Under the weight of heavy tails: A power law perspective on the emergence of outliers in entrepreneurship

Abstract

A fundamental discovery in entrepreneurship is that firm outcomes do not follow a symmetrical Gaussian curve. Instead, most are heavily right-skewed distributions in which a few extreme outliers (e.g., rock star firms like Airbnb, Tesla, and Uber) account for a disproportionate amount of the output. Although past research usually described outcome distributions as shaped following the power law, our study asks the following question: What other less extreme distributions of generalizable firm outcomes exist in entrepreneurship? Our investigation leverages four representative datasets from the U.S., Europe, and Australia, comprising 32 samples with about 22,000 ventures. We implemented a precise data-analytic approach that compares each sample (i.e., empirical distribution) against multiple theoretical distribution shapes to identify the best fit. Results showed that, across nearly all samples, the pure power law was not the dominant distribution. Instead, the annual revenue distribution is shaped as a power law with an exponential cutoff, and the number of employees distribution is shaped lognormally. Combined, these suggest the existence of top-down limitations on the highest performing firms. Accordingly, we offer an agenda for future research focused on (a) identifying and releasing systemic constraints, (b) examining and falsifying the underlying generative mechanisms that cause the emergence of heavy-tailed distributions and the outliers therein, and (c) conducting multi-level, mixed-method studies to investigate how micro-level interactions aggregate into macro-level heavy-tailed distributions. Our paper makes significant contributions to the power law perspective and future efforts to explain and predict the emergence of rock star firms in entrepreneurship.