The Effect of Supplemental Nutrition Assistance Program
Participation on Food Security: A Control Function Approach
Matthew P. Rabbitt
Department of Economics
University of North Carolina at Greensboro
Greensboro, NC 27420
(336)-334-4892
mprabbit@uncg.edu
April, 2013
Preliminary and incomplete draft
Please do not cite
Comments about anything except misspellings and grammatical errors are very welcome
Working Abstract:
The percentage of households experiencing food insecurity in the United States increased
from nearly 11 percent in 2000 to 14.6 percent in 2008 and remained essentially unchanged until
2011 when it reached approximately 15 percent (Coleman-Jensen et al., 2012). Low-income
households are especially susceptible to food hardships and employ a variety of methods to meet
their basic food needs. Some rely on food from emergency food providers while others
participate in one or more of the government food and nutrition assistance programs. SNAP is
the largest food assistance program operated by the USDA, accounting for 73 percent of food
and nutrition spending in fiscal year 2011 (Oliveira, 2012). The goal of the program is to help
low-income households and individuals obtain access to food and a healthful diet. By improving
nutrition and diet, the program also advances other goals, such as improving food security.
Evidence indicates that SNAP participation strongly increases household food consumption and
dietary intakes (Fox et al., 2004); however, evidence regarding food security is ambiguous, with
the unexpected association of SNAP participation increasing food insecurity (Nord et al., 2009).
We use data from the 2001-2008 December CPS Food Security Supplement to examine the
relationship between low-income households’ participation in SNAP and food insecurity. We
advance the literature by developing a control function approach for the behavioral Rasch model
that accounts for self-selection.
I.
Introduction
To be written.
II.
Previous Literature
To be written.
III.
Conceptual Model
We begin with a discussion of Gunderson and Oliveira’s (2001) model of how household
food sufficiency and SNAP participation are determined to motivate the empirical analysis. The
model shows how available household resources lead to the consumption of food and non-food
goods. While the model focuses on food sufficiency, it is also capable of explaining why some
households experience food insecurity. It also suggests how government assistance programs
may improve food insecurity.
Gunderson and Oliveira’s (2001) model extends the traditional household consumption
model to consider household food insecurity. In Gunderson and Oliveira’s model, the household
makes decisions to maximize consumption of food and non-food goods. Household preferences
are represented by a utility function, which incorporates the household’s tastes and preferences
for both goods. Tastes and preferences may be influenced by culture and other demographic
characteristics of the household.
The household faces a single constraint, which limits household consumption. The
household is subject to a financial constraint in which purchases of food and non-food goods
must not exceed total family income. The model assumes total family income is a function of the
sum of all members’ earned income, transfers, other income, and SNAP program benefits. If the
household chooses to participate in the SNAP, total family income is increase by a dollar amount
1
equal to the level of benefits the household is eligible to receive. With an increase in income,
household consumption of food and non-food goods will adjust, as the household determines the
new optimal mix of food and non-food goods.
Gunderson and Oliveira’s model draws on the welfare “stigma” model of Moffitt (1983)
to explain how the household makes its participation decision. The model assumes households
experience a disutility from participation. This disutility is a function of the pecuniary and nonpecuniary costs of participation. Pecuniary costs include time spent filling out an application,
travelling to the administrative office, or recertifying benefit eligibility. Non-pecuniary costs
include the stigma, or distaste a household associates with participation. Sources of stigma
include personal and interpersonal distaste for participation. There are also information costs, as
households must obtain information about eligibility.
The household’s SNAP participation decision requires households to compare the
indirect utility of participation to non-participation. If the indirect utility of participation is
greater than that of non-participation (i.e. the difference is positive.), the household will choose
to participate in SNAP. When the indirect utility is equal for both participation and nonparticipation (i.e. the difference is zero.), the household will be indifferent. The household will
not participate if the indirect utility under participation is less than that for non-participation (i.e.
the difference is negative.). Recall that the optimal levels of food and non-food consumption will
differ depending on the participation decision. The model also assumes that the household will
not face any participation costs if they choose not to participate (i.e. there are no costs to
information gathering if the household chooses not to participate).
2
The model assumes the household chooses consumption levels of food and non-food
goods to maximize its objectives subject to the constraint. Decisions by the household lead to
levels of food consumption that can be compared to a threshold of food insecurity. The food
insecurity threshold is a function of household characteristics that influence food needs, such as
household size, composition, age, and education. With this model, we can examine threats to
household food security.
The model of Gunderson and Oliveira incorporates several threats to household food
security. First, the household is more likely to be food insecure if its members have low labor
