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The Pre-Program Earnings Dip and the Determinants of Participation in a Social Program: Implications for Simple Program Evaluation Strategies

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  • James J. Heckman
  • Jeffrey A. Smith

Abstract

The key to estimating the impact of a program is constructing the counterfactual outcome representing what would have happened in its absence. This problem becomes more complicated when agents self-select into the program rather than being exogenously assigned to it. This paper uses data from a major social experiment to identify what would have happened to the earnings of self-selected participants in a job training program had they not participated in it. We investigate the implications of these earnings patterns for the validity of widely-used before-after and difference-in-differences estimators. Motivated by the failure of these estimators to produce credible estimates, we investigate the determinants of program participation. We find that labor force status dynamics, rather than earnings or employment dynamics, drive the participation process. Our evidence suggests that training programs often function as a form of job search. Methods that control only for earnings dynamics, like the conventional difference-in-differences estimator, do not adequately capture the underlying differences between participants and non-participants. We use the estimated probabilities of participation in both matching estimators and a nonparametric, conditional version of the differences-in-differences estimator and produce large reductions in the selection bias in non-experimental estimates of the effect of training on earnings.

Suggested Citation

  • James J. Heckman & Jeffrey A. Smith, 1999. "The Pre-Program Earnings Dip and the Determinants of Participation in a Social Program: Implications for Simple Program Evaluation Strategies," NBER Working Papers 6983, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:6983
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    References listed on IDEAS

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    1. 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.
    2. Katherine P. Dickinson & Terry R. Johnson & Richard W. West, 1987. "An Analysis of the Sensitivity of Quasi-Experimental Net Impact Estimates of Ceta Programs," Evaluation Review, , vol. 11(4), pages 452-472, August.
    3. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 605-654.
    4. 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.
    5. Edward C. Bryant & Kalman Rupp, 1987. "Evaluating the Impact of Ceta On Participant Earnings," Evaluation Review, , vol. 11(4), pages 473-492, August.
    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. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    8. James Heckman & Neil Hohmann & Jeffrey Smith & Michael Khoo, 2000. "Substitution and Dropout Bias in Social Experiments: A Study of an Influential Social Experiment," The Quarterly Journal of Economics, Oxford University Press, vol. 115(2), pages 651-694.
    9. Bassi, Laurie J, 1984. "Estimating the Effect of Training Programs with Non-Random Selection," The Review of Economics and Statistics, MIT Press, vol. 66(1), pages 36-43, February.
    10. James J. Heckman & Jeffrey A. Smith, 2004. "The Determinants of Participation in a Social Program: Evidence from a Prototypical Job Training Program," Journal of Labor Economics, University of Chicago Press, vol. 22(2), pages 243-298, April.
    11. 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.
    12. James J. Heckman & Jeffrey A. Smith, 1998. "Evaluating the Welfare State," NBER Working Papers 6542, National Bureau of Economic Research, Inc.
    13. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    14. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    15. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
    16. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," Review of Economic Studies, Oxford University Press, vol. 65(2), pages 261-294.
    17. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
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    More about this item

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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