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Empirical nonextensive laws for the county distribution of total personal income and gross domestic product

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  • Ernesto P. Borges

    (Escola Politecnica, Universidade Federal da Bahia, Brazil, and Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil)

Abstract

We analyze the cumulative distribution of total personal income of USA counties, and gross domestic product of Brazilian, German and United Kingdom counties, and also of world countries. We verify that generalized exponential distributions, related to nonextensive statistical mechanics, describe almost the whole spectrum of the distributions (within acceptable errors), ranging from the low region to the middle region, and, in some cases, up to the power-law tail. The analysis over about 30 years (for USA and Brazil) shows a regular pattern of the parameters appearing in the present phenomenological approach, suggesting a possible connection between the underlying dynamics of (at least some aspects of) the economy of a country (or of the whole world) and nonextensive statistical mechanics. We also introduce two additional examples related to geographical distributions: land areas of counties and land prices, and the same kind of equations adjust the data in the whole range of the spectrum.

Suggested Citation

  • Ernesto P. Borges, 2002. "Empirical nonextensive laws for the county distribution of total personal income and gross domestic product," Papers cond-mat/0205520, arXiv.org, revised Jan 2004.
    • Handle: RePEc:arx:papers:cond-mat/0205520
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    Cited by:

    1. Soares, Abner D. & Moura Jr., Newton J. & Ribeiro, Marcelo B., 2016. "Tsallis statistics in the income distribution of Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 158-171.
    2. Rivera-Castro, Miguel A. & Miranda, José G.V. & Borges, Ernesto P. & Cajueiro, Daniel O. & Andrade, Roberto F.S., 2012. "A top–bottom price approach to understanding financial fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1489-1496.
    3. Huang, Siyu & Shi, Yi & Chen, Qinghua & Li, Xiaomeng, 2022. "The growth path of high-tech industries: Statistical laws and evolution demands," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).

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