13
Preexit Benefit Receipt and Employment Histories and Postexit Outcomes of Welfare Leavers

Michele Ver Ploeg

The enactment of time limits, work requirements, and sanctions, among other rules of the Personal Responsibility and Work Opportunity Reconciliation Act of 1996 (PRWORA), caused many observers to wonder how welfare recipients would respond: Would they leave welfare? Would they find jobs? Would they face hardship or would their economic and family situations improve? These questions prompted numerous studies of “welfare leavers,” or those who stopped receiving welfare benefits.

Most of these welfare leaver studies were conducted for monitoring purposes—to inform policymakers and program administrators about the needs and experiences of those who had left welfare. However, some were conducted with the goal of assessing the effectiveness of the reforms; that is, they intended to assess whether the reforms caused those who left welfare to be better off or worse off relative to a comparison group. To make this assessment, the studies usually employed a before-and-after research design, comparing outcomes of welfare leavers before they left welfare to outcomes after they left welfare, or a multiple-cohort design, comparing outcomes of a cohort of people who left welfare prior

The author is grateful to the Wisconsin State Department of Workforce Development and the Institute for Research on Poverty at the University of Wisconsin-Madison for making the data used in this paper available. Ingrid Rothe, Daniel Ross, and Allison Hales-Espeweth of the University of Wisconsin-Madison’s Institute for Research on Poverty, and Barbara Wolfe, Director of IRP deserve special thanks for making the data available and assisting with use of the data. Thanks also to Karl Johnson, who provided research assistance from the project. Finally, the author is grateful to Robert Moffitt for valuable comments on the paper as it developed.



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Studies of Welfare Populations: Data Collection and Research Issues 13 Preexit Benefit Receipt and Employment Histories and Postexit Outcomes of Welfare Leavers Michele Ver Ploeg The enactment of time limits, work requirements, and sanctions, among other rules of the Personal Responsibility and Work Opportunity Reconciliation Act of 1996 (PRWORA), caused many observers to wonder how welfare recipients would respond: Would they leave welfare? Would they find jobs? Would they face hardship or would their economic and family situations improve? These questions prompted numerous studies of “welfare leavers,” or those who stopped receiving welfare benefits. Most of these welfare leaver studies were conducted for monitoring purposes—to inform policymakers and program administrators about the needs and experiences of those who had left welfare. However, some were conducted with the goal of assessing the effectiveness of the reforms; that is, they intended to assess whether the reforms caused those who left welfare to be better off or worse off relative to a comparison group. To make this assessment, the studies usually employed a before-and-after research design, comparing outcomes of welfare leavers before they left welfare to outcomes after they left welfare, or a multiple-cohort design, comparing outcomes of a cohort of people who left welfare prior The author is grateful to the Wisconsin State Department of Workforce Development and the Institute for Research on Poverty at the University of Wisconsin-Madison for making the data used in this paper available. Ingrid Rothe, Daniel Ross, and Allison Hales-Espeweth of the University of Wisconsin-Madison’s Institute for Research on Poverty, and Barbara Wolfe, Director of IRP deserve special thanks for making the data available and assisting with use of the data. Thanks also to Karl Johnson, who provided research assistance from the project. Finally, the author is grateful to Robert Moffitt for valuable comments on the paper as it developed.

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Studies of Welfare Populations: Data Collection and Research Issues to the enactment of PRWORA to outcomes of a cohort of leavers who left welfare after enactment of PRWORA. Both of these designs have weaknesses in drawing causal conclusions.1 Factors outside of welfare, such as the economy, may also change and affect the outcomes of welfare leavers, making it difficult to assess whether outcome changes are due to the reforms or to the other factors using these methods. Another weakness of these methods is that the characteristics of the people leaving welfare at the time of the study, or at the time the cohorts are drawn, may be driving changes in outcomes. For example, if a cohort of leavers is drawn when the caseload is relatively small, the leavers may be comprised primarily of those who have the most barriers to leaving welfare, such as substance abuse, very young children, or little work experience. Their outcomes after leaving may be much different than the outcomes of a cohort of leavers drawn when the caseloads are relatively large, since this cohort may be composed of leavers with fewer barriers to self-sufficiency. This second problem of the composition of the caseload is also a problem even if the leaver studies are only used for monitoring, and not evaluation, purposes. For example, a monitoring study may be conducted to roughly quantify the need for child care services of those who leave welfare. Those who leave welfare in a time when caseloads are just beginning to drop may be able to leave because they had an easy time securing child care, while those who could not easily find childcare may not leave welfare until much later. It would be hazardous to base conclusions about the need for childcare from any single cohort of leavers if one does not know much about that cohort of leavers. The National Research Council report (1999) suggested that as a crude means of standardizing descriptions of the caseload and the outcomes of leavers across time and across areas, outcomes could be stratified by the past welfare receipt history and past work experience of welfare leavers. Standardizing the composition of the caseload and the groups of the leavers would then make comparisons of outcomes of leavers across time and jurisdictions more credible because leavers with similar work and welfare receipt histories would be compared to each other. The purpose of this paper is to classify characteristics of welfare leavers and stayers and their outcomes by their preexit benefit receipt and employment experiences to illustrate one method the leaver studies might use to standardize their results to make comparisons across time and jurisdictions more credible. No attempts to make causal attributions are made in this study. The second section of this study describes the data used. The third section examines the past welfare receipt, employment, and earnings histories of the caseload of AFDC recipients in 1995. Section 4 examines whether and how much welfare leavers work and earn after leaving, whether they return to welfare or use 1   See NRC (1999, 2001) for a more detailed discussion of these weaknesses.

