Misconceptions about multicollinearity in international business research: Identification, consequences, and remedies

Abstract

Collinearity between independent variables is a recurrent problem in quantitative empirical research in International Business (IB). We explore insufficient and inappropriate treatment of collinearity and use simulations to illustrate the potential impact on results. We also show how IB researchers doing quantitative work can avoid collinearity issues that lead to spurious and unstable results. Our six principal insights are the following: first, multicollinearity does not introduce bias. It is not an econometric problem in the sense that it would violate assumptions necessary for regression models to work. Second, variance inflation factors are indicators of standard errors that are too large, not too small. Third, coefficient instability is not a consequence of multicollinearity. Fourth, in the presence of a higher partial correlation between the variables, it can paradoxically become more problematic to omit one of these variables. Fifth, ignoring clusters in data can lead to spurious results. Sixth, accounting for country clusters does not pick up all country-level variation.