United States Food and 3101 Park Center Drive 

Department of Nutrition Room 1014 

Agriculture Service Alexandria, VA 22302 

Decision Demographics

4312 North 39th St

Arlington, VA 22207-4606

(703) 931-9200

Stephen Tordella

*Mathematica Policy Research

USDA Food and Nutrition Service

Family Programs Analysis Branch

Office of Research and Analysis

3101 Park Center Drive, Room 1014

Alexandria, VA 22302

Jenny Laster Genser

This study was conducted under contract number AG-3198-C-10-0010 with the Food and Nutrition 

Service (FNS). The report is available at http://www.fns.usda.gov/fns/research.htm. 

Suggested citation: U.S. Department of Agriculture, Food and Nutrition Service, Office of Research and Analysis, 

Dynamics of Supplemental Nutrition Assistance Program Participation in the Mid-2000s, by James Mabli, et al., 

Project Officer: Jenny Laster Genser, Alexandria, VA: 2011
This report was prepared by Stephen Tordella, Thomas Godfrey, and Priscilla Foran of 

Decision Demographics and James Mabli and Laura Castner of Mathematica Policy Research, 

with additional assistance from John Czajka of Mathematica and Robert Cohen of Decision 

Demographics, for the U.S. Department of Agriculture’s Food and Nutrition Service, Office of 

Research, Nutrition, and Analysis. The authors thank Jenny Genser, Melissa Abelev, Bob 

Dalrymple, Christine Kissmer, Kathryn Law, and Carol Olander of the Food and Nutrition 

Service for providing guidance and reviewing the report. Judy Eargle, Daniel Doyle, Erik 

Cichanowsky, Patrick Benton, and Charles Nelson of the US Census Bureau provided invaluable 

assistance to the project, supporting the acquisition and interpretation of published and 

unpublished 2004 SIPP data.

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and 

activities on the basis of race, color, national origin, age, disability, and where applicable, sex, 

marital status, familial status, parental status, religion, sexual orientation, genetic information, 

political beliefs, reprisal, or because all or part of an individual’s income is derived from any 

public assistance program. (Not all prohibited bases apply to all programs.) Persons with 

disabilities who require alternative means for communications of program information (Braille, 

large point, audiotape, etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice 

and TDD).

To file a complaint of discrimination, write USDA, Director, Office of Adjudication, 1400 

Independence Avenue, SW, Washington, D.C. 20250-9410 or call toll free (866) 632-9992 

(Voice). Individuals who are hearing impaired or have speech disabilities may contact USDA 

through the Federal Relay Service at (800) 877-8339; or (800) 845-6136 (Spanish). USDA is an 

equal opportunity provider and employer.
Table of Contents

Executive Summary i

I. INTRODUCTION 1

A. Background on SNAP 3

B. Previous Research on Dynamics 7

C. Research Objectives 15

D. Data 17

E. Methodological Approach 29

II. DESCRIPTIVE ANALYSES 33

A. Entry into SNAP 35

B. Length of SNAP Participation Spells 65

C. Exiting SNAP 88

D. Re-entry into SNAP 98

E. Summary Measures of SNAP Participation 106

III. SUBGROUP ANALYSES 127

A. SNAP Dynamics Data on Subgroups 128

B. Subgroup Definitions 131

C. Point at which Subgroup Membership is Determined 132

D. Subgroups Trends through Time 134

E. Subgroup Dynamics and their Influence on the Makeup 

of the Caseload 164

IV. SUMMARY AND RECOMMENDATIONS 

FOR FUTURE RESEARCH 169

A. Summary of Key Findings 169

B. Recommendations for Future Research 175

References 181

Appendix A: Data Assessment Memos 

Appendix B: Subgroup Definitions in SNAP Dynamics Research 
This page is intentionally blank for printing purposes. 
List of Figures and Tables

Executive Summary

Figure 1 Average Monthly Entry Rates Among Non-Participating Individuals with Income Under 300 

Percent of Poverty at Some Point In Panel Period, 2004–2006

iv

Figure 2 Comparison of Length of SNAP Participation Spells among Entrants and a Cross-Section of 

Participants

vi

Figure 3 Percentages Re-Entering SNAP, Comparisons over Time vii

Figure 4 Total Time Participants Spent on SNAP During 32-Month Panel viii

Figure 5 Trends in Poverty, the SNAP Caseload, and the Number of Unemployed Individuals, 1990-

2010

x

Table 1 Cumulative Rate of Snap Re-entry within the Panel Period vii

Table 2 Comparison of Primary Measures of SNAP Participation Dynamics ix

Table 3 Changes in Caseload, Unemployment, and Dynamics Patterns over Time xi

Chapter I: Introduction

Figure I.1 Trends in Poverty, the SNAP Caseload, and the Number of Unemployed Individuals, 1990-

2010

7

Figure I.2 Periods of SNAP Caseload Cycle 14

Table I.1 Comparison of Previous Study Time Frames, Data, and Study Objectives with Current Study 13

Table I.2 Summary of the 2004 SIPP Panel 18

Table I.3 Design of the 2004 SIPP Panel 20

Table I.4 Unweighted Count of Persons Below 300% of Poverty by Receipt of SNAP 21

Chapter II: Descriptive Analysis Tables

Figure II.1 Average Monthly Entry Rates, by Year 42

Figure II.2 Average Monthly Replacement Rates, by Year 44

Figure II.3 Percentage of At-Risk Group Experiencing Event, Comparison over Time 63

Figure II.4 Percentage of At-Risk Group who Entered SNAP within Four Months of Experiencing Event, 

Comparison over Time

64

Figure II.5 Percentage of SNAP Entrants who Entered SNAP within Four Months of Experiencing 

Event, Comparison over Time

64

Figure II.6 Median Length of SNAP Participation in Entry Cohort Sample, Comparison over Time 69

Figure II.7 Cumulative Exit Rates for Cross-Sectional Sample, Comparison over Time 83

Figure II.8 Percentage of SNAP Participants Experiencing Event, Comparison over Time 92

Figure II.9 Percentage of SNAP Participants who Exited SNAP within Four Months of Experiencing 

Event, Comparison over Time

93

Figure II.10 Percentages Re-entering SNAP, Comparisons over Time 101

Figure II.11 Total Time on SNAP for Individuals Participating in the Panel Period, Comparisons over 

Time (Median Number of Months)

110

Figure II.12 Characterizing the Length and Frequency of SNAP Participant Spells, Comparisons over 

Time

117

Figure II.13 Changes in the SNAP Turnover Rate over Time 120

Figure II.14 SNAP Caseload, March 2004 to May 2006 121
Figure II.15 SNAP Caseload, March 2004 to May 2006, Excluding Disaster Benefits 122

Table II.1 SNAP Entry Rates by At-Risk Population (Percent) 40

Table II.2 Monthly Entry Rate Comparison over Time (Percent) 41

Table II.3 Average Monthly SNAP Entry Rates and Replacement Rates, by Year 43

Table II.4 Distribution of the Characteristics of the At-Risk Populations and SNAP Entrants 46

Table II.5 Monthly, Wave-Based, and Annual Entry Rates by Individuals in Subgroups 49

Table II.6 Initial Adult SNAP Entry Rate by Age 52

Table II.7 Occurrence of SNAP Entry Trigger Events and Rate of Entry Following Trigger Event 55

Table II.8 Occurrence of SNAP Entry Trigger Events, Mutually Exclusive Categories 58

Table II.9 Overlap in Trigger Events 59

Table II.10 SNAP Monthly Entry Rates Among At-Risk Individuals Experiencing a Trigger Event, by 

Degree of Deviation from Usual Circumstances

61

Table II.11 Life Table of Participation Spells 68

Table II.12 Length of SNAP Spells by Subgroups 71

Table II.13 Life Table of Subsequent Spell Length for the Full Cross-Sectional Sample 76

Table II.14 Subsequent Spell Length for the Full Cross-Sectional Sample by Subgroups 78

Table II.15 Life Table of the Completed Length of SNAP Spells for the Full Cross-Sectional Sample 81

Table II.16 Comparison of Spell Lengths and Exit Rates for Completed Spells Using Cross-Sectional 

Samples

82

Table II.17 Completed Length of SNAP Spells for the Full Cross-Sectional Sample by Subgroups 84

Table II.18 Occurrence of SNAP Exit Trigger Events 91

Table II.19 Occurrence of SNAP Exit Events by Subgroups 95

Table II.20 Life Tables of Spells Off SNAP (Re-Entry Rates) 100

Table II.21 Re-Entry Rates into SNAP, by Subgroups 103

Table II.22 Total Time on SNAP for Individuals Participating in the Panel Period (Percent) 108

Table II.23 Total Time on SNAP for Individuals Participating in the Panel Period (Percent), by Subgroup 112

Table II.24 Characterization of SNAP Participants (Percent) 116

Table II.25 SNAP Turnover Rate 119

Table II.26 Average Monthly SIPP-Based Growth, Replacement, and Exit Rates, 2004 through 2006 124

Table II.27 Average Monthly SIPP-Based Growth, Replacement, and Exit Rates, 2004 through 2006, by 

Subgroup

126

Chapter III: Subgroup Analysis

Table III.1 SNAP Dynamics Studies with Subgroup Data: Time Frames, Data, and Study Objectives 129

Table III.2 Historical Availability of SNAP Dynamics Subgroup Data 130

Table III.3 Varying Definitions of Certain Populations Over Time 132

Table III.4 Subgroup Dynamics In Comparison to Total Population Dynamics 134

Table III.5 Historic Subgroup SNAP Dynamics Data (Tables III.5.1 – 5.10) 135-163

Table III.6 How Subgroup Dynamics Affect the Overall Caseload 165

Chapter IV: Summary and Recommendations for Future Research

Table IV.1 Comparison of Primary Measures of SNAP Participation Dynamics 171

Table IV.2 Comparison of SNAP Participation Dynamics across Selected Subgroups 174
i

EXECUTIVE SUMMARY 

The Supplemental Nutrition Assistance Program (SNAP) is the cornerstone of America’s 

food assistance policy. In fiscal year 2010, an average of over 40 million individuals received 

SNAP benefits each month.1

 The program caseload is not static; each month, new individuals 

enter the program while some participants exit. 

Several factors can lead an individual to enter SNAP. Some individuals may enroll as a result 

of a change in personal financial circumstances. Others who are eligible but do not enroll, may 

later apply for benefits because they recently learned about either the program or their likely 

eligibility. In particular, as state computer systems become better integrated and more advanced, 

individuals may find out they are eligible for a range of services, including SNAP, when they 

had planned to apply for something else. Decisions to apply are also affected by an individual’s 

previous experience with SNAP and their expectations about how long this eligibility period may 

last. 

Once an individual enrolls, several circumstances can affect the length of a participation 

spell. A loss of eligibility, for instance, triggers program exit. In general, SNAP requires 

recipients to report changes in income that make them ineligible. In addition, they must 

periodically report their income and be recertified for eligibility. Some studies—in certain states 

and subgroups—have found spikes in exits that appear to occur at recertification (Ribar, 

Edolhoch, and Liu 2008, Ribar, Edolhoch, and Liu 2009). Other factors that may prompt a 

program exit include failure to comply with program rules, certain life events (moving into group 

quarters, for example), or simply a preference to stop participating. 

Patterns of entry into and exit from SNAP drive caseload patterns. The number of 

participants has increased dramatically since the early 2000s, from an average monthly caseload 

of 17 million in 2000 to more than 40 million in 2010. Since mid-2008, the number of 

participants has set a new record each month, reaching 44.6 million in April 2011. 

The purpose of this study is to investigate SNAP caseload dynamics to understand what 

drives changes in SNAP participation over time. Understanding participation dynamics is critical 

to developing effective SNAP policies. Well-designed studies, for example, can inform 

policymakers about what factors lead individuals to enter and exit SNAP; how long they 

typically participate; and how their participation decisions are affected by changes in individual 

circumstances, overall economic conditions, and program policies. This type of study can also 

help policymakers understand what appear to be anomalies in participation and what happens to 

caseloads during times of different economic and policy environments. 

The current study explores the following research questions on the dynamics of participation 

in SNAP: 

 

1

 Based on data from SNAP Program Operations division [http://www.fns.usda.gov/pd/34SNAPmonthly.htm] 

accessed on August 22, 2011. 
ii

1. What factors lead individuals to enter SNAP? 

2. How long do individuals tend to participate? 

3. What factors lead individuals to exit? 

4. How frequent is program re-entry? 

5. How much do individuals rely on SNAP over time? 

6. How do participation patterns vary by subgroup? 

7. How have participation patterns changed across the time periods covered by previous 

studies of SNAP dynamics? 

In this study, we find that many of the answers to these questions fit easily with what one 

might hypothesize: decreases in income lead people to enter; increases in income lead people to 

exit; and poorer people enter more often and participate for longer. Other findings may not be as 

intuitive, for example entry rates for elderly individuals are among the lowest across all 

subgroups. In addition, fewer than 10 percent of those not currently receiving SNAP benefits 

have received them in the past, but almost half of those who enter have received them at an 

earlier time. Still other findings add to our understanding of phenomena that have been observed: 

for example, in 2004 to 2006, while the economy was improving, the SNAP caseload continued 

to grow. The current study shows that more people entered the program than in the early 2000s 

and people participated longer, with fewer experiencing exit triggers such as increases in 

earnings. 

The current report, then, confirms some of what we know, answers some of what we did not 

know, and provides us with more questions to address in the future. It also, importantly, provides 

details about how long participants remain in the program, how long they stay off SNAP before 

re-entering, how often people re-enter the program, and how much these patterns differ across 

different subgroups. 

The data source for the study is the 2004 Panel of the Survey of Income and Program 

Participation (SIPP), a nationally representative, short-term longitudinal survey that collects 

detailed information on monthly labor force activity, earned income; unearned income, such as 

Social Security and pension payments; cash and non-cash public assistance, and family and 

household composition. It began with approximately 51,000 households that were interviewed 

every four months over a four-year period from 2004 through 2007. The 2004 SIPP panel is 

therefore a very useful data set with which to study SNAP dynamics, including program entry, 

exit, and re-entry. It also enables us to explore how the dynamics of a changing caseload vary by 

individual and family demographic and economic characteristics, as well as how they coincide 

with participants’ changes in employment, income, or family composition. 

 
iii

SNAP Entry 

On average, between 2004 and mid-2006 (the years of the SIPP panel included in this study), 

5 out of every 1,000 individuals in low-income families2 who were not receiving SNAP benefits 

in one month were found to be participating in the next month. This rate represents a slight 

increase over the early 2000s, when 4 out of every 1,000 individuals in low-income families who 

had not been participating entered in the following month. 

The likelihood of entry differed according to the individual’s past and current circumstances. 

For example, for those with an income under 300 percent of poverty at some point in the panel 

period, about 22 of every 1,000 nonparticipants who had not received SNAP benefits in the past 

entered in the next year. By contrast, about 130 of every 1,000 of those who had received 

benefits in the past entered in the next year. Comparing those who entered SNAP with 

nonparticipants who had an income under 300 percent of poverty at some point in the panel 

period also shows the importance of past benefit receipt to program entry. About 47 percent of 

individuals who entered SNAP in this period had previously received benefits, but only 12 

percent of those at risk of entering were previous recipients. 

Family composition and earnings of family members also appear to affect the decision to 

enter SNAP. During the panel period, about three-fourths of entrants were in families with 

children, while only 55 percent of low-income nonparticipants were in families with children. 

About two-thirds of entrants had earnings, relative to almost 80 percent of low-income 

nonparticipants, and only 7 percent of entrants were elderly, compared with 18 percent of lowincome nonparticipants. 

Figure 1 illustrates the difference in the monthly entry rate for several subgroups. That rate is 

the average across 2004 to 2006 of the percentage of individuals with income under 300 percent 

of poverty who were not participating in a given month, yet participated in the following month. 

 

 

2

 Family income under 300 percent of poverty in one or more months during the panel period. 
iv

Figure 1 Average Monthly Entry Rates among Non-Participating Individuals with Income under 

300 Percent of Poverty at Some Point in Panel Period, 2004–2006 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Note: If individuals enter multiple times during the panel period, estimates include each entry 

The most common events that trigger entry into SNAP are related to a drop in family income. 

Among those who entered SNAP in the panel period, 39 percent experienced a decrease in 

family earnings of at least 10 percent in the previous four months; 26 percent experienced at least 

a 10 percent loss in other family income, aside from earnings and income from Temporary 

Assistance to Needy Families. 

Replacement Rates 

Whereas the entry rate measures the number of entrants in relation to the number of lowincome people not participating in SNAP, the replacement rate measures the number of entrants 

in relation to the size of the caseload. The replacement rate is defined as the number of new 

entrants in a given month divided by the number of participants in the previous month’s 

caseload. It is a useful measure for capturing the extent to which the caseload changes from 

month to month. The average monthly replacement rate for 2004 to 2006 was 4.1 percent, 

decreasing from 4.5 percent to 3.7 percent over the course of the study period. The 2004 to 2006 

rates are lower than the rate of 5.4 percent from the early 2000s—a similar number of individuals 

entered over the two time periods, but the size of the caseload was much higher in the mid-2000s 

than it was in the early 2000s. 

Length of SNAP Participation Spells 

More than half of the individuals (58 percent) who entered SNAP during the panel period 

exited the program within one year (see Figure 2). The median spell of participation among new 

0.2

1.7

0.7

0.3

0.4

0.8 0.8

0.5

0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Has not

received

benefits

(18+)

Has

received

benefits

(18+)

In

families

with

children

In

families

without

children

In

families

with

earnings

In

families

without

earnings

Children Nonelderly

adults

Elderly

Percent

All

Individuals
v

entrants lasted about 10 months. This is about 1 month longer than the median spell length 

among new entrants in the early 1990s and about 2 months longer than observed in the early 

2000s. 

Using another way to understand length of spells on SNAP, we derived the spell length of a 

cross-section of individuals participating at a given point in time. We created a cross-sectional 

sample of those who were on SNAP in May 2004, an early point in the panel period, and 

calculated how long participants spent in the program before and after that month. Of this crosssection of participants, close to one-fifth (17 percent) had a spell on SNAP that lasted one year or 

less; more than one-quarter (29 percent) had a spell that lasted two years or less. It takes five 

more years, though, for another quarter of the May 2004 participants to exit the program. In 

other words, half of the cross-section has a spell that lasts seven years or less. This duration, 

however, is much longer than the median spell seen in the mid-1990s through the early 2000s, 

which ranged from 4 to 4.5 years, and is closer to the median spell of the early 1990s of more 

than eight years. 

As with entry rates, spell length varies for individuals with different characteristics. We 

found that new-entrant adults in families without any children, elderly, or disabled members 

have the shortest spells on SNAP (many of these individuals are subject to time limits on their 

SNAP participation). New-entrant elderly individuals with no other family members had the 

longest. This pattern differs markedly from the entry rate patterns, in which elderly individuals 

are the least likely to enter. New entrants in families without earnings, families with no high 

school graduate, and families with Supplemental Security Income all had median spell lengths 

that were longer than average. 

 
vi

Figure 2 Comparison of Cumulative Spell Lengths of SNAP Participation Spells among Entrants 

and a Cross-Section of Participants 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

SNAP Exit 

The most common of the events identified as possible exit triggers is an increase in family 

income—almost two-thirds of participants experienced an increase in family earnings or other 

income. Almost one-quarter of those who experienced an increase in earnings left within four 

months of the increase. Other triggers we examined did not occur as often as the income 

increase, but they were associated with a similar percentage of participants exiting within four 

months. For example, examining change in family composition, we found that for 40 to 50 

percent of participants, a family member (either with or without income) left the household. In 

close to one-quarter of these cases, the participant also left SNAP within four months. 

Of those exiting SNAP, 25 percent did not experience a trigger event related to improved 

financial circumstances or reduced need, as measured by changes in income and family 

composition. However, 75 percent of SNAP exiters experienced at least one trigger event within 

the four-month window, with 43 percent experiencing multiple events. 

The decision to exit SNAP differs for people in different demographic or economic 

circumstances as measured at the start of their spell on the program. Of SNAP participants in 

families with children that experience an increase in earnings, 23 percent exit the program within 

four months. By contrast, 34 percent of participants in families without children who 

experienced such an increase exit within that time. Among those with income under 50 percent 

of poverty at the start of their participation spell, 18 percent exited within four months of 

experiencing an increase in earnings, compared with 30 percent for those who enter SNAP with 

incomes from 100 to 200 percent of poverty. 

38

58

74

8

17

29

0

10

20

30

40

50

60

70

80

90

100

Six months or less One year or less Two years or less

Percentage of Individuals by Spell Lengtn

SNAP Entrants, 2004-2006 Cross-Section of SNAP

Recipients (May 2004)
vii

SNAP Re-entry 

Re-entry is an important aspect of SNAP dynamics. More than half of SNAP participants 

who exited the program in the panel period re-entered within two years. Forty-two percent reentered within one year of exiting, and another 11 percent re-entered within two years of exiting 

(see Table 1). The re-entry rates for 2004-2006 are very similar to those found in the early 1990s, 

but lower than those found in the early 2000s (see Figure 3). 

Table 1 Cumulative Rate of SNAP Re-entry within the Panel Period 

Re-entering SNAP within Panel Period 

Cumulative 

Percent 

Within 6 Months 26 

Within 12 Months 42 

Within 18 Months 48 

Within 24 Months 53 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Figure 3 Percentages Re-entering SNAP, Comparisons over Time 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel; 

 Cody et al. (2007); Gleason et al. (1998) 

42

45

42

11 10 11

0

5

10

15

20

25

30

35

40

45

50

1991-1992 2001-2003 2004-2006

Percentage

Re-entering

 during First Year

Percentage

Re-entering

 during Second Year
viii

Total Time on SNAP during the Panel Period 

Total time on SNAP during the panel period is simply the number of the 32 months in the 

sample that a person receives SNAP benefits. Of the individuals on the panel who received 

SNAP benefits during the panel, about 30 percent were in the program for a total of 8 months or 

less, and 28 percent participated for the entire panel (see Figure 4). The median total time was 18 

months (or 56 percent of the possible 32 months). This finding suggests that individuals depend 

more heavily on SNAP than is indicated by the duration of new spells (median length was 10 

months). 

Figure 4 Total Time Participants Spent on SNAP during 32-Month Panel 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel

SNAP Turnover 

The turnover rate measures the size of the population that receives SNAP benefits at some 

point in the year in relation to the average size of the caseload that year. We estimate the average 

annual turnover rate from 2004 to 2006 as 1.4. Thus, caseworkers whose workload reached 500 

participants in a single month served an average of 700 different participants over the course of 

the year. This suggests that there is only a modest amount of turnover in SNAP participants over 

the course of a year. Even though the average monthly number of individuals receiving benefits 

increased each year from 2004 to 2006, the number of individuals receiving benefits for at least 

one month of the year also increased, producing a constant turnover rate over the period. 

1 to 8 months

30%

9 to 16 months

19%

17 to 24 

months

15%

25 to 32 months

36%
ix

Multiple Spells 

The measure of total time spent on SNAP suggests that many participants with short spells 

re-enter SNAP. When we include spells that occurred prior to the 2004 SIPP panel, about 60 

percent of participants had multiple spells on the program. A smaller percentage of participants 

experienced multiple spells in the mid-2000s than in the early 2000s, and a larger percentage had 

a single long spell (24 months or longer) in the mid-2000s. 

Changes in SNAP Dynamics over Time 

Table 2 presents several of the measures of SNAP dynamics discussed in this report 

alongside the estimates from four earlier reports. Overall, the measures for the mid-2000s are 

more similar to those noted in the early 1990s than they are to those from the mid-1990s through 

the early 2000s. During the 2004-2006 period, individuals enter the program at a slightly higher 

rate than in the early 2000s, they have longer individual spells, fewer multiple spells, and more 

time between spells. The combined participation picture for the mid-2000s, including that of a 

lower replacement rate, is one of less volatility than in the early 2000s. 

Table 2 Comparison of Primary Measures of SNAP Participation Dynamics 

1990-1993 1993-1996 1996-1999 2001-2003 2004-2006 

 

Annual Entry Rate Among All Individuals (Percent) 2.6 NA NA 3.3 3.4 

Replacement Rate (Percent) NA 4.2 3.8 5.4 4.1 

Median Length for Entry Cohort (Months) 9 8 8 8 10 

Median Cross-sectional Completed Spell Length 

(Months) >96 54 54 48 84 

Median Time-Off Between Spells (Months) 20 NA NA 16 20 

Receiving Benefits for Total of Eight Months or Less 

in Panel Period (Percent) 27 NA NA 37 30 

Multiple Spells (Percent) 51 NA NA 63 60 

Average Annual Turnover Rate 1.3 NA NA 1.5 1.4 

 

The growth in the size of the SNAP caseload in the mid-2000s occurred during a time when 

the national economy was improving, at least as measured by the number of individuals living in 

poverty and the unemployment rate. (Figure 5 provides a longer view, tracing the number in 

poverty, number unemployed, and SNAP caseload from 1990-2010.) Although some of the 

growth in the mid-2000s can be attributed to individuals receiving hurricane-related disaster 

benefits in 2005, the number of non-disaster participants also rose throughout that year. The 

regular caseload began to drop early in 2006, and rose again in the latter half of 2006. 
x

Figure 5 Trends in Poverty, the SNAP Caseload, and the Number of Unemployed Individuals, 

1990 - 2010 

Source: SNAP participants: SNAP Summary of Program Operations Data, downloaded on June 29, 2011 from 

http://www.fns.usda.gov/pd/SNAPsummary.htm 

Individuals in poverty: Downloaded on June 15, 2011 from 

http://www.census.gov/hhes/www/poverty/data/historical/hstpov2.xls 

Unemployed individuals: Bureau of Labor Statistics, downloaded on April 19, 2011 from 

http://data.bls.gov/pdq/SurveyOutputServlet

For a caseload to grow, people must be entering the program more often, staying in the 

program longer, or both. In this study, we find that it is both. We noted earlier, though, that the 

dynamics of the mid-2000s align more closely with the dynamics of the early 1990s than other 

periods. Interestingly, the economic circumstances of the early 1990s differed from those of the 

mid-2000s. In the early 1990s, the unemployment rate increased at first and then decreased. In 

the mid-2000s the unemployment rate continually decreased. During other periods of decreasing 

unemployment covered by these dynamics studies, the mid-1990s and late 1990s, participants 

were staying on SNAP for much shorter periods of time than in the mid-2000s. Even in the early 

2000s, while unemployment was increasing, participants were staying on SNAP for shorter 

periods of time than in the mid-2000s. 

 

0

10

20

30

40

50

1985 1990 1995 2000 2005 2010 2015

Millions of People

Below 100% of Poverty

SNAP Participants

Unemployed

Study 

Period
xi

Table 3 Changes in Caseload, Unemployment, and Dynamics Patterns over Time 

 Caseload Change Unemployment Rate 

Median 

Length of 

New Entrant 

Spells 

(months) 

Median Length 

of Completed 

Spells for 

Cross-Section 

of Participants 

(months) 

Median 

Time 

Before ReEntry 

(months) 

 

1991-1993 Increasing Increasing then 

decreasing 9 >96 20 

1993-1996 Decreasing Decreasing 8 54 NA 

1996-1999 Decreasing Decreasing 8 43 NA 

2001-2003 Increasing Increasing 8 48 16 

2004-2006 Increasing Decreasing 10 84 20 

 

Future Directions for SNAP Dynamics Research 

The SIPP is the premier data set with which to study SNAP dynamics. This data set 

facilitates the exploration of how these dynamics vary by individual and family demographic and 

economic characteristics, as well as how they coincide with changes in employment, income, or 

family composition. The 2008 SIPP Panel, whose waves are currently being collected and 

released, will provide the basis for the next study of SNAP dynamics that will cover the 

recessionary period of 2008-2009 and the unprecedented levels of SNAP participation. As of 

June 2011, core SIPP data from Fall 2008 through Summer 2010 has already been made 

available. 

One limitation we faced with the SIPP was the limited information about reasons for SNAP 

entry and exit. The SIPP currently reports these reasons only when transitions occur within the 

reporting period. It misses most of the entry and exit transitions—the ones that occur across 

reporting periods. Revising the instrument to include all months would strengthen the analysis of 

dynamics in future studies. 

Additionally, while SIPP is a longitudinal study of income and program participation, it is 

not focused on the low-income population most likely to participate in SNAP; nor does it include 

a range of variables that can inform our understanding of SNAP dynamics. For example, SIPP 

cannot be used to conduct state-level analysis of SNAP, which is increasingly important with the 

range of state policies in effect. It does not include information about SNAP certification periods, 

either length or timing, and information about some expenses and assets relevant for determining 

eligibility and benefit levels are only available once per year. There would be value in 

conducting a primary data collection effort focused solely on the low-income population and 

engineered to collect a more comprehensive set of individual and family characteristics, as well 

as local and state economic and program policies that are salient to SNAP and changes in SNAP 

participation decisions over time. 
This page is intentionally blank for printing purposes. 
I. INTRODUCTION 

 

The Supplemental Nutrition Assistance Program (SNAP) is the cornerstone of America’s 

food assistance policy. In April 2011, nearly 45 million individuals received SNAP benefits.3

This monthly program caseload is not static; each month, new individuals enter the program 

while some participants exit. Investigating caseload dynamics provides information about what 

factors lead individuals to enter SNAP, how long individuals typically participate, and what 

factors lead them to exit the program. Caseload dynamics studies can show how individuals’ 

participation decisions are affected by changes in individual circumstances, by overall economic 

conditions, and by program policies. 

Typically, studies of program participation dynamics examine measures related to four key 

aspects of participation spells: 

1. Program Entry. Key measures of entry are the number of people entering the program 

over a fixed period of time, such as in a month or year, in relation to the size of the 

population (entry rate) and the number entering in relation to the caseload size 

(replacement rate). Examining changes in program entry and replacement rates over time 

can help to explain overall trends in participant levels. Moreover, examining individuals’ 

circumstances before they enter the program can help identify the factors that appear to 

influence individuals’ participation decisions. 

2. Length of Program Participation Spells. Estimates of the length of participation spells 

can provide valuable insight into the degree to which individuals rely on SNAP once in 

the program. Spell length is measured from a number of perspectives. Entry cohort 

analysis measures the length of stay of individuals who enter SNAP around the same time 

period. Cross-sectional analysis measures the length of stay for those who are 

participating at a specified point in time. The cross-sectional analysis usually indicates 

longer participation spells than the entry cohort because the cross-sectional analysis 

includes the accumulation of entrants that do not exit quickly. Finally, measures of 

turnover and months ever receiving benefits during a period indicate the prevalence of 

multiple spells. 

 

3

 Based on data from SNAP Program Operations division [http://www.fns.usda.gov/pd/34SNAPmonthly.htm] 

accessed on August 22, 2011. 

1
3. Program Exit. Exit rates are the proportions of participants that exit the program over a 

fixed period of time. Like changes in entry rates, changes in exit rates over time can help 

explain changes in caseload size, and an examination of individuals’ circumstances 

around the time of exit can help explain why individuals leave the program. 

4. Program Re-entry. Re-entry patterns measure the extent to which individuals cycle on 

and off a program. These measures tell us about how and why individuals use these 

programs. 

This study examines participation dynamics for SNAP. We describe the characteristics of 

participation spells observed between early 2004 and mid-2006 and show how they have 

changed over time for policy relevant subgroups. This work was conducted in conjunction with 

the analysis presented in the report, ―Determinants of Supplemental Nutrition Assistance 

Program Entry and Exit in the Mid-2000s.‖ That report examines the associations between 

program dynamics and (1) demographic and economic characteristics of families and (2) state 

economic and policy environments and how these associations have changed from the early to 

mid-2000s. The two reports are closely related, despite their different emphases. 

This is a particularly interesting time to study SNAP participation dynamics because the 

number of SNAP participants and the rate of participation among the eligible population 

continued to increase while economic conditions improved. In particular, the current study will 

help us understand if the increase was the result of more individuals entering the program than 

had entered in previous years, current participants staying on longer, or a combination of the two. 

Our results provide evidence that the increase was due to a combination of more entries and 

longer spells. During the mid-2000s, 5 out of every 1,000 low-income individuals4 of all ages not 

receiving SNAP benefits in one month enter the program in the next month. This is higher than 

in the early 2000s, when it was 4 out of every 1,000. Based on economic conditions of the two 

time periods, we might expect the opposite—that rates of entry would be higher in the early 

 

4

 Low-income here is defined as individuals living in families with income below 300 percent of the federal poverty 

level at some point in the panel period. We discuss entry rates for individuals in other income groups in Chapter II. 

2
2000s than in the mid-2000s. Turning to spell length, we find that half of the spells that began in 

mid-2004 ended within 10 months; two-thirds ended within 18 months. In contrast, the median 

spell length of participation for those entering in 2001 was 8 months. 

The rest of this chapter provides background on SNAP, reviews the previous research on the 

dynamics of poverty and SNAP participation, describes the research objectives of this study, 

discusses the data used for the analysis, and presents an overview of the methods employed. 

Chapter II of this report discusses the characteristics of SNAP participation spells observed in the 

2004 to 2006 period. Chapter III presents key results for many policy relevant subgroups and 

compares these findings to earlier time periods. A detailed assessment of the 2004 SIPP panel to 

identify potential problems in the data that could affect estimates of SNAP participation 

dynamics is found in Appendix A. Appendix B tracks how subgroup definitions have been 

modified over time to reflect changes in both data and analytical needs. 

A. Background on SNAP 

SNAP provides monthly benefits that can be used to purchase food in nearly 200,000 

authorized stores across the United States. Eligibility for the program is based primarily on 

financial need; in general, individuals must have income and assets below specified eligibility 

thresholds. Households without elderly or disabled members must have gross income less than 

130 percent of the poverty level, net income less than 100 percent of poverty, and countable 

assets less than $2,000.5

 Households with elderly or disabled members must have net income less 

than 100 percent of poverty and countable assets less than $3,000. 

 

5

 Net income represents the amount of income that households have available to use for food. It equals gross income 

less a standard deduction, an earnings deduction, and deductions for dependent care, medical expenses and shelter 

expenses. Countable assets are primarily financial assets and in some states, some vehicle assets. 

3
Some households are identified as categorically eligible for SNAP, and are not subject to the 

income or asset screens until income exceeds a maximum threshold that varies among states. 

Households in which all members receive Supplemental Security Income (SSI) or cash benefits 

through Temporary Assistance to Needy Families (TANF) are categorically eligible. In addition, 

many states have conferred categorical eligibility status to certain individuals who participate in 

a program funded by Maintenance of Effort or TANF funds. 

Certain individuals are categorically ineligible for SNAP and cannot receive benefits even if 

they pass the income and asset requirements. Legally resident noncitizens must have lived in the 

United States for at least five years to be eligible. Elderly individuals who were both 65 and 

older who were legally resident in August 1996 and children and disabled noncitizens are 

eligible as long as they meet the income and asset requirements. 

A household’s SNAP benefit level equals the maximum SNAP benefit for a household of 

that size less 30 percent of the household’s net income. Maximum benefit levels are the same in 

all states, with the exception of Alaska and Hawaii, where cost of living adjustments are made. 

In the time period of this study, maximum benefits were set equal to the cost of the Thrifty Food 

Plan (TFP), the USDA’s lowest-cost food plan and are updated annually6

. 

Several factors, alone or in combination, may lead an individual to enter the program. Some 

individuals may enroll as a result of a change in personal financial circumstances; others who are 

eligible to begin with may enroll because they recently learned about the program or about their 

own eligibility through program outreach or other sources; still others may enroll because they 

are concurrently enrolled in other public assistance programs, such as (TANF) program or SSI. 

 

6

 The American Recovery and Reinvestment Act (ARRA) raised the maximum SNAP benefit in April 2009, so it is 

not currently tied to the cost of the TFP. This legislation is due to expire in 2013. 

4
Once an individual is enrolled, the length of the participation spell can be affected by 

numerous circumstances. A loss of eligibility, for instance, influences spell length by triggering 

program exit. In general, SNAP households are required periodically to report changes in income 

that may affect their eligibility and to be recertified for eligibility. Thus, individuals whose 

income increases beyond the eligibility limits are likely to exit the program at the time of income 

reporting or recertification. 

In addition to loss of eligibility, the following other factors may prompt program exit: 

Failure to comply with program rules, including reporting requirements and the work 

requirements for nondisabled nonelderly childless adults7 

Life events, such as moving out of state, moving into group quarters, or death 

The household decides that benefits are too low to be worth the effort of complying with 

administrative requirements in the program 

Errors in the administration of the program or determination of benefits 

For most SNAP participants, there are no limits on the number of times they can participate 

in the program or on the total amount of time they can receive benefits as long as they meet the 

eligibility requirements. Thus, individuals whose financial circumstances and other needs 

fluctuate over time may have multiple spells of participation. 

Congress and SNAP administrators modify the program’s rules in response to changing 

economic situations and state needs. Most recently, states have been given increasing flexibility 

to alter program rules and procedures. Key program changes that have occurred in the years just 

prior to or during the study period include the following: 

 

7

 In 2004-2006, nondisabled nonelderly childless adults were subject to time-limited benefits if they were not 

meeting the program’s work requirements or exempt by waiver. In 2009, ARRA allowed states to temporarily 

suspend this time limit. 

5
Changes in Asset Eligibility Rules. States had the option to exclude some or all vehicles 

from the resource test to make SNAP more accessible to families that need vehicles to get 

to work. 

Expanded Categorical Eligibility. In some states, categorical eligibility was extended to 

those receiving noncash benefits through the state’s TANF program.

Outreach. States continued to increase program outreach so that individuals in need of 

assistance knew that SNAP benefits were available and how to apply. 

Changes in Certification Periods. The SNAP certification period is the length of time a 

household has before it must effectively reapply for benefits. Certification periods 

typically range from 3 to 12 months, depending on state guidelines and household 

circumstances. In 2004 to 2006, and in tandem with the changes in reporting 

requirements described next, many states provided longer certification periods for some 

participants. In addition, some offered 24-month certification periods for households in 

which all members were elderly. 

Changes in Reporting Requirements. Reporting requirements govern how a participating 

household must report changes in their income during certification periods. Previous 

SNAP rules required all income changes over $25 to be reported. Recent policy options 

allow states to simplify these rules. The simplified reporting option allows clients not to 

report any changes in income during their certification period, so long as their income 

does not exceed 130 percent of poverty. Status reporting requires a client to report only 

when a household member has a change in jobs, receives a different rate of pay, or shifts 

from part-time to full-time work (or has a similar change in employment status); income 

changes due to different hours of work do not need to be reported. These two policy 

options are not mutually exclusive. 

SSI Combined Application Project (SSI CAP). Some states are simplifying the 

application procedures and benefit calculation for individuals who are receiving SSI 

benefits. Qualified individuals (typically SSI recipients living alone or only with other 

elderly household members) complete a streamlined SNAP application and receive a set 

SNAP benefit based on the limited information they provide, such as shelter expenses. 

Participation in SNAP has more than doubled since the early 2000s. The average monthly 

caseload increased from 17 million in 2000 to 25 million in 2006 and then to over 40 million in 

2010 (Figure I.1). It continues to increase each month, reaching 44.6 million in April 2011. 

Certainly, difficult economic conditions in the early 2000s and in the late 2000s have played a 

substantial role in the increase. However, the number of SNAP participants also increased from 

2004 to 2007, when economic conditions were improving and the number of unemployed 

6
individuals decreased. While assistance for families during major natural disasters such as 

hurricanes in late 2005 increased the caseload, their impact was only temporary. Even 

subtracting out the disaster assistance, the caseload continued to increase. We know from several 

studies (for example, Mabli and Ferrerosa 2010; Klerman and Danielson 2009; Mabli, Martin, 

and Castner 2009) that certain policy changes, such as simplified reporting and longer 

recertification periods, also contributed to the 2004-2007 increase. 

Figure I.1 Trends in Poverty, the SNAP Caseload, and the Number of Unemployed Individuals, 

1990 - 2010 

Source: SNAP participants: SNAP Summary of Program Operations Data, downloaded on June 29, 2011 from 

http://www.fns.usda.gov/pd/SNAPsummary.htm 

Individuals in poverty: Downloaded on June 15, 2011 from 

http://www.census.gov/hhes/www/poverty/data/historical/hstpov2.xls 

Unemployed individuals: Bureau of Labor Statistics, downloaded on April 19, 2011 from 

http://data.bls.gov/pdq/SurveyOutputServlet

B. Previous Research on Dynamics 

This study builds on a variety of previous studies examining the movement of people in and 

out of public assistance programs. Several studies have examined the dynamics of entry into and 

0

10

20

30

40

50

1985 1990 1995 2000 2005 2010 2015

Millions of People

Below 100% of Poverty

SNAP Participants

Unemployed

Study 

Period

7
exit from poverty. These studies are relevant because they use methods similar to those of studies 

examining program participation dynamics, and they track the population generally targeted by 

SNAP. Other studies have examined SNAP participation dynamics specifically. While these 

studies focus primarily on reasons for program entry and exit along with length of program 

participation spells, some also examine program participation over an individual’s lifetime, and 

others identify factors related to caseload growth and decline. 

1. Research on Poverty 

To a substantial degree, the populations eligible for SNAP overlap with the populations that 

are in poverty. Consistent findings emerging from the large body of poverty research are that (1) 

poverty touches many people at some point in their lifetime; (2) close to half of spells of poverty 

end within a year; (3) at any point in time, most people in poverty are in the middle of long-term 

poverty spells; (4) most poverty entries and exits are triggered by changes in employment—for 

various household members in addition to the household head; and (5) black and white 

individuals have markedly different poverty rates. 

Studies of entry into poverty over a person’s lifetime generally use the Panel Study of 

Income Dynamics (PSID) and include work by Duncan and Rogers (1988) and Rank and Hirschl 

(1999). The former focused on children, specifically those up to age 4 at the start of the PSID 

data collection in 1968. The authors found that about one-third of these children entered poverty 

within 15 years, and another 18 percent were near poor (defined as between 100 and 150 percent 

of poverty) during the same period. Twelve percent lived in poverty for 5 or more years. Almost 

80 percent of black children, however, were found to enter poverty for some period in these 15 

years, and almost 47 percent would stay in poverty for 5 or more years. Rank and Hirschl (1999) 

found similarly high probabilities of poverty entry at some point in adult life; they estimated that 

8
by age 40, over one-third of adults (age 20 and over) would experience poverty, and that more 

than half would experience poverty by the time they were 65 years old. Again, the estimates 

varied substantially by race, so two-thirds of black adults could expect to enter poverty by the 

time they were 40 years old and 84 percent by the time they were 65 years old. 

Other researchers also have noted that although a sizeable portion of the population has 

extended poverty spells, poverty spells are short for most people. Long-term spells accumulate 

over time, however, so that even if a small proportion of poverty spells are long, the cumulative 

effect is that in a given month, most of the population in poverty is in the midst of a long spell. 

Duncan and Rogers (1988) estimated that the average spell for children over the 15-year period 

was 1.5 years (0.9 years for non-black children and 5.5 years for black children). Bane and 

Ellwood (1986), who also used the PSID, found that about 45 percent of the population exit 

poverty within a year of entering. At a given point in time, though, the study estimated, slightly 

over 50 percent of the people in poverty would be in a spell that would last 10 or more years. 

McKernan and Ratcliffe (2002), Bane and Ellwood (1986), and Duncan and Rogers (1988) 

examined household events that trigger entry into and exit out of poverty. Using the SIPP panels 

for the early 1990s and late 1990s, McKernan and Ratcliffe found that changes in employment 

were the most important triggers of poverty entry and exit in the late 1990s, although the role 

that earnings played declined between the early and late 1990s. They noted that the very large 

number of poverty entry and exits prompted by an employment change was due, in part, to the 

fact that so many households experienced this event. They found that even after controlling for 

other factors in a multivariate analysis, employment remained the primary influence on poverty 

entry and exit. Using the PSID, both Bane and Ellwood (1986) and Duncan and Rogers (1988) 

not only noted the importance of changes in earnings in relation to entries and exits, but also 

9
showed that any household member’s earnings—not just the head’s—could trigger an entry or 

exit. Indeed, these studies found that the employment of other household members could be just 

as important as, and in the case of poverty exits, even more important than, a change in the 

household head’s earnings. 

According to Bane and Ellwood (1986), entry was also triggered by a birth of a child, the 

onset of a disability, and a shift from a household with two adults to one headed by a single 

female. For poverty exits, additional triggers included an increase in education and a shift from a 

household headed by a single female to one headed by two adults. McKernan and Ratcliffe 

(2002) also found that in the early 1990s, before welfare reform, the shift in marital status of the 

household head played a more prominent role in entries and exits than it did in the late 1990s. 

Iceland (1997) used the PSID to examine factors influencing poverty exits that were 

exogenous to the household, such as changes in the economic structure of metropolitan areas. 

Looking at two periods, 1970-1974 and 1979-1985, he found that a decline in the share of 

manufacturing jobs in metropolitan areas led to a decline in poverty exits for black individuals in 

both periods, and that an increase in the share of jobs in the service industry triggered a decline 

in poverty exits for black individuals during the second period. However, expansion in the 

retail/wholesale industry prompted more poverty exits for black individuals. With the exception 

of the growth in the service industry in the earlier period, which led to a rise in exits for white 

individuals, these changes in economic structure were not significant exit triggers for white 

individuals. 

2. Research on SNAP Participation Dynamics 

Studies of SNAP participation dynamics show that the events triggering SNAP entry and exit 

are similar to those triggering poverty entry and exit, and that patterns of SNAP entry and exit 

10
vary by subgroup, much like patterns of poverty entry and exit. In examining dynamics in the 

mid-1980s, Burstein (1993) found that the most common SNAP entry trigger was a decline in a 

household member’s earnings, and that the most common exit trigger was an increase in a 

household member’s earnings. Similarly, Gleason, Schochet, and Moffitt (1998), which 

investigated SNAP participation dynamics in the early 1990s and Cody, Castner, Mabli, and 

Sykes (2007) studying the early 2000s, also found that a drop in earnings preceded entry more 

often than other triggers. Cody et al. (2007) also found that the effect of a job loss is more 

pronounced for individuals who had not experienced frequent unemployment. 

A recent application of program dynamics to studying the effects of various SNAP policy 

provisions is provided by Ribar, Edelhoch, and Liu (2008). This study used an extraordinarily 

detailed case-level administrative data set on SNAP households in South Carolina to examine 

patterns in the timing of program exits. In particular, the study revealed a very strong influence 

coming from case certification lengths and timing in that households were more likely to leave 

the program during recertification months than in other months, although the authors note that 

the data were not sufficient to tell whether this mostly represented ―cleaning‖ cases that had 

become ineligible or mostly represented administrative barriers to still-eligible cases. 

Many other studies are also relevant to the current study (see Burstein, Patrabansh, Hamilton, 

and Siegel (2009) for a more detailed review). Mills, Dorai-Raj, Peterson, and Alwang (2001) 

examined the factors that influenced program exit decisions of single female-headed families 

with children shortly after the 1996 welfare reform; and Heflin (2004) examined the relationship 

between work status, welfare receipt, and SNAP receipt among women in the post-welfare 

reform era. A collection of recent studies found in Jolliffe and Ziliak (2008) also contains several 

studies of SNAP dynamics, including Moffit and Ribar (2008). They find medium-term earnings 

11
variability to be negatively associated with program participation for low income households, 

and attribute this result partially to the variability in eligibility produced by changes in earnings. 

Collectively this set of studies, particularly the dynamics reports written for FNS (Burstein 

1993; Gleason et al. 1998; and Cody et al. 2007), as well as an analysis by Cody, Gleason, 

Schechter, Satake, and Sykes (2005) of entry and exit rates throughout the 1990s and by Murphy 

and Harrell (1992) of long-term participants in the late 1980s, contributed substantially to our 

understanding of SNAP program dynamics. The following other important findings are generally 

consistent across the studies and confirm many of the results identified above for poverty: 

Household or family composition changes play a significant role in triggering entries, reentries, and exit. 

Most people who enter the program exit within one year. 

At any one point in time, most participants are in the middle of a spell of four or more 

years.8

 

Of those who exit the program, one-third or more re-enter within one year. 

However, these and other studies indicate some noteworthy differences in dynamics from 

one study period to the next. The median spell for persons entering SNAP in the early 1980s 

lasted six months; the early 1990s, nine months; the mid-1990s, eight months; and the early 

2000s, eight months (Burstein 1993, Gleason et al. 1998, Cody et al. 2005, and Cody et al. 2007, 

respectively). Wilde (2001) and Cody et al. (2005) also used the SNAP Control (SNAPQC) data 

to develop similar estimates for 1990-1999; Wilde estimated that the median spell duration for 

new entrants was seven months, while Cody et al. found it to be six months. When examining 

how entry and exit rates contributed to the growth and decline of SNAP caseload, Gleason et al. 

 

8

 Although most people who enter SNAP remain participants for a year or less, the longer term spells accumulate. 

Thus, over time, the cumulative effect is that more participants at a given point in time are in the midst of a longterm spell than in the midst of a short-term spell. 

12
(1998) found that the increase in the caseload in the early 1990s was a result of an increase in the 

duration of SNAP spells, whereas Cody et al. (2005) identified increasing entry rates as the 

larger contributor (though longer spells were found to play a substantial role). Table I.1 

compares the time frames, data, and study objectives across several of these studies. Figure I.2 

illustrates the change in the caseload size in relation to each of these study periods. 

Table I.1 Comparison of Previous Study Time Frames, Data, and Study Objectives with Current 

Study 

Burstein (1993)

Gleason et al. 

(1998) Cody et al. (2005) Cody et al. (2007) Current

Time Period 1983-1986 1990 - 1993 1990-1999 2001 - 2003 2004-2006

Panel(s) 1984 1990, 1991 1990, 1991, 1992, 

1993, 1996

2001 2004

Sample Size 20,000 households 35,000 households 12,000-40,000 

households

31,000 households 51,000 households

Historical SNAP Data 

Used

No Yes No Yes Yes

Descriptive Analysis Entry, exit, 

duration, re-entry, 

entry and exit 

triggers

Entry, exit, 

duration, re-entry, 

entry and exit 

triggers, total time 

on, turnover

Growth, 

replacement, exit, 

duration

Entry, exit, 

duration, re-entry, 

entry and exit 

triggers, growth, 

replacement, total 

time on, turnover

Entry, exit, 

duration, re-entry, 

entry and exit 

triggers, growth, 

replacement, total 

time on, turnover, 

subgroups

Primary At-Risk 

Definition for Entry 

Analysis

Non-participating 

individuals; 

household income 

under 300 percent 

of poverty 

Non-participating 

individuals

N/A Non-participating 

individuals; family 

income under 300 

percent of poverty

Non-participating 

individuals; family 

income under 300 

percent of poverty

Multivariate Analysis N/A Duration, re-entry N/A Entry, duration,

re-entry

Entry, duration, 

re-entry a

a

 The multivariate analysis for the current study is presented in a separate report. 

 

13
 Figure I.2 Periods of SNAP Caseload Cycle 

Note: Fiscal year participation counts are based on data from SNAP Program Operations division.

14
C. Research Objectives 

The goal of this study is to update and extend previous research examining SNAP 

participation dynamics, building primarily on the work of Burstein (1993), Gleason et al. (1998), 

Cody et al. (2005), and Cody et al. (2007). As in Burstein (1993) and in Cody et al. (2007), we 

will limit much of our analysis to the population that is observed to be low income at some point 

in the panel period. 

Our main objective is to describe SNAP dynamics using SIPP data from 2004 through mid2006. This analysis (presented in Chapter II) describes patterns of program entry and exit, and 

provides descriptive statistics on participation spells observed over this period. The key research 

questions explored fall into five categories: 

(1) SNAP Entry 

What are the entry rates for the period covered by the 2004 SIPP panel, for all 

individuals ―at risk‖ of entry and for SNAP subgroups? 

How sensitive are the findings on program entry to the definition of the 

population at risk? 

What trigger events precede SNAP entries? 

What proportion enters SNAP at some point? 

How do the answers to the above research questions compare to the findings in 

the studies for earlier periods? 

 (2) Length of SNAP Participation Spells 

How long are spell lengths for entrants? What is the median time on SNAP after 

program entry? How do these vary among different SNAP subgroups? 

How long are spell lengths for participants when viewed at a specific point in time 

such as a cross-section of participants receiving benefits in the same month? 

How do the answers to the above research questions compare to the findings in 

the studies for earlier periods? 

15
 (3) SNAP Exit 

What are the rates of exit from SNAP, and how do they vary by subgroup? 

What trigger events precede exits? In particular, what percentage of participants 

leave SNAP in the period covered by the 2004 SIPP panel for reasons that are not 

related to improved financial circumstances or reduced need as measured in 

SIPP? 

How do the answers to the above research questions compare to the findings in 

the studies for earlier periods? 

(4) SNAP Re-entry 

What proportion of participants who exit SNAP return to the program within six 

months, within a year, or within two years? What is the median time off SNAP 

between participation spells? How do re-entry patterns vary among different 

subgroups? 

What trigger events precede SNAP re-entries? In particular, what are spell lengths 

of nonparticipation among those who leave for reasons not related to improved 

financial circumstances or reduced need, as measured in SIPP? 

How do the answers to the above research questions compare to the findings in 

the studies for earlier periods? 

(5) Summary Measures 

What proportion of the caseload has a single short spell, single medium-term 

spell, single long spell, or more than one spell during the SIPP panel period? 

What participant characteristics distinguish those who have longer spells, frequent 

spells, or spend a significant proportion of the panel time on SNAP from those 

who only use the program for a single short spell, or a small proportion of the 

panel time? 

What is the turnover rate (the ratio of all participants ever on SNAP during the 

year over the average monthly number of participants) for SNAP participants in 

each year covered by the 2004 SIPP panel? Did the turnover rate change between 

the first few waves of the panel, which was during a period of economic growth, 

and the later waves of the panel, when the economy was starting to weaken? 

What dynamics explain the participation growth that occurred in the mid-2000s? 

What are the replacement and exit rates in this period? 

How do the answers to the above research questions compare to the findings in 

the studies for the earlier periods? 

16
The lifecycle of SNAP cases from entry through duration, exit, and re-entry varies 

significantly among subgroups, and the SNAP caseload can be seen as much as an amalgam of 

subgroups as a single entity. A second objective of the study, therefore, is to present key 

dynamics measures for subgroups for both the current period and earlier study periods. This 

analysis (presented in Chapter III) will address the following research questions: 

How do dynamics of individual subgroups compare with each other? 

What can SNAP dynamics analysis reveal about the unique character of each subgroup? 

How do the dynamics of individual subgroups explain the SNAP caseload evolution in 

terms of rates and absolute size? 

D. Data 

This study relies on data from the 2004 panel of the SIPP. This section provides background 

on the SIPP data and discusses key issues regarding potential response errors in the SIPP. 

1. An Overview of the Survey of Income and Program Participation 2004 Panel 

The SIPP is a short-term longitudinal survey that collects detailed monthly data on labor 

force activity, earned and unearned income, cash and noncash assistance, family and household 

composition, and many additional items. (See Table I.2 for a summary of the 2004 SIPP panel.) 

SIPP follows a representative sample of civilian noninstitutionalized persons over time, 

collecting monthly data by means of interviews conducted at four-month intervals. All members 

of the households interviewed in the first ―wave‖ remain eligible to be interviewed in subsequent 

waves, even if they move away from the original sample address, provided that they remain in 

the survey universe.9

 

9

 The exceptions are (1) children under 15 who move without an accompanying adult panel member, including those 

who enter the foster care system and (2) persons who move to a location that is more than 100 miles from the 

nearest SIPP primary sampling unit. 

17
Table I.2 Summary of the 2004 SIPP Panel 

Purpose Collect income, labor force information, program participation, demographic characteristics 

Design Multistage-stratified sample; longitudinal

Sample Size Approximately 51,000 households interviewed in Wave 1 with a 50 percent sample size 

reduction in Wave 9

Interview Period Households interviewed every four months about previous four months; February 2004 to 

January 2008 

Data Time Period Four months preceding interview: October 2003 – December 2007

Historical Data Program participation (e.g., SNAP benefit receipt) prior to 1st month of household’s panel 

period

Universe Civilian, noninstitutionalized population

Weighting

Full panel weights assigned to those with data (possibly imputed) for full duration of panel or 

who left the universe or died before the end of the panel period; weighted to population 

eligible for SIPP in January 2004; monthly cross-sectional weights available for each wave 

but not used in this analysis

Respondent Household members age 15 and over; proxy interview for unavailable household members

Each interview asks panel members and everyone living with them at the time about their 

activities during the preceding four months. Each interview includes a common set of core 

questions that collect information on household and family composition, personal demographic 

characteristics, employment, income, and participation in a wide range of government assistance 

programs. Periodic ―topical modules‖ collect data on specialized subject areas such as previous 

participation in public assistance programs (also called ―recipiency history‖), employment 

history, citizenship,10 child care costs, assets and liabilities, shelter costs, and work-related 

expenses. 

The length and sample size of SIPP panels has varied over time. The first SIPP panel was 

fielded in 1984 with a sample of nearly 20,000 households interviewed over a period of two and 

one-half years. New panels of generally similar size started in nearly every year between 1984 

 

10 Citizenship became part of the core questions in the 2004 panel. It was a topical module question in all earlier 

panels. 

18
and 1993, before a redesign replaced the overlapping panel design with an abutting panel design 

that allowed larger and generally longer-running panels. A four-year panel with nearly 40,000 

households started in 1996 followed by a three-year panel of about 35,000 households in 2001 

and a four-year panel of about 51,000 households in 2004. 

While the SIPP is fundamentally a longitudinal survey, it is designed to support crosssectional as well as longitudinal analysis. To that end, the initial sample of households is divided 

at random into four equally sized rotation groups that are interviewed on a staggered schedule—

one rotation group per month (Table I.3). For example, the first rotation group is interviewed in 

February, June, and October of each year and asked to provide data for the preceding four 

months (e.g., in October, respondents are asked to provide information on June, July, August and 

September). In addition to distributing the workload evenly over the calendar year and thus 

permitting a set of interviewers to be dedicated to the SIPP, the rotation group design ensures 

that the data collected for any given calendar month are obtained in roughly equal proportions 

from respondents reporting on their activities of one, two, three, and four months ago. 

