{"title":"The Size Distribution Across All \"Cities\": A Unifying Approach","authors":"Kristian Giesen, Jens Suedekum","doi":"10.2139/ssrn.2004229","DOIUrl":null,"url":null,"abstract":"In this paper we show that the double Pareto lognormal (DPLN)\nparameterization provides an excellent fit to the overall US city size distribution, regardless\nof whether �cities� are administratively defined Census places or economically defined\narea clusters. We then consider an economic model that combines scale-independent urban\ngrowth (Gibrat�s law) with endogenous city creation. City sizes converge to a DPLN\ndistribution in this model, which is much better in line with the data than previous urban\ngrowth frameworks that predict a lognormal or a Pareto city size distribution (Zipf�s law).","PeriodicalId":410291,"journal":{"name":"ERN: Analytical Models (Topic)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"45","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Analytical Models (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2004229","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 45
Abstract
In this paper we show that the double Pareto lognormal (DPLN)
parameterization provides an excellent fit to the overall US city size distribution, regardless
of whether �cities� are administratively defined Census places or economically defined
area clusters. We then consider an economic model that combines scale-independent urban
growth (Gibrat�s law) with endogenous city creation. City sizes converge to a DPLN
distribution in this model, which is much better in line with the data than previous urban
growth frameworks that predict a lognormal or a Pareto city size distribution (Zipf�s law).
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