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Decomposition Of The Population Dynamic Theil'S Entropy And Its Application To Four European Countries

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  • D'AMICO, GUGLIELMO
  • DI BIASE, GIUSEPPE
  • MANCA, RAIMONDO

Abstract

In this paper we propose a modification of the Dynamic Theil's Entropy that considers the inequality in the whole population. We decompose it into three addends and we show how to compute them within a Markov model of income evolution. In this way the income inequality can be measured in the whole population and not only among a given number of classes in which the economic agents are classified. The model is implemented with statistics from Eurostat data applied on France, Germany, Greece and Italy. The results reveal different inequality behaviors characterizing the considered European countries.

Suggested Citation

  • 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.
    • Handle: RePEc:hit:hitjec:v:55:y:2014:i:2:p:229-239
    DOI: 10.15057/26967
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    References listed on IDEAS

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    1. Shorrocks, Anthony F, 1984. "Inequality Decomposition by Population Subgroups," Econometrica, Econometric Society, vol. 52(6), pages 1369-1385, November.
    2. Brzezinski, Michal, 2013. "Asymptotic and bootstrap inference for top income shares," Economics Letters, Elsevier, vol. 120(1), pages 10-13.
    3. 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.
    4. Goerlich Gisbert, Francisco J., 2001. "On factor decomposition of cross-country income inequality: some extensions and qualifications," Economics Letters, Elsevier, vol. 70(3), pages 303-309, March.
    5. Quah, Danny T., 1996. "Empirics for economic growth and convergence," European Economic Review, Elsevier, vol. 40(6), pages 1353-1375, June.
    6. Kazuhiko Kakamu & Mototsugu Fukushige, 2009. "Multilevel Decomposition Methods For Income Inequality Measures," The Japanese Economic Review, Japanese Economic Association, vol. 60(3), pages 333-344, September.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Guglielmo D'Amico & Riccardo De Blasis & Philippe Regnault, 2020. "Confidence sets for dynamic poverty indexes," Papers 2006.06595, arXiv.org.
    5. 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.
    6. Jarosław Oczki & Ewa Wędrowska, 2017. "Changes in Household Income Distributions in EU Countries," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 45, pages 299-314.

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    More about this item

    Keywords

    income distribution; dynamic Theil's entropy; fiscal system; welfare 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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