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Studies of Welfare Populations: Data Collection and Research Issues
Part IV
Welfare Leavers and Welfare Dynamics
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Studies of Welfare Populations: Data Collection and Research Issues
12
Studies of Welfare Leavers: Data, Methods, and Contributions to the Policy Process
Gregory Acs and Pamela Loprest
In August 1996, President Clinton signed the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA), making sweeping changes in the system of cash assistance for poor families and creating the Temporary Assistance for Needy Families (TANF) program. Four years after the passage of PRWORA, policy makers, practitioners, and the public continue to ask the ill-defined question, “Did welfare reform work?” Although cash assistance caseloads have dropped dramatically, from 4.4 million in August 1996 to 2.4 million in December, 1999, declining caseloads are not the sole criterion for a successful reform. Indeed, there is concern about the well-being of families who have left welfare: Are families leaving cash assistance postreform worse off than leavers prereform? Are they worse off than they were while receiving aid? To this end, many states and policy researchers, some with federal funding, have conducted and continue to conduct studies of families who have left the welfare rolls, often referred to as “leaver studies.”
Given the proliferation of these studies, this paper attempts to provide guidance for authors and consumers of leaver studies on how to best use and create these studies. Our goals are threefold:
To review the methods used in leaver studies;
To identify preferred practices for those planning to conduct a leaver study; and
To provide guidance to readers in assessing study results and making comparisons across studies.
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To this end, we have examined 49 studies of welfare leavers, including 13 studies funded by the U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation (ASPE).1 They are listed in Table 12–1. Although we have made every attempt to review the body of work on families leaving welfare, these studies are by no means an exhaustive list of research in this area. Although most are explicitly studies of welfare leavers, some are studies of specific state welfare programs and reforms. We include these latter studies because they provide significant amounts of information on welfare leavers. Several of the studies present ongoing work; their findings are preliminary.
This paper is organized into three sections. First, we discuss the value of leaver studies as well as their limitations. Next we discuss what leaver studies should measure, which addresses the question of how to measure economic well-being and how some studies have done so. Finally, we examine methods for conducting a leaver study. This section describes important issues around defining leavers, positives and limitations of administrative and survey data, and how to assess the quality of data used. We hope that information in all these sections will be valuable to both future authors of leaver studies and those who are using them to understand how former welfare recipients are faring.
THE VALUE OF LEAVER STUDIES
Leaver studies can be valuable tools for monitoring the well-being of families who have been exposed to TANF and have left the rolls. Indeed, they can tell policy makers if families who have left welfare are facing problems that can be addressed by policy changes regardless of whether these problems arose as the result of past reforms. Furthermore, although leaver studies may provide only limited information about welfare reform in 1996, the ongoing capacity built by states and the research community will provide a baseline for evaluating future reforms.
Policy researchers and some policy makers also may wish to compare findings across leaver studies; after all, it is tempting to compare the status of leavers across states taking different approaches to welfare reform in order to assess the relative effectiveness of various policies. However, any such comparisons should be made with great caution for two main reasons. First, as we discuss in detail, leaver studies can have important methodological differences. These differences
1
Throughout this report, the term “welfare leaver” refers to someone exiting the Aid to Families with Dependent Children (AFDC) or TANF programs. Note that the 13 ASPE studies cover only 11 study locations because 2 of the locations report findings from different data sources in separate reports.
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include the time period studied, the type of data used, the exact wording and ordering of survey questions, and even the definition of a leaver. Indeed, some leaver studies focus on families leaving welfare in the early to mid-1990s while other report findings from the late 1990s. Findings may differ or differences may be obscured simply because the studies analyze different historical periods. Similarly, some studies focus on the well-being of leavers shortly after they exit welfare while others examine their status several years later.
Second, differences between states, such as in economic opportunities or even the characteristics of welfare recipients themselves, may be even more important than policy differences in accounting for differences in the status of welfare leavers. It would not be surprising to find that leavers in areas where jobs are plentiful fare better than leavers in areas with slack economies regardless of the state’s policy choices. Similarly, differences in the characteristics of state caseloads can affect the status of families leaving welfare. For example, if a state’s welfare recipients are more disadvantaged than those in another state, then its leavers may be more likely to face difficulties after exiting. Finally, if a state pursues policies aimed at encouraging work among current welfare recipients rather than encouraging exits from welfare—for example, through generous earned-income disregards—then leaver studies could miss an important impact of reform: More families are mixing welfare and work. Such families would be ignored in leaver studies because they are still on welfare.
Nevertheless, as long as one keeps in mind these limitations in leaver studies, a well-done leaver study can help policy makers understand the process families go through as they leave welfare and the factors that help them make a successful and long-term transition. Furthermore, leaver studies can help identify challenges faced by leavers and the direction for subsequent policy interventions.
WHAT LEAVER STUDIES SHOULD MEASURE
The primary role of leaver studies is to assess and track the well-being of welfare leavers; associating changes in the well-being of welfare leavers to changes in welfare policy plays a secondary role. Thus, an assessment of leaver studies requires us to address the following questions:
What do we mean by well-being?
