An Extension of the Blinder-Oaxaca Decomposition Technique to Logit and Probit Models

Robert Fairlie

No 1917, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: The Blinder-Oaxaca decomposition technique is widely used to identify and quantify the separate contributions of group differences in measurable characteristics, such as education, experience, marital status, and geographical differences to racial and gender gaps in outcomes. The technique cannot be used directly, however, if the outcome is binary and the coefficients are from a logit or probit model. I describe a relatively simple method of performing a decomposition that uses estimates from a logit or probit model. Expanding on the original application of the technique in Fairlie (1999), I provide a more thorough discussion of how to apply the technique, an analysis of the sensitivity of the decomposition estimates to different parameters, and the calculation of standard errors. I also compare the estimates to Blinder-Oaxaca decomposition estimates and discuss an example of when the Blinder-Oaxaca technique may be problematic.

Keywords: Blinder-Oaxaca decomposition; Probit; Logit; decomposition; race; gender (search for similar items in EconPapers)
JEL-codes: C6 J15 J16 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2006-01
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-lab
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (40)

Published - published in: Journal of Economic and Social Measurement, 2005, 30(4), 305-316

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Related works:
Working Paper: An Extension of the Blinder-Oaxaca Decomposition Technique to Logit and Probit Models (2014)
Working Paper: An Extension of the Blinder-Oaxaca Decomposition Technique to Logit and Probit Models (2014)
Working Paper: An Extension of the Blinder-Oaxaca Decomposition Technique to Logit and Probit Models (2003)
Working Paper: An Extension of the Blinder-Oaxaca Decomposition Technique to Logit and Probit Models (2003)
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