On the city size distribution: A finite mixture interpretation

Hsuan-Li Su

Journal of Urban Economics, 2020, vol. 116, issue C

Abstract: Previous studies have shown that city size is lognormally distributed but have not reached a consensus on the shape of the upper tail. Using three datasets of U.S. cities and empirical distribution function statistics, I show that (1) the entire city size distribution is not lognormally distributed; (2) Zipf’s law does not hold generally; and (3) the power-law tail is robust. I then provide an alternative explanation for the observed fat tail: A mixture of lognormal distributions can generate a power law tail. In fact, this fat tail has its statistical origin in Shaked’s theorem. Finally, I provide an urban growth theory to explain how heterogeneous growth factors form a mixture that shapes the aggregate city size distribution.

Keywords: Gibrat’s Law; Power law; Finite mixture (search for similar items in EconPapers)
JEL-codes: D30 R12 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:juecon:v:116:y:2020:i:c:s0094119019300932

DOI: 10.1016/j.jue.2019.103216

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