How do we measure well-being?
When assessing a family’s overall well-being, policy makers and researchers generally consider five areas: (1) income security, (2) employment, (3) health, (4) living arrangements, and (5) quality of life or hardships. Although one can be “rich and miserable” or “poor and happy,” a family’s financial resources, especially a lack of resources, are an important indicator of well-being. Thus, leaver
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TABLE 12–1 List of Leaver Studies by State
State
Title
General Leaver Studies
Arizona-1*
Arizona Cash Assistance Exit Study: First Quarter 1998 Cohort-Final Report
Arizona-2*
Arizona Cash Assistance Exit Study: Cases Exiting Fourth Quarter 1996
California-Los Angeles County*
Employment and Earnings of Single-Parent AFDC Leavers: Quarter 3 1996 Leavers: PRELIMINARY REPORT
California-San Mateo County*
Examining Circumstances of Individuals and Families who Leave TANF: Assessing the Validity of Administrative Data
District of Columbia*
The Status of TANF Leavers in the District of Columbia — Final Report
Florida
The Family Transition Program: Implementation and Three-Year Impacts of Florida’s Initial Time-Limited Welfare Program
Georgia-1
Transition from Welfare to Work: Findings for the First Year of Temporary Assistance for Needy Families
Georgia-2*
Outcomes for Single-Parent Leavers by Cohort Quarter for Jan-Mar 99: Quarterly Progress Report: PRELIMINARY REPORT
Idaho-1
Project Self-Reliance: TAFI Participant Closure Study (II)
Idaho-2
Differences Between a Surveyed Closed TAFI Case Population and Its “Unreachable” Subpopulation
Illinois-1
How are TANF Leavers Faring? Early Results from the Illinois TANF Closed Case Project
Illinois-2*
Illinois Study of Former TANF Clients: Interim Report
Indiana
The Indiana Welfare Reform Evaluation: Who is On and Who is Off? Comparing Characteristics and Outcomes for Current and Former TANF Recipients
Kentucky
From Welfare to Work: Welfare Reform in Kentucky
Maryland-1
Life After Welfare: An Interim Report
Maryland-2 Life After Welfare: Second Interim Report
Maryland-3
Life After Welfare Reform: Third Interim Report
Massachusetts
How are They Doing? A Longitudinal Study Tracking Households Leaving Welfare Under Massachusetts Reform
Mississippi
Tracking of TANF Clients: First Report of a Longitudinal Study
Missouri-1*
Preliminary Outcomes for 1996 Fourth Quarter AFDC Leavers: Revised Interim Report
Missouri-2*
Chapters 1–4: MRI Project No. 1033–1
Montana
Montana’s Welfare Reform Project: Families Achieving Independence in Montana
New Mexico
Survey of the New Mexico Case Closed AFDC Recipients
New York-1
Leaving Welfare: Findings from a Survey of Former New York City Welfare Recipients
New York-2*
After Welfare: A Study of Work and Benefit in New York State After Case Closing
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Author(s)
Date
Data Used
Karen L.Westra and John Routley
Jan-00
Survey/ Administrative
Karen L.Westra and John Routley
Jul-99
Administrative
Jan-99
Administrative
Anne Moses and David Mancuso
May-99
Administrative
Gregory Acs and Pamela Loprest
Oct-99
Survey/ Administrative
Dan Bloom, Mary Farell, James J.Kemple, and Nandita Verma
Apr-99
Administrative
Georgia Department of Human Resources
Jan-98
Administrative
E.Michael Foster
Administrative
Idaho Department of Health and Welfare
Spring 1998
Survey
Idaho Department of Health and Welfare
Winter 1998
Survey
Steve Anderson, George Julnes, Anthony Halter, David Gruenenfelder, and Linda Brumleve
Aug-99
Survey
George Julnes and Anthony Halter
Mar 00
Survey/ Administrative
David J.Fein
Sep-97
Survey
Scott Cummings and John P.Nelson
Jan-98
Survey
University of Maryland- School of Social Work
Sep-97
Administrative
University of Maryland- School of Social Work
Mar-98
Administrative
University of Maryland- School of Social Work
Mar-98
Administrative
Massachusetts Department of Transitional Assistance
Apr-99
Survey
Jesse D.Beeler, Bill M.Brister, Sharon Chambry, and Anne L.McDonald
Jan-99
Survey/ Administrative
Sharon Ryan
Sep-99
Administrative
Midwest Research Institute
Jun-00
Survey
Montana Department of Public Health and Human Services
Feb-98
Survey
University of New Mexico-Bureau of Business and Economic Research
Sep-97
Survey
Andrew S.Bush, Swati Desai, and Lawrence M.Mead
Sep-98
Survey
Rockefeller Institute
Dec-99
Administrative
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State
Title
North Carolina-1
Evaluation of the North Carolina Work First Program: Initial Analysis of Administrative Data
North Carolina-2
Evaluation of the North Carolina Work First Program: Status of Families Leaving Work First After Reaching the 24-Month Time Limit
Ohio-1 Cuyahoga County
Work After Welfare: Employment in the 1996 Exit Cohort, Cuyahoga County
Ohio-2 Cuyahoga County*
Employment and Return to Public Assistance Among Single, Female Headed Families Leaving AFDC in the Third Quarter, 1996, Cuyahoga County, Ohio
Oklahoma
