Studies of Welfare Populations: Data Collection and Research Issues. Low-Income Population Studies

06/01/2002

We begin this discussion about nonresponse adjustments with a review of two different types of studies often conducted by state welfare agencies. Studies of the low-income population (such as studies of the current welfare population or studies of those who have left welfare rolls) mainly rely on two types of data collection: one collects data directly from administrative records and the other collects data directly from a sample of eligible persons. Some studies use a combination of administrative data and data from survey respondents.

States' welfare systems generally collect administrative data on the demographic characteristics of welfare recipients, the number of adults and children in the welfare case, and the receipt and value of welfare benefits. Many research studies use administrative records, and researchers frequently match the records to data from sources such as the Food Stamp Program and Medicaid. The state Unemployment Insurance files also are used to collect information about employment and earnings for families who have left welfare. Some studies rely on information available in administrative records, and thus do not require any contact with the subjects of the study.

Some states collect data through surveys. These are most often telephone interviews, although some states also conduct in-person interviews to ensure that families without telephones are included. Surveys usually collect information from respondents that is not available in administrative data.

Both types of studies of low-income populations usually suffer from some form of missing data. For example, in studies that include only administrative data collection, persons or families for whom no information is included in the administrative list (used as the sampling frame) have no chance of being included in the sample, and thus will not be represented in the results of the study. In addition, a number of sampled persons, or families, may not have the required data because they were not matched correctly or had no record in other administrative files used to collect outcome data (e.g., earnings data from Unemployment Insurance records). Similarly, surveys that collect data from sampled persons also are subject to underrepresentation due to sampling from incomplete or outdated lists, as well as missing information due to nonresponse. Later, we describe, in more detail, the sources of missing data in the two types of low-income studies.

As mentioned earlier, this paper describes the common procedures used to adjust for nonresponse and noncoverage. These procedures rely on the auxiliary data available for both respondents and nonrespondents. In general, the greater the amount of auxiliary data that can be used for adjustment, the better the adjustment is likely to be. To evaluate the availability and the amount of such data for low-income surveys, we contacted a number of states to inquire about the content and the quality of their administrative data. The main focus of this inquiry was the availability and quality of demographic, socioeconomic, and geographic variables. The results of this survey are provided in a later section. In general, we found that many states have high-quality data for demographic variables such as age, gender, race/ethnicity, and number of children. Welfare income and length of time on welfare seemed to be among the socioeconomic variables of high quality, and county name and zip code were the geographic variables with good-quality data for the states that responded to our survey. In a later section, we show how this information (or any other data source available to states) can be used to adjust for nonresponse in state surveys.

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