Omitted Variable Bias: Examining Management Research With the Impact Threshold of a Confounding Variable (ITCV)

Management research increasingly recognizes omitted variables as a primary source of endogeneity that can induce bias in empirical estimation. Methodological scholarship on the topic overwhelmingly advocates for empirical researchers to employ two-stage instrumental variable modeling, a recommendation we approach with trepidation given the challenges associated with this analytic procedure. Over the course of two studies, we leverage a statistical technique called the impact threshold of a confounding variable (ITCV) to better conceptualize what types of omitted variables might actually bias causal inference and whether they have appeared to do so in published management research. In Study 1, we apply the ITCV to published studies and find that a majority of the causal inference is unlikely biased from omitted variables. In Study 2, we respecify an influential simulation on endogeneity and determine that only the most pervasive omitted variables appear to substantively impact causal inference. Our simulation also reveals that only the strongest instruments (perhaps unrealistically strong) attenuate bias in meaningful ways. Taken together, we offer guidelines for how scholars can conceptualize omitted variables in their research, provide a practical approach that balances the tradeoffs associated with instrumental variable models, and comprehensively describe how to implement the ITCV technique.

Automated summary

Recent management research emphasizes the importance of considering omitted variables as a primary source of endogeneity bias in empirical estimation. Methodological scholarship advocates for the use of two-stage instrumental variable modeling, but researchers approach this recommendation with caution due to associated challenges. Studies utilizing the impact threshold of a confounding variable (ITCV) technique suggest that most causal inference is unlikely to be biased by omitted variables, with only the most pervasive ones impacting causal inference substantively.