Abstract

In 2003, about 56 percent of those eligible to participate in the Food Stamp Program actually participated. The participation rate varied substantially across States, ranging from a high of 83 percent in Oregon to a low of 43 percent

in Massachusetts. Using data for 2003 from the Food Stamp Program Quality Control and Current Population

Survey, this study examined factors that help to explain the variation. Results show that different population characteristics across States are a major factor because different types of eligible people tend to participate at different rates.

States with a higher share of households headed by elderly people had lower rates, while those with a higher share of

households without earnings and headed by nonelderly people had higher participation rates. Yet, substantial variation remained after “standardized” State participation rates were calculated that adjust for these compositional differences. Attempts to further explain these standardized rates by State policies and economic conditions were

unsuccessful, perhaps due to the limited sample size and imprecise measures of policies.

Sources of Variation in State-Level

Food Stamp Participation Rates

Contractor and Cooperator Report No. 37

March 2008

By Scott Cody, Allen Schirm, Elizabeth Stuart, and Laura Castner,

Mathematica Policy Research, Inc., and Alan Zaslavsky, Harvard University

This study was conducted by Mathematica Policy Research, Inc., under a

cooperative research contract with USDA’s Economic Research Service (ERS)

Food and Nutrition Assistance Research Program (FANRP): contract number

43-3AEM-3-80108 (ERS project representative: Kenneth Hanson). The views

expressed are those of the authors and not necessarily those of ERS or

USDA.

iii 

CONTENTS 

Chapter Page 

 EXECUTIVE SUMMARY........................................................................................... ix 

 I INTRODUCTION...........................................................................................................1 

A. THE FOOD STAMP PROGRAM...........................................................................3 

B. PARTICIPATION RATE STUDIES ......................................................................5 

C. RESEARCH OBJECTIVES AND OVERVIEW OF APPROACH........................7 

 II STANDARDIZED PARTICIPATION RATES...........................................................11 

A. PREVIOUS RESEARCH ......................................................................................12 

B. DATA AND METHODS ......................................................................................14 

1. Data ................................................................................................................14 

2. Predictors of FSP Participation......................................................................16 

3. The Logistic Model........................................................................................18 

4. Examples of the Standardization Process ......................................................23 

5. Shrinkage Estimation .....................................................................................26 

C. RESULTS ..............................................................................................................28 

1. Predictors of Participation..............................................................................29 

2. Initial Standardized State Participation Rates................................................30 

3. Household Characteristics Associated with Changes in Rates ......................35 

4. Shrinkage Standardized Participation Rates ..................................................42 

D. CONCLUSIONS....................................................................................................44 

 III UNDERSTANDING VARIATION IN STANDARDIZED STATE 

PARTICIPATION RATES...........................................................................................51 

A. POLICY AND ECONOMIC FACTORS THAT MAY INFLUENCE 

 PARTICIPATION RATES....................................................................................52 
iv 

CONTENTS (continued)

Chapter Page 

 III (continued)

1. FSP Policies ...................................................................................................57 

2. TANF Policies ...............................................................................................61 

3. Medicaid and SSI Policies .............................................................................67 

4. EITC and State Economy...............................................................................68 

B. VARIABLE REDUCTION ...................................................................................69 

1. FSP Accessibility ...........................................................................................70 

2. TANF Eligibility, Benefits, and Accessibility...............................................72 

3. TANF Work Requirements and Time Limits ................................................74 

4. Factor Scores..................................................................................................74 

C. ESTIMATION METHODS...................................................................................76 

D. RESULTS ..............................................................................................................77 

E. CONCLUSIONS....................................................................................................83 

 IV CONCLUSIONS...........................................................................................................87 

A. SUMMARY OF FINDINGS AND SUGGESTIONS FOR FUTURE 

RESEARCH..................................................................................................................87 

B. SUMMARY COMMENTARY.............................................................................89 

1. Interpreting State Participation Rates ............................................................89 

2. Increasing Participation Rates........................................................................90 

 REFERENCES..............................................................................................................91 

 APPENDIX A: VARIANCE ESTIMATION ...........................................................95 

 APPENDIX B: ALTERNATIVE APPROACHES TO ACCOUNTING 

 FOR TANF RECEIPT IN STANDARDIZING 

 PARTICIPATION RATES............................................................103 

 APPENDIX C: SENSITIVITY OF STATE LEVEL MODEL RESULTS 

 TO FACTOR SCORES .................................................................113 

 APPENDIX D: VALUES OF THE STATE POLICY VARIABLES.....................119 
v 

LIST OF TABLES 

Table Page 

II.1 COVARIATES USED IN HOUSEHOLD-LEVEL MODEL......................................17 

II.2 RATES FROM STANDARDIZED RATE EXAMPLE 1............................................24 

II.3 DISTRIBUTIONS FROM STANDARDIZED RATE EXAMPLE 2 ..........................25 

II.4 RATES FROM STANDARDIZED RATE EXAMPLE 2............................................26 

II.5 COEFFICIENT ESTIMATES, LOGISTIC MODEL OF FSP 

PARTICIPATION.........................................................................................................31 

II.6 DIRECT AND INITIAL STANDARDIZED 2003 FSP PARTICIPATION 

RATES ..........................................................................................................................32 

II.7 POSITIVE AND NEGATIVE COMPONENT EFFECTS ON THE DIFFERENCE 

BETWEEN EACH STATE’S DIRECT PARTICIPATION RATE AND THE 

NATIONAL PARTICIPATION RATE .......................................................................38 

 II.8 SHRINKAGE STANDARDIZED 2003 FSP PARTICIPATION RATES ..................43 

 III.1 STATE CHARACTERISTICS USED IN OTHER STUDIES ....................................54 

III.2 FSP POLICIES..............................................................................................................58 

III.3 TANF POLICIES..........................................................................................................63 

III.4 MEDICAID AND SSI POLICIES................................................................................68 

 III.5 STATE EITC AND ECONOMIC MEASURES ..........................................................69 

III.6 POLICIES CONSOLIDATED THROUGH PRINCIPAL COMPONENTS 

ANALYSIS...................................................................................................................71 

III.7 INITIAL COMPONENTS, FSP ACCESSIBILITY.....................................................71 

III.8 FACTOR LOADINGS, FSP ACCESSIBILITY ..........................................................72 

III.9 INITIAL COMPONENTS, TANF ELIGIBILITY, BENEFITS AND 

ACCESSIBILITY .........................................................................................................73 

 III.10 FACTOR LOADINGS, TANF ELIGIBILITY, BENEFITS AND 

ACCESSIBILITY .........................................................................................................74 
vi 

TABLES (continued)

Table Page 

 III.11 INITIAL COMPONENTS, TANF WORK REQUIREMENT AND TIME LIMIT 

POLICIES .....................................................................................................................76 

III.12 FACTOR LOADINGS, TANF WORK REQUIREMENT AND TIME LIMIT 

POLICIES .....................................................................................................................76 

III.13 ALTERNATIVE MODEL SPECIFICATIONS...........................................................78 

 III.14 RESULTS FROM MODELS OF STANDARDIZED PARTICIPATION RATES 

USING ALTERNATIVE APPROACHES TO WEIGHTING 

(MODELS 1, 2 AND 3)................................................................................................79 

 III.15 RESULTS FROM MODELS OF DIRECT PARTICIPATION RATES 

(MODELS 4 AND 5)....................................................................................................82 

 III.16 RESULTS FROM SIMPLIFIED MODEL (MODEL 6)..............................................83 
vii 

LIST OF FIGURES 

Figure Page 

II.1 DIRECT AND INITIAL STANDARDIZED 2003 FSP PARTICIPATION 

RATES WITH 90 PERCENT CONFIDENCE INTERVALS FOR 

STANDARDIZED RATES ..........................................................................................33 

II.2 DIRECT AND INITIAL STANDARDIZED 2003 FSP PARTICIPATION 

RATES WITH 90 PERCENT CONFIDENCE INTERVALS FOR BOTH 

RATES ..........................................................................................................................34

II.3 RELATIVE EFFECTS OF COMPOSITIONAL FACTORS ON THE 

DIFFERENCE BETWEEN EACH STATE’S DIRECT PARTICIPATION 

RATE AND THE NATIONAL PARTICIPATION RATE..........................................40

II.4 INITIAL AND SHRINKAGE STANDARDIZED 2003 FSP PARTICIPATION 

RATES WITH 90 PERCENT CONFIDENCE INTERVALS FOR BOTH RATES...45

II.5 COMPARISON OF STATE PARTICIPATION RATE RANKINGS OFFICIAL 

VERSUS SHRINKAGE STANDARDIZED PARTICIPATION RATES...................46 

 

ix 

EXECUTIVE SUMMARY 

The Food Stamp Program (FSP), the nation’s largest food assistance program, is intended to 

provide a nutritional safety net for the millions of Americans who cannot afford to buy healthy 

food, or enough healthy food, for themselves and their families. For about 25 years, the extent to 

which the program meets this goal has been measured by the national “participation rate” – the 

rate at which FSP-eligible people actually participate in the program. More recently, USDA has 

developed estimates of state-level participation rates. These rates vary substantially from state to 

state, leading policymakers and researchers to wonder why proportionately more eligible 

participate in some states than other states. 

In 2003, while 56 percent of the nation’s eligible population participated in the FSP, 83 

percent of eligibles in Oregon participated, compared with only 43 percent of eligibles in 

Massachusetts. The rates in 22 states were higher than the national rate, and in 16 states, were 

significantly lower. 

This variation is likely to be a function of two factors: the composition of the FSP-eligible 

population in any given state and the characteristics unique to any given state—specifically, its 

policy decisions and economic conditions. We know from studies of national participation rates 

that some groups of eligibles (e.g., childless adults with earnings) are less likely to participate 

than others (e.g., single adults with children). A state may therefore have a low participation rate 

primarily because it has proportionately more childless adults with earnings and/or 

proportionately fewer single adults with children among its eligible population. 

State policies governing the administration of the FSP and other means-tested programs may 

affect the rate at which eligible individuals participate. For example, policies that affect the 

burden associated with participation, such as income reporting requirements and certification 
x 

periods, may affect an eligible individual’s willingness to participate. In addition, given that 

many households enroll in the FSP because they are also enrolling in other assistance programs, 

such as Temporary Assistance for Needy Families (TANF) and Medicaid, a state’s eligibility 

rules for those programs can affect FSP participation rates. Finally, state economic conditions 

may also affect state participation rates. 

In this study, we seek to determine why participation rates vary. We used a two-step 

process to examine several sources of variation in state FSP participation rates. First, we 

estimated “standardized” state participation rates, which adjust for the effects of compositional 

differences across states. Second, we estimated a state-level regression to determine the degree 

to which the remaining variation in the rates can be explained by state policies and economic 

conditions. 

STEP 1: STANDARDIZED PARTICIPATION RATES 

The standardization process, which is based on the difference between the composition of 

each state’s FSP-eligible population and national FSP-eligible population, removes the effects of 

state-to-state differences in the composition of the FSP-eligible population. In other words, the 

process allows us to estimate the FSP participation rate a state would have if its eligible 

population were the same as the national eligible population. For example, given that the elderly 

participate in the FSP at a low rate, what would the participation rate be in Florida, a state with a 

high proportion of elderly residents, if it had the same proportion of elderly residents as the 

nation? 

To compute the standardized state FSP participation rates, we used the 2003 data on the 

number of participants and the number of eligible households that is used to compute the 

national and official state FSP participation rates (Current Population Survey data for estimates 
xi 

of eligible individuals and FSP administrative data for estimates of participants). We used a 

model that estimates the degree to which participation in the program is correlated with various 

household characteristics, including household composition—such as household size and the 

presence of children and elderly people—earnings as a percentage of the poverty line, and the 

size of the food stamp benefit for which the household would be eligible. We then used the 

coefficients from the model to adjust each state’s participation rate on the basis of the 

composition of that state’s FSP-eligible population relative to the national eligible population. 

Figure 1 presents the standardized state FSP participation rates compared with the “direct” 

(that is, unstandardized) participation rate in each state. To reflect the uncertainty with which 

these rates are estimated, the figure also shows 90 percent confidence intervals. The difference 

between the standardized and the direct participation rates reflects the difference between the 

composition of the state and the national eligible populations. A standardized rate that is higher

than the direct rate means that the state’s eligible population is composed of households less

likely to participate in the FSP. A standardized rate that is lower than the direct rate means that 

the state’s eligible population consists of households more likely to participate in the FSP. 

In 28 states, the standardized participation rate is higher than the direct rate, while the 

reverse is true for the remaining 22 states and the District of Columbia. The seven states in which 

the standardized rate falls farthest above the direct rate include Oregon (19 percentage points), 

Maine (17 percentage points), Hawaii and West Virginia (12 percentage points), Nevada and 

Florida (11 percentage points), and Missouri (10 percentage points). These states also tend to 

have have the highest standardized rates. The five states in which the standardized rate falls 

farthest below the direct rate include Alabama (10 percentage points), Michigan (6 percentage 

points), and Rhode Island, Alaska, Mississippi, Minnesota, Mississippi, the District of Columbia, 

and South Carolina (5 percentage points). 
xii 

FIGURE 1 

DIRECT AND INITIAL STANDARDIZED 2003 FSP PARTICIPATION RATES 

10%20%30%40%50%60%70%80%90%

100%110%

Missouri

Oregon

Tennessee

Hawaii

District of Columbia

Oklahoma

Maine

Kentucky

Mississippi

Georgia

Louisiana

South Carolina

Ohio

Michigan

Arizona

West Virginia

Indiana

Minnesota

Alaska

Vermont

Illinois

Nebraska

Arkansas

North Dakota

Iowa

Delaware

South Dakota

Idaho

Alabama

Pennsylvania

Connecticut

Wisconsin

New Mexico

Kansas

Rhode Island

Virginia

Washington

Utah

New York

New Hampshire

Wyoming

Florida

New Jersey

Direct Participation Rate (90 PeConf. Interval)Initial Standardized Participatio(90 Percent Conf. Interval)
xiii

To understand how household characteristics influence the variation in state participation 

rates, we decomposed the difference between each state’s direct participation rate and the 

national participation rate (Figure 2). Factors that push a state’s direct above the national rate are 

shown to the right of the line in the middle of the chart, and factors that push a state’s direct rate 

below the national rate are shown to the left. The difference in the total “length” to the left and 

to the right of the line indicates the general size of the difference between the direct and national 

rates for each state. 

In all states, the two most influential household characteristics include the presence of 

elderly individuals (without children present), and the fact that a household has zero earnings. 

Because the elderly are less likely to participate, states with larger proportions of this population 

have higher standardized participation rates, all else being equal. Because households without 

earnings are more likely to participate, states with larger proportions of this population will have 

lower standardized participation rates, all else being equal. Other characteristics that have a 

relatively large impact on standardized participation rates include the proportion of households 

with earnings between one and 50 percent of poverty, the proportion of households with earnings 

from 51 to 100 percent of poverty, the proportion of child-only households, the proportion of 

households in which children or elderly people receive SSI benefits, and the proportion of 

households headed by an African American. 

Of all states, Missouri has the highest direct participation rate. This high direct participation 

rate is driven by its “fixed effect.” That is, there are factors other than the state’s composition 

that explain Missouri’s high direct participation rate. In addition to the fixed effect, the state’s 

population with no earnings is proportionately larger than it is in the nation as a whole, and this 

contributes to the state’s high direct participation rate because these individuals participate at 

high rates. 
xiv

FIGURE 2 

RELATIVE EFFECTS OF COMPOSITIONAL FACTORS ON THE DIFFERENCE BETWEEN EACH STATE’S 

DIRECT PARTICIPATION RATE AND THE NATIONAL PARTICIPATION RATE 

Note: The figure depicts the composition differences (or fixed effects) that are generally driving the differences 

between the direct and standardized rates rather than the actual value of the difference between the direct 

and standardized rates. The direct and standardized rates are available in Table II.6 

Massachusetts

Nevada

Montana

California

North Carolina

Colorado

Texas

Maryland

New Jersey

Florida

Wyoming

New Hampshire

New York

Utah

Washington

Virginia

Rhode Island

Kansas

New Mexico

Wisconsin

Connecticut

Pennsylvania

Alabama

Idaho

South Dakota

Delaware

Iowa

North Dakota

Arkansas

Nebraska

Illinois

Vermont

Alaska

Minnesota

Indiana

West Virginia

Arizona

Michigan

Ohio

South Carolina

Louisiana

Georgia

Mississippi

Kentucky

Maine

Oklahoma

District of Columbia

Hawaii

Tennessee

Oregon

Missouri

Elderly, No Children Child-Only No Earnings Earnings 1-50% Earnings 51-100% State Fixed Effect Other Other Negative

Direct Rate Lower Direct Rate Higher

National Rate
xv

At the other end of the spectrum is Massachusetts, which has the lowest direct participation 

rate. This is driven by two factors: the age and the poverty level of the FSP-eligible population. 

The state’s elderly population is a proportionately larger than the national elderly population, and 

its population of individuals from one to 50 percent of poverty, and from 51 to 100 percent of 

poverty is proportionally smaller than the percent of individuals in poverty in the national 

population. 

The fact that, in many states, the fixed effect has the strongest influence on the difference 

between the direct and the national participation rate suggests that the highest direct participation 

rates are not due to the composition of those states’ eligible populations but to other factors in 

those states. That is, if the FPS-eligible population in every state were composed of the same 

groups of individuals, the participation rates in these states would still be higher. 

Some states have offsetting compositional characteristics. For example, in Connecticut, the 

factors that would pull the participation rate up—including a population of individuals with zero 

earnings that is proportionately larger than the national population with zero earnings and a 

population of child-only household that is proportionately larger than the national population of 

child-only households—are canceled out by the state’s relatively large elderly population and 

relatively small populations of individuals from one to 50 percent and from 51 to 100 percent of 

poverty.

STEP 2: UNDERSTANDING VARIATION IN STANDARDIZED PARTICIPATION 

RATES 

The standardization process did not eliminate the variation in participation rates; 2003 

standardized participation rates still vary from state to state. This may reflect the influence of 

each state’s economic and policy conditions on participation rates. To examine the relationship 

between the standardized rate and aggregate state characteristics, we estimated a model in which 
xvi

the aggregate state characteristics include measures of state FSP policies (certification periods, 

reporting requirements, accessibility ratings, etc.), TANF policies (transitional FSP benefits, 

earned income disregards, work requirements, and other policies), Medicaid policies, and 

Supplemental Security Income (SSI) policies. The characteristics also include measures of the 

state’s economy, such as the unemployment rate, employment growth, and wages. 

The results from the model suggest that most of the variation is not explained by aggregate 

state characteristics. In other words, standardized participation rates are not, for the most part, 

correlated with most of the FSP and TANF policies we looked at. While there may be a 

relationship between a state’s standardized participation rate and its policies on change reporting 

and program access, the evidence for such a relationship is weak. Moreover, the rates do not 

appear to be correlated with state economic conditions, which could influence an eligible 

individual’s expectations of their future income. 

While the results of this analysis fail to show a strong relationship between participation 

rates and policy/economic characteristics, it does not seem plausible that the variation in 

standardized rates could be explained by random factors alone. On the contrary, one would 

expect that policy and economic conditions would have some influence on the rate at which 

eligible people participate. The failure to identify significant relationships could be explained by 

several factors. First, the policy variables used in the analysis may not accurately reflect the 

policies that are actually in place. It is possible that two states that have adopted the same policy 

may implement it differently, or that the same policy may be implemented differently in different 

areas a state. In addition, sampling error in the participation rate estimates could mask 

meaningful variation in the true state participation rates. Finally, the small number of 

observations may obscure the individual effects of various policy and economic factors that may 

influence state participation rates. Thus, while the results of this analysis suggest that state 
xvii

participation rates vary apart from state policy and economic conditions, states are still likely to 

have some control over their participation rates through the policies and the economic strategies 

they establish. 

CONCLUSION 

As USDA works to increase FSP participation rates, we need to understand why participation 

rates are higher in some states than in others. Part of these differences can be explained by 

differences in the composition of each state’s eligible population. In particular, states whose 

eligible populations contain higher proportions of households with no earnings and households 

headed by African Americans would have higher participation rates, all else being equal, because 

these populations participate at higher rates. Likewise, states whose eligible populations consist 

of higher proportions of elderly individuals tend to have lower participation rates, all else being 

equal, because elderly individuals participate at lower rates. 

However, the variation in state participation rates does not decrease once we account for the 

differences in the compositional differences of the state populations. The standardized 

participation rates vary as much from state to state as the unstandardized rates, with the 

standardized rates ranging from 43 percent in California and Wyoming to 89 percent in Oregon. 

After examining how much of this remaining variation is due to state policies and economic 

conditions, we find that much of this variation remains unexplained. It may be that our measures 

of policies or participation rates are too imprecise to capture the relationships between policies, 

economic conditions, and FSP participation rates. It may also be that limited degrees of freedom 

constrain the model’s ability to identify policy and economic effects. 
1

I. INTRODUCTION 

The Food Stamp Program (FSP) is a safety net for millions of people every month, 

providing eligible low-income households with resources to obtain a more nutritious diet. It is 

the nation’s largest food assistance program in terms of dollars spent. In fiscal year 2005, the 

program provided nearly $29 billion in benefits to a caseload that averaged 26 million people 

each month. Eligibility for the program is based largely on financial need—individuals must 

have income and assets below specified eligibility levels. The income levels and subsequent 

benefit calculations are set at the federal level, with little state-to-state variation, though the asset 

criteria do vary substantially across states. 

The FSP participation rate reflects the percentage of eligible people that actually participate 

in the program. The participation rate has been a standard for assessing the performance of the 

program for about 25 years. In 2003, 56 percent of the nation’s eligible population participated 

(Cunnyngham 2005). However, participation rates vary by state. In 2003, 83 percent of 

eligibles in Oregon participated, compared with only 43 percent of eligibles in Massachusetts 

(Castner and Schirm 2005). Twenty-two states had rates that were higher than the national 

participation rate and 16 states had rates that were significantly lower. 

The goal of the U.S. Department of Agriculture, as expressed through performance targets in 

its fiscal year 2007 budget request, is to reach 68 percent of the eligible population by 2010. 

States will play a key role in helping raise participation rates. Although most program rules are 

set at the federal level, state and local governments share responsibility for administering the 

FSP, and thus share responsibility for increasing participation among the eligible population. 

For states to develop strategies to increase participation and target outreach efforts, they need to 

understand more about what causes the rates to vary across states. 
2

Unfortunately, little is known about the sources of variation in state participation rates. We 

speculate that the variation is likely a function of two types of factors. First, the composition of 

the FSP eligible population in each state could affect its participation rate. We know from 

studies of national participation rates that some groups of eligibles (e.g., childless adults with 

earnings) are less likely to participate than others (e.g., single adults with children). Thus, a state 

may have a low participation rate primarily because it has proportionately more childless adults 

with earnings and/or proportionately fewer single adults with children among its eligible 

population. In other words, we might expect two different states to have different participation 

rates simply because different types of people comprise their respective eligible populations. 

Second, state participation rates may be influenced by state characteristics, specifically 

policy decisions and economic conditions. State policies governing the administration of the 

FSP and other means-tested programs may affect the rate at which eligible individuals 

participate. For example, policies that affect the burden associated with participation, such as 

income reporting requirements and certification periods, may affect an eligible individual’s 

willingness to participate. In addition, given that many households enroll in the FSP because 

they are also enrolling in other assistance programs, such as Temporary Assistance for Needy 

Families (TANF) and Medicaid, a state’s eligibility rules for those programs can affect FSP 

participation rates. In addition, state economic conditions may also affect state participation 

rates. A low-income individual may be more inclined to participate in the FSP if the state 

economy is in recession and the individual senses little hope of increased income in the near term 

than if the state is experiencing an economic boom and the individual sees his or her current 

status as only temporary. 
3

In this report, we seek to determine why participation rates vary. We examine several 

sources of variation in state FSP participation rates using a two-step process. First, we estimate 

“standardized” state participation rates that adjust for the effects of compositional differences 

across states. These standardized rates provide an estimate of what state participation rates 

would be if each state’s eligible population resembled the nation as a whole. In the second step, 

we estimate a state-level regression to determine the degree to which the remaining variation in 

the rates can be explained by state economic circumstances and program policies. 

In the remainder of this chapter, we provide an overview of the FSP, discuss the estimation 

of the existing national and state-level participation rates, and present this study’s research 

objectives. In Chapter II, we discuss the standardized participation rates, the methodology used 

in their estimation, and the policy implications of the rates. In Chapter III, we examine the state 

variation in the standardized rates. Finally, in Chapter IV, we present our conclusions and 

suggestions for future research. 

A. THE FOOD STAMP PROGRAM 

The Food Stamp Act of 1977, as amended, establishes the eligibility criteria for the FSP: 

• FSP Household Definition. Under FSP rules, the FSP household is composed of the 

individuals in the same residential unit who purchase and prepare food together. In 

addition, spouses must apply together, and children under age 22 must file with their 

parents.

• Gross Income Screen. The total income of the FSP household members must be at 

or below 130 percent of the federal poverty guidelines ($1,961 for a family of four in 

the continental United States in fiscal year 2003). Households with elderly or 

disabled members are not subject to the gross income screen.

• Net Income Screen. The FSP allows income deductions for certain household 

expenses related to work, dependent care, medical needs, child support, and shelter. 

After the allowable expenses are deducted from gross income, the resulting net 

income must be at or below 100 percent of the federal poverty guidelines ($1,509 for 

a family of four in the continental United States in fiscal year 2003).
4

• Asset Screen. Households may hold up to $2,000 in countable assets (or $3,000 in 

countable assets if at least one member is elderly or disabled). Countable assets 

include cash and assets that are easily converted to cash. In addition, the values of 

certain vehicles may be counted as assets. Some types of property, such as family 

homes, are not counted.

Certain households are considered categorically eligible for the FSP, and therefore are not 

subject to any of the income or asset screens. FSP households in which all members receive 

Supplemental Security Income (SSI) or cash benefits through TANF are categorically eligible for 

the FSP. In addition, some states have expanded categorical eligibility rules, wherein categorical 

eligibility is conferred based on the receipt of in-kind benefits from federally funded TANF 

programs. 

Other individuals are categorically ineligible for the FSP, and therefore are not eligible to 

participate even if they meet the income and asset requirements. Most legal noncitizens who 

have lived in the United States for under five years are not eligible. In addition, nondisabled 

nonelderly adults living in households without children face time limits unless they work, 

participate in work-related activities (such as work, employment training, or job search), or meet 

one of several conditions that limit their ability or availability to work. 

Once a household is authorized to participate, the monthly benefit is calculated based on a 

federal formula, with very little state variation. In 2003, most states provided benefits through an 

electronic benefit transfer (EBT) system. Benefits may be used at most grocery stores and many 

farmers’ markets, and must be used to purchase food items only. Once participating, individuals 

must periodically recertify for benefits—a process similar to the initial application process. The 

certification period varies according to characteristics of the applicant, but generally ranges from 

3 to 24 months. 
5

B. PARTICIPATION RATE STUDIES 

Participation rates measure the FSP program’s effectiveness at reaching those who are 

eligible. Although rates for the FSP have been measured for the past 25 years, they became 

particularly important after the enactment of the Personal Responsibility and Work Opportunity 

Reconciliation Act of 1996, which disqualified most noncitizens and placed time limits on many 

nondisabled nonelderly childless adults. The rates were used to determine how many individuals 

lost eligibility and how participation patterns changed among those who remained eligible, 

allowing policymakers to identify the groups most impacted by FSP and other assistance 

program changes. Currently, studies of participation rates help researchers identify specific 

groups of people for further analysis and help policymakers target outreach efforts. 

