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Measures of poverty and inequality in the countries and regions of EU

Author

Listed:
  • Nicholas T. Longford

    (SNTL and UPF, Barcelona, Spain)

  • Maria Grazia Pittau

    (Department of Statistics, Sapienza University of Rome)

  • Roberto Zelli

    (Department of Statistics, Sapienza University of Rome)

  • Riccardo Massari

    (Sapienza University of Rome)

Abstract

The European Union Survey on Income and Living Conditions (EU-SILC) is the main source of information about living standards and poverty in the member states of the European Union. It provides reliable statistics at national level but sample sizes do not allow reliable estimates at sub-national level, despite a rising demand from policy makers and local authorities. We provide a comprehensive map of median income, inequality (Gini coefficient and Lorenz curve) and poverty (poverty rates), at country and regional levels, based on the equivalized household income in all the countries in which EU-SILC is conducted. We focus on personal income distribution within regions as opposed to per capita income distribution across regions to give a deeper insight into regional disparities. Small-area estimation is applied to improve estimates in regions with small sample size. Uncertainty of such complex non-linear statistics is assessed by bootstrap methods. Household-level sampling weights are taken into account in both the estimates and their relative bootstrapped standard errors.

Suggested Citation

  • Nicholas T. Longford & Maria Grazia Pittau & Roberto Zelli & Riccardo Massari, 2010. "Measures of poverty and inequality in the countries and regions of EU," Working Papers 182, ECINEQ, Society for the Study of Economic Inequality.
    • Handle: RePEc:inq:inqwps:ecineq2010-182
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    File URL: http://www.ecineq.org/milano/WP/ECINEQ2010-182.pdf
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    References listed on IDEAS

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    1. Julie Le Gallo, 2004. "Space-Time Analysis of GDP Disparities among European Regions: A Markov Chains Approach," International Regional Science Review, , vol. 27(2), pages 138-163, April.
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    Cited by:

    1. Lichner, Ivan & Lyócsa, Štefan & Výrostová, Eva, 2022. "Nominal and discretionary household income convergence: The effect of a crisis in a small open economy," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 18-31.
    2. Vassilis Tselios, 2014. "The Granger-causality between income and educational inequality: a spatial cross-regressive VAR framework," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 221-243, August.
    3. Markéta Hnízdilová & Václav Adamec, 2020. "Per Capita Income Inequality in Czech Households Before, During and After the Economic and Financial Crisis," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 68(5), pages 885-899.
    4. Antošová, Veronika & Stávková, Jana & Birčiaková, Naďa, 2013. "How the Income Situation of Households in the CR Responds to the Economic Development of the Society," MPRA Paper 48778, University Library of Munich, Germany.
    5. Dorota Weziak-Bialowolska, 2016. "Spatial Variation in EU Poverty with Respect to Health, Education and Living Standards," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 125(2), pages 451-479, January.
    6. Silvia PALASCA, 2015. "Living Standards In Europe. A Regional View," EURINT, Centre for European Studies, Alexandru Ioan Cuza University, vol. 2, pages 297-307.
    7. Jana Stávková & Veronika Antošová & Jana Turčínková, 2012. "Influence of education on living conditions of households," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 60(2), pages 367-374.
    8. Birčiaková, Naďa & Antošová, Veronika & Stávková, Jana, 2013. "Has the level of achieved education affected the income of Czech households," MPRA Paper 48846, University Library of Munich, Germany.

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    More about this item

    Keywords

    European regional economics measurement; EU-SILC; Gini coefficient; Poverty rates; Small-area estimation.;
    All these keywords.

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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