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Accounting for Dropouts in Evaluations of Social Experiments

Author

Listed:
  • Heckman, J.
  • Smith, J.
  • Taber, C.

Abstract

This paper considers the statistical and economic justification for one widely-used method of adjusting data from social experiments to account for dropping-out behavior due to Bloom (1984). We generalize the method to apply to distributions not just means, and present tests of the key identifying assumption in this context. A reanalysis of the National JTPA experiment base vindicates application of Bloom's method in this context.
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Suggested Citation

  • Heckman, J. & Smith, J. & Taber, C., 1994. "Accounting for Dropouts in Evaluations of Social Experiments," University of Chicago - Economics Research Center 94-3, Chicago - Economics Research Center.
  • Handle: RePEc:fth:chicer:94-3
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    References listed on IDEAS

    as
    1. Howard S. Bloom, 1984. "Accounting for No-Shows in Experimental Evaluation Designs," Evaluation Review, , vol. 8(2), pages 225-246, April.
    2. Angrist, J.D. & Imbens, G.W., 1991. "Sources of identifying information in evaluation models," Discussion Paper 1991-42, Tilburg University, Center for Economic Research.
    3. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    4. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-318, May.
    5. Arulampalam, W. & Robin A. Naylor & Jeremy P. Smith, 2002. "University of Warwick," Royal Economic Society Annual Conference 2002 9, Royal Economic Society.
    6. James J. Heckman, 1991. "Randomization and Social Policy Evaluation," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
    7. Dubin, Jeffrey A. & Rivers, Douglas, 1993. "Experimental estimates of the impact of wage subsidies," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 219-242, March.
    8. V. Joseph Hotz & Seth Sanders, "undated". "Bounding Treatment Effects in Controlled and Natural Experiments Subject to Post-Randomization Treatment Choice," University of Chicago - Population Research Center 94-2, Chicago - Population Research Center.
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    Citations

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    Cited by:

    1. Weili Ding & Steven F. Lehrer, 2010. "Estimating Treatment Effects from Contaminated Multiperiod Education Experiments: The Dynamic Impacts of Class Size Reductions," The Review of Economics and Statistics, MIT Press, vol. 92(1), pages 31-42, February.
    2. Wiji Arulampalam & Alison Booth & Mark Bryan, 2010. "Are there asymmetries in the effects of training on the conditional male wage distribution?," Journal of Population Economics, Springer;European Society for Population Economics, pages 251-272.
    3. Peter Z. Schochet, 2015. "Statistical Theory for the RCT-YES Software: Design-Based Causal Inference for RCTs," Mathematica Policy Research Reports a0c005c003c242308a92c02dc, Mathematica Policy Research.
    4. 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.
    5. Battistin, Erich & Enrico Rettore, 2003. "Another look at the Regression Discontinuity Design," Royal Economic Society Annual Conference 2003 18, Royal Economic Society.
    6. James J. Heckman, 1995. "Randomization as an Instrumental Variable," NBER Technical Working Papers 0184, National Bureau of Economic Research, Inc.
    7. James J. Heckman & Jeffrey A. Smith, 1995. "Assessing the Case for Social Experiments," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 85-110, Spring.
    8. Steven Lehrer & Weili Ding, 2004. "Estimating Dynamic Treatment Effects from Project STAR," Econometric Society 2004 North American Summer Meetings 252, Econometric Society.

    More about this item

    Keywords

    evaluation;

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

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