Abstract

This study looks at the relationship between food stamp participation and historical earnings over periods of 10-15

years. Earlier research found that households eligible for the Food Stamp Program that had short-term income

declines were less likely to participate than those that had sustained low incomes. This analysis expands on that

research by using a data set that matched historical Social Security earnings records to the 1996 Survey of Income

and Program Participation, allowing examination of the relationship between participation and earnings over a

longer timeframe than available previously. The results show some evidence that historical annual earnings as far

back as 5 years earlier are negatively and significantly associated with households’ decisions to participate in the

Food Stamp Program; that future earnings, which may proxy for earnings expectations, are also negatively and significantly associated with participation; and that monthly income volatility plays an important role. However, because

of weaknesses in the specification of the regression models, findings in this paper are suggestive rather than precise

descriptions of the relationship between longer run income and participation.

Longer Run Earnings and

Food Stamp Participation

Contractor and Cooperator Report No. 46

July 2008

By Sam Elkin and Lesley Turner, The Lewin Group

This study was conducted by The Lewin Group under a cooperative research

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

Assistance Research Program (FANRP): (ERS project representative:

Constance Newman). The views expressed are those of the authors and not

necessarily those of ERS or USDA.
Table of Contents 

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

II. BACKGROUND .................................................................................................................................................3

A. TRENDS IN PARTICIPATION RATES ....................................................................................................................3

B. POTENTIAL REASONS FOR LACK OF PARTICIPATION .........................................................................................5

C. FEDERAL POLICIES AFFECTING PARTICIPATION............................................................................................7

D. SHORT-TERM EARNINGS DECLINES, INCOME VOLATILITY, AND PARTICIPATION .........................................8

III. DATA AND METHODOLOGY .................................................................................................................11

A. DATA SOURCES...............................................................................................................................................11

B. CONSTRUCTING THE ANALYSIS DATA SET ......................................................................................................12

1. Defining Food Stamp Units........................................................................................................................12

2. Focusing on Working-Age Food Stamp Units............................................................................................12

3. Attributing Annual Earnings to Food Stamp Units ....................................................................................13

C. SAMPLES AND OUTCOME MEASURES..........................................................................................................14

D. TYPES OF ANALYSIS CONDUCTED ..............................................................................................................16

IV. DESCRIPTIVE RESULTS..........................................................................................................................17

A. PARTICIPATION RATES ....................................................................................................................................17

B. DESCRIPTIVE STATISTICS ................................................................................................................................18

1. Demographic Characteristics of Participant and Non-Participant Food Stamp Units .............................18

2. Earning Characteristics of Participant and Non-Participant Food Stamp Units ......................................20

3. Eligibility and Benefits of Participant and Non-Participant Food Stamp Units........................................22

4. Characteristics of SIPP Households in which Food Stamp Units are Contained......................................23

5. Characteristics of Participant and Non-Participant Food Stamp Units by Earnings Quartile .................24

C. EARNINGS OVER TIME.................................................................................................................................25

V. REGRESSION RESULTS ...............................................................................................................................29

A. REGRESSION FRAMEWORK..............................................................................................................................29

B. METHODOLOGICAL ISSUES..............................................................................................................................32

C. REGRESSION RESULTS: ELIGIBILITY IN A SPECIFIC MONTH ........................................................................33

D. ELIGIBILITY IN ANY MONTH OF THE YEAR.................................................................................................36

E. INTENSITY OF PARTICIPATION.........................................................................................................................39

VI. CONCLUSIONS AND FUTURE RESEARCH.........................................................................................42

REFERENCES ..........................................................................................................................................................44

APPENDIX A: ADDITIONAL TABLES AND CHARTS.......................................................................................1

APPENDIX B: DESCRIPTION OF THE MID-SIPP..............................................................................................1

A. MID-SIPP SIMULATION APPROACH..................................................................................................................1

1. Step 1: Classify Persons into Food Stamp Units and Reweight.........................................................5

2. Step 2: Determine Each FSU’s Eligibility and Potential Benefits for Every Month.......................7

3. Step 3: Correct Eligibility and Reported Participation Spells for the Seam Problem..................9

APPENDIX C: A COMPARISON OF EARNINGS DATA IN THE SURVEY OF INCOME AND 

PROGRAM PARTICIPATION WITH SOCIAL SECURITY EARNINGS RECORDS.....................................1

A. INTRODUCTION..................................................................................................................................................1

B. PREVIOUS FINDINGS ON ACCURACY OF SURVEY DATA ON EARNINGS..............................................................3

1. Aggregate Comparisons to Benchmark........................................................................................................3

2. Individual-Level Comparisons to Administrative Data................................................................................4

C. DATA ............................................................................................................................................................6

D. ANALYSIS OF RECIPIENCY MATCH ...............................................................................................................8
E. COMPARISON TO PAST PAPERS........................................................................................................................13

F. ANALYSIS OF DIFFERENCES IN EARNINGS MEASURES ....................................................................................15

G. CONCLUSION ..............................................................................................................................................18

REFERENCES CITED IN APPENDIX C..............................................................................................................20

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I. INTRODUCTION 

The Food Stamp Program is one of the government’s key tools for supporting the well-being of 

low-income households and helping them to maintain a sufficient level of nutrition. However, 

benefits are not collected by many of the individuals and families to whom they are available. 

According to estimates published by the U.S. Department of Agriculture (USDA), the average 

monthly individual “food stamp participation rate,” or the share of eligible individuals who 

received food stamps was 65 percent in federal fiscal year (FY) 2005. The household 

participation rate, or share of eligible households receiving benefits, was 59 percent. (Wolkwitz, 

2007). In other words, these estimates suggest that more than 40 percent of households eligible 

for food stamps did not receive them. The participation rate has changed over time, but a 

substantial portion of eligible households have consistently not accessed their benefits. 

With considerable numbers of eligible households – who are low-income and could presumably 

benefit from receiving food stamps – not receiving them, there have been many studies within 

the policy research field that attempt to understand who does and does not participate, and 

what drives this decision. Several of these studies have found that households with short-term 

declines in income are less likely than those with sustained low income to participate in the 

program. For instance, Farrell et al. (2003) used data from the 1996 panel of the Survey of 

Income and Program Participation (SIPP) and found that average earnings of eligible 

households not participating had decreased sharply in the four months prior to the month in 

which they were eligible, and grew substantially faster after the month of eligibility than did the 

average income of participating households. Moreover, the study found evidence that past and 

future income over a longer time period are also significantly related to the likelihood that a 

food stamp eligible household participates in the program. There are several reasons this may 

be the case: for example, a household with higher past income may expect its income to 

rebound in the future and may decide to ‘weather the storm’ instead of applying for food 

stamps, or may expect to only be eligible for low benefits in the future, reducing the payoff it 

can expect by going through the application process. In addition, a household experiencing a 

drop in income may need to learn about program rules and the system for applying for the first 

time. 

From their analysis, Farrell et al. (2003) concluded that “the predictive power of past or future 

income deteriorates as one moves away in either direction from the current period” but that 

there was still a “negative, large and statistically significant” relationship as much as two-and-ahalf years earlier between earnings and participation. They speculated that “earnings in the 

more distant past (three or more years earlier) might have substantial predictive power” and 

suggested as a direction for future research investigating the relationship of longer-term 

historical earnings data and food stamp participation. 

The analysis in this paper attempts to contribute to the research on food stamp participation by 

looking at the effects of longer term earnings on participation. The authors of this paper were 

granted clearance to work with a restricted-access data set that matched historical earnings 

records from Social Security earnings between 1951 and 2000 to the 1996 SIPP. This allows us to 

examine the relationship between food stamp participation and earnings over a much larger 

timeframe than has been available in previous studies and to test the hypothesis suggested by 
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Farrell et al. (2003) that longer-term historical earnings may have a significant effect on 

participation. 

We do not replicate the participation model used in Farrell et al. (2003), and instead rely on the 

determination of eligibility and benefit receipt in the SIPP calculated as part of The SPHERE 

Institute’s MID-SIPP model (MaCurdy and Marrufo, 2006), which corrects for seam bias in the 

SIPP. Our analysis focuses on the significance that current and long run earnings, as measured 

in the Social Security earnings records, play in the decision to participate in food stamps while 

controlling for demographic and family characteristics measured in the SIPP. We also include 

short-term income volatility in our model, which prior research demonstrated as impacting 

participation. Unfortunately, our access to the matched Social Security-SIPP data expired while 

we were conducting analysis and therefore we did not have an opportunity to correct 

weaknesses in our regression models. (These weaknesses are indicated in the body of the 

report.) As a result, findings in this paper are suggestive and indicate areas for useful future 

research, rather than precise descriptions of the relationship between long-run income and 

participation. Nonetheless, we find some evidence that historical annual earnings as far back as 

five years earlier are negatively and significantly associated with households’ decisions to 

participate in food stamps; that future earnings, which may proxy for earnings expectations, are 

also negatively and significantly associated with participation when other characteristics of the 

food stamp unit are controlled for; and, in all cases, that monthly income volatility plays an 

important role as well. 

In the next section, we review in further depth the research literature on food stamp 

participation and the policy context in which to understand participation rates. In the third 

section, we describe our data sources and methodology. Sections IV and V present findings 

from descriptive analysis and regressions. The final section concludes. The appendices contain 

tables with more detailed results, additional explanation of the MID-SIPP model, and an 

analysis of comparisons between earnings data in the SIPP and SER data sets. 
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II. BACKGROUND 

A. Trends in Participation Rates 

In federal fiscal year (FY) 2006, 26.7 million individuals lived in households receiving food 

stamp benefits. In a given month, participating households received an average of $214 in 

benefits.1 However, a substantial portion of individuals and families eligible for food stamps do 

not receive the benefits for which they are eligible. According to one set of estimates, calculated 

by Mathematica Policy Research and published by USDA, the individual “food stamp 

participation rate,” or the share of individuals eligible for food stamps who received them was 

only 65 percent in the average month of FY 2005. The household participation rate, or share of 

eligible households receiving benefits, was 59 percent in the same year. In other words, these 

estimates suggest that more than 40 percent of households eligible for food stamps did not 

receive them. (Wolkwitz, 2007). 

According to the USDA figures, the participation rate has changed over time, with participation 

increasing in 2005. However, a portion of eligible households have consistently not accessed 

their benefits. Exhibit II.1 shows USDA-reported participation rates over a 26-year period. 

While this chart must be interpreted with a degree of care because various occurred changes in 

the methodology for measuring the participation rate over the years, it demonstrates that the 

rate increased in the early 1990s, and fell over the mid- to late-1990s, and that in more recent 

years, participation has been increasing again. In all years, though, the data suggest that about 

one third to one half of eligible households have not participated in the program. 

 

1 USDA Food and Nutrition Service monthly Food Stamp Program data posted online at 

http://www.fns.usda.gov/pd/34fsmonthly.htm (accessed April 29, 2008.)
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Exhibit II.1: Food Stamp Participation Rates, 1980 – 2004 

0%

10%

20%

30%

40%

50%

60%

70%

80%

1980 1985 1990 1995 2000 2005

Individual Participation Rate Household Participation Rate

Source: Wolkwitz, 2007, based on Food Stamp Program Operations data, Food Stamp Program Quality 

Control data, and CPS data. Discontinuities in the two series at 1994 and 1999 reflect changes in 

methodology. For these years, participation rates calculated according to both the older and the newer 

methodologies are shown. Rates calculated according to the different methodologies are not strictly 

comparable. 

Wolkwitz (2007) identifies different household participation rates among various types of 

households. Households with children participate at higher rates, as do households receiving 

Temporary Assistance for Needy Families (TANF) or Supplemental Security Income (SSI), 

while households with elderly members participate at lower rates. Households with lower 

income participate at higher rates, while only about 20 percent of households with income 

above the poverty line participate.2 Households with earnings participate at lower rates as well 

- the household participation rate for households with earnings in FY 2005 was 51 percent. The 

proportion of food stamp participants with earnings has increased over time, particularly 

starting in 1996. (See Exhibit II.2.) 

 

2 Households eligible for food stamps may have monthly incomes up to 130 percent of the poverty line, as 

subject to other eligibility requirements. Some households eligible under categorical eligibility rules may 

have higher income. 
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Exhibit II.2: Percent of Participating Food Stamp Households with Earnings 

0%

5%

10%

15%

20%

25%

30%

35%

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Federal Fiscal Year

Percent of Participating Households With Earned 

Income

Source: Barrett, 2006, based on Food Stamp Program Quality Control data.

B. Potential Reasons for Lack of Participation 

There are many reasons that households may not claim food stamps when they are eligible. 

Bartlett et al. (2004), in a report on findings from the Food Stamp Program Access Study funded 

by USDA, identifies several impediments to participation at various stages of the food stamp 

application process. Eligible households may not know they are eligible and individuals may 

choose to not apply due to personal reasons (like a desire for personal independence, a 

perception of stigma, or a desire for privacy), costs related to applying, or “office policies” 

(defined to include prior bad experience with the program and confusion about how to apply, 

among other things). Eligible households fail to complete the recertification process for similar 

reasons. This section discusses some findings from previous research that gives evidence of 

factors that may be important to consider in understanding the participation rate. 

Lack of awareness. A number of studies find evidence that a large share, and perhaps the 

majority, of non-participating eligibles are not aware they are eligible. In a survey by Ponza et al 

(1999), 72 percent of non-participants considered likely to be eligible reported that they were not 

aware that they were eligible for the program. McConnell and Ponza (1999) cite other surveys 

including the PSID and the SSI/Elderly Cashout Demonstration study and report that “between 

one-third and one-half of non-participants say they think they are ineligible.” The Food Stamp 
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Program Access Study (Bartlett et al., 2004) similarly found that only 43 percent of potentially 

eligible non-participants believed they might be eligible. A small number – four percent – were 

not aware of the program’s existence. 

Personal reasons and stigma. Bartlett et al. (2004) report that 97 percent of eligible but nonparticipating survey respondents who said they would not apply for food stamps if they knew 

were eligible cited personal reasons, the most common being the desire for personal 

independence. Nearly half of these individuals cited a reason the authors categorized as stigma 

of participation. Ponza et al (1999) report that only about a quarter of the 28 percent of nonparticipating households aware of their eligibility for the program responded that stigma or 

related reasons were the main reason for their non-participation. The same survey asked 

questions aimed at identifying perceptions of stigma, and 44 percent of non-participants 

answered “yes” to at least one of these questions (as compared to 38 percent of participants). 

GAO (2004) notes that stigma could be heightened for working families due to the need of the 

program to occasionally verify wages and employment with employers. 

Costs of participation and administrative burden. The time and effort spent initially applying 

for food stamps and later recertifying include the time spent learning about the program, filling 

out forms, gathering documentation to demonstrate eligibility, traveling to the food stamp 

office, and the loss of privacy in divulging personal information. All of these obligations impose 

burdens on food stamp eligible households. These households may judge that the benefits they 

will receive are not worth taking on these burdens. Zedlewski and Brauner (1999, as cited in 

Dion and Pavetti, 2000), using the National Survey of America’s Families, find that ten percent 

of former welfare families and 17 percent of non-welfare families reported leaving the Food 

Stamp Program due to administrative problems or hassles. For working families, the time costs 

of application and recertification may be higher than those for non-working families given 

inflexible work schedules. McKernan and Ratcliffe (2003) find that households where adults 

work traditional hours are less likely to participate in the Food Stamp Program than households 

where adults work nontraditional hours. Bartlett et al. (2004) identify a number of ways in 

which local office policies can affect accessibility to benefits or the burden of applying, 

including (among others) hours of operation, available transportation, bilingual caseworkers, 

child friendliness of the offices, the overall pleasantness of the office, and local office discretion 

over how certification requirements are implemented. 

Failure to complete recertification. In recent years, several studies have found that the length of 

the recertification period – which differs across states, ranging from a few months to over a year 

– has a notable impact on participation. Kornfeld (2002) and Kabbani and Wilde (2003) find that 

frequent recertification is associated with lower caseloads. Other papers have demonstrated that 

frequent recertifications hasten exits; for example, Ribar and Edelhoch (2006) find that families 

in South Carolina have a higher probability of leaving food stamps in a recertification month 

than in other months. The Food Stamp Program Access Study (Bartlett, 2004) estimates that 

more than half of households who were in their recertification month in June 2000 and left the 

Food Stamp Program may still have been eligible (though the study notes that this estimate is 

based on limited data and “should be viewed with caution”). Most states now have a six-month 

reporting or recertification schedule as allowed by the 2002 Farm Bill. 
 7 

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Measurement issues: In addition, mismeasurement in administrative and survey data may 

affect participation rate estimates. Models of food stamp participation using data from surveys 

such as the Survey of Income and Program Participation (SIPP) or Current Population Survey 

(CPS) may be affected by misreporting of earnings and other income. Underreporting of income 

in surveys that are used to calculate participation rates could lead to an overestimate of the 

number of households eligible for benefits and an underestimate of the true participation rate, 

as some of these households are not, in fact, eligible. An analysis by the authors of this paper 

comparing earnings data in the 1996 panel of the SIPP with earnings data in Social Security 

administrative data found substantial number of cases where earnings in the SIPP were 

underreported relative to the administrative data; however, there were also cases where the 

SIPP showed higher earnings than the administrative data and we did not directly measure the 

net impact on the food stamp eligibility determination. This analysis is included as Appendix C.

Imputation of income or assets where such information is missing may also affect the accuracy 

of estimated eligibility. 

Participation may also be misreported. Bollinger and David (2001, as cited in Farrell et al., 2003) 

compared administrative data in three states to responses in the 1984 SIPP and found that 13.8 

percent more households participated than reported that they participated. Based on this, as 

well as higher participation rates found in USDA-reported figures, Farrell et al (2003) estimate 

that their determination of participation rates based on SIPP data may underestimate 

participation rates by 10 to 20 percent. Models such as the MID-SIPP and that used by 

Mathematica Policy Research in measuring the participation rate incorporate data from the 

Food Stamp Quality Control database to correct for underreporting of participation in survey 

data.

C. Federal Policies Affecting Participation 

Several legislative changes over the past decade may have affected food stamp participation 

rates. The 1996 welfare reform legislation, the Personal Responsibility and Work Opportunity 

Reconciliation Act, made most legal immigrants ineligible for food stamps and placed a threemonth limit on food stamp benefit receipt for non-working, able-bodied adults between the 

ages 18 and 49 not living in a household with a dependent child. The Agricultural Research, 

Extension, and Education Act of 1998 and the 2002 Farm Security and Rural Investment Act 

restored eligibility to certain groups of legal immigrants, including children and those who had 

been in the country at least five years. Federal legislation in 2001 allowed a more generous 

shelter deduction and vehicle allowance. The 2002 farm bill also gave options to states for 

program simplification. According to Greenstein (2007), the majority of states subsequently 

opted to simplify program participation by taking such steps as moving to longer (six-month) 

reporting periods with simpler reporting requirements and coordinating income and resource 

definitions with TANF and Medicaid. Additionally, 20 states provide transitional food stamps 

to families who leave TANF without requiring reapplication. 

Food stamp caseloads declined steeply during the second half of the 1990s. Thirty-seven percent 

fewer people received food stamps in the average month of 2000 than in 1994. 3 Exhibit II.1

shows that this was also a period of declining participation rates. Researchers have examined 

 

3 USDA Food and Nutrition Service Food Stamp Program data posted online at 

http://www.fns.usda.gov/pd/fssummar.htm (accessed April 23, 2008.)
 8 

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the extent to which the decline in participation rates reflected welfare reform versus changes in 

the economy or other factors. According to a review of literature by McKernan and Ratcliffe 

(2003), studies have found that between five and 24 percent of the caseload decline from 1994 to 

1998 was due to welfare reform, and 19 to 45 percent was due to the strong economy in the late 

1990s. 

Welfare reform, and the coincident decline in the TANF caseload, potentially impacted the food 

stamp participation rate in various ways. For families participating in TANF, the additional 

administrative burden of applying for food stamps is relatively low (particularly given 

categorical eligibility rules) and the decline in the TANF caseloads suggests that many people 

face a higher burden of applying for food stamps after leaving or if they do not apply for 

welfare. Additionally, many families began to work as a result of the new TANF work 

requirements, and employed families tend to also have lower food stamp participation rates. 

Changing perceptions of welfare may have increased the stigma many potential applicants 

associate with food stamps, and misconceptions about how food stamp rules have changed may 

lead eligible households not to apply. 

Studies have found evidence that those leaving the welfare rolls are less likely to participate in 

the Food Stamp Program. For example, in seven out of eight studies reviewed by Acs and 

Loprest (2001), fewer than half of welfare leavers received food stamps in the quarter after they 

left TANF. Rosenbaum (2006) cites USDA studies that show part of the decline in the 

participation rate was due to difficulties the program experienced in adapting to changes in the 

welfare system following welfare reform. To address the issue of low food stamp participation 

among welfare leavers, the 2002 Farm Bill established a state option for transitional food 

stamps, which allowed current benefit amounts to be locked in place for families leaving 

welfare for six months following exit.

The food stamp participation rate increased between 2002 and 2004. The 2004 increase likely 

reflects state reforms allowed under the 2002 Farm Bill (Greenstein, 2007), while increases in 

earlier years may reflect families adapting to the post-welfare reform system (Rosenbaum, 2006) 

or the economic downturn in the early 2000s. 

D. Short-Term Earnings Declines, Income Volatility, and Participation 

A number of studies consider the role that income volatility plays in program-eligible 

households’ decisions to participate in public programs. Income volatility refers to fluctuations 

in a household’s income over a given time period. There is evidence that income volatility has 

increased over recent decades (see, for example, Dynan et al., 2007) making this consideration 

particularly salient.4 Income volatility could affect the participation decision in several ways. 

