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

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  • Li, Mingliang
  • Tobias, Justin

Abstract

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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    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. Paul Dalziel, 2015. "Regional skill ecosystems to assist young people making education employment linkages in transition from school to work," Local Economy, London South Bank University, vol. 30(1), pages 53-66, February.
    3. 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.
    4. Eicher, Theo S. & García-Peñalosa, Cecilia & Kuenzel, David J., 2018. "Constitutional rules as determinants of social infrastructure," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 182-209.
    5. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    6. Anastasia Dimiski, 2020. "Factors that affect Students’ performance in Science: An application using Gini-BMA methodology in PISA 2015 dataset," Working Papers 2004, University of Guelph, Department of Economics and Finance.
    7. Enrique Moral-Benito, 2010. "Model Averaging in Economics," Working Papers wp2010_1008, CEMFI.
    8. 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.
    9. Enrique Moral-Benito, 2015. "Model Averaging In Economics: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 46-75, February.
    10. Vanina Forget, 2012. "Doing well and doing good: a multi-dimensional puzzle," Working Papers hal-00672037, HAL.

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