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.
Migrants are able to provide firms with knowledge about their country of origin. This can become a valuable source of knowledge for firms in the process of internationalization. Relating to a Knowledge-Based-View perspective, this paper explains how the resource commitment of firms to foreign countries is contingent on immigration from those countries: Immigrants' country knowledge reduces uncertainty and makes the governance of foreign operations more efficient. Moreover, this paper connects the relevance of knowledge for firm internationalization to institutional characteristics in immigrants' home and host countries, both of which policymakers can shape. We test predictions on more than 13,000 observations over a 14-year period (2003-2016). The paper identifies economically significant contingencies of a positive effect of immigration, which are robust to changes in model specification, measurement, and sampling. The results indicate how immigration can shape firms' investments abroad and have implications for developing policy as well as international business theory.
We reconcile the recommendations made by Kalnins (J Int Bus Stud, 2022) on the one hand and by Lindner, Puck and Verbeke (J Int Bus Stud 51(3):283-298, 2020) on the other, on how international business (IB) quantitative researchers should treat multicollinearity. We explain that, in principle, treatment depends on the underlying data generation process, but note that datasets based on any single generation process are rare. In doing so, we broaden the discussion to include how research methods should be selected and robust statistical models built. In addition, we highlight the importance of a comprehensive literature review in selecting appropriate control variables. We also make suggestions on addressing cross-level dependencies and selecting robustness checks to avoid bias in statistical results. Finally, we go beyond regression and include a broader palette of research methodologies building on machine-learning approaches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.