimated State Impacts on Odds of Food Insecurity (Empty Model)

-0.5-0.4-0.3-0.2-0.1

0

0.10.20.30.40.5

TX

NM

OR

CA

MS

AZ

LA

MT

FL

AR

OK

ID

UT

GA

HI

WA

DC

AL

TN 

VT

KS

WV

NV

OH

WY

NE

NC

AK

KY

NY

SC

SD

VA

IL

ME

ND

RI

MO

IN

CO

DE

WI

IA
39

FIGURE 2

Estimated State Impacts on Odds of Food Insecurity, 

Based on Model with Household-Level Variables

-0.5-0.4-0.3-0.2-0.1

0

0.10.20.30.40.5

OR

UT

FL

TX

WA

ID

NM

HI

MT

VT

AK

AZ

GA

KS

CA

WY

NE

OK

CO

MS

MN

OH

TN 

AR

NJ

VA

ME

MD

NV

NH

LA

IN

WI

SD

IL

IA

AL

CT

RI

KY

MO

SC

NC
40

observed characteristics of its population. As expected, the magnitude of these impacts is considerably 

smaller once population differences are taken into consideration. Results illustrate that some, but not all, 

of the interstate differences in food insecurity can be explained by differences in the prevalence of 

households with identified characteristics linked to greater risk. 

Next we look at estimated state impacts, after controlling for state as well as household variables. 

A positive state residual implies that a state has a food insecurity rate higher than expected based on the 

observed characteristics of its population and the observed contextual characteristics. As Figure 3 

illustrates, the remaining state-specific impacts are dramatically lower. At the high end, net of measured 

household and state characteristics, states only confer an added risk of up to 5 percent on the odds of food 

insecurity; at the low end, states only confer an added risk reduction of up to 5 percent. These results 

suggest that almost all of the observed interstate differences in food insecurity can be explained by 

differences in population and contextual characteristics included in this model. 

It is informative to examine how states differ in the relative importance of household and 

contextual variables in explaining high or low levels of food insecurity. Figure 4 illustrates the estimated 

state-specific residuals from each of the three models, looking at the 12 states with the greatest risk of 

food insecurity prior to introducing any controls. As is evident in this figure, states vary greatly in the 

extent to which their high risk of food insecurity can be explained by their particular demographics. In 

Oregon, for instance, there is only a modest decrease in the state-specific impact on the odds of food 

insecurity once household characteristics are controlled.12 In Louisiana, on the other hand, the increased 

risk of food insecurity can be entirely explained by household characteristics. Figure 5 shows a similar 

comparison, this time looking at the 12 states with the lowest risk of food insecurity prior to introducing 

 

12This is consistent with Edwards and Weber (2003), who find, in a study of hunger in Oregon, that little of 

the state’s high hunger rate can be attributed to the demographics of the population. Rather, they find that Oregon’s 

high hunger rate reflects above-average hunger rates among almost all demographic groups, and they recommend 

examining policy and other contextual factors that could play a role. 
41

FIGURE 3

Estimated State Impacts on Odds of Food Insecurity, 

Based on Model with Household-Level and State-Level Variables

-0.5-0.4-0.3-0.2-0.1

0

0.10.20.30.40.5

TX

UT

OR

CA

VT

NM

OH

PA

MN

MT

IL

WY

CT

NJ

TN 

NE

WA

GA

VA

DC

ME

SD

MO

KS

OK

AL

DE

MD

NH

MS

ID

KY

IN

HI

IA

AK

RI

MI

WV

LA

AR

MA
42

FIGURE 4

State Impacts on Odds of Food Insecurity (High Food Insecurity States)

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

TX NM OR CA MS AZ LA MT FL AR 

Empty model Household model Full model
43

FIGURE 5

State Impacts on Odds of Food Insecurity (Low Food Insecurity States)

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

MN CT MD NJ MA MI PA NH IA WI 

Empty model Household model Full model
44

any controls. Again, states vary widely in the extent to which their high performance (that is, low food 

insecurity) can be explained by having a low-risk population. Minnesota, Maryland, and New Hampshire, 

for instance, appear to confer no particular food security benefit after accounting for household 

characteristics, whereas Michigan, Massachusetts, and Pennsylvania continue to fare considerably better 

than average even after controlling for demographics. These kinds of analyses are useful in identifying 

states that fare better or worse given the specific demographics of their residents. 