National Participation Rates. Participation rates for the nation and key subgroups shed 

light on how participation varies by the demographic and economic characteristics of 

households. For example, in 2003, 56 percent of the total eligible population participated. 

However, 74 percent of eligible children participated, while 28 percent of eligible elderly 

individuals participated (Cunnyngham 2005). Similarly, 62 percent of individuals in households 

with no earnings participated, while 47 percent of individuals in households with earnings 

participated. 

These national rates are derived directly from Current Population Survey (CPS) data, FSP 

Statistical Summary of Operations data, and Food Stamp Program Quality Control (FSPQC) 

data. The estimate of the number eligible is derived from a model that applies the FSP eligibility 

rules to the households in the Annual Social and Economic Supplement to the CPS.1

 The 

 1

 The CPS is a survey of approximately 77,000 households providing income and demographic information. 

The eligibility model distributes annual income amounts across months according to patterns observed in other data 

sources, determines the outcome of the gross income test, and then imputes both net income and the outcome of the 

asset test based on a variety of household characteristics (Cunnyngham 2005). 
6

Statistical Summary of Operations data provide counts of individuals and households that were 

issued benefits as well as the total dollar value of the benefits in each month. The FSPQC data 

can be used to estimate the distribution of participants across subgroups.2 The participation rates 

for 2003 do not include people who would fail the program’s income tests but attain eligibility 

through participation in noncash public assistance programs (through the expanded categorical 

eligibility rules). These individuals have been excluded from both the estimates of the number 

eligible and the number participating. 

State Participation Rates. While participation rates at the national level tell us how 

participation varies according to economic and demographic characteristics of the eligible 

population, participation rates at the state level tell us how participation varies according to 

geographic divisions. Castner and Schirm (2005) find substantial variation in participation rates 

from state to state. Although the participation rate for the nation was 56 percent in 2003, 38 

states had rates that were significantly different from the national rate (22 were higher and 16 

were lower). 

The procedure used to estimate official state participation rates builds on the estimates of 

eligibles used for the national participation rates (Castner and Schirm 2006). However, because 

the CPS has very small sample sizes for most states, direct estimates of the number eligible in 

each state are imprecise (e.g., for Arkansas, a state with a typical level of imprecision, one could 

only be confident––at a 90 percent level of confidence––from the direct estimate that the 

participation rate in 2003 was between 50 and 64 percent). To improve precision, Castner and 

Schirm used empirical Bayes shrinkage methods to estimate state participation rates indirectly. 

 2

 The FSPQC sample data are collected by the U.S. Department of Agriculture to assist in determining issuance 

error rates. Included on the file are monthly-level income and demographic variables for all individuals in the 

sampled FSP household. 
7

“Shrinkage” estimates of participation rates are derived by “borrowing strength,” that is, by 

using data from other states or time periods. The assumption underlying shrinkage estimation 

for Arkansas in 2003, for example, is that what happened in other states in 2003 as well as what 

happened in Arkansas and other states in the past is relevant to what happened in Arkansas in 

2003. 

To construct the state participation rates for 2003, Castner and Schirm applied a four-step 

process: 

1. From CPS data and FSP administrative data, derive direct sample estimates of state 

food stamp participation rates for both 2002 and 2003. 

2. Using a regression model, predict state food stamp participation rates based on 

administrative and decennial census data. 

3. Using “shrinkage” methods, average the direct sample estimates and regression 

predictions to obtain preliminary shrinkage estimates of state food stamp 

participation rates. 

4. Adjust the preliminary shrinkage estimates to align the state totals of eligible 

individuals with the national estimate of eligible individuals to obtain final shrinkage 

estimates of state food stamp participation rates. 

On average, this four-step process improved the uncertainty by reducing the interval around 

the direct estimate to approximately three-fifths of its original size (e.g., in Arkansas, the interval 

of uncertainty was reduced so that the chances were 90 in 100 that the true rate was between 58 

and 66 percent). 

C. RESEARCH OBJECTIVES AND OVERVIEW OF APPROACH 

The central objective of this report is to determine why state FSP participation rates vary. 

The overarching research questions addressed are: 

• To what extent is the variation in state participation rates explained by differences in 

the composition of the FSP-eligible population in each state? 
8

• How would states compare in terms of participation rates if we controlled for those 

factors beyond a state’s control? 

• To what extent is the variation explained by differences in state FSP, TANF, and 

Medicaid policies? 

• To what extent is the variation explained by differences in state economic conditions? 

Ultimately, we seek to provide federal and state policymakers with a clearer understanding 

of why participation rates vary as well as a method for comparing state participation rates in a 

way that controls for factors beyond each state’s control. 

To achieve these objectives, we employ a two-stage analysis. In the first stage, we estimate 

standardized state FSP participation rates. The standardization process is intended to make 

participation rates comparable across states by estimating what each state’s participation rate 

would be if its FSP-eligible population resembled that of the nation as a whole. If differences in 

state participation rates were driven entirely by differences in state composition, then we would 

expect the standardized rates to be the same across states. Any variation in the standardized rates 

may therefore be explained by other factors specific to each state. The second stage of the 

analysis is a state-level model examining the degree to which differences in the standardized 

participation rates are explained by differences in state policy and economic characteristics.3

Our results suggest that differences in the composition of state eligible populations drives 

some of the variation in state participation rates. Key compositional factors include the 

proportion of the eligible population that is elderly and the proportion that has zero earnings. 

 3

 An alternative to the two-stage analysis conducted in this study would be to estimate a single hierarchical 

model that includes household and state components. In a hierarchical model, measures of the effects of state-level 

characteristics could be estimated with more precision. While such a model would be feasible, we adopt the twostage approach for two reasons. First, a hierarchical model that does not explicitly control for the composition of 

each state can confound the within-state and between-state effects. With only 51 states, a hierarchical model in this 

context would not have sufficient degrees of freedom to adequately control for state composition. Second, the data 

used in this study are clustered and weighted (that is, they are not independent and identically distributed), and 

estimating hierarchical models with such data introduces significant complexities into the estimation procedures and 

analysis. 
9

Our results also show that the variation in the standardized rates is uncorrelated with state policy 

and economic characteristics. This may suggest that state participation rates are driven by 

factors other than policy and economic conditions. However, it may also suggest that aggregate 

measures of these state characteristics do not capture the meaningful variation in these factors at 

the local level. 

11

II. STANDARDIZED PARTICIPATION RATES 

Differences in state FSP participation rates may result from differences in the types of 

households eligible for food stamps in each state or state policies and economic conditions. In 

this chapter we attempt to remove the first source of variation by answering the question, “To 

what extent do differences in the characteristics of a state’s eligible population relative to the 

national eligible population lead to differences in the state’s participation rate relative to the 

national participation rate?” To do this, we standardize the state participation rates based on the 

difference between the composition of each state’s FSP-eligible population and the national FSP 

eligible population. The standardized rates enable us to compare participation rates across states 

without any differences that result from the observed characteristics of eligible individuals in the 

state. 

Section A of this chapter examines previous research on standardization approaches that 

control for compositional differences as well as predictors of participation in the food stamp 

program. Section B describes the methods used to generate the standardized rates, including the 

data and statistical models. In Section C, we present the standardized rates and divide the 

differences in the direct (that is, unstandardized) and standardized rates into the components due 

to each of the household characteristics for which we standardize. Finally, Section D discusses 

policy implications of the standardized rates, especially as they relate to how well states enroll 

eligible individuals and lessons for how to increase participation rates. Chapter III uses these 

standardized rates to investigate the relationship between participation and state policies and 

economic conditions. 
12

A. PREVIOUS RESEARCH 

The idea of generating some measure of performance that removes compositional 

differences, as our standardized rates do, is not new. This idea has recently been applied in 

studies of health care quality, where the aim is to account for differences in patient 

characteristics that may also affect the outcome of interest when comparing physicians or 

hospitals. Similar issues also arise in education, where researchers are concerned with the 

students’ background characteristics when trying to compare schools’ performance. 

In health care, the standardization is often called “case-mix adjustment.” Case-mix 

adjustment is commonly done to account for differences in the patient characteristics that 

particular doctors or hospitals serve (Goldstein and Spiegelhalter 1996, Christiansen and Morris 

1997). For example, when comparing the mortality rates at different hospitals, the health of the 

patients when they enter the hospital must be taken into account—hospitals that attract many 

critically ill patients may have higher mortality rates than other hospitals, regardless of the 

quality of care those patients receive. These ideas are also used in payment structures that pay 

hospitals (or physicians) amounts that reflect the types of patients they treat. 

Zaslavsky (2001) describes a case-mix adjustment model similar to that used in this report. 

Zaslavsky models the predicted value of some outcome of interest (e.g., mortality) as the sum of 

two components: (1) that due to observed patient characteristics, and (2) that due to the unit 

(e.g., a hospital) at which the patient is treated. As described in more detail below, we use the 

same approach here, modeling FSP participation as a function of household characteristics as 

well as the state in which a household resides. Zaslavsky (2001) also discusses some of the 

statistical issues in using case-mix adjustment methods. 
13

Several studies have examined which factors are correlated with high or low FSP 

participation rates—factors that are likely important when standardizing participation rates. 

According to Cunnyngham (2005), while overall, 56 percent of eligible individuals participated 

in the FSP, the participation rate varies considerably across different types of individuals. Over 

75 percent of eligible children, individuals in the poorest households, and TANF recipients 

participated in the FSP program. In contrast, fewer than one-third of eligible elderly individuals 

and fewer than one-half of eligible individuals in households with earnings participated in the 

FSP. Eligible noncitizens and nondisabled childless adults subject to work registration were also 

relatively less likely to participate in the FSP. 

Other studies have found similar patterns of participation. Gleason et al. (1998) provide a 

review of the literature on FSP participation, finding results generally consistent with 

Cunnyngham (2005). Gleason et al. report that FSP participation rates are "highest among 

nonwhite and nonelderly people, and people living in households that: are low income, include 

children, do not own their own home, are eligible for the highest FSP benefits, have a household 

head that is not well educated, [or] include members who participate in other welfare programs 

such as [Aid to Families with Dependent Children (AFDC)] or Medicaid." 

Farrell et al. (2003) examine in more detail how FSP participation is related to monthly 

income and earnings. They find that many nonparticipating households have had a short-term 

drop in income while other nonparticipating households have low income over a longer time 

period. Farrell et al. conclude that households with long-term low income may not participate in 

the FSP because the same factors that limit income (e.g., low literacy or disability) may also 

hinder their FSP participation. In contrast, nonparticipating households with a short-term drop in 
14

income may not participate in the FSP because those households may believe their drop in 

income is temporary.4

 

B. DATA AND METHODS 

Our approach to standardizing state FSP participation rates involves three steps. First, we 

use a logistic regression model to predict FSP participation among eligible households as a 

function of household characteristics. The coefficients of that model reflect the likelihood that 

eligible households of a given type will participate in the FSP, controlling for other household 

characteristics. Second, using the estimated coefficients for each household characteristic, 

combined with estimates of how each state differs from the nation as a whole with respect to that 

characteristic, we compute initial standardized participation rates. Third, we use shrinkage 

estimation techniques to derive standardized state participation rates that are comparable to the 

official FSP participation rates. 

This section describes the data and estimation techniques used to compute standardized FSP 

participation rates. We first describe the data sources and then discuss the predictors of FSP 

participation our model incorporates. We then discuss the logistic model used to estimate the 

relationship between individual and household characteristics and FSP participation. Finally, we 

describe the steps used to standardize the state FSP participation rates using the results of the 

logistic model. 

1. Data 

To compute the standardized state FSP participation rates, we use the same data from 2003 

on the number of participants and the number of eligible households that is used to compute the 

 4

 Chapter III provides additional details on studies of the determinants of FSP participation. 
15

national and official state FSP participation rates (Cunnyngham 2005; Castner and Schirm 

2005).5

 Using CPS data, we simulate eligibles by comparing the income and other 

characteristics of each household with the FSP rules. Because the CPS does not capture all of 

the information needed to determine whether each sampled household would be eligible for the 

FSP, we estimate the probability that each household is eligible for the FSP, rather than 

classifying a household as eligible or ineligible. Additional details about these methods can be 

found in Cunnyngham (2005). 

The number of participating individuals is derived from the FSPQC. Although the CPS 

inquires about FSP participation, there is substantial underreporting of program participation. 

The FSPQC is collected primarily to monitor FSP payment errors in each state. The data include 

a monthly random sample of active food stamp cases and contain detailed information on the 

income and characteristics of each household in the sample. The FSPQC sample is 

representative at the state level and is weighted to state population counts of program participants 

each month. 

To standardize the state participation rates, we use the same two data sources. That is, we 

use CPS data to estimate the number of eligible households and FSPQC data for the number of 

participants. In order to estimate the logistic regression in a way that utilizes both data sets, we 

estimate the model using a “concatenated data set” that contains all observations of eligible 

households from the CPS and all observations from the FSPQC.6

 The model is estimated using 

variables that are similarly defined in both data sets, plus an additional variable indicating the 

data source (FSPQC or CPS). 

 5

 The data for 2003 was the latest set of data available while this study was conducted. 

6

 To include all observations of eligible households from the CPS, we include all households with a predicted 

probability of food stamp eligibility greater than zero. 
16

One requirement of using this approach of concatenating the two datasets is that the 

populations represented by the CPS and FSPQC need to be the same, other than the fact that the 

FSPQC contains just food stamp participants. The two files need to cover the same geographic 

area, time period, demographic groups, etc. This requirement is also the case when obtaining the 

“official” FSP participation rates that use the FSPQC and CPS (Cunnyngham 2005). Although 

we believe that the two data sources cover the same population overall, as we discuss further in 

Appendix B, there may be subtle differences in the coverage of particular types of households. 

2. Predictors of FSP Participation 

To standardize the state participation rates, we employ a model that estimates FSP 

participation as a function of various household characteristics. Our initial list of covariates 

(household characteristics) was developed after reviewing previous literature on factors 

associated with variation in FSP participation (Kornfeld 2002, Gleason et al. 1998) as well as by 

examining national participation rates (Cunnyngham 2005) to determine which characteristics 

have been associated with large differences in FSP participation rates. 

The set of variables selected for the model of household-level FSP participation includes 

household composition—such as the presence of children or elderly household size—earnings as 

a percentage of the poverty line, and the size of the food stamp benefit for which the household 

would be eligible (expressed as a percentage of the maximum benefit). The full list of variables 

is shown in Table II.1. 

Although TANF is highly predictive of food stamp participation (Cunnyngham 2005), we do 

not include information on TANF receipt in the model for two reasons. First, on a practical 

level, TANF eligibility—and thus TANF participation—varies across states, with states having 

 
17

TABLE II.1 

COVARIATES USED IN HOUSEHOLD-LEVEL MODEL 

General Household Type 

Single Nonelderly Adult (Age 18-59) And One Or More Children, With At Least One Child Age 0-4 

Single Nonelderly Adult And One Or More Children, With No Children Age 0-4 

Multiple Nonelderly Adults And One Or More Children, With At Least One Child Age 0-4 

Multiple Nonelderly Adults And One Or More Children, With No Children Age 0-4 

One Or More Nonelderly Adults, Without Children Or Elderly 

One Or More Elderly, Without Children Or Nonelderly Adults 

One Or More Elderly, With Children Or Nonelderly Adults 

Children Only 

Gender Mix Of Nonelderly Adults In Household 

No Nonelderly Adults In Household 

All Male 

All Female 

Mixed 

Household Size 

Race/Ethnicity Of Household Head 

Hispanic 

Black Non-Hispanic 

White Non-Hispanic 

Other (Includes Individuals Who Chose More Than One Race In The CPS) 

Any Noncitizens In Household 

Any SSI Payments 

SSI To Nonelderly Adult (Age 18-59) 

SSI To Child Or Elderly 

SSI To Both Nonelderly Adult And Child Or Elderly 

No SSI 

Any Household Unemployment Compensation 

Nondisabled Childless Adult Subject To Work Registration, With Earnings 

Nondisabled Childless Adult Subject To Work Registration, Without Earnings 

Household Earnings As Percent Of Poverty 

0 

1-50 

51-100 

101-130 

131+ 

FSP Benefit As Percent Of Maximum 

1-25 

26-50 

51-75 

76-99 

100 

Household Consists Of Single Mother With Earnings < 150 Percent of Poverty And Her Children 
18

different requirements and rules. Thus, a household that receives TANF in one state may not 

necessarily receive TANF in another (for example, a household in Massachusetts may not 

receive TANF if it was located in Texas instead). In this way, the meaning and interpretation of 

the variable depends on the state. Because our model requires us to know what each household’s 

characteristic would be in each state, we cannot include TANF receipt without at least a 

simulation of participation for each household in each state. 

The second reason for excluding TANF receipt from the household-level model is more 

conceptual. Since TANF eligibility—and thus receipt—depends on state policies, TANF receipt 

could be affected by the key analysis variables in the state-level model discussed in Chapter III. 

If we were to standardize participation rates based on a household’s TANF receipt, it would be 

difficult to interpret how state TANF policies affect state FSP participation rates. As a result, we 

estimate the household level model using household characteristics that are not directly 

influenced by the key policy variables examined in the subsequent state-level model.7

 

3. The Logistic Model 

The standardization process is used to remove the effects of differences in the composition 

of FSP eligible populations across states. Intuitively, the standardization process estimates the 

FSP participation rate a state would have if its eligible population were the same as the national 

eligible population. For example, given that the elderly participate in the FSP at a low rate, 

would Florida’s participation rate be higher if it had fewer elderly households in its FSP eligible 

population? This standardization is accomplished for each state by estimating the probability 

that each eligible household in the CPS would participate in the FSP if it was in that state. 

 7

 To address this issue, we also examined participation rates for a subgroup of households most likely to be 

eligible for TANF in all states: low-income single mothers. This group is defined by household characteristics with 

the same meaning across states and can be thought of as a proxy for TANF recipients. See Appendix B for more 

details. 
19

The logic behind the standardization process can be seen by considering a simple example. 

Consider a setting where instead of having many household characteristics that affect 

participation, there were just two types of households that participate in the food stamp program, 

and that each state had at least some of these two types of eligible households. To compute 

standardized participation rates in that setting, we could simply compute the participation rate for 

each household type in each state and then, for each state, average across the household 

participation rates, weighting each household type by the proportions observed in the entire 

nation rather than just in the state. For example, suppose a state’s eligible population was 

composed of 20 percent type 1 households and 80 percent type 2 households, but the national 

eligible population had 50 percent of each type. We would calculate the state’s participation rate 

for the two types of households in that state (assume the rates are 30 percent and 60 percent for 

the two types, respectively). While the actual state participation rate is 

P(participate|type1)P(type1) + P(participate|type2)P(type2) = 0.3*0.2 + 0.6*0.8=0.54, to obtain 

the standardized rate we replace the P(type1) and P(type2) with the national averages and obtain 

0.3*0.5 + 0.6*0.5=0.45. This 45 percent reflects the participation rate the state would have if its 

eligible population looked like the national eligible population, with 50 percent of households of 

each type. This type of standardization is “direct standardization.” 

In reality, there are many characteristics of households that affect participation rates, so the 

direct standardization method is infeasible. But the logic of the method we use remains the 

same. Instead of calculating a participation probability for each household type, we use “indirect 

standardization,” fitting a logistic regression model that predicts household participation 

probabilities given a set of observed household characteristics. This helps smooth the estimated 

probabilities across the covariate values and is particularly helpful if there are small numbers of 

households of a particular type. 
20

The logistic model predicts household-level food stamp participation given the set of 

household characteristics described in above. In particular, a weighted logistic regression model 

is run on the concatenated data set with all FSPQC observations (representing FSP participants) 

and all CPS observations (representing all FSP eligibles), with a “response variable” indicating 

whether each observation is from the FSPQC.8

 This model is expressed as: 

) , 1 log( sis

is

is H

F

F

+= γφ − (1) 

where Fis is an indicator for being in the FSPQC, His are the characteristics of household i in 

state s, and the s γ are state-specific “fixed effects,” described below. The household 

characteristics term reflects the relationship between observed household characteristics and 

participation, while the state fixed effects reflect the different participation probabilities that 

households with the same characteristics would have in different states.9 We assume that the 

relationship between each household characteristic and participation is the same across all states 

(i.e., we do not allow the coefficients of each household characteristic to vary across states). 

As mentioned, this logistic regression is not estimating the probability that we would like. 

We would like to estimate the participation rate R=P/E, where P is the number of participating 

households—households represented by the FSPQC sample—and E is the number of eligible 

households—households represented by the CPS sample. However, using the concatenated data 

set with both FSPQC and CPS households, we define a “success” in the logistic regression as 

being in the FSPQC (being a participant). Because a participating household is represented 

 8

 For this household-level model, the weights for CPS households were equal to their CPS sampling weight 

times their probability of eligibility. The weights for FSPQC households were equal to their FSPQC sampling 

weights. 

9

 To estimate the model we must have one “omitted” state for which we do not estimate a fixed effect; that 

state’s implied fixed effect is 0. 
21

twice in the concatenated data set—once as an eligible household in the CPS and once as a 

participating household in the FSPQC, the logistic regression is instead estimating F=P/(E+P), 

the number of participating households out of the number of eligible households plus the number 

of participating households.10 

We can obtain the quantity that we would like using a simple transformation of the 

probabilities predicted from the logistic regression. The logistic regression yields a predicted 

probability for household i in state s of 

ˆ ˆ

ˆ ˆ

ˆ

1

is s

is s

H

is H

e F

e

φ γ

φ γ

+

+ = +

. To get that household’s predicted 

probability of participation (R), we transform F: R=g(F)=F/(1-F). The formula for the 

participation probability R for household i in state s then reduces to ˆ ˆ His s R e is

φ +γ = . 

One additional complication of this transformation is that the resulting standardized 

participation rates may be greater than 100 percent. Although the probabilities obtained directly 

from the logistic regression (F) are constrained between 0 and 1, the participation rate we 

generate, R, does not have the same constraint, and in fact can be between 0 and infinity. This 

issue is discussed further below as well as in Appendix B, where we discuss an alternative model 

that constrains the rate R to between 0 and 1. 

The next step after fitting the logistic regression model is to calculate the initial standardized 

participation rate for each state. The initial standardized participation rate reflects the predicted 

number of participating individuals over the number of eligible individuals. For each state, one 

at a time, the numerator—the predicted number of participating individuals—is obtained by 

calculating the probability that each household in the nation would participate, if the household 

 10 If the FSPQC and CPS consisted of all participating and all eligible households, rather than samples of such 

households, it would be easy to see that the participating households are in both the FSPQC and the CPS and, thus, 

are in the concatenated data set twice. In reality, where the FSPQC and CPS are samples of participating and 
22

was in that state, and adding those probabilities over all eligible households in the nation. For 

example, when calculating the initial standardized participation rate for New York, for each 

eligible household in the CPS (regardless of the household’s home state), we calculate the 

quantity in Equation 1 with that household’s values for H but New York’s fixed effect ( s γ ). We 

then add those probabilities across all eligible households in the CPS. The denominator, the 

number of eligible households, is the same for every state: the number of eligible households in 

the nation as a whole. Thus, across the states, the only difference in the initial standardized 

participation rates comes through differences in their fixed effects. 

The initial standardized rate for state s can be expressed as:11

∑∑

∑∑

′ ′ = ∈

′

′ ′ = ∈

+

′ ′

= 51

1

51

1

ˆ ˆ

'

s s i

si

s s i

H

si

s

w

ew

r

ssi γφ

 (2) 

The two summation signs in the numerator and denominator refer to adding up the probabilities 

across all households (i) in all states (s'). The numerator has two terms: wsi ′ is the CPS weight, 

which includes the probability of eligibility, while the H ssi e γφ ˆ ˆ

′ + term gives the probability that 

household i would participate if it was in state s. The only difference from a household’s 

 

(continued) 

eligible households, it is unlikely that any individual household would be in both data sets. However, the 

participating households are represented twice, once in the FSPQC and once in the CPS. 

11 For each household i , the weight wis′ is the sum of the CPS sampling weights for each individual in 

household i multiplied by the probability that household i is eligible for food stamps. This yields a standardized 

participation rate among eligible individuals. Note that this weight is different from that used in the logistic 

regression. The regression is estimated over eligible households since FSP participation is a household-decision. 

Therefore, the regression uses a household-level weight. Here, we apply the regression results uniformly to each 

individual in the household to generate a person-level participation rate similar to the official state participation 

rates, which also are person-level. 
23

predicted probability in its own state (s') is the fixed effect, which instead is from state s. The 

sum across all households of the product of these two terms gives the predicted number of 

participating households in state s. The denominator is the sum of the weights across all 

households in all states and thus is the number of eligible households in the nation as a whole. 

This process describes the derivation of household-level participation rates. Individual-level 

participation rates also are generated by multiplying each household’s probability of 

participation by the household size. This gives each individual in a household the household’s 

probability of participation. 

4. Examples of the Standardization Process 

To clarify this method we provide two illustrative examples. For simplicity, in all of these 

examples we assume that all eligible households are in the CPS and all participating households 

are in the FSPQC. In other words, we assume that the sampling weights for the FSPQC and CPS 

are 1 for all households. 

Example 1. Two States and One Household Type. Consider a situation with two states 

and only one type of household (no covariates). Suppose all households in State 1 have a 

participation probability of 0.4 while all households in State 2 have a participation probability of 

0.8, and that there are 400 eligible households in State 1 and 200 eligible households in State 2. 

There are thus 400*0.4=160 participating households in State 1, and 200*0.8=160 participating 

households in State 2. The concatenated data set will thus contain 400+200+160+160=920 

observations: 600 eligible households from the CPS and 320 participating households from the 

FSPQC. 

Since there are no covariates, the logistic regression has just an intercept and a fixed effect 

for State 2. State 1 is the omitted state in the sense that its fixed effect is set to 0: 1 γ = 0 . 
24

Estimating this model on all 920 cases in the concatenated data set, we obtain the following 

parameter estimates: 

log( ) 0.9163 0.6931* 2 1

is

is

H STATE

H

=− + − . 

The estimated fixed effect for State 2 is greater than 0, 2 γˆ > 0 , because State 2 has a higher 

participation rate than State 1. For each state, the initial standardized participation state 

participation rate is obtained using the following formula: 

2 ˆ ˆ

1

2

1

1

His s

s is

s

s is

e

r

′

φ +γ

′ ′ = ∈

′ ′ = ∈

′ =

∑∑

∑∑

, 

which follows from the formula given above for the initial standardized participation rates, 

simplified for the case with two states and all weights equal 1. The sums are over all households 

in the CPS (that is, all eligible households) for each state. In this example, there are 

400+200=600 such households. The direct (that is, unstandardized) and standardized rates are 

presented in Table II.2. 

TABLE II.2 

RATES FROM STANDARDIZED RATE EXAMPLE 1 

State Direct Rate Standardized Rate 

1 40% 40% 

2 80% 80% 

The direct rates are calculated by dividing the number of participating households in each 

state by the number of eligible households in that state. In this example, the standardized rates 

equal the direct rates; without any covariates, the distributions of households are observationally 
25

the same across the two states and the population to which the rates are standardized is the same 

as the population within each state. Thus, there is no standardization required.

