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The distributional effects of COVID-19 around the globe
The COVID-19 crisis has impacted people’s lives around the globe dramatically, hitting different countries asymmetrically due to differences in the health response, in the economic structure, and in the policy response aimed at reducing the effects of the first on the latter
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Also, within countries COVID-19 has exposed old and new divides in society. While the health emergency has initially brought people together, and the solidarity moment has resulted in robust policy responses in some countries, cracks are starting to appear and scars are visible that impact different groups differently.
Elderly people (in particular the frail elderly) are more exposed to the health risk, but it is younger people who have suffered more in the labour market, and adolescents and children who have lost precious time for socialising and learning. Estimates and early evidence show that each month of lockdown shrinks advanced economies by around 2% on an annual basis (OECD, 2020). Developing countries are particularly exposed to the virus due to the weaker healthcare and welfare systems in place, the risks of famine, volatile commodity prices, and the low standards of living which make it much harder for people to keep safe. It has been estimated that 81 percent of the world’s workforce is affected by lockdown measures (ILO, 2020), that poverty could increase globally by half a billion people (Sumner et al., 2020), that Africa will be hit by at least $100 billion in economic costs this year (te Velde, 2020), and that Latin America will experience a contraction of more than 5% of GDP (ECLAC, 2020). While in advanced economies many have switched to working from home, family tasks have not always been shared equally between partners, with women taking on their shoulders a larger share of the new burden of taking care of young children in absence of childcare, monitoring attendance at online lessons and classes, checking homework, preparing meals and attending the extra housekeeping required by more crowded homes. By contrast, in developing economies the restrictions have pushed even more people into the informal sector, wiping out years of slow and painful advances towards extending basic guarantees to all workers. Businesses have gone bankrupt, in some sectors more than in others. Workers in those sectors have seen their prospects reduced dramatically, at a time when mobility between jobs and new vacancy openings in other sectors have also gone down. In Europe, increased reliance on public support is undermined by the dynamics of debt accumulation, which although mitigated by the unprecedented, powerful interventions set out at the EU level (from the ECB’s PEPP to the Next Generation EU recovery instrument), will have to be repaid in the end. This may increase the likelihood of other rounds of austerity in the years to come, with possible dire consequences on the most vulnerable people in society.
In the face of these unprecedented challenges, it is vital that distributional consequences are swiftly analysed, to inform policy changes that are also happening at an unprecedented scale. Taxbenefit microsimulation modelling then becomes a crucial tool. However, tax-benefit models apply the tax and benefit legislation to an observed input population, typically derived from nationally representative survey data. These obviously do not reflect the impact of COVID-19 and related lock-down measures on employment and market incomes. To model the distributional effects of COVID-19, the input data have therefore to be adjusted. This nowcasting exercise is a crucial step that can be undertaken using information from external sources such as government forecasting or expert scenarios, early evidence at the aggregate or semi-aggregate level, or by means of macro models. A similar approach is being used by the European Commission to produce Flash Estimates on changes in poverty and income distribution based on a methodology developed by the University of Essex team (Gasior and Rastrigina, 2017 20 ; Leventi et al, 2017 21 ).
Some results based on the EUROMOD platform have started to appear. In the UK, Bronka et al. (2020) 22 develop an input-output model to estimate the size of the employment shock by industry, distribute the sectoral shock to individual workers according to their characteristics, and then make scenario assumptions about the path of recovery from the crisis. They find that the economy contracts by around a quarter in lock-down, a result confirmed by aggregate data for April 2020, but they also find that the emergency measures put in place by the Government are effective in protecting household incomes, especially at the bottom of the income distribution, where the increased generosity of social assistance schemes even improves the outlook for some individuals. This however comes at a huge cost for the Government, calling into question whether the extended safety net will remain in place for long enough, as well as whether other forms of support will be withdrawn in an effort to reduce the size of the public deficit. Brewer and Tasseva (2020) 23 focus on analysing the distributional impact of the crisis in late April 2020. They also find substantial income losses (around 8% net of the support schemes), confirming the earlier projections of Bronka et al., and little effects on inequality due to the generosity of the emergency measures. A similar pattern – substantial market income losses significantly attenuated by public support schemes at a high cost for the public budget – is found for Ireland (Beirne et al., 2020 24 ), while in Italy the effects on inequality and poverty are projected to be more pronounced, with an increase in the poverty risk of 15 percentage points among individuals affected by the lock-down and more than 8 percentage points considering the overall population (Figari and Fiorio, 2020) 25 .
In a new project, researchers at UNU-WIDER, SASPRI and CeMPA are starting to explore the implications of the COVID-19 crisis for low and middle income countries. Due to lack of timely data, they are following the approach of Bronka et al. (2020) and model the size of the economic shock in the SOUTHMOD countries based on detailed input-output tables and scenario assumptions validated by country experts. They will then use the SOUTHMOD tax-benefit models updated with the most recent policy measures to analyse the distributional and budgetary costs of the crisis. A similar analysis is also carried out for Indonesia using INDOMOD.
Other exercises based on EUROMOD are being undertaken in other countries, and they will be reported in the special COVID-19 section of the new CeMPA website 26 .
20 Gasior K, Rastrigina O (2017). Nowcasting: timely indicators for monitoring risk of poverty in 2014 -2016. EUROMOD Working
Paper EM7/17 21 Leventi C, Rastrigina O, Sutherland H, Navicke J (2017). Nowcasting risk of poverty in the European Union in Atkinson AB, Guio,
AC and Marlier E. (eds) Monitoring social inclusion in Europe. Eurostat: Luxembourg, 353-363. 22 Bronka P, Collado D, Richiardi M (2020). The Covid-19 crisis response helps the poor: The distributional and budgetary consequences of the UK lockdown. Covid Economics 26: 79-106 23 Brewer M, Tasseva I (2020). Did the UK policy response to Covid-19 protect household incomes? EUROMOD Working Paper 12/20 24 Beirne K, Doorley K, Regan M, Roantree B, Tuda D (2020). The potential costs and distributional effect of Covid-19 related unemployment in Ireland. EUROMOD Working Paper 5/20 25 Figari F, Fiorio C (2020). Welfare Resilience in the Immediate Aftermath of the Covid-19 Outbreak in Italy. Covid Economics 8: 92-119 26 https://www.microsimulation.ac.uk/research-and-policy-analysis/covid-19
Prof Matteo Richiardi
Centre for Microsimulation and Policy Analysis
Institute for Social and Economic Research University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ, UK Telephone +44 (0)1206 872957 Visit our website for the latest working papers, events, training and to sign up for news at