Hunger Models 

Our primary results focus on food insecurity. We also consider similar models using food 

insecurity with hunger, an extreme form of food insecurity, as the dependent variable. These models 

allow us to examine the extent to which household and contextual factors are linked to actual reductions 

in food intake, as compared to being linked to less extreme outcomes involving difficulties in meeting 

food-related needs. 

Results for the random intercept model are shown in Table 6. Most of the household-level 

variables that are linked to food insecurity are also linked to hunger, although there are some exceptions. 

As with food insecurity, hunger is more common among households with lower incomes, no employed 

persons, one or more members with a disability, one or more noncitizens, lower educational levels, 

female headship, and renters as compared to homeowners. Unlike food insecurity, there are no significant 

differences in the risk of hunger according to number of children, most race and ethnicity categories 

(including blacks, whites, and Hispanics), or between households in central cities relative to other 

metropolitan areas. On the other hand, differences in the risk of hunger between rural and metropolitan 

areas are more pronounced than are differences in the risk of food insecurity, and households headed by a 

single male have significantly higher risk of hunger than do households headed by a couple, whereas 

there was no comparable difference for these households in the risk of food insecurity. Several of the 

same contextual factors that are linked to food insecurity are likewise linked to hunger. Hunger is more 

prevalent among households in states with higher unemployment, lower wages, higher rent, lower 
45

TABLE 6 

Coefficients and Odds Ratios from Random Intercept Logistic Regression Analysis of Household Hunger (N=

Fixed Effect Coefficient SE Odds Ra

Intercept -4.164*** 0.116 0.016 Level 1 Variables Income

Income/poverty ratio -0.423*** 0.081 0.655 Ratio squared -0.075*** 0.020 0.928 Missing income 0.331*** 0.099 1.392 

Education

High school -0.126* 0.063 0.882 Some college 0.043 0.066 1.044 College degree or more -0.524*** 0.093 0.592 

Race

Black -0.045 0.061 0.996 Hispanic -0.107 0.073 0.899 American Indian 0.233* 0.124 1.262 Asian -0.312** 0.144 0.732 

Housing Tenure

Rent 0.413*** 0.050 1.511 Live without paying -0.129 0.161 0.879 

Location

Central City 0.061 0.053 1.063 Nonmetropolitan -0.177*** 0.057 0.838 Missing -0.289 0.520 0.749 

Number of Children

2 -0.043 0.050 0.958 3 0.087 0.061 1.091 4 or more 0.107 0.076 1.113 

Family Type

Single mother 0.720*** 0.053 2.054 Single father 0.268*** 0.100 1.307 Other household with children 0.199** 0.078 1.220 

Household Characteristics

Any employed in household -0.227*** 0.058 0.797 Any elderly in household -0.269 0.112 0.764 Any disabled in household 0.793*** 0.062 2.210 Any noncitizens in household 0.163** 0.074 1.178 

(table continues) 
46

TABLE 6, continued 

Fixed Effect Coefficient SE Odds Ra

Level 2 Variables Federal Food Programs

Food Stamp recipients per 100 poor persons -0.003 0.003 0.997 Low-income School Breakfast participants per 100 low-income School Lunch participants 0.001 0.004 1.001 Low-income Summer Food Service program participants per 100 lowincome School Lunch participants -0.003 0.004 0.997 Low-income Summer School Lunch participants per 100 low-income School Lunch participants -0.013 0.004 0.987 