Accordingly, no calendar month of data is affected more or less than any other by recall bias or 

other error associated with distance from the interview. 

19
Table I.3 Design of the 2004 SIPP Panel 

Rotation Group Wave Interview Month Data Months

1 1 February 2004 Oct. 2003 - Jan. 2004

2 1 March 2004 Nov. 2003 - Feb. 2004

3 1 April 2004 Dec. 2003 - Mar. 2004

4 1 May 2004 Jan. 2004 - Apr. 2004

1 2 June 2004 Feb. 2004 - May 2004

2 2 July 2004 Mar. 2004 - June 2004

3 2 August 2004 Apr. 2004 – July 2004

4 2 September 2004 May 2004 – Aug. 2004

1 3 October 2004 June. 2004 – Sep. 2004

2 3 November 2004 July. 2004 - Oct. 2004

3 3 December 2004 Aug. 2004 – Nov. 2004

4 3 January 2005 Sep. 2004 - Dec. 2004

1 4 February 2005 Oct. 2004 - Jan. 2005

2 4 March 2005 Nov. 2004 – Feb. 2005

3 4 April 2005 Dec. 2004 - Mar. 2005

4 4 May 2005 Jan. 2005 - Apr. 2005

1 5 June 2005 Feb. 2005 – May 2005

2 5 July 2005 Mar. 2005 – June 2005

3 5 August 2005 Apr. 2005 – July 2005

4 5 September 2005 May 2005 – Aug. 2005

1 6 October 2005 June. 2005 – Sep. 2005

2 6 November 2005 July. 2005 - Oct. 2005

3 6 December 2005 Aug. 2005 – Nov. 2005

4 6 January 2006 Sep. 2005 - Dec. 2005

1 7 February 2006 Oct. 2005 - Jan. 2006

2 7 March 2006 Nov. 2005 – Feb. 2006

3 7 April 2006 Dec. 2005 - Mar. 2006

4 7 May 2006 Jan. 2006 - Apr. 2006

1 8 June 2006 Feb. 2006 – May 2006

2 8 July 2006 Mar. 2006 – June 2006

3 8 August 2006 Apr. 2006 – July 2006

4 8 September 2006 May 2006 – Aug. 2006

1 9 October 2006 June. 2006 – Sep. 2006

2 9 November 2006 July. 2006 - Oct. 2006

3 9 December 2006 Aug. 2006 – Nov. 2006

4 9 January 2007 Sep. 2006 - Dec. 2006

1 10 February 2007 Oct. 2006 - Jan. 2007

2 10 March 2007 Nov. 2006 – Feb. 2007

3 10 April 2007 Dec. 2006 - Mar. 2007

4 10 May 2007 Jan. 2007 - Apr. 2007

1 11 June 2007 Feb. 2007 – May 2007

2 11 July 2007 Mar. 2007 – June 2007

3 11 August 2007 Apr. 2007 – July 2007

4 11 September 2007 May 2007 – Aug. 2007

20
Table I.3, continued

Rotation Group Wave Interview Month Data Months

1 12 October 2007 June. 2007 – Sep. 2007

2 12 November 2007 July. 2007 - Oct. 2007

3 12 December 2007 Aug. 2007 – Nov. 2007

4 12 January 2008 Sep. 2007 - Dec. 2007

Note: The cross-sectional entry analysis focuses on participants in May 2004. The May 2004 data were collected in 

Wave 2. 

2. Constructing a Longitudinal Analysis Weight 

A challenge in using the 2004 SIPP panel to examine SNAP participation dynamics concerns 

the over 50 percent sample cut that was undertaken for budgetary reasons starting after Wave 8. 

Table I.4 tabulates the under-300 percent of poverty population—both total persons below the 

given threshold as well as those receiving SNAP among that population—to offer a more 

accurate portrayal of the relative sample sizes and effects of the sample cuts on dynamics 

analysis. Since the relevant sample for dynamics analysis is those who remain in the SIPP 

universe until the end of the analysis period, this table is limited to Wave 9 of the 2001 panel and 

Waves 8 and 12 of 2004.11

 

Table I.4 Unweighted Count of Persons Below 300% of Poverty by Receipt of SNAP 

Selected Waves Of SIPP 2001 and 2004, Fourth Reference Month

Panel and Wave

SNAP Participant Nonparticipant Total

Number Percent Number Percent Number

2001 Wave 9 4,678 13.9 28,885 86.1 33,563

2004 Wave 8 7,729 16.5 39,019 83.5 46,748

2004 Wave 12 3,453 16.5 17,474 83.5 20,927

Source: Decision Demographics, tabulations of the 2001 and 2004 SIPP panel 

 

11 Not all the people included in this table are part of the dynamics analysis, because some are eliminated due to 

gaps in their SIPP participation. Also, the SNAP participation in this table is strictly cross-sectional, pertaining to 

the fourth reference month, and not yet a representation of spells. 

21
It is clear that the 2004 data through Wave 8 offer a greater opportunity for accurate analysis 

of SNAP dynamics than if we utilized only those who were retained in the 2004 panel through 

Wave 12. The 2004 Wave 12 groups are 55 percent lower than Wave 8, and include about 25 

percent fewer SNAP participants and 40 percent fewer persons overall than 2001 Wave 9. On the 

other hand, the 2004 Wave 8 respondents include 65 percent more SNAP participants and 40 

percent more persons overall than 2001 Wave 9, which will contribute to the accurate portrayal 

of both the total population and that of subgroups. The primary disadvantage to using a shorter 

eight-wave panel is that it is less likely to observe the full SNAP spell length for individuals that 

enter SNAP within the panel and, for those that exit SNAP in the panel, there is a shorter 

window in which to observe them re-entering the program in the panel period. This may lead to 

underestimating the length of SNAP spells and off-SNAP spells. Despite this limitation, we use 

the eight-wave panel for all analyses in order to ensure adequate sample sizes for the estimation 

of SNAP dynamics for subgroups. 

For longitudinal analysis, the SIPP includes two types of weights: longitudinal panel weights 

and calendar year weights. The longitudinal panel weight has a reference period that begins with 

January 2004 and runs through the end of a specified wave, whereas the calendar year weights 

have calendar year reference periods. This study uses a longitudinal panel weight for all analysis. 

The Census Bureau assigns longitudinal weights to persons who have data (reported or imputed) 

for all months of a specified reference period (that is, the period covered by the longitudinal 

weight). The Census Bureau also assigns longitudinal weights to persons who left the survey 

universe (by dying, being admitted to an institution, or moving abroad, primarily) during the 

reference period, providing that they have data for all months that they were in the survey 

22
universe. The longitudinal sample is weighted to represent the population eligible for the SIPP in 

the month to which the longitudinal weight is calibrated (January 2004 for the 2004 panel). 

The Census Bureau produced a longitudinal weight for Waves 1 through 7.12 To analyze an 

eight-wave panel, we produced an eight-wave longitudinal weight. The members of an eightwave panel are a subset of those who have a longitudinal weight for Waves 1 to 7. To produce a 

longitudinal weight for Waves 1 to 8, we adjusted the Wave 1 to 7 weights to compensate for 

differential attrition between Waves 7 and 8 and then calibrated the preliminary weights to Wave 

1 population controls, as is done for all SIPP longitudinal weights. This is described in detail in 

Appendix A. 

3. SIPP Data Challenges 

Since the earliest panel, SIPP users have had to grapple with the potential impact of response 

errors that arise from the SIPP’s design and implementation. We examined the extent to which 

sample loss, seam bias, under-reporting, topical module problems, and SNAP churning are 

apparent in the 2004 SIPP panel. The results for several analyses, which are discussed in detail in 

Appendix A, are summarized below. 

a. Sample Loss 

Sample loss generally occurs when members of a household sampled for the survey either 

cannot be located or refuse to participate. In the 2004 SIPP Panel, about 15 percent of 

households originally sampled did not respond to the Wave 1 interview (this is higher than the 

Wave 1 nonresponse rates from prior SIPP panels, where nonresponse rates ranged from about 5 

percent in 1984 to 13 percent in 2001).13 Among those individuals who were interviewed, 37.7 

percent either left the universe permanently or did not respond to the survey in a given wave 

 

12 The Census Bureau also produced longitudinal weights for Waves 1 to 4; 1 to 10; and 1 to 12. 

13 These estimates are taken from http://www.census.gov/sipp/usrguide/ch2_nov20.pdf. 

23
despite still being a member of the universe by the end of Wave 8 of the 2004 panel, which is the 

―effective‖ end of the survey for our analysis (see Appendix A). 

The SIPP observations used in this study are limited to those that have complete data for 

every month that they are in the SIPP universe through the eighth wave of the survey (these 

observations receive a positive longitudinal weight). Most of these are individuals with reported 

data that are available each of the 32 months in the panel. However, some are observations for 

people that exit the SIPP universe during the panel because of death or exit and re-enter the 

universe during the panel for reasons such as moving into or out of the country, becoming 

institutionalized, etc. Individuals who exit the universe, whether temporarily or for the duration 

of the panel, receive full longitudinal weights (and will be included in the analysis) so long as 

they have complete information for those months that they are in the universe. In this context, 

sample loss involves individuals for whom information is not complete for those months that 

they are in the SIPP universe. This includes individuals who miss one or more interviews while 

still in the SIPP universe but return for subsequent interviews as well as those who simply stop 

responding to the SIPP. We refer to the latter type of sample loss as attrition. 

Our analysis of sample loss in the 2004 SIPP panel (see Appendix A) leads us to conclude 

that there is some evidence of bias from sample loss, but such bias is not a significant concern. 

While over one-third of the Wave 1 sample is not included in the full panel analysis file, the 

longitudinal weights appear to adequately correct for this sample loss when we examine key 

characteristics for January 2004. Indeed, the correction is an improvement from that in the 2001 

SIPP panel. Moreover, the SIPP estimates tend to track estimates from other surveys, such as the 

Annual Social and Economic (ASEC) Supplement, administered as part of the Current 

Population Survey (CPS), relatively closely over the course of the SIPP panel. These findings are 

24
consistent with previous studies examining sample loss in the SIPP (Cody et al. 2007; Cody et al. 

2005; Weinberg 2004). 

b. Seam Bias 

In the SIPP, the ―seam effect‖ reflects the tendency of individuals to report changes in status 

on seams—the months that represent the start or end of each four-month reference period. This 

has important implications for the study of participation dynamics, which is focused primarily on 

individuals’ reported changes in program participation. The seam effect can affect the estimated 

duration of participation spells as well as the timing of program entry and exit relative to other 

changes. 

Our analysis of the 2004 SIPP panel reveals pronounced SIPP seam effects. For SNAP, 69.3 

percent of reported entries occur on the first month of a reference period (the left seam). 

Similarly, 46.6 percent of exits occur on the left seam. If there were no bias, we would expect 

each seam month to account for about 25 percent of reported transitions.14 Hence, this suggests 

that individuals who enter SNAP in a given wave tend to report that they started receiving SNAP 

benefits in the first month of that wave, and individuals who exit in a given wave tend to report 

that they exited in the last month of that wave or the last month of the previous wave. While the 

percent of reported entries at the left seam is slightly larger than in the 2001 panel (69.3 percent 

in 2004 versus 67.5 percent in 2001), the percent of reported exits at the left seam is much 

smaller (46.6 percent in 2004 versus 73.8 percent in 2001). The net effect may be an 

improvement in the reporting of transition events. We attribute this to Census Bureau’s 

introduction of its most extensive dependent interviewing in the 2004 panel, relative to the 2001 

 

14 Because the SIPP sample is split into 4 random rotation groups, with each rotation group having a different fourmonth reference period, seasonal bias or other factors would not affect the distribution of transition events across 

reference months. 

25
panel, in which respondents who had reported receiving SNAP in the previous wave were 

reminded of this fact.15 

Unfortunately, on a given seam month, it is not possible to determine which reported 

transitions are ―real‖ and which actually occurred in a different month. Therefore, we must 

conduct the analysis of participation dynamics in a way that does not rely on the short-term 

timing of transitions. In particular, we use observation ―windows‖ of more than four months to 

determine whether one event, such as a change in income, may trigger entry into or exit from 

SNAP. 

c. Pre-panel Program Participation Data 

We use pre-panel program participation data in our analysis of SNAP dynamics. Unlike 

studies using prior SIPP panels such as Cody et al. (2007) and Gleason et al. (1998), however, 

this data was not collected solely in a Recipiency History Topical Module (RHTM) in the 2004 

panel. The Wave 1 RHTM items on SNAP underwent significant redesign prior to the 2004 

SIPP. Some data were collected throughout the 2004 panel, and other SNAP items in the RHTM 

were improved. 

The Census Bureau redesigned the RHTM in response to a series of recommendations from 

the SIPP Continuous Instrument Improvement Group.16 As in the 2001 panel, the recipiency 

history topical module occurs in Wave 1, only four months after the first reference month of the 

panel. The RHTM redesign resequenced the questions, putting SNAP questions at the end to 

allow probes for categorical eligibility for SNAP based on questions about AFDC/TANF. The 

redesign also changed the nature of the SNAP start month and year questions, and slightly 

 

15 Moore et al. (2009) examined the impact of dependent interviewing procedures on seam bias not just for SNAP 

reporting, but an array of need-based programs and non-need-based characteristics. They found that seam bias had 

declined substantially in the 2004 panel and the decline was attributable to the new dependent interviewing 

procedures. 

16 Moore (2007) provided a complete report on the changes and their impacts on data quality. 

26
adjusted the universe for the RHTM to compensate for previous minor omissions due to 

Computer Assisted Personal Interviewing (CAPI) branching issues, and probed for SNAP start 

dates that came before a recipient’s 18th birthday.

Gleason et al. (1998) found evidence that the RHTM data were problematic in the 1991 

SIPP, while Cody et al. (2007) found fewer problems with the 2001 data. Gleason et al. (1998) 

attributed the problems to the fact that, for the 1991 SIPP panel, the recipiency history data were 

collected in Wave 2, eight months after the first month of the panel, and decided to exclude the 

month 1 spells from the main spell analysis. In contrast, Cody et al. (2007) found that the 2001 

recipiency history data were markedly better than the 1991 data and suitable for analysis for all 

waves. Our analysis, detailed in Appendix A, finds that while some problems persist, the data for 

Waves 1 to 8 of the 2004 SIPP panel appear to be sufficient and useful for dynamics research.17

 

We use the recipiency history data primarily to examine lengths of spells. This includes spell 

length of a cross-section of participants; total time on SNAP; and classifications into short and 

long spells, and multiple and single spells. 

d. One-Month Gaps and SNAP Churning 

Four previous studies (Cody et al. (2007), Cody et al. (2005), Gleason et al. (1998), and 

Burstein (1993)) ―closed up‖ one-month gaps in SNAP participation before conducting analyses 

of SNAP dynamics. That is, they assumed that the respondent made a mistake in reporting and 

did not experience an actual break in participation. Thus, sample members were assumed to have 

received SNAP benefits in a given month if they received benefits in the previous and 

subsequent month. Anecdotal evidence from the states, however, indicates that ―churning,‖ that 

 

17 Due to confidentiality restrictions imposed on the 2004 data, we conducted an analysis of this data within the 

Census Bureau and only the summary data have been presented in Appendix A. 

27
is, short-term nonparticipation in the program during a period of continued eligibility, is 

somewhat common. 

In this study, we examined the prevalence of one- and two-month gaps in SNAP participation 

and characteristics of the SNAP units with such a gap. In assessing whether to continue to close 

one-month gaps in the current analysis, we focused on three possible explanations for short-term 

gaps: 

1. Individuals had a change in circumstances that led them to exit and then another change 

that led them to re-enter, within a very short time period. 

2. Individuals reach the end of their certification period without completing the 

recertification process, leading them to exit the program; then within a month or two, 

reapply and enter back into the program (what we refer to as churning below). 

3. The gap is misreported and participation continued across this period. 

Through our analysis we did not find much empirical evidence to support the first 

explanation. We did find evidence that the gap often occurs about six months into a spell, which 

is consistent with the six-month certification period of 40 percent of participating households in 

2004 (Wolkwitz 2006). We also found that individuals who are more likely to have short gaps in 

participation are also the ones with the shorter participation spells. In other words, they come up 

for recertification more often, and have more opportunities to experience a short-term break in 

participation. These findings, presented in detail in Appendix A, suggest that the gaps may in 

fact be due to churning rather than misreporting. 

After considering the results of this assessment, we decided to close up one month SNAP 

participation gaps in the 2004 panel primarily for two reasons. First, it helps maximize 

comparability of study findings with prior studies of SNAP dynamics. Second, from a SNAP 

policy standpoint, states and policymakers may generally consider the churners to be longer-term 

28
participants, and closing one–months gaps may help policymakers use the study results to learn 

about entries, durations, and triggers among those who are not simply churning. 

e. SIPP and Natural Disasters 

From mid-2004 to late 2005, the southern area of the United States was affected by several 

hurricanes— Charlie, Frances, Ivan, Jeanne, Katrina, Rita, and Wilma. These hurricanes led over 

4.6 million people to seek disaster food assistance, some of whom were evacuees.18 In the SIPP, 

individuals reporting their disaster assistance as SNAP are coded as participants and are included 

in the current study. Their program dynamics, though, are much different than dynamics for 

participants in regular SNAP. For example, their entries may occur at the same time as a 

significant loss of income—the time of the hurricane—rather than in the months following their 

loss of income. And their exits will be related to the expiration of the disaster benefits rather than 

a change in household circumstances. In addition, SIPP respondents who moved out of the area 

may be difficult for the interviewers to find—at the time of disasters, forwarding information 

may not be easily obtained. 

Out of concerns for how the disasters could be affecting our dynamics, we performed several 

calculations with individuals who were residents in these states at the time of the hurricanes 

removed. We found no change upon removal of these individuals. Thus, the estimates provided 

in this report include respondents from these states for all months of the panel period. 

E. Methodological Approach 

Our general methodological approach consists of analyzing the characteristics of 

participation spells observed in the 2004 through 2006 period of the SIPP. This section provides 

 

18 Source: "Disaster Report by Fiscal Year" as of 1-01-11 

http://www.fns.usda.gov/disasters/response/disaster_chart.pdf accessed 8/15/2011 

29
an overview of the methodology used in this report. Additional details on the methodology are 

provided in Chapter II. 

The descriptive analysis of participation dynamics is based on a sample of individuals from 

the 2004 SIPP panel. Alternatively, we could have examined SNAP dynamics of households. 

However, examining SNAP household dynamics is difficult because the composition of a 

household can (and often does) change over time. For example, individuals can move into or out 

of a household, two separate households can merge to form a single household, or a single 

household can split and become more than one household. Because of the challenges posed by 

these changes, and to be consistent with earlier studies of SNAP participation dynamics, this 

report focuses on the dynamics of individuals. 

Our descriptive analysis follows the logic of the chronological contact that a hypothetical 

individual has with SNAP. We begin by examining SNAP entry, then discuss the length of 

participation spells, next discuss the events that lead individuals to exit the program, and finally 

examine whether and when individuals re-enter the program. We also provide summary 

measures of individuals’ overall reliance on SNAP. 

Much of the analysis presented here is consistent with the descriptive analysis of dynamics 

conducted by Cody et al. (2007), Cody et al. (2005), Gleason et al. (1998), and Burstein (1993). 

This consistency facilitates comparisons of SNAP participation dynamics in the mid-2000s with 

those of the mid-1980s, the 1990s, and the early 2000s. In particular, as discussed above, we 

followed the procedure used by these three previous studies to ―close up‖ one-month gaps in 

participation. We also followed approaches similar to theirs for estimating participation 

dynamics, including our approach to defining triggers that could lead to program entry, our 

approach to measuring the distributions of the length of participation spells (both for individuals 

30
newly entering SNAP and for a cross-section of participants in a given month), and our approach 

to defining triggers that could lead to program exit. Indeed, while the Cody et al. (2007) analysis 

of SNAP dynamics using the 2001 SIPP panel developed some assumptions that differed from 

those used in previous studies, the methodology in the current study makes an identical set of 

assumptions to those used in Cody et al. (2007) and thus maximizes comparability between the 

findings from the early and mid-2000s. 

A marked departure from previous studies that was initially made in Cody et al. (2007) and 

we have maintained in the current study is the grouping of individuals by families rather than 

households to determine some of their characteristics, including SNAP participation, income, 

and family composition. That is, we define a person to be a SNAP participant if anyone in 

his/her family is a SNAP participant; the income for any individual is the sum of incomes across 

all family members, and the individual’s family composition is based on all members of the 

family.19 Neither a family or household grouping reflects the actual SNAP unit, which is driven 

by the food purchase and preparation practices of the household members. Immediate family 

members (spouses, children under age 22, and the immediate family members of children under 

age 22) are required to be in the same unit, but other family members and unrelated household 

members may be in separate households. The largest impact of this change is likely for measures 

that look at family characteristics (such as families with earnings or families with elderly 

members). However, comparisons of entry rates using households and families in Cody et al. 

(2007) show very little difference between the two measures. 

 

19 Some members of the family may not be participating, such as certain noncitizens who are ineligible for SNAP, 

but they are counted as participants in this analysis. 

31
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II. DESCRIPTIVE ANALYSIS 

The number of SNAP participants in any given month is the net effect of two separate events: 

program entry and exit. First, individuals enter SNAP, often in response to changes in their 

personal or family circumstances. Second, after receiving benefits for some duration, they exit 

the program, again often in response to changes in personal or household circumstances. The 

dynamics of participation in SNAP, however, are usually characterized not only by entry and 

exit, but by program re-entry. That is, some of those individuals that exit the program re-enter at 

a subsequent date. Examining program re-entry distinctly from program entry adds considerable 

value to our understanding of program dynamics because, as evidenced in prior dynamics 

studies, those individuals that re-enter the program are typically different than those that enter for 

the first time. 

These patterns of entry and exit not only determine the characteristics of the caseload at any 

point in time, but also determine whether the size of the caseload increases or decreases over a 

period of time. For the caseload to increase, as it did from 2004 to early 2006, either more people 

are entering than exiting the program, or people who are entering are participating for longer 

periods of time, or a combination of the two. 

In this chapter, we examine patterns of SNAP dynamics for different cohorts of the U.S. 

population in the mid-2000s.20 While there is no one ―typical‖ SNAP participation spell, we find 

the following participation patterns: 

 

20 As in prior studies of SNAP dynamics, we do not limit ourselves to studying dynamics among the SNAP-eligible 

population. Measuring eligibility precisely is difficult, since most surveys do not collect enough information to 

determine who is eligible for program benefits each month. While several studies have examined participation rates 

among eligible individuals (e.g., Leftin 2010), these studies examine SNAP at one point in time. In this study, we 

are examining patterns over time. Replicating the eligibility determination procedures in a time-series analysis is 

beyond the scope of this study. 

33
About 5 out of every 1,000 nonparticipants with income under 300 percent of poverty at 

some point in the panel period who were not participating at the end of one month 

participate in the next month. 

The entry rate increased from the early-2000s to the mid-2000s. The replacement rate, 

measured as the number of new SNAP entrants in relation to the caseload size, has 

decreased steadily from 2001 to 2006. 

If participation patterns seen in 2004 through 2006 remained constant over time, we 

estimate that 31 percent of adults with income under 300 percent at some point in the 

panel period would participate in SNAP at some time in adulthood. Of those who 

participate as adults, half enter the program by age 30. 

Half of all new entrants leave within 10 months; 58 percent leave within a year.

Participation spells in the mid-2000s are longer than in the early-2000s when half ended 

within 8 months and 62 percent within one year. 

Half of the individuals participating in SNAP in May 2004 have spells lasting at least 7 

years. This is a sizable increase from the early-2000s when half of the individuals 

participating in SNAP in May 2001 had spells lasting less than four years. 

Families with children and income under 300 percent of poverty at some point in the 

panel period are twice as likely to enter SNAP and have longer participation spells 

than families without children. Children living with one adult or multiple unmarried 

adults have over four times the entry rate of children living with married adults. 

Elderly people are much less likely to enter SNAP than other adults, although they 

have longer participation spells than children and younger adults. They are also much 

less likely to re-enter the program once they have left. 

Of those who exit the program, 42 percent return within one year. The rate of re-entry 

is the highest for the poorest families. 

A decrease in family earnings is the most common trigger event that precedes entry, 

while an increase in family earnings is the most common trigger event that precedes 

exit. 

The annual turnover rate during the 2004 panel period was 1.4 each year. About 40 

percent more individuals participated over the course of a year than participated in an 

average month. 

The SNAP caseload increase from the beginning of 2004 to the end of 2005 was 

attributed to greater entry than exit (a higher replacement rate than exit rate).

Participation growth tapered throughout 2005 because of a declining replacement rate 

and increasing exit rate. In 2006 the exit rate exceeded the replacement rate and the 

growth rate became small and negative. 

34
In this chapter, we present the patterns; in the next chapter, we explore how these vary across 

subgroups in more detail and over time. 

A. Entry into SNAP 

For individuals entering SNAP, whether for the first time or not, we generally are interested 

in the following questions, which we address in this section: 

What are the entry rates for the period covered by the 2004 SIPP panel, for all individuals 

―at risk‖ of entry and for SNAP subgroups? Have these changed since the early 2000s 

and earlier periods? 

How sensitive are the findings on program entry to the definition of the population at 

risk? 

What trigger events in their lives lead individuals to enter SNAP? 

How do the answers to the above research questions compare to findings in the studies 

for the earlier periods? 

1. The SNAP Entry Rate 

a. Sample and Methods 

The entry rate, that is, the rate at which individuals enter SNAP over a given period of time, 

is defined as the number at risk of entering who subsequently enter divided by the number at risk 

of entering.21 

To determine the entry rate, we must decide on both the at-risk population and the time 

period over which we wish to measure entry rates. One possibility is to define the at-risk 

population as all individuals. While informative, the entry rates calculated for all individuals tend 

to obscure the differences between changes in the rate among eligibles and changes in the size of 

the eligible population that could enter the program. For instance, a decreasing entry rate could 

 

21 By ―at-risk,‖ we mean individuals who are not receiving SNAP benefits in a given month, and, depending on the 

definition in use, have income under a certain level. The entry rate measure provides us with an estimate of the 

proportion of the nonparticipating population that enters SNAP in a given time period. 

35
reflect a lower tendency for individuals to participate, or it could reflect a shrinking population of 

people that potentially could participate. An alternative measure would be to examine entry rates 

over all individuals that are eligible for benefits. However, such a measure may be too narrow, 

since an individual could be ineligible for SNAP in one month, but eligible and participating two 

months later. 

We develop three definitions of the population of individuals that are ―at risk‖ of entering 

SNAP. These definitions range from strict––in which most or all members of the population are 

likely eligible for SNAP––to the most lenient––one that includes all individuals. By using these 

three definitions, we develop a better understanding of the sensitivity of our rates to our choice 

of at-risk population, and maximize comparability of entry rate estimates in the current study and 

in prior studies of SNAP dynamics. The three definitions are based on income over the full 

analysis period:22 

1. Individuals with monthly income under 100 percent of poverty at some point in the 

analysis period 

2. Individuals with monthly income under 300 percent of poverty at some point in the 

analysis period (the primary measure used by Cody et al. (2007)) 

3. All individuals (the primary measure used by Gleason et al. (1998)) 

The first definition provides entry rates among those likely to be eligible; however, SNAP 

eligibility is not limited to those under poverty, so it has the disadvantage of excluding many 

who would likely be at risk of entering at some point in the panel. This is especially true in the 

current study, relative to prior studies, as the percentage of states that offer policies designed to 

expand eligibility such as broad-based categorical eligibility has increased. The income threshold 

 

22 Ideally, we would measure a person’s income in the same way that it would be measured for the purposes of 

SNAP eligibility determination. However, the SIPP data do not indicate which household members would apply for 

benefits together, so we calculate each person’s income as the sum of the income of all individuals in the family, 

including members of related subfamilies. 

36
of 300 percent of poverty captures individuals likely to be eligible without considering that there 

are some individuals in this group whose income never gets so low that they are truly at risk of 

entering. It has been used in similar studies of entry rates (e.g., Cody et al. 2007 and Burstein 

1993). The third definition places no restrictions on income; this was the definition generally 

used in Gleason et al. (1998) for estimating entry rates in the early 1990s and, while not the 

primary measure in Cody et al. (2007), was estimated for the early 2000s in that study as well. 

In addition to considering multiple definitions of the at-risk population, we consider three 

time periods for computing entry rates. Specifically, we compute: 

(1) Monthly entry rate, which reflects the percentage of all at-risk individuals who enter 

SNAP in the current month after not receiving SNAP benefits during the previous 

two months (at least).23 

(2) Wave-based entry rate, which reflects the percentage of individuals that were not 

receiving SNAP benefits at the end of a SIPP four-month reference period (a 

―wave‖) but that enter SNAP during the subsequent wave. 

(3) Annual entry rate, which reflects among all individuals not participating at the end 

of one reference year, the proportion who participate at some point in the next 

reference year.24 

The monthly entry rate is the easiest to understand, in the sense that it measures how often a 

person moves from not participating in one month to participating in the next. However, the 

annual entry rate may be more useful because it provides a broader view of how often at-risk 

individuals enter the program and the wave-based entry rate may be the most accurate, because it 

accounts for the seam bias that can cause biased distributions in monthly and annual entry rates. 

 

23 The at-risk population is restricted to those who had not received SNAP benefits for the previous two months, 

because of our practice of closing one-month gaps in SNAP participation. Under this practice, we assume that 

sample members received SNAP benefits in a given month if they received SNAP benefits in the previous month 

and also in the subsequent month. In effect, sample members have to be out of the program for two months to be 

considered nonparticipants (and ―at risk‖ of entering the program). Similarly, we close one-month gaps in 

nonparticipation, so that sample members have to be participating in the program for at least two months to be 

considered an entrant. A sample member will be counted as entering the program each time they enter following a 

lapse in participation of at least two months. 

24 This reference year is tied to the SIPP interview schedule. It is a close representation of the calendar year. 

37
To create the entry analysis file, we pulled from the SIPP a sample of person-month 

records—one record for each person for each month that they were in the SIPP universe. We 

then limited the sample to those who were at risk of entering, based on the definitions described 

above. For example, a person whose family income was under 300 percent of poverty at some 

point during the panel period would contribute one record to the second sample described above 

for every month they were not receiving SNAP benefits. Each month they were not receiving 

benefits, they were considered to be at risk of entering. If they subsequently entered the program, 

they would stop contributing to the sample unless they stopped receiving benefits, in which case 

they would once again contribute to the sample. 

Using person months allows us to differentiate between a person who, for example, enters the 

program after two months of being at risk and a person who enters the program after two years of 

being at risk. The former will contribute an entry rate of 100 percent to the sample (entering at 

the first opportunity); the latter will contribute an entry rate of approximately 4 percent to the 

sample (entering after 23 possible opportunities). 

To provide the reader with a sense of the magnitude of the sample sizes for each analysis, we 

provide unweighted counts in most tables. The unweighted counts may be a count of persons 

included in the analysis or counts of person months. Providing sample sizes in person months for 

some tables is necessary because each person in the SIPP sample contributes a different number 

of months to the analysis, depending on the number of months they are not participating in 

SNAP and are thus at-risk of entering. The relative sizes of the populations can be determined by 

comparing the number of person months in each type of analysis. 

38
 b. SNAP Entry among the At-Risk Populations 

We calculate the monthly entry rates using months 3 to 31 of the SIPP panel period so a 

given sample member may contribute up to 29 months of data to the calculation of the rate.25 The 

wave-based entry rates use Waves 2 to 8 (months 5 to 32) of the data, and the annual rates use 

years 1 to 3 (months 13 to 32). 

The monthly SNAP entry rate ranges from 0.4 percent for all individuals to 1.1 percent for 

those whose income dipped below the poverty level at some point during the analysis period (see 

Table II.1). This suggests that for every 1,000 individuals not receiving SNAP benefits at the 

beginning of the month, about 4 enter during the month. When we restrict the population under 

consideration to those whose income was under 300 percent of poverty at some point in the 

analysis period, approximately 5 people in 1,000 will enter during the month.26 If we restrict the 

population even further, to those whose incomes were under poverty at some point in the period, 

approximately 11 in 1,000 will enter in the month. 

 

25 We begin examining the monthly entry rates in month 3 because we require that a person have a two-month spell 

of nonparticipation before they could be considered at risk for entering the program. We end in month 31 because 

we also require that a person have a two-month participation spell to be considered an entrant. In later analyses, we 

will begin in later months so that we can look for events that trigger entry during a period prior to the sample month. 

26 About 72 percent of the population had their income dip below 300 percent of poverty for at least one month in 

the panel period. Thus, results for the entire population are very similar to results for this slightly restricted 

population. 

39
Table II.1 SNAP Entry Rates by At-Risk Population 

All Individuals

Income under 300 

Percent of 

Poverty

Income under 

100 Percent of 

Poverty

(Percent)

Monthly 0.4 0.5 1.1

Wave-based 1.4 2.0 4.1

Annual 3.0 4.2 8.3

(Sample size in 

Person-Months) 

Monthly 1,810,980 1,265,040 499,994 

Wave-based 437,091 305,372 120,750 

Annual 124,587 86,973 34,262 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: At Risk: Not receiving SNAP benefits for at least 2 months 

Reference Months: 3 to 31 for monthly estimates; 5 to 32 for wave-based 

estimates; 12-32 for annual estimates 

Sample: Person months 

Monthly entry rates may appear low because they refer to entry in a given month, rather than 

entry over a period of time. The wave-based entry rate of 2.0 for individuals with income under 

300 percent of poverty suggests that approximately 20 out of every 1,000 of these 

nonparticipants will enter the program in the next four-month wave. Similarly, the annual rate of 

4.2 percent implies that 42 out of every 1,000 nonparticipants with income under 300 percent of 

poverty who are not participating at the end of one year will participate at some point in the next 

year. 

When we restrict the at-risk population to those with income under 100 percent of poverty at 

some time during the analysis period, we find that the monthly, wave-based, and annual entry 

rates were all about twice as large as the rates for those under 300 percent of poverty. 

Approximately 41 out of every 1,000 who were not participating at the beginning of the wave 

will enter during the wave, and approximately 83 out of every 1,000 who were not participating 

at the beginning of the year will enter during the year. 

40
c. Changes in SNAP Entry over Time among the At-Risk Populations 

Entry rates have increased from the early 2000s to the mid-2000s, particularly for individuals 

with income under 300 percent of poverty (Table II.2).27 The monthly entry rate increased from 

0.4 to 0.5 percent for individuals with income under 300 percent of poverty and from 0.9 to 1.1 

for individuals with income under 100 percent of poverty. There were also increases in the wavebased entry rate from 1.8 to 2.0 percent and in the annual entry rate from 4.1 to 4.2 percent for 

individuals with income under 300 percent of poverty. Like the increases in the monthly rate, the 

wave-based and annual rates increased by more for individuals with income under 100 percent of 

poverty. 

Table II.2 Monthly Entry Rate Comparison over Time (Percent) 

Monthly Wave-Based Annual

Subgroup

Early 

1990s 

Early 

2000s 

Mid2000s Mid1980s Early 

1990s 

Early 

2000s 

Mid2000s 

Early 

1990s 

Annual

Early 

2000s 

Annual

Mid2000s 

Annual

All individuals 0.3 0.4 0.4 NA NA 1.4 1.4 2.6 3.3 3.0

Individuals with income 

under 300 percent of 

poverty

NA 0.4 0.5 2.0 2.4 1.8 2.0 NA 4.1 4.2

Individuals with income 

under 100 percent of 

poverty

NA 0.9 1.1 NA NA 3.4 4.1 NA 7.9 8.3

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel for the mid-2000s; Cody et al. (2007) 

for the early 2000s; Gleason et al. (1998) for the early 1990s; Burstein (1993) for the mid-1980s 

We also examine changes in SNAP entry by estimating average monthly entry rates and 

replacement rates over time within the panel period and across the 2001 and 2004 SIPP panels. 

The average monthly entry rate was constant at 0.45 percent from 2001 through 2003, then 

 

27 We also examined whether disaster assistance provided in late 2005 for a number of hurricanes affected the 

results. When calculating the entry rates without the states that were most affected, we found no difference in the 

entry rates. 

41
increased to 0.53 in 2004, and subsequently decreased through 2006 to 0.47 percent (Figure II.1) 

Mirroring the decrease in national unemployment over the same period (not shown), both the 

number of new entrants and the number of individuals at risk of entering decreased from 2004 to 

2006 (Table II.3). The decrease over this period in the entry rate, though, indicates a faster 

decline in the size of the group of entrants than of the at risk group. 

Figure II.1 Average Monthly Entry Rates, by Year 

Note: All averages are over twelve calendar months except 2003 (January through September) 

 and 2006 (January to May). 

0.45 0.45 0.45

0.53 0.51

0.47

0.00

0.25

0.50

0.75

1.00

2001 2002 2003 2004 2005 2006

Percent

42
Table II.3 Average Monthly SNAP Entry Rates and Replacement Rates, by Year 

Individuals 

Entering SNAP

At-Risk 

Individuals in 

Previous Month Entry Rate

SNAP 

Participants in 

Previous Month

Replacement 

Rate

Average 2004 

(June to December) 971,886 183,940,880 0.53 21,816,640 4.5

Average 2005 

(January to December) 937,793 182,393,744 0.51 22,849,719 4.1

Average 2006 

(January to May a)

849,617 182,148,427 0.47 23,096,961 3.7

Average 2004-2006 929,367 182,793,884 0.51 22,599,913 4.1

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel for the mid-2000s. 

Notes: At Risk: Not receiving SNAP benefits for at least 2 months and income under 300 percent of poverty at 

some point in the panel period 

Sample: Person months 

a May 2006 is the last month common to all four rotation groups within the wave. 

Unlike the entry rate, the average monthly replacement rate decreased steadily each year 

from 2001 to 2006 (Figure II.2). The replacement rate measures the number of new SNAP 

entrants in a month divided by the number of participants in the previous month’s caseload. The 

decreasing trend in the replacement rate reflects the decrease in the average monthly number of 

new entrants and the increase in the average monthly number of participants each year (Table 

II.3). 

43
Figure II.2 Average Monthly Replacement Rates, by Year

Note: All averages are over twelve calendar months except 2003 (January through September) 

 and 2006 (January to May). 

Extending the analysis farther back in time, Cody et al. (2005) estimated replacement rates 

throughout the 1990s, but only by panel periods and not by year. The study found that, during the 

caseload growth of the early 1990s, the replacement rate was 5.3 percent and decreased slightly 

throughout that period. Then, in the mid-1990s, a period of caseload decline, the replacement 

rate hovered around 4.2 percent. This was followed by another caseload decline in the late 1990s 

and a replacement rate of 3.8 percent. In the early 2000s, the replacement rate returned to its 

value of the early 1990s, ranging from 5.7 percent in 2001 to 5.0 percent in 2003. Strikingly, the 

current analysis shows the replacement rate (at 3.7 percent in 2006) has decreased to below the 

1990s value despite no decline in the caseload. As we will see in section B, this is due to 

participants staying on the program longer after entering the program. 

d. SNAP Entry among Subgroups 

Prior studies of SNAP participation and SNAP dynamics have found each to vary 

substantially according to characteristics such as age, income, and citizenship (Mabli and 

5.7 5.5

5.0

4.5

4.1 3.7

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

2001 2002 2003 2004 2005 2006

Percent

44
Ferrerosa 2010; Mabli et al. 2009; Cody et al. 2007; Leftin 2010; Gleason et al. 1998). In the 

previous section, we discussed how rates increase as the at-risk population under consideration is 

restricted by income; we now limit most of our analysis to one at-risk population––those with 

monthly income under 300 percent of poverty at some point during the analysis period. As in the 

previous section, our analysis sample consisted of person-month records, so each person in the 

SIPP data contributed a record to the sample for each month they did not participate in SNAP. 

This allowed us to capture entries across all months of the SIPP panel, giving us an average entry 

rate. 

Before we examine the entry rates among subgroups of the population, we first consider the 

characteristics of the at-risk population and entrants as of the reference month (see Table II.4), 

paying particular attention to the subgroup characteristics that differ substantially between those 

two populations. For example, while 11.8 percent of at-risk individuals with income below 300 

percent of poverty at some point in the analysis period had received SNAP benefits in the past, 

47.2 percent of individuals who entered SNAP in this period had previously received them. 

About three-fourths of entrants were in families with children, but only 54.9 percent of our atrisk population was in families with children. Only 7.1 percent of entrants were elderly, 

compared to 18.2 percent of those at risk. The at-risk and SNAP entrant groups also differ 

according to sources of income, with 65.6 percent of SNAP entrants living in families with 

earnings, compared to 78.3 percent of all individuals at risk of entering SNAP; 7.0 percent of 

entrants were living in families with TANF, compared to 0.8 percent of all individuals in the at 

risk group; 17.9 percent of entrants were living in families with SSI, compared to 4.7 percent of 

all individuals at risk of entering SNAP; and 8.0 percent of entrants living in families with no 

income, compared to 2.0 percent of all individuals in the at-risk group. 

45
Table II.4 Distribution of the Characteristics of the At-Risk Populations and SNAP Entrants a

All 

Individuals

For at Least One Month in 2004 Panel

Percent of All 

Subgroup SNAP Entrants

Income under 300 

Percent of Poverty

Income under 100 

Percent of Poverty

Total: All Person-Months 100.0 100.0 100.0 100.0

SNAP Benefit Receipt

Never received SNAP benefits (age 18 and older) 91.0 88.2 83.1 52.8

Previously received SNAP benefits (age 18 and older) 9.0 11.8 16.9 47.2

Family Composition

Individuals in families with children 52.1 54.9 57.3 74.0

Adults in families with children and one adult 2.6 3.3 4.7 8.6

Children in families with children and one adult 3.8 4.9 7.5 16.1

Adults in families with children and multiple adults 2.7 3.4 4.0 9.5

Children in families with children and multiple adults 1.5 1.9 2.5 7.7

Adults in families with children and a married head 23.2 22.7 20.3 15.5

Children in families with children and a married head 18.3 18.6 17.8 16.2

Children in child-only families 0.1 0.2 0.5 0.4

Individuals in families without children 47.9 45.1 42.7 26.0

Individuals in families with elderly members 20.5 20.9 13.2 9.2

Elderly members living alone 4.8 5.8 4.4 1.8

Elderly members living with other elderly individuals 8.3 7.9 3.4 1.7

Elderly members living with non-elderly individuals 7.2 6.8 5.1 5.6

Individuals in families with disabled members 2.8 3.3 4.2 6.6

Individuals in families without any elderly or disabled 

members 24.6 20.9 25.3 10.2

Age and Disability

Nonelderly disabled adults 3.1 3.9 5.1 11.1

Nonelderly nondisabled childless adults 28.2 24.0 27.2 11.3

Age

Children (under age 18) 23.7 25.6 28.3 40.4

Nonelderly adults (age 18 - 59) 58.8 56.2 60.6 52.5

Elderly adults (age 60 and over) 17.5 18.2 11.1 7.1

Sex

Male (age 18 and older) 48.8 47.8 47.1 40.2

Female (age 18 and older) 51.2 52.2 52.9 59.8

Race/Ethnicity b

White, Non-Hispanic 70.4 66.2 59.4 43.1

African American, Non-Hispanic 10.5 11.7 13.6 26.3

Hispanic, all races 13.0 16.2 20.3 23.3

Asian, Non-Hispanic 3.3 2.9 3.4 1.5

Other, Non-Hispanic 2.8 2.9 3.3 5.8

46
Table II.4 continued

All 

Individuals

For at Least One Month in 2004 Panel

Percent of All 

Subgroup SNAP Entrants

Income under 300 

Percent of Poverty

Income under 100 

Percent of Poverty

Education

Individuals in families with HS graduate 94.7 92.6 89.3 83.9

Individuals in families with no HS graduate 5.3 7.4 10.7 16.1

Citizenship

Citizen 94.0 92.7 90.4 94.0

Noncitizen 6.0 7.3 9.6 6.0

Citizen children living with noncitizen adults in the 

family 2.5 3.0 4.1 5.1

Adults in families with citizen adults and citizen 

children 26.1 26.4 24.9 30.8

Children in families with citizen adults and citizen 

children 21.7 23.1 24.2 35.8

Adults in families with noncitizen adults and citizen 

children 1.6 2.1 3.0 2.3

Children in families with noncitizen adults and 

citizen children 1.4 1.9 3.0 3.9

Presence of Income

Individuals in families with no income 1.4 2.0 4.8 8.0

Individuals in families with income 98.6 98.0 95.2 92.0

Presence of Earnings

Individuals in families with earnings 82.4 78.3 73.2 65.6

Individuals in families without earnings 17.6 21.7 26.8 34.4

Presence of TANF 

Individuals in families with TANF 0.6 0.8 1.4 7.0

Individuals in families without TANF 99.4 99.2 98.6 93.0

Other Income

Individuals in families with Social Security income 22.8 24.6 17.4 24.0

Individuals in families without Social Security 

income 77.2 75.4 82.6 76.0

Individuals in families with SSI 3.5 4.7 6.4 17.9

Individuals in families without SSI 96.5 95.3 93.6 82.1

Individuals in families with unemployment 

compensation 1.9 2.3 2.9 4.7

Individuals in families with no unemployment 

compensation 98.1 97.7 97.1 95.3

Sample Size -- Total Person-Months c

1,813,494 1,267,001 500,813 6,716

Sample Size -- Total ever at-risk persons ever in 

category 67,912 48,468 20,657

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: At Risk: Not receiving SNAP benefits for at least 2 months 

47
Reference Months: 3 to 31

Sample: Person months for entry rates; persons for percent of entrants 

Two categories are limited to adults age 18 and over. Data on previous receipt of SNAP benefits is only 

available for adults. We compute the entry rates for male and female adults because we do not expect entry 

rates to vary for male and female children. 

a

 Characteristics as of reference month 

b

 Categories are race alone; respondents who reported multiple races are in the Other, Non-Hispanic 

category 

c The distributions are estimated based on person-months. To assist the reader, we also provide the number 

of persons ever at risk (not receiving SNAP benefits for at least 2 months) in each of the income categories. 

Where we see large differences between the characteristics of the at-risk population and the 

entrants, such as the previous receipt of SNAP benefits, we also expect to see large differences in 

entry rates (see Table II.5). Among at-risk individuals who previously had not received SNAP 

benefits at any time in their adult lives, only about 2 in 1,000 entered the program in a given 

month; over the course of a year, about 22 in 1,000 entered. The entry rate among those who 

previously had received SNAP benefits was much higher––about 17 in 1,000 in a given month 

and 130 in 1,000 in a given year. At-risk individuals in families with children entered at a rate of 

about 7 in 1,000 per month (54 in 1,000 per year), while those without children entered at a rate 

of about 3 in 1,000 per month (26 in 1,000 per year). Individuals in families with SSI were over 

four times as likely to enter each month, with 20 in 1,000 entering per month versus 4 in 1,000 

per month for individuals in families without SSI. A similar differential exists for individuals 

with no income (21 in 1,000 entered per month) and individuals with income (5 in 1,000 entered 

per month). 

48
Table II.5 Monthly, Wave-Based, and Annual Entry Rates by Individuals in Subgroups 

Subgroup Monthly By Wave Annual

Total: All Person-Months 0.5 2.0 4.2

SNAP benefit receipt

Never received SNAP benefits (age 18 and older) 0.2 1.0 2.2

Previously received SNAP benefits (age 18 and older) 1.7 6.6 13.0

Family Composition

Individuals in families with children 0.7 2.7 5.4

Adults in families with children and one adult 1.4 5.1 9.8

Children in families with children and one adult 1.7 6.5 11.9

Adults in families with children and multiple adults 1.5 5.8 10.4

Children in families with children and multiple adults 2.1 8.2 14.2

Adults in families with children and a married head 0.4 1.4 3.1

Children in families with children and a married head 0.5 1.8 3.9

Children in child-only families 1.2 4.1 10.1

Individuals in families without children 0.3 1.2 2.6

Individuals in families with elderly members 0.2 0.9 1.9

 Elderly members living alone 0.2 0.6 1.4

 Elderly members living with other elderly individuals 0.1 0.5 0.8

 Elderly members living with non-elderly individuals 0.4 1.7 3.6

Individuals in families with disabled members 1.0 4.0 8.9

Individuals in families without any elderly or disabled 

members 0.2 1.0 2.4

Age and Disability

Nonelderly disabled adults 1.5 5.5 10.8

Nonelderly nondisabled childless adults 0.2 1.0 2.4

Age

Children (under age 18) 0.8 3.2 6.2

Nonelderly adults (age 18 - 59) 0.5 1.9 4.0

Elderly adults (age 60 and over) 0.2 0.8 1.6

Sex

Male (age 18 and older) 0.3 1.4 3.0

Female (age 18 and older) 0.5 1.9 3.9

Race/Ethnicity a

White, Non-Hispanic 0.3 1.3 2.8

African American, Non-Hispanic 1.2 4.5 8.4

Hispanic, all races 0.8 3.0 6.3

Asian, Non-Hispanic 0.2 0.9 2.1

Other, Non-Hispanic 1.0 4.1 8.8

49
Table II.5, continued

Subgroup Monthly By Wave Annual

Education

Individuals in families with HS graduate 0.5 1.8 3.9

Individuals in families with no HS graduate 1.1 4.2 8.2

Citizenship

Citizen 0.5 2.0 4.2

Noncitizen 0.4 1.7 3.3

Citizen children living with noncitizen adults in the family 0.9 3.6 6.7

Adults in families with citizen adults and citizen children 0.6 2.4 4.8

Children in families with citizen adults and citizen children 0.8 3.1 6.1

Adults in families with noncitizen adults and citizen children 0.6 2.3 4.4

Children in families with noncitizen adults and citizen 

children 1.1 4.3 8.1

Presence of Income

Individuals in families with no income 2.1 6.9 11.9

Individuals in families with income 0.5 1.9 4.0

Presence of Earnings

Individuals in families with earnings 0.4 1.8 3.9

Individuals in families without earnings 0.8 3.0 5.0

TANF Income

Individuals in families with TANF 4.5 17.6 26.0

Individuals in families without TANF 0.5 1.9 4.0

Other Income

Individuals in families with Social Security income 0.5 2.0 4.0

Individuals in families without Social Security income 0.5 2.0 4.2

Individuals in families with SSI 2.0 7.8 14.5

Individuals in families without SSI 0.4 1.7 3.6

Individuals in families with unemployment compensation 1.1 3.4 7.9

Individuals in families with no unemployment compensation 0.5 2.0 4.1

Sample Size: Person-Months 1,265,040 305,372 86,973

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: At Risk: Not receiving SNAP benefits for at least 2 months and income under 300 percent of poverty at 

some point during the panel period 

Reference Months: 3 to 31 for monthly estimates; 5 to 32 for wave-based estimates; and 12-32 for annual 

estimates 

Sample: Person months 

Subgroup Characteristics: As of Month 2 for monthly estimates, Month 4 for wave-based estimates, and 

Month 11 for annual estimates 

Two categories are limited to adults age 18 and over. Data on previous receipt of SNAP benefits is only 

available for adults. We compute the entry rates for male and female adults because we do not expect entry 

rates to vary for male and female children. 

a

 Categories are race alone; respondents who reported multiple races are in the Other, Non-Hispanic 

category 

50
We also examine entry rates by age, race and ethnicity, and education. Consistent with prior 

studies, age was negatively correlated with SNAP entry––about 16 in 1,000 at-risk adults over 

age 60 entered each year, 40 in 1,000 age 18 to 59, and 62 in 1,000 under age 18. The entry rate 

among non-Hispanic African American individuals was three times the entry rate among nonHispanic white individuals, and the entry rate among Hispanic individuals was more than twice 

as high as the entry rate among non-Hispanic white individuals. In addition, the annual entry rate 

among individuals in families in which no one has a high school degree was more than double 

the entry rate among individuals in families with at least one high school graduate.28

 

An alternate approach to understanding entry into SNAP is to examine the age at which 

adults first enter the program. Table II.6 presents estimates of the ages at which a cohort of 

adults initially enter SNAP (if they enter at all).29 We limit this analysis to adults because the 

history of SNAP receipt is not available for children. The cumulative entry rate in the first 

column shows that 8.7 percent of adults participated in SNAP between ages 18 and 30. Overall, 

we estimate that 31.4 percent of these adults would participate in SNAP at some point in their 

adult lives. The cumulative entry rate among entrants shown in the second column suggests that 

15.7 percent of all new entrants would have started participating between the ages of 18 and 20 

 

28 Table II.4 clearly indicates that some categories, such as ―Children in child-only families‖ and ―Individuals in 

families with TANF‖ have small sample sizes. Care must be taken in drawing conclusions for individuals in these 

categories. 

29 The estimates in Table II.6 are based on a cross section of individuals as of month 4 of the SIPP panel period. For 

this sample, we examine whether they had ever received SNAP benefits and, if they had, calculate the age at which 

they first entered the program, using data from the Wave 1 Topical Module and restricted-use SIPP data available at 

the Census Bureau. We then used this information to construct a ―life table‖ for an artificial cohort of individuals 

(see Section B of this chapter for a description of the life table methodology). For every possible age between 18 and 

80, this life table estimates yearly initial entry rates by calculating the percentage of the sample entering SNAP for 

the first time at that age, among those in the sample who were at least that old and who had not entered the program 

at a younger age. These yearly initial entry rates are then translated to cumulative initial entry rates among the full 

sample and among sample members who ultimately entered the program. One important assumption implicit in this 

methodology is that all individuals in the artificial cohort live to at least age 71. Another required assumption is that 

there is a stationarity over time in initial entry rates, since we are using information from a cross section of 

individuals to infer what would happen to a single cohort. 

51
and over half by the time they were age 30. We also infer that 16.8 percent of adult entrants (sum 

of the last three rows in the third column) would have entered for the first time after age 50. 

Table II.6 Initial Adult SNAP Entry Rate by Age 

Age

Cumulative Entry 

Rate

Cumulative Entry 

Rate Among SNAP 

Entrants

Percentage of 

Initial SNAP 

Entrants

18-20 2.5 15.7 15.7

21 to 30 8.7 50.9 35.2

31 to 40 13.2 71.7 20.9

41 to 50 16.6 83.6 11.9

51 to 60 20.0 91.4 7.8

61 to 70 23.2 95.9 4.5

Older than 70 31.4 100.0 4.1

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: At Risk: All adults not receiving SNAP benefits for at least 2 months and income under 300 percent of 

poverty at some point during the panel period 

 Reference Months: 3 to 31 

 Sample: Person months 

 The sample includes only individuals age 18 and older in Month 4. The methodology assumes all individuals 

will live to be at least 71. 

2. Entry Trigger Events 

Prior studies of SNAP dynamics have provided evidence that individuals typically enter 

SNAP in response to a change in their life circumstances––for example, a loss of income or the 

addition of a family member. Although we cannot necessarily identify the direct cause of a 

person’s entry, we can examine his or her family income and the employment status and 

composition of their family immediately preceding entry. Observed changes can help 

policymakers understand the events that may have led that person to enter the program (that is, 

the entry trigger events), and help identify points of intervention to help reduce the need for 

people to enter. 

52
a. Methods 

To examine entry trigger events, we define SNAP entry as participation in SNAP in a given 

month after at least two consecutive months of nonparticipation. To ensure that the period prior 

to entry will be long enough to observe possible entry trigger events, we change our analysis 

period. When we examine potential entry trigger events over a four-month window immediately 

preceding an entry, we limit our sample to months 6 to 31. We define a trigger event as having 

occurred in the window if it changed across any two consecutive months. When we examine 

potential entry trigger events over an eight-month window immediately preceding an entry, we 

limit our sample to months 10 to 31. 

We define our entry trigger events based on previous research (Cody et al. 2007; Gleason et 

al. 1998; and Burstein 1993). Specifically, we include the following trigger events: 

Recently unemployed family member 

Self 

Other family member 

Decrease in family income30 

Earnings (10 percent or more) 

TANF (any decrease) 

Other income (10 percent or more) 

Change in family composition 

Pregnant/new infant in family 

New dependent (non-infant) in family 

Newly separated or divorced 

Other composition change31 

 

30 If we observe a decrease in income during any month of the trigger window, it is considered a trigger event, 

regardless of what happened to income in other months of the trigger window. Thus, if a sample member 

experienced a 10 percent decrease in family income in one month and gained the income back in a subsequent 

month, it is still considered a trigger event. On the other hand, if a sample member experienced a series of 5 percent 

decreases in family income in consecutive months during the trigger window, this is not considered a trigger event. 

53
b. Distribution of Entry Trigger Events in the 2004 Panel 

In Table II.7, we present the primary results of the entry trigger analysis. The first column 

shows the percentage of the at-risk population that experienced each trigger event at some point 

during the sample.32 In the second column, we look forward four months from the time of the 

trigger event and indicate the percentage of those experiencing the trigger event that entered 

SNAP within those four months. In the third column, we look backward four months from SNAP 

entry to find the percentage of entrants who experienced the trigger event prior to entry. 

Similarly, the fourth and fifth columns provide the percentage entering within eight months of 

the trigger event and the percentage of entrants who experienced the trigger event in the eight 

months prior to entry, respectively. By looking forward from the trigger, the ―trigger-centered‖ 

view identifies how often a trigger leads to entry. But this measure alone does not provide 

enough information because a trigger event that does not occur often but usually leads to entry 

when it does occur only helps us identify the trigger events for a small percentage of entrants. By 

looking backward from the entry for the occurrences of trigger events, we develop a clearer 

picture of the impact that each trigger event has on entry into SNAP. 

 

31 This category includes cases that change composition as defined by the groups listed in Table II.4: individuals 

(adults or children) in families with children and one adult; individuals in families with children and a married head; 

individuals in families with children and multiple adults (not married head); children in child-only families; 

individuals in families with elderly members; individuals in families with no elderly members but with disabled 

members; individuals in families without any disabled or elderly members. For example, if a cohabiting couple with 

children marries, the individuals would change from ―individuals in families with children and multiple adults‖ to 

―individuals in families with children and a married head.‖ If a 17-year-old in a family with children and one adult 

turns 18, the individuals would change from ―individuals in families with children and one adult‖ to ―individuals in 

families with children and multiple adults.‖

32 Entry rates among groups with small percentages experiencing the event in the panel period are less reliable (for 

example, those experiencing a decrease in TANF income and those experiencing no trigger event). 

