IDEAS home Printed from https://ideas.repec.org/p/nbr/nberte/0166.html
   My bibliography  Save this paper

Accounting for Dropouts in Evaluations of Social Experiments

Author

Listed:
  • 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
    Note: LS
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/t0166.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Howard S. Bloom, 1984. "Accounting for No-Shows in Experimental Evaluation Designs," Evaluation Review, , vol. 8(2), pages 225-246, April.
    3. Arulampalam, W. & Robin A. Naylor & Jeremy P. Smith, 2002. "University of Warwick," Royal Economic Society Annual Conference 2002 9, Royal Economic Society.
    4. James J. Heckman, 1991. "Randomization and Social Policy Evaluation Revisited," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
    5. 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.
    6. 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.
    7. 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.
    8. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-318, May.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    4. James J. Heckman, 1995. "Randomization as an Instrumental Variable," NBER Technical Working Papers 0184, National Bureau of Economic Research, Inc.
    5. Steven Lehrer & Weili Ding, 2004. "Estimating Dynamic Treatment Effects from Project STAR," Econometric Society 2004 North American Summer Meetings 252, Econometric Society.
    6. 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.
    7. Battistin, Erich & Enrico Rettore, 2003. "Another look at the Regression Discontinuity Design," Royal Economic Society Annual Conference 2003 18, Royal Economic Society.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joshua D. Angrist, 2004. "Treatment effect heterogeneity in theory and practice," Economic Journal, Royal Economic Society, vol. 114(494), pages 52-83, March.
    2. Richard Blundell & Monica Costa Dias, 2009. "Alternative Approaches to Evaluation in Empirical Microeconomics," Journal of Human Resources, University of Wisconsin Press, vol. 44(3).
    3. Ravallion, Martin, 2008. "Evaluating Anti-Poverty Programs," Handbook of Development Economics, in: T. Paul Schultz & John A. Strauss (ed.), Handbook of Development Economics, edition 1, volume 4, chapter 59, pages 3787-3846, Elsevier.
    4. Heckman, James, 2001. "Accounting for Heterogeneity, Diversity and General Equilibrium in Evaluating Social Programmes," Economic Journal, Royal Economic Society, vol. 111(475), pages 654-699, November.
    5. Sianesi, Barbara, 2017. "Evidence of randomisation bias in a large-scale social experiment: The case of ERA," Journal of Econometrics, Elsevier, vol. 198(1), pages 41-64.
    6. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    7. Clint Harris, 2022. "Interpreting Instrumental Variable Estimands with Unobserved Treatment Heterogeneity: The Effects of College Education," Papers 2211.13132, arXiv.org.
    8. Brian Bell & Richard Blundell & John Reenen, 1999. "Getting the Unemployed Back to Work: The Role of Targeted Wage Subsidies," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 6(3), pages 339-360, August.
    9. Battistin, Erich & Rettore, Enrico, 2008. "Ineligibles and eligible non-participants as a double comparison group in regression-discontinuity designs," Journal of Econometrics, Elsevier, vol. 142(2), pages 715-730, February.
    10. Cockx, Bart & Bardoulat, Isabelle, 1999. "Vocational Training: Does it speed up the Transition Rate out of Unemployment ?," LIDAM Discussion Papers IRES 1999032, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    11. Antje Brunner & Jan Pieter Krahnen, 2008. "Multiple Lenders and Corporate Distress: Evidence on Debt Restructuring," Review of Economic Studies, Oxford University Press, vol. 75(2), pages 415-442.
    12. 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.
    13. Emanuela Galasso & Martin Ravallion & Agustin Salvia, 2004. "Assisting the Transition from Workfare to Work: A Randomized Experiment," ILR Review, Cornell University, ILR School, vol. 58(1), pages 128-142, October.
    14. Hidehiko Ichimura & Christopher R. Taber, 2000. "Direct Estimation of Policy Impacts," NBER Technical Working Papers 0254, National Bureau of Economic Research, Inc.
    15. Robert J. LaLonde, 2003. "Employment and Training Programs," NBER Chapters, in: Means-Tested Transfer Programs in the United States, pages 517-586, National Bureau of Economic Research, Inc.
    16. Jeffrey Smith, 2000. "A Critical Survey of Empirical Methods for Evaluating Active Labor Market Policies," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 136(III), pages 247-268, September.
    17. Giorgio Pietro, 2007. "The effect of computer use on earnings in Italy," Empirical Economics, Springer, vol. 33(2), pages 245-262, September.
    18. Manuel Arellano & Stéphane Bonhomme, 2017. "Quantile Selection Models With an Application to Understanding Changes in Wage Inequality," Econometrica, Econometric Society, vol. 85, pages 1-28, January.
    19. Alberto Abadie, 2000. "Semiparametric Estimation of Instrumental Variable Models for Causal Effects," NBER Technical Working Papers 0260, National Bureau of Economic Research, Inc.
    20. James J. Heckman, 2008. "Econometric Causality," International Statistical Review, International Statistical Institute, vol. 76(1), pages 1-27, April.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberte:0166. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.