Abstract

The Food Stamp Program (FSP) is intended to help low-income households afford a nutritionally adequate diet.

Welfare (cash assistance) and FSP policies have changed significantly since the 1990s. This report examines 1990-

2004 data to consider how the policy changes and the changing economic climate have affected the FSP caseload

over time. Results show that the FSP caseload shifted sharply from nearly half receiving cash benefits from

Temporary Assistance for Needy Families (TANF) to less than a fifth. The share of the FSP caseload not receiving

cash benefits from either TANF or Supplemental Security Income (nonpublic assistance, or NPA) increased

sharply. The NPA caseload rose when the economy was weak and was sensitive to reporting requirements for the

FSP (for example, how often participants must be recertified as eligible). The decline in the share of the FSP caseload that receives TANF is not well-explained by the changes in the economy or program policies. The Food Stamp

Program was renamed the Supplemental Nutrition Assistance Program (SNAP) in October 2008.

Determinants of the Food

Stamp Program Caseload

Contractor and Cooperator Report No. 50

January 2009

By Jacob Alex Klerman and Caroline Danielson, RAND

This study was conducted by RAND under a cooperative research contract

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

Research Program (FANRP): contract number 43-3AEM-5-80090 (ERS project

representative: Michele Ver Ploeg). The views expressed are those of the

authors and not necessarily those of ERS or USDA.
Food Stamp Caseload - ii - Klerman and Danielson 

PREFACE 

This document constitutes the final report of the USDA-RAND Cooperative 

Agreement 43-3AEM-5-80090 “Determinants of the Food Stamp Caseload”. This 

work was conducted with the RAND Center for the Study of Social Welfare Policy 

within RAND’s Labor and Population Program. 

This document should be of interest to those trying to understand the 

effect of the Food Stamp Program policy, welfare policy, and the economy on 

the Food Stamp Program caseload, and more broadly on the causes of the wide 

swings in the caseload—overall and its components. 

For more information about this project, contact: 

Jacob Alex Klerman Shelly Ver Ploeg 

Abt Associates Economic Research Service 

55 Wheeler Street 1800 M Street, NW, N2165 

Cambridge, MA 02138 Washington, DC 20036 

(617) 520-2613 (202)694-5372 

Jacob_Klerman@abtassoc.com sverploeg@ers.usda.gov 
Food Stamp Caseload - iii - Klerman and Danielson 

CONTENTS 

Preface.....................................................................ii

Figures......................................................................v

Tables......................................................................vi

Summary....................................................................vii

Acknowledgments.............................................................ix

Acronyms.....................................................................x

I. Introduction..............................................................1

Our Approach...........................................................1

Plan of Paper..........................................................4

Notes on Language......................................................4

II. The Food Stamp Program...................................................5

Purpose and History....................................................5

Eligibility............................................................6

Benefit Computation....................................................7

FSPQC Data.............................................................8

FSP Coverage Rates....................................................10

Broad Caseload Trends.................................................11

III. Forcing Variables......................................................17

A Simple Theory of FSP Participation..................................17

Food Stamp Policies and Procedures....................................21

Welfare Policy........................................................28

The Economy...........................................................30

Other Forcing Variables...............................................32

IV. Econometric Methods.....................................................35

Simulation of fsp take-up.............................................35

Survey and Simple Regression Estimates................................38

Motivating the DoD Estimator..........................................39

Our Specification for the Mean........................................40

Our Specification for the Residuals...................................43

V. Results..................................................................45

Base Model Estimates..................................................45

Sample Inclusion Decisions............................................48

Richer Specification of Policies and the Economy......................50

Benefit Payments Per Capita...........................................57

Simulation Results....................................................58

Discussion............................................................62

VI. Conclusion..............................................................64

Appendix A. FSPQC Data......................................................66

Appendix B. Sources for Forcing Variables...................................73

Included Food Stamp Policies..........................................73

Included Welfare Policies.............................................79

Included Proxies for the Economy......................................80

Other Forcing Variables Included in the Main Models...................80
Food Stamp Caseload - iv - Klerman and Danielson 

Other Controls Included in the Main Models............................81

Food Stamp Program Policies Not Included in the Main Models...........81

Welfare Policies Not Included in the Main Models......................86

References..................................................................89
Food Stamp Caseload - v - Klerman and Danielson 

FIGURES 

Figure 1.1-Normalized FSP Coverage Rate, by Cash Assistance Receipt Sub-Group 

(FFY1994=100) ........................................................... 2

Figure 2.1—National FSP Caseload........................................... 12

Figure 2.2—Normalized FSP Coverage Rate, by Cash Assistance Receipt Sub-Group 

(FFY1994=100) .......................................................... 13

Figure 2.3—Normalized FSP Coverage Rate, by Cash Assistance Receipt Sub-Group 

(FFY2001=100) .......................................................... 14

Figure 2.4—FSP Caseload Shares by Cash Assistance Sub-Group................ 15

Figure 3.1—Short Recertification Periods and the FSP Caseload.............. 25

Figure 3.2—EBT Adoption and the FSP Caseload............................... 27

Figure 3.3—Welfare Reform and the FSP Caseload............................. 29

Figure 3.4-The Unemployment Rate and the FSP Caseload...................... 31
Food Stamp Caseload - vi - Klerman and Danielson 

TABLES 

Table 3.1 Overview of Principal Earlier Studies............................ 18

Table 3.2 Food Stamp Variables in Principal Earlier Studies................ 22

Table 3.3 Welfare Variables in Principal Earlier Studies................... 30

Table 3.4 Economy Variables in Principal Earlier Studies................... 33

Table 5.1 Results for Basic Specification.................................. 46

Table 5.2 Sensitivity to the Inclusion of California....................... 49

Table 5.3 Sensitivity to the Exclusion of Hispanics........................ 51

Table 5.4 Sensitivity to the Inclusion of Lags of the Forcing Variables.... 53

Table 5.5 Log of Benefit Payments Per Capita (vs. Log of Caseload per Capita)

....................................................................... 57

Table 5.6 Log of Benefit Payments Per Capita (vs. Log of Caseload per Capita)

....................................................................... 60

Table B.2 Detailed Food Stamp Policy Coding................................ 78

Table B.3 Statutory Changes to FSP Treatment of Immigrants................. 84
Food Stamp Caseload - vii - Klerman and Danielson 

SUMMARY 

The Food Stamp Program (FSP) is intended to help low-income households 

afford a nutritionally-adequate diet1. Since the 1990s, the FSP caseload has 

varied widely—rising sharply in the early 1990s, dropping sharply in the last 

1990s, and then rising again throughout the 2000s. Welfare and food stamp 

policy changes as well as the changing economic climate are plausible 

candidates for explaining the path of the caseload over time. 

We estimate the impact of these three factors separately on three 

mutually exclusive and exhaustive groups: (i) FSP households receiving cash 

benefit from TANF (formerly AFDC), but not SSI; (ii) FSP households receiving 

cash benefits from SSI, and perhaps TANF; (iii) FSP households receiving cash 

benefits from neither AFDC/TANF, nor SSI-—whom we refer to as NPA/Non-Public 

Assistance. Over our period, 1990 to 2004, the FSP caseload shifted sharply 

from nearly half AFDC/TANF to less than a fifth. The share of the FSP 

caseload receiving SSI increased some. The share of NPA FSP caseload 

increased sharply. 

We find that the explanatory power of our models is concentrated almost 

exclusively in the NPA part of the caseload. In that increasingly important 

subset of the caseload, the economy and FSP policy have the expected effects 

and the effects are large. Specifically, the NPA caseload moves countercyclically (i.e., it rises when the economy is weak) and the NPA caseload is 

quite sensitive to the burden of participating in the FSP—as proxied by short 

certification periods and Simplified Reporting. We find no evidence for 

effects of EBT adoption or outreach expenditures. These qualitative results 

are robust to multiple variations in the specification. Specifically, 

estimating the models excluding Hispanics—as a rough proxy for the effects of 

immigration and PRWORA’s immigration related provisions—does not substantially 

shift the results. Simulations imply that our models explain much of the 

____________ 

1 On October 1, 2008, the Food Stamp Program changed its name to the 

Supplementary Nutrition Assistance Program (SNAP). The new name reflects the 

program’s mission to not only provide food assistance, but also to increase 

nutrition to improve the health and well being of low-income people. However, 

since the report uses data from prior to the name change, the term “Food Stamp 

Program” is used throughout the report. 
Food Stamp Caseload - viii - Klerman and Danielson 

movement in the NPA caseload, but do not explain well the movement in the 

other two caseload components. 
Food Stamp Caseload - ix - Klerman and Danielson 

ACKNOWLEDGMENTS 

This work was conducted under the USDA-RAND Cooperative Agreement 43-

3AEM-5-80090 “Determinants of the Food Stamp Caseload”. Margaret Andrews and 

Shelly Ver Ploeg of USDA-ERS (the project officers for this project), and Dr. 

David Smallwood (Deputy Director of Deputy Director for Food Assistance 

Research in the Food and Rural Economics at the Economic Research Service of 

USDA) have been unusually supportive and helpful. 

At USDA, Bob Dalrymple arranged access to the FSPQC data and Jenny Genser 

provided insights into State policies. Other staff at USDA-FNS providing 

guidance included John Bedwell, Nadine Nichols, and Dan Woodhead. 

Staff at Mathematica Policy Research were uniformly helpful. They 

supplied early years of FSPQC data that were not available on the web site. 

They supplied additional variables that were not included in the public 

distribution file. Finally, they cheerfully and thoroughly answered questions 

about the data. This study would not have been possible without their 

assistance. 

We have benefited from our interaction with a team doing similar analyses 

at the Urban Institute as part of the USDA-Urban Institute study “How are 

State Food Stamp and TANF Policy Choices Affecting Food Stamp Participation.” 

They include Caroline Ratcliffe, Signe-Mary McKernan, and Kenneth Finegold. 

This work is a direct continuation of earlier analyses supported by a 

USDA Small Grants administered by the Institute for Research on Poverty, 

University of Wisconsin, Madison to Caroline Danielson at the University of 

California. That earlier support is gratefully acknowledged. 

We also thank Christine DeMartini for providing programming support, Paul 

Steinberg for providing editorial support, and Christopher Dirks for preparing 

the manuscript. 
Food Stamp Caseload - x - Klerman and Danielson 

ACRONYMS 

Symbol Definition 

AFDC Aid to Families with Dependent Children 

ASEC 

(Current Population Survey) Annual 

Social and Economic Supplement 

CPS Current Population Survey 

EBT Electronic Benefits Transfer 

EITC Earned Income Tax Credit 

ERS Economic Research Service 

FGLS Feasible Generalized Least Squares 

FFY 

Federal Fiscal Year yyyy (October 1, 

yyyy-September 1, yyyy) 

FNS Food and Nutrition Service 

FSP Food Stamp Program 

FSPQC Food Stamp Program Quality Control 

GA General Assistance 

GLS Generalized Least Squares 

GNLLS Generalized Non-Linear Least Squares 

GMM Generalized Method of Moments 

NLGLS Non-Linear Generalized Least Squares 

NLLS Non-Linear Least Squares 

NPA Non-Public Assistance (Food Stamps) 

PA Public Assistance (Food Stamps) 

PRWORA 

Personal Responsibility and Work 

Opportunities Reconciliation Act of 1996 

ORA USDA, Office of Research and Analysis 

QC Quality Control 

SIPP 

Survey of Income and Program 

Participation 
Food Stamp Caseload - xi - Klerman and Danielson 

SSI Supplemental Security Income 

TANF Temporary Assistance to Needy Families 

TFP Thrifty Food Plan 

USDA United States Department of Agriculture 

WNLS Weighted Non-Linear Least Squares 

WLS Weighted Least Squares 

Food Stamp Caseload - 1 - Klerman and Danielson 

I. INTRODUCTION 

The last decade and a half have been a period of major change for the 

Food Stamp Program (FSP). After sharp increases in the early-1990s, the 

overall FSP caseload fell by about a third in the late-1990s and then rose by 

a third in the early-2000s. Furthermore, these wide swings in the overall 

caseload mask strikingly different patterns in the components of the caseload. 

According to this classification of the caseload, the decline in the caseload 

occurred almost exclusively among households receiving food stamp benefits 

along with “welfare” (AFDC/Aid to Families with Dependent Children” and then 

TANF/Temporary Assistance to Needy Families). The increase in the caseload 

occurred almost exclusively among households receiving food stamp benefits, 

but not currently receiving welfare (see Figure 1.1). It seems likely that 

many of them had recently received welfare and continued to receive Food 

Stamps even after leaving welfare (for a job or due to time limits). 

What might have caused these changes in the overall FSP caseload and in 

its components? Theory and previous research leads us to look to both the 

economy and policies, and, indeed, the path of the total FSP caseload closely 

follows the path of the aggregate economy (e.g., the unemployment rate). 

Furthermore, the sharp decline in the welfare component of the FSP caseload is 

coincident with major welfare reform (e.g., PRWORA/Personal Responsibility and 

Work Reconciliation Act of 1996). Finally, the sharp increase in the nonwelfare component of the FSP caseload follows a series of reforms to the FSP 

intended to lower the burden of participation and expanded eligibility. 

OUR APPROACH 

Each of these explanations of caseload change is plausible. It is also 

possible that other, unmeasured, factors played a role. The relative 

importance of these measured factors and their ability to explain the path of 

the aggregate FSP caseload and its components is less clear. Evaluating the 

role of these measured factors and estimating the likely effect of extending 

or repealing welfare or FSP reforms requires an econometric model. This paper 

reports the results of our efforts to estimate such an econometric model. 
Food Stamp Caseload - 2 - Klerman and Danielson 

Figure 1.1-Normalized FSP Coverage Rate, by Cash Assistance Receipt Sub-Group 

(FFY1994=100) 

Source: Tabulations from RAND FSPQC Analysis File 

Such an econometric model begins with data describing the FSP caseload 

itself—the total number of cases and their distribution across cash 

assistance-receipt sub-groups. Specifically, we estimate separate models for 

those receiving welfare (AFDC/TANF, but not SSI), SSI (possibly including 

TANF), and everyone else—who we refer to as the NPA/Non-Public Assistance 

caseload. We compile those data from individual-level data collected as part 

of the Food Stamp Program Quality Control (FSPQC) system. 

An econometric model also requires careful coding of the economic and 

policy forcing variables and how they vary across States and through time. We 

code FSP policy based on USDA summaries, Rosenbaum (2003), and consultation 

with individual States about their policies. Similarly, we code welfare 

policy based on our earlier review of published summaries and consultation 

with individual States (Danielson and Klerman, 2008). With respect to the 

economy, we consider not only the current unemployment rate, but also total 
Food Stamp Caseload - 3 - Klerman and Danielson 

employment and wages per worker. Throughout, we carefully explore the 

dynamics of the effect of these forcing variables on the FSP caseload. A 

naive theoretical approach might posit that only current values of the forcing 

variables affect the current caseload. A richer theoretical approach and our 

earlier empirical work suggests that a more complex specification of the 

forcing variables including not only current values of the forcing values, but 

also carefully-specified lagged values will significantly improve the fit of 

the model and the plausibility of the implied policy responses. 

Finally, an econometric model needs to carefully control for spurious 

effects due to unmeasured factors affecting the FSP caseload. We do so in 

several ways. First, we analyze, not national data, but State-level data. 

Doing so allows us to apply difference-of-differences (DoD) methods. In 

practice, such methods involve including fixed effects (i.e., dummy variables) 

for each State and time period (e.g., year). Including such dummy variables 

controls for time invariant variation across the States and national period 

effects. Even after these controls, there is considerable auto-correlation in 

the residuals; i.e., if the model over-predicts in this period, it is 

extremely likely to over-predict in the next period. Failure to control for 

such auto-correlation leads to overestimation of the precision of the 

estimates. Our approach controls for such autocorrelation and general forms 

of heteroscedasticity. 

We find that the explanatory power of our models is concentrated almost 

exclusively in the NPA part of the caseload. In that increasingly important 

subset of the caseload, the economy and FSP policy have the expected effects 

and the effects are large. Specifically, the NPA caseload moves countercyclically (i.e., it rises when the economy is weak) and the NPA caseload is 

quite sensitive to the burden of participating in the FSP—as proxied by short 

certification periods and Simplified Reporting. We find no evidence for 

effects of EBT adoption or outreach expenditures. These qualitative results 

are robust to multiple variations in the specification. Specifically, 

estimating the models excluding Hispanics—as a rough proxy for the effects of 

immigration and PRWORA’s immigration related provisions—does not substantially 

shift the results. Simulations imply that our models explain much of the 

movement in the NPA caseload, but only poorly explain the movement in the 

other two caseload components. 
Food Stamp Caseload - 4 - Klerman and Danielson 

PLAN OF PAPER 

The balance of this document proceeds in six chapters. The next chapter 

provides an overview of the FSP, its place in the broader American “safety 

net” for the nation’s poor, and our data on FSP caseloads. The third chapter 

describes our “forcing variables”—FSP policy, welfare policy, and the economy. 

The fourth chapter describes our econometric methods. The fifth chapter 

presents the results, for our three sub-groups and for a wide range of 

alternative specifications including dropping Hispanics as a rough test for 

the effects of immigration and welfare reform’s immigration related changes to 

the FSP. The final chapter summarizes our findings, discusses their 

implications for policy, and outlines directions for future research. Four 

appendices discuss in more detail the FSPQC data and the “forcing variables”, 

and present additional regression results. 

NOTES ON LANGUAGE 

We conclude this introduction with some notes on language. We use the 

terms “participants” and “recipients” interchangeably to refer to an 

individual who is certified for and receives food stamps. Note that there can 

be non-participants/non-recipients in a household that receives food stamps 

(e.g., undocumented immigrants, ineligible noncitizens, adults in sanction for 

failure to participate in welfare or FSP welfare-to-work activities). In 

general, food needs of such non-participants are not considered when computing 

the food stamp benefit. 

Also, we use the term “coverage rate” to refer to participants per member 

of the population, whether or not they are (appear to be) eligible for the 

FSP. We reserve the term “participation rate” for the ratio of participants 

to (apparently) eligible members of the population (sometimes referred to as a 

“take-up rate”). We note that this usage differs from the usage in some USDA 

documents (see, for example, Kornfeld, 2002, p. 4-4; Kabbani and Wilde, 2003). 
Food Stamp Caseload - 5 - Klerman and Danielson 

II. THE FOOD STAMP PROGRAM 

This chapter describes our dependent variables, the FSP caseload and 

related outcomes. The chapter begins with an overview of the FSP itself—its 

history and goals and its place in the nation’s social safety net. It then 

discusses our FSPQC data and how we use those data to create rates. Finally, 

it presents recent trends in the—aggregate and disaggregated—caseload. We 

deliberately defer our presentation of the specific policy options allowed 

under federal law, State choices from those options, and the literature on the 

effect of those State choices until the next chapter’s discussion of “forcing 

variables”. 

PURPOSE AND HISTORY 

The FSP is one of the key programs in the federal safety net for lowincome families and the largest of the domestic food and nutrition assistance 

programs administered by USDA/FNS. By statute, the program’s purpose is “to 

permit low-income households to obtain a more nutritious diet by increasing 

their purchasing power” (Food Stamp Act of 1977, as amended, P.L. 95-113). 

The program “serves as the first line of defense against hunger”, enabling 

“low-income families to buy nutritious food” (the language is from 

http://www.fns.usda.gov/fsp/).

The program advances these goals by providing cash equivalents for the 

purchase of food to an average of 22 million individuals per month during the 

period covered by our data. Since the period covered by our data, the Food 

Stamp Program caseload has continued to increase sharply. For a family of 

four in FFY 2008, the maximum FSP benefit was $542 per month. Originally, the 

program provided food “stamps”—i.e., coupons; thus, the program’s name. 

