Socioeconomic determinants of Covid-19 infections and mortality: Evidence from England and Wales
Targeting policy responses to Covid- 19 appropriately is important but requires information such as which groups in society are most affected by the pandemic. This column uses data on Covid-19 infections and mortality for small local areas in England and Wales to study the link of Covid-19 with socioeconomic factors. The findings suggest that areas with larger households, worse levels of self-reported health and a larger fraction of people using public transport have higher infection rates. Areas with an older population, a larger black or Asian population and worse levels of self-reported health have higher mortality rates. Particular attention should hence be paid to reducing the risk of infection on public transport when relaxing lockdown measures.
There is wide variation in Covid-19 infection rates and mortality rates across England and Wales. The local authority with the highest infection rate in early May 2020 was Barrow-in-Furness in the northwest, with over 800 Covid-19 cases per 100,000 people. Nearby Lancaster and South Lakeland as well as the North East region around Gateshead, Sunderland and South Tyneside were further hotspots. Wales also has very high infection rates, but this is mostly due to a higher frequency of testing compared to England.
The picture is different for mortality rates. As of 24 April 2020, the local authority with the highest mortality rate was Hertsmere in Hertfordshire, with 131 deaths per 100,000 people. The neighbouring north London boroughs of Harrow, Barnet and Enfield also had high mortality rates. Out of the 10 local authorities with the highest mortality, six are in Greater London.
Figure 1 Confirmed Covid-19 cases (per 100,000 people)
Source: Author’s calculations using data on the number of confirmed Covid-19 cases from Public Health England (updated 8 May 2020) and Public Health Wales (updated 6 May 2020); population from ONS 2018 population estimates.
Figure 2 Covid-19 deaths per (100,000 people)
Source: Author’s calculations using data on the number of deaths that mention Covid-19 on the death certificate from the ONS weekly deaths dataset up to 24 April 2020.
Health, ethnicity, household size and use of public transport matter
Two studies for New York City (Borjas 2020, Almagro and Orane-Hutchinson 2020) examine the correlation between Covid-19 infections and socioeconomic variables and find that infection rates are higher in zip codes with a larger black population, with on average larger households and with more people working in occupations with a high degree of human interaction.
I use simple correlation and regression analysis to examine this question in England and Wales, looking not only at infections but also at mortality (Sá 2020). Data on the number of deaths that mention Covid-19 on the death certificate are available from the Office for National Statistics (ONS) at a fine level of geographic disaggregation (over 7,000 Middle Layer Super Output Areas). This allows me to include local authority fixed effects in the analysis, capturing local characteristics that have not been included in the model. I find that local areas that have larger households, worse levels of self-reported health and a larger fraction of people using public transport have more Covid-19 infections per 100,000 people. For mortality, household size and use of public transport are less important, but there is a clear relation with age, ethnicity and self-reported health. Local areas with an older population, a larger black or Asian population and worse levels of self-reported health have more Covid-19 deaths per 100,000 people.
Some of these socioeconomic factors were highlighted by Aron and Muellbauer (2020), who note that the number of excess deaths in London – measured as the number of deaths relative to the average number of deaths in the same period in the past five years – increased ahead of other regions, probably due to high population density and a crowded public transport system which make it difficult to social distance. Overman (2020) points out that the economic impacts of Covid-19 are likely to be unequal across different areas of the UK, affecting especially areas where workers cannot easily work from home.
The relation between self-reported health and infections and mortality suggests that encouraging a healthy lifestyle can help prevent the spread of infections and reduce mortality. Also, as many countries now begin to relax lockdown measures, policymakers should pay particular attention to reducing the risk of infection in public transport. This can be done by encouraging people to use other forms of transport, as is being done in the UK, but also by increasing the frequency of services to avoid overcrowding. Businesses should also take the risk of infection in public transport into account when deciding how to get their employees back to the office. Working from home should continue to be encouraged when possible, especially for those who have to travel to work by public transport. This is particularly relevant in London, where more than half of employed workers travel to work by public transport, compared with a national average of 13%.
Almagro, M and A Orane-Hutchinson (2020), “The determinants of the differential exposure to Covid-19 in New York City and they evolution over time”, Covid Economics: Vetted and Real-Time Papers 13, 4 May.
Aron, J and J Muellbauer (2020), “Measuring excess mortality: England is the European outlier in the Covid-19 pandemic”, VoxEU.org, 18 May.
Borjas, G J (2020), “Demographic determinants of testing incidence and Covid-19 infections in New York City neighborhoods”, Covid Economics: Vetted and Real-Time Papers 3, 10 April.
Overman, H (202), “How the UK government should respond to the unequal local economic impacts of COVID-19”, VoxEU.org, 22 April.
Sá, F (2020), “Socioeconomics determinants of Covid-19 infections and mortality: evidence from England and Wales”, Covid Economics: Vetted and Real-Time Papers 22, 26 May 26.