productivity. Lower labor productivity limits the household’s ability to acquire new resources
through labor market activities necessary to purchase food. Reduced labor productivity may be
due to medical conditions, age, or lower education. Second, households that face higher food
prices are more likely to be food insecure. As food prices increase, food consumption will fall
(assuming food is a normal good). Third, food insecurity may be a manifestation of household
preferences for non-food versus food goods. Some households may place a higher value of nonfood goods, choosing to consume greater levels of other goods in place of food. For example, a
household that faces a health shock might reduce food intake for a period of time to increase the
use of medical good and services. Fourth, lower levels of household assets reduce the amount of
available resources to purchase food. Households with few assets may be less capable of
adjusting expenditures to meet their food needs after facing an income shock such as job loss.
Gunderson and Oliveira’s model also allows us to consider how government and
community assistance programs address food insecurity. The SNAP is intended to supplement
the household’s food budget. Benefits are designed to complement a household’s existing food
expenditures and increase total spending on food. Within the model, receipt of SNAP benefits
3
will increase the household’s income, shifting the budget set outward by an amount equal to the
benefit. Household food and non-food consumption will increase, improving the household’s
food and material needs. As food consumption increases, SNAP may improve household food
insecurity. Recall that household food insecurity is determined by comparing food consumption
with a threshold. If households with greater need are more likely to participate in SNAP, the
difference between their level of food consumption and the threshold will be larger. As a result,
these households will require greater benefit levels to transition from experiencing food
insecurity to food security. The benefits they receive may not be sufficient for this an
improvement to manifest. Conversely, the lack of improvement may be due to household
preferences. Households may also adjust their labor supply in response to benefit receipt,
reducing income and increasing food insecurity.
While the model of Gunderson and Oliveira is useful for examining the current question,
it is not without limitations. The current model does not capture changes in food insecurity and
household assets over time. A dynamic model may be better suited for the analysis if households
are more likely experience acute rather than chronic food insecurity. Models that account for
changes over time can capture borrowing and saving practices by the household, along with food
management strategies, such as food stockpiling or binge eating. Uncertainty and risk are also
important elements to consider when analyzing food insecurity. Households may not have
perfect information about future food supplies and income streams. Food insecurity may also
impact household labor productivity, reducing human capital and the amount of energy available
to work.
4
IV.
Data
The data for the empirical analyses come from the 2001-2008 December Current
Population Survey Food Security Supplement (CPS-FSS). The CPS is the source of official
government statistics on employment status and poverty. Approximately 60,000 households are
interviewed each month with data collected on labor force participation status, income, and
household demographic characteristics. The interviewer selects the person who is most
knowledgeable concerning the labor force participation status of the members of the household
and interviews him. CPS households are representative at the state and national levels, of the
civilian, noninstitutionalized population. Occasionally, government agencies sponsor collection
of specialized supplemental data by the CPS.
The Food Security Supplement (FSS) is conducted as a supplement to the CPS for the
Economic Research Service (ERS) of the USDA. It was first included in the April 1995 CPS and
has since been administered every subsequent year. Since 2001, the FSS has been fielded in
December. The purpose of the FSS is to estimate the prevalence of food insecurity in the United
States. Each year the ERS produces estimates of food insecurity in the United States using the
supplement1. All CPS households are asked questions about food expenditures and food
sufficiency. To reduce respondent burden and embarrassment, households with income above
185% of the federal poverty threshold2, or households that do not show signs of food stress3 are
1
The most recent food security report is Coleman-Jensen et al. (2012).
The federal poverty threshold was $17,346 in 2008 for a family of four with two children.
3
The following preliminary screening questions are asked to determine if a household shows signs of food-access
problems:
1. People do different things when they are running out of money for food in order to make their food or their
food money go further. In the last 12 months, since December of last year, did you ever run short of money
and try to make your food or your food money go further?
2. Which of these statements best describes the food eaten in your household-enough of the kinds of food we
want to eat, enough but not always the kinds of food we want to eat, sometimes not enough to eat, or often
not enough to eat?
2
5
not asked the food security questions. All households are asked questions about their food
expenditures and basic food needs, while only those households that answer the food security
questions are asked about participation in government and community food assistance programs.
4.1.
Dependent Variables
The empirical analyses examine responses to a series of food hardship questions as
dependent variables. In each wave of the FSS, a household representative was asked the
following questions regarding themselves, other adults, and the household in general:









