A Simple Law for the Distribution of Long-Term Profit: The Empirical Regularity Behind the 1% of Firms That Capture 73% of Value

• 成果类型: Article
• 署名作者: Wibbens, Phebo D.
• 署名单位: INSEAD Business School
• 刊物名称: ORGANIZATION SCIENCE
• ISSN/ISSBN: 1047-7039
• DOI: 10.1287/orsc.2023.18085
• 发表日期: 2025
• 关键词: long-term firm performance; stochastic modeling; empirical regularities; Industry dynamics; competitive strategy

摘要

Empirical laws and regularities, such as Klepper's shakeout pattern for industry evolution or Gibrat's law for firm size, have shaped our understanding of organizations. Despite decades of research into profit patterns, no such widely applicable empirical regularities have been found for the dependent variable of strategy: long-term value capture. This study reports the discovery of a simple law governing this variable's distribution. A four-parameter normal log-normal (NLN) mixture distribution very well fits observed data of listed firms' 20-year long-term profit (LTP). The distribution correctly describes, for instance, a remarkable asymmetry in value capture: fewer than 1% of all firms in the data set generated 73% of the total LTP. Though the NLN law applies across different industries, geographies, and time periods, its distributional parameters vary. These parameters provide a novel and precise description of differences across settings in economic outcomes, such as the rise of superstars. More broadly, the law's discovery raises profound questions relating to competitive strategy, evolutionary path dependence, the structure of technological opportunity, and social inequality. Code and data for replication are made available.