Developing indices of the economic impact of the Covid-19 pandemic

By Dianna Smith, Graham Moon, Paul Roderick, University of Southampton, UK

It is abundantly clear that the Covid-19 pandemic will have an economic impact into the future. Media commentary is increasingly highlighting this concern. Some industries will recover from shutdown more easily than others; some may not recover in what seems likely to be a deep and long recession. Any economic impact is likely to be geographically uneven, in ways that have long been familiar to geographers.

While much attention has rightly focussed on mapping the Covid-19 pandemic in terms of disease and death, the time is now ripe to think through the likely geography of this emerging economic impact. Though the impact on firms and economic sectors is important, it is also necessary to think about how the economic impact will differentially affect localities and the people who live in them. To do this we need to focus on small areas and consider the sorts of people who might be most impacted in terms of their personal finances, and those who may need most help from the state to make up income and other deficits. We also need to think about how geography may exacerbate these impacts. Geographers have a rich record of developing indices that allow such concerns to be mapped (see further reading). In this blog we outline issues underpinning our development of indices of Covid-related economic insecurity.

The lifecourse and spatial scale

We talk of indices (plural). A first issue to acknowledge is that the economic impact of Covid-19 will be different for people of working age (often with dependent children) compared to older people drawing pensions or other non-work income streams. For the first group, the availability of work is crucial alongside the ability to take up work. For the second group, broader issues come into play concerning access to the resources, including but not limited to the possibility of food poverty, as well as loneliness. The two groups also have access to age-specific benefits that might lessen economic vulnerability. For these reasons, we create separate indices for people of working age and for older people.

‘Small area’ is a second key issue. We might hope that national government will develop responses that will shield us from the economic consequences of Covid-19. Local authorities will undoubtedly be tasked with implementing much of the response. However, the day-to-day experience of the impact will be felt more locally and it is at the local scale that inequalities will be manifest. The pervasiveness of inequality in England is well-established and has already been evident in the patterns of morbidity and mortality associated with the Covid-19 pandemic; we anticipate that it will be equally present in the economic impact. Our indices therefore need to focus at the local scale, not least in the hope that inequality can be addressed through targeted deployment of doubtless finite resources. Prompted by the availability of open data, emerging provision of small area Covid-19 data and our past work on food poverty, we based our indices on Middle Layer Super Output Areas [MSOAs] based on 2011 Census geographies (approximate mean population 7,000). 

Differentiating risk

Thinking through pathways to economic risk, we distinguish three domains that can be measured by available statistical data:

Demographic and health. This domain focuses on the population groups most likely to experience negative economic outcomes from the Covid-19 pandemic and subsequent lockdown. In addition, for the older population, the risk of isolation because of vulnerability to Covid-19 is an issue. We include “BAME” and “Gypsy/Traveller” ethnic groups in both categories, because Joseph Rowntree Foundation research identified these as proxies for greater risk of poverty, and limiting long-term illness and disabilities as well as mobility limitations that may impact travelling to work or resources.

Economic Precarity. Our second domain identifies populations accessing state assistance to deal with economic pressures on their finances. We selected a range of data on benefit claimants from the Department for Work and Pensions (DWP). We also included data on homelessness, on prepaid electricity meters and on sectors of industry that have been particularly impacted by Covid-19-related closures. Work continues on refining this last variable to differentiate the retail sector and include appropriate consideration of the transport sector.

Locational Disadvantage. Our final domain was devised to reflect the possible barriers to income or resource access in our two target populations and access to the means of overcoming those barriers.  We used measures of food store access as a proxy for access to nearby work, measures of access to broadband IT services, and a  user classification of the degree to which local populations are able to access the internet, and possess the necessary technology and the user skills. Work continues on refining these measures and incorporating additional forms of transport poverty. 

