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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.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 7844.

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Date of creation: Aug 2000
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Publication status: published as "Was There a Riverside Miracle? A Framework for Evaluating Multi-Site Programs" Journal of Business and Economic Statistics, Vol. 21, No. 1, pp. 1-11(January 2003)
Handle: RePEc:nbr:nberwo:7844

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  1. Dehejia, Rajeev H., 2005. "Program evaluation as a decision problem," Journal of Econometrics, Elsevier, Elsevier, vol. 125(1-2), pages 141-173.
  2. 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.
  3. 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.
  4. David Card & Alan Krueger, 1990. "Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States," NBER Working Papers 3358, National Bureau of Economic Research, Inc.
  5. Gary Chamberlain & Guido W. Imbens, 1996. "Hierarchical Bayes Models with Many Instrumental Variables," NBER Technical Working Papers 0204, National Bureau of Economic Research, Inc.
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Cited by:
  1. V. Joseph Hotz & Guido W. Imbens & Jacob A. Klerman, 2001. "The Long-Term Gains from GAIN: A Re-Analysis of the Impacts of the California GAIN Program," Working Papers, RAND Corporation Publications Department 01-03, RAND Corporation Publications Department.

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