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Coronabedingte Ungleichheit und Armut in Deutschland: Überschätzt oder unterschätzt?
[The Rise of Inequality and Poverty in Germany During the Pandemic: False Alarm?]

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  • Michael Dauderstädt

Abstract

Recent data from EU-SILC and Destatis seemed to indicate a dramatic increase in German inequality and poverty in the pandemic year 2020. But Destatis had changed its data collection method. Based on several studies, it is likely that the actual rise has been much weaker while the previous data collection by Destatis might have underestimated the true disparities within German society in the past.

Suggested Citation

  • Michael Dauderstädt, 2022. "Coronabedingte Ungleichheit und Armut in Deutschland: Überschätzt oder unterschätzt? [The Rise of Inequality and Poverty in Germany During the Pandemic: False Alarm?]," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 102(1), pages 64-66, January.
    • Handle: RePEc:spr:wirtsc:v:102:y:2022:i:1:d:10.1007_s10273-022-3096-5
    DOI: 10.1007/s10273-022-3096-5
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    References listed on IDEAS

    as
    1. Markus M. Grabka, 2021. "Income Inequality in Germany Stagnating over the Long Term, But Decreasing Slightly during the Coronavirus Pandemic," DIW Weekly Report, DIW Berlin, German Institute for Economic Research, vol. 11(17/18), pages 125-133.
    2. Tanja Buch & Silke Hamann & Annekatrin Niebuhr & Duncan Roth & Georg Sieglen, 2021. "Arbeitsmarkteffekte der Corona-Krise — Sind Berufsgruppen mit niedrigen Einkommen besonders betroffen?," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 101(1), pages 14-17, January.
    3. Kerstin Bruckmeier & Andreas Peichl & Martin Popp & Jürgen Wiemers & Timo Wollmershäuser, 2020. "Covid-19-Krise: Für das Jahr 2020 ist mit keinem Anstieg der Einkommensungleichheit in Deutschland zu rechnen," ifo Schnelldienst Digital, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 1(16), pages 01-03, December.
    4. Charlotte Bartels & Maria Metzing, 2019. "An integrated approach for a top-corrected income distribution," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(2), pages 125-143, June.
    5. Francesca Carta & Marta De Philippis, 2021. "The impact of the COVID-19 shock on labour income inequality: evidence from Italy," Questioni di Economia e Finanza (Occasional Papers) 606, Bank of Italy, Economic Research and International Relations Area.
    6. Carsten Schröder & Theresa Entringer & Jan Goebel & Markus M. Grabka & Daniel Graeber & Hannes Kröger & Martin Kroh & Simon Kühne & Stefan Liebig & Jürgen Schupp & Johannes Seebauer & Sabine Zinn, 2020. "Vor dem Covid-19-Virus sind nicht alle Erwerbstätigen gleich," DIW aktuell 41, DIW Berlin, German Institute for Economic Research.
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    More about this item

    Keywords

    C81; D31; I32;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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