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. 2021 Mar 31;16(3):e0249121.
doi: 10.1371/journal.pone.0249121. eCollection 2021.

Tracking the impact of COVID-19 on economic inequality at high frequency

Oriol Aspachs  1 , Ruben Durante  2   3   4   5 , Alberto Graziano  1 , Josep Mestres  1 , Marta Reynal-Querol  2   3   4   5 , Jose G Montalvo  2   4   5
Affiliations

Tracking the impact of COVID-19 on economic inequality at high frequency

Oriol Aspachs et al. PLoS One. .

Abstract

Pandemics have historically had a significant impact on economic inequality. However, official inequality statistics are only available at low frequency and with considerable delay, which challenges policymakers in their objective to mitigate inequality and fine-tune public policies. We show that using data from bank records it is possible to measure economic inequality at high frequency. The approach proposed in this paper allows measuring, timely and accurately, the impact on inequality of fast-unfolding crises, like the COVID-19 pandemic. Applying this approach to data from a representative sample of over three million residents of Spain we find that, absent government intervention, inequality would have increased by almost 30% in just one month. The granularity of the data allows analyzing with great detail the sources of the increases in inequality. In the Spanish case we find that it is primarily driven by job losses and wage cuts experienced by low-wage earners. Government support, in particular extended unemployment insurance and benefits for furloughed workers, were generally effective at mitigating the increase in inequality, though less so among young people and foreign-born workers. Therefore, our approach provides knowledge on the evolution of inequality at high frequency, the effectiveness of public policies in mitigating the increase of inequality and the subgroups of the population most affected by the changes in inequality. This information is fundamental to fine-tune public policies on the wake of a fast-moving pandemic like the COVID-19.

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Conflict of interest statement

OA, AG, and JM are employees of the Research Department of CaixaBank. There are no patents, products in development or marketed products to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Distribution of monthly net wages: Our sample (CABK) versus the sample of the official wage survey (EES).
Fig 2
Fig 2. Changes in payments between April and February by level of wages in the reference period.
Pre-benefits scenario. Comparing 2020 and 2019.
Fig 3
Fig 3. Diff-in-diffs in payments for each level of salaries in the reference month.
April vs February—2020 vs 2019.
Fig 4
Fig 4. Inequality measures.
(a) Gini index (b) Theil index (α = 1) (c) Lorentz curve: Pre-benefits, 2020 (d) Lorentz curve: Post-benefits, 2020.
Fig 5
Fig 5. Evolution of the Gini index by gender, age and country of origin.
(a) By gender. (b) By age group. (c) By place of birth.
See this image and copyright information in PMC

References

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    1. Chetty Raj, Friedman John N, Hendren Nathaniel, and Stepner Michael. Real-time economics: A new platform to track the impacts of covid-19 on people, businesses, and communities using private sector data. Technical report, NBER WP 27431, 2020.

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