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Wealth distribution and Pareto's law in the Hungarian medieval society

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  • Hegyi, Géza
  • Néda, Zoltán
  • Augusta Santos, Maria

Abstract

The distribution of wealth in the medieval Hungarian aristocratic society is studied and reported. Assuming the wealth of a noble family to be directly related to the size and agricultural potential of the owned land, we take the number of owned serf families as a measure of the respective wealth. Our data analysis reveals the power-law nature of this wealth distribution, confirming the validity of the Pareto law for this society. Since, in the feudal society, land was not commonly traded, our targeted system can be considered as an experimental realization of the no-trade limit of wealth-distribution models. The obtained Pareto exponent (α=0.92–0.95) close to 1, is in agreement with the prediction of such models.

Suggested Citation

  • Hegyi, Géza & Néda, Zoltán & Augusta Santos, Maria, 2007. "Wealth distribution and Pareto's law in the Hungarian medieval society," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 271-277.
    • Handle: RePEc:eee:phsmap:v:380:y:2007:i:c:p:271-277
    DOI: 10.1016/j.physa.2007.02.094
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    References listed on IDEAS

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    1. Fujiwara, Yoshi & Di Guilmi, Corrado & Aoyama, Hideaki & Gallegati, Mauro & Souma, Wataru, 2004. "Do Pareto–Zipf and Gibrat laws hold true? An analysis with European firms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(1), pages 197-216.
    2. Sitabhra Sinha, 2005. "The Rich Are Different!: Pareto Law from asymmetric interactions in asset exchange models," Papers physics/0504197, arXiv.org.
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    Cited by:

    1. Brzezinski, Michal, 2014. "Do wealth distributions follow power laws? Evidence from ‘rich lists’," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 155-162.
    2. Pierpaolo Andriani & Bill McKelvey, 2007. "Beyond Gaussian averages: redirecting international business and management research toward extreme events and power laws," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 38(7), pages 1212-1230, December.
    3. Asif, Muhammad & Hussain, Zawar & Asghar, Zahid & Hussain, Muhammad Irfan & Raftab, Mariya & Shah, Said Farooq & Khan, Akbar Ali, 2021. "A statistical evidence of power law distribution in the upper tail of world billionaires’ data 2010–20," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    4. Blair Fix, 2019. "Energy, hierarchy and the origin of inequality," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-32, April.
    5. Jess Benhabib & Shenghao Zhu, 2008. "Age, Luck, and Inheritance," NBER Working Papers 14128, National Bureau of Economic Research, Inc.
    6. Néda, Zoltán & Gere, István & Biró, Tamás S. & Tóth, Géza & Derzsy, Noemi, 2020. "Scaling in income inequalities and its dynamical origin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    7. Benedikt Fuchs & Stefan Thurner, 2014. "Behavioral and Network Origins of Wealth Inequality: Insights from a Virtual World," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-13, August.
    8. Zoltan Kuscsik & Denis Horvath, 2007. "Statistical properties of agent-based market area model," Papers 0710.0459, arXiv.org.
    9. Gere, István & Kelemen, Szabolcs & Tóth, Géza & Biró, Tamás S. & Néda, Zoltán, 2021. "Wealth distribution in modern societies: Collected data and a master equation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    10. Blair Fix, 2018. "Hierarchy and the power-law income distribution tail," Journal of Computational Social Science, Springer, vol. 1(2), pages 471-491, September.
    11. Fix, Blair, 2019. "Energy, Hierarchy and the Origin of Inequality," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 14(4, April), pages 1-32.
    12. Cui, Jian & Pan, Qiuhui & Qian, Qian & He, Mingfeng & Sun, Qilin, 2013. "A multi-agent dynamic model based on different kinds of bequests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1393-1397.
    13. 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.
    14. Zoltan Neda & Istvan Gere & Tamas S. Biro & Geza Toth & Noemi Derzsy, 2019. "Scaling in Income Inequalities and its Dynamical Origin," Papers 1911.02449, arXiv.org, revised Mar 2020.
    15. Pierpaolo Andriani & Bill McKelvey, 2009. "Perspective ---From Gaussian to Paretian Thinking: Causes and Implications of Power Laws in Organizations," Organization Science, INFORMS, vol. 20(6), pages 1053-1071, December.
    16. Biró, T.S. & Néda, Z., 2018. "Unidirectional random growth with resetting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 335-361.
    17. Hu, Feng-Rung, 2008. "On the estimation of the power-law exponent in the mean-field Bouchaud–Mézard model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(18), pages 4605-4614.

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