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Heavy Tailed, but not Zipf: Firm and Establishment Size in the U.S

Illenin Kondo, Logan Lewis () and Andrea Stella ()

Working Papers from U.S. Census Bureau, Center for Economic Studies

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.

Keywords: Firm size distribution; TFP distribution; Lognormal; Pareto; Zipf’s law; Granularity (search for similar items in EconPapers)
JEL-codes: E24 L11 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2021-07
New Economics Papers: this item is included in nep-bec, nep-com, nep-hme, nep-mac and nep-rmg
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https://www2.census.gov/ces/wp/2021/CES-WP-21-15.pdf First version, 2021 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:21-15

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