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Studies of Welfare Populations: Data Collection and Research Issues other public assistance after leaving, and how self-sufficient they are after they leave. In discussing each of these outcomes, results are presented separately across different types of welfare leavers based on their past welfare receipt and work histories. This section also examines the outcomes of cases classified as “high-barrier” leavers-that is, those who face multiple barriers to gaining self-sufficiency. The outcomes of this group are presented in an attempt to estimate a lower bound on outcomes of leavers. Section 5 examines the importance of past welfare receipt and work history measures in a multivariate setting. Probit models of the probability of leaving welfare and of being employed a year after leaving welfare, controlling for welfare and earnings histories, as well as demographic characteristics of leavers, are estimated. Tobit estimates of post-welfare earnings, controlling for welfare and work histories and demographic characteristics also are given. The coefficients from these models are then used to predict outcomes of different high-barrier groups to assess how cases with multiple barriers to self-sufficiency fare after leaving welfare. This study was undertaken as part of a set of papers that explore the importance of caseload composition factors for outcomes of welfare leavers. Moffitt (this volume: Chapter 14) uses the National Longitudinal Survey of Youth data from 1979 to 1996 to describe the welfare receipt and employment experiences of young women ages 20–29. Stevens (2000) uses AFDC and Unemployment Insurance administrative records from Maryland and draws multiple cohorts of leavers across time periods. The past AFDC and work histories of these cohorts are described and employment outcomes after leaving welfare are compared across cases with different welfare receipt and work experience histories. This study also builds on a series of papers on AFDC leavers in Wisconsin that has been conducted by researchers at the Institute for Research on Poverty at the University of Wisconsin-Madison.2 These reports have examined employment, earnings, and benefit receipt after leaving welfare for a cohort of July 1995 AFDC recipients who left AFDC in the following year. DESCRIPTION OF THE DATA AND KEY VARIABLE DEFINITIONS Data for this study come from the Wisconsin Department of Workforce Development CARES system, which contains information collected through the administration of AFDC and other means-tested programs. These data were matched to earnings and employment data from the state’s UI system. All persons in the data used in this study received AFDC benefits in Wisconsin in July 1995. These cases were tracked with linked administrative data from January 1989 until December 1997, providing up to 9 years of data for each case. 2   See Cancian, M. et al. (1999); Cancian et al., (2000a); and Cancian, M., Haveman, R., Meyer, D.R., and Wolfe, B. (2000b).

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Studies of Welfare Populations: Data Collection and Research Issues Who Is in the Data Set? Every observation in the data set received AFDC-Regular (for single-parent families) in July 1995. The entire caseload at the time numbered 65,017. The following types of cases were eliminated from the data, with the number of cases eliminated (nonsequentially) with the restriction in parentheses: Cases that were open in July 1995 but did not receive any benefits (n=397). Cases where there were no children 18 or younger in July 1995 (n=843). Cases where all eligible children in the case are being cared for by a not-legally responsible relative (n=6,101). Cases where there are two parents (n=907). Cases where a case head is a teen mom—meaning there is an eligible adult under the age of 18 (n=47), or there is no eligible adult and a child is the caretaker (n=254). Cases involving a large family or two conjoined families where a single case head is unidentifiable (n=138). Cases for which UI data were not requested (n=47). Cases where the case head is over 65 years old (n=83). Cases with a male case head (n=1,888). After eliminating these cases, the data set contained 54,518 cases; this is the data set used by Cancian et al. (1999). We further eliminated cases under the age of 21 in 1995. Because we were able to obtain data on AFDC receipt back to July 1989 and UI earnings reports back to January 1989, those under age 21 were eliminated because they were under the age of 15 in 1989 and not reasonably expected to be on AFDC or working. After eliminating these cases, our final number of observations is 48,216. Definition of a Leaver A welfare “leaver” is defined as a case that received AFDC in July 1995 and, over the course of the next year (until August 1996), stopped receiving benefits for 2 consecutive months.3 “Stayers” are those who did not stop receiving benefits for 2 consecutive months during the August 1995–August 1996 period. This period is referred to throughout the paper as the “exit period.” The “preexit period” is between January 1989 and July 1995. The “postexit period” for a leaver begins in the quarter the leaver exited welfare and continues until the last 3   This 2-month definition of a leaver was used in Cancian et al. (1999) and is being used by the leavers studies sponsored by the Office of the Assistant Secretary for Planning and Evaluation in the U.S. Department of Health and Human Services.

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Studies of Welfare Populations: Data Collection and Research Issues quarter of 1997. For a stayer, the postexit period is between July 1996 and the last quarter of 1997. Stayers may have left welfare after August 1996 but did not do so during the exit period. Two alternative definitions of leavers were explored; first, only those who stopped receiving benefits for 3 consecutive months from August 1995 to September 1996 were considered leavers, and a more stringent definition of a leaver considered only those who stopped receiving benefits for 6 consecutive months from August 1995 to December 1996 to be leavers. Caseload composition and outcomes using these definitions are reported in Appendix 13-A. In general, we find only small changes in the demographic composition of the group of leavers under a more restrictive definition of a leaver, that is, one who has stayed off of welfare for 6 consecutive months. The differences in demographic composition between 2-month and 3-month leavers are negligible. Outcomes of leavers change slightly with the more restrictive definition of leavers, as 6-month leavers are less likely to return to welfare and have modestly higher earnings than 2-month and 3-month leavers. Welfare History Variables The cases were categorized into groups based on each case’s past welfare receipt history. This was done as a means to characterize the welfare caseload at the time the sample of leavers was drawn and as a means to standardize comparisons of outcome measures across different types of leavers. Leavers were stratified into groups using monthly AFDC receipt data from July 1989 through December 1997.4 From these data, spells of receipt were counted. A spell began with 1 month of receipt (preceded by a month of no receipt) and ended with 1 consecutive months of nonreceipt. Those enrolled in AFDC in July 1989 were counted as starting a spell, even though they may have already been enrolled in months prior to that. No adjustment was made for this censored data. A month of nonreceipt surrounded by two months of receipt was not counted as an end of a spell. Rather, it was counted as if the spell continued. We implemented this strategy to ensure that a spell actually ended and that the break in receipt was not the result of administrative churning or erroneous reporting. Some cases continued spells after July 1995 and are right censored. No adjustments for these censored data were made. The total number of months on AFDC, the total number of spells, and the average spell length in months (total months of receipt divided by number of spells) were calculated for each observation. Using these measures, all leavers and stayers are classified as short-termers, long-termers, or cyclers. Short-termers have average spell lengths of less than 24 months and fewer than three total 4   Data for November, 1992 are missing for all observations.