54
Table II.7 Occurrence of SNAP Entry Trigger Events and Rate of Entry Following Trigger Event 

Trigger Event 

(Not Mutually Exclusive)

Percent of At-Risk Group 

Experiencing Event at 

Some Point in Panel

Percent of People 

Experiencing 

an Event who 

Entered SNAP 

within 4 Months 

of Experiencing 

the Event

Percent of SNAP 

Entrants who 

Experienced the 

Event in 

Previous 4 Months

Percent of People 

Experiencing

an Event who 

Entered SNAP 

within 8 Months 

of Experiencing 

the Event

Percent of SNAP 

Entrants who 

Experienced the 

Event in 

Previous 8 Months

Change in Family Composition

Pregnant/New infant in family 7.3 4.3 2.3 8.2 5.1

New dependent (non-infant) in family 13.7 4.6 5.3 7.6 8.8

Newly separated or divorced 3.8 5.3 1.8 8.2 3.1

Other composition change 16.7 3.9 6.3 6.7 10.3

Recently Unemployed Family member

Self 15.8 3.9 6.2 6.1 9.7

Other family member 27.6 3.8 11.5 6.1 17.9

Decrease in Family Income

Earnings (10% or more) 75.6 2.8 38.7 4.6 53.0

TANF 1.8 14.2 2.5 21.5 3.4

Other income (10% or more) 75.0 1.7 26.1 3.1 38.0

Experienced no trigger events 8.7 NA 39.3 NA 27.2

Experienced any one trigger event 18.0 NA 31.8 NA 26.1

Experienced multiple events 73.3 NA 28.9 NA 46.7

Experienced any trigger event 91.3 2.5 60.7 4.2 72.8

Sample Size (Person Months) 48,415 197,365 5,492 167,814 4,562

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: At Risk: All individuals not receiving SNAP benefits for at least 2 months and income under 300 percent of poverty at some point in the panel period 

Reference Months: 3 to 31 

Sample: Persons for percent experiencing event at some point; person-months for percent entering within specified number of months of experiencing 

event; persons entering SNAP for percent of entrant

55
Although the identified trigger events play key roles in SNAP entry, with 60.7 percent of all 

entries preceded within four months by at least one of these trigger events, 39.3 percent of entries 

occur without an obvious recent change in circumstances. It could be that the entrants waited 

longer than four months to enter (72.8 percent of entries occur within eight months of a trigger), 

were confident that they could find a job with the improving economy, or were enticed to enter 

because of outreach programs or changes in policy that simplified participation. 

Among those that experienced a trigger, the most common events were related to decreases 

in family income. Among those who entered SNAP, 38.7 percent experienced a decrease in 

family earnings of 10 percent or more, and 26.1 percent experienced a loss in other family 

income (aside from earnings and TANF) during the prior four months. 

A decrease in family income is the most common of the entry triggers, likely due to its 

prevalence in the population with income under 300 percent of poverty at some point during the 

panel. In this at-risk population, 75.6 percent experienced a decrease in family earnings of at 

least 10 percent at some point during the analysis period, and 75.0 percent experienced a 

decrease in other types of family income. Looking at how many of the at-risk individuals entered 

the program (the second column), one sees that that 2.8 percent of those who experienced a loss 

of earnings entered within four months; 1.7 percent of those who experienced a loss of other 

income entered within four months. 

Prior studies of SNAP dynamics have found that triggers that do not occur as often in the atrisk population are more predictive of SNAP entry. That remains true in the current study for 

several triggers. For example, many at-risk individuals experienced a decreased in earnings, but a 

smaller percentage of these individuals subsequently entered SNAP, whereas few at-risk 

individuals experienced a decrease in TANF income, but a much larger percentage of these 

56
individuals subsequently entered SNAP. Similarly, only 7.3 percent of the at-risk population 

experienced a pregnancy or birth in the family at least once during the analysis period, but, 

looking forward, 4.3 percent of the pregnancies and births were associated with entry into SNAP 

within four months. Separations and divorces in the family occurred less often and were 

associated with an entry within four months in 5.3 percent of the cases where it occurred. 

In addition to examining the four-month period prior to entry, we examine the eight-month 

period prior to entry. We know that some events may not lead to immediate or near-term entry 

into SNAP. For example, for the first few months after a job loss, a family may be able to 

survive by drawing down assets. If, however, they are unable to replace the income over time, 

they may ultimately decide to enter SNAP. In this case the job loss would still be the trigger to 

entry, but would not be observed immediately preceding the entry. 

The eight-month window increased the percentage of SNAP entries preceded by a trigger 

event, from 60.7 percent to 72.8 percent. It also increased the percentage of the at-risk population 

that entered SNAP who experienced a trigger event: 2.5 percent entered within four months of 

any trigger and 4.2 percent entered within eight months. 

One difficulty in studying entry rates based on triggers is that triggers can, and often do, 

occur together. For example, the birth of a child may lead a parent to stay home to care for the 

child, leading to a loss of income. In fact, Table II.7 indicates that 28.9 percent of entrants 

experienced multiple events in the four months prior to their entry, an amount that is nearly half 

of the 60.7 percent of entrants that experienced at least one trigger event in the previous four 

months. In the eight months prior to their SNAP entry, 46.7 percent experienced multiple events. 

In Table II.8, we again examine how often some of these same trigger events precede SNAP 

entry by four and eight months, but we order the trigger events to make each row mutually 

57
exclusive. We first identify the entries that were preceded by the unemployment of a family 

member. Next, we identify the entries that were not preceded by the unemployment of a family 

member, but were preceded by a decrease in family income (earnings, TANF, or other income). 

Finally, we identify entries that were not preceded by the unemployment or income decreases, 

but were preceded by a family composition change. We see that 16.9 percent of entrants 

experienced the unemployment of a family member in the four months prior to entry, and 

another 27.4 percent experienced a decrease in earnings that was not through unemployment. 

Table II.8 Occurrence of SNAP Entry Trigger Events, Mutually Exclusive Categories 

Trigger Event

(Mutually Exclusive Order)

Percent of 

At-Risk Group 

Experiencing Event 

at Some Point 

in Panel

Percent of SNAP 

Entrants who 

Experienced the 

Event in 

Previous 4 Months

Percent of SNAP 

Entrants who 

Experienced the 

Event in 

Previous 8 Months

Recently Unemployed Family Member (either self 

or other) 38.1 16.9 25.6

No Unemployment, Decrease in Family 

Income

Decrease in earnings (10% or more) 40.5 27.4 32.7

Decrease in TANF, no decrease in earnings 0.2 0.7 1.0

Other income (10% or more) 11.5 11.5 10.1

No Unemployment, no Decrease in Income, Change in Family 

Composition

Pregnant/New infant in family 0.1 0.7 1.2

New dependent (non-infant) in family 0.3 1.9 1.0

Newly separated or divorced 0.0 0.1 0.1

Other composition change 0.4 1.4 1.0

Sample Size (Person Months) 48,415 5,492 4,562

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: At Risk: All individuals not receiving SNAP benefits for at least 2 months and income under 300 percent of 

poverty at some point in the panel period 

 Reference Months: 3 to 31 

Sample: Person months 

In Table II.9, we combine the triggers into three categories (unemployment, income decrease, 

and change in family composition) and examine the overlap for entrants. That is, we look at the 

58
percentage of the sample that experienced a change in each type of event and the percentages of 

the sample that experienced a change in each pair of events, such as changes in family 

composition and employment or changes in unemployment and income. We see that 14.8 percent 

of entrants experienced a change in family composition in the four months prior to entry, but for 

3.0 percent of entrants it was accompanied by the unemployment of someone in the family, and 

for 10.0 percent of entrants it was accompanied by a decrease in family income. When we 

increase the window to eight months, we find that 23.5 percent of entrants experienced a change 

in family composition, but for 7.4 percent of entrants, the family composition change was 

accompanied by the unemployment of a family member and for 19.4 percent by a decrease in 

family income. Low-income families may be able to withstand the problems caused by a single 

trigger event, but the occurrence of a second trigger event may cause them to turn to SNAP for 

support. 

Table II.9 Overlap in Trigger Events 

Trigger Event

(Mutually Exclusive Order)

Percent of SNAP 

Entrants who 

Experienced the Event 

in Previous 4 Months

Percent of SNAP 

Entrants who 

Experienced the Event 

in Previous 8 Months

Recently unemployed family member 16.9 25.6

Experiencing additional decrease in family income (10% or more) 13.2 22.9

Experiencing change in family composition 3.0 7.4

Decrease in family income (10% or more) 52.8 66.7

With recently unemployed family member 13.2 22.9

Experiencing change in family composition 10.0 19.4

Change in family composition 14.8 23.5

With recently unemployed family member 3.0 7.4

Experiencing additional decrease in family income (10% or more) 10.0 19.4

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: At Risk: All individuals not receiving SNAP benefits for at least 2 months and income under 300 percent of 

poverty at some point in the panel period 

 Reference Months: 3 to 31 

Sample: Person months 

59
The final method we use to examine entry triggers into SNAP is to look at the triggers in the 

context of how often they occur in the family. For example, if changes in family composition are 

common in a household, is that trigger event less likely to lead to SNAP entry than if it is an 

uncommon event? The descriptive evidence in Table II.10, which presents the entry rates 

according to the deviation of the trigger event from the family’s usual circumstances, shows that 

this is largely not the case. We find that 

Monthly entry rates among those who experienced a family composition change in the 

previous four months were higher for those who had experienced multiple composition 

changes in the past 24 months than for those with just one composition change. 

Among those experiencing the unemployment of a family member in the previous four 

months, the highest entry rates were among the individuals for whom this was a more

common experience. Individuals with one or two unemployment spells in the previous 24 

months had entry rates of 3.3 and 3.1, respectively, whereas individuals with 3 or more 

unemployment spells in the previous 24 months had a higher entry rate of 5.2. 

Among those experiencing a decrease in earnings of at least 10 percent in the previous 

four months, the highest entry rates were among the individuals with family earnings that 

fluctuated more often (2 or more times) in the previous 24 months than among the 

individuals with only a single fluctuation. 

60
Table II.10 SNAP Monthly Entry Rates Among At-Risk Individuals Experiencing a Trigger Event, 

by Degree of Deviation from Usual Circumstances 

Trigger Event

Percent of 

SNAP Entrants

Percent of

At-Risk 

Sample

SNAP 

Entry Rate

Change in Family Composition in Previous 4 Months (Sample Size) 32 1,006 NA

In previous 24 months, family experienced

One composition change 40.4 56.6 2.0

More than one composition change 59.6 43.4 3.9

Unemployed Family Member in Previous 4 Months (Sample Size) 58 1,791 NA

Individual unemployed in previous 24 months

1-6 months 68.6 72.8 3.3

7-12 months 25.6 21.2 4.2

13-24 months 5.8 6.0 3.4

Individual's unemployment spells in previous 24 months

1 spell 48.5 52.0 3.3

2 spells 28.2 32.2 3.1

3 or more spells 23.3 15.8 5.2

At Least a 10 Percent Decrease in Earnings in Previous 4 Months (Sample Size) 120 5,917 NA

Number of times family earnings increased or decreased by more than

one standard deviation in previous 24 months

1 fluctuation 3.4 8.0 0.8

2 fluctuations 22.2 20.2 2.2

3 or more fluctuations 74.4 71.8 2.1

Entry Month's Earnings as Percent of Average Earnings in Previous 24 Months

0 to under 50 32.0 16.3 3.9

50 to under 100 21.9 29.7 1.5

100 or more 46.1 54.0 1.7

At Least a 10 Percent Decrease in Other Income in Previous 4 Months 

(Sample Size) 56 5,246 NA

Number of times family earnings increased or decreased by more than

one standard deviation in previous 24 months

1 fluctuation 4.7 14.6 0.3

2 fluctuations 42.0 39.6 1.1

3 or more fluctuations 53.3 45.8 1.3

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: At Risk: All individuals not receiving SNAP benefits for at least 2 months and income under 300 percent of 

poverty at some point in the panel period 

Reference Months: 3 to 31 

Sample: Persons months 

61
c. Changes in the Distribution of Entry Trigger Events from the Early- to Mid-2000s 

There have been changes from the early- to mid-2000s in the occurrence of SNAP entry 

trigger events, the rates of entry following them, and the percentage of SNAP entrants who 

experienced the event in the past four months. Though the percentage of at-risk individuals 

experiencing any trigger event at some point in the panel decreased from 95.9 to 91.3 percent 

from the early- to mid-2000s (Figure II.3), the percentage of people experiencing any trigger 

event who entered SNAP within four months increased slightly from 2.0 to 2.5 percent (Figure 

II.4). Changes over time in the association between trigger events and SNAP participation can 

also be measured by examining SNAP entrants’ recent experiences. The percentage of SNAP 

entrants who experienced the event in the previous four months decreased by ten percentage 

points—from 70.8 to 60.7 percent (Figure II.5). We conclude that trigger events remain key 

determinants of SNAP entry, though they are less common among SNAP entrants in the mid2000s relative to the early-2000s. 

Experiencing a 10 percent decrease in family earnings became less common among both the 

at-risk group and the group of SNAP entrants, but was more strongly associated with SNAP 

entry in the mid-2000s than in the early-2000s (Figures II.3, II.4, and II.5). The percentage of atrisk individuals experiencing a 10 percent decrease in family earnings decreased, from 86.4 

percent in the early-2000s to 75.6 percent in the mid-2000s, and the percentage of individuals 

entering within four months of this change increased from 1.9 to 2.8 percent. While it is 

reasonable to expect that the decrease in the percentage experiencing this event may reflect fewer 

individuals becoming unemployed as the economy improved from the 2001 to 2003 period to the 

2004 to 2006 period, there were slight increases in the percentages of individuals in the at-risk 

group that became unemployed or had a family member become unemployed from the early- to 

62
mid-2000s. Thus, the change in the percentage of individuals experiencing decreases in earnings 

may reflect lowered wages or hours worked among the employed rather than transitions into 

unemployment. 

Figure II.3 Percentage of At-Risk Group Experiencing Event, Comparison over Time

91.3

16.7

3.8

13.7

7.3

75.0

1.8

75.6

27.6

15.8

95.9

17.7

4.5

5.0

10.8

79.2

2.0

86.4

25.6

15.3

0 10 20 30 40 50 60 70 80 90 100

Experienced any trigger event

Other composition change

Newly separated or divorced

New dependent (non-infant) in family

Pregnant/New infant in family

Decrease in other income (10% or more)

Decrease in TANF

Decrease in earnings (10% or more)

Recently unemployed (other family member)

Recently unemployed (self)

Percentage

2001-2003

2004-2006

63
Figure II.4 Percentage of At-Risk Group Who Entered SNAP within Four Months of Experiencing 

Event, Comparison over Time 

Figure II.5 Percentage of SNAP Entrants Who Experienced Event in Previous Four Months, 

Comparison over Time 

2.5

3.9

5.3

4.6

4.3

1.7

14.2

2.8

3.8

3.9

2.0

3.0

7.0

6.0

5.0

2.0

14.0

2.0

4.0

4.0

0.0 5.0 10.0 15.0 20.0

Experienced any trigger event

Other composition change

Newly separated or divorced

New dependent (non-infant) in family

Pregnant/New infant in family

Decrease in other income (10% or more)

Decrease in TANF

Decrease in earnings (10% or more)

Recently unemployed (other family member)

Recently unemployed (self)

Percentage

2001-2003

2004-2006

60.7

6.3

1.8

5.3

2.3

26.1

2.5

38.7

11.5

6.2

70.8

5.6

2.3

2.7

6.1

24.1

2.7

56.0

11.3

5.9

0 10 20 30 40 50 60 70 80 90 100

Experienced any trigger event

Other composition change

Newly separated or divorced

New dependent (non-infant) in family

Pregnant/New infant in family

Decrease in other income (10% or more)

Decrease in TANF

Decrease in earnings (10% or more)

Recently unemployed (other family member)

Recently unemployed (self)

Percentage

2001-2003

2004-2006

64
B. Length of SNAP Participation Spells 

Having examined patterns of SNAP entry and the triggers that lead to entry, we turn to the 

length of stay in the program. We address the following questions: 

How long are participation spells for entrants? What is the median time on SNAP after 

program entry? How do these vary among different SNAP subgroups? 

How long are participation spells for participants when viewed at a specific point in time 

such as a cross-section of participants receiving benefits in the same month? 

How do the answers to the above research questions compare to the findings in the 

studies for earlier periods? 

We examine the length of participation spells by using survival or ―life table‖ analysis to 

estimate the rate at which individuals ended their participation spell in each month following 

program entry. Our analysis uses two different samples of participants—an entry cohort sample 

and a cross-sectional sample. The entry cohort sample includes all individuals who began a spell 

of SNAP participation during a given calendar period, in this case, within the SIPP panel period. 

This allows us to partially answer the first question above, concerning how long the new entrants 

will participate. The cross-sectional sample of SNAP participation includes all individuals 

receiving benefits at a given point in time, regardless of when their participation began, and 

allows us to answer the second and third questions. 

1. Entry Cohort Analysis 

a. Sample and Methods 

The entry cohort analysis uses a sample from the 2004 panel in which each observation 

represents a single participation spell of an individual. We limit our sample to spells that began 

in month 3 or later (and therefore are not left-censored) and allow sample members to contribute 

more than one spell to the analysis. 

65
For each spell, we are able to observe the length of the spell during the panel period, and we 

know whether the spell was still in progress at the end of the panel period (that is, whether the 

spell was right-censored). We also identify characteristics of the individuals during the month 

prior to the start of the spell for our subgroup analysis, similar to those used in the SNAP entry 

subgroup analysis. 

The 2004 SIPP panel contains a total of 12,367 SNAP spells from 10,010 individuals. Most 

of these individuals (almost 79 percent) contributed only one spell to the data set. About 44 

percent of spells are left-censored, including 20 percent that ended within the panel period and 

24 percent that are both left- and right-censored. Another 3 percent of spells are not leftcensored, but began before month 3 or after month 30; including these spells would not give us 

the number of months we need to establish at least two months of participation before we record 

an exit. The remaining 54 percent of spells that form our entry cohort are not left-censored, 

including 30 percent that are neither left- nor right-censored and 23 percent that are rightcensored. 

To estimate spell length, we construct life tables. Life tables allow us to use information we 

have about the length of a spell, while ignoring information we do not have. For example, if an 

individual participated for the last 12 months of the sample, and thus has a right-censored spell, 

we use only the fact that the individual did not exit the program after any of his or her first 11 

months on the program. We then ignore this person beyond month 12 of the life table. We note 

that because the samples on which the spell lengths are based decline as the duration increases, 

the estimates of the hazard rates generally become less precise as duration rises. 

In our life tables we consider participation spells by month. For each month, we show the 

weighted estimates of survivor, hazard, and cumulative exit rates. The hazard rate is the 

66
probability that a spell ends in a particular month, given that it has lasted at least until the 

beginning of that month. The survivor rate is the unconditional probability that a spell remains in 

progress more than a given number of months. Finally, the cumulative exit rate is the 

unconditional probability that a spell ends within a given number of months. The survivor and 

cumulative exit rates total 100 percent. 

For our analysis, we focus on a few summary measures from the life table: the cumulative 

exit probabilities and the median spell length. The cumulative exit probabilities measure the 

proportion of participants who exit SNAP within a given number of months; we focus on 4, 12, 

and 24 months. In the month that the cumulative exit probability reaches 50 percent, we have the 

median spell length––half of all spells are shorter and half of all spells are longer. We provide 

these summary measures for all participants and key subgroups. 

b. Lengths of SNAP Participation in the 2004 Panel and Comparisons over Time 

Table II.11 indicates that most SNAP participants who entered any time after month 3 of the 

panel exited within the next year. The median spell length for these participants was 

approximately 10 months, with 37.5 percent of spells ending within six months and 57.9 percent 

ending within a year. Within two years, 74.3 percent of SNAP participation spells ended, so just 

over one-quarter of spells lasted two years or longer. These results show a slightly longer spell 

length than identified in Cody et al. (2007) for the early 2000s (Figure II.6). They found that the 

median spell length during the early 2000s was eight months, and that 61.4 percent ended within 

one year. Similarly, Cody et al. (2005) measured spell length during the early to late 1990s and 

found that the median length was eight months, and that 64.1 percent of spells ended within a 

year. The 2004 to 2006 spell lengths are also longer than those in 1991 to 1992 in which the 

median length was nine months. 

67
Table II.11 Life Table of Participation Spells 

Month

Number of 

Spells at 

Beginning

of Month

Number 

In-Sample in 

Following Month

Number Exiting 

During Following 

Month Survivor Rate Hazard Rate

Cumulative

Exit Rate

Standard 

Error of 

Survivor 

Rate

(a) (b) (c) (d) (e) (f) (g)

1

a

27,601,828 27,546,465 0 100.0 0.0 0.0 0.1

2 27,546,465 27,521,088 1,653,767 94.0 6.0 6.0 0.3

3 25,867,321 25,664,504 1,427,489 88.8 5.6 11.2 0.4

4 24,237,016 21,924,152 4,005,100 72.5 18.3 27.5 0.6

5 17,919,052 17,494,283 1,232,011 67.4 7.0 32.6 0.6

6 16,262,272 16,050,222 1,168,892 62.5 7.3 37.5 0.6

7 14,881,330 14,701,787 760,091 59.3 5.2 40.7 0.6

8 13,941,695 12,538,231 1,338,059 53.0 10.7 47.0 0.6

9 11,200,172 10,946,789 511,990 50.5 4.7 49.5 0.6

10 10,434,799 10,240,275 397,637 48.5 3.9 51.5 0.6

11 9,842,638 9,599,376 428,706 46.4 4.5 53.6 0.6

12 9,170,670 7,939,961 727,531 42.1 9.2 57.9 0.5

13 7,212,430 6,921,183 265,314 40.5 3.8 59.5 0.5

14 6,655,870 6,568,318 145,131 39.6 2.2 60.4 0.5

15 6,423,187 6,395,717 239,146 38.1 3.7 61.9 0.5

16 6,156,571 5,039,571 350,403 35.5 7.0 64.5 0.4

17 4,689,169 4,495,501 166,336 34.2 3.7 65.8 0.4

18 4,329,165 4,223,766 102,129 33.3 2.4 66.7 0.4

19 4,121,637 4,020,999 107,586 32.4 2.7 67.6 0.4

20 3,913,413 3,128,135 220,092 30.2 7.0 69.8 0.3

21 2,908,042 2,719,471 127,774 28.7 4.7 71.3 0.3

22 2,591,697 2,477,484 68,920 27.9 2.8 72.1 0.3

23 2,408,565 2,314,576 47,010 27.4 2.0 72.6 0.3

24 2,267,567 1,641,016 101,634 25.7 6.2 74.3 0.2

25 1,539,383 1,424,937 30,060 25.1 2.1 74.9 0.1

26 1,394,877 1,263,158 10,194 24.9 0.8 75.1 0.1

27 1,252,964 1,205,697 30,960 24.3 2.6 75.7 0.0

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: Reference Months: 3 to 31 

Sample: SNAP spells 

 a We do not observe exits after one month because we fill one-month gaps in nonparticipation. 

 Column (a) represents the number of SNAP spells that have lasted at least the indicated number of months, 

regardless of when the spell first started. Column (b) indicates the number of the spells from (a) that we 

continue to observe in the following month (that is, spells that are not right censored). Column (c) is the 

number of spells from (b) that exit the SNAP in the following month. The hazard rate (e) is 100*(c)/(b). The 

cumulative exit rate (f) is sum of the previous row’s cumulative exit rate and the product of the current row’s 

hazard rate and previous row’s survivor rate, divided by 100. The survivor rate is 100-(f). For example, Month 

12 indicates that 9,170,670 spells have lasted at least 12 months or longer. We observe the 13th month for 

7,939,961 spells. Of those that we observe, 727,531 end after the 12th month. Then the hazard rate is 

100*727,531/7,939,961 (=9.2). The cumulative exit rate is 53.6+9.2*46.4/100 (=57.9). 

 The change in the number of spells from the first row of the table to the last row reflects losses due to both 

SNAP exits and right censoring. In the higher rows of the table, with the shorter participation spells, more of 

the loss is due to exits, while in the lower rows of the table, with the longer participation spells, more of the 

loss is due to right censoring. For example, at 6 months, column (c) shows that 1.2 million spells end; 

68
subtracting column (b) from column (a) shows that 0.2 million spells are right censored and no longer included 

in the sample. On the other hand, at 24 months, 0.1 million spells end but 0.6 million spells are lost due to 

right censoring. Spells of individuals who leave the universe (for example, due to a move to group quarters, 

exit from the country, or death) before an exit is observed are right-censored and are not incorporated into the 

exit rate. 

Figure II.6 Median Length of SNAP Participation in Entry Cohort Sample, 

 Comparisons over Time 

The rate at which SNAP participants exit the program decreases as length increases, as can 

be seen by examining the hazard rates. Analysis of these rates requires caution because seam bias 

causes them to jump at four-month intervals; however, the rate at these four-month intervals 

generally decreases over time. For example, the hazard rate falls from 18.3 percent in month 4 to 

10.7 percent in month 8, 9.2 percent in month 12, and 7.0 percent in months 16 and 20. 

c. Lengths of SNAP Participation, by Subgroup 

We can compute life tables for key subgroups of participants as well. In Table II.12, we 

present selected results from the subgroup life tables: the median participation spell and 

cumulative exit rates for 4, 12, and 24 months. Then, using the log-rank statistic within each 

category of subgroups (e.g., household composition), we test the significance of the difference in 

the lengths of SNAP receipt. 

9

8 8

10

0

2

4

6

8

10

12

1991-1992 1993-1996 2001-2003 2004-2006

Months

69
There are sharp contrasts in the length of SNAP participation spells among participant 

subgroups. Adults living in families without any children, elderly, or disabled members had the 

shortest spells, with a median length of 7 months, while elderly members with no other family 

members had a median spell that exceeded 23 months. Nonelderly disabled adults had longer 

participation spells than nonelderly, nondisabled, childless adults (median length of 16 and 6 

months, respectively). Furthermore, individuals in families with no earnings, families with no 

high school graduate, and families with SSI all had median spell lengths longer than the average, 

at 12, 13, and 11 months, respectively. 

The subgroup analysis also shows that children living with multiple adults had shorter spell 

lengths than children living with one adult. Children living with a married family head had a 

median spell length of 11 months, and children living with multiple adults had a median spell 

length of 9 months. In contrast, children living with one adult had a median spell length of 12 

months. Similarly, adults living in families with multiple adults, whether married or not, had 

shorter spells than adults living in single-adult families with children. 

Another result provided by the subgroup spell length analysis is that individuals living in 

families with no monthly income had longer median spell lengths than those living at other 

poverty levels. Those in families with no income had a median spell length of 15 months, while 

those living in families with income between 50 and 100 percent of poverty had a median length 

of 12 months, and those living in families with income of more than 200 percent of poverty had a 

median spell length of 6 months. About one-third of individuals in families with no income had a 

SNAP spell that was at least two years, while less than one-fifth of individuals in families with 

income above 200 percent of poverty had a spell that was at least two years. 

70
Table II.12 Length of SNAP Spells by Subgroups 

Subgroup

Sample 

Size

Median 

Participation 

Spell 

(Months)

Cumulative Exit Rate (Percent) Log-Rank Statistic to 

Test Differences 

across Subgroups

4 Months 

or Less

12 Months 

or Less

24 Months 

or Less

All Individuals 6,634 10 27.5 57.9 74.3

Family Composition 149.3***

Individuals in families with children 4,945 10 25.4 57.6 75.0

Adults in families with children and one adult 561 11 19.9 54.9 68.4

Children in families with children and one adult 1,100 12 19.1 50.8 66.4

Adults in families with children and multiple adults 584 8 34.1 67.1 79.5

Children in families with children and multiple adults 512 9 23.1 62.4 79.7

Adults in families with children and a married head 1,028 9 29.9 60.5 78.9

Children in families with children and a married head 1,125 11 26.2 55.4 78.9

Children in child-only families 35 9 29.4 64.8 64.8

Individuals in families without children 1,689 8 33.4 58.5 72.4

Individuals in families with elderly members 626 12 32.1 50.9 63.4

Elderly members living alone 163 >24 17.7 36.8 47.8

Elderly members living with other elderly individuals 120 12 35.6 50.2 51.5

Elderly members living with non-elderly individuals 333 9 36.5 57.0 72.1

Individuals in families with disabled members 448 11 27.5 52.4 64.9

Individuals in families without any elderly or disabled members 615 7 38.4 69.7 85.7

Age and Disability 99.1***

Nonelderly disabled adults 752 16 20.6 47.9 62.6

Nonelderly nondisabled childless adults 668 6 41.1 68.2 84.9

Family Income 211.5***

Under 50 percent of poverty 815 10 24.1 57.6 71.3

50 to under 100 percent of poverty 1,716 12 20.1 50.6 67.4

100 to under 200 percent of poverty 2,281 9 24.6 58.5 77.6

200 or more percent of poverty 1,303 6 45.0 70.3 81.9

71
 Table II.12, continued

Subgroup

Sample 

Size

Median 

Participation 

Spell 

(Months)

Cumulative Exit Rate (Percent) Log-Rank Statistic to 

Test Differences 

across Subgroups

4 Months

or Less

12 Months 

or Less

24 Months 

or Less

Age 27.3***

Children (under age 18) 2,772 11 22.8 54.9 73.9

Nonelderly adults (age 18 - 59) 3,299 8 30.3 60.9 76.5

Elderly adults (age 60 and over) 563 12 32.9 52.5 60.9

Sex 28.3***

Male (age 18 and over) 1,466 8 36.6 65.6 79.7

Female (age 18 and over) 2,396 11 26.6 56.1 71.1

Race/Ethnicity a44.1***

White, Non-Hispanic 3,093 9 29.5 60.3 78.9

African American, Non-Hispanic 1,673 12 26.7 54.3 67.6

Hispanic, all races 1,252 10 26.3 58.3 75.1

Asian, Non-Hispanic 92 20 19.5 40.1 54.1

Other, Non-Hispanic 524 9 22.7 58.9 70.8

Education 47.3***

Individuals in families with HS graduate 5,579 9 28.2 59.6 75.7

Individuals in families with no HS graduate 1,055 13 23.5 49.1 67.4

Citizenship

Citizen 6,302 10 27.2 57.8 74.6

Noncitizen 332 8 31.0 59.9 69.7

Citizen children living with noncitizen adults in the family 305 10 21.4 52.4 78.8

Adults in families with citizen adults and citizen children 2,012 9 28.5 61.4 77.0 48.2***

Children in families with citizen adults and citizen children 2,479 12 21.7 54.6 73.6

Adults in families with noncitizen adults and citizen children 137 8 27.0 60.1 71.7

Children in families with noncitizen adults and citizen children 241 8 31.8 56.3 77.1

72
 Table II.12, continued

Subgroup

Sample 

Size

Median 

Participation 

Spell 

(Months)

Cumulative Exit Rate (Percent) Log-Rank Statistic to 

Test Differences 

across Subgroups

4 Months 

or Less

12 Months 

or Less

24 Months 

or Less

Presence of Income

Individuals in families with no income 524 15 20.9 45.2 67.6 16.0***

Individuals in families with income 6,110 9 28.0 59.0 74.9

Presence of Earnings 122.9***

Individuals in families with earnings 4,291 8 30.2 61.9 78.9

Individuals in families without earnings 2,343 12 22.2 50.3 66.6

Presence of TANF

Individuals in families with TANF 416 10 32.1 58.5 74.1

Individuals in families without TANF 6,218 10 27.1 57.8 74.3

Other Income

Individuals in families with Social Security income 1,664 9 29.6 58.0 71.7 16.2***

Individuals in families without Social Security income 4,970 10 26.8 57.9 75.2

Individuals in families with SSI 1,195 11 28.5 58.7 71.2

Individuals in families without SSI 5,439 10 27.3 57.8 75.1

Individuals in families with unemployment compensation 310 8 31.3 67.2 88.9 7.8***

Individuals in families with no unemployment compensation 6,324 10 27.3 57.4 73.6

 Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: Reference Months: 3 to 31 

Sample: SNAP spells 

Subgroups: Characteristics in month before SNAP spell began 

The log-rank test compares the estimated monthly hazard rate to the expected monthly hazard rate where the expected rate is 

calculated based on the null hypothesis that the hazard rate is the same for each time period of the subgroup category. We do not 

reject the null hypothesis that the distributions are the same across categories if the aggregate difference between the estimated 

and expected hazard rate is small relative to the aggregate variance of the difference. We reject the null hypothesis if the difference 

is large.

a

 Categories are race alone; respondents who reported multiple races are in the Other, Non-Hispanic category 

73
2. Cross-Sectional Analysis 

While a cohort analysis provides a wealth of information about those just entering SNAP, it 

does not inform us about those already participating, so we turn to our cross-sectional sample, 

which consists of all individuals participating in SNAP in a given month. For this group, we 

consider such questions as: How many additional months will they spend in the program? What 

will be the total length of their spell? For the first question, we estimate subsequent spell lengths, 

and for the second, we estimate completed spell lengths. 

a. Sample and Methods 

Our cross-sectional sample includes all individuals receiving SNAP benefits in May 2004.33

It is the month in the second wave of data that all respondents have in common. We then use 

characteristics of individuals in May 2004 to assign them to subgroups, similar to those we use in 

the entry and entry cohort duration analyses. The cross-sectional sample consists of spells. About 

45 percent ended within the panel period, with the remainder being right-censored. 

As with the entry cohort analysis, we use life tables to address the relevant questions. For the 

life table of subsequent spell lengths, May 2004 is treated as month 1 for all cross-sectional 

sample members. For the life table of completed spell lengths, the first month of the spell is 

treated as month 1. 

b. Subsequent Spell Lengths in the 2004 Panel and Comparisons over Time 

For subsequent spell lengths of our cross-sectional sample, average spells were much longer 

than in our new entry cohort, even though we only consider the spell duration from May forward. 

The median subsequent spell length for the cross-sectional sample was over 27 months (the data 

 

33 May 2004 is the fifth panel month for those in rotation group 4, the sixth panel month for those in rotation group 

3, the seventh panel month for those in rotation group 2 and the eighth panel month for those in rotation group 1. 

74
support only the estimation of spell lengths at or less than 27 months), with 30.8 percent having a 

12 month spell length and 46.7 having a 24 month spell length (see Table II.13). This compares 

with a median of 10 months for individuals in the entry cohort sample. 

As in previous studies, we expect to see the longer spell lengths within the cross-sectional 

sample, when compared with the entry cohort (Gleason et al. 1998; Cody et al. 2005; Cody et al. 

2007). For any month of the sample we choose, we will miss many of the short spells that occur 

within the sample period––they are likely to be completed before or begin after our sample 

month. Longer spells, however, are more likely to include our sample month. For this reason, the 

longer spells are more heavily represented in the cross-sectional than in the entry cohort sample. 

75
Table II.13 Life Table of Subsequent Spell Length for the Full Cross-Sectional Sample 

Month

Number of 

Spells at 

Beginning 

of Month

Number 

In-Sample in 

Following 

Month

Number 

Exiting During 

Following 

Month

Survivor 

Rate

Hazard 

Rate

Cumulative

Exit Rate

Standard 

Error of 

Survivor Rate

(a) (b) (c) (d) (e) (f) (g)

1 21,381,163 21,351,297 870,119 95.9 4.1 4.1 0.3

2 20,481,178 20,408,225 793,986 92.2 3.9 7.8 0

.

4

0.4

3 19,614,238 19,570,684 733,405 88.7 3.7 11.3 0

.

5

0.5

4 18,837,280 18,794,091 829,426 84.8 4.4 15.2 0

.

5

0.5

5 17,964,665 17,931,403 545,210 82.2 3.0 17.8 0

.

6

0.6

6 17,386,193 17,357,540 347,603 80.6 2.0 19.4 0

.

6

0.6

7 17,009,937 16,963,446 478,833 78.3 2.8 21.7 0

.

6

0.6

8 16,484,613 16,457,837 473,906 76.1 2.9 23.9 0

.

6

0.6

9 15,983,930 15,982,485 501,941 73.7 3.1 26.3 0

.

7

0.7

10 15,480,544 15,469,065 278,782 72.3 1.8 27.7 0

.

7

0.7

11 15,190,283 15,162,813 400,783 70.4 2.6 29.6 0

.

7

0.7

12 14,762,030 14,720,172 261,489 69.2 1.8 30.8 0

.

7

0.7

13 14,458,683 14,443,520 408,792 67.2 2.8 32.8 0

.

7

0.7

14 14,034,728 14,020,615 362,298 65.5 2.6 34.5 0

.

7

0.7

15 13,658,317 13,650,190 296,881 64.1 2.2 35.9 0

.

7

0.7

16 13,353,309 13,342,828 316,766 62.5 2.4 37.5 0

.

7

0.7

17 13,026,062 13,022,161 247,881 61.4 1.9 38.6 0

.

7

0.7

18 12,774,280 12,757,511 234,437 60.2 1.8 39.8 0

.

7

0.7

19 12,523,073 12,507,925 151,454 59.5 1.2 40.5 0

.

7

0.7

20 12,356,472 12,350,548 338,491 57.9 2.7 42.1 0

.

7

0.7

21 12,012,056 12,010,231 194,432 56.9 1.6 43.1 0

.

7

0.7

22 11,815,800 11,814,077 266,241 55.6 2.3 44.4 0

.

7

0.7

23 11,547,836 11,547,836 217,952 54.6 1.9 45.4 0

.

7

0.7

24 11,329,883 11,317,162 262,637 53.3 2.3 46.7 0

.

7

0.7

25 11,054,525 8,356,784 99,811 52.7 1.2 47.3 0

.

7

0.7

26 8,256,973 5,635,446 29,229 52.4 0.5 47.6 0

.

5

0.5

27 5,606,217 3,040,804 50,657 51.5 1.7 48.5 0

.

0

0.0

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: Reference Months: May 2004 and subsequent months 

Sample: SNAP spells 

 Column (a) represents the number of SNAP spells that have lasted at least the indicated number of months, 

regardless of when the spell first started. Column (b) indicates the number of the spells from (a) that we 

continue to observe in the following month (that is, spells that are not right censored). Column (c) is the 

number of spells from (b) that exit the SNAP in the following month. The hazard rate (e) is 100*(c)/(b). The 

cumulative exit rate (f) is sum of the previous row’s cumulative exit rate and the product of the current row’s 

hazard rate and previous row’s survivor rate, divided by 100. 

 The change in the number of spells from the first row of the table to the last row reflects losses due to both 

SNAP exits and right censoring. In the top rows of the table, with the shorter participation spells, more of the 

loss is due to exits, while in the bottom rows of the table, with the longer participation spells, more of the loss 

76
is due to right censoring. For example, at 6 months, column (c) shows that 0.3 million spells end; subtracting 

column (b) from column (a) shows that less than 30,000 spells are right censored and no longer included in 

the sample. On the other hand, at 27 months, 51,000 spells end but 2.6 million spells are lost due to right 

censoring. Spells of individuals who leave the universe (for example, due to a move to group quarters, exit 

from the country, or death) before an exit is observed are right-censored and are not incorporated into the exit 

rate. 

c. Subsequent Spell Lengths, by Subgroup 

Patterns of subsequent spell lengths among subgroups of the cross-sectional sample (see 

Table II.14) are similar to the patterns for the entry cohort (see Table II.12). Elderly members 

living with no other family members or with only other elderly family members have longer 

participation spells than all other subgroups we examine. Only 17.8 percent of elderly members 

living with no other family members and 15.7 percent of elderly members living with only other 

elderly family members have ended their participation spell within two years. In contrast, 69.7 

percent of individuals in families without any children or elderly or disabled members exit within 

24 months or less; one-third exit within 4 months. Thus, there are a higher proportion of 

individuals in families without children, elderly, or disabled members exiting within 4 months 

than there are elderly individuals living with no other family members or with only other elderly 

family members exiting within 24 months. 

77
Table II.14 Subsequent Spell Length for the Full Cross-Sectional Sample by Subgroups 

Subgroup

Sample 

Size

Median 

Participation 

Spell (Months)

Cumulative Exit Rate (Percent) Log-Rank Statistic 

to Test Differences 

across Subgroups

4 Months 

or Less

12 Months 

or Less

24 Months or 

Less

All Individuals 5,619 > 27 15.2 30.8 46.7

Family Composition 265.3***

Individuals in families with children 4,201 27 15.0 32.1 48.8

Adults in families with children and one adult 632 > 27 11.7 29.9 43.3

Children in families with children and one adult 1,285 > 27 10.0 26.6 41.4

Adults in families with children and multiple adults 402 18 23.4 41.8 56.4

Children in families with children and multiple adults 467 23 15.3 31.6 51.4

Adults in families with children and a married head 635 22 19.0 35.7 52.5

Children in families with children and a married head 757 21 17.6 34.3 56.1

Children in child-only families 23 > 27 10.2 41.0 47.5

Individuals in families without children 1,418 > 27 15.6 26.6 39.8

Individuals in families with elderly members 656 > 27 10.7 19.5 28.3

Elderly members living alone 316 > 27 4.9 10.3 17.8

Elderly members living with other elderly individuals 129 > 27 5.8 8.7 15.7

Elderly members living with non-elderly individuals 198 27 19.5 36.2 48.3

Individuals in families with disabled members 525 > 27 11.0 20.2 35.0

Individuals in families without any elderly or disabled 

members 237 11 33.0 51.3 69.7

Age and Disability 151.3***

Nonelderly disabled adults 930 > 27 9.8 19.6 34.0

Nonelderly nondisabled childless adults 256 11 33.2 51.3 71.1

Family Income 310.7***

Under 50 percent of poverty 1,202 > 27 9.6 25.6 40.8

50 to under 100 percent of poverty 2,127 > 27 9.9 23.2 38.6

100 to under 200 percent of poverty 1,488 20 20.1 36.9 55.9

200 or more percent of poverty 446 10 34.9 55.8 67.1

78
 Table II.14, continued 

Subgroup

Sample 

Size

Median 

Participation 

Spell (Months)

Cumulative Exit Rate (Percent) Log-Rank Statistic 

to Test Differences 

across Subgroups

4 Months 

or Less

12 Months 

or Less

24 Months or 

Less

Age 105.1***

Children (under age 18) 2,532 > 27 13.3 30.0 47.8

Nonelderly adults (age 18 - 59) 2,468 25 18.3 34.5 49.9

Elderly adults (age 60 and over) 619 > 27 8.7 16.0 23.7

Sex 38.0***

Male (age 18 and over) 935 21 23.4 39.8 54.1

Female (age 18 and over) 2,152 > 27 13.4 27.4 41.7

Race/Ethnicity a

62.2***

White, Non-Hispanic 2,372 23 17.4 34.6 51.8

African American, Non-Hispanic 1,756 > 27 10.4 26.8 40.9

Hispanic, all races 965 > 27 15.7 28.9 45.1

Asian, Non-Hispanic 91 > 27 14.3 17.9 29.0

Other, Non-Hispanic 435 20 25.8 39.7 56.3

Education 62.0***

Individuals in families with HS graduate 4,280 25 15.9 33.1 49.7

Individuals in families with no HS graduate 1,339 > 27 12.9 23.6 37.2

Citizenship

Citizen 5,385 > 27 14.6 30.4 46.3 9.0***

Noncitizen 234 20 26.5 38.3 53.6

Citizen children living with noncitizen adults in the family 205 20 15.0 31.4 58.0 6.3**

Adults in families with citizen adults and citizen children 1,545 25 16.2 34.3 49.4 38.2***

Children in families with citizen adults and citizen children 2,343 > 27 12.5 29.4 46.8

Adults in families with noncitizen adults and citizen 

children 108 22 21.7 36.7 52.9

Children in families with noncitizen adults and citizen 

children 156 20 18.1 31.9 57.9

79
 Table II.14, continued 

Subgroup

Sample 

Size

Median 

Participation 

Spell (Months)

Cumulative Exit Rate (Percent) Log-Rank Statistic 

to Test Differences 

across Subgroups

4 Months 

or Less

12 Months 

or Less

24 Months or 

Less

Presence of Income 115.8***

Individuals in families with no income 359 > 27 16.3 32.5 47.6

Individuals in families with income 5,260 > 27 15.1 30.7 46.6

Presence of Earnings 345.2***

Individuals in families with earnings 2,870 19 20.7 39.9 57.9

Individuals in families without earnings 2,749 > 27 8.7 20.3 33.6

Presence of TANF 17.6***

Individuals in families with TANF 1,029 > 27 12.8 28.1 42.7

Individuals in families without TANF 4,590 > 27 15.8 31.5 47.7

Other Income

Individuals in families with Social Security income 1,576 > 27 12.3 26.7 41.3

Individuals in families without Social Security income 4,043 27 16.2 32.3 48.5

Individuals in families with SSI 1,467 > 27 10.7 21.5 35.8

Individuals in families without SSI 4,152 24 16.7 33.9 50.3

Individuals in families with unemployment compensation 125 8 29.7 60.3 68.8 27.6***

Individuals in families with no unemployment 

compensation

5,494 > 27 14.8 30.1 46.1

 Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: Reference Months: May 2004 and subsequent months 

Sample: SNAP spells 

Subgroups: Characteristics in month before SNAP spell began 

The log-rank test compares the estimated monthly hazard rate to the expected monthly hazard rate where the expected rate is 

calculated based on the null hypothesis that the hazard rate is the same for each time period of the subgroup category. We do not 

reject the null hypothesis that the distributions are the same across categories if the aggregate difference between the estimated 

and expected hazard rate is small relative to the aggregate variance of the difference. We reject the null hypothesis if the difference 

is large.

a

 Categories are race alone; respondents who reported multiple races are in the Other, Non-Hispanic category

80
d. Completed Spell Lengths in the 2004 Panel and Comparisons over Time 

We also measured the completed lengths of participation spells (adding receipt prior to May 

2004 to the subsequent receipt) for the cross-sectional sample of participants. Only 8.0 percent of 

participants in May 2004 had a completed spell length of six months or less, 17.4 percent had 

spells that lasted for one year or less, and 28.6 percent had spells that lasted two years or less 

(Table II.15). Half of the cross section of SNAP participants exited the program within seven 

years. The first 25 percent to exit do so within two years; however, an additional five years pass 

before another 25 percent have exited. 

Table II.15 Life Table of the Completed Length of SNAP Spells for the Full Cross-Sectional 

Sample 

Years

Number of Spells at 

Beginning 

of Month

Number 

In-Sample in 

Following Month

Number Exiting 

During 

Following Month Survivor Rate Hazard Rate

Cumulative

Exit Rate

(a) (b) (c) (d) (e) (f)

0.5 21,183,603 21,135,401 1,693,625 92.0 8.0 8.0

1.0 19,441,776 19,302,100 1,972,854 82.6 10.2 17.4

1.5 17,329,247 17,282,052 1,189,134 76.9 6.9 23.1

2.0 16,092,917 16,056,446 1,147,134 71.4 7.1 28.6

3.0 14,909,312 11,393,154 1,341,933 63.0 11.8 37.0

4.0 10,051,221 8,161,423 587,047 58.5 7.2 41.5

5.0 7,574,376 6,806,513 308,469 55.8 4.5 44.2

6.0 6,498,044 5,821,143 435,373 51.6 7.5 48.4

7.0 5,385,770 4,652,415 179,725 49.6 3.9 50.4

8.0 4,472,690 3,940,466 198,700 47.1 5.0 52.9

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: Reference Months: 1 to 31 

Sample: SNAP spells that were underway in May 2004 

Column (a) represents the number of SNAP spells that have lasted at least the indicated number of months, 

regardless of when the spell first started. Column (b) indicates the number of the spells from (a) that we 

continue to observe in the following month (that is, spells that are not right censored). Column (c) is the 

number of spells from (b) that exit the SNAP in the following month. The hazard rate (e) is 100*(c)/(b). The 

cumulative exit rate (f) is sum of the previous row’s cumulative exit rate and the product of the current row’s 

hazard rate and previous row’s survivor rate, divided by 100.

The change in the number of spells from the first row of the table to the last row reflects losses due to both 

SNAP exits and right censoring. In the top rows of the table, with the shorter participation spells, more of the 

loss is due to exits, while in the bottom rows of the table, with the longer participation spells, more of the loss 

is due to right censoring. For example, at 1 year, column (c) shows that 2.0 million spells end; subtracting 

column (b) from column (a) shows that 0.1 million spells are right censored and no longer included in the 

sample. On the other hand, at 7 years, 0.2 million spells end but 0.7 million spells are lost due to right 

censoring. Spells of individuals who leave the universe (for example, due to a move to group quarters, exit 

81
from the country, or death) before an exit is observed are right-censored and are not incorporated into the 

exit rate. 

The estimate of the median completed spell length of seven years for the May 2004 crosssectional sample is longer than the medians found for the mid-1990s and early 2000s (Cody et al. 

2007; Cody et al. 2005), which were 3.5 years and 4 years, respectively (Table II.16).34 The 

median is closer to the Gleason et al. (1998) findings for the early 1990s of 8 years. Figure II.7 

shows the cumulative exit rates for each of the timeframes—the cross-sectional sample from 

2004 closely resembles the 1996 sample for shorter spells, but resembles the 1991 findings for 

the longer spells. 

Table II.16 Comparison of Spell Lengths and Exit Rates for Completed Spells Using CrossSectional Samples 

1991-1993 1993-1996 1996-1999 2001-2003 2004-2006

Median Spell Length 

(Months) >96 54 54 48 84

Cumulative Exit Rate

 6 Months or Less 5.9 NA 8.3 10.1 8.0

12 Months or Less 11.4 NA 16.2 20.8 17.4

24 Months or less 18.4 NA 30.5 27.7 28.6

Sources: 2004-2006: Decision Demographics weighted tabulations of the 2004 SIPP panel; 2001-2003: Cody et al. 

(2007); 1996-1999 Cody et al. (2005); 1993-1996: Cody et al. (2005); 1990-1993 Gleason, et al. (1998) 

 

34 The recipiency history topical module survey instrument underwent several modifications between 2001 and 

2004, including changes in question wording and ordering. Though this may have had an effect on comparisons over 

time, the direction of the effect is unclear. These changes are discussed in Appendix A. 

82
Figure II.7 Cumulative Exit Rates for Cross-Sectional Sample, Comparison over Time 

e. Completed Spell Lengths, by Subgroup 

Not surprisingly, individuals in families without any children, elderly, or disabled members 

have the shortest completed spell length (Table II.17). Many of these individuals are subject to 

time limits. Almost one quarter of them exit within six months and over half exit within two 

years. In contrast, only 13.4 percent of elderly individuals with no other persons in their family 

and 19.9 percent of elderly individuals living in families with only other elderly individuals exit 

within eight years. 

0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7 8

Percent Exiting Within Number of Years

Number of Years

1996

2001

2004

1991

83
Table II.17 Completed Length of SNAP Spells for the Full Cross-Sectional Sample by Subgroups 

Cumulative Exit Rate (Percent)

Subgroup

Sample 

Size

.5 Yrs or 

Less

1 Yr or 

Less

1.5 Yrs 

or Less

2 Yrs or 

Less

3 Yrs or 

Less

4 Yrs or 

Less

5 Yrs or 

Less

6 Yrs or 

Less

7 Yrs or 

Less

8 Yrs or 

Less

All Individuals 5,570 8.0 17.4 23.1 28.6 37.0 41.5 44.2 48.4 50.4 52.9

Family Composition

Individuals in families with children 4,152 7.4 17.1 23.6 29.1 38.3 43.7 46.8 51.6 53.8 56.5

Adults in families with children and one 

adult 632 4.8 12.6 19.3 23.8 33.8 40.4 43.0 48.6 50.1 51.4

Children in families with children and one 

adult 1,261 3.3 10.3 16.3 20.9 30.8 37.1 39.9 45.8 47.2 50.1

Adults in families with children and 

multiple adults 402 13.4 28.4 33.8 38.2 44.8 49.6 51.9 55.3 59.5 61.0

Children in families with children and 

multiple adults 461 6.7 18.7 23.7 28.9 35.7 40.0 44.3 46.8 49.6 54.1

Adults in families with children and a 

married head 635 10.8 23.0 30.0 36.9 47.7 51.7 55.3 58.9 60.9 64.6

Children in families with children and a 

married head 739 10.2 19.0 27.6 35.2 44.7 49.8 53.2 59.3 61.9 65.0

Children in child-only families 22 10.5 17.2 17.2 17.2 26.9 32.1 32.1 32.1 32.1 32.1

Individuals in families without 

children 1,418 9.8 18.5 21.5 27.0 32.7 34.6 35.9 38.1 39.5 41.3

Individuals in families with elderly 

members 656 5.9 12.9 14.5 18.6 21.1 22.0 23.3 24.0 25.7 27.8

Elderly members living alone 316 1.4 4.0 5.2 8.8 9.1 10.0 10.9 11.4 12.1 13.4

Elderly members living with other 

elderly individuals 129 5.3 7.1 7.1 8.6 9.3 10.0 10.0 14.2 14.2 19.9

Elderly members living with nonelderly individuals 198 11.9 26.8 30.0 34.4 41.9 42.4 45.4 46.3 50.8 52.0

Individuals in families with disabled 

members 525 6.0 11.2 14.6 19.5 27.0 29.2 30.7 34.4 36.0 36.4

Individuals in families without any elderly 

or disabled members 237 24.0 41.8 47.1 56.0 65.0 68.7 69.9 72.7 72.7 78.9

84
Table II.17, continued

Cumulative Exit Rate (Percent)

Subgroup

Sample 

Size

.5 Yrs or 

Less

1 Yr or 

Less

1.5 Yrs 

or Less

2 Yrs or 

Less

3 Yrs or 

Less

4 Yrs or 

Less

5 Yrs or 

Less

6 Yrs or 

Less

7 Yrs or 

Less

8 Yrs or 

Less

Age and Disability

Nonelderly disabled adults 930 4.9 11.0 14.5 18.7 25.7 28.0 29.7 32.8 35.4 36.0

Nonelderly nondisabled childless adults 256 24.6 40.5 47.4 57.8 68.8 72.3 74.2 74.2 75.5 78.2

Family Income

Under 50 percent of poverty 1,186 3.6 10.8 16.5 21.4 29.9 35.4 38.3 43.5 45.4 47.6

50 to under 100 percent of poverty 2,107 3.9 10.6 15.9 21.1 28.9 32.1 34.3 37.3 40.6 42.4

100 to under 200 percent of poverty 1,481 11.2 24.4 30.2 35.3 45.3 51.1 54.8 59.9 61.0 64.3

200 or more percent of poverty 444 24.1 39.6 44.2 53.7 63.4 67.5 69.7 72.8 72.8 77.3

Age

Children (under age 18) 2,483 6.1 14.5 21.1 26.7 35.8 41.3 44.6 49.8 51.8 55.0

Nonelderly adults (age 18 – 59) 2,468 10.5 21.6 27.4 33.1 42.2 46.5 48.8 52.7 54.9 56.7

Elderly adults (age 60 and over) 619 4.5 9.9 11.0 14.7 15.6 16.8 18.4 19.3 20.0 22.4

Sex

Male (age 18 and over) 935 14.5 27.8 33.0 39.4 48.0 51.5 53.1 57.5 61.0 63.5

Female (age 18 and over) 2,152 7.2 15.8 20.6 25.6 33.1 37.0 39.3 42.4 43.8 45.5

Race/Ethnicity a

White, Non-Hispanic 2,355 9.7 20.4 26.2 34.0 44.1 48.4 51.0 56.4 58.5 61.0

African American, Non-Hispanic 1,740 4.7 12.6 18.4 21.6 29.4 34.3 36.8 40.9 42.5 44.0

Hispanic, all races 954 9.6 20.2 25.2 30.6 35.4 39.1 41.5 44.8 46.9 51.5

Asian, Non-Hispanic 91 0.8 2.3 2.3 6.6 20.0 26.5 26.5 26.5 26.5 26.5

Other, Non-Hispanic 430 11.4 18.5 27.2 30.1 42.5 49.9 55.3 57.2 60.6 62.8

Education

Individuals in families with HS graduate 4,247 8.7 19.2 25.2 30.8 40.0 44.9 47.9 52.3 54.6 57.0

Individuals in families with no HS 

graduate 1,323 5.8 11.8 16.5 21.6 27.7 31.3 32.9 36.4 37.6 40.2

85
Table II.17, continued

Subgroup

Cumulative Exit Rate (Percent)

Sample 

Size

.5 Yrs or 

Less

1 Yr or 

Less

1.5 Yrs 

or Less

2 Yrs or 

Less

3 Yrs or 

Less

4 Yrs or 

Less

5 Yrs or 

Less

6 Yrs or 

Less

7 Yrs or 

Less

8 Yrs or 

Less

Citizenship

Citizen 5,336 7.6 17.1 22.9 28.4 36.9 41.5 44.1 48.3 50.1 52.6

Noncitizen 234 15.0 24.2 27.5 32.5 38.2 41.8 45.7 50.2 54.7 58.6

Citizen children living with noncitizen 

adults in the family 200 8.7 18.0 27.2 38.0 44.9 49.6 52.9 57.4 60.7 66.1

Adults in families with citizen adults and 

citizen children 1,545 8.3 19.9 26.5 32.0 41.8 46.8 49.1 53.1 55.4 57.0

Children in families with citizen adults 

and citizen children 2,300 5.4 14.2 20.8 26.0 35.3 40.8 44.0 49.1 50.8 53.8

Adults in families with noncitizen adults 

and citizen children 108 13.1 20.1 25.4 30.2 34.8 44.1 55.1 65.1 71.6 79.0

Children in families with noncitizen adults 

and citizen children 151 11.5 17.7 23.6 34.0 41.3 48.1 52.9 61.3 71.8 81.0

Presence of Income

Individuals in families with no income 355 10.3 19.5 28.0 33.3 38.8 44.0 44.9 50.8 51.9 51.9

Individuals in families with income 5,215 7.9 17.3 22.8 28.3 36.9 41.4 44.1 48.2 50.3 52.9

Presence of Earnings

Individuals in families with earnings 2,836 11.2 24.3 30.6 36.8 48.2 53.8 57.2 62.2 64.2 67.1

individuals in families without earnings 2,734 4.3 9.5 14.4 19.2 24.1 27.4 29.3 32.5 34.5 36.6

Presence of TANF

Individuals in families with TANF 1,019 5.3 15.2 20.4 25.5 35.1 40.0 41.6 44.2 44.8 46.4

Individuals in families without TANF 4,551 8.7 18.0 23.8 29.4 37.5 41.9 44.8 49.4 51.7 54.4

86
Table II.17, continued

Subgroup

Cumulative Exit Rate (Percent)

Sample 

Size

.5 Yrs or 

Less

1 Yr or 

Less

1.5 Yrs 

or Less

2 Yrs or 

Less

3 Yrs or 

Less

4 Yrs or 

Less

5 Yrs or 

Less

6 Yrs or 

Less

7 Yrs or 

Less

8 Yrs or 

Less

Other Income

Individuals in families with Social 

Security income 1,573 6.6 15.2 19.8 25.3 29.8 31.9 35.0 38.2 40.1 43.8

Individuals in families without Social 

Security income 3,997 8.5 18.2 24.3 29.8 39.5 45.0 47.5 52.0 54.1 56.2

Individuals in families with SSI 1,463 5.5 11.6 16.0 19.5 25.1 26.7 29.3 32.4 33.9 35.4

Individuals in families without SSI 4,107 8.9 19.4 25.5 31.6 41.0 46.8 49.5 54.2 56.5 59.6

Individuals in families with 

unemployment compensation 125 19.7 33.1 44.4 57.6 60.8 71.3 80.7 80.7 80.7 80.7

Individuals in families with no 

unemployment compensation 5,445 7.7 17.0 22.6 27.9 36.4 40.9 43.4 47.7 49.7 52.2

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: a Categories are race alone; respondents who reported multiple races are in the Other, Non-Hispanic category 

Reference Months: 1 to 31 

 Sample: SNAP spells that were underway in May 2004

87
C. Exiting SNAP 

Our analysis of SNAP spell lengths tells us how long individuals stay in SNAP before they 

exit. We now turn to examining the life events that might lead to their exit in order to estimate 

how many individuals leave SNAP in the period covered by the 2004 SIPP panel for reasons that 

are not related to improved financial circumstances or reduced need as measured in SIPP. To 

answer this question, we will determine (1) what types of events most often precede a SNAP 

exit; (2) how often an exit is preceded by an observed trigger event; and (3) whether trigger 

events differ with characteristics of the participants. 

