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On social inequality: Analyzing the rich–poor disparity

Author

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  • Eliazar, Iddo
  • Cohen, Morrel H.

Abstract

From the Old Testament to the Communist Manifesto, and from the French Revolution to the Occupy Wall Street protests, social inequality has always been at the focal point of public debate, as well as a major driver of political change. Although being of prime interest since Biblical times, the scientific investigation of the distributions of wealth and income in human societies began only at the close of the nineteenth century, and was pioneered by Pareto, Lorenz, Gini, and Pietra. The methodologies introduced by these trailblazing scholars form the bedrock of the contemporary science of social inequality. Based on this bedrock we present a new quantitative approach to the analysis of wealth and income distributions, which sets its spotlight on the most heated facet of the current global debate on social inequality—the rich–poor disparity. Our approach offers researchers highly applicable quantitative tools to empirically track and statistically analyze the growing gap between the rich and the poor.

Suggested Citation

  • Eliazar, Iddo & Cohen, Morrel H., 2014. "On social inequality: Analyzing the rich–poor disparity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 148-158.
    • Handle: RePEc:eee:phsmap:v:401:y:2014:i:c:p:148-158
    DOI: 10.1016/j.physa.2014.01.033
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    References listed on IDEAS

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    1. Alfarano, Simone & Milaković, Mishael & Irle, Albrecht & Kauschke, Jonas, 2012. "A statistical equilibrium model of competitive firms," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 136-149.
    2. repec:cup:cbooks:9781107013445 is not listed on IDEAS
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    Cited by:

    1. Viktor Stojkoski & Petar Jolakoski & Arnab Pal & Trifce Sandev & Ljupco Kocarev & Ralf Metzler, 2021. "Income inequality and mobility in geometric Brownian motion with stochastic resetting: theoretical results and empirical evidence of non-ergodicity," Papers 2109.01822, arXiv.org.
    2. Inoue, Jun-ichi & Ghosh, Asim & Chatterjee, Arnab & Chakrabarti, Bikas K., 2015. "Measuring social inequality with quantitative methodology: Analytical estimates and empirical data analysis by Gini and k indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 184-204.
    3. Fontanari, Andrea & Taleb, Nassim Nicholas & Cirillo, Pasquale, 2018. "Gini estimation under infinite variance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 256-269.
    4. Chatterjee, Arnab & Chakrabarti, Anindya S. & Ghosh, Asim & Chakraborti, Anirban & Nandi, Tushar K., 2016. "Invariant features of spatial inequality in consumption: The case of India," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 169-181.
    5. Palestini, Arsen & Pignataro, Giuseppe, 2016. "A graph-based approach to inequality assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 455(C), pages 65-78.
    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.

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