“I/We worried whether my/our food would run out before I/we got money to buy more.”
Was that often, sometimes, or never true for you/your household in the last 12 months?
“We couldn’t afford to eat balanced meals.” Was that often, sometimes, or never true for
you in the last 12 months?
In the last 12 months, did you or other adults in the household ever cut the size of your
meals or skip meals because there wasn’t enough money for food?
How often did this happen-almost every month, some months but not every month, or in
only 1 or 2 months?
In the last 12 months, did you ever eat less than you felt you should because there wasn’t
enough money for food?
In the last 12 months, were you ever hungry, but didn’t eat, because you couldn’t afford
enough food?
In the last 12 months, did you lose weight because you didn’t have enough money for
food?
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?
How often did this happen-almost every month, some months but not every month, or in
only 1 or 2 months?
For households with children, the respondent was also asked questions regarding the food
hardships that children might have experienced:



“We relied on only a few kinds of low-cost food to feed our children because we were
running out of money to buy food.” Was that often, sometimes, or never true for you in
the last 12 months?
“We couldn’t food our children a balanced meal because we couldn’t afford that.” Was
that often, sometimes, or never true for you in the past 12 months?
“The children were not eating enough because we just couldn’t afford enough food.” Was
that often, sometimes, or never true for you in the last 12 months?
6





