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Zipf’s law for cities: an empirical examination

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  • Ioannides, Yannis Menelaos
  • Overman, Henry G.
Abstract
We use data for metro areas in the United States, from the US Census for 1900 - 1990, to test the validity of Zipf's Law for cities. Previous investigations are restricted to regressions of log size against log rank. In contrast, we use a nonparametric procedure to calculate local Zipf exponents from the mean and variance of city growth rates. This also allows us to test for the validity of Gibrat's Law for city growth processes. Despite variation in growth rates as a function of city size, Gibrat's Law does hold. In addition the local Zipf exponents are broadly consistent with Zipf's Law. Deviations from Zipf's Law are easily explained by deviations from Gibrat's Law.

Suggested Citation

  • Ioannides, Yannis Menelaos & Overman, Henry G., 2003. "Zipf’s law for cities: an empirical examination," LSE Research Online Documents on Economics 583, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:583
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    References listed on IDEAS

    as
    1. Overman, Henry G. & Ioannides, Yannis M., 2001. "Cross-Sectional Evolution of the U.S. City Size Distribution," Journal of Urban Economics, Elsevier, vol. 49(3), pages 543-566, May.
    2. Xavier Gabaix, 1999. "Zipf's Law for Cities: An Explanation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(3), pages 739-767.
    3. Huriot,Jean-Marie & Thisse,Jacques-François (ed.), 2000. "Economics of Cities," Cambridge Books, Cambridge University Press, number 9780521641906, January.
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    More about this item

    Keywords

    Zipf's Law; Gibrat's Law; urban growth; estimation of Brownian motion. JEL classification codes: R00;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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