IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/28864.html
   My bibliography  Save this paper

The particle system model of income and wealth more likely to imply an analogue of thermodynamics in social science

Author

Listed:
  • Angle, John

Abstract

The Inequality Process (IP) and the Saved Wealth Model (SW) are particle system models of income distribution. The IP’s social science meta-theory requires its stationary distribution to fit the distribution of labor income conditioned on education. The Saved Wealth Model (SW) is an ad hoc modification of the particle system model of the Kinetic Theory of Gases (KTG). The KTG implies the laws of gas thermodynamics. The IP is a particle system similar to the SW and KTG, but less closely related to the KTG than the SW. This paper shows that the IP passes the key empirical test required of it by its social science meta-theory better than the SW. The IP’s advantage increases as the U.S. labor force becomes more educated. The IP is the more likely of the two particle systems to underlie an analogue of gas thermodynamics in social science as the KTG underlies gas thermodynamics.

Suggested Citation

  • Angle, John, 2011. "The particle system model of income and wealth more likely to imply an analogue of thermodynamics in social science," MPRA Paper 28864, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:28864
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/28864/1/MPRA_paper_28864.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Adrian Dragulescu & Victor M. Yakovenko, 2000. "Statistical mechanics of money," Papers cond-mat/0001432, arXiv.org, revised Aug 2000.
    2. Scalas, Enrico & Gallegati, Mauro & Guerci, Eric & Mas, David & Tedeschi, Alessandra, 2006. "Growth and allocation of resources in economics: The agent-based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 86-90.
    3. Anirban Chakraborti & Bikas K. Chakrabarti, 2000. "Statistical mechanics of money: How saving propensity affects its distribution," Papers cond-mat/0004256, arXiv.org, revised Jun 2000.
    4. Chatterjee, Arnab & K. Chakrabarti, Bikas & Manna, S.S, 2004. "Pareto law in a kinetic model of market with random saving propensity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(1), pages 155-163.
    5. Arnab Chatterjee & Bikas K. Chakrabarti & S. S. Manna, 2003. "Pareto Law in a Kinetic Model of Market with Random Saving Propensity," Papers cond-mat/0301289, arXiv.org, revised Jan 2004.
    6. S. Ispolatov & P.L. Krapivsky & S. Redner, 1998. "Wealth distributions in asset exchange models," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 2(2), pages 267-276, March.
    7. A. Chakraborti & B.K. Chakrabarti, 2000. "Statistical mechanics of money: how saving propensity affects its distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 17(1), pages 167-170, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Düring, Bertram & Matthes, Daniel & Toscani, Giuseppe, 2008. "A Boltzmann-type approach to the formation of wealth distribution curves," CoFE Discussion Papers 08/05, University of Konstanz, Center of Finance and Econometrics (CoFE).
    2. Patriarca, Marco & Chakraborti, Anirban & Germano, Guido, 2006. "Influence of saving propensity on the power-law tail of the wealth distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 723-736.
    3. Bagatella-Flores, N. & Rodríguez-Achach, M. & Coronel-Brizio, H.F. & Hernández-Montoya, A.R., 2015. "Wealth distribution of simple exchange models coupled with extremal dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 168-175.
    4. Luquini, Evandro & Montagna, Guido & Omar, Nizam, 2020. "Fusing non-conservative kinetic market models and evolutionary computing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    5. N. Bagatella-Flores & M. Rodriguez-Achach & H. F. Coronel-Brizio & A. R. Hernandez-Montoya, 2014. "Wealth distribution of simple exchange models coupled with extremal dynamics," Papers 1407.7153, arXiv.org.
    6. Aydiner, Ekrem & Cherstvy, Andrey G. & Metzler, Ralf, 2018. "Wealth distribution, Pareto law, and stretched exponential decay of money: Computer simulations analysis of agent-based models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 278-288.
    7. Maldarella, Dario & Pareschi, Lorenzo, 2012. "Kinetic models for socio-economic dynamics of speculative markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 715-730.
    8. Boghosian, Bruce M. & Devitt-Lee, Adrian & Johnson, Merek & Li, Jie & Marcq, Jeremy A. & Wang, Hongyan, 2017. "Oligarchy as a phase transition: The effect of wealth-attained advantage in a Fokker–Planck description of asset exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 476(C), pages 15-37.
    9. Costas Efthimiou & Adam Wearne, 2016. "Household Income Distribution in the USA," Papers 1602.06234, arXiv.org.
    10. Fei Cao & Sebastien Motsch, 2021. "Derivation of wealth distributions from biased exchange of money," Papers 2105.07341, arXiv.org.
    11. Chakrabarti, Anindya S. & Chakrabarti, Bikas K., 2010. "Statistical theories of income and wealth distribution," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 4, pages 1-31.
    12. 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.
    13. Ghosh, Asim & Chatterjee, Arnab & Inoue, Jun-ichi & Chakrabarti, Bikas K., 2016. "Inequality measures in kinetic exchange models of wealth distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 465-474.
    14. Shu-Heng Chen & Sai-Ping Li, 2011. "Econophysics: Bridges over a Turbulent Current," Papers 1107.5373, arXiv.org.
    15. D. S. Quevedo & C. J. Quimbay, 2019. "Piketty's second fundamental law of capitalism as an emergent property in a kinetic wealth-exchange model of economic growth," Papers 1903.00952, arXiv.org, revised Mar 2019.
    16. Adams Vallejos & Ignacio Ormazabal & Felix A. Borotto & Hernan F. Astudillo, 2018. "A new $\kappa$-deformed parametric model for the size distribution of wealth," Papers 1805.06929, arXiv.org.
    17. Sitabhra Sinha, 2005. "The Rich Are Different!: Pareto Law from asymmetric interactions in asset exchange models," Papers physics/0504197, arXiv.org.
    18. Lima, Hugo & Vieira, Allan R. & Anteneodo, Celia, 2022. "Nonlinear redistribution of wealth from a stochastic approach," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    19. Huang, Jing & Wang, Yougui, 2014. "The time-dependent characteristics of relative mobility," Economic Modelling, Elsevier, vol. 37(C), pages 291-295.
    20. Ignacio Ormazábal & F. A. Borotto & H. F. Astudillo, 2017. "Influence of Money Distribution on Civil Violence Model," Complexity, Hindawi, vol. 2017, pages 1-15, November.

    More about this item

    Keywords

    Inequality Process; Kinetic Theory of Gases; labor income distribution; particle system; Saved Wealth Model; social science analogue of thermodynamics;
    All these keywords.

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:28864. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.