Through the 1990s and into the 2000s, States gradually converted from paper 

“stamps” to Electronic Benefit Transfers (EBT)—an ATM like card (see 

discussion in the next chapter and the table of State-by-State adoption dates 

in Appendix B). Today, essentially all benefits are provided through EBT. 
Food Stamp Caseload - 6 - Klerman and Danielson 

The roots of the FSP date back to a series of pilot programs begun in 

1961 and then the Food Stamp Act of 1964 leading to a program in 22 States.2 

The amendments of 1971 (PL 91-671) made the program national with uniform 

eligibility standards and benefits. The current funding structure of the 

program dates to the Food Stamp Act of 1977.3 Specifically, the federal 

government pays all benefits and splits the cost of administration with the 

States. 

Most FSP features are common across the States. However, as we discuss 

in detail in the next chapter, this is less true today than it was in the mid1990s. Alongside devolution of authority in designing other programs (e.g., 

welfare/AFDC/TANF; see Table 2.1 above), since the mid-1990s, there has been 

increasing State discretion in the operation of the FSP. 

ELIGIBILITY 

FSP eligibility and benefit computation rules are quite complex.4 We 

discuss the aspects that have varied over time and across States in more 

detail in the next chapter. 

In brief, most families are eligible for benefits if they have gross 

incomes less than 130 percent of the monthly poverty line ($2,238 for a family 

of four in FFY 2008), net countable income less than 100 percent of the 

poverty line for households with no elderly or disabled members ($1,721 for a 

family of four in FFY 2008), and less than $2,000 in countable assets ($3,000 

for households with elderly or disabled members). The treatment of 

automobiles relative to this asset standard varies across time and States. 

Households receiving benefits or services from welfare (AFDC/TANF), cash 

income from Supplemental Security Income (SSI), or in some places cash income 

from General Assistance (GA) are categorically eligible (i.e., automatically, 

irrespective of other program eligibility requirements, and without filling 

____________ 

2 On the earlier history of the FSP, see Hoynes and Schanzenbach (2007). 

3 See http://www.fns.usda.gov/fsp/rules/Legislation/history.htm for a 

more detailed history of the program. 

4 This discussion draws heavily on the overview in Poikolainen (2005). 

Cunnyngham (2005) also explores this issue, including a careful analysis of 

changes in national FSP policy over time. 
Food Stamp Caseload - 7 - Klerman and Danielson 

out another application), although any cash income from these sources is 

counted against their FSP benefit5. 

In addition to these income and asset rules, families must meet certain 

non-financial eligibility standards to participate in the FSP. Only citizens 

and legal residents are eligible to participate in the FSP. Also, since the 

passage of PRWORA in 1996 some recent immigrants have been barred from 

participation, although several laws passed subsequently narrowed the grounds 

for exclusion.6 Also since 1996, certain nondisabled, nonelderly, adults 

living in households without children are subject to work participation 

requirements or time limits on benefits. 

After they are initially certified to receive benefits, FSP participants 

must file regular reports to verify that they are still eligible for the 

program. In addition, they must be recertified periodically (usually, but not 

always, in person). As we discuss in the next chapter, there is considerable 

variation through time and across States in the details of these requirements. 

Estimating the effect of those reporting rules on participation and the recent 

increase in the caseload is a major goal of this study. 

BENEFIT COMPUTATION 

The maximum benefit to which a household is entitled is based on the cost 

of the Thrifty Food Plan (TFP). The TFP is based on an “economical and 

nutritious diet”, adjusted for household size and composition and is $542 per 

month for a family of four in FFY 2008. 

A household’s benefit is computed on the basis of net monthly income, 

which is equal to gross income less a standard deduction, and where 

applicable, deductions for earned income (20 percent of earnings), dependent 

____________ 

5 Prior to 2001, only households receiving cash AFDC/TANF, SSI, or GA 

benefits were categorically eligible. However, regulations published in 

November 2000 required States to confer categorical eligibility on families 

receiving benefits or services at least 50 percent funded by TANF, and gave 

them the option of conferring categorical eligibility on families receiving 

benefits or services less than 50 percent funded by TANF. 

6 For a summary of the current exclusions, see 

http://www.fns.usda.gov/fsp/Rules/Memo/02/POLIMGRT.HTM. We note that the 

largest immigrant-receiving states typically put in place replacement food 

assistance programs for legal immigrants made ineligible for the federal 

program (Zimmerman and Tumlin, 1999). However, such participants are not 

included in the federal FSPQC data that we analyze. 
Food Stamp Caseload - 8 - Klerman and Danielson 

care expenses, medical expenses for elderly or disabled participants, child 

support payments, and excess shelter costs exceeding 50 percent of income). A 

household’s monthly benefit is computed by subtracting 30 percent of the net 

income from the maximum benefit. This formula implies that benefits are 

reduced by 30 cents for every additional dollar of net unearned income and 

slightly less for earned income. 

FSPQC DATA 

This document reports analyses of data generated as part of the FSP 

Quality Control (FSPQC) process. Because the federal government pays all 

benefits, States lack strong financial incentives to properly review 

applications. Therefore, to ensure payment accuracy, federal statute requires 

States to participate in the carefully designed and implemented Food Stamp 

Program Quality Control review program (hereafter, FSPQC7). 

Under the FSPQC, State auditors draw a stratified random sample of all 

FSP cases8, where the strata are States or sub-State regions. The exact 

number of cases varies with the State’s total caseload and other factors, but 

is usually between 25 and 200 per month. We refer to the records generated as 

part of that audit as the Food Stamp Program Quality Control data, or simply 

as the FSPQC data. In recent years, the resulting national file includes 

about 50,000 records. The unit of audit is a household. The total number of 

individuals in an annual file is in excess of 100,000. Mathematica Policy 

Research (MPR), under contract to the USDA, prepares a version of these files 

for public release. 

Our analysis file begins with the files distributed by MPR. We augment 

them with a variable on reporting period available in FFY2003 and FFY2004 that 

____________ 

7 For more detail on FSPQC, see GAO (2001) and Rosenbaum (2000). The 

latter article cites Section 16(c) of the Food Stamp Act (7 U.S.C § 2025(c)), 

which provides the statutory basis for the QC system. The food stamp 

regulations at 7 C.F.R. §§ 273.2(d)(2), 275.1 - 275.4, 275.10 - 275.14, and 

275.21 and the Food Stamp Program Quality Control Review Handbook (FNS 

Handbook 310) provide much more detailed information on QC procedures. For a 

discussion of the strengths and weaknesses of the food stamp QC system and 

related penalties and incentives see Affholter and Kramer (1987). 

8 Certain households are excluded from the FSPQC sample, notably those 

receiving disaster benefits. Also, ineligible households were dropped from 

the edited QC data file starting in FFY 2003. 
Food Stamp Caseload - 9 - Klerman and Danielson 

MPR also supplied to us directly, but is not on the standard public use 

distribution file. We consider only the 50 States and the District of 

Columbia. We thus drop Guam and the U.S. Virgin Islands. We also drop all 

sampled households and individuals who are ex post deemed ineligible for the 

FSP program. 

Federal regulations precisely specify the contents of the FSPQC data and 

its collection methods (USDA, 2003), provide the detailed instructions for the 

reviewers, and reproduce the (then current) data collection form (reproduced 

in Appendix A of this document). The intention is that the FSPQC auditor will 

collect sufficient information to verify that the benefit was properly 

computed. As such, the FSPQC data include detailed demographic, labor market, 

assets, and other FSP eligibility information. The exact information 

collected has varied over time. Appendix A provides a review of year-to-year 

changes. 

Individual-level public use FSPQC files are available back to 1980. They 

include most of the information from the FSPQC review. Information that would 

reveal the identity of individuals is suppressed. The file also includes the 

calendar month to which the review referred and weights to control for the 

sampling probabilities and to align the microdata with the administrative 

totals for participant households in each State. Poikolainen and Ewell (2005) 

provide documentation for the 2004 data file.9 Other reports in this series 

provide documentation for the data files for other years. For FFY 1996 

through the FFY 2004, the files are on the MPR web site (http://host4.MPRmpr.com/fns/fnsqcdata/download.htm). For earlier years, MPR provided files 

directly to RAND.10

____________ 

9 It should be noted that the actual file does not align exactly with 

this documentation. Specifically, the file contains many variables not 

described in the Code Book. In particular, the file appears to include the 

date the case was first opened (as used in analysis of FSP program dynamics), 

but that variable is not described in the Code Book. 

10 Apparently no reviews were conducted in the District of Columbia in 

March, July, August, and September of 1995. Our analysis file fills in the 

missing information by linear interpolation from adjacent months in which 

reviews were conducted. 

The data also have some true zeros. In some States, in some months, 

there simply were no reviewed cases in some of our three sub-groups. In those 

cases, we set the counts to zero. This, however, is not appropriate for the 

conditional rates (e.g., the error rate). These missing data are especially 

problematic since we specify those variables as twelve month trailing 
Food Stamp Caseload - 10 - Klerman and Danielson 

While the primary objective of the QC system is to assess the accuracy of 

eligibility determinations and benefit calculations, researchers also use it 

as a source of detailed information on FSP participant characteristics. From 

these data, MPR produces an annual report on the characteristics of FSP 

participants (e.g., Wolkwitz and Leftin, 2008) and a report on FSP 

participation rates (e.g., Wolkwitz, 2008). In addition, these data have been 

used for other microsimulation efforts (Rosso and Fowler, 2000; Brinkley and 

Ewell, 2000) and for econometric analyses (e.g., Gleason, et al, 2001; Kabbani 

and Wilde, 2003; Kornfeld, 2002). 

FSP COVERAGE RATES 

The FSPQC data provide information on the number of people participating 

in the FSP. However, our concept of interest is the coverage rate—which we 

define as participants per member of the population. Note that this “FSP 

Coverage Rate” differs from the “FSP Participation Rate” computed annually by 

MPR (e.g., Wolkwitz, 2008). In this report, we define the Participation Rate 

as the ratio of participants to eligible individuals. Our analysis considers 

the FSP coverage rate, not the “FSP Participation Rate”. We defer to Chapter 

4 and our motivation of our methods, our discussion of the FSP Participation 

Rate, issues with computing it, and why we do not use it. 

Computing coverage rates raises several issues. First, we need to define 

participant groups of interest. Here and in the econometric analyses that 

follow, we consider three groups defined by participation in cash benefit 

programs: (i) FSP households receiving cash benefit from TANF (formerly 

AFDC), but not SSI; (ii) FSP households receiving cash benefits from SSI, and 

perhaps TANF; (iii) FSP households receiving cash benefits from neither 

AFDC/TANF, nor SSI. 

This grouping corresponds to our understanding of likely policy effects 

on the FSP caseload. As we discuss in the next chapter, much of the late1990s caseload decline appears to be closely related to the corresponding 

sharp shrinkage in the welfare caseload. To understand the importance of 

 

averages. Thus, one missing month forces us to drop twelve months of data. 

To address this problem, we interpolate such information between months in 

which there were observed cases and then take the average over months in which 

we actually compute an outcome and months for which we interpolate an outcome. 
Food Stamp Caseload - 11 - Klerman and Danielson 

changes in the welfare program for the FSP caseload, we disaggregate by cash 

assistance receipt status.11 

Second, to compute a rate, we need to assign an appropriate denominator. 

Consistent with our interest in “coverage rates”, not “participation rates”; 

we define the denominators based on simple population counts; i.e., 

independent of cash assistance receipt status or income level within agegender specific groups. 

Third, we need a source for these population statistics. We extract the 

population statistics from the “Bridged Population Estimates” of the National 

Center for Health Statistics (NCHS) (DHHS, 2003; Ingram et al., 2003; see also 

see http://www.cdc.gov/nchs/data/series/sr_02/sr02_135.pdf). We use these 

NCHS estimates rather than the more conventional Census estimates because they 

provide a data series which is consistent across the 1990s and early 2000s. 

The 2000 Census enumerated many more people than would have been expected 

based on population projections from the 1990 Census. These increases were 

particularly notable for Hispanics. The official Census series cross-over 

from a 1990 Census base to a 2000 Census base in 2001. The cross-over leads 

to a discontinuity in population totals. As much as possible, we want a 

consistent series; and, ideally, one based on the more recent 2000 Census. 

The NCHS population estimates address this issue. 

BROAD CASELOAD TRENDS 

Having described the FSP, the source of the data on cases, and the source 

of the data on the overall population, we turn to caseload trends. The size 

of the total FSP caseload has varied widely over the last fifteen years (see 

Figure 2.1). The number of FSP participants rose sharply in the early 1990s, 

peaking in 1994 at 28 million. The caseload dropped sharply thereafter to 

____________ 

11 We note that States report biannual Public Assistance (PA) and NonPublic Assistance (NPA) cases and persons to the USDA. These data are useful, 

but not ideal, for pursuing the analysis of this report because they do not 

disaggregate by type of public assistance received, and because non-public 

assistance households and persons are classified as such if even one household 

member is receiving food stamps in the absence of public assistance (see 

Danielson and Klerman, 2006). 
Food Stamp Caseload - 12 - Klerman and Danielson 

under 17 million in July 2000. Since then, the FSP caseload has risen sharply 

to over 29 million in August 2008.12 

Figure 2.1—National FSP Caseload 

Source: FNS-388 administrative counts. 

This variation in overall case count over our study period hides widely 

diverging coverage rate trends. It is these diverging trends that motivate 

the disaggregated analysis below. Figure 2.2 plots those trends; specifically 

for our three groups it plots coverage rates relative to the FSP caseload in 

FFY1994 (approximate the peak of the total FSP caseload). 

____________ 

12 FSP participation reached an all-time high in November 2005, reaching 

nearly 30 million. However, an estimated 4 million received disaster benefits 

in the wakes of Hurricanes Katrina, Rita, and Wilma. 
Food Stamp Caseload - 13 - Klerman and Danielson 

Figure 2.2—Normalized FSP Coverage Rate, by Cash Assistance Receipt Sub-Group 

(FFY1994=100) 

Note: Rates are 12-month trailing averages of the computed coverage rate 

defined in the text, then divided by the FFY 1994 rate and multiplied by 100. 

Source: Tabulations from RAND FSPQC Analysis File 

Between 1994 and 2001—i.e., peak to trough of the total caseload, cases 

combining Food Stamps and participation in welfare dropped by over 60 percent. 

In contrast, cases combining Food Stamps and SSI dropped by less than 20 

percent, and the Food Stamps-only caseload dropped by 30 percent. 

Since 2001, the relative patterns look very different (see Figure 2.3). 

Cases combining Food Stamps and welfare continue to drop—another 10 percent 

from the 1994 level, about 15 percent from the 2001 level. Cases combining 

Food Stamps and SSI rose back to the 1994 level, which is nearly a 15 percent 

increase from the 2001 level. Finally, cases with Food Stamps but neither 

welfare nor SSI explode, 20 percent from their 1994 level and over 60 percent 

from their 2001 level. 
Food Stamp Caseload - 14 - Klerman and Danielson 

Figure 2.3—Normalized FSP Coverage Rate, by Cash Assistance Receipt Sub-Group 

(FFY2001=100) 

Note: Rates are 12-month trailing averages of the computed coverage rate 

defined in the text, then divided by the FFY 2001 rate and multiplied by 100. 

Source: Tabulations from RAND FSPQC Analysis File 

The net result of these diverging participation trends has been a radical 

shift in the composition of the FSP caseload (See Figure 2.4). Through its 

peak, the FSP caseload was about half welfare recipients. In 2004, it is 

about one-sixth welfare recipients (48 percent to 17 percent13). In contrast, 

the share of combining SSI and food stamps has increased by half (from 13 

percent to 21 percent) and the share with no cash assistance has increased by 

nearly two-thirds (from 38 percent to 62 percent). 

____________ 

13 The proportion of FSP households receiving welfare has continued to 

fall, reaching only 12 percent by 2007. 
Food Stamp Caseload - 15 - Klerman and Danielson 

Figure 2.4—FSP Caseload Shares by Cash Assistance Sub-Group 

Source: Tabulations from RAND FSPQC Analysis File. 

Clearly, there is plenty to explain. Simple inspection of the preceding 

three figures, however, suggests several conjectures about the causes of the 

caseload variation: 

• FSP Policy: A series of policy changes in the early 2000s lowered the 

compliance burden of participating in the FSP and expanded 

eligibility, especially for those not categorically eligible for the 

program. 

• Welfare Policy: Federal welfare reform passed in 1996; State efforts 

under waivers preceded federal welfare reform; and State efforts to 

implement the provisions of federal welfare reform lasted for several 

years after 1996. Moving welfare recipients off the welfare caseload 
Food Stamp Caseload - 16 - Klerman and Danielson 

meant, initially at least, increasing the burden of FSP participation 

for these leavers. 

• Case Conversion: Many welfare leavers may have “converted” their 

combined welfare/Food Stamp cases to non-welfare Food Stamp cases. 

Offsetting increases in the FSP benefit, along with policies aimed at 

reducing the burden of participation among those not categorically 

eligible for the FSP, may have partially offset the increased burden 

of participation. 

• The Economy: The path of the aggregate caseload follows the 

unemployment rate. The economy boomed in the late 1990s, followed by 

a recession in the early 2000s. 

In the next chapter we provide a simple theoretical structure for these 

conjectures, we then describe the policy context and the specific policies 

(including the path of the economy), and how we measure them. 
Food Stamp Caseload - 17 - Klerman and Danielson 

III. FORCING VARIABLES 

In this chapter, we describe our “forcing variables”; i.e., the variables 

that might potentially shift the FSP caseload. Our discussion begins with a 

simple theoretical economic model of program participation. This simple 

theoretical economic model motivates the three classes of forcing variables 

that we consider in our empirical work: States’ formal FSP policies and 

procedures, States’ welfare policies, and the economy. Following our 

development of the theory, we describe the specific policies, discuss the 

expected effect, review the previous literature, and detail our methodological 

approach. 

We organize our review of the previous literature by class of forcing 

variable. Table 3.1 provides a summary of the principal earlier studies of 

the determinants of the FSP caseload. Appendix B provides detail on our 

coding of State policies and dates of changes in those policies. 

A SIMPLE THEORY OF FSP PARTICIPATION 

What determines the size of the FSP caseload and the path it follows over 

time? Our approach is motivated by the standard economic theory of program 

participation (e.g., Keane and Moffitt, 1998; Grogger, Karoly, and Klerman, 

2002). Families choose a bundle of work and program participation to maximize 

their utility (consumption of goods, leisure, compliance burden, and stigma 

from program participation), subject to program rules and available labor 

market options. 

Clearly anything that affects eligibility is likely to affect 

participation. In addition, this perspective suggests that not everyone who 

is eligible for the FSP and other transfer programs will actually participate 

(see GAO, 2004, for a discussion similar to the one presented below). 

Participation requires considerable effort. Indeed, studies suggest that to 

submit an initial application often requires several visits to the welfare 

office and about six hours of the applicant’s time (GAO, 1999, 2004; Bartlett 

and Burstein, 2004). Similarly, recertifications are reported to take several 

hours and an in-person visit during regular working hours to a welfare office 

(GAO, 1999; USDA, 1999). 
Food Stamp Caseload - 18 - Klerman and Danielson 

Table 3.1 

Overview of Principal Earlier Studies 

Authors 

Participation Data 

(Period) Notes 

Wallace and Blank 

(1999) 

CPS 

(1980-1996) 

DV: Total; non-AFDC 

Gleason, et al (2001) FSPQC (1992-1999) Linear time trends but no time fixed 

effects/some models. 

Other variables: Medicaid Policies. 

Kornfeld (2002) FSPQC (1987-1999) State trends: Some models 

Models estimated for subgroups. 