Family Health and Well-Being In Oklahoma: An Exploratory Analysis of TANF Cases Closed and Denied October 1996-November 1997
Pennsylvania
TANF Closed-Case Telephone Survey
South Carolina-1
Former Clients of South Carolina’s New Welfare Program: Trends and Issues in Surveys to Date
South Carolina-2
Survey of Former Family Independence Program Clients: Cases Closed During April Through June 1997
South Carolina-3
Survey of Former Family Independence Program Clients: Cases Closed During July Through September 1997
Tennessee
Summary of Surveys of Welfare Recipients Employed or Sanctioned for Noncompliance
Texas
Texas Families in Transition: The Impacts of Welfare Reform Changes in Texas: Early Findings
Virginia
Fairfax Welfare Reform Evaluation Study
Washington-1
Conversations with 65 Families
Washington-2
Washington’s TANF Single-Parent Families Shortly After Welfare
Washington-3
Washington’s TANF Single-Parent Families After Welfare
Washington-4*
A Study of Washington State TANF Leavers and TANF Recipients
Washington-5*
A Study of Washington State TANF Leavers and TANF Recipients
Wisconsin-1
Post-Exit Earnings and Benefit Receipt Among Those Who Left AFDC in Wisconsin
Wisconsin-2
Employment and Earnings of Milwaukee County Single Parent AFDC Families: Establishing Benchmarks for Measuring Employment Outcomes
Wisconsin-3
Survey of Those Leaving AFDC or W-2: January to March 1998 Preliminary Report
Wyoming
A Survey of Power Recipients
Sanctioned Leavers
Iowa
Iowa’s Limited Benefit Plan: Summary Report
Michigan
A Study of AFDC Case Closures Due to JOBS Sanctions: April 1996 AFDC Case Closures
New Jersey
Survey of WFNJ/TANF Case Closed to Sanction
*Assistant Secretary for Planning and Evaluation (ASPE) funded study.
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Author(s)
Date
Data Used
Maximus
May-99
Administrative
Maximus
May-99
Survey
Claudia Coulton, Marilyn Su, Neil Bania, and Edward Wang
Administrative
Claudia Coulton and Nandita Verma
May-99
Administrative
Lynda Williams
Sep-98
Survey
Pennsylvania Bureau of Program Evaluation
Feb-98
Survey
Donald M.Klos
Survey
South Carolina Department of Social Services
12-Jun-98
Survey
South Carolina Department of Social Services
9-Oct-98
Survey
Center for Manpower Studies
Mar-98
Survey
Texas Department of Human Services
Dec-98
Survey
Carole Kuhns, Danielle Hollar, and Renee Loeffler
Survey
City of Seattle Department of Housing and Human Services
Mar-98
Survey
Washington Department of Social and Health Services
Jul-98
Survey
Washington Department of Social and Health Services
Jan-99
Survey
Jay Ahn
Feb-00
Administrative
Debra Fogerty and Shon Kraley
Feb-00
Survey
Marcia Cancian, Robert Haveman, Thomas Kaplan, and Barbara Wolfe
Oct-98
Administrative
University of Wisconsin- Milwaukee, Employment and Training Institute
Administrative
Institute for Research on Poverty-University of Wisconsin
13-Jan-99
Survey
Western Management Services
May-98
Survey
Thomas M.Fraker
May-97
Survey
Laura Colville, Gerry Moore, Laura Smith, and Steve Smucker
May-97
Survey
New Jersey Division of Family Development, Bureau of Quality Control
Mar-98
Survey
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studies should collect and present information on a family’s income.2 In addition to earned income, the studies should consider cash from friends and family, including child support payments, as well as public assistance in the form of cash and near-cash aid such as food stamps.
Because a central goal of PRWORA is to move families from welfare to work, it is also important to consider their employment situation. Employment should be measured at a point in time as well as over a period of time. For example, there can be a great deal of difference in how many leavers are working in a specific month compared to how many have worked at any point over the past year. Having both sets of data allows for broader understanding of employment among leavers.
Leaver studies also should collect data on the number of hours that leavers work and how much their jobs pay. Additional information about jobs is also beneficial, including whether their jobs have regular hours or schedules, whether adult leavers hold multiple jobs, what noncash benefits they receive, what the costs of working are (transportation, child care, job-related expenses such as work clothes or uniforms), and what skills are required for their jobs.
Health status and access to health insurance and health care also are important indicators of well-being. In addition to ascertaining the health status of adult leavers and their children, it is also important to ask whether the members of a leaver’s family have health insurance coverage and what the sources of that coverage are (public programs such as Medicaid, employer-sponsored health plans, or other sources). Although insurance is generally a good indicator of access to health care, it is also useful to directly determine if a leaver can obtain medical attention when needed.
