American Journal of Modern Physics

| Peer-Reviewed |

An Econophysical Approach of Polynomial Distribution Applied to Income and Expenditure

Received: 16 March 2014    Accepted: 08 April 2014    Published: 10 April 2014
Views:       Downloads:

Share This Article

Abstract

Polynomial distribution can be applied to dynamic systems in certain situations. Macroeconomic systems characterized by economic variables such as income and wealth can be modelled similarly using polynomials. We extend our previous work to data regarding income from a more diversified pool of countries, which contains developed countries with high income, developed countries with middle income, developing and underdeveloped countries. Also, for the first time we look at the applicability of polynomial distribution to expenditure (consumption). Using cumulative distribution function, we found that polynomials are applicable with a high degree of success to the distribution of income to all countries considered without significant differences. Moreover, expenditure data can be fitted very well by this polynomial distribution. We considered a distribution to be robust if the values for coefficient of determination are higher than 90%. Using this criterion, we decided the degree for the polynomials used in our analysis by trying to minimize the number of coefficients, respectively first or second degree. Lastly, we look at possible correlation between the values from coefficient of determination and Gini coefficient for disposable income.

DOI 10.11648/j.ajmp.20140302.18
Published in American Journal of Modern Physics (Volume 3, Issue 2, March 2014)
Page(s) 88-92
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Dynamic Systems, Polynomial Distribution, Mean Income, Cumulative Distribution Function, Coefficient of Determination, Expenditure (Consumption)

References
[1] E. Oltean, F.V. Kusmartsev, “A polynomial distribution applied to income and wealth distribution”, Journal of Knowledge Management, Economics, and Information Technology, 2013, Bucharest, Romania
[2] A. Dragulescu, V. M. Yakovenko, “Statistical mechanics of money, income, and wealth: A short survey”, URL: http://www2.physics.umd.edu/~yakovenk/econophysics.html
[3] A. Dragulescu, V.M. Yakovenko, “Statistical mechanics of money”, Eur. Phys. J. B 17, 2000, pp.723-729.
[4] A. Dragulescu, V.M. Yakovenko, “Evidence for the exponential distribution of income in the USA”, Eur. Phys. J. B 20, 2001, pp. 585-589.
[5] A. C. Silva, V. M. Yakovenko, “Temporal evolution of the “thermal” and “superthermal” income classes in the USA during 1983–2001”, Europhys. Lett., 69 (2), 2005, pp. 304–310.
[6] K. E. Kurten and F. V. Kusmartsev, “Bose-Einstein distribution of money in a free-market economy”, Physics Letter A Journal, EPL, 93, 28003, 2011
[7] F. V. Kusmartsev, “Statistical mechanics of economics”, Physics Letters A 375, 2011, pp. 966–973
[8] E. Oltean, F. V. Kusmartsev, “A study of methods from statistical mechanics to income distribution”, ENEC, Bucharest, 2012
[9] D. Coes, “Interpreting Brazilian Income Distribution Trends”, University of New Mexico, 2006, URL: http://www.economics.illinois.edu/seminars/conferences/baer/program
[10] http://www.nscb.gov.ph/stats/mnsds/mnsds_decile.asp.
[11] I. Dhamani, “Income Inequality in Singapore: Causes,Consequences and Policy Options”, National University of Singapore, 2008. URL: http://www.scb.se/Pages/TableAndChart____226030.aspx
[12] http://www.scb.se/Pages/TableAndChart____226030.aspx (accessed December 2013)
[13] http://www.ubos.org/UNHS0910/chapter6_%20patterns%20and%20changesin%20income%20.html
[14] http://www.ons.gov.uk/ons/search/index.html?newquery=household+income+by+decile&newoffset=0&pageSize=50&sortBy=&applyFilters=true
[15] http://www.progressorcollapse.com/the-rise-of-income-inequality-during-thatcher-neoliberal-policies-in-britain/
Author Information
  • Department of Physics, Loughborough University, Loughborough, the UK

Cite This Article
  • APA Style

    Elvis Oltean. (2014). An Econophysical Approach of Polynomial Distribution Applied to Income and Expenditure. American Journal of Modern Physics, 3(2), 88-92. https://doi.org/10.11648/j.ajmp.20140302.18

    Copy | Download

    ACS Style

    Elvis Oltean. An Econophysical Approach of Polynomial Distribution Applied to Income and Expenditure. Am. J. Mod. Phys. 2014, 3(2), 88-92. doi: 10.11648/j.ajmp.20140302.18

    Copy | Download

    AMA Style

    Elvis Oltean. An Econophysical Approach of Polynomial Distribution Applied to Income and Expenditure. Am J Mod Phys. 2014;3(2):88-92. doi: 10.11648/j.ajmp.20140302.18

    Copy | Download

  • @article{10.11648/j.ajmp.20140302.18,
      author = {Elvis Oltean},
      title = {An Econophysical Approach of Polynomial Distribution Applied to Income and Expenditure},
      journal = {American Journal of Modern Physics},
      volume = {3},
      number = {2},
      pages = {88-92},
      doi = {10.11648/j.ajmp.20140302.18},
      url = {https://doi.org/10.11648/j.ajmp.20140302.18},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajmp.20140302.18},
      abstract = {Polynomial distribution can be applied to dynamic systems in certain situations. Macroeconomic systems characterized by economic variables such as income and wealth can be modelled similarly using polynomials. We extend our previous work to data regarding income from a more diversified pool of countries, which contains developed countries with high income, developed countries with middle income, developing and underdeveloped countries. Also, for the first time we look at the applicability of polynomial distribution to expenditure (consumption). Using cumulative distribution function, we found that polynomials are applicable with a high degree of success to the distribution of income to all countries considered without significant differences. Moreover, expenditure data can be fitted very well by this polynomial distribution. We considered a distribution to be robust if the values for coefficient of determination are higher than 90%. Using this criterion, we decided the degree for the polynomials used in our analysis by trying to minimize the number of coefficients, respectively first or second degree. Lastly, we look at possible correlation between the values from coefficient of determination and Gini coefficient for disposable income.},
     year = {2014}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - An Econophysical Approach of Polynomial Distribution Applied to Income and Expenditure
    AU  - Elvis Oltean
    Y1  - 2014/04/10
    PY  - 2014
    N1  - https://doi.org/10.11648/j.ajmp.20140302.18
    DO  - 10.11648/j.ajmp.20140302.18
    T2  - American Journal of Modern Physics
    JF  - American Journal of Modern Physics
    JO  - American Journal of Modern Physics
    SP  - 88
    EP  - 92
    PB  - Science Publishing Group
    SN  - 2326-8891
    UR  - https://doi.org/10.11648/j.ajmp.20140302.18
    AB  - Polynomial distribution can be applied to dynamic systems in certain situations. Macroeconomic systems characterized by economic variables such as income and wealth can be modelled similarly using polynomials. We extend our previous work to data regarding income from a more diversified pool of countries, which contains developed countries with high income, developed countries with middle income, developing and underdeveloped countries. Also, for the first time we look at the applicability of polynomial distribution to expenditure (consumption). Using cumulative distribution function, we found that polynomials are applicable with a high degree of success to the distribution of income to all countries considered without significant differences. Moreover, expenditure data can be fitted very well by this polynomial distribution. We considered a distribution to be robust if the values for coefficient of determination are higher than 90%. Using this criterion, we decided the degree for the polynomials used in our analysis by trying to minimize the number of coefficients, respectively first or second degree. Lastly, we look at possible correlation between the values from coefficient of determination and Gini coefficient for disposable income.
    VL  - 3
    IS  - 2
    ER  - 

    Copy | Download

  • Sections