 

Ziliak, Gundersen, and 

Figlio (2003) 

Agg. FSP (1980-

1999) 

Dynamics: Four lags of dependent 

variable 

See also Figlio, Gundersen, and 

Ziliak (2000). 

Kabbani and Wilde 

(2003) 

FSPQC (1990-2000) DV: Also caseload w/ and w/o 

earnings, error rate 

Dynamics: Some models 

State trends: Some models 

Currie and Grogger 

(2001) 

March CPS (1980-

1998) 

State trends: Some models 

Models estimated for subgroups 

Danielson and Klerman 

(2006) 

Aggregate FSP data 

(1990-2004) 

FE: State trends 

Hanratty (2006) SIPP (1996-2003) DV: Income eligible, legal 

residents, with children 

(gross income <130% FPL; 

net income <100% FPL), stratifying 

1-parent/2-parent 

Ratcliffe, McKernan, 

and Feingold (2008) 

SIPP (1996-2003) DV: Low income (<170% FPL); plus 

households with children; single 

female headed households with 

children; 2-adults with children; 

ABAWD (18-50, not disabled, no 

children) 

Klerman, and Danielson 

(2008; this paper) 

FSPQC (1990-2004) DV: Total, non-Hispanics 

State trends: Some models 

Key: DV-dependent variable; FE-fixed effects; FPL-federal poverty line 

Note: Unless indicated in “Notes” column all studies use the caseload 

(usually with some normalization for population) as the DV/Dependent 

variable, and all use econometric methods including fixed effects for 

State and year, but no dynamic terms (i.e., lagged dependent 

variables). 
Food Stamp Caseload - 19 - Klerman and Danielson 

Data: FSPQC/Food Stamp Program Quality Control data, Agg. FSP/USDA 

Aggregate FSP Caseload Data (FNS 388 and/or FNS 388A), CPS/March 

Current Population Survey, SIPP/Survey of Income and Program 

Participation. 

Not surprisingly, therefore, several studies have found that up to 40 

percent of the eligible population does not participate because of the 

perceived burden of applying. Bartlett and Burstein (2004) report that among 

eligible FSP non-participants, 25 percent report that the “they would have to 

answer questions that were too personal”, 40 percent report that “the 

application process required too much paperwork”, 22 percent report that it 

would require “too much time away from work”, 15 percent report that it would 

require “too much time away from child care or elder care responsibilities”, 

and 13 percent report that “it was too difficult to get to the food stamp 

office”.14 

Given this burden, the smaller the expected FSP benefit, the less likely 

it will be that an eligible family will participate. McConnell, Ponza, and 

Cohen (1999) find that more than a third of those eligible report that they do 

not participate because they believe they are eligible for only a small 

benefit amount. Bartlett and Burstein (2004) report that 37 percent of 

eligible non-participants report making such a calculus. Emphasizing the link 

between AFDC/TANF participation and FSP participation, Bartlett and Burstein 

report that over two-thirds of this group reported that applying for food 

stamp benefits was not worth the effort because they believed they were not 

eligible for cash under TANF. 

In addition, in the United States, participation in welfare programs, 

including the FSP, carries two types of “stigma”. First, there is external 

stigma. People who participate in welfare programs (including the FSP) may be 

looked down upon by others. The more obvious it is to others that someone 

participates in a welfare program, the higher the stigma. Consistent with 

this perspective, several studies have found that a quarter of eligible nonparticipants who had received food stamps in the past reported some form of 

____________ 

14 For earlier studies with similar results, see Ponza et al., 1999; 

McConnell and Ponza, 1999; Bartlett, et al., 1992; see also Zedlewski and 

Brauner, 1999. 
Food Stamp Caseload - 20 - Klerman and Danielson 

disrespectful treatment while using food stamps in a store (GAO, 2004; 

Bartlett and Burstein, 2004).15 

Second, there is internal stigma. People prefer not to participate in 

welfare programs, even if no one else knows. Bartlett and Burstein (2004) 

term this “a desire for personal independence” (emphasis in the original). 

This is one interpretation of comments that people do not participate in the 

FSP because they can “get by without it”; i.e., the small benefit is not worth 

the “cost” in stigma (combined with the compliance burden). According to 

Bartlett and Burstein (2004), this group includes 89 percent of eligible nonparticipants. In addition, they report that 64 percent of eligible nonparticipants “do not like to rely on government assistance”. 

Some have claimed that both types of stigma increased with the welfare 

reform of the mid-1990s. On the other hand, the adoption of EBT in the early 

1990s may have lowered stigma. While Food Stamps were quite distinctive, EBT 

cards look similar to a credit or debit card. We note that Bartlett and 

Burstein (2004) find no evidence of an increase in external stigma from 1996 

to the early 2000s. 

Finally, an improving economy will also lower the caseload by improving 

labor market opportunities. As earned income rises, the FSP benefit falls. 

Some people’s income will rise so as to make them ineligible. Others, now 

eligible for only a smaller benefit and considering the burden and stigma of 

application, will decide not to apply or recertify. Consistent with this 

perspective, nearly all previous studies have found that the FSP caseload is 

extremely sensitive to the economy (see Table 3.4, below). Also consistent 

with this behavioral perspective, Martini (1992) finds that participation 

increases with the size of the benefit a household would receive. 

Beyond this conventional economic model of program participation, there 

is some evidence that FSP participation is depressed by misunderstand of 

eligibility. It appears that some welfare leavers, legal immigrants, and U.S. 

born children of illegal immigrants do not realize that they are eligible 

(Bartlett, 2004). Given variation over time in exactly which immigrants are 

eligible for the FSP, this confusion is not surprising. 

____________ 

15 For older evidence that is consistent with this more recent evidence, 

see McConnell and Nixon, 1996; Ponza et al, 1999; see also the focus group 

evidence in Ponza and McConnell, 1996. 
Food Stamp Caseload - 21 - Klerman and Danielson 

Given this simple theoretical perspective, we turn to a discussion of the 

specific forcing variables we include in our regression specifications, how we 

measure them, and the previous literature on their effects on the FSP 

caseload. 

FOOD STAMP POLICIES AND PROCEDURES 

The FSP benefit is not “free money”. Participation requires considerable 

time, effort, and expense (e.g., bus fare) from recipients. Our theoretical 

model suggests that as program “policies” and “procedures” make participation 

less onerous (i.e., lower costs of participation or less stigma; e.g., 

simplified reporting, EBT), participation in a program becomes more attractive 

and enrollment would be expected to rise; conversely, as program rules become 

more onerous (e.g., shorter recertification intervals, work requirements for 

ABAWDs), participation in a program becomes less attractive and enrollment 

would be expected to fall. 

Unlike the cash assistance program (AFDC/TANF), food stamp benefit levels 

and (most) eligibility requirements have been uniform across the continental 

United States since 1971 (in Alaska and Hawaii benefit levels are higher to 

account for a higher cost of living in those two states). Thus, using our 

difference-of-differences (DoD) econometric methods, we cannot estimate the 

effects of food stamp benefit levels (or other national policies). However, 

in the last decade FSP authorizing legislation and USDA regulations have 

granted States discretion over a widening set of FSP policies. 

Table 3.2 summarizes the treatment of FSP policy in the principal earlier 

studies. The table also summarizes the significance pattern of the results. 

Unless otherwise noted, each statistically significant result is in the 

direction predicted by our simple theoretical framework. See Table 3.1 for 

the period and caseload data source in each of these studies. 

Reporting Requirements: Once a household is deemed eligible for food 

stamps, it has several ongoing compliance requirements. Many of the details 

of those requirements are now State options. Furthermore, changes in 

regulation and statute have shifted the options open to the States over the 

1990s and early 2000s. 

• Recertification: Once a household is deemed eligible for food 

stamps, it is certified for a specific number of months, with the 

length of the certification period depending on characteristics of 
Food Stamp Caseload - 22 - Klerman and Danielson 

the household and on State policy. Once this period ends, the 

household must be recertified. At recertification, a 

representative of the household meets with the FSP caseworker and 

provides documentation of income, household composition, residence, 

vehicles (if not excluded), assets, and changes in child support 

obligations. While this recertification was once almost always 

done in person, more recently, States have the option of allowing 

it to occur by mail. A mail recertification option considerably 

lowers the compliance burden. 

Table 3.2 

Food Stamp Variables in Principal Earlier Studies 

Authors FSP Vars 

Wallace and Blank (1999) None 

Gleason, et al (2001) Recert*** 

Kornfeld (2002) ER***, EBT**, Recert*** 

Ziliak, Gundersen, and Figlio (2003) ER, EBT, ABAWD Waiver** 

Kabbani and Wilde (2003) Frequent Recert***, EBT**, Outreach 

Expenditures**, Monthly Reporting*** 

Currie and Grogger (2001) EBT (* for married no kids and 

rural), Recert (*** for single heads 

and ** for rural) 

Danielson and Klerman (2006) EBT*** (implementation dummy), ER 

(overpayments, current/lag)***, 

ABAWD-W, Simp (slope**), Trans (twoyear spline**) 

Hanratty (2006) Recert*** (for earners), Simp*** (1-

parent only), 

Ratcliffe, McKernan, and Finegold 

(2008) 

Recert***, Simp 

Abbreviations: Recert/Recertification Interval, Rep/Reporting interval, 

ER/FSP Error Rate, EBT/EBT Implementation, Simp/Simplified Reporting, 

Trans/Transitional Benefits. 

Significance levels are coded conventionally: “*” Statistically significant 

at the 10 percent level, “**” at the 5 percent level, and “***” at the 

1 percent level. 
Food Stamp Caseload - 23 - Klerman and Danielson 

• Periodic Reporting: For most of the 1980s, FSP participants were 

required to report monthly on their income and family situation. 

Beginning in 1988, States were allowed (and encouraged) to shift to 

quarterly reporting; i.e., a household had to file a report once 

every three months, rather than every month. 

• In addition, November 2000 regulations allowed States to shift to 

simplified reporting for cases with earned income. The Farm 

Security and Rural Investment Act of 2002 (the “2002 Farm Bill”) 

extended that option to almost all households.16 Under simplified 

reporting, recertification periods can be six months in length and 

changes during the certification period need not be reported unless 

they raise the household’s income above 130 percent of the federal 

poverty line. By the end of FFY 2003, 42 States had adopted 

simplified reporting (CBPP, 2003; NAPIPM, 2004; USDA, 2004). 

• Finally, States can opt to reduce the reporting burden for 

households not included in simplified reporting. For example, only 

if a household member changes his or her job does the household 

need to report a change (so-called “status reporting”). Or States 

can define changes to income that are necessary to report more 

broadly—e.g., $100 per month rather than $10 per month. 

State choices about reporting and certification intervals have important 

implications for their error rates and those implications have shifted 

considerably over the last decade. See Dean and Rosenbaum (2002) for a 

discussion. In brief, through about 2000, shorter reporting and 

recertification periods led to lower error rates through two pathways. 

• Prior to 2000, QC reviewers were to compare benefits paid against 

actual earnings. FSP participants were supposed to report changes 

in earnings, but in qualitative interviews, caseworkers state that 

____________ 

16 The 2002 Farm Bill changes can be found at Section 4109 of the Farm 

Bill amending Section 6(c)(1) of the Food Stamp Act and codified at 7 U.S.C. 

2015(c)(1). They were effective October 1 2002. 

Trippe et al. (2004) States: “Missouri and Louisiana were two of the 

earliest States to adopt the option, implementing it for earners in May and 

August 2001, respectively, and expanding it to nonearners under the 2002 Farm 

Bill. Ohio implemented simplified reporting for earners in July 2002, and 

Arizona first implemented it for both earners and nonearners in January 2003.” 
Food Stamp Caseload - 24 - Klerman and Danielson 

changes are rarely reported. Thus, changes in earnings that should 

have been reported were often not reported, resulting in an FSPQC 

error. Required (monthly or quarterly) reporting and certification 

assured that the information the State had was more current and 

therefore more likely to correspond to what the QC reviewer would 

find—leading to fewer QC errors. Consistent with this pathway, 

USDA encouraged States with high error rates to shorten their 

reporting intervals in order to lower their error rates. 

• By raising the burden of participation, States could induce some of 

the more error prone cases (in particular, cases with earnings) to 

leave the FSP caseload. 

This changed in the early-2000s. Changes to statutes and regulations 

lowered the probability of a penalty. States are now penalized if their 

combined error rates are above the national average for two years in a row, 

rather than the one-year period used earlier (CBPP, 2005). Furthermore, beyond 

lowering the compliance burden for FSP participants, under simplified 

reporting the relevant information was the information as of the earlier 

report. There was thus no reason to collect information more frequently. 

Thus, adopting simplified reporting lowered error rates, lowered recipients’ 

compliance burden, and—because there were fewer reports—lowered the 

administrative burden on the State welfare departments. 

Not surprisingly, States have moved quickly to adopt simplified 

reporting. Figure 3.1 plots the (weighted) fraction of the FSPQC sample, in 

each month, in a state that has adopted short recertification periods, a 

standard proxy for these changes in the administrative burden of 

participation. Short recertification periods increase the burden of 

participation and would be expected to lower the caseload. Figure 3.1 also 

includes the aggregate caseload. The inverse relation of the two time series 

is striking. The timing is almost exactly coincident with the initial rise, 

the late-1990s drop, and the early-2000 rise in the caseload. 

The existing literature suggests strong effects of the compliance burden 

on the FSP caseload. Currie and Grogger (2001) find that long recertification 

intervals increase the FSP caseload. Kornfeld (2002) finds that frequent 

recertification periods (defined as the proportion of the caseload whose 

reporting period is one to three months) lower the FSP caseload. Kabbani and 

Wilde (2003) find strong evidence that shorter recertification periods cut the 
Food Stamp Caseload - 25 - Klerman and Danielson 

FSP caseload and some evidence that monthly reporting requirements have a 

similar effect. Hanratty (2006) finds that three month recertification 

periods for earners reduce the probability of participation among both single 

and two-parent families that meet income tests. Finally, Ratcliffe, McKernan, 

and Finegold (2008) find that longer recertification periods increase the 

probability of participation across all types of low-income households they 

examine 

Figure 3.1—Short Recertification Periods and the FSP Caseload 

SOURCE: Tabulations from RAND FSPQC Analysis File. 

Following the earlier literature, we code this concept using the fraction 

of cases (in a cash assistance sub-group) with short certification periods. 

We define “short” as three months or less. We compute this fraction directly 

from the information in our FSPQC data files. We would have liked also to 

include reporting requirements (e.g., monthly, quarterly). However, that 
Food Stamp Caseload - 26 - Klerman and Danielson 

information is only available in the FSPQC data for 2003 and 2004 and 

sporadically in earlier years. 

Electronic Benefit Transfer (EBT): Our theoretical discussion emphasized 

the non-monetary “costs” of participating in the FSP. Among those costs is 

the “stigma” induced by using food “stamps” and thereby revealing 

participation in a “welfare” program (broadly defined). EBT cards look like 

regular credit or debit cards. The transition to EBT should therefore have 

reduced that “stigma”. 

With PRWORA, States were required to convert from paper food “stamps” to 

EBT cards. Twelve States have put only FSP benefits (and in 2 cases, also 

WIC) on the EBT card. Other States have put TANF and sometimes other cash 

assistance on the card.17 

Figure 3.2 plots EBT adoption and the FSP caseload. EBT adoption would 

be expected to raise the FSP caseload. The previous literature has found 

mixed results on the effects of EBT adoption. Currie and Grogger (2001), 

Kornfeld (2002), and Danielson and Klerman (2006) find a small positive effect 

on the caseload. Kabbani and Wilde (2003) and Ziliak, Gunderson, and Figlio 

(2003) find no effect. Ratcliffe, McKernan, and Finegold (2008) find that the 

introduction of EBT increases participation among two-adult households with 

children, but not other types of households. 

We tabulate information on EBT adoption from FNS (available at 

http://www.fns.usda.gov/fsp/ebt/ebt_status_report.htm). 

Nutrition Education and Outreach Initiatives: There is considerable 

evidence that some eligible families do not participate in the FSP because 

they do not realize that they are eligible. Bartlett and Burstein (2004) 

report that almost all eligible non-participants are aware of the FSP, and 

many have participated in the past. However, about half do not realize that 

they are probably now eligible and there is considerable confusion as to 

eligibility requirements for individuals not on AFDC/TANF.18 

____________ 

17 Four States implemented EBT Statewide before the passage of PRWORA. 

The earliest was Maryland in April 1993. The last State completed conversion 

in October 2003 (USDA, 2004). 

18 For older evidence that is consistent with this perspective that lack 

of knowledge is important, see Coe, 1983, GAP, 1988; Hollenbeck and Ohls, 

1984; see also GAO, 1999. Daponte, Sanders, and Taylor (1999) provide random 

assignment evidence. 
Food Stamp Caseload - 27 - Klerman and Danielson 

Figure 3.2—EBT Adoption and the FSP Caseload 

SOURCE: Tabulations from RAND FSPQC Analysis File. 

In response to the sharp decline in the FSP caseload in the late 1990s 

and reports of increased use of food banks and food pantries (GAO, 1999), USDA 

allocated funds for FSP outreach (GAO, 2004). We explored proxying for these 

efforts as expenditures per capita (as in Kabbani and Wilde, 2003 and 

Ratcliffe, McKernan, and Finegold (2008). 

Other Policies: In Appendix B, we review several other policies for 

which there is inter-State variation, but which we do not include in our 

estimated models. Those policies include outreach and education efforts, 

Transitional Food Stamps, Asset Policies, and Immigrant Policies. In the 

appendix, we discuss this decision. In brief, there are few degrees of 
Food Stamp Caseload - 28 - Klerman and Danielson 

freedom available and on a priori grounds we do not expect these policies to 

have effects as large as the included policies. In general, these decisions 

are supported by results from the previous literature (also surveyed in the 

appendix. 

WELFARE POLICY 

Because well over half of the FSP caseload (and much more in the early 

1990s) is composed of households that also receive, or are potentially 

eligible to receive welfare (AFDC/TANF), welfare policies are likely to have 

an impact on the FSP caseload. The effect of welfare policies is complex. 

Welfare usually conveys categorical eligibility for FSP benefits. In 

addition, many people who leave welfare remain income-eligible for FSP 

benefits (see the discussion of Transitional Food Stamps in Appendix B and the 

citations there). Some leavers will remain in the non-welfare part of the FSP 

caseload. The fact that few of them appeared to do so was of considerable 

concern in the late 1990s (e.g., prompting an official Report to Congress; 

USDA, 2001). The role of welfare reform in causing the decline in the FSP 

caseload in the late 1990s is a focus of this study. 

The period covered by our data (1990-2004) was a period of sweeping 

change in welfare policy. At the beginning of the period, State welfare 

programs were strongly constrained by federal regulations and included benefit 

structures that strongly discouraged combining work with welfare, weak work 

requirements, and no time limits (see Grogger, Karoly, and Klerman, 2002). 

This changed via waivers from US DHHS to States in the 1990s and culminated 

with all States’ implementation of TANF programs between 1996 and 1998. The 

transition to TANF substantially altered States’ operation of their cash 

assistance programs. PRWORA, the legislation that launched the TANF program, 

devolved responsibility for program design to the States and strongly 

encouraged them to reduce their caseloads. Time limits on benefits, strong 

sanctions for non-compliance with program requirements, and increased 

financial incentives to combine work and welfare characterized most States’ 

TANF programs. 

Figure 3.3 plots broad welfare reform proxies and the FSP caseload. The 

welfare reform measures rise sharply in the mid-1990s. Actual implementation 

of these reforms (e.g., strengthened sanctions and time limits) continued over 

the next few years. Welfare reform made welfare program participation more 
Food Stamp Caseload - 29 - Klerman and Danielson 

burdensome. It would thus be expected to lower the FSP caseload. These 

reforms thus might explain some of the sharp drop in the FSP caseload over 

that period. 