One goal of welfare reform is to foster stable families, but the strain of balancing a job and child care may be profound on low-income single mothers. Thus, it is also important to understand if leavers’ families are breaking up, with children being sent off to live with friends or relatives. Similarly, leavers may struggle to maintain independent households, so a leaver study also should determine whether leavers are “crowding in” with friends or relatives. Alternatively, leavers may be forming stable two-adult households either through marriage or cohabitation.
It is also important to assess if leavers are facing hardships that cannot be captured by examining income alone. Thus, leaver studies also should consider whether leavers must struggle to meet their families’ nutritional needs, pay their bills, or live in substandard housing. In addition, policy makers are concerned
2
Collecting reliable income information can be challenging. Generally the only way to obtain information on income is to ask people a detailed series of questions which was done for the March supplements to the Current Population Survey, and few leaver studies do this. In fact, only four of the studies we examine provide information on total family income (Arizona, Illinois, Missouri, and Washington).
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about the impact of welfare reform on children. To assess child well-being, leaver studies could gather information about children’s school performance and behavioral problems, for example. Some studies also have gathered information on leaver families’ involvement in the child welfare system.
Furthermore, leaver studies can examine how a leaver’s status changes over time. This information helps to answer the question of whether a leaver’s situation is improving during the transition off welfare and whether he or she is achieving self-sufficiency. Specifically, studies should try to learn whether leavers experience earnings growth over time and whether their use of public program benefits wanes over time.
Finally, it is also useful for leaver studies to fit their findings into a broader context. For example, even if leavers report high incidences of hardships, it is important to be able to know whether they are worse off since leaving welfare than before leaving welfare. Another approach is to compare leavers’ outcomes to other groups, such as current welfare recipients or other low-income families who never received welfare, to better interpret how well they are faring.
Taken together, these five areas—income security, employment, health, living arrangements, and quality of life or hardships—can describe the well-being of TANF leavers. In addition, states should think about how to tailor their leaver studies to garner information that is of specific interest to them.
LEAVER STUDY METHODS
Defining Welfare Leavers
The first issue all leaver studies must address is, “Who is a leaver?” A leaver clearly is someone who was receiving welfare and then stopped receiving welfare, but precisely how to define this term can vary.
It is not uncommon for a welfare case to be closed for administrative reasons—for example, the adult in the unit failed to appear for a recertification meeting. Sometimes cases closed for this reason reopen within a matter of weeks. These “leavers” were neither trying to exit welfare nor were they “forced off by a formal sanction. To avoid including these “administrative closures,” studies can require that a case remains closed for a certain period of time before the case is considered to be a leaver. Many studies follow a definition that requires closure for 2 months before inclusion in the sample of leavers. Others require only 1 month. One might expect that studies using a 1-month definition would have higher returns to welfare and lower employment than those using 2-month definitions, all else equal. Interestingly, we find no clear pattern across the two definitions, (as shown in Table 12–2). This could be because all else is not equal, and there are many other differences across these studies that could affect outcomes. Only Arizona-1 actually provides outcome numbers for both definitions in the same data. Although this is only one study, it does show that first-quarter returns
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TABLE 12–5 Leaver Studies Using Surveys
State/Study
Exit Cohort
Timing of Survey Postexit
Sample Size
Response Rate (%)
Type of Survey
Respondents Paid
Leaver Studies:
Arizona
1Q98
12–18 months
821
72
Phone/in person
Yes
District of Columbia
4Q98
1 year
277
61
Phone/in person
Yes
Idaho-1
3rd and 4th Q97
6 months
477
17
Mail
No
Idaho-2
3rd and 4th Q97
10 months
53
47
Mail
No
Illinois-1
December 1997 or June 1998
4–11 months
427
31
Phone
Yes
Illinois-2
December 1998
6–8 months
514
51
Phone/in person
Yes
Kentucky
January-November 1997
1–11 months
560
17
Phone
No
Massachusetts
1st and 2nd Q97
3 monthsa
341
53
In person
Yes
Michigan
July 1998
12 months
126
85
In person
No
Mississippi
1Q98
6 months
405
87
Phone/mail/in person
No
Missouri-2
4Q98
30 months
878
75
Phone/in person
Yes
Montana
March 1996–September 1997
1–18 months
208
c
Phone
No
North Carolina-2
July 1998
5 months
315
77
Phone
Yes
New Jersey
February-October 1998
n.a.
453
45
In person
No
New Mexico
July 1996–June 1997
n.a.
88
12
Mail
No
New York-1
November 1997
6 months
126
22
Phone
No
Oklahoma
October 1996–November 1997
2–20 months
292
53
Phone
Yes
Pennsylvania
March 1997–January 1998
1–11 months
169
47
Phone
No
South Carolina-1
n.a.
n.a.