Economic Policies

Low-income tax burden 0.013 0.012 1.013 Overall tax burden 0.024 0.027 1.024 

Economic Attributes

Unemployment rate 0.101** 0.040 1.106 Poverty rate -0.014 0.016 0.986 Average wages per job ($1000s) -0.035*** 0.008 0.966 Median rent ($100s) 0.222*** 0.054 1.249 

Social Attributes

Percentage nonmovers -0.211*** 0.006 0.810 

Survey Year

1999 -0.210*** 0.765 0.811 2000 0.128 0.080 1.137 April 2001 0.149* 0.086 1.161 December 2001 0.066 0.086 1.068 

Random Effect 

Variance 

Component DF Chi-Squ

Intercept Variance 0.020 239 267.68 *p<.1 **p<.05 ***p<.01 Note: The following variables have been centered around their grand mean: Food Stamp recipients per 100 poor persons, ratio of Schoto School Lunch participants, ratio of low-income Summer Food Service program recipients to School Lunch participants, low-incomburden, unemployment rate, poverty rate, average wages per job, median rent, percentage nonmovers. 

aReference categories are less than high school, white, homeowner, metropolitan county (not central city), one child, married. 
47

residential stability, and lower participation in the Summer School Lunch program. In the case of 

unemployment and residential stability, the magnitude of the relationships to hunger are substantially 

larger than are the relationships to food insecurity. Unlike food insecurity, we find no significant impact 

of the Summer Food Service program (as distinct from the Summer School Lunch program) or of the tax 

burden on low-income households. 

We also estimate a random slopes model predicting hunger, shown in Table 7. The contextual 

factors that were significant in the random intercept model continue to be significant in this model. 

However, we find almost no contextual factors that moderate the relationship between income and 

hunger. This differs from our analogous food insecurity model, in which Food Stamp participation, 

unemployment rates, and the tax burden on low-income households all appear to moderate the link 

between near-poverty and/or low-income status and the odds of food insecurity. 
48

TABLE 7 

Coefficients and Odds Ratios from Random Slope Logistic Regression Analysis of Household Hunger (N= 70

Fixed Effect Coefficient SE Odds Ra

Intercept -4.647*** 0.153 0.0095Level 1 Variables Income

Poverty 1.881*** 0.152 6.560 Near poverty (1–1.3 of poverty line) 1.569*** 0.189 4.802 Low income (1.3–1.85 of poverty line) 1.216*** 0.189 3.374 Missing poverty 1.009*** 0.252 2.743 

Education

High school -0.120* 0.063 0.887 Some college 0.026 0.066 1.026 College degree or more -0.770 0.094 0.463 

Race

Black -0.018 0.061 0.982 Hispanic -0.074 0.073 0.929 American Indian 0.241* 0.125 1.273 Asian -0.292** 0.144 0.747 

Housing Tenure

Rent -0.499*** 0.050 0.607 Live without paying -0.032 0.161 0.969 

Location

Central City 0.067 0.054 1.069 Nonmetropolitan -0.140** 0.056 0.869 Missing -0.229 0.521 0.795 

Number of Children

2 -0.003 0.050 0.997 3 0.167*** 0.061 1.182 4 or more 0.220*** 0.076 1.246 

Family Type

Single mother 0.787*** 0.054 2.197 Single father 0.297*** 0.097 1.346 Other household with children 0.255*** 0.078 1.290 

Household Characteristics

Any employed in household -0.255*** 0.058 0.775 Any elderly in household -0.221* 0.116 0.802 Any disabled in household 0.823*** 0.063 2.277 Any noncitizens in household 0.212*** 0.074 1.236 

