Abstract
Whereas the creation and emergence of high-growth firms are central topics in entrepreneurship research, senior scholars lament the absence of a comprehensive theory to explain and predict this rare but important phenomenon. One approach that remains untested, however, is the use of power-law distributions to explain key growth measures in nascent and high-growth firms. A unique statistical property of power laws is that these distributions are usually caused by a single generative mechanism that drives outcomes at all levels of analysis. In this paper we explore this possibility, by testing how well power laws explain entrepreneurial growth, and proposing “opportunity tension” as a generative mechanism for that growth. We test the model using MATLAB, to construct semi-parametric bootstrap estimates for maximum likelihood fit with a power-law model, based on data from the Panel Study of Entrepreneurial Dynamics II and Inc. 5000. We find significant support for our hypothesis, i.e. we find a universal scaling exponent of ~1.75 for multiple growth measures including outcomes of nascent ventures and hyper-growth firms. Our results provide an intriguing foundation for a scale-free theory of new venture performance.
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