Figure 3.3—Welfare Reform and the FSP Caseload 

SOURCE: Tabulations from RAND FSPQC Analysis File. 

Table 3.3 summarizes the treatment of welfare policy in the principal 

earlier FSP studies. Grogger, Karoly, and Klerman (2002) summarize the 

treatment of welfare policy in the welfare reform literature. 

In our empirical work, we considered two ways to specify welfare reform. 

Our primary approach includes high-level summaries of reform; specifically, 

the number of waivers and the time since implementing a State’s new TANF 

program. We also considered specifications using specific state policies. In 

work with state welfare caseloads (Danielson and Klerman, 2008), we generated 

plausible estimates of the effects of individual policies. We explored 
Food Stamp Caseload - 30 - Klerman and Danielson 

similar models here with FSPQC data, but the results were not precisely 

estimated. 

Table 3.3 

Welfare Variables in Principal Earlier Studies 

Authors Welfare Vars 

Wallace and Blank (1999) BL, AFDC-UP***, AFDC-W**, AFDC 

caseload*** 

Gleason, et al (2001) Dis, Sanc, TL 

Kornfeld (2002) Dis*, TL, Sanc (between ns and 

***), Family Cap*** 

Ziliak, Gundersen, and Figlio (2003) BL, AFDC- W** (pre reform), TANF 

I, AFDC caseloads 

Kabbani and Wilde (2003) AFDC-W, TANF-I 

Danielson and Klerman (2006) AFDC-W (slope and spline), TANF-I 

(slope and spline)***, ln(max BL) 

Hanratty (2006) AFDC-W** (2-parent only), TANF-I* 

(1-parent only), BL** (2-parent 

only, “wrong sign”) 

Ratcliffe, McKernan, and Finegold 

(2008) 

BL, Dis, Sanction*, TL; 

Results for all households; more 

effects for sub-populations 

(defined on presence of children 

and number of adults) 

Abbreviations: AFDC-W/AFDC Waiver, TANF-I/TANF Implementation, BL/Welfare 

Benefit Level, Sanc/Sanction, Dis/Earned Income Disregard, TL/Time 

Limit, AFDC-UP/AFDC Unemployed Parent. 

Significance levels are coded conventionally: “*” Statistically significant 

at the 10 percent level, “**” at the 5 percent level, and “***” at the 

1 percent level. 

THE ECONOMY 

There is broad consensus that the economy affects the FSP caseload (USDA, 

1999). The correlation is unmistakable in a simple time series plot (e.g., 

Figure 3.4). 

That the economy and the FSP caseload track is not surprising. We would 

expect a better economy to make work, more hours of work, and higher wages 

easier to find and more attractive and, thus to raise potential earnings. In 
Food Stamp Caseload - 31 - Klerman and Danielson 

some cases, the improved labor market conditions will lead to earnings so high 

as to make the family ineligible for food stamps. In other cases, earnings 

will remain low enough to leave the family still eligible for some, but 

smaller, FSP benefit. Some of those families will decide that the costs 

(compliance, loss of privacy, stigma, work requirements) are not worth the 

smaller benefit. Thus, we would expect FSP participation to fall when the 

economy booms. 

Figure 3.4-The Unemployment Rate and the FSP Caseload 

SOURCE: Tabulations from RAND FSPQC Analysis File. 

The period we examine opened with a recession, followed by a long and 

robust expansion, a mild recession, and finally an expansion (see Figure 3.4). 

Given the coincidence of welfare reform, FSP policy and procedure changes, and 

the robust economic expansion of the 1990s, controlling for policies and the 
Food Stamp Caseload - 32 - Klerman and Danielson 

economy is crucial if we want to accurately estimate the effects of the 

policies of interest. 

Table 3.4 summarizes the treatment of the economy and the pattern of 

significance in earlier studies. Previous research typically proxies for the 

economy using the unemployment rate. That literature finds that FSP caseloads 

are strongly counter-cyclical (Hanson and Gundersen, 2002). However, the 

fraction of the caseload change explained by the economy varies widely-from 

about a fifth (Currie and Grogger, 2001) to close to half (Figlio, Gundersen 

and Ziliak, 2000; Gleason et al., 2001). This wide variation in the estimated 

effects of the economy is also found in the literature on the welfare caseload 

(see Haider, Klerman, and Roth, 2003). 

OTHER FORCING VARIABLES 

As we discuss in the next chapter, we control directly for broad 

demographic shifts. Two other time-varying forcing variables are worthy of 

discussion: The minimum wage, and the Earned Income Tax Credit (EITC). 

Minimum Wage: A higher minimum wage could have either a negative or a 

positive effect on the caseload. A higher minimum wage may induce businesses 

to substitute away from (unskilled) labor, thus decreasing employment and 

thereby the caseload; at the same time, the minimum wage jobs that remain are 

better paying, thus reducing caseload. In net, the effect on welfare 

caseloads is ambiguous. Previous research has found a negative effect of the 

minimum wage on the welfare caseload: CEA (1999) estimates an elasticity of 

between -0.25 and -0.52, while Grogger (2004) also finds a negative impact of 

the minimum wage on welfare use among single mothers with children over nine. 

We use the State-level minimum wage and, following CEA (1999), compute the 

real monthly earnings of an individual working at the minimum wage for 30 

hours a week. 

Earned Income Tax Credit (EITC): There were also major expansions in the 

federal Earned Income Tax Credit (EITC) during this period. Those changes 

substantially increased incentives to find employment among low-income 

families in the 1990s. Previous research finds large effects of the EITC on 

employment and the welfare caseload (Grogger, 2004; Meyer and Rosenbaum, 

2001). Unfortunately (as noted earlier), the methods we use that enable us to 

estimate the effect of FSP and welfare policy changes on the caseload do not 

allow us to estimate the effect of nationwide policy changes. A minority of 
Food Stamp Caseload - 33 - Klerman and Danielson 

States had a state earned income tax credit during this period. They are much 

smaller and we do not consider them here. 

Table 3.4 

Economy Variables in Principal Earlier Studies 

Authors Econ Vars 

Wallace and Blank (1999) UR (L2)***, ln(median wage)***, 

ln(20th quintile of wage 

distribution) 

Gleason, et al (2001) UR (L1)***, Mean Income (L1)***, 

PR (L1**), MMW (L1)*** 

Kornfeld (2002) UR(L2)***, EGR, ln(wage), ln(wage 

20th percentile)** 

Ziliak, Gundersen, and Figlio (2003) UR(L4)*** (t and t-4), EGR(L4), 

Ratio of 50th to 10th Income 

Percentile 

Kabbani and Wilde (2003) UR(L2)**, EGR 

Currie and Grogger (2001) UR 

Danielson and Klerman (2006) UR (L1)***, Per cap employment** 

(L1), Total per cap worker 

earnings*** (L1), ln(min wage 

earnings) 

 

Hanratty (2006) UR** (*** 1-parent), service 

wage** (2-parent only) 

Ratcliffe, McKernan, and Finegold 

(2008) 

None 

Abbreviations: UR/Unemployment Rate, EGR/Employment Growth Rate, 

PR/Poverty Rate, MMW/Mean Manufacturing Wage. Lag structure indicated 

in parentheses: No parentheses/no lags, others Ln/n-lags. 

Significance levels are coded conventionally: “*” Statistically significant 

at the 10 percent level, “**” at the 5 percent level, and “***” at the 

1 percent level. 

Food Stamp Caseload - 35 - Klerman and Danielson 

IV. ECONOMETRIC METHODS 

Having described our data for FSP participation and the forcing 

variables, we now describe our methods. We begin with a broad overview of the 

approaches used in the existing literature relative to the DoD econometric 

methods we use. We then describe our methods in detail. 

SIMULATION OF FSP TAKE-UP 

USDA’s semi-official estimates of policy impact are derived from a 

simulation model maintained by MPR based on the March Current Population 

Survey.19 That model is used to generate annual estimates of FSP coverage 

rates and the effect of policy changes (e.g., Cunnyngham, 2005). Rather than 

estimating participation directly (as we do below), the MPR simulation model 

takes a two-step approach. In the first step, MPR estimates the number of 

individuals eligible to participate in the FSP. In the second step, they 

estimate the participation rate, the fraction of those eligible who actually 

participate. 

For two reasons, this approach is quite attractive. 

1. As noted by Cunnyngham (2005), the participation rate is of 

intrinsic interest. It can plausibly be used as a performance 

measure as defined in the Government Performance and Results Act 

of 1993. Specifically, while the FSPQC error rate focuses on 

people who get food stamps who should not (or who get too much 

food stamp benefits), the participation rate focuses on the extent 

to which those who should/could get food stamps actually get them. 

Nevertheless, USDA does not use the participation rate as an 

official performance measure. The required data are not available 

in a sufficiently timely manner. USDA’s Program Access Index—the 

ratio of participants to the population at or below 125 percent of 

the federal poverty line, as measured in the American Community 

Survey—as a performance measure. 

____________ 

19 On microsimulation models in general, see Citro and Hanushek (1991). 

On the MPR efforts see: http://www.MPR-mpr.com/welfare/math-2.asp. 
Food Stamp Caseload - 36 - Klerman and Danielson 

2. As Cunnyngham (2005 and earlier years) shows, the methods are 

quite powerful. They allow the precise simulation of the effect 

of past and potential future policy changes—even policy changes 

that affect only a small part of the FSP population or that were 

adopted, simultaneously, nationwide. 

Nevertheless, their simulations methods are, for several reasons, not 

perfect; and the econometric methods we apply below are complementary. The 

most notable problem with the simulation approach is that the data are not 

sufficient to support the approach. MPR’s simulation model requires 

information on eligibles and participants. For each group, the model requires 

enough information to establish eligibility under current and alternative FSP 

policies. 

Consider first information on participants. The FSPQC data (the same 

data we use below) provide relatively high quality information on actual 

participants. The main issue is that inasmuch as alternative policies would 

require new information on those currently receiving Food Stamps, the current 

FSPQC sample data collection instrument may not collect the right information. 

Thus, for example the pre-1996 FSPQC data does not include detailed 

information on time since entering the U.S. for noncitizens. This complicates 

the estimation of the likely or actual effect of the immigrant reforms. 

Now consider the information on eligibles. Here the problem is much more 

severe. By definition, FSP administrative data files (e.g., the FSPQC data) 

do not have information on eligible non-participants. Since estimating 

participation rates requires counts of eligible individuals, MPR uses the 

March Current Population Survey (CPS), Annual Demographic File. The CPS and 

other surveys are known to under-count participants in means-tested programs, 

including FSP participants, and there is some evidence that the underreporting is becoming more severe.20 This is why MPR uses the CPS to generate 

information on eligibles, but not on participants. 

The CPS data, however, have several problems. 

1. The March CPS simply does not have all of the information required 

to impute eligibility. There is almost no information on assets. 

____________ 

20 See the survey in Hotz and Scholz, 2000. See also Primus, et al, 

1999; Marquis and Moore, 1990; Bolinger and David, 1997; Wheaton and 

Gianarelli, 2000; Klerman, Ringel, and Roth, 2005; Bavier, 2000. 
Food Stamp Caseload - 37 - Klerman and Danielson 

Information on citizenship and immigration is limited. 

Information on work and other activities is not sufficient to 

verify satisfaction of the ABAWD rules. The Census household does 

not exactly correspond to the Food Stamp Unit (and no direct 

information is available on what the Food Stamp Unit would be). 

The CPS does not have enough information to compute net income. 

2. The March CPS uses an annual income concept. The FSP uses a 

monthly income concept. 

3. The model must implicitly assume that the information provided is 

exactly correct. We know that FSP participation is under-reported 

(Resnick, et al, 2004). It seems likely that other information is 

sometimes inaccurate. For example, welfare/AFDC/TANF and 

Supplemental Security Income (SSI) provide categorical eligibility 

for the FSP, but they are themselves under-reported in the CPS by 

about 25 percent (Klerman, Ringel, and Roth, 2004). 

MPR is well aware of these issues. Appendix C to their report 

(Cunnyngham, 2005, and earlier years) provides a careful discussion of their 

approach to each of these issues. They plausibly impute the missing 

information from other data sources (often the Survey of Income and Program 

Participation (SIPP); but also the Panel Study of Income Dynamics (PSID), and 

other administrative data) and plausible assumptions. Their work is careful 

and the approach is sensible. However, no one would deny that the resulting 

estimates of eligibles are imperfect. 

Beyond these problems with the data, the simulation approach implicitly 

assumes that the household and individual characteristics leading to 

eligibility are not themselves affected by program policies and regulations. 

This seems implausible.21 The basic premise of PRWORA was that when faced 

with different incentives, more individuals would choose to work and leave 

welfare.22 That is also an implication of any economic model of program 

participation (e.g., Keane and Moffitt, 1998; Grogger, Karoly, and Klerman, 

____________ 

21 See Kornfeld, 2002, p. 5-3 for a similar argument. 

22 See USDA (2001, p. 31, fn. 5) “The changes in eligibility rules may 

explain even more of the fall in the number eligible if some of the increase 

in income and assts of these groups is a result of welfare reform changes 

encouraging work.” 
Food Stamp Caseload - 38 - Klerman and Danielson 

2002). Such changes in behavior would change income and thus FSP 

eligibility.23

It follows that the simulation results do not usually incorporate the 

effects of any behavioral response to policy changes. In the language of tax 

modeling, they are therefore “static estimates” rather than “dynamic 

estimates” (e.g., Mankiw and Weinzierl, 2005; CBO, 2005). Ideally, any policy 

effect estimates would be dynamic; i.e., they would include changes in 

behavior that lead to changes in eligibility. 

Finally, these simulation methods are not informative for policies that 

change the conditions of participation, but not eligibility. Thus, any 

effects of EBT (which might lower stigma) or reporting and recertification 

policy changes (which might lower burden) are not captured by the 

participation rate estimates. MPR’s simulation methods provide no way to 

disentangle the effects of such changes on take-up. 

The problems raised by the last two points are not that simulation 

methods are incompatible with behavioral responses. Given estimates of 

behavioral responses, a simulation model could incorporate such responses.24 

However, the current MPR models do not incorporate such responses. Part of 

the problem is probably the lack of appropriate estimates of such behavioral 

parameters (e.g., how much participation would increase for every hour the 

application and recertification burden were reduced). 

SURVEY AND SIMPLE REGRESSION ESTIMATES 

An alternative to this simulation-based approach is estimation of 

econometric models.25 For a given observed policy change, compute the change 

in the number of participants—while attempting to hold all else equal. The 

last caveat is crucial and challenging. The conventional approach is to 

include a long list of proxies for individual characteristics. This approach 

____________ 

23 Econometric estimators that stratify on family structure are also open 

to this criticism. We note that the evidence that policy affects work and 

welfare participation is strong. The evidence that policy affects family 

structure (e.g., marital status, the presence of children) is much weaker. 

For a comprehensive review, see Grogger, Karoly, and Klerman, 2004). 

24 See for example MATH STEWARD (Jacobson, et al, 2001). 

25 Random assignment experiments would be another option. See Grogger, 

Karoly, and Klerman (2002) on the relation of econometric estimates and random 

assignment studies for welfare policy. 
Food Stamp Caseload - 39 - Klerman and Danielson 

is potentially attractive for the analysis of individual level survey data 

(e.g., from the CPS or the SIPP). 

However, this conventional approach is problematic from at least two 

perspectives. First, we know that program participation is seriously underreported in survey data (e.g., SIPP or CPS; see Cunnyngham, 2005). If the 

dependent variable is not properly measured, it seems likely that the 

estimated effects will also be problematic. Formally, if under-reporting did 

not differ by individual characteristics, we would be less concerned about 

under-reporting (with appropriate functional forms, it would only affect the 

constant). However, by matching CPS data on Californians to California 

administrative data that records program participation, Klerman, Ringel, and 

Roth (2004) show that for welfare and Medicaid, under-reporting itself varies 

with covariates. The same is likely to be true for the FSP. 

Second, while survey data are rich in individual characteristics, it is 

not clear that those characteristics are valid regressors. We would not want 

to condition on (i.e., include as a regressor) any characteristic that might 

itself be affected by the forcing variables. This line of argument suggests 

that neither family structure, nor family income, are valid regressors. 

Granting this point, the advantage of the additional information in the survey 

data nearly disappears. 

MOTIVATING THE DOD ESTIMATOR 

Instead, we follow the natural experiments literature (e.g., Meyer, 

1995). Specifically, we regress aggregate coverage rates at the State-month 

level (stratified by demographic characteristics) directly on the policies 

themselves. We control for other factors using dummy variables for State and 

calendar year and month. Such methods are known as “difference-of-difference” 

(DoD) methods. 

The name “difference-of-differences” derives from a simple pre-/post 

experiment/control design for a binary policy implemented only in the postperiod and only in the experimental group, in the absence of random assignment 

(Cook and Campbell, 1979; Shadish, Cook, and Campbell, 2001). In that design, 

the DoD estimate is computed in three steps. First, one computes the change 

over time (“after” minus “before”) in the treatment group. That first 

difference is the naïve estimate of the impact of the policy. It eliminates 

any persistent difference in the treatment group. Second, one computes the 
Food Stamp Caseload - 40 - Klerman and Danielson 

change over time in the control group. This second difference gives an 

estimate of the pure time effect, which is implicitly assumed to be common 

across the experimental and control groups. Finally, subtract the change over 

time in the control group from the change over time in the treatment group, 

eliminating any pure time effects. This last number is the DoD estimate. It 

controls both for persistent differences across units (treatment/control, or 

in our case, States) and pure time effects (that are common to the treatment 

and control groups). 

Our approach generalizes this simple DoD estimator to a regression 

framework (Meyer, 1995). Rather than truly differencing, we include dummy 

variables for each State and time period (see below for the exact 

specification of the time effects). Such dummy variables can be shown to be 

the functional equivalent of differencing. These dummy variables control for 

any time invariant variation across States and any nationwide variation across 

time periods. Including dummy variables in a regression allows us to consider 

multiple, non-binary policies, adopted at various times, by various States. 

Furthermore, the regression specification allows the inclusion of other 

controls and more robust estimates of the appropriate standard errors. 

OUR SPECIFICATION FOR THE MEAN 

Consistent with the aggregate nature of our data, and like the previous 

research, we take a reduced-form approach; that is, we analyze the effect of 

the FSP and welfare reforms themselves and of the economy on the total 

caseload and on the components of interest. We do not model at the individual 

level, entry and exit, or eligibility and take-up conditional on eligibility. 

Our approach begins with the conventional difference-of-differences 

specification: 

(IV.1) [ ] [ ] sggtsgg tsgg

tsg

tsg

tsg X tf N

M

y ,, , ,,

,,

,,

,, log , ++++== εθμβα

The g subscript indicates cash assistance receipt sub-groups, the s subscript 

denotes States, and the t subscript indexes time measured in months. Every 

term has a g subscript to emphasize that our estimates totally stratify by 

sub-group; in practice, these are the three sub-groups defined and discussed 

in Chapter 2. The coverage rate is defined as the ratio of participants, M, 
Food Stamp Caseload - 41 - Klerman and Danielson 

to the population, N. In our specifications, we specify the log of this 

coverage rate as our dependent variable. Note that we are not modeling the 

participation rate. We therefore do not try to restrict the population to 

some estimate of eligibles. 

In the equation, X represents the K forcing variables (e.g., FSP 

policies, welfare policies, and the economy) and β the corresponding K

regression coefficients, μ is a vector of 51 State dummies (for the 50 States, 

plus the District of Columbia), f[t,θ] is our specification for time effects 

(see below), and ε is a residual. 

