which these measures are put. We note five considerations. First, when conducting an experimental evaluation of a program, the criteria for judging data sources is whether they yield different estimates of program impact, which generally depends on differences in income (employment) between treatment and control groups. In this case, errors in measuring the level of income between treatment and control groups could have little effect on the evaluation. Alternatively, suppose one’s objective is to describe what happened to households who left welfare. In this case, researchers will be interested in the average levels of postwelfare earnings (or employment). We discuss results from Kornfeld and Bloom (1999) where UI data appear to understate the level of income and employment of treatments and controls in an evaluation of the Job Training Partnership Act (JTPA), but differences between the two groups appear to give accurate measures of program impacts. Depending on the question of interest, the UI data may be suitable or badly biased.

Second, surveys, and possibly tax return data, can provide information on family resources while UI data provide information on individual outcomes. When assessing the well-being of case units who leave welfare, we often are interested in knowing the resources available to the family. When thinking about the effects of a specific training program, we often are interested in the effects on the individual who received training.

Third, data sets differ in their usefulness in measuring outcomes over time versus at a point in time. UI data, for example, make it relatively straightforward to examine employment and earnings over time, while it is impossible to do this with surveys unless they have a longitudinal design.

Fourth, sample frames differ between administrative data and surveys. Researchers can not use administrative data from AFDC/TANF programs, for example, to examine program take-up decisions because the data only cover families who already receive benefits. Surveys, on the other hand, generally have representative rather than targeted or “choice-based” samples.

Fifth, data sources are likely to have different costs. These include the costs of producing the data and implicit costs associated with gaining access. The issue of access is often an important consideration for certain sources of administrative data, particularly data from tax returns.

The remainder of this paper is organized as follows: We characterize the strengths and weaknesses of income and employment measures derived from surveys, with particular emphasis on national surveys, from UI wage records, and from tax returns. For each data source, we summarize the findings of studies that directly compare the income and employment measures derived from that source with measures derived from at least one other data source. We conclude the paper by identifying the “gaps” in existing knowledge about the survey and administrative data sources for measuring income and employment for low-income and



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