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Studies of Welfare Populations: Data Collection and Research Issues spells throughout the preexit period; long-termers have average spell lengths of 24 or more months and fewer than 3 total spells; and cyclers have three or more spells, regardless of average spell length. The exact cutoff points of these classifications are somewhat arbitrary, however, under this definition, long-termers are those who have spent at least a third of the time we observe them on welfare and short-termers are those who have spent less than one-third of the time on welfare.5 In general, we expect that short-termers face the fewest barriers to self-sufficiency. We expect that long-termers have the most barriers to self-sufficiency. Cyclers are expected to be somewhere between them. Therefore, we expect that short-termers will be less dependent on assistance and have better labor market outcomes after leaving than long-termers and we expect outcomes of cyclers to be somewhere between them. The AFDC receipt data only include administrative records from the state of Wisconsin. Some cases may have moved to Wisconsin just before the exit period and started spells then. These may include a mix of long-term, cycler, and short-term welfare users. However, because we cannot track welfare receipt in other states, these cases are classified as short-termers. Similarly, the definitions do not account for the age of the case head (except that all were at least 15 in 1989). Those who are younger have fewer years of “exposure” to welfare and are likely to have fewer and shorter spells compared to older recipients. Work History Variables Earnings information from Unemployment Insurance records from first quarter 1989 to fourth quarter 1997 are used in this study. A variable for the percentage of quarters with any earnings in the preexit period was created and used to stratify outcomes (number of quarters from 1989 to 1995 with positive earnings divided by total number of quarters between first quarter 1989 and third quarter 1995). The percentage of quarters with earnings was divided into the following categories to make comparisons feasible: (1) those who had never worked in the preexit period; (2) those who had worked at least one quarter but no more than 25 percent of the quarters in the preexit period; (3) those who had worked more than 25 percent of the quarters but not more than 50 percent of the quarters; (4) those who had worked more than 50 percent of the quarters but not more than 75 percent of the quarters; and (5) those who had worked more than 75 percent of the quarters. Each outcome of interest is also stratified by these categories of work history. Again, earnings records from other states are not available for those who move into Wisconsin. Also, no standardization for the age of the case head was 5   Alternative definitions were examined and the caseload compositions based on those definitions are reported in Table 13-B1 in Appendix 13-B.

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Studies of Welfare Populations: Data Collection and Research Issues made in this measure. The youngest welfare recipients in July 1995 are likely to have worked fewer quarters than older recipients. Thus, we expect the average age of groups with less work experience to be lower than the average age of groups with more work experience. Postleaving Outcome Measures Three types of outcomes for welfare leavers were examined: (1) public assistance receipt, such as whether the case returned to welfare and whether the case received other public assistance benefits (food stamps and medical assistance); (2) earnings and employment after leaving; and (3) total income, from earnings and public assistance benefits after leaving. The entire sample was tracked through administrative records through December 1997. For each leaver, there are at least five quarters of data on earnings and public assistance receipt after leaving. Outcomes of both leavers and stayers are reported.6 Some outcomes are reported relevant to the quarter the leaver stopped receiving AFDC, such as earnings in the first quarter after exit. For leavers, the actual calendar year quarter of these earnings will vary according to when the leaver stopped receiving welfare. For stayers, the first quarter after initial exit is the third quarter, 1996, the second quarter after exit is the fourth quarter 1996, and so on. Data Limitations This study relies solely on administrative records from the CARES system and matched UI records from the state of Wisconsin. These data have important limitations. First, only records from Wisconsin are included in this study. If a case moved into or out of Wisconsin, information about the case when not in the state is not available. Second, good information on how many of these movers might be in the data file at some point is not available. Administrative data are available on those in the case unit and not on others who might be living in the same household as the unit. For example, earnings of a cohabitating partner are not available, nor are data on living arrangements. Third, errors may occur during the process of matching the CARES data to the UI data may occur if Social Security numbers are reported erroneously or if there are duplications in the data reported to the UI system from employers. Finally, with specific regard to UI data, not all jobs are covered in the Unemployment Insurance system (for example, self-employed persons or federal government employees) or recorded 6   Outcomes of leavers who did not return to AFDC in the follow-up period also were examined. As expected, these “continuous leavers” had better outcomes than those who returned to AFDC.

