Constance Newman

Erik Scherpf

Economic

Research

Service

Economic 

Research

Report 

Number 156

September 2013

United States Department of Agriculture

Supplemental Nutrition Assistance 

Program (SNAP) Access at the 

State and County Levels

Evidence From Texas SNAP Administrative 

Records and the American Community Survey
Economic Research Service 

www.ers.usda.gov

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Recommended citation format for this publication:

Newman, Constance and Erik Scherpf. Supplemental Nutrition Assistance Program

(SNAP) Access at the State and County Levels: Evidence From Texas SNAP 

Administrative Records and the American Community Survey, ERR-156, U.S. 

Department of Agriculture, Economic Research Service, September 2013.

Cover artwork provided by: Shutterstock
United States Department of Agriculture

Economic 

Research 

Service

Economic

Research

Report

Number 156

September 2013

Abstract

This report links Supplemental Nutrition Assistance Program (SNAP) administrative 

records from Texas to the American Community Survey (ACS) to estimate SNAP access 

rates for geographic and demographic subgroups, at both the State and county levels. The 

linked data allow us to measure SNAP participation more accurately than is possible with 

household survey data while also allowing us to estimate SNAP eligibility in the population. The large sample size of the ACS enables us to provide a breakdown of access rates 

by subgroups within the State and for demographic subgroups within the largest counties 

in Texas. The report provides Texas SNAP administrators with a profile of individuals who 

may benefit from expanded outreach. 

Keywords: SNAP, Supplemental Nutrition Assistance Program, American Community 

Survey, ACS, Texas, households with children, food access, SNAP participation, eligibility, 

food assistance programs

Acknowledgments

The authors want to thank the following individuals for their contributions to this report: Graton 

Gathright, Amy O’Hara, Benjamin C. Harris, and Margaret R. Jones, Census Bureau; Mark Prell, 

David Smallwood, and Margaret Andrews, Economic Research Service-USDA; Bob Dalrymple, 

Food and Nutrition Service-USDA; Karen E. Cunnyngham, Mathematica Policy Research; Laura 

Wheaton, The Urban Institute; Nikolas Mittag, The University of Chicago; Caroline Danielson, 

Public Policy Institute of California; and seminar participants at USDA’s Economic Research 

Service, the National Association for Welfare Research and Evaluation 2012 conference, and 

the American Association of SNAP Directors 2012 conference. Thanks also to our editor, Dale 

Simms, and our designer, Wynnice Pointer-Napper, Economic Research Service-USDA.

Constance Newman, cnewman@ers.usda.gov

Erik Scherpf escherpf@ers.usda.gov

Supplemental Nutrition Assistance 

Program (SNAP) Access at the State 

and County Levels

Evidence From Texas SNAP Administrative 

Records and the American Community Survey
ii

SNAP Access at the State and County Levels: Evidence From Texas, ERR-156

Economic Research Service/USDA

Contents

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

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

Related Research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Linking the ACS and Administrative Records To Estimate SNAP Access. . . . . . . . . . 6

Estimating SNAP Eligibility. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Immigrants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

College and Graduate School Students. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

SNAP Unit Income. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Modeling Eligibility. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

County-Level Access Rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Appendix: Detailed Characteristics of the Administrative Data, the ACS Sample, 

and the Linked Data—PIK Rates and Comparability of Samples. . . . . . . . . . . . . . 31

A. Texas Administrative Data Description. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

B. Comparison of PIK’ed and Non-PIK’ed Samples in the ACS Data. . . . . . . . . . . . . 32

C. Linked Data—Comparability of Samples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
A report summary from the Economic Research Service

ERS is a primary source 

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analysis from the U.S. 

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providing timely information on economic and policy 

issues related to agriculture, 

food, the environment,and 

rural America. www.ers.usda.gov

United States Department of Agriculture

September 2013

Find the full report 

at www.ers.usda.

gov/publications/erreconomic-researchreport/err-156.aspx

In fiscal year 2012 (October 1, 2011 through September 30, 2012), about 1 in 4 Americans 

participated in at least 1 of the United States Department of Agriculture’s (USDA) 15 domestic 

food assistance programs. Over 70 percent of USDA’s total 2012 outlays went to these 

programs, and 73 percent of food assistance spending went to the Supplemental Nutrition 

Assistance Program (SNAP). Knowing the demographic characteristics of those who receive 

SNAP benefits and those who, among eligible individuals, do not is important for assessing and 

improving program performance.

What Is the Issue?

While State-level estimates of SNAP participation rates are published annually by USDA’s 

Food and Nutrition Service, the data required to produce more detailed estimates within a State 

(i.e., at a county level) have previously not been available. Such estimates would be useful as 

States decide where to focus outreach expenditures. These detailed participation rates would 

also be helpful in assessing administrative performance in local areas and in guiding administrative policies and procedures. This report measures access to SNAP using a new approach 

that links State-level SNAP administrative records and the American Community Survey 

(ACS), which surveys a sample of about 2 million U.S. households each year on various demographic factors. The report provides estimates of detailed SNAP access rates for geographic 

areas (counties and congressional districts) and demographic subgroups in Texas during 2008 

and 2009.

What Did the Study Find?

By linking a State’s administrative data on SNAP participation to the ACS, this report demonstrates how a wide range of “access” rates (analogous to participation rates) can be estimated 

across various demographic and geographic groupings. SNAP access is estimated for subgroups 

in Texas distinguished by age, citizenship status, disability status, education, employment 

status, work experience, health insurance coverage, household composition variables, household 

language, previous location of residence, race, receipt of various forms of income or governConstance Newman

Erik Scherpf

Economic

Research

Service

Economic 

Research

Report 

Number 156

September 2013

United States Department of Agriculture

Supplemental Nutrition Assistance 

Program (SNAP) Access at the 

State and County Levels

Evidence From Texas SNAP Administrative 

Records and the American Community Survey

Constance Newman and Erik Scherpf

Supplemental Nutrition Assistance 

Program (SNAP) Access at the State 

and County Levels

Evidence From Texas SNAP Administrative 

Records and the American Community Survey
ment assistance, rural/urban residency, school enrollment status, and veteran status. Among the more policyrelevant findings: 

• The statewide access rate was estimated to be 62.8 percent of eligible individuals. 

• Among the 25 Texas counties sufficiently large enough to support estimation, the SNAP access rate ranged 

from a low of almost 5 out of every 10 eligible individuals (45.7 percent in Denton County) to a high of 

almost 8 out of 10 (78.2 percent in Hidalgo County).

• Among the 32 Texas congressional districts in 2008-09, the lowest rate of SNAP access was 36.8 percent 

in the 7th District near Houston while the highest rate was 76.9 percent in the 15th District (partially adjacent to Texas’ southern border). 

• Elderly individuals (age 60+) who lived alone, or only with other elderly individuals, had an especially low 

rate of access to SNAP. The access rate of elderly individuals who lived with at least one non-elderly individual in the household is still lower than the statewide average for all individuals—although significantly 

higher than that of elderly individuals living alone.

• Consistent with prior national estimates, households with children had substantially higher access rates in 

Texas than households without children. Among households with children, couples had significantly lower 

access rates than female heads or even multiple-adult (noncouple) households, and couples with children 

made up the largest eligible subgroup of all households, by household composition, in the State.

• A lack of facility with English may be an important barrier to SNAP participation. With its large Hispanic 

population, this is a particular policy concern in Texas. According to the Census Bureau’s classification 

of linguistic isolation among Spanish speakers, “isolated” Spanish-speaking households exhibited access 

rates 13 percentage points lower than “non-isolated” Spanish-speaking households.

How Was the Study Conducted?

The study uses data from the American Community Survey (ACS) collected in 2009 by the U.S. Census 

Bureau, linked to 2008-09 administrative data from Texas SNAP files, which contain the universe of SNAP 

recipients in Texas. With a sample size of about 2 million U.S. households each year, the ACS is a nationally 

representative survey of the population of the United States designed to support statistical analysis of small 

spatial areas. We applied State and Federal program rules to simulate SNAP eligibility for individuals in the 

linked sample with information available in the ACS. We also employed a weighting method that takes into 

account sample loss from unmatchable records. 

We use the term “access rates” to clearly distinguish our estimates from the official State SNAP participation 

rates published by USDA’s Food and Nutrition Service (FNS). We produce these estimates using a different 

methodology in terms of the underlying data sources used, the approach to estimating program eligibility, and 

the time period examined. The access rates estimated in this report measure access (or participation) among 

eligible individuals based on annual income measures, whereas the participation rate measures participation 

among eligible individuals in a representative month. Our measure is also distinct from a third, more timely 

but approximate measure of participation—the Program Access Index.

www.ers.usda.gov
1

SNAP Access at the State and County Levels: Evidence From Texas, ERR-156

Economic Research Service/USDA

Supplemental Nutrition Assistance Program 

(SNAP) Access at the State and County Levels

Evidence From Texas SNAP Administrative Records 

and the American Community Survey

Constance Newman and Erik Scherpf

Introduction

A key measure of the performance of the Supplemental Nutrition Assistance Program (SNAP) is 

the extent to which assistance reaches its target population. Annual estimates of monthly State and 

national participation, or access, rates—the proportion of eligible individuals who receive SNAP 

benefits—are prepared on behalf of USDA’s Food and Nutrition Service (FNS), the agency that 

administers SNAP at the Federal level (Leftin et al., 2011; Cunnyngham et al., 2011; USDA Food 

and Nutrition Service, 2011a). These estimates of SNAP access at the State and national levels have 

been critical for assessing and improving program performance. However, more detailed estimates 

of SNAP access within a State could aid program administrators in targeting outreach efforts to 

underserved populations and areas and in administrative reform.

This report presents a profile of SNAP access in Texas during 2008 and 2009. Using SNAP administrative records linked to the American Community Survey (ACS), we estimate patterns of SNAP 

access for geographic areas and subpopulations within Texas. The results presented here, although 

specific to Texas, could be replicated for other States as SNAP administrative records are made 

available. We call our estimates “access” rates as opposed to “participation” rates in order to distinguish them from official estimates of participation since the goals and basic methodology differ.1

Established methods for estimating SNAP participation rates at the national and State levels use 

both administrative records and household survey data (Cunnyngham et al., 2011; Leftin et al., 

2011). They use aggregate participant counts available from administrative databases, such as the 

FNS National Data Bank, to obtain the number of SNAP participants for the numerator of the 

rate. The denominator—the estimated total number of eligible individuals (in the country or in a 

State)—is based on household survey data, especially the Current Population Survey (CPS). 

Our approach also employs SNAP administrative records and household survey data: we simulate 

SNAP eligibility with the ACS and measure program access with State administrative records. 

However, instead of using each data source independently to obtain aggregate counts, as in the studies 

noted above, we link the two at the individual level to determine whether an individual estimated to be 

1Although in this report “access” rates and “participation” rates both measure the proportion of program participants 

in the estimated program-eligible population, we maintain the terminological distinction to emphasize that the rates 

derived in this report differ in important ways from the official State SNAP participation rates published by FNS (see, 

for instance, Cunnyngham et al. (2011)). These differences include the underlying data sources used and the method by 

which program eligibility is estimated. Most notably, the access rates estimated in this report measure the proportion of 

program participants among individuals estimated to be eligible on the basis of annual income measures, whereas the 

official State participation rates measure the proportion of participants among eligible individuals in a representative 

month. The use of the term “access rate” is not meant to imply the presence of any type of barrier to program entry but 

merely the receipt of SNAP benefits.
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Economic Research Service/USDA

eligible for SNAP in the ACS actually received benefits in the administrative records.2 With this linked 

microdata, we are able to estimate SNAP access rates for almost any desired variable grouping, limited 

only by what is available in the ACS. For this report, we present SNAP access rates for select demographic, economic, and geographic subgroups. Microdata also afford the flexibility to produce detailed 

cross-tabulations, such as for demographic characteristics at the county level. To illustrate, we also 

present access rates by select demographic characteristics within the seven largest counties in Texas.3

Because SNAP receipt has been found to be substantially under-reported in household survey data, 

we use administrative records, rather than the ACS, to measure access. Meyer and Goerge (2011) 

found “false negative” reporting rates of 32 percent in Illinois and 37 percent in Maryland.4 Such 

under-reporting of program receipt can lead to substantially under-estimated participation rates. 

Moreover, as Meyer and Goerge show, household characteristics that are important in explaining 

SNAP participation are also related to the probability of misreporting SNAP participation. 

Estimated participation rates for demographic subgroups may therefore be even less reliable.

The available SNAP administrative records contain information on individuals who received 

program benefits in Texas. By supplementing household survey data with administrative records, we 

obtain a sample that, when appropriately weighted, is representative of the entire State population 

for that year. Household survey data also provide the demographic information necessary to estimate 

program eligibility and to characterize SNAP participants and nonparticipants in a detailed way. 

Only two household surveys in the United States are large enough to support State-level estimates: 

the ACS and the CPS. While the CPS contains more information on each sampled household that 

would be useful for estimating program eligibility, we opted for the ACS because its larger sample 

size supports both State-level and, crucially, county/district-level estimates of SNAP access.5 The 

resulting linked microdata set thus provides rich demographic information on both participating and 

nonparticipating households, while addressing the misreporting of SNAP receipt found in the ACS. 

In this report, we estimate SNAP eligibility according to the standard definition. Many States, 

including Texas, have adopted an alternative set of eligibility rules, known as broad-based categorical eligibility (BBCE), that apply to certain applicants in lieu of the standard eligibility rules. 