Example 2. Two States and Two Household Types. Now consider two states and two 

types of households. Assume that the proportions of Type 1 households in the population of 

eligible households is different between the states. Although the states have the same 

participation rates for each type of household, the unstandardized rates are different because of 

different compositions of the eligible households in the two states (Table II.3). 

TABLE II.3 

DISTRIBUTIONS FROM STANDARDIZED RATE EXAMPLE 2 

 State 1 State 2 

Number of eligible households 400 200 

Percent of HH of Type 1 (Number) 20% (80) 80% (160) 

HH Type 1: Participation Probability 

 (Number of Participants) 40% (32) 40% (64) 

HH Type 2: Participation Probability 

 (Number of Participants) 70% (224) 70% (28) 

In this case, the concatenated data set will have a total of 948 observations: 600 from the 

CPS, and 32+224+64+28=348 from the FSPQC. Fitting a logistic regression to the concatenated 

FSPQC and CPS data, the estimated model is: 

is logit F(H , ) 0.9163 0.5596* 0* 2 s TYPE STATE =− + − . 

State 1 is again the omitted state, and thus 1 γ is set to 0. In this case, because both states 

have the same participation probabilities for the two household types, 2 γ is estimated to be 0. 

The direct and standardized rates for the two states are presented in Table II.4. Even though 

the participation probabilities for each household type are the same in the two states, the different 

compositions of the eligible population in the two states make the direct participation rates quite 
26

different. However, the standardized rates are equal, reflecting the fact that the state 

participation rates would be the same if the two states had the same eligible populations. We 

also note that if the “TYPE” variable is omitted from the model, the standardized rates will be 

equal to the direct rates because there would be no apparent difference between the distributions 

of households in the two states according to the model: the model does not account for 

differences that may exist between states in household characteristics that are not included in the 

model. 

TABLE II.4 

RATES FROM STANDARDIZED RATE EXAMPLE 2 

State Direct Rate Standardized Rate 

1 64% 58% 

2 46% 58% 

5. Shrinkage Estimation 

So far, we have discussed how to estimate two sets of FSP participation rates: 

(1) The direct rates are computed as the number of FSP participants divided by the 

estimated number of FSP-eligible individuals in each state, and 

(2) The initial standardized rates reflect the direct rates adjusted for compositional 

differences (based on the results of the logistic model) 

Two other sets of the state FSP participation rates are: 

(1) The official rates obtained when Bayesian shrinkage estimation techniques are 

applied to the direct rates, and 

(2) The shrinkage standardized rates obtained when Bayesian shrinkage estimation 

techniques are applied to the initial standardized rates. 
27

The difference between the direct estimate of the FSP participation rate and the initial 

standardized estimate of the FSP participation rate for a state reflects the effects of adjusting for 

compositional differences in state FSP eligible populations. That is, a state’s initial standardized 

rate may be higher than its direct participation rate because it has a proportionally large 

population of eligible individuals with a low propensity to participate in the FSP. However, 

comparing the initial standardized FSP participation rates to the official state participation rates 

published by USDA (Castner and Schirm 2005) could be misleading because the official rates 

are derived using Bayesian shrinkage methods to improve the precision of the estimated rates. 

To obtain standardized rates that are comparable to the official rates, we applied shrinkage 

estimation methods to the initial standardized participation rates. 

To derive shrinkage estimates of standardized rates, we estimated a regression model to 

predict the standardized FSP participation rates as a function of state characteristics. Examples 

of characteristics included in the model are the poverty rates for key populations such as the 

elderly and children, average adjusted gross income (from tax return data), the percentage of 

households that rent their living quarters, and the percentage of households receiving 

unemployment compensation. We then use shrinkage estimation to average the standardized 

participation rates with the regression predictions. The shrinkage estimation gives more weight 

to those standardized rates with lower variance (that is, those rates where we have more 

confidence in the estimate).12 

 12 See Castner and Schirm (2006) for more details on shrinkage estimation techniques. 
28

The resulting shrinkage estimates of the standardized FSP participation rates are not only 

more comparable to the official state FSP participation rates, but also more precise in that they 

have narrower confidence intervals. The shrinkage techniques reduce the total variation in 

standardized participation rates by 37 percent.13 

In the following section, we discuss all four types of participation rates. We present the 

direct and initial standardized rates for two reasons. First, we can only derive estimates of the 

effects of compositional differences on state participation rates by comparing the direct rates 

with the initial standardized rates. While we would prefer to distinguish the differences between 

the official participation rates and the shrinkage standardized rates, such a distinction would be 

misleading because of the statistical modeling used in applying shrinkage methods.14 Second, 

for the same reason, we use the initial standardized participation rates in the state-level model 

(discussed in Chapter III). We present the official rates and the shrinkage standardized rates for 

two reasons. First, the shrinkage standardized rates are more comparable to the official 

participation rates, and second, the shrinkage standardized rates are more precise estimates of the 

standardized state participation rates. 

C. RESULTS 

This section presents the results of the standardization process. We first describe the results 

of the logistic model to identify predictors of FSP participation. We then present initial 

standardized participation rates and describe how state compositional factors affect participation 

 13 See Section C, Table II.11 for more details. 

14 The shrinkage estimation process uses regression techniques to incorporate data from other states, time 

periods, and data sources. We do not use the shrinkage estimates of the direct rates as the basis for the standardized 

rates because the standardization process would be likely to simply uncover the adjustments made through the 

shrinkage estimation’s regression. Similarly, we do not use the shrinkage estimates of the standardized rates as the 

basis for the state-level model because the state-level model would simply uncover the adjustments made through 

the shrinkage process. 
29

rates. Finally, we present the shrinkage participation rates and compare them with the official 

participation rates. 

1. Predictors of Participation 

The logistic model allows us to examine the household characteristics that are associated 

with FSP participation. Table II.7 presents the coefficient values obtained from the logistic 

regression of participation on the set of household characteristics in Table II.1, indicating the 

characteristics that are associated with FSP participation. One caution in interpreting these 

coefficients is that this was a multivariate model and that the full set of characteristics are not 

mutually exclusive. Thus, for example, when determining the predicted participation rate for a 

household composed of a white single mother with one child under age 4, we would add the 

coefficients for household type 1, with all female adults, two people, white non-Hispanic 

household head, plus the appropriate coefficients for the household earnings and FSP benefit. 

Nonetheless, the results are generally consistent with previous studies that examine the 

determinants of FSP participation, summarized in Section B. In Table II.5 we see that 

characteristics associated with higher levels of participation include having one or more children 

(particularly those between the ages of 0 and 4), having no nonelderly adults in the household or 

having only male or only female adults, having a child or elderly individual in the household 

receiving SSI, and having low household earnings (particularly below 100 percent of the poverty 

line). The primary factors associated with lower levels of participation include having an elderly 

person in the household and being a nondisabled childless adult subject to work registration.15 

 15 The positive coefficient on the variable indicating that there is a noncitizen in the household—suggesting that 

households with noncitizens participate at higher rates than households without noncitizens—is counterintuitive. 

Conventional wisdom suggests that noncitizens are less likely to participate in the FSP. This may be explained by 

issues of survey “coverage” for the CPS; the CPS could have an undersample of noncitizens. If so, the CPS likely 

would yield an underestimate of the number of eligible households with noncitizens, which would artificially inflate 
30

2. Initial Standardized State Participation Rates 

Table II.6, Figure II.1, and Figure II.2 present the initial standardized state FSP participation 

rates (in Figure II.1 and Figure II.2, the states are sorted by the direct participation rate). The 

table and figures also show the direct FSP participation rates for each state, estimated as the 

number of participating households in the state (according to the FSPQC) divided by the number 

of eligible households in the state (according to CPS data). Both of these rates represent the 

percentage of eligible individuals participating in the FSP in each state. To reflect the 

uncertainty with which these rates are estimated, we also provide 90 percent confidence 

intervals.16 For simplicity, Figure II.1 only shows the confidence intervals for the standardized 

rates; Figure II.2 presents the same participation rates as Figure II.1, but it also includes the 

confidence intervals for the direct participation rates.17 All confidence intervals were derived 

using a jackknife procedure followed by smoothing using a generalized variance function 

(Wolter 1985).18 

The standardized and direct participation rates presented here both effectively include the 

state fixed effects. That is, the standardized rates reflect what each state’s participation rate 

would be if that state resembled the nation as a whole in terms of the composition of eligible 

households, but still had a state fixed effect. The differences between the standardized and the 

 

(continued) 

the implied participation rate for this group and lead our model to predict the presence of noncitizens as a factor that 

increases the probability of participation in the FSP. 

16 In actuality, the direct rates also have confidence intervals associated with them. For simplicity we present 

only the confidence intervals for the standardized rates. 

17 Formal tests of the significance of the difference between direct and standardized participation rates would 

need to account for the correlation between these rates. 

18 See Appendix A for more details on the variance estimation procedure. 
31

direct participation rates, therefore, reflect the differences between the compositions of the state 

and national eligible populations. 

TABLE II.5 

COEFFICIENT ESTIMATES, LOGISTIC MODEL OF FSP PARTICIPATION 

Coefficient Standard Error 

Intercept -4.0299 (0.0147) 

General Household Type 

Single Nonelderly Adult (Age 18-59) And One Or More Children, With At 

Least One Child Age 0-4 Omitted 

Single Nonelderly Adult And One Or More Children, With No Children Age 

0-4 -0.2444 (0.0017) 

Multiple Nonelderly Adults And One Or More Children, With At Least One 

Child Age 0-4 0.6200 (0.0038) 

Multiple Nonelderly Adults And One Or More Children, With No Children 

Age 0-4 0.2529 (0.0039) 

One Or More Nonelderly Adults, Without Children Or Elderly 0.3512 (0.0041) 

One Or More Elderly, Without Children Or Nonelderly Adults -2.2898 (0.0076) 

One Or More Elderly, With Children Or Nonelderly Adults -1.6976 (0.0046) 

Children Only -0.4541 (0.0077) 

Gender Mix Of Nonelderly Adults In Household 

No Nonelderly Adults In Household 1.1429 (0.0071) 

All Male 0.2858 (0.0024) 

All Female 0.3016 (0.0022) 

Mixed Omitted 

Household Size -0.0519 (0.0005) 

Race/Ethnicity Of Household Head 

Hispanic -0.0341 (0.0015) 

Black Non-Hispanic 0.2180 (0.0011) 

White Non-Hispanic Omitted 

Other 0.0902 (0.0023) 

Any Noncitizens In Household 0.1320 (0.0021) 

SSI Payments 

SSI To Nonelderly Adult (Age 18-59) -0.0814 (0.0025) 

SSI To Child Or Elderly 1.8735 (0.0019) 

SSI To Both Nonelderly Adult And Child Or Elderly 1.4191 (0.0064) 

No SSI Omitted 

Any Household Unemployment Compensation 0.4082 (0.0031) 

Nondisabled Childless Adult Subject To Work Registration, With Earnings -0.8370 (0.0036) 

Nondisabled Childless Adult Subject To Work Registration, Without Earnings -0.5524 (0.003) 

Household Earnings As % Of Poverty Line 

0 2.7805 (0.0139) 

1-50 2.8413 (0.0140) 

51-100 2.0805 (0.0140) 

101-130 1.3732 (0.0141) 

131+ Omitted 

FSP Benefit As % Of Maximum 

1-25 0.1132 (0.0016) 

26-50 0.5153 (0.0016) 

51-75 0.5442 (0.0016) 

76-99 0.5201 (0.0015) 

100 Omitted 

Household Consists Of Single Mother and Her Children, with Earnings < 150% 

Of Poverty Line 0.7087 (0.0031) 
32

TABLE II.6 

DIRECT AND INITIAL STANDARDIZED 2003 FSP PARTICIPATION RATES 

Direct 

Participation 

Rate 

Initial Standardized 

Participation Rate 

Lower Bound of 

Confidence Interval of 

Standardized Rate (90%) 

Upper Bound of 

Confidence Interval of 

Standardized Rate (90%)

Alabama 54.1 43.9 37.3 50.5 

Alaska 61.5 56.3 46.4 66.2 

Arizona 65.0 62.8 54.2 71.5 

Arkansas 60.1 56.7 48.9 64.6 

California 45.3 42.5 38.3 46.8 

Colorado 45.5 46.5 40.5 52.5 

Connecticut 53.8 54.0 45.7 62.4 

Delaware 54.8 63.0 52.4 73.6 

District of Columbia 74.3 69.7 58.6 80.9 

Florida 48.9 59.7 53.4 66.1 

Georgia 67.5 65.8 56.3 75.4 

Hawaii 79.1 91.4 77.5 100.0 

Idaho 54.2 62.3 51.8 72.8 

Illinois 60.6 67.0 58.0 75.9 

Indiana 63.6 69.8 60.1 79.5 

Iowa 57.2 62.6 54.0 71.3 

Kansas 53.0 52.3 44.6 60.0 

Kentucky 68.9 66.7 57.2 76.1 

Louisiana 66.2 63.3 54.3 72.2 

Maine 69.9 87.1 74.8 99.5 

Maryland 48.1 45.3 38.3 52.2 

Massachusetts 40.5 39.3 32.4 46.2 

Michigan 65.1 59.0 50.5 67.5 

Minnesota 63.1 57.9 49.1 66.7 

Mississippi 67.9 62.7 53.7 71.6 

Missouri 89.5 99.7 86.3 100.0 

Montana 44.6 46.2 40.5 52.0 

Nebraska 60.5 67.9 56.9 79.0 

Nevada 41.0 52.0 43.5 60.4 

New Hampshire 49.7 54.7 45.0 64.5 

New Jersey 48.7 48.9 44.2 53.5 

New Mexico 53.0 53.0 45.8 60.3 

New York 50.2 51.9 46.7 57.0 

North Carolina 45.4 43.3 36.5 50.0 

North Dakota 57.8 60.6 50.6 70.7 

Ohio 65.2 63.4 54.9 71.9 

Oklahoma 73.0 69.3 59.7 78.9 

Oregon 85.7 105.1 90.3 100.0 

Pennsylvania 54.0 56.7 48.9 64.6 

Rhode Island 51.9 46.5 40.2 52.7 

South Carolina 65.9 61.4 53.1 69.7 

South Dakota 54.3 57.0 46.1 67.8 

Tennessee 83.1 86.1 73.5 98.6 

Texas 47.4 47.1 43.5 50.7 

Utah 50.9 58.8 49.2 68.5 

Vermont 60.8 67.9 55.0 80.8 

Virginia 51.5 54.6 46.4 62.8 

Washington 51.4 55.2 47.3 63.2 

West Virginia 64.9 76.9 66.5 87.2 

Wisconsin 53.3 49.5 41.8 57.2 

Wyoming 49.2 51.0 42.3 59.8 
33 

FIGURE II.1 

DIRECT AND INITIAL STANDARDIZED 2003 FSP PARTICIPATION RATES 

WITH 90 PERCENT CONFIDENCE INTERVALS FOR STANDARDIZED RATES 

10%20%30%40%50%60%70%80%90%

100%110%

Missouri

Oregon

Tennessee

Hawaii

District of Columbia

Oklahoma

Maine

Kentucky

Mississippi

Georgia

Louisiana

South Carolina

Ohio

Michigan

Arizona

West Virginia

Indiana

Minnesota

Alaska

Vermont

Illinois

Nebraska

Arkansas

North Dakota

Iowa

Delaware

South Dakota

Idaho

Alabama

Pennsylvania

Connecticut

Wisconsin

New Mexico

Kansas

Rhode Island

Virginia

Washington

Utah

New York

New Hampshire

Wi

Direct Participation RInitial Standardized Pa(90 Percent Conf. Inte
34 

FIGURE II.2 

DIRECT AND INITIAL STANDARDIZED 2003 FSP PARTICIPATION RATES 

WITH 90 PERCENT CONFIDENCE INTERVALS FOR BOTH RATES 

10%20%30%40%50%60%70%80%90%

100%110%

Missouri

Oregon

Tennessee

Hawaii

District of Columbia

Oklahoma

Maine

Kentucky

Mississippi

Georgia

Louisiana

South Carolina

Ohio

Michigan

Arizona

West Virginia

Indiana

Minnesota

Alaska

Vermont

Illinois

Nebraska

Arkansas

North Dakota

Iowa

Delaware

South Dakota

Idaho

Alabama

Pennsylvania

Connecticut

Wisconsin

New Mexico

Kansas

Rhode Island

Virginia

Washington

Utah

New York

New Hampshire

Wyoming

Flid

Direct Participation Rate (9Conf. Interval)Initial Standardized Partici(90 Percent Conf. Interval)
35

A standardized rate higher than the direct estimate means that the state has an eligible 

population composed of households less likely to participate in the FSP. For 28 states, the 

standardized participation rate is higher than their direct estimate, while the reverse is true for the 

remaining 23 states.19 The states with the largest increases from direct participation rate to 

standardized participation rate include Oregon (19 percentage points), Maine (17 percentage 

points), Hawaii and West Virginia (12 percentage points), Nevada and Florida (11 percentage 

points), and Missouri (10 percentage points). The states with the highest direct rates tend to also 

have the highest standardized rates. 

A standardized rate lower than the direct estimate means that the state has an eligible 

population composed of households more likely to participate in the FSP. The five states with 

the largest decreases from direct participation rate to standardized participation rate include 

Alabama (10 percentage points), Michigan (6 percentage points), and Rhode Island, Alaska, 

Mississippi, Minnesota, Mississippi, the District of Columbia, and South Carolina (5 percentage 

points). 

3. Household Characteristics Associated with Changes in Rates 

To understand how household characteristics influence the variation in state participation 

rates, we decompose the difference between each state’s direct participation rate and a national 

participation rate. Using the coefficients from the logistic regression, combined with national 

values for the covariates, we derive the national participation rate implied by the model. For 

each state, we then compute a series of n incremental standardized rates (where n is the number 

of covariates in the logistic model) replacing the national mean for the each covariate with the 

 19 Because the participation rates are standardized to the national population, and because different states have 

different sized populations, we would not expect the standardization process to lead to an equal number of increases 

and decreases in participation rates. 
36

actual state value for that covariate. For example, we can compute one incremental standardized 

rate for Alabama using the national means for all covariates except household size, and a second 

rate using the national means for all covariates except the proportion of households headed by a 

Hispanic, etc.20 The difference between the national rate and each incremental rate reflects the 

component effect for each state. Specifically, they show the factors that cause each state’s direct 

rate to differ from the national participation rate. 

The magnitude of the component effect is driven by two factors: (1) the size of the 

coefficient from the logistic model, and (2) the magnitude of the difference between the state 

value of the covariate and the national value of the covariate. Therefore, even if a state looks 

markedly different from the nation as a whole along a given covariate, the effect of that 

difference may be small if the coefficient for that covariate is small. 

To interpret component effects, we must keep in mind the purpose of the standardization 

process. For each state, the standardization process estimates what the participation rate would 

be if the state eligible population was the same as the national population. As stated above, a 

state’s standardized rate could be higher than the national rate because its population is 

composed of households less likely to participate in the FSP. This can happen in two ways: (1) 

relative to the nation as a whole, a state could have a larger proportion of households that 

participate at low rates, or (2) relative to the nation as a whole, a state could have a smaller 

proportion of households that participate at high rates. Households that participate at low rates 

are those with negative coefficients from the logistic model; households that participate at high 

rates are those with positive coefficients from the logistic model. 

 20 This process included a national value of the state fixed effects, computed as the weighted average of the 

individual state fixed effects, where a state’s weight was proportional to the size of its FSP eligible population. 
37

Table II.7 summarizes the positive and negative coefficients from the logistic model.21 Thus, a 

state with a larger than national proportion of households with multiple, nonelderly adults, one or 

more children at least one of whom is age 0 to 4 will have a higher direct participation rate, all 

else being equal. This higher direct participation rate will lead their standardized rate to be lower 

than their direct rate, all else being equal. Likewise, a state with a larger than national proportion 

of households with single, nonelderly adults, one or more children none of which are age 0 to 4 

will have a lower direct participation rate, all else being equal. This lower participation rate will 

lead their standardized rate to be higher than their direct rate, all else being equal. 

Although for this exercise we change each household characteristic one at a time, in reality 

households have a number of different characteristics and it would be impossible to change one 

aspect of the state’s eligible population (e.g., the percentage of households composed of a single 

adult and multiple children) without also affecting the distribution of other eligible household 

characteristics (e.g., the earnings distribution). Nonetheless, these differentiations allow us to 

investigate generally which household characteristics drive the differences between direct and 

standardized rates. 

The household characteristics that have the largest effect on the standardization are those 

where the difference between the state and national composition, combined with the coefficient, 

are the largest. The two most influential characteristics across all states are the proportion of 

households that have elderly individuals and no children and the proportion of households that 

have zero earnings. Because the elderly are less likely to participate (as evidenced by the 

negative coefficient on the proportion of households with elderly and no children) states with 

 21 The coefficients presented in Table II.7 are the same as those presented in Table II.5. Note that for 

covariates that are part of a categorization (such as household composition and earnings), the size and direction of 

the covariate is a function of the omitted category. Omitted categories are identified in Table II.5. 
38

TABLE II.7 

POSITIVE AND NEGATIVE COMPONENT EFFECTS ON THE DIFFERENCE BETWEEN EACH STATE’S DIRECT 

PARTICIPATION RATE AND THE NATIONAL PARTICIPATION RATE 

Positive Effects 

(Higher Proportions Lower 

Standardized Participation Rates) 

Negative Effects 

(Higher Proportions Increase 

Standardized Participation Rates) 

Covariate Coefficient Covariate Coefficient 

Proportion of Households with Multiple 

Nonelderly Adults And One Or More Children, 

With At Least One Child Age 0-4 

0.6200 

Proportion of Households with Single Nonelderly 

Adult and One Or More Children, With No 

Children Age 0-4 

-0.2444 

Proportion of Households with Multiple 

Nonelderly Adults And One Or More Children, 

With No Children Age 0-4 

0.2529 

Proportion of Households with One Or More 

Elderly, Without Children Or Nonelderly Adults -2.2898 

Proportion of Households with One Or More 

Nonelderly Adults, Without Children Or Elderly 0.3512 Proportion of Households with One Or More 

Elderly, With Children Or Nonelderly Adults -1.6976 

Proportion of Households with No Nonelderly 

Adults In Household 1.1429 Proportion of Households with Children Only -0.4541 

Proportion of Households with All Adults Male 0.2858 Household Size -0.0519 

Proportion of Households with All Adult Female 0.3016 Proportion of Households Headed by an Hispanic -0.0341 

Proportion of Households Headed by a Black 

Non-Hispanic 0.2180 Proportion of Households with SSI To Nonelderly Adult (Age 18-59) -0.0814 

Proportion of Households Headed by Other Race 0.0902 Nondisabled Childless Adult Subject To Work 

Registration, Without Earnings -0.5524 

Any Noncitizens In Household 0.1320 

Proportion of Households with SSI to Child or 

Elderly 1.8735 

Proportion of Households with SSI To Both 

Nonelderly Adult And Child Or Elderly 1.4191 

Proportion of Households with Unemployment 

Compensation 0.4082 

Proportion of Households with Earnings at 0 

Percent of Poverty 2.7805 

Proportion of Households with Earnings between 

1-50 Percent of Poverty 2.8413 

Proportion of Households with Earnings between 

51-100 Percent of Poverty 2.0805 

Proportion of Households with Earnings between 

101-130 Percent of Poverty 1.3732 

Proportion of Households with FSP Benefit 

between 1-25 Percent of Maximum 0.1132 

Proportion of Households with FSP Benefit 

between 56-50 Percent of Maximum 0.5153 

Proportion of Households with FSP Benefit 

between 51-75 Percent of Maximum 0.5442 

Proportion of Households with FSP Benefit 

between 76-99 Percent of Maximum 0.5201 

Percentage of Households Consisting of Single 

Mother with Earnings < 150% Of Poverty 0.7087 
39

larger proportions of this population have higher standardized participation rates, all else being 

equal. Because households without earnings are more likely to participate (as evidenced by the 

positive coefficient on the proportion of households with earnings at zero percent of poverty), 

states with larger proportions of this population will have lower standardized participation rates, 

all else being equal. Other components with relatively large impacts on standardized 

participation rates include: the proportion of households with earnings between 1 and 50 percent 

of poverty, the proportion of households with earnings between 51 and 100 percent of poverty, 

the proportion of households that are child-only, the proportion of households with SSI for 

children or elderly, and the proportion of households headed by an African American. 

Figure II.3 summarizes the component effects in each state by comparing the direct

participation rate with the national participation rate. The relative effects in Figure II.3 reflect 

the change in the participation rate due to moving from the national mean to the state mean for 

each particular factor, holding all else constant (in Figure II.3, states are sorted by the direct 

participation rate). These relative impacts are shown for the proportion of households with 

elderly and no children, the proportion of households with no earnings, the proportion of 

households with earnings between 1 and 50 percent of poverty, the proportion of households 

with earnings between 51 and 100 percent of poverty, the proportion of households that are 

child-only, and the state fixed effects. For each state, the other component effects that have 

positive and negative impacts on the standardized rates are grouped together. Factors that lead a 

state’s direct rate to be higher than the national rate are shown to the right of the line in the 

middle of the chart, and factors that lead a state’s direct rate to be lower are shown to the left. 

The difference in the total length to the left and to the right of the line indicates the general size 

of the difference between the direct and national rates for each state. 
40

FIGURE II.3 

RELATIVE EFFECTS OF COMPOSITIONAL FACTORS ON THE DIFFERENCE BETWEEN EACH STATE’S 

DIRECT PARTICIPATION RATE AND THE NATIONAL PARTICIPATION RATE 

Note: The figure depicts the composition differences (or fixed effects) that are generally driving the differences 

between the direct and standardized rates rather than the actual value of the difference between the direct 

and standardized rates. The direct and standardized rates are available in Table II.6 

Massachusetts

Nevada

Montana

California

North Carolina

Colorado

Texas

Maryland

New Jersey

Florida

Wyoming

New Hampshire

New York

Utah

Washington

Virginia

Rhode Island

Kansas

New Mexico

Wisconsin

Connecticut

Pennsylvania

Alabama

Idaho

South Dakota

Delaware

Iowa

North Dakota

Arkansas

Nebraska

Illinois

Vermont

Alaska

Minnesota

Indiana

West Virginia

Arizona

Michigan

Ohio

South Carolina

Louisiana

Georgia

Mississippi

Kentucky

Maine

Oklahoma

District of Columbia

Hawaii

Tennessee

Oregon

Missouri

Elderly, No Children Child-Only No Earnings Earnings 1-50% Earnings 51-100% State Fixed Effect Other Other Negative

Direct Rate Lower Direct Rate Higher

National Rate
41

Missouri has the highest direct participation rate among all states. In Missouri, the state 

fixed effect is the driving component explaining why Missouri’s rate is among the highest. That 

is, there are factors other than compositional components that explain Missouri’s high 

participation rate. The state’s population of individuals with no earnings, which is 

proportionately larger than the nation as a whole, also contributes to the state’s high participation 

rate because these individuals participate at high rates. 

On the other end of the spectrum is Massachusetts, which has the lowest direct participation 

rate. The rate in Massachusetts is lower than the nation as a whole in large part because of noncompositional factors specific to Massachusetts (that is, the state fixed effect). Additionally, the 

state has a proportionately larger-than-national population of elderly, and proportionately 

smaller-than-national populations of individuals between 1 and 50 percent and 51 and 100 

percent of poverty. 

