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Heavy tailed but not Zipf: Firm and establishment size in the United States

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  • Illenin O. Kondo
  • Logan T. Lewis
  • Andrea Stella

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 US economy, using confidential Census Bureau data. These findings hold even far in the upper tail and suggest that heterogeneous firm models should more systematically explore deviations from Zipf's law.

Suggested Citation

  • Illenin O. Kondo & Logan T. Lewis & Andrea Stella, 2023. "Heavy tailed but not Zipf: Firm and establishment size in the United States," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 767-785, August.
    • Handle: RePEc:wly:japmet:v:38:y:2023:i:5:p:767-785
    DOI: 10.1002/jae.2976
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    Cited by:

    1. ARATA Yoshiyuki, 2023. "Zipf's Law without the Stationarity Assumption," Discussion papers 23085, Research Institute of Economy, Trade and Industry (RIETI).
    2. Xian Jiang & Hannah Rubinton, 2024. "The Adoption of Non-Rival Inputs and Firm Scope," Working Papers 2024-005, Federal Reserve Bank of St. Louis, revised Mar 2024.
    3. Xian Jiang & Hannah Rubinton, 2024. "The Adoption of Non-Rival Inputs and Firm Scope," CESifo Working Paper Series 11028, CESifo.
    4. Yoshiki Ando, 2024. "Dynamics of High-Growth Young Firms and the Role of Venture Capitalists," PIER Working Paper Archive 24-012, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

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