COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Ages and Lifetimes of U.S. Firms: Why Businesses Should NOT be Treated Like People – Axtell

October 30, 2017 @ 4:30 pm – 5:45 pm
Exploratory Hall, Room 3301, Fairfax Campus
Joseph Marr


Robert Axtell, PhD
Computational Social Science Program, Department of Computational and Data Sciences,
College of Science
Department of Economics, College of Humanities and Social Sciences
Krasnow Institute for Advanced Study
George Mason University

Computational Public Policy Lab
Krasnow Institute for Advanced Study and Schar School of Policy and Government

External Professor, Santa Fe Institute (
External Faculty, Northwestern Institute on Complex Systems (
Scientific Advisory Council, Waterloo Institute for Complexity and Innovation (

Ages and Lifetimes of U.S. Firms: Why Businesses Should NOT be Treated Like People

Monday, October 30,  4:30-5:45
Exploratory Hall, Room 3301

ABSTRACT: Over the last 150 years American corporations have acquired many rights associated with individual citizens, such as free speech, the ability to make campaign contributions, and so on. In this talk I will quantify the age-related demographic properties of U.S. business firms and argue that the peculiar nature of firm aging suggests that businesses are very much unlike individual people. Specifically, using data on all 6 million U.S. firms having employees, I document that firm ages are discrete Weibull-distributed while firm lifetimes follow a closely-related distribution. Further, the hazard rates associated with firm survival are monotone declining according to a power law. From this the expected remaining lifetime can be computed and it will be demonstrated that this INCREASES as firms age. Specifically, while a new firm in the U.S. can expect to live for about 15 years, a firm that has survived 50 years can expect to live for 30 more. Finally, conditioning on firm size leads to even more extreme results: increasing firm size by a decade cuts the hazard rate in half. In sum these results suggest that firm aging is very different from biological aging and makes analogies between firms and people both quantitatively inaccurate and qualitatively wrong-headed. Technically, this talk will focus on the application of conventional demographic techniques to economic and financial data, including failure/survival analysis with censored data.

Rob Axtell earned an interdisciplinary Ph.D. degree at Carnegie Mellon University, where he studied computing, social science, and public policy. His teaching and research involves computational and mathematical modeling of social and economic processes. Specifically, he works at the intersection of multi-agent systems computer science and the social sciences, building so-called agent-based models for a variety of market and non-market phenomena.

His research has been published in the leading scientific journals, including Science and the Proceedings of the National Academy of Sciences, USA, and reprised in Nature, and has appeared in top disciplinary journals (e.g., American Economic Review, Computational and Mathematical Organization Theory, Economic Journal), in general interest journals (e.g., PLOS One) and in specialty journals (e.g., Journal of Regulatory Economics, Technology Forecasting and Social Change.) His research has been supported by American philanthropies (e.g., John D. and Catherine T. MacArthur Foundation, Institute for New Economic Thinking) and government organizations (e.g., National Science Foundation, Department of Defense, Small Business Administration, Office of Naval Research, Environmental Protection Agency). Stories about his research have appeared in major magazines (e.g., Economist, Atlantic Monthly, Scientific American, New Yorker, Discover, Wired, New Scientist, Technology Review, Forbes, Harvard Business Review, Science News, Chronicle of Higher Education, Byte, Le Temps Strategique) and newspapers (e.g., Wall St. Journal, Washington Post, Los Angeles Times, Boston Globe, Detroit Free Press, Financial Times). He is co-author of Growing Artificial Societies: Social Science from the Bottom Up (MIT Press) with J.M. Epstein, widely cited as an example of how to apply modern computing to the analysis of social and economic phenomena.