In the past 12 months, did you ever cut the size of any of the children’s meals because
there wasn’t enough money for food?
In the past 12 months, were the children ever hungry but you just couldn’t afford more
food?
In the last 12 months, did any of the children ever skip a meal because there wasn’t
enough money for food?
How often did this happen-almost every month, some months but not every month, or in
only 1 or 2 months?
In the last 12 months, did any of the children ever not eat for a whole day because there
wasn’t enough money for food?
Each question was converted to a binary variable according to the methods suggested in Bickel
et al. (2000).
The 18 food hardship questions constitute the food security scale. These questions inquire
about the experiences and behaviors of households having difficulty meeting their food needs.
Behaviors and experiences captured by the food security questions include the anxiety or
perception that the food eaten by adults and children is inadequate in quality or quantity, and
reported instances of reduced food intake, or its consequences for adults and children.
Consequences vary from feelings of hunger to losing weight. All questions specify that a
behavior or condition must be due to a lack of economic resources. For example, the questions
about hunger make it clear that the reason must be a lack of “enough money for food,” not
hunger due to dieting or time constraints. In our empirical investigation, we examine responses
to the first 10 questions concerning adults in the household and also examine all of the questions
concerning adults and children.
Our descriptive analyses use the household’s food security status to compare the
prevalence of food insecurity across sub-populations. A household is classified into one of the
food security status categories based on its score on the food security scale, which is determined
by the household’s overall pattern of responses to the food security questions. Each category
7
represents a meaningful range of severity of food insecurity defined by the underlying food
security scale. These categories capture specific behaviors and conditions associated with a
certain level of food insecurity.
Households are food secure if the respondent reports no food hardships or if he reports
only one or two food hardships. These households either have no reported food access problems
or typically report feelings of anxiety over the quantity of food available in the household. There
is little indication of changes to diets or levels of food consumption. A household is classified as
food insecure if the respondent reports three or more food hardships. Food insecure households
are further classified as having either low or very low food security; however, classification rules
differ for households with and without children.
Households without children are classified as having low food security if the respondent
reports three to five food hardships. Low food security households have reported multiple food
access problems and few, if any, indications of reduced food intake. If the respondent reports six
or more food hardships the household is classified as having very low food security. These
households show multiple signs of disrupted eating patterns and reduced food intake.
Households with children are classified as having low food security if the respondent
reports three to seven food hardships, including conditions among both adults and children.
These households may be further classified as having very low food security if the respondent
reports eight or more food hardships.
4.2.
Explanatory Variables
The explanatory variables in our empirical analyses primarily describe the resources and
needs of households. These include several measures of economic resources. In each wave,
8
respondents were asked about the households’ and household members’ participation in one or
more of the government food and nutrition assistance programs. We use the respondents’ reports
to form a measure of the household’s participation in SNAP within the past 12 months.
In some alternative specifications, we replace SNAP participation within the past 12
months with SNAP participation in the past 30 days. SNAP participation in the previous 30 days
is less subject to recall bias and potentially better aligned with the mindset of the respondent
when answering the food security questions. If the household respondent reports indications of
food hardships that are associated with the month in which the FSS is conducted, then SNAP
receipt within the past 30 days is a more appropriate measure of the program’s impact on food
security.
We also include measures of the respondent’s labor force participation status. All of our
models include indicators for whether the respondent was employed during the preceding month.
Our analyses incorporate measure of short and long-term economic resources. These
measures include binary indicators for total family income and home ownership. Total family
income includes household income from the labor market, pensions, dividends, interest, social
security payments, and any other money received by family members in the past 12 months. In
some alternative specifications we convert the binary income measures into an indicator for
income at or below 100% of the federal poverty threshold. These indicators describe potential
household assets. The wealth measures are directly useful as measures of household resources,
but also indirectly valuable as possible controls for SNAP program eligibility.
In addition to economic resources, we also include controls for resources in the form of
help from social networks. Our models use two binary measures that indicate whether the
9
respondent reported using a community food pantry or soup kitchen within the past 12 months.
We also control for the respondent’s own human capital resources using measures of his or her
educational attainment.
Our models also include other demographic, geographic and economic conditions as
controls. These include measures of the respondent’s age, race/ethnicity, and immigration status.
We also include measures of the number of persons in the household, age of the youngest and
oldest person in the household, urban residence and state unemployment rate. Finally, indicators
are included for state of residence and wave of interview.
4.3.
Instrumental Variables
To be written.
4.4.
Analysis Sample
We restrict our analyses to households with income below 130% of the federal poverty
threshold. These households are gross income eligible for the SNAP. This reduces the sample to
59,367 households. We further exclude households that do not provide usable responses to one
or more of the questions used to form the explanatory variables, leaving a final analysis sample
of 57,630 households. Households are separated into four mutually exclusive and exhaustive
groups based on household structure and age. This practice is common within the SNAP
participation literature and allows us to examine important policy groups separately. The groups
represent two parent, single parent, adults age 17 to 59 without children, and elderly adults (age
> 59) without children households. The final analysis sample consists of 9,964 two parent,
13,999 single-parent, 17,573 other adult, and 16,205 elderly households. All of the statistical
analyses use survey weights that adjust for the survey’s sampling design and non-response.
10
Table 1 lists the proportions of households within a food security status category for each
wave of the CPS-FSS considered. The estimates indicate the proportion of food secure
households remained relatively constant until 2008, when it declined. During the same period,
the proportion of households experiencing low food security remained relatively constant, while
the number of households experiencing very low food security increased. Recall that households
with very low food security report more than six or eight food hardships, depending on if
children are present. These households show signs of disruptions in eating patterns and some
members experience hunger.
Estimates from Table 1 also indicate the proportion of households tended to increase over
the analysis window. Single parent households represented the largest proportion of SNAP
participants, followed by two parent, other adult and elderly households. This is likely a result of
the limited financial resources and increased food requirement for single parent households.
Single parent households also tend to be headed by a female (Table 3). Participation by elderly
and single parent households increased over the analysis window, while participation by two
parent households followed a similar pattern for all households, declining slightly in at the year.
Participation for other adult households reached a peak in 2005, then leveled off until increasing
slightly in 2008.
Table 2 presents estimates of the proportion of households with different SNAP
participation histories. For all types of households, food security is lower for program
participants, reinforcing the concerns about self-selection (i.e. households with greater need are
more likely to participate.). SNAP participating two parent households appear to be more likely
to be food insecure, along with single parent, other adult and elderly households. Other adult
households have the lowest food security, followed by single parents, two parents, and elderly
11
households. This is likely an artifact of limited financial assets. Other adult and single parent
households have the lowest income and are likely to own a home (Table 3).
V.
Econometric Specification
Estimation of the effect of SNAP participation on household food insecurity is
complicated by the fact that food insecurity is a latent (unobservable) trait. We use the Rasch
model (Rasch, 1960) to examine this relationship. The central idea behind this approach is that
multiple outcomes that can be observed (i.e. reports of food hardships) all derive from a single,
underlying latent variable, such as food insecurity. We assume that a household compares food
expenditures and the food security threshold with answering questions about food hardships. Let
household i’s underlying continuous index of food hardship or deprivation by θi, with the
property that higher values of the index correspond to greater levels of hardship. We do not
observe θi directly. Suppose, however, that we have j continuous indicators, Yi*j , that are related
to θi such that each of them depends on the index and some random measurement error, νij. We
can express the relationship between the indicators and the underlying latent index as
Yi*j  i  i j ,
(1)
where νij is distributed according to a Type 1 Extreme Value (EV1) distribution. Equation (1)
describes a factor analytic relationship in which the hardship index θi is the underlying common
factor and the factor loading is normalized to one.
Up to this point, we have expressed the model in terms of a set of continuous indicators
of food hardships (the Yi*j variables); however, the actual indicators are discrete variables. Like
12
Wilde and Nord (2005), Ribar et al. (2006) and Depolt et al. (2009), we assume the continuous
indicators, Yi*j , are related to the binary responses as follows:
 1  if  Yi*j   j

