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. 2023 Mar 15:1–29. Online ahead of print. doi: 10.1007/s10888-022-09560-8

COVID-19 and income inequality: evidence from monthly population registers

Nikolay Angelov 1,2, Daniel Waldenström 3,4,
PMCID: PMC10015130  PMID: 37360569

Abstract

We measure the distributional impact of the COVID-19 pandemic using newly released population register data in Sweden. Monthly earnings inequality increased during the pandemic, and the key driver is income losses among low-paid individuals while middle- and high-income earners were almost unaffected. In terms of employment, as measured by having positive monthly earnings, the pandemic had a larger negative impact on private-sector workers and on women. In terms of earnings conditional on being employed, the effect was still more negative for women, but less negative for private-sector workers compared to publicly employed. Using data on individual take-up of government COVID-19 support, we show that policy significantly dampened the inequality increase, but did not fully offset it. Annual total market income inequality, which also includes capital income and taxable transfers, shows similar patterns of increasing inequality during the pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10888-022-09560-8.

Keywords: Pandemic, Income inequality, Earnings, Government policy

Electronic supplementary material

Below is the link to the electronic supplementary material.

(PDF 1.22 MB) (1.2MB, pdf)

Footnotes

We are grateful for comments from Cecilia Öst and two anonymous referees. Financial support from the Swedish Research Council is gratefully acknowledged.

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Contributor Information

Nikolay Angelov, Email: nikolay@angelov.cc.

Daniel Waldenström, Email: daniel.waldenstrom@ifn.se.

References

  1. Adams-Prassl A, Boneva T, Golin M, Rauh C. Inequality in the impact of the coronavirus shock: Evidence from real time surveys. J. Public Econ. 2020;189:104245. doi: 10.1016/j.jpubeco.2020.104245. [DOI] [Google Scholar]
  2. Almeida V, Barrios S, Christl M, De Poli S, Tumino A, van der Wielen W. The impact of COVID-19 on households’ income in the EU. Journal of Economic Inequality. 2022;19:413–431. doi: 10.1007/s10888-021-09485-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alvaredo F. A note on the relationship between top income shares and the gini coefficient. Econ. Lett. 2011;110:274–277. doi: 10.1016/j.econlet.2010.10.008. [DOI] [Google Scholar]
  4. Angelov, N., Waldenström, D.: The Impact of COVID-19 on Economic Activity: Evidence from Administrative Tax Registers. IZA Policy Paper No. 179 (2021)
  5. Blundell R, Costa Dias M, Joyce R, Xu X. COVID-19 And inequalities. Fisc. Stud. 2020;41:291–319. doi: 10.1111/1475-5890.12232. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Carta, F., De Phillips, M.: The Impact of the COVID-19 Shock on Labour Income Inequality: Evidence from Italy. Occational Paper, Banca d’Italia (2021)
  7. Casarico, A., Lattanazio, S.: The heterogeneous effects of COVID-19 on labor market flows: Evidence from administrative data. Covid Economics, 152–174 (2020) [DOI] [PMC free article] [PubMed]
  8. Clark, A. E., D’Ambrosio, C., Lepinteur, A.: The Fall in Income Inequality During COVID-19 in Four European Countries. Journal of Economics Inequality, forthcoming (2021) [DOI] [PMC free article] [PubMed]
  9. Crossely TF, Fisher P, Low H. The heterogeneous and regressive consequences of COVID-19: Evidence from high quality panel data. J. Public Econ. 2021;193:104334. doi: 10.1016/j.jpubeco.2020.104334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Farré L, Fawaz Y, González L, Graves J. Gender inequality in paid and unpaid work during COVID-19 times. Rev. Income Wealth. 2022;68:323–347. doi: 10.1111/roiw.12563. [DOI] [Google Scholar]
  11. Firpo SP, Fortin NM, Lemieux T. Unconditional quantile regression. Econometrica. 2008;77:953–973. [Google Scholar]
  12. Firpo SP, Fortin NM, Lemieux T. Decomposing wage distributions using recentered influence function regression. Econometrics. 2018;6:1–40. doi: 10.3390/econometrics6020028. [DOI] [Google Scholar]
  13. Gottschalk P, Smeeding T. Handbook of Income Distribution, chapter Empirical Evidence on Income Inequality in Industrialized Countries. Cambridge: Cambridge University Press; 2000. pp. 261–307. [Google Scholar]
  14. Hupkau C, Petrongolo B. Work, care and gender during the COVID-19 crisis. Fisc. Stud. 2020;41:623–651. doi: 10.1111/1475-5890.12245. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. IMF: Fiscal Monitor April 2021, Washington D.C. (2021)
  16. O’Donoghue C, Sologon DM, Kyzyma I, McHale J. Modelling the distributional impact of the COVID-19 crisis. Fisc. Stud. 2020;41:321–336. doi: 10.1111/1475-5890.12231. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Rognlie, M.: Deciphering the Fall and Rise in the Net Capital Share: Accumulation or Scarcity?. Brook. Pap. Econ. Act., 1–54 (2015)
  18. Stantcheva S. Inequalities in the times of the pandemic. Econ. Policy. 2022;37:5–41. doi: 10.1093/epolic/eiac006. [DOI] [Google Scholar]

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(PDF 1.22 MB) (1.2MB, pdf)

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