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Blinder–Oaxaca decomposition for Tobit models

Blinder–Oaxaca decomposition for Tobit models In this article, a decomposition method for Tobit models is derived, which allows the differences in observed outcome variables between two groups to be decomposed into a part that is explained by differences in observed characteristics and a part attributable to differences in the estimated coefficients. Monte Carlo simulations demonstrate that in the case of censored dependent variables this decomposition method produces more reliable results than the conventional Blinder–Oaxaca decomposition for linear regression models. Finally, our method is applied to a decomposition of the gender wage gap using German data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Economics Taylor & Francis

Blinder–Oaxaca decomposition for Tobit models

Applied Economics , Volume 42 (12): 7 – May 1, 2010
7 pages

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References (7)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1466-4283
eISSN
9999-7004
DOI
10.1080/00036840701721612
Publisher site
See Article on Publisher Site

Abstract

In this article, a decomposition method for Tobit models is derived, which allows the differences in observed outcome variables between two groups to be decomposed into a part that is explained by differences in observed characteristics and a part attributable to differences in the estimated coefficients. Monte Carlo simulations demonstrate that in the case of censored dependent variables this decomposition method produces more reliable results than the conventional Blinder–Oaxaca decomposition for linear regression models. Finally, our method is applied to a decomposition of the gender wage gap using German data.

Journal

Applied EconomicsTaylor & Francis

Published: May 1, 2010

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