Date of Award
12-19-2005
Degree Type
Closed Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Finance
First Advisor
Jayant R. Kale - Chair
Second Advisor
Richard D. Phillips
Third Advisor
Martin F. Grace
Fourth Advisor
James E. Owers
Abstract
In this study we contribute to the ongoing research on the rationales for corporate diversification. Using plant-level data from the U.S. Census Bureau, we examine whether combining several lines of business in one entity leads to increased productive efficiency. Studying the direct effect of diversification on efficiency allows us to discern between two major theories of corporate diversification: the synergy hypothesis and the agency-cost hypothesis. To measure productive efficiency, we employ a non-parametric approach—a test based on Varian’s Weak Axiom of Profit Maximization (WAPM). This method has several advantages over other conventional measures of productive efficiency. Most importantly, it allows one to perform the efficiency test without relying on assumptions about the functional form of the underlying production function. To the best of our knowledge, this study is the first application of the WAPM test to a large sample of non-financial firms. The study provides evidence that business segments of diversified firms are more efficient compared to single-segment firms in the same industry. This finding suggests that the existence of the so-called ‘diversification discount’ cannot be explained by efficiency differences between multi-segment and focused firms. Furthermore, more efficient segments tend to be vertically integrated with others segments in the same firm and to have been added through acquisitions rather than grown internally. Overall, the results of this study indicate that corporate diversification is value-enhancing, and that it is not necessarily driven by managers’ pursuit of their private benefits.
DOI
https://doi.org/10.57709/1058997
Recommended Citation
Emm, Ekaterina E., "Efficiency Implications of Corporate Diversification: Evidence from Micro Data." Dissertation, Georgia State University, 2005.
doi: https://doi.org/10.57709/1058997