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α: calibrated so average coauthorship-adjusted count equals average raw count
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.