Technically, most households that qualify under BBCE rules are categorically eligible for SNAP 

if they have received a benefit from either Temporary Assistance for Needy Families (TANF) 

or TANF-related Maintenance-of-Effort (MOE) programs. Qualifying benefits can range from 

receiving a TANF- or MOE-funded brochure to being referred to a social services “800” number. 

In this report, we present results using the standard eligibility rules in order to facilitate comparisons 

with past estimates (for Texas) and future estimates using this methodology for other States (which 

may not have similar BBCE policies as in Texas).

2See the Appendix for a description of the Texas administrative data.

3Microdata also open up the possibility of modeling the participation decision. However, we do not pursue that in this 

report. 

4False negatives are defined as survey reports of nonreceipt that are contradicted by the administrative records. Rates 

of false positives—reports of receipt not substantiated by the administrative records—are much lower: 0.8 percent in Illinois and 0.5 percent in Maryland.

5The final sample of the 2009 ACS was roughly 1.9 million households (excluding group quarters), including 306,081 

in Texas alone. By contrast, final sample sizes in the CPS come to about 60,000 households.
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Related Research

This report contributes to the literature on SNAP participation by analyzing the individual’s participation decision using household survey data linked to administrative records. Previous studies 

have used linked administrative records mainly to explore underreporting of SNAP participation in 

household surveys (Meyer and Goerge, 2011; Taeuber et al., 2005; and Taeuber et al., 2004, Marquis 

and Moore, 1990; Bollinger and David, 1997). This study is among the first to estimate SNAP 

access using SNAP administrative microdata linked to a household survey.6

Other research has used both administrative records and household survey data to estimate participation rates at the State and national levels (Cunnyngham et al., 2011; Leftin et al., 2011; USDA 

Food and Nutrition Service, 2011a). Leftin et al. (2011) estimate a participation rate at the national 

level by year and by broad demographic subgroupings; Cunnyngham et al. (2011) estimate Statelevel participation rates for all eligible individuals and the working poor; and USDA (2011a) 

provides an annual Program Access Index (PAI).

For the numerator of the participation rate, these studies use aggregate participant counts from 

administrative databases, such as the USDA Food and Nutrition Service (FNS) National Data Bank. 

For the denominator, the studies use household survey data, typically the CPS, to estimate the total 

number of eligible individuals in a given geographic region. While this line of research combines 

administrative data on SNAP participation with household survey data, it does not link the two data 

sources at the individual or household level, as is done here. Instead, the emphasis is on obtaining an 

accurate estimate of an overall participation rate at the national and State levels and for a few broad 

demographic subgroups. 

Cunnyngham et al. (2011) and Leftin et al. (2011) both employ the same methodology for estimating 

the number of eligible individuals (see box, “Related Research on SNAP Participation/Access”). 

They simulate eligibility criteria that are not available, or poorly measured, in the CPS with the aid 

of other data sources, primarily the Survey of Income and Program Participation (SIPP) conducted 

by the U.S. Census Bureau. This includes the (household) composition of the SNAP unit, asset 

eligibility for SNAP, TANF receipt, Social Security’s Supplemental Security Income (SSI), and net 

income. The researchers calibrate simulated TANF and SSI participants to match administrative 

data, and they adjust SNAP participant totals for benefits issued in response to disasters or issued in 

error. To obtain more precise estimates of participation rates at the State level, Cunnyngham et al. 

(2011) use a Bayesian shrinkage estimator, which averages direct sample estimates of State participation rates with the predicted rates from regression models using 3 years of pooled cross-section 

data.7 This estimator improves the precision of their annual State-level estimates at the expense of 

introducing some bias.

The reports by Cunnyngham et al. (2011) are released with a 2-year timelag and therefore would 

not be a timely measure of access with which to evaluate States’ performance each year. To this 

6Meyer and Goerge’s 2011 study used administrative records from Illinois and Maryland linked to the ACS, but they 

did not attempt to approximate SNAP-eligible households other than to restrict the sample to households with income 

below twice the poverty line.

7Peterson et al. (2010) also employ the ACS to simulate eligibility in Puerto Rico, and they supplement ACS data with 

administrative data from Puerto Rico’s Nutrition Assistance Program, a program that is similar to SNAP. Their approach 

largely follows that of Cunnyngham et al. (2011) and Leftin et al. (2011) in that they simulate monthly income, SNAP 

unit composition, assets, and other expenses used to calculate net income.
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Study Primary data 

sources

Estimation of 

SNAP eligibility 

criteria

Coverage Estimation of 

Participation Rate

Leftin et al. 

(2011)

1. SNAP program 

operations data

2. CPS ASEC

Comprehensive 

simulation of 

eligibility criteria 

using CPS and 

other data sources

National level 

for demographic 

subgroups

Aggregates 

from different 

data sources in 

numerator and 

denominator.

Cunnyngham et al.

(2011)

1. SNAP program 

operations data

2. CPS ASEC

Comprehensive 

simulation of 

eligibility criteria 

using CPS and 

other data sources

State level for the 

entire population 

and for the 

working poor

Aggregates 

from different 

data sources in 

numerator and 

denominator.

Peterson et al. 

(2010)

1. SNAP program 

operations data

2. ACS

Comprehensive 

simulation of 

eligibility criteria 

using ACS

State level 

(Puerto Rico only) 

Aggregates 

from different 

data sources in 

numerator and 

denominator.

USDA PAI 1. SNAP program 

operations data

2. ACS

Estimated number 

of individuals 

under 125% 

poverty using 

ACS

State level Aggregates 

from different 

data sources in 

numerator and 

denominator.

Taueber 

(2005)

1. Maryland 

SNAP 

administrative 

records

2. ACS 

(Supplementary 

Survey 2001)

General estimate 

of some eligibility 

criteria

State level 

(Maryland only)

Linked microdata 

used to estimate 

proportion 

of “eligible” 

individuals who 

participated.

Fellowes and 

Berube 

(2005)

1. County-level 

SNAP operations

2. Decennial 

Census

General estimate 

of some eligibility 

criteria

Select urban 

counties and 

MSAs

Aggregates 

from different 

data sources in 

numerator and 

denominator.

FRAC 

(2011)

1. State SNAP 

program 

operations data

2. ACS (3-year)

General estimate 

of some eligibility 

criteria

Select cities and 

counties

Aggregates 

from different 

data sources in 

numerator and 

denominator.

Related Research on SNAP Participation/Access
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end, the PAI was devised to meet the statutory timeframe requirement to pay performance rewards 

for a fiscal year prior to the end of the following fiscal year (USDA, 2011a). Like the participation rate estimated in Cunnyngham et al. (2011), the numerator for each State PAI is the State-level 

SNAP participant count taken from the National Data Bank (with some adjustments for recipients 

of Disaster SNAP and the Food Distribution Program on Indian Reservations). The denominator is 

based on the number of individuals estimated in the ACS to have annual incomes below 125 percent 

of the Federal poverty level in that State. Hence, the PAI is an imprecise measure of program access. 

It does, however, have the virtue of being produced in a timely manner and appears to track reasonably well the State participation rates reported in Cunnyngham et al. (2011).

Taeuber et al. (2005) use SNAP administrative records and the 2001 ACS (Supplemental Survey 

(SS01)) to estimate a SNAP participation rate for a single State (Maryland). They use administrative records to obtain a count of SNAP participants and the ACS to simulate the number of 

eligible households. Their approach to estimating eligibility is more straightforward than that of 

Cunnyngham et al. (2011) and Leftin et al. (2011). In particular, they do not attempt to simulate 

monthly income and implement a simplified “resource and vehicle test”’ that excludes from eligibility households that owned two or more vehicles. 

Two studies have developed local measures of SNAP access. Fellowes and Berube (2005) estimate 

participation rates for select major metropolitan areas and counties. For this finer level of geographical detail, the authors rely on the 2000 Decennial Census Public Use Microsample (PUMS) to 

obtain counts of estimated eligible households for their denominator. The Food Research and Action 

Center (2011) also derives a local measure of SNAP access. Their focus is on access in large cities, 

estimating eligibility by calculating the number of people with annual incomes below 130 percent of 

the Federal poverty level. Their measure is thus an indicator of access among a low-income population similar to the PAI (USDA, 2011a).

To attain a measure of program access that is both timely and accurate, our approach to estimating 

eligibility seeks to strike a balance between the more rigorous approaches of Cunnyngham et al. 

(2011), Leftin et al. (2011), and Peterson at al. (2010) and the more pragmatic approaches of Taeuber 

et al. (2005), Fellowes and Berube (2005), the Food Research and Action Center (2011), and USDA 

(2011a). This report does not aim to produce State-level estimates of participation rates directly 

comparable to those produced in other FNS reports, which attempt to simulate all aspects of SNAP 

eligibility guidelines, including the monthly accounting period. However, we do go further in 

applying SNAP eligibility rules than the PAI (USDA, 2011a). 

A few key differences between this and previous studies are worth noting. By merging the two 

data sources, we are necessarily restricting the universe of Texas SNAP recipients in the administrative records to the Texas subsample in the ACS. As a result, we use sample weights to obtain an 

estimate of the number of SNAP participants in Texas, which is subject to sampling error.8 This 

estimate of the number of SNAP participants is therefore less precise than the participant count 

obtained directly from the universe of participants. The linked data do, however, allow us to estimate access rates for more detailed demographic and geographic subsamples. The results provide 

a better understanding of the factors associated with a household’s decision to participate, or not 

participate, in SNAP.

8The estimates are also subject to measurement error to the extent that we have records not assigned a Protected 

Identification Key (PIK) by Census. But otherwise, this approach greatly reduces measurement error in SNAP receipt by 

linking to administrative records of participation. We also use the ACS replicate weights to estimate standard errors. 
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Linking the ACS and Administrative Records 

To Estimate SNAP Access

To construct the sample for this study, we merged SNAP administrative records for calendar years 

2008 and 2009 to the Texas subsample of the 1-year 2009 ACS.9 The Texas subsample of the ACS 

comprises all individuals residing in Texas at the time of the ACS interview. We omitted individuals 

who were matched to the Texas SNAP administrative records but were interviewed for the ACS 

outside of Texas.10 Our study therefore examines SNAP access among “current” Texas residents.11

Individuals living in group quarters were also excluded. Our final unweighted matched sample was 

made up of 286,236 individuals. Full documentation on the ACS sample design is available at the 

U.S. Census Bureau (2009).12

We merge individual records from the two data sources using a Protected Identification Key 

(PIK). PIKs are internal Census Bureau identifiers based in part on a person’s Social Security 

number (SSN). PIKs allow researchers to match an individual’s records across data sources 

without compromising a respondent’s personally identifiable information, such as SSNs. The 

Census Bureau assigns PIKs to individual records in demographic surveys and in administrative 

records using the agency’s Person Identification Validation System (PVS).13 This system uses 

information in the source file (e.g., Social Security numbers, addresses, names, and dates of birth) 

to search for the person’s corresponding record in the Numerical Identification File maintained by 

the Social Security Administration. 

Assigning PIKs to SNAP administrative records is facilitated by the fact that SNAP applicants must 

provide a SSN to the local SNAP office. The ACS, by contrast, does not collect the SSNs of respondents so assigning a PIK must rely on probabilistic matches based on names and addresses. As a 

result, a larger proportion of individuals in the administrative records were assigned a PIK than in 

the ACS (see the Appendix for more information regarding the rate at which individuals received 

PIKs in the two data sources). 

9Income questions in the ACS refer to the 12 months prior to the survey interview, and ACS interviews throughout 

the calendar year. To determine whether the individual participated in SNAP over that same period, it was necessary to 

merge 2 calendar years of administrative records (2008 and 2009) to the 1-year 2009 ACS. This means that for nearly the 

entire 2009 sample, the ACS reference period for income includes at least part of the calendar year 2008 (e.g., for those 

interviewed in January 2009, the reference will include all of calendar year 2008). 

10This would occur if in the 12 months prior to his or her ACS interview an individual had received SNAP benefits in 

Texas but had since moved out of the State. These movers account for 4.5 percent of all ACS-interviewed individuals who 

were matched in the Texas data. Although we obtained matches to the Texas SNAP administrative records for some of 

these “movers” (i.e., those who lived in Texas at some point during the SNAP reference period but not at the time of the 

ACS interview), we did not include them in the sample because we were not able to adequately account for all eligible 

“movers” as well. 

11Because interviews in the ACS are fairly evenly distributed throughout the calendar year, the sample of “current” 

residents is representative of a State’s population for that year.

12See the Appendix for more description of the Texas administrative data and the implications of the use of PIKs for 

our ACS and linked ACS-Texas samples.

13An analysis of the PVS by the National Opinion Research Center (NORC) revealed that Texas had the seventh lowest 

share of respondents assigned a PIK among all States in the 2009 ACS (NORC, 2011). The NORC study also revealed 

that respondents who could not be validated in the 2009 ACS were more likely to be noncitizens, of Hispanic origin, and 

to reside in households where English was not the primary language. These factors likely account for Texas’ poor validation rate. 
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We matched the ACS to the Texas administrative files at the individual level, but estimated eligibility at the household or, more precisely, the SNAP unit level (which we define in detail below).14

Consistent with USDA reports such as Cunnyngham et al. (2011) and USDA (2011a), we also estimated access rates at the individual level. However, we adopt the rule that if any member of an ACS 

SNAP unit is matched to the SNAP administrative records, we assign eligibility and receipt to every 

member of that ACS SNAP unit provided individual are not disqualified on the basis of citizenship 

status, etc. This approach increases the probability of finding a match in the administrative records 

because an indirect match can occur even if some members of the SNAP unit were not assigned 

a PIK in the ACS. Defining a match at the SNAP unit level yields a higher match rate relative to 

matching individual records. However, this approach means that larger SNAP units in the ACS are 

more likely to be matched to the administrative records than smaller SNAP units. 