As with entry trigger events, we cannot directly identify the reason individuals stop receiving 

SNAP benefits, but we can examine their life circumstances (and changes in their life 

circumstance) immediately around the time they leave the program to try to understand more 

about why they exited. In this analysis, we use SIPP data on SNAP participants’ circumstances 

to define a set of trigger events that are analogous to the entry trigger events discussed in Section 

A. We then examine the extent to which these exit trigger events precede SNAP exits. 

1. Methods 

We choose our exit and entry triggers in much the same way, primarily based on the triggers 

found relevant in Cody et al. (2007), Gleason et al. (1998), and Burstein (1993). We define exits 

only for the at-risk population, which consists of all individuals who participated in SNAP for 

the previous two months. We examine SNAP exits in sample months 6 through 31 of the SIPP 

panel period and allow individuals to contribute more than one observation to the data set.35 We 

 

35 An exit in month 6 is defined as participating in SNAP in month 5 and not participating in month 6. Thus, while 

exits are defined in months 6 to 31, they are based on participation data from months 5 to 30. 

88
define exiting SNAP as not receiving SNAP benefits in the sample month or the following 

month.36 

We look for exit trigger events that occurred during the sample month or any of the three 

previous months. Unlike the definitions used for entry trigger events, we shift the window to 

include the sample month, because some participants may exit SNAP in the same month that 

their circumstances change. In entry, the circumstances may need to have changed for a person 

to become eligible, but they may exit knowing that circumstances are about to change (for 

example, that he or she is starting a new job the following week). 

We use the following trigger events in this analysis: 

Increase in family earnings (10 percent or more) 

Increase in other family income (10 percent or more) 

Departure of family member without income 

Departure of family member with income 

New adult family member 

New child family member 

2. Distribution of Exit Trigger Events Among All SNAP Participants in the 2004 Panel 

The most common exit trigger event was an increase in family income, with almost twothirds of the sample experiencing the event at some time during the panel (Table II.18). About 

one-quarter of the participants who experienced an increase in earnings left within four months 

of the increase. 

 

36 Because of our practice of closing up both one-month gaps in participation and one-month spells, a true exit must 

consist of two months of participation followed by two months of nonparticipation. 

89
Other trigger events such as family composition changes were less common than increases in 

income, but the triggers still led to similar exit rates among SNAP participants. For example, 

43.3 percent of SNAP participants had a family member with income leave; 48.1 percent of 

SNAP participants had a family member without income leave. In each case, close to one-quarter 

of the participants who experienced a decrease in family size left within four months of the 

decrease. 

Twenty-five percent of SNAP exiters did not experience a trigger event related to improved 

financial circumstances or reduced need, as measured by changes in income and family 

composition. Seventy-five percent of SNAP exiters experienced at least one trigger event within 

the four-month window, with 43.2 percent experiencing multiple events. Mirroring the incidence 

of the trigger events among SNAP participants, SNAP exiters were most likely to have 

experienced an increase in earnings (46.4 percent) relative to other trigger events. 

90
Table II.18 Occurrence of SNAP Exit Trigger Events 

Trigger Event

Percent of SNAP 

Participants that 

Experienced

the Event

during the Panel

Percent of Persons that 

Experienced the Trigger

then Exited SNAP within 

4-Month Window

Percent of Persons Exiting 

SNAP that Experienced

the Trigger

within Previous

4 Months

Increase in Income

Earnings 63.9 24.0 46.4

Other income 63.8 17.5 32.0

Change in Family Composition

Family Size Decreases

 Member without income leaves 48.1 23.1 24.7

 Member with income leaves 43.3 22.2 22.0

Family Size Increases

 New Child 16.1 18.6 5.0

 New Adult 19.2 23.5 7.8

Other 2.9 13.5 0.7

Distribution of Trigger Events 100.0 NA 100.0

Experienced no trigger events 11.6 NA 25.0

Experienced any single trigger event 19.0 NA 31.8

Experienced multiple events 69.4 NA 43.2

Experienced any trigger event 88.4 21.2 75.0

Leaves the Sample a

1.9 100.0 2.8

Sample Size 9,894 149,773 5,726

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: At Risk: Individuals receiving SNAP benefits for at least 2 months 

Reference Months: 3 to 31 

Sample: Persons for percent experiencing event at some point during panel; person-months of participants 

for percent exiting within specified number of months of experiencing event; persons exiting SNAP for 

percent of exiters 

a Individuals who die, are institutionalized, enter the armed forces and live in army barracks, or leave the 

country are removed from the SIPP sample. We assume that any one of these reasons would also lead to 

SNAP exit and assign the sample exit as the exit trigger. 

3. Changes in the Distribution of Exit Trigger Events Among All SNAP Participants 

from the Early- to Mid-2000s 

A slightly smaller percentage of SNAP participants experienced exit trigger events in the 

mid-2000s than in the early-2000s. About 88.4 percent of participants experienced any trigger 

event during the 2004-2006 panel, down from 90.6 percent during the 2001-2003 panel (Figure 

91
II.8). Though the percentage of participants who experienced an increase in earnings decreased 

over time, from 72.3 to 63.9 percent, the percentage who experienced an increase in other 

income increased from 58.3 to 63.8 percent. Despite this event, and others, becoming more 

common among SNAP participants, the percentages of SNAP participants experiencing the 

trigger event who exited within four months decreased over time for every type of trigger (Figure 

II.9). 

Figure II.8 Percentage of SNAP Participants Experiencing Event, Comparison over Time 

88.4

19.2

16.1

43.3

48.1

63.8

63.9

90.6

17.7

15.8

44.2

46.7

58.3

72.3

0.0 25.0 50.0 75.0 100.0

Experienced any trigger event

Family size increases (new adult)

Family size increases (new child)

Family size decreases

(member with income leaves)

Family size decreases

(member without income leaves)

Increase in other income

Increase in earnings

2001-2003

2004-2006

92
Figure II.9 Percentage of SNAP Participants Who Exited SNAP within Four Months of 

Experiencing Event, Comparison over Time 

4. Distribution of Exit Trigger Events among Subgroups in the 2004 Panel 

Exit rates differed based on the participants’ characteristics in the month before the spell 

began (Table II.19). Twenty-three percent of SNAP participants in families with children that 

experience an increase in earnings exit the program within four months, compared to 34.3 

percent of participants in families without children. Among participants in families with children, 

this percentage varies from 17.8 percent for children in single-adult families to 21.4 percent for 

children in families with multiple adults to 24.4 percent for children in families with a married 

head. For adults in these families, this percentage varies from 20.4 percent for adults in singleadult families to around 27 percent for adults in families with multiple adults and adults in 

families with a married head. Not surprisingly, there is also a differential by income, with 17.9 

21.2

23.5

18.6

22.2

23.1

17.5

24.0

24.6

31.8

20.3

26.1

25.3

20.7

26.7

0.0 25.0 50.0

Experienced any trigger event

Family size increases (new adult)

Family size increases (new child)

Family size decreases

(member with income leaves)

Family size decreases

(member without income leaves)

Increase in other income

Increase in earnings

2001-2003

2004-2006

93
percent of participants with income under 50 percent of poverty exiting SNAP within four 

months of experiencing an increase in earnings, compared to 40.3 percent of participants with 

income above 200 percent of poverty—with the comparison to poverty measured prior to the 

increase. 

Changes in family size (in either direction) are strongly associated with SNAP exit for 

participants in families with elderly members, especially elderly members living with nonelderly individuals (43.4 and 44.8 percent, respectively, among those with a decrease in family 

size). Indeed, the probability of exiting SNAP following a decrease in family size increases with 

participants’ age, from 22.1 percent for children, 33.4 percent for nonelderly adults, to 48.4 

percent for elderly adults. There is also a striking gender differential, with 46.3 percent of males 

exiting SNAP after experiencing a decrease in family size, compared to 29.7 percent of females. 

94
Table II.19 Occurrence of SNAP Exit Events by Subgroups 

Percentage of Individuals Exiting SNAP within 4 Months of the Trigger Event 

(Not Mutually Exclusive)

Subgroup None

Increase in 

Earnings

Increase in Other 

Income

Increase in 

Family Size

Decrease in 

Family Size Any Trigger a

All Individuals 14.1 24.0 17.5 22.2 28.9 21.4

Family Composition

Individuals in families with children 14.5 22.6 17.2 19.8 27.2 20.2

Adults in families with children and one adult 11.4 20.4 14.9 21.4 35.0 17.3

Children in families with children and one adult 10.8 17.8 13.3 18.1 23.0 15.0

Adults in families with children and multiple adults 18.6 26.8 20.8 27.0 28.8 24.9

Children in families with children and multiple adults 15.6 21.4 15.4 19.6 19.0 18.6

Adults in families with children and a married head 17.3 26.6 22.3 18.3 36.7 25.1

Children in families with children and a married head 17.8 24.4 20.0 19.4 25.5 22.6

Children in child-only families 21.3 0.0 13.6 5.2 0.0 13.7

Individuals in families without children 13.3 34.3 19.2 35.4 43.3 28.5

Individuals in families with elderly members 10.1 25.0 15.2 32.0 43.4 23.6

 Elderly members living alone 5.6 15.5 4.4 28.6 0.0 13.4

Elderly members living with other elderly individuals 6.5 0.0 12.9 0.0 26.8 17.0

 Elderly members living with non-elderly individuals 18.4 27.1 26.0 40.9 44.8 30.2

Individuals in families with disabled members 10.2 30.5 14.7 35.0 40.1 24.1

Individuals in families without any elderly or disabled members 28.9 40.2 31.1 36.9 52.6 36.9

Age and Disability

Nonelderly disabled adults 9.4 19.1 14.8 21.2 20.9 17.7

Nonelderly nondisabled childless adults 27.9 42.0 34.1 42.4 60.5 39.9

Family Income

Under 50 percent of poverty 8.3 17.9 11.7 10.6 10.4 14.7

50 to under 100 percent of poverty 10.2 21.0 12.3 22.0 20.9 17.2

100 to under 200 percent of poverty 18.5 29.7 26.1 24.3 31.7 27.9

200 or more percent of poverty 31.1 40.3 32.9 40.2 42.4 37.7

95
Table II.19, continued

Percentage of Individuals Exiting SNAP within 4 Months of the Trigger Event 

(Not Mutually Exclusive)

Subgroup None

Increase in 

Earnings

Increase in Other 

Income

Increase in 

Family Size

Decrease in 

Family Size Any Trigger a

Age

Children (under age 18) 13.8 20.9 15.7 18.6 22.1 18.4

Nonelderly adults (age 18 - 59) 15.7 27.3 20.1 25.3 33.4 24.6

Elderly adults (age 60 and over) 8.9 20.9 12.5 29.6 48.4 21.1

Sex

Male (age 18 and over) 18.5 33.2 25.0 32.3 46.3 30.7

Female (age 18 and over) 12.5 24.0 16.8 22.7 29.7 21.4

Race/Ethnicity b

White, Non-Hispanic 15.7 28.5 21.9 26.6 34.7 25.6

African American, Non-Hispanic 11.9 18.6 12.5 17.3 22.0 17.0

Hispanic, all races 14.6 23.8 15.9 22.8 27.2 20.0

Asian, Non-Hispanic 8.1 17.8 9.3 6.8 37.1 14.8

Other, Non-Hispanic 16.3 25.0 20.8 20.2 24.7 24.1

Education

Individuals in families with HS graduate 15.4 24.9 18.6 23.7 29.0 22.4

Individuals in families with no HS graduate 10.0 18.7 12.6 16.5 28.0 16.6

Citizenship

Citizen 13.9 23.7 17.7 21.4 28.6 21.2

Noncitizen 19.0 29.2 13.4 35.5 35.7 25.8

Citizen children living with noncitizen adults in the family 19.0 25.2 18.0 23.8 20.9 21.9

Adults in families with citizen adults and citizen children 15.1 24.5 20.0 20.8 33.2 22.7

Children in families with citizen adults and citizen children 13.1 20.5 15.9 16.9 21.8 18.0

Adults in families with noncitizen adults and citizen children 18.1 25.0 10.1 25.8 18.2 19.9

Children in families with noncitizen adults and citizen children 19.9 25.4 13.4 36.5 18.2 21.4

96
Table II.19, continued

Percentage of Individuals Exiting SNAP within 4 Months of the Trigger Event 

(Not Mutually Exclusive)

Subgroup None

Increase in 

Earnings

Increase in Other 

Income

Increase in 

Family Size

Decrease in 

Family Size Any Trigger a

Presence of Income

Individuals in families with no income 13.6 22.8 15.2 28.1 24.6 19.9

Individuals in families with income 14.2 24.1 17.7 21.6 29.0 21.5

Presence of Earnings

Individuals in families with earnings 19.1 25.3 21.8 24.8 32.2 24.2

Individuals in families without earnings 9.1 21.1 12.5 18.5 18.1 16.9

Presence of TANF

Individuals in families with TANF 10.0 17.3 12.2 21.7 24.6 16.0

Individuals in families without TANF 15.0 25.4 18.7 22.3 30.2 22.7

Other Income

Individuals in families with Social Security income 13.1 24.6 16.1 25.4 29.1 22.1

Individuals in families without Social Security income 14.5 23.8 17.9 21.6 28.8 21.2

Individuals in families with SSI 10.6 21.3 14.9 24.8 24.8 19.1

Individuals in families without SSI 15.5 24.4 18.3 21.5 30.3 22.0

Individuals in families with unemployment compensation 20.9 34.0 24.0 3.9 38.8 29.9

Individuals in families with no unemployment compensation 14.0 23.4 17.3 22.6 28.7 21.0

Sample size (person-months) 16,186 3,335 2,118 469 574 5,799 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: At Risk: Individuals receiving SNAP benefits for at least 2 months 

Reference Months: 3 to 31 

Sample: Person-months of those experiencing trigger event 

Subgroups: Characteristics in month before SNAP spell began 

a This column also includes the trigger event of leaving the sample due to death, institutionalization, entry into the 

armed forces and living in army barracks, or exit from the country. We assume that any one of these reasons would 

also lead to SNAP exit and assign the sample exit as the exit trigger. Thus, the exit rates for this group are 100 

percent. 

b

 Categories are race alone; respondents who reported multiple races are in the Other, Non-Hispanic category 

97
D. Re-entry into SNAP 

Re-entry is the final stage of SNAP participation dynamics that we examine.37 In our earlier 

entry analysis, we noted that about half of all adults entering SNAP had prior participation spells 

as adults. In this section, we focus solely on those individuals with prior receipt––both those who 

had a second (or more) spell within the panel and those who experienced their second (or more) 

spell as an adult. The questions we address are: 

What proportion of participants who exit SNAP return to the program within six months, 

within a year, or within two years? What is the median time off SNAP between 

participation spells? How do re-entry patterns vary among different subgroups? 

How do the answers to the above research questions compare to the findings in the 

studies for earlier periods? 

Once again, we use life tables, but in this case we examine the duration of spells off SNAP 

following a spell of participation. The exit rate from these "off spells" is the re-entry rate back 

into SNAP. 

1. Sample and Methods 

The sample consists of individuals who received SNAP benefits during the panel period and 

exited before the end of the panel period. Individuals could contribute more than one observation 

to the analysis. The data consist of 6,353 off-spell observations, contributed by 5,185 unique 

individuals. Any off spell that began in or after month 4 and before month 31 of the panel is 

included in the sample—95 percent of off spells began in this time frame. About 62 percent of 

the remaining off-spell observations are right-censored. We refer back to original new spell to 

determine the subgroup of the spell off SNAP and the subsequent re-entry, if it occurs. 

 

37 This re-entry analysis overlaps to some extent with the entry analysis presented in Section B, since much of the 

entry analysis was not limited to those who had never previously received SNAP benefits. The entry analysis used 

information from some individuals who re-entered SNAP. However, it did not use information regarding when 

individuals had last exited the program and did not analyze the duration of time to re-entry. In the re-entry analysis 

in this section, by contrast, we analyze the duration of time between when the individuals exited the program and 

when (and if) they re-entered. 

98
Subgroups for off spells and re-entry rates are determined as of the month preceding the original 

―new‖ SNAP entry. 

Because we have fewer than three years available to us in this SIPP panel period, we have a 

limited follow-up period for the re-entry analysis. The maximum possible number of months of 

follow-up information is 27, though the sample for which we have more than 24 months of 

follow-up data is relatively small. However, our analysis shows that re-entry rates fell off rapidly 

during the first 24 months of off-spells, suggesting that most SNAP participants who re-entered 

the program did so within two years of exiting. 

2. SNAP Re-entry in the 2004 Panel and Comparisons over Time 

As was the case in previous studies, many respondents in our SIPP sample returned to SNAP 

after exiting. More than half (52.9 percent) who exited returned within two years, of those, half 

had already returned within six months of their exit (Table II.20). Of those who ended a 

participation spell, 42.1 percent re-entered during their first year off the program and another 

10.8 percent re-entered during the second year.38 Similarly, Gleason et al. (1998) found that, in 

the early 1990s, 42.4 percent re-entered in their first year off the program and 11.3 percent reentered in their second year, and Cody et al. (2007) found that 45.0 percent re-entered in their 

first year off and 10.4 percent re-entered in the second year (Figure II.10). 

 

38 As in the life tables of SNAP spell lengths in the previous section, because the samples on which the re-entry rates 

are based decline as the duration increases, the estimates of the hazard rates become less precise as duration rises. 

99
Table II.20 Life Tables of Spells Off SNAP (Re-entry Rates) 

Month

Number of Spells 

at Beginning of 

Month

Number InSample in 

Following Month

Number Reentering During 

Following 

Month

Survivor 

Rate

Hazard 

Rate

Cumulative

Re-entry Rate

Standard 

Error of 

Survivor Rate 

1 24,720,427 24,633,366 0 100.0 0.0 0.0 0.1

2 24,633,366 24,559,799 1,220,276 95.0 5.0 5.0 0.3

3 23,339,523 22,471,829 1,181,790 90.0 5.3 10.0 0.5

4 21,290,039 19,196,469 2,600,404 77.8 13.5 22.2 0.6

5 16,596,065 16,080,927 461,532 75.6 2.9 24.4 0.6

6 15,619,395 15,181,039 411,058 73.6 2.7 26.4 0.6

7 14,769,981 14,144,820 355,624 71.7 2.5 28.3 0.7

8 13,789,197 12,313,851 1,082,723 65.4 8.8 34.6 0.7

9 11,231,128 10,784,390 230,953 64.0 2.1 36.0 0.6

10 10,553,437 10,221,110 181,015 62.9 1.8 37.1 0.6

11 10,040,095 9,651,544 216,643 61.5 2.2 38.5 0.6

12 9,434,901 8,260,878 480,196 57.9 5.8 42.1 0.6

13 7,780,681 7,546,775 162,616 56.6 2.2 43.4 0.6

14 7,384,159 7,009,759 93,288 55.9 1.3 44.1 0.6

15 6,916,471 6,657,127 76,689 55.2 1.2 44.8 0.6

16 6,580,438 5,606,889 188,598 53.4 3.4 46.6 0.5

17 5,418,291 5,191,837 40,782 53.0 0.8 47.0 0.5

18 5,151,055 4,869,080 114,638 51.7 2.4 48.3 0.5

19 4,754,441 4,426,194 60,161 51.0 1.4 49.0 0.5

20 4,366,032 3,646,278 130,614 49.2 3.6 50.8 0.4

21 3,515,664 3,249,007 30,803 48.7 0.9 51.3 0.4

22 3,218,203 2,946,196 13,759 48.5 0.5 51.5 0.4

23 2,932,437 2,637,639 12,280 48.3 0.5 51.7 0.3

24 2,625,359 1,688,897 40,967 47.1 2.4 52.9 0.2

25 1,647,929 1,442,114 38,107 45.9 2.6 54.1 0.2

26 1,404,007 1,206,275 0 45.9 0.0 54.1 0.1

27 1,206,275 920,093 5,630 45.6 0.6 54.4 0.0

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: Reference Months: 4 to 31 

Sample: Nonparticipation spells following a participation spell 

Column (a) represents the number of SNAP spells that have lasted at least the indicated number of 

months, regardless of when the spell first started. Column (b) indicates the number of the spells from (a) 

that we continue to observe in the following month (that is, spells that are not right censored). Column (c) 

is the number of spells from (b) that exit the SNAP in the following month. The hazard rate (e) is 

100*(c)/(b). The cumulative exit rate (f) is sum of the previous row’s cumulative exit rate and the product 

of the current row’s hazard rate and previous row’s survivor rate, divided by 100. 

 

100
Figure II.10 Percentages Re-entering SNAP, Comparisons over Time 

3. SNAP Re-Entry, by Subgroup 

Individuals who had long participation spells also tend to have had high re-entry rates. In our 

subgroup analysis for re-entry, we measure the subgroup characteristics based on the original 

spell that preceded their non-participation spell (Table II.21). Among individuals in the poorest 

families (those with monthly income under 50 percent of poverty in their non-participation 

spell), almost three-fifths re-entered within two years of exiting. However, among those who 

lived in families with income at least two times the poverty line, slightly over two-fifths reentered within two years. Similarly, we find that individuals in families that had no earners were 

likely to re-enter SNAP sooner than those in families that had earners. 

We also see substantial differences in re-entry rates by age and by family composition. Half 

of the individuals in families with children re-entered SNAP within 16 months of exiting. In 

42.4

45.0

42.1

11.3 10.4 10.8

0

5

10

15

20

25

30

35

40

45

50

1991-1992 2001-2003 2004-2006

Percentage

Re-entering

 during First Year

Percentage

Re-entering

 during Second Year

101
contrast, fewer than half of the individuals living in families without children re-entered within 

two years of exiting. In contrast to previous findings, in this panel, adults living in single-parent 

families with children had longer periods off SNAP (median of 24 months) than adults in 

families with children and non-married multiple adults (median of 16 months). Adults living in 

families with children and married adults had a median off-SNAP length of greater than 27 

months. 

The elderly are an exception to the general pattern of longer participation spells being 

associated with quick reentry. Although elderly individuals tended to have long participation 

spells, once they exited the program, they often did not re-enter (33.2 percent re-entered the 

program within two years, compared to 48.8 percent of nonelderly adults). It could be that the 

elderly individuals moved in with other family members who could support them or made other 

financial arrangements that did not lend to re-entering SNAP. 

Noncitizens have shorter spells than citizens (median of 13 months versus 20 months, 

respectively. Median off-spell lengths are much shorter for children in families with citizen 

adults and citizen children than for adults in these types of families (13 and 24 months, 

respectively). The differential is similar for children in families with noncitizen adults and citizen 

children, and adults in these families (12 months and greater than 27 months, respectively), 

though these two groups are based on small sample sizes. 

102
Table II.21 Re-Entry Rates into SNAP, by Subgroups 

Subgroup Sample Size

Median Time 

to Re-entry 

(Months)

Cumulative Rate of Re-entry (Percent)

Log-Rank Statistic 

to Test Differences 

across Subgroups

4 Months

or Less

12 Months 

or Less

24 Months or 

Less

All Individuals 6,054 20 22.2 42.1 52.9

Family Composition 127.4***

Individuals in families with children 4,726 16 23.6 45.4 55.7

Adults in families with children and one adult 521 24 21.0 41.4 52.8

Children in families with children and one adult 1,043 13 28.2 48.6 60.6

Adults in families with children and multiple adults 589 16 22.8 44.6 53.6

Children in families with children and multiple adults 537 12 30.2 51.1 62.7

Adults in families with children and a married head 950 > 27 15.9 36.5 46.8

Children in families with children and a married head 1,063 13 24.5 49.8 57.9

Children in child-only families 23 4 52.1 76.3 100.0

Individuals in families without children 1,328 > 27 17.1 30.8 42.9

Individuals in families with elderly members 425 > 27 18.5 30.9 43.6

Elderly members living alone 111 > 27 19.6 38.4 42.8

Elderly members living with other elderly individuals 65 > 27 21.8 24.3 27.8

Elderly members living with non-elderly individuals 242 > 27 18.2 29.5 47.7

Individuals in families with disabled members 397 > 27 17.3 33.6 46.5

Individuals in families without any elderly or disabled 

members 506 > 27 15.9 29.1 40.7

Age and Disability 20.2***

Nonelderly disabled adults 652 18 21.8 42.6 54.1

Nonelderly nondisabled childless adults 553 > 27 14.6 27.5 41.0

Family Income 135.4***

Under 50 percent of poverty 714 12 31.8 53.9 59.0

50 to under 100 percent of poverty 1,481 12 30.7 51.0 58.0

100 to under 200 percent of poverty 2,319 24 17.5 39.0 50.4

200 or more percent of poverty 1,235 > 27 14.8 31.0 42.6

103
Table II.21, continued

Subgroup Sample Size

Median Time 

to Re-entry 

(Months)

Cumulative Rate of Re-entry (Percent)

Log-Rank Statistic 

to Test Differences 

across Subgroups

4 Months

or Less

12 Months 

or Less

24 Months or 

Less

Age 80.7***

Children (under age 18) 2,666 13 27.4 49.8 60.2

Nonelderly adults (age 18 - 59) 2,984 25 18.6 37.2 48.8

Elderly adults (age 60 and over) 404 > 27 16.3 28.8 33.2

Sex 18.7***

Male (age 18 and over) 1,317 > 27 16.4 33.2 42.5

Female (age 18 and over) 2,071 24 19.7 38.7 51.0

Race/Ethnicity a

57.5***

White, Non-Hispanic 2,850 > 27 17.6 34.0 46.8

African American, Non-Hispanic 1,471 12 28.3 52.5 57.8

Hispanic, all races 1,170 16 23.9 44.2 55.8

Asian, Non-Hispanic 64 17 28.5 50.0 52.9

Other, Non-Hispanic 499 16 21.7 48.8 63.7

Education 29.8***

Individuals in families with HS graduate 5,186 22 20.7 40.3 51.2

Individuals in families with no HS graduate 868 12 30.5 52.3 62.3

Citizenship

Citizen 5,739 20 22.0 41.6 52.8

Noncitizen 315 13 24.5 49.0 55.1

Citizen children living with noncitizen adults in the family 303 16 27.1 47.5 57.1

Adults in families with citizen adults and citizen children 1,901 24 19.0 39.5 50.1 21.1***

Children in families with citizen adults and citizen children 2,386 13 26.1 48.7 59.2

Adults in families with noncitizen adults and citizen children 133 > 27 25.6 39.8 48.6

 

104
Table II.21, continued

Subgroup Sample Size

Median Time 

to Re-entry 

(Months)

Cumulative Rate of Re-entry (Percent)

Log-Rank Statistic 

to Test Differences 

across Subgroups

4 Months

or Less

12 Months 

or Less

24 Months or 

Less

Presence of Income

Individuals in families with no income 310 16 27.6 45.5 74.8 11.0***

Individuals in families with income 5,744 20 21.9 41.9 51.5

Presence of Earnings 21.4***

Individuals in families with earnings 4,320 24 20.9 40.6 50.5

individuals in families without earnings 1,734 16 25.4 46.0 59.4

Presence of TANF 21.7***

Individuals in families with TANF 643 13 30.8 48.4 60.3

individuals in families without TANF 5,411 20 21.0 41.3 52.0

Other Income

Individuals in families with Social Security income 1,503 25 19.4 40.7 49.5 24.5***

Individuals in families without Social Security income 4,551 19 23.0 42.6 53.9

Individuals in families with SSI 1,167 15 24.2 47.8 60.5 4.1**

Individuals in families without SSI 4,887 23 21.7 40.7 51.2

Individuals in families with unemployment compensation 244 > 27 11.0 44.3 49.1

Individuals in families with no unemployment compensation 5,810 20 22.6 42.0 53.1

 Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

 Notes: At Risk: Individuals receiving SNAP benefits for at least 2 months 

 Reference Months: 4 to 31 

 Sample: Nonparticipation spells following a participation spell 

 Subgroups: Characteristics in month before SNAP spell began 

a

 Categories are race alone; respondents who reported multiple races are in the Other, Non-Hispanic category 

 The log-rank test compares the estimated monthly hazard rate to the expected monthly hazard rate where the expected rate is calculated based on 

the null hypothesis that the hazard rate is the same for each time period of the subgroup category. We do not reject the null hypothesis that the 

distributions are the same across categories if the aggregate difference between the estimated and expected hazard rate is small relative to the 

aggregate variance of the difference. We reject the null hypothesis if the difference is large. 

105
E. Summary Measures of SNAP Participation 

Thus far in the analysis, we have examined each step of a participant’s contact with SNAP: 

entry, duration on the program, exit, and, finally, re-entry. Now, we explore the total experience 

with the program, and how it has changed over time, using several additional measures: 

Total time on. What proportion of the 32-month period covered by the SIPP panel period 

does an individual spend on SNAP? What participant characteristics distinguish those 

who spend a significant proportion of the panel time on SNAP from those who only use 

the program for a small proportion of the panel time? 

Spell type. What proportion of the caseload has a single short spell, single medium-term 

spell, single long spell, or more than one spell during the SIPP panel period? 

Turnover Rate. What is the turnover rate (the ratio of all participants ever on SNAP 

during the year over the average monthly number of participants) for SNAP participants 

in each year covered by the 2004 SIPP panel? Did the turnover rate change between the 

first few waves of the panel, which was during a period of economic growth, and the later 

waves of the panel, when the economy was starting to weaken? 

Replacement Rates and Exit Rates. What is the number of new SNAP entrants in a 

month divided by the number of participants in the previous month’s caseload? And how 

many SNAP participants exit the program each month? What dynamics explain the 

participation growth that occurred in the mid-2000s? 

These additional measures of program dynamics help to summarize individual experiences in 

SNAP and to interpret aggregate caseload trends. In particular, they address an important policy 

research question of the current study: what dynamics explain the participation growth that 

occurred in the mid-2000s? We answer this question below by decomposing the participation 

growth into changes over time in several summary measures. 

1. Total Time on SNAP 

The total time during which a participant receives benefits over the course of the panel period 

provides an additional measure of an individual’s SNAP participation behavior. From our earlier 

analysis of the entry cohort, we know that the median duration is ten months. However, we also 

106
know that 25 percent of participants that leave the program return within six months and 50 

percent return within twenty months. Thus, the measure of spell duration alone may provide 

misleading information about their total reliance on the program. Because a measure of the total 

time in the program combines information about an individual’s spell length and re-entry, it 

provides a more accurate measure of program dependence. By including the time spent in prepanel participation spells, it is also a more comprehensive measure. However, our total-time-on 

measure is still limited by right censoring, because we do not know when many of the spells will 

end. This will lead us to underestimate the dependence on the program by some participants. 

We calculate the total time on as the number of months during the SIPP panel period that 

each individual received SNAP benefits. Table II.22 shows the distribution for the full sample 

and for those with at least one month of participation. 

107
Table II.22 Total Time on SNAP for Individuals Participating in the Panel Period (Percent) 

Excluding Pre-Panel Data Including Pre-Panel Data

Number of Months All Individuals

Individuals with at 

Least One Month of 

SNAP During Panel 

Period All Individuals

Individuals with at 

Least One Month of 

SNAP During Panel 

Period

No Receipt of SNAP in Panel 

Period 81.6 0.0 81.6 0.0

1 to 4 3.0 16.4 2.9 15.5

5 to 8 2.5 13.8 2.3 12.3

9 to 12 1.9 10.5 1.8 9.6

13 to 16 1.5 8.1 1.3 7.0

17 to 20 1.4 7.7 1.2 6.5

21 to 24 1.3 7.3 1.1 6.0

25 to 28 1.6 8.5 1.1 6.1

29 to 32 5.1 27.7 3.0 16.2

33 to 36 NA NA 0.1 0.5

37 to 40 NA NA 0.1 0.3

41 to 44 NA NA 0.1 0.5

45 to 48 NA NA 0.1 0.6

49+ NA NA 3.5 18.9

Mean months 3.3 18.0 7.2 39.0

Median months 0.0 17.0 0.0 20.0

Sample Size 51,379 9,894 51,379 9,894

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

 Notes: Reference Months: 1 to 32 

Sample: All individuals and participants 

a. Total Time on SNAP in the 2004 Panel 

We found that 18.4 percent of the full population received SNAP at some point during the 

32-month panel period. Of those who participated, 16.4 percent had contact with the program for 

less than five months, and 30.2 percent had contact with the program for less than nine months. 

In addition, 27.7 percent of those who received SNAP did so for the entire 32 months of the 

panel period. If we also add the time spent in SNAP before the panel period, we find that spell108
length for 20.8 percent of those who participated at least once during the panel lasted longer than 

32 months.39 

Table II.22 also shows that the median total time in SNAP among participants during the 

panel period was 17 months out of a possible 32. Thus, re-entry into the program led the average 

participant to spend more than 50 percent of the 32-month period in the program. 

b. Changes in Total Time on SNAP from the Early-1990s to the Early-2000s to the 

Mid-2000s 

The median total time on SNAP was 20 months in the early 1990s (Gleason et al. 1998) and 

15 months in the early 2000s (Cody et al. 2007); it then rose quickly to 17 months in the mid2000s (Figure II.11).40 The variation in these estimates is due to the net effects of two elements 

of program dynamics: how long individuals spend on the program and how long participants 

who exit the program spend off the program before re-entering. Compared to the early-1990s, 

individuals in the early-2000s who entered SNAP in the panel stayed on for fewer months 

(median of 8 months versus 9 months) but, once exited, re-entered the program more quickly 

(median of 16 months versus 20 months). Compared to the early-2000s, individuals who entered 

SNAP in the 2004 panel stayed on SNAP longer (median of 10 months versus 8 months) but, 

once exited, stayed off the program longer as well before re-entering (median of 20 months 

versus 16 months). Finally, the median total time on was longer in the early-1990s than in the 

mid-2000s. While the early-1990s and the mid-2000s were similar in the median duration of 

SNAP participants that enter SNAP in the panel (9 and 10 months, respectively) and in median 

 

39 The information concerning previous participation tells us the start date of the spell that was in progress in Month 

1 of the 2004 SIPP panel, not about start and end dates of spells that ended prior to the sample. Thus, in Table II.22 

we do not see an increase in the number receiving SNAP benefits, but rather in the length of time that some 

participants received SNAP benefits. 

40 The length of the SIPP panel period can affect the median total time on estimate if individuals enter SNAP 

disproportionately toward the end, relative to the beginning, of the SIPP panel, thereby right-censoring the SNAP 

spell length for these individuals. Because the three SIPP panel periods considered above different in length, this 

potential bias should be acknowledged when comparing the three estimates of total time on SNAP. 

109
length of time spent off the program prior to re-entering (20 months for both periods), 

participants who were participating in SNAP in the first wave of the panel had longer median 

SNAP spells-- more than 8 years in the early-1990s, compared to 7 years in the mid-2000s. 

Figure II.11 Total Time on SNAP for Individuals Participating in Panel Period, 

Comparisons over Time (Median Number of Months) 

c. Total Time on SNAP among Subgroups in the 2004 Panel 

The percentages of individuals who received SNAP at some point during the 32-month 2004 

SIPP panel period varied greatly across subgroups (Table II.23). For individuals in families with 

children, 23.3 percent received SNAP at some point in the panel, compared to 11.5 percent for 

those without children. SNAP receipt in the panel decreases by age, with 26.8 percent of 

children, 16.5 percent of non-elderly adults, and 9.0 percent of elderly adults receiving SNAP in 

the panel. The differential by income is even more striking, with SNAP receipt in the panel 

ranging from 51.9 percent of individuals in families with income below 50 percent of poverty, 

compared to 5.5 percent of individuals in families with income above 200 percent of poverty. 

The percentage of individuals without earnings, at 29.0 percent, is about twice as large as the 

20

15

17

0

4

8

12

16

20

24

Months

1991 to 1994

2001 to 2003

2004 to 2006

110
percentage of individuals with earnings, as is the percentage of individuals without and with 

income (42.8 and 17.5 percent, respectively). Finally, 55.9 percent of individuals in families with 

SSI received SNAP at some point in the panel, compared to 16.0 percent of individuals in 

families without SSI. 

111
Table II.23 Total Time on SNAP for Individuals Participating in the Panel Period (Percent), 

by Subgroup a

All Individuals

Individuals with at Least One Month 

of SNAP During Panel Period

Subgroup Sample Size

Percentage 

Receiving 

SNAP in 

Panel Sample Size Median

All Individuals 51,379 18.4 9,894 17

Family Composition

Individuals in families with children 29,748 23.3 7,315 19

Adults in families with children and one adult 2,081 41.8 925 23

Children in families with children and one adult 3,959 49.2 2,031 22

Adults in families with children and multiple 

adults 1,647 36.3 642 16

Children in families with children and multiple 

adults 1,405 51.0 748 20

Adults in families with children and a married 

head 10,085 12.3 1,306 14

Children in families with children and a married 

head 10,394 14.8 1,612 16

Children in child-only families 177 28.5 51 12

Individuals in families without children 21,631 11.5 2,579 15

Individuals in families with elderly members 10,797 9.3 1,035 19

Elderly members living alone 3,324 11.0 418 32

Elderly members living with other elderly 

individuals 4,092 4.8 198 27

Elderly members living with non-elderly 

individuals 3,196 12.8 401 12

Individuals in families with disabled members 1,978 38.1 785 24

Individuals in families without any elderly or 

disabled members 8,856 8.6 759 8

Age and Disability

Nonelderly disabled adults 2,643 45.6 1,281 26

Nonelderly nondisabled childless adults 10,123 8.4 829 8

Family Income

Under 50 percent of poverty 3,329 51.9 1,787 27

50 to under 100 percent of poverty 5,680 48.0 2,990 25

100 to under 200 percent of poverty 13,792 20.7 2,923 13

More than 200 percent of poverty 26,941 5.5 1,439 8

Age

Children (under age 18) 15,935 26.8 4,442 20

Nonelderly adults (age 18 - 59) 25,679 16.5 4,504 16

Elderly adults (age 60 and over) 9,765 9.0 948 24

112
Table II.23, continued

All Individuals

Individuals with at Least One Month 

of SNAP During Panel Period

Subgroup Sample Size

Percentage 

Receiving 

SNAP in 

Panel Sample Size Median

Sex

Male (age 18 and older) 23,885 16.1 4,037 16

Female (age 18 and older) 27,494 20.5 5,857 19

Race/Ethnicity b

White, Non-Hispanic 34,325 12.2 4,472 15

African American, Non-Hispanic 6,963 37.7 2,727 22

Hispanic, all races 6,665 24.2 1,799 17

Asian, Non-Hispanic 1,313 10.1 142 26

Other, Non-Hispanic 2,113 32.0 754 16

Education

Individuals in families with HS graduate 44,878 16.0 7,496 16

Individuals in families with no HS graduate 6,501 35.1 2,398 24

Citizenship

Citizen 48,209 18.8 9,419 17

Noncitizen 3,170 13.3 475 16

Citizen children living with noncitizen adults in 

the family 1,659 25.7 447 16

Adults in families with citizen adults and citizen 

children 12,609 19.8 2,623 17

Children in families with citizen adults and citizen 

children 14,408 26.9 4,018 17

Adults in families with noncitizen adults and 

citizen children 803 22.1 213 15

Children in families with noncitizen adults and 

citizen children 1,044 30.2 345 15

Presence of Income

Individuals in families with no income 1,659 42.8 763 19

Individuals in families with income 49,720 17.5 9,131 17

Presence of Earnings

Individuals in families with earnings 36,902 14.7 5,674 13

Individuals in families without earnings 14,477 29.0 4,220 24

Presence of TANF

Individuals in families with TANF 1,357 84.8 1,167 28

Individuals in families without TANF 50,022 16.5 8,727 16

113
Table II.23, continued

All Individuals

Individuals with at Least One Month 

of SNAP During Panel Period

Subgroup Sample Size

Percentage 

Receiving 

SNAP in 

Panel Sample Size Median

Other Income

Individuals in families with Social Security 

Income

13,082 17.9 2,358 18

Individuals in families without Social Security 

Income 38,297 18.5 7,536 17

Individuals in families with SSI 3,328 55.9 1,949 24

Individuals in families without SSI 48,051 16.0 7,945 16

Individuals in families with unemployment 

compensation

1,637 21.7 400 10

Individuals in families with no unemployment 

compensation

49,742 18.3 9,494 18

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: Reference Months: 1 to 32 

 Sample: All individuals and participants 

a

 Excluding Pre-Panel Data 

b

 Categories are race alone; respondents who reported multiple races are in the Other, Non-Hispanic 

category 

The median total time in SNAP also differs across subgroups (Table II.23). Individuals in 

families with children spent 19 out of 32 months on SNAP, compared to individuals in families 

without children who spent 15 months. The median total time on SNAP decreases with income, 

with individuals with income under 50 percent of poverty having spent 27 out of 32 months and 

individuals with income over 200 percent of poverty having spent 8 months. There are also 

sizable differentials by education (16 and 24 months respectively, for individuals in families with 

and without a high school graduate); by earnings (13 and 24 months respectively, for individuals 

in families with and without earnings); by SSI (24 and 16 months, respectively, for individuals 

with and without SSI); and by presence of unemployment compensation (10 and 18 months, 

respectively, for individuals with and without unemployment compensation). 

114
2. Characterizing SNAP Participants by Spell Type 

A weakness of the total-time-on measure is that it does not tell us whether individuals 

participate in SNAP continuously or whether they move on and off the program. An alternative 

way of summarizing participants’ SNAP experiences is to learn more about those who had single 

spells by identifying individuals as (1) single-spell, short-term participants; (2) single-spell, 

medium-term participants; (3) single-spell, long-term participants; or (4) multiple-spell 

participants. 

a. Characterizing SNAP Participants by Spell Type in the 2004 Panel 

Following the analysis of Cody et al. (2007) and Gleason et al. (1998), we define the four 

groups as follows: 

Short-term participants are those with a single participation spell that lasted 8 months or 

less. 

Medium-term participants are those with a single participation spell that lasted between 9 

and 23 months 

Long-term participants are those with a single participation spell that lasted 24 months or 

longer 

Multiple-spell participants are those with more than one participation spell during the 

panel period 

We perform this characterization for our cross-sectional sample. The first column of Table 

II.24 categorizes recipients into single-spell recipients and multiple-spell recipients based on 

their receipt only in the current panel. For the duration of receipt, though, it adds on the pre-panel 

duration of any spells that were in progress at the beginning of the panel period. In the second 

column, we extend this method to also include spells that both began and ended prior to the panel 

period. Using this new information leads many single-spell participants to be classified as 

multiple-spell participants—when we include the information from the pre-panel period, we find 

115
that nearly 60 percent of participants had multiple spells. Of those who had single spells, most 

had either short spells (eight months or less) or long spells (more than two years). 

Table II.24 Characterization of SNAP Participants (Percent) 

Number Of Months

Include Pre-Panel Data 

On Duration Of 

Beginning Spell a

Include Pre-Panel Data On 

Previous Spells And Duration Of 

Beginning Spell b

Single-Spell Participants

Short-Term (1 to 8 Months) 25.3 12.1

Medium-Term (9 to 23 Months) 17.4 8.8

Long-Term (24+ Months) 35.8 19.3

Multiple Spell Participants 21.5 59.8

Sample Size 10,010 10,010

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: Reference Months: 1 to 32 

Sample: All individuals and participants 

a

Pre-panel data are used to determine the duration of the spells that are ongoing at the beginning of 

the panel; however, pre-panel data about participation in spells that ended before the start of the panel 

are not included. 

b

Pre-panel data on both spells that were ongoing at the beginning of the panel and spells that ended 

before the beginning of the panel are included. 

b. Changes in the Characterization of SNAP Participants by Spell Type from the 

Early-1990s to the Early-2000s to the Mid-2000s 

The percentage of SNAP participants with multiple spells increased from 51.3 percent in the 

early-1990s (Gleason et al. 1998) to 62.7 percent in the early-2000s (Cody et al. 2007) and then 

decreased to 59.8 percent in the mid-2000s (Figure II.12). Among single-spell participants, there 

was a sizable shift from long-term spells (more than two years) in the early-1990s to short- and 

medium-term spells (collectively less than two years) in the early-2000s. This was followed by a 

second and much smaller shift from short-term spells (8 months or less) in the early-2000s to 

medium- and long-term spells (collectively more than 8 months) in the mid-2000s. As a result, 

the percentage of medium-term spells (9 to 23 months) increased steadily across the three 

periods, though they were the least common of all spell types in the 2000s. 

116
Figure II.12 Characterizing the Length and Frequency of SNAP Participant Spells, 

Comparisons over Time 

3. Turnover Rate 

The two previous summary measures of SNAP participation were from the point of view of 

individual participants. We next summarize SNAP participation from the perspective of the 

program, by presenting estimates of the SNAP turnover rate during calendar years 2004, 2005, 

and part of 2006. We also compare these turnover rates to estimated turnover rates for periods 

from the 1980s, 1990s, and early 2000s. 

If the overall SNAP caseload remains relatively constant, the turnover rate is a useful 

measure of how often individuals move into and out of the system. With a low turnover rate, the 

program will handle the same participants over long periods of time with few participants 

entering or exiting in a given month. With a high turnover rate, by contrast, the program will 

handle large numbers of individuals, even if the number of cases they have to handle remains 

steady. In any given month, there will be many new faces in the SNAP office, and many others 

who had participated in the past will no longer participate. 

2.9 3.2

42.6

51.3

17.5

6.5

13.4

62.7

12.1 8.8

19.3

59.8

0

25

50

75

100

Single

Short-Term

(1 to 8 Months)

Single

Medium-Term

(9 to 23 Months)

Single

Long-Term

(24+ Months)

Multiple Spells

Percentage of Participants

1991 to 1994

2001 to 2003

2004 to 2006

117
a. SNAP Turnover Rate in the 2004 Panel 

The turnover rate measures the size of the population that has come into contact with SNAP 

over the course of a year in relation to the average size of the caseload. We calculate it as the 

total number of individuals who received SNAP benefits during the year, divided by the mean 

number receiving SNAP benefits in a month. 

We estimate the SNAP turnover rate to be 1.4 in each of calendar years 2004 and 2005, and 

also 1.4 in the 12 months from June 2005 to May 2006 (see Table II.25). Thus, for each year, 

caseworkers who had a caseload size of 500 in a single month handled an average of 700 

different cases over the course of the year. This suggests that there is only a modest amount of 

turnover in the SNAP caseload over the course of a year. While there was an increase across 

years in the number of individuals receiving benefits in at least one month of the year, the 

average monthly number of individuals receiving benefits also increased each year, leading to a 

constant turnover rate over the 2.5 years. 

118
Table II.25 SNAP Turnover Rate 

(A) (B) (C)

Total Receiving 

Benefits in At Least 

One Month

Average Monthly 

Number Receiving 

Benefits

Turnover Rate 

(A/B)

2004 Panel

January 2004-December 2004 30,129,134 21,501,977 1.4

January 2005-December 2005 31,663,862 23,088,912 1.4

June 2005-May 2006 a

31,757,586 23,200,443 1.4

January 2004-May 2006 39,533,424 22,434,857 1.8

2001 Panel

January 2001-December 2001 24,549,821 16,269,571 1.5

January 2002-December 2002 25,819,693 17,204,142 1.5

October 2002-September 2003 26,445,119 18,351,314 1.4

January 2001-September 2003 35,687,585 17,223,082 2.1

1991 Panel

January 1991-December 1991 1.3

January 1992-December 1992 1.3

1984 Panel

January 1984-December 1984 1.4

January 1985-December 1985 1.4

Source: 2004: Decision Demographics, weighted tabulations of the 2004 SIPP panel; 2001: Cody et al. 

(2007); 1991: see Gleason et al. (1998) p 98 text discussion; 1984: Burstein et al. (1993). 

 Notes: Calendar Months: January 2004 to May 2006 

 Sample: SNAP spells 

 a

 May 2006 is the last month common to all four rotation groups within the wave. 

b. Changes in the SNAP Turnover Rate over Time 

The SNAP turnover rate has not changed very much from 1984 to 2006. Figure II.13 presents 

the rate for 1984 and 1985 from Burstein et al. (1993); for 1991 and 1992 from Gleason et al. 

(1998); for 2001, 2002, and 2003 from Cody et al. (2007); and 2004, 2005, and 2006 from the 

current study. The turnover rate was lower in both 1991 and 1992 (1.3 percent) than in both 1984 

and 1985 (1.4 percent), possibly due to the increase in the duration of spells from the mid-1980s 

to the early 1990s (Gleason et al. 1998). In 2001 and 2002 it was 1.5 percent, then decreased and 

stabilized at 1.4 from 2003 to 2006. 

119
Figure II.13 Changes in the SNAP Turnover Rate over Time 

There is no clear discernable association with the state of the national economy, at least from 

considering changes in the number of unemployed individuals in the U.S. population over time. 

The decrease in the turnover rate between 2002 and 2003 coincides with the start of a decline in 

the number of unemployed individuals in 2003—an unemployment trend that continued through 

2006. This would suggest a negative relationship between the turnover rate and the strength of 

the economy. However, unemployment was highest in 1991 and 1992, relative to the early- to 

mid-2000s, at a time when the turnover rate was lowest, which would suggest a positive 

relationship between the turnover rate and the strength of the economy. 

4. Decomposition of the SNAP Participation Growth in the Mid-2000s 

The number of SNAP participants has increased dramatically over the last ten years, 

including from 2004 to the end of early 2006 at a time when the economy was improving. Figure 

II.14 presents the number of SNAP participants from March 2004 to May 2006. The spikes in 

September 2004 and September through November 2005 represent the increase due to the 

1.4 1.4

1.3 1.3

1.5 1.5

1.4 1.4 1.4 1.4

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1984 1985 1991 1992 2001 2002 2003 2004 2005 2006

120
provision of disaster benefits following Hurricanes Charlie, Frances, Ivan, and Jeanne in 

Alabama and Florida and Hurricanes Katrina, Rita, and Wilma in the Gulf states. The number of 

participants increased from March 2004 through December 2005 and began decreasing in early 

2006. 

The decrease at the end of the period was not simply due to the termination of disaster 

benefits several months after the hurricanes. Figure II.15 presents the number of SNAP 

participants from March 2004 to May 2006 excluding individuals receiving disaster benefits in 

any month and state. The number of participants increased from March 2004 through December 

2005 and began decreasing in early 2006. In the decomposition analysis below, we use the 2004 

SIPP panel to examine what program dynamics contributed to the slowdown in growth toward 

the end of 2005 and what dynamics contributed to the decrease in participants (the negative 

growth) in early 2006. 

Figure II.14 SNAP Caseload, March 2004 to May 2006 

Source: SNAP Quality Control Data for 1994 through 1996 

22,000,000

24,000,000

26,000,000

28,000,000

30,000,000

32,000,000

121
Figure II.15 SNAP Caseload, March 2004 to May 2006, Excluding Disaster Benefits 

Source: SNAP Quality Control Data for 1994 through 1996 

a. Methods 

The substantial growth between 2004 and the end of 2005 can be the result of increases in the 

entry rate and/or decreases in the exit rate. Similarly, the caseload decline in early 2006 can be 

the result of decreases in the entry rate and/or increases in the exit rate. Following the 

methodology of Cody et al. (2005), we calculate each month’s percentage change in the caseload 

(the growth rate), replacement rate, and exit rate and average them over the SIPP survey period. 

Each month’s replacement rate is defined as the number of entrants, et, in month t divided by the 

total number of participants, pt-1, in month t-1.Each month’s exit rate, nt, is defined as the number 

of exiters, xt, in month t (participants in month t-1 not participating in month t) divided by the 

total number of participants in month t-1, pt-1. Combined, these rates reveal how the caseload 

changes over time. In each month t, the total number of participants can be calculated as pt = pt-1

- xt + et. That is, the number of participants in the current month t is equal to the number of 

participants in the previous month minus those who exited the program between month t-1 and 

22,000,000

24,000,000

26,000,000

28,000,000

30,000,000

32,000,000

122
month t, and plus those individuals who entered the program between months t-1 and t. 

Similarly, the growth rate can be computed as gt = rt - nt, with the growth in SNAP participation 

across two months equal to the replacement rate minus the exit rate. Because the total number of 

participants in the previous month is in the denominator of both the replacement rate and the exit 

rate, the growth rate is defined as the difference between the number of new entrants in month t

and the number of exiters in month t, relative to the number of participants in the previous 

month.

We compute the monthly growth rate, replacement rate, and exit rate from March 2004 to 

May 2006. March 2004 is the earliest month for which we have at least two months of data for 

individuals in all four rotation groups. Similarly, May 2006 is the last month for which we have 

two months of data from individuals in all four rotation groups. We then decompose the 

percentage change in the average growth rate from the base period of March 2004 to December 

2004 to each of the two subsequent periods (January 2005 to December 2005 and January 2006 

to May 2006). The decomposition allocates the share of the change in growth rate to changes in 

the replacement rate and exit rate. 

b. Decomposition of Caseload Changes from 2004 to 2006 

The average monthly growth rate was positive in 2004 and 2005, but decreased and became 

negative in early 2006 (Table II.26). The average monthly replacement rate decreased over the 

three periods, while the average monthly exit rate remained constant from 2004 to 2005 and then 

increased from 2005 to 2006. Thus, the decomposition reveals two main findings. First, the 

caseload increased in 2004 and 2005 because the replacement rate was greater than the exit rate 

and the caseload decreased in early 2006 when the exit rate ultimately exceeded the replacement 

rate. Second, the slowdown in caseload growth (i.e., the decrease in the positive growth rate) 

123
from 2004 to 2005 was due solely to the decrease in the replacement rate. By contrast, 62 percent 

of the decrease in the growth rate from 2005 to 2006 was explained by the increase in the exit 

rate and 38 percent by the decrease in the replacement rate. 

When we exclude from the SIPP sample individuals living in the five states affected most by 

Hurricanes Katrina and Rita (Alabama, Florida, Louisiana, Mississippi, and Texas), the 

replacement rates remain more or less the same as in the full sample (with all states), but there is 

a smaller increase in the exit rate in early 2006. The result is a similar growth rate in 2004 and 

2005 as in the full sample of states, but a smaller (negative) growth rate in early 2006 (-0.2 

percent instead of -0.7 percent). 

Table II.26 Average Monthly SIPP-Based Growth, Replacement, and Exit Rates, 2004 through 

2006

Percent Change In Growth Rate 

(Relative To First Period)

Explained By Change In:

Period a

Average 

Monthly 

Growth Rate

Average Monthly 

Replacement 

Rate

Average 

Monthly 

Exit Rate

Replacement

Rate

Exit

Rate

All States

March 2004 to December 2004 1.1 4.7 3.6 -- --

January 2005 to December 2005 0.5 4.1 3.6 1.02 -0.02

January 2005 to May 2006 a

-0.7 3.7 4.3 0.38 0.62

Excluding Alabama, Florida, Louisiana, Mississippi, and Texas

March 2004 to December 2004 1.0 4.7 3.7 -- --

January 2005 to December 2005 0.4 4.1 3.7 0.99 0.01

January 2005 to May 2006 a

-0.2 3.6 3.8 0.78 0.22

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: a May 2006 is the last month common to all four rotation groups within the wave 

c. Decomposition of Caseload Increase from 2004 to 2006, by Subgroup 

We re-estimated average monthly growth rates, exit rates, and replacement rates, and 

performed the decomposition analysis, for eight policy relevant subgroups: individuals with and 

124
without children; individuals in families with income below 100 percent of poverty or at or 

above 100 percent of poverty; individuals in families with or without earnings; and individuals in 

families with or without elderly members. The results are presented in Table II.27. The main 

findings for the full sample (with all states) include: 

Individuals in families with children had a similar growth rate in 2004 as individuals in 

families without children (1.0 percent and 1.2 percent, respectively). However, the exit 

rate increased from 2004 to 2005 for individuals in families with children, but decreased 

sizably for those in families without children. This resulted in a growth rate that was three 

times as large for individuals in families without children in 2005 than for individuals in 

families with children. Individuals in families without children then decreased sharply 

from 2005 to 2006. 