For example, in programs where participants must report income changes, higher income 

volatility will mean a higher reporting burden. As mentioned earlier, recent literature has 

focused on the effects of recertification periods and other administrative requirements. Farrell et 

al. (2003) focus on a different effect of income volatility: households seeing a sudden decline in 

 

4 The literature is not unanimous that income volatility is increasing; for example, an analysis by the 

Congressional Budget Office (2007) found little change in the variability of individual earnings over the 

past 20 years. Dynan et al. (2007) provide a review of the literature on the topic and a comparison of their 

own findings with those of the Congressional Budget Office. 
 9 

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income may expect it to be temporary and may expect higher income (and therefore an end to 

their eligibility for food stamps) within a short period of time. 

In general, the expected relationship between income volatility and participation in the Food 

Stamp Program is that higher volatility will be associated with lower participation rates. In 

other words, given two households with the same average income over a particular time period, 

a household with stable, moderately low income is more likely to participate than a household 

with the same average income but with short periods of extremely low income balanced with 

short periods of higher income. Farrell et al (2003) derive this thesis from the permanent income 

hypothesis (Friedman, 1957). The strong version of that hypothesis states that expected longterm income, rather than current income, determines an individual’s or household’s 

consumption at a given time. Households smooth consumption over time, and do not reduce 

consumption due to a downturn in income or increase consumption due to an increase in 

income unless it is expected to be more than a transitory change. A weaker version of the 

hypothesis simply acknowledges that expected future income and past income are important 

determinants of current consumption. Applied to the question of how income affects 

participation in the Food Stamp Program, the hypothesis is that someone who expects an 

increase in income will be less willing to bear the time and effort costs of applying for benefits 

or of recertification. 

Limitations on the permanent income hypothesis for low-income households are well known. 

Food stamp eligibility rules contain asset limits, and so an eligible household will have few, if 

any assets. For such households, smoothing consumption over time would require borrowing 

against expected future income, and low-income households may face “liquidity constraints” 

preventing them from doing so. Nonetheless, there are several reasons why expected future 

income might affect Food Stamp Program participation. First, a household expecting higher 

income in the future may decide to temporarily batten down. An increase in income will reduce 

the amount of benefits the household can receive in the future or make the household ineligible, 

lowering the benefits of applying for food stamps in the current time period. A household 

experiencing a sudden drop in income may need to learn about program rules and the system 

for applying for the first time. Further, the reporting burdens may be higher for a household 

with fluctuating income. (During the period of analysis of this study, 1997 and 1998, changes in 

monthly income of more than $25 had to be reported to the program, though state rules have 

changed since the 2002 Farm Bill.) 

In addition, despite the limited assets of eligible households, they may nonetheless be able to 

smooth consumption over time without accessing food stamps. These households may have 

accumulated assets not reported to the program, they may be able to tap into home equity, or 

they may have access to resources that do not factor into the asset test such as ability to borrow 

from or rely on friends or family outside the food stamp household or other rainy day 

strategies. 

Farrell et al.’s findings supported the weak version of the permanent income hypothesis, at least 

with regards to near-term income. They found that “before the months leading up to the 

reference month, mean income of non-participating, food-stamp eligible households fell by 

much more than mean income of participant households; similarly, their mean income grew 

much more rapidly after the reference month ... This is consistent with the premise that 
 10 

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expectations of higher future income explain why some non-participant households do not 

participate.” The remainder of this paper explores whether earnings over a longer-term period 

than that analyzed by Farrell et al. also bear a significant relationship to participation. 
 11 

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III. DATA AND METHODOLOGY 

A. Data Sources 

The descriptive and statistical analyses in this report are derived from a restricted access data 

set consisting of the 1996 SIPP matched to the Social Security Administration’s Summary 

Earnings Records (SER). The SIPP is a multi-panel, longitudinal survey of households and is 

commonly used in analyses of food stamp participation (e.g., McKernan and Ratcliffe, 2003). 

The 1996 panel includes data covering income and program participation over a four-year 

period, which limits the timeframe over which analysis can be conducted. However, the 

matched data set contains historical earnings data from the SER matched to SIPP individuals, 

making possible analyses of the association between data contained in the SIPP panel and 

earnings over a longer historical period. 

The 1996 SIPP followed a sample of approximately 37,000 households over a period of four 

years. Households were grouped into four “rotation groups” that were approximately equal in 

size and were interviewed every four months on a staggered schedule (i.e., the first rotation 

group was interviewed in one month, the second rotation group was interviewed in the next, 

etc.). One group was interviewed in each of the 48 months the panel covers. At each interview, 

SIPP household members were asked retrospectively about their income and program 

participation for the preceding four months. Thus information on the income and program 

participation for respondents is available for the entire 48 month period.5 The first interviews 

were conducted between April and July of 1996 and the final interviews were conducted in 

December 1999 through March 2000. The SIPP contains detailed household and individual-level 

information on demographic characteristics, assets, income, and program participation. 

The SER is a restricted-use data set maintained by the Social Security Administration that 

contains information on individuals’ annual earnings and quarters of employment. The SER 

includes only “covered earnings” – that is, earnings to which the Social Security payroll tax 

applied – reported through payroll tax records, up to the taxable maximum, for the years 

between 1951 and 2000. 6 Earnings of individuals working in uncovered sectors, including some 

state and local government workers, some long-term federal government workers, domestic 

workers who were not paid substantial amounts by any particular household, and some 

agricultural workers, do not show up in the SER. Matched individuals with no earnings 

recorded in the SER for a year – including children who were too young to have earnings over 

the period covered by the SIPP panel – were ascribed a value of zero earnings. Even individuals 

with no earnings in any year were included in the matched data set and have SER earnings 

records of zero in all years. 

The data set used in this analysis matched the earnings records in the SER to SIPP data for 

individuals in SIPP households for whom a valid social security number (SSN) was reported to 

the SIPP. If a SIPP respondent did not provide a social security number (SSN) or provided an 

 

5 The SIPP Users’ Guide contains detailed information on the design, sampling methodology, and 

structure of the SIPP. Available at: http://www.sipp.census.gov/sipp/usrguide/sipp2001.pdf 

6Maximum taxable earnings for Social Security taxes were $62,700 in 1996 and rose to $76,200 by 2000. See 

the Social Security Administration’s website for a historical listing of maximum taxable earnings 

(http://www.ssa.gov/planners/maxtax.htm, accessed July 5, 2007). 
 12 

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incorrect SSN, he or she was not matched to the SER.7 Additionally, the version of the SER data 

set available to Lewin researchers only included SER earnings records for SIPP respondents 

participating in the first SIPP interview. Thus, individuals who joined SIPP households 

following the first interview were not matched to the SER. 

B. Constructing the Analysis Data set 

Preparing the SIPP-SER data set for this analysis consisted of assigning SIPP individuals to 

“food stamp units” (FSU), which serve as the unit of analysis, and then attributing annual 

earnings to the FSU from the SER. This process was complicated by several factors, including 

monthly changes in household composition in the SIPP and incomplete matching between the 

SIPP and SER. Because of our study’s focus on earnings, we limited the analysis data set to units 

with at least one working-age member. 

1. Defining Food Stamp Units 

Food stamp eligibility and benefit determination is based on the FSU, which includes members 

of a household who purchase and prepare food together. The FSU can be different from both 

the household and the family as food expenses may not be shared among all members of a 

household or family, and individuals from separate families may prepare food together within 

a household. For ease of discussion, however, we will often use the terms “household” and 

“FSU” interchangeably. 

We grouped individuals in the SIPP into FSUs based on the MID-SIPP model, developed by The 

SPHERE Institute. The model was designed to simulate the effects of changes in food stamp 

policies using SIPP data. In defining FSUs and estimating food stamp eligibility, participation, 

and benefit amounts, it corrects for “seam bias” in the SIPP (i.e., the tendency for respondents to 

underreport changes in characteristics during the four months asked about in a single interview, 

and over-report changes between the periods asked about in consecutive interviews). The MIDSIPP model is described at more length in Appendix B; for a full description, see MaCurdy and 

Marrufo (2006). This paper uses the MID-SIPP’s assignment of individuals in the SIPP into 

FSUs, monthly eligibility determinations, and estimated benefit levels. FSU participation in the 

Food Stamp Program is determined using reported receipt of food stamps by SIPP individuals, 

but adjusted by the MID-SIPP model to account for seam bias. In some cases, this results in a 

FSU receiving benefits in a non-eligible month. However, for the analyses that follow, a FSU is 

only considered to be a FSU participant if it is eligible and receiving benefits in a given month.8 

2. Focusing on Working-Age Food Stamp Units 

As this report focuses on the relationship between earnings and participation, we focus our 

analysis on FSUs that contain at least one working-aged member. FSUs without a working-age 

member are therefore excluded from the analysis data set. We define working aged individuals 

to be those who are between 18 and 65 years of age. 

 

7 During the match, SSA validated social security numbers using the Enumeration Verification System. 

8 The eligibility and benefit data provided to Lewin by The SPHERE Institute do not incorporate the MIDSIPP’s calibration to the Food Stamp Quality Control Data. 
 13 

444516 

3. Attributing Annual Earnings to Food Stamp Units 

SIPP sample members may move into and out of households and create new FSUs from month 

to month due to divorces, marriages, children leaving for or returning from college, etc.9

However, since the SER contains annual rather than monthly earnings data, it is necessary to 

ascribe individuals to a particular FSU for the year in order to attribute their annual earnings to 

one. We do so based on their FSU membership in June of the calendar year. For example, if an 

individual is residing in FSU A in January through June and moves to FSU B in July, his or her 

annual earnings will be attributed to FSU A.10 We treat each year as a separate observation, and 

individuals not part of any FSU in June are excluded from our analysis set for that year. 

Because of various complications in working with a data set that combines the monthly SIPP 

data and the annual SER data, it is necessary to restrict the analysis data set in several 

additional ways: 

• We only include FSUs that have at least one working aged member who was present in 

the SIPP for all months during the calendar year. There are several reasons why an 

individual may be present in the SIPP for some months of a year but not others. Most 

notably, the SIPP (like other longitudinal surveys) suffers from attrition and members of 

the sample who participate in one interview may not participate in interviews in later 

months. Of all SIPP individuals in the 1996 panel, 39 percent were lacking at least one 

month of data in 1997, and for 1998, this figure is 42 percent. Since we cannot break 

down the SER’s annual earnings data by month, to increase the comparability of the two 

data sources, we only use cases where the SIPP-reported earnings used in the 

determination of the FSU’s eligibility is available for all 12 months in the calendar year. 

Those who attrite may be lower income (Weinberg, 2003), and may be more likely to fail 

to report participation (Bollinger and David, 2001, as cited in Farrell et al., 2003), which 

may affect descriptive statistics from our sample. However, it is not clear how attriters 

differ, if at all, from other households with regard to the relationship between earnings 

and participation. 

• We restrict our analysis to FSUs present in 1997 and 1998. Due to the staggered structure 

of SIPP interviews across the four rotation groups, 12 months of data for the years 1996 

and 1999 are not available for many SIPP individuals.11 We exclude these years from our 

analysis. 

• Finally, we exclude a number of FSUs because of incomplete matching between the SIPP 

and the SER. In particular, we drop from our analysis FSUs without any working aged 

members successfully matched to the SER. For these FSUs, it would be impossible to 

 

9 Once an individual has joined a SIPP household, he or she will be included in subsequent surveys, 

regardless of whether he or she remains in the original household. 

10 June was used because it is in the middle of the year and therefore more likely than months closer to 

the beginning or end of the year to represent the household composition present in the most months of 

the year. 

11 This is because the half of all households that were first interviewed in June or July of 1996 were only 

asked about the preceding four months (February through May or March through June 1996). Similarly, 

the quarter of all households last interviewed in December of 1999 were only asked about the preceding 

four months (August through November 1999). 
 14 

444516 

track earnings over time. In 1997 and 1998, 6 percent of working-aged adults in a FSU 

were not matched to the SER.12 As discussed above, these may have been individuals 

that did not provide a SSN, individuals who provided an invalid SSN, or individuals not 

present at the first SIPP interview. 

Our final analysis data set consists of 33,499 FSU-year observations.13 

C. Samples and Outcome Measures 

This study investigates the reasons why FSUs identified as eligible for food stamps decide to 

participate in the Food Stamp Program or not, and in particular, whether current, past, and 

future earnings have a significant association with participation. Therefore, the analysis focuses 

on FSUs eligible for food stamps. Determining the sample of eligible FSUs within our analysis 

data set is complicated by the fact that eligibility for the Food Stamp Program is calculated on a 

monthly basis, but our analysis focuses on the annual earnings contained in the SER. We 

perform analysis on two different samples selected to represent food stamp eligibility in a year, 

based on two different definitions: 

 

(1) FSUs eligible in a specific month of the year 

(2) FSUs eligible in at least one month of the year 

The first sample, consisting of FSUs eligible for the Food Stamp Program in one specific month 

in the year, is based off a more strict definition of eligibility. The second sample, by definition, 

contains all FSUs belonging to the first sample, as well as FSUs eligible in other months. 

It should be noted that the specific month chosen for the first definition of eligibility in our 

descriptive analysis differs from the month chosen for our regression analysis. For the 

descriptive analysis, we use June as the specific month to define eligibility (the same month we 

used to assign individuals to FSUs for the year). For the regression analysis examining the 

relationship between income and participation, we use December as the specific month to 

define eligibility.14 

 

12 Of individuals present in the SIPP for all of 1997, irrespective of age, 12,060 did not match to the SER; 

this figure is 12,957 for 1998. 

13 Given the nature of the dataset, it would have been preferable to correct standard errors for FSUs 

present as observations in both 1997 and 1998 to allow for within-FSU dependence over time. However, 

the dataset did not identify repeated observations of FSUs (identifiers of FSUs were not consistent from 

year to year). While it would have been possible to identify repeated observations through different 

means, it was infeasible in the period of time in which we had access to the matched dataset. As a result, 

measurements of standard errors may be somewhat too small, although as there are at most two 

observations of each FSU (small relative to the total number of FSUs) this effect is likely to be small. 

14 December was initially chosen for the regression analysis due to considerations regarding an alternate 

model we ultimately did not use. Once the decision was made to discard that model, it would admittedly 

have been preferable to rerun the analysis using June, to be consistent with the descriptive analysis. 

However, time constraints on our access to the restricted SIPP-SER dataset prevented us from doing so at 

that point. 
 15 

444516 

The measures of participation we use correspond to the definitions of eligibility defining each 

sample. Where the sample is defined by eligibility in a specific month, we examine whether the 

FSU participates in that month. Where the sample is defined by eligibility in any month during 

the year, we examine whether the FSU participates in any month of the year. We also use a third 

measure of participation when the sample is defined by eligibility in any month of the year: 

whether the FSU participates in none, some, or all of the months in which it is eligible during 

the calendar year. We refer to this as the “intensity” of participation. 

Together, the different definitions of eligibility and participation characterize three models used 

in this study. These are summarized in Exhibit III.1. 

Exhibit III.1: Measures of Eligibility and Participation 

Model Eligibility Definition (sample) Participation Definition (outcome) 

1. The FSU is eligible for food stamps 

in a specific month 

Sample sizes: 5,316 (June) or 

4,444(December)

The FSU is participating in the Food Stamp Program in 

that specific month (June or December) 

2. The FSU is eligible for food stamps 

in at least one month in the calendar 

year 

Sample size: 7,598 

The FSU is participating in the Food Stamp Program in at 

least one month that it is also eligible during the calendar 

year 

3. Same as Model 2 The FSU is participating for every month it is eligible for 

the Food Stamp Program –or– the FSU is participating for 

some of the months it is eligible for the program –or– the 

FSU is participating for none of the months it is eligible for 

the program 

The two samples can be expected to have somewhat different characteristics. In particular, FSUs 

eligible for longer periods of time would be expected to make up a higher percentage of the 

sample defined by eligibility in a single month.15 Similarly, FSUs meeting the corresponding 

participation definition may be expected to have longer spells of participation in the Food 

Stamp Program than those meeting the second criteria. Our assumptions about how this will 

affect responsiveness to changes earnings are somewhat ambiguous. On the one hand, those 

with longer eligibility spells may face lower impediments to participation from factors such as 

newly learning about the program and how to apply for food stamps, and therefore, may be 

more likely to enter the Food Stamp Program when faced with a sudden loss of earnings. On 

the other hand, it may be that a change in earnings within a long eligibility spell would be less 

likely to lead a FSU that had already made the decision to participate to stop participating or 

one that had decided not to participate to begin participating. 

 

15 This is a commonly recognized feature of spell analysis. Of the food stamp eligibility spells captured in 

sample Model 2, the longer ones are more likely than the shorter ones to be ongoing in the specific month 

used to define eligibility in Model 1. 
 16 

444516 

D. Types of Analysis Conducted 

This paper presents the results of both descriptive and regression analysis of the samples. After 

presenting eligibility and participation characteristics of the two samples, we discuss 

descriptive statistics comparing demographic, economic, and other characteristics of FSUs that 

participate with those that do not. We also examine households by their quartile of current 

earnings (i.e., in the year they are determined to be eligible), likewise comparing participants 

and non-participants within each quartile. This allows us to see whether differences between 

households that use food stamps and those that do not vary according to earnings levels. We 

then examine the earnings of FSUs meeting our two eligibility criteria, comparing total earnings 

measured in the SER among all FSU members up to 15 years prior to when a FSU meets the 

eligibility criteria and up to two years in the future. 

We use regression analysis to examine the extent to which past, current, and future earnings are 

related to participation decisions. While descriptive statistics allow us to observe how the 

earnings of participants and non-participants differ, other characteristics associated with 

participation or nonparticipation may explain some, or all, of any relationship between earnings 

and participation decisions we observe. Using regression analysis, we are able to control for 

differences in observable characteristics, using the broad range of characteristics measured by 

the SIPP as well as the historical earnings and employment information provided by the SER, to 

see if the association between earnings over time and participation in the Food Stamp Program 

remains significant. We examine three outcomes using regression analysis as described in 

Exhibit III.1 – whether a FSU participates in the program in a particular month (December), 

whether a FSU participates during any month in the year, and whether the FSU participates for 

all, some, or none of the months it is eligible for benefits. 

It must be noted that our analysis contains a potential source of bias in that, as discussed in the 

previous section, 6 percent of working-aged adults in the FSUs do not have corresponding 

records in the SER. This could bias our results in two ways – by excluding entire FSUs that have 

no working-aged members matching to the SER and by reducing the earnings we observe for 

FSUs that have at least one working-aged member matching to the SER but other members that 

do not match. Data analysis we performed on the entire SIPP sample show some differences 

between working-age individuals not matched to the SER and those that are matched – e.g., 

those that are not matched are a little younger on average (38 years old, compared with 40), 

more likely to be Hispanic (17 percent versus 9 percent), and have somewhat lower average 

earned and unearned income – but differences with regards to food stamp participation are not 

large. Of those not matched to the SER, 23 percent are eligible for food stamps, compared to 19 

percent of those matched; and among those eligible 33 percent of the non-matched individuals 

received food stamps compared to 37 percent of the matched individuals. 
 17 

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IV. DESCRIPTIVE RESULTS 

A. Participation Rates 

As shown in Exhibit IV.1, of the FSUs in the sample defined based on eligibility for food stamps 

in any month of the year, approximately 37 percent participated in the Food Stamp Program at 

some point during that year. Similarly, in the sample defined based on eligibility for the 

program in a specific month (June), 36 were participating in that month.16 There were higher 

participation rates in 1997 than in 1998 (in the sample defined by eligibility in any month of the 

year, 39 percent of eligible FSUs participated in 1997, compared to 35 percent in 1998), but these 

differences are not large. 

USDA-reported food stamp participation rates (Wolkwitz, 2007) in 1997 and 1998 indicate that 

57.5 and 54.2 percent of eligible households participated in the Food Stamp Program, 

respectively. Our model finds lower rates of participation. A primary reason for this difference 

is that the eligibility and benefit data provided from the MID-SIPP do not incorporate the MIDSIPP’s calibration to the Food Stamp Program Quality Control (FSP-QC) data set. In contrast, 

both the USDA-reported figures and figures in other reports based on the full MID-SIPP model 

are calibrated to that data set. Background data provided by The SPHERE Institute show that 

the number of food stamp participants in the MID-SIPP model before calibration represents 

approximately 78 percent of the number of participants found using the FSP-QC data. The fact 

that fewer food stamp participants are captured in the SIPP accounts for about half of the 

difference between the rates we find and those found using the FSP-QC data. 

Another factor contributing to our rates being lower than USDA’s is that eligibility 

determination based on the SIPP may lead to lower participation rates than eligibility 

determination based on the CPS. The methodological appendix of Wolkwitz (2007) shows that 

in 1997 and 1998, individual participation rates calculated from the SIPP were lower than the 

CPS-based individual participation rates. Other potential sources of differences include the 

different models used to determine eligibility and the particular sample used by this study 

(FSUs with working-age adults that matched to the SER).17

 

16 Given that the sample eligible specifically in June contains proportionally more FSUs eligible for longer 

periods than the other sample (who are generally more disadvantaged that shorter-term eligibles), we 

might expect it to have a higher participation rate. It is unclear why we did not find more of a difference 

in the participation rates of the samples. One contributing factor is that the definition of participation for 

this sample will exclude FSUs who receive food stamps in months other than June, while the other 

sample will capture FSUs that participate in some months but not others. However, it is unlikely that this 

is the sole explanation. 

17 Although it would not be an additional source of differences between the USDA estimate and our own, 

it is worth noting that survey measurement error may also affect our estimated participation rates. 

Comparing earnings data in the 1996 panel of the SIPP with the SER, we found that of the 17.5 percent of 

working-aged individuals in the SIPP who reported no earnings in a year, about a fifth (3.3 percentage 

points) had positive annual earnings in the matched administrative data, averaging about $5,600. 

Further, for individuals with positive but relatively modest earnings in the SIPP (less than $20,000) and 

earnings shown in the administrative data, the administrative data showed earnings that were 10 percent 

or more higher than was reported on the SIPP in at least one-third of cases. If the administrative data are 
 18 

444516 

As Exhibit IV.1 shows, participation rates are not constant across quartiles of current earnings. 

