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

• 成果类型: Article
• 署名作者: Gelbach, Jonah B.
• 署名单位: University of Pennsylvania
• 刊物名称: JOURNAL OF LABOR ECONOMICS
• ISSN/ISSBN: 0734-306X
• DOI: 10.1086/683668
• 发表日期: 2016
• 页码: 509-543
• 关键词: wage gap; identification; IMPACT; decline

摘要

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 instrumental variables, the fact that my decomposition nests the Oaxaca-Blinder decomposition, and a Hausman test result.