IDEAS home Printed from https://ideas.repec.org/p/irs/iriswp/2007-01.html
   My bibliography  Save this paper

Extreme incomes and the estimation of poverty and inequality indicators from EU-SILC

Author

Listed:
  • VAN KERM Philippe

Abstract

Micro-data estimates of welfare indices are known to be sensitive to observations from the tails of the income distribution. It is therefore customary to make adjustments to extreme data before estimating inequality and poverty statistics. This paper systematically evaluates the impact of such adjustments on indicators estimated from the EU-SILC (Community Statistics on Income and Living conditions) which is expected to become the reference source for comparative statistics on income distribution and social exclusion in the EU. Emphasis is put on the robustness of cross-country comparisons to alternative adjustments. Results from a sensitivity analysis considering both simple, classical adjustments and a more sophisticated approach based on modelling parametrically the tails of the income distribution are reported. Reassuringly, ordinal comparisons of countries are found to be robust to variants of data adjustment procedures. However, data adjustments are far from innocuous. Cardinal comparisons of countries reveal sensitive to the treatment of extreme incomes, even for seemingly small adjustments.

Suggested Citation

  • VAN KERM Philippe, 2007. "Extreme incomes and the estimation of poverty and inequality indicators from EU-SILC," IRISS Working Paper Series 2007-01, IRISS at CEPS/INSTEAD.
    • Handle: RePEc:irs:iriswp:2007-01
    as

    Download full text from publisher

    File URL: https://liser.elsevierpure.com/en/publications/extreme-incomes-and-the-estimation-of-poverty-and-inequality-indi
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Frank A. Cowell & Maria-Pia Victoria-Feser, 2002. "Welfare Rankings in the Presence of Contaminated Data," Econometrica, Econometric Society, vol. 70(3), pages 1221-1233, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Frank Cowell & Maria-Pia Victoria-Feser, 2003. "Distribution-Free Inference for Welfare Indices under Complete and Incomplete Information," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 1(3), pages 191-219, December.
    2. Cristina Blanco Pérez & Xavier Ramos, 2010. "Polarization And Health," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(1), pages 171-185, March.
    3. Frank A. Cowell & Emmanuel Flachaire, 2014. "Statistical Methods for Distributional Analysis," Working Papers halshs-01115996, HAL.
    4. Rossello, Damiano, 2015. "Ranking of investment funds: Acceptability versus robustness," European Journal of Operational Research, Elsevier, vol. 245(3), pages 828-836.
    5. Frank Cowell & Maria-Pia Victoria-Feser, 2007. "Robust stochastic dominance: A semi-parametric approach," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 5(1), pages 21-37, April.
    6. Carlos Sánchez-González & Rosa M. García-Fernández, 2020. "A Multivariate Indicator to Compute Middle Class Population," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(1), pages 1-14, January.
    7. Sotomayor, Orlando, 2008. "The Distribution of Household Income in Brazil: Unequal and Immutable?," World Development, Elsevier, vol. 36(7), pages 1280-1293, July.
    8. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & Hussain, Saiful Izzuan, 2021. "Measuring income inequality: A robust semi-parametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    9. Frank A Cowell & Maria-Pia Victoria-Feser, 2001. "Distributional Dominance with Dirty Data," STICERD - Distributional Analysis Research Programme Papers 51, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    10. Jenkins, Stephen P., 2022. "Getting the Measure of Inequality," IZA Discussion Papers 14996, Institute of Labor Economics (IZA).
    11. Stanislaw Maciej Kot, 2016. "Estimates Of The World Distribution Of Personal Incomes Based On Country Sample Clones," GUT FME Working Paper Series A 41, Faculty of Management and Economics, Gdansk University of Technology.
    12. Stéphane Guerrier & Samuel Orso & Maria-Pia Victoria-Feser, 2018. "Parametric Inference for Index Functionals," Econometrics, MDPI, vol. 6(2), pages 1-11, April.
    13. Beat Hulliger & Tobias Schoch, 2014. "Robust, distribution-free inference for income share ratios under complex sampling," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(1), pages 63-85, January.
    14. Cowell, Frank & Victoria-Feser, Maria-Pia, 2001. "Robust Lorenz curves : a semi-parametric approach," LSE Research Online Documents on Economics 2155, London School of Economics and Political Science, LSE Library.
    15. Higgins, Sean & Lustig, Nora, 2016. "Can a poverty-reducing and progressive tax and transfer system hurt the poor?," Journal of Development Economics, Elsevier, vol. 122(C), pages 63-75.
    16. Frank A. Cowell & Philippe Kerm, 2015. "Wealth Inequality: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(4), pages 671-710, September.
    17. Frank A. Cowell & Maria-Pia Victoria-Feser, 2008. "Modelling Lorenz Curves: Robust and Semi-parametric Issues," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 13, pages 241-253, Springer.
    18. Espen Villanger, 2003. "The effects of disasters on income mobility: Bootstrap inference and measurement error simulations," CMI Working Papers WP 2003:6, CMI (Chr. Michelsen Institute), Bergen, Norway.
    19. Giuseppe Munda, 2012. "Intensity of preference and related uncertainty in non-compensatory aggregation rules," Theory and Decision, Springer, vol. 73(4), pages 649-669, October.
    20. Cristina Blanco-Perez, 2012. "Rethinking the Relative Income Hypothesis," SOEPpapers on Multidisciplinary Panel Data Research 501, DIW Berlin, The German Socio-Economic Panel (SOEP).

    More about this item

    Keywords

    social indicators; poverty and inequality; extreme incomes; parametric tail; EU-SILC;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:irs:iriswp:2007-01. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Philippe Van Kerm (email available below). General contact details of provider: https://edirc.repec.org/data/cepsslu.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.