Individuals in families with income below 100 percent of poverty had replacement rates, 

exit rates, and growth rates that were similar to the full sample—the decrease over time in 

the replacement rate and the increase over time in exit rate produced a decreasing growth 

rate that became negative in early 2006. Though the same was true for individuals in 

families with income at 100 percent of poverty or greater, the replacement rates and exit 

rates were about twice as large as those for individuals in families with less income. This 

indicates greater turnover among those with more income. 

Individuals in families with earnings had a relatively large positive growth rate in 2005, 

whereas individuals in families without earnings had a small negative growth rate in 

2005. The 2006 growth rate for individuals in families with earnings was the largest 

negative growth rate among all subgroups, while the 2006 growth rate for individuals in 

families without earnings was the only positive growth rate among all subgroups. 

Consistent with the full sample, individuals in families with elderly members and 

individuals in families without elderly members experienced a decrease in the 

replacement rate from 2004 to 2006. Unlike the latter group, however, individuals in 

families with elderly members experienced a decrease in the exit rate from 2004 to 2005. 

This resulted in the largest growth rate in 2005 among all subgroups for individuals in 

families with elderly members—one that was three time as large as for individuals in 

families without elderly members. 

125
Table II.27 Average Monthly SIPP-Based Growth, Replacement, and Exit Rates, 2004 through 

2006, by Subgroup 

Percent Change In Growth Rate 

(Relative To First Period)

Explained By Change In:

Period a

Average 

Monthly 

Growth Rate

Average Monthly 

Replacement 

Rate

Average 

Monthly 

Exit Rate

Replacement

Rate

Exit

Rate

Individuals in families with children

 March 2004 to December 2004 1.0 4.5 3.5 -- --

 January 2005 to December 2005 0.3 4.0 3.7 0.74 0.26

 January 2005 to May 2006 -0.6 3.7 4.2 0.39 0.61

Individuals in families without children

 March 2004 to December 2004 1.2 5.3 4.1 -- --

 January 2005 to December 2005 1.0 4.4 3.4 4.70 -3.70

 January 2005 to May 2006 -1.0 3.7 4.7 0.36 0.64

Individual in families with income below poverty

 March 2004 to December 2004 0.6 3.1 2.5 -- --

 January 2005 to December 2005 0.0 2.6 2.6 0.77 0.23

 January 2005 to May 2006 -1.2 2.4 3.5 0.21 0.79

Individuals in families with income 100 percent of poverty or greater

 March 2004 to December 2004 2.6 7.2 4.6 -- --

 January 2005 to December 2005 1.1 6.2 5.1 0.64 0.36

 January 2005 to May 2006 -0.6 5.3 5.9 0.54 0.46

Individuals in families with earnings

 March 2004 to December 2004 1.2 5.7 4.5 -- --

 January 2005 to December 2005 0.9 5.2 4.3 1.58 -0.58

 January 2005 to May 2006 -1.3 4.6 6.0 0.27 0.73

Individuals in families without earnings

 March 2004 to December 2004 0.9 3.6 2.7 -- --

 January 2005 to December 2005 -0.1 2.8 2.9 0.83 0.17

 January 2005 to May 2006 0.1 2.6 2.5 -1.77 2.77

Individuals in families with elderly members

 March 2004 to December 2004 2.1 5.2 3.1 -- --

 January 2005 to December 2005 1.2 3.2 2.0 2.26 -1.26

 January 2005 to May 2006 0.0 3.0 3.0 0.21 0.79

Individuals in families without elderly members

 March 2004 to December 2004 0.9 4.7 3.7 -- --

 January 2005 to December 2005 0.4 4.2 3.8 0.80 0.20

 January 2005 to May 2006 -0.8 3.8 4.5 0.39 0.61

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: a May 2006 is the last month common to all four rotation groups within the wave

126
III. SUBGROUP ANALYSES 

 

This chapter compares SNAP dynamics data from the 1984, 1991, 2001, and 2004 SIPP 

panels for nine subgroups that are important in understanding SNAP caseload dynamics. In 

Chapter II, dynamic events and spells lengths were examined for all persons and individuals in 

different subgroups. In this chapter, each subgroup is portrayed on its own and contrasted with 

the characteristics of the total SNAP caseload. 

The typical lifecycle of SNAP cases from entry through duration, exit, and re-entry varies 

substantially among subgroups. This section addresses the following research questions: 

What can the compilation of SNAP dynamics research reveal about each subgroup’s 

unique character? 

How do the dynamics of individual subgroups explain the SNAP caseload evolution in 

terms of rates and absolute size? 

The subgroups included comprise the main groups of concern to the FNS, and the main 

groups that drive the SNAP caseload: 

Single parents 

Children of single parents 

Married adults with children 

Children of married adults 

Elderly adults 

Non-elderly nondisabled childless adults 

Noncitizens 

People living in families with earnings 

People living in families with TANF income 

127
This chapter reviews the series of studies that we draw on to describe subgroups, provides a 

key to data availability, and provides an historic profile of the nine designated subgroups. 

A. SNAP Dynamics Data on Subgroups 

The principal studies of SNAP dynamics that have data on subgroups are a series of reports 

written for FNS: Burstein (1993); Gleason et al. (1998); and Cody et al. (2007), as well as an 

analysis by Cody et al. (2005) of entry and exit rates throughout the 1990s.41 The time periods, 

SIPP panels, sample sizes, universes, and types of analysis are all shown in Table III.1. Over 

time, the descriptive analysis topics and the life table analyses of SNAP spells by subgroup have 

been expanded. 

Not all of the analyses described in Table III.1 are repeated for subgroups, or for all 

subgroups at each juncture. Table III.2 illustrates the historical availability of SNAP dynamics 

data for our selected subgroups. Rather than by author, this table is organized by SIPP panel: 

1984, 1991, and 2001. The current study of the 2004 panel covers all of the subgroups for all 

topics. 

 

41 We have omitted data for the 1990s--from Cody (2005)--from the balance of this chapter because the subgroup 

data are severely restricted in both subject content and coverage. 

128
Table III.1 SNAP Dynamics Studies with Subgroup Data: Time Frames, Data, and Study Objectives a 

Burstein (1993)

Gleason et al. 

(1998) Cody et al. (2007) Current

Time Period 1983-1986 1990 - 1993 2001 - 2003 2004-2006

Panel(s) 1984 1990, 1991 2001 2004

Sample Size 20,000 households 35,000 households 31,000 households 51,000 households

Historical SNAP Data Used No Yes Yes Yes

Descriptive Analysis Entry, exit, duration, reentry, entry and exit 

triggers

Entry, exit, duration, reentry, entry and exit 

triggers, total time on, 

turnover

Entry, exit, duration, reentry, entry and exit 

triggers, growth, 

replacement, total time 

on, turnover

Entry, exit, duration, reentry, entry and exit 

triggers, growth, 

replacement, total time 

on, turnover, subgroups

Primary At-Risk Definition for 

Entry Analysis

Non-participating 

individuals; household 

income under 300 

percent of poverty

Non-participating 

individuals

Non-participating 

individuals; family 

income under 300 

percent of poverty

Non-participating 

individuals; family 

income under 300 

percent of poverty

Topics for life table analysis 

of SNAP spells by subgroup

New spells, off spells 

(re-entry)

New spells, off spells 

(re-entry), subsequent 

spells of cross-sectional 

sample

New spells, off spells 

(re-entry), subsequent 

spells of cross-sectional 

sample

New spells, off spells 

(re-entry), subsequent 

and completed spells of 

cross-sectional sample

129
Table III.2 Historical Availability of SNAP Dynamics Subgroup Data 

At Risk Population and 

Entry Rates

Spell Length, Median 

Months, and Spell Exit 

Rate at

4, 12, 24 Months b

Exit Triggers

Re-entry (Median 

Months) and

Re-entry Rate

at 4, 12, 24 Months

SIPP Panel Years 2001 1991 a1984 2001 1991 1984 2001 1984 2001 1991 1984 c

Single parents x x x x x x x x x x x

Children of single parents x x x x x x x x x x x

Married adults with children d

x x x x x x x x x x x

Children of married adults d

x x x x x x x x x x x

Elderly adults x x x x x x x

Individuals in childless families without elderly or 

disabled members x x x x x x x x x x x

Noncitizens x x x x x x x

People living in households with earnings x x x x x x x x x x x

People living in households with TANF income x x x x

Source: 2001 from Cody (2007), 1991 from Gleason (1998), 1984 from Burstein (1993) 

Notes: The availability of the data for the current 2004 study is identical to that of 2001. 

a 1991 analysis applied to the total population rather than the under 300 percent of poverty population as in 1984 and 2001.

b

 1984 data include only new spells, 1991 data adds cross sectional sample data on subsequent spell length without medians, and 2001 further 

adds those medians and completed spell length data. 

 c 1984 data lack the median and the re-entry rate at 24 months. 

 d For 1984 and 1991, multiple adults-present households are presented instead of married adults households; see text for implications.

130
The analysis using the 1984 panel includes only six subgroups, whereas the analysis from the 

1991 panel has data available for all subgroups except those living in households with TANF 

income.42 For the study using the 2001 panel, all nine subgroups have a full complement of data 

with the exception of life table analyses of completed SNAP spells of the cross-sectional sample. 

Note also that there are no trigger data available for subgroups in 1991. While preparing the 

2004 analysis, we went back to the original 2001 analysis files and replicated many measures to 

ensure accuracy and compatibility of the 2001 and 2004 data. In this process we also expanded 

the 2001 life table analysis of completed SNAP spells of the cross-sectional sample from the 

total population to include subgroups as well. Those data are included in this chapter’s tables. 

B. Subgroup Definitions 

As is often the case with studies that evolve over time, subgroup definitions have been 

modified and revised to reflect changes in both data and analytical needs. Understanding how the 

definitions have changed is important to properly assessing how dynamics have changed. We 

provide a brief summary of the changes in Table III.3 and present the differences in more detail 

in Appendix B. 

 

42 In 1996, welfare reform replaced AFDC with TANF, but that there was no subgroup analysis for AFDC 

households for the 1991 panel. 

131
Table III.3 Varying Definitions of Certain Populations Over Time 

Burstein (1993) Gleason et al. (1998) Cody et al. (2007) Current

At-risk population for 

entry rates

Nonparticipating 

population with 

income under 300 

percent of poverty at 

some point in the 

panel period

Total 

nonparticipating 

population

Nonparticipating 

population with 

income under 300 

percent of poverty at 

some point in the 

panel period

Nonparticipating 

population with 

income under 300 

percent of poverty at 

some point in the 

panel period

Household/Family 

Composition

Based on 

relationships and 

characteristics within 

the entire household 

or dwelling unit

Based on 

relationships and 

characteristics within 

the entire household 

or dwelling unit

Based on 

relationships and 

characteristics within 

the family—all those 

related to the 

household head

Based on 

relationships and 

characteristics within 

the family—all those 

related to the 

household head

C. Point at which Subgroup Membership is Determined 

For some types of subgroup analyses, subgroup membership must be determined at a 

particular point in time, even though it may actually change during the specific period being 

analyzed. For example, if a participating family starts its spell with earnings, but then loses the 

earnings, their participation spell will continue, but they will have changed from a subgroup with 

earnings to a subgroup without earnings. We do not want to divide their spell into two distinct 

spells, one for the period in which they had earnings and one for the period in which they did 

not, so we need to choose one. Below, we explain the point in time at which we place individuals 

into certain subgroups for each of the measures. 

At-risk and entrants 

Subgroup membership for the at-risk population and SNAP entrants is determined on a 

month-by-month basis. For these measures only, the subgroup assignment is allowed to change 

on a monthly basis if respondents’ characteristics change. Each month, from month 3 to month 

31, individuals are placed into the at-risk subgroup based on their current characteristics. We 

then examine the subsequent month to see if they entered. We identify those who enter in the 

132
subsequent month as new entrants and classify them based on their previous month 

characteristics. For example, of all the person months in which individuals met the definition of 

at-risk, 3.3 percent were single parents. Of all the persons in every month examined who entered 

SNAP, 8.6 percent were single parents. 

Entry rates 

For entry rates, we determine each individual’s subgroup membership as of the month 

preceding entry for monthly entry rates, as of the last month preceding the wave of entry for 

wave-based entry rates, and as of the last month preceding the year of entry for annual entry 

rates. 

New SNAP spells 

We determine the subgroup of new spells in the same manner as the monthly entry rate: the 

month preceding SNAP entry. 

Subsequent and completed spells of the cross-sectional sample 

The cross-sectional samples consist of all SNAP spells active as of a given month. For 2001 

and 2004 panels, this month is May 2001 and 2004, respectively, the first common month in the 

second wave. Subgroup determinations are also made as of that month. 

Re-entry rates 

Re-entry rates are the result of calculations of the end of a new SNAP spell and the initiation 

of another SNAP spell. We refer back to original new spell to determine the subgroup of the 

spell off SNAP and the subsequent re-entry, if it occurs. Subgroups for off spells and re-entry 

rates are determined as of the month preceding the original ―new‖ SNAP entry. 

133
Exit triggers 

Since exit trigger events use a four-month reference period, the subgroup for exit trigger 

analysis is determined four months previous to exiting a SNAP spell. 

D. Subgroups Trends through Time 

In the sections below, we discuss how dynamics differ within subgroups across time and in 

comparison to the total population. Table III.4 provides a brief summary of how the dynamics 

compare to the total population. 

Table III.4 Subgroup Dynamics In Comparison to Total Population Dynamics 

Entry Rates New-entrant spells Subsequent/completed 

spells for cross-section Time before Re-entry

Single Adults with Children Much higher Longer Longer Longer 

Children of Single Parents Much higher Longer Longer Shorter

Married Adults with 

Children Lower Shorter Shorter Longer

Children of Married 

Parents Higher Longer Shorter Shorter

Elderly Much lower Longer Much longer Longer

Nonelderly, nondisabled, 

childless adults Much lower Much shorter Much shorter Longer

Noncitizens Mixed Shorter Shorter Mixed

Individuals in Families with 

Earnings Lower Shorter Shorter Longer

Individuals in Families with 

TANF

Very much 

higher Mixed Longer Shorter

1. Total Population 

 Table III.5.1 for the total population and total SNAP spells sets the stage for the other groups 

(Tables III.5.2-5.10), and provides the comparative base. Some of the measures in the tables 

represent percentages of this total, and they appear in this table as 100.0 percent. 

134
Table III.5.1 Historic Subgroup SNAP Dynamics Data: Total Population 

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

At-risk populations and SNAP entrants

 Percent of the at-risk population 100.0 100.0 100.0 100.0

 Percent of entrants 100.0 100.0 100.0 100.0

Entry rates

 Monthly -- 0.3 0.4 0.5

 Wave-based 2.0 -- 1.8 2.0

 Annual -- 2.6 4.1 4.2

Spell length of new spells

 Median months 6 9 8 10

 Cumulative exit at 4 months or less 41.1 32 32.6 27.5

 Cumulative exit at 12 months or less 68.1 57 61.4 57.9

 Cumulative exit at 24 months or less 80.3 71 74.1 74.3

 Percent of overall spells at 1 month a

-- -- 100.0 100.0

 Percent of overall spells at 4 months -- -- 100.0 100.0

 Percent of overall spells at 12 months -- -- 100.0 100.0

 Percent of overall spells at 24 months -- -- 100.0 100.0

Subsequent spell length for cross-sectional sample

 Median months -- -- 19 > 27 

 Cumulative exit at 4 months or less -- 12 20.3 15.2

 Cumulative exit at 12 months or less -- 27 40.0 30.8

 Cumulative exit at 24 months or less -- 43 57.4 46.7

 Percent of overall spells at 1 month a

-- -- 100.0 100.0

 Percent of overall spells at 4 months -- -- 100.0 100.0

 Percent of overall spells at 12 months -- -- 100.0 100.0

 Percent of overall spells at 24 months -- -- 100.0 100.0

Completed spell length for cross-sectional sample

 Cumulative exit at 6 months or less -- -- 10.1 8.0

 Cumulative exit at 1 year or less -- -- 20.8 17.4

 Cumulative exit at 2 years or less -- -- 34.6 28.6

 Cumulative exit at 4 years or less -- -- 51.5 41.5

 Cumulative exit at 8 years or less -- -- 62.7 52.9

 Percent of overall spells at 1 month a

-- -- 100.0 100.0

 Percent of overall spells at 1 year -- -- 100.0 100.0

 Percent of overall spells at 2 years -- -- 100.0 100.0

 Percent of overall spells at 4 years -- -- 100.0 100.0

 Percent of overall spells at 8 years -- -- 100.0 100.0

135
Table III.5.1 Total Population, continued

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

Re-entry rates

 Median nonparticipation spell in months -- 20 16 20

 Cumulative re-entry at 4 months or less 11.6 25 24.0 22.2

 Cumulative re-entry at 12 months or less 38.3 42 45.0 42.1

 Cumulative re-entry at 24 months or less -- 54 55.4 52.9

Exit triggers

 Increase in earnings 28.6 -- 26.7 24.0

 Increase in other income 13.0 -- 20.7 17.5

 Increase in family size -- -- 26.9 22.2

 Decrease in family size -- -- 29.4 28.9

 Any trigger -- -- 24.8 21.4

"--" indicates data fields that cannot be completed 

Source: Decision Demographics weighted tabulations of the 2004 SIPP panel, 2001 SIPP panel data, Cody 

(2007), 1991 SIPP panel data from Gleason (1998) 

Notes: 

a

 Percent of Overall Spells at Beginning of Period is the subgroup percent of the totals shown in life 

table columns (a) in Tables II.12, II.14, II.16, and II.23 for 2004. Column (a) represents the number 

of SNAP spells that have lasted at least the indicated number of months, regardless of when the 

spell first started. These panels show the percent of the total spells across all groups accounted for 

by this subgroup at each juncture. 

Subgroup determinations for 2001 and 2004. At-risk and entrants: person month basis, reference 

month. Entry rates: month preceding entry for monthly entry rates; last month preceding the wave 

of entry for wave-based entry rates; and last month preceding the year of entry for annual entry 

rates. New SNAP spells: the month preceding new SNAP entry. Re-entry rates: the month 

preceding the original ―new‖ SNAP entry. Subsequent and completed spells of the cross-sectional 

sample: May 2001 or May 2004, the first common month in the second wave. Exit triggers: four 

months previous to exiting a SNAP spell. Similar determinations were made for 1991 and 1986. 

2. Single Parents 

Single parent- adults make up about three percent of the population at risk, but account for 

nine percent of SNAP entrants in 2001 and 200443—a result of an entry rate that is about three 

times the level found in the total population. They account for a larger share of the at-risk 

population in 2001 and 2004 than they did in 1984 and 1991, and also a larger share of new 

entrants. 

 

43 For brevity’s sake, data coming from each of the four studies will be simply referred to by the initial year of the 

SIPP panel on which it is based: findings based on Burstein, et al. are referred to simply as 1984 data; Gleason, et al. 

findings will go by 1991, Cody, et al. will be 2001, and the current study as 2004. 

136
Single parents’ median new spell length is consistently longer than for the total population, 

by as much as seven months in 1991 and as little as one month in 2004. Exits from new SNAP 

spells occur at a rate that is slower than for the total population, which corresponds to the longer 

median spell length. Of the years examined, 1991 had the lowest 24-month cumulative exit rate, 

at 59 percent; 2004 had the highest, at 69 percent. 

The subsequent spell length measures how many additional months recipients will spend in 

the program from that common month forward. The subsequent spell length, as measured by 

cumulative exit percentages, was substantially longer in 1991 than it was during the 2001 SIPP, 

and 2004 is between those two extremes. At 12 months, 15 percent had exited in 1991, while the 

2001 and 2004 cumulative exit rates of 35 and 30 percent, respectively, were close to the values 

for the total population. As a proportion of overall SNAP spells of various lengths, single 

parents’ shares gradually increase with increasing spell length. This is consistent with other 

research that observed the longer spells of assistance among this subgroup and their children. 

Re-entry among single parents occurs at a slightly slower rate than for the full population in 

2004 (24 months versus 20 months for the median nonparticipation spell), though it was slightly 

faster for single parents in 2001 than for the full population (14 months versus 16 months for the 

median nonparticipation spell). The percentage of single parents re-entering within two years 

was similar to the percentage for the total population for 1991, 2001, and 2004. 

The most common single parent exit trigger in 2001 and 2004 was a decrease in family size. 

This trigger is also more frequently found among single parents in comparison to the total 

population exit triggers. Increases in earned or unearned income occurred less frequently among 

single parents in comparison to the total population exit triggers. 

137
Table III.5.2 Historic Subgroup SNAP Dynamics Data: Single Parents a

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

At-risk populations and SNAP entrants

 Percent of the at-risk population 2.4 1.6 3.2 3.3

 Percent of entrants 4.3 4.7 9.2 8.6

Entry rates

 Monthly -- 0.9 1.3 1.4

 Wave-based 3.6 -- 4.8 5.1

 Annual -- 7.0 9.8 9.8

Spell length of new spells

 Median months 9 16 11 11 

 Cumulative exit at 4 months or less 27.4 17 25.5 19.9

 Cumulative exit at 12 months or less 55.3 44 55.4 54.9

 Cumulative exit at 24 months or less 65.7 59 69.0 68.4

 Percent of overall spells at 1 month b

-- -- 9.2 8.6

 Percent of overall spells at 4 months -- -- 9.4 8.8

 Percent of overall spells at 12 months -- -- 11.0 9.2

 Percent of overall spells at 24 months -- -- 14.4 10.1

Subsequent spell length for cross-sectional sample

 Median months -- -- 23 > 27 

 Cumulative exit at 4 months or less -- 5 17.1 11.7

 Cumulative exit at 12 months or less -- 15 35.1 29.9

 Cumulative exit at 24 months or less -- 30 53.6 43.3

 Percent of overall spells at 1 month b

-- -- 12.9 11.3

 Percent of overall spells at 4 months -- -- 13.5 11.5

 Percent of overall spells at 12 months -- -- 14.1 11.5

 Percent of overall spells at 24 months -- -- 14.3 12.2

Completed spell length for cross-sectional sample

 Cumulative exit at 6 months or less -- -- 6.4 4.8

 Cumulative exit at 1 year or less -- -- 15.9 12.6

 Cumulative exit at 2 years or less -- -- 29.8 23.8

 Cumulative exit at 4 years or less -- -- 47.2 40.4

 Cumulative exit at 8 years or less -- -- 58.1 51.4

 Percent of overall spells at 1 month b

-- -- 13.0 11.4

 Percent of overall spells at 1 year -- -- 13.6 11.8

 Percent of overall spells at 2 years -- -- 13.9 12.1

 Percent of overall spells at 4 years -- -- 14.2 12.7

 Percent of overall spells at 8 years -- -- 13.6 12.5

138
Table III.5.2 Single Parents, continued

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

Re-entry rates

 Median nonparticipation spell in months -- 22 14 24 

 Cumulative re-entry at 4 months or less 16.2 25 27.0 21.0

 Cumulative re-entry at 12 months or less 41.0 36 47.4 41.4

 Cumulative re-entry at 24 months or less -- 55 57.8 52.8

Exit triggers

 Increase in earnings 26.0 -- 22.2 20.4

 Increase in other income 2.7 -- 14.3 14.9

 Increase in family size -- -- 27.3 21.4

 Decrease in family size -- -- 41.9 35.0

 Any trigger -- -- 19.1 17.3

"--" indicates data fields that cannot be completed 

Source: Decision Demographics weighted tabulations of the 2004 SIPP panel, 2001 SIPP panel data, Cody 

(2007), 1991 SIPP panel data from Gleason (1998) 

Notes: 

a

The ―single parents‖ subgroup actually corresponds to two different definitions: in 1984 and 1991 

it is adults in households with children and one adult, while in 2001 and 2004 it is adults in families

with children and one adult. Please refer to the text for a further exploration of these distinctions. 

b

 Percent of Overall Spells at Beginning of Period is the subgroup percent of the totals shown in life 

table columns (a) in Tables II.12, II.14, II.16, and II.23 for 2004. Column (a) represents the number 

of SNAP spells that have lasted at least the indicated number of months, regardless of when the 

spell first started. These panels show the percent of the total spells across all groups accounted for 

by this subgroup at each juncture. 

Subgroup determinations for 2001 and 2004. At-risk and entrants: person month basis, reference 

month. Entry rates: month preceding entry for monthly entry rates; last month preceding the wave 

of entry for wave-based entry rates; and last month preceding the year of entry for annual entry 

rates. New SNAP spells: the month preceding new SNAP entry. Re-entry rates: the month 

preceding the original ―new‖ SNAP entry. Subsequent and completed spells of the cross-sectional 

sample: May 2001 or May 2004, the first common month in the second wave. Exit triggers: four 

months previous to exiting a SNAP spell. Similar determinations were made for 1991 and 1986. 

3. Children of Single Parents 

Children of single parents make up about five percent of the population at risk, but account 

for 16 to 17 percent of SNAP entrants in both 2001 and 2004. This is a result of entry rates that 

are consistently three times the rates for the total population at risk. In 2001 and 2004, these 

children form a larger share of the at-risk population than they did in 1984 and 1991, but also 

represent a much larger percentage of entrants. 

139
Children of single parents’ median new spell length is consistently longer than the total 

population, by as much as six months in 1984 but as little as 2 months in 2004. In each of the 

time periods examined, about half of the children’s spells end within one year. 

The subsequent spell length changed substantially over the time periods. In 1991, 14 percent 

of children of single parents participating early in the panel period exit within a year. In 2001, 

over a third exit within a year, indicating generally shorter subsequent spells. In 2004, fewer 

were exiting within a year than in 2001, but more than in 1991. Similarly, the completed spell 

length for those that were participating at a given point in time, was longer in 2004 than in 2001, 

with fewer exiting in 2004 at 1 year, 2 years, 4 years, and 8 years. In 2004, half leave within 8 

years while in 2001, over half leave within 4 years. At the start of subsequent and completed 

spells in 2004, children of single parents accounted for 22 percent of all spells, and by 24 months 

this grows to nearly 25 percent. 

In 2001 and 2004, re-entry among children of single parents occurs faster than among the 

total population, though in 1991 the rates for the children and total population were fairly 

similar. The children’s median time off SNAP after a spell was 12 months in 2001, 4 months less 

than for the total population, and 13 months in 2004, 7 months less than for the total population. 

Generally, children of single parents are more dependent on SNAP than their parents, which 

implies that single parent families with more children are more dependent on SNAP. 

The most common exit trigger for children of single parents in 2001 and 2004 was a decrease 

in family size, reflecting the pattern set by their parents, although fewer children experienced this 

as a trigger in 2004 than in 2001 (a 10-percentage point difference). Indeed, all of the listed exit 

triggers declined in frequency among this group. 

140
Table III.5.3 Historic Subgroup SNAP Dynamics Data: Children of Single Parents a

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

At-risk populations and SNAP entrants

 Percent of the at-risk population 3.6 2.3 5.0 4.9

 Percent of entrants 9.8 8.3 17.4 16.1

Entry rates

 Monthly -- 1.1 1.6 1.7

 Wave-based 5.5 -- 5.9 6.5

 Annual -- 10.2 11.1 11.9

Spell length of new spells

 Median months 12 13 12 12

 Cumulative exit at 4 months or less 24.0 19 23.2 19.1

 Cumulative exit at 12 months or less 50.7 49 50.9 50.8

 Cumulative exit at 24 months or less 61.3 64 67.3 66.4

 Percent of overall spells at 1 month b

-- -- 17.4 16.1

 Percent of overall spells at 4 months -- -- 18.2 17.0

 Percent of overall spells at 12 months -- -- 22.5 18.8

 Percent of overall spells at 24 months -- -- 27.4 21.0

Subsequent spell length for cross-sectional sample

 Median months -- -- 22 > 27 

 Cumulative exit at 4 months or less -- 4 16.8 10.0

 Cumulative exit at 12 months or less -- 14 35.6 26.6

 Cumulative exit at 24 months or less -- 27 55.7 41.4

 Percent of overall spells at 1 month b

-- -- 25.4 22.2

 Percent of overall spells at 4 months -- -- 26.7 23.1

 Percent of overall spells at 12 months -- -- 27.9 23.6

 Percent of overall spells at 24 months -- -- 27.4 25.0

Completed spell length for cross-sectional sample

 Cumulative exit at 6 months or less -- -- 6.8 3.3

 Cumulative exit at 1 year or less -- -- 16.0 10.3

 Cumulative exit at 2 years or less -- -- 31.5 20.9

 Cumulative exit at 4 years or less -- -- 51.2 37.1

 Cumulative exit at 8 years or less -- -- 64.5 50.1

 Percent of overall spells at 1 month b

-- -- 25.3 22.0

 Percent of overall spells at 1 year -- -- 26.2 23.2

 Percent of overall spells at 2 years -- -- 26.7 24.1

 Percent of overall spells at 4 years -- -- 26.3 25.3

 Percent of overall spells at 8 years -- -- 23.8 23.5

141
Table III.5.3 Children of Single Parents, continued

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

Re-entry rates

 Median nonparticipation spell in months -- 20 12 13

 Cumulative re-entry at 4 months or less 14.5 27 29.5 28.2

 Cumulative re-entry at 12 months or less 49.9 39 50.1 48.6

 Cumulative re-entry at 24 months or less -- 53 64.8 60.6

Exit triggers

 Increase in earnings 24.9 -- 20.5 17.8

 Increase in other income 7.0 -- 14.9 13.3

 Increase in family size -- -- 25.0 18.1

 Decrease in family size -- -- 34.6 23.0

 Any trigger -- -- 18.1 15.0

"--" indicates data fields that cannot be completed 

Source: Decision Demographics weighted tabulations of the 2004 SIPP panel, 2001 SIPP panel data, Cody 

(2007), 1991 SIPP panel data from Gleason (1998) 

Notes: 

a

 The children of single parents subgroup actually corresponds to two different definitions: in 1984 

and 1991 it is children in households with children and one adult, while in 2001 and 2004 it is 

children in families with children and one adult. Please refer to the text for a further exploration of 

these distinctions. 

b

 Percent of Overall Spells at Beginning of Period is the subgroup percent of the totals shown in life 

table columns (a) in Tables II.12, II.14, II.16, and II.23 for 2004. Column (a) represents the number 

of SNAP spells that have lasted at least the indicated number of months, regardless of when the 

spell first started. These panels show the percent of the total spells across all groups accounted for 

by this subgroup at each juncture. 

Subgroup determinations for 2001 and 2004. At-risk and entrants: person month basis, reference 

month. Entry rates: month preceding entry for monthly entry rates; last month preceding the wave 

of entry for wave-based entry rates; and last month preceding the year of entry for annual entry 

rates. New SNAP spells: the month preceding new SNAP entry. Re-entry rates: the month 

preceding the original ―new‖ SNAP entry. Subsequent and completed spells of the cross-sectional 

sample: May 2001 or May 2004, the first common month in the second wave. Exit triggers: four 

months previous to exiting a SNAP spell. Similar determinations were made for 1991 and 1986. 

4. Multiple or Married Adults with Children44

Married adults with children make up 23 percent of the population at risk, but represent only 

18 and 16 percent of new entrants in 2001 and 2004, respectively. This is a result of entry rates 

that are consistently lower than for the total population. 

 

44 This category refers to married adults with children in 2001 and 2004 and adults in households with multiple 

adults (either married or unmarried) and children in 1984 and 1991. See Appendix B for implications. 

142
Their new spells are slightly shorter than the spells for the total population—the median is 

typically the same or one month less than for the full population, and a larger percentage exit 

within the specified points in time, across all years examined. 

The subsequent spell lengths for multiple or married adults with children are similar in 1991 

and 2004, with just about one-fifth of those participating in a given month participating for 

another 4 months or less and over one-third participating for another 12 months or less. In 2001, 

the subsequent spell length is shorter than the other two periods, with half having exited within 

12 months. Similarly, for the completed spell length, participants exit at a slower rate in 2004 

than in 2001, with almost two-thirds exiting within four years in 2001 and just over half exiting 

in four years in 2004. 

The share of long spells that married adults with children account for is smaller than those for 

single parents because their percentage of overall SNAP spells consistently drops with increasing 

spell length. For subsequent and completed spells lasting at least one month in the 2004 crosssectional sample, they account for 12 percent of all spells. For spells of 24 months, the share 

declines slightly to 11 percent, and among those with completed spells lasting at least eight 

years, married adults account for less than seven percent. 

Their median time off SNAP after a spell is the maximum measurable, over 27 months in 

2004, and their cumulative re-entry rates are lower than the total population. These patterns are 

slightly more pronounced in 2001 than in 2004. 

The most common exit trigger for married adults with children in 2001 and 2004 was a 

decrease in family size, followed by increases in earned income. Exits due to higher earnings and 

income declined between 2001 and 2004. 

143
Table III.5.4 Historic Subgroup SNAP Dynamics Data: Married Adults with Children a

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

At-risk populations and SNAP entrants

 Percent of the at-risk population 32.9 28.7 22.7 22.7

 Percent of entrants 36.0 34.8 17.5 15.5

Entry rates

 Monthly -- 0.4 0.3 0.4

 Wave-based 2.2 -- 1.4 1.4

 Annual -- 2.9 3.5 3.1

Spell length of new spells

 Median months 5 8 8 9

 Cumulative exit at 4 months or less 47.2 36 38.1 29.9

 Cumulative exit at 12 months or less 72.9 63 68.2 60.5

 Cumulative exit at 24 months or less 87.3 76 78.3 78.9

 Percent of overall spells at 1 month b

-- -- 17.5 15.5

 Percent of overall spells at 4 months -- -- 16.9 15.1

 Percent of overall spells at 12 months -- -- 14.8 14.2

 Percent of overall spells at 24 months -- -- 10.7 12.2

Subsequent spell length for cross-sectional sample

 Median months -- -- 12 22

 Cumulative exit at 4 months or less -- 20 27.8 19.0

 Cumulative exit at 12 months or less -- 38 50.1 35.7

 Cumulative exit at 24 months or less -- 56 66.7 52.5

 Percent of overall spells at 1 month b

-- -- 12.6 12.1

 Percent of overall spells at 4 months -- -- 11.6 11.8

 Percent of overall spells at 12 months -- -- 10.3 11.0

 Percent of overall spells at 24 months -- -- 9.3 10.6

Completed spell length for cross-sectional sample

 Cumulative exit at 6 months or less -- -- 15.4 10.8

 Cumulative exit at 1 year or less -- -- 28.3 23.0

 Cumulative exit at 2 years or less -- -- 43.0 36.9

 Cumulative exit at 4 years or less -- -- 62.4 51.7

 Cumulative exit at 8 years or less -- -- 76.3 64.6

 Percent of overall spells at 1 month b

-- -- 12.7 12.2

 Percent of overall spells at 1 year -- -- 11.9 11.8

 Percent of overall spells at 2 years -- -- 11.4 10.9

 Percent of overall spells at 4 years -- -- 9.1 9.0

 Percent of overall spells at 8 years -- -- 7.7 6.8

144
Table III.5.4 Married Adults with Children, continued

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

Re-entry rates

 Median nonparticipation spell in months -- 18 > 24 > 27

 Cumulative re-entry at 4 months or less 12.4 27 18.5 15.9

 Cumulative re-entry at 12 months or less 37.2 44 39.3 36.5

 Cumulative re-entry at 24 months or less -- 54 45.9 46.8

Exit triggers

 Increase in earnings 30.6 -- 29.3 26.6

 Increase in other income 15.4 -- 28.5 22.3

 Increase in family size -- -- 22.3 18.3

 Decrease in family size -- -- 32.9 36.7

 Any trigger -- -- 29.5 25.1

"--" indicates data fields that cannot be completed 

Source: Decision Demographics weighted tabulations of the 2004 SIPP panel, 2001 SIPP panel data, Cody 

(2007), 1991 SIPP panel data from Gleason (1998) 

Notes: 

a

 The married adults with children subgroup actually corresponds to two distinct definitions of 

subgroups as noted above; in 1984 and 1991 it is adults living in a household with other adults and 

children, while in 2001 and 2004 it is adults in families with children and a married head. Please 

refer to the text for a further exploration of these distinctions. 

b

 Percent of Overall Spells at Beginning of Period is the subgroup percent of the totals shown in life 

table columns (a) in Tables II.12, II.14, II.16, and II.23 for 2004. Column (a) represents the number 

of SNAP spells that have lasted at least the indicated number of months, regardless of when the 

spell first started. These panels show the percent of the total spells across all groups accounted for 

by this subgroup at each juncture. 

Subgroup determinations for 2001 and 2004. At-risk and entrants: person month basis, reference 

month. Entry rates: month preceding entry for monthly entry rates; last month preceding the wave 

of entry for wave-based entry rates; and last month preceding the year of entry for annual entry 

rates. New SNAP spells: the month preceding new SNAP entry. Re-entry rates: the month 

preceding the original ―new‖ SNAP entry. Subsequent and completed spells of the cross-sectional 

sample: May 2001 or May 2004, the first common month in the second wave. Exit triggers: four 

months previous to exiting a SNAP spell. Similar determinations were made for 1991 and 1986. 

5. Children of Multiple or Married Adults45 

Children of married adults make up 19 percent of the population at risk (compared to 5 

percent for children of single parents) and 16 percent of new entrants in 2001 and 2004. Because 

children of married adults enter SNAP at lower levels and rates compared with the general atrisk population, they account for about the same share of new entrants as children of single 

 

45 This category refers to children of married adults in 2001 and 2004 and children in households with multiple 

adults (either married or unmarried) and children in 1984 and 1991. See Appendix B for implications. 

145
parents. Their median new spell length in 2004 is 11 months, one more than the total population. 

In 2001 it is the same length as the total—eight months. 

Their cumulative exit percentages are about equal to the total population for new spells; 

however they exit subsequent and completed spells of the cross-sectional sample at a slightly 

higher rate than the total. 

The median subsequent spell length for children of married adults increased from 16 to 21 

months between 2001 and 2004. The 2004 patterns of cumulative exit rates closely parallel those 

of 1991, which probably had about the same median subsequent spell length. Over half of 

subsequent spells in 1991 and 2004 ended by 24 months, whereas in 2001, two-thirds of such 

spells ended by 24 months. As a proportion of overall SNAP spells of various lengths, children 

of married adults’ shares gradually decreases with increasing spell length. 

Longer completed spell lengths result from lower exit rates. Table III.5.5 shows that 

cumulative exit rates for children of married adults were lower in the 2004 analysis period 

compared with the 2001 for all lengths of spells except six months. 

Re-entry for children of married adults occurs at a somewhat higher rate than for the overall 

population. Their median time off SNAP after a spell was 12 months in 2001 and 13 months in 

2004. About one-quarter of the subgroup re-enters within four months, and about 60 percent reenter within 24 months. Generally, children of married adults are more dependent on SNAP than 

their parents, which implies that families with more children are more dependent on SNAP than 

smaller families. 

Income increases and family size increases affected children of married adults the most in 

2001. By 2004, the frequency of these triggers had declined somewhat, and the most common 

exit trigger for children of married adults was a decrease in family size. 

146
III.5.5 Historic Subgroup SNAP Dynamics Data: Children of Married Adults a

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

At-risk populations and SNAP entrants

 Percent of the at-risk population 26.7 22.0 19.1 18.6

 Percent of entrants 34.5 33.2 16.0 16.2

Entry rates

 Monthly -- 0.5 0.4 0.5

 Wave-based 2.6 -- 1.6 1.8

 Annual -- 3.7 3.9 3.9

Spell length of new spells

 Median months 6 12 8 11

 Cumulative exit at 4 months or less 40.4 30 31.3 26.2

 Cumulative exit at 12 months or less 70.9 52 65.2 55.4

 Cumulative exit at 24 months or less 84.5 69 78.5 78.9

 Percent of overall spells at 1 month b

-- -- 15.9 16.2

 Percent of overall spells at 4 months -- -- 16.1 16.1

 Percent of overall spells at 12 months -- -- 15.2 17.0

 Percent of overall spells at 24 months -- -- 11.2 12.2

Subsequent spell length for cross-sectional sample

 Median months -- -- 16 21

 Cumulative exit at 4 months or less -- 15 23.0 17.6

 Cumulative exit at 12 months or less -- 32 44.8 34.3

 Cumulative exit at 24 months or less -- 52 67.9 56.1

 Percent of overall spells at 1 month b

-- -- 11.8 13.3

 Percent of overall spells at 4 months -- -- 11.4 13.2

 Percent of overall spells at 12 months -- -- 10.8 12.8

 Percent of overall spells at 24 months -- -- 9.4 11.4

Completed spell length for cross-sectional sample

 Cumulative exit at 6 months or less -- -- 8.9 10.2

 Cumulative exit at 1 year or less -- -- 22.1 19.0

 Cumulative exit at 2 years or less -- -- 36.8 35.2

 Cumulative exit at 4 years or less -- -- 57.6 49.8

 Cumulative exit at 8 years or less -- -- 70.6 65.0

 Percent of overall spells at 1 month b

-- -- 11.5 13.0

 Percent of overall spells at 1 year -- -- 11.7 12.8

 Percent of overall spells at 2 years -- -- 11.4 12.4

 Percent of overall spells at 4 years -- -- 10.6 11.4

 Percent of overall spells at 8 years -- -- 9.2 9.6

147
Table III.5.5 Children of Married Adults, continued

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

Re-entry rates

 Median nonparticipation spell in months -- 13 12 13

 Cumulative re-entry at 4 months or less 11.7 28 28.4 24.5

 Cumulative re-entry at 12 months or less 39.2 50 52.1 49.8

 Cumulative re-entry at 24 months or less -- 61 60.1 57.9

Exit triggers

 Increase in earnings 26.1 -- 28.7 24.4

 Increase in other income 10.2 -- 27.1 20.0

 Increase in family size -- -- 26.8 19.4

 Decrease in family size -- -- 18.8 25.5

 Any trigger -- -- 27.8 22.6

"--" indicates data fields that cannot be completed 

Source: Decision Demographics weighted tabulations of the 2004 SIPP panel, 2001 SIPP panel data, Cody 

(2007), 1991 SIPP panel data from Gleason (1998) 

Notes: 

a

 The children of married adults subgroup actually corresponds to two distinct definitions of 

subgroups as noted above; in 1984 and 1991 it is children living in a household with multiple adults, 

while in 2001 and 2004 it is children in families with children and a married head. Please refer to 

the text for a further exploration of these distinctions. 

b

 Percent of Overall Spells at Beginning of Period is the subgroup percent of the totals shown in life 

table columns (a) in Tables II.12, II.14, II.16, and II.23 for 2004. Column (a) represents the number 

of SNAP spells that have lasted at least the indicated number of months, regardless of when the 

spell first started. These panels show the percent of the total spells across all groups accounted for 

by this subgroup at each juncture. 

Subgroup determinations for 2001 and 2004. At-risk and entrants: person month basis, reference 

month. Entry rates: month preceding entry for monthly entry rates; last month preceding the wave 

of entry for wave-based entry rates; and last month preceding the year of entry for annual entry 

rates. New SNAP spells: the month preceding new SNAP entry. Re-entry rates: the month 

preceding the original ―new‖ SNAP entry. Subsequent and completed spells of the cross-sectional 

sample: May 2001 or May 2004, the first common month in the second wave. Exit triggers: four 

months previous to exiting a SNAP spell. Similar determinations were made for 1991 and 1986. 

6. Elderly Adults 

Elderly adults have low SNAP entry rates, but relatively long spells and relatively low 

cumulative exit rates. In fact, if we compare spells that last at least 4 months to those that last at 

least 24 months, we find that the elderly represent a greater share of participants in the 24-month 

spells (11.8 percent for new spells) than in the 4-month spells (6.9 percent for new spells). More 

generally, in examining new entrant spells and spells from cross-section of participants, the 

longer the spell, the greater the proportion attributable to elderly adults. These findings are 

148
consistent across the 1991, 2001, and 2004 panels. Elderly individuals have been a growing 

portion of the population at risk of entering SNAP, accounting for over 18 percent in 2004 

compared to 17 percent in 1991 and 16 percent in 2001. However, only seven percent of SNAP 

entrants were elderly in 2004 and six percent elderly in earlier periods. 

The median new spell length for elderly adults is 10 months in 1991, compared to 12 months 

in 2001 and 2004, while the corresponding figures for the total population are 9, 8, and 10 

months. Elderly spell lengths increase between 1991 and 2001 when spell lengths for the total 

population decline. In 2004, elderly spells remain longer. 

In 2001 and 2004, when we include the longer spells that are found in the cross-sectional 

samples, the length of elderly spells and the slower pace of their cumulative exit are striking. The 

median subsequent spell length for elderly adults is over 24 months in 2001 and over 27 months 

in 2004. In 2004, the cumulative exit rates for elderly adults are about half those of the total 

population, and less than a quarter of their subsequent spells end by 24 months. The 2001 period 

shows the highest rate of exit of the elderly among the years examined, while the 2004 period 

shows the lowest. Elderly adults have consistently lower cumulative exit rates at every point in 

time for the 2004 analysis period compared with the 2001. When considering spells of different 

lengths, the elderly account for higher proportions of longer spells, however these proportions 

are still less than the percent they make up of the at-risk population. 

Once the elderly exit SNAP, they are also slow to rejoin the program. In 1991, 2001 and 

2004, their median time off SNAP is the longest measurable, and their cumulative re-entry rates 

at 12 and 24 months are 20 to 30 percent lower than the total population. 

149
Decrease in family size is the strongest trigger for elderly exits in both 2001 and 2004. 

Ironically, increase in family size is the second strongest exit trigger, possibly because it may 

bring an increase in family income. 

150
III.5.6 Historic Subgroup SNAP Dynamics Data: Elderly Adults (Age 60 or Older)

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

At-risk populations and SNAP entrants

 Percent of the at-risk population -- 16.4 17.3 18.2

 Percent of entrants -- 5.9 6.0 7.1

Entry rates

 Monthly -- 0.1 0.2 0.2

 Wave-based -- -- 0.6 0.8

 Annual -- 1.1 1.3 1.6

Spell length of new spells

 Median months -- 10 12 12

 Cumulative exit at 4 months or less -- 33 30.2 32.9

 Cumulative exit at 12 months or less -- 57 50.1 52.5

 Cumulative exit at 24 months or less -- 67 60.9 60.9

--

 Percent of overall spells at 1 month a

-- -- 6.0 7.1

 Percent of overall spells at 4 months -- -- 6.2 6.9

 Percent of overall spells at 12 months -- -- 7.1 8.3

 Percent of overall spells at 24 months -- -- 9.1 11.8

Subsequent spell length for cross-sectional sample

 Median months -- -- > 24 > 27 

 Cumulative exit at 4 months or less -- 11 13.8 8.7

 Cumulative exit at 12 months or less -- 25 30.3 16.0

 Cumulative exit at 24 months or less -- 34 40.0 23.7

 Percent of overall spells at 1 month a

-- -- 9.7 9.2

 Percent of overall spells at 4 months -- -- 10.0 9.6

 Percent of overall spells at 12 months -- -- 10.6 10.6

 Percent of overall spells at 24 months -- -- 12.2 12.2

Completed spell length for cross-sectional sample

 Cumulative exit at 6 months or less -- -- 6.9 4.5

 Cumulative exit at 1 year or less -- -- 13.4 9.9

 Cumulative exit at 2 years or less -- -- 20.8 14.7

 Cumulative exit at 4 years or less -- -- 28.5 16.8

 Cumulative exit at 8 years or less -- -- 38.7 22.4

 Percent of overall spells at 1 month a

-- -- 9.8 9.2

 Percent of overall spells at 1 year -- -- 10.1 9.6

 Percent of overall spells at 2 years -- -- 11.0 10.6

 Percent of overall spells at 4 years -- -- 14.4 11.7

 Percent of overall spells at 8 years -- -- 17.7 15.3

151
Table III.5.6 Elderly Adults, continued

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

Re-entry rates

 Median nonparticipation spell in months -- > 30 > 24 > 27 

 Cumulative re-entry at 4 months or less -- 21 22.8 16.3

 Cumulative re-entry at 12 months or less -- 30 35.9 28.8

 Cumulative re-entry at 24 months or less -- 38 42.8 33.2

Exit triggers

 Increase in earnings -- -- 16.5 20.9

 Increase in other income -- -- 20.4 12.5

 Increase in family size -- -- 22.0 29.6

 Decrease in family size -- -- 37.7 48.4

 Any trigger -- -- 22.0 21.1

"--" indicates data fields that cannot be completed 

Source: Decision Demographics weighted tabulations of the 2004 SIPP panel, 2001 SIPP panel data, Cody 

(2007), 1991 SIPP panel data from Gleason (1998) 

Notes: 

a

 Percent of Overall Spells at Beginning of Period is the subgroup percent of the totals shown in life 

table columns (a) in Tables II.12, II.14, II.16, and II.23 for 2004. Column (a) represents the number 

of SNAP spells that have lasted at least the indicated number of months, regardless of when the 

spell first started. These panels show the percent of the total spells across all groups accounted for 

by this subgroup at each juncture. 

Subgroup determinations for 2001 and 2004. At-risk and entrants: person month basis, reference 

month. Entry rates: month preceding entry for monthly entry rates; last month preceding the wave 

of entry for wave-based entry rates; and last month preceding the year of entry for annual entry 

rates. New SNAP spells: the month preceding new SNAP entry. Re-entry rates: the month 

preceding the original ―new‖ SNAP entry. Subsequent and completed spells of the cross-sectional 

sample: May 2001 or May 2004, the first common month in the second wave. Exit triggers: four 

months previous to exiting a SNAP spell. Similar determinations were made for 1991 and 1986. 

7. Individuals in Childless Families without Elderly or Disabled Members 

Non-elderly, non-disabled childless adults accounted for 20 to 22 percent of the at-risk 

population in 1991, 2001, and 2004—that is, one-fifth of low-income nonparticipants. However, 

they account for just one-tenth of program entrants in 2001 and 2004, as indicated by their lower 

than average entry rates. In 2001 and 2004, periods after the implementation of welfare reform, 

those from among this group who were unemployed, not participating in a work activity, or 

exempt because of high unemployment faced restrictive time limits and were eligible for SNAP 

for only 3 months out of any 36-month period. This put them in the at-risk pool without the 

152
ability to enter. This does not explain the even greater disparity in 1991, though, when 20 percent 

of nonparticipants are in the subgroup, but they represent only 5 percent of entrants. 

The median new spell length in 2004 is seven months, three months less than for the total 

population. Their cumulative exit percentages are consistently higher than for the total 

population, with almost three-quarters exiting within 12 months in 2001 and over two-thirds 

exiting within 12 months in 2004. 

The median subsequent spell length for non-elderly, non-disabled childless adults was only 5 

months in 2001 and 11 months in 2004. In 2001, nearly 50 percent exit by month four, and only 

13 percent do not exit their subsequent spells by 24 months. Although 2004 shows lower rates of 

exit, with one-third exiting within four months, it remains larger than the one-fifth who exit 

within four months in 1991, before the time limits were put in place. We see similar patterns in 

the completed spell length, with generally longer spells in 2004 than in 2001, but still much 

shorter than for the total population. Non-elderly, non-disabled adults account for five percent of 

overall subsequent and completed spells as of the first month. Among spells that last two years, 

they account for three percent, and they make up less than two percent of spells that last eight 

years. 

In addition to brief spells on SNAP, non-elderly non-disabled childless adults also have 

longer spells off the program before re-entry. Their median time off SNAP was 27 months in 

1991, and longer than the maximum calculated in 2001 and 2004. 

Virtually all shown triggers precede exits more often for the non-elderly, non-disabled adults 

than for the total population in 2001 and 2004. Twice the proportion of this subgroup 

experienced each of the exit triggers than the total population in 2001, and in 2004 nearly 75 

percent more experienced such triggers. 

153
III.5.7 Historic Subgroup SNAP Dynamics Data: Individuals in Childless Families Without Elderly 

or Disabled Members a

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

At-risk populations and SNAP entrants

 Percent of the at-risk population 13.5 20.1 21.9 20.9

 Percent of entrants 8.1 4.9 11.0 10.2

Entry rates

 Monthly -- 0.1 0.2 0.2

 Wave-based 1.2 0.1 0.9 1.0

 Annual -- 0.8 2.4 2.4

Spell length of new spells

 Median months 5 4 5 7

 Cumulative exit at 4 months or less 48.1 52 46.6 38.4

 Cumulative exit at 12 months or less 78.2 76 73.7 69.7

 Cumulative exit at 24 months or less 87.4 78 82.7 85.7

 Percent of overall spells at 1 month b

-- -- 10.9 10.1

 Percent of overall spells at 4 months -- -- 9.9 9.1

 Percent of overall spells at 12 months -- -- 6.9 7.0

 Percent of overall spells at 24 months -- -- 6.2 6.3

Subsequent spell length for cross-sectional sample

 Median months -- -- 5 11

 Cumulative exit at 4 months or less -- 19 47.9 33.0

 Cumulative exit at 12 months or less -- 40 71.6 51.3

 Cumulative exit at 24 months or less -- 55 86.6 69.7

 Percent of overall spells at 1 month b

-- -- 3.9 5.2

 Percent of overall spells at 4 months -- -- 2.8 4.3

 Percent of overall spells at 12 months -- -- 1.8 3.6

 Percent of overall spells at 24 months -- -- 1.2 2.9

Completed spell length for cross-sectional sample

 Cumulative exit at 6 months or less -- -- 32.3 24.0

 Cumulative exit at 1 year or less -- -- 56.8 41.8

 Cumulative exit at 2 years or less -- -- 76.9 56.0

 Cumulative exit at 4 years or less -- -- 90.8 68.7

 Cumulative exit at 8 years or less -- -- 94.3 78.9

 Percent of overall spells at 1 month b

-- -- 4.0 5.3

 Percent of overall spells at 1 year -- -- 3.0 4.3

 Percent of overall spells at 2 years -- -- 1.7 3.5

 Percent of overall spells at 4 years -- -- 0.8 2.6

 Percent of overall spells at 8 years -- -- 0.6 1.7

154
Table III.5.7 Individuals in Childless Families Without Elderly or Disabled Members, continued

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

Re-entry rates

 Median nonparticipation spell in months -- 27 > 24 > 27 

 Cumulative re-entry at 4 months or less 7.8 8 16.1 15.9

 Cumulative re-entry at 12 months or less 32.1 40 32.5 29.1

 Cumulative re-entry at 24 months or less -- 47 42.9 40.7

Exit triggers

 Increase in earnings 28.6 -- 53.3 40.2

 Increase in other income 13.9 -- 42.9 31.1

 Increase in family size -- -- 52.0 36.9

 Decrease in family size -- -- 57.4 52.6

 Any trigger -- -- 49.3 36.9

"--" indicates data fields that cannot be completed 

Source: Decision Demographics weighted tabulations of the 2004 SIPP panel, 2001 SIPP panel data, Cody 

(2007), 1991 SIPP panel data from Gleason (1998) 

Notes: 

a This category can be found consistently in the 1991, 2001, and 2004 data. In this 2004-based 

report, we also present data for ―non-elderly, nondisabled, childless adults‖ which is determined on 

a person basis, a slightly different definition because such individuals could be living with an elderly 

or disabled person. 

b

 Percent of Overall Spells at Beginning of Period is the subgroup percent of the totals shown in life 

table columns (a) in Tables II.12, II.14, II.16, and II.23 for 2004. Column (a) represents the number 

of SNAP spells that have lasted at least the indicated number of months, regardless of when the 

spell first started. These panels show the percent of the total spells across all groups accounted for 

by this subgroup at each juncture. 

Subgroup determinations for 2001 and 2004. At-risk and entrants: person month basis, reference 

month. Entry rates: month preceding entry for monthly entry rates; last month preceding the wave 

of entry for wave-based entry rates; and last month preceding the year of entry for annual entry 

rates. New SNAP spells: the month preceding new SNAP entry. Re-entry rates: the month 

preceding the original ―new‖ SNAP entry. Subsequent and completed spells of the cross-sectional 

sample: May 2001 or May 2004, the first common month in the second wave. Exit triggers: four 

months previous to exiting a SNAP spell. Similar determinations were made for 1991 and 1986. 

8. Noncitizens 

Noncitizens can only be tracked in the 1991, 2001, and 2004 dynamics studies. In 2001 and 

2004, the percentage of entrants that they account for is nearly half what it is in 1991, before 

restrictions on the eligibility of legally resident noncitizens took effect. Their entry rates are 

lower than those of the total population by 15 to 20 percent in 2004. 

Generally, noncitizens’ median spell lengths are shorter than the total population, and they 

exit all types of spells more rapidly. Their median new spell length in 2004 is eight months, two 

155
months less than for the total population. Subsequent and completed spells of the cross-sectional 

sample tell a similar story, where their spells are relatively short, and their cumulative exit 

relatively fast, especially in the lower spell lengths. As a percent of all spells, noncitizens 

dropped between 2001 and 2004, when they accounted for a consistent five to six percent of 

total. 

Of the years examined, noncitizens median spells off SNAP are shortest in 2004 and longest 

in 1991. In 1991 and 2001, they re-entered at a slower pace than the total population; in 2004, 

they re-entered more quickly than the total population. 

Changes in family size or earnings were exit triggers for 30 to 35 percent of noncitizens in 

2004, rates that were higher than those experienced by the total population, especially the 

increase in family size. Triggers preceded exits by noncitizens more often in 2004 than in 2001. 

