In recent years many states have made substantial changes to their Aid to Families with Dependent Children (AFDC) programs on Federal 1115 Waivers, and the recent enactment of the Personal Responsibility and Working Opportunity Reconciliation Act of 1996 ensures that many more, large changes will be implemented in the not too distant future. The anticipated growth in state-level experimentation makes it especially important to improve our understanding of state-level factors behind historical growth in AFDC caseloads, for several reasons.
First, a better understanding of the impact of changes in the state economy will help states better prepare for the fiscal implications of economic recessions and recoveries under block grants.
Second, a better understanding of state-level factors behind program growth will help states design, and understand the implications of, program changes. This analysis cannot provide significant insight on the impacts of reforms such at time limits, work requirements or various restrictions on benefits because there has been little historical experience relevant to these reforms. However, the analysis can provide valuable insights on the effects of changes in basic program parameters for cash assistance, and how changes in programs such as Medicaid, SSI and Food Stamps interact with AFDC. We have more historical experience with these changes and while some research has already been done in this area, the results have been mixed or inconclusive. This analysis will help determine how realistic it is to expect proposed program changes to substantially reduce welfare dependency without impoverishing children.
Third, a better understanding of state-level factors behind historical growth will also improve our ability to establish baseline levels of program participation in a state for comparison to actual levels of participation under a state reform. As states implement reforms under the new welfare law, it will be very important to separate the impacts of those reforms on caseloads from the effects of environmental factors. Currently, caseloads are declining in most states. Are these due to legislated and administrated reforms, improvement in the economy, or some combination?
For these reasons, the Office of the Assistant Secretary for Planning and Evaluation (ASPE) in the U.S. Department of Health and Human Services has a strong interest in promoting research aimed at better understanding the determinants of AFDC participation at the state level. As part of their effort in this area, ASPE has contracted with The Lewin Group, Inc. to analyze the relationship between state AFDC caseload growth and (1) the strength and structure of the state economy; (2) demographic trends; and (3) changes in the structure of AFDC and other public assistance programs using state-level data to estimate pooled time-series models of program participation. This is the project's final report.
It is important to keep in mind that the purpose of this effort was not to build a better forecasting model. Many efforts to model AFDC participation, especially using time-series data at the national or state level, are motivated by the need to project future caseloads and expenditures. Such efforts place a priority on building a model that fits the historical series well, using whatever predictors are available. They do not necessarily require a more fundamental understanding of the relationship between the predictors and the historical series. In contrast, we place greater emphasis on developing and assessing explanatory variables that derive from theoretical considerations on the overall fit of the model.
While our findings on AFDC participation are of interest in their own right, they also serve to illustrate the potential of the methodology we use, as well as its weaknesses. We have previously applied the methodology to studies of participation in SSA's disability programs, Social Security Disability Insurance (SSDI) and Supplemental Security Income (SSI), and obtained much stronger findings about the effects of labor markets and general assistance programs on program participation than had been found in earlier studies. The methodology could also be applied to state-level indicators of the well-being of children or other vulnerable groups. Tobin (1994) has, for instance, applied the methodology to understanding determinants of the poverty rate with some success.(1) Gaylin and McLanahan (1995) have applied the methodology to studying determinants of out-of-wedlock births. The approach we take views state-level events as "natural experiments" that allow the examination of their impacts on key outcome variables. For the AFDC program, the level of experimentation has increased markedly in recent years and is likely to mushroom in the next few years. Interest in this methodology is likely to grow as a result.
Our work builds on the strengths of several previous studies that have used the same general approach (see Chapter 2). With the assistance of staff at ASPE and the Administration for Children and Families (ACF), we undertook an ambitious effort to develop a quarterly, state-level data set for the 1979-94 period. This effort has been rewarded with some results that are substantially stronger than previous results, especially concerning the impact of business cycles and of changes in basic program parameters (maximum monthly benefits, the average tax and benefit reduction rate, and the gross income limit). In other ways, however, we have not made as much progress in understanding the determinants of caseload growth as we would have liked. While we better understand the substantial caseload fluctuations during the period studied, an underlying trend remains unexplained, and we have very limited new findings concerning the effects of other state programs and policies on AFDC participation. Nonetheless, our findings clearly demonstrate the promise of this approach, and they could no doubt be strengthened through: expansion of the database to the post-1994 period and to earlier years; further development of some explanatory variables; and further experimentation with the model's specification and methods of estimation.
We estimate separate participation models for the Basic and Unemployed Parent (UP) programs. We focus on caseload models for each program, but also estimate total recipient and child recipient models. In addition, we estimate an average monthly benefit model for the combined programs.
Because the focus of this study is participation at the state level, we have also used our "best" estimated model to analyze the history of program participation in four selected states over the sample period: California, Florida, Maryland, and Wisconsin. We chose these states in part because their caseload histories have been carefully analyzed by others. California and Florida are both large states with rates of caseload growth during the latter part of our sample period that are well above the national rate. Maryland's caseload declined substantially relative to the national caseload during the middle part of the period. Wisconsin's caseload was essentially stable in the last few years of the sample, when caseloads in most other states were growing substantially. This exercise serves two purposes: determining how well the model, built on the varied experiences of all states, fits individual state caseload series, and improving our understanding about the causes of caseload growth in these states.