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An agent-based model of the observed distribution of wealth in the United States

Author

Listed:
  • Hunter A. Vallejos

    (Georgia Institute of Technology)

  • James J. Nutaro

    (Oak Ridge National Laboratory)

  • Kalyan S. Perumalla

    (Oak Ridge National Laboratory)

Abstract

Pareto cautiously asserted that the wealth and income distributions which bear his name are universal, basing his argument on observations of this distribution in many different types of economies. In this paper, we present an agent based model (and a scalable approximation of it) in a closely related spirit. The central feature of this model is that wealth enables an individual to secure more wealth. Specifically, the important and novel feature of this model is its ability to simultaneously produce both the Pareto distribution observed in empirical data for the top 10% of the population and the exponential distribution observed for the lower 90%. We show that the model produces these distributions of wealth when initialized with an equitable distribution. Then, using historical data, we initialize the model with US wealth shares in 1988 and show that the model tracks wealth share changes from 1988 to 2012. Simulations to 2088 project that the top 0.01% of the population will possess more than 70% of the total wealth in the economy.

Suggested Citation

  • Hunter A. Vallejos & James J. Nutaro & Kalyan S. Perumalla, 2018. "An agent-based model of the observed distribution of wealth in the United States," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 641-656, October.
    • Handle: RePEc:spr:jeicoo:v:13:y:2018:i:3:d:10.1007_s11403-017-0200-9
    DOI: 10.1007/s11403-017-0200-9
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    References listed on IDEAS

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    Cited by:

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    6. Patrick Mellacher & Timon Scheuer, 2021. "Wage Inequality, Labor Market Polarization and Skill-Biased Technological Change: An Evolutionary (Agent-Based) Approach," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 233-278, August.

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