Determinants of AFDC Caseload Growth. 1. Overview


Most states have developed their own AFDC caseload forecasting models, which are typically used for budget and staffing projections. A 1990 study conducted by the state of Oregon documents the variety of techniques used by the fifty states in forecasting their AFDC caseloads (Oregon State Department of Human Resources, 1990). According to this study, most states use a simple trend analysis (fitting a linear relationship to past series of caseload values) to predict future values for the AFDC caseload. Several states, however, use a multivariate regression framework, incorporating demographic, economic, and programmatic factors, to forecast their AFDC caseloads.

We reviewed state models for two reasons. First, individual state models may reveal important determinants of the AFDC caseload that have not been examined in other studies. Second, our plan called for using the model we developed to simulate caseload series in a few selected states. For this analysis, we wanted to select states that have well-developed AFDC models so that we could compare the results of our model to findings from the state models. In addition, we expect that the developers of the state models would be helpful in interpreting our findings.

The state models discussed here provide additional examples of how particular factors may be measured and incorporated in our model of caseload growth. The models also identify important state-specific policy changes that occurred during the time period we will examine. The MinnesotaCare program in Minnesota and the FIP program in Washington are examples. To the extent that such policy changes are identified, they can be included in our model of caseload growth. Given the fact that we will not be able to identify all such policy changes for all states, the state models that do provide information on policy changes will allow us to test the sensitivity of our results to the omission of state-specific policy variables.

Below, we describe selected state models that go beyond trend analysis or ARIMA models(7), incorporating factors believed to influence AFDC caseload growth in a multivariate regression framework. The models we describe are those used by Florida, Maryland, Minnesota, Oregon, Texas, and Washington. In addition, we discuss a report by Barnow (1988) that presents a guide for states to use in developing their own AFDC caseload models, using New Jersey as an example.