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A Structural Analysis of the Correlated Random Coefficient Wage Regression Model

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  • Christian Belzil

    ()
    (GATE - Groupe d'analyse et de théorie économique - CNRS : UMR5824 - Université Lumière - Lyon II - Ecole Normale Supérieure Lettres et Sciences Humaines)

  • Jörgen Hansen

    (Department of Economics - Concordia University)

Abstract

We estimate a finite mixture dynamic programming model of schooling decisions in which the log wage regression function is set in a random coefficient framework. We also analyze the determinants of 3 counterfactual experiments (a college attendance subsidy, a high school graduation subsidy and an overall decrease in the rate of time preference) and examine a proposition often claimed in the "Average Treatment Effects" literature; that the discrepancy between OLS and IV estimates of the returns to schooling may be explained by the relatively higher returns experienced by those affected by exogenous policy changes. We find that the average return to experience upon entering the labor market (0.0863) exceeds the average return to schooling (0.0576) and we find more cross-sectional variability in the returns to experience than in the returns to schooling. Labor market skills (as opposed to taste for schooling) appear to be the prime factor explaining schooling attainments. We find little evidence in favor of a positive correlation between reactions induced by an exogenous experiment and the individual specific returns to schooling.

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Bibliographic Info

Paper provided by HAL in its series Post-Print with number halshs-00201350.

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Date of creation: 2007
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Publication status: Published, Journal of Econometrics, 2007, 140, 2, pp. 827-848
Handle: RePEc:hal:journl:halshs-00201350

Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00201350/en/
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Keywords: Random Coefficient; Returns to Schooling; Comparative Advantages; Dynamic Programming; Dynamic Self-Selection;

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  1. Eckstein, Zvi & Wolpin, Kenneth, 1998. "Youth Employment and Academic Performance in High School," IZA Discussion Papers 18, Institute for the Study of Labor (IZA).
  2. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
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  13. James J. Heckman & Edward J. Vytlacil, 2000. "Local Instrumental Variables," NBER Technical Working Papers 0252, National Bureau of Economic Research, Inc.
  14. Jörgen Hansen & Christian Belzil, 2010. "Structural Estimates of the Intergenerational Education Correlation," Working Papers id:2892, eSocialSciences.
  15. Thierry Magnac & David Thesmar, 2002. "Identifying Dynamic Discrete Decision Processes," Econometrica, Econometric Society, vol. 70(2), pages 801-816, March.
  16. Card, David, 1999. "The causal effect of education on earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 30, pages 1801-1863 Elsevier.
  17. Christian Belzil & Jörgen Hansen, 2002. "Unobserved Ability and the Return to Schooling," CIRANO Working Papers 2002s-19, CIRANO.
  18. Jean-Pierre Florens & James Heckman & Costas Meghir & Edward Vytlacil, 2002. "Instrumental variables, local instrumental variables and control functions," CeMMAP working papers CWP15/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  19. Michael P. Keane & Kenneth I. Wolpin, 1995. "The career decisions of young men," Working Papers 559, Federal Reserve Bank of Minneapolis.
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  21. Stephen V. Cameron & James J. Heckman, 1998. "Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts of American Males," Journal of Political Economy, University of Chicago Press, vol. 106(2), pages 262-333, April.
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