Blinder-Oaxaca Decomposition for Tobit Models

Thomas Bauer, Mathias Sinning

    Research output: Contribution to journalArticle


    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.
    Original languageEnglish
    Pages (from-to)1569-1575
    JournalApplied Economics
    Issue number12
    Publication statusPublished - 2010


    Dive into the research topics of 'Blinder-Oaxaca Decomposition for Tobit Models'. Together they form a unique fingerprint.

    Cite this