Yi j  
*

0  if  Yi j   j .
(2)
This specification of the relationship between the latent continuous indicator and the observed
categorical response is the same used in standard probit and logit models. The thresholds, or δj
terms, are estimated as part of the multivariate model and take on different values for each type
of hardship. Higher values of the thresholds indicate the associated condition is less likely to be
met. Higher thresholds also imply that the index has to be larger for the condition to be met,
which indicates a greater severity of food insecurity. Given equations (1) and (2), the probability
that household i’s respondent answers “yes” to the jth food hardship question is
P  Yi j  1| i ,  j  
exp  i   j 
1  exp  i   j 
(3)
where exp( ) is the exponential function, θi is the hardship index, and δj is the threshold or
“calibration” parameter. Equations (1)-(3) constitute a Rasch measurement model which
describes how multiple discrete food hardship indicators are related to the underlying hardship
index, θi. This is the model used by the USDA to produce annual statistics on the prevalence of
food insecurity in the United States.
5.1.
Behavioral Rasch Model
While the Rasch measurement model is useful for describing the prevalence of food
insecurity, it is not able to analyze the relationship between SNAP participation and food
insecurity. The Rasch model can be extended to include a behavioral component using a
13
Generalized Linear Mixture Model (GLMM) approach. Under this framework, the hardship
index is specified as
i  sSi  X ' Xi  ei
(4)
where Si is a SNAP program participation indicator, Xi is a vector of observed control variables
related to food insecurity, βS is a scalar coefficient, βX is a matrix of coefficient, and ei is i.i.d.
normally distributed with zero mean and unknown variance σ2. At first glance, this may appear
to be an overly restrictive assumption; however, this distribution represents the remainder after
all other observable household characteristics are taken into account (Opsomer et al., 2003).
An additional assumption of the Rasch model that carries over to the Behavioral Rasch
model is conditional independence of the errors in the responses. This means that the probability
of a “yes” response to a given hardship question for a given value of θi does not depend on the
response to another question. Therefore, responses to the hardship questions are only related by
the common food hardship index. If two events are independent, the probability both occur is the
product of the probabilities that each event occurs. Similarly, with conditional independence, the
conditional probability for a response vector is simply the product of the responses for each
question. By stacking the household’s responses  Yi j  into a vector Yi , the probability of
observing the responses is
P  Yi  yi | Xi ,Si  

