IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp1585.html
   My bibliography  Save this paper

A Structural Analysis of the Correlated Random Coefficient Wage Regression Model with an Application to the OLS-IV Puzzle

Author

Listed:
  • Belzil, Christian

    (Ecole Polytechnique, Paris)

  • Hansen, Jörgen

    (Concordia University)

Abstract

We estimate a finite mixture dynamic programming model of schooling decisions in which the log wage regression function is set within a correlated random coefficient model and we use the structural estimates to perform counterfactual experiments. We show that the estimates of the dynamic programming model with a rich heterogeneity specification, along with simulated schooling/wage histories, may be used to obtain estimates of the average treatment effects (ATE), the average treatment effects for the treated and the untreated (ATT/ATU), the marginal treatment effect (MTE) and, finally, the local average treatment effects (LATE). The model is implemented on a panel of white males taken from the National Longitudinal Survey of Youth (NLSY) from 1979 until 1994. We find that the average return to experience upon entering the labor market (0.059) exceeds the average return to schooling in the population (0.043). The importance of selectivity based on individual specific returns to schooling is illustrated by the difference between the average returns for those who have not attended college (0.0321) and those who attended college (0.0645). Our estimate of the MTE (0.0573) lies between the ATU and ATT and exceeds the average return in the population. Interestingly, the low average wage return is compatible with the occurrence of very high returns to schooling in some subpopulation (the highest type specific return is 0.13) and the simulated IV estimates (around 0.10) are comparable to those very high estimates often reported in the literature. The high estimates are explained by the positive correlation between the returns to schooling and the individual specific reactions. Moreover, they are not solely attributable to those individuals who are at the margin, but also to those individuals who would achieve a higher grade level no matter what. The structural dynamic programming model with multi-dimensional heterogeneity is therefore capable of explaining the well known OLS/IV puzzle.