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Studies of Welfare Populations: Data Collection and Research Issues when they legally should be. As a result, some cases that appear to have no earnings may in fact have earnings from jobs. Hotz and Scholz (this volume: Chapter 9) review studies of underreporting in the UI system. In the Wisconsin data, some cases cannot be tracked with the administrative records from the postexit period (for example, those who move into or out of the state as described). These cases, “disappearers,” make up 3.7 percent of the total of 54,518 cases. Other cases appear in some but not all quarters. These “partial disappearers” make up 13.6 percent of the total caseload. Cases that disappear are used in the analysis unless otherwise noted. Cases not appearing in UI records for a quarter are assumed to have zero earnings for that quarter. Cases not appearing in public assistance records were assumed to not be receiving benefits. THE WELFARE RECEIPT AND WORK HISTORIES Because of dynamics in policy, economic conditions, and other social factors, the characteristics of those who receive welfare (and leave welfare) at one period may be quite different from the characteristics of those who receive (and leave welfare) at another time period. For example, during periods of high unemployment, the caseload may include many cases that have lots of work experience and have not received welfare very often, but who cannot find a job in a slack economy. On the contrary, during economic booms, these types will probably move into jobs and off welfare, leaving those with the most barriers to employment and self-sufficiency on the rolls. In this section, we describe the welfare receipt and work histories of the caseload of AFDC recipients with a sample of leavers drawn in July 1995. Welfare Histories of the Caseload in July 1995 Table 13–1 provides the distribution of the total number of months of AFDC benefit receipt for the full caseload overall and separately by the number of spells of receipt during the time frame. (To abbreviate, we call this total-time-on, or TTO.) Column 1 shows TTO for the entire caseload. This column shows that a majority of the caseload in July 1995 received benefits for more than 2 years and that a large portion (nearly 38 percent) received benefits for at least 5 of the 6 years in the preexit period. This is not surprising given that at any point in time, the caseload will be made up disproportionately of long-term beneficiaries. (See Bane and Ellwood, 1994, for a discussion of welfare dynamics.) The bottom row of Table 13–1 shows the overall distribution of the number of spells of the caseload in July 1995. The majority of cases had only one spell (57.2 percent) and just over a quarter had 2 spells (25.8 percent). The fraction of those with three or more spells is quite small; as only 14 percent fell into this category. Moffitt (this volume: Chapter 14) found that of those who were ever on AFDC of the 10 years of NLSY data used in the study, 48 percent had only one

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Studies of Welfare Populations: Data Collection and Research Issues TABLE 13–1 Distribution of Total-Time-On AFDC in Months Between 7/89 to 7/95 by Number of Spells of AFDC Receipt Over Entire Period (percent distribution) Total-Time-On (months) All Number of Spells 0 1 2 3+ 0 3.1 3.1 — — — 1–6 4.8 — 7.9 1.3 0.0 7–12 6.7 — 8.9 5.5 1.7 13–18 5.7 — 5.4 7.3 5.2 19–24 6.3 — 5.6 7.7 7.8 25–36 11.2 — 8.6 13.7 19.6 37–48 11.9 — 9.3 13.6 22.2 49–60 12.6 — 8.4 16.5 25.6 61+ 37.7 — 46.0 34.4 17.9 Total percent with number of spells   3.1 57.2 25.8 13.9 NOTE: Total number of observations=48,216. Maximum number of months=71. spell of receipt and only 8 percent had 4 or more spells. Thus, both of these studies show a small amount of turnover in the caseload. Table 13–1 also reports the distribution of TTO by the number of spells of benefit receipt. Of those who had only one spell, 46 percent had a long spell of more than 5 years. The rest of those with only one spell are distributed fairly evenly across the TTO scale. For those with 2 spells, a smaller fraction received welfare for more than 5 years (34 percent). Those with two spells are, however, more concentrated in the categories of 2–6 years of benefit receipt than those with only one spell. Finally, those with three spells of receipt are concentrated primarily in the range of 2–5 years of benefit receipt. Two-thirds, 67 percent, of those with at least 3 spells received benefits for a total of 2–5 years. Table 13–2 is a slight variation on Table 13–1. Instead of reporting the total number of months of benefit receipt, Table 13–2 reports the average spell length (ASL) of benefit receipt.7 The first column gives the overall distribution of ASL. There is a cluster (26 percent) of the caseload with an ASL of more than 5 years. However, the majority of the caseload have ASLs of between half a year and 3 years. The distribution of ASL for those with one spell is the same as in Table 13– 1. For those with two spells of benefit receipt, more than half have ASLs of 2 to 7   ASL was calculated as the TTO measure divided by the total number of spells.

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Studies of Welfare Populations: Data Collection and Research Issues TABLE 13–2 Distribution of Average AFDC Receipt Spell Length in Months Between 7/89 to 7/95 by Number of Spells of AFDC Receipt Over Entire Period (percent distribution) Average Spell Length (months) All Number of Spells 0 1 2 3+ 0 3.1 3.1 — — — 1–6 7.7 — 7.9 6.8 10.4 7–12 13.8 — 8.9 15.0 35.1 13–18 10.8 — 5.4 13.7 30.2 19–24 10.1 — 5.6 13.6 24.4 25–36 18.1 — 8.6 51.0 0.0 37–48 5.3 — 9.3 0.0 0.0 49–60 4.8 — 8.4 0.0 0.0 61+ 26.3 — 46.0 0.0 0.0 Total percent with number of spells   3.1 57.2 25.8 13.9 3 years. For those with three spells, 11 percent have an ASL of less than half a year. An additional 35 percent have ASLs of less than a year. Thus, 45 percent of cases have short spells of benefit receipt on a relatively infrequent basis. However, 55 percent of those with three spells have ASLs of 1 to 2 years. To capture the two concepts of average spell length and total number of spells in a less cumbersome way, three categories of welfare recipients were created: cyclers (more than two spells), short-termers (fewer than two spells and TTO of less than 2 years), and long-termers (fewer than two spells and TTO of 2 or more years). Table 13–3 illustrates the distribution of the caseload in July 1995 across these three categories. More than half the sample (55 percent) are long-term welfare users. Nearly a third (31 percent) are short-term users, and nearly 14 percent of the sample are cyclers. Moffitt (this volume: Chapter 14) found about one-third of the women ever on AFDC were cyclers, between 37 and 58 percent were long-termers, and be- TABLE 13–3 Long-termer, Short-termer, and Cycler Status (percent distribution)   Overall Leaver Stayer Long-termer 55.3 42.9 66.7 Short-termer 30.8 39.1 23.1 Cycler 13.9 18.0 10.2