Participation among households in the first quartile of earnings is 12 percentage points higher 

than that of all households. 18 In fact, only in the fourth quartile of earnings is participation 

substantially lower than for the entire sample. FSUs in the first quartile had zero earnings, those 

in the second earned $2,474 on average, those in the third earned $10,528, and FSUs in the 

fourth quartile earned, on average, $22,670. (See Appendix Exhibit A.1) 

Exhibit IV.1: Food Stamp Participation Rates 

 Total Eligible FSUs Participants Participation Rate 

Any eligibility in specific month (June) 5,316 1,939 36% 

Any eligibility in June 1997 2,484 1,113 39% 

Any eligibility in June 1998 2,468 826 34% 

Any eligibility in year 7,598 2,783 37% 

Any eligibility in 1997 4,068 1,567 39% 

Any eligibility in 1998 3,530 1,216 35% 

First quartile of annual earnings 2,351 1,156 49% 

Second quartile 1,424 664 47% 

Third quartile 1,874 681 36% 

Fourth quartile 1,949 282 14% 

Eligible for 12 months 2,487 1,519 61% 

Note: Data are weighted. 

B. Descriptive Statistics 

4. Demographic Characteristics of Participant and Non-Participant Food Stamp 

Units 

We next examine FSU characteristics, comparing participant households with non-participant 

households. We present statistics from the sample of FSUs that were eligible for the Food Stamp 

Program in at least one month during the year; as discussed above, this sample has more shortterm eligibles than the sample eligible specifically in June. To ascribe individual-level 

characteristics such as race or education level to the household, we identify a “head” of each 

FSU, which we define as the oldest working aged FSU member that matched to the SER, and 

treat that individual as a representative of the FSU. We also use the “head” concept in our 

analysis of FSU composition. 

Exhibit IV.2 provides an overview of the composition of participating FSUs, compared to those 

that do not participate. Participating FSUs are more likely to be female-headed and have 

 

more reliable than the SIPP, such underreporting on the SIPP would inflate the estimate of eligible 

households. On the other hand, there were also cases where the SIPP showed higher income than the 

administrative data; we did not directly measure the net impact on the food stamp eligibility 

determination. See Appendix C for more discussion of earnings comparisons between the SIPP and SER. 

18 The first quartile contains families with zero earnings only. As over 25 percent of the sample of eligible 

FSUs had zero current earnings, the first quartile is larger, and the second quartile is smaller, than the 

third and fourth quartiles. 
 19 

444516 

children present. Of FSUs that had children, participating FSUs are also more likely to have 

younger children. 

Exhibit IV.2: Composition of FSUs Eligible for Food Stamp Benefits in at Least One 

Month during the Year 

 Non-Participants Participants Total Eligible 

Female headed 53.2% 71.3% 59.5% 

Number of FSU members 2.2 2.8 2.4 

Any children present 41.9% 65.7% 50.2% 

Of those FSUs with children present: 

Youngest child is between 0 and 3 years old 13.2% 24.0% 17.0%

Youngest child is between 4 and 10 years old 31.7% 52.8% 39.1% 

Youngest child is eleven or more years old 55.1% 23.2% 43.9% 

Any elderly present 2.2% * *

Number of observations 4,815 2,783 7,598 

Notes: Data are weighted. Asterisks indicate results suppressed due to sample size considerations. 

Results are suppressed when there are less than 75 observations in a cell. In addition, if a result is 

suppressed in the “non-participants” or “participants” column, the result is also suppressed in the 

“total eligible” column. 

Additionally, participating FSUs are more likely to be headed by individuals who are non-white 

(38 versus 24 percent) and who have not received a high school diploma or GED (42 versus 28 

percent). These individuals are also less likely to have a college or advanced degree (3 versus 15 

percent). However, these individuals were similar in age to the heads of non-participating 

households. 

Exhibit IV.3: Characteristics of FSU Heads, 

FSUs Eligible for Food Stamp Benefits in at Least One Month during the Year 

 Non-Participants Participants All Eligible FSUs 

Age of head of household (average) 41 40 40 

Race 

White 76.0% 62.3% 71.2% 

Black 19.8% 32.9% 24.4% 

Native American 2.2% * * 

Asian/Pacific Islander 2.0% * * 

Hispanic origin 13.3% 15.5% 14.1% 

Education Level 

Less than high school diploma or GED 28.1% 41.6% 32.8% 

High school diploma or GED 34.6% 34.0% 34.4% 

Some college, no degree 22.6% 18.4% 21.4% 

College/graduate/professional degree 14.7% 3.2% 11.7% 

 

Number of observations 4,815 2,783 7,598 

Notes: Data are weighted. Asterisks indicate results suppressed due to sample size considerations. 

Results are suppressed when there are less than 75 observations in a cell. In addition, if a result is 

suppressed in the “non-participants” or “participants” column, the result is also suppressed in the 

“total eligible” column. 
 20 

444516 

5. Earning Characteristics of Participant and Non-Participant Food Stamp 

Units 

Overall, FSUs that participate in the Food Stamp Program in at least one month during the year 

are more disadvantaged than eligible non-participants (Exhibit IV.4). Summing earnings 

reported in the SER across all FSU members (that have a record in the SER), there are large 

differences in earnings between participants and non-participants, both in the current year and 

over time. Since our sample groups together FSU observations from 1997 and 1998, we denote 

earnings over time in relation to the observation year; thus, earnings in the year of observation 

are displayed as t = 0, which represents annual earnings in 1997 or in 1998 depending on when 

we observe the FSU. Likewise, earnings at t - 1 represent either annual earnings in 1996 or 1997, 

depending on which year the observation is drawn from, and so on. Historical averages of 

earnings do not include the current year, so, for example, the 2-year average shown in the table 

is the average of earnings in t – 1 and t - 2. 

Our measure of historical earnings for a FSU is the sum of the historical earnings of its current 

members. Although it is certainly plausible that the composition of FSUs has changed over time 

and that some of its current members had not lived together in years in the past, it is the current 

members’ past earnings that are relevant to the factors hypothesized to affect participation – 

e.g., expectations of future earnings of the FSU and resources not factored into food stamp 

eligibility. 

As shown in the exhibit, on average, participating households earned $7,545 less than 

households that did not participate. This difference in average earnings does not simply appear 

in the current year, but is fairly consistently present when we examine historical earnings. 
 21 

444516 

Exhibit IV.4: FSU Earnings According to SER Records, FSUs Eligible for Food Stamp 

Benefits in at Least One Month during the Year 

 Non-Participants Participants Difference All Eligible FSUs 

Current year earnings 

Earnings t = 0 $12,891 $5,346 $7,545 $10,248 

 

Historical earnings 

Earnings t – 1 $12,226 $4,660 $7,566 $9,576 

Earnings 2 year average $11,990 $4,592 $7,397 $9,398 

Earnings 5 year average $11,281 $4,735 $6,546 $8,988 

Earnings 10 year average $10,859 $4,841 $6,017 $8,751 

 

Future earnings 

Earnings t + 1 $14,191 $6,881 $7,310 $11,630 

Earnings 2 year average $14,693 $7,440 $7,252 $12,152 

 

Quarters with earnings 

t = 0 2.6% 1.7% 0.8% 2.3% 

2 year total 5.0% 3.0% 2.0% 4.3% 

5 year total 12.1% 7.5% 4.6% 10.5% 

10 year total 23.1% 15.2% 8.0% 20.3% 

Lifetime total 60.6% 42.1% 18.5% 54.1% 

Lifetime years with earnings 18.2% 14.1% 4.1% 16.7% 

 

Number of observations 4,815 2,783 -- 7,598 

Notes: Data are weighted. Dollar amounts presented in 1996 dollars, adjusted for inflation using 

the CPI-W. 

Background calculations, which we do not present here, show that in the more restrictive 

sample of households eligible for benefits specifically in June (rather than any month in the 

year), the trends are quite similar. Earnings for both participants and non-participants are lower 

in this sample, which is consistent with our assumption that this sample contains a higher 

proportion of households eligible for food stamps for longer time periods and who therefore are 

likely to be more disadvantaged. However, the percentage difference in earnings between 

participants and non-participants in this sample is remarkably similar to the percentage 

difference in the sample based on eligibility in any month of the year. For example, Exhibit IV.4 

shows that current year earnings of participants in the sample based on eligibility in any month 

in the current year is 59 percent lower than earnings of non-participants. Our background 

calculations find that in the sample based on eligibility in June, participants earned $3,596, or 58 

percent less than the $8,525 earned by non-participating FSUs. 

Self-reported earnings in the SIPP (not shown in a table) have a similar pattern to the earnings 

reported in the SER. For the sample of FSUs eligible for benefits in at least one month, annual 

SIPP earnings differ by approximately $7,000 between participants and non-participants 

($12,477 versus $5,416, respectively). Taking into consideration not just earnings but total 

income from all sources measured in the SIPP, the difference between participating and nonparticipating FSUs shrinks to $5,233 ($15,190 versus $10,184, respectively), mainly due to the 

large amount of transfer income participating households receive from programs like TANF 
 22 

444516 

($2,756). Analyses of the sample of FSUs eligible in June yield similar findings, although 

earnings were again lower across participants and non-participants.

Consistent with these differences in earnings, eligible FSUs that receive food stamps have 

members that worked fewer quarters in the current year, previous year, and past 2, 5, and 10 

years than eligible FSUs that did not receive benefits.19 It is important to note that the SER only 

contains information on covered sectors of employment; thus, a household may have a small 

number of quarters of employment because there were no members with earnings for the 

majority of the year or because household members worked and received earnings in 

uncovered sectors. 

The heads of FSUs that participated in the Food Stamp Program were similar in age to nonparticipants but these individuals worked fewer years (14.1 versus 18.2) and quarters (42.1 

versus 60.6 total) than did non-participants.20 This may indicate that households that choose to 

receive food stamps in the current year experienced more variation in their earnings over time, 

though we did not investigate the extent to which years or quarters with earnings occurred in 

spells. 

6. Eligibility and Benefits of Participant and Non-Participant Food Stamp Units 

As shown in Exhibit IV.5, households that received food stamps were on average eligible for 

almost 10 months out of the year, while eligible non-participants were only eligible for benefits 

for a little over half the year. FSUs participating in the program were also eligible for larger 

benefits than households that were eligible but did not participate. 

Exhibit IV.5: Food Stamp Eligibility and Participation, 

FSUs Eligible for Food Stamp Benefits in at Least One Month during the Year 

Notes: Data are weighted. Dollar amounts presented in 1996 dollars, adjusted for inflation using 

the CPI-W. 

 

19 If non-participating FSUs were headed by older members (with more prime-aged working years) or 

contained more adult members (who could have contributed quarters of work to the FSU) than 

participating FSUs, it would be a potential explanation for the differences found between the two groups. 

However, as shown in Exhibits IV.2 and IV.3, neither the age of the FSU head nor the number of adult 

members differ substantially according to participation status. 

20 The SER only contains records of earnings and quarters covered for the years between 1951 and 2000. 

To the extent that FSUs contain individuals aged 65 or older that had earnings prior to 1951, we will 

underestimate the total years of earnings for all FSU members. 

 Non-Participants Participants All Eligible FSUs 

Months of eligibility for Food Stamp Program 6.2 9.8 7.5 

Average months of participation in Food Stamp 

Program 0 8.7 3.1 

Average benefit amount for which FSU is eligible 

(only in eligible months; as measured in MIDSIPP model) $126 $165 $140 

 

Number of observations 4,815 2,783 7,598 
 23 

444516 

7. Characteristics of SIPP Households in which Food Stamp Units are Contained 

Next, we examine the characteristics of the SIPP households that include eligible FSUs. A SIPP 

household may contain multiple FSUs and eligibility for each is determined independently. 

Still, additional resources may exist within the household that are available, to some degree, to 

members of an eligible FSU. In fact, 15 percent of non-participating FSUs belonged to a SIPP 

household that received food stamp income within the year. This suggests a household’s total 

resources, including food stamps received by another FSU within the household, may provide 

one explanation for why a FSU eligible for the program chooses not to participate. 

When we compare the SIPP-measured income received by the households that participants and 

non-participants belonged to, the differences are striking. The households that included nonparticipating FSUs reported annual income 59 percent higher than households containing 

participating FSUs, and were more likely to receive earnings. Additionally, non-participating 

FSUs were more likely to belong to a “non-family” household (i.e., a household consisting 

entirely of unrelated individuals; approximately one third of non-participating FSUs versus 

approximately one quarter of participants). Non-participating FSUs were also more likely to 

belong to a married family household (37 versus 25 percent) while a larger portion of 

participating FSUs belonged to a single-female headed household (48 versus 24 percent). 
 24 

444516 

Exhibit IV.6: Household Type and Income by Source According to SIPP Records, 

FSUs Eligible for Food Stamp Benefits in at Least One Month during the Year 

NonParticipants Participants Difference All Eligible 

FSUs 

Income 

Total household income $23,695 $14,922 $8,773 $20,622 

Percent households with earnings 84.1% 65.7% -- 77.6% 

Household earnings for receivers $22,589 $13,149 $9,440 $19,791 

Percent households with food stamp income 6.2% 99.9% -- 39.0% 

Household food stamp income for receivers $1,256 $1,684 -$428 $1,640 

Percent households with TANF income 4.2% 41.6% -- 17.3% 

Household TANF income for receivers $2,332 $2,968 -$636 $2,867 

Percent households with SSI income 14.9% 32.5% -- 21.1% 

Household SSI income for receivers $4,559 $4,389 $170 $4,467 

 

Type of Household 

Married family 37.3% 24.6% -- 32.9% 

Non-married family 

Male household head * * -- * 

Female household head 23.6% 48.4% -- 32.3% 

Non-family 

Male household head 18.1% 9.6% -- 15.1% 

Female household head 14.6% 12.9% -- 14.0% 

Group quarters * * -- * 

 

Number of observations 4,815 2,783 7,598 

Notes: Data are weighted. Asterisks indicate results suppressed due to sample size considerations. 

Results are suppressed when there are less than 75 observations in a cell. In addition, if a result is 

suppressed in the “non-participants” or “participants” column, the result is also suppressed in the 

“total eligible” column. Share of food stamp participants receiving benefits is slightly below 100% 

because household food stamp income measured in the SIPP is not adjusted for seam bias in the 

same way as participation data from the MID-SIPP model. Dollar amounts presented in 1996 

dollars, adjusted for inflation using the CPI-W. 

8. Characteristics of Participant and Non-Participant Food Stamp Units by 

Earnings Quartile 

Finally, we divide the sample of FSUs eligible for benefits at any month in the year by quartile 

of earnings in that year.21 As shown earlier in Exhibit IV.1, participation rates differ 

substantially across quartiles, with about half of eligible FSUs in the first quartile (who had no 

earnings in the current year) participating compared to only one out of seven in the fourth 

quartile. This is consistent with other studies (such as Wolkwitz, 2007) that show households 

with earnings participating at lower rates than households without earnings. It probably largely 

reflects a combination of the higher level of need among households with lower earnings and 

the higher level of benefits for which these FSUs are eligible. 

 

21 Because approximately 30 percent of all eligible FSUs had zero earnings reported in the SER, each 

quartile does not contain exactly 25 percent of the sample. In particular, Q1 contains 31 percent of FSUs 

and Q2 contains 19 percent. Q3 and Q4 both contain approximately 25 percent of FSUs each. 
 25 

444516 

Appendix Exhibit A.1 presents FSU characteristics by quartile. Participants and non-participants 

in the same quartile are relatively similar for some characteristics, such as age and education 

level. Nonetheless, there are consistent (if sometimes modest) differences in characteristics 

between participants and non-participants across quartiles. For example, in all quartiles, 

participants are somewhat more likely to be non-white, and to have a head of household with 

less than a high school diploma. A higher proportion of participant FSUs in each quartile also 

have a head of household who is female, although in the top quartile the proportion is very 

similar between participant and non-participant FSUs. 

 

When we examine average earnings over a 10 year period, a more substantial difference 

between participants and non-participants emerges. In each quartile, 10-year historical earnings 

are between 27 and 40 percent lower for participants than non-participants 

In contrast, differences in total current year income as measured in the SIPP are much smaller 

between participants and non-participants; this is primarily due to higher transfer income 

(including food stamp benefits) received by participants. Notably, in the bottom quartile, where 

FSUs had zero earnings reported in the SER in the year of observation, the SIPP shows nonparticipants as having received close to $3,000 in earnings in the current year. Additionally, the 

households these FSUs belonged to had additional resources ($16,480 in income in the current 

year for participants; $11,172 in income for non-participants). This suggests, non-participants 

may have received earnings in uncovered employment or lived in households where resources 

were shared informally. See Appendix C for further discussion of differences in earnings data in 

the SIPP and the SER. 

C. Earnings over time 

Farrell et al. (2003) examined monthly earnings for participants and non-participants and found 

that the earnings of households not receiving food stamps had a V-shaped pattern – i.e., a sharp 

drop in average earnings in the four months leading up to the month in which they were 

eligible, followed by rapid growth in average earnings after the month of eligibility. Those that 

did participate in the Food Stamp Program had consistently low earnings. In this section we 

look at the earnings patterns of participants and non-participants over time using our model 

and the annual earnings data from the SER. Like Farrell et al. (2003) we find consistently lower 

earnings among participants than non-participants, but we do not find the same V-shaped 

pattern. 

Exhibit IV.7 depicts annual earnings over time for FSUs eligible for benefits, using the sample 

based on eligibility in any month in the year, and combining FSUs observed in 1997 and 1998.22

All amounts are adjusted for inflation and presented as 1996 dollars. In the year of eligibility (t 

= 0, representing either 1997 or 1998) the difference in earnings between households receiving 

food stamps and those that did not was $7,545, a sizeable difference, considering that average 

earnings for all eligible FSUs in the year of eligibility was $10,248. Differences in average 

earnings between participants and non-participants persist and are substantial over the 

 

22 We also examine the two years separately, and, as shown in Appendix Exhibit A.2, trends over time for 

both years are very similar. 
 26 

444516 

previous 15 years, showing that current participation rates bear a relationship with earnings 

from more than a decade earlier.23

Exhibit IV.7: Annual Earnings by Participation Status, FSUs Eligible in at Least One 

Month during the Year 

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

$16,000

$18,000

t-15 t-14 t-13 t-12 t-11 t-10 t-9 t-8 t-7 t-6 t-5 t-4 t-3 t-2 t-1 t=0 t+1 t+2 t+3

Non-Participant Participant

Notes: Data are weighted. t = 0 indicates 1997 or 1998. All amounts presented in constant 1996 

dollars, adjusted for inflation using the CPI-W. 

For both participants and non-participants, trends in earnings are substantially affected by 

economic trends and/or life-cycle factors, but there are some notable differences between the 

groups. Approximately seven years prior to the year of eligibility, the earnings of participating 

households begin to decrease, in comparison to earnings in the prior year (t - 8), and continue 

declining for most of the subsequent years before the year of eligibility. Conversely, nonparticipants experience a growth in earnings over this period. In particular, over this time 

period, non-participants saw their earnings increase by 21 percent, from $11,047 to $13,323. 

Participants experienced an 11 percent decline in earnings between t - 7 and t - 1, followed by an 

increase to $5,377 in the year of eligibility, which is 2 percent larger than earnings in t - 7. 

However, these differences in growth rates turn out to largely reflect the fact that participants 

have lower earnings to begin with. Appendix Exhibits A.3 through A.6 show historical earnings 

by earning quartile in t = 0. When the food stamp units are grouped based on earnings in this 

manner, it turns out that growth rates in earnings in the seven years leading up to food stamp 

eligibility are similar or higher among participants than non-participants. This is shown in 

 

23 Some of the growth in both series may be due to aging of members of the two groups over time; in 

particular, some individuals within the FSUs in the two groups may not have been old enough have 

earnings in the earlier years in the period covered. 
 27 

444516 

Exhibit IV.8. That exhibit also compares growth ending the year before eligibility to separate 

out the impact of short-term decreases in earnings that led to temporary eligibility in t = 0; in 

the top quartile, earnings growth is higher for non-participants, but only by a small amount. 

Exhibit IV.8: Earnings Trends over Time, FSUs Eligible for Food Stamp Benefits in at 

Least One Month during the Year, By Quartile 

 Non-participants Participants Total eligible 

Changes from t - 7 to t = 0 

Lowest Quartile 

Earnings t – 7 $4,768 $3,067 $3,924 

Earnings t = 0 $0 $0 $0 

Percent change -100% -100% -100% 

 

Second Quartile 

Earnings t – 7 $6,681 $3,977 $5,422 

Earnings t = 0 $2,558 $2,439 $2,503 

Percent change -62% -39% -54% 

 

Third Quartile 

Earnings t – 7 $8,566 $6,354 $7,740 

Earnings t = 0 $10,351 $9,541 $10,049 

Percent change 21% 50% 30% 

 

Highest Quartile 

Earnings t – 7 $18,327 $13,614 $17,652 

Earnings t = 0 $27,921 $22,290 $27,115 

Percent change 52% 64% 54% 

Changes from t - 7 to t = -1

Lowest Quartile 

Earnings t – 7 $4,768 $3,067 $3,924 

Earnings t = -1 $793 $472 $633 

Percent change -83% -85% -84% 

 

Second Quartile 

Earnings t – 7 $6,681 $3,977 $5,422 

Earnings t = -1 $4,570 $2,985 $3,832 

Percent change -32% -25% -29% 

 

Third Quartile 

Earnings t – 7 $8,566 $6,354 $7,740 

Earnings t = -1 $10,177 $7,484 $9,170 

Percent change 19% 18% 18% 

 

Highest Quartile 

Earnings t – 7 $18,327 $13,614 $17,652 

Earnings t = -1 $24,856 $17,790 $23,844 

Percent change 36% 31% 35% 

Notes: Data are weighted. We examine future earnings at t + 2, rather than t + 3; because this is 

the last year we observe earnings for all sample members. 
 28 

444516 

The differences in the overall growth rates between participants and non-participants occur 

because they are distributed differently among the quartiles. Underlying data (not shown in the 

table) indicate that 42 percent of participants fall into the lowest quartile, compared to only 25 

percent of non-participants; conversely, ten percent of participants fall into the highest quartile, 

compared to 35 percent of non-participants. 

