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COVID-19 IMPACTS ON DESTITUTION IN THE UK

Published online by Cambridge University Press:  28 July 2020

Arnab Bhattacharjee  and
Elena Lisauskaite
Arnab Bhattacharjee
Affiliation:
Heriot-Watt University and National Institute of Economic and Social Research. E-mail: a.bhattacharjee@hw.ac.uk.
Elena Lisauskaite
Affiliation:
National Institute of Economic and Social Research. E-mail: e.lisauskaite@niesr.ac.uk.
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Abstract

We use microsimulation combined with a model of the COVID-19 impacts on individuals and households to obtain projections of households in destitution in the United Kingdom. The projections are estimated at two levels: aggregate quarterly for the UK, for all quarters of 2020; and annual for 2020 differentiated by region, sector and household demographics. At the aggregate level, destitution is projected to be about three times higher than the non-COVID counterfactual level in 2020Q2, as well as substantially higher than the non-COVID case for the remainder of the year. This increased destitution is initially largely due to the effect on the self-employed, and as the Furlough scheme is drawn down, also on the unemployed. Impacts upon different regions and sectors vary widely, and so do variations across different household types. The sectors particularly affected are construction and manufacturing, while London and its closely connected regions (South East and the Midlands) are most severely affected. Single adult households suffer the most, and the adverse effects increase with number of children in the household. That the effects upon youth remain high is a particularly worrying sign, and very high increases in destitution are also projected for 25–54 year olds and the elderly (75 years and older). Further, severe adverse effects are projected for sections of society and the economy where multiple impacts are coincident. Robust and sustained mitigation measures are therefore required.

Keywords

COVID-19 crisisdestitutionunemploymentself-employedregionssectorsE24I32C553J82L00R11
Type
Research Article
Information
National Institute Economic Review , Volume 253 , August 2020 , pp. R77 - R85
Copyright
© National Institute of Economic and Social Research, 2020

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Footnotes

We are grateful to the Trussell Trust for research funding and for enhancing the policy context of this work. We thank Cyrille Lenoël, Craig Thamotheram and Garry Young for access to the data from the NiGEM model, Filip Sosenko and Justin van de Ven for various advices and assistance with implementing the LINDA microsimulation model, and Glen Bramley, Jagjit Chadha, Adrian Pabst, Thomas Weekes and Garry Young for helpful comments. We also thank two anonymous reviewers for comments that helped improve upon the article. The usual disclaimer applies.

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