For many states, the largest factor explaining the difference between the direct and national 

participation rates is the state fixed effect. This finding suggests that the highest participation 

rates are not due to compositional differences but to something unique about the state. That is, if 

every state had the same composition, the participation rates in these states would still be higher. 

Some states have offsetting compositional characteristics. For example, in Connecticut, the 

factors leading to a higher participation rate, including a proportionately larger-than-national 

population of individuals with zero earnings, and a proportionately larger-than-national 

population of child-only households, which would pull the direct rate up, are canceled out by the 

state’s relatively large elderly population and relatively small populations of individuals between 

1 and 50 percent and 51 and 100 percent of poverty. 
42

4. Shrinkage Standardized Participation Rates 

Bayesian shrinkage techniques were used to improve our confidence in the estimated 

standardized participation rates and generate rates that are comparable to the official FSP 

participation rates. Table II.8 compares the shrinkage standardized rate with the direct and initial 

standardized participation rates. For several states, the shrinkage process leads to large changes 

in the estimate of the participation rate (as high as 17 percentage points in Missouri); on average, 

the shrinkage process leads to a 4 percentage point change (increase or decrease) in the estimate 

of the standardized participation rate. 

The difference between the initial standardized participation rate and the shrinkage 

participation rate reflects the fact that the initial standardized participation rates carry a high 

degree of uncertainty. Missouri has a relatively high variance associated with its initial 

standardized participation rate; the 90 percent confidence interval on Missouri’s initial 

standardized participation rate of 99.7 percent is +/-13 percentage points (the third largest 

confidence interval among all states). As a result, the shrinkage process gives more weight to the 

regression-predicted participation rate for Missouri. 

The shrinkage standardized rates are substantially more precise than the initial standardized 

rates. For example, the 90 percent confidence interval on Missouri’s shrinkage standardized 

participation rate of 82.7 percent is +/-7 percentage points, much less than the +/-13 percentage 

points associated with the initial standardized rate. Across all states, the average 90 percent 

confidence interval falls from +/-7 percentage points for the initial standardized rates to +/-5 

percentage points for the shrinkage standardized rates. The smaller confidence intervals are 

apparent in Figure II.4, which compares the initial standardized participation rates (and their 90 
43

TABLE II.8 

SHRINKAGE STANDARDIZED 2003 FSP PARTICIPATION RATES 

 Direct Participation Rate

Initial Standardized 

Participation Rate 

Shrinkage Standardized 

Participation Rate 

Difference between Initial and 

Shrinkage Standardized Rates

Alabama 54.1 43.9 48.5 +4.6 

Alaska 61.5 56.3 55.4 -0.9 

Arizona 65.0 62.8 66.6 +3.8 

Arkansas 60.1 56.7 58.6 +1.9 

California 45.3 42.5 43.3 +0.7 

Colorado 45.5 46.5 50.4 +4.0 

Connecticut 53.8 54.0 56.9 +2.9 

Delaware 54.8 63.0 55.2 -7.8 

District of Columbia 74.3 69.7 67.2 -2.5 

Florida 48.9 59.7 57.6 -2.1 

Georgia 67.5 65.8 63.0 -2.8 

Hawaii 79.1 91.4 82.7 -8.8 

Idaho 54.2 62.3 55.5 -6.8 

Illinois 60.6 67.0 66.2 -0.8 

Indiana 63.6 69.8 69.7 -0.1 

Iowa 57.2 62.6 61.9 -0.7 

Kansas 53.0 52.3 55.6 +3.4 

Kentucky 68.9 66.7 68.8 +2.1 

Louisiana 66.2 63.3 67.9 +4.6 

Maine 69.9 87.1 82.6 -4.6 

Maryland 48.1 45.3 51.8 +6.5 

Massachusetts 40.5 39.3 44.1 +4.8 

Michigan 65.1 59.0 63.5 +4.5 

Minnesota 63.1 57.9 54.4 -3.5 

Mississippi 67.9 62.7 57.3 -5.4 

Missouri 89.5 99.7 82.7 -17.0 

Montana 44.6 46.2 48.8 +2.6 

Nebraska 60.5 67.9 60.7 -7.2 

Nevada 41.0 52.0 50.5 -1.4 

New Hampshire 49.7 54.7 56.9 +2.1 

New Jersey 48.7 48.9 47.8 -1.0 

New Mexico 53.0 53.0 51.5 -1.5 

New York 50.2 51.9 50.9 -1.0 

North Carolina 45.4 43.3 43.6 +0.4 

North Dakota 57.8 60.6 50.3 -10.3 

Ohio 65.2 63.4 64.4 +1.1 

Oklahoma 73.0 69.3 71.7 +2.4 

Oregon 85.7 105.1 88.6 -16.5 

Pennsylvania 54.0 56.7 54.2 -2.5 

Rhode Island 51.9 46.5 46.8 +0.3 

South Carolina 65.9 61.4 63.3 +1.9 

South Dakota 54.3 57.0 49.3 -7.7 

Tennessee 83.1 86.1 87.0 +0.9 

Texas 47.4 47.1 47.3 +0.2 

Utah 50.9 58.8 59.5 +0.7 

Vermont 60.8 67.9 59.3 -8.6 

Virginia 51.5 54.6 54.6 -0.1 

Washington 51.4 55.2 63.0 +7.8 

West Virginia 64.9 76.9 80.4 +3.5 

Wisconsin 53.3 49.5 49.1 -0.4 

Wyoming 49.2 51.0 43.3 -7.8 

Note: The standardization process does not restrict participation rates to be 100 percent or lower, and so, as is the case with the 

official participation rates, some estimated rates are over 100 percent. Appendix B details an alternative model that restricts 

participation rates to be less than 100 percent. Because that alternative model relies on unreasonable assumptions, we do not use 

it for the primary results. 
44

percent confidence intervals) with the shrinkage standardized participation rates (and their 90 

percent confidence intervals). (As with Figures II.1 and II.2, the states are sorted by their direct 

participation rates, which are not shown.) 

Figure II.5 compares the state rankings and their 90 percent confidence intervals based on the 

official FSP participation rates and the shrinkage standardized participation rates. In general, 

states with high rankings according to the official rates tend to have high rankings according to 

the shrinkage standardized rates (and states with low rankings among the official rates tend to 

have low rankings among the shrinkage standardized rates). For most states, there is substantial 

overlap between the confidence intervals for the two rankings, suggesting that the 

standardization process does not substantially affect where a state’s participation rate falls in 

respect to other states.22 In 10 states, there is a difference in rank of more than 10 between the 

official and shrinkage standardized participation rates. The three states whose rankings are 

affected by the standardization process the most are Florida, Utah, and New Hampshire. 

D. CONCLUSIONS 

For most states, the standardized FSP participation rates are not substantially different from 

unstandardized estimates. Moreover, the state rankings are not substantially different when 

comparing rankings derived from the shrinkage standardized rates with the official participation 

rate rankings. This leads us to conclude that while standardization can adjust the participation 

rates for differences in the composition of each state’s eligible population, it is factors other than 

the composition of the eligible population that explain why a state’s rate differs from the national 

average. The next chapter explores whether the variation not explained by compositional 

differences can be explained by differences in state policies or economic conditions. 

 22 A formal test of the difference between a state’s ranking would need to account for the correlation between 

the rankings. 
45 

FIGURE II.4 

INITIAL AND SHRINKAGE STANDARDIZED 2003 FSP PARTICIPATION RATES 

WITH 90 PERCENT CONFIDENCE INTERVALS FOR BOTH RATES 

10%20%30%40%50%60%70%80%90%

100%110%

Missouri

Oregon

Tennessee

Hawaii

District of Columbia

Oklahoma

Maine

Kentucky

Mississippi

Georgia

Louisiana

South Carolina

Ohio

Michigan

Arizona

West Virginia

Indiana

Minnesota

Alaska

Vermont

Illinois

Nebraska

Arkansas

North Dakota

Iowa

Delaware

South Dakota

Idaho

Alabama

Pennsylvania

Connecticut

Wisconsin

New Mexico

Kansas

Rhode Island

Virginia

Washington

Utah

New York

New Hampshire

Wyoming

Florida

New Jersey

Shrinkage Participation Rate (9Conf. Interval)Initial Standardized Participati(90 Percent Conf. Interval)
46

FIGURE II.5 

COMPARISON OF STATE PARTICIPATION RATE RANKINGS OFFICIAL VERSUS SHRINKAGE 

STANDARDIZED PARTICIPATION RATES 

1 1 3

1 1 4

1 2 3 Official FSP Participation Rate

1 2 5 Shrinkage Standardized FSP Participation Rate

2 3 6

2 3 6

3 4 12

7 11 23

3 5 8

2 5 6

4 6 15

7 10 18

4 7 15

2 6 7

5 8 17

1 4 6

5 9 16

7 9 16

6 10 16

7 7 12

7 11 18

11 16 24

6 12 19

10 15 24

6 13 19

17 30 43

7 14 19

10 17 26

7 15 20

7 8 14

7 16 22

8 12 19

11 17 24

16 23 33

13 18 25

10 14 22

14 19 25

8 13 19

12 20 29

16 25 38

14 21 27

14 22 32

14 22 28

10 18 27

14 23 30

23 33 41

18 24 33

11 19 27

20 25 35

13 20 29

Vermont

Washington

Minnesota

Iowa

Nebraska

Arkansas

Ohio

Illinois

Mississippi

Alaska

Georgia

Indiana

Arizona

Kentucky

Oklahoma

South Carolina

Michigan

Maine

Louisiana

West Virginia

Hawaii

Oregon

Tennessee

Missouri

District of Columbia
47

Figure II.5 (continued) 

Note: The numbers within the shaded regions reflect the states’ ranking under that set of participation rates. The 

numbers at the beginning and end of the shaded regions reflect the endpoints of the 90 percent confidence 

intervals around the ranking. 

20 26 35

32 44 49

22 27 35

21 28 38

22 28 37

32 42 48

Official FSP Participation Rate 23 29 39

Shrinkage Standardized FSP Participation Rate 22 32 40

23 39 39

23 34 41

24 31 39

21 31 40

24 32 40

19 29 41

24 33 40

19 26 36

26 34 40

37 47 50

25 35 44

31 41 49

24 36 44

27 36 46

27 37 44

29 40 48

32 38 46

33 46 48

33 39 47

43 49 51

35 40 49

26 35 43

37 41 48

39 46 49

37 42 48

17 24 34

35 43 49

30 37 45

36 44 49

30 39 47

35 45 49

14 21 32

37 46 49

37 45 48

38 47 50

18 27 37

37 48 51

42 51 51

44 49 51

45 50 51

42 50 51

30 38 47

47 51 51

41 48 51

Alabama

Kansas

Wisconsin

Virginia

Pennsylvania

Delaware

Idaho

Connecticut

Rhode Island

South Dakota

New Mexico

North Dakota

Montana

North Carolina

Maryland

Texas

California

Florida

New York

Colorado

Utah

Nevada

Massachusetts

New Jersey

New Hampshire

Wyoming

49

The standardized participation rates presented here can help states better understand their 

direct participation rate. They can help states determine whether their rate is above or below the 

national average rate due to compositional factors or due to some other characteristics within the 

state. In an environment where USDA is encouraging states to increase participation rates, these 

results can help states identify which populations they can focus on to help raise their own rates. 

For instance, a state with a relatively large population of low income elderly—a population that 

participates at a low rate—may choose to focus outreach efforts on those households. 

Alternatively, states with relatively small populations of households with low earnings between 1 

and 50 percent of poverty—a population that participates at a high rate—may choose to focus 

outreach efforts on other populations. 

51

III. UNDERSTANDING VARIATION IN STANDARDIZED 

STATE PARTICIPATION RATES 

The standardized participation rates for 2003 presented in Chapter II provide an estimate of 

what state participation rates would be if each state’s eligible population resembled the nation as 

a whole. These standardized participation rates still vary from state to state, which may reflect 

the influence of each state’s economic and policy conditions. That is, even if two states had 

eligible populations with identical characteristics, one state’s economy or FSP policies may lead 

more eligible individuals to participate. 

In this chapter, we explore the relationship between state economic and policy conditions 

and the standardized FSP participation rates. We examine whether states with stricter policies 

and/or stronger economies tend to have lower standardized participation rates, while states with 

more lenient policies or weaker economies tend to have higher standardized participation rates. 

The results suggest that these policy and economic factors do not explain most of the 

variation in state participation rates—either standardized or unstandardized, which could provide 

evidence that participation is elastic relative to individual characteristics, but inelastic relative to 

policies and macroeconomic conditions. However, we also caution that the results are based 

only on 51 observations (for the 50 states and the District of Columbia). Given this small 

number of observations, policy and economic differences would need to lead to substantially 

large differences in standardized participation rates to be found significant by our model. 

Moreover, our results rest on our ability to accurately measure the strictness of a state’s policy 

regime. Because it is difficult to measure how state policies are implemented at the local level, 

and because it is difficult to construct meaningful measures of state policies, our results may also 

suggest that better measurement of state policies is necessary. 
52

Section A of this chapter explores the various policy and economic factors that may 

influence state FSP participation rates. Section B discusses options for measuring policies, while 

section C describes our methodological approach. Section D discusses the results of our 

analysis, and Section E provides some concluding remarks. 

A. POLICY AND ECONOMIC FACTORS THAT MAY INFLUENCE 

PARTICIPATION RATES 

An individual’s decision to participate in the FSP can be affected by both policy and 

economic conditions. If a given state’s policy regime increases the amount of effort needed for 

an eligible individual to enroll in the program, the individual may be less likely to apply for 

benefits. In a sense, the costs of applying outweigh the expected benefits. Similarly, if a state’s 

economy is strong, the eligible individual’s perceived need for benefits may be reduced; they 

may believe their future income will be sufficient and applying for benefits is unnecessary. 

Numerous other studies have examined the role that economic and policy factors play in 

driving state FSP (as well as TANF) participation trends. Studies by Gleason et al. (2000) and 

Ziliak et al. (2000) conclude that levels of food stamp participation by state are strongly 

influenced by state economic conditions. Both studies concluded that economic growth at the 

state level drove almost 50 percent of the decline in the FSP caseload after 1996. Ziliak et al. 

estimate that a 1 percentage point increase in the unemployment rate will lead to a 2.3 percentage 

point increase in the FSP caseload after one year. Both studies find that changes in state 

AFDC/TANF policies have some power in explaining changes in the number of FSP 

participants. Wilde et al. (2000) also find that state economic factors contribute substantially to 

the decline in the FSP caseload, but they attribute a smaller portion of the decline (35 percent) to 

those factors. Kornfeld (2002) also examined factors explaining changes in FSP caseloads, 

finding that participation among several adult households with children is most sensitive to 
53

economic changes. In addition, Kornfeld examined the role played by state FSP policies in 

determining participation levels and concluded that caseloads are sensitive to whether a state has 

high error rates, short certification periods, and electronic benefit transfer (EBT) systems. 

Because these studies focus on the number of participants rather than on participation rates, 

they generally do not suggest whether a given economic or policy factor makes the number of 

participants in a given state higher (lower) because it creates more (fewer) eligible households or 

because it encourages (discourages) participation by eligible households. Nevertheless these 

studies provide good guidance on what state factors may influence FSP participation patterns. 

We identified five broad state characteristics that could affect state FSP participation rates: 

(1) FSP policies, (2) TANF policies, (3) Medicaid/SSI policies, (4) Earned Income Tax Credit 

(EITC) policies, and (5) economic characteristics. Many studies have looked at the role that 

these factors play in driving participation in public assistance programs. Table III.1 summarizes 

which of these factors were found to significantly influence participation in the FSP and TANF 

programs. 

In the remainder of this section, we discuss the state characteristics explored in this study. 

For most variables, we measure the variable such that lower values reflect more lenient policies 

(i.e., policies that would lead to higher participation rates, all else being equal), and higher values 

reflect stricter policies (i.e., policies that would lead to lower participation rates, all else being 

equal). Consistently measuring the variables allows us to consolidate policies using principal 

components analysis (discussed in Section B).23

 23 See Appendix D for state-level values of the variables included in the study. 
54

TABLE III.1 

STATE CHARACTERISTICS USED IN OTHER STUDIES

FSP Studies

Kornfeld (2002)

Wilde et al 

(2000)

Currie et al 

(2001)a

Ziliak et al 

(2002)a

Bartlett et al 

(2004)

McKernan et al 

(2003)

Outcome

State per capita 

caseload

ln(state per capita 

caseload)

HH-level FSP 

participation 

ln(state per capita 

caseload)

Probability HH 

completes 

application

HH-level FSP 

participation 

1. FSP Policy

EBT

Presence of statewide EBT 9* 9* 9 9 9

Certification Periods and Reporting Requirements

FSP Error Rate 9* 9

Certification Periods 9* 9 9 9*

Monthly/Quarterly Reporting required 9

ABAWDs

Percent ABAWD Waived/subject to time limits 9* 9 9*

Other

Fingerprinting required of all applicants 9*

Number of 3rd party verification forms required 9

E&T requirements for non-TANF households 9

2. TANF Policy

FSP-Related Rules

Transitional Benefits

FSP Disqualifications/sanctions 9* 9 9

Eligibility

Income Eligibility Threshold

Earned Income Disregard 9* 9

Treatment of In-Kind Income

Asset Limits

Eligibility Rule Stringency (Factor)

Benefits

Max TANF ben - family of 3 (sometimes ln of ben) 999

Benefit while working in Month 12, 24

Accessibility

Family Cap 9* 9

Diversion Payments 9 9

Job Search Requirement 9

Behavior Related rules (Factor)

Time Limits and Work Requirements

Time Limit 9 9

Sanctions 9* 9

Work Requirement

3. Mediciad/SSI

Medicaid

Max Income for Medicaid among Age-Eligible Children

Max Age for Medicaid among Income Eligible Children

Whether num. Expansions adopted above national median

Medicaid Eligibility Thres. By child age group

Medicaid Eligibility for SSI Recipients

Flag if Family's Youngest Child Eligible for Medicaid 9

SSI

Maximum SSI Benefit (log)

4. EITC

Calculated state EITC

Refundable State EICTC

5. State Economy

Unemployment Rate 9* 9* 9 9* 9*

Employment Growth 9 9 9

Log Real Minimum Wage 9

Log 20th percentile wage 9*

Mean State Income

Mean Wage Manufacturing Industry

6. Other State Characteristics

Political Affiliation of Governor 9* 9* 9

Political Affiliation of Legislature 9* 9* 9

Region 9* 9*
55

Table III.1 (continued)

FSP Studies (Continued

Clarke et al 

(2004)a

Kabbani and 

Wilde (2002)

Mikelson and 

Lerman (2004)

Gleason et al. 

(2001) Yelowitz (2002)

Outcome

Change in State 

FSP Caseload

State per capita 

caseload

HH-level FSP 

participation 

Caseload as % of 

1991 Caseload

HH-level FSP 

participation

FSP Policy

EBT

Presence of statewide EBT 9* 9

Certification Periods and Reporting Requirements

FSP Error Rate

Certification Periods 9* 9*

Monthly/Quarterly Reporting required

ABAWDs

Percent ABAWD Waived/subject to time limits

Other

Fingerprinting required of all applicants

Number of 3rd party verification forms required

E&T requirements for non-TANF households

TANF Policy

FSP-Related Rules

Transitional Benefits 9*

FSP Disqualifications/sanctions 9*

Eligibility

Income Eligibility Threshold

Earned Income Disregard 9*

Treatment of In-Kind Income

Asset Limits

Eligibility Rule Stringency (Factor)

Benefits

Max TANF ben - family of 3 (sometimes ln of ben)

Benefit while working in Month 12, 24

Accessibility

Family Cap

Diversion Payments

Job Search Requirement 9*

Behavior Related rules (Factor)

Time Limits and Work Requirements

Time Limit 9*

Sanctions 9*

Work Requirement 9*

Mediciad/SSI

Medicaid

Max Income for Medicaid among Age-Eligible Children 9*

Max Age for Medicaid among Income Eligible Children 9*

Whether num. Expansions adopted above national median 9

Medicaid Eligibility Thres. By child age group 9

Medicaid Eligibility for SSI Recipients

Flag if Family's Youngest Child Eligible for Medicaid

SSI

Maximum SSI Benefit (log)

EITC

Calculated state EITC

Refundable State EICTC 9*

State Economy

Unemployment Rate 9 9* 9 9*

Employment Growth 9 9*

Log Real Minimum Wage

Log 20th percentile wage

Mean State Income 9*

Mean Wage Manufacturing Industry 9*

Other State Characteristics

Political Affiliation of Governor 9*

Political Affiliation of Legislature

Region
56

Table III.1 (continued)

TANF Studies SSI Studies

Fender et al 

(2002)a

Fender et al 

(2002)a Grogger (2000)a

McKernan et al. 

(2000)

DeJong and 

Graefe (2002) Schmidt (2004)

Outcome

Casload Entry 

(Hypothesized 

Effects)

Caseload Exit 

(Hypothesized 

Effects)

HH-level TANF 

participation 

HH-level 

Employment HH Migration

HH-level SSI 

receipt flag

FSP Policy

EBT

Presence of statewide EBT

Certification Periods and Reporting Requirements

FSP Error Rate

Certification Periods

Monthly/Quarterly Reporting required

ABAWDs

Percent ABAWD Waived/subject to time limits

Other

Fingerprinting required of all applicants

Number of 3rd party verification forms required

E&T requirements for non-TANF households

TANF Policy

FSP-Related Rules

Transitional Benefits 9*

FSP Disqualifications/sanctions

Eligibility

Income Eligibility Threshold 9

Earned Income Disregard 9

Treatment of In-Kind Income 9

Asset Limits 9 9

Eligibility Rule Stringency (Factor) 9*

Benefits

Max TANF ben - family of 3 (sometimes ln of ben) 9 9 9

Benefit while working in Month 12, 24 9

Accessibility

Family Cap 9

Diversion Payments 9

Job Search Requirement 9

Behavior Related rules (Factor) 9*

Time Limits and Work Requirements

Time Limit 9 9*

Sanctions 9 9

Work Requirement 9 9*

Mediciad/SSI

Medicaid

Max Income for Medicaid among Age-Eligible Children

Max Age for Medicaid among Income Eligible Children

Whether num. Expansions adopted above national median

Medicaid Eligibility Thres. By child age group

Medicaid Eligibility for SSI Recipients 9

Flag if Family's Youngest Child Eligible for Medicaid

SSI

Maximum SSI Benefit (log) 9

EITC

Calculated state EITC 9

Refundable State EICTC 9

State Economy

Unemployment Rate 9 9* 9*

Employment Growth

Log Real Minimum Wage 9 9

Log 20th percentile wage

Mean State Income

Mean Wage Manufacturing Industry

Other State Characteristics

Political Affiliation of Governor

Political Affiliation of Legislature

Region

a

Significance not available
57

1. FSP Policies 

State policy choices on FSP eligibility rules and procedures can potentially explain much of 

the variation in state participation rates. Studies of variation in state FSP policies are relatively 

new since, prior to the mid 1990s, few differences in FSP policies existed across states. As 

shown in Table III.2, we examine the following four types of state FSP policies: (1) statewide 

implementation of EBT cards, (2) requirements for reporting changes in income, (3) rules 

governing the eligibility of Able Bodied Adults without Dependents (ABAWDs), and (4) 

program accessibility. 

EBT. Several studies, including Kornfeld (2002), Wilde et al. (2000) and Kabbani and 

Wilde (2002) have found significant relationships between state FSP participation patterns and 

whether a state has implemented EBT. EBT cards, which operate much like debit cards, 

replaced the old Food Stamp coupons. States began adopting EBT in the late 1990s and its use is 

expected to increase FSP participation because it reduces the stigma associated with being a 

program participant. By 2003, the focus of this study, all but five states had implemented their 

EBT program statewide. For our analysis, we use a 0/1 flag to identify states with statewide 

EBT programs before 2003.24 

Certification Periods and Reporting Requirements. Recent changes to policies that 

affect FSP certification periods and reporting requirements may affect participation rates. The 

FSP certification period is the length of time a household has before it must effectively reapply 

for benefits. Certification periods typically range from 3 to 12 months, depending on the state 

guidelines and household circumstances. 

 24 Data on EBT policies comes from USDA (2003). 
58

TABLE III.2 

FSP POLICIES 

Policy Measure 

EBT 

EBT Flag (0/1) if EBT implemented statewide prior to 2003 

Certification Period, Reporting Requirement

Certification Periods Average period for households with earnings and for single-mother 

households 

Reporting Requirements Three flags (0/1), the first indicating the use of simplified or quarterly 

reporting, the second indicating the use of status or change reporting, and the 

third reflecting the combination of the first two flags 

ABAWD Rules

ABAWDs Subject to Work 

Requirements 

Flag (0/1) indicating at least some ABAWDs in the state are subjected to the 

work requirements 

Accessibility

Categorical Eligibility Flag (0/1) if state confers categorical eligibility to non-cash TANF households 

Vehicle Rules Relative leniency of state vehicle asset rules, or calculated countable vehicle 

value for a prototypical FSP unit 

Fingerprinting Flag (0/1) if state requires fingerprinting to apply for FSP benefits 

Application Page Length Measure of application page length 

Number of Visits Typical number of visits required to apply (in 2000) 

State FSP Outreach Efforts Flag (0/1) if state has special outreach activities 

Reporting requirements govern how a participating household must report changes to 

income in between recertification months. Traditional FSP rules require all income changes over 

$25 to be reported, although recent policy options allow states to make these rules more lenient. 

States have begun to lengthen certification periods for many clients, including those with volatile 

household income (such as households with earnings). Moreover, states can adopt a set of 

options on when clients report income changes (USDA 2003): 
59

• The Simplified Reporting option relieves clients from reporting any changes in 

income during their certification period, as long as their income does not exceed 130 

percent of poverty. 

• The Quarterly Reporting option allows clients to report their income every three 

months, regardless of whether it changes within that three month period. 

• Status reporting requires a client to report only when a household member has a 

change in jobs, receives a different rate of pay, or shifts from part time to full time (or 

has a similar change in employment status); income changes due to different hours of 

work do not need to be reported. 

• States can also adopt rules allowing clients to not report changes less than $100. 

States can combine these policies in ways that require more or less reporting from the client. For 

example, a state may adopt the lenient simplified reporting option, but may also require status 

reporting from clients. 

Changes to certification periods and reporting requirements might influence FSP 

participation rates if they make it easier to enroll in the program (or, in many cases, easier to stay 

enrolled in the program). Some previous studies have tried to measure the impact of these 

changes on FSP participation. Kornfeld (2002) and Ziliak et al. (2000) both used the FSP error 

rate as a proxy for how restrictive state certification and reporting rules are. The rationale behind 

using error rates is that states with more restrictive certification periods and reporting 

requirements (that is, states with rules more like the traditional rules) would have lower error 

rates, while states with more lenient certification periods and reporting requirements may have 

higher error rates. However, the error rate may be an imprecise proxy for certification and 

reporting periods because it only indirectly captures the factors at play, and it is not clear a priori

whether high error rates should be correlated with high participation rates or low participation 

rates. Other studies have used measures of the average certification period, either overall or for 

certain subgroups, but few studies have examined newer options on simplified reporting 

procedures. 
60

For our analysis, we use five variables to capture changes in certification periods and 

reporting requirements.25 For certification periods, we use each state’s average certification 

period for households with earnings as well as their average certification period for singlemother households. We suspect that, all else being equal, states where these certification periods 

tend to be longer will have higher participation rates. 