We keep non-PIK’ed individuals who are assigned to an ACS SNAP unit containing at least one 

other unit member with a valid PIK in the ACS. If no member of a SNAP unit has a valid PIK, 

and therefore the entire unit is unmatchable, then each record belonging to that SNAP household is 

dropped from our sample. Overall, we lose about 6.5 percent of our sample by dropping these nonPIK’ed observations. Since the probability that a record receives a PIK is nonrandom, we model the 

probability that an individual is a member of a unit in which at least one member received a PIK and 

adjust the person weight by the inverse of the predicted probability of having a PIK in the ACS (see 

appendix table B2).

14In this report, we use the terms “SNAP unit” and “unit” interchangeably. The “unit” is defined according to SNAP 

rules of what constitutes a household unit for eligibility purposes, but not all units are in SNAP.
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Estimating SNAP Eligibility

A SNAP unit is defined as a group of individuals who live together and who customarily purchase 

food and prepare meals together. The household identified in the ACS does not necessarily correspond well to this definition. Large households, especially ones with unrelated individuals and 

related subfamilies—such as a sister and her children—may have multiple SNAP units living in one 

ACS household. We consequently do not strictly rely on the ACS definition of a household. 

Instead, we group individuals in units that reflect more closely the definition of a SNAP unit, 

adhering as closely as possible to the mandatory rules for unit formation specified in the Federal 

regulations. In particular, we attempt to ensure that spouses (and partners, although they are not 

explicitly included in the Federal guidelines) are kept in the same SNAP unit. Children under 

the age of 22 must remain in the same unit as their parents. If no parent is present in the household, however, individuals who are under the age of 22, but at least 18 years old, can form their 

own SNAP unit. Other children under the age of 18 who do not have a parent in the household are 

assigned to a SNAP unit with another adult relative (over 18 years of age).15 We retain the ACS classification of subfamilies as separate units as long as doing so does not violate one of the rules for 

unit formation, such as when the oldest member in a subfamily is under 22 years old and their parent 

is also present in the household. 

One of the Federal guidelines for SNAP unit formation that is difficult to observe using the ACS 

is whether individuals in a household purchase and prepare food together. Indeed, this guideline 

is likely difficult to verify even for a SNAP caseworker. As a result, we apply only the mandatory rules for unit formation based on family relationships and age, and assume that, subject to 

the mandatory rules mentioned above, households will form the smallest units possible. The most 

significant consequence of this approach is that adult children (where adult is defined as age 22 

or over if a parent is present; otherwise 18 or over) form their own SNAP unit, together with their 

spouse or children, separate from any parents present in the household. Similarly, adult siblings of 

the ACS household head form their own units, with spouses and children, to the extent that we are 

able to infer these relationships.16

Such an approach is justified for two reasons. First, because the poverty thresholds used to determine eligibility reflect economies of scale in the household (e.g., two adults who each fall below the 

poverty threshold individually might exceed the threshold if they were to pool their incomes in a 

two-person unit), we assume adults maximize their chances of qualifying for SNAP by forming the 

smallest units allowable. This approach implicitly assumes that whether they actually purchase and 

prepare food together cannot be verified. Second, administrative data on SNAP participants suggests 

that this type of behavior occurs. Keeping households and subfamilies, as defined in the ACS, 

relatively intact results in a much lower proportion of one-person units than is found in the SNAP 

administrative data.

Finally, because the SNAP units that we construct differ in some cases from the ACS household, we 

also designate a SNAP unit “reference person’’ who may differ from the household reference person 

in the ACS. When the ACS reference person is present in the SNAP unit, that person is designated 

15We also attempt to identify the likely children of individuals who are not the ACS household head.

16Unlike the Current Population Survey, the ACS does not explicitly identify household interrelationships, which are 

apparent only to the extent that they can be inferred from members’ relationship to the head.
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as the SNAP unit reference person. For SNAP units that are formed from a larger ACS household 

but do not contain the reference person from the ACS household, we select a reference person for 

that SNAP unit based on an algorithm that takes into account the individual’s age and relationship to 

the ACS household reference person, with the restriction that the SNAP unit reference person must 

be 18 or older.17

Immigrants

There are many special SNAP eligibility rules that apply to noncitizens who are authorized to be 

in the country depending on whether they are refugees, how long they have been in the country, 

their age, and other factors. However, the ACS does not allow us to identify whether an individual is 

authorized to reside in the country legally. We can, however, use other characteristics that are likely 

to indicate that they are legal residents. Among noncitizens who are potentially eligible for SNAP, 

we can identify the following groups (USDA, 2011b): (1) individuals who have been in the country 

for more than 5 years; (2) individuals who are less than 18 years old regardless of date of entry; 

(3) individuals who are receiving disability benefits (SSI receipt is the proxy), regardless of date of 

entry; (4) elderly individuals born in or before 1931 who arrived before 1996 (for SNAP eligibility, 

individuals are defined as elderly starting at age 60); (5) individuals who have a military connection 

(either served in the military or had a parent or spouse who served); and (6) refugees and “special 

immigrants” if their ancestry and year of entry is likely to make them eligible. 

For any noncitizens not covered under the potential eligibility criteria above, we use an estimate from 

Passel and Cohn (2010) to determine the share that would likely be unauthorized. Passel and Cohn 

estimate that 1.6 million unauthorized migrants resided in Texas in 2009, which equates to 59 percent 

of the noncitizen population. However, the share of noncitizens who did not fall into one of the potential eligibility categories above was 16 percent of all noncitizens in the sample. Since 16 percent is 

much less than 59 percent, we estimate that all of the remaining 16 percent of individual noncitizens 

were unauthorized. This may mean that we are underestimating unauthorized noncitizens, but they are 

also less likely to have received a PIK, so they are probably less represented in the data overall. 

These estimates of noncitizen eligibility do not greatly affect the overall share of SNAP-eligible 

persons in the population because the number of noncitizens is small relative to the whole population. However, we try to apply as many criteria as possible from the available data in the hope of 

improving the estimates of SNAP access for authorized noncitizens and for counties or congressional districts that have high shares of noncitizens.

College and Graduate School Students

Able-bodied students between ages 18 and 49 who attend college or graduate school are eligible for 

SNAP benefits under certain situations.18 We were able to model the following situations that would 

allow them to apply: (1) if the student has a child less than 6 years old, (2) if the student works more 

than 20 hours per week, or (3) if the student is a single parent to any child or children between ages 

6 and 12. We are not able to know for sure if all individuals are single parents to children of the 

relevant age range, but we estimate these SNAP-eligible students based on relationship variables in 

the ACS and SNAP unit composition.

17In the ACS, the household reference person can be as young as 15 years old. 

18For the full set of conditions that allow college or graduate students to be eligible for SNAP, see http://www.fns.usda.

gov/snap/applicant_recipients/students.htm.
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SNAP Unit Income

We calculate SNAP unit income as the sum of personal income received by each unit member, 

including any income from earnings, public assistance, SSI, or retirement income. In addition, we 

include in the total income for the SNAP unit a prorated share of the income of SNAP unit members 

who are themselves ineligible due to being unauthorized by our estimate. The prorating involves 

dividing the income of ineligible individuals equally among all household members and deducting 

from total income a share for each ineligible individual.19

To estimate eligibility, we apply the USDA FNS monthly income guidelines for fiscal years 2008 

and 2009.20 These are the income levels below which a SNAP unit would be eligible (see more 

explanation of eligibility criteria below). Since ACS respondents are surveyed in different months 

of the calendar year 2009, their reference periods for SNAP eligibility in the past year differ across 

the sample by month interviewed. For each SNAP unit, we calculate their income guideline as a 

weighted average of relevant income guidelines, where the weights are determined by the number of 

months in each fiscal year spanned by the unit’s ACS reference period. 

SNAP eligibility is determined on a monthly basis, but income in the ACS is measured annually. 

Other studies that estimate SNAP eligibility with this limitation model monthly income using aggregate employment data from the Bureau of Labor Statistics (BLS) together with microdata from the 

Survey of Income and Program Participation (SIPP) (Leftin et al., 2011, Cunnyngham et al., 2011). 

Because the results of this and future reports are to be made available in a short timeframe, we have 

opted to rely on a single data source and therefore cannot simulate monthly income. 

Other analysts have addressed this issue by raising the gross and net income guidelines by a relevant 

multiplier in order to account for the tendency of monthly income to be more volatile than annual 

income (Marks et al., 2011). Two National Research Council reports have noted the possibility of 

using the ratio of average monthly poverty to annual poverty as a way to estimate income eligibility for the National School Lunch Program when annual income data are the only type available (National Research Council, 2010). We have chosen not to raise the thresholds by the ratio 

of average monthly poverty to annual poverty. Without prior work to guide us on the relationship 

between monthly and annual SNAP income eligibility, any multiplier that we would adopt would 

necessarily be somewhat arbitrary. As a result, we use the ACS annual income and express it as a 

ratio to the annual poverty thresholds. 

Modeling Eligibility

The test of eligibility is based on Federal rules in which most SNAP units must pass two income 

tests, one that applies to their gross income (gross income test) and one that applies to their gross 

income net of several deductions (net income test). 21 However, if all adult members of a SNAP unit 

19For example, to calculate the prorated income for a household with two adult earners who are ineligible noncitizens 

and two children who are citizens, the incomes of the adult earners would be divided into four equal shares, one share for 

each member of the household. Total income would be calculated by deducting two of the four shares from the household’s total income. In this example, the children’s SNAP unit income would be the sum of their two shares from each of 

the parent’s earnings.

20The FNS income guidelines are akin to, but not the same as, the poverty thresholds produced by the U.S. Census 

Bureau. For FNS guidelines, see http://www.fns.usda.gov/snap/government/FY09_Income_Standards.htm. 

21For Federal eligibility requirements, see http://www.fns.usda.gov/snap/applicant_recipients/eligibility.htm
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are on TANF, the unit members are eligible for SNAP (it is only reported for adults in the ACS). To 

measure TANF receipt, we rely on the ACS variable for general cash assistance, which is most likely 

TANF but may also include general assistance from the State.22

We also treat units in which all adult members report SSI receipt as SNAP-eligible. We use SSI 

receipt as a proxy for receipt of a set of specific disability benefits that confer eligibility, but which 

are not available in ACS. This measure of SNAP eligibility is conservative because many individuals 

with a disability do not receive SSI but do receive other disability-based benefits. Moreover, as with 

reported SNAP receipt, SSI and TANF are known to be underreported in the ACS.

All other SNAP units must pass the net income test, and all non-elderly, non-disabled SNAP units 

must pass both the net and gross income tests. We do not exclude from eligibility non-elderly, 

able-bodied adults without dependents (commonly referred to as ABAWDS) because on April 1, 

2009, the American Recovery and Reinvestment Act (ARRA, or the “Stimulus Act’”) temporarily 

allowed States to suspend the time limits for SNAP that applied to this group. Moreover, we do 

not attempt to impose possible time limits for respondents whose ACS reference period includes 

the pre-ARRA period. 

The gross income test requires that monthly gross income be no greater than 130 percent of the 

Federal poverty guideline while the net income test requires that monthly income, after allowable 

deductions, be no greater than 100 percent of the Federal poverty guideline. We estimate most of the 

required deductions used to arrive at the FNS definition of net income: the standard deduction, the 

earned income deduction, and the excess shelter cost deduction. For the earned income deduction, 

we deduct 20 percent of earned income, including prorated earned income for ineligible individuals. 

For households that do not contain elderly or disabled members, shelter costs in excess of half of 

adjusted income (subject to a limit of $459 in 2009) are deducted from gross income.23 Households 

with an elderly or disabled member are not subject to this shelter cap. Shelter costs include rent or 

mortgage payments, condominium fees, property taxes, property insurance, and utilities. The shelter 

costs are prorated to exclude the shares that would be paid by SNAP-ineligible household members.

We are not able to estimate several deductions from gross income that are allowed under the 

program due to lack of data in the ACS. One is the dependent care deduction, which allows households to deduct out-of-pocket costs for the care of a child or other dependent in order for household members to work, seek work, or attend school. Households are allowed to deduct the medical 

expenses of elderly members over $35 (per month). Another deduction from gross income that we 

are not able to consider is legally obligated child support payments made to or for a non-household 

member. Nationally, in 2009, 3.9 percent of participating SNAP households claimed a dependent 

care deduction, 3.7 percent claimed a medical expense deduction, and 1.8 percent claimed a child 

support deduction (Leftin et al., 2010). Although we do not have these numbers for Texas residents, the national figures provide a rough idea of the share of Texas households that may have 

used these deductions. 

We do not incorporate estimates of asset holdings into the eligibility estimates. ACS provides data 

on the number of vehicles in a household, but it does not have information on which household 

members use the cars or the value of the cars. Texas’ SNAP vehicle policy is to exempt a $15,000 

22Texas does not have a general assistance program, so, for this case, the ACS variable for general assistance represents 

TANF alone. 

23This is the limit on the excess shelter cost deduction that applies in 48 States, including Texas.
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fair market value from one vehicle and then follow Federal rules for counting remaining vehicles. 

We follow the steps of other analysts and use the number of vehicles as a rough proxy for assets 

(Peterson et al., 2010; Taeuber et al., 2005). In particular, SNAP units with two or more vehicles are 

not eligible in our model. 
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Results

We present results on SNAP access rates for demographic, unit composition, economic, and 

geographic characteristics.24 For each characteristic, we report the access rate and the proportion 

of eligible individuals within each category (the associated standard errors are reported in parentheses). This allows us to compare the access rates while also accounting for the relative importance 

of the given subgroup in the SNAP-eligible population. Below, we highlight some of the significant 

differences in access rates within each category (at the 10-percent level of significance) and identify 

possible target groups for outreach efforts. 