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Studies of Welfare Populations: Data Collection and Research Issues tween 23 and 44 percent were short-termers, depending on how these two concepts were defined. Using Maryland administrative data on the AFDC/TANF caseload from 1985–1998 and linked UI data, Stevens (2000) disaggregated the AFDC/TANF caseload from Baltimore City into four birth cohorts and observed each of the cohorts for a ten-year period. He also divided the caseload into the long-termer, short-termer, and cycler distinctions and found more short-term welfare recipients than long-term welfare recipients. About 50 percent of those on welfare during the time span were short-termers while about one-third were long-termers, which is almost exactly the reverse of findings from the Wisconsin data. In another study that used the Maryland data and similar definitions of dependence, but that examined 11 birth cohorts of women, the percent of the caseload that was short-termers ranged between 44–67 percentage, the percent that was longer-termers ranged from 35 to 47 percent, and the percent that were cyclers ranged from 3–19 percent (Moffitt and Stevens, 2001). Except for two birth cohorts, the percent of short-termers was always greater than the percentage of long-termers. The results of the Maryland studies that show more short-termers than long-termers in the caseload compared to results from the Wisconsin data that show more long-termers illustrate the point about compositional factors of different caseloads at different times. Given these different compositions, we might expect Maryland leavers to have better postexit outcomes than Wisconsin leavers who have greater welfare dependency, with all, else being equal. The Work Histories of the Caseload in July 1995 A principal emphasis of the 1996 welfare reforms was to push welfare recipients into work and work-related activities. Not surprisingly, most studies of welfare leavers focus on the work outcomes of leavers, whether they have and keep jobs, what their wages are, and how their wages change as they work more. As recipients leave welfare, we would expect those with more work experience to have better outcomes. To assess whether this hypothesis is correct, we have classified the entire caseload in July 1995, by the number and percentage of quarters between January 1989 and July 1995, in which the case had nonzero UI wage reports. Table 13–4 shows the distribution of prior work experience. We find that most of the caseload did not have much work experience during this time period. Less than a quarter of the caseload (21 percent) had worked more than half the quarters. Nearly 20 percent had no reported earnings during the time frame, 34 percent had earnings in less than 25 of the quarters, and 26 percent worked between 25 and 50 percent of the time between January 1989 and July 1995. What is the relationship between work history and welfare receipt history? Table 13–5 shows the distribution of work history across short-termer, long-termer, and cycler status. The table shows that those who cycle on and off welfare have the most work experience. Only 6 percent of cyclers had never worked in the

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Studies of Welfare Populations: Data Collection and Research Issues much less likely to leave welfare than other cases. Although their employment rates are not vastly different from all other leavers, their earnings are substantially different. High-barrier cases that are eligible for SSI are likely to have even greater problems making it on their own, according to these predictions. For other types of high-barrier cases, employment may not be a significant problem for them; however, earnings do seem to be a problem. Results found here should supplement similar simulations conducted in Cancian et al. (2000b), where much wider differences in predicted outcomes between high-barrier cases and low-barrier cases were found. The Cancian et al. definitions of high-barrier cases are more restrictive than definitions used here. CONCLUSIONS The purpose of this paper is to illustrate the importance of characterizing the composition of the caseload at the time the welfare leavers sample is drawn. The paper also aims to exemplify one method of standardizing results across different types of leavers with different benefit receipt and work histories in order to make the studies more comparable across time and across areas. In general, we find that past welfare receipt history matters a great deal for outcomes, but not always as expected. We also find that those with more work experience prior to leaving were more likely to leave welfare and were much more successful in gaining employment and earnings after leaving welfare. We described the composition of the caseload during the time the leavers sample was drawn according to their prior work and benefit receipt. Results presented in that section show that a significant portion of the caseload received AFDC benefits for at least 5 of the 6 years in the preobservation period. Most of the cases on AFDC in 1995 had fewer than two spells of benefit receipt in the preexit period. Only 14 percent had three or more spells of receipt. The caseload was divided into three groups: long-termers, short-termers, and cyclers. Under these definitions, 55 percent of the caseload were long-termers, 31 percent were short-termers, and 14 percent were cyclers. The caseload was also broken down by past work experience, as measured by the percentage of quarters in the preexit period with UI earnings. Twenty percent of the caseload did not work at all in the preexit period, 60 percent worked at least one quarter but no more than half the quarters, and 25 percent worked for more than half the quarters. Crossing work history with welfare receipt history, we found that those who had received benefits the longest had the least amount of work experience. Short-termers had the most work experience. Cyclers had the least amount of work experience. We also showed outcomes by past benefit receipt and work experience. The first outcome examined was the proportion of cases that left welfare. Results showed that higher percentages of cyclers and short-termers left welfare than long-termers. Results also showed that higher portions of leavers were found in the groups with the most work experience. For those who left welfare, two sets of