  P Y
J
 j1
ij
ij
 1| Xi ,Si  1  P  Yi j  1| Xi ,Si 
Y
1Yi j  1  ei 
   dei
 
where φ( ) is the standard normal density. The product of responses will run over 10 items for
all households and an additional eight items for households with children. Equation (5) and
14
(5)
independence of the observations leads to a likelihood function that is the product of the
probabilities of responses for all N observations.
5.2.
Endogenous Behavioral Rasch Model
Up to this point we have implicitly assumed that the SNAP program participation
variable (Si) is exogenous. The voluntary nature of the SNAP participation decision suggests that
program participation may be endogenous. Households experiencing food hardships are more
likely to participate. We account for the household’s behavior by modeling the participation
decision as
Si  I  X ' Xi  Z' Zi  u i  0 
(6)
where Xi represents the standard control variables included in the food hardship model, Zi is a set
of variables that are only associated with the program participation decision, αx and αz are
matrices of coefficients, and ui is a stochastic error-component that is assumed to be i.i.d.
normal. The resulting model is consistent with a standard probit model for the decision to
participate in SNAP. Correlation may exist between the unobservable determinants of food
hardships and program participation, such as household food needs, tastes and preferences for
food that may potentially bias the analysis results. This can be formalized by assumes the errorcomponent in equation (4) can be decomposed into ui and e*, such that ei = ui + e*. Under this
specification, ui generates correlation between the program participation variable (Si) and the
food hardship indicators, which can lead to biased estimates.
To consistently estimate the model parameters when the program participation decision is
endogenous, the estimation strategy must be able to account for all observable and unobservable
variables related to the participation decision and the observed hardship indicators. We use the
15
nonlinear endogenous switching framework4 proposed by Terza (2009). An incarnation of this
framework is the ability to consistently estimate models with an endogenous treatment. Under
the fully parametric specification of this framework, one can specify the joint distribution of the
observed hardship indicators (the Yij’s) and the program participation variable (Si). The method
relies on the assumption that the unobservable variables follow a specified distribution,
conditional on the exogenous and identifying instrumental variables. This is equivalent to the
error-component assumption made in equation (6). Based on this assumption and equations (5)
and (6), we can write the likelihood function as follows:

