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Accounting For Heterogeneous Returns In Sequential Schooling Decisions

  • Gema Zamarro

    ()

    (CEMFI, Centro de Estudios Monetarios y Financieros)

This paper presents a method for estimating returns to schooling that takes into account that returns may be heterogeneous among agents and that educational decisions are made sequentially. A sequential decision model is interesting because it explicitly considers that the level of education of each individual is the result of previous schooling choices and so, the variation of supply-side instruments over time will emerge as a source of identification of the desired parameters. A test for heterogeneity in returns from sequential schooling decisions is developed and expressions for Marginal Treatment Effects are obtained in this context. Returns are estimated and tested from cross-sectional data from a Spanish household survey that contains rich family background information and useful instruments. This data is stratified by level of education and so estimators are adapted to take this feature into account. Finally, this methodology is used to analyze possible effects of the 1970 reform of the Spanish education system.

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Paper provided by CEMFI in its series Working Papers with number wp2006_0609.

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Date of creation: Jun 2006
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Handle: RePEc:cmf:wpaper:wp2006_0609
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  1. Zamarro, Gema, 2010. "Accounting for heterogeneous returns in sequential schooling decisions," Journal of Econometrics, Elsevier, vol. 156(2), pages 260-276, June.
  2. Belzil, Christian, 2007. "The return to schooling in structural dynamic models: a survey," European Economic Review, Elsevier, vol. 51(5), pages 1059-1105, July.
  3. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
  4. Comay, Yochanan & Melnik, A & Pollatschek, M A, 1973. "The Option Value of Education and the Optimal Path for Investment in Human Capital," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(2), pages 421-35, June.
  5. 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.
  6. Belzil, Christian & Hansen, Jorgen, 2007. "A structural analysis of the correlated random coefficient wage regression model," Journal of Econometrics, Elsevier, vol. 140(2), pages 827-848, October.
  7. James J. Heckman & Edward J. Vytlacil, 2000. "Local Instrumental Variables," NBER Technical Working Papers 0252, National Bureau of Economic Research, Inc.
  8. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, 05.
  9. Taber, Christopher R., 2000. "Semiparametric identification and heterogeneity in discrete choice dynamic programming models," Journal of Econometrics, Elsevier, vol. 96(2), pages 201-229, June.
  10. Belzil, Christian & Hansen, Jörgen, 2002. "Unobserved Ability and the Return to Schooling," IZA Discussion Papers 508, Institute for the Study of Labor (IZA).
  11. Carneiro, Pedro & Hansen, Karsten T. & Heckman, James J., 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College Choice," IZA Discussion Papers 767, Institute for the Study of Labor (IZA).
  12. Belzil, Christian, 2004. "On the Specification of Mincerian Wage Regressions with Heterogeneity, Non-Linearity, Non-Separability, and Heteroskedasticity," IZA Discussion Papers 1083, Institute for the Study of Labor (IZA).
  13. Heckman, James J. & Lochner, Lance John & Todd, Petra E., 2005. "Earnings Functions, Rates of Return and Treatment Effects: The Mincer Equation and Beyond," IZA Discussion Papers 1700, Institute for the Study of Labor (IZA).
  14. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, vol. 69(5), pages 1127-60, September.
  15. Joshua Angrist, 2004. "Treatment Effect Heterogeneity in Theory and Practice," Econometric Society 2004 North American Winter Meetings 186, Econometric Society.
  16. Manski, Charles F & Lerman, Steven R, 1977. "The Estimation of Choice Probabilities from Choice Based Samples," Econometrica, Econometric Society, vol. 45(8), pages 1977-88, November.
  17. Empar Pons & Maria Teresa Gonzalo, 2001. "Returns to Schooling in Spain. How Reliable Are IV Estimates?," Working Papers 446, Queen Mary University of London, School of Economics and Finance.
  18. Donald W. K. Andrews & Marcia M. A. Schafgans, 1998. "Semiparametric Estimation of the Intercept of a Sample Selection Model," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 497-517.
  19. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
  20. Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
  21. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
  22. Christopher R. Taber, 2001. "The Rising College Premium in the Eighties: Return to College or Return to Unobserved Ability?," Review of Economic Studies, Oxford University Press, vol. 68(3), pages 665-691.
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