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Studies of Welfare Populations: Data Collection and Research Issues outcomes were examined: benefit receipt after exit (return to AFDC, food stamps, or Medicaid) and employment status and earnings after exit. Results show that the cycler, short-termer, and long-termer distinction is an important distinction for benefit receipt outcomes. Long-termers were much more likely than short-termers and cyclers to return to welfare, and a higher proportion of long-termers continued to receive food stamps and Medicaid after leaving than short-termers. Benefit receipt outcomes after leaving did vary by work experience prior to leaving welfare, but the differences were not large. On the other hand, employment and earnings outcomes after leaving varied substantially across prior work experience strata. As expected, those who had worked more prior to leaving welfare had higher employment rates and higher earnings after leaving. Employment and earnings outcomes also varied by prior AFDC benefit receipt, but not as drastically. Surprisingly, long-termers had better employment outcomes than short-termers. Long-termers were more likely to be employed after leaving and their earnings were higher after leaving than short-termers. Cyclers’ employment rates and earnings did not differ greatly from those of long-termers. The final part of the paper examines how important past benefit receipt distinctions and work experience distinctions are for these outcomes when other background characteristics of the cases are controlled. The probability of leaving welfare and the probability of ever being employed in the year after leaving welfare were estimated. Earnings after leaving were also predicted for welfare leavers. The primary finding in this section is that prior work experience was a consistently strong predictor of success. The percentage of quarters worked in the preexit period was positively associated with the probability of leaving welfare and the probability of employment after leaving. Quarters worked and average wages in the preexit period were both positive and strong predictors of quarterly earnings after leaving welfare. We also found that past welfare receipt distinctions were important predictors of the probability of leaving welfare. Short-termers were significantly more likely to leave welfare than long-termers and in general, results consistently show that those who had received AFDC longer were less likely to leave AFDC. The cycler distinction was not a strong predictor of the probability of leaving welfare, although there is some evidence that those with one spell of benefit receipt were less likely to leave welfare than those with no prior spells of receipt. The probability of being employed after leaving is, surprisingly, positively related to the length of time spent on welfare prior to the preexit period. Average spell length and long-termer status were both positive and strong predictors of the probability of employment after leaving welfare. For this outcome, the cycler distinction was an important predictor of employment as cyclers were significantly more likely to be employed than short-termers. Spell length is positively associated with earnings after leaving as well. Long-termer status is associated with higher earnings after leaving. Furthermore, average spell length is positively associated with earnings after leaving. The

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Studies of Welfare Populations: Data Collection and Research Issues number of welfare receipt spells were significant predictors of earnings after leaving. The coefficient for each category of number of spells (one spell, two or three spells, or four or more spells) is negative and statistically significant compared to those with no prior spells. The results that long-termers worked more quarters and had higher earnings after leaving than short-termers and cyclers is contrary to expectations that previous dependency levels would be negatively correlated with employment outcomes. A good explanation for these results is not clear. In summary, we conclude that in examining the outcomes of welfare leavers, it is important to characterize the caseload by their past work experience and by their past benefit receipt history because outcomes vary widely across different work experience and benefit receipt backgrounds. Work history background is especially important, we find, as the outcomes vary greatly according to different work experience groups. In terms of past benefit receipt history, the long-term versus short-term distinction is an important one. Distinctions by the number of spells of receipt show mixed results—sometimes this distinction matters, sometimes it does not. REFERENCES Acs, G., and P.Loprest 2001 Initial Synthesis Report of the Findings ofASPE’s “Leavers” Grants. Washington, DC: The Urban Institute. Available: http://aspe.hhs.gov/hsp/leavers99/synthesis01/ Bane, M.J., and D.T.Ellwood 1994 Welfare Realities: From Rhetoric to Reform. Cambridge, MA: Harvard University Press. Blank, R.M., and P.Ruggles 1994 Short-term recidivism among public-assistance recipients. American Economic Review 84(2):49–53. Cancian, M.R., Haveman, T.Kaplan, and B.Wolfe 1999 Post-Exit Earnings and Benefit Receipt Among Those Who Left AFDC in Wisconsin. Institute for Research on Poverty Special Report No. 75. Cancian, M.R., Haveman, T.Kaplan, I.Rothe, and B.Wolfe 2000a Before and After TANF: The Utilization of Noncash Public Benefits by Women Leaving Welfare in Wisconsin. Institute for Research on Poverty. Cancian, M.R., Haveman, D.R.Meyer, and B.Wolfe 2000b Before and After TANF: The Economic Well-Being of Women Leaving Welfare. Institute for Research on Poverty Special Report No. 77. Moffitt, R.A. 1992 Incentive effects of the U.S. welfare system: A review. Journal of Economic Literature 30:1–61. Moffitt, R.A., and J.Roff 2000 The Diversity of Welfare Leavers. Background paper to Policy Brief 00–02. Welfare, Children, and Families: A Three-City Study. Working Paper 00–01. Baltimore, MD: Johns Hopkins University.

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Studies of Welfare Populations: Data Collection and Research Issues Moffitt, R.A., and D.Stevens 2001 Changing Caseloads: Macro Influences and Micro Composition. Unpublished paper presented at conference, Welfare Reform Four Years Later: Progress and Prospects, Federal Reserve Bank of New York, November 17. National Research Council 1999 Evaluating Welfare Reform: A Framework and Review of Current Work. R.A.Moffitt and M.L.Ver Ploeg, eds. Commission on Behavioral and Social Sciences and Education. Committee on National Statistics. Washington, DC: National Academy Press. 2001 Evaluating Welfare Reform in an Era of Transition. Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs, Robert A.Moffitt and Michele Ver Ploeg, Editors. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: National Academy Press. Stevens, David W. 2000 Welfare, Employment and Earnings. Memorandum prepared for the Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs, Committee on National Statistics. University of Baltimore, MD, October.