N 
 J
1  e*  

L  , , ,  | data    Si      q i j SSi  X ' Xi  u i   j    i  de*i    u i  du i
  
i 1   ' W   j1

i
(7)
 ' Wi  J
*





1 e

        1  Si       q i j  SSi  X ' Xi  u i   j    i  de*i    u i  du i 
  
   j1






where qi j  2Yi j  1, 5 Wi   Xi , Zi  and    X ,  Z . The likelihood function in (7) can be
estimated via maximum likelihood methods and produces consistent estimates for all of the
model parameters when the program participation variable is endogenous. The parameter
estimate for λ is equivalent to a factor loading parameter with the property that its nullity is a
sufficient statistic for exogeneity of the program participation variable. For estimation purposes,
we develop customized software using the ado/Mata language in Stata.
VI.
Results
To be written.
4
This approach is based on the work of Olsen (1980), who showed for the linear outcome equation that Heckman’s
(1978) dummy endogenous treatment estimator can be derived without assuming joint normality of the errors.
5
We are able to make this simplifying assumption because the logistic distribution is symmetric.
16
VII.
Conclusion
To be written.
17
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19
IX.
Appendix
Table 1: Food Hardships for Households with Income Below 130% of the Federal Poverty Threshold by
Year
Year
2001
2002
2003
2004
2005
2006
2007
2008
0.683
0.210
0.108
0.668
0.211
0.121
0.682
0.206
0.112
0.667
0.212
0.122
0.673
0.201
0.126
0.675
0.196
0.129
0.662
0.202
0.137
0.615
0.211
0.174
0.611
0.294
0.096
0.639
0.279
0.082
0.640
0.277
0.083
0.616
0.293
0.091
0.640
0.266
0.094
0.644
0.275
0.081
0.620
0.266
0.114
0.587
0.282
0.131
0.568
0.306
0.127
0.536
0.335
0.129
0.535
0.328
0.137
0.524
0.333
0.143
0.558
0.315
0.127
0.551
0.304
0.144
0.564
0.286
0.150
0.502
0.305
0.193
0.701
0.157
0.142
0.668
0.149
0.182
0.669
0.167
0.164
0.644
0.171
0.185
0.637
0.167
0.196
0.666
0.146
0.189
0.655
0.172
0.173
0.603
0.165
0.232
0.810
0.128
0.062
0.805
0.121
0.074
0.832
0.103
0.065
0.820
0.115
0.065
0.821
0.108
0.072
0.818
0.112
0.070
0.781
0.127
0.092
0.765
0.125
0.111
All Households:
Food Secure
Low Food Security
Very Low Food Security
Two-Parent HH:
Food Secure
Low Food Security
Very Low Food Security
Single-Parent:
Food Secure
Low Food Security
Very Low Food Security
Other Adult, No
Childrena:
Food Secure
Low Food Security
Very Low Food Security
Elderly, No Childrenb:
Food Secure
Low Food Security
Very Low Food Security
Calculated using data from the 2001-2008 December Food Security Supplement.
All calculations are weighted.
a
The other adult group includes adults age 18 to 59 without children.
b
A household is classified as elderly if the head’s age is greater than 59.
20
Table 2: Food Hardships and SNAP Participation for Households with Income Below 130% of the Federal
Poverty Threshold
SNAP
All
Participant
Non-Participant
Two-Parent:
Food Secure
Low Food Security
Very Low Food Security
SNAP Participation, past 12 months
0.624
0.279
0.097
0.255
0.468***
0.373***
0.159***
-
0.677
0.247
0.076
-
0.541
0.314
0.145
0.487
0.465***
0.358***
0.177***
-
0.613
0.272
0.145
-
0.653
0.162
0.185
0.202
0.422***
0.236***
0.343***
-
0.712
0.143
0.145
-
0.807
0.117
0.076
0.157
0.601***
0.219***
0.180***
-
0.846
0.098
0.056
-
Single-Parent:
Food Secure
Low Food Security
Very Low Food Security
SNAP Participation, past 12 months
Other Adults, No Childrena:
Food Secure
Low Food Security
Very Low Food Security
SNAP Participation, past 12 months
Elderly, No Childrenb:
Food Secure
Low Food Security
Very Low Food Security
