Heavy tailed but not Zipf: Firm and establishment size in the United States
利用美国人口普查局保密数据，研究发现企业规模分布更符合对数正态分布或对数正态与非齐普夫帕累托分布的卷积，而非传统假设的帕累托分布，这对异质性企业模型有重要启示。
Summary 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.