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Studies of Welfare Populations: Data Collection and Research Issues APPENDIX 13-A DESCRIPTION OF LEAVERS AND OUTCOMES ACROSS DIFFERENT DEFINITIONS OF LEAVERS In this appendix, the definition of a leaver is modified to see how sensitive the composition and outcomes of leavers are to the definition used in the paper. Specifically, the requirement that a leaver must have stopped receiving AFDC for 2 consecutive months to be considered a leaver is made more restrictive. We try two additional definitions; first, that a leaver must have discontinued receiving benefits for 3 consecutive months to be considered a leaver, and second, that a leaver must have discontinued receiving benefits for 6 consecutive months to be considered a leaver. These definitions were operationalized as follows: All cases received AFDC in July 1995. Leavers under the 2-month definition stopped receiving AFDC for 2 consecutive months between August 1995 and July 1996. (June 1996 was the last month a case may have received AFDC and still be considered a leaver if the case did not receive welfare in July and August of 1996.) Leavers under the 3-month definition stopped receiving AFDC for 3 consecutive months between August 1995 and July 1996. (June 1996 was the last month a case may have received AFDC and still be considered a leaver if the case did not receive welfare in July, August, and September of 1996.) Leavers under the 6-month definition stopped receiving AFDC for 6 consecutive months between August 1995 and July 1996. (June 1996 was the last month a case may have received AFDC and still be considered a leaver if the case did not receive welfare in July through December 1996.) Using these definitions, Table 13-A1 shows how the composition of the leaver and stayer groups vary across the three definitions. Table 13-A2 shows how some key outcomes of leavers vary across the different definitions. A brief summary of these two tables is reported here. With a more restrictive definition of a leaver, a smaller portion of the caseload, not surprisingly, qualifies as a leaver. With the 3-month definition, 45.1 percent are leavers compared to 48.1 percent for the 2-month definition. For the 6-month definition, only 41.1 percent are classified as leavers. The characteristics of leavers under the more restrictive definition change only slightly. There are few differences in the characteristics of 2-month leavers and 3-month leavers. The differences are very small across all the demographic and past work and welfare receipt history variables. There are small differences in the demographic composition of 6-month leavers and 2-month leavers. A higher proportion (2.5 percentage points) of 6-month leavers are white than 2-month leavers. Six-month leavers are slightly less likely to come from Milwaukee County than 2-month leavers (38.7 percent compared to 42.4 percent). Six-month leavers are slightly more likely to be short-termers than 2-month leavers (41.2 percent compared to 39.1 percent) and slightly less likely to be long-termers (40.7 percent compared to 42.9 percent). Six-month leavers have, in general, spent a little less time on

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Studies of Welfare Populations: Data Collection and Research Issues TABLE 13-A1 Characteristics of Welfare Leavers Under Different Definitions of “Leaver”   Leaver=2 Months off Leaver=3 Months off Leaver=6 Months off Characteristic Leaver Stayer Leaver Stayer Leaver Stayer Total number 23,207 25,009 21,742 26,474 19,796 28,420 Percent of sample 48.1 51.9 45.1 54.9 41.1 58.9 Race/ethnicity   % black 32.2 48.1 31.7 52.2 29.7 52.2 % Hispanic 6.4 7.1 6.5 7.1 6.4 7.1 % white 61.4 44.8 61.8 40.7 63.9 41.7 Age of case head   % <26 37.1 35.5 37.1 35.6 37.1 35.7 % 27–31 25.2 23.7 25.1 23.8 25.1 23.9 % 32–41 30.9 32.2 30.8 32.2 30.8 32.1 % 42+ 6.8 8.6 6.9 8.4 7.0 8.2 Education of case head   % less than high school 36.1 50.2 35.4 50.0 34.4 49.7 % high school diploma 45.3 37.7 45.6 37.9 46.0 38.2 % some college 18.6 12.1 19.0 12.1 19.6 12.2 County of residence   Milwaukee County 42.4 65.3 40.7 65.4 38.7 65.1 Other urban county 35.6 24.1 36.5 23.9 37.6 24.0 Rural 22.0 10.6 22.7 10.6 23.6 10.8 Percent with child on SSI 8.1 13.1 7.9 13.0 7.6 12.9 Age of youngest child   % 0 to 1 years 27.1 29.2 27.2 28.9 27.4 28.7 % 2 to 4 years 31.4 31.0 31.2 31.2 31.1 31.3 % 5 to 11 years 29.7 29.9 29.6 30.0 29.4 30.0 % 12 or older 11.8 9.9 12.0 9.9 12.1 9.9

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Studies of Welfare Populations: Data Collection and Research Issues   Leaver=2 Months off Leaver=3 Months off Leaver=6 Months off Characteristic Leaver Stayer Leaver Stayer Leaver Stayer Total number 23,207 25,009 21,742 26,474 19,796 28,420 Percent of sample 48.1 51.9 45.1 54.9 41.1 58.9 Welfare History (7/89 to 7/95)   % short-termer 39.1 23.1 40.0 23.2 41.2 23.6 % long-termer 42.9 66.7 41.9 66.3 40.7 65.4 % cycler 18.2 10.2 18.1 10.5 18.1 11.0 Percent of time on welfare (7/89 to 7/95)   0<=x<25% of time 23.5 11.3 24.3 11.3 25.3 11.5 25<=x<50% of time 20.4 14.3 20.7 14.3 21.1 14.5 50<=x<100% of time 43.7 45.4 43.2 45.7 42.3 46.2 Always on 12.4 29.0 11.8 28.6 11.3 27.8 Mean AFDC spell length 7/89 to 7/95 (in months) 27.5 (23.2) 41.2 (25.2) 26.9 (23.0) 40.9 (25.2) 26.3 (22.8) 40.4 (25.2) Percent of quarters with earnings (1/89 to 7/95)   Never worked 14.0 25.1 14.2 24.3 14.4 23.5 0<x<=25% 30.0 37.6 29.6 37.5 29.3 37.2 25<x<=50% 28.0 23.1 27.9 23.4 27.7 23.9 50<x<=75% 17.7 10.6 17.9 10.8 18.2 11.2 More than 75% of quarters 10.3 3.6 10.3 3.9 10.5 4.2

