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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.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0166.

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Date of creation: Sep 1994
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Publication status: published as Review of Economics and Statistics, Vol. 80, no. 1 (1998): 1-14.
Handle: RePEc:nbr:nberte:0166

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References

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  1. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-18, May.
  2. 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.
  3. Joshua D. Angrist & Guido W. Imbens, 1991. "Sources of Identifying Information in Evaluation Models," NBER Technical Working Papers 0117, National Bureau of Economic Research, Inc.
  4. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-75, March.
  5. V. Joseph Hotz & Seth Sanders, . "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.
  6. Arulampalam, W. & Robin A. Naylor & Jeremy P. Smith, 2002. "University of Warwick," Royal Economic Society Annual Conference 2002 9, Royal Economic Society.
  7. Gary Burtless, 1985. "Are targeted wage subsidies harmful? Evidence from a wage voucher experiment," Industrial and Labor Relations Review, ILR Review, Cornell University, ILR School, vol. 39(1), pages 105-114, October.
  8. James J. Heckman, 1991. "Randomization and Social Policy Evaluation," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
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Cited by:
  1. Ding, Weili & Lehrer, Steven F., 2009. "Estimating Treatment Effects from Contaminated Multi-Period Education Experiments: The Dynamic Impacts of Class Size Reductions," CLSSRN working papers clsrn_admin-2009-43, Vancouver School of Economics, revised 22 Jul 2009.
  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. Mark Bryan & Wiji Arulampalam & Alison Booth, 2004. "Are There Asymmetries In The Effects Of Training On The Conditional Male Wage Distribution?," Royal Economic Society Annual Conference 2004 115, Royal Economic Society.
  5. James J. Heckman, 1995. "Randomization as an Instrumental Variable," NBER Technical Working Papers 0184, National Bureau of Economic Research, Inc.
  6. 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.
  7. Steven Lehrer & Weili Ding, 2004. "Estimating Dynamic Treatment Effects from Project STAR," Econometric Society 2004 North American Summer Meetings 252, Econometric Society.

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