Basic and UP Models
Although some previous researchers have examined the combination of Basic and UP participation, the predominant approach is to consider these programs separately. There are sound methodological reasons for considering them separately, especially given the fact that the UP program became mandatory in 1990, and because data for doing so are readily available -- although potential interactions between the programs should be recognized. Hence, we have developed separate participation models for these programs. It should be recognized, however, that cases in one program may shift, over time, into the other program. We have allowed for potential interactions in only a very simple way -- by including dummy variables for the presence and type (6-month or 12-month) of UP program in the Basic participation equations.
Household Demographic Variables
Past efforts have commonly used the number of female-headed households with children under the age of 18 or similar measures as explanatory variables, rather than more general population measures such as the size of the female or male population in a specific age group. There is reason to believe, however, that such variables themselves are sensitive to economic factors, and controlling for such variables may result in an understatement of the impact of changes in economic variables on AFDC participation. We examine this issue by estimating models with and without such variables included.
Age Distribution of the Population
We did not find any studies using aggregate data at either the national or state level that directly examined the impact of changes in the age distribution of the population on AFDC participation, although there is evidence from studies using individual data that participation varies with the age of the household head. During the time period we are examining, there was a substantial change in the age distribution of the population that is most at risk for AFDC -- those between 16 and 45 -- as the Post World War II "baby boom" generation completed its entrance into this age group earlier in the period and began to age out of the age group by the end of the period. In our previous work in modeling participation in the Social Security Administration's disability programs over the same period, we have taken this factor into account and found it to be important (Lewin-VHI, 1995b and 1995c).
Labor Market Variables
The specifications and findings in the literature we have reviewed suggest that the unemployment rate and some measure of average hourly earnings will be key explanatory variables and that the full impact of changes in these variables on AFDC participation will be realized only after four to six quarters have passed, especially for the unemployment rate. As discussed above, even longer lags may be important. State-level data for such variables are available although only at the annual level. Earnings adjustments to reflect federal taxes and earned income tax credits are feasible, although they have rarely been done in previous studies, and we have followed suit. Previous studies have not considered industry level measures of employment, but such measures may more accurately reflect the effects of business cycles on the segment of the labor market that is most relevant to potential AFDC recipients.
Measuring AFDC Benefits
Most studies use a maximum monthly benefit variable for a household of specified size, and some adjust this variable for Food Stamps. Failure to adjust for Food Stamps results in an overstatement of the effect of an increase in the benefit of the family's income. The AFDC benefit variable is sometimes combined with the earnings variable into a single variable, but we think this is a mistake if it is feasible to start with separate variables and test the restriction that combining the variables implies. One study that is methodologically similar to this study also uses a benefit reduction rate and obtains strong results (Moffitt, 1986). Hence, we were encouraged to develop a similar measure for this study.
Simultaneous Determination of AFDC Benefits
While most studies have assumed that the AFDC benefit variable is exogenous to participation, there are theoretical reasons and empirical evidence to support the hypothesis that increases in participation create down-ward fiscal and political pressure on the level of benefits. If so, the coefficient of the benefit variable may understate the effect of an increase in benefits on participation. Correction of this problem requires use of an instrumental variables technique, but it may be difficult to find the right instruments. This is complicated when more than one benefit variable is used, as in our models. While this issue may be an important one, we have neglected it here in favor of addressing other issues that seemed more important and easily dealt with.
Average Benefits per Case
We originally had planned to focus on participation in our analysis, ignoring the issue of average benefits per case. Some previous studies have, however, examined average benefits per case and found some evidence of sensitivity to economic factors -- even though average benefits are largely determined by maximum benefit levels. We decided to develop a model of average benefits per case in order to more fully understand the impacts of the business cycle and other economic factors on AFDC expenditures.
The literature we reviewed has not examined the impact of changes in other state programs on AFDC participation, with the exception of Medicaid. The findings from the Yelowitz (1993) study on Medicaid expansions encouraged us to think we would find a similar Medicaid impact. Based on our previous analysis of SSI participation, it seemed likely that impacts of changes in other programs on AFDC might be identified using the pooled state methodology.