SNAP Participation, past 12 months
Calculated using data from the 2001-2008 December CPS Food Security Supplement.
All calculations are weighted.
*** indicates a P-value <0.001 for a Wald test of equal means for SNAP participants and non-participants.
a
The other adult group includes adults age 18 to 59 without children.
b
A household is classified as elderly if the head’s age is greater than 59.
21
Table 3: Means for Households with Income Below 130% of the Federal Poverty Threshold by Household
Type
Other
TwoSingleAdults, No Elderly, No
Total
Parent
Parent
Childrena
Childrenb
SNAP Program Participation:
Received SNAP Benefits, Past 12
Months
0.267
0.255
0.487
0.202
0.157
(0.442)
(0.436)
(0.500)
(0.401)
(0.364)
0.253
(0.435)
0.229
(0.420)
0.464
(0.499)
0.186
(0.389)
0.158
(0.365)
Outreach Spending Per Poor Person
0.040
(0.109)
0.036
(0.100)
0.039
(0.106)
0.044
(0.116)
0.040
(0.107)
Broad Based Categorical Eligibility
0.255
(0.436)
0.270
(0.444)
0.252
(0.434)
0.258
(0.438)
0.244
(0.429)
One Vehicle Per Household Exempt
0.078
(0.269)
0.067
(0.250)
0.076
(0.266)
0.080
(0.271)
0.085
(0.279)
One Vehicle Per Adult Exempt
0.224
(0.417)
0.251
(0.434)
0.226
(0.418)
0.212
(0.409)
0.219
(0.414)
All Vehicles Exempt
0.417
(0.493)
0.352
(0.477)
0.429
(0.495)
0.438
(0.496)
0.424
(0.494)
Female
0.613
(0.487)
0.424
(0.494)
0.851
(0.356)
0.490
(0.500)
0.659
(0.474)
Black, Non-Hispanic
0.215
(0.411)
0.114
(0.318)
0.342
(0.474)
0.214
(0.410)
0.168
(0.374)
Other Race, Non-Hispanic
0.053
(0.224)
0.069
(0.254)
0.039
(0.193)
0.064
(0.244)
0.043
(0.203)
0.205
0.413
0.240
0.137
0.121
Received SNAP Benefits, Past 30 days
Instrumental Variables:
Respondent Demographics:
Hispanic
22
(0.404)
(0.492)
(0.427)
(0.344)
(0.326)
47.803
(19.311)
38.155
(11.216)
36.123
(12.254)
39.523
(13.331)
72.725
(7.603)
2658.052
(1985.854)
1581.585
(987.487)
1455.012
(1055.007)
1739.797
(1031.007)
5346.743
(1105.559)
Employed
0.401
(0.490)
0.584
(0.493)
0.538
(0.499)
0.473
(0.499)
0.093
(0.291)
High School Graduate
0.341
(0.474)
0.345
(0.475)
0.387
(0.487)
0.323
(0.468)
0.320
(0.467)
College Graduate
0.059
(0.235)
0.056
(0.229)
0.028
(0.166)
0.104
(0.305)
0.038
(0.192)
Non-Citizen, Immigrant
0.128
(0.334)
0.311
(0.463)
0.124
(0.330)
0.107
(0.309)
0.041
(0.198)
HH income less than 100% FPL
0.689
(0.463)
0.635
(0.482)
0.785
(0.411)
0.734
(0.442)
0.591
(0.492)
Own Home
0.385
(0.487)
0.477
(0.499)
0.246
(0.430)
0.267
(0.443)
0.576
(0.494)
Food from Food Pantry Past 12 Months
0.133
(0.339)
0.129
(0.335)
0.176
(0.381)
0.144
(0.351)
0.086
(0.281)
Ate at Kitchen Past 12 Months
0.019
(0.135)
0.009
(0.093)
0.014
(0.119)
0.035
(0.185)
0.010
(0.099)
Count of individuals in HH
2.594
(1.740)
4.817
(1.473)
3.637
(1.431)
1.607
(0.855)
1.404
(0.661)
Age Oldest in Household
49.592
(19.134)
41.222
(12.410)
38.354
(12.987)
41.454
(14.413)
73.195
(7.571)
Age
Age-squared
Household Economic Resources:
Community Resources:
Household Demographics:
23
Age Youngest in Household
32.658
(27.791)
5.459
(4.815)
6.453
(5.153)
36.240
(13.694)
67.955
(14.254)
Urban Household
0.765
(0.424)
0.774
(0.418)
0.795
(0.403)
0.789
(0.408)
0.709
(0.454)
State Unemployment Rate
5.344
(0.991)
5.428
(0.979)
5.366
(0.987)
5.315
(1.008)
5.306
(0.980)
N
57,630
9,964
13,888
17,573
16,205
Economic Conditions:
Calculated using data from the 2001-2008 December CPS Food Security Supplement.
All calculations are weighted.
Standard deviations are in parenthesis.
a
The other adult group includes adults age 18 to 59 without children.
b
A household is classified as elderly if the head’s age is greater than 59.
24