in a welfare program can address whether the welfare population is representative of the entire population of those living at some fraction of the poverty level.

If one is interested in school-age children, computerized school data provide a base population for understanding the selection issues. One example is to link the 6- to 12-year-old population and their School Lunch Program (SLP) information to Food Stamp administrative data to understand who uses Food Stamps and what population the administrative data actually represent. Because SLP eligibility is very similar to Food Stamps (without the asset test), such data could provide a very good idea of Food Stamp participation. The criticism that administrative data only tracks individuals while they are in the program is true. Extending this a bit, administrative data, in general, only track individuals while they are in some administrative data set. Good recent examples of addressing this issue are the TANF leaver studies being conducted by a number of states. They are linking records of individuals leaving TANF with UI and other administrative data, as well as survey data, to fill in the data that welfare agencies typically have on these individuals—data from the states’ FAMIS or MMIS systems. Especially when we are studying welfare or former welfare recipients, it is likely that these individuals appear in another administrative data set—Medicaid, Food Stamps, child support, WIC, or child care, to name a few. Although participation in some of these is closely linked to income maintenance, as we have learned in the recent past, there is also enough independence from income maintenance programs to provide useful post-participation information. Finally, if they are not in any of these social programs databases, they are likely to be in the income tax return databases or in credit bureau databases, both now becoming data sets used more commonly for social research (Hotz et al., 1999).

A more thorny problem may be situations in which an individual or a family leaves the jurisdiction where administrative data were collected. We may be “looking” for them in other databases when they may have moved out of the county or state (or country) in which the data were collected. The creation of national-level data sets may help to address this problem simply through a better understanding of mobility issues, if not actually linking data from multiple states to better track individuals or families.

It is certainly possible that two administrative databases will label an individual as participating in two programs that should be mutually exclusive. For example, in our work in examining the overlap of AFDC or TANF and foster care, we find that children are identified as living with their parents in an income maintenance case when they are actually living with foster parents. Although these records eventually may be reconciled for accounting purposes (on the income maintenance side), we do need to accurately capture the date that living in an AFDC grant ended and living in foster care began. Foster care administrative data typically track accurately where children live on a day-to-day basis. Therefore, in studying these two programs, it is straightforward to truncate the AFDC record when foster care begins. However, one would want to “overwrite” the

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