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Entropy based European income distributions and inequality measures

Author

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  • Villas-Boas, Sofia B.
  • Fu, Qiuzi
  • Judge, George
Abstract
In this paper, instead of likelihood based methods that are fragile under model uncertainty, we use entropy based methods on time-ordered household income data to recover income distribution information on European countries and obtain an inequality income measure. For information recovery, we use a family of information theoretic entropy divergence measures to obtain income probability density functions and the corresponding inequality measures, which reflect how European country based economic behavioral systems are performing, and in terms of dynamics have changed over time.

Suggested Citation

  • Villas-Boas, Sofia B. & Fu, Qiuzi & Judge, George, 2019. "Entropy based European income distributions and inequality measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 686-698.
    • Handle: RePEc:eee:phsmap:v:514:y:2019:i:c:p:686-698
    DOI: 10.1016/j.physa.2018.09.121
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    References listed on IDEAS

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    1. Roine, Jesper & Waldenström, Daniel, 2014. "Long-Run Trends in the Distribution of Income and Wealth," Working Paper Series 1021, Research Institute of Industrial Economics.
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    7. James B. McDonald, 2008. "Some Generalized Functions for the Size Distribution of Income," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 3, pages 37-55, Springer.
    8. Thomas Piketty & Emmanuel Saez, 2003. "Income Inequality in the United States, 1913–1998," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(1), pages 1-41.
    9. Judge,George G. & Mittelhammer,Ron C., 2012. "An Information Theoretic Approach to Econometrics," Cambridge Books, Cambridge University Press, number 9780521869591, September.
    10. Duangkamon Chotikapanich & William E. Griffiths & Gholamreza Hajargasht & Wasana Karunarathne & D. S. Prasada Rao, 2018. "Using the GB2 Income Distribution," Econometrics, MDPI, vol. 6(2), pages 1-24, April.
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    Cited by:

    1. Judge, George, 2023. "Information Recovery in Complex Economic Systems," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt4jj70102, Department of Agricultural & Resource Economics, UC Berkeley.
    2. G.-Fivos Sargentis & Theano Iliopoulou & Panayiotis Dimitriadis & Nikolaos Mamassis & Demetris Koutsoyiannis, 2021. "Stratification: An Entropic View of Society’s Structure," World, MDPI, vol. 2(2), pages 1-22, March.

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    More about this item

    Keywords

    Income probability distribution function; Micro income data; Information theoretic methods; Cressie–Read divergence; Entropy maximization; Pareto’s law; (PDFs);
    All these keywords.

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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