2,002b
77
Phone/in person
No
South Carolina-2
2Q97
1 year
391
76
Phone/in person
No
South Carolina-3
3Q97
1 year
403
76
Phone/in person
No
Tennessee
n.a.
n.a.
2,500
51
Phone
No
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Texas
November 1997
6 months
1,396
42
Phone/mail
No
Virginia
n.a.
n.a.
171
46
Phone
No
Washington-1
n.a.
65
c
In person
No
Washington-2
December 1997–March 1998
12–18 months
560
31
Phone
No
Washington-5
October 1998
6 months
987
72
Phone/in person
Yes
Wisconsin-3
1Q98
6–9 months
375
69
d
No
Wyoming
n.a.
n.a.
200
32
Phone
No
Caseload Studies:e
Indiana
n.a.
n.a.
847
71
Phone/in person
No
Iowa
n.a.
n.a.
162
85
In person
No
Washington-3
n.a.
n.a.
592
52
Phone
No
aThis study surveyed respondents every 3 months for a year. The study includes the results of the interviews at months 3 and 12.
bThis study is an analysis of five surveys performed in South Carolina. These five surveys have a total sample of 2002 cases. The overall response rate of these five surveys was 77 percent.
cResponse rate not reported.
dSurvey mode not described.
eThese studies took a random sample of people who began receiving benefits when Temporary Assistance for Needy Families (TANF) was implemented in the state. At the time of the survey, these recipients may or may not have been receiving TANF benefits.
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leavers without telephones are included in the study. Three studies (two from Idaho and one from New Mexico) used mail surveys; this method is not recommended because the common problems with all surveys (described as follows) are magnified in mail surveys.
Overall, the strength of survey data is the breadth of information they contain. However, survey data have their own shortcomings. First, surveys rely on respondents to answer questions accurately and truthfully.7 Second, survey data are collected for only a sample of welfare leavers; therefore, any assessment of the well-being of leavers based on surveys is subject to sampling error. Finally, and potentially most seriously, even if the sample of leavers accurately reflects all leavers, not all sampled families will respond to the survey. That is, a researcher only will be able to contact and interview a subset of the original sample. If the leavers who respond to the survey are very different from the nonrespondents, then the survey data will suffer from nonresponse bias and not accurately represent the status of leavers. The best way to reduce nonresponse bias is to have a high response rate. A large literature is available on increasing response rates (see Cantor and Cunningham, this volume: Chapter 2; Singer and Kulka, this volume: Chapter 4, and Weiss and Bailar, this volume: Chapter 3). (See Table 12–5 for response rates in the leaver studies examined here.)
Getting the Most Out of a Leaver Study
Both administrative and survey data have their shortcomings, but combining data from these two sources provides a rich description of the overall well-being of leavers. As Table 12–1 shows, eight studies use both survey and administrative data to study the same cohort of leavers.8 In the following sections, we describe steps researchers can take to examine the accuracy of employment information from administrative data and assess the accuracy and representativeness of survey data. None of these techniques can completely address the potential shortcomings in the data, but if they are employed, they can help readers weigh the findings reported in any given leaver study.
Do UI Records Understate Employment by Welfare Leavers?
With the exception of Missouri, all leaver studies using UI wage records to examine employment only link into a single state’s UI system. Consequently, leavers that move out of state or work outside of their home state will not appear
7
For a discussion of measurement in error in surveys of low-income populations, see Mathiowetz et al. (this volume: Chapter 6).
8
Four states (Arizona, DC, Illinois, and Mississippi) present findings from both survey and administrative data in the same report; another four states (Missouri, North Carolina, Washington, and Wisconsin) present their findings from these two data sources in separate reports.
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TABLE 12–6 Employment of Welfare Leavers: Comparison of Administrative and Survey Data
Employment Rate (%)
State/Study
Exit Cohort
Timing of Survey
Survey Data
Administrative Data*
Arizona
1Q98
12–18 months
57.0
50.0
District of Columbia
4Q98
12 months
60.3
n.a.
Illinois
December 1998
6–8 months
63.2
55.0
Missouri
4Q98
30 months
65.0
58.0
Washington
October 1998
6–8 months
59.0
57.0
*Based on employment rate from the fourth postexit quarter.
SOURCE: See Appendix B for a complete listing of the leavers studies referenced.
in the data.9 Furthermore, not all jobs are covered by state UI systems so there will be no record of work for a leaver who works in an uncovered job. If a leaver study uses both administrative and survey data and has asked surveyed leavers about their employment status, one can assess the extent of this potential underreporting.
Five jurisdictions use surveys of TANF leavers to ask the leavers themselves about their current employment status. The responses of leavers generally refer to employment about 6 months to a year after exit. Table 12–6 compares these self-reported employment rates with fourth quarter post exit employment rates computed from administrative data. The surveys consistently find higher employment rates than those reported in UI wage records; in general they are about 7 percentage points higher. The Illinois survey presents some instructive information. In its administrative records, Illinois finds that 30 percent of leavers never worked over the first four postexit quarters. In its survey, Illinois finds that only 15 percent of leavers say they have never worked since exiting TANF.