The statewide access rate among all individuals in Texas was 62.8 percent in 2009 (table 1). By 

race, individuals who identified as Black or African American alone had the highest access rate 

at 71.3 percent. Biracial or multiracial individuals had nearly as high an access rate (70 percent). 

Individuals who identified as White alone had an access rate just below the State average (61.5), 

while only about one in three eligible individuals who identified as Asian alone participated in the 

program, by far the lowest access rate among the racial groups. About two-thirds of individuals of 

Hispanic origin participated, a rate that was about 8 percentage points higher than for individuals of 

non-Hispanic origin (58.2 percent). Significantly more individuals of Hispanic origin than of nonHispanic origin were eligible for SNAP (54.5 percent vs. 45.5 percent). 

More than three-quarters (76.5 percent) of eligible children in Texas participated in the program. 

As in previous research, children (age 0-17) were more likely to participate in SNAP than adults. 

Among adults, individuals between the ages of 30 and 39 were more likely to participate than older 

adults (age 40 or over) and younger adults (age 18 to 29). Nearly two-thirds (63.3 percent) of eligible 

adults in their thirties participated in the program. By contrast, only about two in five (43.2 percent) 

eligible adults age 60 or older participated in the program. These participation patterns by age are 

consistent with findings on participation rates nationally. At 40 percent, children also accounted for 

the largest proportion, by age, of eligible individuals in Texas. 

The access rate among individuals in SNAP units in which the most educated member had 

completed some college (but did not earn a 4-year degree) was nearly 2 percentage points higher 

than in units where the most educated member did not finish high school (65.4 vs. 63.6 percent). 

Individuals in units in which the most educated member did not finish high school, held only a high 

school diploma, or had attained some college each accounted for approximately 30 percent of the 

eligible population. The access rate was significantly lower (41.5 percent) for individuals in units in 

which the most educated member held at least a bachelor’s degree. However, individuals from these 

units only made up about 8 percent of the SNAP-eligible population in Texas.

Individuals from small (1 to 2 member) and medium (3 to 4 members) sized units accounted for 

fairly equal proportions of the eligible population in Texas (37.3 and 35.2 percent, respectively), 

while individuals from large units (5 or more members) accounted for just over one-quarter of the 

eligible population (27.5 percent). Those from medium and large units, however, were significantly 

more likely to participate. The access rate of individuals from large and medium units was 79.3 and 

72.8 percent, respectively; from small units, the rate was only 41.2 percent (table 1). 

24In this report, we include the results based on the standard definition of eligibility and using Texas administrative 

data for identifying SNAP participants. Results using the expanded definition of eligibility, and results using survey 

reports of participation.
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Consistent with previous findings in the literature, individuals in units with children were more 

likely to participate in SNAP than were individuals in childless units (table 2, fig. 1). This finding 

alone, however, masks heterogeneity within these unit types. Among units with children, individuals from female-headed units and from multiple-adult units (not headed by a married or 

cohabiting couple) participated at the highest rates (79.4 and 82.1 percent, respectively). However, 

individuals in female-headed units with children accounted for nearly 23 percent of eligible 

individuals in Texas, whereas those in multiple-adult units with children accounted for about 4 

Table 1

Texas SNAP access rates by demographic characteristics, 2008-09

Access rate Percent eligible

Estimate SE Estimate SE

Texas 62.8 (0.36) 100.0

Race

White alone 61.5 (0.41) 65.3 (0.37)

Black or African American alone 71.3 (0.70) 16.9 (0.23)

American Indian or Alaska Native alone 60.5 (3.97) 0.7 (0.06)

Asian alone 31.9 (2.34) 2.6 (0.09)

Some other race alone 63.3 (1.18) 12.2 (0.28)

Two or more races 70.0 (1.85) 2.3 (0.10)

Hispanic origin

Hispanic origin 66.6 (0.54) 54.5 (0.32)

Non-Hispanic origin 58.2 (0.47) 45.5 (0.32)

Age

0 to 17 76.5 (0.48) 39.7 (0.19)

18 to 29 56.4 (0.62) 19.4 (0.17)

30 to 39 63.3 (0.81) 13.1 (0.13)

40 to 49 55.0 (0.61) 9.6 (0.13)

50 to 59 46.8 (0.89) 7.3 (0.12)

60 to 69 43.2 (0.85) 5.1 (0.10)

70+ 39.4 (0.81) 5.8 (0.08)

Highest educational attainment of SNAP unit member

Some high school 63.6 (0.71) 30.9 (0.36)

High shool diploma 65.2 (0.68) 31.1 (0.36)

Some college 65.4 (0.61) 29.8 (0.37)

College degree 41.5 (1.28) 8.2 (0.21)

SNAP unit size

Small (1 to 2 members) 41.2 (0.42) 37.3 (0.35)

Medium (3 to 4 members) 72.8 (0.59) 35.2 (0.45)

Large (5 or more members) 79.3 (0.83) 27.5 (0.44)

Notes: Estimates are based on authors’ calculations using the 2009 ACS and Texas SNAP administrative records. Standard 

errors (SE), displayed in parentheses, were calculated using the method of successive differences and 80 replicate weights.

Source: ERS tabulations of the 2009 American Community Survey (ACS) and 2008-09 Supplemental Nutrition Assistance 

Program (SNAP) administrative records.
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percent of eligible individuals.25 Male-headed units with children participated at a lower rate (72.8 

percent) and made up a small proportion of the eligible population (3.2 percent). Individuals in 

units consisting of couples (either married or cohabiting with a partner) with children accounted 

for the largest proportion of the eligible population. About two in five eligible individuals in Texas 

(38.5 percent) lived in this type of unit in 2009, and 72 percent of the individuals in these units 

participated in the program. 

25Some (married or cohabiting) couples may be mischaracterized as “multiple adult” units. These are likely to be 

“other relatives” or nonrelatives of the ACS reference person, for whom we were not able to identify a partner or spouse 

match based on the information provided in the ACS. 

Table 2

Texas SNAP access rates by individual and SNAP unit characteristics, 2008-09 

Access rate Percent eligible

Estimate SE Estimate SE

SNAP unit type

Couple with children 72.0 (0.69) 38.5 (0.40)

Couple without children 44.5 (0.90) 7.8 (0.19)

Female head with children 79.4 (0.63) 22.7 (0.35)

Female head without children 37.2 (0.72) 12.5 (0.17)

Male head with children 72.8 (1.93) 3.2 (0.13)

Male head without children 27.2 (0.79) 10.4 (0.19)

Multiple adults with children 82.1 (1.97) 3.9 (0.18)

Multiple adults without children 58.8 (3.76) 1.0 (0.06)

Children in SNAP unit

Children under age 6 only 74.2 (1.10) 15.3 (0.30)

Children age 6-17 only 70.4 (0.73) 25.4 (0.35)

Children under age 6 and over 79.8 (0.87) 27.6 (0.39)

No children 36.4 (0.38) 31.7 (0.29)

Citizenship status 

U.S. citizen 65.2 (0.34) 83.9 (0.20)

Non-citizen 50.0 (0.88) 16.1 (0.20)

Military veterans or active duty

No unit members 63.4 (0.37) 93.7 (0.16)

At least one unit member 54.3 (1.42) 6.3 (0.16)

School enrollment status

Not enrolled 63.8 (0.36) 93.7 (0.12)

Currently enrolled 47.7 (1.08) 6.3 (0.12)

Language and linguistic isolation

English only 61.5 (0.48) 43.9 (0.32)

Spanish not isolated 70.3 (0.66) 35.3 (0.41)

Spanish isolated 57.3 (1.03) 16.0 (0.32)

Other not isolated 36.6 (1.98) 3.2 (0.14)

Other isolated 40.8 (3.70) 1.5 (0.10)

Notes: Estimates are based on authors’ calculations using the 2009 ACS and Texas SNAP administrative records. Standard 

errors (SE), displayed in parentheses, were calculated using the method of successive differences and 80 replicate weights.

Source: ERS tabulations of the 2009 American Community Survey (ACS) and 2008-09 Supplemental Nutrition Assistance 

Program (SNAP) administrative records.
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In each type of unit, access rates were significantly lower when no children were present. Overall, 

individuals in units with children were about twice as likely as individuals in childless units to 

participate in SNAP (75.1 percent vs. 36.4 percent). Childless units still accounted for nearly onethird (31.7 percent) of the eligible population. Individuals in units with only very young children 

(under age 6) were more likely to participate than those in units with only older children (ages 6 to 

17). Individuals in units with both older and younger children, however, had the highest access rate, 

at nearly 80 percent (table 2). 

About one-sixth of SNAP-eligible individuals in Texas belonged to a unit with at least one noncitizen 

member (16.1 percent). Not surprisingly, individuals in these units had lower access rates than those 

in units made up entirely of U.S. citizens (50 percent vs. 65.2 percent).

Individuals living in linguistically isolated, non-English speaking units had lower access rates than 

individuals in units that were not linguistically isolated (fig. 2). The U.S. Census Bureau defines a 

linguistically isolated household as one in which no member age 14 or older speaks only English or 

one in which no member speaks English “very well.”26 Spanish-speaking, isolated individuals had 

an access rate of 57.3 percent, lower than that of Spanish-speaking, non-isolated individuals (70.3 

percent). Individuals in isolated, Spanish-speaking units made up 16 percent of eligible individuals 

in 2009, while individuals in non-isolated, Spanish-speaking units accounted for more than onethird of all eligible individuals in Texas (35.3 percent). Hence, more than half of SNAP-eligible individuals in Texas lived in Spanish-speaking units in 2009, whether isolated or non-isolated.

Among single-adult SNAP units, individuals in female-headed units had a much higher access 

rate: 64.4 percent compared to 38 percent for male-headed units (table 3). Female-headed units 

accounted for 72 percent of the eligible single-adult population. This difference in access rates 

between male- and female-headed single-adult units is, as table 2 also reveals, largely driven by 

their different propensities to have children in the unit. Conditional on children in the unit, the 

difference in access rates between male- and female-headed single-adult units narrowed to 72 and 

26Linguistic isolation is a household-level variable in the ACS so that if a household is categorized as linguistically 

isolated, all individuals in that household are considered linguistically isolated.

Figure 1

SNAP access rates by household composition in Texas, 2009

SNAP = Supplemental Nutrition Assistance Program.

Note: An access rate is the percent of SNAP participants among individuals estimated to be eligible for SNAP receipt.

Source: Authors’ calculations using 2009 ACS and Texas SNAP administrative data for 2008-09.

Couple with children

Couple without children

Female head with children

Female head without children

Male head with children

Male head without children

Multiple adults with children

Multiple adults without children

0 10 20 30 40 50 60 70 80 90

Percent
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79 percent, respectively. However, male-headed single-adult units made up only 4.8 percent of the 

eligible population in units with children. 

Among eligible multiple-adult units, three-quarters of eligible individuals lived in married couple 

units. However, these individuals had lower access to SNAP than those in other multiple-adult 

units.27 When looking just at units with children, individuals in multiple-adult nonmarried units 

were again more likely to participate (81.7 percent) than individuals in multiple-adult married 

units (70.2 percent). And individuals in multiple-adult married units with children made up nearly 

half (47 percent) of the SNAP-eligible population in units with children in Texas in 2009. 

Among units with an elderly member (age 60 or older), access rates differed according to whether 

the elderly lived alone in the household, with other elderly individuals, or with other non-elderly 

individuals (fig. 3). In this case, we are measuring the participation rates of individuals in SNAP 

units with an elderly individual according to the presence of other elderly or non-elderly members in 

the ACS household and who may belong to a different SNAP unit. The rationale here is not to determine whether elderly individuals are alone in a SNAP unit, since many can form units separate from 

other household members, but whether they are on their own in the residence. Elderly individuals 

who lived alone or with other elderly individuals had very low access rates (35.9 and 33.7 percent, 

respectively). Access rates were much higher in elderly units if at least one non-elderly individual 

was living in the household (53.5 percent). Elderly individuals who lived alone made up 28.1 percent 

of all eligible individuals in units in which at least one elderly person resided. Because these are, by 

definition, single-person units while the others contain multiple individuals, units in which an elderly 

person lives alone account for a much larger proportion of all elderly units. 

27Married households are defined here as those in which the reference person is married. The ACS only provides 

information on household members’ relationship to the reference person. In cases where the SNAP reference person 

differs from the ACS household reference person (i.e., because the latter is not present in the SNAP household), we have 

attempted to infer a marital relationship. For example, if a SNAP household contains the child of the ACS reference person as well as the son/daughter-in-law of the ACS reference person, we assume that this is a married couple even though 

the relationship between these two household members is not directly established in the ACS. Similarly, if two parents, or 

parents-in-law, are present, we assume they are a couple. Last, if the SNAP household reference person is a sibling, and 

an adult “other relative” (the ACS does not directly identify brother/sister-in-laws) of the opposite sex is present, they are 

also considered to be a couple.

Figure 2

SNAP access rates by household language and linguistic isolation in Texas, 2009

SNAP = Supplemental Nutrition Assistance Program.

Note: Note: An access rate is the percent of SNAP participants among individuals estimated to be eligible for SNAP receipt.

Source: Authors’ calculations using 2009 ACS and Texas SNAP administrative data for 2008-09.