To capture the various reporting requirement options, we created three variables. The first is 

a 0/1 flag where a value of 1 indicates that the state has not adopted the simplified reporting or 

quarterly reporting options.26 The second is a 0/1 flag indicating that the state has adopted a 

change reporting or status reporting requirement. The third variable is the interaction of the first 

two since states with a value of 1 for both of the other reporting requirement variables are more 

strict than states with a value of one for either but not both of those variables. 

ABAWD Restrictions. The Personal Responsibility and Work Opportunity Reconciliation 

Act of 1996 (PRWORA) required ABAWDs to meet work requirements or face time-limited 

FSP benefits. Many aspects of the ABAWD rules can vary by state, but because the ABAWD 

population is a small portion of the overall eligible population, and because we have limited 

degrees of freedom in our analysis model, we wanted to minimize the number of variables used 

to measure ABAWD policies. The only ABAWD policy we examine is whether any ABAWD 

in a given state is subject to the work requirements. States can exempt large portions of the 

ABAWD population by waiving geographic regions with high unemployment and by applying 

the “15 percent exemption,” which allows states to exempt specific segments of the ABAWD 

population. Some states can combine these two options and exempt all ABAWDs in their state 

 25 Data on certification period and reporting requirement policies comes from USDA (2003). 

26 Since simplified reporting and quarterly reporting are considered more lenient than the traditional change 

reporting option, they are given the lower value in the flag. This lower value makes measuring this policy consistent 

with other policies where higher values are expected to lead to lower participation rates. 
61

from the work requirement. We created a 0/1 flag to indicate whether some ABAWDs are 

subjected to the work requirements. We expect states in which some ABAWDs are subjected to 

the work requirements to have lower participation rates, all else being equal. 

Program Accessibility. States and localities have control over policies and procedures that 

can make the FSP more or less accessible. Examples include: 27

• Categorical Eligibility. Some states confer categorical eligibility to households that 

receive non-cash TANF benefits. We created a 0/1 flag to indicate whether the state 

confers categorical eligibility to non-cash TANF households (the value is set to 0 if 

the state does confer categorical eligibility).

• Vehicular Asset Rules. States can change the rules for determining how the value of 

vehicles is treated when determining whether a household is eligible for benefits. We 

rank state vehicular asset rules as being lenient, moderate, or strict.

• Fingerprinting. Some states require FSP applicants to be fingerprinted, potentially 

increasing the burden associated with applying. We created a 0/1 flag to indicate 

whether the state requires fingerprinting. 

• Application Page Length. Longer FSP applications may make it more burdensome 

to apply for benefits. We constructed a variable reflecting the number of pages in the 

state FSP application. 

• Number of Visits. State rules may lead individuals in some states to make more trips 

to the local FSP office to apply than individuals in other states. We constructed a 

variable reflecting the number of visits typically required of an applicant to complete 

the application process. 

• FSP Outreach. States may have specific outreach campaigns to increase participation 

among eligible individuals. We created a 0/1 variable to indicate whether the state 

has an outreach program in place (the value is set to 0 if the state does have an 

outreach program). 

2. TANF Policies 

In 2003, about 17 percent of FSP households also participated in the TANF program 

(Cunnyngham and Brown 2004). In most states, the TANF application process is closely linked 

 27 Information on categorical eligibility and outreach efforts is based on USDA (2003). Information on 

vehicular rules comes from the Welfare Rules Database. Information on fingerprinting rules, application page length 

and number of office visits reflect conditions in 2000 and are based on O’Brien (2000). 
62

to the FSP application process. Assuming that individuals make their decision to participate in 

the FSP and TANF jointly, policies that discourage TANF participation may also discourage FSP 

participation. Much research has been conducted to date examining the effects that state TANF 

rules have on FSP and TANF participation. Several of these studies, including Kornfeld (2002), 

Kabbani and Wilde (2002), and Gleason et al. (2001), have found that stricter state TANF 

policies lead to smaller FSP caseloads. 

We have identified five broad classes of TANF policies: (1) FSP-related TANF policies, (2) 

TANF eligibility policies, (3) benefit levels, (4) accessibility policies, and (5) time limits and 

work requirements. Table III.3 summarizes the variables we examine in this study.28

FSP-Related TANF Policies. There are two key FSP-related TANF policies that could 

affect state FSP participation rates. The first is transitional benefits, which allows households 

leaving TANF for work to continue receiving food stamps for a set period, such as three months. 

The potential effect of transitional benefits on state participation rates is unclear. The policy 

continues to give FSP benefits to newly employed people who would have left the FSP 

otherwise, which could lead to higher participation rates. However, in the long run, the goal of 

the policy is to move individuals off of public assistance and into employment. If successful, 

this would lead more individuals to become ineligible, reducing both the number of participants 

and the number of eligibles, resulting in a lower participation rate for those remaining eligible. 

To measure transitional benefits, we use a 0/1 flag to indicate which states do not provide 

transitional benefits. 

 28 Data on TANF policies comes from the Welfare Rules Database. 
63

TABLE III.3 

TANF POLICIES 

Policy Measure 

FSP-Related Policies 

Transitional benefits 

(FSP) 

Flag (0/1) indicating no FSP transitional benefits 

FSP sanctions Flag (0/1) indicating FSP sanctions policy 

Eligibility 

Income Threshold Eligibility threshold, household of 3 

Earned Income 

Disregard 

Percent of earnings not disregarded for a family of 3 

Benefits 

Maximum Benefit Maximum Benefit, family of 3 

Family Cap Flag (0/1) indicating that the state has a family cap provision 

Accessibility 

Diversion Payments Measure of relative size of diversion payments 

Application 

Requirements 

Separate flags (0/1) indicating whether job search, drug testing, parenting classes, 

school attendance and immunization are required at application 

Citizenship 

Requirements 

Measure of stringency of citizenship requirements 

Time Limits and Work Requirements

Lifetime Time Limit Measure of duration of lifetime time limits 

Intermittent Time Limit Flag (0/1) indicating whether the state has an intermittent time limit 

Time Limit Exemption 

Policies 

Separate flags (0/1) indicating whether the state does not exempt individuals who 

are ill/incapacitated from the time limits, and whether the state does not exempt 

mothers of children age 3 to 12 months from the time limits. 

Time Limit Extensions Flag (0/1) indicating the state does not allow time limits to be extended 

Qualifying Activities Measure of the number of activities that can qualify as meeting the work 

requirement 

Minimum Hours in Work 

Activities 

Flag (0/1) indicating the state requires 30 or more hours in a work activity to meet 

the work requirement. 

Start of Work 

Requirements 

Flag (0/1) indicating the state does work requirements apply before participants’ 3rd

month of benefits. 

Work Requirement 

Exemption Policies 

Separate flags (0/1) indicating whether the state does not exempt individuals who 

are ill/incapacitated from the work requirements, and whether the state does not 

exempt mothers of children older than 6 months from the work requirements. 

Severe Sanction Amount The most severe sanction for a family of 3. 

Severe Sanction Length A variable classifying states by the duration of the most severe sanction (1 = long, 5 

= short) 

Difference between 

Severe and First 

Sanctions 

A measure of the difference between the most severe sanction and the first sanction 

for a family of 3. 
64

The second FSP-related TANF policy sanctions FSP benefits for individuals who do not 

comply with TANF work requirements. We would expect FSP sanctions to lower participation 

rates if they discourage eligible clients from participating. Also, if the policy successfully moves 

participants from welfare to work, then as with transitional benefits, the policy could also lower 

participation rates. We use a 0/1 flag to identify states that have FSP sanction policies as part of 

their TANF program. 

Eligibility. There are numerous TANF eligibility rules that vary from state to state. These 

include: the income eligibility threshold, the amount of earnings that can be disregarded when 

determining eligibility, the treatment of in-kind income, asset limits, eligibility status of pregnant 

women, citizenship requirements, and activities (such as job search, parenting classes and drug 

screening) that are required to be eligible. Given that our model has limited degrees of freedom, 

we chose to focus on the two eligibility rules that affect the most applicants: the income 

eligibility threshold and the earned income disregard. To measure the income eligibility 

threshold such that higher values reflect stricter policies, we rescaled the 2003 state income 

eligibility thresholds.29 Similarly, to measure the earned income disregard such that higher 

values reflect stricter policies, we compute the percentage of income that would not be 

disregarded for a family of three in each state.30

Benefits. The maximum TANF benefit is set by states and varies widely. In 2003, the 

maximum benefit for a family of three ranged from $164 in Alabama to $923 in Alaska. The 

median state benefit was $396. Our model uses the maximum benefit for a family of three, 

 29 For each state, the measure of the income eligibility threshold is computed as (2000-THRESH)/20, where 

THRESH is the income eligibility threshold for a family of three. The minimum value was 18.0, reflecting the state 

with the highest threshold; the maximum value was 89.9, reflecting the state with the lowest threshold. 

30 We used the percent of earnings that would be disregarded in each state for a family of three working 20 

hours a week at the minimum wage. To rescale the variable such that higher values reflect stricter policies, we 

calculate the percent of income that would not be disregarded. 
65

rescaled such that higher values reflect lower benefits.31 In addition to the maximum benefit, we 

include a flag (0/1) for whether the state has a family cap policy limiting benefits to children 

born to ongoing TANF clients. 

Accessibility. There are several TANF rules that can make the application process more or 

less accessible. These include: 

• Diversion payments. Diversion payments are intended to steer applicants away from 

the TANF program. Our model uses the maximum diversion payment in each state. 

High values are treated as strict since they are more likely to reduce the number of 

program entrants. 

• Application requirements. Many states require TANF applicants to complete certain 

activities before their application can be approved. We included separate flags (0/1) 

for the following five application requirements: (1) applicants conduct job search, (2) 

applicant’s minor children attend school, (3) children are required to be immunized, 

(4) drug and/or alcohol screening and/or treatment is required of most/all applicants, 

and (5) applicants with children must attend parenting classes. 

• Citizenship rules. We created a 0/1 flag to distinguish states with more lenient 

policies governing the eligibility of noncitizens from states with stricter policies. 

Lenient states (flag = 0) are those in which all nonexempt noncitizens in country for 

more than five years are eligible and where there is some state funding for noncitizens 

entering more recently (there are 24 lenient states). Strict states (flag = 1) are the 

remaining states. 

Time Limits and Work Requirements. TANF requires beneficiaries to participate in 

work-related activities and places a time limit on how long individuals can receive benefits. 

States have many policies that govern these time limits and work requirements, including: 

• Duration of lifetime time limits. States can limit the total number of months 

individuals can receive TANF. We rescaled the state lifetime time limits such that 

higher values reflect stricter rules.32 A state with no time limit is given a value of 0. 

 31 For each state, the measure of benefit is computed as (1000-MAXBEN)/10, where MAXBEN is the 

maximum benefit for a family of three. The minimum value is 7.7, reflecting the state with the highest maximum 

benefit, and the maximum value is 83.6, reflecting the state with the lowest maximum benefit. 

32 Our lifetime time limit variable is computed as (120-m) where m is the number of months for the state 

lifetime time limit. The resulting variable ranges from 0 to 99. 
66

• Intermittent time limit. Some states also have an intermittent time limit for TANF 

receipt (e.g., a state may limit an individual to 24 months of benefits until they have 

36 months of ineligibility). We use a flag (0/1) to indicate whether a state has any 

intermittent time limit. 

• Time limit exemptions. We constructed two separate flags to reflect the states’ 

policies on time limit exemptions. The first flag indicates whether the state exempts 

individuals who are ill or incapacitated (a value of 1 means the state does not exempt 

these individuals). The second flag indicates whether the state exempts mothers with 

children ages 3 to 12 months (a value of 1 means the state does not exempt these 

individuals). 

• Time limit extensions. Some states allow time limits to be extended for individuals 

who have complied with the work requirement policies but who can not find 

employment. We use a flag (0/1) to identify states that have no extension policies. 

• Number of qualifying activities. The number and type of activities that meet TANF 

work requirements varies from state to state. We use the number of work 

requirement activities, rescaled such that high values reflect fewer work 

requirements.33

• Minimum hours of work activity. States can set the number of hours that an 

individual must participate in a work activity in order to meet the work requirement. 

We use a flag (0/1) to identify states with a minimum of 30 hours or more. 

• Start of work requirement. States can establish how soon after entry into the TANF 

program the work requirement applies. We use a flag (0/1) to identify states whose 

work requirement applies before participants’ third month of benefits. 

• Work requirement exemptions. We constructed two separate flags to reflect the 

states’ policies on work requirement exemptions. The first flag indicates whether the 

state exempts individuals who are ill or incapacitated (a value of 1 means the state 

does not exempt these individuals). The second flag indicates whether the state 

exempts mothers with children older than 6 months (a value of 1 means the state does 

not exempt these individuals). 

• Most severe sanction amount. States can sanction individuals for noncompliance in 

the work requirements. We compute the state’s most severe sanction amount for a 

family of three. 

• Duration of most severe sanction. States can also establish the duration that their 

most severe sanction applies. We classified states into five groups based on the 

duration of severe sanctions.34 

 33 We measure the number of qualifying activities as (5-n), where n is the number of qualifying activities. The 

resulting variable ranges from 0 to 3. 

34 Severe sanctions are classified as: 1=one month or until compliance; 2=2-5 months; 3=6-11; months; 

4=12=36 months; and 5=permanent. 
67

• Difference between severe and first sanction. States may apply initial sanctions that 

are lower than their most severe sanction. We measure the difference between the 

most severe sanction and the initial sanction in each state. 

The TANF program has numerous other rules that affect program eligibility, but these other 

rules are not examined in this study. The limited number of observations in our model requires 

us to limit the number of covariates included. Some TANF rules, such as rules for asset 

eligibility, are captured indirectly through the state FSP rules.35 Other TANF rules, such as rules 

governing the eligibility of pregnant women, the treatment of deemed income, and the treatment 

of in-kind income, likely affect such a small portion of the FSP eligible population that they 

many not affect state FSP participation rates. 

3. Medicaid and SSI Policies 

State Medicaid policies may also affect state FSP participation rates. Gleason et al. (2002) 

found that FSP caseloads are affected by state Medicaid policies. They used two measures of 

Medicaid policies: (1) the maximum age at which income-eligible children automatically qualify 

for Medicaid benefits, and (2) the maximum income at which age-eligible children are 

automatically eligible for benefits. Yelowitz (2001) examined state expansions to 

Medicaid/SCHIP over the 1990s, measuring eligibility thresholds for children in various age 

groups and examining whether the state had adopted more than the median number of outreach 

expansions. Uniform data on state Medicaid outreach rules—similar to the measures used by 

Yelowitz—is not available for the 2003 analysis period. To capture state variation in Medicaid 

rules, we use the state SCHIP eligibility threshold for children (Table III.4). 

Few studies have examined the role that SSI policies can play in driving FSP participation. 

While SSI is a federal program, many states will supplement the federal SSI benefit with 

 35 Many states have aligned their FSP asset rules to be consistent with their TANF asset rules. 
68

additional benefits. We capture this state variation with a variable that reflects the maximum 

federal plus state SSI benefit to an eligible individual in each state. 

TABLE III.4 

MEDICAID AND SSI POLICIES 

Policy Measure 

Medicaid 

SCHIP eligibility Measure of eligibility threshold (percent of poverty) for children in SCHIP 

SSI 

Maximum Benefit Maximum federal + state SSI benefit for an individual 

4. EITC and State Economy 

Few studies have examined the role of state earned income tax credit programs in 

determining FSP or TANF participation. The federal EITC, which is uniform across states, is a 

refundable tax credit, meaning people with earnings receive the credit even if it exceeds their tax 

liability. Several states have additional earned income credits, some of which are refundable and 

some of which are not. The availability of the state credit would raise household incomes, 

potentially reducing the likelihood that eligible individuals participate in the FSP. Mikelson and 

Lerman (2004) found that a 0/1 indicator for whether a state’s EITC was refundable was 

negatively correlated with a household’s FSP participation decision. Fender et al. (2002) use 

state rules to compute the credit for a prototypical family. For this study, we use a measure 

similar to that constructed by Fender et al., reflecting the state EITC amount for a prototypical 

family (Table III.5). We do not account for whether the EITC is refundable (in 2003, the EITC 

was refundable in 13 of the 16 states with state EITCs). 

Another factor that could affect state FSP participation rates is the overall state economy. 

Eligible individuals may be less likely to participate in the FSP if the state economy is strong, 
69

since their expectations for future earnings may be higher. We use three separate measures of 

the state economy: 

• Unemployment Rate. We use the overall state unemployment rate in 2003 

• Employment Growth Rate. We measure the change in state employment from 

January 2002 to January 2003. 

• 20th Percentile Wage. We measure the 20th percentile hourly wage in the state. 

TABLE III.5 

STATE EITC AND ECONOMIC MEASURES 

Policy Measure 

State EITC 

State EITC A calculated state EITC amount for a prototypical family 

State Economy 

Unemployment rate Unemployment rate for analysis year 

Employment 

Growth Rates 

Employment growth over a fixed period 

20th Percentile Wage 20th Percentile Wage 

B. VARIABLE REDUCTION 

The model to measure the effects of state policies on state participation rates has only 51 

observations—one for each state in 2003. To conserve degrees of freedom, we used principal 

components analysis to combine policies for inclusion in the model. Principal components 

analysis is a variable reduction technique that combines policies that covary across states. 

Principal components analysis takes n variables and constructs n principal components, where 

the first component is the linear combination of variables that contribute the most to the total 

variation, the second principal component (which is uncorrelated with the first) contributes the 

most to the residual variation, and so on (Harman 1967). The principal components are simply a 

weighted linear combination of variables with no assumption of causal influence (Hatcher 1994). 
70

We then identify y—the number of principal components needed to explain most of the variation 

in reflected in the n variables. We transform the principal components into y factors. 

Following Hatcher (1994), we wanted factors formed from at of least three variables, so we 

conducted principal components analysis on sets of related policies where there are at least five 

separate policy measures (assuming two measures will “drop out” of the analysis). We applied 

principal components analysis to three sets of similar variables: (1) FSP accessibility, (2) TANF 

eligibility, benefits, and accessibility, and (3) TANF work requirements and time limits (Table 

III.6).36 All of the variables included in these three groups have been defined such that higher 

values are expected to lead to lower participation rates, all else being equal. While these policies 

have different metrics, an initial step of the principal components analysis converts each variable 

to have a mean of zero and a standard deviation of one. 

1. FSP Accessibility 

Table III.7 shows the initial six components constructed from the six FSP accessibility 

policies. Two components have an eigenvalue greater than one (meaning they account for the 

variance of more than one variable). Combined, these two components explain 49 percent of the 

total variation observed in the six FSP accessibility policies. 

 36 Hatcher (1994) also recommends that the minimum number of observations included in the analysis should 

be five times the number of variables. Because we have a fixed number of observations—51—this suggests that we 

should have a maximum of 10 variables in each group. Two of the three groups have fewer than 10 variables; the 

third group—TANF Work Requirements and Time Limits—has 13 variables. However, we were not able to explain 

more variation across policies by limiting this third group to 10 variables. 
71

TABLE III.6 

POLICIES CONSOLIDATED THROUGH PRINCIPAL COMPONENTS ANALYSIS 

FSP Accessibility 

TANF Eligibility, Benefits and 

Accessibility 

TANF Work Requirements and 

Time Limits 

Categorical Eligibility 

Vehicle Rules 

Fingerprinting 

Application Page Length 

Number of Visits 

State FSP Outreach Efforts 

Income Threshold 

Maximum Benefit 

Family Cap 

Diversion Payments 

Job Search Application 

Requirements 

School Attendance Application 

Requirements 

Immunization Application 

Requirements 

Drug Testing Application 

Requirements 

Parenting Class Application 

Requirements 

Citizenship Requirements 

Lifetime Time Limit 

Intermittent Time Limit 

Time Limit Exemption for 

Ill/Incapacitated 

Time Limit Exemption for Parents 

with Young Children 

Time Limit Extensions 

Qualifying Activities 

Minimum Hours in Work 

Activities 

Start of Work Requirements 

Work Requirement Exemption for 

Ill/Incapacitated 

Work Requirement Exemption for 

Parents with Young Children 

Severe Sanction Amount 

Severe Sanction Length 

Difference between Severe and 

First Sanctions 

TABLE III.7 

INITIAL COMPONENTS, FSP ACCESSIBILITY 

Principal 

Component Eigenvalue 

Cumulative Variation 

Explained 

1 

2 

3 

4 

5 

6 

1.55787087 

1.36907502 

0.9565634 

0.79851673 

0.71415116 

0.60382282 

0.2596 

0.4878 

0.6473 

0.7803 

0.8994 

1.0000 
72

We retained the two components with eigenvalues greater than one.37 Table III.8 shows the 

results of the verimax rotation of these components. Since this is an orthogonal rotation, the 

coefficients—called loadings—in Table III.8 reflect the bivariate correlation between the 

variable and the factor. Variables with loadings greater than 0.4 are flagged as “meaningful” (we 

ignore variables that have loadings greater than 0.4 on more than one component). We can then 

use the meaningful variables to interpret the factors. Thus, Factor 1 represents the number of 

visits required and FSP outreach policies, and Factor 2 represents the categorical eligibility and 

vehicular asset policies. 

TABLE III.8 

FACTOR LOADINGS, FSP ACCESSIBILITY 

Input Variable Factor 1 Factor 2 

Categorical Eligibility 

Vehicle Rules 

Fingerprinting 

Application Page Length 

Number of Visits 

State FSP Outreach Efforts 

-0.50 

0.40 

-0.56 

0.49 

-0.67* 

0.72* 

0.64* 

0.71* 

0.43 

0.43 

0.17 

0.31 

2. TANF Eligibility, Benefits, and Accessibility 

To reduce the number of TANF-related variables, we explored using principal components 

analysis over three alternative combinations of TANF eligibility, benefits, accessibility, and time 

limit/work requirement policies: (1) one large group containing all policies, (2) two separate 

groups (one for eligibility, benefits, and accessibility policies and one for time limit/work 

requirement policies), and (3) three separate groups (one for eligibility and benefits, one for 

 37 As an alternative to keeping those components with an eigenvalue greater than 1, Hatcher (1994) suggests 

keeping those components that explain at least 70 percent of the total variation. In this case, that would suggest 
73

accessibility, and one for time limit/work requirement policies). Using two separate groups was 

preferred to using only one group because it yielded factors that were easier to interpret. For 

example, when we used only one large group of all TANF policies, one factor was loaded by 

income eligibility, application requirement, work requirement exemption, and sanction amount 

policies. If this factor were shown to influence standardized state participation rates, it would be 

difficult to interpret what policy issues were actually affecting the rates. Because a key goal of 

this process is variable reduction, using two separate groups was preferred to using only one 

group because it yielded fewer total factors. 

Table III.9 shows the eigenvalues of the initial components extracted for the TANF 

eligibility, benefits, and accessibility variables. Three components have an eigenvalue greater 

than 1, explaining 59 percent of the total variance observed in these policies. Table III.10 shows 

the factor loadings for these variables. Factor 1 is interpreted as reflecting income eligibility, 

benefit, and citizenship policies; Factor 2 is interpreted as family cap, school attendance, and 

immunization policies; and Factor 3 is interpreted as reflecting drug testing and parenting class 

application requirements. 

TABLE III.9 

INITIAL COMPONENTS, TANF ELIGIBILITY, BENEFITS AND ACCESSIBILITY 

Principal Component Eigenvalue Cumulative Variation Explained 

1 

2 

3 

4 

5 

6 

7 

2.97152437 

1.57898459 

1.36665214 

0.95301365 

0.86001204 

0.70149325 

0.48894638 

0.2972 

0.4551 

0.5917 

0.6870 

0.7730 

0.8432 

0.8921 

 

(continued) 

keeping the first four components. Because the goal of this process is to conserve degrees of freedom, we chose to 

keep only the two components with eigenvalues greater than 1. 
74

8 

9 

10 

0.40641409 

0.37107541 

0.30188408 

0.9327 

0.9698 

1.0000 

TABLE III.10 

FACTOR LOADINGS, TANF ELIGIBILITY, BENEFITS AND ACCESSIBILITY 

Input 

Variable Factor 1 Factor 2 Factor 3 

Income Threshold 

Maximum Benefit 

Family Cap 

Diversion Payments 

Job Search Application Requirements 

School Attendance Application Requirements 

Immunization Application Requirements 

Drug Testing Application Requirements 

Parenting Class Application Requirements 

Citizenship Requirements 

0.70 * 

0.75 * 

0.11 

-0.75 * 

0.43 * 

0.11 

0.60 

0.19 

-0.11 

0.64 * 

0.27 

0.39 

0.66 * 

0.16 

-0.19 

0.84 * 

0.80 * 

0.22 

0.40 

0.16 

-0.18 

0.10 

-0.50 

-0.17 

0.36 

0.17 

0.11 

0.67 * 

0.88 * 

0.10 

3. TANF Work Requirements and Time Limits 

For the 13 TANF work requirement and time limit policies, 6 principal components have an 

eigenvalue greater than 1 (Table III.11). Keeping six factors, Factor 1 reflects time limit 

policies, Factor 2 reflects sanction amounts, Factor 3 reflects work requirement exemptions, 

Factor 4 reflects the number of qualifying activities, Factor 5 reflects work requirement 

extensions and the start of the work requirement, and Factor 6 reflects the presence of 

intermittent time limits and the length of sanctions (Table III.12). 

4. Factor Scores 

To use the results of the principal components analysis, we want to create variables that 

reflect each state’s position relative to others along the factors that have been extracted. There 

are two approaches to constructing these variables (Hatcher 1994). Factor scores are optimally 

weighted linear combinations of all variables in each group of policies. Factor-based scores are 
75

unweighted linear combinations of just those variables that demonstrate meaningful loadings. 

We constructed factor scores such that the summary factors reflect all policies examined. 
76

TABLE III.11 

INITIAL COMPONENTS, TANF WORK REQUIREMENT AND TIME LIMIT POLICIES 

Principal 

Component Eigenvalue 

Variation 

Explained 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

2.48634807 

2.26003143 

1.44667304 

1.25522381 

1.10302537 

1.02725068 

0.94024893 

0.72886667 

0.57647948 

0.44687639 

0.3155546 

0.24896106 

0.16446044 

0.1913 

0.3651 

0.4764 

0.5729 

0.6578 

0.7368 

0.8091 

0.8652 

0.9095 

0.9439 

0.9682 

0.9873 

1.0000 

TABLE III.12 

FACTOR LOADINGS, TANF WORK REQUIREMENT AND TIME LIMIT POLICIES 

Input Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 

Lifetime Time Limit 

Intermittent Time Limit 

Time Limit Exemption for Ill/Incapacitated 

Time Limit Exemption for Young Children 

Time Limit Extensions 

Qualifying Activities 

Minimum Hours in Work Activities 

Start of Work Requirements 

Work Requirement Exemption for 

Ill/Incapacitated 

Work Requirement Exemption for Young 

Children 

Severe Sanction Amount 

Severe Sanction Length 

Difference between Severe and First 

Sanctions 

0.74 * 

-0.32 

0.73 * 

0.87 * 

0.19 

0.60 

-0.10 

-0.90 

0.20 

-0.17 

0.00 

0.23 

0.90 

0.40 

-0.80 

0.12 

-0.10 

-0.10 

-0.9 

-0.67 

-0.60 

0.16 

0.12 

0.81 * 

-0.50 

0.89 * 

0.40 

0.36 

0.33 

-0.11 

0.18 

0.80 

0.40 

0.33 

0.76 * 

0.71 * 

0.23 

-0.15 

0.80 

0.31 

-0.50 

-0.39 

-0.30 

-0.30 

0.86 * 

-0.45 

0.90 

-0.10 

0.38 

-0.60 

0.00 

-0.20 

0.11 

-0.70 

0.80 

0.13 

0.82 * 

-0.70 

0.17 

-0.71 * 

-0.10 

-0.30 

0.26 

0.11 

-0.16 

0.70 

0.61 * 

0.40 

0.00 

-0.90 

-0.40 

-0.24 

-0.24 

0.14 

-0.24 

-0.22 

0.71 * 

-0.70 
77

The principal components analysis enables us to reduce 29 FSP and TANF policy variables 

to 11 factor scores, a substantial reduction considering the state level model will be estimated 

over only 51 observations. As discussed in the next section, we conducted sensitivity analyses of 

the factor scores, replacing each set of factors with all component policy variables and with those 

component policy variables that have the highest loadings.

