US city size distribution: Robustly Pareto, but only in the tail
AbstractWe establish empirically using three different definitions of US cities that the upper tail obeys a Pareto law and not a lognormal distribution. We emphasize estimation of a switching point between the body of the city size distribution (which includes most cities) and its upper tail (which includes most of the population). For the 2000 Census Places data, in particular, our preferred model suggests that switching from a lognormal to a Pareto law occurs within a narrow confidence interval around population 60,290, with a corresponding Pareto exponent of 1.25. Most cities obey a lognormal; but the upper tail and therefore most of the population obeys a Pareto law. We obtain qualitatively similar results for the upper tail with the Area Clusters data of Rozenfeld et al. (2011), and the US Census combined Metropolitan and Micropolitan Areas data, though the shape of that distribution at smaller sizes is sensitive to the definition used.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Urban Economics.
Volume (Year): 73 (2013)
Issue (Month): 1 ()
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Web page: http://www.elsevier.com/locate/inca/622905
Gibrat’s law; Zipf’s law; Pareto law; Upper tail; Mixture of distributions; Switching regressions; Urban evolution; Urban hierarchy;
Find related papers by JEL classification:
- D30 - Microeconomics - - Distribution - - - General
- D51 - Microeconomics - - General Equilibrium and Disequilibrium - - - Exchange and Production Economies
- J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers
- R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
- C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models
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