1. An important limiting factor in applying the methodology to indicators of well-being is that reliable state-level measures of many indicators do not exist. ASPE and others are promoting efforts to improve measurement of state-level indicators going forward, but these will not be helpful for supporting the type of modeling reported on here until at least several years into the future. Existing data may permit development of reasonably reliable estimates of some state-level indicators retrospectively.
2. A few recent studies have found evidence that suggests business cycles impact family structure. Moffitt (1994) includes the state unemployment rate and variables reflecting employment in specific sectors in his study of female headship that uses pooled CPS data from 1968 to 1989. Unemployment was significantly and positively related to female headship among black females, but not among white females. Blank and Ruggles (1996) include the state unemployment rate in duration models of AFDC eligibility and AFDC participation. Unemployment reduces the likelihood that a spell of AFDC eligibility will end due to a change in family composition. However, Moffitt (1995) finds little consistent evidence that AFDC benefits have had a substantial impact on out-of-wedlock childbearing.
3. Throughout this document we use the word "family" to refer to AFDC assistance units, as defined by Federal and state regulations, although some units contain individuals who are not legally related to each other.
4. This second requirement is currently being phased-in and holds true only for those children born after September 30, 1983.
5. We are grateful to Aaron Yelowitz for providing the Medicaid expansion variable. See Yelowitz (1994).
6. Note that "other things constant" implicitly includes the AFDC earnings cut-off, which is fully determined by wages and the other program parameters.
7. See Yelowitz (1994) and Moffitt (1992) for more complete discussion of these theoretical points.
8. This refers to requirements that are beyond those of the state's JOBS program.
9. Cromwell, et.al, (1986) found that the same change results in a 1.8 percent caseload increase after a year, using a similar methodology. The Congressional Budget Office estimates a 1.7 percent increase using a time-series model (CBO, 1993). The findings we report here are based on a model that uses changes in the unemployment rate as the only labor market variable. Adding another business cycle variable, trade employment per capita, essentially increases the estimated effect of the business cycle.
10. Since completing this work, we have become aware of very recent analyses of the impact of Zebley on SSI child participation and the share of SSI child participation growth accounted for by transfers from AFDC, by Garrett and Glied (1997). There preliminary results indicate that about half of the SSI child growth caused by Zebley is accounted for by transfers from AFDC.
11. Hoynes (1996) has also obtained strong and convincing results in a duration analysis of AFDC cases in California, using local labor market variables.
12. Tom Corbett is more pessimistic. In correspondence to us, he writes: "The reality is that the notion of a national program where a few relatively easily measured economic, demographic, and program parameters could predict caseload had virtually disappeared by the 1990s."
13. The problem is analogous to the problem with fixed weight price indices, such as the Consumer Price Index, which become a poorer and poorer measure of inflation as actual consumption bundles drift away from the market basket used to determine the weights. The proposed solution is equivalent to a Divisia ("chain-linked") price index.
14. The examples are California (northern vs. southern), Florida (Dade County -- Miami area -- vs. the rest of the state), and Maryland (Prince George's County vs. the rest of the state).
15. Another way to define the participation rate in a state would be to divide the caseload by a weighted sum of the population in various age groups, using national participation rates by age in a base year as weights. We use the logarithm of this denominator, which we call "expected participation" as an explanatory variable in our participation models.
16. For example, Nebraska's caseload increased by 260 percent from 1979.4 to 1980.4, but the base was very small (60 families) and the per capita increase was high, but not out of line with the experience in other states during the period.
17. Steven Thompson reported that Regional Financial Associates in West Chester, PA, has obtained data from state employment departments for all 50 states. We had previously explored wage data available by state and industry from the Department of Labor and determined that data for specific low-wage, low-skill industries were missing in many states and many years.
18. We originally had planned to estimate a simultaneous model for the maximum monthly benefit variable, following Shroder (see Lewin-VHI, 1996), but abandoned this effort after expanding the number of benefit parameters in the model. One idea for generating instruments is to use the share of adults (voters) in various age groups; presumably elderly voters are less supportive of benefits for young families than are younger voters. Another possibility is the Federal share of the AFDC payment; the higher the Federal share, the less would be the impact of a change in the caseload on taxpayers.
19. Testing whether the presence or absence of an UP program shifts the parameters for the Basic model merits further discussion because there are really three separate regimes to consider. All States have been required to have UP programs since October of 1990, and this period clearly represents one regime. Many States adopted UP programs voluntarily well before 1990. Pre-1990 observations for these States could be grouped with post-1990 observations for all States, but the fact that these States had voluntary programs, along with the fact that these State programs did not necessarily comply with Federal rules that came into effect in 1990, makes it possible that these two groups of observations are not comparable. Hence, we will begin by treating them as separate regimes. Observations for States with no UP programs constitute the third possible regime. For this study we examined the comparability of results for the UP programs across the first two regimes in an informal way.