(table continues) 
49

TABLE 7, continued 

Fixed Effect Coefficient SE Odds Ra

Level 2 Variables Federal Food Programs

Food Stamp recipients per 100 poor persons 0.0002 0.005 1.000 Low-income School Breakfast participants per 100 low-income School Lunch participants 0.002 0.008 1.002 Low-income Summer Food Service program participants per 100 lowincome School Lunch participants -0.007 0.007 0.993 Low-income Summer School Lunch participants per 100 low-income School Lunch participants -0.019*** 0.007 0.981 

Economic Policies

Low-income tax burden -0.006 0.021 0.994 Overall tax burden 0.123** 0.053 1.131 

Economic Attributes

Unemployment rate 0.206*** 0.078 1.131 Poverty rate 0.011 0.030 1.011 Average wages per job ($1000s) -0.035** 0.015 0.966 Median rent ($100s) 0.293*** 0.100 1.340 

Social Attributes

Percentage nonmovers -0.040*** 0.012 0.961 

Survey Year

1999 -0.089 0.160 0.915 2000 0.368** 0.161 1.445 April 2001 0.575*** 0.166 1.777 December 2001 0.265 0.174 1.303 

Cross-Level Interactions Poverty* Food Stamp recipients per 100 poor persons -0.005 0.006 0.995 Poverty* Low-income School Breakfast participants per 100 low-income School Lunch participants -0.001 0.010 0.999 Poverty* Low-income Summer Food Service program participants per 100 lowincome School Lunch participants 0.005 0.009 1.005 Poverty*Low-income Summer School Lunch participants per 100 low-income School Lunch participants 0.009 0.009 1.009 Poverty* Low-income tax burden 0.021 0.028 1.021 Poverty* Overall tax burden -0.116* 0.066 0.890 Poverty* Unemployment rate -0.147 0.097 0.863 Poverty* Poverty rate -0.028 0.038 0.972 

(table continues) 
50

TABLE 7, continued 

Fixed Effect Coefficient SE Odds Ra

Poverty* Average wages per job ($1000s) -0.000 0.019 1.00 Poverty* Median rent ($100s) -0.094 0.124 0.910 Poverty* Percentage nonmovers 0.024 0.015 1.024 Poverty*1999 -0.244 0.192 0.783 Poverty*2000 -0.332* 0.197 0.717 Poverty*April 2001 -0.718*** 0.207 0.488 Poverty*December 2001 -0.294 0.210 0.745 Near poverty* Food Stamp recipients per 100 poor persons -0.005 0.009 0.995 Near poverty* Low-income School Breakfast participants per 100 low-income School Lunch participants -0.0004 0.135 1.000 Near poverty* Low-income Summer Food Service program participants per 100 low-income School Lunch participants 0.016 0.012 

1.016 

Near poverty*Low-income Summer School Lunch participants per 100 lowincome School Lunch participants 0.005 0.011 1.005 Near poverty* Low-income tax burden 0.024 0.037 1.024 Near poverty* Overall tax burden -0.129 0.089 0.879 Near poverty* Unemployment rate -0.020 0.134 0.980 Near poverty* Poverty rate -0.061 0.053 0.941 Near poverty* Average wages per job ($1000s) -0.008 0.026 0.992 Near poverty* Median rent ($100s) 0.011 0.017 1.011 Near poverty* Percent non-movers 0.021 0.022 1.021 Near poverty*1999 -0.136 0.243 0.873 Near poverty*2000 -0.468* 0.264 0.626 Near poverty*April 2001 -0.421 0.273 0.656 Near poverty*December 2001 -0.343 0.286 0.710 Low income* Food Stamp recipients per 100 poor persons -0.008 0.008 0.992 Low income* Low-income School Breakfast participants per 100 low-income School Lunch participants -0.004 0.013 0.996 Low income* Low-income Summer Food Service program participants per 100 low-income School Lunch participants 0.002 0.011 1.002 Low income*Low-income Summer School Lunch participants per 100 lowincome School Lunch participants 0.008 0.011 1.008 Low income* Low-income tax burden 0.051 0.035 1.052 Low income* Overall tax burden -0.146* 0.083 0.864 

