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Cited by 581

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

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

Open access
Gelbach, Jonah B.·2016·Journal of Labor Economics
DOI 10.1086/683668
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Abstract

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.

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Metadata

Authors
Gelbach, J. B.
Venue
Journal of Labor Economics
Publisher
University of Chicago Press
Published
2016-04
Volume
34
Issue
2
Pages
509-543
ISSN
  • 0734-306X (ISSN-L)
  • 1537-5307
DOI
10.1086/683668
OpenAlex
W3123966045
Cited by
581
References
6
FWCI
36.68
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: When Do Covariates Matter? And Which Ones, and How Much? https://app.cochat.ai/papers/10.1086/683668
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Gelbach, J. B. (2016). When Do Covariates Matter? And Which Ones, and How Much? Journal of Labor Economics. https://doi.org/10.1086/683668

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.