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Returns to Schooling and Bayesian Model Averaging: A Union of Two Literatures


  • Li, Mingliang
  • Tobias, Justin


In this paper, we review and unite the literatures on returns to schooling and Bayesian model averaging. We observe that most studies seeking to estimate the returns to education have done so using particular (and often different across researchers) model specifications. Given this, we review Bayesian methods which formally account for uncertainty in the specification of the model itself, and apply these techniques to estimate the economic return to a college education. The approach described in this paper enables us to determine those model specifications which are most favored by the given data, and also enables us to use the predictions obtained from all of the competing regression models to estimate the returns to schooling. The reported precision of such estimates also account for the uncertainty inherent in the model specification. Using U.S. data from the National Longitudinal Survey of Youth (NLSY), we also revisit several "stylized facts" in the returns to education literature and examine if they continue to hold after formally accounting for model uncertainty.

Suggested Citation

  • Li, Mingliang & Tobias, Justin, 2004. "Returns to Schooling and Bayesian Model Averaging: A Union of Two Literatures," Staff General Research Papers Archive 12011, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genres:12011

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    References listed on IDEAS

    1. Light, Audrey, 1998. "Estimating Returns to Schooling: When Does the Career Begin?," Economics of Education Review, Elsevier, vol. 17(1), pages 31-45, February.
    2. Dreze, Jacques H. & Richard, Jean-Francois, 1983. "Bayesian analysis of simultaneous equation systems," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 9, pages 517-598 Elsevier.
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    10. James Heckman & Edward Vytlacil, 2001. "Identifying The Role Of Cognitive Ability In Explaining The Level Of And Change In The Return To Schooling," The Review of Economics and Statistics, MIT Press, vol. 83(1), pages 1-12, February.
    11. Bernt Bratsberg & Dek Terrell, 1998. "Experience, Tenure, and Wage Growth of Young Black and White Men," Journal of Human Resources, University of Wisconsin Press, vol. 33(3), pages 658-682.
    12. Justin L. Tobias, 2003. "Are Returns to Schooling Concentrated Among The Most Able? A Semiparametric Analysis of The Ability--earnings Relationships," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(1), pages 1-29, February.
    13. 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.
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    Cited by:

    1. Ley, Eduardo & Steel, Mark F.J., 2012. "Mixtures of g-priors for Bayesian model averaging with economic applications," Journal of Econometrics, Elsevier, vol. 171(2), pages 251-266.
    2. Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney, 2012. "Bayesian model averaging in the instrumental variable regression model," Journal of Econometrics, Elsevier, vol. 171(2), pages 237-250.
    3. Enrique Moral-Benito, 2010. "Model Averaging in Economics," Working Papers wp2010_1008, CEMFI.
    4. Steel, Mark F. J., 2017. "Model Averaging and its Use in Economics," MPRA Paper 81568, University Library of Munich, Germany.
    5. León-González, Roberto & Montolio, Daniel, 2015. "Endogeneity and panel data in growth regressions: A Bayesian model averaging approach," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 23-39.
    6. Vanina Forget, 2012. "Doing well and doing good: a multi-dimensional puzzle," Working Papers hal-00672037, HAL.

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