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Do dropouts suffer from dropping out? Estimation and prediction of outcome gains in generalized selection models

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Author Info
Mingliang Li (Department of Economics, University of California, Irvine, USA)
Dale J. Poirier (Department of Economics, University of California, Irvine, USA)
Justin L. Tobias (Department of Economics, University of California, Irvine, USA)

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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. Copyright © 2004 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/jae.731
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File URL: http://qed.econ.queensu.ca:80/jae/2004-v19.2/
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Publisher Info
Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 19 (2004)
Issue (Month): 2 ()
Pages: 203-225
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:jae:japmet:v:19:y:2004:i:2:p:203-225

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  1. Manski, C.F., 1990. "The Selection Problem," Working papers 90-12, Wisconsin Madison - Social Systems.
  2. Vijverberg, Wim P. M., 1993. "Measuring the unidentified parameter of the extended Roy model of selectivity," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 69-89. [Downloadable!] (restricted)
  3. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-18, May.
  4. James J. Heckman & Edward J. Vytlacil, 2000. "Local Instrumental Variables," NBER Technical Working Papers 0252, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  5. 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.
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  6. Rajeev Dehejia, 1999. "Program Evaluation as a Decision Problem," NBER Working Papers 6954, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  7. 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. [Downloadable!] (restricted)
  8. Nobile, Agostino, 2000. "Comment: Bayesian multinomial probit models with a normalization constraint," Journal of Econometrics, Elsevier, vol. 99(2), pages 335-345, December. [Downloadable!] (restricted)
  9. 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, Blackwell Publishing, vol. 64(4), pages 487-535, October. [Downloadable!] (restricted)
  10. 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. [Downloadable!] (restricted)
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  11. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-23, May. [Downloadable!] (restricted)
  12. Koop, Gary & Poirier, Dale J., 1997. "Learning about the across-regime correlation in switching regression models," Journal of Econometrics, Elsevier, vol. 78(2), pages 217-227, June. [Downloadable!] (restricted)
  13. Chib, Siddhartha & Hamilton, Barton H., 2002. "Semiparametric Bayes analysis of longitudinal data treatment models," Journal of Econometrics, Elsevier, vol. 110(1), pages 67-89, September. [Downloadable!] (restricted)
  14. 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. [Downloadable!] (restricted)
  15. Heckman, James J & Honore, Bo E, 1990. "The Empirical Content of the Roy Model," Econometrica, Econometric Society, vol. 58(5), pages 1121-49, September. [Downloadable!] (restricted)
  16. Heckman, James J. & Vytlacil, Edward J., 2000. "The relationship between treatment parameters within a latent variable framework," Economics Letters, Elsevier, vol. 66(1), pages 33-39, January. [Downloadable!] (restricted)
  17. 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, 06. [Downloadable!] (restricted)
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  18. Chib, Siddhartha & Hamilton, Barton H., 2000. "Bayesian analysis of cross-section and clustered data treatment models," Journal of Econometrics, Elsevier, vol. 97(1), pages 25-50, July. [Downloadable!] (restricted)
  19. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages S19-40, Suppl. De. [Downloadable!] (restricted)
  20. James J. Heckman & Jeffrey A. Smith, 1998. "Evaluating the Welfare State," NBER Working Papers 6542, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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