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Studies of Welfare Populations: Data Collection and Research Issues TABLE 13-A2 Outcomes of Welfare Leavers Under Different Definitions of “Leaver”   Leaver=2 Months off Leaver=3 Months off Leaver=6 Months off Characteristic Leaver Stayer Leaver Stayer Leaver Stayer Total number 23,207 25,009 21,742 26,474 19,796 28,420 Percent of sample 48.1 51.9 45.1 54.9 41.1 58.9 Number returned to AFDC 6,753   4,831   2,753   Percent returned to AFDC 29.1 N/A 22.2 N/A 13.9 N/A Average earnings over 4 quarters after leavinga 1,642.1 (1,628.3) 786.1 (1,124.6) 1,677.7 (1,658.5) 804.2 (1,127.8) 1,733.2 (1,686.0) 825.4 (1,136.4) Median earnings over 4 quarters after leavinga 1,311.0 199.0 1,371.9 226.5 1,459.9 259.8 Average income from earnings, AFDC, food stamps, over 4 quarters after leavinga 2,193.6 (1,556.3) 2,301.5 (1,128.1) 1,980.1 (1,636.9) 2,304.3 (1,125.9) 1,956.0 (1,673.3) 2,298.9 (1,129.8) Median income over 4 quarters after leavinga 1,864.0 2,184.6 1,817.1 2,189.1 1,761.6 2,180.3 Number of quarters with earnings after exitb   % did not work 22.8 33.8 22.6 28.9 21.8 28.0 % worked 1–3 quarters 22.4 32.5 24.3 37.2 23.6 37.0 % worked 4+ quarters 54.7 37.7 53.1 33.9 54.6 35.0 NOTE: Standard deviations reported in parentheses. aDoes not include disappearers. bPlease note that one more quarter postexit is observed under the 2- and 3-month definitions than under the 6- month definition. The latest quarter a 2- or 3-month leaver could have exited would be third quarter 1996, whereas the latest quarter a 6-month leaver could have exited is fourth quarter 1996.

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Studies of Welfare Populations: Data Collection and Research Issues welfare prior to the exit period than 2-month leavers. This is as expected, because the group of 6-month leavers is probably composed of cases that are more self-sufficient than the group of 2-month leavers. There are only negligible differences in the work histories of 2-month, 3-month, and 6-month leavers. As expected, 6-month leavers have better outcomes than 3-month and 2-month leavers. Only 13.9 percent of 6-month leavers returned to AFDC, compared to 22.2 percent of 3-month leavers and 29.1 percent of 2-month leavers. The mean and median earnings in the first year after exit of 6-month leavers are higher than those of 3-month and 2-month leavers. The mean and median earnings in the first year after exit for 6-month leavers are $1,733 and $1,460. For 3-month leavers, the mean and median are $1,678 and $1,372. For 2-month leavers, the mean and median are $1,642 and $1,311. Somewhat surprisingly, 6-month leavers did not work much more than 2-month leavers. However, one less quarter after exit is observed for 6-month leavers than for 2-month leavers, so little emphasis is put on this result.

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Studies of Welfare Populations: Data Collection and Research Issues APPENDIX 13-B SUPPLEMENTARY TABLES TABLE 13-B1 Distributions of Long-termer, Short-termer, and Cycler Welfare Histories by Alternative Definitions (percent distribution)   Definition 1a Definition 2b Definition 3c Definition 4d Definition 5e Full sample   Long-termer 76.7 67.8 61.2 55.3 36.9 Short-termer 9.4 18.3 24.9 30.8 49.2 Cycler 13.9 13.9 13.9 13.9 13.9 By leaver status   Stayers   Long-termer 84.2 77.8 72.4 66.7 47.7 Short-termer 5.7 12.1 17.5 23.1 42.1 Cycler 10.2 10.2 10.2 10.2 10.2 Leavers   Long-termer 68.7 57.0 49.1 42.9 25.2 Short-termer 13.3 25.0 32.9 39.1 56.8 Cycler 18.0 18.0 18.0 18.0 18.0 NOTE: All cyclers are those who have had three or more spells regardless of average spell length. aDefinition 1: Average spell length-6 months=short-termer; average spell length>6=long-termer. bDefinition 2: Average spell length-12 months=short-termer; average spell length>12=long-termer. cDefinition 3: Average spell length-18 months=short-termer; average spell length>18=long-termer. dDefinition 4: Average spell length-24 months=short-termer; average spell length>24=long-termer. eDefinition 5: Average spell length-36 months=short-termer; average spell length>36=long-termer.

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Studies of Welfare Populations: Data Collection and Research Issues TABLE 13-B2 Earnings of Leavers in Quarters Without AFDC Receipt (includes disappearers)   1st Quarter After Exit 2nd Quarter After Exit 3rd Quarter After Exit 4th Quarter After Exit 5th Quarter After Exit All leavers   N 19,912 18,803 18,987 19,375 19,815 Mean earnings 1,370 1,656 1,745 1,827 1,860 Median earnings 1,245 1,381 1,290 1,385 1,382 Short-term welfare user   N 8,610 7,766 7,832 7,955 8,113 Mean earnings 1,575 1,647 1,687 1,741 1,787 Median earnings 1,017 1,117 1,127 1,154 1,185 Long-term welfare user   N 8,264 7,729 7,795 7,955 8,139 Mean earnings 1,697 1,762 1,801 1,893 1,927 Median earnings 1,399 1,408 1,421 1,524 1,528 Cycler   N 3,545 3,308 3,360 3,465 3,563 Mean earnings 1,656 1,702 1,752 1,871 1,874 Median earnings 1,381 1,352 1,338 1,553 1,482 TABLE 13-B3 Different Definitions of High-Barrier Cases   Definition 1a Definition 2b Definition 3c Definition 4d Definition 5e Definition 6f Number 1,410 421 1,723 361 2,484 3,292 Percent of total sample 2.9 2.1 3.6 0.7 5.2 6.8 Number of leavers 344 27 443 87 506 1,225 Percent in high-barrier definition who left AFDC 24.4 6.4 25.7 24.1 20.4 37.2 aDefinition 1=Basic high-barrier definition: Did not finish high school, received AFDC for more than 48 months in 72 months prior to exit, had at least one child under the age of 5, worked four or fewer quarters in the preexit period. bDefinition 2=Same as #1 except did not work at all in the preexit period. cDefinition 3=Same as #1 except worked fewer than eight quarters in the preexit period. dDefinition 4=Same as #1 except had at least one child under the age of 1. eDefinition 5=Only qualification is case head received SSI. fDefinition 6=Only qualification is one child in case received SSI.