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Effects of network topology on wealth distributions

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  • Diego Garlaschelli
  • Maria I. Loffredo

Abstract

We focus on the problem of how wealth is distributed among the units of a networked economic system. We first review the empirical results documenting that in many economies the wealth distribution is described by a combination of log--normal and power--law behaviours. We then focus on the Bouchaud--M\'ezard model of wealth exchange, describing an economy of interacting agents connected through an exchange network. We report analytical and numerical results showing that the system self--organises towards a stationary state whose associated wealth distribution depends crucially on the underlying interaction network. In particular we show that if the network displays a homogeneous density of links, the wealth distribution displays either the log--normal or the power--law form. This means that the first--order topological properties alone (such as the scale--free property) are not enough to explain the emergence of the empirically observed \emph{mixed} form of the wealth distribution. In order to reproduce this nontrivial pattern, the network has to be heterogeneously divided into regions with variable density of links. We show new results detailing how this effect is related to the higher--order correlation properties of the underlying network. In particular, we analyse assortativity by degree and the pairwise wealth correlations, and discuss the effects that these properties have on each other.

Suggested Citation

  • Diego Garlaschelli & Maria I. Loffredo, 2007. "Effects of network topology on wealth distributions," Papers 0711.4710, arXiv.org, revised Jan 2008.
  • Handle: RePEc:arx:papers:0711.4710
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    References listed on IDEAS

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    1. Albert Henderson, 1999. "Information science and information policy: The use of constant dollars and other indicators to manage research investments," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(4), pages 366-379.
    2. Caldarelli, Guido, 2007. "Scale-Free Networks: Complex Webs in Nature and Technology," OUP Catalogue, Oxford University Press, number 9780199211517, Decembrie.
    3. Corrado Di Guilmi & Mauro Gallegati & Edoardo Gaffeo, 2003. "Power Law Scaling in the World Income Distribution," Economics Bulletin, AccessEcon, vol. 15(6), pages 1-7.
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    Cited by:

    1. Kemp, Jordan T. & Bettencourt, Luís M.A., 2022. "Statistical dynamics of wealth inequality in stochastic models of growth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    2. Bertotti, M.L. & Chattopadhyay, A.K. & Modanese, G., 2017. "Stochastic effects in a discretized kinetic model of economic exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 724-732.
    3. Max Greenberg & H. Oliver Gao, 2024. "Twenty-five years of random asset exchange modeling," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(6), pages 1-27, June.
    4. Hoppe, K. & Rodgers, G.J., 2015. "A microscopic study of the fitness-dependent topology of the world trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 64-74.

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