Percent

English only

Spanish, not isolated

Spanish, isolated

Other, not isolated

Other, isolated

0 10 20 30 40 50 60 70 80
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There is a similar disparity in SNAP access among non-elderly, disabled individuals depending on 

whether they were living alone. The access rate for individuals in non-elderly, disabled households 

was higher if other people were in the household (67.7 percent vs. 50.6 percent if living alone). 

As expected, access rates were low among individuals in units without children and with at least 

one nondisabled member age 18-49, especially if they were living alone (25.9 percent). This group 

roughly corresponds to the definition of ABAWDs, who face restrictions on the total number of 

months that they can receive SNAP. (Those restrictions were relaxed in April 2009 when ARRA 

took effect, but States were not required to locate those ABAWDS previously denied eligibility and 

inform them of ARRA eligibility restoration.) 

Table 3

Texas SNAP access rates by household composition, 2008-09

Access rate Percent eligible

Estimate SE Estimate SE

Single-adult units

Male adult 38.0 (0.84) 28.0 (0.39)

Female adult 64.4 (0.52) 72.0 (0.39)

Multiple-adult units

Married couple 65.4 (0.61) 75.7 (0.55)

Other multiple-adult unit 77.5 (0.99) 24.3 (0.55)

Units with children

Single male adult 72.2 (1.96) 4.8 (0.20)

Single female adult 79.3 (0.63) 33.3 (0.49)

Multiple adults married 70.2 (0.75) 46.9 (0.53)

Multiple adults other 81.7 (1.17) 15.0 (0.43)

Elderly individuals in unit

Living alone 35.9 (1.05) 28.1 (0.54)

Living with only other elderly 33.7 (1.35) 20.8 (0.59)

Living with at least one non-elderly 53.5 (1.05) 51.1 (0.75)

Disabled non-elderly in unit

Living alone 50.6 (1.75) 8.4 (0.35)

Not living alone 67.7 (0.90) 91.6 (0.35)

ABAWD units

Single-person unit 25.9 (0.75) 72.3 (0.96)

Multi-person unit 49.9 (1.69) 27.7 (0.96)

Units with citizen children and noncitizen adults

All other units 61.1 (0.34) 75.6 (0.35)

Citizen child with noncitizen adult 68.2 (0.93) 24.4 (0.35)

Notes: Estimates are based on authors’ calculations using the 2009 American Community Survey and Texas SNAP 

administrative records. Standard errors (se), displayed in parentheses, were calculated using the method of successive 

differences and 80 replicate weights. ABAWD = Able-Bodied Adults Without Dependents.

* This variable uses the original ACS household description to determine if an elderly person was living alone or with other 

individuals, even if he or she is in different units for SNAP eligibility consideration.

Source: ERS tabulations of the 2009 American Community Survey (ACS) and 2008-09 Supplemental Nutrition Assistance 

Program (SNAP) administrative records.
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Individuals in units that included at least one noncitizen adult and one citizen child participated at 

a slightly higher rate (68.2 percent) than other units (61.1 percent). Individuals in units with citizen 

children and noncitizen parents accounted for about a quarter of the eligible population in Texas in 

2009 (table 3). 

Individuals in units with stronger labor force attachment had better access to SNAP than individuals 

in units with little to no labor force attachment (table 4). This is evident in the results that look at 

differences in the work experience of the unit’s primary worker (the member who had the most 

work experience) and in the summary of overall SNAP unit employment status. Fifty-two percent of 

eligible individuals lived in SNAP units in which the primary worker worked all of the year. Nearly 

68 percent of individuals in these units participated in SNAP. Individuals in units whose primary 

worker had weaker attachment to the labor market—working only either most or some of the year, or 

last working more than 1 year ago—were less likely to participate in SNAP, especially if participation in the labor market was more than 5 years ago (52.2 percent). Individuals in these units made up 

18 percent of the eligible population in Texas in 2009.

The “unit employment status” variable presents additional information about the recent labor force 

attachment of the SNAP unit as a whole. It measures whether (1) anyone was employed and how 

much of the year they worked; (2) all members were either unemployed or not in the labor force; 

or 3) no member was in the labor force, of which a large share of units include elderly individuals. 

Access rates of units with an unemployed person (65 percent) differed markedly from those in which 

no unit members were in the labor force (54 percent), probably due to the fact that the latter category 

includes elderly individuals, while the unemployed category is likely to include working-age adults 

in need of work. Those units that had someone employed for all, most, or some of the year had 

access rates that ranged between 60.2 and 67.4 percent.28 Interestingly, the individuals in units in 

which at least one person was employed all year made up more than half of eligible individuals in 

28The percentage eligible for those in households in which “at least one person was employed and worked all year” 

differs from the percent eligible for those in households in which the primary worker “works year round” by whether the 

worker was employed or not at the time of the interview.

Figure 3

SNAP access rates of households with elderly individuals in Texas, 2009

SNAP = Supplemental Nutrition Assistance Program.

Note: Note: An access rate is the percent of SNAP participants among individuals estimated to be eligible for SNAP receipt.

Source: Authors’ calculations using 2009 ACS and Texas SNAP administrative data for 2008-09.

Percent

Living alone

Living with only other elderly

Living with at least one non-elderly

0 10 20 30 40 50 60
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Texas (51.2 percent). The second largest group was individuals in units in which all members were 

out of the labor force, accounting for about a quarter (25.6 percent) of eligible individuals.

In previous research, units with stronger labor force attachment have been found to have lower SNAP 

participation rates than those with weaker attachment. One reason our results may differ is that 

we have not separated out elderly individuals or units that are predominantly composed of elderly 

members. These units tend to have weak attachment to the labor force and low participation rates. We 

Table 4

Texas SNAP access rates by economic characteristics, 2008-09

Access rate Percent eligible

Estimate SE Estimate SE

Work experience of primary worker

Worked year round 67.7 (0.60) 51.9 (0.33)

Worked most of year 61.8 (1.04) 12.6 (0.27)

Worked some of year 60.3 (1.05) 10.7 (0.21)

Last worked 1-5 years ago 59.5 (1.18) 6.9 (0.18)

Last worked over 5 years ago 52.2 (0.68) 18.0 (0.24)

SNAP unit employment status

At least 1 employed worked all year 67.4 (0.59) 51.2 (0.34)

At least 1 employed worked most of year 60.6 (1.27) 9.1 (0.23)

At least 1 employed worked some of year 60.2 (1.54) 5.6 (0.16)

All either unemployed or not in labor force 64.7 (1.27) 8.5 (0.21)

All not in labor force 54.3 (0.56) 25.6 (0.30)

Health insurance

Private health coverage 53.8 (0.70) 17.5 (0.26)

Public health coverage 71.0 (0.41) 46.7 (0.26)

No coverage 56.5 (0.63) 35.8 (0.27)

Public assistance

Received none 61.6 (0.37) 93.7 (0.20)

Received some 80.7 (1.23) 6.3 (0.20)

Supplemental Social Income

Received none 63.1 (0.38) 92.7 (0.15)

Received some 58.8 (1.01) 7.3 (0.15)

Social Security

Received none 63.9 (0.40) 86.4 (0.21)

Received some 55.5 (0.76) 13.6 (0.21)

Self-employment income

Received none 62.5 (0.38) 91.0 (0.24)

Received some 66.2 (1.26) 9.0 (0.24)

Salary/wage income

Received none 55.5 (0.67) 30.3 (0.33)

Received some 66.0 (0.47) 69.7 (0.33)

Notes: Estimates are based on authors’ calculations using the 2009 ACS and Texas SNAP administrative records. Standard 

errors (se), displayed in parentheses, were calculated using the method of successive differences and 80 replicate weights.

Source: ERS tabulations of the 2009 American Community Survey (ACS) and 2008-09 Supplemental Nutrition Assistance 

Program (SNAP) administrative records.
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have also included matched individuals in our sample who were not determined to be eligible by our 

model. Methodological differences also explain the somewhat divergent results. In particular, we are 

summarizing labor force attachment over the entire 12-month ACS reference period, rather than in any 

particular month. For individuals with short spells in the program, they would be more likely to participate in those months in which they were out of work or between jobs.

Individuals who lacked private health insurance coverage (either no coverage or coverage by public 

health insurance) made up 82.5 percent of the eligible population in 2009 (table 4). Individuals with 

public health insurance coverage had a higher access rate (71 percent) than those with either private 

coverage (54 percent) or no coverage (56.5 percent). 

Receipt of TANF (Temporary Assistance for Needy Families) by all adult members in the unit 

confers to the entire unit categorical SNAP eligibility. Despite this, individuals in units with any 

member receiving public assistance accounted for only about 6 percent of the SNAP-eligible population.29 In most every case, recipients of public assistance also qualify for SNAP benefits, even if 

they do not meet the Federal rules for eligibility. The low proportion of public assistance recipients 

among SNAP-eligible units is the result of both low participation in cash assistance programs and 

under-reporting of such cash assistance in the ACS. As expected, this group had a high SNAP access 

rate of 81 percent (table 4). Their relatively high access rate may also be due to a common application form and caseworkers making these individuals aware of their SNAP eligibility when they apply 

for public assistance or TANF.

Individuals in units with some SSI receipt accounted for 7.3 percent of the eligible population and 

had an access rate of 59 percent. The access rate for SSI recipients is affected by a program called 

Texas’ SSI Combined Application Project in which SSI recipients who are not receiving SNAP are 

identified electronically through Social Security Administration assistance and encouraged to apply 

via a simplified SNAP application. 

Access rates were lower in units in which a member received some Social Security income. This is 

perhaps not surprising since Social Security receipt is not based on need. Social Security benefits 

serve three different groups of people: retirees, work disabled, and survivors of workers. Most Social 

Security beneficiaries are retired. As a result, most Social Security recipients are older adults who 

were shown to have the lowest SNAP access rate of all age groups. 

Access rates in units with some earnings were just over two-thirds, regardless of whether those earnings came from self-employment income or from wages. The access rate for individuals in wageearning units was 66 percent, and this group made up 70 percent of the eligible population (table 4).

Rural residents had slightly higher access to SNAP than did their urban counterparts (65.3 percent 

versus 62.3 percent). However, 84 percent of all eligible individuals in Texas were urban residents 

(table 5). 

Similarly, access rates by the metropolitan status of current residence do not exhibit large differences. Access rates were lower in the central city of a metropolitan statistical area (MSA)30 than in 

the remainder of the MSA or outside the MSA (61.3 percent relative to 63.4 percent and 67.6 percent, 

29As previously mentioned, the ACS does not distinguish between TANF and other forms of cash public assistance. 

All are subsumed under one “public assistance” category.

30A metropolitan statistical area refers to a densely populated geographic region with close economic ties. Specific 

delineations are defined by the U.S. Office of Management and Budget. 
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Table 5

Texas SNAP access rates by geography, 2008-09

Access rate Percent eligible

Estimate SE Estimate SE

Metro status of current residence

Central city of MSA 61.3 (0.54) 53.6 (0.28)

Remainder of MSA 63.4 (0.64) 33.2 (0.25)

Outside of MSA 67.6 (0.93) 13.1 (0.17)

Area of residence

Rural 65.3 (0.83) 16.3 (0.26)

Urban 62.3 (0.38) 83.7 (0.26)

Residence 12 months ago

In Texas 63.3 (0.35) 97.3 (0.13)

In other State 39.3 (3.05) 1.9 (0.11)

Outside U.S. 20.9 (3.26) 0.8 (0.06)

Congressional district

1 66.0 (1.64) 2.9 (0.10)

2 65.6 (2.06) 2.5 (0.10)

3 41.0 (2.44) 2.3 (0.12)

4 66.6 (1.81) 2.9 (0.10)

5 63.4 (2.19) 2.6 (0.12)

6 59.9 (2.02) 2.9 (0.12)

7 36.8 (3.48) 1.5 (0.08)

8 64.8 (2.27) 2.7 (0.11)

9 58.4 (2.08) 3.8 (0.15)

10 55.9 (2.33) 2.7 (0.12)

11 64.9 (1.79) 2.7 (0.09)

12 58.6 (2.31) 2.7 (0.10)

13 59.7 (2.23) 2.3 (0.08)

14 64.5 (2.02) 2.5 (0.09)

15 76.9 (1.35) 5.4 (0.15)

16 70.6 (1.41) 4.2 (0.13)

17 61.4 (1.96) 2.9 (0.09)

18 57.5 (1.89) 4.4 (0.18)

19 64.3 (1.83) 2.7 (0.08)

20 66.4 (1.76) 3.8 (0.13)

21 56.3 (2.51) 2.0 (0.11)

22 57.2 (2.85) 2.3 (0.11)

23 70.5 (1.75) 3.8 (0.14)

24 52.0 (3.03) 2.2 (0.13)

25 61.9 (2.21) 3.2 (0.11)

26 58.1 (2.49) 2.7 (0.12)

27 71.7 (1.66) 4.5 (0.11)

28 75.7 (1.47) 5.5 (0.14)

29 58.5 (2.60) 3.6 (0.16)

30 64.4 (1.94) 4.5 (0.15)

31 56.4 (2.33) 2.8 (0.11)

32 48.9 (3.05) 2.8 (0.14)

Notes: Estimates are based on authors’ calculations using the 2009 American Community Survey and Texas SNAP 

administrative records. Standard errors (se), in parentheses, were calculated using the method of successive differences and 

80 replicate weights. MSA = Metro Statistical Area, as defined by the U.S. Office of Management and Budget.

Source: ERS tabulations of the American Community Survey (ACS) and 2008-09 Supplemental Nutrition Assistance Program 

(SNAP) administrative records.
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respectively). Most SNAP-eligible individuals lived in an MSA, with over half of them residing in 

central cities (54 percent).