156
III.5.8 Historic Subgroup SNAP Dynamics Data: Noncitizens

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

At-risk populations and SNAP entrants

 Percent of the at-risk population -- 5.8 6.4 7.3

 Percent of entrants -- 11.2 6.7 6.0

Entry rates

 Monthly -- 0.5 0.5 0.4

 Wave-based -- -- 1.9 1.7

 Annual -- 3.7 4.7 3.3

Spell length of new spells

 Median months -- 8 7 8

 Cumulative exit at 4 months or less -- 37 42.9 31.0

 Cumulative exit at 12 months or less -- 59 69.3 59.9

 Cumulative exit at 24 months or less -- 69 78.3 69.7

--

 Percent of overall spells at 1 month a

-- -- 6.8 6.0

 Percent of overall spells at 4 months -- -- 6.6 6.5

 Percent of overall spells at 12 months -- -- 5.5 5.4

 Percent of overall spells at 24 months -- -- 6.0 5.5

Subsequent spell length for cross-sectional sample

 Median months -- -- 12 20

 Cumulative exit at 4 months or less -- 8 31.5 26.5

 Cumulative exit at 12 months or less -- 22 50.1 38.3

 Cumulative exit at 24 months or less -- 43 64.9 53.6

 Percent of overall spells at 1 month a

-- -- 7.4 5.0

 Percent of overall spells at 4 months -- -- 6.8 4.8

 Percent of overall spells at 12 months -- -- 6.0 4.1

 Percent of overall spells at 24 months -- -- 6.0 4.0

Completed spell length for cross-sectional sample

 Cumulative exit at 6 months or less -- -- 15.5 15.0

 Cumulative exit at 1 year or less -- -- 29.4 24.2

 Cumulative exit at 2 years or less -- -- 35.9 32.5

 Cumulative exit at 4 years or less -- -- 54.4 41.8

 Cumulative exit at 8 years or less -- -- 71.3 58.6

 Percent of overall spells at 1 month a

-- -- 7.5 5.1

 Percent of overall spells at 1 year -- -- 7.0 4.6

 Percent of overall spells at 2 years -- -- 6.8 4.6

 Percent of overall spells at 4 years -- -- 7.5 4.7

 Percent of overall spells at 8 years -- -- 4.9 4.4

157
Table III.5.8 Noncitizens, continued

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

Re-entry rates

 Median nonparticipation spell in months -- > 30 23 13

 Cumulative re-entry at 4 months or less -- 27 25.4 24.5

 Cumulative re-entry at 12 months or less -- 36 44.0 49.0

 Cumulative re-entry at 24 months or less -- 46 50.4 55.1

Exit triggers

 Increase in earnings -- -- 29.3 29.2

 Increase in other income -- -- 23.7 13.4

 Increase in family size -- -- 20.9 35.5

 Decrease in family size -- -- 29.6 35.7

 Any trigger -- -- 28.3 25.8

"--" indicates data fields that cannot be completed 

Source: Decision Demographics weighted tabulations of the 2004 SIPP panel, 2001 SIPP panel data, Cody 

(2007), 1991 SIPP panel data from Gleason (1998) 

Notes: 

a

 Percent of Overall Spells at Beginning of Period is the subgroup percent of the totals shown in life 

table columns (a) in Tables II.12, II.14, II.16, and II.23 for 2004. Column (a) represents the number 

of SNAP spells that have lasted at least the indicated number of months, regardless of when the 

spell first started. These panels show the percent of the total spells across all groups accounted for 

by this subgroup at each juncture. 

Subgroup determinations for 2001 and 2004. At-risk and entrants: person month basis, reference 

month. Entry rates: month preceding entry for monthly entry rates; last month preceding the wave 

of entry for wave-based entry rates; and last month preceding the year of entry for annual entry 

rates. New SNAP spells: the month preceding new SNAP entry. Re-entry rates: the month 

preceding the original ―new‖ SNAP entry. Subsequent and completed spells of the cross-sectional 

sample: May 2001 or May 2004, the first common month in the second wave. Exit triggers: four 

months previous to exiting a SNAP spell. Similar determinations were made for 1991 and 1986. 

9. People in Families with Earnings 

People in families with earnings represent a majority of the population at risk (78.3 percent). 

Such a dominant group might be expected to conform to patterns found for the general 

population, but the opposite applies. 

People in families with earnings enter SNAP at slightly lower rates than the full population. 

Their median new spell length in 2004 is 8 months, two months less than for the total population. 

In earlier years, their new spell length is zero to one month less than for the total population. 

Their cumulative exit percentages are consistently higher than for the total population for new 

spells, especially for subsequent and completed spells of the cross-sectional sample. 

158
People in families with earnings experience subsequent and completed spells that are shorter 

than for the total population. The median subsequent spell length for people in families with 

earnings was only 16 months in 2001 and 19 months in 2004, compared to the subsequent spell 

lengths for the total population of 19 months and over 27 months. Their percentage of overall 

SNAP spells consistently drops with increasing spell length. 

People in families with earnings experienced median spells off SNAP before re-entry that 

were about four months longer than those among the total population. Their median time off 

SNAP was 24 months in 1991 and 2004, and 21 months in 2001. 

Changes in family size or earnings were exit triggers for 25 to 30 percent of people in 

families with earnings in 2004, rates that were somewhat higher than those experienced by the 

total population. Decrease in family size was a trigger for over 30 percent of the subgroup in 

2004, and an increase in family size triggered exit at the same level in 2001. 

159
III.5.9 Historic Subgroup SNAP Dynamics Data: People Living in Families with Earnings 

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

At-risk populations and SNAP entrants

 Percent of the at-risk population 80.0 85.1 81.5 78.3

 Percent of entrants 79.2 73.0 69.2 65.6

Entry rates

 Monthly -- 0.3 0.4 0.4

 Wave-based 2.0 -- 1.5 1.8

 Annual -- 2.4 3.8 3.9

Spell length of new spells

 Median months 5 8 8 8

 Cumulative exit at 4 months or less 47.8 36 35.2 30.2

 Cumulative exit at 12 months or less 76.8 63 65.9 61.9

 Cumulative exit at 24 months or less 87.9 76 78.7 78.9

 Percent of overall spells at 1 month a

-- -- 69.2 65.5

 Percent of overall spells at 4 months -- -- 68.6 64.3

 Percent of overall spells at 12 months -- -- 63.4 58.8

 Percent of overall spells at 24 months -- -- 56.0 47.2

Subsequent spell length for cross-sectional sample

 Median months -- -- 16 19

 Cumulative exit at 4 months or less -- 20 24.7 20.7

 Cumulative exit at 12 months or less -- 40 46.8 39.9

 Cumulative exit at 24 months or less -- 58 64.7 57.9

 Percent of overall spells at 1 month a

-- -- 55.7 53.7

 Percent of overall spells at 4 months -- -- 53.0 51.3

 Percent of overall spells at 12 months -- -- 50.4 46.7

 Percent of overall spells at 24 months -- -- 47.4 43.5

Completed spell length for cross-sectional sample

 Cumulative exit at 6 months or less -- -- 12.9 11.2

 Cumulative exit at 1 year or less -- -- 25.4 24.3

 Cumulative exit at 2 years or less -- -- 41.1 36.8

 Cumulative exit at 4 years or less -- -- 58.2 53.8

 Cumulative exit at 8 years or less -- -- 69.1 67.1

 Percent of overall spells at 1 month a

-- -- 55.7 53.4

 Percent of overall spells at 1 year -- -- 53.9 51.6

 Percent of overall spells at 2 years -- -- 51.1 48.3

 Percent of overall spells at 4 years -- -- 48.4 43.8

 Percent of overall spells at 8 years -- -- 46.7 38.3

160
Table III.5.9 People Living in Families With Earnings, continued

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

Re-entry rates

 Median nonparticipation spell in months -- 24 21 24

 Cumulative re-entry at 4 months or less 12.0 20 20.8 20.9

 Cumulative re-entry at 12 months or less 36.4 39 41.5 40.6

 Cumulative re-entry at 24 months or less -- 51 51.2 50.5

Exit triggers

 Increase in earnings 30.1 -- 26.9 25.3

 Increase in other income 21.2 -- 23.8 21.8

 Increase in family size -- -- 31.4 24.8

 Decrease in family size -- -- 31.0 32.2

 Any trigger -- -- 26.3 24.2

"--" indicates data fields that cannot be completed 

Source: Decision Demographics weighted tabulations of the 2004 SIPP panel, 2001 SIPP panel data, Cody 

(2007), 1991 SIPP panel data from Gleason (1998) 

Notes: 

a

 Percent of Overall Spells at Beginning of Period is the subgroup percent of the totals shown in life 

table columns (a) in Tables II.12, II.14, II.16, and II.23 for 2004. Column (a) represents the number 

of SNAP spells that have lasted at least the indicated number of months, regardless of when the 

spell first started. These panels show the percent of the total spells across all groups accounted for 

by this subgroup at each juncture. 

Subgroup determinations for 2001 and 2004. At-risk and entrants: person month basis, reference 

month. Entry rates: month preceding entry for monthly entry rates; last month preceding the wave 

of entry for wave-based entry rates; and last month preceding the year of entry for annual entry 

rates. New SNAP spells: the month preceding new SNAP entry. Re-entry rates: the month 

preceding the original ―new‖ SNAP entry. Subsequent and completed spells of the cross-sectional 

sample: May 2001 or May 2004, the first common month in the second wave. Exit triggers: four 

months previous to exiting a SNAP spell. Similar determinations were made for 1991 and 1986. 

10. People in Families with TANF Income 

People in families with TANF Income can only be tracked in 2001 and 2004 dynamics 

studies.46 They are a very small subgroup within the population at risk at under one percent, at 

least partly because the most TANF participants already participate in SNAP due to categorical 

eligibility. However, people in families with TANF account for seven percent of SNAP entrants 

as reflected in entry rates that are six to ten times those of the general population. 

 

46 The earlier studies also lacked information about people in families with Aid to Families with Dependent Children 

(AFDC). AFDC was the cash welfare program that existed prior to the 1996 welfare reform, which replaced AFDC 

with TANF. 

161
Their median new spell length in 2004 is ten months, the same as for the total population, but 

in 2001, it is four months longer than for the total population. The patterns are consistent for the 

subsequent and completed spell length of the cross-sectional sample—the findings are similar 

between people with TANF income and the full population in 2004, but people in families with 

TANF income have longer spells and are slower to exit in 2001 than the total population. 

When considering the percent of overall spells accounted for by people with TANF income, 

they clearly make up a higher percentage of longer spells than shorter. This applies to new spells 

as well as subsequent and completed spells. This relationship is stronger in 2001 than in 2004. 

People in families with TANF stayed off SNAP for a shorter time than the general 

population. Their median time off SNAP was 12 months in 2001 and 13 months in 2004, 

compared to 16 and 20 months for the total population. Just over 60 percent re-enter within 24 

months in 2004. Compared to the total population, they re-enter at higher rates, especially in the 

first four months, reflecting how quickly things can change for this subgroup. 

Changes in family size are the most common exit triggers, affecting nearly one in four spells 

in 2004. Triggers generally have a lesser impact in 2001 among this subgroup, and all triggers in 

both periods affect this subgroup to a lesser extent than the total population. 

162
III.5.10 Historic Subgroup SNAP Dynamics Data: People Living in Families with 

 TANF Income 

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

At-risk populations and SNAP entrants

 Percent of the at-risk population -- -- 0.7 0.8

 Percent of entrants -- -- 6.3 7.0

Entry rates

Monthly -- -- 4.3 4.5

 Wave-based -- -- 16.6 17.6

 Annual -- -- 26.3 26.0

Spell length of new spells

 Median months -- -- 12 10

 Cumulative exit at 4 months or less -- -- 27.1 32.1

 Cumulative exit at 12 months or less -- -- 51.7 58.5

 Cumulative exit at 24 months or less -- -- 65.0 74.1

 Percent of overall spells at 1 month a

-- -- 6.3 7.1

 Percent of overall spells at 4 months -- -- 6.8 7.2

 Percent of overall spells at 12 months -- -- 6.8 7.4

 Percent of overall spells at 24 months -- -- 9.2 8.9

Subsequent spell length for cross-sectional sample

 Median months -- -- > 24 > 27 

 Cumulative exit at 4 months or less -- -- 12.6 12.8

 Cumulative exit at 12 months or less -- -- 31.7 28.1

 Cumulative exit at 24 months or less -- -- 50.0 42.7

 Percent of overall spells at 1 month a

-- -- 23.9 19.9

 Percent of overall spells at 4 months -- -- 25.8 20.3

 Percent of overall spells at 12 months -- -- 27.3 20.8

 Percent of overall spells at 24 months -- -- 28.2 21.6

Completed spell length for cross-sectional sample

 Cumulative exit at 6 months or less -- -- 5.7 5.3

 Cumulative exit at 1 year or less -- -- 10.6 15.2

 Cumulative exit at 2 years or less -- -- 19.8 25.5

 Cumulative exit at 4 years or less -- -- 37.4 40.0

 Cumulative exit at 8 years or less -- -- 54.6 46.4

 Percent of overall spells at 1 month a

-- -- 23.6 19.9

 Percent of overall spells at 1 year -- -- 24.7 20.5

 Percent of overall spells at 2 years -- -- 27.8 20.6

 Percent of overall spells at 4 years -- -- 30.7 20.8

 Percent of overall spells at 8 years -- -- 32.4 22.2

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Table III.5.10 People Living in Families with TANF Income, continued

SIPP Panel

SNAP Dynamics Topic 1984 1991 2001 2004

Re-entry rates

 Median nonparticipation spell in months -- -- 12 13

 Cumulative re-entry at 4 months or less -- -- 35.3 30.8

 Cumulative re-entry at 12 months or less -- -- 55.9 48.4

 Cumulative re-entry at 24 months or less -- -- 64.9 60.3

Exit triggers

 Increase in earnings -- -- 18.0 17.3

 Increase in other income -- -- 10.7 12.2

 Increase in family size -- -- 12.6 21.7

 Decrease in family size -- -- 19.0 24.6

 Any trigger -- -- 15.2 16.0

"--" indicates data fields that cannot be completed 

Source: Decision Demographics weighted tabulations of the 2004 SIPP panel, 2001 SIPP panel data, Cody 

(2007), 1991 SIPP panel data from Gleason (1998) 

Notes: 

a

 Percent of Overall Spells at Beginning of Period is the subgroup percent of the totals shown in life 

table columns (a) in Tables II.12, II.14, II.16, and II.23 for 2004. Column (a) represents the number 

of SNAP spells that have lasted at least the indicated number of months, regardless of when the 

spell first started. These panels show the percent of the total spells across all groups accounted for 

by this subgroup at each juncture. 

Subgroup determinations for 2001 and 2004. At-risk and entrants: person month basis, reference 

month. Entry rates: month preceding entry for monthly entry rates; last month preceding the wave 

of entry for wave-based entry rates; and last month preceding the year of entry for annual entry 

rates. New SNAP spells: the month preceding new SNAP entry. Re-entry rates: the month 

preceding the original ―new‖ SNAP entry. Subsequent and completed spells of the cross-sectional 

sample: May 2001 or May 2004, the first common month in the second wave. Exit triggers: four 

months previous to exiting a SNAP spell. Similar determinations were made for 1991 and 1986. 

E. Subgroup Dynamics and their Influence on the Makeup of the Caseload 

We discussed earlier that the overall caseload can grow if more people are entering, if 

participants are staying on longer, or both. These types of changes can vary across subgroups, 

though, and if either the subgroup is large or the changes are extreme, the subgroup changes can 

have a noticeable effect on changes for the overall population. In this section, we discuss how the 

dynamics of the subgroups between 2001 and 2004 affect their total distribution within the 2004 

participant pool. The specific dynamics we discuss are displayed in Table III.6. In most cases, 

the reasons behind the increases and decreases are different across the subgroups. 

164
Table III.6 How Subgroup Dynamics Affect the Overall Caseload 2001-2004 

Change in 

Percentage of 

CrossSectional 

Population 

(percentage 

points)

Percentage of AtRisk PopulationMonthly Entry 

Rate (percent)

Median New 

Spell Length 

(months)

Median Time 

Before Re-Entry 

(months)

2001 2004 2001 2004 2001 2004 2001 2004

Total Population NA 100.0 100.0 0.4 0.5 8 10 16 20

Single Adults with Children -1.6 3.2 3.3 1.3 1.4 11 11 14 24

Children of Single Parents -3.2 5.0 4.9 1.6 1.7 12 12 12 13

Married Adults with Children -0.5 22.7 22.7 0.3 0.4 8 9 >24 >27

Children of Married Parents +1.5 19.1 18.6 0.4 0.5 8 11 12 13

Elderly -0.5 17.3 18.2 0.2 0.2 12 12 >24 >27

Nonelderly, nondisabled, 

childless adults +1.3 21.9 20.9 0.2 0.2 5 7 >24 >27

Noncitizens +2.4 6.4 7.3 0.5 0.4 7 8 23 13

Individuals in Families with 

Earnings -2.0 81.5 78.3 0.4 0.4 8 8 21 24

Individuals in Families with TANF -4.0 0.7 0.8 4.3 4.5 12 10 12 13

Single adults with children declined as a percentage of total participants. Their entry rate 

increased between the two time periods, as did the percentage of the at-risk population that they 

represent. However, their median spell length stayed the same while the spell length overall 

increased. This contributed to a decrease in the total percentage of participants that were single 

parents with children. 

Children of single adults declined as a percentage of total participants. Their entry rate 

increased, although they represented a slightly smaller portion of the at-risk population. Their 

median spell length, though, like their parents’ spell length, did not change, while the spell 

length for the total population increased. This contributed to a decrease in the percentage of 

participants represented by children of single adults. 

Married adults with children decreased as a percentage of total participants. Their entry 

rate increased, but they represented a smaller share of the at-risk population. Also, their spell 

165
length increased slightly, but not as much as for the total population. This contributed to a 

decrease in the percentage of participants that are married adults with children. 

Children of married parents increased as a percentage of total participants. Their entry rate 

increased and their percentage of the at-risk population did not change. Their median 

participation spell length also increased more than did the median spell length of the total 

population. All of these contributed to their increase as a percentage of the participant pool. 

Elderly individuals decreased as a percentage of total participants. Their entry rate remained 

the same while the percentage of the at-risk population that they represented increased, both of 

which would contribute to an increase in the percentage of participants that are elderly. However, 

their median spell length did not change, while the median spell length among all participants 

increased. This netted as a decrease in the percentage of participants that are elderly. 

Nonelderly, nondisabled, childless adults increased as a percentage of total participants. 

Their entry rate remained the same and their percentage of the at-risk population decreased, 

which would have contributed to a decrease in the percentage of participants that they represent. 

However, their median spell length increased by almost 50 percent. This contributed to an 

increase in the percentage of total participants that are nonelderly, nondisabled, childless adults. 

Noncitizens decreased as a percentage of total participants. Their entry rate decreased, 

although the percentage of the at-risk population that are noncitizens increased. Their median 

spell length, though, increased by a smaller amount than for the total population. This 

contributed to the decrease in the percentage of participants that are noncitizens. 

Individuals in families with earnings decreased as a percentage of total participants. Their 

entry rate remained the same but their percentage of the at-risk population decreased. Their 

median spell length did not increase, while the length increased for the total population. These 

166
contributed to the ultimate decrease in the percentage of total participants that live in families 

with earnings. 

Individuals in families with TANF income decreased as a percentage of total participants. 

Their entry rate increased and their percentage of the at-risk population increased. However, 

their median spell length decreased while it increased for the total population. This contributed to 

their decrease in the percentage of participants. 

 

 

 

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IV. SUMMARY AND RECOMMENDATIONS FOR FUTURE RESEARCH 

 

The purpose of this study was to investigate SNAP caseload dynamics to understand what 

drives changes in SNAP participation over time. Over much of our sample period, caseloads 

increased substantially, as they did during the time of the Cody et al. (2007) study of the early 

2000s and the Gleason et al. (1998) study of the early 1990s. To help explain these changes, we 

examined what factors lead individuals to enter SNAP, how long individuals typically 

participate, and what factors lead them to exit the program. We have also compared our findings 

with those reported in the earlier studies. Future comparisons with this study, using the 2008 

SIPP Panel, will examine the unprecedented levels of participation during the economic 

slowdown of the late 2000s. 

In this chapter we summarize our main set of findings and discuss a direction for future 

research on SNAP dynamics. 

A. Summary of Key Findings 

The annual entry rate among individuals with income less than 300 percent of poverty in one 

or more months of the panel period increased from 4.1 in the early-2000s to 4.2 percent in the 

mid-2000s and the monthly entry rate increased from 0.4 to 0.5 percent (see Table IV.1) 

(estimates for earlier periods are not available for this population). In addition, the median spell 

length for those who enter in the panel period is two months longer, on average, in the mid2000s compared to the mid- to late-1990s and the early 2000s. Those who exited re-enter later, 

on average, than in the early-2000s, which leads to fewer participants with multiple spells in the 

mid-2000s, compared to the early-2000s. Trigger events also changed. While decreases in 

income remain the predominant trigger to entry, and increases in income remain the predominant 

169
trigger to exit, the percentage of SNAP entrants that have experienced any entry trigger in the 

past four months decreased by close to 10 percentage points and the percentage of SNAP exiters 

who experienced any trigger in the past four months decreased by almost 9 percentage points. 

1. SNAP Entry 

On average, in 2004 to 2006, 5 out of every 1,000 nonparticipants with income under 300 

percent of poverty at some point in the panel period who are not participating at the end of one 

month participate in the next month (4 out of 1000 when we did not limit the income). However, 

the likelihood of entry differs according to the family situation. Individuals who received 

benefits in the past are much more likely to enter than those who had not received benefits; 

individuals in families with children are more likely to enter than individuals in families without 

children; individuals in families without income are more likely to enter than individuals in 

families with income; and individuals in families with SSI are more likely to enter than 

individuals in families without SSI. 

170
Table IV.1 Comparison of Primary Measures of SNAP Participation Dynamics 

1991-1993 1993-1996 1996-1999 2001-2003 2004-2006

Annual Entry Rate Among Individuals 

with Income Under 300 Percent of 

Poverty (Percent) NA NA NA 4.1 4.2

Monthly Entry Rate Among Individuals 

with Income Under 300 Percent of 

Poverty (Percent)

NA NA NA 0.4 0.5

Replacement Rate (Percent) NA 4.2 3.8 5.4 4.1

Median Duration for Entry Cohort 

(Months) 9 8 8 8 10

Median Cross-sectional Completed 

Spell Length (Months) > 96 54 54 48 84

Median Time-Off (Months) 20 NA NA 18 20

Total Time On (Percent with Eight 

Months or Less) 27 NA NA 37 30

Multiple Spells (Percent) 51 NA NA 63 60

Turnover Rate 1.3 NA NA 1.5 1.4

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Although trigger events continue to play key roles in SNAP entry as in prior studies of SNAP 

dynamics, with 61 percent of all entries preceded within four months by at least one of these 

trigger events, 39 percent of entries occur without an obvious recent change in circumstances. 

The most common events that triggered entry into SNAP were related to decreases in family 

earnings. Among those who entered SNAP in the panel period, 39 percent experienced a 

decrease in family earnings of at least 10 percent in the four months prior to their entry. The 

incidence of this trigger event, however, was sizably smaller than in the early-2000s when it was 

56 percent. 

2. SNAP Spell Length 

Most individuals who enter SNAP during the panel period exit within one year. The median 

participation spell among new entrants is about ten months. However, among a cross-section of 

those who are participating in a given month early in the panel (May 2004), the median 

171
subsequent spell is more than 27 months. This is a sizable increase from May 2001 when it was 

19 months. 

As with entry rates, spell duration varies according to the characteristics of the individuals. 

Individuals in families without earnings have longer spells than individuals in families with 

earnings. Additionally, children have longer spells than non-elderly adults, but elderly generally 

have the longest spells. This is in contrast to entry rates, where we see that elderly are the least 

likely to enter. The groups who stay on longer were similar to those in the early-2000s. 

3. SNAP Exit 

The most common trigger event associated with a SNAP exit was an increase in family 

income, with almost two-thirds of participants experiencing an increase of at least 10 percent. 

Slightly less than one-quarter of participants left SNAP within four months of the increase. Other 

events, though not as common, including the departure of a family member, led to a similar 

percentage exiting within four months. 

4. SNAP Re-entry 

As in the early-2000s, more than half of SNAP participants in the mid-2000s who exited the 

program in the panel period re-entered SNAP within two years. Although we cannot be sure who 

enters following the panel period, it appears that most people who re-enter SNAP do so within 

two years of exiting. 

5. Subgroups 

In Table IV.2, we bring together several of our measures for selected subgroups. The 

subgroups with the higher entry rates (children, individuals in families with children, individuals 

in families without earnings, individuals in families without a high school graduate, individuals 

in families with SSI, individuals in families with no income) also have longer median 

172
participation spells, have a shorter period before re-entry, and are less likely to exit after 

experiencing an identified trigger event. These subgroups also tend to have longer median 

duration of subsequent spells for the cross section of participants in May 2004, except that 

individuals in families with children have shorter subsequent spells than individuals in families 

without children, and individuals in families with and without income both have long subsequent 

spell lengths. The elderly enter less frequently, but participate longer, are less likely to leave 

following a trigger event, and are less likely to re-enter once they exit. 

173
Table IV.2 Comparison of SNAP Participation Dynamics across Selected Subgroups 

Subgroup (of Individuals in families with 

income under 300 percent of poverty at 

some point in the panel period)

Monthly Entry 

Rate

Median 

Duration of 

SNAP 

Spells, New 

Entry 

Sample 

(Months)

Median 

Duration of 

Subsequent 

Spell Length, 

Cross 

Sectional 

Sample 

(Months)

Probability of 

Exit Given Any 

of Identified 

Triggers

Re-entry: 

Median Duration 

of 

NonparticipationSpell (Months)

All Individuals 0.4 10 >27 21.4 20.0

Children 0.8 11 >27 18.4 13

Nonelderly adults 0.5 8 25 24.6 25

Elderly 0.3 12 >27 21.1 >27

Individuals in families with children 0.7 10 27 20.2 16.0

Individuals in families without children 0.3 8 >27 28.5 >27

Individuals in families with earnings 0.4 8 19 24.2 24.0

Individuals in families without earnings 0.8 12 >27 16.9 16.0

Individuals in families with HS graduate 0.5 9 25 22.4 22.0

Individuals in families without HS 

graduate 1.1 13 >27 16.6 12.0

Individuals in families with SSI 2.0 11 >27 19.1 15.0

Individuals in families without SSI 0.4 10 24 22.0 23.0

Individuals in families with no income 2.1 15 >27 19.9 16.0

Individuals in families with income 0.5 9 >27 21.5 20.0

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

6. Summary Measures 

The total time on SNAP is measured by the percentage of the 32 months in the sample that a 

person spends receiving SNAP benefits. Given that nearly 60 percent of participants had multiple 

spells in the panel, total time on SNAP is a useful complement to examining the length of SNAP 

spells. Of the 18 percent of individuals in the panel that received SNAP benefits during the 

174
panel, 30 percent had 8 months or less of contact with SNAP and over 27 percent received 

benefits during the entire panel. The median total time on SNAP in the panel—18 months—

indicates that the median spell duration of ten months for new entrants discussed earlier does not 

provide the complete picture of a person’s benefit receipt. 

The increase in the number of SNAP participants in 2004 and 2005 was attributed to a 

replacement rate that was greater than the exit rate. This reversed, however, in early 2006 and the 

caseload began to decline. The slowdown in growth in 2004 and 2005 was due solely to a 

decrease in the replacement rate, while the ultimate reversal in growth in early 2006 was due 

mostly to an increase in the exit rate. 

B. Recommendations for Future Research 

The SIPP panel allows us to estimate the dynamics of participation including rates of entry, 

exit, and re-entry for any panel period or set of months within the panel. The richness of the data 

also facilitates the exploration of how these dynamics vary by individual and family 

demographic and economic characteristics, particularly how they coincide with changes in 

employment, income, or family composition. But attributing changes in SNAP participation to 

changes in employment (or income or family composition) requires a set of assumptions about 

the two longitudinal series. First, both the change in participation and the change in employment 

need to be reported accurately in the months in which they occurred. We know from the analysis 

of seam bias in Appendix A that a disproportionate percentage of reported changes in 

participation and employment occur on the seams between waves, making it difficult to establish 

when the changes actually occurred. Second, a maximum, and reasonable, length of time 

between the changes needs to be assumed. For example, changes in earnings and employment 

are fairly coincident, with earnings decreasing usually in the same month or the month after a job 

175
loss occurs. It is reasonable to expect, however, that changes in participation can lag several 

months behind changes in employment or income. Individuals who experience a job loss, for 

example, may believe they can find new work quickly or continue to draw down on savings 

before deciding to enter SNAP. Similarly, unemployed SNAP participants who obtain a new job 

may wait a month or two prior to leaving SNAP to assess better whether the employment will be 

permanent and to their liking. Thus, to establish associations between changes in life events and 

changes in SNAP participation, one must decide on a reasonable window of time that precedes 

the start or end of a SNAP spell in which to examine changes in these factors. Finally, even when 

the data is reported accurately and a reasonable window for observing changes is established, 

knowing which event actually triggered program entry or exit is difficult. Changes in family 

composition and income, for example, often occur together, such as when an employed sister 

comes to live temporarily with her brother’s family. It is not possible to differentiate whether the 

SNAP exit is due to the increase in income or to the fact that an additional adult member is now 

present who can drive to stores with lower food prices or help cook cheaper meals rather than 

buying expensive prepared foods. Thus, examining longitudinal series of participation and 

employment, income, and family composition can only go so far in explaining why people enter 

and exit the program over time. Ideally, we want to know from the respondents themselves why 

they elected to participate or not participate in a given month. We believe this is a fruitful area of 

future research either through the use of improved SIPP data or through the collection of primary 

data. 

1. Modifying SIPP to Obtain Reported Reasons for All SNAP Entries and Exits 

Those who developed the SIPP instrument also understand the importance of asking 

respondents why program entries and exits occurred, as both the 2001 and 2004 SIPP panels 

176
contain self-reported data on the reasons for entering and exiting SNAP. The 10 self reported 

reasons for entering include: (1) new child (or other dependent) or pregnancy, (2) separation or 

divorce, (3) job loss or wages reduced, (4) loss or reduction of other income, (5) became disabled 

or otherwise unable to work, (6) no change—just decided it was time, (7) no change—just heard 

about the program, (8) need to recertify, (9) became disabled—other.47 Respondents are asked to 

identify all reasons that apply to their situation; the SIPP provides up to two reasons for up to 

two SNAP entries within each wave. However, in our analysis we discovered that the question is 

only asked of those who have an entry within the wave. It is not asked of those who were not 

participating at the end of one wave but were participating at the beginning of the next wave. A 

similar problem exists with the two reasons for up to two SNAP exits within each wave. Because 

the largest percentage SNAP entries and exits occur across waves, rather than within waves, we 

do not have answers to these questions for most individuals who transition onto and off of 

SNAP. As a result, the information cannot be used in our analysis. 

Because it is so important to know not just how many and what type of SNAP transitions are 

made, but why they are made, it would be useful to work with the Census Bureau to revise future 

SIPP instruments to ask this question of all SNAP entrants and exiters. It would not only be 

feasible to change the instrument in this way, but with the start of dependent interviewing in the 

2004 panel, it is now easier for an interviewer to identify whether the respondent made a SNAP 

transition across waves. Because the 2008 SIPP data collection is currently underway, these 

changes would need to be incorporated into the re-engineered survey that is still being 

 

47 The categories changed from the 2001 SIPP panel. The 2001 panel categories were: (1) needed the money, (2) 

pregnancy/birth of child, (3) began receiving for another dependent (e.g., grandchild), (4) separated or divorced 

from spouse/partner, (5) loss of job/wages/other income (own or partner’s), (6) loss of other support income, (7) just 

learned about the program, (8) just got around to applying, (9) became disabled, and (10) other. 

177
developed. The re-engineered SIPP panel, with annual interviews, will tentatively be fielded in 

early 2014 and collect data on calendar year 2013. 

2. Collecting National or State Data on SNAP Dynamics 

We believe modifying the SIPP instrument in this way would greatly improve the usefulness 

of the data for understanding the relationship between trigger events and SNAP transitions. The 

SIPP, however, is not a survey focused solely on SNAP dynamics and we believe there would be 

value in such a design and data collection effort, particularly in a survey that can be repeated 

over time. To demonstrate the usefulness of this type of survey, we consider a characteristic that 

in both the current study and prior studies of SNAP dynamics, as well as related SNAP 

participation research, has consistently been found to be strongly associated with SNAP entry 

and, among participants, the length of time spent on the program—prior participation. Entry rates 

are much higher and spell lengths are longer for individuals that have previously participated in 

the program. But what is it about prior participation that changes the benefit-cost calculus for 

individuals who are considering entering or exiting the program? Several possibilities include: 

Knowledge of eligibility rules. Individuals who have been on the program previously 

likely have a better understanding of the income and asset tests, the necessary 

documentation, and reporting procedures and will know when they are likely to be 

eligible in future months and years. 

Knowledge of application procedure. Many economic models of SNAP participation 

specify a set of costs associated with applying. These may be time costs such as spending 

part of a morning at the application office or financial costs such as paying to travel to the 

application office or foregoing a morning or afternoon’s pay from one’s job or even 

having to pay for childcare while applying in person. The costs for individuals who have 

participated in SNAP previously may be smaller than the costs for those who have never 

participated—they may have a better sense of how long the activities take and be able to 

carve out the appropriate amount of time rather than assuming the activities will take a 

full day. 

Tie to the workforce and economy. Individuals who have participated in SNAP 

previously may be more vulnerable to fluctuations in the economy, either through wages 

178
and employment income or through budgeting and savings behavior. If prior SNAP 

participants are less likely to save each month, for example, then they may be affected 

more by downturns in the economy, leading them to re-enter the program. 

Family instability. Individuals who have participated in SNAP previously may have less 

stable family compositions. Individuals may join and leave families more often, leading 

them to re-enter SNAP. 

Program policies. Individuals that have participated in the program previously likely 

know program policies designed to promote outreach such as community-based outreach 

services; expand eligibility such as broad-based categorical eligibility; or ease reporting 

such as simplified or reduced income reporting requirements. Knowing these programs 

and policies exist may decrease the perceived ―costs‖ to entering the program or 

continuing to participate in the program. 

A survey that collects information from SNAP new entrants, as well as continuing 

participants, can include sets of questions designed to identify which of these (and other) 

characteristics of prior participants differ from those of individuals new to the program. For 

example, a question for SNAP participants in states with simplified reporting for earners could 

be whether, before applying, they knew about the policy and in what ways they valued it or 

thought they might value it in the future (say when they returned to employment, if currently 

unemployed). 

The survey could be conducted in a set of states varying not only by program policies, but by 

economic conditions. In addition to obtaining an employment history for each household 

member, as done in SIPP, questions can be included that ask about the perceived employment 

possibilities for those members in the next three to six months. SNAP participation typically lags 

several months behind changes in the unemployment rate. Researchers have not identified 

whether this is because (1) local labor demand for low income individuals—the eligible 

population to receive SNAP benefits—typically lags behind the demand for higher-income (or 

more highly educated) workers or (2) nonemployed individuals in a strengthening economy 

marked by a falling unemployment rate need several months of market indicators to convince 

179
themselves to begin undertaking a costly job search endeavor. Knowing how SNAP participants 

perceive changes in the aggregate economy could help in understanding environmental, rather 

than individual or family, factors that may influence program entry and exit decisions. 

The primary data collection effort for the longitudinal design could be either a national 

survey or a several state ―case study‖. In either case the sample frame would consist of an extract 

of newly entered households. Households would be interviewed initially within the first month of 

entering SNAP and would then complete a follow up interview six months to one year later. 

Based on the estimates in the current study, this would allow the follow up interviews to consist 

of more than 50 percent of households that are no longer on the program after one year. The 

survey would minimize burden on state offices by requiring only a small set of variables needed 

to contact the new SNAP participants. 

 

 

180
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Ribar, David C., Marilyn Edolhoch, and Qiduan Liu. ―Watching the Clocks: The Role of Food

Stamp Recertification and TANF Time Limits in Caseload Dynamics.‖ Journal of Human 

Resources 43(1): 208–239, 2008. 

Weinberg, Daniel. "Data Issues Affecting the Calculation of Alternative Poverty Measures." 

Paper presented at the National Research Council Workshop on Experimental Poverty 

Measures, Washington, DC, June 15-16, 2004. 

Wilde, Parke, ―Food Stamp Program Entry and Exit in the 1990s.‖ Washington, DC: U.S. 

Department of Agriculture. Paper Presented to Association of Public Policy Analysis and 

Management Fall Research Conference, November 2001. 

Wolkwitz, , ―Characteristics of Food Stamp Households, Fiscal Year 2005,‖ report submitted to 

the U.S. Department of Agriculture, Food and Nutrition Service. Washington, DC: 

Mathematica Policy Research, Inc., September 2006. 

182
APPENDICES 

 
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Appendix A 

Longitudinal File Assessment, Recipiency History Topical Module Assessment, 

and Analysis of One- and Two-Month Gaps 

The analysis was conducted to identify potential problems in the data that could affect estimates 

of SNAP participation dynamics. Our assessment identifies several problems in the 2004 SIPP 

panel—most of which are similar to problems observed in previous panels. While these problems 

exist, we believe the 2004 SIPP data can still be used to generate reasonable estimates of SNAP 

participation dynamics. However, some adjustments to the data that were made in the study of 

SNAP dynamics in Cody et al. (2007) will also be required here. As was the case in that study, 

the estimates of participation dynamics in the current study must be considered in the context of 

the potential bias created by these problems. 

I. OVERVIEW 

The analysis of the dynamics of participation in SNAP is based on data from the 2004 SIPP 

panel. In general, we use these data to examine participation dynamics in two ways: 

(1) Descriptive Analysis of Program Entry, Duration, and Exit. We conduct a 

descriptive analysis of patterns of entry into SNAP, the duration of participation spells, 

and patterns of exit from and re-entry into the program. As in previous dynamics 

studies, a central tool used in this analysis is the life table, which examines the 

distribution of participation spells by their duration. We also will examine how the 

incidence of trigger events such as changes in income or changes in household size are 

correlated with program entry and program exit.

(2) Multivariate Analysis of Program Entry, Duration, and Re-Entry. We conduct 

multivariate modeling of program entry, spell duration, and program re-entry. This 

analysis examines how trigger events affect participation dynamics while controlling 

for other individual characteristics. For each individual, we examine whether the trigger 

event is a deviation from the individual’s usual circumstances.

These analyses use the SIPP data longitudinally. For most individuals in our analysis file, we 

have 32 months of monthly data on income, program participation, and household 

characteristics. With this information, we compute entry and exit statistics for each month and 

examine whether triggers occurring in one month are associated with program participation 

changes in a subsequent month. 

The analysis presented in this Appendix is intended to identify problems that may bias our 

estimates of participation dynamics. Some problems in the data can make the data less 

representative of the populations of interest such as those individuals receiving SNAP benefits. 

Other problems can introduce erroneous information and false changes into an individual’s 

information over time. These problems can create biased estimates of dynamics. 

A-1
Aside from this brief Overview, our assessment of the SIPP data is presented in six parts: 

Section II discusses the development of new analysis weights based on eight waves of 

the survey. Because the SIPP panel contained longitudinal panel weights for Waves 1 

to 4, 1 to 7, 1 to 10, and 1 to 12, but not for Waves 1 to 8, we had to construct a new 

panel weight that used the original seven-wave panel weight on the file along with 

information about and characteristics of panel members who either left the survey 

universe after Wave 7 or attrited from the survey after Wave 7. 

Section III examines potential bias stemming from sample loss and seam reporting in the 

SIPP. While the 2004 SIPP panel suffered from high rates of sample loss, the rates 

were comparable to recent SIPP panels. The full panel analysis weights created by the 

Census Bureau tend to correct for this sample loss and estimates of general 

population characteristics appear unbiased. The “seam effect” in the 2004 SIPP is 

pronounced, as it was in previous panels, with most of the key changes of interest to 

this study occurring on the seams between SIPP Waves. Therefore, estimates of 

changes over time must allow for the fact that many changes may be observed on a 

seam month instead of the month that they actually occurred. Defining trigger event 

variables using four or eight months of data, as in the prior studies’ continues to allow 

for this. 

Section IV examines inconsistencies in the SIPP data. One key problem identified in the 

data is that program participation is underreported in throughout the panel. This 

affects measures of participation in the first four months of the panel and measures of 

program entry in the second four months. Other problems we identified include the 

way that some assistance units (such as SNAP units) are constructed, and a high rate 

of extremely short participation spells. 

Section V examines how SIPP estimates of SNAP participation compare with those 

from administrative data sources. Estimates of SNAP participants in the SIPP differ 

from administrative estimates in two key ways: (1) SIPP data have proportionately 

too many adults, and (2) SIPP data have proportionately too many Hispanic 

individuals and American Indians. We believe that the higher proportions of adults 

and Hispanic individuals is related to the mistake identified in the 2001 SIPP panel 

data, that SIPP makes ineligible adult noncitizens appear to be receiving SNAP 

benefits along with their children (when, in reality, only their children are 

participating). 

Section VI examines data from the principal SIPP topical modules that are used in our 

analysis: data on recipiency history and employment history from Wave 1. Due to 

confidentiality restrictions imposed on the 2004 data, we conducted this analysis 

within the Census Bureau. The Wave 1 Recipiency History Topical Module (RHTM) 

items on SNAP underwent significant redesign prior to the 2004 SIPP. Some data 

were collected throughout the 2004 panel, and other SNAP items in the RHTM were 

improved, although the net effect on dynamics analysis was small. Overall, the results 

of this evaluation place the 2004 RHTM data within a normal range of what can be 

A-2
expected from the 2001 and 1991 data, and the data appear to be reliable and not in 

need of adjustment. Data from the employment history module also appear useful for 

our analysis. 

Section VII examines gaps in SNAP participation in the 2004 SIPP panel. We examine 

the incidence of gaps of different sizes; the lengths of spells prior to and following the 

gaps; characteristics associated with having a gap; and other data features related to 

gaps. The analysis was conducted to help inform the decision about whether to recode 

one- or even two-month gaps in participation by closing them up. We found evidence 

that one- and especially two-month gaps could be due not only to misreporting but 

also to actual program churning, with individuals exiting and re-entering the program 

soon thereafter. We consulted with FNS and decided to close one-month gaps in 

participation, consistent with prior studies. 

We believe that the problems in the 2004 SIPP data can be addressed either by making 

adjustments in the data or by identifying the potential bias resulting from the problems and 

interpreting the results of the analysis accordingly. In the end, while these problems likely 

introduce error into our results, we still believe that the analysis yields useful and informative 

estimates of the dynamics of participation in SNAP. 

II. WEIGHT CONSTRUCTION 

The primary analysis sample for this project will consist of original sample members who did not 

attrite through the first eight waves of the 2004 SIPP panel. There was a 50 percent sample cut 

following Wave 8, so a longer panel would provide a substantially smaller sample. The Census 

Bureau produced a longitudinal weight for Waves 1 through 7. To analyze an eight-wave panel, 

we have produced a longitudinal weight for Waves 1 to 8. The members of an eight-wave panel 

are a subset of those who have a longitudinal weight for Waves 1 to 7. To produce a longitudinal 

weight for Waves 1 to 8, we adjusted the Wave 1 to 7 weight for the somewhat smaller (by about 

4.5 percent) sample of persons with data for Waves 1 through 8. The adjustment is intended to 

compensate for attrition between Waves 7 and 8. 

Constructing the Eight-Wave Weight 

The Census Bureau assigns longitudinal weights to persons who have data (reported or imputed) 

for all months of the panel reference period (that is, the period covered by the longitudinal 

weight). The Census Bureau also assigns longitudinal weights to persons who left the survey 

universe (by dying, being admitted to an institution, or moving abroad, primarily) during the 

panel reference period, providing that they have data for all months that they were in the survey 

universe. In adjusting the Wave 1 to 7 longitudinal weight to apply to a Wave 1 to 8 sample, we 

had to account for universe leavers regardless of how they are handled in subsequent analyses. 

Universe leavers, by definition, are not subject to attrition, so there is no need to adjust their 

seven-wave weights. That is, those whom we identify as universe leavers have retained their 

seven-wave panel weight as their eight-wave panel weight. 

A-3
Table A.1 displays how the initial SIPP sample size is reduced from the 110,065 sample 

members in Wave 1 to the 68,810 individuals with eight-wave panel weights. First, we keep only 

those 72,065 panel members with seven-wave panel weights. Next, we partition this group into 

(1) those individuals who retain their original weight (meaning that their eight-wave panel 

weight is equal to their seven-wave panel weight); (2) those individuals whose seven-wave panel 

weight is adjusted to produce the eight-wave weight; and (3) those individuals who do not 

receive an eight-wave panel weight. 

Table A.1 Obtaining Eight-Wave Longitudinal Sample from Initial Sample, 2004 Panel 

 

Unweighted 

Count 

Unweighted 

Percentage 

 

Total Sample (Wave 1) 110,446 100.0 

 

Panel Members with Wave 1 to 7 Panel Weights 72,065 65.3 

Panel Members That Receive Wave 1 to 8 Panel Weight 68,810 62.3 

 Retain original panel weight 2,557 2.3 

 Present for all of Wave 7 and coded as leaving the universe in Wave 8 163 0.2 

 Present for part of Wave 7 (1-3 months) 165 0.2 

 Not present for any of Wave 7 2,229 2.0 

 Receive Adjusted Weight 66,253 60.0 

 

No missing months of data in Waves 2 through 6 and four months of data for 

Wave 8 66,162 59.9 

 

One or more missing months of data in Waves 2 through 6 and four months of 

data for Wave 8 91 0.1 

 

Panel Members That Do Not Receive Wave 1 to 8 Panel Weight (Due to Missing 

Months in Wave 8 and Not Coded as Leaving Universe in Wave 8) 3,255 3.0 

 

Panel Members without Wave 1 to 7 Panel Weights 38,381 34.8 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

Notes: 

 These counts exclude infants born to panel members after Wave 1. 

To retain their original weight, panel members must have been present for all of Wave 7 and 

have been coded as leaving the universe in Wave 8, or present for some or none of Wave 7. 

(Over 90 percent of panel members who had missing data in Wave 7 also had missing data in 

Wave 8 and 99 percent of panel members who had no data in Wave 7 also had no data in Wave 

8.) To receive an adjusted weight, a panel member must have four months of data for both 

Waves 7 and 8. Panel members with a seven-wave weight who have four full months of Wave 7 

data, but fewer than four months of Wave 8 data and no indication of having left the universe, 

are assigned no eight-wave weight. 

In all, 2,557 panel members retained their seven-wave panel weights (their “original” weights) as 

their eight-wave panel weights; 66,253 panel members received adjusted weights; and 3,255 

panel members received no weights for the eight-wave panel. As described below, individuals in 

this last group, however, were used in adjusting the weights of the panel members that did 

A-4
receive adjusted weights when identifying how the characteristics of those that attrited from the 

survey differed from those that did not attrite. 

The weighting adjustment was carried out in two stages. In the first stage we calculated and 

applied an adjustment factor to compensate for differential attrition between Waves 7 and 8. In 

the second weighting stage we calibrated the preliminary weights to Wave 1 population controls, 

as is done for all SIPP longitudinal weights. 

The first stage adjustment consisted of estimating a regression model of the association between 

(1) the likelihood of being in Wave 7 and not attriting in or before Wave 8 and (2) a set of 

individual- and family-level characteristics including race and ethnicity, labor market 

participation status, family income to poverty ratio, and indicators for receipt of SNAP, 

Supplemental Security Income (SSI), TANF, Medicaid, or unemployment compensation. The 

dependent variable was a binary variable coded to 1 if the individual did not attrite between 

Waves 7 and 8 (those who would receive adjusted weights) and to 0 if the individual did (those 

who would receive no weight). Predicted propensities not to attrite were then used to develop 

adjustment factors that correct for attrition bias. Specifically, the adjustment factor is the inverse 

of the predicted probability of remaining in the sample between Waves 7 and 8 and is applied to 

the seven-wave panel weight. The weighted sum of observations after the adjustment is equal to 

the weighted sum of the observations included in the propensity model. 

The second stage in the reweighting procedure was a calibration to bring the weighted sample 

into agreement with independent population controls for January 2004. All SIPP longitudinal 

weights are calibrated to population controls for the common month of Wave 1 (January 2004), 

so this was desirable for the eight-wave panel weight that we developed. The population controls 

divide the population by age, sex, race, Hispanic origin, and relationship to the household 

reference person. The demographic controls were based on the Census Bureau’s population 

estimates while the relationship controls were based on a Census Bureau tabulation of the 

Current Population Survey. 

Assigning Panel Weights to Children Born after the Start of the Panel 

Once the weight adjustment and calibration was completed, we identified which panel members 

with positive eight-wave panel weights had children who were born after the start of the panel, 

and we assigned longitudinal weights derived from their mothers, fathers, or guardians. We used 

a method identical to that using the 2001 panel in Cody et al. (2007). 

For any infant born after January 2004 (or born in January 2004 but not listed as a household 

member in that month), we assigned an eight-wave panel weight according to a scheme that gave 

priority to the mother’s weight, as detailed below. 

If the child’s mother (biological or adoptive) was present at any point, we assigned the mother’s 

eight-wave weight to the child except when one parent (either the mother or the father) was an 

original member of the panel and the other parent joined the SIPP household after Wave 1. If the 

father joined the household after Wave 1, we assigned one-half the mother’s weight. If the 

mother joined the household after Wave 1, we assigned one-half the father’s weight. This 

A-5
strategy of assigning half-weights in some cases was designed to increase the number of sample 

infants who received panel weights. It should not affect the weighted number of infants 

significantly. In all cases, weights were assigned without regard to whether they were positive 

versus zero. If the appropriate weight for a child was the mother’s weight and the mother’s 

weight happened to be zero, then the assignment of a zero weight to the child was appropriate as 

well. 

If an infant’s biological or adoptive mother was never present, we assigned the weight from the 

child’s father, guardian, or household reference person. If the child’s father (biological or 

adoptive) was present, we assigned the eight-wave weight (including values of zero) from the 

father. If neither parent was present, but someone in the household was identified as the child’s 

guardian, we assigned the eight-wave weight from the guardian. If no one was identified as the 

child’s guardian, we assigned the eight-wave weight from the household reference person.1

 We 

followed this sequence regardless of the values of the weights. 

Children who were adopted after January 2004 were eligible to receive panel weights but only if 

they were also born after January 2004. Adopted children born in or before January 2004 were 

treated the same way as other persons who moved in with panel members after Wave 1; they 

could not be assigned panel (longitudinal) weights, but their data contributed to the family and 

household characteristics of panel members in the months that they shared such membership. In 

addition, while present they received cross-sectional weights. In total, 2,023 children born 

between Wave 1 and the end of Wave 8 were assigned positive eight-wave weights. Of this latter 

total, 1,783 (or 88.1 percent) received positive weights from their mothers, 122 (6.0 percent) 

received positive weights from their fathers, and 118 (5.8 percent) received positive weights 

from nonparental guardians. No one received positive weights from household reference persons 

who were not identified as their guardians. 

III. SAMPLE LOSS AND SEAM REPORTING 

In this section, we explore the effects of sample loss and seam bias on SIPP-based estimates of 

participation dynamics. We then examine how response patterns in Wave 1 differ from patterns 

in Wave 2 and later. The key findings discussed in this section include: 

• The rates of sample loss in the 2004 panel are high, even relative to previous panels. 

Almost half of the individuals responding in the first wave of the SIPP are excluded 

from the dynamics analysis because they stop participating or otherwise have 

incomplete data. However, when we look at key individual characteristics, there is no 

evidence of substantial bias resulting from this sample loss. 

• Patterns of seam reporting are evident across many characteristics. Among SNAP 

participants, over two thirds of program entries and half of program exits occur on the 

first month of a wave. Seam reporting patterns are apparent among potential trigger 

events such as changes in employment and changes in other income. As in prior 

SNAP dynamics analyses, this suggests that analyses of participation spell durations 

 

1

 A child could receive a weight from a stepparent only through either of these last two alternatives. 

A-6
and analyses of trigger events need to allow for the fact that seam reporting can affect 

short term transitions. 

A. Sample Loss 

 Sample loss generally occurs when members of a household sampled for the survey either 

cannot be located or refuse to participate. In the 2004 SIPP Panel, about 15 percent of 

households originally sampled did not respond or could not be identified for the Wave 1 

interview (this is higher than the Wave 1 nonresponse rates from prior SIPP panels, where 

nonresponse rates ranged from about 5 percent in 1984 to 13 percent in 2001).2

 Among those 

individuals who were interviewed, 37.7 percent stopped participating in the survey by the end of 

eighth wave of the 2004 panel (the “effective” end of the survey for our analysis). 

This section examines the extent of sample loss and the implications that it may have for analysis 

of participation dynamics. If the individuals who left the SIPP sample are different from the 

individuals who do not leave, then estimates generated from the SIPP could be biased. The 

longitudinal “full panel” analysis weights described in the previous section are designed by the 

Census Bureau in part to address this bias. In this section, we examine weighted SIPP estimates 

to identify evidence of bias, and conclude that for broad characteristics, the SIPP weights appear 

to account for sample loss. 

The SIPP observations used in the study of participation dynamics are limited to those that have 

complete data for every month that they are in the SIPP universe through the eighth wave of the 

survey (these observations receive the full panel weights). Most of these are individuals with 

reported data are available each of the 32 months in the panel. However, some are cases that 

missed one wave of SIPP interviews and had the missing data from that wave imputed based on 

responses in the previous and subsequent waves. Others are observations for people that enter or 

exit the SIPP universe during the panel for reasons such as birth/death, moving into or out of the 

country, becoming institutionalized, etc. Individuals who enter/exit the universe receive full 

panel weights (and are included in the analysis) so long as they have complete information for 

those months that they are in the universe. It should be noted that some individuals that leave the 

SIPP universe do return later in the panel. 

In this context, sample loss (sampled individuals excluded from the analysis) are individuals that 

do not have complete information for those months that that they are in the SIPP universe. This 

includes individuals who stop responding to the SIPP (for instance, people who move and cannot 

be located, or people who refuse to participate in the SIPP), as well as individuals for whom at 

least two consecutive waves of the SIPP are missed, and therefore missing wave imputations 

cannot be completed. We refer to these two types of sample loss as attrition. 

Table A.2 shows the total sample loss in the SIPP among individuals interviewed in Wave 1.3

 

Of the 110,446 individuals responding in Wave 1, 37.7 percent are lost due to attrition. The 

remaining sample of 68,810 reflects those individuals who receive a full panel weight (for 

Waves 1 to 8) and are included in the analysis of program participation dynamics. We note that 

 

2

 These estimates are taken from http://www.census.gov/sipp/usrguide/ch2_nov20.pdf. 3

 Sample loss due to nonresponse in Wave 1 is not reflected in this table. 

A-7
this table excludes the 2,023 infants that were born between the end of Wave 1 and the end of 

Wave 8 because they do not have Wave 1 characteristics. The total sample size including these 

infants is thus 70,833 individuals. 

Differential rates of sample loss are apparent when we look at broad population characteristics 

such as income and age. Table A.2 shows that rates of sample loss are higher among those with 

lower income. While the overall combined sample loss rate is 37.7 percent, the sample loss rate 

for individuals below 10 percent of poverty is 49.2 percent. If not correctly accounted for, this 

type of differential sample loss could lead to biased estimates of participation dynamics. Table 

A.2 shows that total sample loss rates are highest among individuals age 19 to 39—44.0 percent 

of all individuals and 49.1 percent of individuals below 100 percent of poverty in this age group 

leave the sample. The age group with the second highest sample loss rates are those under age 

19—39.4 percent of all individuals, and 44.4 percent of individuals below 100 percent of poverty 

leave the sample. 

Table A.2 Sample Loss Rates, by Select Characteristics in 2004 SIPP Panel a

Initial 

Sample 

Size 

Percent of Initial 

Sample Lost 

Remaining 

Sample Size 

 

Total 110,446 37.7 68,810 

 

Income as Percent of Poverty b

 Under 10 Percent 3,916 49.2 1,989 

 10 to Less Than 50 Percent 4,531 45.7 2,461 

 50 to Less Than 100 Percent 9,060 39.1 5,521 

 100 to Less Than 150 Percent 10,863 37.8 6,754 

 150 to Less Than 200 Percent 10,880 38.3 6,713 

 200 to Less Than 300 Percent 18,979 36.9 11,972 

 300 to Less Than 400 Percent 15,169 35.5 9,784 

 400 to Less Than 500 Percent 11,488 34.7 7,498 

 500 Percent or More 25,372 36.8 16,031 

 

Age (All Individuals) 

 Under 19 31,255 39.4 18,949 

 19 to 39 30,508 44.0 17,079 

 40 to 64 35,241 34.5 23,085 

 65+ 13,442 28.0 9,683 

 Total 110,446 37.7 68,796 

 

Age (Individuals with Income less than 100 Percent of Poverty) 

 Under 19 6,820 44.4 3,795 

 19 to 39 5,218 49.1 2,657 

 40 to 64 4,162 37.5 2,600 

 65+ 1,307 29.7 919 

 Total 17,507 43.0 9,971 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

A-8
Notes: a These estimates exclude infants born after Wave 1, but before the end of Wave 8 of the 

panel. A subset of these infants who had mothers, fathers, or guardians with positive eightwave weights was assigned positive eight-wave panel weights. In all, 2,023 infants were 

assigned positive eight-wave panel weights, for a total sample size of 70,833 individuals. 

b

 Income and age characteristics are taken from the first panel month. 

Comparing cross-sectional and full panel estimates of the population in January 2004 can 

provide some insight into the Census Bureau’s ability to adjust for sample loss in their analysis 

weights. While full panel estimates only include those individuals with data for every month they 

are in the SIPP universe in Waves 1 to 8, cross sectional estimates include all individuals present 

in January 2004, regardless of subsequent SIPP response status. Because the full panel weights 

are calibrated to January 2004 population characteristics, full panel estimates of January 2004 

characteristics should be similar to cross-sectional estimates if those full panel estimates are 

unbiased by sample loss. Differences between cross-sectional and full panel estimates, on the 

other hand, reflect potential bias introduced by sample loss. 

Table A.3 presents estimates of key characteristics that are analyzed in the study of participation 

dynamics. In this table, estimates derived from the cross-sectional weights are compared with 

estimates derived from the full panel weights. While for most characteristics, such as SNAP 

participation, the full panel estimates appear similar to the cross sectional estimates, some 

estimates are different. Estimates of the number of Asian, non-Hispanic individuals are lower 

when using the full panel weights, but there is general agreement between the sets of weighted 

estimates for individuals in the other race and ethnicity categories. Estimates of individuals 

employed 35+ hours in some weeks are lower when using the full panel weights, though the sets 

of weighted estimates are similar for those that worked full time in all weeks. Finally, estimates 

of individuals receiving SSI benefits in January 2004 are higher when using the full panel 

weights. The magnitudes of all of these differences, however, are smaller than those in the 2001 

panel. Indeed, participation in SNAP in the 2001 panel was greater using the full panel weight 

than the cross-sectional weight, whereas SNAP participation in the 2004 panel shows the 

estimates to be nearly equivalent using each set of weights. 

