The CPS and SIPP are vital data sets for understanding the functioning of low-wage labor markets and the effects of antipoverty programs. These data get high marks on many of the concerns mentioned in the introduction. They have a national sampling frame covering program participants and nonparticipants that make these data valuable for developing a broad perspective on developments in low-wage labor markets. An example of this type of study is Primus et al. (1999), which uses CPS data to show that AFDC/TANF and Food Stamp Program participation rates have declined considerably faster than poverty rates between 1993 and 1997. They further report that incomes of poor single mothers fell between 1995 and 1997 (after rising between 1993 and 1995), and that the safety net is lifting fewer children from poverty than in the past. Concerns arise with this study, some of which are mentioned in the text that follows. Nonetheless, the CPS and the SIPP are the only data sets that would allow analysts to address the important issues that Primus et al. examine on a national scale.
The other national data sets that have been used to analyze the employment and income status of low-income populations are the National Longitudinal Survey (particularly the National Longitudinal Survey of Youth 1979) and the PSID. Both of these data sets have the additional feature that they are longitudinal surveys so that one can obtain information on earnings and employment status over time for the same person (and household). (4) The PSID has surveyed, until very recently, its respondents and the "splitoffs" of initial respondent households on an annual basis since 1968. Similarly, until 1994 the NLSY79 conducted annual surveys of a random sample of individuals who were 14-21 years of age in 1979. Both of these surveys gathered detailed information on labor market earnings and employment status of respondents, earnings and some employment information on other adult household members, and some information on other sources of income, including income from various public assistance programs. One of the advantages of longitudinal data sets such as SIPP, PSID, and NLSY is that they allow one to monitor the entry into and exit from welfare or other social programs and the factors related to welfare dynamics, including changes in earnings and family structure.
The CPS, SIPP, and PSID, in addition to having nationally representative samples, focus on households as the unit of analysis, and include information on all adult household members.(5) Given the general presumption that families pool resources, data sets that focus on families or households (and include information on cohabiting partners) are valuable. A calculation in Meyer and Cancian (1998) illustrates the usefulness of having data on family, as well as individual, incomes. Their study examines the economic well-being of women in the 5 years after leaving AFDC. They show that in the first year upon exit from AFDC, 79 percent of the women have incomes below the poverty line, but when family income is considered, a smaller number, 55.5, have income below the (correspondingly larger) poverty line. After 5 years, 64.2 percent of the women still have incomes below the poverty line, while only 40.5 percent of the broader family unit had income below the poverty line.
The nationally representative surveys provide information on multiple sources of income, especially in the SIPP, either through separate questions or prompting of specific income sources. By asking specific questions about, for example, welfare receipt or food stamps, the data identify participants and (eligible) nonparticipants, so the data can be used to study program entry effects.
The national surveys also measure income and employment in a comparable fashion both over time and across geographical locations, though in January 1994 the way that earnings information was elicited in the CPS was changed (Polivka, 1997).(6)
Another strength of the nationally representative surveys is that questions can be modified to reflect changing circumstances. For example, the U.S. Census Bureau periodically conducts cognitive interviews of respondents to the CPS in order to assess how they responded to different CPS income- and welfare-related questions. Such studies are used to determine which of the CPS questions were confusing and how respondents interpreted questions. Results from these cognitive interviews are used to improve the way questions are asked, with the goal of improving the quality of the data on key variables such as income and program participation. 7 Typically, this sort of sophisticated assessment can only be done on large-scale, national surveys.
To summarize, there are several potential strengths of using survey data to measure income and employment. These include the following:
- Surveys can provide representative samples for specific populations and generally include data for other family members.
- Surveys typically provide demographic data and data on other characteristics of households (such as educational attainment). They also may gather detailed information on many distinct income sources.
- National surveys provide consistent information across states and localities.
- Surveys can be flexible, so their developers can control what information is collected about income and employment, and this information can be improved over time
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