AFDC end date so that one would not use the wrong date if one were to analyze the overlap between AFDC and another program, such as WIC, where the participation date may be less accurate than in the foster care program.
Basic reliability issues also arise. For example, some administrative databases do a less than acceptable job of identifying the demographic characteristics of an individual. At a minimum, data entry errors may occur in entering gender or birth dates (3/11/99, instead of 11/3/99). Also, data on workers’ determination of race/ethnicity might not be self-reported, or race/ethnicity might not be critical to the business of the agency, although this is often a concern of external parties. In some cases, when one links two administrative data files, the race/ethnicity codes for an individual do not agree. This discrepancy may be a particular problem when the data files cover time periods that are far apart, because some individuals do change how they label themselves and the labels used by agencies may change (Scott, 2000). Linking administrative data with birth certificate data—often computerized for decades in many states—or having another source of data can help address these problems. We will discuss this issue below when we discuss record linkage in detail (Goerge, 1997).
As mentioned earlier, the pull files provided by government agencies are often not cumulative files and most often only span a limited time period. For most social research, longitudinal data are required, and continuous-time data—as opposed to repeated, cross-sectional data—are preferred, again depending on the question. Although these pull files may contain some historical information, this is often kept to a minimum to limit the file size. The historical information is typically maintained for the program’s unit of administration. For TANF, this is the family case. For Food Stamps, it is the household case. In either program, the historical data for the individual member of the household or family are not kept in these pull files. The current status typically is recorded in order to accurately calculate the size of the caseload. Therefore, to create a “clean” longitudinal file at the individual level, one must read each monthly pull file in order to recreate the individual’s status history. Using a case history for an individual would be inaccurate. An example is the overlap between AFDC and foster care discussed earlier. The case history for the family—often that of the head of the household, and which may continue after the child enters foster care—would not accurately track the child’s income maintenance grant participation. More on this topic is discussed in the following sections.
The state of the art in addressing the most pressing policy issues of the day is to use administrative data and survey methods to obtain the richest, most accurate