We also compared the distribution of individuals by monthly income when using the crosssectional and full panel weights (Table A.4). The decile values of the distributions of total family 

income are similar in the cross-sectional and full panel estimates for all income levels at or above 

the 30th percentile, but differ slightly below this level. The 10th percentile value for the 

distribution of persons by total family income is about $63 higher in the full panel estimates—

$863 compared with $800 in the cross-sectional estimates. For the 20th percentile value, the 

difference is about $45. This indicates that the bottom 20 percent of the distribution is skewed 

towards lower income amounts in the full panel estimates. Because both sets of weights produce 

similar estimates of the lower end of the distribution of family earnings, the differences in the 

lower tail of the total family income distribution may suggest that the full panel weights do not 

fully account for the disproportionate loss of individuals with various sources of unearned 

income from the sample.

 

A-9
Table A.3 Sample Members with Cross-Sectional versus Eight-Wave Panel Weights, 

 by Demographic and Economic Characteristics 

Characteristic 

Cross-Sectional 

Estimates a

Eight-Wave Panel 

Estimates b

Eight-Wave Panel 

as Percent of 

Cross-Section 

 

Weighted Sample Size 286,946,523 286,947,893 

 

Age 

 Under 19 77,165,656 77,447,492 100.4% 

19 - 39 83,312,915 83,026,062 99.7% 

40 - 64 92,001,033 92,007,420 100.0% 

65 + 34,466,919 34,466,919 100.0% 

 

Race/Ethnicity 

White Alone Non-Hispanic 194,446,211 194,233,808 99.9% 

Black Alone Non-Hispanic 34,714,271 34,684,986 99.9% 

Hispanic 39,970,249 40,080,248 100.3% 

Asian/Pacific Islander Alone Non-Hispanic 9,596,865 9,320,373 97.1% 

Other Non-Hispanic 8,218,928 8,628,478 105.0% 

 

Employment Status 

No Time on Layoff, No Time Looking for Work 

Employed 35+ hours all weeks 108,138,895 108,301,671 100.2% 

Employed 35+ hours some weeks 2,178,602 2,054,707 94.3% 

Employed 1-34 hours per week 26,764,265 27,635,709 103.3% 

Some Time Laid Off and/or Looking for Work 

Employed 35+ hours some weeks 1,232,417 1,182,253 95.9% 

Employed 1-34 hours some weeks 780,982 744,601 95.3% 

No time working 8,568,485 8,278,540 96.6% 

Did Not Work, Not Laid Off, Not Looking for Work 77,928,515 77,300,784 99.2% 

With Job, Did Not Work 1,174,497 1,265,453 107.7% 

 

Disability Status 

Had work-preventing physical/mental/health condition 14,149,288 14,291,465 101.0% 

Had work-limiting physical/mental/health condition 22,662,542 23,057,411 101.7% 

 

Participants of Government Assistance Programs 

TANF 3,279,271 3,252,198 99.2% 

SNAP 19,493,044 19,674,845 100.9% 

SSI 6,118,361 6,527,958 106.7% 

Social Security 43,087,062 43,519,941 101.0% 

Veterans Disability Payments 3,165,465 3,126,532 98.8% 

WIC 5,602,640 5,726,281 102.2% 

 

 

A-10
Table A.3, continued 

Characteristic 

Cross-Sectional 

Estimates a 

Eight-Wave Panel 

Estimates b 

Eight-Wave Panel 

as Percent of 

Cross-Section 

Persons In Households of this Size 

1 30,169,310 30,649,722 101.6% 

2 76,235,546 76,385,546 100.2% 

3 55,576,998 54,580,261 98.2% 

4 61,975,099 62,791,252 101.3% 

5+ 62,989,570 62,541,112 99.3% 

 

Persons In Families of this Type 

Two Parent Families 183,830,585 185,365,750 100.8% 

Families headed by Single Female 35,637,232 34,750,640 97.5% 

Families headed by Single Male 67,478,706 66,831,502 99.0% 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

Notes: a The column labeled "Cross-Section" provides weighted counts of persons in the January 2004 crosssection sample. 

b

 The column labeled "Eight-Wave Panel" provides weighted counts in January 2004 of persons with eightwave panel weights. 

A-11
Table A.4 Decile Values of Selected Monthly Income Distributions for January 2004, 

 using Cross-Sectional and Eight-Wave Panel Weights 

Income Distribution 

CrossSectional 

Estimates a

Eight-Wave 

Panel 

Estimates b

Eight-Wave 

Panel as 

Percent of 

Cross-Section 

 

Persons by Total Family Income 

10th percentile 800 863 107.9% 

20th percentile 1,537 1,582 102.9% 

30th percentile 2,208 2,250 101.9% 

40th percentile 2,954 2,982 100.9% 

50th percentile 3,759 3,790 100.8% 

60th percentile 4,710 4,732 100.5% 

70th percentile 5,871 5,885 100.2% 

80th percentile 7,403 7,393 99.9% 

90th percentile 10,062 9,903 98.4% 

100th percentile 81,877 81,877 100.0% 

 

Persons by Family Earnings 

10th percentile 0 0 -- 

20th percentile 0 0 -- 

30th percentile 1,200 1,212 101.0% 

40th percentile 2,050 2,083 101.6% 

50th percentile 2,944 2,989 101.5% 

60th percentile 3,956 4,000 101.1% 

70th percentile 5,100 5,172 101.4% 

80th percentile 6,612 6,629 100.3% 

90th percentile 9,112 9,031 99.1% 

100th percentile 77,550 77,550 100.0% 

 

Persons Age 60 or Older by Family Social Security 

10th percentile 0 0 -- 

20th percentile 372 367 98.7% 

30th percentile 646 647 100.2% 

40th percentile 854 861 100.8% 

50th percentile 1,024 1,024 100.0% 

60th percentile 1,202 1,200 99.8% 

70th percentile 1,414 1,419 100.4% 

80th percentile 1,658 1,655 99.8% 

90th percentile 1,975 1,971 99.8% 

100th percentile 11,105 11,105 100.0% 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

Notes: a The column labeled "Cross-Section" provides weighted counts of persons in the January 2004 crosssection sample. 

b

 The column labeled "Eight-Wave Panel" provides weighted counts in January 2004 of persons with eightwave panel weights. 

 

 

A-12
While there are differences apparent in estimates of the January 2004 population between the full 

panel and cross-sectional estimates, we are most concerned about whether these differences 

affect estimates over the life of the panel. Therefore, a second way to examine the effects of 

sample loss is to compare SIPP full panel estimates to estimates from an independent source at 

different points over the life of the panel. In Table A.5 (below), we compare the distribution of 

population characteristics in the SIPP with the distribution observed in the Annual Social and 

Economic (ASEC) supplement, administered as part of the Current Population Survey (CPS) 

each March. The SIPP and ASEC estimates are similar in 2004, and for many characteristics, the 

SIPP captures changes over time as they are observed in the CPS. However, estimates of some 

characteristics trend away from estimates in the CPS. Key differences include: 

• Both SIPP and ASEC estimates of the proportion of individuals under age 19 decline 

over time, and estimates of individuals over age 40 tend to increase over time, but the 

changes in the SIPP proportions of those over age 40 are larger than those in the 

ASEC. 

• ASEC estimates of the white, non-Hispanic population decrease over time, while 

those from the SIPP increase very slightly over time. At the same time, SIPP 

estimates of the Hispanic population decrease over time, while those from the ASEC 

increase. 

• SIPP estimates of individuals receiving SNAP benefits increase over time by a greater 

amount than estimates from ASEC. 

• SIPP estimates of adults receiving SSI increase over time; the ASEC estimate stays 

constant. 

• SIPP estimates of adults receiving Social Security increases over time, while 

estimates from ASEC remain relatively constant. 

• SIPP estimates of the proportion of individuals living in two parent families increase 

from January 2004 to January 2005 and subsequently remain fairly constant, while 

ASEC estimates decrease. 

If we assume that the ASEC estimates are unbiased estimates of the full population over time, 

then we can conclude that there is some bias in the SIPP estimates, but that bias is relatively 

small. Given this small magnitude, we would not anticipate that bias in SIPP-based estimates of 

these characteristics over time would lead to meaningful bias of estimates of program dynamics. 

In summary, our analysis of sample loss in the 2004 SIPP panel leads us to conclude that there is 

some evidence of bias from sample loss, but such bias is not a significant concern. While over 

one-third of the Wave 1 sample is not included in the full panel analysis file, the full panel 

weights appear to adequately correct for this sample loss when we examine key characteristics 

for January 2004. Indeed, the correction is an improvement from that in the 2001 SIPP panel. 

Moreover, the SIPP estimates tend to track ASEC estimates relatively closely over the course of 

the SIPP panel. 

A-13
It should still be stressed that while there is no large bias in the characteristics we examined, it is 

still a concern that the full panel is substantially smaller than the original 2004 panel sample. 

Having a smaller set of observations leads to reduced precision in the estimates of participation 

patterns and in estimates of what factors affect entry and exit. Several aspects of the 2004 panel 

work to our advantage, however. First, the sample sizes are larger at the start of the 2004 panel 

than at the start of the 2001 panel. Second, the panel did not suffer from a large-scale cut 

between Waves 1 and 2 as did the 2001 panel. Third, there was a smaller rate of sample loss in 

the 2004 panel, (once the Census Bureau’s 2001 panel sample cut is excluded from the 2001 

sample loss calculation). Thus, while we have reduced precision levels using the full panel 

relative to using the month 1 cross-section, this reduction is smaller here than in prior studies of 

SNAP dynamics. 

A-14
Table A.5 Comparison of Percent Distribution of Population Characteristics, SIPP and ASEC, 2004-20061

Jan 2004 SIPP vs. 

Mar 2004 ASEC 

Jan 2005 SIPP vs. 

Mar 2005 ASEC 

SIPP a ASEC 

SIPP 

Minus 

ASEC SIPP ASEC 

SIPP 

Minus 

ASEC 

Age 

Under 19 27.0 26.9 0.1 26.7 26.7 0.0 19 - 39 28.9 29.1 -0.1 28.3 28.8 -0.6 

40 - 64 32.1 32.0 0.1 32.7 32.4 0.3 65 + 12.0 12.0 0.0 12.3 12.1 0.2 

Gender 

Male 48.8 49.0 -0.1 48.7 49.0 -0.4 Female 51.2 51.0 0.1 51.3 51.0 0.4 

Marital Status 

Married 42.3 42.1 0.2 42.4 42.0 0.4 Divorced 7.9 7.6 0.4 7.9 7.7 0.3 

Separated 1.5 1.6 -0.1 1.4 1.7 -0.3 Widowed 5.0 4.8 0.2 4.9 4.8 0.2 Never Married 43.3 44.0 -0.7 43.3 43.9 -0.6 

Race/Ethnicity 

White Non-Hispanic 67.7 67.6 0.1 67.8 67.1 0.7 Black Non-Hispanic 12.1 12.1 0.0 12.1 12.1 0.0 

Hispanic 14.0 14.0 -0.1 13.8 14.4 -0.6 Asian/Pacific Islander 3.2 4.1 -0.8 3.2 4.2 -1.0 American Indian 3.0 2.2 0.8 3.0 2.2 0.8 

Participants of Gov't Assistance Programs 

TANF 

All Adults and Children 1.9 2.1 -0.2 2.0 1.8 0.3 

SNAP 

All Adults and Children 8.2 7.2 1.0 9.2 7.6 1.7 All Children b 4.1 5.6 -1.5 4.9 5.8 -0.9 
Table A.5, continued

Jan 2004 SIPP vs. 

Mar 2004 ASEC 

Jan 2005 SIPP vs. 

 Mar 2005 ASEC 

SIPP a ASEC 

SIPP 

Minus 

ASEC SIPP ASEC 

SIPP 

Minus 

ASEC 

Participants of Gov't Assistance Programs, cont’d

SSI 

All Adults 2.0 1.8 0.2 2.4 1.8 0.5 

Social Security 

All Adults 14.7 14.1 0.6 15.3 14.1 1.2 

Veterans Disability Payments 1.1 0.8 0.3 1.2 0.9 0.3 WIC 2.0 1.3 0.7 2.1 1.3 0.9 

Persons In Families of this Type 

Two Parent Families 64.6 64.0 0.6 65.4 63.5 1.9 Families headed by Single Female 23.3 23.3 0.0 23.3 23.4 -0.1 

Families headed by Single Male 12.1 12.8 -0.7 11.4 13.1 -1.7 

Source: Decision Demographics tabulations of the 2004 SIPP Panel and March 2004-2006 ASEC Notes: 

a SIPP estimates generated using eight-wave panel weights. b Mar ASEC estimates of SNAP children were based on HFOODNO * HSUP-WGT. 
B. Seam Bias 

 In the SIPP, the “seam effect” reflects the tendency of individuals to report changes in status on 

seams—the months that represent the start or end of each four-month reference period. This has 

important implications for the study of participation dynamics, which is focused primarily on 

individuals’ reported changes in program participation. The seam effect can affect the estimated 

duration of participation spells as well as the timing of program entry and exit relative to other 

changes. In this section, we examine patterns of seam reporting to determine which transitions are 

affected. Unfortunately, on a given seam month, it is not possible to determine which reported 

transitions are “real” and which actually occurred in a different month. Therefore, we must conduct the 

analysis of participation dynamics in a way that does not rely on the short-term timing of transitions. 

Table A.6 shows the distribution of key transition events by the months that those transitions are 

reported in the SIPP.4

 For most events, the transitions disproportionately occur on seam months. For 

example, 69.3 percent of reported entries into SNAP occur on the first month of a reference period (the 

left seam). Similarly, 46.6 percent of exits occur on the left seam. If there were no bias, we would 

expect each seam to account for about 25 percent of reported transitions.5

 Hence, this suggests that 

individuals who enter SNAP in a given wave tend to report that they started receiving SNAP benefits 

in the first month of that wave, and individuals who exit in a given wave also tend to report that they 

exited in the first month of that wave. All else being equal, this would have the effect of lengthening 

estimated spell durations. While the percent of reported entries at the left seam is slightly larger than in 

the 2001 panel (69.3 percent in 2004 versus 67.5 percent in 2001), the percent of reported exits at the 

left seam is much smaller (46.6 percent in 2004 versus 73.8 percent in 2001). The net effect may be an 

improvement in the reporting of transition events. 

 

4

 Left seams from Wave 1 and right seams from Wave 8 are excluded from the estimates in Table A.6. In Wave 1, the leftseam transitions cannot be observed since there are no data on the month prior to the left seam. In Wave 8, while rightseam transitions can be observed for some of the sample, our longitudinal panel weights are representative through Wave 8 

of the sample only. 

5

 Because the SIPP sample is split into 4 random rotation groups, with each rotation group having a different four-month 

reference period, seasonal bias or other factors would not affect the distribution of transition events across reference 

months. 

A-17
Table A.6 Distribution of Transition Events by Reference Month a,b,c 

 

Percent of Transitions from Prior Month 

to Indicated Month 

Transition Event Total Month 1 Month 2 Month 3 Month 4 

 

SNAP 

Entry 30,497,124 69.3 7.8 10.2 12.7 

Exit 25,388,310 46.6 22.1 14.6 16.6 

 

Employment among 

Persons 16 and Over 

Entry 91,249,690 45.6 17.1 17.7 19.6 

Exit 82,297,555 43.1 17.9 19.3 19.6 

 

More than 5% Change in Income 

among Persons 16 and Older 

Earnings 775,754,126 55.9 14.0 14.1 16.0 

TANF 9,137,706 66.9 12.2 9.4 11.5 

SSI 25,525,713 86.2 5.4 3.7 4.6 

 

Household Composition 

Different Household (Total) 91,909,075 29.2 23.1 23.2 24.5 

Different Household (Adult) 67,910,066 28.9 22.9 23.3 24.9 

Different Household (Child 

Under 15) 23,999,010 29.9 23.9 22.8 23.4 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

Notes: a Left-censored spells, spells beginning in Wave 1, and newborns upon entry are excluded from the entry estimates. 

b Right-censored spells and spells ending in Wave 8 are excluded from the exit estimates. 

c Changes in income and household composition in Wave 1 are excluded. 

 

Transitions in other events that could affect SNAP entry and exit also are subject to seam bias. For 

example, among all individuals just less than half of job entries and exits occur on the left-seam. 

Additionally, individuals disproportionately report changes in earnings of more than 5 percent on left 

seams. The seam effect for changes in earnings is less than that of changes in TANF or SSI. For 

TANF, 66.9 percent of changes greater than 5 percent are reported on the left seam, while for SSI, 86.2 

percent of changes greater than 5 percent are reported on the left seam. Changes in household size 

appear to be only slightly affected by seam reporting when we examine trends for all individuals. 

To determine whether patterns of seam reporting are associated with characteristics of the SIPP 

household, Table A.7 compares left seam reporting of transition events for different subgroups. For 

individuals who report receipt of SNAP at some point during the 2004 panel, rates of seam reporting 

reflect those of the population as a whole. However, when we limit the analysis to individuals whose 

household respondent for the wave changed (and only examining those waves where the household 

respondent is different), seam reporting for most transitions approaches 100 percent. The seam effect is 

higher for both SNAP entries and exits when a proxy responds for the individual than for all 

individuals. Finally, the seam effect is higher for records where the entire month is imputed, relative to 

the full sample. 

A-18
Table A.7 Rates of Seam Reporting For Select Subgroups, 2004 SIPP Panel a,b,c 

Interview Status (Adults only) 

Transition Event 

All 

Individuals 

Adults 

Receiving 

SNAP Benefits 

During Panel 

Individuals in 

Households 

with Change 

in Household 

Respondent 

Interview 

(Self) Proxy 

Individual’s 

Month 

Imputed 

 

SNAP 

Entry 69.3 69.9 93.4 7,934,787 72.7 89.8 

Exit 46.6 47.4 91.7 3,972,087 53.6 70.5 

 

Employment among Persons 16 and 

Over 

Entry 45.6 44.0 96.1 20,354,727 45.8 56.2 

Exit 43.1 41.9 96.8 17,183,742 46.5 70.3 

 

More than 5% Change in Income 

among Persons 16 and Older 

Earnings 55.9 52.4 97.6 246,051,837 58.5 63.6 

TANF 66.9 65.9 97.2 4,732,601 75.5 84.3 

SSI 86.2 86.1 98.8 14,604,675 87.2 91.0 

 

Household Composition 

Different Household (Total) 29.2 28.4 43.1 13,455,787 29.4 35.8 

Different Household (Adult) 28.9 28.4 41.8 13,447,333 29.1 35.7 

Different Household (Child Under 15) 29.9 29.9 49.4 8,454 70.9 100.0 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

Notes: a Left-censored spells, spells beginning in Wave 1, and newborns upon entry are excluded from the entry estimates. 

b Right-censored spells and spells ending in Wave 8 are excluded from the exit estimates. 

c Changes in income and household composition in Wave 1 are excluded. 

 

The extent of seam bias in the 2004 SIPP panel constrains our ability to examine how program 

participation is affected by the timing of other events. Because of seam reporting, an observed 

transition could have occurred in the reported month or in any other month of a wave. Indeed, it is 

even possible that a trigger event that precedes a SNAP transition is reported after that transition is 

reported. As a result, as in prior SNAP dynamics analyses, our analysis accounts for the SIPP seam 

effect. We use trigger “windows” of 4 and 8 months to capture transition events that may have been 

reported on a seam. Finally, we conduct a sensitivity analysis excluding those groups with the largest 

seam bias (namely, individuals in households where the respondent changed in the wave, and 

individuals whose information was imputed). 

 

A-19
IV. DATA INCONSISTENCIES 

 

In this section, we provide a summary of key inconsistencies we identified in the 2004 SIPP panel 

data. Inconsistencies may reflect reporting errors or data processing errors, and using data with errors 

could lead to biased estimates of participation dynamics. We examine two types of inconsistencies. 

First, we describe cross-sectional inconsistencies, where some information for a respondent or 

household conflicts with other information in the same month. Second, we describe longitudinal 

inconsistencies, where a respondent’s information in one month is inconsistent with information 

provided in a subsequent month. 

 Key findings from the analysis of data inconsistencies include: 

• As in the 2001 panel, we have found conflicting information for some individuals about 

whether or not the individual was employed in a given month. We assessed the implications 

of using the same recode of the SIPP’s employment summary variable in 2004 as we used 

in 2001 and continued to use it in 2004. 

• Some SNAP and TANF assistance units have conflicting information on the amount of 

benefits received in that unit. Moreover, in some cases, it is difficult to determine whether 

the one unit observed in the SIPP actually represents two separate assistance units. We 

developed an algorithm to resolve these inconsistencies based on a set of simple 

assumptions. 

• Underreporting of program participation is significantly greater in Wave 1 than in Wave 2. 

One possible explanation of this behavior is that respondents are “learning” how to respond 

accurately to the survey instrument. This could bias estimates of participation spells that are 

active in the first wave, as well as estimates of spells that start in the second wave. 

A. Cross-Sectional Inconsistencies 

There are two key cross-sectional inconsistencies we identified in the 2004 SIPP data. The first is 

a set of inconsistencies that arise in determining which individuals are employed, unemployed, and out 

of the labor force in a given month. The second is a set of inconsistencies that arise in the formation of 

SNAP and TANF assistance units in the SIPP data. 

1. Employment Status Indicators 

 In 2006, using the 2001 SIPP panel, Mathematica identified a number of problems with key 

variables that determine employment status. The problems led some individuals to have conflicting 

information on whether they were employed, and if so, the number of hours that they worked. 

Mathematica worked with Census Bureau staff to investigate these issues and determined that there 

were multiple reasons for these inconsistencies. As a result, for many of these inconsistencies it was 

difficult to determine which variables should be trusted and which should be ignored or modified. 

After a comprehensive assessment of the SIPP’s employment status summary variable, RMESR, and 

other variables such as hours worked and job start and end dates, Mathematica decided on a recoding 

procedure that preserved the employment status value from RMESR for most of the sample, but 

recoded it using additional information from the “usual hours worked per week” variable RMHRSWK 

to form a new employment summary measure EMPCAT. 

A-20
Using the 2004 panel we cross-tabulated in month 1 of the panel the employment summary variable 

EMPCAT with the SIPP employment summary variable RMESR and usual hours worked per week 

variable RMHRSWK to identify how many sample members would have their employment status 

recoded if EMPCAT was used in place of RMESR in the current study. We found that: 

• Except for one person, all sample members (about 69.5 percent of the sample) that would 

be categorized as employed using RMESR continue to be categorized as employed using 

EMPCAT. The one individual categorized as employed using EMPCAT but unemployed 

using RMESR has (1) an RMESR value that indicates that they did not have a job all month 

and spent at least one but not all weeks on layoff or looking for work; and (2) a 

RMHRSWK value that indicates he or she worked 1 to 34 hours at least one week but not 

all weeks and worked 0 hours the remaining weeks. 

• Using EMPCAT, 3.0 percent of the unweighted sample, or a set of 3,253 individuals, is 

unemployed in month 1. Using RMESR, 2.3 percent, or a set of 2,539 individuals, is 

unemployed in this month. The individuals coded as unemployed using EMPCAT, but not 

using RMESR, reported usually working 0 hours per week in the month. 

• All sample members that would be coded as out of the labor force using RMESR continue 

to be categorized as out of the labor force using EMPCAT. This makes up about 27.6 

percent of the sample. 

The employment status variables are important to our analysis because we investigate the role that 

changes in employment status have on program entry and exit. Based on this tabulation (and similar 

tabulations in other months of the panel), we utilize EMPCAT in place of RMESR to define monthly 

employment measures. We believe all except one of the sample members affected by this choice of 

variable can reasonably be considered unemployed given that they reported working zero hours in a 

usual week in the month. This also maintains comparability with the results in Cody et al. (2007) based 

on the analysis of the 2001 SIPP panel. 

2. Assistance Unit Formation Problems 

 In some cases, individuals who are members of the same SNAP or TANF assistance unit have 

information that conflicts with other members of that unit.6

 In terms of SNAP units, we identified a 

handful of cases with one of two inconsistencies: 

(1) The program unit had extra SNAP benefits. In each SNAP unit, the SIPP is only 

supposed to record the amount of benefits on one person’s record – that of the unit reference 

person. However, in some units, the benefit amount is assigned to multiple people—the unit 

reference person as well as one or more non-reference persons. Often, the individuals in the 

same unit have SNAP benefits of different amounts. It is unclear why these inconsistencies 

occur. It could be that the benefit amount assigned to non-reference persons is superfluous 

(and should be ignored), that the unit’s benefits have been pro-rated across multiple unit 

members (and should be summed across members of the unit), or that the unit has been 

incorrectly formed (and should be split into two units). 

 

6

 In this section, we refer to “assistance units” as those members of a dwelling unit that receive benefits under the same 

case. We use the term “household” to refer to the SIPP household. 

A-21
(2) The SNAP unit reference person was a participant in another SNAP unit. The SIPP 

records include “pointers” that indicate, for each individual, which other household member 

is their SNAP unit reference person. In some households, some members point to one 

individual as the unit reference person, who in turn points to another individual as the unit 

reference person. This second reference person is also covered by SNAP and points to 

him/herself as the reference person of his/her unit. In these cases, it could be that there is 

only one SNAP unit and the first set of pointers is erroneous. Alternatively, it could be that 

the household has multiple units with two reference persons, and the pointer on the first 

reference person is erroneous. 

Each month, between 1.6 and 5.3 percent of SNAP households (unweighted) has one of these two 

inconsistencies (Table A.8). Similar problems exist for TANF units, where the same SIPP procedures 

for identifying unit reference persons and income are used. For TANF units, the rates of 

inconsistencies are higher than for SNAP units—ranging from 3.4 to 6.9 percent of TANF units 

(unweighted) each month. 

The percentage of SNAP units with inconsistencies is fairly constant at around 1.6 percent from month 

1 to month 4, then jumps to 4.6 percent in month 5 and remains fairly constant (ranging from 4.0 to 5.3 

percent) from month 5 onward. Concerned about the jump between Waves 1 and 2, which is larger 

than the jump we saw in the 2001 SIPP panel, we performed additional diagnostic tabulations on the 

cases with inconsistencies. We concluded that the introduction of dependent interviewing in the 2004 

panel, in conjunction with changes in interview status from “self” to “proxy” between waves, may be 

responsible for the largest proportion of the inconsistencies—the reporting of two SNAP benefit 

amounts within one SNAP unit. 

As an example, consider an actual case that we observed in the data in which a household contains a 

married couple. In the first wave, both persons 101 and 102 respond as “self”, with person 101 

identifying him- or herself as the SNAP unit head and thus is coded as the sole recipient of the SNAP 

benefit amount. In Wave 2, person 102 responds as “self” and also serves as a proxy for person 101 

who does not respond. In this wave, person 102 identifies him- or herself as the SNAP unit head and is 

coded with the benefit amount. Then, according to the order of questions in the SIPP interviewer’s 

manual, the dependent interviewing causes the SIPP interviewer to ask about the previous participation 

of person 101 before asking who is covered in person 102’s SNAP unit. The interviewer asks “[Person 

101] was getting SNAP benefits before, are they still?” Person 102 responds in the affirmative that 

person 101 is still receiving SNAP, and the interviewer proceeds to ask questions about the level of 

benefits of person 101. Finally, after attributing a benefit amount to person 102 and person 101 (which 

is sometimes reported as the same amount and sometimes not), the interviewer asks who is covered by 

the benefit in Wave 2 and person 102 responds they both are. The result is that person 101 and 102 are 

both coded as being in the unit headed by person 102, but both of them have a positive benefit amount 

when there should only be one benefit amount per unit. 

The effect of dependent interviewing on the increase in inconsistencies between Wave 1 and 2 appears 

to be compounded by an increase use of proxy respondents in Wave 2. The increase in the use of proxy 

respondents may be due to Wave 1 interviews being conducted in person and Wave 2 interviews being 

conducted by telephone, as the SIPP tries to do only one in-person interview per year (and Wave 1 

interviews are always in person). 

A-22
Table A.8 Frequency of within-Unit Inconsistencies for SNAP and TANF, 

 2004 SIPP Panel, Unweighted 

Reference 

Month 

Households 

with 

SNAP 

Percentage 

of SNAP 

Households 

with Problems 

Households 

with 

TANF 

Percentage 

of TANF 

Households 

with Problems 

 

1 3,245 1.6% 645 3.6% 

2 3,311 1.6% 644 3.4% 

3 3,382 1.6% 659 3.5% 

4 3,435 1.6% 676 3.8% 

5 3,407 4.6% 661 5.7% 

6 3,343 4.0% 625 6.2% 

7 3,369 4.2% 624 5.9% 

8 3,392 4.0% 622 5.5% 

9 3,362 5.2% 620 5.2% 

10 3,317 4.7% 600 4.7% 

11 3,330 4.4% 589 4.4% 

12 3,343 4.4% 581 4.5% 

13 3,395 4.9% 580 4.5% 

14 3,340 4.6% 565 4.4% 

15 3,341 4.3% 565 4.4% 

16 3,346 4.2% 558 4.8% 

17 3,362 5.0% 564 6.9% 

18 3,306 4.5% 538 6.7% 

19 3,291 4.3% 537 6.7% 

20 3,315 4.1% 543 6.6% 

21 3,405 5.0% 542 6.3% 

22 3,375 4.4% 521 6.1% 

23 3,394 4.2% 524 5.7% 

24 3,399 4.2% 525 6.3% 

25 3,417 5.3% 523 4.6% 

26 3,335 4.6% 505 5.0% 

27 3,317 4.4% 494 4.7% 

28 3,305 4.1% 494 4.7% 

29 3,228 5.1% 497 4.2% 

30 3,157 4.5% 470 4.3% 

31 3,153 4.4% 465 4.1% 

32 3,138 4.3% 460 4.3% 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

These inconsistencies are a concern in part because they could affect our estimates of the number 

of SNAP units experiencing certain events, and they could affect our estimates of potential TANF 

trigger events. We developed basic assumptions to resolve these inconsistencies. In units where 

multiple individuals have income, we assume that there is only one program unit (not multiple units for 

each person with income), and we assume that the only income to be counted is that of the designated 

reference person. In cases where one reference person points to another reference person, we again 

assume that there is only one program unit, and that the individual that points to him/herself is the 

reference person of the unit. 

B. Longitudinal Inconsistencies 

A-23
In this section, we discuss differences in underreporting of program participation between Wave 1 

and Wave 2, our approach to handling participation spells that are censored by transitions into and out 

of the SIPP universe (and discuss a small number of inconsistent records that are observed), and how 

we handle extremely short participation spells. 

1. Differences between Wave 1 and Wave 2 Responses 

 In the 2004 Panel (as well as in the 2001 and 1996 Panels), patterns of responses between 

Waves 1 and 2 suggest a “learning” of the SIPP instrument. Underreporting of program participation is 

substantially larger in Wave 1 than Wave 2. It could be that individuals in Wave 1 are more likely to 

underreport—intentionally or unintentionally—because they are not familiar with the interview 

procedures; then as they learn the procedures, they provide more accurate information. This type of 

response pattern has two key implications for the analysis of participation dynamics. First, it would 

lead to an under-representation of participation spells in the first wave of the SIPP (and the participants 

that report may be systematically different from the participants that do not report). Second, it would 

lead to an overcount of new spells in the second wave of the SIPP. Unfortunately, this problem cannot 

be fixed in the data. Instead, we can at best use sensitivity analysis to explore how our estimates of 

participation dynamics might be affected by the differences between Wave 1 and Wave 2 responses. 

Table A.9 shows how underreporting of program participation is consistently larger in Wave 1 than 

Wave 2. For each wave, we examine the number of participants in the common month of that wave. 7 

For example, the number of individuals reporting SNAP receipt in the common month of Wave 1 

(January 2004) is 14.8 percent below administrative totals for that month, while the number in the 

common month of Wave 2 (May 2004) is only 9.7 percent below administrative totals. The difference 

between Wave 1 and Wave 2 is larger for SNAP and TANF than for employment. 

 

7

 The common month refers to the one month of each Wave that is common to each SIPP rotation group. Using the 

common month allows us to simplify the analysis to reflect one month of each Wave. 

A-24
Table A.9 Difference in Participation Levels between Waves 1 and 2 of the 2004 SIPP Panel 

 January 2004 May 2004 

Transition 

Event 

SIPP 

 (Wave 1) 

Administrative 

Data 

Percent 

Difference 

SIPP 

(Wave 2) 

Administrative 

Data 

Percent 

Difference 

Difference in 

Wave 1 and 

Wave 2 

Differences 

 

SNAP 

Participants 

Total 19,493,044 22,869,339 -14.8 21,498,317 23,817,622 -9.7 -5.0 

Adult 10,386,132 11,240,416 -7.6 11,680,169 11,809,981 -1.1 -6.5 

Children 

(under age 

18) 

9,106,912 11,628,923 -21.7 9,818,148 12,007,641 -18.2 -3.5 

 

TANF 

Participants 

Total 3,279,271 4,758,264 -31.1 3,469,136 4,687,755 -26.0 -5.1 

Mothers 972,767 1,152,179 -15.6 965,392 1,129,221 -14.5 -1.1 

Children 

(under age 

6) 

2,306,505 3,606,085 -36.0 2,503,744 3,558,534 -29.6 -6.4 

 

Employment 128,418,606 151,880,200 -15.4 134,383,726 151,880,200 -11.5 -3.9 

 

Source: Decision Demographics, tabulations of the 2004 SIPP panel; the 2004 SNAP QC data, exclusive of Guam and the 

Virgin Islands; the March 2004 ASEC data (for employment "administrative" estimates). TANF estimates, exclusive 

of Guam, Puerto Rico, and the Virgin Islands, from http://www.acf.hhs.gov/programs/ofa/datareports/caseload/caseload_current.htm 

Notes: 

SIPP Estimates generated using cross-sectional weights. Employment reflects all individuals with earnings and is 

compared with estimates from the CPS. The CPS estimates represent "ever-had" earnings during the previous 

year. 

The increase in reported SNAP participation between Waves 1 and 2 can be the result of a number of 

factors. It could be that individuals who are present in both waves report participation at higher rates in 

Wave 2. However, it could also be the case that individuals who enter the SIPP sample in Wave 2 (for 

instance, individuals that move into a SIPP household) are disproportionately SNAP participants. 

Finally, it could be that weighting adjustments in Wave 2 disproportionately increase the weights of 

SNAP participants. 

Table A.10 decomposes the observed change in SNAP participation from wave to wave by these 

various factors. For each wave, we estimate the number of SNAP participants in the common month 

(using cross-sectional weights). The estimated number of participants increased by 2.0 million between 

January and May 2004, increased by 759 thousand between May and September 2004, increased by 

623 thousand between September 2004 and January 2005, and increased by 482 thousand between 

January 2005 and May 2005. We decompose these changes into three categories: (1) changes in 

reporting of SNAP participation status among individuals present in the common months of the current 

and previous wave, (2) reporting among individuals who are present in the current wave but were not 

in the SIPP sample in the common month of previous wave,8

 and (3) residual growth. Residual growth 

 

8

 Individuals are considered absent from the SIPP sample in a common month even if they were present in other months of 

that or prior Waves. For example, an individual can respond in Wave 2, not respond in Wave 3 and respond again in Wave 

A-25
is the difference between the observed change and the sum of the first two categories. This growth can 

be explained by changes made to individuals’ weights across waves to account for movement of other 

individuals into and out of the SIPP sample. 

The estimates in Table A.10 support the theory that individuals are learning the SIPP survey between 

Waves 1 and 2. The large increase in participation between Waves 1 and 2 is driven by a relatively 

large number of people who did not report participation in Wave 1 but reported participation in Wave 

2. It does not appear that the change is driven by SNAP participants entering the SIPP sample in Wave 

2, or by weighting adjustments between the two waves. The number of individuals present in both 

waves that report participation in Wave 2 but not Wave 1 (4.7 million) is higher than the analogous 

number observed in subsequent Waves (between 3.3 and 3.8 million). At the same time, the number 

reporting SNAP participation in Wave 1 but not Wave 2 (3.1 million) is more consistent with the 

analogous number observed in subsequent Waves (3.1 million to 3.2 million). The net increase due to 

changes in reporting status among individuals present in both Waves is 1.6 million, higher than the net 

increase observed in later waves. 

 4. For this analysis, we would consider that individual absent from the sample in Wave 3. Note that there are 157,742 

(weighted) individuals in Wave 2 who were not present in the common month of Wave 1. These individuals were present 

in at least one other month of Wave 1 besides the common month. 

A-26
Table A.10 Decomposition of Changes in SNAP Participation Estimates across the Common Months 

of Waves 1 through 5 in the 2004 SIPP 

Jan. 2004 May 2004 Sept. 2004 Jan. 2005 May 2005 

 

Total Participants in Wave 19,499,943 21,524,129 22,283,027 22,906,394 23,388,476 

Net Increase Over Prior Wave 2,024,186 758,898 623,367 482,082 

 

Decomposition of Change 

In SNAP Now but Not in Prior Wave 4,703,331 3,752,364 3,633,309 3,274,338 

Not in SNAP Now but Was in Prior Wave 3,085,339 3,159,764 3,213,709 3,082,983 

Net increase in reporting 1,617,991 592,600 419,600 191,355 

 

Participants Not in Sample Prior Wave, Not New 

to Sample This Wave2

 157,742 990,727 1,339,603 1,215,884 

Participants, New to Sample This Wave 334,319 338,156 312,172 387,570 

Subtotal 492,061 1,328,883 1,651,774 1,603,454 

 

Combined net growth 2,110,052 1,921,483 2,071,374 1,794,809 

 

Residual growth -85,866 -1,162,585 -1,448,007 -1,312,727 

 

SNAP Reporting Rates 

Percent Not in Sample Prior Wave, Not New This 

Wave with SNAP 20.1 8.3 8.8 8.1 

Percent New to Sample This Wave with SNAP 9.1 10.5 9.8 11.6 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

Notes: 

1. A positive residual growth would suggest a relatively greater increase in weights among participants than 

nonparticipants. The observed negative residual growth implies a smaller increase in weights among 

participants than among nonparticipants.

2. There are 157,742 (weighted) individuals in Wave 2 who were not present in the common month of Wave 1. 

These individuals were present in at least one other month of Wave 1 besides the common month. 

A small number of SNAP participants in Wave 2 are individuals that were not in sample in the 

common month of Wave 1 but were not new to the sample in Wave 2. In other words, there are 

observations for these individuals for a different month of Wave 1. As would be expected, analogous 

numbers of participants in later waves are larger because there is more of an opportunity for 

individuals to leave the sample and return. The number of SNAP participants that are new to the SIPP 

sample in Wave 2 is consistent with the number observed in later waves. 

In short, there appears to be some factor that leads individuals to underreport program participation in 

Wave 1, and correct that underreporting in Wave 2. This could lead to biased estimates of program 

participation dynamics if the spells that are unreported in Wave 1—in particular, spells that begin in 

Wave 1 but are unreported—are systematically different than those spells that are reported. In our 

analysis, we test the sensitivity of our results to whether or not Wave 1 spells are included. 

A-27
V. COMPARISONS OF SIPP CHARACTERISTICS OF SNAP PARTICIPANTS WITH 

ADMINISTRATIVE DATA 

We compared the characteristics of SNAP participants in the SIPP with the characteristics of 

participants in administrative data. The results show that SIPP-based estimates of SNAP participants 

are similar to those in the SNAP-QC data. Table A.11 compares characteristics of SNAP participants 

in the SIPP with characteristics in the SNAPQC administrative data for calendar years 2004 through 

2006.9

 In general, the SIPP data align closely to the SNAPQC data. However, there are some notable 

differences: 

• The age distribution of SNAP participants in the SIPP is inconsistent with the distribution 

in the SNAP QC data. In the SIPP, there are proportionately more SNAP adults and fewer 

SNAP children than in the SNAP QC. This is a known problem with the SIPP data. It is 

due, in part, to the fact that the SIPP unit formation procedures do not permit child-only 

SNAP units to exist. Instead, the SIPP forces adults to be part of the SNAP unit. This 

problem exists in the 1996 and 2001 SIPP panels since the prevalence of child-only units 

increased after many adult non-citizens became categorically ineligible for SNAP in 1997. 

Unfortunately, it is not possible to determine which units should be child-only units. After 

reviewing the 2001 panel in 2006, we requested that the Census Bureau make changes to 

their programs, but any changes would not have been in time to affect the 2004 panel. The 

differences between SIPP and SNAP QC are smaller, however, in the 2004 panel than in 

the 2001 panel for both adults and children. SIPP data have proportionately more Hispanic 

participants and proportionately fewer white SNAP participants than the QC data. This may 

be due, in part, to the fact that ineligible noncitizen adults are grouped into child-only units 

by the SIPP unit formation procedures. 

• SIPP data have proportionately fewer households with zero earnings than SNAP QC data. 

This may reflect several factors. The difference could reflect a tendency of SNAP 

participants to report income to the SIPP but not to the SNAP QC. Additionally, seam bias 

and other SIPP factors could lead to high earnings and SNAP benefits in the same month 

for some participants. 

• The proportion of individuals in SNAP households with TANF ranges from 3 to 4 

percentage points lower in the SIPP than in the SNAP QC. In the 2001 panel, TANF 

receipt ranged from 4 to 8 percentage points lower in the SIPP than in the SNAP QC. 

 

9

 Because May 2006 is the last month in the eighth wave for which all four rotation groups have data, we estimated the 

monthly average for 2006 from January to May 2006. 

A-28
Table A.11 SNAP Participants by Characteristics in Administrative Data and in the SIPP 

Monthly Average, 2004 Monthly Average, 2005 Monthly Avera

SNAP QC SIPP Difference SNAP QC SIPP Difference SNAP QC SIPP 

Age  0-4 17.0 14.8 -2.3 17.0 14.0 -3.0 17.0 1 5-17 33.1 30.2 -3.0 32.8 30.1 -2.7 32.2 2

 18-59 41.7 45.9 4.2 41.8 46.2 4.4 42.1 4

 60+ 8.1 9.2 1.1 8.4 9.7 1.3 8.8 1

 Unknown 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

Race/Ethnicity  White Non-Hispanic 43.0 38.4 -4.6 43.4 38.1 -5.3 42.5 3 Black Non-Hispanic 33.1 31.9 -1.3 32.6 31.3 -1.3 33.4 3 Hispanic 19.4 22.7 3.3 19.8 23.0 3.2 19.4 2 Asian/Pacific Islander 2.5 1.7 -0.8 2.2 2.1 -0.1 2.5  American Indian 1.5 5.3 3.8 1.5 5.4 3.9 1.7  Unknown 0.4 0.0 -0.4 0.4 0.0 -0.4 0.5 SNAP Unit Benefit Amount  $1 - 10 3.0 3.4 0.4 2.9 3.8 0.9 3.5  $11-25 1.7 1.9 0.2 1.5 1.7 0.2 1.5 

 $26-$75 7.0 9.2 2.2 6.7 8.6 1.9 6.8  $76-$150 19.3 19.3 0.0 17.6 19.3 1.7 11.9 1 $151-$200 7.1 9.4 2.2 8.6 8.8 0.1 14.1  $201-$250 9.0 10.7 1.7 7.7 9.1 1.3 8.2 

 $251-$300 12.8 12.9 0.1 13.3 13.4 0.1 12.5 1 $301-$350 7.2 6.5 -0.7 7.2 6.7 -0.5 6.5  $351-$400 11.5 10.0 -1.5 11.8 10.1 -1.7 11.5 1 $401-$450 4.4 4.4 0.0 4.2 3.1 -1.1 4.3  $451+ 17.1 9.5 -7.5 18.3 12.3 -6.0 19.3 1
Table A.11, continued

Monthly Average, 2004 Monthly Average, 2005 Monthly Avera

SNAPQC SIPP Difference SNAPQC SIPP Difference SNAPQC SIPP 

SNAP Unit Earnings  $0 60.3 54.9 -5.5 60.1 52.9 -7.2 59.1 5 $1-199 2.8 1.7 -1.1 2.9 1.2 -1.7 2.5  $200-399 3.4 2.8 -0.5 3.4 2.5 -0.9 3.6  $400-599 4.4 3.9 -0.5 4.0 3.8 -0.2 4.3  $600-799 5.3 4.7 -0.7 5.2 4.2 -1.0 5.5  $800-999 5.4 4.4 -1.0 5.2 4.3 -1.0 5.3  $1000-1199 5.3 5.3 0.0 5.0 5.8 0.7 5.1  $1200-1399 4.3 4.9 0.6 4.7 5.2 0.5 4.3  $1400-1599 3.4 3.5 0.1 3.2 3.8 0.6 3.1  $1600-1999 3.6 4.7 1.2 4.1 5.2 1.1 4.4  $2000-2499 1.3 3.2 1.9 1.7 4.3 2.5 2.1  $2500+ 0.6 6.0 5.5 0.4 7.0 6.6 0.7 SNAP Unit TANF Benefits  $0 79.1 82.0 2.9 80.6 84.2 3.6 82.2 8 $1-25 0.1 0.0 0.0 0.0 0.1 0.0 0.1 

 $26-$75 0.1 0.1 0.0 0.1 0.1 0.0 0.1 

 $76-$150 0.4 0.7 0.3 0.3 0.4 0.1 0.2  $151-$200 1.5 2.2 0.6 1.3 2.4 1.1 1.2  $201-$250 1.8 1.8 -0.1 1.7 1.5 -0.2 1.8  $251-$300 2.2 2.2 0.0 1.9 1.7 -0.2 1.8  $301-$350 1.7 1.9 0.2 1.6 1.0 -0.6 1.4  $351-$400 2.3 1.3 -1.0 2.0 1.2 -0.8 2.2  $401-$450 1.3 1.2 -0.1 1.5 1.6 0.1 1.4  $451+ 1.4 0.9 -0.5 1.6 0.7 -0.9 1.4 

Covered by TANF b,c Total 20.9 18.0 -2.9 19.4 15.8 -3.6 17.8 1 Adult 7.0 7.4 0.4 6.4 6.9 0.5 6.0  Child 14.0 10.6 -3.4 13.0 8.9 -4.1 11.8 
Table A.11, continued 

Monthly Average, 2004 Monthly Average, 2005 Monthly Avera

FSPQC SIPP Difference FSPQC SIPP Difference FSPQC SIPP 

Covered by SSI2 Total 12.4 14.4 2.0 12.7 14.9 2.1 13.0 1 Adult 11.0 12.5 1.5 11.3 13.0 1.7 11.5 1

 Child 1.5 1.9 0.4 1.5 1.9 0.4 1.4 

 Source: Decision Demographics, tabulations of the 2004 SIPP panel; the 2004 SNAP QC data, exclusive of Guam and the Virgin Isla

http://www.acf.hhs.gov/programs/ofa/data-reports/caseload/caseload_current.htm 

 Notes: 

a Through May. b Imputed adult/child among persons with missing age in SNAP QC. c TANF assigned to all persons in the SNAP unit. 
VI. TOPICAL MODULE DATA 

We examined data from the principal SIPP topical modules that are used in our analysis: data on 

recipiency history from Wave 1 and data on employment history from Wave 1. Due to 

confidentiality restrictions imposed on the 2004 data, we conducted this analysis within the 

Census Bureau and only the summary data have been published. This section presents our 

assessment of the data in these topical modules. 

The Wave 1 Recipiency History Topical Module (RHTM) items on SNAP underwent 

significant redesign prior to the 2004 SIPP. Some data were collected throughout the 2004 

panel, and other SNAP items in the RHTM were improved, although the net effect on 

dynamics analysis is small. The RHTM data continue to be useful for dynamics analysis. 

Data from the employment history appear useful for our analysis. 

1. Recipiency History 

In this section, we first outline factors that may have changed the nature of the 2004 RHTM 

compared with the 2001 panel and earlier panels, and then present the results of tests carried out 

on the data similar to those performed with the 2001 data. 

The Census Bureau redesigned the RHTM in response to a series of recommendations from the 

SIPP Continuous Instrument Improvement Group. Moore (2007) provided a complete report on 

the changes and their impacts on data quality. As in the 2001 panel, the recipiency history topical 

module occurs in Wave 1, only four months after the first reference month of the panel. The 

RHTM redesign resequenced the questions, putting SNAP questions at the end to allow probes 

for categorical eligibility for SNAP based on questions about AFDC/TANF. The redesign also 

changed the nature of the SNAP start month and year questions, and slightly adjusted the 

universe for the RHTM to compensate for previous minor omissions due to CAPI branching 

issues, and probed for SNAP start dates that came before a recipient’s 18th birthday. 

Resequencing and Inclusion in Core 

Individuals in Wave 1 who reported in the SIPP core questionnaire that they received SNAP in 

the first reference month (month one) were subsequently asked in the topical module to provide 

the month and year in which their SNAP recipiency began, probing for “left-censored” SNAP 

spells that began prior to the SIPP panel. Moore notes that as part of the 1996 redesign, these 

questions were moved from the RHTM to the SIPP core questionnaire. Not noted by Moore is 

the fact that these month and year started SNAP items have been repeated in every wave of the 

2004 and 2008 SIPP panels. Whenever new respondents were found (e.g., new spouses, 

newborns, new members of splinter households, etc.) in 2004 Waves 2-12 Core interviews, and 

they reported SNAP receipt in the first reference month, they were asked when that spell of 

SNAP began. These Wave 2-12 response data, however, were neither edited nor published. The 

Census Bureau did provide these additional data for us to use within the Bureau. However, they 

could not be employed in our dynamics analysis since that analysis universe is limited to those 

who have a Wave 1-8 longitudinal panel weight, thus omitting any respondent who was absent in 

Wave 1. 

A-32
Rephrasing 

Another possibly significant change has to do with the phrasing of the left-censored start date for 

spells active in month 1, changing the focus from the initial application date to the initial receipt 

date. Moore outlines those changes as follows: 

In the 2001 panel, these questions were of the following form (e.g., for Food Stamps): 

>FBEG120< (2001) 

When did [NAME/you] apply for the Food Stamps you received? 

CIIG’s review (SIPP Continuous Instrument Improvement Group, 1998) found the 2001 

approach questionable on two main grounds. First, it did not really focus on or attempt to 

make clear to respondents the central concept of interest – the start of the spell of receipt 

that continued into the wave 1 reference period. And second, it offered no hint that its 

intent was to capture the respondent’s past receipt as an adult, and not as a child 

beneficiary, or as a child covered by benefits received by a parent. Thus, CIIG 

recommended major changes to these items, the intent of which was to clarify – and, in 

the case of the “as an adult” criterion, make explicit – these key concepts. This was 

accomplished in a series of up to four questions, as follows: 

>FBEG120< (2004) 

When did [NAME/you] start receiving Food Stamps? 

(if the reported date is before the person’s 18th birthday, ask FBEG120A; otherwise, skip 

to FBEG120B) 

>FBEG120A< (2004) 

When did [NAME/you] start receiving Food Stamps on [his/her/your] own, or in 

[his/her/your] own name? 

>FBEG120B< (2004) 

And [has/have] [NAME/you] received Food Stamps every month since then? 

(if “yes,” then end the series, as the spell start date has been established; otherwise, 

continue with FBEG120C) 

>FBEG120C< (2004) 

When did [NAME/you] start receiving Food Stamps CONTINUOUSLY, every month 

[.../through [month]]? 

If respondents accurately noted the question phrasing subtleties concerning application and 

receipt dates in the 2001 and 2004 panels, then the 2001 spells could be expected to be somewhat 

longer due to the gap between the application and receipt dates. This is explored further below. 

No Top-Coding 

Another factor that may affect SNAP spell length is a lack of top-coding on the Census internal 

versions of year initiated SNAP. Top-coding affects public-use datasets; however, due to 

confidentiality restrictions on month data in the RHTM, we assessed the data using Census 

internal datasets in the analysis of a number of RHTM variables. The following is a list of 

internal variables included; the month variables are exclusively internal, the year variables are 

equivalent to their public use counterparts but not top-coded, and the year allocation flags are 

identical in the public use data. 

A-33
EFBG120Y Year started SNAP receipt for active month 1 spells 

 AFBG120Y Allocation flag for EFBG120Y 

 

EFBG120M Month started SNAP receipt for active month 1 spells 

 AFBG120M Month applied for EFBG120M 

EFSSTRYR Year first received SNAP 

 AFSSTRYR Allocation flag for EFSSTRYR 

EFSSTRMN Month first received SNAP 

 AFSSTRMN Allocation flag for EFSSTRMN 

EFRMRMN Month ... started the job or business 

 AFRMRMN Allocation flag for EFRMRMN 

ELSTWRKM Month last worked at a paid job or business 

 ALSTWRKM Allocation flag for ELSTWRKM 

EPRVJBMN Month last worked at a paid job or business 

 (Before first reference month) 

 APRVJBMN Allocation flag for EPRVJBMN 

Two variables--EFBG120Y Year started SNAP receipt for active month 1 spells, and 

EFSSTRYR Year first received SNAP could be affected by lack of top-coding. However, only 

the top three percent of cases are top-coded in the public use files. Also, the change does not 

affect the medians or percentiles reported in the dynamics analysis life tables. 

Allocation of reference person dates to others in the SNAP unit 

RHTM history questions were asked only of sample members age 18+ authorized to receive the 

benefits (SNAP “reference persons”) in any month of the reference period. The left-censored 

start date questions were restricted to those with active month 1 SNAP spells. In this study, 

which uses the individual as the unit of analysis, we need to identify the start dates of leftcensored spells for all members of recipient households (including both SNAP reference persons 

and other household members). We adopted the same approach employed by Gleason, et al. as 

well as Cody, et al. to determine the start date of a left-censored spell for an individual who was 

not the SNAP reference person but who lived in the same household as the SNAP reference 

person during the first panel month. In general, this approach assigns parents’ start dates to 

children, and assumes adults in the SNAP household had the same start date as the reference 

person unless they moved in after the reference person’s start date.10

 

10 Specifically, the four rules are: (1) If the individual was a child of a SNAP reference person and less than age 18, 

the child’s start date was the start of the reference person’s spell if the spell began after the child was born, and the 

child’s birth date otherwise. (2) If the individual was more than age 18, we determined when the individual and the 

SNAP reference person moved into their current residence using information in the Wave 2 topical module. The 

spell start date of the individual was then calculated using information on the calendar order of the move dates and 

the spell start date. For example, if the individual moved into his or her month 1 address after the reference person 

did but before the reference person’s spell started, the spell start date of the individual was set to the start date of the 

reference person’s spell. In some instances, we also used Wave 2 topical module information on the prior residence 

of household members before they moved into their current address. (3) If the individual was a child of an adult in 

the household who was not a reference person but has SNAP, the start date of the child was the start date of the 

parent (as calculated using rule 2) if the child was born before that start date. (4) If the individual was a child 

A-34
Results of Testing 

Gleason, et al. found evidence that the RHTM data were problematic in the 1991 SIPP, while 

Cody, et al. found fewer problems with the 2001 data. Gleason, et al. attributed the problems to 

the fact that, for the 1991 SIPP panel, the recipiency history data were collected in Wave 2, eight 

months after the first month of the panel, and decided to exclude the month 1 spells from the 

main spell analysis. In contrast, Cody, et al. found that the 2001 recipiency history data were 

markedly better than the 1991 data and suitable for analysis for all waves. Our analysis, detailed 

below, finds that while some problems persist, the data for Waves 1-8 of the 2004 SIPP panel 

appear to be sufficient for dynamics research. 

All of the RHTM data in Tables A.12-21 are reported only for panel members to whom we have 

assigned a Wave 1-8 longitudinal panel weight with the addition of newborns who join SIPP 

households during the study period. Those newborns, who appear in any table that is not limited 

to Wave 1 spells, have been assigned their parents’ or a surrogate’s weight. Some tables include 

both weighted and unweighted data for those with longitudinal weights so that the sufficiency of 

the sample can be assessed. Other tables show only weighted results. 

Table A.12 shows some basic parameters of the 2004 SIPP sample of SNAP recipients, 

emphasizing the extent to which the start date of the month 1 spell is missing or imputed. We 

found an unweighted sample of just over 5,000 active SNAP spells in month 1, substantially 

more than the 2,700 found in 2001, which ensures an adequate sample size. Reference persons 

and other members of the SNAP units increased by 85 percent, corresponding directly to the total 

persons increase. Table A.12 shows that all of the SNAP reference persons have information 

available about when they began receiving SNAP, although those data were imputed for onethird of them. Of SNAP reference persons who had imputed start dates, nearly 70 percent had 

only an imputed start month, having provided the starting year (this represents those for whom 

only month is imputed divided by all those for whom date is imputed). Another way to look at 

the reporting is that only ten percent of reference persons required full imputation of SNAP 

starting date. 

 unrelated to any household member, then we used rule 2 if the child was at least age 15, and rule 1 otherwise. We 

used this procedure because the residence history questions were not asked of children who were less than age 15. 

A-35
Table A.12 Extent of Missing and Imputed Start Dates of Month 1 Spells by SNAP Reference Person Status 

 Number Perce All Persons SNAP Reference Persons Other Members of the SNAP Unit All Persons SN

Reference 

 Weighted Sample Size Weighted Sample Size Weighted Sample Size Weighted Sample Size Weighted SNAP Participants in Month 1 19,094,136 5,070 7,892,927 2,143 11,201,209 2,927 100.0 100.0 100.0 

Begin Date Provided 18,238,654 4,874 7,892,927 2,143 10,345,728 2,731 95.5 96.1 100.0 Missing Begin Date 855,481 196 0 0 855,481 196 4.5 3.9 0.0 

SNAP Participants in Month 1 with Begin Date Provided 18,238,654 4,874 7,892,927 2,143 10,345,728 2,731 100.0 100.0 100.0 

Imputed Begin Date 5,549,304 1,465 2,612,728 711 2,936,576 754 30.4 30.1 33.1 

SNAP Participants in Month 1 with Imputed Begin Date 5,549,304 1,465 2,612,728 711 2,936,576 754 100.0 100.0 100.0 

Only Month is Imputed 2,839,564 752 1,814,110 488 1,025,453 264 51.2 51.3 69.4 

Source: Decision Demographics tabulations of Wave 1 of the 2004 SIPP Panel. 
Table A.12 also shows that we were unable to assign a start date to 7.6 percent of other members 

of SNAP units compared to 6.1 percent in the 2001 panel. This is either because we don't know 

when the person moved into the month 1 SNAP household or we don't know, with certainty, the 

order of the move dates and the spell start date. Spell dates for household members who were not 

reference persons were imputed either because their pre-panel residence information was 

imputed or because the spell start dates of the SNAP reference person were imputed. Imputation 

patterns among the other members of SNAP units for whom we have provided start dates are 

similar to those of the reference persons, with most having only the month imputed. This 

similarity is expected since other unit members were assigned data from reference persons. 