Nearly all individuals in our sample remained in Texas for the 12-month reference period covered by 

the ACS. However, access rates were much lower among the small fraction that moved to Texas from 

another State or country during that period. It may be that, following a long-distance move, eligible 

individuals do not get around to applying for SNAP benefits right away. 

The congressional districts in the urban areas of Dallas (the 3rd and 32nd Districts) and Houston 

(the 7th) exhibited the lowest access rates (though they are not significantly different from each 

other). The three congressional districts that make up the southern portion of the State (the 15th, 

27th, and 28th) had the highest access rates, at roughly three-quarters or more each (fig. 4, table 5).

Figure 4

SNAP access rates in Texas by congressional district, 2008-09

SNAP = Supplemental Nutrition Assistance Program.

Note: Note: An access rate is the percent of SNAP participants among individuals estimated to be eligible for SNAP receipt.

Source: Authors’ calculations using 2009 ACS and Texas SNAP administrative data for 2008-09.

SNAP access by Texas

Congressional Districts,

2008-09

70.51% - 79.4%

62.41% - 70.5%

52.71% - 62.4%

38.3% - 52.7%
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County-Level Access Rates

Table 6 shows access rates for the 25 largest Texas counties (out of 254). The results, even for this 

subsample of Texas counties, illustrate the uneven nature of SNAP access across the State. While 

some counties exhibit access rates well below the State average of about 63 percent, others have 

access rates of more than 75 percent. Program administrators might take note of counties on either 

end of the distribution of county access rates. For example, Denton is the only county of the 25 

largest with an estimated access rate below 50 percent. At the other end of the distribution, Hidalgo, 

Jefferson, and Webb Counties had access rates of nearly 80 percent in 2009. 

The ACS sample for Texas is also large enough to allow us to estimate access rates by various 

demographic characteristics within seven of the largest counties (table 7). This additional level of 

granularity provides program administrators with the most nuanced picture yet of SNAP access 

in their State. It permits them to identify both where and for whom barriers to program access are 

most acute. 

Table 6

Texas SNAP access rates by select county, 2008-09

Access rate Percent eligible

Estimate SE Estimate SE

Texas 62.8 (0.36) 100.0 --

County 66.0 (1.64) 2.9 (0.10)

Bell 54.7 (3.63) 1.5 (0.09)

Bexar 64.2 (1.29) 9.3 (0.23)

Brazoria 62.5 (3.49) 1.0 (0.09)

Brazos 55.9 (3.49) 1.0 (0.06)

Cameron 74.5 (1.99) 4.1 (0.12)

Dallas 57.3 (1.48) 14.7 (0.30)

Denton 45.7 (4.03) 1.8 (0.11)

Ector 70.3 (3.51) 0.9 (0.06)

El Paso 71.0 (1.30) 6.0 (0.18)

Fort Bend 50.8 (3.53) 1.6 (0.10)

Galveston 64.7 (3.01) 1.3 (0.08)

Grayson 74.3 (3.40) 0.6 (0.05)

Harris 55.1 (0.96) 21.4 (0.36)

Hidalgo 78.2 (1.33) 7.8 (0.19)

Jefferson 75.8 (2.59) 1.6 (0.09)

Lubbock 63.7 (3.01) 1.6 (0.07)

McLennan 64.4 (3.25) 1.5 (0.07)

Montgomery 57.3 (3.96) 1.7 (0.10)

Nueces 66.4 (2.90) 2.2 (0.11)

Potter 65.2 (4.21) 0.8 (0.07)

Smith 65.3 (3.96) 0.9 (0.08)

Tarrant 58.9 (1.42) 8.1 (0.22)

Travis 60.2 (2.14) 5.0 (0.15)

Webb 76.0 (2.27) 2.4 (0.09)

Williamson 60.3 (4.49) 1.3 (0.10)

Notes: Estimates are based on authors’ calculations using the 2009 ACS and Texas SNAP administrative records. Standard 

errors (se), in parentheses, were calculated using the method of successive differences and 80 replicate weights.

Source: ERS tabulations of the 2009 American Community Survey (ACS) and 2008-09 Supplemental Nutrition Assistance 

Program (SNAP) administrative records.
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Table 7

Texas SNAP access rates by select county and demographic characteristics, 2008-09

Access rate

Bexar Dallas El Paso Harris Hidalgo Tarrant Travis Other

Race

White alone 63.3 53.2 71.2 50.9 78.1 55.8 51.3 63.1

Black or African American alone 74.4 70.5 66.1 68.2 - 72.0 76.8 72.8

American Indian or Alaska Native 

alone 65.8 59.8 79.0 71.3 75.3 24.6 71.6 54.8

Asian alone 44.7 29.2 3.1 31.5 28.1 36.4 13.5 35.6

Some other race alone 62.5 51.0 73.7 50.5 79.6 55.2 77.0 70.1

Two or more races 65.8 62.1 75.7 71.3 72.4 69.2 72.5 71.5

Hispanic origin

Hispanic origin 66.9 56.9 72.7 54.7 79.8 56.3 70.8 70.2

Non-Hispanic origin 56.7 57.6 52.8 55.7 37.4 60.8 47.8 59.9

Age

0 to 17 81.3 69.4 84.8 67.2 87.8 71.8 76.9 79.2

18 to 29 54.6 50.5 65.9 46.8 75.3 53.5 51.4 59.2

30 to 39 67.4 53.9 71.5 54.4 81.9 57.4 55.9 67.7

40 to 49 54.1 49.6 68.5 46.0 70.4 47.7 52.9 57.4

50 to 59 43.8 45.9 51.2 44.5 53.5 43.5 36.2 47.9

60 to 69 45.4 39.4 51.3 35.2 59.2 30.5 33.8 45.1

70+ 34.3 35.7 48.8 39.2 56.2 32.9 40.7 38.1

Highest education of unit members

Some high school 62.6 54.3 72.4 55.7 79.6 56.2 67.8 66.7

High school diploma 69.2 61.6 70.7 57.7 78.7 66.1 65.6 65.7

Some college 66.2 64.1 73.2 58.7 78.5 60.9 65.4 66.0

College degree 37.5 34.5 57.9 34.4 65.2 38.3 27.3 45.9

Unit size

Small (1 to 2 members) 40.2 37.0 47.6 36.4 51.8 37.4 33.6 43.2

Medium (3 to 4 members) 75.1 64.5 80.4 60.4 84.9 69.5 77.1 76.7

Large (5 or more) 87.2 73.0 89.2 70.8 91.0 70.5 73.9 81.9

Military veterans or active duty

No household members 65.0 57.7 72.7 55.3 78.8 58.6 60.6 65.3

At least one household member 54.4 47.0 46.3 51.1 52.3 63.1 52.2 55.7

Language and linguistic isolation

English only 61.4 62.0 58.3 59.3 58.5 62.1 55.4 62.4

Spanish not isolated 69.2 61.1 76.2 60.5 79.3 61.5 74.8 73.1

Spanish isolated 46.6 49.4 66.5 45.6 80.0 50.0 64.7 60.0

Other not isolated 48.6 27.6 24.2 37.1 48.9 46.0 26.9 37.0

Other isolated 77.1 37.9 - 36.3 9.7 63.4 14.3 39.2

Notes: Estimates are based on authors’ calculations using the 2009 American Community Survey and Texas SNAP administrative records. 

Standard errors (se) are available upon request.

Source: ERS tabulations of the 2009 American Community Survey (ACS) and 2008-09 Supplemental Nutrition Assistance Program (SNAP) 

administrative records.
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For example, Hidalgo and El Paso Counties both exhibited higher than average access rates (78.2 

and 71.0 percent, respectively), and both had similarly low access rates among individuals in units 

that spoke only English (58.5 and 58.3 percent, respectively) and high rates among individuals in 

Spanish-speaking, non-linguistically isolated units (79.3 and 76.2 percent, respectively). Yet the 

gap between linguistically isolated and non-isolated units is different in the two counties (fig. 5). 

In Hidalgo County, where individuals in linguistically isolated Spanish-speaking units participate 

at a rate of 80 percent, there is no significant gap. In El Paso County, however, the gap is nearly 

10 percentage points (and statistically significant). What factors might account for the apparent 

success of Hidalgo County in reaching linguistically isolated Spanish-speaking units in need relative to El Paso County?

Figure 5

SNAP access rates of Spanish-speaking isolated households in Texas by county, 2009

Percent

SNAP = Supplemental Nutrition Assistance Program.

Note: Note: An access rate is the percent of SNAP participants among individuals estimated to be eligible for SNAP receipt.

Source: Authors’ calculations using 2009 ACS and Texas SNAP administrative data for 2008-09.

Bexar Dallas El Paso Harris Hidalgo Tarrant Travis

0

20

40

60

80

100

Spanish isolated

Spanish not isolated
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Conclusion

This report provides an indepth profile of SNAP access in Texas and demonstrates the power of 

linked SNAP administrative and American Community Survey data. Policymakers and program 

administrators want detailed information about how SNAP is serving the needs of families in their 

States and counties, and this linked microdata can provide it.

For many subpopulations in Texas in 2009, the report shows that patterns of SNAP access were 

similar to national estimates of participation for those subpopulations. However, by presenting a 

more disaggregated picture of SNAP access in the State, it also uncovers some new results. For 

example, the familiar gradient in age was evident in Texas: access rates declined with age, and 

access rates for the elderly (age 60 and over) were nearly 25 percentage points lower than for children (age 17 and under). Lower SNAP participation among the elderly has been well documented 

nationally. The more detailed tabulations in this study reveal that, in Texas, it was the elderly who 

live alone or only with other elderly individuals who had much lower SNAP access rates in 2009. 

The access rate of elderly individuals living with at least one other non-elderly individual actually 

corresponds to the statewide average for all individuals. 

Similarly in line with national estimates, units with children had substantially higher SNAP access 

rates in Texas than units without children in 2009. Among units with children, couples had significantly lower access than female heads or even multiple-adult (non-couple) units, and they made up 

the largest eligible subgroup, by unit type, in the State. Conversely, among units without children, 

couples had a higher access rate than female- and male-headed units. 

This report also highlights SNAP access among subpopulations not previously considered at either 

the State or national level. For example, our results indicate that a lack of facility with English may 

be an important barrier to SNAP access. With its large Hispanic population, this is a particular 

policy concern in Texas. The results here show a large difference in SNAP access according to 

the Census Bureau’s classification of linguistic isolation among Spanish speakers, with “isolated” 

Spanish-speaking units exhibiting access rates 13 percentage points lower than “non-isolated” 

Spanish-speaking units. Differing rates of program access between these two groups were evident 

across counties as well. 

Finally, this report provides results at a finer level of geographic detail than has previously been 

available. It reveals substantial variation in access rates across both congressional districts and counties in Texas. In addition, estimates of SNAP access for demographic subpopulations within counties 

can allow program officials to identify geographic differences in SNAP access among vulnerable 

demographic subpopulations.

Using Texas, this report presents a new approach to estimating SNAP access at the State and 

substate levels. By linking State SNAP administrative records and household survey data at 

the person level, we are able to characterize SNAP access for more detailed subpopulations 

and geographic areas than has been done previously. The linked microdata offer a great deal 

of flexibility in characterizing SNAP access, and the results presented in this report do not 

exhaust the possibilities. 

An important application of the linked microdata that is not exploited in this report would be to 

use it in a model of the participation decision among eligible individuals in a multivariate setting. 

Because SNAP participation is more accurately identified in the linked data, the modeling approach 
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would better isolate the characteristics associated with nonparticipation among eligible individuals. 

Another potentially useful effort would be to analyze how households with episodic receipt differ 

from those with either longer term receipt or those with no receipt, based on the monthly SNAP 

receipt information in the Texas administrative data. Although in this report we have employed the 

standard definition of eligibility, future research on SNAP access could model as eligible those who 

appear to qualify for SNAP under broad-based categorical eligibility. 

Estimates at this level of detail provide program officials and policymakers with a new tool to assess 

and improve program performance. As more State-level SNAP administrative records become available, detailed tabulations like these can be produced on an annual basis for other States, enabling 

States to monitor SNAP access for specific subpopulations and regions over time. 
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References

Bollinger, C.R., and M.H. David. 1997. “Modeling Discrete Choice with Response Error: Food 

Stamp Participation,” Journal of American Statistical Association 92(439): 827-835.

Cody, S., L. Castner, J. Mabli, and J. Sykes. 2007. Dynamics of Food Stamp Program Participation, 

2001-2003. USDA Food and Nutrition Service; Office of Research, Nutrition, and Analysis. 

http://www.mathematica-mpr.com/publications/PDFs/DynamicsFSP01-03.pdf

Cody, S., P. Gleason, B. Schechter, M. Satake, and J. Sykes. 2005. Food Stamp Program entry and 

exit: An analysis of participation trends in the 1990s. Washington, DC. http://mathematica-mpr.

com/publications/pdfs/FSPentryexit.pdf

Cunnyngham, K.E., L.A. Castner, and A.L. Schirm. 2011. Empirical Bayes Shrinkage Estimates 

of State Supplemental Nutrition Assistance Program Participation Rates in 2006-2008 for All 

Eligible People and the Working Poor. Alexandria, VA.

Danielson, C., and J.A. Klerman. 2006. Why Did the Food Stamp Caseload Decline (and Rise)? 

Effects of Policies and the Economy. http://www.irp.wisc.edu/publications/dps/pdfs/dp131606.pdf

Dickert-Conlin, S., K. Fitzpatrick, and L. Tiehen. 2010. The Downs and Ups of the SNAP Caseload: 

What Matters?

