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Income inequality dynamic measurement of Markov models: Application to some European countries

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  • D'Amico, Guglielmo
  • Di Biase, Giuseppe
  • Manca, Raimondo

Abstract

In this paper we present a methodology for measuring income inequality dynamically within a Markov model of income evolution. The proposed methodology requires knowledge of the evolution of the population and the averages and medians of the incomes in a country and allows the computation of dynamic inequality indices. The methodology is supported with statistics from Eurostat data applied on France, Germany, Greece and Italy.

Suggested Citation

  • D'Amico, Guglielmo & Di Biase, Giuseppe & Manca, Raimondo, 2012. "Income inequality dynamic measurement of Markov models: Application to some European countries," Economic Modelling, Elsevier, vol. 29(5), pages 1598-1602.
    • Handle: RePEc:eee:ecmode:v:29:y:2012:i:5:p:1598-1602
    DOI: 10.1016/j.econmod.2012.05.019
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    7. Xander Koolman & Eddy van Doorslaer, 2004. "On the interpretation of a concentration index of inequality," Health Economics, John Wiley & Sons, Ltd., vol. 13(7), pages 649-656, July.
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    Cited by:

    1. Guglielmo D'Amico & Filippo Petroni & Philippe Regnault & Stefania Scocchera & Loriano Storchi, 2019. "A copula based Markov Reward approach to the credit spread in European Union," Papers 1902.00691, arXiv.org.
    2. Guglielmo D'Amico & Riccardo De Blasis & Philippe Regnault, 2020. "Confidence sets for dynamic poverty indexes," Papers 2006.06595, arXiv.org.
    3. Guglielmo D’Amico & Philippe Regnault, 2018. "Dynamic Measurement of Poverty: Modeling and Estimation," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(2), pages 305-340, November.
    4. Nikolaos Stavropoulos & Alexandra Papadopoulou & Pavlos Kolias, 2021. "Evaluating the Efficiency of Off-Ball Screens in Elite Basketball Teams via Second-Order Markov Modelling," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
    5. Guglielmo D’Amico & Philippe Regnault & Stefania Scocchera & Loriano Storchi, 2018. "A Continuous-Time Inequality Measure Applied to Financial Risk: The Case of the European Union," IJFS, MDPI, vol. 6(3), pages 1-16, June.
    6. D'Amico, Guglielmo & Di Biase, Giuseppe & Manca, Raimondo, 2014. "Decomposition Of The Population Dynamic Theil'S Entropy And Its Application To Four European Countries," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 55(2), pages 229-239, December.
    7. D’Amico, Guglielmo & Scocchera, Stefania & Storchi, Loriano, 2018. "Financial risk distribution in European Union," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 252-267.
    8. Guglielmo D’Amico & Alex Karagrigoriou & Veronica Vigna, 2024. "Forecasting the Power Generation Mix in Italy Based on Grey Markov Models," Energies, MDPI, vol. 17(9), pages 1-16, May.
    9. Guglielmo D’Amico & Giuseppe Di Biase & Raimondo Manca, 2015. "Measuring Income Inequality: An Application Of The Population Dynamic Theil'S Entropy," Accounting & Taxation, The Institute for Business and Finance Research, vol. 7(1), pages 103-114.
    10. Guglielmo D'Amico & Stefania Scocchera & Loriano Storchi, 2021. "Randentropy: a software to measure inequality in random systems," Papers 2103.09107, arXiv.org.

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    More about this item

    Keywords

    Income distribution; Dynamic Theil's Entropy; Multistate model; Economic policies;
    All these keywords.

    JEL classification:

    • E64 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Incomes Policy; Price Policy
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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