Abstract
This paper explores the use of regression analysis in conducting research on corporate governance. We highlight that combinations of governance mechanisms together act to mitigate the shareholder-manager agency problem, and examine the implications of this idea for designing empirical research on corporate governance using regression models. We outline the consequences of the omitted variable problem that arises if linkages between governance mechanisms are ignored. We describe two research studies to illustrate how linear regression and logistic regression may be used to examine the complex interlinkages among multiple governance mechanisms. These studies demonstrate approaches to model specification issues that arise in governance research, and also highlight how research designs may be constructed to avoid violation of important assumptions of the regression model.
We thank Jean-Luc Arregle, Sayan Chatterjee, Michel Ghertman, Scott Masten, Shaker Srivinasan and other participants in the “Statistical Models for Strategic Management” conference at Edhec, Nice, as well as John Easterwood for valuable comments on this paper.
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Seth, A., Bowden, S. (1997). Regression Analysis and Governance. In: Ghertman, M., Obadia, J., Arregle, JL. (eds) Statistical Models for Strategic Management. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2614-5_13
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DOI: https://doi.org/10.1007/978-1-4757-2614-5_13
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