IDEAS home Printed from https://ideas.repec.org/p/bdi/opques/qef_806_23.html
   My bibliography  Save this paper

Labour income inequality and in-work poverty: a comparison between euro area countries

Author

Listed:
  • Giulia Bovini

    (Bank of Italy)

  • Emanuele Ciani

    (Bank of Italy)

  • Marta De Philippis
  • Stefania Romano

    (Bank of Italy)

Abstract

We study inequality in gross labour income among the working-age population, comparing Italy to the other main euro area countries. We use EU-SILC data between 2008 and 2018, the longest period without time breaks. We show that inequality in individual labour income is higher in Italy than in France and Germany. This is mainly a consequence of the lower employment rate, i.e. of the higher share of working-age individuals with no labour income, rather than of wider earnings disparities among workers. Inequality in equivalised household labour income is also higher in Italy than in France in Germany because a lower employment rate translates into a larger share of single or no-earner households. In line with these findings, while in Italy low-earning workers are relatively few, they face a greater risk of poverty than in France or Germany, since they more often live in households where other members are not employed or have low-work-intensity jobs. These results stress the importance of jointly considering earnings and employment dynamics when analysing labour income inequality, low-pay work, and in-work poverty.

Suggested Citation

  • Giulia Bovini & Emanuele Ciani & Marta De Philippis & Stefania Romano, 2023. "Labour income inequality and in-work poverty: a comparison between euro area countries," Questioni di Economia e Finanza (Occasional Papers) 806, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_806_23
    as

