Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/35398
Authors: 
Elder, Todd E.
Goddeeris, John H.
Haider, Steven J.
Year of Publication: 
2009
Series/Report no.: 
IZA discussion papers 4159
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 naive 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.
Subjects: 
Decompositions
discrimination
JEL: 
J31
J24
J15
J16
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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