The simulations demonstrate the ability of a pooled analysis to provide insights into the causes of caseload growth. Drawing on the experiences of all states, as the model does, and comparing simulations for individual states to national simulations using a single model, provides information about caseload growth in individual states that might easily be missed in an individual state time-series analysis.
At the same time, however, the simulations for individual states demonstrate limitations of the model, at least as currently constructed. Most of these limitations stem from measurement problems -- factors thought by many to be important determinants of caseload growth at the state level are not adequately measured at the state level. Some improvements can be made in this area, as outlined in Chapter 1, and we expect that making these improvements would add significantly to the model's ability to capture the determinants of caseload growth. One factor that will become increasingly important as we move forward, and that will be especially difficult to measure, is the many detailed and varied changes that states are currently making to their welfare programs.
Other changes to the model might also make it substantially more powerful. Given the interaction between the Basic and UP programs that is known in California, exploring ways to model this interaction might be fruitful. Alternatively, it may be better to simply combine the caseloads and model them together. As Michael Wiseman has pointed out, the big issue is whether or not a mother and her children end up on welfare, not whether there is a second parent in the household.(11) This would also solve the specification problem that arises because of the absence of the UP program in many states for a significant part of the sample.(12) The findings for individual states and the comments on them also suggest that efforts to disaggregate the caseload by characteristics such as race/ethnicity and citizenship would be very useful. It might also be useful to divide the largest states into sub-state areas for the analysis, such as Dade County vs. the rest of Florida and northern vs. southern California.(13) Further consideration of asymmetries in business cycle effects also seems warranted. A number of other ideas have been suggested by reviewers of the model, as discussed at the end of Chapter 1.
Improvements to the model would likely increase the extent to which the state-level factors in the model and dummy variables for federal program changes account for growth and cyclical variation in the national time series. It would be a mistake, however, to focus exclusively on improving the fit of the national series. Significant gains in understanding caseload growth are more likely to be achieved if future efforts focus on the fundamental measurement and structural issues rather than goodness of fit per se. That has been our approach, and we think it has been very worthwhile. The strength of our findings, especially concerning business cycle effects and program changes, have impressed the many experts in welfare research who have reviewed our work and have convinced us that further efforts to improve the model would be rewarding.