    Download full text from publisher

    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2023-0806/QEF_806_23.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Eran B. Hoffmann & Davide Malacrino & Luigi Pistaferri, 2022. "Earnings dynamics and labor market reforms: The Italian case," Quantitative Economics, Econometric Society, vol. 13(4), pages 1637-1667, November.
    2. Karagiannis, Elias & Kovacevic', Milorad, 2000. "A Method to Calculate the Jackknife Variance Estimator for the Gini Coefficient," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(1), pages 119-122, February.
    3. Anthony F. Shorrocks, 1983. "The Impact of Income Components on the Distribution of Family Incomes," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 98(2), pages 311-326.
    4. Manuel Arellano & Stéphane Bonhomme & Micole De Vera & Laura Hospido & Siqi Wei, 2022. "Income risk inequality: Evidence from Spanish administrative records," Quantitative Economics, Econometric Society, vol. 13(4), pages 1747-1801, November.
    5. Andrea Brandolini & Alfonso Rosolia & Roberto Torrini, 2011. "The distribution of employees’ labour earnings in the European Union: Data, concepts and first results," Working Papers 198, ECINEQ, Society for the Study of Economic Inequality.
    6. Michele Raitano, 2016. "Income Inequality in Europe Since the Crisis," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 51(2), pages 67-72, March.
    7. Ruta Aidis & Saul Estrin & Tomasz Mickiewicz, 2012. "Size matters: entrepreneurial entry and government," Small Business Economics, Springer, vol. 39(1), pages 119-139, July.
    8. Adamopoulou Effrosyni & Bobbio Emmanuele & Philippis Marta De & Giorgi Federico, 2019. "Reallocation and the Role of Firm Composition Effects on Aggregate Wage Dynamics," IZA Journal of Labor Economics, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 8(1), pages 5-22, June.
    9. Andrea Brandolini & Piero Cipollone & Paolo Sestito, 2001. "Earnings Dispersion, Low Pay and Household Poverty in Italy, 1977-1998," Temi di discussione (Economic working papers) 427, Bank of Italy, Economic Research and International Relations Area.
    10. Emanuele Ciani & Roberto Torrini, 2019. "The Geography of Italian Income Inequality: Recent Trends and the Role of Employment," Politica economica, Società editrice il Mulino, issue 2, pages 173-208.
    11. Elias Karagiannis & Milorad Kovacevic', 2000. "A Method to Calculate the Jackknife Variance Estimator For the Gini Coefficient," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(1), pages 119-122, February.
    12. Andrea Brandolini & Eliana Viviano, 2016. "Behind and beyond the (head count) employment rate," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(3), pages 657-681, June.
    13. Andrea Brandolini & Romina Gambacorta & Alfonso Rosolia, 2018. "Inequality amid income stagnation: Italy over the last quarter of a century," Questioni di Economia e Finanza (Occasional Papers) 442, Bank of Italy, Economic Research and International Relations Area.
    14. Barton H. Hamilton, 2000. "Does Entrepreneurship Pay? An Empirical Analysis of the Returns to Self-Employment," Journal of Political Economy, University of Chicago Press, vol. 108(3), pages 604-631, June.
    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. Francesca Carta, 2020. "Timely Indicators for Inequality and Poverty Using the Italian Labour Force Survey," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(1), pages 41-65, May.
    2. Heshmati, Almas, 2004. "A Review of Decomposition of Income Inequality," IZA Discussion Papers 1221, Institute of Labor Economics (IZA).
    3. Franecsca Carta, 2019. "Timely indicators for labour income inequality," Questioni di Economia e Finanza (Occasional Papers) 503, Bank of Italy, Economic Research and International Relations Area.
    4. George Djolov, 2014. "A Note on the Estimation of the Gini Index," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 8(3), pages 237-256, August.
    5. Karoly, Lynn & Schröder, Carsten, 2015. "Fast methods for jackknifing inequality indices," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 37(1), pages 125-138.
    6. Kuan Xu, 2007. "U-Statistics and Their Asymptotic Results for Some Inequality and Poverty Measures," Econometric Reviews, Taylor & Francis Journals, vol. 26(5), pages 567-577.
    7. Mazzi Gian Luigi & Mitchell James & Carausu Florabela, 2021. "Measuring and Communicating the Uncertainty in Official Economic Statistics," Journal of Official Statistics, Sciendo, vol. 37(2), pages 289-316, June.
    8. Carsten Schröder & Yolanda Golan & Shlomo Yitzhaki, 2014. "Inequality and the time structure of earnings: evidence from Germany," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 12(3), pages 393-409, September.
    9. David E. A. Giles, 2004. "Calculating a Standard Error for the Gini Coefficient: Some Further Results," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(3), pages 425-433, July.
    10. Xiaofeng Lv & Gupeng Zhang & Xinkuo Xu & Qinghai Li, 2017. "Bootstrap-calibrated empirical likelihood confidence intervals for the difference between two Gini indexes," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(2), pages 195-216, June.
    11. Armitage, Seth & Hou, Wenxuan & Liu, Xianda & Wang, Cong, 2021. "Law, Endowment and Inequality in Access to Finance," Finance Research Letters, Elsevier, vol. 39(C).
    12. Jordi Arcarons & Samuel Calonge, 2015. "Inference tests for tax progressivity and income redistribution: the Suits approach," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 13(2), pages 207-223, June.
    13. Ivica Petrikova, 2022. "The Effects of Local-Level Economic Inequality on Social Capital: Evidence from Andhra Pradesh, India," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 34(6), pages 2850-2877, December.
    14. Deng, Wei & Liang, Qiao Zhuan & Fan, Pei Hua, 2019. "Complements or substitutes? Configurational effects of entrepreneurial activities and institutional frameworks on social well-being," Journal of Business Research, Elsevier, vol. 96(C), pages 194-205.
    15. Xiaofeng Lv & Gupeng Zhang & Xinkuo Xu & Qinghai Li, 2017. "Bootstrap-calibrated empirical likelihood confidence intervals for the difference between two Gini indexes," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(2), pages 195-216, June.
    16. Fatih Guvenen & Luigi Pistaferri & Giovanni L. Violante, 2022. "Global trends in income inequality and income dynamics: New insights from GRID," Quantitative Economics, Econometric Society, vol. 13(4), pages 1321-1360, November.
    17. Yoonseok Lee & Donggyun Shin, 2016. "Measuring Social Tension from Income Class Segregation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 457-471, July.
    18. Qin, Yongsong & Rao, J.N.K. & Wu, Changbao, 2010. "Empirical likelihood confidence intervals for the Gini measure of income inequality," Economic Modelling, Elsevier, vol. 27(6), pages 1429-1435, November.
    19. Yoonseok Lee & Donggyun Shin, 2013. "Measuring Social Unrest Based on Income Distribution," Center for Policy Research Working Papers 160, Center for Policy Research, Maxwell School, Syracuse University.
    20. Wang, Dongliang & Zhao, Yichuan & Gilmore, Dirk W., 2016. "Jackknife empirical likelihood confidence interval for the Gini index," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 289-295.

    More about this item

    Keywords

    working-age population; employment rate; inequality; in-work poverty;
    All these keywords.

    JEL classification:

    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J30 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - General
    • J82 - Labor and Demographic Economics - - Labor Standards - - - Labor Force Composition

    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:bdi:opques:qef_806_23. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bdigvit.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.