Heavy Tailed, but not Zipf: Firm and Establishment Size in the U.S.
July 2021
Working Paper Number:
CES-21-15
Abstract
Heavy tails play an important role in modern macroeconomics and international economics.
Previous work often assumes a Pareto distribution for firm size, typically with a shape parameter approaching Zipf's law. This convenient approximation has dramatic consequences for the importance of large firms in the economy. But we show that a lognormal distribution, or better yet, a convolution of a lognormal and a non-Zipf Pareto distribution, provides a better description of the U.S. economy, using confidential Census Bureau data. These findings hold even far in the upper tail and suggest heterogeneous firm models should more systematically explore deviations from Zipf's law.
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:
estimation,
econometric,
macroeconomic,
estimating,
finance,
monopolistic,
larger firms,
tariff,
economically,
elasticity,
wholesale,
gdp,
distribution,
employment distribution
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Census of Manufactures,
Standard Industrial Classification,
Total Factor Productivity,
Company Organization Survey,
American Immigration Council,
Federal Reserve Bank,
Financial, Insurance and Real Estate Industries,
Longitudinal Business Database,
Federal Reserve System,
North American Industry Classification System,
CDF,
TFPR,
Federal Reserve Board of Governors,
Business Dynamics Statistics
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