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Unobserved Ability and the Return to Schooling

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  • Christian Belzil

    ()

  • Jörgen Hansen

    ()

Abstract

We estimate a structural dynamic programming model of schooling decisions with unobserved heterogeneity in school ability and market ability on a sample taken from the National Longitudinal Survey of Youth (NLSY). Both the instantaneous utility of attending school and the wage regression function are estimated flexibly. The null hypothesis that the local returns to schooling are constant is strongly rejected in favor of a convex wage regression function composed of 8 spline segments. The local returns are very low until grade 11 (1% per year or less), increase to 3.7% in grade 12 and exceed 10% only from grade 14 to grade 16. The average return increases smoothly from 0.4% (grade 7) to 4.6% (grade 16). The convexity of the log wage regression function implies that those who obtain more schooling also experience higher average returns. We strongly reject the null hypothesis that unobserved market ability is uncorrelated with realized schooling attainments, which underlies many previous studies that have used OLS to estimate the return to schooling. The correlation between realized schooling and market ability is found to be positive and is consistent with the existence of a positive "Ability Bias". À partir d'un échantillon tiré du National Longitudinal Survey of Youth (NLSY), nous estimons un modèle de programmation dynamique des choix d'éducation en présence d'hétérogénéité non observée dans les capacités scolaires et aptitudes sur le marché de l'emploi. L'utilité instantanée de la fréquentation scolaire ainsi que la fonction de salaire sont évaluées de façon flexible. L'hypothèse nulle que les rendements marginaux de l'éducation sont constants est catégoriquement rejetée en faveur d'une fonction de salaire convexe composée de huit segments de fonction d'approximation spline. Les rendements marginaux s'avèrent être très faibles jusqu'à la onzième année (1 % ou moins par an), augmentent jusqu'à 3,7 % pour la douzième année et dépassent les 10 % pour les années 14 à 16. Le rendement moyen augmente uniformément de 0,4 % (7ème année) à 4,6 % (16ème année). La convexité de la fonction de régression logarithmique du salaire implique que ceux qui atteignent un plus haut niveau de scolarisation obtiennent également de meilleurs rendements moyens sur le marché de l'emploi. Nous rejetons l'hypothèse nulle selon laquelle les aptitudes non observées sur le marché du travail ne sont pas corrélées avec les niveaux d'éducation atteints. Ce résultat va à l'encontre de ceux obtenus dans plusieurs études antérieures qui estimaient le rendement de l'éducation par la méthode des MCO. Nous trouvons une corrélation positive entre le niveau de scolarité atteint et les aptitudes sur le marché de l'emploi, confirmant ainsi l'existence d'un «Biais d'aptitude» positif.

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Bibliographic Info

Paper provided by CIRANO in its series CIRANO Working Papers with number 2002s-19.

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Date of creation: 01 Feb 2002
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Handle: RePEc:cir:cirwor:2002s-19

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Keywords: Return to Schooling; Dynamic Programming; Ability Bias; Discount Rate Bias; Rendements de l'éducation; Programmation dynamique; Biais d'aptitude; Biais de taux d'escompte;

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References

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  1. Christian Belzil & Jörgen Hansen, 2002. "A Structural Analysis of the Correlated Random Coefficient Wage Regression Model," CIRANO Working Papers 2002s-07, CIRANO.
  2. Whitney K. Newey & James L. Powell & Francis Vella, 1998. "Nonparametric Estimation of Triangular Simultaneous Equations Models," Working papers 98-6, Massachusetts Institute of Technology (MIT), Department of Economics.
  3. Christian Belzil & Jörgen Hansen, 2002. "Unobserved Ability and the Return to Schooling," CIRANO Working Papers 2002s-19, CIRANO.
  4. 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.
  5. Douglas Staiger & James H. Stock, 1994. "Instrumental Variables Regression with Weak Instruments," NBER Technical Working Papers 0151, National Bureau of Economic Research, Inc.
  6. Narayana R. Kocherlakota, 1995. "The equity premium: it's still a puzzle," Discussion Paper / Institute for Empirical Macroeconomics 102, Federal Reserve Bank of Minneapolis.
  7. Eckstein, Zvi & Wolpin, Kenneth, 1998. "Youth Employment and Academic Performance in High School," CEPR Discussion Papers 1861, C.E.P.R. Discussion Papers.
  8. Lang, Kevin, 1993. "Ability Bias, Discount Rate Bias and the Return to Education," MPRA Paper 24651, University Library of Munich, Germany.
  9. Keane, Michael P & Wolpin, Kenneth I, 1997. "The Career Decisions of Young Men," Journal of Political Economy, University of Chicago Press, vol. 105(3), pages 473-522, June.
  10. 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.
  11. Audrey Light & Wayne Strayer, 2000. "Determinants of College Completion: School Quality or Student Ability?," Journal of Human Resources, University of Wisconsin Press, vol. 35(2), pages 299-332.
  12. Belzil, Christian & Hansen, Jörgen, 2001. "Heterogeneous Returns to Human Capital and Dynamic Self-Selection," IZA Discussion Papers 272, Institute for the Study of Labor (IZA).
  13. Richard Blundell & James Powell, 2001. "Endogeneity in nonparametric and semiparametric regression models," CeMMAP working papers CWP09/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  14. Christian Belzil & Jörgen Hansen, 2001. "Estimating the Intergenerational Education Correlation from a Dynamic Programming Model," CIRANO Working Papers 2001s-20, CIRANO.
  15. 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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