. "9 Measuring Employment and Income for Low-Income Populations with Administrative and Survey Data." Studies of Welfare Populations: Data Collection and Research Issues. Washington, DC: The National Academies Press, 2001.
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Studies of Welfare Populations: Data Collection and Research Issues
gather data for targeted groups, such as current or former welfare recipients, and at the state or local level.1 Although survey data continue to be important, the use of administrative data sources to measure income and employment has grown dramatically over the past 30 years. Data on wages and salaries from state Unemployment Insurance (UI) systems, for example, have been used to measure the earnings and employment of individuals that participated in state AFDC/TANF programs, manpower training, and other social programs. Data on earnings (and employment) from Social Security Administration (SSA) records have been linked with the records of welfare and social program participants.
What type of data one uses to measure income and employment among current and past welfare participants and welfare-eligible households may have important consequences for implementing and evaluating recent welfare reforms. Recent debates between the states and the federal government, for example, over employment targets and associated sanctions mandated under PRWORA hinged crucially on exactly how the fraction of a state’s caseload that is employed would be measured. Furthermore, the conclusions of several recent assessments of the impacts of welfare reform and caseload decline appear to depend on how income and employment of welfare leavers and welfare-eligible populations are measured.2
In this paper we assess the strengths and weaknesses of using survey or administrative data to measure the employment and income of low-income populations. We review a number of studies, most of which have been conducted in the past 10–15 years,3 that assess the comparability of income and employment measures derived from surveys and administrative records. Clearly the primary criterion for evaluating data sources is their accuracy or reliability. Ideally one would compare the income and employment measures derived from either surveys or administrative data sources with their true values in order to determine which source of data is the most accurate.
Unfortunately this ideal is rarely achieved. One seldom, if ever, has access to the true values for any outcome at the individual level. At best, one only can determine the relative differences in measures of a particular outcome across data sources. In this paper, we try to summarize the evidence on these relative differ-
Often these samples are gathered in the context of evaluations of specific welfare or training programs.
See, for example, studies by Primus et al. (1999), Cancian et al. (1999), and Rolston (1999) for a flavor of how this debate hinges on measurement issues.
Several earlier studies compared employment measures for low-income populations across alternative data sources, most notably the study by Greenberg and Halsey (1983) with data from the SIME/DIME Experiments. Given changes over time in such things as Unemployment Insurance coverage and response rates in surveys, we focus on the most recent studies available to maximize the relevance of our findings for the measurement of these outcomes for current and future studies.