Differences in future earnings between participating FSUs and those not receiving benefits are 

large, although for both groups, earnings increase after the year of eligibility. This may be a 

result of the United States’ economic expansion during the late 1990s, as t + 1 through t + 3 

represent either 1998 through 2000 or 1999 through 2001. 

To check whether the overall trends reflect our sample selection, we also look at earnings over 

time for the sample based on FSU eligibility in a single month. We still observe a sizeable 

difference in earnings in the current and past years, although earnings for both groups are 

lower in all years. (These figures are not presented in a table.) This is not surprising; FSUs 

eligible in a given month can be expected to, on average, have consistently lower earnings in the 

current year as the sample excludes households included in the other sample who had only 

temporary dips in earnings in any other month. 

Notably, we did not find the V-shaped pattern found by Farrell et al. (2003).24 There are several 

potential reasons why this is the case. Most notably, our annual earnings measure smoothes 

over much of the variation contained in the monthly earnings data. However, analysis of 

background data provided by the authors of Farrell et al. (2003) suggests that this does not fully 

explain the differences between their results and ours; taking annual averages of their monthly 

data does not completely eliminate the dip in earnings of non-participants in the year of 

eligibility. Other possible factors include that unlike Farrell et al., our eligibility model (from the 

MID-SIPP) smoothes over changes in eligibility and participation due to seam bias; and that 

Farrell et al. measure earnings as a percent of the poverty line, essentially measuring earnings 

relative to household size in a way our model does not. 

Despite not finding the V-shape, it is worth noting that within similar earnings groups, 

participants saw higher earning growth (or lower earning declines) in recent years than nonparticipants, a finding that is consistent with the predictions of the permanent income 

hypothesis. The difference in growth rates reflects the fact that among food stamp units with 

similar earnings in t = 0, non-participants had higher earnings in the past. Such households may 

expect higher earnings in the future and therefore be less likely to take on the costs of 

participating in the Food Stamp Program. The regression analysis discussed below takes a more 

sophisticated approach to controlling for differences in earnings levels, and controls for other 

characteristics of food stamp units as well. 

 

24 We did find a V-shaped pattern in the lowest and second-lowest quartiles (Appendix Charts A2 and 

A3), where average earnings of non-participants declined more sharply in t = 0 than average earnings of 

participants, and earnings for both participants and non-participants rebounded in t = 1. In the lowest 

quartile, this is almost definitional, as the lowest quartile was defined by the set of FSUs with zero 

earnings in t = 0. (More than one quarter of FSUs – 31 percent – had zero earnings in t = 0.) 
 29 

444516 

V. REGRESSION RESULTS 

When we compared the earnings histories of working-age participants and non-participants in 

the previous section, we found that participants have lower earnings in the current and past 

years. These households are more likely to have children, are headed by individuals with lower 

levels of education, and are more likely to be female headed. We found that in years leading up 

to being eligible for Food Stamps, participants on average had stable or decreasing earnings, 

while non-participants experienced an increase in earnings, but that this trend did not hold for 

all subgroups. 

With regression analysis, we are interested in disentangling the association between FSU 

characteristics, current earnings, and long-run earnings. We test the hypothesis that higher 

long-term earnings may lead some households not to participate in the Food Stamp Program 

despite a short-term downturn that makes them eligible for at least one month in the year we 

calculate eligibility. This could be because their past earnings or other factors related to past 

earnings lead them to expect higher earnings in the future. Additionally, these households may 

have been better able to accumulate assets that are unreported or do not factor into the food 

stamp eligibility determination. Previous research has shown that month-to-month fluctuations 

in income play a large role in the decision to participate (Farrell et al., 2003). We investigate the 

additional question of whether longer term earnings and earnings patterns also help to explain 

the decision to participate. We find some evidence that past earning levels are related to a 

household’s decision to participate even after taking into account current earnings and earnings 

volatility, measured by the length of eligibility for the program. 

A. Regression Framework 

We employ three regression models to examine the decision to participate in the Food Stamps 

Program as was summarized in Exhibit III.1. To recapitulate: 

Model (1) examines the probability that a FSU participates in a specific month, conditional on 

eligibility in the month. In this model, we use December as the specific month for defining 

eligibility and participation.25 There are 4,444 FSUs that were eligible for benefits in December. 

This model offers a standardized method of determining eligibility and participation as we 

know that all FSUs faced the same decision of whether to participate in this particular month. 

Two consequences of using December as the reference month are worth noting. First, since the 

participation decision in question is at the end of the year, current year earnings and other 

independent variables related to the current year (such as months of earnings) can properly be 

interpreted as primarily occurring prior to the time the participation decision was made. 

Second, as discussed in the methodology section of this paper, FSUs eligible for longer periods 

are a higher proportion of this sample than a sample based on eligibility in any month of the 

year, as FSUs with short eligibility spells are less likely to be represented in the eligible 

population in a given month. 

 

25 December was chosen as the month for defining eligibility and participation for the regressions in an 

early stage of the analysis. Due to our access to the matched SIPP-SER data set expiring, we did not have 

an opportunity to redo the regression analysis using June as the reference month, which would have been 

consistent with our descriptive analysis. 
 30 

444516 

Model (2) examines the probability that a FSU participates in any month in the year, 

conditional on any eligibility in the year. In this model we use a broader definition of 

eligibility and participation and examine the probability of any participation in the calendar 

year, conditional on any eligibility in the year. While our data contain individuals who receive 

food stamps in months where they are not eligible, for this model we only consider a FSU to be 

participating in a month if it is also eligible in the month. There are 7,598 FSUs that were eligible 

for benefits at least one month in the calendar year. 

Model (3) examines the intensity of participation. Our third model examines the probability 

that a FSU receives food stamps in all months in which it is eligible (full participation); or in 

only some of the months in which it is eligible (partial participation); or in no months in which 

it is eligible (no participation), conditional on any eligibility in the year. This model uses a 

categorical dependent variable to determine the association between FSU characteristics and 

earnings and the decision to fully or partially participate, relative to no participation. 

We use probit models in (1) and (2), reporting the marginal effects evaluated at the mean value 

of the independent variables. For binary variables, such as an indicator for having a female FSU 

head, the coefficient reported represents the change in probability of participation based on a 

discrete change from 0 (not having a female head) to 1 (having a female head) in the 

independent variable. The coefficient for continuous variables represents the percentage point 

change in the probability of participation associated with a one-unit change in the independent 

variable, at its mean. In all cases, we take the natural log (ln) of earnings and average earnings. 

As a result, our findings estimate the percentage point change in the probability of participation 

based on a one percent change in earnings. 

Model (3) uses a multinomial probit model (with no participation as the excluded category), 

also reporting marginal effects. All regressions are weighted using weights developed as part of 

the MID-SIPP model to properly reflect that model’s assignation of individuals to FSUs, and 

robust standard errors are reported. 

Independent Variables 

The SIPP and SER data sets allow us to include a rich set of independent variables in the 

regressions. We include measures of earnings, as reported in the SER, over different time 

periods to see whether past earnings play a role in households’ expectations of future income 

and in the decision to participate. We include earnings in the year of observation (t = 0, which 

will represent either 1997 or 1998), average earnings in the two prior years (t - 1 to t - 2), three to 

five years before the observation year (t - 3 to t - 5) and six to ten years before the observation 

year (t - 6 to t - 10).26 We also include future earnings in some models (t + 1 to t + 2) as a proxy 

of expectations of future earnings levels. 

 

26 Averages over discrete periods were taken to limit the number of (presumably closely related) earnings 

variables among the set of independent variables. As our intention is to test the hypothesis suggested in 

Farrell et al. (2003) that earning more than two years in the past may help explain participation, we look 

separately at historical earnings within the two most recent years and historical earnings from more than 

two years earlier. Earnings six to ten years in the past are included to distinguish between moderate-term 

and long-term historical earnings. 
 31 

444516 

Using the MID-SIPP’s calculations, we include indicators for the length of eligibility the FSU 

experienced in the year of eligibility: whether the household was eligible for benefits one to four 

months, five to eight months, or nine to 11 months, relative to being eligible for the entire year. 

While earnings and eligibility are highly correlated, the measure of current earnings we include 

is annual, so adding information on eligibility allows us to examine variability in eligibility (and 

by proxy variability in a household’s earnings), holding the level of the current year’s annual 

earnings constant. For example, this could help to compare two hypothetical households with 

$18,000 of earnings in the current year, where one received $1,500 per month for 12 months and 

the other received $9,000 per month for two months and $0 for the remaining months. While 

both households have the same current income, their monthly income and eligibility for the 

food stamps differs greatly, and including indicators for the length of eligibility allows the 

regression specification to capture these differences. In addition, we include a variable for 

months of earnings in the year, which also provides information on earnings variability. 

We also take into consideration the average benefit amount that the household could have 

received in months of eligibility, since households are likely to weigh the costs associated with 

participating against potential benefits. Although benefits are highly correlated with earnings 

and with household composition and should be a part of the regression model, the data we are 

using only include annual earnings (from the SER) and household composition in a particular 

month (which in out model is June). Since both of these factors may change through the year, 

the average benefit amount a FSU is eligible for will capture additional information that may be 

related to the decision to participate. In addition, including them allows the model to 

distinguish between the effects of earnings and household composition on participation that 

operate through changes in the average benefit level over the year, and effects of earnings and 

household composition on composition that operate independently of the benefit level. 

In some models, we include an indicator for whether the household was observed in 1998, to 

see whether general macroeconomic conditions were related to participation. We also include 

demographic characteristics of the FSU head, including race, gender, age and age-squared 

(which is a proxy for experience and allows the regression model to reflect a non-linear 

relationship that reflects the fact that past a certain point, increases in age have diminishing 

returns), level of education, and years of earnings (since 1951). All of these variables were 

calculated using data from the SIPP except for years of earnings, which we generate using the 

SER. 

We use a number of indicators relating to FSU composition to see whether the presence of 

elderly or child members is related to participation. We include indicators for the number of 

children present and the age of the youngest child. All household composition variables are 

based on FSU membership in June. Finally, we include an indicator for whether the FSU 

belonged to a household which received TANF benefits during the year. Model (1) and (2) also 

include the number of months that the FSU received earnings during the year, calculated using 

SIPP data. 

The key relationship we are studying is whether past, current, and future earnings are related to 

participation decisions, under the hypothesis that past or future earnings may be related either 

to their expectations for future earnings or to the availability of resources not factored into food 

stamp eligibility determination, which in turn influence a household’s decision of whether to 
 32 

444516 

participate in the Food Stamp Program given a downturn in earnings. It should be noted, 

however, that as is generally the case with regression analysis, a finding of a relationship 

between two variables – in this case past or future income and food stamp participation – may 

have alternate explanations. For example, if particular earnings patterns are somehow 

correlated with characteristics associated with perceiving stigma in food stamp participation (in 

ways not controlled for using the variables included in our regression models), a relationship 

may be found that has nothing to do with expected future earnings or resource availability. 

B. Methodological Issues 

The results presented below must be viewed in light of a number of caveats. Due to the large 

portion of households with zero earnings, our specification of earnings variables in the 

regressions limits the interpretation of coefficient estimates. Additionally, while we do include 

age and age-squared in our models that include our full set of covariates, we do not take into 

account the fact that some FSUs may be headed by individuals too young to have received 

earnings in the t - 10 or later years. These problems, which we were not able to correct while we 

still had access to the data, are discussed below. 

Earnings Variables. We use the natural log of earnings to determine the percentage point 

change in probability of food stamp participation associated with a percentage change in 

earnings, since a dollar amount change in earnings may have different effects depending on the 

level of total earnings. For instance, a $1,000 increase in earnings for a household earning 

$10,000 would likely be welcomed, but would probably have less of an impact than the same 

increase for a household earning $2,000. In all cases when a FSU received zero earnings, the 

natural log of earnings was set to zero. In the year of eligibility, approximately 30 percent of 

food stamp eligible households received zero earnings. 

In a preferable specification, we would disentangle the effects on participation of marginal 

changes in positive earnings from the effects of changing employment status (i.e., going from no 

earnings to some earnings, or vice versa). To do so, we would replace the natural log of 

earnings included in our models with two terms: 1) an indicator for whether a FSU had any 

earnings, and 2) the interaction term of this indicator and the natural log of earnings. The 

specification would state: 

P(participation) = + + 22110 − EaEEaa 2 )1(* ) ) )

Where E1 represents the natural log of earnings and E2 is an indicator that takes the value of 1 if 

a FSU had positive earnings and 0 if it did not. With this model, 1 a

) would capture the impact of 

a marginal increase in earnings on participation among those FSUs with earnings, and 2 a

) would 

measure the relationship between working at all and participating in the Food Stamp Program. 

As they do not have the interaction term to disentangle these two effects, our models should not 

be interpreted as a precise measurement of the marginal effect of earnings on participation, and 
 33 

444516 

we suggest viewing our findings instead merely as an indication of the likely significance of 

past earnings only. 27

Age of FSU Members. As discussed above, although we control for the age of a FSU’s oldest 

working-aged member, taking into account potential experience (age-squared), we do not 

include any controls to take into account whether the FSU contained members old enough to 

have received earnings over the time horizons we examine. One solution to this problem would 

be to include age splines in our regressions, which control for this. 

C. Regression Results: Eligibility in a Specific Month 

Exhibit V.1 shows the results of the regressions using Model (1). For our sample of households 

eligible for benefits in December, we first examine the bivariate relationship between current 

earnings and participation (Column 1). We find the expected negative and significant 

relationship. Adding past and future earnings, (Column 2), we see that some of this relationship 

is captured by earnings in other years, as the coefficient for current earnings decreases when we 

include earnings received in the past and future. Current earnings remain significantly 

associated with the decision to participate. Additionally, average earnings between one and 

two years in the past and earnings between three and five years in the past are significantly 

related to the decision to participate. These findings suggest that for two FSUs with the same 

earnings in the current year, households who had lower earnings in past years (at least, going 

back five years) are more likely to receive food stamp benefits when eligible. Future earnings 

are included, under the hypothesis that actual future earnings will proxy for households’ 

expectations of their future earnings, and future earnings are indeed significantly associated 

with participation, though in the opposite direction as anticipated; the positive coefficient 

suggests households with higher expected earnings are more likely to participate. This is an 

interesting finding; however, it may simply indicate that households that receive higher 

earnings in the future have certain attributes which also make them more likely to participate. 

Indeed, this hypothesis is supported by the results in Column 3. 

 

27 Another methodological issue with regards to earnings relates to potential mismeasurement of 

earnings in both the SIPP and the SER. Disagreement between the SER and SIPP with regards to earnings 

is discussed in Appendix C. Such disagreement could affect regression results in two ways. First, as the 

sample of those eligible for food stamps is determined based on SIPP data, individuals with earnings 

shown in the SER may be included in the sample if they do not have earnings (or have low earnings) 

shown in the SIPP. To the extent that there are FSUs with relatively high SER earnings included in the 

sample, who do not participate in the Food Stamp Program because they are not in actuality eligible, it 

may bias our regression findings with regards to the relationship between current, past, or future 

earnings and participation towards the negative. Appendix C shows that about 3 percent of working-age 

individuals with no earnings shown in the SIPP have earnings in the SER, but that on average these 

earnings are low, so this bias will probably be limited. Second, if FSUs with high earnings in the SIPP are 

excluded from the sample but actually have lower earnings (as reflected in the SER), there would only be 

a bias if this group differs non-randomly from low-earning FSUs who do not overreport on the SIPP. 

Findings in Appendix C are suggestive that this impact is likely to be small as well, as we find that 

individuals with zero earnings in the SER but positive earnings on the SIPP do not differ noticeably in 

demographic or income characteristics from the SIPP population as a whole. 
 34 

444516 

Column 3 includes our full set of control variables. The length of time a FSU was eligible for the 

Food Stamp Program is significantly related to participation. When we include indicators for 

the period of time a FSU was eligible, the negative coefficients on these variables and declining 

magnitude as eligibility length increases suggest that among households with the same average 

income and earnings in the current year, those who were eligible for a larger portion of the year 

– and thus had more consistently low income – were more likely to receive benefits than those 

that experienced both high income and income low enough to qualify them for food stamps in 

December. Specifically, shorter periods of eligibility are related to a lower probability of 

participation. This is consistent with the findings of Farrell et al. (2003), who showed that nonparticipants experienced a higher variability in monthly income, characterized by a dip during 

the month of eligibility, than non-participants, who, on average, had consistently low income. 

Race and gender are significantly associated with participation – households with non-white 

heads are also more likely to participate, all else equal, as are female headed FSUs and FSUs 

headed by older working-age adults, although the magnitudes of these effects are small. 

Households headed by individuals with lower education levels are more likely to participate, 

although FSUs headed by adults without a high school diploma are less likely to receive 

benefits in this month than households headed by adults with a high school diploma. As the 

composition of a FSU determines, to a large degree, benefit levels, it is not surprising that when 

we include information on the number and age of children and presence of elderly members 

(results not shown), benefit levels are no longer related to participation. As we would expect, 

families that live in households receiving cash assistance (TANF) are significantly more likely to 

receive food stamps. This may be somewhat of a mechanical effect, as families who apply for 

welfare are often applying for food stamps at the same time. Finally, the number of months that 

a household received any earnings,28 regardless of the level, is also significantly and negatively 

related to participation. In this model, in fact, the number of months of earnings in the year is 

one of the largest predictors of participation in December. Here, we know that 11 out of the 12 

months preceded the month for which we measure participation. 

When these control variables are added, significance among the earnings variables changes. The 

significant relationship between current earnings and participation remains. However, the 

relationship between earnings in the recent past (t - 1 to t - 2) and participation is no longer 

significant. The relationship between participation and past earnings in years t - 3 through t - 5 

remains significant at a 95% confidence level. Future earnings are again negative and 

significantly associated with receipt of food stamps, supporting our hypothesis that difference 

in characteristics were influencing the positive association in Column (2), since once we include 

measures of household characteristics, the sign once again is in the expected direction. 

As discussed before, we expect that selecting on eligibility in a particular month will result in a 

group of households to be more disadvantaged, on average, than the group selected on 

eligibility for food stamps in any month in a year. We find that for these households, current 

income and changes in current income, as well as expectations for income in the near future are 

more important factors in the decision to participate than low earnings levels in the past. 

 

28 This is the only case where we develop a measure of earnings using the SIPP rather than the SER. This 

is because the SER only contains information on annual earnings and the number of quarters in a year in 

covered employment. Thus, it misses a good deal of information on monthly variation in earnings. 
 35 

444516 

Although it is difficult to determine which of these factors has the strongest association with 

participation, these findings are consistent with those of Farrell et al. (2003). 

Exhibit V.1: Regression Results, Dependent Variable Food Stamp Receipt in December, 

Conditional on Eligibility in December 

 (1) (2) (3) 

Earnings 

Earnings t = 0 -0.022 -0.015 -0.010 

(0.002)** (0.003)** (0.004)** 

Av. Earnings t - 1 to t - 2 -0.012 -0.004 

 (0.004)** (0.004) 

Av. Earnings t - 3 to t - 5 -0.012 -0.007 

 (0.003)** (0.003)* 

Av. Earnings t - 6 to t - 10 0.003 0.006 

 (0.003) (0.003) 

Av. Earnings t + 1 to t + 2 0.009 -0.012 

 (0.003)** (0.003)** 

Months of Eligibility in Year 

1 to 4 months -0.288 

 (0.019)** 

5 to 8 months -0.197 

 (0.019)** 

9 to 11 months -0.101 

 (0.021)** 

Year = 1998 -0.017 

 (0.016) 

Average FS Amt in Year -0.022 

 (0.016) 

FSU head demographics 

Race = nonwhite 0.039 

 (0.019)* 

Hispanic -0.045 

 (0.023) 

Sex = Female 0.089 

 (0.019)** 

Age 0.029 

 (0.005)** 

Age squared -0.000a 

 (0.000)** 

Less than HS degree 0.104 

 (0.036)** 

HS degree/GED 0.135 

 (0.036)** 

Some college, no degree 0.110 

 (0.040)** 

Years of earnings -0.001 

 (0.001) 
 36 

444516 

Exhibit V.1 (Cont’d) 

(1) (2) (3) 

FSU composition 

Number of adults 0.073 

 (0.019)** 

1 or 2 children 0.090 

 (0.031)** 

3 or 4 children 0.205 

 (0.044)** 

5 or more children 0.183 

 (0.075)* 

Any elderly members -0.078 

 (0.052) 

Youngest child <1 0.074 

 (0.073) 

Youngest child 1 to 3 0.071 

 (0.039) 

Youngest child 4 to 6 0.067 

 (0.037) 

Youngest child 7 to 10 -0.006 

 (0.036) 

Any HH AFDC/TANF receipt 0.355 

 (0.023)** 

Average months earnings in year -0.522 

 (0.259)* 

Observations 4444 4444 4444 

Notes: Data are weighted. Robust standard errors in parentheses. Marginal effects at mean of 

independent variable reported. * significant at 5%; ** significant at 1%; a less than 0.0005 in 

absolute value

D. Eligibility in Any Month of the Year 

We next examine the probability of participation in the Food Stamp Program at any point in a 

year, conditioning on any food stamp eligibility in the year (Exhibit V.2). Again, we find a 

significant and negative relationship between current income and participation. Including 

additional information in Column (2), we find results similar to Model (1), including a 

significant and negative association between earnings in the recent past (averaged over one to 

two years in the past) and participation as well as between earnings three to five years in the 

past and participation. As in Model (1), future earnings and participation show a positive 

relationship, which, when controls are added, reverses signs. 

