Determinants of AFDC Caseload Growth. b. Vital Statistics


The number of female headed households with children under the age of 18 has proven to be a strong predictor of AFDC Basic participation in previous studies. Unfortunately, reliable estimates of the number of female headed households at the state level are unavailable. Therefore, we relied on variables constructed from state-level vital statistics on marriages, divorces, and out-of-wedlock births to proxy for female-headed households in our Basic models.(6) The quarterly series used in the model are interpolated from the annual data provided by the Natality, Marriage, and Divorce Statistics Branch, National Center for Health Statistics. We also tried a "marriage market" variable in some models.

We expect the changes in the vital statistics variables to result in changes in the number of female headed households and, consequently, have an impact on the Basic caseload. We estimated Basic participation models with and without these data to determine whether the coefficients of business cycle variables are sensitive to their inclusion. It could be that business cycle effects work, in part, through their effects on formation of female-headed households. We also explore the effects of these factors in our UP models because changes in the number of two-parent households are expected to affect UP participation. In addition to the current values of these variables, we experimented with both multiple lag and lagged moving average specifications. In the results reported we use the moving average of the four previous quarters.

At least one previous study of participation in the AFDC has included a measure of "marriage market conditions" as an explanatory variable (Shroder, 1995). The variable used is a mixture of a demographic variable and an economic variable: the log of the number of women between the ages of 15 and 64 in the state divided by the number of employed men in the state. We obtained annual state-level employment data by sex from the Bureau of Labor Statistics (BLS) and annual state population data by age and sex from the Bureau of the Census to create an annual series. The quarterly series included in the models is interpolated from this annual series. We experimented with a few variants of this variable, including lag specifications and the moving average of the last four quarters.