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Accounting for Dropouts in Evaluations of Social Experiments

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  • James Heckman
  • Jeffrey Smith
  • Christopher Taber

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.

Suggested Citation

  • James Heckman & Jeffrey Smith & Christopher Taber, 1994. "Accounting for Dropouts in Evaluations of Social Experiments," NBER Technical Working Papers 0166, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0166
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    References listed on IDEAS

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    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," Harvard Institute of Economic Research Working Papers 1568, Harvard - Institute of Economic Research.
    3. Angrist, J.D. & Imbens, G.W., 1991. "Sources of Identifying Information in Evaluation Models," Harvard Institute of Economic Research Working Papers 1568, Harvard - Institute of Economic Research.
    4. 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.
    5. James J. Heckman, 1991. "Randomization and Social Policy Evaluation Revisited," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
    6. Arulampalam, W. & Robin A. Naylor & Jeremy P. Smith, 2002. "University of Warwick," Royal Economic Society Annual Conference 2002 9, Royal Economic Society.
    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.
    9. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-318, May.
    10. Gary Burtless, 1985. "Are Targeted Wage Subsidies Harmful? Evidence from a Wage Voucher Experiment," ILR Review, Cornell University, ILR School, vol. 39(1), pages 105-114, October.
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    Citations

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    Cited by:

    1. Peter Z. Schochet, "undated". "Statistical Theory for the RCT-YES Software: Design-Based Causal Inference for RCTs," Mathematica Policy Research Reports a0c005c003c242308a92c02dc, Mathematica Policy Research.
    2. 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.
    3. Erich Battistin & Enrico Rettore, 2003. "Another look at the regression discontinuity design," CeMMAP working papers CWP01/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. 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, vol. 23(1), pages 251-272, January.
    5. James J. Heckman, 1995. "Randomization as an Instrumental Variable," NBER Technical Working Papers 0184, National Bureau of Economic Research, Inc.
    6. Steven Lehrer & Weili Ding, 2004. "Estimating Dynamic Treatment Effects from Project STAR," Econometric Society 2004 North American Summer Meetings 252, Econometric Society.
    7. 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.
    8. Richter, André & Robling, Per Olof, 2013. "Multigenerational e ffects of the 1918-19 influenza pandemic in Sweden," Working Paper Series 5/2013, Stockholm University, Swedish Institute for Social Research.
    9. 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.

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    More about this item

    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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