Data and Code for: The Effect of Omitted Variables on the Sign of Regression Coefficients

Summary:  View help for Summary We show that, depending on how the impact of omitted variables is measured, it can be substantially easier for omitted variables to flip coefficient signs than to drive them to zero. This behavior occurs with “Oster’s delta” (Oster 2019), a widely reported robustness measure. Consequently, any time this measure is large—suggesting that omitted variables may be unimportant—a much smaller value reverses the sign of the parameter of interest. We propose a modified measure of robustness to address this concern. We illustrate our results in four empirical applications and two meta-analyses. We implement our methods in the companion Stata module regsensitivity.

Funding Sources:  View help for Funding Sources National Science Foundation (1943138)

Subject Terms:  View help for Subject Terms Identification; Treatment effects; Partial Identification; Sensitivity Analysis; Unconfoundedness

JEL Classification:  View help for JEL Classification
      C14 Semiparametric and Nonparametric Methods: General
      C18 Methodological Issues: General
      C21 Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
      C25 Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
      C51 Model Construction and Estimation

Data Type(s):  View help for Data Type(s) experimental data; observational data

Collection Notes:  View help for Collection Notes This paper re-analyzes datasets from 14 replication packages.

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