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

28 Pages Posted: 12 May 2004

See all articles by Justin L. Tobias

Justin L. Tobias

University of California, Irvine - Department of Economics

Mingliang Li

SUNY at Buffalo - Department of Economics

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

Tobias, Justin L. and Li, Mingliang, Returns to Schooling and Bayesian Model Averaging: A Union of Two Literatures. Journal of Economic Surveys, Vol. 18, No. 2, pp. 153-180, April 2004. Available at SSRN: https://ssrn.com/abstract=526853

Justin L. Tobias

University of California, Irvine - Department of Economics

3151 Social Science Plaza
Irvine, CA 92697-5100
United States

Mingliang Li (Contact Author)

SUNY at Buffalo - Department of Economics ( email )

Buffalo, NY 14260
United States
716-645-2121 Ext. 435 (Phone)
716-645-2127 (Fax)

HOME PAGE: http://www.acsu.buffalo.edu/~mli3/

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