Power law distributions in entrepreneurship: Implications for theory and research

• We examine more than 12,000 nascent, young, and hyper-growing firms.
• We examine the distribution of key variables in entrepreneurship theories.
• Forty eight of 49 variables are power law rather than normally distributed.
• Variables should be assumed as power law distributed unless proven otherwise.
• Power law distributions challenge and offer opportunities for entrepreneurship research.

A long-held assumption in entrepreneurship research is that normal (i.e., Gaussian) distributions characterize variables of interest for both theory and practice. We challenge this assumption by examining more than 12,000 nascent, young, and hyper-growth firms. Results reveal that variables which play central roles in resource-, cognition-, action-, and environment-based entrepreneurship theories exhibit highly skewed power law distributions, where a few outliers account for a disproportionate amount of the distribution's total output. Our results call for the development of new theory to explain and predict the mechanisms that generate these distributions and the outliers therein. We offer a research agenda, including a description of non-traditional methodological approaches, to answer this call.