However, adding our full set of control variables, as shown in Column 3, yields somewhat 

different results from Model (1). Belonging to the 1998 cohort of FSUs (rather than the 1997 

cohort) is significantly and negatively related to participation. (In Model (1) it had also been 

negatively related to participation, but the coefficient was smaller and not significant.) This may 

reflect the generally favorable macroeconomic conditions in the United States during the latter 

half of the 1990s that helped reduce poverty rates and unemployment among low-income 

groups, and also may reflect changing perspectives on program participation following welfare 
 37 

444516 

reform in the mid-1990s. The different findings of significance in the two models may be related 

to the fact that the second model contains proportionally fewer long-term eligibles than the first 

sample and therefore potentially includes more households who may be expected to have 

benefited from the income and job growth that occurred during that period. 

The length of eligibility for food stamps in the year and many demographic characteristics, 

including the race, sex, age, and education of the FSU head remain significantly associated with 

participation. However, unlike in Model (1), the age of the youngest child in the FSU is 

significantly and positively associated with participation, indicating that, holding all else 

constant, families with younger children are more likely to participation. 

Also dissimilar to Model (1), in our second model, only earnings between one and two years 

prior to t = 0 and future earnings are remain significantly related to participation. This may be 

due to the fact that the Model (2) sample includes a larger portion of short-term eligible 

households than the first sample, and that for households that have only recently become 

eligible for food stamps, earnings in the recent past are more important than current earnings in 

the decision to participate. 

Exhibit V.2: Regression Results, Dependent Variable Any Food Stamp Participation in 

Year, Conditional on Eligibility in at Least One Month during the Year 

 (1) (2) (3) 

Earnings 

Earnings t = 0 -0.024 -0.009 0.001 

(0.001)** (0.003)** (0.003) 

Av. Earnings t - 1 to t - 2 -0.016 -0.010 

 (0.003)** (0.003)** 

Av. Earnings t - 3 to t - 5 -0.011 -0.003 

 (0.003)** (0.003) 

Av. Earnings t - 6 to t - 10 -0.002 0.001 

 (0.002) (0.003) 

Av. Earnings t + 1 to t + 2 0.006 -0.013 

 (0.003)* (0.003)** 

Months of Eligibility in Year 

1 to 4 months -0.354 

 (0.016)** 

5 to 8 months -0.215 

 (0.015)** 

9 to 11 months -0.087 

 (0.018)** 

Year = 1998 -0.029 

 (0.013)* 

Average FS Amt in Year -0.002 

 (0.008) 
 38 

444516 

Exhibit V.2 (cont’d) 

 (1) (2) (3) 

FSU head demographics 

Race = nonwhite 0.059 

 (0.016)** 

Hispanic -0.044 

 (0.018)* 

Sex = Female 0.086 

 (0.014)** 

Age 0.027 

 (0.004)** 

Age squared -0.000a 

 (0.000)** 

Less than HS degree 0.119 

 (0.026)** 

HS degree/GED 0.110 

 (0.025)** 

Some college, no degree 0.105 

 (0.027)** 

Years of earnings 0.001 

 (0.001) 

FSU composition 

Number of adults 0.073 

 (0.013)** 

1 or 2 children 0.092 

 (0.023)** 

3 or 4 children 0.216 

 (0.033)** 

5 or more children 0.172 

 (0.060)** 

Any elderly members -0.059 

 (0.046) 

Youngest child <1 0.184 

 (0.054)** 

Youngest child 1 to 3 0.122 

 (0.030)** 

Youngest child 4 to 6 0.099 

 (0.029)** 

Youngest child 7 to 10 0.025 

 (0.028) 

Any HH AFDC/TANF receipt 0.482 

 (0.018)** 

Average months earnings in year -0.257 

 (0.185) 

Observations 7598 7598 7598 

Notes: Data are weighted. Robust standard errors in parentheses. Marginal effects at mean of 

independent variable reported. * significant at 5%; ** significant at 1%; a less than 0.0005 in 

absolute value
 39 

444516 

E. Intensity of Participation 

Finally, we examine whether certain factors may be related to a household’s decision to 

participate in the Food Stamp Program immediately or wait a certain number of months before 

participating, rather than not participating at all. Exhibit V.3 displays results from the 

multinomial analysis of participation, examining the probability of partial participation, relative 

to no participation and the probability of full participation, relative to no participation. We 

define partial participation as participation in the program for between 1 and 99 percent of 

eligible months and full participation as participation for 100 percent of eligible months. This 

allows us to examine how earnings and other characteristics are related to the intensity of 

benefit use among eligible households. Thus, a household eligible for four months that 

participates all four months would be considered to have the same intensity of participation as a 

household eligible for 12 months that participates all 12 months. 

As Exhibit V.3 displays, the association between earnings, FSU head and household 

characteristics, and food stamp participation differ remarkably between FSUs receiving benefits 

for a portion of eligible months and those receiving benefits during their full period of 

eligibility. While earnings in the recent past (between t - 1 to t - 2) are significantly and 

negatively related to full participation, these factors are not related to partial participation. 

Current and future earnings, length of eligibility, ethnicity, education, the age of the youngest 

child, and receipt of welfare are significantly related to partial participation. Our coefficient 

estimates for current earnings are positive, which may indicate unobservable characteristics, 

such as motivation or resourcefulness, that are associated both with the ability to navigate the 

food stamp application process and with higher earnings. The results for full participation 

mirror those found in Exhibit V.2, although in many cases, the magnitude of the effects are 

somewhat smaller. The lack of significant coefficient estimates for past earnings for those with 

partial participation may suggest that the findings in Exhibits V.1 and V.2 are largely driven by 

those who participate for the full period of eligibility. 
 40 

444516 

Exhibit V.3: Multinomial Regression Results, Probability of Partial or Full Participation, 

Relative to No Participation, Conditional on Eligibility in at Least One Month during the 

Year 

 (1) (2) (3) 

 Partial Full Partial Full Partial Full 

Earnings 

Earnings t = 0 -0.003 -0.021 0.000 -0.009 0.005 -0.004 

(0.009)** (0.001)** (0.002) (0.002)** (0.002)* (0.002) 

Av. Earnings t - 1 to t - 2 -0.002 -0.014 0.000 -0.010 

 (0.002) (0.002)** (0.002) (0.002)** 

Av. Earnings t - 3 to t - 5 -0.001 -0.010 0.001 -0.004 

 (0.002) (0.002)** (0.002) (0.002) 

Av. Earnings t - 6 to t - 10 -0.003 -0.000 0.001 0.001 

 (0.001) (0.002) (0.002) (0.002) 

Av. Earnings t + 1 to t + 2 -0.000 0.006 -0.006 -0.007 

 (0.002) (0.002)** (0.002)** (0.002)** 

Months of Eligibility in Year 

1 to 4 months -0.188 -0.156 

 (0.010)** (0.014)** 

5 to 8 months -0.076 -0.134 

 (0.009)** (0.012)** 

9 to 11 months -0.006 -0.084 

 (0.011) (0.013)** 

Year = 1998 -0.014 -0.013 

 (0.008) (0.010) 

Average FS Amt in Year 0.009 -0.012 

 (0.005) (0.006) 

FSU head demographics 

Race = nonwhite 0.012 0.047 

 (0.010) (0.013)** 

Hispanic -0.026 -0.016 

(0.010)* (0.015) 

Sex = Female 0.017 0.068 

 (0.009) (0.011)** 

Age 0.004 0.024 

 (0.003) (0.003)** 

Age squared -0.000 -0.000a 

 (0.000) (0.000)** 

Less than HS degree 0.042 0.078 

 (0.017)* (0.022)** 

HS degree/GED 0.020) 0.092 

 (0.016) (0.022)** 

Some college, no degree 0.032 0.076 

 (0.018) (0.024)** 

Years of earnings 0.001 0.000 

 (0.001) (0.001) 
 41 

444516 

Exhibit V.3 (cont’d) 

 (1) (2) (3) 

 Partial Full Partial Full Partial Full 

FSU composition 

Number of adults 0.010 0.060 

 (0.009) (0.011)** 

1 or 2 children 0.024 0.068 

 (0.015) (0.019)** 

3 or 4 children 0.036 0.185 

 (0.022) (0.032)** 

5 or more children -0.016 0.196 

 (0.030) (0.058)** 

Any elderly members -0.023 -0.039 

 (0.026) (0.035) 

Youngest child <1 0.065 0.120 

 (0.036 (0.051)* 

Youngest child 1 to 3 0.046 0.075 

 (0.020)* (0.026)** 

Youngest child 4 to 6 0.024 0.076 

 (0.018) (0.025)** 

Youngest child 7 to 10 0.016 0.009 

 (0.018) (0.022) 

Any HH AFDC/TANF 

receipt 0.120 0.362 

 (0.015)** (0.019)** 

Notes: Data are weighted. Robust standard errors in parentheses. Marginal effects at mean of 

independent variable reported. * significant at 5%; ** significant at 1%; a less than 0.0005 in 

absolute value
 42 

444516 

VI. CONCLUSIONS AND FUTURE RESEARCH 

The analysis in this paper investigated the hypothesis that earnings in periods other than the 

current month may bear on a food stamp-eligible household’s decision as to whether to receive 

benefits. Building off Farrell et al (2003), we have discussed this relationship within the 

theoretical framework of a weak version of the permanent income hypothesis that suggests that 

households facing temporary declines in income may be less likely to participate because of 

expectations of higher income, reliance on resources not factoring into food stamp eligibility, or 

potentially higher costs of participation for households with fluctuating incomes. Farrell et al 

(2003) found evidence that monthly income volatility helps explain why some eligible 

households do not claim benefits; households with only short-term downturns in income are 

less likely to receive benefits than others with consistently low income. Results from our 

regression models are consistent with this finding and show that FSUs that are eligible for food 

stamps in only part of the year, rather than all 12 months, are less likely to participate in the 

program. 

Our analysis goes beyond this to look at historical earnings from as much as ten years before the 

year in which the food stamp unit is determined to be eligible, as well as future earnings over 

the following two years. Indeed, we find evidence of a relationship between past earnings and 

the participation decision. First, as Exhibit IV.7 showed, among food stamp units eligible in a 

given year, those who participate in the program that year consistently had lower income on 

average than those who do not participate for at least 15 years earlier. Second, our regression 

models provide some evidence that even controlling for current earnings, lower earnings as far 

back as five years are associated with a higher likelihood that an eligible food stamp unit 

receives food stamps; in some of the models (including one with the full set of independent 

variables included) higher average earnings between three and five years before the year of 

eligibility is significantly associated with lower participation. To put it another way, for two 

eligible households with the same current earnings, the household with lower income as much 

as five years earlier is more likely to receive benefits. To different extents in our different 

models, this holds when controlling for consistency of eligibility over the months of the current 

year and for demographic factors. Further, we find that higher earnings two years into the 

future, which may proxy for expected earnings, are also significantly associated with lower 

participation in models with the full set of independent variables. 

However, because of weaknesses in the specification of our regression models, it remains 

unclear how important prior and expected income are as determinants. Among these 

weaknesses, which have been indicated throughout the report, our specification did not 

disentangle the effects on participation of marginal changes in positive earnings from the effects 

of having some versus no earnings (particularly important given that approximately 30 percent 

of food stamp eligible households in our dataset had zero earnings in the year of eligibility), and 

did not take into account whether the FSU contained members old enough to have received 

earnings over the time horizons we examine. Consequently, findings in this paper should be 

considered suggestive rather than precise descriptions of the relationship between long-run 

income and participation. 

While our suggestive findings are consistent with our hypothesis that higher past incomes may 

lead a household to be less likely to participate because of expectations of future income, other 
 43 

444516 

resources available or high costs of participation, we reiterate that other hypotheses could also 

explain these findings, including that past earnings are correlated with characteristics not 

controlled for in our model, or not otherwise observable, such as stronger perceptions of stigma. 

Future research, using differently specified models, could perhaps better determine the 

magnitude of the association between past earnings and participation. In particular, future 

research could correct the specification errors that we were not able to correct during the period 

during which we had access to the matched SIPP-SER data set. In addition, analysis of data 

covering different periods than what we analyzed could determine whether similar 

relationships exist under different policy and economic conditions. In particular, following the 

2002 Farm Bill, most states have simplified the systems for applying for food stamps and for 

reporting information related to continuing eligibility used in the food stamp application and 

reporting process, and many have adopted transitional food stamp programs for families 

leaving welfare. Increases in the participation rate in recent years (particularly 2004) suggest 

that these efforts have had some success in increasing the number of eligible low-income 

households who receive benefits. If so, it is likely that these steps have also reduced the 

relationship between income volatility and food stamp participation, and may have also 

changed the relationship between past earnings and current participation. As later SIPP panels 

have been matched to earnings data, it would be possible to conduct similar analysis to ours in 

a more recent period. 
 44 

444516 

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438966 

APPENDIX A: ADDITIONAL TABLES AND CHARTS 

Exhibit A.1: FSU Characteristics by Quartile of Earnings, FSUs Eligible in at Least One Month du

Q1 Q2 Q3 

NonParticipants Participants Total Non- Participants Participants Total Non- Participants Participants Total 

FSU Head Characteristics Female 55.0% 70.0% 62.5% 57.2% 81.5% 68.5% 60.5% 72.8% 65.1% Age 49.3 45.3 47.3 37.6 36.3 37.0 37.2 35.3 36.5 Race 

White 77.0% 62.0% 69.5% 70.3% 58.7% 64.9% 76.4% 62.7% 71.3% Black 19.1% 34.2% 26.6% 23.9% 36.4% 29.7% 19.6% 31.0% 23.8% Native American * * * * * * * * * 

Asian/Pacific Islander * * * * * * * * * 

Hispanic Origin 12.7% 17.0% 14.9% 11.0% 12.8% 11.8% 14.2% 14.7% 14.4% 

Education Level 

Less than high school diploma or GED 46.3% 50.8% 48.5% 27.0% 39.5% 32.8% 23.6% 31.8% 26.6% High school diploma or GED 24.6% 29.2% 26.9% 36.3% 36.0% 36.4% 39.0% 37.3% 38.4% Some college, no degree 17.4% 15.3% 16.4% 24.0% 17.8% 21.1% 24.1% 23.1% 23.8% 

College/graduate/professional degree 11.8% * 8.2% 12.7% * 9.7% 13.3% * 11.2% 

FSU Earnings 

Current year earnings 

Earnings t = 0 $0 $0 $0 $2,604 $2,474 $2,543 $10,528 $9,705 $10,220 

Historical earnings 

Earnings t – 1 $793 $472 $633 $4,570 $2,985 $3,832 $10,177 $7,484 $9,170 

Earnings 2 year average $1,249 $727 $990 $4,964 $3,086 $4,089 $9,904 $7,097 $8,855 Earnings 5 year average $2,579 $1,592 $2,089 $5,721 $3,634 $4,749 $9,120 $6,682 $8,209 Earnings 10 year average $3,999 $2,408 $3,209 $6,181 $3,831 $5,087 $8,639 $6,296 $7,763 

Future earnings 

Earnings t + 1 $948 $690 $820 $5,195 $5,066 $5,135 $11,789 $11,292 $11,603 

Earnings 2 year average $1,416 $1,067 $1,243 $5,930 $5,919 $5,925 $12,395 $11,851 $12,191 
438966 

Q1 Q2 Q3 

NonParticipants Participants Total Non- Participants Participants Total Non- Participants Participants Total 

Work History Working quarters covered t = 0 0.0 0.0 0.0 2.4 2.0 2.2 3.7 3.4 3.6 

Total working quarters covered 46.5 33.8 40.2 51.1 38.8 43.4 61.3 49.7 57.0 Years of earnings 15.5 12.2 13.9 16.8 13.8 15.4 18.0 15.4 17.0 

Food Stamp Eligibility and Participation Months of eligibility 9.4 11.3 10.3 8.6 10.8 9.6 6.0 9.0 7.1 Average months of benefits 0.0 10.1 5.0 0.0 9.3 4.3 0.0 7.9 2.9 

Percent of eligible months 0.0% 86.0% 42.7% 0.0% 78.8% 36.7% 0.0% 67.0% 25.0% Average monthly benefit (in eligible months) $93 $128 $110 $120 $197 $156 $117 $182 $141 

FSU Income Reported in the SIPP $2,903 $622 $1,771 $5,649 $3,587 $4,689 $12,312 $10,087 $11,481 

Total Income $8,680 $7,163 $7,927 $8,693 $8,359 $8,537 $13,872 $13,066 $13,571 Earnings $2,903 $622 $1,771 $5,649 $3,587 $4,689 $12,312 $10,087 $11,481 Transfer Income $1,444 $3,873 $2,649 $647 $2,877 $1,686 $226 $1,372 $654 

Property Income $87 $23 $55 $39 $24 $32 $53 $13 $38 Household Income Reported in the SIPP 

Household Income Reported in the SIPP $16,480 $11,172 $13,845 $20,432 $14,510 $17,674 $22,565 $17,220 $20,567 

Percent households with earnings 43.6% 24.0% 33.9% 84.8% 85.6% 85.2% 99.1% 96.6% 98.1% Household earnings for receivers $17,823 $11,761 $15,689 $17,934 $9,131 $13,815 $19,814 $13,855 $17,623 Percent households with food stamp income 8.5% 100.0% 53.9% 8.4% 100.0% 51.0% 4.3% 99.6% 39.9% 

Household food stamp income for receivers $1,261 $1,543 $1,521 $1,582 $2,036 $1,996 $1,394 $1,722 $1,701 

Percent households with TANF income 5.1% 38.3% 21.6% 6.3% 53.7% 28.4% 3.1% 36.1% 15.4% 

TANF income for receivers $2,695 $3,504 $3,408 $2,303 $3,044 $2,956 $2,876 $2,171 $2,259 

Percent households with SSI income 33.6% 53.9% 43.7% 17.0% 28.3% 22.2% 7.5% 11.2% 8.8% Household SSI income for receivers $5,122 $4,237 $4,580 $4,280 $5,282 $4,874 $3,414 $3,959 $3,671 
438966 

Q1 Q2 Q3 

NonParticipants Participants Total Non- Participants Participants Total Non- Participants Participants Total 

Type of Household Married Family 28.6% 15.5% 22.1% 21.3% 17.9% 19.7% 26.7% 31.8% 28.6% Non-married family 

Male household head * * * * * * * * * Female household head 19.5% 38.5% 28.9% 27.6% 60.0% 42.7% 30.1% 55.6% 39.7% 

Non-family 

Male household head 24.8% 16.7% 20.8% 25.4% * 19.4% 20.0% * 14.0% Female household head 21.8% 24.8% 23.3% 19.5% * 14.6% 17.9% * 12.9% 

Group quarters * * * * * * * * * Number of observations 1,195 1,156 2,351 760 664 1,424 1,193 681 1,874 

Notes: Data are weighted. Asterisks indicate results suppressed due to sample size considerations. Results are suppressed when th

in a cell. In addition, if a result is suppressed in the “non-participants” or “participants” column, the result is also suppressed in the appropriate quartile. 
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Exhibit A.2: Annual Income by Participation Status and Year, 

FSUs Eligible in at Least One Month during the Year 

$0

$2,000$4,000$6,000$8,000

$10,000$12,000$14,000$16,000

t-15 t-14 t-13 t-12 t-11 t-10 t-9 t-8 t-7 t-6 t-5 t-4 t-3 t-2 t-1 t = 0 t+2 t+2 t+3

Non-Participants (97) Non-Participants (98) Participants (97) Participants (98

Notes: Data are weighted. t = 0 indicates 1997 or 1998. All amounts presented in constant 1996 dollars, adjusted for inflation us
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Exhibits A3 through A6: Annual Income by Earnings Quartile in t = 0, FSUs Eligible in at Least One Mo

A.3: Lowest Quartile A.4: Second Quartile A.5: Third Quart

A.6: Highest Quartile 

Notes: Data are weighted. t = 0 indicates 1997 or 1998. All amounts presented in constant 1996 dollars, adjusted for inflation us

$0

$5,000

$10,000$15,000$20,000$25,000$30,000

t-15 t-14 t-13 t-12 t-11 t-10 t-9 t-8 t-7 t-6 t-5 t-4 t-3 t-2 t-1 t=0 t+1 t+2 t+3

Non-Participants Participants

$0

$5,000

$10,000$15,000$20,000$25,000$30,000

t-15 t-14 t-13 t-12 t-11 t-10 t-9 t-8 t-7 t-6 t-5 t-4 t-3 t-2 t-1 t=0 t+1 t+2 t+3

Non-Participants Participants

$0

$5,000

$10,000$15,000$20,000$25,000$30,000

t-15 t-14 t-13 t-12 t-11 t-10 t-9 t-

Non

$0

$5,000

$10,000$15,000$20,000$25,000$30,000

t-15 t-14 t-13 t-12 t-11 t-10 t-9 t-8 t-7 t-6 t-5 t-4 t-3 t-2 t-1 t = 0 t+2 t+2 t+3

Non-Participants Participants
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APPENDIX B: DESCRIPTION OF THE MID-SIPP 

This appendix reprints sections of MaCurdy, Thomas and G. Marrufo, Food Assistance for the 

Working Poor: Simulating the Impact of the Nutrition Tax Credit on the Food Stamp Program relevant 

to the analysis in this paper. In particular it reprints the sections describing the construction of 

Food Stamp Units within the SIPP; determination of eligibility, potential benefits, and 

participation spells; and correction for the seam bias. MaCurdy and Marrufo go on to describe 

the calibration of the MID-SIPP to the Food Stamp Program Quality Control data set; however, 

that section is not reprinted here as the MID-SIPP data used in this analysis came from an 

earlier version of the model where participation spells were determined separately from 

calibration, and the data used in this report did not incorporate this calibration. 

A. MID-SIPP Simulation Approach 

Our simulation approach involves seven steps, described in this section. As a model for 

simulating the effects of Food Stamps, the central element of our approach is to construct a 

representative sample of “food stamp units” (FSUs) drawn from SIPP and to use the information 

available for each FSU to emulate its FSP eligibility and participation over designated time 

horizons. We infer eligibility assuming particular assignment rules governing FSU formations, 

eligibility rules, benefit levels and FSP reporting requirements. 

