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A Structural Analysis of the Correlated Random Coefficient Wage Regression Model

  • Christian Belzil
  • Jörgen Hansen

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. The model allows for absolute and comparative advantages in the labor market and assumes that the population is composed of 8 unknown types. Overall, labor market skills (as opposed to taste for schooling) appear to be the prime factor explaining schooling attainments. The estimates indicate a higher cross-sectional variance in the returns to experience than in the returns to schooling. From various simulations, we find that the sub-population mostly affected by a counterfactual change in the utility of attending school is composed of individuals who have any combination of some of the following attributes; absolute advantages in the labor market, high returns to experience, low utility of attending school and relatively low returns to schooling. Unlike what is often postulated in the average treatment effect literature, the weak correlation (unconditional) between the returns to schooling and the individual reactions to treatment is not sufficient to reconcile the discrepancy between OLS and IV estimates of the returns to schooling often found in the literature. Nous estimons un modèle de programmation dynamique des choix en éducation dans lequel la fonction de régression logarithmique du salaire dépend de coefficients aléatoires. Ce modèle permet de tenir compte des avantages absolus et comparés des individus sur le marché de l'emploi et part du principe que la population est composée de huit types inconnus. Dans l'ensemble, les qualifications sur le marché du travail (par opposition au goût de s'instruire) semblent être le principal facteur permettant d'expliquer les niveaux de scolarité. Nos estimations indiquent une plus forte variance transversale dans les rendements de l'éducation. À partir de plusieurs simulations, nous trouvons que la sous-population qui se trouve le plus affecté par un changement contrefactuel dans le niveau d'utilité de la fréquentation scolaire est celle composée d'individus possédant une combinaison des attributs suivants : avantage absolu sur le marché du travail, rendements élevés de l'expérience, faible niveau d'utilité par rapport à la fréquentation scolaire et rendements de l'éducation relativement bas. Contrairement à ce qui est souvent postulé dans la littérature, la corrélation faible (non conditionnelle) entre les rendements de l'éducation et les réactions individuelles aux traitements n'est pas une condition suffisante pour réconcilier les différences que l'on retrouve souvent dans la littérature entre les résultats des estimations par MCO et par VI des rendements de l'éducation.

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Paper provided by CIRANO in its series CIRANO Working Papers with number 2002s-07.

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Length: 36 pages
Date of creation: 01 Jan 2002
Date of revision:
Handle: RePEc:cir:cirwor:2002s-07
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  1. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects and Econometric Policy Evaluation," NBER Technical Working Papers 0306, National Bureau of Economic Research, Inc.
  2. David Card, 2000. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," NBER Working Papers 7769, National Bureau of Economic Research, Inc.
  3. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
  4. Robert M. Sauer, 2004. "Educational Financing and Lifetime Earnings," Review of Economic Studies, Oxford University Press, vol. 71(4), pages 1189-1216.
  5. 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.
  6. Willis, Robert J & Rosen, Sherwin, 1979. "Education and Self-Selection," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages S7-36, October.
  7. Costas Megir & Martin Palme, 2001. "The effect of a social experiment in education," CEE Discussion Papers 0014, Centre for the Economics of Education, LSE.
  8. Thierry Magnac & David Thesmar, 2002. "Identifying Dynamic Discrete Decision Processes," Econometrica, Econometric Society, vol. 70(2), pages 801-816, March.
  9. 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.
  10. Joshua Angrist & Alan Krueger, 1990. "Does Compulsory School Attendance Affect Schooling and Earnings?," Working Papers 653, Princeton University, Department of Economics, Industrial Relations Section..
  11. Christian Belzil & J�rgen Hansen, 2002. "Unobserved Ability and the Return to Schooling," Econometrica, Econometric Society, vol. 70(5), pages 2075-2091, September.
  12. Charles F. Manski & John V. Pepper, 1998. "Monotone Instrumental Variables with an Application to the Returns to Schooling," NBER Technical Working Papers 0224, National Bureau of Economic Research, Inc.
  13. Eckstein, Z. & Wolpin, K.I., 1997. "Youth Employment and Academic Perfomance in High School," Papers 24-97, Tel Aviv.
  14. Michael P. Keane & Kenneth I. Wolpin, 1995. "The career decisions of young men," Working Papers 559, Federal Reserve Bank of Minneapolis.
  15. 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.
  16. Belzil, Christian & Hansen, Jörgen, 2003. "Structural Estimates of the Intergenerational Education Correlation," IZA Discussion Papers 973, Institute for the Study of Labor (IZA).
  17. 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.
  18. Wooldridge, Jeffrey M., 1997. "On two stage least squares estimation of the average treatment effect in a random coefficient model," Economics Letters, Elsevier, vol. 56(2), pages 129-133, October.
  19. Hotz, V.J. & Miller, R.A., 1991. "Conditional Choice Probabilities and the Estimation of Dynamic Models," GSIA Working Papers 1992-12, Carnegie Mellon University, Tepper School of Business.
  20. Kenneth I. Wolpin & Mark R. Rosenzweig, 2000. "Natural "Natural Experiments" in Economics," Journal of Economic Literature, American Economic Association, vol. 38(4), pages 827-874, December.
  21. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
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  23. 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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