C. ESTIMATION METHODS 

The basic model for examining the degree to which standardized participation rates vary 

with respect to state policy and economic conditions is: 

s s ss rP E e ′ = α β + +

where, 

sr′ = 2003 standardized participation rate for state s (a participation rate of 50 percent is 

measured as 0.50) 

Ps = a vector of policy variables for FSP, TANF, Medicaid, SSI, and EITC rules in state s 

in 2003 

Es = a vector of economic conditions in state s in 2003 

This model is estimated with one observation per state. For the basic model, we weight the 

individual state participation rate estimates by the inverse of the variance of those participation 

rate estimates. This weighting allows standardized rates for which we have more confidence to 

have more weight in the regression results. Our variance estimates were derived using jackknife 

estimation techniques and then smoothed with a generalized variance function (GVF). 

Table III.13 lists the various specifications of this model. In addition to the basic model 

(Model 1), we estimated the following alternative specifications: 

• Alternative Weighting: To examine the effects of weighting, we estimate two 

alternative versions. Model 2 is identical to Model 1, except the individual state 

observations are unweighted. Model 3 is also identical to Model 1, except it is 
78

weighted using the initial jackknife variance estimates (that is, the variance estimates 

before being smoothed with the GVF). 

• Direct Participation Rates: To examine how the correlation between state policy 

conditions and participation rates differ between standardized and direct 

(unstandardized) participation rates, we replicate Models 1 and 2 using the direct 

participation rates (Models 4 and 5). 

• Simplified Model: To examine whether the limited degrees of freedom constrains the 

model, we estimate a simplified version of Model 1 using a reduced set of covariates 

(Model 6). 

TABLE III.13 

ALTERNATIVE MODEL SPECIFICATIONS 

Basic 

Model 

Alternative 

Weighting 

Direct 

Participation 

Rates 

Simplified 

Model 

 1 2 3 4 5 6 

Standardized Participation Rate 3 3 3 3

Direct Participation Rate 3 3 

Weighted Model (GVF) 3 3 3

No Weights 3 3 

Weighted Model (Jackknife) 3 

Degrees of Freedom 23 23 23 23 23 37 

D. RESULTS 

In general, the variance in participation rates cannot be explained by differences in state 

policies or economic conditions. This holds true whether we are examining the variation in 

standardized or direct participation rates, which suggests that participation rates vary for reasons 

other than state program policies or aggregate state economic conditions. 

Table III.14 presents the results of Model 1. Only two policies have a significant 

relationship with the standardized participation rates: (1) the state FSP policy on change 
79

TABLE III.14 

RESULTS FROM MODELS OF STANDARDIZED PARTICIPATION RATES 

USING ALTERNATIVE APPROACHES TO WEIGHTING (MODELS 1, 2 AND 3) 

 Model 1 

(Weighted, GVF) 

Model 2 

(Unweighted) 

Model 3 

(Weighted, Jackknife) 

Coefficient 

Standard 

Error Coefficient 

Standard 

Error Coefficient 

Standard 

Error 

 

Intercept 1.0305 (0.4071) 1.1373 (0.4932) 0.8668 (0.4529) 

 

FSP Policies 

EBT 0.0721 (0.0575) 0.0856 (0.0677) 0.0603 (0.0658) 

Certification Period 

(Earnings) 0.0134 (0.0341) 0.0216 (0.0372) 0.0173 (0.0390) 

Certification Period 

(Single Mothers) -0.0042 (0.0412) -0.0179 (0.0458) -0.0071 (0.0464) 

Reporting Requirements 0.0758 (0.1002) 0.1067 (0.1236) 0.0688 (0.1097) 

Change Reporting 0.1188* (0.0521) 0.0811 (0.0668) 0.1153 (0.0591) 

Reporting x Change -0.0596 (0.1314) -0.0613 (0.1608) -0.0462 (0.1417) 

ABAWD Rules -0.0884 (0.0615) -0.0610 (0.0738) -0.096 (0.073) 

Accessibility Policies 

Factor 1 -0.0005 (0.0280) -0.0067 (0.0331) 0.0008 (0.0288) 

Factor 2 -0.0732* (0.0284) -0.0604 (0.0374) -0.0589 (0.0292) 

 

TANF Policies 

Transitional FSP Benefits -0.0178 (0.0658) 0.0086 (0.0890) -0.0334 (0.0732) 

FSP Sanctions -0.0417 (0.0707) -0.0789 (0.0775) -0.007 (0.0782) 

Earned Income Disregard -0.0001 (0.001) -0.0001 (0.0012) 0.0002 (0.0012) 

Eligibility, Benefits, and 

Accessibility 

Factor 1 -0.0370 (0.0335) -0.0218 (0.0399) -0.037 (0.0369) 

Factor 2 -0.0205 (0.0250) -0.0344 (0.0304) -0.0242 (0.0277) 

Factor 3 -0.0131 (0.0274) -0.0086 (0.0329) -0.0055 (0.0294) 

Work Requirements and Time 

Limits 

Factor 1 -0.0331 (0.0328) -0.0601 (0.0367) -0.0366 (0.0366) 

Factor 2 -0.0269 (0.0275) -0.0145 (0.0334) -0.0251 (0.0276) 

Factor 3 -0.0148 (0.0261) 0.0006 (0.0322) 0.0002 (0.0311) 

Factor 4 0.0046 (0.0244) 0.0106 (0.0289) -0.0007 (0.0255) 

Factor 5 0.0037 (0.0262) 0.0121 (0.0287) 0.0053 (0.0293) 

Factor 6 0.0014 (0.0313) 0.0022 (0.0393) 0.0068 (0.0312) 

 

Other Programs 

Medicaid/SCHIP Eligibility -0.0004 (0.0007) 0.0000 (0.0008) 0.0001 (0.0007) 

SSI Maximum Benefit -0.0005 (0.0005) -0.0006 (0.0005) -0.0005 (0.0006) 

EITC 0.0000 (0.0001) -0.0001 (0.0001) -0.0001 (0.0001) 

 

Economic Indicators 

Unemployment Rate -0.0262 (0.0288) -0.0107 (0.0339) -0.0225 (0.0345) 

20th Percentile Wage 0.0003 (0.0427) -0.0194 (0.0520) -0.001 (0.0466) 

Employment Growth Rate -0.0132 (0.0134) -0.0184 (0.0154) -0.0131 (0.0146) 

 

Adjusted R-Square -0.0637 -0.1458 -0.0578 

* Significant at the 95% level of confidence. 
80

reporting, and (2) FSP accessibility Factor 2 (reflecting categorical eligibility and vehicular 

assets). The coefficient on the change reporting variable indicates that states with a change or 

status reporting policy have a participation rate that is almost 12 percentage points higher than 

states that do not. The coefficient on the FSP accessibility Factor 2 variable suggests that states 

with more strict categorical eligibility and vehicular asset rules tend to have lower FSP 

participation rates. 

These policy effects are sensitive to the weighting approach we use in our models. The 

significant relationships are apparent only when we estimate the model weighted with the GVFbased variance estimates of the participation rates. The GVF estimation process smoothes the 

more volatile jackknife-based estimates of the participation rate variances, lowering the variance 

estimates for several states that have small sample sizes and hence relatively large jackknifebased variance estimates for their participation rates. Because we weight by the inverse of the 

variance, lowering the variance increases the weight these states get in Model 1 (variance 

smoothed) relative to Model 3 (variance not smoothed). However, these states still have lower 

relative weight in Model 3 than they do in Model 2, where each state is given equal weight. 

In other words, these policy effects are apparent only when we give more weight to states 

where we have more confidence in the participation rate; they are not apparent if we treat each 

participation rate equally, and they are not apparent if we give extremely small weights to states 

where we are less confident in their participation rates. Because we believe the GVF-based 

variance estimates are the best estimates of the participation rate variances, we are inclined to 

interpret these policy effects as potentially meaningful. However, because the effects are 

sensitive to the model weighting, caution should still be used in drawing policy implications 

from these results. 
81

We used F-tests to examine whether the key policies taken as a group could explain the 

variation in state participation rates. In particular, we conducted F-tests grouping all FSP 

policies, all TANF policies, all other program policies and all economic conditions. The F-tests 

indicated that even when taken as a group, these policy variables are not significantly correlated 

with standardized participation rates. 

In general, the lack of significance among the covariates suggests that after standardizing 

state participation rates, aggregate state characteristics do not explain the residual variation in 

these rates. However, even before the standardization process, these variables are not correlated 

with the direct participation rates. Table III.15 presents the results from models estimated using 

the direct participation rates. No state policy or economic characteristic is significantly related to 

the direct state participation rates. Thus, regardless of whether state participation rates are 

standardized for the composition of state eligible populations, the variation in participation rates 

cannot be explained by variation in state characteristics. 

It is possible that the lack of significant relationships between state characteristics and state 

participation rates could be driven by the limited degrees of freedom. With only 51 observations, 

there may not be enough variation in rates to be explained by the numerous covariates. We 

estimated a simplified version of Model 1 with a reduced set of covariates. In the simplified 

version, we estimated the model without selected FSP policy variables, without any factor-based 

variables, and without covariates representing Medicaid and SSI policies. The results from the 

simplified version are consistent with other models (Table III.16). When we reduce the number 

 
82

TABLE III.15 

RESULTS FROM MODELS OF DIRECT PARTICIPATION RATES (MODELS 4 AND 5) 

 Model 4 

(Weighted, GVF) 

 Model 5 

(Unweighted) 

 

Coefficient 

Standard 

Error 

 

Coefficient 

Standard 

Error 

 

Intercept 0.7915 (0.3632) 0.8276 (0.4272) 

 

FSP Policies 

EBT 0.0210 (0.0510) 0.0131 (0.0586) 

Certification Period (Earnings) 0.0270 (0.0304) 0.0329 (0.0322) 

Certification Period 

(Single Mothers) 

-0.0204 (0.0372) -0.0364 (0.0397) 

Reporting Requirements 0.1055 (0.0873) 0.1658 (0.1071) 

Change Reporting 0.0766 (0.0467) 0.0370 (0.0578) 

Reporting x Change -0.1498 (0.1120) -0.1848 (0.1393) 

ABAWD Rules -0.0408 (0.0541) -0.0198 (0.0639) 

 

Accessibility Policies 

Factor 1 0.0253 (0.0243) 0.0199 (0.0287) 

Factor 2 -0.0534 (0.0267) -0.0372 (0.0324) 

 

TANF Policies 

Transitional FSP Benefits -0.0252 (0.0607) 0.0064 (0.0771) 

FSP Sanctions -0.0290 (0.0626) -0.0687 (0.0671) 

Earned Income Disregard 0.0001 (0.0009) 0.0003 (0.0010) 

Eligibility, Benefits, and Accessibility 

Factor 1 -0.0247 (0.0304) -0.0092 (0.0345) 

Factor 2 -0.0165 (0.0220) -0.0263 (0.0263) 

Factor 3 -0.0003 (0.0236) 0.0102 (0.0285) 

 

Work Requirements and Time Limits 

Factor 1 -0.0113 (0.0290) -0.0328 (0.0318) 

Factor 2 -0.0332 (0.0250) -0.0278 (0.0289) 

Factor 3 -0.0145 (0.0229) -0.0060 (0.0279) 

Factor 4 0.0130 (0.0205) 0.0160 (0.0251) 

Factor 5 -0.0063 (0.0226) -0.0057 (0.0248) 

Factor 6 -0.0117 (0.0279) -0.0137 (0.0341) 

 

Other Programs 

Medicaid/SCHIP Eligibility -0.0007 (0.0007) -0.0004 (0.0007) 

SSI Maximum Benefit -0.0003 (0.0004) -0.0003 (0.0005) 

EITC 0.0000 (0.0001) 0.0000 (0.0001) 

 

Economic Indicators 

Unemployment Rate 0.0081 (0.0247) 0.0199 (0.0294) 

20th Percentile Wage -0.0014 (0.0383) -0.0093 (0.045) 

Employment Growth Rate -0.0051 (0.0114) -0.0146 (0.0133) 

 

Adjusted R-Square -0.0650 -0.2598 

* Significant at the 95% level of confidence. 
83

TABLE III.16 

RESULTS FROM SIMPLIFIED MODEL (MODEL 6) 

Model 6 

(Unweighted) 

 Coefficient Standard Error 

 

Intercept 0.7105 (2.7923) 

FSP Policies 

EBT 0.0367 (0.8142) 

Certification Period (Earnings) -0.0014 (0.1839) 

Certification Period 

(Single Mothers) 

-- -- 

Reporting Requirements 0.0462 (0.6121) 

Change Reporting 0.0694 (1.6453) 

Reporting x Change -0.0711 (0.7906) 

ABAWD Rules -- -- 

Accessibility Policies 

Factor 1 0.0102 (0.6016) 

Factor 2 -0.0332 (1.8026) 

 

TANF Policies 

Transitional FSP Benefits 0.0304 (0.6524) 

FSP Sanctions 0.0081 (0.1532) 

Earned Income Disregard 0.0009 (1.1687) 

Eligibility, Benefits, and Accessibility 

Factor 1 -- -- 

Factor 2 -- -- 

Factor 3 -- -- 

Work Requirements and Time Limits 

Factor 1 -- -- 

Factor 2 -- -- 

Factor 3 -- -- 

Factor 4 -- -- 

Factor 5 -- -- 

Factor 6 -- -- 

 

Other Programs 

Medicaid/SCHIP Eligibility -- -- 

SSI Maximum Benefit -- -- 

EITC -- -- 

 

Economic Indicators 

Unemployment Rate -0.0020 (0.1023) 

20th Percentile Wage -0.0269 (1.1571) 

Employment Growth Rate -0.0069 (0.6361) 

 

Adjusted R-Square -0.0243 

 

* Significant at the 5% level of confidence. 
84

of covariates the variation in key FSP and TANF policies and in economic conditions is still not 

significantly correlated with the variation in standardized participation rates.38 

In short, the state participation rates are not correlated with most or all state policy and 

economic characteristics. These findings hold regardless of how individual states are weighted, 

whether the standardized or direct participation rates are used, and how the individual state 

policies are measured. 

E. CONCLUSIONS 

Even after standardizing the state participation rates to control for differences in the 

composition of each state’s eligible population, variation remains in the rates at which eligible 

individuals participate in the FSP. However, these patterns of this variation across states are not 

well explained by other aggregate state characteristics. State participation rates do not appear 

correlated with most FSP and TANF policies that might affect the ease with which people enter 

the program. While there may be a relationship between states’ policies on change reporting and 

program access and their standardized participation rate, the evidence for such a relationship is 

far from concrete. Moreover, the rates do not appear correlated with state economic conditions 

that could influence eligible individuals’ expectations of future income. 

 38 We explored other possible explanations for the lack of a significant relationship between policy variables 

and standardized participation rates. First, to test for multicolinearity, we regressed the standardized participation 

rate against each covariate in Model 1 separately. The results suggest that multicolinearity is not influencing the 

results. In these separate models, correlations were insignificant for all variables except the FSP Access Factor 2 

variable and SSI maximum benefits. It is also possible that the factor scores derived from the principal components 

analysis do not accurately capture the meaningful variation in state policies. To test the sensitivity of the results to 

these factor scores, we estimated the basic model replacing the factor scores with individual policy measures. Even 

in these models, however, the individual policy measures are not significantly correlated with the standardized state 

participation rates; no component policy is shown to be significantly related to state participation rates. This holds 

even for the FSP access component policies, which were shown to be significant in Model 1. We also estimated a 

version of the model in which we replaced the factor scores with selected component polices. Specifically, we used 

the policies that had the largest loading for each factor. Again, these individual policies are not significantly related 

to the state participation rates (as in Model 1, policies on change reporting are shown to be significantly related to 

participation rates). Appendix C contains the estimates from these alternative models. 
85

While the results of this analysis fail to show a strong relationship between policy and 

economic characteristics and participation rates, it is hard to believe that the variation in 

standardized rates would be explained by random factors alone. One would expect that state 

policies and procedures would have some influence over the rate at which eligible people 

participate. The failure to identify significant relationships could be explained by several factors: 

• Similar policies may be implemented differently. It is possible that two states 

adopting the same policy may implement that policy in a different way. For instance, 

two states may adopt the FSP simplified reporting option. In one state, the rules for 

simplified reporting may be made clear to participants and hence participating in the 

FSP is made easier. In the other state, the rules for simplified reporting may be 

presented in a confusing fashion, leading participants to be more frustrated and 

confused with the program. Such problems could lead to shorter spells among 

participants and could discourage others from applying. Thus, if two policies 

measured similarly in this study are implemented differently, then our model will not 

capture the meaningful variation in those policies.

• Aggregate measures may mask meaningful local variations. Just as the same policy 

could be implemented differently in two different states, the same policy may be 

implemented differently within a state. This is particularly true in states where 

county social service agencies have authority to establish FSP rules and procedures. 

Thus, the actual policy in place in areas with high concentrations of FSP-eligible 

households—if available—would be a better measure of FSP policy than the statelevel measure. Similarly, economic conditions at the local level may be masked by 

the aggregate economic measures used in the model. 

• State procedures may be more important than state policies. Our model examines 

the influence of key state policies governing eligibility and benefits for the FSP, 

TANF, and other programs. However, procedures for accepting applications and 

conducting outreach—both of which vary at the local level—may be more important 

determinants of state participation rates than the policies examined here.

• Imprecision in participation rate estimates. The estimates of state participation rates 

have substantial error associated with them. Sampling error could mask meaningful 

variation in the true state participation rates. While we attempt to account for this by 

weighting observations, sampling error in the participation rates could nevertheless 

affect our ability to identify significant relationships. Moreover, the participation 

rates could be affected by measurement errors stemming from underreporting, 

undercoverage, and simulation error, further affecting our ability to identify 

significant relationships.

• Limited degrees of freedom. The fact remains that the models are estimated over 

only 51 observations. This limited number of observations may mask the individual 

effects of various policy and economic factors that may influence state participation 
86

rates. Examining the variation across states and over time may allow researchers to 

better identify the effects of each of these factors.

Thus, while the results of this analysis suggest that state participation rates vary independent 

of state policy and economic conditions, states still likely have some control over their 

participation rates. The challenge is to find better measures of state policies and possibly of state 

participation rates, and examine state participation rates over time. 
87

IV. CONCLUSIONS 

A. SUMMARY OF FINDINGS AND SUGGESTIONS FOR FUTURE RESEARCH 

As USDA works to increase FSP participation rates, we need to understand why participation 

rates are higher in some states than in others. Part of these differences can be explained by 

differences in the composition of each state’s eligible population. In particular, states whose 

eligible populations contain higher proportions of households with no earnings and households 

headed by African Americans would have higher participation rates, all else being equal, because 

these populations participate at higher rates. Likewise, states whose eligible populations consist 

of higher proportions of elderly individuals tend to have lower participation rates, all else being 

equal, because elderly individuals participate at lower rates. 

However, the variation in state participation rates does not decrease once we account for the 

differences in the compositional differences of the state populations. The standardized 

participation rates vary as much from state to state as the unstandardized rates, with the 

standardized rates ranging from 43 percent in California and Wyoming to 89 percent in Oregon. 

After examining how much of this remaining variation is due to state policies and economic 

conditions, we find that much of this variation remains unexplained. It may be that our measures 

of policies or participation rates are too imprecise to capture the relationships between policies, 

economic conditions, and FSP participation rates. It may also be that limited degrees of freedom 

constrain the model’s ability to identify policy and economic effects. 

The research presented here is the first attempt we know of to standardize state FSP 

participation rates. Thus, our suggestions for future studies include exploring modifications to 

the techniques presented, which may provide additional insights about why we do not find a 

reduction in the variation of the rates from the standardization process. Our suggestions are to: 
88

• Explore Alternative Standardization Techniques. The procedures for generating the 

standardized FSP participation rates presented here are based on a logistic model of 

FSP participation. As discussed in Appendix B, this model implied participation rates 

of over 100 percent for some subgroups. An alternative estimation approach—the 

semi-logistic model—constrains the participation rates to be less than 100 percent and 

thus that approach generated standardized rates that differed substantially from those 

from the logistic approach. While we believe the logistic approach to be the better of 

the two—see Appendix B for more details—more work should be done evaluating 

alternative approaches for standardizing the FSP participation rates. In particular, 

more work should be done to understand why some groups have very high predicted 

participation rates, including some over 100 percent. The high rates could be 

attributed to sampling error, undercoverage in the CPS sample, problems with the 

model that simulates FSP eligibility, or all of these factors. 

• Measure Participation Rates in Other Datasets. Microdata files from the American 

Community Survey (ACS) are currently being released by the Census Bureau. The 

ACS will improve researchers’ abilities to generate precise estimates of state 

characteristics. However, the information collected in the ACS differs from that 

collected in the CPS. Future research should examine state FSP participation rates 

derived from ACS data, and explore any differences between those rates and the CPSbased participation rates.

• Improve State Measures. Existing measures of state policies for the FSP and other 

programs may not capture the meaningful cross-state variations in these programs, 

and they may also mask meaningful within-state variations. Better measures of state 

policies would enable researchers to explore the relationship between policies and 

outcomes. In addition, researchers could explore including measures beyond those 

used in previous studies. 

• Examine Multiple Years of Participation Rates. The limited number of observations 

likely constrains our ability to identify policy and economic effects on state 

participation rates. Future research should examine multiple years of participation 

rates to better identify these effects. By using multiple years, researchers will have 

more variation in rates to examine relative to the variation in policy and economic 

factors. Multiple years of data may also allow researchers to better understand the 

timing of the effects of policy changes on participation rates. 

We expect that research on the determinants of FSP participation rates will continue to be 

important in the coming years. Increasing flexibility with respect to state FSP policies, new 

efforts to “modernize” state FSP programs, and a continued emphasis on increasing participation 

rates will certainly combine to affect participation rates in the future. As a result, efforts to 

refine estimation techniques and to measure state policies accurately will allow researchers and 

policymakers to better understand those factors that drive state FSP participation rates. 
89

B. SUMMARY COMMENTARY 

While our findings do not fully explain the variation observed in state participation rates, we 

still demonstrate that a state’s participation rates can be influenced by the composition of its 

FSP-eligible population. These findings have implications for how policy makers interpret state 

participation rates and how states achieve increases in participation rates. 

1. Interpreting State Participation Rates 

Each year, USDA publishes official state participation rates, which are often interpreted as a 

measure of state performance. For instance, state policymakers and advocates for low-income 

families cite their state’s rank in the distribution of participation rates as evidence that their FSP 

is performing well or poorly. However, the findings from this analysis raise the question: if 

state participation rates are a function of the composition of the eligible FSP population, which is 

beyond a state’s control, do the rates really reflect performance?

Although the answer to this question is not definitive, it is useful to compare participation 

rates across states because the results still tell policymakers and advocates whether their state 

ranks among the top, middle, or bottom states. Nevertheless, because we know that variation in 

rates reflects factors both within and outside of each state’s control, the results of a cross-state 

comparison of rates should be interpreted with caution. That is, the fact that a state ranks among 

the top states in participation rates could reflect high performance, or an FSP-eligible population 

that participates at high rates, or both. 

Caution should also be used when comparing state participation rates to the national 

participation rate target. Each year in its budget request to Congress, USDA establishes a 

performance target for the national participation rate. In the fiscal year 2007 request, the target 

was 68 percent. However, USDA has never intended this target to be a benchmark for states. 
90

The results of our analysis underscore this approach because a state’s ability to reach a target 

participation rate would depend on the composition of the state’s FSP-eligible population. 

2. Increasing Participation Rates 

A motivation for this study was to help identify policies that states could use to increase FSP 

participation rates. The results of our state-level analysis, however, suggest that state 

participation rates are not affected by measurable differences in state FSP policies (or other 

program policies). As discussed in Chapter III, we are reluctant to conclude that program 

policies have little impact on participation rates. Rather, we conclude that one possible 

interpretation is that with only 51 observations, the policy measures used here do not reflect the 

meaningful variation in program policies and procedures from state to state. 

Despite the fact that we do not identify significant policy effects, this study’s results can still 

help inform state efforts to increase FSP participation rates, particularly by examining which 

household characteristics are associated with FSP participation, and combining that with 

measures of state population characteristics. States can then identify which populations should 

receive targeted attention in efforts to provide outreach and increase participation rates. With 

disproportionately large shares of eligible elderly individuals, states like Florida, Connecticut, 

and Massachusetts may decide to focus efforts on raising participation rates among these 

individuals. 

An alternative use of these findings would be to identify those populations that may yield 

the biggest return in terms of increased participation rates. Since eligible individuals without 

earnings have a higher propensity to participate, efforts targeting these groups could yield larger 

gains in FSP participation rates. Of course, such a strategy may come at the expense of efforts to 

reach populations that have low propensities to participate, and that may not be a desired 

outcome. 
91

REFERENCES 

Castner, Laura A. and Allen L. Schirm. “Reaching Those in Need: State Food Stamp 

Participation Rates in 2003.” Report submitted to the U.S. Department of Agriculture, Food 

and Nutrition Service. Washington, D.C.: Mathematica Policy Research, November 2005. 

Castner, Laura A., and Allen L. Schirm. “Empirical Bayes Shrinkage Estimates of State Food 

Stamp Participation Rates in 2002 and 2003 for All Eligible People and the Working Poor.” 

Report submitted to the U.S. Department of Agriculture, Food and Nutrition Service. 

Washington, D.C.: Mathematica Policy Research, July 2006. 

Christiansen, C.L. and Carl M. Morris. “Improving the Statistical Approach to Health Care 

Provider Profiling.” Annals of Internal Medicine, 1997, 127: 764-768. 

Cunnyngham, Karen. “Food Stamp Program Participation Rates: 2003.” Alexandria, VA: U.S. 

Department of Agriculture, Food and Nutrition Service, July 2005. 

Cunnyngham, Karen and Beth Brown. “Characteristics of Food Stamp Households: Fiscal Year 

2003.” Alexandria, VA: U.S. Department of Agriculture, Food and Nutrition Service, 

November 2004. 

Farrell, Mary, Michael Fishman, Matthew Langley, and David Stapleton. “The Relationship of 

earnings and income to food stamp participation: A longitudinal analysis.” Report prepared 

by the Lewin Group and the Cornell Center for Policy Research, submitted to the U.S. 

Department of Agriculture Economic Research Service. Washington, D.C., November 

2003. 

Fay, Robert E. III and Roger A. Herriot. “Estimates of Income for Small Places: An Application 

of James-Stein Procedures to Census Data.” Journal of the American Statistical Association 

1979, 74 (366): 269-277. 