After allocating all individuals in SIPP into FSUs – essentially structuring the SIPP sample into a 

sample of FSUs -- we create monthly data for each FSU describing its economic and demographic 

circumstances. For a given policy regime, we calculate FSP eligibility for each FSU for each month. 

A policy regime is a set of eligibility and benefit rules that determine: 

• Which individuals are eligible for FSP, including work and immigration status 

• Benefit/eligibility determination criteria including: 

 Gross and net income cutoffs 

 Treatment of income by source 

 Deductions and exclusions 

 Asset limitations 

 Treatment of housing and utility costs 

• Reporting plans 

 Retrospective or prospective budgeting 

 Budget period 

 Reporting period (monthly, quarterly, semi-annual) 

 Reporting type (fixed interval or change) 

 Recertification period 

If eligible, we use these monthly data to calculate benefits for which the FSU is eligible. The 

assignment algorithms for eligibility maintain the critical assumption that FSUs do not change their 

behavior – altering their earnings or family structure – in response to changes in FSP reporting 

regimes. A model of FSP participation then determines whether an FSU takes up food stamp 

benefits. If it does, we track monthly changes in circumstances relevant for continued eligibility. 
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In doing so, our analysis creates a longitudinal data set depicting the FSP eligibility and 

participation experiences for individual FSUs. These data can be used to project outcomes in 

four domains: 

1) Eligibility for program benefits, 

2) Duration of eligibility and ineligibility, 

3) Collection of food stamp benefits, and 

4) Administrative activity as measured by number of mandated reports. 

These outcomes produce a rich description of FSUs’ experiences under each policy regimes. 

Comparison across regimes using our MID-SIPP model allows policymakers to weigh the 

differences in costs associated with different policy regimes. 

Exhibit B.1 provides an overview of how these simulation tasks are accomplished in the seven 

steps. The remainder of the chapter describes each of these seven steps. 
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Exhibit B.1a: Steps in the Simulation Approach Part I 
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Exhibit B.1b: Steps in the Simulation Approach Part II 
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1. Step 1: Classify Persons into Food Stamp Units and Reweight 

The first step of our simulation is to convert the SIPP into observations based on FSUs, assigning all 

people in the SIPP to an FSU. We assign individuals to an FSU consistent with the way the Food 

Stamp program would consider the household or family if they applied for Food Stamps. Thus, 

an FSU is a group of people, not necessarily related, who purchase and prepare meals together. In 

most cases, an FSU includes all members of a household, but sometimes households composed of 

unrelated individuals or multiple families can split into more than one FSU when food expenses 

are not shared by all members. 

Because the SIPP does not provide detailed information on how household members divide their 

food expenditures and activities, we make these assignments by applying the rules stipulated by the 

Code of Federal Regulations, 7 U.S.C 2012 3(I). In particular, we adopt the following assignment 

rules: 

(a) A single family household forms one FSU; 

(b) Unrelated subfamilies are assigned to different FSUs, except in the following cases: 

 Unmarried couples, and 

 Foster children younger than 19 years and their guardians; 

(c) Related subfamilies are assigned to different FSUs, with the following exceptions: 

 Parents and their children under age 22, and 

 Guardians and children under age 18; 

(d) Elderly and disabled persons are allowed to file separately when total income of 

the remaining household members is less than 165% of poverty; 

(e) Post-secondary students working less than 20 hours per week are separated to form 

their own FSU.1

Given these allocation rules, MID-SIPP’s default approach is to divide households into the 

maximum number of FSUs allowable by the FSP regulations. Of course, there are undoubtedly 

instances when more than one related family live in the same residence, buy meals together, and 

should be combined in one FSU. Hence, an alternative approach would be to aggregate all 

families living in the same household into a single FSU rather than separate FSUs as done in our 

base analysis. The simulation framework is flexible and could readily incorporate alternative FSU 

assignments. Our tests indicate that aggregating all families living in the same household into one 

FSU decreases the number of FSUs by nearly 8%. All findings presented here use the default FSU 

allocation described above. 

Because we follow FSUs over multiple months, the structure of an FSU can change over time. In 

the current simulation model, we treat any change in an FSU’s composition that potentially 

alters its eligibility as the formation of new FSUs—or “split-offs”—each one with a different identity 

from the original unit. For example, if a husband leaves a husband-wife-child FSU, then two new 

FSUs are formed: an FSU composed of a mother and child and another composed of a male adult. 

Consequently, an individual can belong to more than one FSU over the course of the year. 

 

1 According to FSP rules, income from post-secondary students working less than 20 hours a week is not 

counted in FSU income and is excluded when calculating FSP benefits. 
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For all FSUs appearing in our sample in any month covered by the sample period, we generate 

statistics on eligibility and benefits for subpopulations of FSUs distinguished by their 

characteristics, such as working-poor FSUs (those primarily supported by low-wage earnings) 

and female-headed FSUs with children. When examining these numbers, bear in mind that the 

length of time for which an FSU collects or remains eligible for Food Stamps does not necessarily 

reflect the length of time for which individuals within that FSU collect benefits or remain eligible.2 

The focus on FSUs, rather than individuals, and the particular strategy for assigning FSUs, are 

adopted as our base case. However, the MID-SIPP simulation framework can readily calculate 

statistics for individuals rather than for FSUs. As noted above, it can also accommodate a variety 

of other approaches for defining and tracking divided FSUs. 

The empirical analysis for the baseline MID-SIPP relies on SIPP data from the 1997 federal fiscal 

year, incorporating complete histories for all available FSUs during this sample period. We 

exclude an FSU when (I) it is missing crucial data within the sample period—e.g., data exists for 

April and June but not for May; (ii) none of its members are in SIPP in the first month of the 

sample period, and (iii) the FSU leaves the SIPP through attrition (as opposed to splitting) 

before the end of the sample period. Thus, our simulations keep FSUs that stay in SIPP 

throughout the sample period or that are generated from a splitting FSU whose members 

remain in SIPP throughout the period. 

The latter two exclusion rules preserve the SIPP’s longitudinal sample design and avoid the 

need to make sophisticated adjustments to SIPP weights to account for sample attrition.3 To 

account for the exclusion of units with missing crucial data (our first exclusion rule), we adjust 

the monthly household SIPP weights. We construct a common monthly adjustment factor equal 

to the number of FSUs in the SIPP observed in a particular month divided by the number of 

FSUs in our sample observed in the same month. We then apply this adjustment to all units in 

our simulation sample. This calculation essentially assumes that the crucial data are missing at 

random. This minor adjustment does not affect the composition of households in the sample. 

As shown in Table 2.1, the distribution of households in different categories in our sample 

using re-scaled weights approximates the raw SIPP distributions. 

Even after controlling for attrition and missing data, we still need to ensure that the SIPP data 

yields a “representative” population of FSUs for projecting annual statistics. Given a revolving 

population of FSUs over time, including FSU split-offs that by definition are observed for only 

part of the sample period, there are many options for defining a representative population. For 

 

2 For example, assume the original FSU was eligible for FSP from January to March and that the new 

mother-child FSU remains eligible after the husband leaves in March until the end of the year. In this 

case, both mother and child would have a 12 month long eligibility spell. Instead, we assume these 

individuals constitute two different FSUs with durations of 3 and 9 months respectively. However, in 

both cases we calculate correctly each individual’s number of months on FSP within a year. 

3 The original design of the SIPP called for an initial selection of a nationally representative sample of 

households, with all adults in those households being interviewed once every 4 months over a 32-month 

period. For subsequent waves, the SIPP includes in its sample all other adults living with original sample 

members. By including all FSUs ever formed by members who remain in the SIPP throughout the period, 

we are also including in our sample all original sample members and adults living with them through the 

period. 
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the analysis presented here, our simulations include the population of all individuals observed 

in September 1997 and all FSUs formed by these individuals during the fiscal year. In this way, 

including split-offs in our population permits computation of annual figures that account for 

families who were eligible for short intervals during the period of analysis. In the analyses 

below, we use weights for the latest month an FSU is seen in our sample period to create a 

sample of FSUs that would be nationally representative for the end of the period if no FSU splitoffs had been created within the sample period. 

2. Step 2: Determine Each FSU’s Eligibility and Potential Benefits for Every 

Month 

At the completion of Step 1, we have a sample of FSUs developed from the SIPP data. For each 

FSU specified in Step 1, the next step is to impute this FSU’s program eligibility and the level of 

benefits it is eligible for on a monthly basis. As the second step in our framework, the eligibility and 

potential benefit imputation applies the set of Food Stamp rules defined under the policy regime to 

be simulated. Given a specification for FSUs and rules prescribed for an FSP policy regime, we 

impute each FSU’s program eligibility and level of benefits from FSU level data on gross and net 

income, FSU size and composition, financial assets minus deductions, vehicle assets minus 

deductions, and categorical eligibility status. 

SIPP supplies on a monthly basis much of the information needed to conduct monthly gross and net 

income tests and the resource test, as well as tests related to the demographic structure of the FSU 

(such as citizenship). We are also able to assign disability status on a monthly basis to apply the 

appropriate FSP rules for FSUs that contain disabled persons. We classified individuals as 

disabled if they received non-earned income due to disability, including non-elderly individuals 

receiving Supplemental Security Income (SSI). 

Unfortunately, data on an FSU’s assets and some deductible expenses are available only once a 

year in special topical modules (in Waves 3, 6, 9 and 12).4 We adopt the following rules to assign assets 

and expenses to the other months in the panel: 

(a) Monthly averages reported in Wave 3 are assigned to all months comprising 

Waves 1, 2, 3, and 4; 

(b) Monthly averages reported in Wave 6 are assigned to all months included in 

Waves 5, 6, and 7; 

(c) Monthly averages reported in Wave 9 are assigned to all months making up 

Waves 8, 9, and10; and 

(d) Monthly averages reported in Wave 12 are assigned to all months included in 

Waves 11 and 12. 

Income tests require information on both FSU-level gross income and admissible deductions. 

We calculate gross income by summing all sources of earnings and income for all adult members 

included in the unit. For earned income, we include all wages and salaries of employees, as well as any 

 

4 The sample in the model data set excludes households with incomplete asset information in the 

corresponding topical module. We excluded these households from the model data set during the year 

there was not topical module information available, but retained them in the remaining years. For 

example, a household missing asset information in only 1996 is not included in our sample for 1996, but it 

is included in our samples for 1997 through 1999. 
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net income from self-employment. For unearned income, we account for all types of financial and 

property income, social security, foster child payments, and all transfers from means-tested programs 

except Food Stamps. Similarly, we make the appropriate deductions to calculate net income, 

following the rules of the policy regime. To anchor our results in known FSP information, we start 

from existing policy, so to calculate net income, we deduct the following: 

• A state-based standard deduction of $134, 

• 20% of the FSU’s earned income (the “earned income deduction”), 

• Dependent care expenses up to a $175 maximum per dependent and up to $200 per 

children younger than two, 

• Legally mandated child support payments, 

• Medical expenses in excess of $35 if there is an elderly or disabled person in the FSU, 

and 

• An excess shelter deduction. For the excess shelter deduction, we subtract the monthly 

rent and utility expenses above 50% of the FSU’s net income after applying all remaining 

deductions. Following the rules, we limit the excess shelter deduction to a maximum of 

$250 if there are no elderly or disabled members in the FSU.

For an FSU passing the income and demographic-structure tests in a month, we assess whether the 

asset tests exclude it from benefits. In calculating assets, we aggregate amounts in checking 

accounts, savings accounts, stocks, bonds, and mutual funds for all members of the FSU, as well as 

savings in IRA and Keogh Accounts after deducting withdrawal penalties. Again, our example case 

uses existing rules, so we apply the follow tests: Eligible households with an elderly member 

(60 or older) cannot have counted liquid assets above $3,000, whereas eligible households without 

an elderly member are restricted to counted liquid assets of no more than $2,000.5 Counted liquid 

assets include cash, checking and savings accounts, savings certificates, stocks and bonds, IRAs and 

Keogh plans (less early withdrawal penalties), and nonrecurring lump-sum payments like 

insurance settlements. In addition, the equity value of property not producing income consistent 

with its value (such as recreational property) is included in this measure. We accounted for vehicles 

by applying the following three rules: 1) for the first vehicle, or any vehicle used to commute to 

work, any market value above $4,650 was counted; 2) for other vehicles, the higher of either any fair 

market value above $4,650 or any equity was counted; and 3) for vehicles used to produce income or 

to transport disabled persons, all value was excluded from the resource test according to regulation 

273.8, Section (e)(3)(I). A similar set of calculations is done for each alternative policy regime. 

At this stage in the simulation, our benefit calculations assume that any eligible FSU applies and 

collects full potential benefits in that month. This is treated as a reference or baseline calculation, 

since not all eligible FSUs will in fact take-up benefits. At any take-up less than 100%, this reference 

case will by definition be an over-estimate of the benefits actually collected. (In Step 5, we address 

benefit take-up rates to simulate participation among those eligible.) 

Reporting rules are handled as follows: When an FSU first becomes eligible or becomes eligible 

after a period of non-certification, we treat the first month of eligibility as the certification time 

and assign to the FSU the level of benefits calculated at the time of certification and certify it for the 

assumed reporting period. At the end of this certification period, we assume that FSUs remaining 

 

5 In the case of units containing only persons on SSI, TANF, or General Assistance (GA), asset eligibility is 

automatic. 
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eligible reapply for the benefits and receive payments consistent with conditions at the time of 

recertification. If a reporting regime requires households to report changes, then we presume 

that FSUs perfectly abide by the prescribed rules and caseworkers immediately adjust eligibility 

and benefits accordingly. If an FSU becomes ineligible in a month, then we treat it as reapplying 

at its first opportunity if and when eligibility reoccurs. 

These steps create a simulated panel of FSUs observed on a monthly basis, with eligibility status 

and benefit levels directly computed from the information contained in SIPP. The next step is to 

adjust this panel of data to account for SIPP reporting inaccuracies that cause distortions in the 

data. 

 

3. Step 3: Correct Eligibility and Reported Participation Spells for the Seam 

Problem 

The SIPP interviews households three times each year. At each interview, the respondent is 

asked about family members’ circumstances during the previous four months. In a recall 

survey such as this, individuals are more likely to report that changes in circumstances occurred 

at the beginning of the first month or at the end of the last month of an interview period. Thus, 

the survey structure induces a disproportionate number of changes in income, asset levels, and 

program participation reported to occur at the ‘seam’ between two interview periods, yielding 

artificial breaks in the profiles of variables used to impute FSP eligibility and benefits, and also 

of the participation variable. Known as the seam problem, this factor potentially contaminates 

analyses of dynamic behavior estimated with data from longitudinal surveys. Thus we first 

adjust the eligibility, benefit level and participation variables for the seam problem. 

To better understand the seam problem and our approach to address it, consider an eight 

month time line: 

 Month 1 2 3 4 5 6 7 8 

Interview I I 

Eligibility: Reported E E E E NE NE NE NE 

Actual E E E E E NE NE NE 

Suppose a SIPP family is interviewed (I) in months 4 and 8. In each month, a family is in one of 

two states: eligible for FSP (E) or not eligible (NE). A seam problem arises if the family in 

period 8 reports a change in circumstances as though it occurred in month 4, when in fact it 

occurred sometime after month 4 but before month 8. For the example above, we know from 

the interview that the family was in state E in month 4 and in state NE in month 8. Because of 

the seam problem, the family may report termination in month 4, even though the duration of 

the family’s eligibility spell may have extended beyond month 4 (through month 5 in the above 

example). The seam problem can affect reported participation itself as well as reports on 

income and other information used to determine eligibility. In our simulation analysis, the 

seam problem is particularly an issue for the estimation of duration distributions for eligibility, 

denoted f (•). If the family’s spell started in month 1 and the family misreports the spell 

ending in month 4, then the estimated value of f(4) is too high, because we have counted the 

family as f(4) instead of f(5). 
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The MID-SIPP compensates for the distortions induced by the seams in SIPP.6 In essence, the 

analysis adjusts for this estimation error by specifying a “smooth” functional form for f(•) that 

redistributes part of the occurrence of events in period 4 to periods 5, 6, and 7 in a way 

consistent with patterns in the data. In particular, we use a logit function to fit the probability 

that a spell that lasted t-1 months would end in the following month, allowing for a rich set of 

demographic covariates and a flexible function that captures duration dependence. This 

conditional probability is called the hazard rate at t. Using this logit specification, we 

constructed the likelihood function for all observed spells, distinguishing completed spells, 

censored spells and spells ending on a seam month. To account for potential misreporting of 

termination at the seam month, the likelihood of a spell that ended in a seam at t-1, is specified 

as the conditional probability that it will end at t, or at t+1 or at t+2, which is simply the sum of 

the hazard rates at t, t+1 and t+2.7 The estimated hazard rates resulting from this method do 

not have the spikes at seam months (4, 8 or 12) followed by unusually low values in the 

subsequent months (5, 9 or 13) seen in the empirical hazard rates. 

At the end of Step 3, we have a seam-corrected panel data set of SIPP FSUs observed on a 

monthly basis, with monthly estimations of eligibility and potential benefits. Because we want 

to make projections about the Food Stamp Program at the national level, before this data set is 

ready for analysis, we must derive weights so that our projections are nationally representative 

and match administrative totals.

 

6 Appendix A demonstrates the relevance of the seam problem in SIPP and describes our approach to 

compensate for the distorting influence of artificial patterns induced by seams. 

7 See Appendix A for a detailed description of the estimation methods used to correct the seam problem. 
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APPENDIX C: A COMPARISON OF EARNINGS DATA IN THE SURVEY OF INCOME 

AND PROGRAM PARTICIPATION WITH SOCIAL SECURITY EARNINGS RECORDS 

A. Introduction 

The Census Bureau’s Survey of Income and Program Participation (SIPP), which was designed 

“to provide accurate and comprehensive information about the income and program 

participation of individuals and households in the United States,”1 has been one of the primary 

sources of data on the economic situations of low-income individuals and families. Among the 

strengths of the SIPP for use in social research are its longitudinal structure, which allows 

researchers to analyze data on individuals over time, and the detailed data it collects on 

program benefits. As a result, it is a valuable tool for researching participation in low-income 

programs. 

Given its broad use in these areas, knowing the level of accuracy of earnings data in the SIPP is 

of substantial interest to policy researchers and other social scientists. Inaccurately measured 

earnings can lead to distorted conclusions about program participation. For example, the main 

body of this paper uses the SIPP to investigate why a substantial share of individuals who are 

eligible for food stamps do not participate in the programs. If survey data fails to capture 

earnings by some individuals, research based on those data could be confounded by the fact 

that those individuals may appear from the survey data to be eligible for food stamps when in 

fact their income is high enough to make them ineligible. 

This appendix investigates the accuracy of earnings data from the 1996 panel of the SIPP. The 

analysis is based on a data set in which SIPP data are matched to Social Security administrative 

earnings records. This matching of data provides the opportunity to compare earnings amounts 

reported by SIPP respondents to earnings amounts in the Social Security earnings records. Such 

a comparison can provide information on the extent of nonsampling error, and in particular 

measurement error, in the SIPP’s earnings data. 

There are several well-acknowledged sources of potential nonsampling error in survey data on 

earnings.2 Nonresponse—either failure of part of the sample to participate in the survey as a 

whole, or nonresponse to particular questions—can lead to biases in statistics generated with 

the survey data if the nonresponsive portion of the sample is not randomly determined. In 

longitudinal surveys like the SIPP, attrition is a particular problem, where members of the 

sample who participate in one wave of the survey do not participate in later waves. According 

to Weinberg (2003), attrition from the 1996 panel exceeded 25 percent by the end of the panel’s 

second year, and sample loss reached as high as 35.5 percent in the last wave of the panel 

(Bureau of the Census, 2001). The Census Bureau performed various adjustments to the SIPP 

data to account for nonresponse in the 1996 SIPP, including weighting adjustments when entire 

households do not respond to the survey and imputations of data when one person in a 

household does not respond. (Bureau of the Census, 2001). 

 

1 Bureau of the Census (2001). 

2 For a general overview of sources of survey measurement error, see Bound, Brown, and Mathiowetz 

(2000). For a general overview of issues with the SIPP, see Weinberg (2003).
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Survey respondents may also misremember amounts they have earned in past months, 

particularly if they do not refer to records to confirm their memory or if they work in jobs in 

which earnings vary from month to month. Given its four-month wave structure, the SIPP 

suffers from “seam bias” in which reported earnings change more in consecutive months 

between waves (the “seam”) than within waves because respondents tend to report similar 

earnings for all four months they are asked about in each questionnaire, causing an artificial 

concentration of changes at each new interview. 

Respondents may misunderstand the questions; for example – some responders may report 

after-tax earnings even though the SIPP requests information on gross income – or may not 

have sufficient knowledge to accurately answer the question if, for example, one member of the 

household reports his or her own employment and earnings as well of the employment and 

earnings of all other household members. In addition, respondents may deliberately misreport 

under certain circumstances, such as if they have “off-the-books” earnings that they do not 

want to report to the interviewer.

In addition, the SIPP’s design may lead to some inaccuracies. The SIPP only collects information 

about two jobs in each wave, plus additional earnings from “moonlighting” and earnings from 

self-employment. While some attempt is made to record total earned income from additional 

jobs this variable is likely to involve more measurement error as it represents a total of possibly 

several jobs over a four-month period. 

The basic method of this analysis will be to compare individual-level earnings records from the 

1996 SIPP with the earnings records for the same individuals in the Social Security data. 

Administrative data are often considered more accurate than survey data, and the usual 

assumption made in these types of studies is that the administrative data provide an accurate 

external measure of earnings. Discrepancies are consequently attributed to error in the survey 

data. However, the Social Security earnings records are also subject to certain sources of error 

and limitations, particularly when being treated as a representation of all earnings among the 

general population instead of being used within their intended programmatic purposes. Social 

Security data will not contain data on any earnings not reported to SSA, such as “off-the-books” 

earnings or some earnings not covered by the Social Security system. While some respondents 

may be reluctant to report off-the-books earnings in their responses to the SIPP, the survey is 

likely to capture more such earnings than the administrative data, and in these cases the survey 

data will be more accurate. In addition, the administrative data set to which we had access, 

Summary Earnings Record, does not contain “uncovered” earnings to which the Social Security 

payroll tax does not apply, such as earnings above the taxable maximum (around $70,000 

during the years covered by the 1996 SIPP panel) or earnings from certain types of jobs. (Other 

sets of Social Security administrative data contain uncovered earnings.) Interpreting differences 

between earnings as represented in the two data sources should take into account error in both 

data types. 