Further, a supplemental study by Wisconsin’s Department of Workforce Development (1998) examines how much employment is missed using UI wage records by comparing administrative and survey data on families leaving welfare in the first quarter of 1998. This study finds that out of the 375 surveyed leavers, 85 percent reported employment information consistent with administrative
9
It may be possible to obtain employment and earnings information on leavers who work “out of state” by matching program data to UI data from neighboring states, but this may be too costly and time consuming for the expected benefit. Alternatively, several researchers and states have contemplated using data on the National Directory of New Hires maintained by the Office of Child Support Enforcement (OCSE) of the U.S. Department of Health and Human Services. This database contains information on the employment and earnings of all newly hired workers in the United States. To this date, however, OCSE has not allowed anyone to use these data for research purposes.
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records. Among the leavers who reported that they had worked in the survey but did not show up in Wisconsin’s UI data, 38 percent claimed to be working in temporary jobs that may not be reported to the UI system. Another 32 percent worked as housekeepers, childcare workers, farmhands, or in other jobs in which they may be considered self-employed and/or for which employers may not file UI reports. Ten percent explicitly stated they were self-employed and 17 percent had left the state.
Are Respondents Answering Survey Questions Accurately?
Survey data are based on self-reported information from respondents. If respondents intentionally or unwittingly provide inaccurate information, the survey findings may not reflect the well-being of leavers. When surveys gather information that duplicates information available through administrative sources, it is possible to compare a respondent’s answer to the administrative report to assess accuracy. For example, a survey may ask, “In the year since you exited welfare, have you ever received food stamps?” Because this information is reported in administrative data, it is possible to see if survey respondents are providing reliable information. In general, studies that compare survey and administrative findings on common areas find fairly close agreement, as shown in Table 12–7 . Finding similar results using survey and administrative data does not guarantee that all other survey responses are accurate; however, if the findings were different, it would undermine the confidence one would have in the survey results.
Of course, the real value of surveys is their ability to obtain information unavailable in administrative records, and for such items it is not possible to obtain external validation. This can be particularly challenging when trying to determine whether a leaver is better off since exit than before. For example, a welfare leaver interviewed 9 months after exit may not recall the trouble he or she had paying the rent prior to leaving welfare. One way to examine the importance of recall problems is to supplement a leaver study with a survey of families still on welfare. The Washington state study is the only study we review that conducts a “stayer” analysis. Surprisingly, while other surveys (Arizona and Illinois) that ask about food security find that leavers generally report the same or lower levels of insecurity prior to exit than after exiting, Washington finds that current recipients actually report higher rates of food insecurity than leavers.
How Representative Are Survey Respondents of Leavers in General?
As we discussed, nonresponse bias is a potentially significant problem for surveys of welfare leavers. Indeed, if the leavers who did not respond to the survey (either because they could not be located or because they refused to participate) are appreciably different from respondents, then survey data will
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TABLE 12–7 Post-Temporary Assistance for Needy Families (TANF) Exit Program Participation: Comparing Administrative and Survey Data Findings
Point in Time
Since Exit
State
Administrative (%)
Survey (%)
Administrative (%)
Survey (%)
Welfare
District of Columbiaa
18.8
18.8
21.1
24.6
Illinois-2c
17.5
13.7
28.9
18.5
Missouri-1b
20.5
14.0
44.0
31.0
Washington-4c
16.0
19.0
23.4
n.a.
Food Stamps
District of Columbiaa
37.9
40.8
n.a.
55.2
Illinois-2c
34.2
32.9
56.0
44.1
Missouri-1b
40.1
47.0
81.0
83.0
Washington-4c
40.0
n.a.
n.a.
50.0
Medicaidd
Arizona-1a
36.9
39.0
71.7
n.a.
District of Columbiaa
47.5
53.8
n.a.
n.a.
Illinois-2c
47.4
46.9
68.8
n.a.
Missouri-1b
n.a.
33.0
n.a.
n.a
Washington-4c
39.6
53.3
n.a.
n.a.
aThe periods of follow-up for Arizona and the District of Columbia’s survey data are 12–18 months and 12 months, respectively. The administrative data are reported for the fourth quarter after exit.
bThe period of follow-up for Missouri’s survey is 30 months. However, only 12 months of administrative data are available. The administrative data reported are for the fourth quarter after exit.
cThe period of follow-up for Illinois’s and Washington’s survey data is 6–8 months. The administrative data reported are for the third quarter after exit.
dData reported for adults.
paint a misleading picture of the well-being of TANF leavers. In general, the higher the response rate to a survey, the less concerned one is about its representativeness. (Table 12–4 shows response rates.)
Differences in response rates can affect outcomes for welfare leavers as measured by surveys. We report these results separately for surveys with high, moderate, and low response rates. In general, we would expect respondents to lead more stable lives than nonrespondents and to be more eager to share good news with survey takers. To the extent that nonresponse bias is a problem in these surveys, we would expect surveys with lower response rates to generally show that welfare leavers are better off. Note, however, that even in a survey with a 75-percent response rate, the nonresponse bias may be profound.