Fender, Lynne, Singe-Mary McKernan and Jen Bernstein, “Linking State TANF and Related 

Policies to Outcomes: Preliminary Typologies and Analysis, Final Report.” Washington, 

DC: The Urban Institute, June 2002. 

Figlio, D., C. Gunderson and J. Ziliak, “The Effects of the Macroeconomy and Welfare Reform 

on Food Stamp Caseloads,” American Journal of Agriculture Economics. 82:635-4, 2000. 

Gleason, Philip, Peter Schochet and Robert Moffitt, “The Dynamics of Food Stamp Program 

Participation in the Early 1990s.” Report submitted to the U.S. Department of Agriculture, 

Food and Nutrition Service. Princeton, NJ: Mathematica Policy Research, April 1998. 

Gleason, Philip, Carole Trippe, Scott Cody and Jacqueline Anderson, “The Effects of Welfare 

Reform on the Characteristics of the Food Stamp Population.” Report submitted to the U.S. 

Department of Agriculture, Economic Research Service. Washington, DC: Mathematica 

Policy Research, March 2000. 
92

Goldstein, H. and D.J. Spiegelhalter. “League Tables and Their Limitations: Statistical Issues in 

Comparisons of Institutional Performance.” Journal of the Royal Statistical Society, Series 

A, 1996, 159 (3): 385-443. 

Griffiths, Richard and Khandaker Mansur. “Current Population Survey State-Level Variance 

Estimation.” Paper presented at the Federal Committee on Statistical Methodology 2001 

Conference. Washington, D.C.: U. S. Bureau of the Census, 2001. 

Harman, H. H. Modern factor analysis (2nd ed.). Chicago: University of Chicago Press, 1967.

Hatcher, L. 1994. A step-by-step approach to using the SAS system for factor analysis and 

structural equation modeling. SAS Institute Inc., Cary, NC, 1994 

Kabbani, Nader S. and Parke Wilde, “Short Recertification Periods in the U.S. Food Stamp 

Program: Causes and Consequences.” Working Paper. New York: American University of 

Beirut. Draft April 11, 2002. 

Kirlin, John and Christopher Logan, “The Effects of EBT Customer Service Waivers on Food 

Stamp Program Recipients. April 2002. ERS Report No. FANRR23. 

Kim, Jae-On and Charles W. Mueller. Factor Analysis: Statistical Methods and Practical Issues. 

Newbury Park, CA: Sage Publications. 1978. 

Koch, Gary G., Susan S. Atkinson, and Maura E. Stokes. “Poisson Regression.” In Samuel Kotz 

and Norman L. Johnson, eds., Encyclopedia of Statistical Sciences. New York: John Wiley 

and Sons, Inc., 1986: 32-41. 

Kornfeld, Robert, “Explaining Recent Trends in Food Stamp Program Caseloads.” Report 

submitted to the U.S. Department of Agriculture, Economic Research Service. Cambridge, 

MA: Abt Associates Inc., March 2002. 

O’Brien, Doug, Kimberly Prendergast, Eleanor Thompson, Marcus Fruchter, and Halley Torres 

Aldeen. “The Red Tape Divide: State-by-State Review of Food Stamp Applications.” 

Chicago: America’s Second Harvest, 2000. 

O’Malley, Alistair J., and Alan M. Zaslavsky. “Variance-covariance Functions for Domain 

Means of Ordinal Survey Items.” Survey Methodology, vol. 31, no. 2, December 205, pp. 

169-182. 

Otto, M.C. and W.R. Bell, “Sampling Error Modelling of Poverty and Income Statistics for 

States.” Proceedings of the Section on Government Statistics, American Statistical 

Association, 1995, pp. 160-165. 

Rao, J.N.K, C.F.J. Wu, and K. Yue. “Some Recent Work on Resampling Methods for Complex 

Surveys.” Survey Methodology, vol. 18, no. 2, December 1992, pp. 209-217. 

U.S. Department of Agriculture (USDA), “Food Stamp Program: State Options Report, Second 

Edition.” Alexandria, VA: Food and Nutrition Service, U.S. Department of Agriculture, 

May 2003. 
93

Wilde, Parke, Peggy Cook, Craig Gunderson, Mark Nord and Laura Tiehen, “The Decline in 

Food Stamp Program Participation in the 1990s.” Washington, DC: U.S. Department of 

Agriculture, Economic Research Service, June 2000. 

Wolter, Kirk M. Introduction to Variance Estimation. New York: Springer-Verlag, Springer 

Series in Statistics, 1985. 

Yelowitz, Aaron S. “Gateways into the Food Stamp Program.” Focus, Vol. 22, No. 2, Summer 

2002. 

Zaslavsky, Alan M. “Issues in Case-Mix Adjustment of Measures of the Quality of Health 

Plans.” 1998 Proceedings of the Section on Government Statistics and the Section on Social 

Statistics. Alexandria, VA: American Statistical Association, 1998, 56-64. 

Zaslavsky, Alan M. “Statistical Issues in Reporting Quality Data: Small Samples and Case-Mix 

Variation.” International Journal for Quality in Health Care 13(6): 481-88, 2001. 

Ziliak, James P., Craig Gundersen, and David Figlio. “Welfare Reform and Food Stamp 

Caseload Dynamics.” Discussion Paper No. 1215-00. Madison, WI: Institute for Research 

on Poverty, November 2000. 

95

APPENDIX A 

VARIANCE ESTIMATION 

97

This appendix provides details on the techniques used to estimate the variance of the 

standardized participation rates presented in Chapter II. Because the standardized rates are based 

on samples of participating and eligible households, they are measured with error. Thus, in 

addition to presenting the standardized rates themselves, we also provide estimates of their 

sampling variability. The estimated variances of the standardized state participation rates 

(shown in Table II.8 and Figure II.1) are calculated using a two-step process. We first calculate 

variance estimates using a jackknife procedure, and then smooth those variances using a 

generalized variance function (GVF). Those two steps are described in detail in this section.

A. JACKKNIFE-BASED VARIANCES 

We first use a jackknife variance estimation procedure (Rao, Wu, and Yue 1992) to obtain 

preliminary, rough estimates of the variance of the standardized state rates. The method used is 

similar to that described in Castner and Schirm (2006), but extended to this setting with the 

concatenated data set of both CPS and FSPQC observations. 

The following steps define the jackknife procedure used: 

1. Define eight jackknife groups for each data set. For the CPS, the jackknife groups 

are defined by the eight rotation groups, each of which consists of a set of 

households (actually, housing units) that begin the CPS at the same time.39 For the 

FSPQC, we created eight random groups within state, month, and sampling strata (as 

appropriate, depending on each state’s QC sample design). Our process of assigning 

FSPQC jackknife groups ensures that for each state, households are evenly 

distributed across months and sampling strata within each jackknife group. 

2. Calculate the standardized state-level rates using the full concatenated data set, as 

described in the main text. For state i, call the resulting standardized state rate Ri . 

3. For each jackknife group (k=1 to 8), drop all observations from group k from the data 

set (i.e., drop CPS jackknife group k and FSPQC jackknife group k). Using this 

reduced dataset, estimate the standardized state FSP participation rates as described 

 39 These sets of households are called rotation groups because the households are in the CPS for four months, 

rotate out for eight months, and then rotate back in for four months, after which time they are dropped from the CPS. 
98

in the text. For state i, call the resulting standardized state rate R ki )(, . The “(k)” 

subscript indicates that jackknife group k has been excluded. 

4. By excluding each of the eight jackknife groups in turn we obtain eight alternative 

estimates for the standardized state FSP participation rate of state i. We use Ri as 

the estimated standardized state participation rate, and estimate the variance of Ri by 

measuring the variability among the eight estimates according to: 

∑=

= −

8

1

2

)(, .)( 8

7 )(

k

RV i RR iki 

The factor 7/8 enters this expression because the R ki )(, are obtained from samples that are 

only 7/8 the size of the full concatenated data set for state i, and hence are expected to be more 

variable than Ri (by a factor of 8/7). Our jackknife estimate of the standard error of Ri is 

obtained by taking the square root of ) (RV i . The resulting estimated jackknife standard errors of 

the standardized state participation rates are presented in Table A.1. 

B. SMOOTHED VARIANCE ESTIMATES 

Given the CPS design, we can use only eight jackknife groups, which results in relatively 

imprecise estimates of the variance. We thus use a generalized variance function (GVF) to 

model the variances as a function of the rates and sample sizes, “borrowing strength” across 

states through the use of the model. The final variance estimate for each state is a precision 

weighted average of the state’s jackknife and GVF variance estimates. 

We use a GVF to smooth the variances because the individual states’ variances are 

measured with relatively large uncertainty, but we believe that states with similar rates and 

sample sizes should have similar variances. This is similar in spirit to the shrinkage done to 

obtain the official food stamp participation rates by smoothing across states with similar 

characteristics (Castner and Schirm 2006) and is related to the Fay-Herriott model (Fay and 
99

Herriot 1979), which combined direct and model-based estimates to facilitate calculation of 

statistics for small geographic areas. 

GVF’s are described in detail in Wolter (1985), and the use of a GVF with the CPS is 

described in Otto and Bell (1995) and Griffiths and Mansur (2001). O’Malley and Zaslavsky 

(2005) use a method of combining direct and model-based estimates of variances similar to that 

used here. In O’Malley and Zaslavsky (2005) the smoothing was done across survey items 

(variables) and survey respondents (observations), whereas in our situation we have just one 

variable per observation and our modeling and smoothing is done just across states 

(observations). 

We explored a variety of GVF models, including those suggested by Wolter (1985) and Otto 

and Bell (1995) for use with the CPS. After a model selection process that included diagnostic 

plots and summary statistics, we determined that the following GVF specification provided the 

best fit to the data: 

,

2

i

i

i

i

i

i e

N

R

c

N

R baV +++=

where ),,0(~

2

i Ne σ Vi is the estimated variance, Ri is the standardized state-level food stamp 

participation rate, and Ni is the combined FSPQC and CPS sample size for state i. The chosen 

GVF model is essentially the same as that used by Griffiths and Mansur (2001), which is of the 

same form as the official CPS GVF, divided by the state sample sizes. 
100

TABLE A.1

STANDARDIZED 2003 FSP PARTICIPATION RATE VARIANCE ESTIMATES

Jackknife Variance 

GVF 

Variance Smoothed Variance 

Shrinkage 

Variance 

Alabama 0.0012 0.0018 0.0016 0.0010 

Alaska 0.0021 0.0043 0.0036 0.0021 

Arizona 0.0018 0.0029 0.0028 0.0010 

Arkansas 0.0023 0.0023 0.0023 0.0009 

California 0.0005 0.0014 0.0007 0.0005 

Colorado 0.0008 0.0019 0.0013 0.0008 

Connecticut 0.0265 0.0026 0.0026 0.0009 

Delaware 0.0023 0.0045 0.0041 0.0012 

District of Columbia 0.0087 0.0046 0.0046 0.0023 

Florida 0.0008 0.0021 0.0015 0.0009 

Georgia 0.0019 0.0035 0.0034 0.0014 

Hawaii 0.0075 0.0072 0.0072 0.0021 

Idaho 0.0047 0.0041 0.0041 0.0015 

Illinois 0.0030 0.0030 0.0030 0.0009 

Indiana 0.0053 0.0034 0.0035 0.0010 

Iowa 0.0060 0.0027 0.0028 0.0011 

Kansas 0.0019 0.0022 0.0022 0.0008 

Kentucky 0.0069 0.0033 0.0033 0.0010 

Louisiana 0.0033 0.0030 0.0030 0.0011 

Maine 0.0044 0.0057 0.0056 0.0014 

Maryland 0.0015 0.0018 0.0018 0.0011 

Massachusetts 0.0028 0.0017 0.0018 0.0010 

Michigan 0.0022 0.0027 0.0027 0.0010 

Minnesota 0.0068 0.0028 0.0029 0.0010 

Mississippi 0.0028 0.0029 0.0029 0.0016 

Missouri 0.0045 0.0070 0.0066 0.0017 

Montana 0.0006 0.0026 0.0012 0.0008 

Nebraska 0.0040 0.0045 0.0045 0.0012 

Nevada 0.0051 0.0026 0.0027 0.0009 

New Hampshire 0.0021 0.0042 0.0035 0.0012 

New Jersey 0.0004 0.0020 0.0008 0.0005 

New Mexico 0.0014 0.0021 0.0019 0.0010 

New York 0.0006 0.0018 0.0010 0.0006 

North Carolina 0.0016 0.0017 0.0017 0.0008 

North Dakota 0.0063 0.0037 0.0037 0.0013 

Ohio 0.0020 0.0028 0.0027 0.0008 

Oklahoma 0.0032 0.0034 0.0034 0.0010 

Oregon 0.0073 0.0081 0.0080 0.0022 

Pennsylvania 0.0023 0.0023 0.0023 0.0009 

Rhode Island 0.0007 0.0024 0.0015 0.0007 

South Carolina 0.0009 0.0031 0.0025 0.0009 

South Dakota 0.0080 0.0042 0.0043 0.0013 

Tennessee 0.0068 0.0058 0.0058 0.0019 

Texas 0.0003 0.0016 0.0005 0.0004 

Utah 0.0057 0.0034 0.0035 0.0011 

Vermont 0.0073 0.0061 0.0061 0.0012 

Virginia 0.0039 0.0025 0.0025 0.0009 

Washington 0.0031 0.0023 0.0023 0.0013 

West Virginia 0.0063 0.0039 0.0040 0.0016 

Wisconsin 0.0049 0.0022 0.0022 0.0009 

Wyoming 0.0017 0.0040 0.0028 0.0012 
101

We obtain parameter estimates of a, b, and c using iteratively reweighted least squares to fit 

the model using the 51 states, with the jackknife variance estimates modeled as a function of the 

standardized FSP participation rates, the combined CPS and FSPQC sample sizes.40 The 

following parameter estimates were obtained: a=0.00; b=-1.66; c=9.61. 

The resulting parameter estimates are then used to produce a predicted variance for each 

state, Vm . The final variance estimates are a weighted average of the jackknife-based variances 

(Vj ) and the GVF model-based variances (Vm ), where each is weighted according to its 

precision. Specifically, we use the following formula (O’Malley and Zaslavsky 2005): 

)( , 22

2 2

jm

VV mjjm RVar

σσ

σσ

+

+ =

where Vj and Vm are the jackknife and (GVF) model-based estimates of the variance, 

respectively, and 2 σ j and 2 σ m are their respective variances. The model-based variance ( 2 σ m ) is 

obtained from the GVF model. The variance of the jackknife estimate is estimated by 

2 2

7

2

σ = Vjj . This estimate is obtained under an assumption of normality, using the fact that the 

jackknife estimate of the variance, Vj , is a seventh-degree of freedom estimate of the variance, 

and the coefficient of variation of a chi-square distribution is /2 df , where df is the number of 

degrees of freedom. 

The final variance estimate is a weighted average of the jackknife and model-based variance 

estimates, where the weights reflect the relative precision of the two estimates. Table A.1 shows 

the three sets of variance estimates: the jackknife-based variances, the GVF model-based 

 40 We used the Stata reg command to fit the model. 
102

variances, and the smoothed (final) variances, which are a weighted average of the two previous 

columns. Additionally, we present in Table A.1 variance estimates of the shrinkage estimates 

(see Castner and Schirm (2006) for more details on the variance estimation in the shrinkage 

estimation process). 
103

APPENDIX B 

ALTERNATIVE APPROACHES TO ACCOUNTING FOR TANF RECEIPT IN 

STANDARDIZING PARTICIPATION RATES 

105

As discussed in Chapter II, accounting for TANF receipt in the standardization process 

presents a complex problem. TANF eligibility and receipt is highly correlated with food stamp 

receipt. FSP-eligible TANF recipients participate at high rates (Cunnyngham 2005), and we 

would like to standardize participation rates to account for the fact that some states have more 

TANF participants than others. However, eligibility for TANF varies across states and thus there 

is no consistent way of defining the TANF-eligible population across the entire country. Since 

our model requires us to know what each individual’s characteristics would be in each state, we 

would need to know their TANF participation status in the 50 states and the District of 

Columbia; since this cannot be observed, it can only be estimated through simulation. 

As an alternative, we investigated the link between likely TANF receipt and food stamp 

program participation by examining the participation of a group of eligible households likely to 

be eligible for TANF in every state: low-income single mothers. By controlling the 

participation rates for the composition of low income single mothers in each state, we would 

indirectly control for the fact that TANF participants are more likely to participate in the FSP, 

without having to account for the fact that TANF policies lead to different levels of participation 

among single mothers in each state (a relationship that could then be explored in the state-level 

model investigating variation in standardized participation rates). In this appendix, we discuss 

the complicated issues surrounding efforts to estimate participation rates for single mothers. In 

the end, we conclude that problems with the data result in estimated single mothers participation 

rates—and potentially overall FSP participation rates—that may be too high. 

A. PARTICIPATION RATES OF LOW-INCOME SINGLE MOTHERS 

One way of investigating the link between TANF and food stamp receipt would be to 

generate standardized food stamp participation rates for TANF recipients only, as was done for 

all food stamp eligible households in the main text. However, since TANF eligibility rules vary 
106

by state, and since we know only if a household is TANF-eligible in the state in which they live 

(and lack detailed income information in the FSPQC and CPS files), we cannot predict which 

households would be TANF-eligible in all states. 

To get around this, we instead consider a group of households that are likely to be TANFeligible in all states, but which are defined by observed household characteristics that do not 

depend on the state in which a household resides. The group we consider consists of low-income 

single mothers and their children, and in particular, households composed of a female single 

adult and one or more children under age 17, with household earnings below 150 percent of the 

poverty line. Because these households are likely to be eligible for TANF regardless of the state 

in which they live, we can use them to investigate how states’ FSP participation rates differ 

among the (likely) TANF-eligible population. 

To examine the state participation rates of this group of low-income single mothers, we 

modified the logistic model described in the main text. In particular, we include an additional 

term, αs , which allows the participation rate for low-income mothers to vary by state. In the 

same way that the s γ terms capture the variation in the overall food stamp participation rates 

across states, the αs terms capture the variation across states among the subgroup of low-income 

single mothers. With this extended model, the standardized state-level rates for the overall 

population are calculated in the same way described in the main text (with the αs term added in 

for the low-income single mothers households). Similarly, the standardized state-level rates for 

low-income single mothers are calculated in the same way, but with the summations over just the 

low-income single mothers in the eligible population. 
107

B. PARTICIPATION RATES EXCEEDING 100 PERCENT 

A key difficulty in examining the food stamp participation rate among this group of single 

mothers is that the participation rate is very high. The direct estimate of the national 

participation rate for this group is 97 percent. In fact, in some states the direct estimate of the 

participation rate for this group is estimated to be over 150 percent (Table B.1). These direct 

rates are calculated simply as the number of participating single-mother households in a state 

(estimated using the FSPQC data) divided by the number of eligible single-mother households in 

the state (estimated using the CPS data). These rates would imply that in some states there are 

more single mother households participating in the food stamp program than are eligible. The 

estimation of participation rates greater than 100 percent is not an unheard of occurrence when 

estimating FSP participation rates of particular subgroups or states; in fact the 2003 estimated 

participation rate of TANF recipients was 126 percent (Cunnyngham 2005). 
108

TABLE B.1

DIRECT AND STANDARDIZED 2003 FSP PARTICIPATION RATES FOR SINGLE MOTHERS

Direct Participation Rate 

Logistic Standardized 

Participation Rate 

Semi-Logistic Standardized 

Participation Rate 

Alabama 100.4 93.3 80.1 

Alaska 154.1 147.7 96.2 

Arizona 118.4 140.0 87.5 

Arkansas 117.3 115.0 100.0 

California 68.1 64.2 58.1 

Colorado 91.4 102.3 76.8 

Connecticut 150 148.2 84.7 

Delaware 131.2 164.2 89.5 

District of Columbia 95.7 91.1 78.5 

Florida 70.0 71.0 73.4 

Georgia 112.3 128.6 91.5 

Hawaii 105.7 125.9 88.9 

Idaho 121.5 139.0 95.8 

Illinois 118.3 133.2 92.3 

Indiana 141.1 151.1 97.5 

Iowa 103.5 115.9 73.0 

Kansas 83.2 87.2 83.2 

Kentucky 97.5 100.0 77.5 

Louisiana 108.2 112.3 86.9 

Maine 94.7 97.2 89.5 

Maryland 121.9 127.4 80.9 

Massachusetts 108.0 119.5 79.6 

Michigan 95.8 84.2 82.4 

Minnesota 101.1 92.8 97.7 

Mississippi 133.8 151.3 87.9 

Missouri 169.9 188.5 96.6 

Montana 73.0 80.0 68.0 

Nebraska 110.6 127.0 87.0 

Nevada 74.6 82.4 71.4 

New Hampshire 107.9 137.3 91.6 

New Jersey 109.7 109.5 81.3 

New Mexico 110.4 109.0 83.0 

New York 78.9 79.0 73.2 

North Carolina 78.2 75.7 67.4 

North Dakota 114.2 116 87.6 

Ohio 106.2 97.6 81.6 

Oklahoma 139.6 160.8 91.9 

Oregon 158.9 184.6 100.0 

Pennsylvania 115.1 109 86.1 

Rhode Island 101.1 102.2 80.7 

South Carolina 140.9 136.9 95.0 

South Dakota 156.3 201.0 98.0 

Tennessee 159.4 149.3 94.4 

Texas 87.2 98.4 70.2 

Utah 96.5 119.4 81.9 

Vermont 110.4 106.7 91.0 

Virginia 93.6 105.9 76.6 

Washington 90.6 92.1 84.0 

West Virginia 91.0 95.7 86.5 

Wisconsin 92.5 98.9 93.3 

Wyoming 92.2 117.4 83.6 
109

Participation rates that exceed 100 percent for single mothers could reflect several factors. 

First, sampling variability in the FSPQC and CPS data could lead the number of participants in 

FSPQC data to exceed the number of eligibles in CPS data, particularly if the “true” participation 

rate among this group approaches 100 percent. Second, due to errors in the sampling frame, the 

CPS could underrepresent low-income single mothers, and this coverage problem may not be 

completely accounted for in the sampling weights. Third, the simulation model used to estimate 

eligibility in the CPS data could underestimate the true number of eligible single mothers in each 

state. 

C. THE SEMI-LOGISTIC MODEL 

Because the logistic regression model does not constrain the standardized participation rates 

to fall under 100 percent and because of the very high direct rates for the low-income single 

mother households (often greater than 100 percent), the standardized rates for that group also end 

up often being over 100 percent. This section describes an alternative model, the “semilogistic,” which restricts all participation rates to be between 0 and 100 percent. For example, in 

Connecticut, there are more low-income single mothers in the FSPQC data than in the CPS, 

which implies that the number of participating households of that type is larger than the number 

of eligible households of that type. The semi-logistic model assumes that this predicted rate over 

100 percent is due just to chance: because of variability in the households selected for the CPS 

and FSPQC samples, we happened to get more low-income single mothers in the FSPQC than in 

the CPS. Whether or not this assumption is reasonable is discussed further below, after we give 

the details of the semi-logistic model. 

The semi-logistic model is very similar to the logistic, with just a small change to the “link 

function” that relates the response variable (in the FSPQC data) to the predictor variables (the 

household characteristics and state fixed effects). In particular, the semi-logistic model is: 
110

is

is

F log( ) 1 2F

= ++ H m is s is s φ γ α −

The difference between this and the logistic model is simply the 2*F in the denominator on the 

left-hand side; the denominator of the logistic model link function is 1-F rather than 1-2*F. 

The standardized state rates are calculated in the same way as described for the logistic 

model, with just one small change as a result of a new form for the probability of participation. 

The resulting formula for the standardized state rates is: 

51 ˆ ˆ ˆ ˆ ˆ ˆ

1

51

1

( /(1 )) Hm Hm is s is s is s is s

is

s is

s

is

s is

w e e

r

w

′′ ′′ φγ α + + φγ α + +

′

′ ′ = ∈

′

′ ′ = ∈

+

′ =

∑∑

∑∑

To generate the rate for low-income single mothers, the summations are done over only the 

households that consist of a low-income single mother, rather than over the full population of 

eligible households. 

Because of the modified link function used in the semi-logistic model, the resulting 

standardized rates are lower than 100 percent for all states. However, the use of the semi-logistic 

model relies on strong assumptions about the nature of the data. In particular, the semi-logistic 

model assumes that any participation rate greater than 100 percent is due to sampling variability 

only: that just by chance, we see more participating single mothers in the FSPQC than we see 

eligible single mothers in the CPS, and that there are no systematic biases in the resulting data. 

Through investigation of the data we determined that this is an unrealistic assumption. 

Although there are no known biases in the FSPQC or CPS data, it is likely that the sampling 

process of either data set or the assignment of probabilities of eligibility in the CPS may lead to 

either an underestimate of the number of eligible single mother households in the CPS or an 

overestimate of the number of single mothers participating households in the FSPQC. Because 

of this, we chose to use the logistic model rather than the semi-logistic. Although it results in 
111

some participation rates greater than 100, we were not comfortable with the assumptions that 

would be necessary to use the semi-logistic model instead. 

D. ACCOUNTING FOR TANF 

Ultimately, we did not adopt our initial approach for accounting for the differences across 

states in the rates of TANF receipt. The approach would have included a state fixed effect 

specific to single mother FSP participation, and that fixed effect would be accounted for in the 

estimation of the state-level model. However, because the fixed effects from the logistic model 

implied participation rates well over 100 percent for single mothers, we concluded that the 

results did not pass the standard of face validity. While we could generate rates that appeared 

more realistic using the semi-logistic regression, the underlying assumption that the rates 

exceeded 100 percent due solely to sampling variability could not be supported. 

As a result, our model for standardizing the state FSP participation rates does not directly 

address the fact that FSP participation is correlated with TANF participation, and TANF 

participation varies by state. Our model includes a control for the proportion of each state’s 

eligible population that are single mothers, but the model assumes the propensity for single 

mothers to participate in the FSP does not vary by state. Our state-level model is designed to 

capture some of the variation in state TANF policies by allowing total FSP participation rates to 

vary by TANF policy. However, the model does not estimate the relationship separately for 

single mothers and other eligibles. Since TANF households are only 17 percent of the FSP 

caseload, it is possible that the effects of these policies on the single mothers is obscured when 

we estimate the model for the overall FSP eligible population. 

Implicit in this discussion is the finding that the standardized participation rates presented in 

Chapter II are based, in part, on rates for single mothers that exceed 100 percent. That is, if the 

models estimated rates for single mothers that were lower than 100 percent, the overall 
112

standardized FSP participation rate would also likely fall. Until we have a better understanding 

of the reasons that participation rates exceed 100 percent, we cannot fully interpret this problem. 

In the end, the standardized participation rates presented in Chapter II represent our best estimate 

of the standardized rates. Moreover, our state-level model discussed in Chapter III assumes that 

even if the standardized rates are overstated due to this problem, the variation across states in 

these standardized rates is correctly estimated. This assumption may or may not hold true if the 

factors leading to participation rates that exceed 100 percent also vary by state. 
113

APPENDIX C 

SENSITIVITY OF STATE LEVEL MODEL RESULTS TO FACTOR SCORES 

115

To examine whether individual policy measures explain more variation in state participation 

rates than the factor scores, we estimated models by replacing the factor scores with policy 

measures. Given the small number of observations in the model, we could not estimate one 

model by replacing all factor scores with policy measures. Model 7 replaces the FSP 

accessibility factors with policy measures; Model 8 replaces the TANF eligibility, benefits, and 

accessibility factors with policy measures; and Model 9 replaces the TANF work requirement 

and time limit factors with policy measures. The results from Models 7, 8, and 9 are presented in 

Table C.1. Model 10 replaces all factor scores with those individual policy measures that had the 

highest loading for each factor. The results from Model 10 are presented in Table C.2. 