Fellowes, M., and A. Berube. 2005. Leaving Money (and Food) on the Table: Food Stamp 

Participation in Major Metropolitan Areas and Counties. http://www.brookings.edu/~/media/

research/files/reports/2005/5/childrenfamilies fellowes/20050517_foodstamp.pdf

Food Research and Action Center. 2011. SNAP Access in Urban America: A City-by-City Snapshot. 

http://frac.org/wp-content/uploads/2011/01/urbansnapreport_jan2011.pdf

Kornfeld, R. 2002. Explaining Recent Trends in Food Stamp Program Caseloads: Final Report. 

http://naldc.nal.usda.gov/download/48203/PDF

Leftin, J., E. Eslami, and M. Strayer. 2011. Trends in Supplemental Nutrition Assistance Program 

Participation Rates: Fiscal Year 2002 to Fiscal Year 2009. Alexandria, VA: http://www.fns.

usda.gov/ora/MENU/Published/snap/FILES/Participation/Trends2002-09.pdf

Leftin, J., A. Gothro, and E. Eslami. 2010. Characteristics of Supplemental Nutrition Assistance 

Program Households: Fiscal Year 2009. Alexandria, VA. http://www.fns.usda.gov/ora/menu/

Published/snap/FILES/Participation/2009Characteristics.pdf

Mabli, J., S. Tordella, L. Castner, T. Godfrey, and P. Foran. 2011. Dynamics of Supplemental 

Nutrition Assistance Program Participation in the Mid-2000s. Alexandria, VA. http://www.fns.

usda.gov/ora/MENU/Published/snap/FILES/Participation/DynamicsMid2000.pdf

Marks, J.Y., J.B. Isaacs, T.M. Smeeding, and K.A. Thornton. 2011. Wisconsin Poverty Report: 

Technical Appendix for 2009. http://www.irp.wisc.edu/research/WisconsinPoverty/pdfs/

WIPovTechnicalAppendix_Sept2010.pdf
30

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Marquis, K.H., and J.C. Moore. 1990. “Measurement Errors in SIPP Program Reports.” https://

www.census.gov/srd/papers/pdf/rsm2010-01.pdf

Meyer, B.D., and R.M. Goerge. 2011. “Errors in Survey Reporting and Imputation and their 

Effects on Estimates of Food Stamp Program Participation.” http://papers.ssrn.com/sol3/papers.

cfm?abstract_id=1824261

National Opinion Research Center (NORC). 2011. Assessment of the U.S. Census Bureau’s Person 

Identification Validation System. Vol. 20814. http://www.norc.org/PDFs/May 2011 Personal 

Validation and Entity Resolution Conference/PVS Assessment Report FINAL JULY 2011.pdf

National Research Council. 2010. Developing and Evaluating Methods for Using American 

Community Survey Data to Support the School Meals Program: Interim Report. A L. Schirm 

and N. J. Kirkendall, eds. Washington, DC: The National Academies Press. http://www.nap.edu/

catalog.php?record_id=12917

Passel, J.S., and D. Cohn. 2010. U.S. Unauthorized Immigration Flows Are Down Sharply Since 

Mid-Decade. Pew Hispanic Center. Washington, DC. http://www.pewhispanic.org/2010/09/01/

us-unauthorized-immigration-flows-are-down-sharply-since-mid-decade/

Peterson, A., B.E. Moulton, E.O. Smith, and C. Wilson. 2010. Implementing Supplemental Nutrition 

Assistance Program in Puerto Rico: A Feasibility Study. Alexandria, VA. http://www.fns.usda.

gov/ora/MENU/Published/snap/FILES/ProgramDesign/PuertoRico.pdf

Taeuber, C.M., J. Staveley, and R. Larson. 2005. Modeling Participation in the Maryland Food 

Stamp Program Using Census Data and Administrative Records. Baltimore, MD. http://www.

ubalt.edu/jfi/jfi/reports/Foodstampdenominatorpaper_final_Jun06l.pdf

Taeuber, C.M., D.M. Resnick, S.P. Love, J. Staveley, P. Wilde, and R. Larson. 2004. Differences 

in Estimates of Food Stamp Program Participation Between Surveys and Administrative 

Records. Baltimore, MD. http://cdm16352.contentdm.oclc.org/cdm/compoundobject/collection/

p15224coll6/id/1438/rec/7

U.S. Census Bureau. 2009. “ACS 2009 1-year Accuracy of the Data (US).” http://www.census.gov/

acs/www/Downloads/data_documentation/Accuracy/ACS_Accuracy_of_Data_2009.pdf 

U.S. Department of Agriculture, Food and Nutrition Service. 2011a. Calculating the SNAP Program 

Access Index: A Step-By-Step Guide 2010. http://www.fns.usda.gov/ora/menu/Published/snap/

FILES/Other/PAI2010.pdf

U.S. Department of Agriculture, Food and Nutrition Service. 2011b. SNAP Guidance on Non-Citizen 

Eligibility. http://www.fns.usda.gov/snap/government/pdf/Non-Citizen_Guidance_063011.pdf

Ziliak, J.P., C. Gundersen, and D.N. Figlio. 2003. “Food Stamp Caseloads over the Business Cycle,” 

Southern Economic Journal 69(4): 903-19.
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Appendix: Detailed Characteristics of the Administrative 

Data, the ACS Sample, and the Linked Data—PIK Rates 

and Comparability of Samples

A. Texas Administrative Data Description

The Texas administrative data that are used for analysis at Census include information on the sex, 

birth year, SNAP benefit amount, and household composition of individual SNAP participants by 

month from January 2008 through December 2009. Prior to using the data for analysis, Census 

attaches a Protected Identification Key or PIK from the personally identifiable information (PII) 

originally provided to Census, and the PII is removed from the data in that process. 

Two variables on the “persons” file indicate an individual’s case number (“caseid”) and the client 

number (“clnum”). It is our understanding that an individual’s client number followed the client if 

he or she changed households (or “caseids”), for the most part. This appears to be the case, though 

some individuals were apparently given new client numbers, perhaps when administrators could not 

locate their original ones. 

PIKs, Case IDs, and Client Numbers

We merge person records from the two data sources using a Protected Identification Key (PIK). 

PIKs are internal Census Bureau identifiers based in part on a person’s Social Security Number 

(SSN). PIKs allow researchers to match person records across data sources without compromising 

a respondent’s personally identifiable information, such as SSNs. The Census Bureau assigns PIKs 

to person records in demographic surveys and in administrative records using the agency’s Person 

Identification Validation System (PVS). This system uses information in the source file (e.g., Social 

Security numbers, addresses, names and dates of birth) to search for the person’s corresponding 

record in the Numerical Identification File maintained by the Social Security Administration. 

In a small number of administrative records, PIKs that Census assigned to the Texas data are associated with multiple client numbers (“clnum”). This, together with guidance provided to us by 

researchers familiar with these data, suggests that the same individual has been assigned different 

client numbers in the administrative records over time. The client number is supposed to follow an 

individual if he or she cycles in and out of the program, but it appears that is not always feasible. For 

other PIKs, the case ID (“caseid”) differs from month to month, but the client number (“clnum”) 

does not. This suggests that the same person is being successfully followed in the administrative 

data and that he or she has moved into different households.

In only 0.56 percent of unique PIKs, the client number changes for some months (26,915 out of 

4,769,893 unique PIKs). And in only 10 percent of these PIKs (or 2,752 out of 26,915 PIKs) were 

other important characteristics such as sex or birth year found to be different. Thus, out of all PIKs 

(almost 5 million), only 2,752 appear to represent a different person, where half of the 2,752 are 

probably correct, and the other half are in error. In other words, since they are duplicates, one of the 

two observations is probably correct. 

In order to match individuals in the American Community Survey (ACS), we need to treat the PIK 

as the best representative of unique individuals (as opposed to using the client number variable). In 

table A1, we compare the distributions of individuals across age, sex, and a few other variables using 
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the PIK’ed individuals by client number and individuals by the PIK, which essentially differs by 

1,376 observations (half of the 2,752 described in the previous paragraph).

The Extent of Individuals Missing PIKs

Although we use the PIK as our ultimate identifier in order to match to individuals in the ACS, 

we need to use the client number as an identifier in order to assess the extent of missing PIKs. 

Thus, treating client number as the individual identifier, there are 4,849,914 unique individuals in 

the combined 2008-2009 data set. Of these, 83,624 individuals do not have a PIK, or 1.72 percent 

(table A1).31

There are 1,992,385 unique case units in the data. In less than 0.3 percent of case units (5,700) did 

no individual have a PIK. In only 1.8 percent of all case units did 1 person or more not have a PIK 

(37,049), and in the remaining 97.9 percent of case units, all individuals had a PIK (1,949,636). 

Individual and SNAP Unit Characteristics 

by PIK Status in the Texas Data

Using the client line number as the individual identifier, individuals who were not PIK’ed were 

largely children (74.7 percent), and slightly more than half were males (52.7 percent) (table A1). 

Individuals with no PIK had a higher share of Hispanics (60.4 percent) compared to individuals 

who did have a PIK (51.7 percent), and individuals with no PIK had a lower share of Whites (14.0 

percent) and Blacks (18.6 percent) compared to individuals with a PIK (24.3 percent and 21.2 

percent, respectively). Individuals with no PIK had an average of 9.6 months in the program, while 

individuals with a PIK had an average of 11.7 months in the program. Average benefit amounts were 

higher for individuals without a PIK. 

Among case units in which no one was PIK’ed, 86 percent were case units that had only one person 

(table A2). This is logical because there is only person to match in one-person case units. The larger 

the case unit, the lower the rate of no one in the unit being assigned a PIK. 

B. Comparison of PIK’ed and Non-PIK’ed Samples 

in the ACS Data

The American Community Survey (ACS) is an annual survey that provides basic information about 

the U.S. population at small geographic levels. For survey evaluation purposes, the U.S. Census 

Bureau has assigned individuals in some of the ACS survey samples a Protected Identification Key 

or “PIK.” The PIK allows analysts to link those individuals to other survey and administrative data.

This section describes the extent and characteristics of the 2009 ACS sample with and without PIKs. 

Out of the 306,081 individuals who lived in Texas while taking the ACS, 35,502 (or 11.6 percent) 

were not assigned a PIK. 

31Alternatively, we treated each separate combination of “caseid” and “clnum” as a unique individual. With that definition, the total number of individuals was 5,169,510. Of these, 85,623 individuals did not have a PIK, or 1.66 percent.
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Table A1

Characteristics of individuals in the Texas administrative data, 2008-2009, by PIK status

Client Number as Identifier PIK as ID

Individuals with a PIK or not PIK’ed Not PIK’ed All All

––—————— Percentage —————––— 

Sex

Male 43.3 52.7 43.5 43.4

Female 56.7 47.3 56.5 56.7

Total 100 100 100 100 

Age

0-17 52.6 74.7 53.0 52.2

18-29 16.8 11.3 16.7 17.0

30-39 10.5 7.5 10.5 10.6

40-49 7.6 3.3 7.5 7.7

50-59 5.8 1.6 5.7 5.8

60-69 3.4 0.8 3.4 3.4

70+ 3.3 0.8 3.2 3.3

Total 100 100 100 100

Race

White 24.3 14.0 24.2 24.3

Black 21.2 18.6 21.1 21.1

Hispanic 51.7 60.4 51.9 51.8

American Indian or Alaskan Native 0.4 0.5 0.4 0.4

Asian or Pacific Islander 1.3 2.5 1.3 1.3

Omitted from record 1.1 4.1 1.2 1.1

Total 100 100 100 100

Case unit size

1 person 15.2 5.8 15.0 14.9

2 persons 17.2 19.0 17.2 16.6

3 persons 22.1 24.5 22.1 21.9

4 persons 20.9 21.4 20.9 21.1

5 or more persons 24.7 29.4 24.8 25.5

Total 100 100 100 100

Length of time in SNAP (months)

Mean 11.7 9.6 11.7 11.8

Median 12 8 11 12

Benefits ($)

Mean 360 396 361 362

Median 343 367 344 345

Observations 4,766,290 83,624 4,849,914 4,769,893

Note: PIK = Protected Identification Key.

Source: ERS tabulations of Texas Supplemental Nutrition Assistance Program administrative records at the U.S. Bureau 

of the Census.
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Table B1 shows PIK rates by selected individual characteristics and table B2 shows results from a 

probit model of the probability of an individual getting a PIK based on individual and household 

composition factors. 

Since SNAP program participation is at the SNAP unit level, we are able to assign participation to 

individuals who live in a household in which at least one person has a PIK and is linked to the Texas 

administrative data. We keep the non-PIK’ed individuals data in the ACS data for the calculation of 

SNAP units and eligibility, and then we drop individuals who do not have a PIK and do not live with 

anyone with a matching PIK.

Compared to PIK’ed individuals, those without a PIK in the ACS had a higher share of young people 

and a slightly lower share of Whites (table B3). Those individuals without a PIK had a much higher 

share of Hispanics (52.1 percent) than did individuals with a PIK (29.8 percent). Individuals without 

a PIK were more likely to be living in larger SNAP units: 24 percent of individuals without a PIK 

lived in SNAP units with 5 or more members, while 15.5 percent of individuals with a PIK lived in 

such units. Individuals without a PIK had a lower share of two-person SNAP units compared to individuals with a PIK (19 percent compared to 29.5 percent). In all of the other SNAP unit size categories, the two groups had similar shares.

Table B4 shows the results for characteristics of SNAP case units by whether all members had a 

PIK, at least one member had a PIK, or no member had a PIK. The last column shows the distribution of characteristics for all units.