(table continues) 
51

TABLE 7, continued 

Fixed Effect Coefficient SE Odds Ra

Low income* Unemployment rate -0.144 0.121 0.866 Low income* Poverty rate -0.030 0.048 0.970 Low income* Average wages per job ($1000s) -0.005 0.025 0.995 Low income* Median rent ($100s) -0.251 0.162 0.778 Low income* Percentage nonmovers 0.030 0.021 1.030 Low income*1999 0.160 0.244 1.174 Low income*2000 -0.105 0.249 0.900 Low income*April 2001 -0.362 0.264 0.696 Low income*December 2001 -0.044 0.266 0.957 Random Effect 

Variance 

Component DF Chi-Squa

Intercept variance 0.005 238 208.314 “Poverty” variance 0.029 238 216.242 “Near poverty” variance 0.019 238 214.204 “Low income” variance 0.030 238 227.677 “Missing poverty” variance 0.097 238 225.925 *p<.1 **p<.05 ***p<.01 Note: The following variables have been centered around their grand mean: Food Stamp recipients per 100 poor persons, ratio of Schoto School Lunch participants, ratio of low-income Summer Food Service program recipients to School Lunch participants, low-incomburden, unemployment rate, poverty rate, average wages per job, median rent, percentage nonmovers. aReference categories are less than high school, white, homeowner, metropolitan county (not central city), one child, married.
52

CONCLUSIONS 

This report examines interstate variation in household food security. Using a multilevel 

framework, we have identified several contextual dimensions that appear linked to food security among 

households: the availability and accessibility of federal nutrition assistance programs, policies affecting 

economic well-being of low-income families (focusing here on tax policy), economic characteristics of 

communities, and social characteristics of communities. Together, these constitute key elements of what 

we term the food security infrastructure—a set of programs, policies, and community attributes that affect 

the availability, accessibility, and affordability of food and the extent to which resources are available to 

households to meet their food-related needs. 

Overall, our findings lend strong support to the concept of a food security infrastructure that 

promotes household food security among community members. We find some evidence supporting a role 

for each of the dimensions, sometimes in affecting the overall risk of food insecurity and sometimes in 

moderating the detrimental impact of low household income. This framework is useful in understanding 

food insecurity, and has important implications for efforts to ameliorate food-related hardships. We 

document potentially important roles for the Food Stamp and summer meal programs in reducing the risk 

of food insecurity among families with children, suggesting that efforts to enhance the accessibility of 

these programs could be beneficial. Further, we demonstrate that policies outside of the nutrition area 

(here, tax policy) can either help or hinder families’ ability to meet food-related needs. This suggests that 

efforts to increase food security would benefit from a broad focus on the range of policies that affect 

family economic well-being, rather than a narrow focus limited to nutrition assistance programs. We 

highlight the relationship between economic characteristics and household food security outcomes, thus 

confirming the importance of quality job opportunities as a component of the food security infrastructure. 

And we document a large and very robust link between median rent and food insecurity, which suggests 

that efforts to ensure affordable housing would be a vital part of efforts to increase food security. Finally, 

our findings with regard to a connection between residential stability in the community and reduced 
53

prevalence of food insecurity suggest that social capital may play an important role. Furthermore, we find 

that most of the factors that are linked to food insecurity are also linked to hunger, a more extreme 

outcome. 

Contrary to our expectations, we find no evidence that any of the state characteristics moderate 

the heightened risk of food insecurity associated with being poor. However, our results do suggest that 

families who are above the poverty line yet still economically vulnerable may be better able to maintain 

food security in the context of a more accessible Food Stamp program, a lower tax burden for households 

in the lower income ranges, and a stronger labor market. This pattern of results suggests that there may be 

a particularly vulnerable segment of the population, those who are in precarious financial circumstances 

yet who have not fallen into poverty, whose ability to meet food-related needs is most affected by the 

economic and policy context. 