Suggested Citation

  • Belzil, Christian & Hansen, Jörgen, 2005. "A Structural Analysis of the Correlated Random Coefficient Wage Regression Model with an Application to the OLS-IV Puzzle," IZA Discussion Papers 1585, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp1585
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp1585.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Willis, Robert J & Rosen, Sherwin, 1979. "Education and Self-Selection," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages 7-36, October.
    2. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    3. Thierry Magnac & David Thesmar, 2002. "Identifying Dynamic Discrete Decision Processes," Econometrica, Econometric Society, vol. 70(2), pages 801-816, March.
    4. Pedro Carneiro & James J. Heckman, 2002. "The Evidence on Credit Constraints in Post--secondary Schooling," Economic Journal, Royal Economic Society, vol. 112(482), pages 705-734, October.
    5. 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.
    6. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    7. Christian Belzil & Jörgen Hansen, 2002. "Unobserved Ability and the Return to Schooling," Econometrica, Econometric Society, vol. 70(5), pages 2075-2091, September.
    8. 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.
    9. James Heckman & Edward Vytlacil, 1998. "Instrumental Variables Methods for the Correlated Random Coefficient Model: Estimating the Average Rate of Return to Schooling When the Return is Correlated with Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 33(4), pages 974-987.
    10. Eckstein, Z. & Wolpin, K.I., 1997. "Youth Employment and Academic Perfomance in High School," Papers 24-97, Tel Aviv.
    11. 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.
    12. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    13. 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.
    14. Meghir, Costas & Palme, Marten, 2001. "The Effect of a Social Experiment in Education," SSE/EFI Working Paper Series in Economics and Finance 0451, Stockholm School of Economics.
    15. 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.
    16. Stephen V. Cameron & Christopher Taber, 2004. "Estimation of Educational Borrowing Constraints Using Returns to Schooling," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 132-182, February.
    17. Belzil, Christian & Hansen, Jörgen, 2003. "Structural Estimates of the Intergenerational Education Correlation," IZA Discussion Papers 973, Institute of Labor Economics (IZA).
    18. Wooldridge, Jeffrey M., 2003. "Further results on instrumental variables estimation of average treatment effects in the correlated random coefficient model," Economics Letters, Elsevier, vol. 79(2), pages 185-191, May.
    19. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    20. Stephen V. Cameron & James J. Heckman, 1998. "Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts," NBER Working Papers 6385, National Bureau of Economic Research, Inc.
    21. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christian Belzil, 2008. "Testing the Specification of the Mincer Wage Equation," Annals of Economics and Statistics, GENES, issue 91-92, pages 427-451.
    2. Belzil, Christian & Hansen, Jörgen, 2008. "Calibration and IV Estimation of a Wage Outcome Equation in a Dynamic Environment," IZA Discussion Papers 3528, Institute of Labor Economics (IZA).
    3. Heckman, James J. & Lochner, Lance J. & Todd, Petra E., 2006. "Earnings Functions, Rates of Return and Treatment Effects: The Mincer Equation and Beyond," Handbook of the Economics of Education, in: Erik Hanushek & F. Welch (ed.), Handbook of the Economics of Education, edition 1, volume 1, chapter 7, pages 307-458, Elsevier.
    4. Laurent Lequien, 2007. "Education in France during World War II and Subsequent Mortality," Working Papers 2007-06, Center for Research in Economics and Statistics.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christian Belzil, 2008. "Testing the Specification of the Mincer Wage Equation," Annals of Economics and Statistics, GENES, issue 91-92, pages 427-451.
    2. 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.
    3. Belzil, Christian, 2007. "The return to schooling in structural dynamic models: a survey," European Economic Review, Elsevier, vol. 51(5), pages 1059-1105, July.
    4. Aakvik, Arild & Salvanes, Kjell G. & Vaage, Kjell, 2003. "Measuring Heterogeneity in the Returns to Education in Norway Using Educational Reforms," IZA Discussion Papers 815, Institute of Labor Economics (IZA).
    5. James J. Heckman, 2005. "Micro Data, Heterogeneity and the Evaluation of Public Policy Part 2," The American Economist, Sage Publications, vol. 49(1), pages 16-44, March.
    6. Pedro Carneiro & James J. Heckman & Edward J. Vytlacil, 2011. "Estimating Marginal Returns to Education," American Economic Review, American Economic Association, vol. 101(6), pages 2754-2781, October.
    7. John K. Dagsvik & TorbjØrn HÆgeland & Arvid Raknerud, 2011. "Estimating the returns to schooling: a likelihood approach based on normal mixtures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(4), pages 613-640, June.
    8. Christian Belzil & Jörgen Hansen, 2004. "Earnings Dispersion, Risk Aversion And Education," Research in Labor Economics, in: Accounting for Worker Well-Being, pages 335-358, Emerald Group Publishing Limited.
    9. Mario Fiorini, 2012. "Fostering Educational Enrolment Through Subsidies: The Issue Of Timing," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 741-772, August.
    10. James J. Heckman, 2008. "The Principles Underlying Evaluation Estimators with an Application to Matching," Annals of Economics and Statistics, GENES, issue 91-92, pages 9-73.
    11. Rojas, Eugenio & Sánchez, Rafael & Villena, Mauricio G., 2016. "Credit constraints in higher education in a context of unobserved heterogeneity," Economics of Education Review, Elsevier, vol. 52(C), pages 225-250.
    12. Ge, Suqin, 2013. "Estimating the returns to schooling: Implications from a dynamic discrete choice model," Labour Economics, Elsevier, vol. 20(C), pages 92-105.
    13. Belzil, Christian & Hansen, Jörgen & Liu, Xingfei, 2011. "Dynamic Skill Accumulation, Comparative Advantages, Compulsory Schooling, and Earnings," IZA Discussion Papers 6167, Institute of Labor Economics (IZA).
    14. Christian Belzil & Jörgen Hansen, 2001. "Heterogeneous Returns to Human Capital and Dynamic Self-Selection," CIRANO Working Papers 2001s-10, CIRANO.
    15. Li, Mingliang & Tobias, Justin L., 2011. "Bayesian inference in a correlated random coefficients model: Modeling causal effect heterogeneity with an application to heterogeneous returns to schooling," Journal of Econometrics, Elsevier, vol. 162(2), pages 345-361, June.
    16. Christian Belzil & J. Hansen, 2010. "The distinction between dictatorial and incentive policy interventions and its implication for IV estimation," Working Papers hal-00463877, HAL.
    17. Heckman, James J. & Urzúa, Sergio, 2010. "Comparing IV with structural models: What simple IV can and cannot identify," Journal of Econometrics, Elsevier, vol. 156(1), pages 27-37, May.
    18. Aakvik, Arild & Salvanes, Kjell G. & Vaage, Kjell, 2010. "Measuring heterogeneity in the returns to education using an education reform," European Economic Review, Elsevier, vol. 54(4), pages 483-500, May.
    19. Binelli, Chiara & Menezes-Filho, Naercio, 2019. "Why Brazil fell behind in college education?," Economics of Education Review, Elsevier, vol. 72(C), pages 80-106.
    20. Martin Nybom, 2017. "The Distribution of Lifetime Earnings Returns to College," Journal of Labor Economics, University of Chicago Press, vol. 35(4), pages 903-952.

    More about this item

    Keywords

    treatment effects; dynamic self-selection; dynamic programming; returns to schooling; random coefficient;
    All these keywords.

    JEL classification:

    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor
    • J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp1585. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.