Table 12–8 shows employment and earnings information from survey data by response rate. Out of the nine surveys with high response rates, seven report
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TABLE 12–8 Employment Earnings of Employed Welfare Leavers: Survey Data Findings by Survey Response Rate
State
Hours Worked
Earnings
Panel A: Response Rate Greater Than 70%
Arizona-1
#
Average wage: $7.52
Indiana
61% worked 35 or more hours a week
40.7% earned $7 or more an hour
Michigan
#
53.2% earned $400 or more a month
Mississippi
Average number of hours worked: 35
Average wage: $5.77
Missouri-2
Average number of hours worked: 39
##
North Carolina
37.9% worked 40 or more hours
Median monthly salary: $849.76
South Carolina-2
Average number of hours worked: 36
Average wage: $6.44
South Carolina-3
Average number of hours worked: 36
Average wage: $6.45
Washington-5
Average number of hours worked: 36
Average wage: $7.70
Panel B: Response Rate Between 50% and 70%
District of Columbia
Average number of hours worked: 36
Average wage: $8.74
Illinois-2
Median number of hours worked: 37
Median wage: $7.42
Massachusetts
#
63.3% income $250 or more a weeka
Oklahoma
Average number of hours worked: 34
Average wage: $6.15
Tennessee
35% worked full time
Average wage: $5.67
Washington-3
Average number of hours worked: 36
Average wage: $8.09
Wisconsin-3
57% worked 40 or more hours a week
Average wage: $7.42
Panel C: Response Rate Less Than 50%
Idaho-1
40% worked 30 or more hours a week
21% earned $7 or more an hour
Illinois-1
Average number of hours worked: 35.8
Median wage: $7.11
Kentucky
73.5% worked 35 or more hours
40.9% earned $7 an hour or more
Montana
47% worked 21 or more hours
##
New Mexico
74.6% worked 30 or more hours
29% earned $7 or more an hour
New York-1
40% worked 35 or more hours
##
Pennsylvania
62% worked 30 or more hours
59% earned $6.50 or more an hour
Texas
Average numbers of hours worked: 34
Average wage: $6.28
Virginia
#
Median monthly salary: $1,160
Washington-2
Average hours worked: 34
Average wage: $8.42
Wyoming
#
83% earned $7.50 or more an hour
aAverage weekly earning for full-time work is $305.
# Hours worked not reported.
## Earnings not reported.
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information on hours worked, with five reporting the average number of hours worked by employed leavers. These five studies find that leavers work an average of 35 to 39 hours per week. Five studies report average hourly earnings: They range from $5.77 to $7.70. Among the studies with response rates of between 50 and 70 percent, four report average or median hours worked per week, and they show that employed leavers work between 34 and 37 hours per week. Among low-response-rate studies, three report average hours, and they, too, find an average of about 35 hours per week. The range of hourly wage rates reported in low-and moderate-response-rate studies runs from a low of $5.67 in Tennessee to a high of $8.74 in the District of Columbia.
Researches use two relatively straightforward techniques to assess the extent of nonresponse bias in surveys of welfare leavers. The first technique involves using administrative data on the entire survey sample and comparing respondents to nonrespondents. The second involves using the survey data to compare the characteristics of easily located and interviewed leavers with those of leavers that were “hard to find.”10
First, consider how administrative data can help uncover potentially important non-response bias in survey data. Three studies, the District of Columbia (DC), Missouri, and South Carolina, have compared administrative information on survey respondents and nonrespondents to see if nonrespondents appear to be very different from respondents. Missouri (Dunton, 1999) finds that nonrespondents tend to have less education and lower quarterly earnings than respondents. South Carolina (Edelhoch and Martin, 1999) compares the reasons for TANF exit for survey respondents and nonrespondents and finds that respondents are significantly less likely to have their cases closed because of a sanction and significantly more likely to have their cases closed because of earned income than nonrespondents. These comparisons suggest that findings from these studies may present too sunny a picture of the status of welfare leavers. On the other hand, DC’s leaver study finds that nonrespondents are slightly younger, have younger children, and have had shorter spells of receipt than nonrespondents. Overall, however, DC finds that respondents are fairly similar to nonrespondents.
Another technique to gauge the importance and potential biases of nonresponse involves examining differences among respondents, comparing survey responses from respondents who were easy to contact and quickly agreed to be surveyed to the responses of hard-to-contact and reluctant responders.11 This
10
One also can attempt to do an ex post facto study of nonrespondents. This is rather costly and involves painstaking efforts to locate nonrespondents to the initial survey and interviewing them. None of the studies reviewed here attempt this; however, Mathematica Policy Research is conducting such a nonrespondent study in Iowa. The organization’s goal is to locate and interview 15 nonrespondents.
11
Groves and Wissoker (1999) use a similar approach for examining nonresponse bias in the National Survey of America’s Families.