In some models, we also include State-specific linear time trends. The 

econometric model then becomes: 

(IV.2) [ ] [ ] sgsggtsgg tsgg

tsg

tsg

tsg X tft N

M

y ,, ,, ,,

,,

,,

,, log , +++++== εθδμβα

We note that there is some controversy about their inclusion in the 

literature. Wallace and Blank (1999) have argued that the time series are too 

short to estimate such linear time trends. Thus, the effect of their 

inclusion is to soak up true policy effects. 

Consistent with earlier work, we consider both contemporaneous forcing 

variables (i.e., the policies and the economy), X and their lags (e.g., 

Wallace and Blank, 1999; Klerman and Haider, 2004; Danielson and Klerman, 

2006, 2008; Ratcliffe, McKernan, and Finegold, 2008). The conventional 

approach in such aggregate models is to model a policy as either “not in 

place” or “in place”. Implicitly, the effect of the policy is assumed to be a 

step function. No effect yesterday; full effect today. For several reasons, 

this conventional specification seems implausible: 

1. Many policies require a bureaucracy for full implementation. 

Putting that bureaucracy into place is rarely instantaneous. 

For example, programs to enable ABAWDs to fulfill their work 

requirements must be put in place. 

2. In general, the target population’s behavioral response to 

policy changes depends on the spread of knowledge about the 

policy and confidence that it will be applied. For example, in 

order for the stigma of program participation to be reduced, 

potentially eligible populations must come to know about the 

replacement of paper coupons with the EBT card. See Wemmerus, 
Food Stamp Caseload - 42 - Klerman and Danielson 

and Gottleib (1999) who find that changes in the vehicle asset 

test continued to affect the caseload for 18 months. 

3. Klerman and Haider (2004) and Haider and Klerman (2004) show 

that if a policy affects the flows onto and off welfare, the 

effect on the caseload stock will be a distributed lag, with the 

lag lasting several years. 

Given this critique of the conventional approach, we follow Danielson and 

Klerman (2006, 2008) in specifying a flexible functional form for the effect 

of the policies on the FSP caseload. Our specification includes three parts. 

First, we allow for the conventional one-time effect at implementation. 

Second, we allow for a linear effect over the first three years. Finally, we 

allow for a different linear effect in the fourth and following years. Both 

on a priori grounds and given past experience with this specification, we 

expect no immediate effect, an intermediate effect (i.e., the first through 

third years) that varies with the particular program, and no additional effect 

in the long-term (in the fourth and following years). 

For the case of the economy, the previous literature has sometimes 

included lagged values, and those lagged values often substantially increased 

the estimated total effect of the economy (See CEA, 1999; Figlio, Gundersen, 

and Ziliak, 2000; Klerman and Haider, forthcoming; Ziliak, Figlio, Davis, and 

Connolly, 2000; Kornfeld 2002). In connection with the FSP caseload in 

particular, see Schoeni’s (2001) comments on Currie and Grogger (2001). He 

argues that the typical parameterization of economic variables likely 

underestimates the true proportion of the decline resulting from the economy. 

To capture lagged effects of the economy on caseload stocks, we include for 

each measure the moving average of the current and previous eleven month’s 

values and the one-year lag of this average. 

Our motivation for the DoD estimator implies time dummies for every 

period (i.e., months). Here, we take a more parsimonious specification. We 

estimate a national linear trend within each calendar year (January to the 

following January; requiring the trends to overlap) and then a seasonal effect 

for each calendar month (taking January as the excluded month). Thus, we 

estimate not Yx12 parameters, but Y+1+11 parameters (where Y is the number of 

federal fiscal years in the data). 

Finally, we do not include lagged dependent variables. We note that much 

of the FSP (and welfare) literature does include such variables (e.g., Figlio, 
Food Stamp Caseload - 43 - Klerman and Danielson 

Gundersen, and Ziliak, 2000; Ziliak, Gundersen, and Figlio, 2001). Our rich 

specification of the effect of earlier changes in policies and the economy 

should capture the same effects intended to be captured by lagged dependent 

variables. Furthermore, we have argued elsewhere (Klerman and Haider, 2004) 

that such models are likely to be mis-specified; in addition econometric 

issues lead the estimates to be inconsistent. Finally, we note that policy 

estimates tend to be severely muted in models that include lagged dependent 

variables (see the direct comparisons in Wilde et al., 2000). It appears that 

the lagged dependent variables are soaking up much of the true policy 

effect.26

OUR SPECIFICATION FOR THE RESIDUALS 

Our dependent variable is the coverage rate for a group in a State-year. 

We include fixed effects for State and time. There should thus be no 

components of variance at the State or year level. Nevertheless, for two 

reasons, it seems unlikely that the data are independently and identically 

distributed (i.i.d.). 

First, time-series data usually have autocorrelated residuals. Inasmuch 

as there are unobserved factors that affect the coverage rate, those 

unobserved factors are likely to be autocorrelated. That autocorrelation in 

the unobserved factors will induce autocorrelation in the residuals. We 

addressed this problem by estimating AR(12) models 

Second, State populations vary widely. Some conventional econometric 

analyses therefore suggest weighting by the inverse of the State population. 

Alternatively, the FSPQC sample sizes and coverage rates vary. Some 

conventional econometric analyses would therefore suggest weighting by the 

sampling variability for a binomial variable (pq/N) or the equivalent 

expression when the dependent variable is the log of a binomial variable (see 

Maddala, 1983). 

For the second reason, Dickens (1990) argues that this will be 

appropriate only if there are not important unmeasured variables at the Stateyear level. On a priori grounds such unmeasured variables seem likely and 

____________ 

26 We note that Bell (2001) has a slightly different reading of this 

literature; with which we disagree. 
Food Stamp Caseload - 44 - Klerman and Danielson 

Dickens’ analyses suggest that weighting usually induces more error into the 

estimated standard errors (and in the parameters). Following Dickens we do 

not weight the data. Instead, we equally weigh each State-year observation. 

Nevertheless, some component of variance due to FSPQC sample size and the 

coverage rate, and generic heteroscedasticity also seems likely. A superpopulation perspective suggests that, inasmuch as populations vary in size, 

the variance of the residual will vary inversely. To correct the standard 

errors for such heterogeneity—and any other heterogeneity, our preferred 

results apply a Huber-Eicker-White approach to correcting the standard errors 

for arbitrary heteroscedasticity. 

This approach includes no formal controls for heterogeneity; e.g., 

variation in income or race/ethnicity across States and years. Given that we 

include State fixed effects and that any such drift in heterogeneity is likely 

to be small, the lack of formal controls for heterogeneity is unlikely to 

induce significant bias. 

It is also possible that the possibility that the individual terms are 

heteroscedastic due to the varying State population, sampling rates, weighted 

sampling, and different numbers of reviews. The FSPQC sample sizes are chosen 

to assure a minimum level of precision in the resulting estimated error rates. 

It follows that the sampling variance of the estimate of the coverage rate is 

approximately constant across States and time. Thus, heteroscedasticity due 

to sampling variability is unlikely to have a substantial effect on the 

computed standard errors or the parameter estimates. We correct for any such 

heteroscedasticity by using robust (i.e., Huber-Eicker-White) standard errors 

(as calculated by Stata). 
Food Stamp Caseload - 45 - Klerman and Danielson 

V. RESULTS 

Having described our data, the forcing variables, and our econometric 

methods, in this chapter we present our results. The first section 

discusses our basic results. The second section considers results of 

some alternative specifications and their implications for the 

interpretation of our basic results. The third section considers the 

expanded sets of covariates. The fourth second considers models for FSP 

benefits paid. The fifth section develops the implications of our 

parameter estimates via a series of simulations. A final section 

presents some summary comments about the modeling results. We defer our 

summary of the substance until the next chapter. 

BASE MODEL ESTIMATES 

Table 5.1 presents the results for our basic specification, which 

we refer to below as the “Base Model”. The dependent variable is the 

log of cases per capita. Thus, the parameter estimates can be 

interpreted as percentage changes in the caseload. We estimate the 

models totally stratified by TANF (and no SSI); SSI (possibly also 

TANF), and no TANF and no SSI (labeled “NPA” for non-public assistance”. 

Results for these three sub-groups are reported in the three columns. 

All estimates were generated using the Stata xtgee command. We 

report the parameter estimates, standard errors, and conventional 

indicators for statistical significance (“*” for 5 percent; “**” for 1 

percent). The standard errors are computed using an AR(12) 

specification to control for autocorrelation in our monthly data. In 

addition, to make our standard errors robust to heteroscedasticity and 

other forms of serial correlation we use robust (i.e., Huber-EickerWhite) standard errors; and, following Bertrand, Duflo, and Mullianathan 

(2004), we also compute the standard errors clustering on state (both 

are implied by Stata’s “robust” option in xtgee). 

These results implicitly make two decisions about the estimation 

sample: (i) they exclude California and (ii) they include Hispanics. 

In the next section, we explain these choices and explore the 

sensitivity of our results to them. 
Food Stamp Caseload - 46 - Klerman and Danielson 

 

Table 5.1 

Results for Basic Specification 

Note: Dependent variable is log cases per capita. 

All models include dummy variables for state and calendar month, 

and fiscal year splines (not reported). 

8450 observations (50 states x 169 months; excluding California). 

Standard errors in parentheses; computed using AR(12) 

specification, clustering on state, and robust standard errors. 

* significant at 5%; ** significant at 1%. 

The first three rows give the effects of contemporary economic 

conditions: the unemployment rate, the log of total employment, and the 

log of real wages per worker, respectively. To control for seasonality, 

each of these variables is measured as the average over the current 

month and the previous 11 months. The next four rows give the effects 

of our FSP policies: short certification periods, simplified report, 

adoption of EBT, and outreach expenditures, respectively. The final 

three rows give proxies for welfare reform (a dummy for any AFDC waiver, 

a dummy for TANF implementation), and the log of the state minimum wage. 

The source and exact definitions of each of the variables are given in 

Appendix B. 

Having described the basic structure of the tables, we turn to the 

substance. The results are striking and confirm the utility of 

 TANF SSI NPA 

unemp_0 0.034 -0.015 0.019 

(0.029) (0.023) (0.012) 

l_temp_0 -1.689 -1.169 -1.764 

(1.300) (0.916) (0.471)** 

l_Rwpw_0 0.141 -0.333 -0.345 

(0.423) (0.273) (0.190) 

cert_short -0.516 0.424 -0.197 

(0.407) (0.325) (0.047)** 

SR_imp -0.174 0.088 0.073 

(0.115) (0.055) (0.020)** 

EBT_pct -0.014 -0.006 0.027 

(0.053) (0.043) (0.017) 

outreach_pc 0.001 -0.016 0.003 

(0.007) (0.011) (0.005) 

tanf -0.101 -0.176 -0.031 

 (0.051)* (0.050)** (0.023) 

waiv -0.026 -0.005 -0.003 

(0.032) (0.031) (0.018) 

l_Rmin_wage_0 -0.496 -0.269 0.134 

(0.390) (0.311) (0.168) 
Food Stamp Caseload - 47 - Klerman and Danielson 

stratifying by cash receipt status. The only thing that significantly 

affects either the TANF caseload or the SSI caseload is TANF 

implementation. That effect is negative and large—about 10 percent for 

the FSP TANF caseload and 18 percent for the FSP SSI caseload. 

Consistent with our earlier theoretical discussion, the expected 

effect of TANF implementation on TANF is clear. TANF clearly limited 

eligibility and increased burden. We expect to find and do find that 

TANF lowers the FSP TANF caseload. 

The expected effect of TANF on SSI is less clear. On the one hand, 

TANF might be expected to depress the FSP SSI caseload because the 

PRWORA legislation that created TANF also limited eligibility for SSI 

(e.g., some immigrants, some with alcohol or drug addiction). On the 

other hand, PRWORA gave states an even stronger incentive to move cases 

from TANF to SSI. With PRWORA, TANF benefits went from a federal-state 

split to a block grant, while the federal government continued to pay 

all SSI benefits. This might have led us to expect an increase in the 

SSI caseload. These results suggest that the first effect dominated. 

Results for most of the economic variables have the expected, 

counter-cyclical, sign, but are not close to statistical significance 

(the estimate for unemployment for SSI has the “wrong sign”, but note 

that it is less than one standard deviation from zero). There is no 

evidence of any FPS policy effects. This is consistent with our 

conjecture that TANF and SSI participation is determined by the even 

more onerous rules for participation in those programs, rather than FSP 

rules. 

In contrast, several of our regressors shift the NPA caseload. As 

expected, the non-cash assistance is counter-cyclical. Our 

specification includes three proxies for the economy. All three proxies 

suggest that the FSP caseload is counter-cyclical; i.e., the FSP 

caseload increases when the unemployment rate rises and when either 

total employment or real wages fall. Unlike previous studies, our 

results are significantly different from zero at conventional 

significance levels, only for the log of total employment (p<0.01). 

However, we note that the point estimates for the unemployment and real 

wages are also close to conventional statistical significance (p=0.11 

and p=0.07). 
Food Stamp Caseload - 48 - Klerman and Danielson 

Furthermore, the coefficients are substantively large. The 

variable is specified in logs, so the parameter is interpreted as an 

elasticity. Thus, a one percent decrease in total employment is 

estimated to increase the FSP NPA caseload by 1.8 percent. The 

coefficient for the unemployment rate is also large. A one percentage 

point increase in the unemployment rate is estimated to increase the FSP 

NPA caseload by nearly 2 percent. Finally, a one percent decrease in 

real wages is estimated to increase the FSP NPA caseload by about a 

third of a percentage point. 

Interpreting the effects of these substantively related and 

statistically correlated is difficult. Below, we present results of 

simulation estimates that help us to understand the total effects and 

their ability to explain the observed path of the caseload. 

Similarly, lowering the burden of participation in the FSP NPA 

increases the caseload. Specifically, a ten percent change in the 

fraction of the population subject to short certification periods (less 

than 3 months) pushes down the caseload by about 2 percent. Over time, 

the national fraction of the caseload subject to short-certification 

periods varies by about 30 percentage points, so this might explain 

about 6 percent of a caseload increase or decrease. In addition, a 

State’s adopting Simplified Reporting pushes up the caseload by about 7 

percent. There is no statistically significant effect of EBT adoption 

or outreach expenditures. 

SAMPLE INCLUSION DECISIONS 

As noted earlier, this Base Model specification makes two sample 

inclusion decisions. In this section, we explain those choices and 

explore the sensitivity of our estimates to them. 

Our Base Model excludes California. Since 1974, California has 

“cashed out” Food Stamps for its SSI population; i.e., rather than 

getting Food Stamps according to the FSP formula, all California SSI 

recipients receive a fixed increment to their SSI payment, regardless of 

their other income. Over this period, that increment was approximately 

$10. This SSI FSP cash-out implies that we would expect the California 

SSI caseload to react differently to our forcing variables. In as much 
Food Stamp Caseload - 49 - Klerman and Danielson 

as there is substitution between the SSI, TANF, and NPA caseloads, we 

might expect some effects on the other two categories. 

Table 5.2 presents results both for our Base Model and for a model 

that includes California. As might be expected given that California is 

only one “State” among 51, the estimated coefficients barely change. 

Patterns of statistical significance are unchanged. Limited inspection 

of other results suggests that this qualitative finding is not limited 

to this particular model specification. We conclude that our results 

are not sensitive to the exclusion of California. Excluding California 

gives purer estimates. Therefore, in the balance of the paper, we 

report results excluding California. 

Table 5.2 

Sensitivity to the Inclusion of California 

Note: Dependent variable is log cases per capita. 

All models include dummy variables for state and calendar month, 

and fiscal year splines (not reported). 

Base Model: 8450 observations (50 states x 169 months; excluding 

California). 

Results w/California: 8619 observations (51 states x 169 months). 

Standard errors in parentheses; computed using AR(12) 

specification, clustering on state, and robust standard errors. 

* significant at 5%; ** significant at 1%. 

Our Base Model includes Hispanics. The 1996 welfare reform 

(PRWORA/Personal Responsibility and Work Opportunities Reconciliation 

 TANF SSI NPA 

California Excluded Included Excluded Included Excluded Included 

unemp_0 0.034 0.017 -0.015 -0.021 0.019 0.024 

(0.029) (0.034) (0.023) (0.022) (0.012) (0.013) 

l_temp_0 -1.689 -1.759 -1.169 0.247 -1.764 -1.714 

(1.300) (1.314) (0.916) (1.532) (0.471)** (0.455)**

l_Rwpw_0 0.141 0.310 -0.333 -0.601 -0.345 -0.380 

(0.423) (0.558) (0.273) (0.368) (0.190) (0.205) 

cert_short -0.516 -0.533 0.424 0.359 -0.197 -0.192 

(0.407) (0.466) (0.325) (0.352) (0.047)** (0.047)**

SR_imp -0.174 -0.186 0.088 0.091 0.073 0.075 

(0.115) (0.110) (0.055) (0.046) (0.020)** (0.020)**

EBT_pct -0.014 -0.029 -0.006 0.045 0.027 0.037 

(0.053) (0.063) (0.043) (0.062) (0.017) (0.019) 

outreach_pc 0.001 0.007 -0.016 -0.004 0.003 0.004 

(0.007) (0.008) (0.011) (0.017) (0.005) (0.005) 

tanf -0.101 -0.097 -0.176 -0.205 -0.031 -0.028 

 (0.051)* (0.050) (0.050)** (0.054)** (0.023) (0.023) 

waiv -0.026 -0.030 -0.005 -0.034 -0.003 0.007 

(0.032) (0.034) (0.031) (0.034) (0.018) (0.020) 

l_Rmin_wage_0 -0.496 -0.491 -0.269 -0.621 0.134 -0.017 

(0.390) (0.393) (0.311) (0.542) (0.168) (0.213) 
Food Stamp Caseload - 50 - Klerman and Danielson 

Act) substantially shrunk eligibility for the FSP among legal 

immigrants. Later legislation reversed some of those changes. 

Our difference-of-differences approach cannot directly estimate the 

effects of those national changes in eligibility. We note, however, 

that the overwhelming fraction of affected legal immigrants are 

Hispanics. As a rough proxy for the effects of these immigrant 

provisions, we re-ran our Base Model excluding Hispanics both from the 

caseload and from the population counts. 

Table 5.3 presents those results as well as our base case results. 

Again, the results are relatively robust. For FSP NPA cases, the effect 

of the unemployment rate increases slightly, so that it is now 

significant at p=0.05; the effect of total employment decreases slightly 

(in absolute value), with no effect on the level of statistical 

significance. The expected effects of excluding Hispanics are on the 

TANF coefficient. There is essentially no change in this coefficient in 

the FSP TANF and FSP SSI caseloads. There is a small decrease (in 

absolute value) in the FSP NPA caseload. This is the expected direction 

of effect, but the coefficient is far from statistical significance in 

either specification. We conclude that the effects we find are not 

being driven by PRWORA’s immigrant provisions. 

RICHER SPECIFICATION OF POLICIES AND THE ECONOMY 

As we discussed in Chapter 3, our earlier theoretical and empirical 

work and other empirical work on the FSP caseload has found some lagged 

effects of policy and the economy. Earlier theoretical and empirical 

work suggests that the TANF and waiver dummy variables are poor 

summaries of the interstate and over-time variation in AFDC and TANF 

policy. A richer model would include proxies for the details of state 

AFDC and TANF policy. 