Combining variables

We used standard approaches to combine our variables into domain–specific measures and into overall indices (see further reading). The locational disadvantage index is common to both elderly people and the working age population. The ten highest-risk MSOAs in each domain were as follows:

The Demographic and Health domain and Economic Precarity domain pick out similar areas, the Locational Disadvantage domain does not. The latter highlights rural or small-town areas where people have relatively further to travel to work or to food retail and may lack a decent internet service or the skills or inclination to use that service to overcome barriers of distance. The former identify mainly urban disadvantage. These associations are slightly reduced for older people.

These issues have consequences for the calculation of overall composite indices. In practice composite indices would best be driven by local choices and priorities and could reflect differential weighting of the three component indices or indeed of individual variables. Some idea of the overall picture for the two demographic groups can however be gauged by the following maps which use unweighted data for all variables.

Overall index of risk of economic insecurity related to Covid-19 in the working age population

Overall index of risk of economic insecurity related to Covid-19 for the older population population

Where next?

The indices presented here suggest a local geography of the economic impact of Covid-19 that is highly differentiated and more detailed than that implied by studies that focus just on local authorities or selected areas.  They take our understanding down to the local levels at which people are likely to experience unequal impacts. They do confirm that the economic impact of the pandemic is likely to concentrate on post-industrial areas and on the rural and coastal left-behind. This impact will exacerbate the negative economic consequences likely to flow from Brexit and a decade of austerity.  Those communities that are already disadvantaged will experience additional further pressures. We are making the indices, the domain scores and the underlying variables openly available so that users can produce local maps and use data to make evidence-based resource allocation decisions that target need. We will be continuing to refine both the selection of variables and the indices as more data become available and we move from anticipating the economic consequences of the pandemic to actual experience.


About the authors:

Dianna Smith is a health geographer with a research focus on population health and social inequalities linked to local environments, with a focus on food and alcohol environments and food insecurity. Her expertise includes small-area estimation, co-development of toolkits with public sector/civil society stakeholders and close collaboration with local government.  She is co-PI on the NIHR ARC project, Wessex FRIEND, and leads CITISCAPE, a citizen science study based in Southampton.

Graham Moon is a health geographer, social epidemiologist and quantitative social scientist. He is Emeritus Professor of Human Geography at the University of Southampton and has longstanding research interests in health-related behaviour, psychiatric care, small area estimation and the application of multilevel modelling. 

Paul Roderick is a public health clinical academic and Professor of Public Health at the University of Southampton since 2007.  His main research interests  are in chronic disease epidemiology (especially chronic kidney and chronic liver disease) and inequalities in  health and health care.  He has had a longstanding and successful collaboration with health geographers.

Suggested Further Reading

Exeter, D.J., Zhao, J., Crengle, S., Lee, A., Browne, M. (2017) The New Zealand Indices of Multiple Deprivation (IMD): A new suite of indicators for social and health research in Aotearoa, New Zealand. PLoS ONE, 12 (8), art. no. e0181260. https://doi.org/10.1371/journal.pone.0181260

Green, M.A., Daras, K., Davies, A., Barr, B., Singleton, A. (2018)  Developing an openly accessible multi-dimensional small area index of ‘Access to Healthy Assets and Hazards’ for Great Britain, 2016. Health and Place, 54, pp. 11-19. https://doi.org/10.1016/j.healthplace.2018.08.019

Page, N., Langford, M., Higgs, G. (2019) Exploring spatiotemporal variations in public library provision following a prolonged period of economic austerity: A GIS approach. Area. https://doi.org/10.1111/area.12575

Shortt, N.K., Richardson, E.A., Mitchell, R., Pearce, J. (2011) Re-engaging with the physical environment: A health-related environmental classification of the UK. Area, 43 (1), pp. 76-87. https://doi.org/10.1111/j.1475-4762.2010.00960.x

Smith, D., Thompson, C., Harland, K., Parker, S., Shelton, N. (2018) Identifying populations and areas at greatest risk of household food insecurity in England. Applied Geography, 91, pp. 21-31. https://doi.org/10.1016/j.apgeog.2017.12.022

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