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approach is based on the idea that “hard to interview” cases fall on a continuum between the “easy to interview” and nonrespondents. If the hard to interview are very different from the easy to interview in ways that are important to the study, it is likely that nonrespondents are even more different, and nonresponse bias is likely to be a big problem.
Only DC explicitly uses this technique. DC finds that hard-to-interview cases are neither clearly better nor worse off than the easy-to-interview cases; rather, their experiences are more diverse. For example, easy-to-interview cases are slightly more likely to work than hard-to-interview cases but among those who work, the hard-to-interview have higher hourly wages. In a supplementary study, Missouri (1999) compares employment and earnings among survey respondents in the Kansas City area based on the timing of response. Missouri finds that respondents among the final third of completed interviews are slightly less likely to work than respondents in the first two-thirds of completed interviews (88.5 versus 91.4 percent). The harder to interview also have lower monthly incomes ($935 versus $1,094).
Although we have described several techniques researchers can use to assess the potential for nonresponse bias in leaver studies, the best way to guard against nonresponse bias is to have a high response rate. Even though these techniques cannot rule out the possibility of significant nonresponse bias, they do provide readers with a sense of the potential size and direction of the bias. Interestingly, however, we find that surveys with moderate response rates (50 to 70 percent) report findings that are fairly similar to those with higher response rates (more than 70 percent).
CONCLUSION
Leaver studies are useful tools for monitoring the well-being of families that have been exposed to TANF and have left the rolls. They can help policy makers identify the problems that families who have left welfare are facing, and the ongoing capacity built by states and the research community will provide a baseline for formulating and evaluating future reforms.
This paper examines the methodologies used in a large set of leaver studies, identifies preferred practices for conducting such studies, and discusses the implications of research methods for the interpretations of the findings reported in these studies.
Leaver studies rely on two types of data: (1) linked administrative records from welfare programs, other low-income assistance programs, and state unemployment insurance systems, and (2) survey data. The quality of the information garnered from administrative data depends on how well the data systems are linked as well as the coverage of these systems. In general, leaver studies do not describe the methods they used to link data from multiple sources. Furthermore, although the employment of former welfare recipients is an important outcome,
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this information comes from state UI records. Even with a perfect match to welfare program data, state UI records will understate the level of employment of welfare leavers because a nontrivial portion of jobs are not reported to the state’s UI system (jobs out of state, self-employment, as well as some domestic and agricultural work).
Surveys of leavers provide a broader set of information than administrative data on the well-being of families that have left welfare. However, the quality of survey data depend on the accuracy of the information garnered from respondents and the representativeness of the completed survey sample. Indeed, it is reasonable to expect that leavers who can be located and who choose to respond to a survey may be better off than other leavers.
Leaver studies that examine the same cohort of leavers using both administrative and survey data present a more complete picture of the status of leavers than studies that rely on only a single source. Although both sources have their limitations, combining information from the two sources can help researchers and policy makers to better assess the findings. For example, it is useful to obtain information on employment and program participation in surveys that is also available in administrative data. The survey data can be used to assess the extent of underreporting of employment in UI wage records, while the administrative data on program participation can be used to assess if respondents are responding accurately to survey questions.
In addition, nonresponse bias is potentially an important problem in leaver studies. By using administrative data available for both survey respondents and nonrespondents, researchers can gauge the extent to which respondents differ from leavers in general. In addition, one can also obtain a sense of the extent of nonresponse bias by comparing the responses of easily interviewed cases with those of cases that were hard to locate or initially refused to respond.
Finally, states can build on these studies by repeating them for new cohorts of leavers or by following existing cohorts over time. Studying new cohorts allows comparison of whether the status of leavers is changing as policies become more fully implemented and time limits are reached. Reinterviewing or analyzing administrative data for the same cohort of leavers as time passes provides information on whether employment is becoming more stable, earnings are rising, and economic hardship is decreasing—in short, whether the well-being of leavers is improving over time.
REFERENCES
Dunton, Nancy 1999 Non-Response Analysis: Missouri Leavers Survey. Unpublished tables and presentation at the Fall 1999 Outcomes Grantee Meeting of the U.S. Department of Health and Human Services Office of the Assistant Secretary for Planning and Evaluation, Washington, DC, October 25–26.
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Edelhoch, Marilyn, and Linda Martin 1999 Analysis of Response Rates and Non-Response Bias in Surveys. Unpublished tables and presentation at the Fall 1999 Welfare Outcomes Grantee Meeting of the U.S. Department of Health and Human Services Office of the Assistant Secretary for Planning and Evaluation, Washington, DC, October 25–26.
Groves, Robert, and Douglas Wissoker 1999 No. 7: Early Nonresponse Studies of the 1997 National Survey of America’s Families. National Survey of America’s Families Methodology Working Paper. Washington, DC: The Urban Institute.
Wisconsin Department of Workforce Development 1998 Differences between AFDC and W-W Leavers Survey Data for January–March 1998 and Wisconsinís UI Wage Records for 1998. Department of Workforce Development MEP Folio Brief 09–99, October 19.
Representative terms from entire chapter:
administrative data
