Unexplained Gaps and Oaxaca-Blinder Decompositions

28 Pages Posted: 13 May 2009  

Todd E. Elder

Michigan State University

John Goddeeris

Michigan State University

Steven J. Haider

Michigan State University - Department of Economics; IZA Institute of Labor Economics

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Abstract

We analyze four methods to measure unexplained gaps in mean outcomes: three decompositions based on the seminal work of Oaxaca (1973) and Blinder (1973) and an approach involving a seemingly naïve regression that includes a group indicator variable. Our analysis yields two principal findings. We show that the coefficient on a group indicator variable from an OLS regression is an attractive approach for obtaining a single measure of the unexplained gap. We also show that a commonly-used pooling decomposition systematically overstates the contribution of observable characteristics to mean outcome differences when compared to OLS regression, therefore understating unexplained differences. We then provide three empirical examples that explore the practical importance of our analytic results.

Keywords: decompositions, discrimination

JEL Classification: J31, J24, J15, J16

Suggested Citation

Elder, Todd E. and Goddeeris, John and Haider, Steven J., Unexplained Gaps and Oaxaca-Blinder Decompositions. IZA Discussion Paper No. 4159. Available at SSRN: https://ssrn.com/abstract=1402506

Todd E. Elder (Contact Author)

Michigan State University ( email )

110 Marshall-Adams Hall
Department of Economics
East Lansing, MI 48824
United States
517-355-0353 (Phone)

John Goddeeris

Michigan State University ( email )

East Lansing, MI 48824
United States

Steven J. Haider

Michigan State University - Department of Economics ( email )

East Lansing, MI 48824
United States

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

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