Welfare Resilience at the Onset of the COVID‐19 Pandemic in a Selection of European Countries: Impact on Public Finance and Household Incomes

• Published date: 01 June 2022
• Author: Olga Cantó,Francesco Figari,Carlo V. Fiorio,Sarah Kuypers,Sarah Marchal,Marina Romaguera‐de‐la‐Cruz,Iva V. Tasseva,Gerlinde Verbist
• Date: 01 June 2022
• DOI: http://doi.org/10.1111/roiw.12530

© 2021 The Authors. Review of Income and Wealth published by John Wiley & Sons Ltd on behalf of

International Association for Research in Income and Wealth

293

WELFARE RESILIENCE AT THE ONSET OF THE COVID- 19 PANDEMIC

IN A SELECTION OF EUROPEAN COUNTRIES: IMPACT ON PUBLIC

FINANCE AND HOUSEHOLD INCOMES

by Olga Cantó

Universidad de Alcalá and EQUALITAS

FranCesCO Figari

University of Insubria,ISER University of Essex,CeRP Collegio Carlo Alberto and Dondena

CarlO V. FiOriO*

University of Milan,Irvapp- FBK and Dondena

sarah Kuypers, sarah MarChal and gerlinde Verbist

Herman Deleeck Centre for Social Policy,University of Antwerp

Marina rOMaguera- de- la- Cruz

Universidad Antonio de Nebrija

AND

iVa V. tasseVa

London School of Economics and Political Science

This paper assesses the impact on household incomes of the COVID- 19 pandemic and governments’

policy responses in April 2020 in four large and severely hit EU countries: Belgium, Italy, Spain and

the UK. We provide comparative evidence on the level of relative and absolute welfare resilience at

the onset of the pandemic, by creating counterfactual scenarios using the European tax- benefit model

EUROMOD combined with COVID- 19- related household surveys and timely labor market data. We

find that income poverty increased in all countries due to the pandemic while inequality remained

broadly the same. Differences in the impact of policies across countries arose from four main sources:

the asymmetric dimension of the shock by country, the different protection offered by each tax- benefit

system, the diverse design of discretionary measures and differences in the household level circum-

stances and living arrangements of individuals at risk of income loss in each country.

JEL Codes: D31, H55, I32

Keywords: COVID- 19, household incomes, tax- benefit microsimulation, income protection, cross-

country comparison

Review of Income and Wealth

Series 68, Number 2, June 2022

DOI: 10.1111/roiw.12530

This is an open access article under the terms of the Creative Commons Attribution License, which

permits use, distribution and reproduction in any medium, provided the original work is properly

cited.

*Correspondence to: Carlo V. Fiorio, Department of Economics, Management and Quantitative

Methods (DEMM), University of Milan, Via Conservatorio, 7 20122 Milan, Italy and Irvapp-FBK

and Dondena (carlo.fiorio@unimi.it).

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Review of Income and Wealth, Series 68, Number 2, June 2022

294

© 2021 The Authors. Review of Income and Wealth published by John Wiley & Sons Ltd on behalf of

International Association for Research in Income and Wealth

1. intrOduCtiOn

The COVID- 19 pandemic has led to a worldwide economic downturn worse

than the one that characterized the 2008 Great Recession. The potential impact

on GDP, although mostly unpredictable still today without a clear knowledge of

the further development of the health emergency, can lead to a massive slump in

economic development (Dorn et al., 2020). OECD estimates for the initial direct

impact of the crisis revealed a decline in annual GDP growth of around 2 percent-

age points for each month of economic shutdown (OECD, 2020a). Focusing on

the situations faced by workers, the International Labour Organization estimated

initially a rise in global unemployment of between 3 percent and 13 percent, with

underemployment expected to increase on a large scale and the decline in eco-

nomic activity and travel limits impacting both manufacturing and services (ILO,

2020).

To limit the spread of the virus governments across Europe restricted or com-

pletely shut down non- essential economic activities. These containment measures

resulted in unprecedented demand and supply shocks. Between the first and sec-

ond quarter of 2020, GDP fell drastically in some European countries— by 11.8

percent in Belgium, 13 percent in Italy, 17.9 percent in Spain and 18.8 percent in

the UK, making them some of the worst affected countries economically in

Europe.1

The picture described above, as well as the lessons of previous recessions such

as the one of 2008, suggest that the downturn due to the COVID- 19 pandemic will

overshadow European economies for years to come, through a legacy of unemploy-

ment, public debt and long- lasting impacts on household incomes (Jenkins et al.,

2013). As Saez and Zucman (2020, p. 1) rightly point out, governments “can prevent

a very sharp but short recession from becoming a long- lasting depression” by acting

1See Eurostat’s indicator “GDP and main components (output, expenditure and income)

[NAMQ_10_GDP].”

Note: We thank Silvia De Poli, Adrián Hernández Martín, Manos Matsaganis, Holly Sutherland

and the participants to the EUROMOD Research Workshop (2020) and the OECD- WISE seminar

(2020). The support of the Collegio Carlo Alberto is acknowledged. Olga Cantó and Marina

Romaguera- de- la- Cruz acknowledge support from the Comunidad de Madrid— H2019/HUM- 5793

and from the Spanish Ministerio de Ciencia e Innovación— PID2019- 104619RB- C41. Sarah Kuypers,

Sarah Marchal and Gerlinde Verbist acknowledge support from the Belgian Federal Public Service

Social Security. We use EUROMOD (version I3.0+) which is developed and managed by the Institute

for Social and Economic Research (ISER) at the University of Essex, in collaboration with the

European Commission— JRC Seville and national teams. We are indebted to Holly Sutherland and the

many people who have contributed to the development of EUROMOD. The process of extending and

updating EUROMOD is financially supported by the European Union Programme for Employment

and Social Innovation “Easi” (2014- 2020). We make use of the following microdata: the Italian and

Spanish versions of the SILC data made available by ISTAT and INE; EU- SILC made available by

EUROSTAT; the Corona Study data made available by the Universities of Antwerp and Hasselt; the

Family Resources Survey and Understanding Society COVID- 19 Study made available by the UK

Department of Work and Pensions (DWP) via the UK Data Service; the Spanish Annual Labour Force

Survey (INE), register data on monthly Social Security affiliates (Ministerio de Inclusión Social y

Migraciones) and information on monthly benefits from Servicio Público de Empleo Estatal (SEPE,

Ministerio de Trabajo y Economía Social). Data providers bear no responsibility for the analysis or

interpretation of the data reported here. Any mistakes are the authors’ only.

Funding information: Open Access Funding provided by Universita degli Studi di Milano within

the CRUI-CARE Agreement. [Correction added on 21st May 2022, after first online publication: CRUI

funding statement has been added.]