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Assessing the Case for Social Experiments

Author

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  • James J. Heckman
  • Jeffrey A. Smith

Abstract

This paper analyzes the method of social experiments. The assumptions that justify the experimental method are exposited. Parameters of interest in evaluating social programs are discussed. The authors show how experiments sometimes serve as instrumental variables to identify program impacts. The most favorable case for experiments ignores variability across persons in response to treatments received and assumes that mean impacts of a program are the main object of interest in conducting an evaluation. Experiments do not identify the distribution of program gains unless additional assumptions are maintained. Evidence on the validity of the assumptions used to justify social experiments is presented.

Suggested Citation

  • 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.
  • Handle: RePEc:aea:jecper:v:9:y:1995:i:2:p:85-110
    Note: DOI: 10.1257/jep.9.2.85
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    File URL: http://www.aeaweb.org/articles.php?doi=10.1257/jep.9.2.85
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    References listed on IDEAS

    as
    1. Heckman, J.J. & Hotz, V.J., 1988. "Choosing Among Alternative Nonexperimental Methods For Estimating The Impact Of Social Programs: The Case Of Manpower Training," University of Chicago - Economics Research Center 88-12, Chicago - Economics Research Center.
    2. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    3. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-318, May.
    4. 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.
    5. Ashenfelter, Orley & Card, David, 1985. "Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs," The Review of Economics and Statistics, MIT Press, vol. 67(4), pages 648-660, November.
    6. 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.
    7. Kydland, Finn E & Prescott, Edward C, 1991. " The Econometrics of the General Equilibrium Approach to Business Cycles," Scandinavian Journal of Economics, Wiley Blackwell, vol. 93(2), pages 161-178.
    8. Barnett,William A. & Powell,James & Tauchen,George E. (ed.), 1991. "Nonparametric and Semiparametric Methods in Econometrics and Statistics," Cambridge Books, Cambridge University Press, number 9780521424318, October.
    9. Andrews, Donald W K, 1991. "Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models," Econometrica, Econometric Society, vol. 59(2), pages 307-345, March.
    10. Gary Burtless & Larry L. Orr, 1986. "Are Classical Experiments Needed for Manpower Policy," Journal of Human Resources, University of Wisconsin Press, vol. 21(4), pages 606-639.
    11. Barnett,William A. & Powell,James & Tauchen,George E. (ed.), 1991. "Nonparametric and Semiparametric Methods in Econometrics and Statistics," Cambridge Books, Cambridge University Press, number 9780521370905, October.
    12. Ashenfelter, Orley C, 1978. "Estimating the Effect of Training Programs on Earnings," The Review of Economics and Statistics, MIT Press, vol. 60(1), pages 47-57, February.
    13. Thomas Fraker & Rebecca Maynard, 1987. "The Adequacy of Comparison Group Designs for Evaluations of Employment-Related Programs," Journal of Human Resources, University of Wisconsin Press, vol. 22(2), pages 194-227.
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    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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