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Do Dropouts Suffer from Dropping Out? Estimation and Prediction of Outcome Gains in Generalized Selection Models

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  • Li, Mingliang
  • Poirier, Dale J
  • Tobias, Justin

Abstract

In this paper we describe methods for predicting distributions of outcome gains in the framework of a latent variable selection model. We describe such procedures for Student-t selection models and a finite mixture of Gaussian selection models. Importantly, our algorithms for fitting these models are simple to implement in practice, and also permit learning to take place about the non-identified cross-regime correlation parameter. Using data from High School and Beyond, we apply our methods to determine the impact of dropping out of high school on a math test score taken at the senior year of high school. Our results show that selection bias is an important feature of this data, that our beliefs about this non-identified correlation are updated from the data, and that generalized models of selectivity offer an improvement over the ``textbook'' Gaussian model. Further, our results indicate that on average dropping out of high school has a large negative impact on senior-year test scores. However, for those individuals who actually drop out of high school, the act of dropping out of high school does not have a significantly negative impact on test scores. This suggests that policies aimed at keeping students in school may not be as beneficial as first thought, since those individuals who must be induced to stay in school are not the ones who benefit significantly (in terms of test scores) from staying in school.

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Bibliographic Info

Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number 12013.

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Date of creation: 01 Jan 2003
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Publication status: Published in Journal of Applied Econometrics 2003, vol. 19, pp. 203-225
Handle: RePEc:isu:genres:12013

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Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070
Phone: +1 515.294.6741
Fax: +1 515.294.0221
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Web page: http://www.econ.iastate.edu
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References

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  1. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, Econometric Society, vol. 66(5), pages 1017-1098, September.
  2. Koop, Gary & Poirier, Dale J., 1997. "Learning about the across-regime correlation in switching regression models," Journal of Econometrics, Elsevier, Elsevier, vol. 78(2), pages 217-227, June.
  3. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, American Economic Association, vol. 80(2), pages 313-18, May.
  4. Dehejia, Rajeev H., 2005. "Program evaluation as a decision problem," Journal of Econometrics, Elsevier, Elsevier, vol. 125(1-2), pages 141-173.
  5. James Heckman & Justin L. Tobias & Edward Vytlacil, 2003. "Simple Estimators for Treatment Parameters in a Latent-Variable Framework," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 748-755, August.
  6. Heckman, James J & Smith, Jeffrey, 1997. "Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 64(4), pages 487-535, October.
  7. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, Econometric Society, vol. 62(2), pages 467-75, March.
  8. James J. Heckman & Edward J. Vytlacil, 2000. "Local Instrumental Variables," NBER Technical Working Papers 0252, National Bureau of Economic Research, Inc.
  9. Vijverberg, Wim P. M., 1993. "Measuring the unidentified parameter of the extended Roy model of selectivity," Journal of Econometrics, Elsevier, Elsevier, vol. 57(1-3), pages 69-89.
  10. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc.
  11. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 8(S), pages S19-40, Suppl. De.
  12. Heckman, James J. & Vytlacil, Edward J., 2000. "The relationship between treatment parameters within a latent variable framework," Economics Letters, Elsevier, Elsevier, vol. 66(1), pages 33-39, January.
  13. Manski, C.F., 1989. "Nonparametric Bounds On Treatment Effects," Working papers, Wisconsin Madison - Social Systems 8909, Wisconsin Madison - Social Systems.
  14. Nobile, Agostino, 2000. "Comment: Bayesian multinomial probit models with a normalization constraint," Journal of Econometrics, Elsevier, Elsevier, vol. 99(2), pages 335-345, December.
  15. Bjorklund, Anders & Moffitt, Robert, 1987. "The Estimation of Wage Gains and Welfare Gains in Self-selection," The Review of Economics and Statistics, MIT Press, vol. 69(1), pages 42-49, February.
  16. James J. Heckman & Jeffrey A. Smith, 1998. "Evaluating the Welfare State," NBER Working Papers 6542, National Bureau of Economic Research, Inc.
  17. Chib, Siddhartha & Hamilton, Barton H., 2002. "Semiparametric Bayes analysis of longitudinal data treatment models," Journal of Econometrics, Elsevier, Elsevier, vol. 110(1), pages 67-89, September.
  18. Dale J. Poirier, 1995. "Intermediate Statistics and Econometrics: A Comparative Approach," MIT Press Books, The MIT Press, The MIT Press, edition 1, volume 1, number 0262161494, December.
  19. Poirier, Dale J., 1998. "Revising Beliefs In Nonidentified Models," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 14(04), pages 483-509, August.
  20. Chib, Siddhartha & Hamilton, Barton H., 2000. "Bayesian analysis of cross-section and clustered data treatment models," Journal of Econometrics, Elsevier, Elsevier, vol. 97(1), pages 25-50, July.
  21. Manski, C.F., 1990. "The Selection Problem," Working papers, Wisconsin Madison - Social Systems 90-12, Wisconsin Madison - Social Systems.
  22. Heckman, James J & Honore, Bo E, 1990. "The Empirical Content of the Roy Model," Econometrica, Econometric Society, Econometric Society, vol. 58(5), pages 1121-49, September.
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Cited by:
  1. Li, Mingliang & Tobias, Justin L., 2011. "Bayesian inference in a correlated random coefficients model: Modeling causal effect heterogeneity with an application to heterogeneous returns to schooling," Journal of Econometrics, Elsevier, Elsevier, vol. 162(2), pages 345-361, June.
  2. Nalan Basturk, 2014. "On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14," Tinbergen Institute Discussion Papers 14-085/III, Tinbergen Institute.
  3. Chib, Siddhartha & Jacobi, Liana, 2007. "Modeling and calculating the effect of treatment at baseline from panel outcomes," Journal of Econometrics, Elsevier, Elsevier, vol. 140(2), pages 781-801, October.

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