Table 5.4 reports results for richer specification that includes 

lagged variables of each of the policy and economic variables (as well 

as our Base Model). Specifically, for each of our three sub-groups 

(TANF/SSI/NPA), we present four models: (A) our Base Model; (B) a 

model with lags of the economic variables; (C) a model with lags of the 

economic variables and the policy variables; and (D) a model with 

specific welfare policies and their lags. 
Food Stamp Caseload - 51 - Klerman and Danielson 

Table 5.3 

Sensitivity to the Exclusion of Hispanics 

Note: Dependent variable is log cases per capita. 

All models include dummy variables for state and calendar month, 

and fiscal year splines (not reported). 

Base Model: 8450 observations (50 states x 169 months; excluding 

California). 

Standard errors in parentheses; computed using AR(12) 

specification, clustering on state, and robust standard errors. 

* significant at 5%; ** significant at 1%. 

Consider first the results with lags of the economic variables 

(i.e., Model B vs. Model A). Specifically, the contemporaneous effect 

is the average of the current month and the eleven previous months. The 

lagged effect is the effect of the previous twelve months. One can thus 

think about these results are “the last year” and “the year before 

that”. With one exception, our earlier finding of no economic effects 

on the FSP TANF and FSP SSI caseloads continues to hold. The exception 

is the lagged value of wages per worker in the SSI caseload. The point 

estimate is as expected counter-cyclical (i.e., negative; lower wages 

cause higher FSP caseloads) and statistically significant at p=0.05. 

Turning now to the FSP NPA caseload where our Base Model did have 

statistically significant economic effects, we find a richer story. The 

unemployment rate whose contemporaneous effect is not quite significant 

in the Base Model has a strongly statistically significant lagged 

 TANF SSI NPA 

Hispanics Included Excluded Included Excluded Included Excluded 

unemp_0 0.034 0.047 -0.015 -0.010 0.019 0.024 

(0.029) (0.025) (0.023) (0.023) (0.012) (0.012)* 

l_temp_0 -1.689 -1.630 -1.169 -1.269 -1.764 -1.715 

(1.300) (1.417) (0.916) (0.935) (0.471)** (0.505)**

l_Rwpw_0 0.141 0.226 -0.333 -0.340 -0.345 -0.352 

(0.423) (0.484) (0.273) (0.273) (0.190) (0.216) 

cert_short -0.516 -0.524 0.424 0.426 -0.197 -0.195 

(0.407) (0.423) (0.325) (0.292) (0.047)** (0.050)**

SR_imp -0.174 -0.176 0.088 0.063 0.073 0.071 

(0.115) (0.118) (0.055) (0.054) (0.020)** (0.022)**

EBT_pct -0.014 -0.019 -0.006 -0.016 0.027 0.027 

(0.053) (0.055) (0.043) (0.043) (0.017) (0.017) 

Outreach_pc 0.001 0.004 -0.016 -0.016 0.003 0.003 

(0.007) (0.007) (0.011) (0.011) (0.005) (0.005) 

Tanf -0.101 -0.107 -0.176 -0.181 -0.031 -0.022 

 (0.051)* (0.053)* (0.050)** (0.047)** (0.023) (0.023) 

Waiv -0.026 -0.025 -0.005 -0.000 -0.003 0.000 

(0.032) (0.033) (0.031) (0.028) (0.018) (0.019) 

l_Rmin_wage_0 -0.496 -0.568 -0.269 -0.310 0.134 0.207 

(0.390) (0.393) (0.311) (0.310) (0.168) (0.176) 
Food Stamp Caseload - 52 - Klerman and Danielson 

effect. Furthermore, computing a “long run effect” as the sum of all of 

the terms, we see a sharp increase from 0.019 to 0.038 (=0.009+0.029). 

Thus, a two-year one percentage point increase in the unemployment rate 

is estimated to increase the NPA FSP caseload by 3.8 percent. Given 

that we observe swings in the unemployment rate of over 3 percent in our 

data, this is a potentially large effect. 
Food Stamp Caseload - 53 -Klerman and Danielson 

 Table 5.4 

Sensitivity to the Inclusion of Lags of the Forcing Variables 

 TANF SSI Model A B C D A B C D A unemp_0 0.034 0.040 0.029 0.028 -0.015 -0.064 -0.064 -0.064 0.019 0.0

(0.029) (0.026) (0.028) (0.031) (0.023) (0.041) (0.039) (0.040) (0.012) (0.0

unemp_1 -0.024 -0.027 -0.014 0.101 0.093 0.101 0.0

 (0.041) (0.041) (0.039) (0.058) (0.056) (0.059) (0.0

l_temp_0 -1.689 -1.988 -1.453 -1.612 -1.169 -1.012 -1.053 -0.923 -1.764 -1.5

(1.300) (1.301) (1.236) (1.342) (0.916) (0.950) (0.923) (0.979) (0.471)** (0.4

l_temp_1 0.019 0.088 0.194 0.814 0.796 1.134 0.1

 (1.207) (1.184) (1.055) (1.453) (1.414) (1.498) (0.2

l_Rwpw_0 0.141 0.155 0.496 0.539 -0.333 -0.312 -0.105 -0.108 -0.345 -0.4

(0.423) (0.531) (0.561) (0.502) (0.273) (0.192) (0.252) (0.242) (0.190) (0.1

l_Rwpw_1 0.000 0.359 0.364 -0.728 -0.513 -0.540 -0.2

 (0.469) (0.506) (0.511) (0.303)* (0.269) (0.256)* (0.1

cert_short -0.516 -0.515 -0.500 -0.505 0.424 0.449 0.434 0.492 -0.197 -0.1

(0.407) (0.412) (0.400) (0.382) (0.325) (0.328) (0.311) (0.273) (0.047)** (0.0

SR_imp -0.174 -0.173 -0.199 -0.196 0.088 0.087 0.095 0.088 0.073 0.0

(0.115) (0.115) (0.119) (0.119) (0.055) (0.054) (0.058) (0.059) (0.020)** (0.0

SRslp 0.004 0.004 -0.001 -0.001 

 (0.005) (0.005) (0.002) (0.003) 

SRslp3 -0.180 -0.190 0.022 0.035  (0.021)** (0.036)** (0.021) (0.023) EBT_pct -0.014 -0.015 -0.005 -0.008 -0.006 0.002 0.026 0.022 0.027 0.0

(0.053) (0.054) (0.051) (0.046) (0.043) (0.042) (0.044) (0.046) (0.017) (0.0

EBTslp -0.005 -0.003 -0.002 -0.003 

 (0.005) (0.004) (0.002) (0.002) 

EBTslp3 -0.009 -0.002 -0.002 -0.002 

 (0.005) (0.005) (0.002) (0.002) 

outreach_pc 0.001 0.002 -0.001 -0.002 -0.016 -0.016 -0.017 -0.016 0.003 0.0

(0.007) (0.007) (0.007) (0.006) (0.011) (0.011) (0.011) (0.011) (0.005) (0.0

tanf -0.101 -0.099 -0.075 -0.033 -0.176 -0.176 -0.185 -0.182 -0.031 -0.0

(0.051)* (0.051) (0.059) (0.041) (0.050)** (0.049)** (0.054)** (0.069)** (0.023) (0.0

tanfslp -0.025 -0.017 -0.011 -0.011  (0.004)** (0.007)* (0.004)* (0.006) tanfslp3 0.001 0.002 -0.004 -0.003 

 (0.006) (0.007) (0.003) (0.003) 

waiv -0.026 -0.026 -0.028 -0.016 -0.005 -0.002 0.020 0.017 -0.003 -0.0

(0.032) (0.032) (0.029) (0.036) (0.031) (0.032) (0.041) (0.046) (0.018) (0.0

waivslp -0.001 -0.000 -0.004 -0.003 

 (0.002) (0.002) (0.002) (0.002) 

l_Rmin_wage_0 -0.496 -0.522 -0.087 0.018 -0.269 -0.094 0.115 0.014 0.134 0.18

(0.390) (0.387) (0.349) (0.391) (0.311) (0.315) (0.317) (0.382) (0.168) (0.1

div -0.059 -0.048  (0.064) (0.050) divslp -0.003 0.004 
Food Stamp Caseload - 54 -Klerman and Danielson 

 (0.005) (0.003) divslp3 -0.008 -0.001  (0.005) (0.001) brr_13p20w 0.005 0.022  (0.053) (0.068) brr_13p20wslp 0.002 0.006  (0.010) (0.003) brr_13p20wslp3 -0.003 -0.001  (0.008) (0.001) sanc_adXff 0.059 0.096  (0.046) (0.094) sanc_adXffslp -0.003 -0.000  (0.004) (0.004) sanc_adXffslp3 -0.009 0.003  (0.004)* (0.002) sanc_ffXff -0.212 0.015  (0.167) (0.059) sanc_ffXffslp -0.021 0.001  (0.012) (0.002) sanc_ffXffslp3 0.009 0.001  (0.006) (0.002) tl_ad 0.013 -0.009  (0.060) (0.056) tl_ad_slp 0.000 -0.000  (0.001) (0.001) tl_hitXad 0.031 -0.260  (0.161) (0.173) tl_hitXad_slp 0.001 0.001  (0.001) (0.001) tl_ff -0.054 -0.024  (0.029) (0.044) tl_ff_slp -0.002 -0.001  (0.002) (0.001) tl_hitXff -0.255 -0.071  (0.227) (0.059) tl_hitXff_slp 0.001 -0.000  (0.001) (0.000) l_Rmaxben_0 0.175 -0.067  (0.551) (0.249) 

Note: Dependent variable is log cases per capita. 

All models include dummy variables for state and calendar month, and fiscal year splreported). Base Model: 8450 observations (50 states x 169 months; excluding California). Standard errors in parentheses; computed using AR(12) specification, clustering on sstandard errors. * significant at 5%; ** significant at 1%. 
Food Stamp Caseload - 55 - Klerman and Danielson 

Shifts in the other economic variables are less dramatic. In our base 

specification, (the log of) total employment was the only economic variable 

that was different from zero at conventional statistical levels. In this 

enriched specification, its lag is not statistically significant. 

Furthermore, the long-run effect drops slightly from 

–1.764 to -1.421; i.e. a one percent decrease in employment increase the FSP 

NPA caseload by 1.4 percent. 

With respect to wages per worker, in the Base Model it was borderline 

significant. With the addition of a lag, the main effect is significant at 

the 5 percent level. Furthermore, the long-run effect grows from –0.345 to –

0.681; i.e., a one percent decline in wages increases the FSP NPA caseload by 

0.7 percent. 

Finally, we note that, each of these economic results is relatively 

stable as we enrich the specification of the policy variables (i.e., Model C 

and Model D). 

To Model B that includes lagged economic effects, Model C adds two lags 

of some of the policy variables. Specifically, building on the approach of 

Danielson and Klerman (2008), the first lag of the policy variables is a 

linear change in the policy over the first three years since implementation; 

while the second lag is the linear change in the policy over the second three 

years since implementation. (See Appendix C for the exact specification.) 

Consider first the FSP policies and the FSP NPA caseload results where the 

Base Model found statistically significant effects of short certification 

periods and Simplified Reporting. There is no evidence of lagged effects for 

any of the variables. If anything, the long run effects shrink slightly. 

Turning to the FSP TANF and FSP SSI caseloads where previously there was no 

effect, we note that anomalous result of a large and statistically significant 

effect of Simplified Reporting at the second lag. This lagged effect is in 

addition to a large, but not statistically significant, contemporaneous 

effect. This lagged effect appears to be due to small samples. This term 

models the change in the effect of Simplified Reporting for implementations 

four to six years after implementation. We have very few observations of 

Simplified Reporting after it was in place for more than three years. We 

therefore believe that this result is spurious. 

Turning now to the welfare variables, there are small changes with the 

inclusion of lags (Model C). For the FSP TANF caseload, the significant TANF 
Food Stamp Caseload - 56 - Klerman and Danielson 

term is now the first lag, but the long-run effect is nearly unchanged. For 

the FSP SSI caseload, there is a small, but statistically significant effect 

at one lag, and the long-run effect grows slightly. For the FSP NPA caseload, 

there is a small, but statistically significant, effect at two lags. With 

respect to waivers, all of the effects continue to be statistically 

insignificant. 

Finally, Model D includes proxies for individual TANF policies. The 

included policies are presence of a welfare diversion program (“div”), a 

generous benefit reduction rate (“brr_12p20w”, coded as one if a family of 

three remains eligible for any benefit, after on welfare for more than a year, 

while working 30 hours per week at the state’s 20th percentile wage), sanction 

policy (gradual sanction, progressing to a full family sanction—“sanc_adXff”—

and immediate, full family sanction—“sanc_ffXff”), time limit policy (“tl”: 

clock ticks in this month for adults only–“tl_ad”; adults could have reached 

the time limit in this month–“tl_hitXad”; and the corresponding variables for 

a full family time limit “tl_ff” and “tl_hitXff”), and the log of welfare 

benefit for a family of three (“l_Rmaxben”). These policy variables are 

coded as a dummy variable for the policy in place, a linear slope with time 

since implementation, and a linear spline beginning 3 years after 

implementation (with no suffix, a “_slp”, and a “slp3” suffix, respectively). 

Inspection of these results shows no clear pattern. A priori, we would 

most expect to find an effect for the FSP TANF caseload and we did find such 

effect using DHHS-ACF TANF caseload data (Danielson and Klerman, 2008). 

Nevertheless, the only statistically significant effect is a small effect for 

the second lag of a gradual sanction. There are no statistically significant 

effects of the SSI caseload. Surprisingly, there are slightly more 

statistically significant effects for the FSP NPA caseload. Again, there is a 

small effect of the second lag of a gradual sanction. In addition, there is a 

moderate effect of reaching an adult only time limit. None of these effects 

are large and the pattern of effects does not seem to be easily interpretable. 

We conclude either that these data will not support disaggregated analyses of 

policies or that there are no such effects (above and beyond the FSP TANF and 

waiver variables). The former interpretation seems more plausible. 
Food Stamp Caseload - 57 - Klerman and Danielson 

BENEFIT PAYMENTS PER CAPITA 

Previous analyses have focused on the caseload. Survey reports of 

benefits received are notoriously imprecise. In contrast, our FSPQC data has 

high quality information on benefit amounts. Table 5.5 reports estimates for 

our Base Model, where instead of taking the dependent variable to be the log 

of the caseload per capita, we take the dependent variable to be the log of 

benefit payments per capita (labeled “Model E”). For comparison, we also 

include in the table results for our base model, with the log of the caseload 

per capita as the dependent variable. 

Table 5.5 

Log of Benefit Payments Per Capita (vs. Log of Caseload per Capita) 

Note: Dependent variable is log cases per capita or log of benefit per capita. 

All models include dummy variables for state and calendar month, and 

fiscal year splines (not reported). 

Base Model: 8450 observations (50 states x 169 months; excluding 

California). 

Standard errors in parentheses; computed using AR(12) specification, 

clustering on state, and robust standard errors. 

* significant at 5%; ** significant at 1%. 

In Table 5.5, the pattern of significance is similar between the caseload 

models and the benefit models. In the FSP TANF model for the caseload, the 

only significant term was for TANF implementation. In the benefit model, the 

point estimate increases slightly, but the standard error increases even more. 

 TANF SSI NPA 

DV Caseload Benefit Caseload Benefit Caseload Benefit 

unemp_0 0.034 0.018 -0.015 -0.042 0.019 0.006 

(0.029) (0.045) (0.023) (0.040) (0.012) (0.017) 

l_temp_0 -1.689 -2.033 -1.169 -1.917 -1.764 -2.259 

 (1.300) (1.753) (0.916) (1.563) (0.471)** (0.579)**

l_Rwpw_0 0.141 0.405 -0.333 -0.517 -0.345 -0.494 

(0.423) (0.589) (0.273) (0.346) (0.190) (0.207)* 

cert_short -0.516 -0.768 0.424 0.979 -0.197 -0.184 

 (0.407) (0.627) (0.325) (0.546) (0.047)** (0.067)**

SR_imp -0.174 -0.233 0.088 0.191 0.073 0.095 

 (0.115) (0.167) (0.055) (0.101) (0.020)** (0.028)**

EBT_pct -0.014 -0.026 -0.006 -0.004 0.027 0.034 

(0.053) (0.068) (0.043) (0.065) (0.017) (0.024) 

outreach_pc 0.001 0.003 -0.016 -0.033 0.003 0.001 

 (0.007) (0.009) (0.011) (0.012)** (0.005) (0.009) 

tanf -0.101 -0.113 -0.176 -0.324 -0.031 -0.049 

 (0.051)* (0.064) (0.050)** (0.116)** (0.023) (0.046) 

waiv -0.026 -0.028 -0.005 -0.029 -0.003 -0.036 

(0.032) (0.041) (0.031) (0.055) (0.018) (0.029) 

l_Rmin_wage_0 -0.496 -0.257 -0.269 -0.229 0.134 0.288 

(0.390) (0.544) (0.311) (0.507) (0.168) (0.281) 
Food Stamp Caseload - 58 - Klerman and Danielson 

In net, the parameter is only borderline significant (p=0.08). In the SSI 

model for the caseload, again the only significant term was TANF 

implementation. In the model for the FSP benefit amount, that term continues 

to be significant (and nearly twice as large). In addition, there is an 

anomalous effect of outreach expenditures per capita; they lower the FSP SSI 

caseload. We have no explanation for this result. 

Finally, we turn to the FSP NPA caseload results. In the model for the 

caseload, there were significant effect of total employment, short 

certification periods, and Simplified Reporting. That pattern of significance 

carries over to FSP benefit amount. The only change is that the magnitude of 

the unemployment effect declines (from borderline significant to clearly 

insignificant) and the magnitude of the total employment effect and the wages 

per worker also rise moderately. 

We conclude that FSP benefit expenses move together with the caseload. 

This is not surprising. We had, however, expected that economic effects would 

be larger for FSP benefits than for the FSP caseload: Changes that make more 

people eligible would also be expected to decrease the earnings of those 

already eligible further increasing benefits. We find little evidence of any 

such effect. 

SIMULATION RESULTS 

The previous sections have presented results for a range of models for 

the components of the FSP caseload (and for FSP benefits paid). Our 

discussion has considered various ways to interpret the magnitude of the 

effects. Despite our efforts, it remains difficult to interpret the results—

especially given the substantive and statistical inter-relation of the 

variables. In addition, our initial interest in these models was partially 

spurred by our desire to understand the wide swings in the FSP caseload over 

the last decade and a half. The parameter estimates reported in the previous 

sections are not directly informative about the causes of the swings in the 

caseload. 

To address each of these concerns and thereby to improve our 

understanding of the implications of our parameter estimates, in this section, 

we report results of simulations based on our parameter estimates. 

Specifically, we summarize the effects of the included policies and the 
Food Stamp Caseload - 59 - Klerman and Danielson 

economy on each of the three caseloads analyzed in this report by means a set 

of counterfactuals: 

1. What would the FSP TANF, FSP SSI, and FSP NPA caseloads have been 

if no state had three-month certification periods for any case? 

2. What would these caseloads have been if no state had spent funds 

on FSP outreach, or had implemented EBT and simplified reporting? 

3. What would these caseloads have been if no AFDC waivers had been 

approved and the legislation creating the TANF program had never 

been passed? 

4. And finally, what would these caseloads have been if the economy 

had never boomed in the mid- and late-1990s or stagnated in the 

early 2000s? 

We simulate these counterfactuals using our Base Model (i.e., Table 5.1) 

and then for our model with lags of the economic variables (i.e., Table 5.4, 

Model B). Table 5.6 presents the results of this simulation exercise. 

Interpreting the table requires considerable explanation. Consider first 

the top panel. It presents results from the Base Model for the period FFY94-

FFY01 using the Base Model. This is the period of the sharp decrease in the 

caseload. Reading across the top row (labeled “Actual”), we see that the FSP 

TANF caseload declined 64 percent, the FSP SSI caseload declined 6 percent, 

and the FSP NPA caseload declined 20 percent. 

