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Was There a Riverside Miracle? A Framework for Evaluating Multi-Site Programs

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  • Rajeev Dehejia

Abstract

This paper uses data from the Greater Avenues for Independence (GAIN) initiative to discuss the evaluation of programs that are implemented at multiple sites. Two frequently used methods are to pool the data or to use fixed effects (an extreme version of which estimates separate models for each site). The former approach, however, ignores site effects. Though the latter estimates site effects, it lacks a framework for predicting the impact in subsequent implementations of the program (e.g., will a new implementation resemble Riverside or Alameda?). I develop a model for earnings that lies between these two extremes. For the GAIN data, I show that most of the differences across sites are due to differences in the composition of participants. I show also that uncertainty regarding 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.

Suggested Citation

  • Rajeev Dehejia, 2000. "Was There a Riverside Miracle? A Framework for Evaluating Multi-Site Programs," NBER Working Papers 7844, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:7844
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    References listed on IDEAS

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    1. James J. Heckman & Jeffrey Smith, 2000. "The Sensitivity of Experimental Impact Estimates (Evidence from the National JTPA Study)," NBER Chapters,in: Youth Employment and Joblessness in Advanced Countries, pages 331-356 National Bureau of Economic Research, Inc.
    2. Card, David & Krueger, Alan B, 1992. "Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States," Journal of Political Economy, University of Chicago Press, vol. 100(1), pages 1-40, February.
    3. Chamberlain, Gary & Imbens, Guido, 1996. "Hierarchical Bayes Models with Many Instrumental Variables," Scholarly Articles 3221489, Harvard University Department of Economics.
    4. Dehejia, Rajeev H., 2005. "Program evaluation as a decision problem," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 141-173.
    5. V. Joseph Hotz & Guido W. Imbens & Julie H. Mortimer, 1999. "Predicting the Efficacy of Future Training Programs Using Past Experiences," NBER Technical Working Papers 0238, National Bureau of Economic Research, Inc.
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    Cited by:

    1. V. Joseph Hotz & Guido W. Imbens & Jacob A. Klerman, 2000. "The Long-Term Gains from GAIN: A Re-Analysis of the Impacts of the California GAIN Program," NBER Working Papers 8007, National Bureau of Economic Research, Inc.
    2. V. Joseph Hotz & Guido W. Imbens & Jacob A. Klerman, 2000. "The Long-Term Gains from GAIN: A Re-Analysis of the Impacts of the California GAIN Program," NBER Working Papers 8007, National Bureau of Economic Research, Inc.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

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