Determinants of AFDC Caseload Growth. 1. Determining the Final Specifications


We present two sets of estimates for the UP participation models. In the first set -- the "full-period" estimates -- we use data for only the 19 states that have data for the whole sample period -- a total of 19 x 60 = 1,140 observations. In the second set -- the "post-mandate" estimates -- we use data for 49 states for the last 16 quarters of the sample period, during which all states were required to have UP programs -- a total of 784 observations. The District of Columbia, which had an UP program for the entire period, is excluded from both samples due to questionable data for the dependent variable in two quarters. Mississippi is excluded from the post-mandate sample for a similar reason.(12) For the latter sample we only estimate a caseload model.

As with the Basic program, we searched through many specifications for the full-period models prior to the specification reported here. The search was conducted in parallel with the search for the Basic equations, for the caseload equation only. In general, we searched in the same way as for the Basic equations, except that we elected to retain the same program parameter specification as in the Basic equation for comparison purposes.

We focused our search efforts on the labor market variables because prior research has demonstrated that these variables are more important for the UP program than for the Basic program. Nonetheless, we settled on the same two variables, the unemployment rate and trade employment per capita, for the final specification. The only difference between the specification of these variables in the Basic and UP equations is that the distributed lag for trade employment in the UP equation is first-order (linear) instead of second (quadratic), and the maximum lag length is six instead of ten.

For the post-mandate estimates, we started with the final specification from the full-period estimates, minus the dummies for the early years. We subsequently changed the specification in a few respects, as discussed later, but the two models are very similar in specification. We could not use the Parks method to estimate this model because the sample period is too short, so we applied only the WLS method. We discuss the findings for the full-period model first, then present the post-mandate estimates.