Since the Census Bureau only collects start dates from the SNAP reference person and we 

assigned start dates to all others in SNAP units through the process described above, our analysis 

of underlying data quality is focused primarily SNAP reference persons—their imputations were 

done by the Census Bureau and are present in the original dataset. The imputations of Other 

Members of the SNAP Unit were carried out by us, based largely on the reference person data. 

Generally, the reference person left-censored start date information is of high quality and 

demonstrates reasonable patterns. Table A.13 presents the elapsed length of the month 1 spells, 

as of month 1, for SNAP reference persons. Over 93 percent of reference persons have valid 

spell lengths. 

Table A.13 Elapsed Length of Month 1 Spells and Imputation Status 

 

SNAP 

Reference Persons 

 

Weighted 

Estimate 

Sample 

Size 

Weighted 

Percent 

 

All Persons 

Number of Elapsed Years Spell Was in Progress (up to month 1) 7,892,927 2,143 100.0 

Less than Zero (began after month 1) 521,664 126 6.6 

Zero (began in month 1) 384,625 87 4.9 

Less than 1 Year 2,378,174 612 30.1 

1 Year 855,753 232 10.8 

2 Years 513,253 143 6.5 

3 Years 467,423 133 5.9 

4 Years 484,365 120 6.1 

5 Years 262,435 80 3.3 

More than 5 Years 2,025,235 610 25.7 

 

A-37
Table A.13, continued

Imputed Begin Date 

Number of Elapsed Years Spell Was in Progress (up to month 1) 2,612,728 711 100.0 

Less than Zero (began after month 1) 287,122 67 11.0 

Zero (began in month 1) 3,214 1 0.1 

Less than 1 Year 377,174 102 14.4 

1 Year 269,815 72 10.3 

2 Years 184,912 53 7.1 

3 Years 145,951 40 5.6 

4 Years 179,210 47 6.9 

5 Years 106,014 28 4.1 

More than 5 Years 1,059,316 301 40.5 

 

Nonimputed Begin Date 

Number of Elapsed Years Spell Was in Progress (up to month 1) 5,280,199 1,432 100.0 

Less than Zero (began after month 1) 234,542 59 4.4 

Zero (began in month 1) 381,411 86 7.2 

Less than 1 Year 2,000,999 510 37.9 

1 Year 585,937 160 11.1 

2 Years 328,340 90 6.2 

3 Years 321,473 93 6.1 

4 Years 305,155 73 5.8 

5 Years 156,421 52 3.0 

More than 5 Years 965,919 309 18.3 

 

Only Imputed Begin Month 

Number of Elapsed Years Spell Was in Progress (up to month 1) 1,814,110 488 100.0 

Less than Zero (began after month 1) 244,052 53 13.5 

Zero (began in month 1) 3,214 1 0.2 

Less than 1 Year 185,393 52 10.2 

1 Year 205,423 53 11.3 

2 Years 145,159 40 8.0 

3 Years 102,030 26 5.6 

4 Years 131,667 36 7.3 

5 Years 93,486 25 5.2 

More than 5 Years 703,687 202 38.8 

 

Source: Decision Demographics tabulations of Wave 1 of the 2004 SIPP Panel. 

About 7 percent of the SNAP reference persons' month 1 spells started after month 1, resulting in 

a negative elapsed duration. This is the same level documented by Cody, et al., while Gleason, et 

al. found about 10 percent of reference persons having negative durations in the 1991 panel. 

Negative durations occur when the SIPP-core-based questions reveal that an individual was 

receiving SNAP in month 1, but the response to the question about the starting date of the month 

1 spell was after month 1. Negative durations are explored further in Table A.14. 

As documented in the analysis of Table A.12, 70 percent of imputations are of month only, since 

the respondents provided a year response. Table A.13’s bottom panel, which shows elapsed years 

for month-only imputations, suggests that respondents have difficulty remembering the specific 

A-38
starting month for spells that began further in the past. The modal category of month-only 

imputations, at 39 percent, is those who have been on SNAP more than five years. About 56 

percent of month-only imputations have been on SNAP for three years or more, compared with 

only 33 percent of non-imputed cases. 

Table A.14 explores patterns among the seven percent of reference persons who have negative 

durations. About half of those who have a negative duration provided the illogical answer 

themselves, suggesting that a within-instrument logic test could potentially eliminate some 

errors. Among those with an imputed begin date, 85 percent only had the month imputed. 

However, it appears that the SIPP month imputation routine has problems that have persisted 

from the 2001 panel into 2004. In both panels, about 95 percent of imputations set the start date 

well after Wave 1; in fact, the month of December (regardless of rotation group) accounted for 

the vast majority of negative spells, while only one record in either SIPP had an imputed start 

month equal to month 1 (see Table A.13 panels 2 and 4). Cody, et al. suggested that perhaps the 

Census Bureau meant to impute December of the previous year, but did not alter the year 

variable. Although the time for revising the instrument logic and imputation routines for the 

2008 SIPP Wave 1 RHTM has already passed, we are providing this information to the SIPP 

staff to help improve procedures for the 2014 Re-engineered SIPP panel. 

Table A.15 returns to an analysis of normal SIPP spells that began before the 2004 panel—leftcensored spells—comparing elapsed durations of all persons with those of SNAP reference 

persons. Among the SNAP reference persons whose month 1 spells began before month 1, 23 

percent have been in progress for 6 months or less, and 35 percent have been in progress for 1 

year or less, patterns very similar to those from 2001. At the other extreme of duration, 29 

percent have been in progress for more than 6 years, virtually the same as 2001. The median 

elapsed time for reference persons was 2.3 years, and the mean elapsed time was 5.3 years, both 

over 10 percent higher than they were in 2001 when the median was 2.1 and mean 4.8. The large 

difference between the mean and median reflects the predominance of long spells. Some of the 

growth in the mean spell could be due to the lack of top-coding in internal 2004 data, but it does 

not seem out of range compared to the growth in the median. As expected, the elapsed spell 

durations were greater for reference persons than for all persons because some household 

members join SNAP households after the spell starts. 

A-39
Table A.14 Month 1 Spells that Began after Month 1 and Imputation Status 

 

SNAP 

Reference Persons 

 

Weighted 

Estimate 

Sample 

Size 

Weighted 

Percent 

 

All Persons 

Spell Began After Month 1 (negative elapsed length) 521,664 126 100.0 

1 month after Month 1 113,234 28 21.7 

2 months after Month 1 58,311 15 11.2 

3 months after Month 1 64,310 17 12.3 

After Wave 1 285,809 66 54.8 

 

Imputed Begin Date 

Spell Began After Month 1 (negative elapsed length) 287,122 67 100.0 

1 month after Month 1 7,066 2 2.5 

2 months after Month 1 0 0 0.0 

3 months after Month 1 0 0 0.0 

After Wave 1 280,056 65 97.5 

 

Nonimputed Begin Date 

Spell Began After Month 1 (negative elapsed length) 234,542 59 100.0 

1 month after Month 1 106,168 26 45.3 

2 months after Month 1 58,311 15 24.9 

3 months after Month 1 64,310 17 27.4 

After Wave 1 5,753 1 2.5 

 

Only Imputed Begin Month 

Spell Began After Month 1 (negative elapsed length) 244,052 53 100.0 

1 month after Month 1 7,066 2 2.9 

2 months after Month 1 0 0 0.0 

3 months after Month 1 0 0 0.0 

After Wave 1 236,986 51 97.1 

 

Source: Decision Demographics tabulations of Wave 1 of the 2004 SIPP Panel. 

A-40
Table A.15 Elapsed Length of Month 1 Spells Beginning Before Month 1 

 All Persons 

SNAP 

Reference Persons 

 Percent 

Cumulative 

Percent Percent 

Cumulative 

Percent 

 

Number of Elapsed Years 

0.5 or less 24.0 24.0 22.7 22.7 

> 0.5 - 1.0 13.7 37.8 12.4 35.1 

> 1.0 - 1.5 7.5 45.3 7.5 42.7 

> 1.5 - 2.0 4.8 50.1 4.3 47.0 

> 2.0 - 3.0 8.5 58.6 7.2 54.2 

> 3.0 - 4.0 7.5 66.0 7.1 61.3 

> 4.0 - 5.0 5.8 71.9 6.0 67.4 

> 5.0 to 6.0 3.7 75.6 3.7 71.0 

> 6.0 to 10 10.2 85.8 10.4 81.4 

More than 10 14.2 100.0 18.6 100.0 

 

Median 2.0 2.3 

Mean 4.4 5.3 

 

Source: Decision Demographics tabulations of Wave 1 of the 2004 SIPP Panel. 

Emulating Cody, et al. as well as Gleason, et al., we used five “in-sample” methods to assess the 

accuracy of the recipiency history information. The results of this analysis are discussed below. 

This analysis, presented in Tables A.16 through 21, is based on SNAP spells among all persons. 

1. Compare the proportion of spells that began in month 1 with the proportion of 

spells that began in other panel months. First, we determine whether the proportion 

of month 1 spells that began in month 1 is similar to the proportion of ongoing spells 

that began in each of the other panel months (Table A.16). If the Wave 1 topical 

module information is accurate, then the month 1 proportions should be similar to the 

proportions in the other panel months, particularly those that correspond to the first 

month of each wave (e.g. months 5, 9, 13, 17, 21, 25, and 29). The proportion of 

month 1 spells starting in month 1 is 5.3 percent, substantially lower than the average 

13.1 percent observed in the first months of other waves. The corresponding figures 

for 2001 panel are the same at 5.3 percent of month 1, while 16.3 percent of cases 

began in first months of later waves, so the 2004 data show some improvement. This 

still suggests that the recipiency history data do not accurately identify which spells 

start in month 1. If we assume spells with a negative elapsed duration actually started 

in Month 1, the percent starting in month 1 increases to 11.3 percent, much closer to 

the average observed in other waves, although given the imputation patterns 

discussed above, this is probably an incorrect assumption. 

2. Compare spell durations by start month. We compared the cumulative duration of 

non-left-censored month 1 spells (spells with zero elapsed duration in month 1) to 

four other samples, including:

• Non-left-censored spells that started after month 1 

A-41
• Non-left-censored spells that started in month 5 

• Month 1 spells that began after month 1 (i.e. negative elapsed duration) 

• Month 1 spells that began one to six months prior to the first panel month (i.e. 

left-censored elapsed duration of one to six months) 

Non-left-censored spells starting in month 1 have somewhat longer participation 

spells than non-left-censored spells starting after month 1 (Table A.17). The median 

spell duration for month 1 spells with zero duration is 10 months, compared with 8 

for non-left censored spells starting after month 1. Non-left censored spells starting in 

month 5 as well as those of negative duration also have a median length of 10. These 

medians are fairly close to one another given their different universes, closer than 

those of either Cody, et al. or Gleason, et al. 

In the analysis conducted by Gleason, et al., with the 1991 panel, the difference 

between month 1 spells and other spells was substantially greater (Table A.18). The 

median spell length for month 1 spells with zero elapsed duration was 28 months 

compared with 8 months for other non-left censored spells. This led Gleason, et al. to 

conclude the data on spells with zero elapsed duration in month 1 were not usable. 

Given that we find smaller disparities than even the 2001 panel data, we conclude the 

cases starting in month 1 could be added to the analysis of participation spells, 

following the 2001-based practice. Any bias present in the month 1 spells is not likely 

to affect overall estimates when these observations are combined with spells starting 

in subsequent months, particularly given the relatively small unweighted number of 

such spells: 223 vs. 8,164. Nor are the negative elapsed durations likely to affect 

overall estimates disproportionately. The 2004 left-censored spells with durations of 1 

to 6 months as of month 1 had longer median length (19 months) than Cody, et al. at 

14 months, but not as long as Gleason, et al. at 23 months for 1991. 

A-42
Table A.16 SNAP Spells Beginning in that Panel Month as a Percentage of on-Going Spells 

On-Going 

SNAP Spells 

SNAP Spells 

that Began in 

this Month 

SNAP Spells 

that Began in 

this Month 

Panel Month Weighted Weighted Weighted 

 

Month 1a 

 Excluding Negative Durations 17,103,716 910,274 5.3 

 Including Negative Durations 18,238,654 2,045,212 11.2 

Month 2 19,530,931 652,346 3.3 

Month 3 20,005,992 683,666 3.4 

Month 4 20,288,019 675,329 3.3 

Month 5 22,113,502 3,706,650 16.8 

Month 6 21,467,043 248,528 1.2 

Month 7 21,315,809 434,824 2.0 

Month 8 21,337,411 671,263 3.1 

Month 9 22,104,877 2,864,597 13.0 

Month 10 21,736,244 397,537 1.8 

Month 11 21,799,812 659,601 3.0 

Month 12 21,858,577 663,241 3.0 

Month 13 22,929,466 2,819,279 12.3 

Month 17 23,463,029 2,944,386 12.5 

Month 21 23,472,666 3,058,548 13.0 

Month 25 23,996,255 3,028,845 12.6 

Month 29 23,680,708 2,709,042 11.4 

 

Average, Months 5, 9, 13, 17, 21, 25, and 29 23,108,643 3,018,764 13.1 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

Notes: a Month 1 spells that were reported to have started after the first panel month were assumed 

to be non-left-censored spells that began in month 1. 

A-43
Table A.17 Weighted Cumulative Exit Rate for Five Samples of Spells, by Duration 

Non-Left-Censored Spells Left Censored Spell 

Duration of Spell 

(in Months) 

Month 1 Spells 

with Zero Elapsed 

Duration 

Non-Left Censored 

Spells Starting 

After Month 1 

Non-Left Censored 

Spells Starting in 

Month 5 

Month 1 Spells with 

Negative Elapsed 

Durations 

Month 1 Spells with 

Elapsed Durations of 

1 to 6 Months 

 

1 5.0 6.3 4.6 2.7 0.0 

2 6.1 12.4 7.3 6.4 1.0 

3 9.9 18.0 9.9 10.6 2.6 

4 20.8 32.4 31.7 21.8 4.2 

5 25.1 37.4 37.5 28.6 9.7 

6 34.5 41.8 38.5 29.4 13.5 

7 36.7 45.4 42.0 35.9 19.2 

8 46.4 50.7 46.8 48.1 21.8 

9 49.1 52.8 49.2 49.2 27.8 

10 50.8 54.6 50.7 50.8 31.5 

11 52.9 57.1 52.7 52.5 35.0 

12 55.1 61.2 58.3 53.5 37.3 

13 55.1 62.7 58.9 56.5 39.4 

14 55.1 63.7 60.8 57.7 41.5 

15 55.7 64.9 61.1 58.7 43.8 

16 60.7 67.0 65.3 59.0 44.5 

17 61.2 68.1 66.6 59.0 46.3 

18 62.2 68.8 66.6 59.9 47.5 

19 62.2 69.6 66.8 60.2 51.1 

20 66.0 71.9 69.9 62.4 51.8 

21 66.0 73.3 70.2 62.6 52.5 

22 66.0 74.1 70.5 62.6 54.1 

23 66.0 74.6 70.9 62.6 55.4 

24 67.2 75.9 72.9 64.4 57.1 

25 68.6 77.0 74.0 65.3 57.7 

26 68.6 77.3 74.1 65.3 58.5 

27 69.4 77.9 74.7 65.3 59.6 

28 69.8 79.1 66.3 60.7 

29 72.4 79.5 66.3 62.5 

30 74.2 79.5 66.3 62.6 

 

Sample Size 223 8,164 897 283 1,028 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

Notes: a Median duration bolded. 

A-44
Table A.18 Median Spell Durations for Five Samples of Non-Left Censored Spells in 2004, 

2001, and 1991 SIPP Data 

 

Month 1 

Spells with 

Zero Elapsed 

Duration 

Non-Left 

Censored 

Spells 

Starting After 

Month 1 

Non-Left 

Censored 

Spells 

Starting in 

Month 5 

Month 1 

Spells with 

Negative 

Elapsed 

Durations 

Month 1 

Spells with 

Elapsed 

Durations of 

1 to 6 Months 

 

2004 SIPP Data 10 8 10 10 19 

2001 SIPP Data (Cody, et al.) 12 8 8 19 14 

1991 SIPP Data (Gleason, et al.) 28 8 8 16 23 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel; Cody, et al.; Gleason, et al. 

3. Compare spell durations by elapsed pre-panel time. We compared the cumulative 

duration during the panel of left-censored month 1 spells (spells that that started prior 

to the panel). We compare these durations across five subgroups defined by their 

duration prior to the panel:

• Month 1 spells with an elapsed duration of 1 to 6 months 

• Month 1 spells with an elapsed duration of 7 to 12 months 

• Month 1 spells with an elapsed duration of 13 to 24 months 

• Month 1 spells with an elapsed duration of more than 24 months 

• All month 1 spells that began before month 1 (all left-censored spells) 

The results show that left-censored spells with longer pre-panel durations have longer 

durations within the panel (Table A.19). The median spell length during the panel for 

spells with elapsed pre-panel durations of 1 to 6 months is 17 months; the median 

spells for pre-panel durations of 7 to 12 months and 13 to 24 months are 29 and 30 

months, while the median for spells with pre-panel durations of more than 24 months 

is more than 30 months. This pattern is expected since people with spells that were in 

progress for a long time prior to the start of the panel are less likely to stop 

participating. 

The 2004 panel spells for these groups are significantly longer (five to 13 or more 

months longer) than for the 2001 panel in every category. The median of all leftcensored spells in 2001 was 24 months, whereas in 2004, half of the cases had not yet 

departed by the 30-month mark. This marks a partial return to patterns found by 

Gleason, et al. that pertain to the 1991 panel. In the Gleason, et al. study, the median 

within-panel duration of spells with elapsed durations of 1 to 6 months was 23 

months, and spells for the other subgroups were longer still. 

A-45
Table A.19 Weighted Cumulative Exit Rates for Month 1 Left-Censored Spells by Panel Month 

and Elapsed Duration a

 

Duration of Spell 

(in Months) 

Month 1 Spells with 

Elapsed Durations of 

1 to 6 Months 

Month 1 Spells 

with Elapsed 

Durations of 7 to 

12 Months 

Month 1 Spells 

with Elapsed 

Durations of 13 to 

24 Months 

Month 1 Spells with 

Elapsed Durations 

of More than 

24 Months 

All LeftCensored Spells 

 

1 2.0 1.1 0.2 0.3 0.8 

2 5.0 1.2 0.6 0.4 1.6 

3 7.7 2.1 1.5 1.0 2.8 

4 17.4 7.5 8.1 7.1 9.7 

5 24.5 12.1 8.9 8.1 12.7 

6 26.5 15.1 10.5 9.1 14.3 

7 30.1 17.1 12.9 10.1 16.2 

8 35.1 20.4 19.2 13.1 20.1 

9 37.8 22.2 22.7 13.6 21.8 

10 39.7 24.8 24.1 14.0 22.9 

11 40.2 25.1 26.1 14.9 23.8 

12 43.0 27.0 28.5 17.6 26.3 

13 45.1 29.1 30.6 18.8 28.0 

14 45.7 30.6 31.4 19.2 28.6 

15 45.7 31.4 32.5 19.5 29.1 

16 48.4 32.1 35.3 22.6 31.7 

17 50.4 34.8 37.9 23.8 33.4 

18 52.5 35.0 38.6 24.0 34.2 

19 53.7 35.0 39.7 24.3 34.7 

20 56.4 39.0 42.1 27.4 37.8 

21 57.2 41.4 42.8 28.7 39.0 

22 58.2 41.7 43.5 28.8 39.5 

23 58.7 41.7 43.5 30.0 40.2 

24 59.9 43.4 46.2 32.3 42.2 

25 61.0 44.3 46.8 33.3 43.1 

26 61.8 44.3 47.8 33.9 43.8 

27 62.7 44.6 48.2 34.0 44.1 

28 63.1 48.9 49.3 36.0 45.9 

29 64.2 50.0 49.6 36.5 46.6 

30 64.6 51.7 50.0 36.5 47.0 

 

Sample Size 1,028 559 533 2,248 4,368 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

Notes: a Median duration bolded. 

4. Examine “artificial” cohort of left-censored spells. We compared a cohort of leftcensored spells in month 1 with a cohort of “artificial” left-censored spells. The 

artificial left-censored spells were created from all active spells in month 12 that 

started between panel months 2 and 12. We compared the duration (subsequent to 

month 12) of the artificial cohort spells to the duration (subsequent to month 1) of 

month 1 spells that were reported to begin 1 to 11 months before the panel. The 

A-46
distributions of spell durations should be similar for the two samples if the start dates 

in the topical module are accurate. 

The duration of artificial left-censored spells is shorter than the duration of month 1 

left censored spells (Table A.20). For the artificial cohort, the median spell length is 

20 months, and for the month 1 cohort, the median spell length is 26 months. This is a 

differential of six months between the two cohorts, halfway between the differentials 

found by Cody, et al. and Gleason, et al. 

In the 1991 data, Gleason, et al. found that the artificial cohort spells were shorter 

than the Wave 1 cohort spells by nine months, and concluded that individuals in the 

Wave 1 cohort were under-reporting their pre-panel duration. The nine-month 

disparity between the artificial and month 1 cohorts represents median spells for the 

artificial cohort of 15 months and the median spells for the month 1 cohort of 24 

months. 

With the 2001 panel, Cody, et al. found the smallest differential, and in the opposite 

direction: the median spell for the 2001 artificial cohort was 21 months and the 

median spell for the month 1 cohort was 18. Buoyed by the similarity between the 

two cohorts, Cody, et al. concluded that the 2001 data quality was good. The distinct 

historic and economic contexts of the three panels, and indeed of the two individual 

cohorts within each panel, have the potential to create significant differentiation in 

addition to whatever unique qualities the SIPP instruments may have created. With 

results from three panels available, it appears that the 2004 patterns are within a 

reasonable range. 

A-47
Table A.20 Weighted Cumulative Exit Rates for Artificial 

 and Month 1 Left-Censored Spells 

Duration of Spell 

Artificial LeftCensored Spells 

Month 1 Left Censored 

Spells with Elapsed 

Durations of 1 to 11 months 

 

1 0.0 0.0 

2 0.5 0.6 

3 0.9 1.7 

4 1.8 2.7 

5 5.4 6.3 

6 10.3 8.8 

7 13.5 12.6 

8 17.5 14.3 

9 19.6 18.3 

10 22.8 21.0 

11 26.9 23.7 

12 33.9 26.0 

13 36.6 29.3 

14 38.8 31.7 

15 41.2 34.0 

16 45.3 36.2 

17 46.9 37.6 

18 48.0 38.9 

19 48.9 41.8 

20 52.8 42.7 

21 55.1 44.0 

22 56.4 45.5 

23 57.3 47.2 

24 59.5 48.7 

25 61.4 49.7 

26 61.8 50.5 

27 62.9 51.4 

28 64.9 52.5 

29 65.6 54.2 

30 65.6 54.7 

 

Sample Size 1,790 1,541 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

Notes: a Median duration bolded. 

5. Compare elapsed duration with subsequent duration. Finally, we compare the 

cumulative elapsed spell duration of month 1 spells that began before month 1 with 

the cumulative spell duration of these spells during the panel. Assuming that the 

distribution of SNAP spell durations has not changed over time and that the economic 

and policy climates were constant, these spells should be, on average, in the middle of 

their spells in month 1. Thus, the backward and forward spell duration distributions 

should be similar if this “stationarity” assumption is valid and if the Wave 1 topical 

module recipiency history information is accurate. 

A-48
The 2004 panel forward spell durations are shorter than backward spell durations 

(Table A.21). About 26 percent cases had exited their forward spells within one year, 

whereas only 22 percent of spells track backwards for one year or less. The 

differential rises to over ten percentage points by 30 months, when 47 percent of 

forward spells had ended and only 36 percent of spells track backwards for 30 months 

or less. Neither forward nor backward durations reach the median level of spells 

within 30 months. 

Cody, et al. also found forward spells to be shorter than backward. The median 

duration of 2001-panel forward spells (24 months) was shorter than the median of 

backward spells (more than 30 months). Both these findings are counter to what was 

observed in the 1991 data by Gleason, et al. Gleason, et al. found that the forward 

durations were longer than the backward durations, which they said added support for 

their conclusion that respondents reported their pre-panel durations to be shorter than 

they actually were. 

Conclusion 

These five in-sample approaches to assessing the topical module data demonstrate that the 

recipiency history data could be materially different from within-panel measures of SNAP 

participation begin dates. The recall demands for within-panel measures are limited to the 

immediate past four months, the SIPP reference period, and even within that short time 

respondents display considerable seam bias in their responses. For the RHTM, however, the 

majority of left-censored spells required greater recall on the part of respondents. One effect of 

the recall demands is evident in the rising level of month-only imputations with rising leftcensored spell length—respondents simply cannot remember back that far. Also, when 

comparing backward with forward spells, we can expect that the backward spells—all leftcensored—are not measured as accurately as the forward spells. 

While spells with negative elapsed duration probably are mainly caused by imputation error, 

there are still some spells that appear to be the result of reporting problems. One solution would 

be to treat spells with negative elapsed duration as spells that start in month 1. Doing so would 

increase the proportion of spells starting in month 1 to be more in line with the proportion of 

spells starting in the first month of other waves. Table A.17 showed that spells with negative 

elapsed duration in the 2004 panel have nearly the same median duration as other non-left 

censored spells. That, and the fact that they are a minority of such spells (3.5 percent), means 

that their inclusion is unlikely to bias results. 

Spells with zero elapsed duration are also potentially problematic. The proportion of spells active 

in month 1 that are reported to start in month 1 is small relative to the proportions starting in the 

first month of subsequent waves. Part of this disparity may be attributed to the fact that Wave 1 

participation is under-reported in general. Nevertheless, given the small number of such spells, 

and that the median durations of these spells is virtually the same as for spells starting in later 

months, we included these spells in the dynamics analysis. 

A-49
Two changes in the character of the SNAP RHTM data also had the potential to bias the results: 

the lack of top-coding and the change in phrasing in favor of SNAP receipt date rather than 

application date. Overall length of spells increased in 2004 compared with 2001, however both 

the median and mean spell length increased by about ten percent. Had the lack of top-coding had 

a significant effect, the mean spell length would have increased more than the median. The 

change in phrasing should have resulted in 2004 spells being shorter than 2001 spells, other 

things being equal. Any change due to phrasing change probably would have been small, limited 

to the gap between application and receipt among successful SNAP applicants in 2001. If there 

was such a depressing influence on the change in spell length from 2001 to 2004, it cannot be 

identified—perhaps the ten percent overall increase in spell length between the two panels would 

have been higher otherwise. 

Overall, the results of this evaluation place the 2004 RHTM data within a normal range of what 

can be expected from the 2001 and 1991 data, and the data appear to be reliable and not in need 

of adjustment. 

A-50
Table A.21 Weighted Forward and Backward Spell Duration 

 Distributions of Month 1 Left-Censored Spells 

Duration of Spell 

Backward Elapsed 

Duration (Cumulative 

Distribution) 

Forward (Within Panel) 

Cumulative Exit Rates 

 

1 0.8 0.8 

2 1.4 1.6 

3 2.3 2.8 

4 9.0 9.7 

5 10.8 12.7 

6 12.1 14.3 

7 13.4 16.2 

8 17.0 20.1 

9 18.0 21.8 

10 18.8 22.9 

11 19.7 23.8 

12 21.9 26.3 

13 22.9 28.0 

14 23.3 28.6 

15 23.6 29.1 

16 26.0 31.7 

17 27.1 33.4 

18 27.4 34.2 

19 27.7 34.7 

20 29.9 37.8 

21 30.9 39.0 

22 31.0 39.5 

23 31.7 40.2 

24 33.5 42.2 

25 34.1 43.1 

26 34.5 43.8 

27 34.6 44.1 

28 35.7 45.9 

29 36.1 46.6 

30 36.1 47.0 

 

Sample Size 4,368 4,368 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

Notes: Median duration bolded. 

A-51
2. Employment History 

 In the Wave 1 topical module, respondents are asked about their employment history. 

Individuals who are unemployed in the first month of the panel are asked about any previous 

employment. In the 2001 panel there was some evidence that the information was biased, 

showing lower rates of historic employment for individuals in month 1 than later in the panel. 

There is no such bias apparent in the 2004 panel, so the employment history data should be 

useful in constructing measures of “usual circumstances.” 

Table A.22 examines the proportion of individuals unemployed in month 1 that were employed 

six and 12 months prior. It also shows analogous rates for individuals unemployed in months 12, 

24 and 32. In general, the proportions with prior employment for the month 1 unemployed and 

for unemployed individuals later in the panel are comparable and within an expected range of 

variation. There is a small proportion (3.1 percent) of month 1 unemployed individual for whom 

employment history cannot be determined. Given the falling unemployment rates during the 

period as measured by the CPS, we might expect to find rising proportions employed in the past 

six or 12 months. While this is true for percent employed all of the previous six months among 

employed adults, the other employment series fluctuate somewhat. Since the CPS and SIPP are 

independent instruments and the unemployment rate is not directly comparable with the 

employment measures over the past six and 12 months, this can be expected. 

We believe that the employment history topical module data provide a sound basis for creating 

measures of usual circumstances. For instance, for a given individual, we can determine whether 

being unemployed month 6 was typical or not typical relative to the past year by combining their 

employment information during the first 5 months with either (a) the start date of their job(s) in 

month 1 (reported in the core data), if employed, or (2) their employment history from the topical 

module data). These estimates can be used to assess the relationship between usual 

circumstances and participation dynamics. 

Table A.22 Distribution of Adults by Employment History

Percent of Adults 

2004 SIPP 2004 SIPP 2004 SIPP 2004 SIPP 

Month 1 Month 12 Month 24 Month 32 

Full Panel Full Panel Full Panel Full Panel 

 Weights Weights Weights Weights 

 

Average Unemployment Rate (CPS) 5.9 5.5 4.9 4.4 

 

Total Not Employed Adults (Age 18-75) 63,629,346 59,394,785 59,036,322 58,521,043 

Percent Employed within Previous 6 Months 15.9 19.7 18.5 15.3 

Percent Employed within Previous 12 Months 23.5 25.0 26.8 25.1 

Don't Know 3.1 0.0 0.0 0.0 

 

Total Employed Adults (Age 18-75) 135,786,722 138,282,664 138,558,462 140,087,470 

Percent Employed All of Previous 6 Months NA 89.5 89.6 90.6 

Percent Employed All of Previous 12 Months NA 82.4 81.8 82.3 

 

Source: Decision Demographics tabulations of the 2004 SIPP Panel. 

 

A-52
VII. GAPS IN SNAP PARTICIPATION 

This section presents the results of our assessment of “gaps” in SNAP participation in the 2004 

SIPP panel. We examine the incidence of gaps of different sizes; the lengths of spells prior to 

and following the gaps; characteristics associated with having a gap; and other data features 

related to gaps. The analysis was conducted to help inform the decision of whether to recode 

one-month gaps, or even two-month gaps, in participation by “closing them up.” 

Incidence of Gaps and Distribution across Reference Months 

In the 2004 SIPP panel, 2.7 million families participating in SNAP had a one- or two-month gap 

in their participation spell (686 families, unweighted) (Table A.23). This makes up 7 percent of 

all families participating in SNAP. The number with at least one one-month gap (1.6 million) is 

larger than the number with at least one two-month gap (1.2 million). (Because some families 

have both a one- and a two-month gap within the panel, these numbers sum to an amount greater 

than the 2.7 million families. No families have three or more one-month gaps or three or more 

two-month gaps, though some have a mix of one- and two-month gaps. 

A-53
Table A.23 Percentage of SNAP Persons with One- and Two-Month Gaps 

Percentages Weighted Counts Un

Number of OneMonth Gaps Number of Two-Month Gaps 

Number of OneMonth Gaps Number of Two-Month Gaps 

Number of OneMonth Gaps 

 0 1 2 3+ 0 1 2 3+ 0 

0 93.06 2.61 0.13 0.00 0 36,205,545 1,014,983 50,346 0 0 911 3.57 0.33 0.02 0.00 1 1,387,415 126,904 9,415 0 1 32 0.21 0.08 0.00 0.00 2 81,329 31,170 0 0 2 

3+ 0.00 0.00 0.00 0.00 3+ 0 0.00 0 0 3+ 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP Panel. 
The majority of one-month gaps (68 percent) occur in the fourth month of the wave (Table 

A.24)—the month for which we expect the most accurate reporting. Each SIPP interview takes 

place in the month following the end of the wave. So, for example, the Wave 1 interview takes 

place in what could be referred to as month 5. The respondent is most likely to report the month 

4 information accurately because it was only one month prior to the interview. 

Table A.24 Distribution of One-Month and Two-Month Gaps 

within the Reference Period 

Weighted 

Number Percentage 

One-Month Gaps 

 Gap at month 1 113,768 7 

 Gap at month 2 263,719 15 

 Gap at month 3 181,286 10 

 Gap at month 4 1,189,959 68 

 

Two-Month Gaps 

 Gap at month 1 153,446 12 

 Gap at month 2 524,121 41 

 Gap at month 3 575,074 44 

 Gap at month 4 39,938 3 

 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP Panel. 

On the other hand, the information reported for the beginning of the next wave is likely to be the 

least accurate, which could lead to over-reporting of one-month gaps. Continuing with the 

example above, even if a person reports SNAP participation in months 1 to 3 of Wave 1, and no 

SNAP participation in month 4 of Wave 1, their interview for month 5 (month 1 of Wave 2) does 

not occur until four months later. If the respondent returned to SNAP in the middle of Wave 2, 

they may erroneously report participation for all months of Wave 2. (The seam bias analysis 

clearly shows that people do tend to report in this way.) Therefore, it may be that a gap did, in 

fact, begin in the fourth reference month, but it may be less likely that the gap truly ended after 

one month. The gaps in months 1 to 3, however, are likely to have been accurately reported, as 

they do not occur on a seam month. 

Most two-month gaps occur in the middle (months 2 and 3) or the end (months 3 and 4) of the 

wave (Table A.24). Forty-one percent of two-month gaps start in month 2 and 44 percent start in 

month 3. If we use the same argument above, that recall bias is present in the data, then the twomonth gaps starting in month 2 should be the most accurate, as the start and end date do not 

occur on a seam month of the wave. The two-month gaps starting in month 3 would suffer from 

the same recall problem, namely that while the gap start months is accurate, the end months 

(month 4) might be inaccurate if recall bias affected responses for the next wave. 

Taken as a whole, Table A.24 provides fairly reassuring evidence that the start months of oneand two-month gaps are being reported accurately, though it is uncertain whether the end months 

are accurate as well. 

A-55
Duration of Participation Spells With and Without Closing Gaps 

To assess the implication of closing gaps on the average spell length of SNAP participation for 

participants with gaps, we estimate lengths of SNAP spells prior to and following the gap. We 

then close the gap and re-estimate the average spell length. We first focus on one-month gaps 

and then describe whether closing up two-month gaps yields similar conclusions (Table A.25). 

Table A.25 Duration of Spells with Gaps and Once Gaps are Closed 

One-Month Gap Two-Month Gap 

Length of spell prior to gap 

 Average if not left-censored 5.7 7.3 

 Percent left-censored 34.3 47.8 

Length of spell following gap 

 Average 7.2 7.8 

 Average if not right-censored 6.2 5.4 

 Percent right-censored 64.2 69.8 

Length of spell if gap closed a

 Average 19.7 22.8 

 Average if not left-censored 15.0 18.9 

 Average if not right-censored 14.5 15.9 

 Average if not left- or right-censored 10.8 14.7 

 Percent right- or left-censored 75.5 79.7 

 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP Panel. 

Notes: a The second column contains estimates for closing both one and two-month gaps. 

The average length of spells preceding the gap, among non-left-censored spells, is 5.7 months. A 

non-left-censored spell is one that begins after the respondent’s first month in the panel. We 

focus on non-left-censored spells because they comprise the majority of SNAP spells prior to the 

gap, with 65.7 percent of these spells being non-left-censored, and because spell lengths for leftcensored spells can only be estimated using SIPP topical module data that are confidential, and 

that are somewhat different in nature than the monthly, within-panel observations of SNAP 

participation. 

The average length of spells following the gap is 7.2 months. While all spells following the gap 

are by definition not left-censored because they begin in the month after the gap, almost twothirds of them are right-censored, meaning that the respondent exits the SIPP panel while still 

participating in SNAP. The average length of the non-right-censored spells following the gap is 

6.2 months. The shorter average spell length among non-right-censored spells reflects the fact 

that the end of the SIPP panel is more likely to interrupt longer spells that extend past the panel 

period, rather than spells that are completed within the SIPP panel period. 

The average spell length, among non-left-censored spells, once the gap is closed is 15.0 months. 

We note that all non-left-censored spells prior to the gap remain non-left-censored spells once 

the gap is closed. Thus, closing one-month gaps more than doubles the average spell length of 

non-left-censored spells for individuals with one month gaps. This is a sizable difference, but the 

impact on the average spell length among all SNAP participants, including those without gaps, is 

much smaller because less than 4 percent of participants have one-month gaps. 

A-56
The average length of non-left-censored spells prior to the gap of 5.7 months is interesting 

because it is approximately equal to a certification period of 6 months, which was the 

certification period for about 40 percent of SNAP participating households in 2005 and over half 

of households with earnings or with children.11 This finding supports the idea that one-month 

gaps truly reflect churning as states have described, with cases being closed due to failure to 

recertify and participants re-entering shortly after re-applying. 

For participants with two-month gaps, the average is 7.3 months prior to the gap (among nonleft-censored spells), 7.8 months following the gap (including both right-censored and non-rightcensored spells), and 18.9 months once both the two-month gaps and the one-month gaps are 

closed. While the increase in the length of the spell once the gap is closed is similar in magnitude 

to the one-month gap case, the length of the spell prior to the two-month gap is longer than the 

spell length prior to the one-month gap. This may suggest that there are different types of 

participants with two-month gaps than with one-month gaps. 

Characteristics of Participants with and Without Gaps 

Tabulating characteristics of SNAP participants with and without participation gaps of one to 

two months shows that individuals with gaps are more likely to have characteristics associated 

with shorter recertification periods (Table A.26). For example, individuals with gaps 

disproportionately live in families without elderly members as opposed to families with elderly 

members. We find that 94 percent of individuals with a one-month gap live in families without 

elderly members, compared to 81 percent of individuals without gaps. And, the certification 

period for households without elderly members is 10 months while the certification period for 

households with elderly members is 20 months.12 

 

11 Barrett, Allison. “Characteristics of Food Stamp Households: Fiscal Year 2005.” Alexandria, VA: U.S. 

Department of Agriculture, Food and Nutrition Service, September 2006. 12 Barrett, Allison. “Characteristics of Food Stamp Households: Fiscal Year 2005.” Alexandria, VA: U.S. 

Department of Agriculture, Food and Nutrition Service, September 2006. 

A-57
Table A.26 Characteristics of Persons with and without Gaps 

 No Gaps 

One 1-Month 

Gap 

One 2-Month 

Gap Multiple Gaps 

Total persons 36,205,545 1,387,415 1,014,983 299,164 

 

Average percentage of months in SIPP panel spent 

participating in SNAP 56.0 68.0 73.0 61.0 

Average family benefit $184 $199 $224 $244 

 

Percent in families with children 71.0 80.0 86.0 81.0 

Percent in families without children 29.0 20.0 14.0 19.0 

 

Percent in families with elderly 17.0 7.0 6.0 7.0 

Percent in families without elderly 83.0 93.0 94.0 93.0 

 

Percent in families of size 1 15.0 14.0 12.0 17.0 

Percent in families of size 2 16.0 17.0 14.0 8.0 

Percent in families of size 3 or more 69.0 69.0 74.0 75.0 

 

Percent in families with earned income 59.0 60.0 66.0 79.0 

Percent in families without earned income 41.0 40.0 34.0 21.0 

 

Percent in families with unearned income 71.0 67.0 71.0 38.0 

Percent in families without unearned income 29.0 33.0 29.0 62.0 

 

Percent in families with SSI 10.0 7.0 7.0 2.0 

Percent in families without SSI 90.0 93.0 93.0 98.0 

 

Percent in families with Social Security 23.0 17.0 9.0 14.0 

Percent in families without Social Security 77.0 83.0 91.0 86.0 

 

Percent in families with unemployment 

compensation 4.0 3.0 2.0 6.0 

Percent in families without unemployment 

compensation 96.0 97.0 98.0 94.0 

 

Percent in families with TANF 11.0 11.0 24.0 12.0 

Percent in families without TANF 89.0 89.0 76.0 88.0 

 

Percent in families with income increase in gap 

month -- 20.0 25.0 27.0 

Percent in families with income increase in month 

prior to gap -- 26.0 26.0 42.0 

 

Percent in families with income at or below poverty 52.0 58.0 60.0 58.0 

Percent in families with income above poverty 48.0 42.0 40.0 42.0 

 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP Panel. 

We find this same relationship for most other subgroups: characteristics associated with gaps are 

also associated with shorter certification periods. This includes families with children, with 

earned income, without SSI, and without Social Security. However, we find little association 

between living in a family with TANF or unemployed compensation and experiencing a gap. 

A-58
The percentage of individuals with an increase in family income at or just prior to the break in 

participation is slightly higher among individuals with a two-month gap than a one-month gap 

(Table A.26). It is also higher among individuals with multiple gaps (of either one- or twomonth) than a single one- or two-month gap. 

Timing of Changes in Individuals’ Family Characteristics 

In Table A.27 we compare how characteristics of participating individuals differ before, during, 

and after the gap. We determine how often an individual with a one- or two-month gap in 

participation experiences a change in his or her family characteristics around the time of the gap 

(before the gap to within the gap, before the gap to after the gap, or within the gap to after the 

gap). This information is presented in the first column of Table A.27. This compares to the 

percentage of individuals that experience such changes within a participation spell (indicating the 

change may not lead to a break in participation) in the second column or during a longer gap in 

participation of three or more months in the third column. 

Table A.27 Timing of Changes in Family Characteristics (Percent) 

Characteristics 

In Families with 1- or 2-

Month Gap Experiencing 

Change Across Gap a

Families with no Gap 

that had Change 

within Spell 

In Families with 3- or 

More Month Gap 

Experiencing Change 

Across Longer Gap a

 

Increase in family size 4.5 15.6 23.6 

Decrease in family size 5.4 11.6 22.5 

No change in family size 90.6 78.1 59.7 

 

Increase in benefit level 28.2 21.1 29.8 

Decrease in benefit level 28.2 19.4 28.6 

No change in benefit level 69.9 73.7 66.6 

 

Increase in number of children 4.0 11.9 14.4 

Decrease in number of children 3.1 11.6 18.2 

No change in number of children 93.0 80.3 71.1 

 

Increase in number of elderly 0.1 2.9 4.2 

Decrease in number of elderly 0.2 1.4 3.9 

No change in number of elderly 99.7 96.2 92.0 

 

Increase in earned income 22.7 52.4 65.0 

Decrease in earned income 30.2 46.6 59.6 

No change in earned income 56.7 41.2 26.0 

 

Increase in unearned income 27.1 55.2 66.6 

Decrease in unearned income 24.3 50.6 54.5 

No change in unearned income 57.1 36.8 23.6 

A-59
Table A.27, continued

Characteristics 

In Families with 1- or 2-

Month Gap Experiencing 

Change Across Gap a 

Families with no Gap 

that had Change 

within Spell 

In Families with 3- or 

More Month Gap 

Experiencing Change 

Across Longer Gap a 

 

Increase in SSI 8.3 16.6 21.0 

Decrease in SSI 3.4 13.1 14.6 

No change in SSI 88.9 81.3 74.5 

 

Increase in Social Security Income 4.8 17.9 21.3 

Decrease in Social Security Income 4.4 12.5 15.5 

No change in Social Security Income 91.0 79.4 72.8 

 

Increase in unemployment compensation 5.5 6.0 11.7 

Decrease in unemployment compensation 0.5 6.5 5.7 

No change in unemployment compensation 94.0 92.3 85.8 

 

Increase in TANF 5.3 12.3 14.0 

Decrease in TANF 9.1 12.5 13.3 

No change in TANF 88.5 84.7 79.8 

 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP Panel. 

Notes: a Characteristic changes across any two of the three time periods: before gap, within gap, following gap. 

We learn from the table that changes in circumstances are common within spells. Twelve to 15 

percent of individuals in families with no gaps in participation have a change within the spell in 

the number of family members or the number of children. Forty-five to 55 percent of these 

individuals have a change within the spell in earned or unearned income. 

However, changes are even more prevalent, around the gap, for individuals in families with a 

relatively long gap in participation (three or more months). We generally accept that individuals 

with these long gaps are truly experiencing a gap in participation. Around 55 to 65 percent of 

these individuals have a change in earned income or a change in unearned income; close to onequarter have a change in the family size. 

If the individuals in families with shorter gaps were also exiting and re-entering because of 

changes in circumstances, we would expect to see a relatively high prevalence of these changes 

in the first column. In other words, we would expect them to look more like the people with 

longer gaps than the people with shorter gaps. However, only about 5 percent of these 

individuals have changes in family size and 23 to 30 percent have changes in earned income or 

changes in unearned income. We must note that the opportunities for such changes are fewer for 

individuals in families with these shorter gaps relative to individuals in the other two columns—

the changes are measured over at most four months (one month before the gap, up to two months 

of the gap, and one month after the gap) for individuals in the first column. The changes can be 

captured over longer periods in the other columns. Although the difference in the number of 

months over which the changes can be captured is so different across columns, the percentages 

A-60
of changes in circumstances for the individuals in families with one- and two-month gaps as to 

suggest that a change in circumstances is not the cause of their participation gap. 

Amount of Income Change Around Participation Gap 

Because many individuals in the panel experience changes in income throughout the panel, it is 

useful to look at the magnitudes of changes in selected variables. Table A.28 presents the 

magnitude of income changes just prior to a one- or two-month gap in participation (from two 

months before the gap to one month before the gap), at the gap (from the month prior to the gap 

to the month(s) of the gap), or after the gap (the last month of the gap to the first month of the 

new spell). 

Table A.28 is the first table to indicate that some of the individuals with gaps in participation 

may actually be gaps related to changes in circumstances. In particular, we see that 40.2 percent 

of individuals experience a change in earnings just before the gap, 30.9 percent at the gap, and 

43.9 percent at the end of the gap. Major changes at any time during these months could trigger 

SNAP exits and entry, although changes of a few dollars would probably not lead to an exit or 

entry. In fact, we see that about half of the gaps with changes in earnings were changes of at least 

$400. The distribution of magnitudes for changes in other income types is more widely 

distributed, often with the highest percentages of changes at the smallest and highest amounts. 

Table A.28 Amount of Income Change around Participation Gap 

 Percent of Gaps with Individuals Experiencing Change in Month Income Amount a

Change in Monthly Income Month Prior to Gap Month(s) of Gap Month Following Gap 

Earned Income 40.2 30.9 43.6 

 $1-200 9.4 8.2 12.7 

 $201-400 8.3 6.9 4.3 

 $401+ 22.5 15.8 26.6 

Unearned Income 26.8 29.8 37.9 

 $1-200 16.0 15.2 17.6 

 $201-400 1.4 7.8 7.2 

 $401+ 9.4 6.8 13.1 

SSI 3.3 3.7 7.1 

 $1-200 1.4 1.4 2.1 

 $201-400 0.2 0.2 0.4 

 $401+ 0.2 0.5 0.9 

Social Security 3.3 2.8 6.5 

 $1-200 1.3 1.3 3.4 

 $201-400 0.0 0.2 0.0 

 $401+ 1.9 1.3 3.1 

Unemployment Compensation 2.6 1.3 6.0 

 $1-200 0.2 0.4 0.5 

 $201-400 1.0 0.5 1.8 

 $401+ 1.4 0.5 3.7 

 

A-61
Table A.28, continued 

 Percent Of Gaps With Individuals Experiencing Change In Month Income Amount a

Change in Monthly Income Month Prior to Gap Month(s) of Gap Month Following Gap 

TANF 4.7 10.7 6.3 

 $1-200 1.3 5.5 0.9 

 $201-400 0.5 3.5 4.1 

 $401+ 2.9 1.7 1.4 

 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP Panel. 

Notes: a Individuals experience change from the month prior to the column heading to the month identified in the 

column heading. 

Conclusion 

To avoid overestimating the entry rate, researchers conducting the previous studies opted to 

“close up” one-month gaps (not two-month gaps), assuming that the respondent made a mistake 

in reporting and did not experience an actual break in participation. Anecdotal evidence from the 

states, however, indicates that “churning,” short-term nonparticipation in the program, is 

somewhat common. 

 

In assessing whether to close one-month gaps in this current analysis, we focused on three 

possible explanations for short-term gaps: 

1. Individuals had a change in circumstances that led them to exit and then another 

change that led them to re-enter, within a very short time period 

2.Individuals hit the end of their certification period without completing the 

recertification process, leading them to exit the program; then within a month or two, 

they reapplied and entered back into the program (what we refer to as churning 

below). 

3.The gap is misreported and participation continued across this period. 

If we had found solid evidence that the first case was most prevalent, we would have suggested 

not closing the gaps, as the exits and entries would then appear real. Aside from the last table, 

concerning the magnitudes of the changes, however, we did not find much evidence to support 

this. Table A.27 showed us that individuals with short-term gaps do not seem to experience a 

similarly high level of changes in circumstances as seen with those with longer gaps. 

Thus we were left trying to identify if the gaps are due to churning or misreporting. From Table 

A.24, we find that most one-month gaps are reported in the last month of the wave – the month 

we expect to be most accurately reported. Although reporting bias in the next wave may lead to 

an underrepresentation of the gap, it appears likely that there was a gap. From Table A.25 we see 

that the gap often occurs about six months into a spell, which is consistent with the six-month 

certification period of 40 percent of participating households. Then, in Table A.26, we find that 

individuals who are more likely to have short gaps in participation are also the ones with the 

A-62
shorter participation spells. In other words, they come up for recertification more often, and have 

more opportunities to experience a short-term break in participation. 

Because the tabulations are consistent with an explanation of churning, and the highest incidence 

is in a month that we expect to be most accurate, we believe that the gaps may in fact be due to 

churning rather than misreporting. 

The question that remained, then, was whether to close the gaps or not. On one hand, the gaps 

appear to be true breaks in participation—individuals exited the program and re-entered very 

quickly. On the other hand, the exits and entries were not triggered by changes in circumstances, 

but instead by an end of the certification period. If we were to close the one-month gaps, our 

analysis would include longer single spells and examine trigger events only around their entry 

before the long spell. If we were not to close the one-month gaps, we would have multiple 

shorter spells and will include triggers for individuals who did not experience a change in 

circumstances that led to their entry. In other words, closing the gap would result in longer 

median participation spells, lower entry rates, and an entry trigger analysis based on a more 

reasonable set of entries. Not closing the gaps would result in shorter median participation spells 

and have an entry trigger analysis that has been diluted by families that entered without a change 

in circumstances. Given the percentage of individuals with gaps in Table A.23, however, this 

dilution is likely to be minimal. 

While methodologically we believe either approach is appropriate and defensible, we decided in 

consultation with FNS to close the gaps. If states and policymakers generally consider the 

churners to be longer-term participants, then closing the gaps allows the analysis to focus on 

entries, durations, and triggers among those who are not simply churning. 

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B-1 

Appendix B 

Subgroup Definitions in SNAP Dynamics Research 

 

In this appendix, we provide information about how selected subgroup definitions have evolved 

across a series four SNAP dynamics research reports. 

In Chapter III, we present SNAP dynamics data for the total population and nine subgroups from 

Burstein 1993; Gleason et al. 1998; Cody et al. 2007; and the current project. The reports use 

data from SIPP panels that started in 1984, 1991, 2001, and 2004 respectively, and this appendix 

will refer to each study by the date the SIPP panel started. Some of those subgroups are affected 

by evolution in their definitions. Poverty status differentials affect all of the 1991 data, including 

subgroups, while the use of family versus household data affected subgroup determinations 

starting in 2001. 

Universe as defined by the official poverty level 

In the 1991 data, the population at risk of SNAP entry was the total population of person-months 

that was not in the SNAP program. In the 1984, 2001, and 2004 data, there is a further restriction 

on the universe at risk of entry, which is that it includes only those below 300 percent of poverty 

level. Poverty status restrictions only affect the at-risk and entering population tabulations. For 

the life-table analyses of SNAP spells, that makes up the majority of the data presented in 

Chapter III. All spells are included regardless of the ratio of income to poverty. 

 

Households, families, and subgroup determination 

The second distinction has to do with the Census concepts of household or family. The 1984 and 

1991 data use the Census household—all persons living in an occupied housing unit—to define 

and construct subgroups. In 2001 and 2004, however, Census family units are used to define and 

construct subgroups. The primary family in a SIPP household is defined as including all persons 

who are related to the head of that household, including members of related subfamilies in the 

household. It could be argued that members of a family are more likely to pool resources and eat 

together and form a SNAP unit, and that the family is the more relevant unit of analysis. We 

present data for four subgroups in Chapter III for whom this is a particularly important issue. The 

first two subgroups are: 

• Single parents 

• Children of single parents 

 

The single parents subgroup actually corresponds to two different definitions: in 1984 and 1991 

it is adults in households with children and one adult, while in 2001 and 2004 it is adults in 

families with children and one adult. Similarly, the children of single parents subgroup 

corresponds to the following two definitions: in 1984 and 1991 it is children in households with 
B-2 

children and one adult, while in 2001 and 2004 it is children in families with children and one 

adult. While a single parent with children is probably the modal formation in these definitions, 

there are many variations of household and family relationships that can correspond to people 

age 18 and over being part of the same household or family as people under 18. It is those simple 

age tests that determine whether a 1984 or 1991 household, or a 2001 or 2004 family, qualifies to 

be part of these subgroups. 

 

The next two subgroups that we track in Chapter III are: 

• Married adults with children 

• Children of married adults 

 

In 1986 and 1991, those subgroups were unavailable. Rather, the following two subgroups were 

provided, based on household-level tests: 

• Adults in households with children and multiple adults 

• Children in households with children and multiple adults 

 

Married adults with children and children of married adults are family constructs that probably 

comprise the modal categories of households in which multiple adults and children who share a 

household find themselves. Again, there are many other variations of household and family 

relationships that can correspond to multiple people age 18 and over being part of the same 

household as people under 18, and it is those simple tests of age and sharing of a household that 

determine the subgroup. Thus, the 1986 and 1991 subgroups were more broadly constituted. 

 

In the 2001 and current 2004-based analysis, four mutually-exclusive subgroup definitions have 

been employed at the family level. The first two subgroup titles appear to be close to what was 

provided in 1986 and 1991 for households, but by separating the married contingent from the 

others, we actually create two sets of subgroups that are fairly different from each other. 

• Adults in families with children and multiple adults 

• Children in families with children and multiple adults 

• Adults in families with children and a married head 

• Children in families with children and a married head 

 

Table B.1 provides at-risk population, SNAP entrants, and SNAP entry rates for the above four 

categories in 2004. It is clear that families with married heads and children account for the 

majority of the population at risk, but it is also clear that the SNAP entry rates and the percent of 

entrants accounted for by the families headed by other multiple adults are disproportionately 

high. The wave-based SNAP entry rates are about four times higher for the multiple adults 

categories, and the same differentials applied to the 2001-based analysis. 
B-3 

 

Table B.1 At-Risk Populations, SNAP Entrants, and SNAP Entry Rates, 2004 Panel 

Subgroup 

At Risk: 

Income under 

300 Percent of 

Poverty 

Percent of 

All SNAP 

Entrants 

Wave-Based 

SNAP Entry Rate 

 

Total: All Person-Months 100.0 100.0 2.0 

 

Family Composition 

Adults in families with children and multiple adults 3.4 9.5 5.8 

Children in families with children and multiple adults 1.9 7.7 8.2 

Adults in families with children and a married head 22.7 15.5 1.4 

Children in families with children and a married head 18.6 16.2 1.8 

 

Source: Decision Demographics, weighted tabulations of the 2004 SIPP panel 

Notes: At Risk: Not receiving SNAP benefits for at least 2 months and income under 300 percent of poverty at 

some point during the panel period 

Reference Months: 3 to 31 

Sample: Person months for entry rates; persons for percent of entrants 

a

 Characteristics as of reference month; for wave-based estimates, as of Month 4 

Subgroup determinations. At-risk and entrants: person month basis, reference month. Entry rates: last 

month preceding the wave of entry for wave-based entry rates. 

In Chapter III, we limited the 2001 and 2004 data to 

• Married adults with children (Adults in families with children and a married head) 

• Children of married adults (Children in families with children and a married head) 

Therefore, the categories that we label married adults with children, and children of married 

adults in these time series tables represent different populations for the 1986 and 1991 panel 

analyses than they do in the 2001 and 2004 panels. The household- versus family-level construct 

is also relevant for three other subgroups in Chapter III: 

• Individuals in childless families without elderly or disabled members 

• People living in families with earnings 

• People living in families with TANF income 

 

Each of these subgroups is constituted slightly differently at the family level than at the 

household level. A household may include multiple families, so its characteristics will represent 

the combination of the families. 

Additionally, the first category called “able-bodied childless adults” in 1986, and “individuals in 

households with only able-bodied prime-age childless adults” in 1991, but both concepts referred 

to individuals in households that contained no children, elderly, or disabled. The differences for 
B-4 

these groups between the first two and second two studies are minor, especially in comparison to 

the adults and children subgroup distinctions mentioned above. For those who are interested in 

contrasting nonelderly disabled adults with nonelderly nondisabled childless adults as 

individuals, there are new subcategories in the main body of the current study that pertain to 

those groups defined as individuals.