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Regional Income Distribution in the European Union: A Parametric Approach

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  • Spasova, Tsvetana

    (University of Basel)

Abstract

This work studies trends in income distributions and inequality in the Euro- pean Union using data from the European Union Statistics on Income and Living Conditions. We model the income distribution for each country under a Dagum distribution assumption and using maximum likelihood techniques. We use pa- rameter estimates to form distributions for regions defined as finite mixtures of the country distributions. Specifically, we study the groups of new" and old" countries depending on the year they joined the European Union. We provide formulae and estimates for the regional Gini coefficients and Lorenz curves and their decomposition for all the survey years from 2007 through 2011. Our esti- mates show that the new" European Union countries have become richer and less unequal over the observed years, while the old" ones have undergone a slight increase in inequality which is however not significant at conventional levels.

Suggested Citation

  • Spasova, Tsvetana, 2019. "Regional Income Distribution in the European Union: A Parametric Approach," Working papers 2019/18, Faculty of Business and Economics - University of Basel.
  • Handle: RePEc:bsl:wpaper:2019/18
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    References listed on IDEAS

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    1. Chotikapanich, Duangkamon & Griffiths, William E. & Rao, D. S. Prasada, 2007. "Estimating and Combining National Income Distributions Using Limited Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 97-109, January.
    2. Gordon Anderson & Maria Grazia Pittau & Roberto Zelli & Jasmin Thomas, 2018. "Income Inequality, Cohesiveness and Commonality in the Euro Area: A Semi-Parametric Boundary-Free Analysis," Econometrics, MDPI, vol. 6(2), pages 1-20, March.
    3. Carmelo Garcia Perez & Mercedes Prieto Alaiz, 2011. "Using the Dagum model to explain changes in personal income distribution," Applied Economics, Taylor & Francis Journals, vol. 43(28), pages 4377-4386.
    4. Branko Milanovic, 2012. "Global inequality recalculated and updated: the effect of new PPP estimates on global inequality and 2005 estimates," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(1), pages 1-18, March.
    5. Xavier Sala-i-Martin, 2006. "The World Distribution of Income: Falling Poverty and … Convergence, Period," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(2), pages 351-397.
    6. Stefano Filauro, 2017. "European incomes, national advantages: EU-wide inequality and its decomposition by country and region," EERI Research Paper Series EERI RP 2017/05, Economics and Econometrics Research Institute (EERI), Brussels.
    7. Christian Kleiber, 2008. "A Guide to the Dagum Distributions," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 6, pages 97-117, Springer.
    8. Duangkamon Chotikapanich & William E. Griffiths & D. S. Prasada Rao & Vicar Valencia, 2012. "Global Income Distributions and Inequality, 1993 and 2000: Incorporating Country-Level Inequality Modeled with Beta Distributions," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 52-73, February.
    9. Nicholas T. Longford & Maria Grazia Pittau & Roberto Zelli & Riccardo Massari, 2012. "Poverty and inequality in European regions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1557-1576, January.
    10. Van Kerm, Philippe & P. Jenkins, Stephen, 2011. "Patterns of persistent poverty: evidence from EU-SILC," ISER Working Paper Series 2011-30, Institute for Social and Economic Research.
    11. Gholamreza Hajargasht & William E. Griffiths & Joseph Brice & D.S. Prasada Rao & Duangkamon Chotikapanich, 2012. "Inference for Income Distributions Using Grouped Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 563-575, May.
    12. Lambert, Peter J & Aronson, J Richard, 1993. "Inequality Decomposition Analysis and the Gini Coefficient Revisited," Economic Journal, Royal Economic Society, vol. 103(420), pages 1221-1227, September.
    13. Frank A. Cowell & Russell Davidson & Emmanuel Flachaire, 2015. "Goodness of Fit: An Axiomatic Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 54-67, January.
    14. Aristei, David & Perugini, Cristiano, 2010. "Preferences for redistribution and inequality in well-being across Europe," Journal of Policy Modeling, Elsevier, vol. 32(2), pages 176-195, March.
    15. Fabrizi, Enrico & Ferrante, Maria Rosaria & Pacei, Silvia & Trivisano, Carlo, 2011. "Hierarchical Bayes multivariate estimation of poverty rates based on increasing thresholds for small domains," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1736-1747, April.
    16. Monique Graf & Desislava Nedyalkova, 2014. "Modeling of Income and Indicators of Poverty and Social Exclusion Using the Generalized Beta Distribution of the Second Kind," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(4), pages 821-842, December.
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    More about this item

    Keywords

    income distribution; finite mixtures; inequality; Gini decomposition; European Union;
    All these keywords.

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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