Following a distinction established by Pedace and Bates (2000), the analysis has two separate 

stages. The first stage looks at agreement and disagreement between the two data sources in 

their identification of who has any earnings (regardless of the amount) and who has no 

earnings. The second stage looks just at those individuals that both data sources identify as 
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having earnings, and looks at discrepancies in the amounts of earnings shown in the two data 

sets. 

After this introduction, the next section provides a brief discussion of past literature on the 

accuracy of survey data on earnings. The third section discusses the data used in the analysis. 

The fourth and fifth sections present the results of the analysis. The final section concludes. 

B. Previous Findings on Accuracy of Survey Data on Earnings 

This discussion highlights some of the key studies that have looked at the accuracy of annual 

earnings data in national surveys. Several very helpful reviews of literature on the accuracy of 

survey data exist. Among these are Bound, Brown, and Mathiowetz (2000) and Moore, Stinson, 

and Welniak (1997) and Hotz and Scholz (2002); we rely heavily on these three papers.3

Studies of the accuracy of income and earnings data take two main forms: “benchmark” 

comparisons of aggregate statistics derived from the survey to some other aggregate measure, 

and comparisons of individual-level responses to independent individual-level data such as 

administrative records. 

1. Aggregate Comparisons to Benchmark 

Two relatively recent studies compare aggregate earnings data derived from the SIPP and the 

Census’s Current Population Survey (CPS) to each other and to independent benchmarks 

derived from the Bureau of Economic Analysis’s National Income and Product Accounts 

(NIPA), the database used to create GDP and other national income statistics. Both studies find 

that the SIPP tends to miss earnings data, while the CPS is closer to the benchmark. 

Coder and Scoon-Rogers (1996) find that aggregate wage and salary income in the CPS March 

supplement is closer to the benchmark derived from the NIPAs than aggregate wage and salary 

income from the SIPP. Wages and salaries in the 1990 panel of the SIPP equal 92 percent of the 

benchmark, while wages and salaries measured in the 1990 CPS equal 97 percent of the 

benchmark. On the other hand, The SIPP is closer to the benchmark on many other measures of 

income, including self-employment income, where the SIPP data in 1990 equals 78 percent of 

the benchmark as compared to 66 percent in the CPS.4

Roemer (2000) takes a similar approach using more recent data to show comparisons for years 

from 1990 to 1996. Roemer finds the SIPP’s measurement of wages and salaries in 1996 to equal 

91 percent of the NIPA benchmark and its measurement of self-employment earnings to equal 

69 percent of the benchmark; taken together, SIPP-measured earnings are 88 percent of the 

benchmark. In comparison, March CPS measurements in 1996 as a percentage of the benchmark 

 

3 Table 1 of Bound, Brown, and Mathiowetz (2000) summarizes findings from 18 studies assessing error 

in survey measurements of earnings. The paper can currently be found at 

http://www.psc.isr.umich.edu/pubs/pdf/rr00-450.pdf (accessed February 7, 2007). 

4 In discussing these results which show survey-based aggregates below the NIPA benchmark, Moore, 

Stinson, and Welniak (1997) “urge caution” in concluding that survey respondents underestimate their 

income. They note that there are several other potential sources of differences, such as time frame or 

definitions used. 
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are 102 percent for wages and salaries, 53 percent for self-employment earnings, and 96 percent 

for earnings taken together. SIPP wages and salaries remain consistently around 90 percent to 

91 percent of the NIPA benchmark, which Roemer describes as “perhaps disappointing” 

because of attempts to improve collection of wage data in the 1996 panel. The SIPP in general 

shows lower aggregate income and a higher number of people receiving that income than the 

CPS, and based on distributional estimates, Roemer hypothesizes that the higher burden of 

responding to the SIPP leads to fewer higher-wage earners participating in the survey. 

2. Individual-Level Comparisons to Administrative Data 

Validation studies comparing survey data on earnings to administrative data typically use IRS, 

Social Security, or employer records as the administrative data sources. As mentioned earlier, 

the typical assumption in such studies is that the administrative data are accurate and therefore 

differences between the survey data and the administrative data are interpreted as survey error. 

Some exceptions exist. Many of these studies find survey data to overstate earnings at lower 

earnings levels, and to understate earnings at higher levels. 

A validation study of the Panel Study of Income Dynamics (PSID) looked at a sample of 

employees at a single manufacturing firm and compared their responses to a PSID-like 

interview to data from the employers’ records. The study found very small differences in mean 

earnings – for example, Duncan and Hill (1985) report that mean earnings for 1981 and 1982 

from the interviews were underreported by less than one percent for annual earnings and by 

about four percent for hourly earnings. Average absolute differences were substantially bigger 

(seven to nine percent for annual earnings; 14 to 16 percent for hourly earnings), suggesting 

little bias in responses but more substantial random error. A follow up study (Rodgers, Brown, 

and Duncan (1993)) also found little bias, though higher random error.5

Coder (1992) compared earnings of married couple SIPP respondents to IRS records. The 

comparison was limited to those couples who could be matched to the tax returns, and 

excluded couples with zero earnings in both data sources. Little difference was found (a simple 

correlation of 0.83), but Moore, Stinson, and Welniak characterize the sample as “very 

restricted”.6 Hendrick, King, and Bienias (1997) make a similar comparison among a data set 

matching the 1990 SIPP panel to IRS data as part of an analysis of nonresponse bias in the SIPP, 

using a different sample (matched cases with non-zero earnings in both SIPP and IRS). They 

find that SIPP-measured earnings are lower than earnings measured with the IRS returns, and 

that there is “SIPP overestimation at the low earnings level and/or SIPP underestimation at the 

high earnings level”. 

Roemer (2000), discussed above for its comparisons with NIPA-based benchmarks, also looked 

at matched CPS-IRS data. With this comparison, Roemer concludes that both underreporting 

and overreporting of earnings occur in the CPS relative to IRS data, with net underreporting 

only occurring at the top end of the income spectrum. Roemer did not analyze a similar match 

between the SIPP and the IRS data. 

 

5 Findings summarized in Bound, Brown, and Mathiowetz (2000) and Moore, Stinson, and Welniak 

(1997). 

6 Findings summarized in Bound, Brown, and Mathiowetz (2000) and Moore, Stinson, and Welniak 

(1997).
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Looking at a data set matching the CPS to Social Security earnings for 1977 and 1978, Bound 

and Krueger (1991) find high correlations between the two measurements of earnings, and little 

bias in the errors, despite a high variance in differences in the earnings measures. For men, 

errors are larger at the lower end of the earnings spectrum (as measured in the Social Security 

data), which a later analysis by Bollinger (1998) finds to be due to a small group of low earners 

either overreporting their earnings in the CPS or for whom earnings are not captured in the 

Social Security data set.7

Abowd and Stinson (2003) diverge from the typical operational assumption that administrative 

data are fully accurate and question the extent to which differences between SIPP-measured 

annual earnings and Social Security earnings data (as contained in the Detailed Earnings 

Records file, or DER) are due to errors in the survey versus errors in the administrative data. 

They look at matches in earnings from particular jobs, as opposed to matches between persons.8

They find that a somewhat higher proportion of variance in earnings in the DER is attributable 

to error than in the SIPP.9

In our analysis we have chosen to follow closely a paper by Pedace and Bates (2000). Pedace 

and Bates worked with a data set matching the 1992 SIPP panel to Social Security administrative 

data on earnings from the Summary Earnings Record (SER). They distinguish between accuracy 

in recipiency—i.e., accurate reporting in the survey data whether individuals have received any 

earnings or not—and accuracy in reporting earning amounts, and point out that studies like 

Roemer (2000) and Coder (2002) find underreporting of amounts, but little understatement of 

recipiency. In their own data analysis, they find a high level of agreement between the SIPP and 

the SER in whether individuals receive earnings (90 percent of matched cases either have 

positive earnings in both data sets or have zero earnings in both data sets) but a substantial 

amount of differences in amounts. Cases in which there are positive earnings in the SIPP and 

zero earnings in the SER are about twice as common as cases where there are zero earnings in 

the SIPP and positive earnings in the SER, which the authors see as evidence of a “systematic 

failure to capture earnings on the SER.” They find substantial misreporting of amounts; where 

both data sets have positive earnings, the SIPP values are on average $459 lower than the SER 

values. At the lower end of the earnings spectrum (as measured by the SER), the SIPP showed 

higher earnings than the SER, but at higher earnings level ($20,000 and above) – and in total 

overall – the SER showed higher earnings. Econometric models show that a number of 

demographic factors and types of occupations affect the accuracy of reporting (or at least the 

match between SIPP-reported data and the SER.) 

 

7 Findings of both papers summarized in Bound, Brown, and Mathiowetz (2000) and Moore, Stinson, and 

Welniak (1997). 

8 The DER contains all wages and other compensation reported on the IRS’s W-2 form. Therefore, unlike 

the SER, it contains earnings reported to the IRS regardless of whether the earnings are covered by the 

Social Security system. The DER also contains earnings separated out by different jobs, while the SER 

only contains total annual earnings from all jobs for each individual. Abowd and Stinson’s methodology 

takes advantage of the job-by-job earnings records. 

9 Abowd and Stinson construct a model that differentiates between “shared effects” in the two earnings 

measures, which they treat as noise in true earnings, and “separate effects” from one data source or the 

other which they treat as error attributable to that source. 
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Bridges, Del Bene, and Leonesio (2002) perform a similar analysis using data from the 1992 and 

1993 SIPP panels to the DER. Their findings are similar, and also show SIPP earnings to be 

higher than DER earnings for lower earners and lower for higher earners. 

Later in the appendix, we compare our findings to Pedace and Bates’s; more detail on their 

paper is included in that discussion. 

C. Data 

The public-use data from the 1996 SIPP panel include monthly information on households, 

families, and individuals over 48 months, including detailed monthly demographic, program, 

employment, and health characteristics for a nationally representative sample. SSA and Census 

have matched these SIPP public use files to administrative records on earnings (including selfemployment earnings) using Social Security Numbers (SSNs). 

The bulk of our analysis looks at individuals in this matched data set and compares the annual 

earnings reported for these individuals in the SER with their annual earnings from the 1996 

SIPP panel.10 However, it is not possible to make comparisons for every individual in the data 

set for each year. As a result, we constructed an analysis sample for each year consisting of just 

those individuals where such a comparison is possible. We generally focus on working-age 

individuals, which we define as ages 18 through 65. However, where we compare our findings 

to past papers—primarily Pedace and Bates (2000)—we instead follow their convention of 

looking at individuals 15 and older. 

Availability of annual earnings data. One factor limiting our sample is that earnings data from the 

SER are only available on an annual basis, while the SIPP contains monthly earnings data. To 

make a comparison between the two, we construct annual earnings variables for the SIPP by 

summing over the 12 months of each year. This is only possible when there are 12 months of 

data. As a result, we do not include an individual in our analysis for a given year unless 12 

months of SIPP earnings data are available for that individual. 

There are several reasons why there may not be 12 months of data for an individual in a year. 

Some are related to the SIPP’s interview structure. An original sample of households is selected 

to participate in the SIPP, and these households are interviewed periodically at the end of 

several four month “waves.” (There was a total of 12 waves for the 1996 SIPP.) However, 

households may not be stable from wave to wave: individuals originally in a SIPP household 

may move out (for example, due to divorce or a child leaving for college) or new individuals 

may move in (for example, due to marriage or the birth of a new child). A substantial portion of 

individuals who participate in interviews at the beginning of each SIPP panel do not participate 

in some or all of later interviews, a situation known as attrition. Further, in 1996 and 1999 many 

individuals lack at least one month of data due to the SIPP's interview schedule: only two of the 

four staggered rotation groups, representing approximately 50 percent of SIPP participants, 

were first interviewed early enough that the time period they asked about (i.e., the previous 

four months) included January 1996. Similarly, for one of the four staggered rotation groups, 

 

10 Earnings data include earnings from self-employment. It is possible that there are larger discrepancies 

between the two data sources with regards to self-employment earnings than with regards to wages and 

salaries, due to differences in how the two data sets classify income types as self-employment earnings. 
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the last interview occurred in December 1999, too early to report the earnings they had received 

in that month.11

The 1996 SIPP panel includes about 116,000 individuals that were included in the survey at least 

once. On average, for each year we drop about half of these individuals from our sample 

because they do not contain earnings in at least one month of the year. (See Exhibit C.1.) 

Exhibit C.1: SIPP Participants With Less Than 12 Months of Earnings Data, By Year 

1996 1997 1998 1999 

Total individuals in SIPP 115,996 115,996 115,996 115,996 

# individuals with <12 months earning 

data 76,200 44,973 49,483 44,631 

% of individuals in SIPP with <12 months 

earning data 65.7% 38.8% 42.7% 38.5% 

Remaining individuals 39,796 71,023 66,513 71,365 

Non-match with SER. As discussed above, for many records in the SIPP, SSA was not able to 

make a successful match to the SER. This primarily occurred for two reasons. First, in many 

cases no valid Social Security Number was provided during a SIPP interview. Second, the SIPPSER match provided to us for analysis only contained a match of individuals who were present 

in SIPP households during the first interview. Thus, no SER record was available to us for 

sample members who entered households after the first interview. As shown in Exhibit C.2, 

dropping the non-matched individuals reduces the remaining sample size by between 5,700 and 

13,000 each year. 

 

11 See Bureau of the Census (2001) for more information. 
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Exhibit C.2: SIPP Participants Not Matching to SER, By Year 

 1996 1997 1998 1999 

Total individuals remaining from Exhibit 

C.1 39,796 71,023 66,513 71,635 

# individuals not matching to SER data 5,734 12,060 12,957 10,710 

% of individuals not matching to SER 

data (as percent of total individuals in the 

SIPP) 

4.9% 10.4% 11.2% 9.2% 

Remaining individuals 34,062 58,963 53,556 60,925 

Addendum: Remaining individuals as a 

percentage of total individuals in the SIPP 

(115,996) 

29.4% 50.8% 46.2% 52.5% 

There are on an average nearly 52,000 individuals matching to the SER each year, leaving 

207,506 person-year observations (i.e., repeats of individuals across the different years). These 

observations serve as the analysis sample for most of the analysis in this appendix. Of these, 

114,927 observations are of working age individuals between 18 and 65 years of age. 

D. Analysis of Recipiency Match 

The first question we address is whether SIPP and SER match in identifying earners and nonearners. Exhibit C.3 shows a cross-tabulation of working-aged (18-65) individuals identified in 

the 1996 SIPP as having or not having earnings (including self-employment earnings) in the two 

data sets. 

Exhibit C.3: Comparison of Earners and Non-Earners as Identified in the SIPP and SER 

Positive earnings in SER Zero Earnings in SER Total

Positive earnings in SIPP 75.5% 7.0% 82.5%

Zero earnings in SIPP 3.3% 14.2% 17.5%

Total 78.9% 21.1% 100.0%

Notes: 114,927 person-year observations in total. Sample includes working-age (18-

65) individuals in the 1996 SIPP panels (covering 1996-1999). Individuals are 

included for each year where 12 months of earnings data are available in the SIPP 

and where matches were made to the Summary Earning Records data set. 

In 89.7 percent of observations, the SIPP and SER match in whether their data show an 

individual as having any earnings in the year; in the other 10.3% of cases, one of the data sets 

shows the individual as having earning in a year while the other shows the individual as 

having no earnings in the same year. About two-thirds of the non-matching cases (or 7.0 percent 

of cases overall) are cases in which the SIPP shows the individual as having earnings in the year 

but the SER does not. In the other third of non-matching cases (or 3.3 percent of cases overall) 

the SER shows earnings for the individual but the SIPP does not. 
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There are several potential explanations for the mismatched cases. Where the SIPP shows 

earnings not present in the SER, some individuals worked jobs not covered by the Social 

Security system, such as some state or local government workers and some long-term federal 

government workers, domestic workers who do not get paid substantial amounts by any 

particular household, and some agricultural workers. Others likely had “off-the-books” 

earnings that were not reported to the IRS, but that they nonetheless reported to SIPP 

interviewers. Another possible cause is survey error, where respondents mistakenly reported 

earnings for themselves or a household member where no earnings actually existed. Where 

earnings were present in the SER but nothing was reported in the SIPP, the most likely 

explanation is that the individual had earnings, but these earnings were not reported during the 

SIPP interview. 

While we are unable to directly identify which of these sources were at play, data in the SIPP do 

allow us to look at the characteristics of individuals for whom the two data sources do not 

agree. Exhibit C.4 compares the demographic characteristics of working-aged adults with zero 

earnings in only one or the other data source to those with zero earnings shown in both and to 

the whole sample of working-aged adults. 

Exhibit C.4: Descriptive Demographic Statistics of Individuals With Zero Earnings Shown 

in the SER or SIPP – Working Aged Adults, 1996-1999 

Individuals with 

zero earnings in 

the SER but 

positive earnings 

in the SIPP 

Individuals with 

zero earnings in 

the SIPP but 

positive earnings 

in the SER 

Individuals 

with zero 

earnings in 

both the SIPP 

and SER Full sample 

Number of individuals in the sample 7,998 3,821 16,301 114,927 

Age 

Average age 42.3 36.2 46.3 40.0 

18-25 10.6% 38.2% 10.1% 15.5% 

26-35 18.8% 15.4% 14.7% 22.9% 

36-45 28.2% 15.9% 20.1% 27.1% 

46-55 27.7% 13.1% 21.4% 20.9% 

56-65 14.7% 17.4% 33.7% 13.6% 

Race 

White 82.8% 71.4% 80.7% 84.3% 

Black 12.8% 21.2% 14.5% 11.4% 

American Indian 1.3% 2.2% 1.3% 1.1% 

Asian Pacific Islander 3.1% 5.2% 3.5% 3.2% 

Ethnicity 

Hispanic 12.4% 15.5% 10.6% 9.5% 

Non-Hispanic 87.6% 84.5% 89.4% 90.5% 

Sex 

Female 49.9% 57.7% 68.9% 51.1% 

Male 50.1% 42.3% 31.1% 48.9% 
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Individuals with 

zero earnings in 

the SER but 

positive earnings 

in the SIPP 

Individuals with 

zero earnings in 

the SIPP but 

positive earnings 

in the SER 

Individuals 

with zero 

earnings in 

both the SIPP 

and SER Full sample 

Relationship to Reference Person 

Reference Person 48.4% 30.6% 45.0% 51.0% 

Spouse 29.6% 24.2% 36.2% 28.8% 

Child 12.0% 36.6% 12.3% 14.4% 

Grandchild * * * 0.4% 

Parent * * 1.0% 0.4% 

Sibling 1.2% * 1.1% 0.8% 

Other relative 1.0% 2.1% 1.4% 1.1% 

Foster child * * * * 

Unmarried partner 1.8% * 1.3% 1.7% 

House/roommate 1.0% * 0.6% 0.9% 

Roomer/boarder * * * * 

Other non-relative * * 0.5% 0.5% 

Type of Household 

Stable household 

Married family 66.5% 66.4% 66.1% 67.0% 

Family, male head of household 3.9% 3.5% 2.9% 3.1% 

Family, female head of household 10.3% 19.4% 14.2% 11.4% 

Non-family, male head of 

household 8.4% 3.3% 6.3% 7.0% 

Non-family, female head of 

household 6.2% 3.1% 6.4% 5.9% 

Group quarters * * * 0.2% 

Change in household type 5.4% 

Married family 1.6% * 1.4% 2.0% 

Family, male head of household * * 0.5% 0.7% 

Family, female head of household 1.0% * 1.0% 1.2% 

Non-family, male head of 

household * * * 0.9% 

Non-family, female head of 

household * * * 0.7% 

Group quarters * 0.0% * 0.0% 

* Results are suppressed when there are less than 75 observations in a cell. 

“Head of household” and “reference person” are used interchangeably to refer to 

the owner or renter on record of the household. 

As the table shows, those with zero earnings in the SIPP but with earnings in the SER are on 

average younger than the other groups shown. More than one-third fall into the 18 to 25 range 

(38.2 percent, compared to only 15.5 percent in the sample overall and about 10 to 11 percent of 

those with zero SER earnings). They are more likely to be black (21.2 percent, compared to 11.4 

percent in the overall sample), more likely to be Hispanic (15.5 percent compared to 9.5 percent 

in the overall sample), and are more likely to live in a female headed household than the other 

groups (19.4 percent lived in female-headed “stable” households compared to 11.4 percent in 
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the sample as a whole; “stable” here means that the household composition did not change over 

the course of the year). 

In comparison, the demographic characteristics of the group of people with zero earnings in the 

SER and positive earnings in the SIPP are close to the characteristics of the full sample. Three 

quarters fall within the middle age categories (74.8 percent are in the 26-55 range, compared to 

70.9 percent in the full sample, 56.2 percent of those with no earnings in either source, and 44.4 

percent of those with zero SIPP earnings but positive SER earnings). They are somewhat more 

likely than the overall sample to be Hispanic (12.4 percent versus 9.5 percent). 

Those with no earnings shown by either data source also have similar demographic 

characteristics to the sample as a whole. This group, however, is substantially more likely to be 

older – they have the highest average age, and 33.7 percent fall into the 56-65 age range, 

compared to 13.6 percent of the overall sample – and is more likely to be female (68.9 percent 

compared to 51.1 percent in the sample as a whole). 