Now consider the two columns labeled “NPA”. If we impose no change in 

the economy from February 1992 forward, our model suggests that the FSP NPA 

caseload would only have declined by 2 percentage points (vs. the 20 

percentage points it actually declined). We interpret this simulation to 

imply that the robust economic expansion in the 1990s alone explain 18 

percentage points (=20-2) of the decline. The right panel reports that these 

18 percentage points are 88 percent (=18/20, before rounding) of the observed 

decline; i.e., the three economic variables together explain almost all of the 

decline in the FSP NPA caseload in this period. 

Considering the other “Concepts”, imposing no Short Certifications during 

this period implies a decline 5 percentage points less than actual; which is 

24 percent of the observed decline. Adoption of Simplified Reporting 

(combined with the other FSP policy changes—EBT and outreach expenditures) 

actually pushed the caseload up 2 percentage points. Finally, the adoption of 

TANF pushed the FSP NPA caseload down 2 percentage points. 
Food Stamp Caseload - 60 - Klerman and Danielson 

 

Table 5.6 

Log of Benefit Payments Per Capita (vs. Log of Caseload per Capita) 

Period Model Concept 

Simulated Caseload 

Change Percent Explained

 TANF SSI NPA TANF SSI NPA 

FFY94-FFY01 Base Actual -64% -6% -20% 

 Short Cert. -61% -9% -15% 5% -44% 24%

 Simp. Rep. -63% -6% -22% 1% 1% -11%

 TANF -60% 12% -18% 6% 289% 13%

 Economy -56% 2% -2% 13% 136% 88%

 Total 25% 383% 114%

 Econ Lags Actual -64% -6% -20% 

 Short Cert. -61% -9% -15% 5% -46% 23%

 Simp. Rep. -63% -7% -23% 1% -10% -12%

 TANF -60% 12% -17% 6% 289% 13%

 Economy -57% 21% 5% 10% 440% 123%

 Total 22% 673% 148%

FFY01-FFY04 Base Actual -7% 16% 68% 

 Short Cert. -13% 19% 58% -85% -20% 14%

 Simp. Rep. 8% 8% 58% 222% 48% 15%

 TANF -7% 16% 68% 0% 0% 0%

 Economy -19% 13% 54% -194% 18% 20%

 Total -57% 46% 49%

 Econ Lags Actual -7% 16% 68% 

 Short Cert. -13% 19% 58% -94% -21% 14%

 Simp. Rep. 8% 8% 58% 220% 48% 15%

 TANF -7% 16% 68% 0% 0% 0%

 Economy -18% 8% 51% -168% 52% 25%

 Total -42% 78% 53%

Note: Simulations using parameters from Base Model (Table 5.1=Table 5.4 Model 

A) and Econ Lags Model (Table 5.4 Model B). 

”Simulated Decline” gives the implied decline in the caseload holding 

this “Concept” at its value at the start of the period. 

”Percent Explained” is the percent of the total decline explained by 

this “Concept”. 

A “Concept” is a group of variables. “Simp. Rep.” Includes the effect 

of Simplified Reporting, EBT, and outreach. 

“Total” sums all of the estimated effects. 

Effects of the economy are based on periods defined by the turning 

points in the unemployment rate (rather than the turning points in the 

caseload; specifically starting in 2/1992 and 10/2000). 

The last row of this block sums the percentages explained. Thus, the 

regressors included in our model explain 114 percent of the observed decline; 

i.e., even ignoring the year fixed effects, our model slightly over-explains 

the decline. We consider this to be a success of the model. 
Food Stamp Caseload - 61 - Klerman and Danielson 

The next panel presents results for the same period, using the richer 

model with lags of the economic variables. With more economic variables, the 

economy alone over-explains the decline (123 percent). As expected, the other 

effects are nearly unchanged. With the other variables, the explained 

variation is 148 percent of the observed variation (i.e., half again too 

large). We defer our discussion of this over-explanation until we discuss the 

simulation results for the models for FSP TANF caseloads over this same 

period. 

The models for the FSP TANF caseload over this period are much less 

successful at explaining the decline. The observed decline is even larger—64 

percent. However, TANF explains only 4 percentage points of that decline. 

The economy explains only 8 percentage points of that decline. All together 

we explain less than a quarter of the decline (22 percent). The models with 

lagged economic variables actually explain less of the decline. That we 

cannot explain the decline in the TANF caseload is a relatively standard 

result in the TANF literature. Observed variables clearly under-explain the 

TANF caseload decline. TANF appears to have involved some other changes that 

are not well proxied by our simple dummy for TANF implementation. Even in 

richer models that succeed in estimating plausible impacts of individual TANF 

policies (e.g., Danielson and Klerman, 2008), most of this FSP TANF caseload 

decline remains unexplained. Klerman and Haider (2004) appear to find larger 

effects of the economy for California data using hazard models. They explain 

the difference in terms of the lag structure induced by entry and exit. The 

results here are not consistent with that explanation. 

Such unmeasured dimensions of welfare reform are a likely explanation for 

the over-explanation of the FSP NPA models. Those unmeasured dimensions of 

welfare reform probably pushed some people off of TANF, and onto the FSP NPA 

caseload. They would thus push up the FSP NPA caseload, eliminating (some of) 

the over-explanation of the FSP NPA caseload decline. 

For SSI, we have the opposite problem. We massively over-explain the 

caseload decline (383%; nearly four times). The richer model with lags of the 

economic variables is even worse (673%). The explanation of this overexplanation appears to be that there is little to explain. The actual decline 

is only 6 percent. Thus, the moderate effects we do estimate swamp the actual 

increase. In addition, it seems plausible that some of the unmeasured TANF 

policies pushed up the SSI caseload. In particular, several states had active 
Food Stamp Caseload - 62 - Klerman and Danielson 

policies to help TANF cases (where the state paid all of the costs) to qualify 

for SSI (where the federal government paid all of the costs). 

Results for the later period are different. Over this period caseload 

trends diverged. The FSP TANF caseload declined by another 7 percent. The 

SSI caseload increased by 16 percent. Finally, the FSP NPA caseload increased 

by 68 percent. 

Our model for the FSP NPA caseload explains about half of the observed 

increase (49% for the Base Model; 53% for the model with lags of the economic 

variables). The explanation appears to be nearly equal parts the decrease in 

short certification periods and the increase in Simplified Reporting, and a 

slightly larger contribution of the weakening economy (14%, 15% and 20% 

respectively, in the Base Model). We consider this to be a relatively 

successful estimation/simulation result. 

Results for the FSP SSI caseload are similarly successful overall, but 

the individual results seem implausible. The Base Model explains 46 percent 

of the increase in the SSI caseload; while the model with lags of the economic 

variables explains 78 percent. The economic effects themselves seem 

plausible. However, the model attributes large effects to short certification 

periods and Simplified Reporting. We argued on a priori grounds that it 

seemed unlikely that these FSP policies would affect the SSI caseload. 

Furthermore, while substantively large, these corresponding regression 

coefficients were not statistically different from zero. 

The FSP TANF caseload results appear to have similar issues. The model 

implies that the FSP TANF caseload would grow. It actually shrinks. Again, 

statistically insignificant regression coefficients are inducing large implied 

changes in the caseload. The economy is estimated to have pushed the caseload 

up another 11 percentage points. This seems plausible. Again, it appears 

that unmeasured features of TANF continued to push the TANF caseload down, 

even when the economy alone would have pushed it up. 

DISCUSSION 

This chapter has reported results from a Base Model for the disaggregated 

FSP caseload—TANF, SSI, and NPA, separately. It has also reported several 

sensitivity analyses for these models. Overall, we find that the basic 

pattern of results is robust across the various specifications, but the 
Food Stamp Caseload - 63 - Klerman and Danielson 

patterns are very different across the components of the caseload. We defer 

discussion of the substantive findings to the next and final chapter. 
Food Stamp Caseload - 64 -

VI. CONCLUSION 

We have used FSPQC data to estimate the determinants of the FSP caseload 

and FSP benefits per capita for three mutually exclusive and exhaustive 

groups: (i) FSP households receiving cash benefit from TANF (formerly AFDC), 

but not SSI; (ii) FSP households receiving cash benefits from SSI, and perhaps 

TANF; (iii) FSP households receiving no cash benefits from either AFDC/TANF or 

SSI (NPA/Non-Public Assistance). Over our period, 1990 to 2004, the FSP 

caseload shifted sharply from nearly half AFDC/TANF to just over a sixth. The 

share of the FSP caseload receiving SSI increased somewhat. The share of NPA 

FSP caseload increased sharply. 

We find that the explanatory power of our models is concentrated almost 

exclusively in the NPA part of the caseload. In that increasingly important 

subset of the caseload, the economy and FSP policy have the expected effects 

and the effects are large. Specifically, the NPA caseload moves countercyclically (i.e., it rises when the economy is weak) and the NPA caseload is 

quite sensitive to the burden of participating in the FSP—as proxied by short 

certification periods and Simplified Reporting. We find no evidence for 

effects of EBT adoption or outreach expenditures. These qualitative results 

are robust to multiple variations in the specification. Specifically, 

estimating the models excluding Hispanics—as a rough proxy for the effects of 

immigration and PRWORA’s immigration related provisions—does not substantially 

shift the results. Simulations imply that our models explain much of the 

movement in the NPA caseload, but do not explain well the movement in the 

other two caseload components. 

These results are qualitatively similar to estimates based on survey data 

from the Survey of Income and Program Participation (Hanratty, 2006, and 

Ratcliffe, McKernan, and Finegold, 2008). In particular, those estimates 

imply that shorter certification periods reduce participation among families 

that are likely eligible for the FSP. Ratcliffe, McKernan, and Finegold 

(2008) also find no effect of EBT and outreach spending on participation. 

Our results differ in that we find that portions of the FSP caseload are 

higher in states that implemented simplified reporting. However, the effect 

we find is concentrated in the portion of the caseload not receiving SSI or 

TANF cash assistance. Thus it appears that our approach of focusing on 
Food Stamp Caseload - 65 -

caseloads by type of program participation rather than by household 

composition may enable us to detect policy effects in a time period of rapid 

change in participation in means-tested assistance programs. 

Our results may also differ simply because we use administrative and 

census data that more accurately track FSP caseloads and populations than do 

survey data. The FSPQC data also have larger samples of participants than 

does the SIPP. Against this accuracy we trade off the ability to target 

eligible households. The two approaches—one using survey data, the other 

using administrative data—are in this sense complementary. 
Food Stamp Caseload - 66 - 

APPENDIX A. FSPQC DATA 
Food Stamp Caseload - 67 - Klerman and Danielson 
Food Stamp Caseload - 68 - Klerman and Danielson 
Food Stamp Caseload - 69 - Klerman and Danielson 

Food Stamp Caseload - 71 - Klerman and Danielson 

Complete documentation for the FSPQC Public Use Files for 1996-2004 is 

available on the MPR web site (http://host4.MPRmpr.com/fns/fnsqcdata/download.htm; n.b., all years are federal fiscal years). 

It appears that the basic data collection instrument was stable from FFY1990 

to early FFY 1998; specifically October 1989 through December 1997. 

Thereafter, the form has changed several times. PRWORA’s block granting 

resulted in the end of joint the integrated (FSP/AFDC quality control system. 

With the end of that system, some earlier items were deleted, allowing room 

for new items. Furthermore, the major PRWORA (1996) changes to the FSP 

required the collection of new information. Therefore, several variables were 

added or changed in FFY 1999/April 1999 and in FFY2001/November 2000. These 

new variables included information on ABAWD status, citizenship status, and 

vehicle ownership.27 

Then for FFY2003/October 2002 file, the form was revised again. 

Variables for which there was difficulty collection reliable data were 

dropped. In addition, the FSPQC stopped collecting information on ineligible 

households, since complete data was not available. 

As far back as FFY1995, MPR reports problems with the coding of 

citizenship status (as well as deemed AFDC and wage income) that they 

addressed through a set of standardized recodes (beginning in FFY1996; the 

FFY1995 recodes were performed by hand). With the FFY1998 changes, MPR began 

to report that the ABAWD and citizenship variables appeared to be unreliable. 

As of the FFY2001 file, MPR deemed these two variables to be reliable. 

MPR also notes that the Years of Education variables have very high 

missing rates (at least for FFY1998 when the problem was first noted, through 

FFY2001). In addition, with the FFY2003 file, MPR notes problems with the 

____________ 

27 Of lesser interest to this study there were also new items on shelter 

costs, allotment adjustments, homeless shelter deduction, energy assistance, 

and State diversion payments. 
Food Stamp Caseload - 72 - Klerman and Danielson 

Race variable in some States (including New York in FFY2003 and Delaware in 

FFY2004). 
Food Stamp Caseload - 73 - Klerman and Danielson 

APPENDIX B. SOURCES FOR FORCING VARIABLES 

In this appendix, we provide a detailed discussion of our coding of the 

forcing variables. We begin with a formal description of changes to the FSP. 

We then discuss welfare policy, the economy, and the other forcing variables 

included in our models. Finally, we briefly discuss the reasons we excluded 

several other FSP policies from our preferred models. 

INCLUDED FOOD STAMP POLICIES 

Between 1996 and 2006, the FSP has been shaped by a series of authorizing 

statutes. The annual series “Characteristics of Food Stamp Households” 

published by the Office of Analysis, Nutrition, and Education within USDA 

includes a summary of statutory changes over the previous year; Cunnyngham 

(2005) collates these changes. Box B.1 reproduces those summaries. 
Food Stamp Caseload - 74 - Klerman and Danielson 

Box B.1 

Statutory Changes

FFY1996 PRWORA was enacted on August 22, 1996 and made the following 

changes to the FSP: 

• States were required to implement EBT for the issuance of 

Food Stamp benefits by October 1, 2002. 

• The household definition was expanded to include married 

children and children who are parents, under age 22, who 

also live in the household. 

• Most permanent resident aliens are ineligible to 

participate in the FSP. 

• Most ABAWDs who are not working are only eligible for 3 

months of benefits in a 36-month period. 

• The age at which a student’s earnings begin to be included 

in the household’s income was lowered from 22 years to 18 

years. 

• The maximum monthly benefit was lowered from 103 percent 

of the TFP to 100 percent. 

• The fair market value limit for vehicles increased from 

$4,600 to $4,650. 

• The deduction for shelter costs was increased to $247 from 

$250, starting January 1, 1997 

FFY1998 In 1997, Congress passed the Balanced Budget Act, which took 

effect in fiscal year 1998. This legislation allows States to 

exempt 15 percent of the unemployed, able-bodied, childless 

adults from the FSP time limits imposed by the Personal 

Responsibility and Work Opportunity Reconciliation Act of 1996. 

In addition, the Balanced Budget Act increased funds for the 

Food Stamp Employment and Training Program, while restricting 

how the funds could be used and making them available until 

expended. States were required to earmark 80 percent of their 

federal food stamp employment and training funds to provide 

approved work or training programs for childless, able-bodied 

18- to 50-year-olds. 
Food Stamp Caseload - 75 - Klerman and Danielson 

FFY1999 Effective November 1, 1998, the Agricultural Research, Extension 

and Education Reform Act of 1998 restored eligibility to some 

permanent resident aliens. 

FFY2000 Effective July 1999, categorical eligibility was expanded to 

include households in which all members are authorized to 

receive benefits from means-tested programs funded over 50 

percent by TANF and/or State Maintenance of Effort (MOE) funds. 

At a State’s option, other programs that further the purposes of 

welfare reform may also be used to confer categorical 

eligibility. Thus, a family who leaves welfare to work but 

still receives transitional assistance, such as child support 

subsidies or transportation assistance, may still be considered 

categorically eligible for the FSP. States began implementing 

expanded definitions of categorical eligibility throughout 

Fiscal Year 2000. 

FFY2001 New regulations implemented in January 2001 excluded from the 

asset test any vehicle with equity below $1,500, and exempted 

from the equity test one vehicle per adult in every household as 

well as any vehicles used by a teenager to drive to work or 

school. If there are no qualifying adults or teenagers in the 

household, one vehicle is still exempted from the equity test. 

For vehicles exempt from the equity test but not excluded 

entirely from the asset test, any fair market value exceeding 

$4,650 is counted toward the asset limit. For any remaining 

vehicles, the higher of either any fair market value in excess 

of $4,650 or any equity is counted. 
Food Stamp Caseload - 76 - Klerman and Danielson 

FFY2001 In addition, the FY 2001 Agricultural Appropriations Act 

(enacted in September 2000 but not effective until July 1, 2001) 

allowed States to use TANF vehicle rules in place of food stamp 

rules if the TANF rules were more generous. Further, a broader 

interpretation of categorical eligibility rules was announced in 

July 1999 and implemented on November 21, 2000. This new 

interpretation requires States to confer categorical eligibility 

on families receiving or certified as eligible to receive 

benefits or services that are at least 50 percent funded by TANF 

or Maintenance of Effort (MOE) funds. (States have the option of 

conferring categorical eligibility on families receiving or 

certified to receive benefits or services that are less than 50 

percent funded by TANF/MOE. They may also confer categorical 

eligibility on households where one member receives the benefit 

or service, but the State determines that the whole household 

benefits.) Categorically eligible households are exempt from 

the asset test and the net income test. They are subject to a 

200 percent of poverty gross income test. These changes were 

designed to make it easier for low–income workers to keep a 

vehicle and still receive food stamps. 

The FY 2001 Agricultural Appropriations Act increased the 

maximum allowable shelter deduction on March 1, 2001. States 

were directed to apply the higher shelter deduction when 

certifying or recertifying households on or after March 1. 

FFY2002 <none> 

FFY2003 The 2002 Farm Security and Rural Investment Act restored 

eligibility for qualified noncitizens who are receiving 

disability payments, have lived in the United States for five 

years as a legal immigrant beginning on the date of entry, or 

are under 18. The legislation also raised the asset limit to 

$3,000 for households with disabled members, and adjusted the 

standard deduction to vary by household size and be indexed each 

year for inflation 
Food Stamp Caseload - 77 - Klerman and Danielson 

Certification Periods: We compute the fraction of cases with 

certification periods of 3 months or less directly from the FSPQC dataset. 

This approach is the one taken in Kabbani and Wilde (2003) although we modify 

it by computing the fraction with short certification periods within each of 

our subgroups. 

Program Outreach: FNS provided yearly total amounts States spent on 

outreach for FFY1990-2005. For each state, we divide the annual number by the 

population total, and allocate this per capita amount equally among the twelve 

months of the year. Twenty-two states had no outreach expenditures over the 

entire period. Among those with positive expenditures (in FFY2005, 21 states), 

the mean FFY2005 expenditure was $269,814, while the median expenditure was 

$46,950. 

Electronic Benefits Transfer: PRWORA required that all states implement 

EBT by October 1, 2002. FNS reports the timing of statewide implementation of 

EBT programs at http://www.fns.usda.gov/fsp/ebt/ebt status report.htm. We 

compute the percent of FSP issuance made via EBT using forms FNS-46, “Issuance 

Reconciliation Report” and FNS-338, “State Issuance and Participation 

Estimates”. Statewide implementation of EBT occurred between April 1993 and 

June 2004. 

FFY2004 The 2002 Farm Security and Rural Investment Act restored 

eligibility to certain qualified noncitizens. The eligibility 

of qualified noncitizens who are receiving disability payments 

or who have lived in the United States for 5 years as a legal 

immigrant beginning on the date of entry was restored in fiscal 

year 2003. The eligibility of noncitizen children was restored 

October 1, 2003. 

