Evaluating Welfare Reform in an Era of Transition (2001) / Chapter Skim
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Appendix C: The Statistical Power of National Data to Evaluate Welfare Reform
Appendix C: The Statistical Power of National Data to Evaluate Welfare Reform
Pages 209-220
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From page 209... ...
variation to identify the overall effects as well as specific components of welfare reforms that have occurred over the last 20 years. As summarized in Chapter 2, a number of recent studies have used the cross-state variation in waivers granted to states in their administration of the AFDC program to assess the extent to which these particular reforms could account for the decline in the AFDC caseloads that occurred during the l990s, as well as trends in labor force participation, earnings and poverty rates among welfare-prone groups in the population (see, e.g., Bartik and Eberts, 1999; Blank, 1997, 1999; Council of Economic Advisers, 1997; Figlio et al., 2000; Moffitt, 1999; Schoeni and Blank, 2000; Ziliak and Figlio, 2000; Ziliak et al., 2000~.
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From page 210... ...
Other dependent variables include the unemployment rate, the lagged unemployment rate, state indicators, and state trends to account for other factors that may explain the cross-state and temporal variation in outcomes. The substate demographic cell versions of these models also included education status, age, and waiver by education interaction terms.
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Family cap -10.58 4.751 CEA Log(AFDC rate) Earnings disregard -4.569 4.318 CPS AFDC rate Any waiver -1.007 0.3673 CPS Doubledb AFDC rate Any waiver -1.007 0.2597 CPS-Disaggregated AFDC rate Waiver by education -1.67 0.6064 < 12 interaction CPS-Disaggregated AFDC rate Waiver by education = -0.947 0.6064 12 interaction CPS-Disaggregated AFDC rate Waiver by education = -0.662 0.6064 13-15 interaction CPS-Disaggregated AFDC rate Waiver by education = -0.751 0.6065 16+ interaction CPS Annual Weeks Any waiver 9.837662 8.766234 Worked CPS Annual Hours Any waiver 13.72197 10.49327 Worked CPS Annual Earnings Any waiver 27.68749 16.33189 CPS Weekly Earnings Any waiver 16.84836 11.89296 aDue to different definitions of the dependent variable CEA and CPS AFDC effect estimates are not directly comparable; see discussion for details.
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In this paper we condition on the observed values of the variance estimates and independent variables. We then explore the power over plausible ranges of effect sizes and total sample sizes.2 THE POWER OF CEA MODELS TO DETECT WAIVER EFFECTS AND COMPONENT EFFECTS ON AFDC CASELOADS The CEA models use the log of the AFDC case rate as the dependent variable.
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whether the state disregarded some amount of earnings of an assistance unit when calculating the AFDC monthly cash benefit. In addition to an indicator for waiver status or a collection of component indictors as independent variables, the models include state indicators and trends, unemployment rate and lagged unemployment rate, and the log of the maximum AFDC benefit for a family of three.
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For the CEA models, only the any waiver effect has substantial power for this observed effect size, sample size, state sample allocation, and pattern of waivers across the states and over time. The roughly 60 percent power at the observed effect size (5.75)
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Figure C-2 presents power curves for a model with a single any waiver indicator and a model that replaces the any waiver indicator by the indicators for the interactions of any waiver with different levels of educational attainment. Additional independent variables are state indicators and trends, unemployment rate, lagged unemployment rate, and the log of the maximum AFDC benefit for a family of three.
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This result suggests that these outcome measures would be difficult to use for welfare reform evaluation unless the reform was expected to have a substantially larger effect on these measures than waivers or a larger data set was available. OBTAINING MORE POWER Despite the seemingly important changes that waivers brought to the welfare system, the above analyses imply that the basic CEA analysis barely has enough statistical power to detect an effect of waivers on the size of state AFDC caseloads.
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A doubling of the CPS sample size would add substantially to the ability to measure the effects of welfare reform. However, a simple doubling of the sample sizes for each state is not necessarily the optimal way to allocate a doubling of sample size.
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With further improvements in disaggregation and increased sample sizes, this style of analysis could increase its contribution to the reform analysis portfolio of methods. REFERENCES Bartik, Timothy, and Randall Eberts 1999 Examining the effect of industry trends and structure on Welfare caseloads.
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Figlio 2000 Geographic Differences in AFDC and Food Stamp Caseloads in the Welfare Reform Era. Working Paper # 180, Joint Center for Poverty Research, University of Chicago and Northwestern University (May)
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,' M 20 30 Figure Cat: Power for the CPS Mode! if Sample Size Were Any Waiver ~ M ~ H Any Waiver ~ / %%' /~ L Percent Channe in AFDC Caseload
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