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The evolution of poverty in the EU-28: a further look based on multivariate tail dependence

Author

Listed:
  • César Garcia-Gomez

    (University of Valladolid)

  • Ana Pérez

    (University of Valladolid)

  • Mercedes Prieto-Alaiz

    (University of Valladolid)

Abstract

This paper proposes a graphical tool based on the copula function, namely the mul-tivariate tail concentration function, to represent the dependence structure on the tailsof a multivariate joint distribution. We illustrate the use of this function to measuredependence between poverty dimensions. In particular, we analyse how multivariate taildependence between the dimensions of the AROPE rate evolved in the EU-28 between2008 and 2018. We nd evidence of lower tail dependence in all EU-28 countries, al-though this dependence is time-varying over the period analysed and the e ect of theGreat Recession on this dependence is not homogeneous over all countries.

Suggested Citation

  • César Garcia-Gomez & Ana Pérez & Mercedes Prieto-Alaiz, 2022. "The evolution of poverty in the EU-28: a further look based on multivariate tail dependence," Working Papers 605, ECINEQ, Society for the Study of Economic Inequality.
    • Handle: RePEc:inq:inqwps:ecineq2022-605
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    File URL: http://www.ecineq.org/milano/WP/ECINEQ2022-605.pdf
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    References listed on IDEAS

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    1. Manner, Hans & Segers, Johan, 2011. "Tails of correlation mixtures of elliptical copulas," LIDAM Reprints ISBA 2011002, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Koen Decancq, 2014. "Copula-based measurement of dependence between dimensions of well-being," Oxford Economic Papers, Oxford University Press, vol. 66(3), pages 681-701.
    3. Sweeting, Paul, 2013. "Calculating and communicating tail association and the risk of extreme loss ‐ Abstract of the London Discussion," British Actuarial Journal, Cambridge University Press, vol. 18(1), pages 73-83, March.
    4. Roman Matkovskyy, 2020. "A measurement of affluence and poverty interdependence across countries: Evidence from the application of tail copula," Bulletin of Economic Research, Wiley Blackwell, vol. 72(4), pages 404-416, October.
    5. Matkovskyy, Roman, 2019. "Centralized and decentralized bitcoin markets: Euro vs USD vs GBP," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 270-279.
    6. Suman Seth & Maria Emma Santos, 2019. "On the Interaction Between Focus and Distributional Properties in Multidimensional Poverty Measurement," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(2), pages 503-521, September.
    7. François Bourguignon & Satya R. Chakravarty, 2019. "The Measurement of Multidimensional Poverty," Themes in Economics, in: Satya R. Chakravarty (ed.), Poverty, Social Exclusion and Stochastic Dominance, pages 83-107, Springer.
    8. Koen Decancq, 2020. "Measuring cumulative deprivation and affluence based on the diagonal dependence diagram," METRON, Springer;Sapienza Università di Roma, vol. 78(2), pages 103-117, August.
    9. Schmid, Friedrich & Schmidt, Rafael, 2007. "Multivariate conditional versions of Spearman's rho and related measures of tail dependence," Journal of Multivariate Analysis, Elsevier, vol. 98(6), pages 1123-1140, July.
    10. Cyril Caillault & Dominique Guegan, 2005. "Empirical estimation of tail dependence using copulas: application to Asian markets," Quantitative Finance, Taylor & Francis Journals, vol. 5(5), pages 489-501.
    11. Frahm, Gabriel, 2006. "On the extremal dependence coefficient of multivariate distributions," Statistics & Probability Letters, Elsevier, vol. 76(14), pages 1470-1481, August.
    12. Sweeting, Paul & Fotiou, Fotis, 2013. "Calculating and communicating tail association and the risk of extreme loss," British Actuarial Journal, Cambridge University Press, vol. 18(1), pages 13-72, March.
    13. Genest, Christian & Nešlehová, Johanna G. & Rémillard, Bruno, 2017. "Asymptotic behavior of the empirical multilinear copula process under broad conditions," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 82-110.
    14. Jean-Yves Duclos & Luca Tiberti, 2016. "Multidimensional poverty indices: A critical assessment," Cahiers de recherche 1602, CIRPEE.
    15. Yannick Malevergne & Didier Sornette, 2006. "Extreme Financial Risks : From Dependence to Risk Management," Post-Print hal-02298069, HAL.
    16. Reboredo, Juan C. & Tiwari, Aviral Kumar & Albulescu, Claudiu Tiberiu, 2015. "An analysis of dependence between Central and Eastern European stock markets," Economic Systems, Elsevier, vol. 39(3), pages 474-490.
    17. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    18. Manner, Hans & Segers, Johan, 2011. "Tails of correlation mixtures of elliptical copulas," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 153-160, January.
    19. Rafael Schmidt & Ulrich Stadtmüller, 2006. "Non‐parametric Estimation of Tail Dependence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 307-335, June.
    20. Genest, Christian & Nešlehová, Johanna, 2007. "A Primer on Copulas for Count Data," ASTIN Bulletin, Cambridge University Press, vol. 37(2), pages 475-515, November.
    21. Frahm, Gabriel & Junker, Markus & Schmidt, Rafael, 2005. "Estimating the tail-dependence coefficient: Properties and pitfalls," Insurance: Mathematics and Economics, Elsevier, vol. 37(1), pages 80-100, August.
    22. A. B. Atkinson & F. Bourguignon, 1982. "The Comparison of Multi-Dimensioned Distributions of Economic Status," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 49(2), pages 183-201.
    23. Hua, Lei & Joe, Harry, 2011. "Tail order and intermediate tail dependence of multivariate copulas," Journal of Multivariate Analysis, Elsevier, vol. 102(10), pages 1454-1471, November.
    24. César García‐Gómez & Ana Pérez & Mercedes Prieto‐Alaiz, 2021. "Copula‐based analysis of multivariate dependence patterns between dimensions of poverty in Europe," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(1), pages 165-195, March.
    25. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    26. Supper, Hendrik & Irresberger, Felix & Weiß, Gregor, 2020. "A comparison of tail dependence estimators," European Journal of Operational Research, Elsevier, vol. 284(2), pages 728-742.
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    Cited by:

    1. César García Gómez & Ana Pérez & Mercedes Prieto-Alaiz, 2024. "Changes in the Dependence Structure of AROPE Components: Evidence from the Spanish Region," Hacienda Pública Española / Review of Public Economics, IEF, vol. 248(1), pages 21-51, March.

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    More about this item

    Keywords

    Multivariate tail dependence; Copula; Poverty; AROPE rate; Europe.;
    All these keywords.

    JEL classification:

    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe

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