In these alternative models, the individual policy measures are not significantly correlated 

with the standardized state participation rates. No component policy is shown to be significantly 

related to state participation rates. This holds even for the FSP access component policies, which 

were shown to be significant in Model 2. 
116

TABLE C.1 

RESULTS FROM MODELS OF STANDARDIZED PARTICIPATION RATES USING ALL POLICIES IN 

PLACE OF FACTOR SCORES (MODELS 7, 8, AND 9) 

 Coefficient Standard Error 

 

FSP Accessibility (Model 7) 

Categorical Eligibility -0.1577 (0.0811) 

Vehicle Rules -0.0062 (0.0529) 

Fingerprinting -0.1259 (0.1357) 

Application Page Length -0.0019 (0.0038) 

Number of Visits -0.0118 (0.0618) 

State FSP Outreach Efforts -0.0663 (0.0827) 

 

Adjusted R-Square (Model 7) -0.1498 

 

TANF Eligibility, Benefits and Accessibility (Model 8) 

 

Income Threshold -0.0040 (0.0028) 

Maximum Benefit 0.0030 (0.0047) 

Family Cap 0.0028 (0.0629) 

Diversion Payments 0.0000 (0.0000) 

Job Search Application Requirements 0.0347 (0.0779) 

School Attendance Application Requirements -0.0886 (0.0739) 

Immunization Application Requirements 0.0418 (0.0679) 

Drug Testing Application Requirements -0.0024 (0.0758) 

Parenting Class Application Requirements -0.0491 (0.0772) 

Citizenship Requirements -0.1452 (0.0704) 

 

Adjusted R-Square (Model 8) -0.0843 

 

TANF Work Requirements and Time Limits (Model 9) 

Lifetime Time Limit 0.0014 (0.0019) 

Intermittent Time Limit -0.0531 (0.0791) 

Time Limit Exemption for Ill/Incapacitated 0.0006 (0.1026) 

Time Limit Exemption for Parents with Young Children -0.1251 (0.1124) 

Time Limit Extensions 0.0397 (0.0919) 

Qualifying Activities -0.0265 (0.0376) 

Minimum Hours in Work Activities -0.0565 (0.0857) 

Start of Work Requirements 0.0652 (0.1178) 

Work Requirement Exemption for Ill/Incapacitated 0.0071 (0.0748) 

Work Requirement Exemption for Parents -0.0060 (0.1040) 

Severe Sanction Amount 0.0000 (0.0003) 

Severe Sanction Length -0.0140 (0.0263) 

Difference between Severe and First Sanctions -0.0003 (0.0003) 

 

Adjusted R-Square (Model 9) -0.2429 

 
117

TABLE C.2 

RESULTS FROM MODELS OF STANDARDIZED PARTICIPATION RATES USING SELECTED POLICIES 

IN PLACE OF FACTOR SCORES (MODEL 10) 

 Coefficient Standard Error 

 

Intercept 0.5368 (0.6199) 

FSP Policies 

EBT 0.1302 (0.0681) 

Certification Period (Earnings) 0.0133 (0.0330) 

Certification Period (Single Mothers) -0.0039 (0.0364) 

Reporting Requirements 0.0881 (0.1046) 

Change Reporting 0.1233* (0.0562) 

Reporting x Change -0.0082 (0.1236) 

ABAWD Rules -0.0523 (0.0594) 

Accessibility Policies 

Vehicle Rules -0.0378 (0.0421) 

State FSP Outreach Efforts -0.0653 (0.0699) 

 

TANF Policies 

Transitional FSP Benefits 0.0132 (0.0667) 

FSP Sanctions 0.0095 (0.0681) 

Earned Income Disregard 0.0008 (0.0011) 

Eligibility, Benefits, Accessibility 

Maximum Benefit 0.0030 (0.0032) 

Diversion Payments 0.0000 (0.0000) 

School Attendance Application Requirements -0.0698 (0.0540) 

Parenting Class Application Requirements 0.0397 (0.0588) 

Work Requirement/Time Limit 

Time Limit Exemption for Parents -0.1374 (0.0669) 

Time Limit Extensions 0.0602 (0.0638) 

Qualifying Activities -0.0357 (0.0259) 

Work Requirement Exemption for Ill/Incapacitated 0.0352 (0.0580) 

Severe Sanction Amount -0.0119 (0.0190) 

Severe Sanction Length -0.0001 (0.0002) 

 

Other Programs 

Medicaid/SCHIP Eligibility 0.0004 (0.0007) 

SSI Maximum Benefit -0.0006 (0.0005) 

EITC -0.0001 (0.0001) 

 

Economic Indicators 

Unemployment Rate -0.0066 (0.0271) 

20th Percentile Wage 0.0226 (0.0405) 

Employment Growth Rate -0.0235 (0.0142) 

 

Adjusted R-Square -0.0972 

 

119

APPENDIX D 

VALUES OF THE STATE POLICY VARIABLES 

121

In Chapter III we describe the state policy measures explored in this study. We define most 

of the measures such that lower values reflect more lenient policies (i.e., policies that would lead 

to higher participation rates, all else being equal), and higher values reflect stricter policies (i.e., 

policies that would lead to lower participation rates, all else being equal). 

In this appendix, we provide the values of these measures by state. 
122

State

Had Not 

Implemented 

Statewide EBT

Average 

Certification Period 

for Households with 

Earnings

Average 

Certification Period 

for Single Mother 

Households

Not Adopted 

Simplified 

Reporting or 

Quarterly 

Reporting

Has Adopted 

Status or 

Change 

Reporting

Some ABAWDs 

are Subect to 

Work 

Requirements

Alabama 0 11.59 10.29 0 0 0

Alaska 0 6.62 6.89 1 1 1

Arizona 0 8.66 8.52 0 1 1

Arkansas 0 11.9 11.87 0 0 0

California 1 11.84 11.78 0 0 1

Colorado 0 5.65 6.04 0 0 0

Connecticut 0 7.53 7.82 0 1 1

Delaware 1 6.08 5.92 0 1 0

District of Columbia 0 6.1 6.81 0 1 0

Florida 0 5.13 5.08 0 0 0

Georgia 0 6.51 7.01 0 1 1

Hawaii 0 11.78 11.8 1 0 1

Idaho 0 7.57 7.68 1 1 1

Illinois 0 12.1 11.32 0 1 0

Indiana 1 6.82 7.13 0 1 1

Iowa 1 11.1 10.76 0 0 0

Kansas 0 11.84 11.83 0 0 1

Kentucky 0 6.43 7.11 0 0 1

Louisiana 0 11.41 9.99 0 1 0

Maine 1 7.32 7.59 0 0 1

Maryland 0 6.68 7.26 0 0 1

Massachusetts 0 6.46 9.24 1 0 1

Michigan 0 7 8.6 0 0 0

Minnesota 0 11.92 11.94 1 0 0

Mississippi 1 10.88 9.69 1 0 0

Missouri 0 6.15 6.23 0 0 0

Montana 1 11.71 11.73 0 0 1

Nebraska 1 4.63 4.52 0 0 0

Nevada 1 5.64 6 1 1 1

New Hampshire 0 6.13 6 0 1 1

New Jersey 0 6.14 6.79 0 1 1

New Mexico 0 10.54 9.08 0 1 1

New York 0 7.17 8 0 1 1

North Carolina 0 3.94 5 0 0 1

North Dakota 0 7.79 8 1 1 1

Ohio 0 5.97 6 0 1 1

Oklahoma 0 11.13 9.93 0 1 1

Oregon 0 7.31 7.38 1 1 0

Pennsylvania 0 11.47 11.38 0 1 1

Rhode Island 0 10.85 11.49 1 1 1

South Carolina 0 11.62 11.83 0 1 1

South Dakota 0 13.87 13.9 1 0 0

Tennessee 0 6 5.72 0 0 0

Texas 0 5.44 5.4 0 1 0

Utah 0 3.89 5.03 1 1 0

Vermont 0 7.93 8.27 0 1 1

Virginia 1 5.97 5.84 0 1 1

Washington 0 4.74 4.81 1 1 0

West Virginia 1 6.49 8.62 0 1 0

Wisconsin 0 6.39 6.45 0 1 1

Wyoming 0 3.6 3.92 1 1 1

VALUES OF STATE POLICY MEASURES

TABLE D.1

Note: Most variables measured so that lower variables reflect more lenient policies and higher values reflect stricter policies. More 

information about each variable definition can be found in Chapter III.

FSP Policies

Certification Period Reporting Requirements
123

State

Does Not 

Confer 

Categorical 

Eligibility

Ranking of 

Vehicle Asset 

Rules (Lenient, 

Moderate, Strict)

Requires 

Fingerprinting

Application 

Page Length

Number of Visits 

Typically 

Required to 

Complete 

Application

Does Not 

Have 

Outreach 

Program

Alabama 1 1 0 19 1 1

Alaska 0 1 0 6 0 0

Arizona 0 2 1 14 0 0

Arkansas 0 2 0 8 0 0

California 0 2 1 21 1 1

Colorado 0 2 0 20 0 1

Connecticut 0 1 0 16 0 0

Delaware 0 2 0 12 0 1

District of Columbia 1 1 0 15 0 1

Florida 0 2 0 1 1 0

Georgia 0 2 0 5 1 0

Hawaii 0 2 0 11 1 1

Idaho 0 2 0 4 0 1

Illinois 1 2 0 5 1 1

Indiana 0 2 0 2 1 0

Iowa 1 1 0 10 1 1

Kansas 0 1 0 10 0 1

Kentucky 0 1 0 0 1 0

Louisiana 0 1 0 6 0 1

Maine 0 2 0 6 0 1

Maryland 0 1 0 23 1 0

Massachusetts 0 2 0 8 0 0

Michigan 0 1 0 16 0 1

Minnesota 0 2 0 36 1 0

Mississippi 0 2 0 21 1 1

Missouri 0 2 0 4 0 1

Montana 0 2 0 17 1 1

Nebraska 0 2 0 14 0 1

Nevada 0 2 0 8 1 1

New Hampshire 0 1 0 10 0 0

New Jersey 0 1 0 16 1 0

New Mexico 0 2 0 6 1 1

New York 0 2 1 8 1 0

North Carolina 0 1 0 15 0 1

North Dakota 0 2 0 24 0 1

Ohio 0 1 0 8 0 1

Oklahoma 0 2 0 20 0 1

Oregon 0 1 0 10 1 0

Pennsylvania 1 2 0 16 1 0

Rhode Island 1 3 0 28 0 1

South Carolina 0 1 0 5 0 0

South Dakota 0 2 0 20 0 1

Tennessee 0 2 0 2 1 0

Texas 1 2 1 4 1 0

Utah 0 2 0 12 0 1

Vermont 0 1 0 11 0 0

Virginia 1 3 0 14 0 1

Washington 0 2 0 6 1 0

West Virginia 0 2 0 33 0 1

Wisconsin 0 1 0 19 1 1

Wyoming 0 1 0 3 1 1

TABLE D.1

VALUES OF STATE POLICY MEASURES

Note: Most variables measured so that lower variables reflect more lenient policies and higher values reflect stricter policies. More 

information about each variable definition can be found in Chapter III.

FSP Policies

Accessibility Policies
124

State

Does Not 

Provide 

Transitional 

Benefits FSP Sanctions

Income 

Eligibility 

Threshold for 

Household of 

Size 3 (scaled)

Percentage of 

Earned Income 

Not Disregard for 

Family of Size 3

Maximum Benefit 

for Family of Size 

3 (Scaled)

Family Cap 

Policy

Alabama 1 0 89.75 0 83.6 0

Alaska 1 0 35.4 43 7.7 0

Arizona 0 0 70.7 55 65.3 1

Arkansas 1 0 86.05 100 79.6 1

California 1 0 50.95 23 29.6 1

Colorado 0 0 74.45 33 64.4 0

Connecticut 1 0 58.25 0 45.7 1

Delaware 1 0 78.6 47 66.2 1

District of Columbia 1 0 73.05 20 62.1 0

Florida 1 1 80.35 26 69.7 1

Georgia 1 0 74.3 47 72 1

Hawaii 1 0 17.95 20 43 0

Idaho 1 1 67.6 60 69.1 0

Illinois 1 0 75.7 33.3 60.4 1

Indiana 1 0 81.1 25 71.2 1

Iowa 1 0 46.95 40 57.4 0

Kansas 1 1 74.05 47 57.1 0

Kentucky 1 0 54.55 0 73.8 0

Louisiana 1 0 82 0 76 0

Maine 1 1 48.85 31 51.5 0

Maryland 0 0 70.45 60 52.7 1

Massachusetts 0 1 64.6 35 38.2 1

Michigan 1 1 61.3 41 54.1 0

Minnesota 1 0 48.55 62 46.8 1

Mississippi 1 1 77.1 0 83 1

Missouri 1 0 72.1 11 70.8 0

Montana 1 0 56.2 39 49.3 0

Nebraska 0 0 63.4 80 63.6 1

Nevada 1 0 44 0 65.2 0

New Hampshire 1 0 60.95 50 37.5 0

New Jersey 1 0 68.2 0 57.6 1

New Mexico 1 0 47.2 35 61.1 0

New York 0 0 66.65 38 42.3 0

North Carolina 0 0 62.5 0 72.8 1

North Dakota 1 1 37.4 28 52.3 1

Ohio 1 1 51 20 62.7 0

Oklahoma 1 0 64.8 35 70.8 1

Oregon 0 0 69.2 50 49.7 0

Pennsylvania 0 0 66.15 50 59.7 0

Rhode Island 1 0 36.1 29 44.6 0

South Carolina 1 0 68.75 50 79.5 1

South Dakota 1 1 65.35 63 51.7 0

Tennessee 1 1 48.55 64 81.5 1

Texas 1 0 79.95 7 78.7 0

Utah 1 0 71.35 38 52.6 0

Vermont 1 0 49.95 48 36.1 0

Virginia 0 0 36.4 47 68 1

Washington 1 1 45.5 50 45.4 0

West Virginia 1 0 62.35 60 54.7 0

Wisconsin 1 0 26.9 100 37.2 0

Wyoming 1 1 73 51 66 1

TANF Policies

VALUES OF STATE POLICY MEASURES

TABLE D.1

Note: Most variables measured so that lower variables reflect more lenient policies and higher values reflect stricter policies. More 

information about each variable definition can be found in Chapter III.

FSP Related Policies Eligibility Benefits
125

State

Maximum 

Diversion 

Payment

Job Search 

Requirement

Minor 

Children 

Attend 

School

Children 

Required to 

Be 

Immunized

Drug 

Screening for 

Most or All

Parenting 

Class 

Requirement

Strict 

Citizenship 

Rules

Alabama 0 1 0 0 0 0 1

Alaska 2769 0 0 0 0 1 0

Arizona 1041 0 1 1 0 0 0

Arkansas 612 1 1 1 0 0 1

California 4000 0 1 1 0 0 0

Colorado 1000 0 1 1 0 0 0

Connecticut 1629 0 0 0 0 0 0

Delaware 1500 0 1 1 1 1 0

District of Columbia 1137 1 0 0 0 0 1

Florida 1000 0 1 1 0 0 1

Georgia 0 1 1 1 1 1 0

Hawaii 4560 0 0 0 0 0 0

Idaho 927 1 1 1 1 0 1

Illinois 0 0 1 0 1 1 0

Indiana 0 1 1 1 0 0 1

Iowa 0 0 1 0 1 1 1

Kansas 0 1 0 0 0 0 1

Kentucky 1300 0 1 0 1 0 1

Louisiana 960 0 1 1 1 1 1

Maine 1455 0 0 1 0 1 0

Maryland 1419 1 1 1 1 1 0

Massachusetts 0 0 1 1 0 0 1

Michigan 0 0 1 1 0 0 1

Minnesota 0 0 0 0 0 0 0

Mississippi 0 0 1 1 1 0 1

Missouri 0 1 0 0 0 0 0

Montana 0 0 0 1 1 1 1

Nebraska 0 0 1 0 0 1 0

Nevada 0 1 1 1 1 1 1

New Hampshire 0 0 0 0 0 0 1

New Jersey 1550 1 1 1 1 0 0

New Mexico 1500 0 1 1 1 0 0

New York 1000 0 1 0 1 0 0

North Carolina 816 1 1 1 1 1 1

North Dakota 0 1 1 1 0 1 1

Ohio 1000 1 0 0 0 1 1

Oklahoma 876 0 1 1 1 0 1

Oregon 0 0 0 0 1 0 0

Pennsylvania 0 0 0 0 1 0 0

Rhode Island 0 0 0 0 0 0 0

South Carolina 0 1 1 0 1 1 1

South Dakota 966 0 1 1 0 0 1

Tennessee 0 0 1 1 1 1 1

Texas 1000 0 1 1 1 1 1

Utah 1422 0 1 0 0 0 0

Vermont 0 1 0 0 1 1 0

Virginia 1280 0 1 1 0 0 1

Washington 1500 0 0 0 0 0 0

West Virginia 1359 0 1 1 0 1 0

Wisconsin 1600 1 1 0 0 1 0

Wyoming 0 0 1 0 0 0 1

TABLE D.1

VALUES OF STATE POLICY MEASURES

Accessibility

Application Requirements

TANF Policies

Note: Most variables measured so that lower variables reflect more lenient policies and higher values reflect stricter policies. More 

information about each variable definition can be found in Chapter III.
126

State

Duration of 

Lifetime Time 

Limits

Intermittent 

Time Limit

No Exemption 

for Ill or 

Incapacitated

No Exemption for 

Mothers with 3- to 12-

Month Children

No Time Limit 

Extenstion

Alabama 60 0 1 1 0

Alaska 60 0 1 1 1

Arizona 60 0 1 1 0

Arkansas 96 0 0 0 0

California 60 0 0 1 0

Colorado 60 0 1 1 1

Connecticut 99 0 0 0 0

Delaware 84 0 0 1 0

District of Columbia 0 0 0 0 0

Florida 72 1 0 0 0

Georgia 72 0 1 1 0

Hawaii 60 0 0 0 1

Idaho 96 0 1 1 1

Illinois 60 0 0 1 1

Indiana 96 0 1 1 0

Iowa 60 0 1 1 1

Kansas 60 0 1 1 0

Kentucky 60 0 1 1 0

Louisiana 60 1 1 1 0

Maine 0 0 0 0 0

Maryland 60 0 0 1 1

Massachusetts 0 1 0 0 0

Michigan 0 0 0 0 0

Minnesota 60 0 1 1 0

Mississippi 60 0 1 1 0

Missouri 60 0 0 1 1

Montana 60 0 1 1 0

Nebraska 60 1 1 0 1

Nevada 60 0 1 1 0

New Hampshire 60 0 1 1 0

New Jersey 60 0 0 1 0

New Mexico 60 0 1 1 1

New York 0 0 0 0 0

North Carolina 60 1 1 1 1

North Dakota 60 0 1 1 1

Ohio 60 1 1 1 0

Oklahoma 60 0 1 1 1

Oregon 0 1 0 0 0

Pennsylvania 60 0 0 1 0

Rhode Island 60 0 0 1 1

South Carolina 60 1 0 1 1

South Dakota 60 0 1 1 1

Tennessee 60 1 0 0 0

Texas 60 1 0 1 0

Utah 84 0 1 1 0

Vermont 0 0 0 0 0

Virginia 60 1 1 1 1

Washington 0 0 0 0 0

West Virginia 60 0 1 1 1

Wisconsin 60 0 1 0 0

Wyoming 60 0 1 1 1

TABLE D.1

VALUES OF STATE POLICY MEASURES

Note: Most variables measured so that lower variables reflect more lenient policies and higher values reflect stricter 

policies. More information about each variable definition can be found in Chapter III.

Time Limits and Work Requirements

TANF Policies

Time Limit Exemptions
127

State

Measure of 

Qualifying 

Activities 

(Scaled)

Minimum 

Hours of 

Work 

Activity

Work 

Requirement 

Prior to Third 

Month of 

Benefits

No Exemption 

for Ill or 

Incapacitated

No Exemption 

for Mothers with 

Children 6 

Months or Older

Most Severe 

Sanction 

Amount for 

Family of Size 

3

Duration of 

Most Severe 

Sanction 

(Scaled)

Difference 

Between 

Severe and 

First 

Sanction

Alabama 0 1 1 0 0 164 3 123

Alaska 0 1 1 0 0 923 1 553.8

Arizona 3 1 1 1 1 347 1 260.25

Arkansas 0 1 1 0 1 204 1 153

California 1 1 1 0 0 136 3 0

Colorado 0 0 1 1 0 356 2 267

Connecticut 1 0 1 0 0 543 2 407.25

Delaware 3 0 1 0 1 338 5 225.446

District of Columbia0 1 1 0 0 81 30

Florida 0 1 1 0 1 303 2 0

Georgia 0 1 1 1 0 280 5 210

Hawaii 1 1 1 0 1 570 2 0

Idaho 1 1 1 1 1 309 5 0

Illinois 0 1 0 0 0 396 2 198

Indiana 1 0 1 0 1 288 1 198

Iowa 2 1 1 1 1 426 3 0

Kansas 0 1 0 0 0 429 2 0

Kentucky 0 1 1 0 0 262 1 174.66667

Louisiana 0 1 1 1 0 240 1 188

Maine 0 1 1 0 0 236 3 0

Maryland 3 0 0 0 0 473 1 0

Massachusetts 0 0 1 1 1 618 1 618

Michigan 0 1 1 0 1 459 1 0

Minnesota 0 1 1 0 0 532 1 478.8

Mississippi 0 1 0 0 0 170 5 0

Missouri 010 0 0 73 20

Montana 0 1 1 1 1 507 1 403

Nebraska 0 1 1 0 1 364 4 0

Nevada 0 1 1 0 0 348 5 232

New Hampshire 0 1 1 0 0 258 1 189

New Jersey 0 1 1 0 1 424 2 322

New Mexico 0 1 0 0 0 389 3 291.75

New York 0 1 1 0 0 192.3333333 3 83.333333

North Carolina 0 1 0 1 0 272 5 204

North Dakota 0 0 1 0 1 477 4 288

Ohio 0 0 1 0 0 373 3 305

Oklahoma 0 1 1 1 1 292 1 0

Oregon 0 0 1 1 1 503 1 453

Pennsylvania 0 0 1 0 0 403 5 316

Rhode Island 0 1 1 0 0 147 1 42

South Carolina 0 1 1 0 0 205 1 0

South Dakota 0 1 1 0 1 483 1 483

Tennessee 2 1 1 0 1 185 2 0

Texas 3 1 1 0 0 108 3 0

Utah 2 0 1 1 1 474 1 374

Vermont 0 1 1 0 0 225 1 150

Virginia 0 1 1 0 0 320 3 0

Washington 3 1 1 0 1 218 1 112

West Virginia 0 1 0 0 0 453 3 302.151

Wisconsin 0 1 0 1 1 628 5 422

Wyoming 0 1 1 1 1 340 1 0

TABLE D.1

VALUES OF STATE POLICY MEASURES

Work Requirement Exemptions

TANF Policies

Time Limits and Work Requirements

Note: Most variables measured so that lower variables reflect more lenient policies and higher values reflect stricter policies. More information about each 

variable definition can be found in Chapter III.
128

State

SCHIP 

Eligibility 

Level as 

Percent of 

Poverty

Medicaid 

Expansion

SSI Maximum 

Benefit EITC

Unemployment 

Rate

20th Percentile 

Wage

Employment 

Growth Rate

Alabama 200 0 512 0 5.683 8.08 1.0

Alaska 200 1 874 0 7.517 10.1 -1.0

Arizona 200 1 512 0 5.633 8.6 -0.5

Arkansas 200 1 512 0 5.592 7.43 2.0

California 250 0 692 0 6.667 8.47 -1.0

Colorado 185 0 548 0 5.700 9.65 -0.5

Connecticut 300 0 747 0 5.100 9.88 3.0

Delaware 200 1 512 0 4.083 9.14 0.0

District of Columbia 200 1 512 1051 6.692 10.03 -1.5

Florida 200 1 512 0 5.233 8.2 0.0

Georgia 235 0 512 0 4.583 8.7 0.5

Hawaii 200 1 517 0 3.967 8.71 0.0

Idaho 150 1 565 0 5.333 7.82 2.0

Illinois 185 0 512 210.2 6.567 8.77 -2.0

Indiana 200 0 512 252.24 5.025 8.77 1.0

Iowa 200 1 534 273.26 4.350 8.72 -1.5

Kansas 200 1 512 630.6 4.867 8.19 -5.0

Kentucky 200 0 512 0 5.792 8.08 -1.3

Louisiana 200 1 512 0 6.275 7.24 2.0

Maine 200 1 522 206.84 4.800 8.56 -1.5

Maryland 300 1 512 840.8 4.342 9.92 2.0

Massachusetts 200 0 641 630.6 5.592 9.89 -1.0

Michigan 200 0 526 0 7.100 8.9 0.0

Minnesota 275 0 593 1387.32 4.558 9.95 0.0

Mississippi 200 1 512 0 6.208 7.76 -1.0

Missouri 300 0 512 0 5.283 8.66 -2.0

Montana 150 0 512 0 4.458 7.43 0.9

Nebraska 185 0 519 0 3.825 8.39 1.7

Nevada 200 1 548 0 5.183 8.36 5.0

New Hampshire 300 0 539 0 4.192 9.9 -2.0

New Jersey 350 0 543 840.8 5.742 9.81 0.3

New Mexico 235 0 512 0 6.042 7.49 0.7

New York 250 0 599 1261.2 6.183 8.63 -1.0

North Carolina 200 0 512 0 6.275 8.23 -4.0

North Dakota 140 1 512 0 3.617 7.86 -2.0

Ohio 200 0 512 0 6.033 8.42 1.5

Oklahoma 185 1 565 210.2 5.383 7.78 0.7

Oregon 170 1 514 210.2 7.858 8.18 4.0

Pennsylvania 235 0 539 0 5.600 8.76 0.0

Rhode Island 250 1 576 1051 5.175 8.75 0.0

South Carolina 150 0 512 0 6.467 8.09 2.0

South Dakota 200 1 527 0 3.342 8.07 0.7

Tennessee 200 1 512 0 5.250 8.33 -3.0

Texas 200 1 512 0 6.592 7.56 0.0

Utah 200 0 512 0 5.250 7.95 -1.0

Vermont 300 1 570 1345.28 4.175 9.01 0.7

Virginia 200 0 512 0 3.917 8.76 -1.0

Washington 250 1 539 0 7.300 8.97 3.0

West Virginia 200 1 512 0 5.967 7.47 0.5

Wisconsin 200 0 596 588.56 5.500 8.95 1.7

Wyoming 133 1 522 0 4.092 7.97 1.0

VALUES OF STATE POLICY MEASURES

TABLE D.1

Note: Most variables measured so that lower variables reflect more lenient policies and higher values reflect stricter policies. More information about 

each variable definition can be found in Chapter III.

Other Programs and Policies

Other Programs Economic Indicators