FFY2005 <none> 

FFY2008 The 2008 Farm Bill included renaming the program to SNAP, 

increasing the minimum deduction and adjusting for inflation, 

removing the cap on the dependent care deduction, increasing 

the standard deduction and adjusting for inflation, indexing 

the asset limits, and excluding retirement and educational 

savings. 
Food Stamp Caseload - 78 - Klerman and Danielson 

Simplified Reporting: Once a household is deemed eligible for food 

stamps, it is certified for a period of months, the length of the 

certification period depending on characteristics of the household and state 

policy. Once that period ends, the household must be recertified (i.e., 

current income and family structure must be documented to prove continued 

eligibility for food stamps). Even between certifications, the household is 

required to report changes in circumstances that may affect eligibility or the 

benefit level. States may choose to require households to report changes 

within 10 days of occurrence (“incident reporting”) or at specified intervals 

(“periodic reporting”). The latter reduces the burden on households with 

frequent changes in income. The simplified system further reduces the burden 

of periodic reporting by requiring households to report changes that happen 

during a certification period only when their income rises above 130 percent 

of the federal poverty line. 

We use a conservative measure of implementation of simplified reporting: 

States that chose reporting intervals of at least three months (the modal 

policy specifies six months) and that included at least households with earned 

income (and often other households as well) in the simplified system are coded 

as having implemented simplified reporting. By the end of FFY2004, 45 states 

had adopted simplified reporting as we measure it (Rosenbaum, 2003; National 

Association for Program Information and Performance Measurement, 2004; USDA, 

2004).28 Implementation occurred between September 2000 and July 2005. 

Table B.2 lists the month and year of adoption of EBT and simplified 

reporting. We assume if a policy is implemented in a month, it is in effect 

throughout the entire month. 

Table B.2 

Detailed Food Stamp Policy Coding 

State 

Electronic Benefits 

Transfer Implemented 

Statewide 

Simplified Reporting 

Implemented 

Alabama Nov-97 Aug-04 

Alaska Jun-98 Jan-04 

Arizona Aug-99 Jan-04 

Arkansas Apr-98 Nov-03 

California Jun-04 Jul-04 

Colorado Feb-98 Nov-03 

____________ 

28 We verified this information and filled in gaps by examining states’ 

on-line FSP policy manuals and by making calls to state officials. 
Food Stamp Caseload - 79 - Klerman and Danielson 

Connecticut Oct-97 Jan-03 

Delaware Sep-03 Mar-02 

District of Columbia Oct-98 May-01 

Florida Oct-98 Aug-03 

Georgia Nov-98 Oct-02 

Hawaii Aug-98 

Idaho Feb-98 Nov-03 

Illinois Nov-97 Nov-03 

Indiana Mar-02 Jun-03 

Iowa Oct-03 Jan-04 

Kansas Mar-97 Oct-02 

Kentucky Nov-99 Nov-03 

Louisiana Dec-97 Sep-00 

Maine Jun-03 Jun-03 

Maryland Apr-93 Oct-01 

Massachusetts Oct-97 Oct-02 

Michigan Jul-01 Aug-01 

Minnesota Oct-98 

Mississippi Oct-02 May-05 

Missouri May-98 Sep-04 

Montana Jul-02 Apr-02 

Nebraska Sep-02 Jun-03 

Nevada Jul-02 Oct-04 

New Hampshire Jan-99 Jul-05 

New Jersey Jun-99 Jul-01 

New Mexico Aug-95 Jan-04 

New York Feb-01 Oct-02 

North Carolina Jun-99 Jun-03 

North Dakota Mar-97 

Ohio Oct-99 Nov-03 

Oklahoma Jan-98 Mar-02 

Oregon May-98 Aug-03 

Pennsylvania Sep-98 Jun-03 

Rhode Island Oct-98 Dec-04 

South Carolina Dec-95 Aug-02 

South Dakota Mar-97 

Tennessee Aug-99 Sep-01 

Texas Nov-95 Apr-03 

Utah Apr-96 

Vermont Oct-98 Nov-02 

Virginia Jul-02 Jul-03 

Washington Nov-99 Oct-02 

West Virginia Jun-03 May-01 

Wisconsin Oct-00 Jul-03 

Wyoming Jan-00 

INCLUDED WELFARE POLICIES 

Statutory changes summarized in Box B.1 implies that the implementation 

of TANF affected FSP caseloads both directly by changing eligibility and the 

attractiveness of the program and indirectly by beginning a process of 

unbundling welfare from food stamps receipt. We include two high-level welfare 

reform variables that capture the date of implementation of any statewide 

waiver to AFDC policies granted in the early 1990s and the date of 
Food Stamp Caseload - 80 - Klerman and Danielson 

implementation of states’ TANF programs. We draw the dates of implementation 

from Crouse (1999); this is also the coding used in CEA (1999). 

INCLUDED PROXIES FOR THE ECONOMY 

Following previous research that indicates that richer measures of the 

economy increase the share of observed change attributable to the economy, we 

use three measures of the economy: monthly State-level unemployment rates 

(multiplied by 100) from the Bureau of Labor Statistics’ series A; monthly 

State-level total covered employment as recorded by the Quarterly Census of 

Employment and Wages (QCEW) Program that the BLS conducts in cooperation with 

the States; and quarterly State-level total earnings per worker, as recorded 

by the QCEW. We divide total employment by the total estimated population in a 

State and month and multiply by 100. 

Following Klerman and Haider (2003), we model the effect of the economy 

using current and lagged values, as well as interactions of these values. The 

current and lagged measures are moving averages of the previous year’s (i.e., 

of the twelve monthly) values and moving averages of the values one year 

previous. 

OTHER FORCING VARIABLES INCLUDED IN THE MAIN MODELS 

Two other time-varying forcing variables are worthy of discussion: The 

minimum wage, and the Earned Income Tax Credit (EITC). 

Minimum Wage: A higher minimum wage could have either a negative or a 

positive effect on the caseload. A higher minimum wage may induce businesses 

to substitute away from (unskilled) labor, thus increasing the caseload; at 

the same time, the minimum wage jobs that remain are better paying, thus 

reducing caseload. In net, the effect on welfare caseloads is ambiguous. 

Previous research has found a negative effect of the minimum wage on the 

welfare caseload: CEA (1999) estimates an elasticity of between -0.25 and -

0.52, while Grogger (2004) also finds a negative impact of the minimum wage on 

welfare use among single mothers with children over nine. We use the Statelevel minimum wage and, following CEA (1999), compute the real monthly 

earnings of an individual working at the minimum wage for 30 hours a week. 

Earned Income Tax Credit (EITC): There were also major expansions in the 

federal Earned Income Tax Credit (EITC) during this period. Those changes 
Food Stamp Caseload - 81 - Klerman and Danielson 

substantially increased incentives to find employment among low-income 

families in the 1990s. Previous research finds large effects of the EITC on 

employment and the welfare caseload (Grogger, 2004; Meyer and Rosenbaum, 

2001). Unfortunately (as noted earlier), the methods we use that enable us to 

estimate the effect of FSP and welfare policy changes on the caseload do not 

allow us to estimate the effect of nationwide policy changes. A minority of 

States had an earned income tax credit during this period. They are much 

smaller than the federal credit, and we do not consider them here. 

OTHER CONTROLS INCLUDED IN THE MAIN MODELS 

The specification includes a national year spline with knot points in 

October of each year, fixed effects for each State, State-specific linear year 

trends (in some specifications), and indicators for each calendar month. 

FOOD STAMP PROGRAM POLICIES NOT INCLUDED IN THE MAIN MODELS 

As noted in Chapter 3, we explored several other FSP policies, but did 

not include them in our final models. We excluded them because our time 

series of cross-sections approach has only a limited number of degrees of 

freedom. On a priori grounds we concluded that the effect of these variables 

was likely to be small. Limited specification searches were consistent with 

that a priori expectation. Here we describe these other policies. 

Payment Error Rates: FNS penalizes States for over- and underpayments. 

According to the GAO (2001), in FFY 2000 States overpaid food stamp recipients 

about $976 million and underpaid them about $360 million, with errors making 

up about 9 percent of total payments. About half of the errors were 

determined to have occurred because food stamp workers made mistakes; the 

other half were determined to have occurred because recipients deliberately or 

inadvertently provided inaccurate information (GAO, 2001). States are 

penalized equally for over-payments and under-payments and the types of errors 

are positively correlated. 

In a simple model, high overpayments would be expected to lead to higher 

caseloads (Kornfeld, 2002). Some people who should not be getting food stamps 

get them. Some people who should be getting smaller FSP benefits and might 

therefore choose not to remain in the program, are getting larger benefits and 
Food Stamp Caseload - 82 - Klerman and Danielson 

therefore remain enrolled. Conversely, high underpayments might be expected 

to lead to lower caseloads. 

The literature, plausibly, argues for a different pathway (Rosenbaum, 

2000). Consider what happens if for exogenous reasons (e.g., poor 

supervision, large fraction of the caseload working) the combined error rate 

rises. Fearing federal penalties, States take actions to lower their error 

rates. Some of the actions are formal and captured in our earlier discussion 

of certification and reporting periods. Other measures are informal (i.e., 

there was no change in official policy that would be captured in the “State 

Options Report”, but there was a de facto change in policy). Caseworkers feel 

the pressure and respond in ways that push down the caseload (e.g., shorter 

certification periods, more scrutiny of those with earned income). 

The previous literature has found mixed results on the effects of error 

rates. Kornfeld (2002) finds an effect; Ziliak, Gundersen, and Figlio (2003) 

does not. Danielson and Klerman (2006) find that overpayments are associated 

with a higher overall caseload, but no relationship between underpayments and 

the caseload. Hanratty (2006) and Ratcliffe, McKernan, and Finegold (2008) 

and do not include payment errors in their models. 

On a priori grounds, it seems likely that the reporting and 

recertification requirements are the crucial policy choice. Error rates 

themselves are not a direct cause of caseload levels. 

Transitional Benefits: Clearly, leaving welfare is a change of 

circumstances; and, it is a change of circumstances of which the State welfare 

department is (in principle) aware. Furthermore, families sometimes leave 

because of a change in circumstances that would affect their FSP benefit 

(e.g., employment, marriage). Thus, leaving welfare would often trigger a 

requirement to reevaluate eligibility for the FSP (but see 7 C.F.R. 

273.12(f)(3)). Given that compliance burden, we would expect FSP enrollment 

to drop sharply with welfare exit. 

Because of the stigma of participation, we would expect a drop in FSP 

participation associated with welfare exits even if families remain nominally 

eligible. In as much as people leave welfare to escape demeaning contact with 

the welfare office and the stigma of welfare participation, we would expect 

those people to also exit the FSP at the same time. Indeed, studies of 

welfare leavers find sharp drops in FSP participation among leavers (Zedlewski 

and Brauner, 1999; Dion and Pavetti, 2000; Quint and Widom, 2001; Rafferty, 
Food Stamp Caseload - 83 - Klerman and Danielson 

Zedlewski, and Gruber, 2001; Miller, Redcross, and Henrichson, 2002). 

Similarly, studies of welfare exit often find that the exiting family remained 

nominally eligible, though often for a relatively low benefit (GAO, 1999; 

Zedlewski and Brauner, 1999; Zedlewski and Gruber, 2001). 

To address concerns about drops in FSP participation with welfare exit, 

2001 USDA regulations allowed States to provide up to three months of 

Transitional Food Stamps. During this period, the food stamp benefit remains 

what it was before the case closed, adjusted for the loss of the TANF benefit 

(unless the household requests that its benefit adjusted, or the State opts to 

use information from another program in which the household participates to 

calculate the benefit). The 2002 Farm Bill extended the period during which 

States could offer such Transitional Food Stamps to five months. 

Before FFY 2003, only New York had adopted transitional benefits (for the 

three months allowed under the pre-2002 regulations). With the 2002 Farm 

Bill, more States adopted Transitional Food Stamps. By the end of FY 2003, 13 

States had adopted transitional benefits (CBPP, 2003; USDA, 2004). 

We would expect transitional benefits to increase the total FSP caseload 

by increasing its non-welfare portion. However, welfare leavers as a fraction 

of the FSP caseload are small, especially given the short period of 

eligibility. It thus seems unlikely that Transitional Food Stamps will have a 

major effect on the aggregate FSP caseload. Furthermore, our data includes 

only a very short window in which transitional benefits might have been 

adopted, so our power to detect any effects would be low. 

Asset Policies: In regulations in January 2001, new vehicle rules were 

implemented excluding vehicle equity below $1,500 and one vehicle per adult. 

Also, starting in 2001, states were allowed to adopt TANF vehicle asset rules 

if they were more generous than FSP rules (Table B.1). We do not consider 

asset policies here. 

Immigrant Restrictions: The 1996 PRWORA legislation made many permanent 

residents (i.e., legal immigrants who were not citizens) ineligible for food 

stamps (Zimmerman and Tumlin, 1999). (Undocumented immigrants had never been 

eligible for the FSP.) Later legislation restored eligibility for some 

noncitizens (see Table B.3; USDA, 2000; Stavrianos, Cody, and Lewis, 1997) 

(see Table B.3). 
Food Stamp Caseload - 84 - Klerman and Danielson 

Table B.3 

Statutory Changes to FSP Treatment of Immigrants 

Legislation Effective Date Provision 

PRWORA (7/1996) 10/1996 Barred most legal immigrants 

(exempted groups included 

refugees (for 7 years), 

those with 40 quarters of 

work, and military personnel 

and their families). 

Agricultural Research, 

Extension and Education 

Reform Act (AREERA) of 1998 

11/1/1998 Restored eligibility for 

elderly, disabled, and 

children who were in the US 

August 22, 1996. 

Farm Security and Rural 

Investment Act (Farm Security 

Act) of 2002 

10/1/2002 Restored eligibility for 

qualified noncitizens 

receiving disability 

benefits. 

 4/1/2003 Restored eligibility to 

qualified noncitizens who 

have lived legally in the US 

for over five years. 

 10/1/2003 Restored eligibility for 

qualifying non-citizen 

children 

Beyond the de jure effect on eligibility, there are claims that PRWORA’s 

limitation on benefits to immigrants and other anti-immigrant moves during 

this period had an overall “chilling effect” even on unaffected immigrants 

(e.g., the citizen children of noncitizen immigrants; see Fix and Zimmerman, 

1999; Fix and Passel, 1999; Borjas, 2001). Note that even without a “chilling 

effect”, we might expect some drop in FSP cases, even among those in 

households with immigrants who remain eligible. Making some individuals in a 

household ineligible for food stamps lowers the FSP benefit. The 

administrative burden of applying for and remaining in the FSP, however, 

remains the same. Some households might decide that the administrative burden 

is not worth the now lower FSP benefit (see Kornfeld, 2002, for a similar 

argument). 

Note that these were national program changes, so their effect cannot be 

estimated directly using our DoD methods. Many States instituted programs to 

restore “food stamps” to legal immigrants (see Zimmerman and Tumlin, 1999). 

Those were State programs. Participants in those programs do not appear in 

our federal FSPQC data. In contrast, they would probably appear in surveydata based analyses. 
Food Stamp Caseload - 85 - Klerman and Danielson 

Finally, note that noncitizens make up a relatively small proportion of 

the caseload (9 percent in 1996, prior to enactment of PRWORA; and 6 percent 

in 2007). In some analyses in the body of this report, we proxy for the 

effect of these changes by dropping all Hispanics (relatively few blacks or 

whites are immigrants). When we do so, we drop Hispanics from both the 

numerator (the count of program participants) and the denominator (the 

estimate of the population). 

Able Body Adults Without Dependents (ABAWDs): The 1996 PRWORA 

legislation required States to require able-bodied childless adults who do not 

have 20 hours per week in an approved activity—work or an approved training 

program—to receive FSP benefits for 3 months in 36. The original 1996 PRWORA 

legislation allowed States to waive the 3-month cutoff if no jobs were 

available. The 1997 Balanced Budget Act (effective October 1, 1997) gave 

States the additional authority to waive the three-month cutoff for an 

additional 15 percent of their ABAWD caseload. In practice, this appears to 

mean that States could extend the cutoff to five or even six months, if they 

so chose (see CBPP, December 7, 2000). 

As Czajka et al. (2001) has shown (see also Savrianos, Cody, and Lewis, 

1997; Genser, 1999), only a small fraction (1.3 percent) of the FSP caseload 

is potentially subject to ABAWD regulations and very few cases have been 

sanctioned. Thus, on a priori grounds it seems extremely unlikely that ABAWD 

regulations have affected caseloads even at the level of disaggregation 

considered here. 

Other Policies: The State Options Reports (USDA, 2001, 2002, 2003, 2004) 

also provide information on State policies with respect to simplified 

definitions of income and resources, expanded categorical eligibility, 

simplified housing costs, simplified standard utility allowance, simplified 

determination of deductions, child support expense income exclusion, State 

option FSP for immigrants, simplified FSP for TANF households, wage 

supplements, education and training pledge States, employment and training 

sanction periods, comparable disqualification, child support-related 

disqualification, drug felony disqualification, and electronic application 

filing. On a priori grounds, we assume that these policies do not have large 

effects on the caseload. 
Food Stamp Caseload - 86 - Klerman and Danielson 

WELFARE POLICIES NOT INCLUDED IN THE MAIN MODELS 

Our main models only include high-level summaries of welfare policy: The 

maximum welfare benefit, any AFDC waiver, and time since TANF implementation. 

We also considered specific welfare policies. We also explored several other 

variables. Specifically, we considered the following detailed welfare 

policies. 

Maximum Benefit: We computed this as the natural log of the average of 

the previous year’s maximum benefit level for a family of three divided by 

100. Maximum benefits are drawn from the dataset created for CEA (1999) and 

updated with information from Urban Institute’s Welfare Rules Database, the 

State Policy Documentation Project, the fifth TANF Report to Congress and 

information provided by the U.S. Department of Labor (CBPP/CLASP, 1999; OFA, 

2003; Urban, 2001a). 

Specific TANF Policies: Table B.4 summarizes our coding of these TANF 

policies. We considered four major policy determinants of the welfare 

caseload: time limits on aid receipt, the rate at which the benefit declines 

as earnings increase (“financial incentives”), penalties for non-compliance 

with work-related aspects of welfare programs (“sanctions”), and programs 

aimed at assisting families facing a temporary need to avoid becoming part of 

the welfare caseload (“diversion”). We estimated a set of models including 

these detailed welfare policies. While largely of the right sign, they are 

imprecisely estimated. 
Food Stamp Caseload - 87 - Klerman and Danielson 

Table B.4 

State Welfare Reform Policies 

N. States 

Financial incentives 

Earnings-eligible for TANF if single parent 

with two children working 30 hours a week at 

state 20th percentile wage 17 

Not eligible (AFDC policy) 34 

Work-related sanctions 

Grant eliminated at first instance of noncompliance with TANF requirements 12 

Grant gradually eliminated if non-compliance is 

on-going 25 

Grant reduced if adult is non-compliant (AFDC 

policy)29 15 

Time limits 

No time limit (AFDC policy)30 5 

Grant is reduced by adult’s share if time limit 

reached 5 

Grant is eliminated if adult reaches time limit 42 

Diversion 

Families can apply for a short-term, lump-sum 

payment in lieu of TANF to meet emergency needs 28 

No diversion program (AFDC policy) 23 

Note: Row entries indicate the number of states that ever implemented each 

policy type, so the rows in the sanctions and time limit sections of the table 

may sum to more than 51. 

Source: Urban Institute, Welfare Rules Database. 

____________ 

29 Note that three states altered the AFDC policy by reducing the 

family’s grant by a fixed amount or percentage of the grant. We include these 

states in the partial sanction category because the incentives both policies 

present case heads are not substantially different. 

30 We count states that nominally have a time limit, but that offer 

substitute programs or time limit extensions to all adults who reach it, as 

having no time limit. 

Food Stamp Caseload - 89 - Klerman and Danielson 

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