Table A2

Case unit characteristics in the Texas administrative data, 2008-2009, by PIK status

All in unit 

PIK’ed

At least 

one in unit 

PIK’ed

No one 

in unit 

PIK’ed All

Case unit size Percent

1 person 39.1 0.0 86.3 38.5

2 persons 21.0 23.4 9.5 21.0

3 persons 17.6 27.0 3.1 17.7

4 persons 12.3 22.4 0.8 12.4

5 or more persons 10.1 27.2 0.4 10.4

Total 100 100 100 100

Units with children

No children 39.2 3.2 38.7 38.5

At least one child 0-17 years 60.9 96.8 61.3 61.5

Total 100 100 100 100

Units with elderly

No elderly 84.2 96.2 89.5 84.4

At least one elderly person, 60+ 15.8 3.8 10.5 15.6

Total 100 100 100 100

Observations 1,949,636 37,049 5,700 1,992,385

Note: PIK = Protected Identification Key.

Source: ERS tabulations of Texas Supplemental Nutrition Assistance Program administrative records at the U.S. Bureau 

of the Census.
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Table B1

ACS Texas 2009 PIK rates by selected characteristics

Individual characteristics

Percent assigned 

a PIK

Male 88.30

Female 88.50

Has less than high school diploma 84.32

Has high school diploma 88.40

Has some college 92.23

Has college degree or post-grad 90.81

Age 0 to 15 85.33

Age 16 to 29 84.60

Age 30 to 39 85.47

Age 40 to 49 90.16

Age 50 to 59 92.34

Age 60 to 69 93.52

Age 70 and over 93.61

Non-citizen 58.54

Speaks only English at home 92.51

Speaks Spanish at home 81.11

English poor or none 64.20

White alone 89.51

Black alone 88.21

American Indian or Alaska Native alone 84.50

Asian alone 87.16

Other race alone 78.17

Two or more races 88.23

Hispanic 81.49

Non-Hispanic 91.77

Did not work 88.32

Worked all year full time 90.31

Out of the labor force 88.79

Unemployed 86.72

Self-reported disability 91.60

Urban 88.18

Rural 88.95

Observations 308,061

Note: PIK = Protected Identification Key.

Source: ERS tabulations using The American Community Survey at the U.S. Bureau of the Census.
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Table B2

Probit model of the probability of being assigned a PIK for ACS 2009, Texas residents

PIK Probit Model

Variable Coefficient SE of Coef.

Unit type Couple 0.191*** (0.019)

Female head 0.170*** (0.015)

Male head and multiple adult

Children Only children under 6 0.364*** (0.018)

Only children age 6 to 17 0.343*** (0.013)

Children under 6 and over 6 0.536*** (0.017)

Citizenship Any noncitizen in unit -0.437*** (0.015)

Elderly Number of elderly in unit 0.303*** (0.019)

Number of nonelderly adults in unit 0.151*** (0.014)

Disability Unit member with self-reported disability 0.233*** (0.014)

Rural Rural (not urban) 0.000824 (0.012)

Highest education Has less than high school diploma -0.0513** (0.016)

Has high school diploma -0.114*** (0.013)

Has some college

Has college degree -0.0791*** (0.014)

Race All members white only 0.0271 (0.023)

All members Black only -0.0110 (0.027)

All members Amer. Indian or Alaskan Native only -0.231*** (0.069)

All members Asian only -0.0239 (0.035)

All members other race only -0.107*** (0.027)

At least some members 2 or more races

Hispanic origin All members of Hispanic origin 0.130*** (0.018)

At least one member non-Hispanic

Work experience Primary worker worked all year 0.0427* (0.018)

Primary worker worked most of year 0.0698** (0.022)

Primary worker worked some of year 0.0844*** (0.024)

Non one in unit worked during year

Poverty status Poverty index under 100 -0.162*** (0.016)

Poverty index 100-129 -0.102*** (0.020)

Poverty index 130-199 -0.101*** (0.016)

Poverty index 200 +

English language All unit members speaks English at home -0.0422* (0.017)

All unit members speak English poorly -0.519*** (0.020)

Survey mode Survey mode—by CAPI

Survey mode—by mail 0.872*** (0.012)

Survey mode—by CATI -0.253*** (0.011)

Constant 0.737*** (0.039)

Observations 305,799

Note: z-statistics not shown, * p<0.05, ** p<0.01, and *** p<0.001. CAPI = Computer-assisted personal interviewing. 

CATI = Computer-assisted telephone interviewing. PIK = Protected Identification Key.

Source: ERS tabulations of The American Community Survey at the U.S. Bureau of the Census.
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Table B3

Individual characteristics of Texas residents in the 2009 ACS by PIK’ed status

Individuals with a PIK or not PIK’ed Not PIK’ed All

Sex

Male 48.3 48.7 48.3

Female 51.7 51.3 51.7

Total 100 100 100

Age

0-17 22.7 29.7 23.5

18-29 16.5 23.0 17.3

30-39 12.4 16.1 12.8

40-49 14.2 11.8 13.9

50-59 14.4 9.1 13.8

60-69 10.4 5.5 9.9

70+ 9.4 4.9 8.9

Total 100 100 100

Race

White 78.4 70.0 77.4

Black 9.8 10.1 9.9

American Indian or Alaskan Native 0.9 1.2 0.9

Asian 3.9 4.3 3.9

Native Hawaiian or Pacific Islander 0.1 0.1 0.1

Other race 6.9 14.3 7.7

Total 100 100 100

Hispanic

Hispanic 29.8 52.1 32.4

Non-Hispanic 70.2 47.9 67.6

Total 100 100 100

SNAP unit size

1 person 20.2 21.9 20.4

2 persons 29.5 19.4 28.3

3 persons 15.8 15.7 15.7

4 persons 19.0 19.4 19.0

5 or more persons 15.5 23.7 16.5

Total 100 100 100

Observations 4,766,290 83,624 4,849,914

Note: PIK = Protected Identification Key.

Source: ERS tabulations using the American Community Survey at the U.S. Bureau of the Census.
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C. Linked Data—Comparability of Samples

In this section, we describe the comparability of the linked sample to the PIK’ed sample from the 

Texas SNAP administrative records. Not all of the individuals linked by PIK had participated in 

SNAP before the ACS interview date, and not all of them had participated within the previous 12 

months. The total number of individuals who were matched and found to have participated within 12 

months before the ACS interview was 33,209 individuals (table C1). 

We assign SNAP participation to 10,070 additional individuals who were not linked by PIK but who 

lived in the same SNAP unit as at least one other linked individual who met the SNAP participation period criteria. That represents 23 percent of all of the individuals we estimate to have received 

SNAP benefits. 

Table C1 shows characteristics of SNAP participants by match type—whether directly matched by 

their own PIK or indirectly matched by a PIK’ed SNAP unit member—in the unweighted linked 

data. Males were more highly represented among indirectly linked individuals, as were Hispanics. 

Individuals between age 30 and 49 were more highly represented among indirectly linked individuals than they were among directly linked ones. But, generally, the highest shares among both 

groups were of young people.

Table C2 shows the characteristics of SNAP participants in the weighted linked data compared to 

those in the PIK’ed Texas administrative data (using PIK as the individual identifier). The distributions for directly linked weighted and indirectly linked weighted individuals are also shown. 

Table B4

Case unit characteristics of Texas residents in the 2009 ACS, by PIK status

All in unit 

PIK’ed

At least 

one in unit 

PIK’ed

No one 

in unit 

PIK’ed All

Case unit size

1 person 44.5 0 66.5 42.9

2 persons 31.3 26.2 17.0 29.8

3 persons 10.4 23.4 6.5 11.0

4 persons 9.1 24.0 5.9 10.0

5 or more persons 4.8 26.4 4.1 6.4

Total 100 100 100 100

Units with children

No children 74.0 21.7 81.3 70.6

At least one child 0-17 years 26.0 78.3 18.7 29.4

Total 100 100 100 100

Units with elderly

No elderly 68.6 89.9 79.5 71.1

At least one elderly person, 60+ 31.4 10.1 20.5 28.9

Total 100 100 100 100

Observations 123,020 11,039 11,677 145,736

Note: PIK = Protected Identification Key.

Source: ERS tabulations of Texas Supplemental Nutrition Assistance Program Administrative Records at the U.S. Bureau 

of the Census.
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Table C1

Characteristics of Texas SNAP participants in linked data by match type

Individual characteristics

Directly linked 

to TX Admin data 

by own PIK

Indirectly linked to 

TX Admin data 

by other SNAP 

unit member PIK

Total SNAP 

participants

Sex

Male 41.6 52.0 44.0

Female 58.4 48.0 56.0

Total 100 100 100

Age

0-17 46.1 33.4 43.1

18-29 19.2 22.3 19.9

30-39 10.4 20.5 12.8

40-49 7.7 12.6 8.8

50-59 6.6 6.8 6.7

60-69 4.8 3.1 4.4

70+ 5.3 1.3 4.4

Total 100 100 100

Race

White 25.9 21.1 24.8

Black 19.1 14.4 18.0

Hispanic 52.8 62.7 55.1

Total 100 100 100

SNAP unit size

1 person 15.8 - 12.1

2 persons 14.0 11.4 13.5

3 persons 18.1 19.8 18.5

4 persons 21.2 24.2 21.9

5 or more persons 30.9 44.7 34.1

Total 100 100 100

Observations 33,209 10,070 43,279

Note: PIK = Protected Identification Key. SNAP = Supplemental Nutrition Assistance Program.

Source: ERS tabulations using the American Community Survey at the U.S. Bureau of the Census.
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Table C2

Characteristics of Texas SNAP participants in the linked data (weighted and unweighted) 

and in the Texas administrative data by PIK status

Individual characteristics

PIK’ed

TX Admin

with PIK as ID

Linked sample

by PIK

and SNAP 

participant

Linked sample, 

directly linked 

SNAP 

participant

Linked sample, 

indirectly linked 

SNAP 

participant

––—————— Weighted —————––— 

Sex

Male 43.4 44.5 41.9 52.4

Female 56.7 55.5 58.1 47.6

Total 100 100 100 100

Age

0-17 52.2 44.7 48.0 34.5

18-29 17.0 21.1 20.3 23.8

30-39 10.6 13.2 10.3 22.2

40-49 7.7 8.4 7.3 11.7

50-59 5.8 5.4 5.6 4.9

60-69 3.4 3.5 3.9 2.1

70+ 3.3 3.6 4.5 0.8

Total 100 100 100 100

Race

White 24.3 21.1 22.4 17.3

Black 21.1 19.1 20.4 15.1

Hispanic 51.8 57.8 55.0 66.1

Total 100 100 100 100

Case unit size

1 person 14.9 11.9 15.8 -

2 persons 16.6 12.6 13.1 11.0

3 persons 21.9 18.4 18.2 19.2

4 persons 21.1 22.4 21.5 24.9

5 or more persons 25.5 34.7 31.4 44.9

Total 100 100 100 100

Observations 4,769,893 4,469,342 3,362,490 1,106,852

Note: PIK = Protected Identification Key. SNAP = Supplemental Nutrition Assistance Program.

* These categories are not mutually exclusive. They are old definitions that we use here for comparison purposes across 

rows, but they should not be interpreted to sum to 100 within the column as the other variables do. 

Source: ERS tabulations of Texas Supplemental Nutrition Assistance Program administrative records at the U.S. Bureau of 

the Census and the American Community Survey.
41

SNAP Access at the State and County Levels: Evidence From Texas, ERR-156

Economic Research Service/USDA

Compared to the samples of PIK’ed individuals in the Texas data, the weighted linked Texas-ACS 

samples have about the same share of males (43.3 and 44.5 percent, respectively) and a lower share 

of children, age 0 to 17 (44.7 percent), compared to the Texas administrative data (52.2 percent). The 

weighted linked data have similar shares of Whites and Blacks (differing from the Texas data by 

no more than 3 percentage points across each group) and a higher share of Hispanics (57.8 percent) 

compared to the share of Hispanics in the Texas data (51.8 percent). The weighted linked data have 

a lower share of individuals in case units with fewer than four members and a higher share of individuals in SNAP units of four or more. 

Table C3 shows the distributions of the Texas administrative data and the weighted and unweighted 

linked data at the case unit level. The shares across both the weighted and unweighted linked 

data are similar to each other. The weighted linked data have a lower share of one-person units 

(33 percent), compared to 39 percent in the administrative data, and lower shares of two-member 

and three-member units. The weighted linked data have fewer units with children—58.7 percent, 

compared to 60.9 percent in the SNAP administrative data—and about the same share of units with 

at least one elderly member as do the administrative data.

Table C3

Case unit characteristics in the linked data and Texas administrative data, 2008-2009, 

by PIK status

All in Unit 

PIK’ed

TX Admin

Linked data, 

SNAP 

participants

Linked data, 

SNAP 

participants

Unweighted Weighted

Case unit size

1 person 39.1 33.2 33.0

2 persons 21.0 18.4 17.4

3 persons 17.6 16.8 17.1

4 persons 12.3 15.0 15.5

5 or more persons 10.1 16.6 17.1

Total 100 100 100

Units with children

No children 39.2 42.9 41.3

At least one child 0-17 years 60.9 57.1 58.7

Total 100 100 100

Units with elderly

No elderly 84.2 79.7 83.1

At least one elderly person, 60+ 15.8 20.3 16.9

Total 100 100 100

Observations 1,949,636 15,797 1,613,606

Note: PIK = Protected Identification Key.

Source: ERS tabulations of Texas Supplemental Nutrition Assistance Program administrative records at the U.S. Bureau 

of the Census.