When Do Covariates Matter? And Which Ones, and How Much?

40 Pages Posted: 26 Jun 2009 Last revised: 16 Sep 2014

See all articles by Jonah B. Gelbach

Jonah B. Gelbach

University of California, Berkeley - School of Law; NYU School of Law

Date Written: August 28, 2014


Authors often add covariates to a base model sequentially either to test a particular coefficient’s “robustness” or to account for the “effects” on this coefficient of adding covariates. This is problematic, due to sequence-sensitivity when added covariates are intercorrelated. Using the omitted variables bias formula, I construct a conditional decomposition that accounts for various covariates’ role in moving base regressors’ coefficients; I also provide a consistent covariance formula. I illustrate this conditional decomposition with NLSY data in an application that exhibits sequence-sensitivity. Related extensions include IV, the fact that my decomposition nests the Oaxaca-Blinder decomposition, and a Hausman-test result.

Keywords: decompositions, black-white wage gap, omitted variables

JEL Classification: C01, C13, C20, J31, J71

Suggested Citation

Gelbach, Jonah B., When Do Covariates Matter? And Which Ones, and How Much? (August 28, 2014). Journal of Labor Economics, Forthcoming, Available at SSRN: https://ssrn.com/abstract=1425737 or http://dx.doi.org/10.2139/ssrn.1425737

Jonah B. Gelbach (Contact Author)

University of California, Berkeley - School of Law ( email )

215 Boalt Hall
Berkeley, CA 94720-7200
United States

NYU School of Law ( email )

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