Determinants of AFDC Caseload Growth. 6. Other Programs and Laws


Very few of the variables in the category of new programs and laws had statistically significant coefficients in the specifications we tried, and the signs of several of these coefficients are opposite that expected. In contrast to the several strong findings for some of the demographic, labor market, and program variables reported above, none of the findings reported below stand out as particularly strong or convincing. It is important to keep in mind that they are a product of a specification search over many variables.


The final specification of the Basic participation models includes two Medicaid variables. "Medicaid expansion" is the share of children under 19 in the state eligible for Medicaid under the Medicaid expansions that began in 1988. The variable is zero before 1988. The second variable is the Medicaid expansion variable multiplied by the share of children under 19 in the state who were AFDC recipients in 1987 (average monthly child recipients), the pre-expansion year. Both variables are entered as changes in the current quarter.

We expected the Medicaid expansion variable when included alone to have a negative coefficient, especially given the strong findings reported by Yelowitz (1994) from his analysis of the same expansions using CPS data for individuals linked to state eligibility and expenditure data.(10) Instead, however, we found a marginally significant, positive coefficient. We added the interaction term on the hypothesis that the expansion would have a larger negative effect (or smaller positive effect) in states in which a large share of children were already on AFDC and, therefore, covered by Medicaid. The coefficient of the interaction variable is consistent with this hypothesis. In fact, the combined coefficients imply that the estimated effect of the expansion is positive in states where the share of children on AFDC in 1987 is below 14.6 percent, and negative in states where the share is larger.(11) Most states are in the former category, but a few are in the latter -- including California (15.9 percent).

One other notable feature of the findings is that it is only the current value of the variables that is statistically significant. We expected some lag in the effects of the expansions, but found that lagged values of the expansion variables did not have significant effects even when the current values were omitted from the equation.

One hypothesis about why the estimated effect of the expansion is positive for most states is that efforts to enroll newly eligible individuals into Medicaid also encouraged enrollment in AFDC. This does not explain, however, why the findings are at odds with those of Yelowtiz.

We also estimated models including a measure of the value of Medicaid benefits as an explanatory variables, but never found this variable to be statistically significant.

SSI children

The coefficient of the log of current SSI child beneficiaries is positive in all participation equations and significant in most. We were unsure about inclusion of this variable; on the one hand, it might capture the effects of shifts in AFDC children onto SSI after Sullivan v. Zebley, implying a negative coefficient, but on the other hand it might proxy for unobserved factors affecting participation in both programs in the same direction, which would imply a positive coefficient.

Unemployment Insurance

The log of the percent of unemployed persons who are insured has statistically significant, positive coefficients in all equations. As with the SSI child variable, there are conflicting sign expectations: increases in the share of insured unemployed should reduce the share of unemployed persons who meet the AFDC means test, suggesting a negative coefficient, but the variable might also proxy for unobserved factors that have the same effect on both the share insured and AFDC caseloads.

Abortion Restrictions

Two abortion dummies are included in the reported models (parental notification or consent requirements, and Medicaid funding restrictions), both lagged one period. The coefficient on the dummy for restrictions on Medicaid payments for abortions was more consistently significant than that on the parental consent/notification dummy. Both are negative, suggesting that the negative hypothesized effect of such restrictions on conceptions exceeds the positive effect on births for babies already conceived. The estimated coefficients imply that parental notification or consent requirements reduce caseloads by 0.2 percent and Medicaid funding restrictions reduce them by 0.3 percent. The estimated effects on the number of child recipients are larger -- reductions of 0.3 and 0.5 percent, respectively -- presumably because fertility reductions occur among AFDC mothers as well as potential AFDC mothers.

As with the findings for the family cap, these estimates are surprisingly strong, especially because the estimated effects occur after just two quarters. It may be that these effects are due to other efforts in the states that adopted these restrictions to reduce fertility and AFDC participation.

SSA Allowance Rates

Another variable in this category with a significant coefficient is our measure of SSA's administrative tightening of initial allowances from 1977 to 1978. According to our estimate, reductions in the allowance rate during this period resulted in increases in AFDC participation in 1979. This finding would be substantially strengthened if the sample period was extended back through 1978 and a strong effect were found in that year as well.

General Assistance

The final variable in this category is the measure we developed for cuts in state general assistance (GA) programs. Given the strong findings we obtained for the impact of these cuts on SSI participation in earlier research, we had expected to find some effect for AFDC even though the connection between AFDC and GA is more tenuous than that between SSI and GA. The coefficient of this variable, including lagged values, was not significant in any of the equations we tried. Given the size of the cuts that occurred and the success we had in using this variable in SSI models, we conclude that the GA cuts during this period had at most very small impacts on AFDC participation. States that cut their GA benefits may have been successful in assuring that AFDC-eligible families who sought GA, which is financed entirely from state and local revenues, obtained AFDC benefits, which are partially funded by the federal government, even before the GA cuts occurred. From our earlier work, it appears that the effect of the GA cuts on SSI applications and awards was high because the effort required to apply and the uncertain result discouraged them GA recipients from applying earlier. AFDC eligibility is easier to determine than SSI eligibility and, consequently, the determination process is much simpler. It might also be that those states which cut their GA programs also tightened their AFDC eligibility requirements or screens.