One way to infer the direction of possible entry effects is to examine the impact of welfare reform on the exit behavior of recipient cases. As Moffitt (1993) noted, when welfare reform changes the benefits and potential earnings of welfare recipients and applicants, exit effects (effects on the probability of exiting welfare for recipient cases) are likely to be of the opposite sign as entry effects:
The conventional, "static" theory suggests that potential applicants as well as recipients continually compare two variables in making decisions to apply or exit: potential earnings in the private labor market, and the welfare benefit. Empirical research has strongly confirmed this theory, for welfare benefits and potential earnings have been shown repeatedly to have strong positive and negative effects, respectively, on the probability of being on AFDC at a point in time and on the probability of entering the rolls; and the probability of exiting the rolls has been shown to be negatively affected by benefits and positively affected by potential earnings.
For example, the imposition of time limits would tend to lower the expected value of welfare benefits, leading both to higher rates of exits by welfare recipients and to lower rates of application for welfare.(2)
Detecting exit effects in a particular direction may help to infer the direction of entry effects, but obtaining estimates of the size of entry effects requires the analysis of time-series data on applications to a state's welfare program. For example, if the data and analytic resources were available, monthly levels of applications could be studied over a multiyear period, adjusting for time-varying factors such as local unemployment rates, population changes, and the implementation of new policies (such as expansions of eligibility for welfare). To calculate application rates, applications could be compared to the population of potential welfare applicants, which could be estimated (at least in large states) from household survey data. Entry effects would be measured as the extent to which adjusted rates of application differ following the implementation of a welfare reform package. Exit effects could also be measured using aggregate time-series data on the size of the caseload of ongoing welfare recipients. Unfortunately, as with most estimates obtained from nonexperimental analyses, these estimates of entry and exit effects would probably be somewhat sensitive to the control variables included and the statistical assumptions underlying an evaluator's model of applicant behavior.
Moffitt (1992) proposed an experimental approach for measuring entry effects. This approach would involve randomly assigning the welfare reform and control policies to a large number of different sites, then comparing entry and exit rates in the sites. Moffitt notes that such an approach has many practical problems, including the difficulty of obtaining enough sites, the problem of cross-site migration, and the challenge of maintaining stable policies in each site for more than a very limited period of time. He concludes that, to obtain estimates of entry effects, a more feasible method is nonexperimental approaches that use administrative data.