 Readers should exercise caution in their interpretation of the apparent relationship between 

summer meals and food security. While we find statistically significant coefficients on both summer meal 

variables (state-level Summer Food Service program participation and state-level Summer School Lunch 

program participation), two aspects of our findings suggest caution. First, we find that state-level Summer 

School Lunch participation (though not Summer Food Service program participation) is also linked to 

lower odds of food insecurity among childless households, a result with no theoretical justification, 

suggesting that the former may be proxying for other unmeasured state attributes. Second, we find no 

evidence that summer meal programs have a greater impact on food security for poor, near-poor, or lowincome households—those for which such programs are most relevant—than for higher income 

households. This differs from our findings for state-level Food Stamp participation and for the lowincome tax burden, where the only significant relationships to food security are among near-poor and 

low-income households, a more theoretically consistent pattern. Further analysis of the relationship 

between summer meal programs and food insecurity is essential to understanding the potential benefits of 

such programs. 
54

A second focus of our analysis was to determine the extent to which cross-state variation in food 

insecurity could be explained by the household and state characteristics in our model. We find that both 

types of variables are important in explaining cross-state variation, although their relative importance 

varies among states. Overall, it appears that the bulk of the interstate differences in food security can be 

explained by cross-state differences in both demographics and contextual characteristics. 

These findings have important implications for our understanding of interstate variation in food 

security rates. In particular, our results illustrate that a high (or low) food insecurity rate can imply very 

different things in different states. In some states, it merely indicates that a state has a high-risk 

population (more poverty, more single-mother households, etc); in other states, it reflects unexpected food 

insecurity despite a lower-risk population. It would be valuable to provide estimates of “excess food 

insecurity” as a way of identifying states that fare better or worse than would be expected based on the 

characteristics of their residents. Efforts to strengthen the food security infrastructure may be particularly 

valuable in states that have unexpectedly high rates of food insecurity given the characteristics of their 

residents. 
55

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APPENDIX TABLE 1 

Empty Model

Fixed Effect Coefficient SE Odds Ra

Intercept -1.728*** 0.020 0.178 Random Effect 

Variance 

Component DF Chi-Squ

Intercept 0.071 254 938.313 
59

APPENDIX TABLE 2 

Random Intercept Model with Household Variables Only

Fixed effect Coefficient SE Odds Ra

Intercept -2.197*** 0.061 0.11Level 1 Variables Income

Income/poverty ratio -0.477*** 0.042 0.62Ratio squared -0.047*** 0.009 0.954 Missing income -0.016 0.049 0.984 

Education

High school -0.120*** 0.039 0.887 Some college -0.166*** 0.040 0.847 College degree or more -0.698*** 0.050 0.498 

Race

Black 0.167*** 0.036 1.182 Hispanic 0.143** 0.041 1.154 American Indian 0.361*** 0.077 1.434 Asian 0.008 0.073 1.008 

Housing Tenure

Rent 0.419*** 0.027 1.52Live without paying 0.030 0.082 1.03

Location

Central city 0.097*** 0.031 1.102 Nonmetropolitan -0.067** 0.031 0.935 Missing -0.300 0.260 0.740 

Number of Children

2 0.031 0.028 1.032 3 0.174*** 0.034 1.190 4 or more 0.259*** 0.044 1.296 

Family Type

Single mother 0.508*** 0.030 1.662 Single father 0.053 0.053 1.055 Other household with children 0.112*** 0.042 1.118 

Household Characteristics

Any employed in household -0.178*** 0.038 0.837 Any elderly in household -0.308*** 0.062 0.735 Any disabled in household 0.679*** 0.041 1.972 Any noncitizens in household 0.105** 0.042 1.11

(table continues)
60

APPENDIX TABLE 2, continued

Random Effect 

Variance 

Component DF Chi-Squ

Intercept 0.040 254 543.18