Individuals for whom the SER showed income but where no income was reported in the SIPP 

are noticeably less likely to be the SIPP’s household reference person.12 In cases where the SER 

shows earnings but the SIPP does not, only 30.6 percent of the individuals are the reference 

person, compared to 51.0 percent in the overall sample. On the other hand, 36.6 percent of these 

individuals are children of the reference person, compared to 14.4 percent of the sample overall. 

Even though all members of the household are supposed to respond to the SIPP questionnaire 

themselves, this finding is supportive of the idea that one member of the household may be 

responding on behalf of his or her children and misreporting the children’s earnings. 

Exhibit C.5 shows income and earnings statistics for the same groups of individuals. 

 

12 The SIPP reference person is the owner or renter on record of the household. Other household members 

are classified according to their relationship to the reference person. When individuals are not available 

to complete an interview, the household reference person is most often the household member that will 

complete the interview for them. We use the terms “reference person” and “head of household” 

interchangeably in this paper. 
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Exhibit C.5: Descriptive Earnings, Income, and Industry Statistics of Individuals With 

Zero Earnings Shown in the SER or SIPP – Working Aged Adults, 1996-1999 

Individuals with 

zero earnings in 

the SER but 

positive earnings 

in the SIPP 

Individuals with 

zero earnings in 

the SIPP but 

positive earnings 

in the SER 

Individuals with 

zero earnings 

in both the 

SIPP and SER Full sample 

Number of individuals in the sample 7,998 3,821 16,301 114,927 

Job industry 

Agricultural work 2.1% * 0.9% 1.7% 

Domestics 1.8% * * 0.5% 

Government 12.2% 0.0% * 4.4% 

 

Annual income, mean (SIPP) $26,174 $4,733 $6,810 $26,046 

Nonearnings Income $2,419 $4,733 $6,810 $2,551 

Earnings $23,755 $0 $0 $23,495 

Earnings as a percent of total income 90.8% 0.0% 0.0% 90.2% 

Percent with zero earnings 0.0% 100.0% 100.0% 17.2% 

Transfer income $160 $448 $982 $216 

Percent with zero income from any source 

(SIPP) 0.0% 40.0% 16.0% 3.6% 

Annual earnings, mean (SER) $0 $5,550 $0 $20,397 

Percent with zero earnings (SER) 100.0% 0.0% 100.0% 20.7% 

Years of earnings, mean (SER) 22.6 19.6 22.1 22.9 

Total earnings, 1937 to date, mean (SER) $107,265 $163,398 $115,335 $307,052 

Average annual earnings over lifetime, mean 

across individuals in sample (SER) $4,747 $8,357 $5,230 $13,391 

Zero years of earnings (SER) 4.31% 0% 7.33% 1.31% 

Quarters covered, mean (SER) 0 2.20 0 2.97 

 

Income and Earnings (SIPP) 

Total Household Income, mean $58,136 $42,447 $39,873 $57,016 

Household Earnings $51,944 $32,787 $27,126 $50,988 

Non-Earnings Income $6,192 $9,661 $12,747 $6,028 

Earnings for receivers $51,954 $43,570 $40,811 $53,971 

Household Food Stamp Income $154 $392 $383 $151 

FS Income for receivers $1,534 $2,051 $1,721 $1,554 

Household AFDC Income $138 $371 $299 $111 

AFDC Income for receivers $3,290 $3,302 $3,302 $2,752 

Household SSI Income $209 $546 $991 $272 

Income for receivers $3,750 $4,653 $4,712 $4,286 

Percent of households with public 

assistance 13.0% 25.0% 31.4% 13.1% 

Percent of households with zero 

earnings 0.0% 24.2% 32.8% 5.4% 

Percent of months in which household 

had earned income 90.7% 68.1% 58.6% 88.2% 

Percent of households with zero income 0.0% * 0.8% 0.2% 

* Results are suppressed when there are less than 75 observations in a cell. 
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As with the demographic patterns, earnings and income patterns appear to be much closer to 

the general sample for the group with no earnings in the SER than for the group with no 

earnings in the SIPP. Exhibit C.5 shows that for individuals for whom no earnings appear in the 

SER, but for whom earnings do appear in the SIPP, the SIPP shows annual income of $26,174 on 

average, very close to the $26,046 average income for the full sample. Earnings are also similar, 

with average annual earnings of $23,495 shown by the SIPP for the entire sample and $23,755 

for those with earnings in the SIPP but not the SER. In contrast, while the SER shows average 

annual earnings of $20,397 in the overall sample for those with earnings, for those with no 

earnings shown in the SIPP the SER shows average annual earnings of only $5,550. This appears 

consistent with the generally younger age of this group.

Interestingly, the similarities between the group with no SER earnings and the overall sample 

even holds when looking at years of earnings identified in the SER; despite having no SER 

earnings in the year of the match, individuals in this group have 22.6 other years of earnings 

recorded in the SER on average, compared to 22.9 years of earnings in the SER for the sample as 

a whole. Unsurprisingly given that they are on average younger, the group with no SIPP 

earnings has fewer years of SER earnings on average (19.6). 

Exhibit C.5 also shows average non-earnings income identified in the SIPP for individuals and 

the households they reside in. Again, the SIPP earnings but not SER earnings is very close to the 

general sample, with $6,192 in non-earnings household income on average compared to $6,028 

on average in the sample as a whole. Those with no SIPP-reported earnings but with earnings in 

the SER have $9,661 in average non-earnings income reported in the SIPP. Those with no 

earnings shown in either data source have the most non-earnings income on average, $12,747. A 

similar pattern is seen among the share of households the SIPP identifies as receiving public 

assistance income. 

Data in the SIPP allows us to look at the industries in which people with earnings shown in the 

SIPP but not the SER work. There are three general industries where we might expect to see a 

number of people working in jobs not covered by the Social Security system: agriculture, 

domestic services, and government jobs. As Exhibit C.5 shows, these industries are more highly 

represented among workers with no SER earnings than among the sample as a whole. In total, 

16.1 percent of these workers work in these three broad industries, as compared to only 6.5 

percent in the sample as a whole. 

E. Comparison to Past Papers

As mentioned in the discussion of literature, several recent papers have made similar 

comparisons between the SIPP and Social Security Administrative data. Exhibit C.6 shows a 

comparison between our findings and the findings of two such papers. Pedace and Bates (2000) 

looked at a match between the 1992 SIPP and the SER. Bridges, Del Bene, and Leonesio (2002) 

perform a comparison between the 1993 SIPP panel and the DER. The biggest difference 

between the SER and the DER relevant to the analysis of earnings recipiency is that the DER 

includes earnings not covered by the Social Security system while the SER does not. 
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Both of these papers look at the population age 15 and above, instead of the working-aged 

population. As a result, we reran the comparison for this population and show the results in the 

table below. 

Exhibit C.6: Comparison of Earners and Non-Earners as Identified in the SIPP and SER 

Among Individuals 15 Years or Older; Findings from Three Papers 

Pedace and Bates (2000) 

1992 

Positive earnings in SSA data Zero earnings in SSA data Total 

Positive earnings in SIPP 61.0% 6.4% 67.4% 

Zero earnings in SIPP 3.6% 29.0% 32.6% 

Total 64.6% 35.4% 100.0% 

Bridges, Del Bene, and Leonesio (2002) 

1993 

Positive earnings in SSA data Zero earnings in SSA data Total 

Positive earnings in SIPP 65.4% 5.4% 70.7% 

Zero earnings in SIPP 3.2% 26.1.% 29.3% 

Total 68.5% 31.5% 100.0% 

Elkin and Turner (2007) 

1996-199913

Positive earnings in SSA data Zero earnings in SSA data Total 

Positive earnings in SIPP 63.4% 6.4% 69.8% 

Zero earnings in SIPP 3.8% 26.4% 30.3% 

Total 67.1% 32.9% 100.0% 

The table shows that the three studies are quite consistent. Pedace and Bates found 

disagreement in the identification of earnings between the two sources in 10.0 percent of cases, 

while the current study finds disagreement in 10.2 percent of cases. A conclusion is that we do 

not find any evidence that changes in SIPP methodology in 1996 panel notably improved 

identification of earnings recipients in the SIPP. 

Bridges, Del Bene, and Leonesio found discrepancies between the two data sources in 8.6 

percent of cases. The lower number may be largely due to the DER’s inclusion of non-covered 

jobs. Indeed, most of the difference occurs in the category where the SIPP showed income but 

the SSA data did not; Bridges, Del Bene, and Leonesio’s figure is one percentage point lower 

than that found in Pedace and Bates and our own analysis. It is interesting to note that this one 

percentage point difference is consistent with our industry analysis above. We found that 16.1 

percent of working-aged people with earnings in the SIPP but not the SER work in industries 

 

13 By combining the four years of data, our figures essentially represent a (weighted) average over the 4 

years. Background data shows that there is a small amount of variance across years – e.g., the portion of 

the sample with earnings in the SIPP but not the SER ranges from 6.1 percent in 1998 to 7.0 percent in 

1996 – but the overall pattern is relatively consistent. 
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with a disproportionate number of uncovered jobs (agriculture, domestic services, or the 

government sector). If we assume the same proportion holds for the 6.4 percent of those over 

age 15 identified in the same category in Exhibit C.6, and we further assume that for people 

with no earnings shown in the SER all jobs in these industries were indeed uncovered, that 

would account for one percentage point, leaving 5.4 percentage points unaccounted for – the 

same percentage Bridges, Del Bene, and Leonesio found. This is not to say that all such jobs are 

uncovered, but rather that our findings from the SER are consistent with the implication from 

Bridges, Del Bene, and Leonesio’s findings that about 16 percent (one-sixth) of cases where the 

SIPP shows earnings but the SER does not are due to uncovered jobs. 

F. Analysis of Differences in Earnings Measures 

Exhibit C.7 shows the distribution of earnings amounts among the analysis population as 

measured in the two data sources. For each data source, only individuals with earnings of 

$60,000 or less are included in the distribution shown in the chart. (Given that the chart includes 

only these individuals, the percentage of individuals with zero earnings may not match what 

was shown in earlier table.) 

Exhibit C.7: Distribution of Individuals By Earnings Reported in the SIPP and the SER 

(Individuals with less than $60,000) 

The pattern evident from Exhibit C.7 is that a higher percentage of SER cases have no earnings 

or earnings less than $9,000, while a higher percentage of SIPP fall into the range of roughly 

0%

5%

10%

15%

20%

25%

$0 

$1 to $3,000

$3,001 to $6,000

$6,001 to $9,000

$9,001 to $12,000

$12,001 to $15,000

$15,001 to $18,000

$18,001 to $21,000

$21,001 to $24,000

$24,001 to $27,000

$27,001 to $30,000

$30,001 to $33,000

$33,001 to $36,000

$36,001 to $39,000

$39,001 to $42,000

$42,001 to $45,000

$45,001 to $48,000

$48,001 to $51,000

$51,001 to $54,000

$54,001 to $57,000

$57,001 to $60,000

Earnings Range

Percentage of Individuals Falling Into Earnings Range

SIPP

SER
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$9,000 to $36,000 range.14 Above that range, the two are roughly similar, not differing by more 

than 0.3 percentage points in either direction.15 In other words, the SER shows more individuals 

at very low annual earning levels, while the SIPP shows more individuals with middle-range 

(and lower-middle range) earnings. This is consistent with prior research that found that among 

lower-end earners the SIPP showed more earnings than the SER. 

Exhibit C.8 looks at discrepancies between earnings reported in the SER and earnings reported 

in the SIPP. Only individuals for whom both the SER and SIPP show earnings are included. It 

follows a table from Pedace and Bates, who assumed that the SER administrative data 

represented truth, and used ranges defined by earnings as measured by the SER. The left-hand 

columns show that there is a high frequency of substantial discrepancies. In only 62 percent of 

cases overall is the SIPP earnings amount within 25 percent of the SER earnings amount, and in 

only 21 percent of cases is it within five percent of the SER earnings amount. In general, 

accuracy increases as SER earnings rise; in about three-quarters of cases between $20,000 and 

$50,000 in SER earnings, the SIPP value is within 25 percent of the SER value, and in close to 

half of the cases in this range it is within 10 percent. (The top category, showing SER earnings 

above $50,000 is most likely anomalous because of upper limits on covered earnings in the SER 

and top-coding in the SIPP.) These findings generally mirror Pedace and Bates’s although we 

find somewhat smaller frequencies of the larger discrepancies. (For example, in the $15,000 to 

$20,000 range, we find 43 percent of cases where earnings are within ten percent of each other 

and 72 percent where they are within 25 percent of each other; Pedace and Bates found 48 

percent and 78 percent respectively.) This may suggests a small increase in accuracy of the 

earnings amounts in the 1996 SIPP panel as compared to the 1992 previous panel. 

The right-hand columns, showing means and medians, provide a sense of the directionality of 

misreporting. The positive medians show that individuals with up to $15,000 in SER earnings 

more often report earnings in the SIPP that are higher than what is shown in the SER, but that 

pattern flips above $15,000 where individuals more often report lower earnings in the SIPP than 

in the SER. However, in all categories up to $45,000, the means are greater than the medians, 

showing that on the whole the discrepancies tend to be bigger in cases where SIPP earnings are 

higher than SER earnings. The differences between means and medians are particularly stark in 

the lower earnings ranges, which may reflect earnings in jobs not covered by Social Security 

and/or “off-the-books” jobs. These findings are somewhat different from Pedace and Bates; the 

averages we found were greater in all categories; and the absolute value of the median 

discrepancies they found were in most categories greater than the absolute values of the 

discrepancies we found. 

 

14 As found earlier, there are more cases with zero earnings shown in the SER than cases with zero 

earnings shown in the SIPP. Removing the zero earning cases from the distribution emphasizes more 

strongly the pattern that a higher share of cases in the SER show earnings less than $9,000, and a higher 

share of cases in the SIPP show earnings between $9,000 and $36,000. 

15 The pattern does not change notably if the individuals with zero earnings in each data source are 

omitted. 
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Exhibit C.8: Distribution of Discrepancies, SER as Base (Individuals 15 Years Old and 

Above) 

Percent within: Difference (SIPP-SER) 

5% of SER 10% of SER 25% of SER Mean Median N 

SER EARNINGS CATEGORY 

$1 to >$4,999 7.75% 14.23% 30.16% 4,799 579 14,524 

$5,000 to >$9,999 13.63% 24.46% 48.52% 3,404 375 10,634 

$10,000 to >$14,999 20.87% 36.90% 64.50% 2,309 68 9,704 

$15,000 to >$19,999 24.44% 43.08% 72.02% 1,466 -336 9,438 

$20,000 to >$24,999 26.60% 45.14% 74.17% 673 -708 8,668 

$25,000 to >$29,999 27.67% 47.13% 76.83% -201 -1,167 7,359 

$30,000 to >$34,999 28.06% 48.12% 77.70% -1,264 -1,813 6,421 

$35,000 to >$39,999 27.59% 47.54% 78.20% -2,259 -2,399 5,164 

$40,000 to >$44,999 27.80% 47.82% 76.61% -2,306 -2,625 4,015 

$45,000 to >$49,999 28.07% 47.64% 77.56% -3,837 -3,628 3,142 

$50,000 and above 19.31% 33.09% 61.36% 6,921 -2,375 12,985 

Total sample 20.62% 35.80% 62.12% 2,121 -258 92,054 

Given the findings of Abowd and Stinson (2003), whose model suggested that earnings data 

contained within SSA data contains at least as much error as the SIPP’s, we also present the 

same distribution table using the SIPP instead of the SER as the base (Exhibit C.9). The left-hand 

columns, showing the distribution of the frequency of discrepancies of various sizes, show 

essentially the same pattern. For most earnings categories, and for each threshold, the 

frequencies are slightly higher when the SER is the base, possibly partially reflecting higher 

average SIPP earning amounts, which if distributed broadly would provide a larger base 

against which percent differences are measured. On the other hand, the right-hand columns 

differ substantially. Median discrepancies are positive for all income categories (again, ignoring 

the anomalous top category). Mean discrepancies are also positive for all categories up to 

$45,000, but for the $45,000 to $50,000 category is slightly negative. Mean discrepancies are 

generally smaller than was found using the SER as the base. Median discrepancies are also 

smaller at the higher earnings categories. 
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Exhibit C.9: Distribution of Discrepancies, SIPP as Base (Individuals 15 Years Old and 

Above) 

Percent within:

Difference (SERSIPP) 

5% of SIPP 10% of SIPP 25% of SIPP Mean Median N 

SIPP EARNINGS CATEGORY 

$1 to >$4,999 9.45% 17.33% 36.35% 1,575 215 12,011 

$5,000 to >$9,999 13.95% 24.94% 49.98% 1,150 95 10,351 

$10,000 to >$14,999 18.69% 33.06% 59.45% 957 229 10,923 

$15,000 to >$19,999 21.68% 38.05% 64.45% 1,109 543 10,569 

$20,000 to >$24,999 24.02% 41.01% 67.97% 1,260 743 9,610 

$25,000 to >$29,999 24.41% 41.47% 69.14% 1,312 961 8,121 

$30,000 to >$34,999 26.60% 44.54% 70.95% 786 926 6,782 

$35,000 to >$39,999 27.53% 45.69% 70.83% 484 989 5,176 

$40,000 to >$44,999 26.80% 45.09% 72.18% 251 1,222 4,127 

$45,000 to >$49,999 28.70% 46.58% 70.87% -408 927 3,059 

$50,000 and above 21.62% 36.15% 63.36% -24,605 -6,263 11,325 

Total sample 20.49% 35.17% 60.61% -2,121 258 92,054 

A few observations stand out when comparing this table to the previous one. First, in the lowest 

earnings categories ($0 to $5,000 and $5,000 to $10,000) differences are a little more frequent 

using the SIPP as the base, but in the rest of the categories the opposite is true; differences are 

more frequent when categorized by SER earnings. In particular, above $10,000 discrepancies of 

more than 25 percent are more common by between 4 and 8 percentage points when 

categorized by SER earnings. Differences are bigger in absolute value among most categories of 

earnings when the SER is used as the base than when the SIPP is used as the base. Further, 

when the SER is used as the base, differences exhibit a striking declining pattern in both mean 

and median differences. When SIPP is used as a base, there is less variance among the mean and 

median differences, which do not exhibit a clear trend. This suggests errors are more randomly 

distributed when SIPP earnings are used as the base, though consistently positive by a small 

amount. 

G. Conclusion 

The findings of this paper are largely consistent with much of the previous literature. Findings 

from our analysis of the match in recipiency between the SIPP and the Social Security 

administrative data are very close to those in Pedace and Bates (2000) and Bridges, Del Bene, 

and Leonesio (2002), with all three papers showing the two data sources to match on earnings 

recipiency in about 90 percent of cases, and showing that cases where an individual has 

earnings in the SIPP but not the SER are more common than the opposite. For individuals with 

earnings shown in both the SIPP and SER, we found that – at least when using the 

administrative data as the base – the SIPP shows higher earning amounts for the lower end of 

earners than the administrative data does; this too is consistent with many prior studies, but is 

not as evident when the survey data are used as the base. 

With regards to the analysis of recipiency, two types of mismatch warrant explanation. First, 

among cases where individuals reported earnings in the SIPP but did not have earnings in the 

SER, Pedace and Bates had suggested that “this type of recipiency error involves a systematic 
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failure to capture earnings on the SER.” Our findings suggest that this is still the case. Out of the 

6.4 percent of cases that fall into this category, about one percentage point appears to be in 

uncovered jobs, and more accurate data may be found in the DER. Other possible sources of 

mismatch include misreporting by SIPP respondents, errors in the SER data set (such as failure 

on the part of the employer to report earnings for some employees), and “off-the-books” 

earnings captured by the SIPP but not the SER. It is also possible that some SIPP respondents 

provided incorrect SSNs, either to SIPP interviewers or to employers, leading to an incorrect 

match. Overall, this group with earnings in the SIPP but not the SER does not differ noticeably 

in its demographic or income characteristics from the SIPP population as a whole. One 

interesting finding that may warrant further investigation is that the number of years of 

earnings contained in the SIPP for this group does not notably differ from the general 

population, suggesting that whatever factors led to the individual to have no earnings in the 

SER did not pertain to previous years. 

The group with positive SER earnings and no SIPP earnings, on the other hand, differs from the 

general SIPP population, most notably in that they are younger and less likely to be the 

reference person in a household. Earnings are also substantially lower among this group. These 

factors suggest some of this type of mismatch is attributable to a group that has earnings that 

were reported to Social Security, but who were not directly interviewed for the SIPP. Instead, 

the reference persons in their households may have responded on their behalf and misreported 

that they had no earnings or employment. In addition, some SIPP respondents may omit shortterm jobs when responding to the survey, which would show up as low earnings amounts on 

the SER. This group’s lower earnings and higher public assistance recipiency rates may also 

support the idea that misreporting on the SIPP may be more common among lower-income 

households. 

Some of the same factors leading to mismatches in identifying earnings recipiency may also be 

at play among individuals with earnings shown in both data sources. For example, individuals 

working in uncovered jobs for part of the year and covered jobs for the rest of the year would 

show up as having lower earnings in the SER than in the SIPP. This can account for some of the 

cases where the SIPP shows higher earnings than the SER. Overall, we found that people report 

lower earnings on the SIPP than are shown in the SER. Categorizing individuals into earnings 

categories on the basis of SIPP data did not confirm the finding that individuals at the low end 

of the earnings spectrum report more on the SIPP than on the SER; errors appear more 

randomly distributed when the SIPP earnings categorization is used. 

Given limitations on our access to the data, we were not able to look more closely at the sources 

of discrepancies. Future research, preferably using the DER instead of the SER, could look 

separately at how characteristics differ between cases where earnings in the Social Security data 

are higher and cases where earnings in the SIPP data are higher, broken out by different 

earnings categories. Such analysis could be run twice – once using SIPP earnings as the basis of 

the earnings categories, and once using the Social Security earnings data as the basis. If different 

patterns emerge, it may be informative as to whether discrepancies are more likely attributable 

to the survey or administrative data. 
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2007. 

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