Study of State Demographic, Economic, and Programmatic Variables and Their Impact on the Performance-Based Child Support Incentive System

In passing CSPIA, Congress mandated a study of the economic and demographic characteristics of states and how they affect performance, calling on the Secretary of the Department of Health and Human Services (DHHS) to recommend adjustments to ensure that the relative performance of the states is measured from a baseline that takes account of such variables. This study provides the underlying data for the Secretary’s report. Specifically, the study seeks to answer two questions: * * What economic, demographic, and programmatic factors are associated with the performance of state child support enforcement programs? If empirical work identifies factors that affect performance and are outside the control of child support agencies, how could DHHS amend the incentive system to account for the factors with the goal of improving the system’s equity? In answering the first question, we expanded the scope of the project beyond the original Congressional request and included an analysis of programmatic factors—such as staffing levels and award establishment processes. This was necessary because we needed to consider the associations of all potential determinants of performance in order to generate unbiased estimates of the effects of economic and demographic factors. Underlying the study are the performance data reported by states in FY 1999 and 2000. OCSE used the FY 1999 data as a baseline. The FY 2000 incentive payments were based on a combination of the old incentive formula (2/3 of the incentive payment) and the new formula (1/3 of the payment). We assembled state-level data on over 50 economic, demographic, and programmatic variables that have theoretical relationships with child support performance. The variables include state rates of poverty, unemployment, non-marital births, migration and incarceration. We also considered IV-D program spending and staff levels and other program features that experts believe affect performance. We then developed a number of statistical models to explore and estimate the independent effects, if any, of each of these theorized determinants of child support performance. We developed a distinct statistical model for each of CSPIA’s five measures. We then applied the models results to the incentive policy. Specifically, we show how adjustments could be made to state scores for each performance measure.