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Was there a Riverside miracle? An hierarchical framework for evaluating programs with grouped data

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  • Rajeev H. Dehejia

    ()
    (Columbia University - Department of Economicss)

Abstract

This paper uses data from the Greater Avenues for Independence (GAIN) demonstration to discuss the evaluation of programs that are implemented at multiple sites. Two frequently used methods are pooling the data or using fixed effects (an extreme version of which estimates separate models for each site). The former approach, however, ignores site effects. Though the latter incorporates site effects, it lacks a framework for predicting the impact of subsequent implementations of the program (e.g., will a new implementation resemble Riverside or Alameda?). I present an hierarchical model that lies between these two extremes. For the GAIN data, I demonstrate that the model captures much of the site-to-site variation of treatment effects, but has less uncertainty than a model which estimates treatment effects separately for each site. I also show that uncertainty in predicting site effects is important: when the predictive uncertainty is ignored, the treatment impact for the Riverside sites is significant, but when we consider predictive uncertainty, the impact for the Riverside sites is insignificant. Finally, I demonstrate that the model is able to extrapolate site effects with reasonable accuracy, when the site for which the prediction is being made does not differ substantially from the sites already observed. For example, the San Diego treatment effects could have been predicted based on observable site characteristics, but the Riverside effects are consistently underestimated.

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Bibliographic Info

Paper provided by Columbia University, Department of Economics in its series Discussion Papers with number 0102-15.

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Length: 40 pages
Date of creation: 2002
Date of revision:
Handle: RePEc:clu:wpaper:0102-15

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Cited by:
  1. Guido Imbens & Jeffrey Wooldridge, 2008. "Recent developments in the econometrics of program evaluation," CeMMAP working papers CWP24/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Arpino, Bruno & Mealli, Fabrizia, 2011. "The specification of the propensity score in multilevel observational studies," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1770-1780, April.
  3. Donald M. Pianto & Sergei Soares, 2004. "Use Of Survey Design For The Evaluation Of Social Programs: The Pnad And Peti," Anais do XXXII Encontro Nacional de Economia [Proceedings of the 32th Brazilian Economics Meeting] 133, ANPEC - Associação Nacional dos Centros de Pósgraduação em Economia [Brazilian Association of Graduate Programs in Economics].
  4. Carlos A. Flores & Oscar A. Mitnik, 2013. "Comparing Treatments across Labor Markets: An Assessment of Nonexperimental Multiple-Treatment Strategies," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1691-1707, December.
  5. V. Joseph Hotz & Guido W. Imbens & Jacob A. Klerman, 2006. "Evaluating the Differential Effects of Alternative Welfare-to-Work Training Components: A Re-Analysis of the California GAIN Program," NBER Working Papers 11939, National Bureau of Economic Research, Inc.
  6. Hunt Allcott, 2012. "Site Selection Bias in Program Evaluation," NBER Working Papers 18373, National Bureau of Economic Research, Inc.
  7. Dehejia, Rajeev, 2013. "The porous dialectic: Experimental and non-experimental methods in development economics," Working Paper Series UNU-WIDER Research Paper , World Institute for Development Economic Research (UNU-WIDER).

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