How the UK government should respond to the unequal local economic impacts of COVID-19
How the UK government should respond to the unequal local economic impacts of COVID-19
As governments respond to the economic crisis caused by COVID-19, a key question is how the impact will differ across local economies and how policy should respond (Baldwin and Weder di Mauro 2020). In the short run, if we know which areas are hardest hit, this may help better target support. Longer term, we should worry that those areas hardest hit may suffer persistent relative declines in population, employment rates or earnings for years after the recession ends (Stuart and Hershbein 2019). But the analysis below suggests that it will be difficult to predict both which areas will be hardest hit and to formulate timely place-based responses for the UK. Given this, at least initially, government should focus on the key characteristics that are likely to determine how hard an area is hit and respond through existing policy levers.
Can we predict the local economic impacts of the crisis?
Figure 1 looks at two factors that may help determine local impacts in the UK. The map on the left, from Faggio and Silva (2014), plots the share of self-employed for urban areas, showing larger shares in London and the South East. On the right, a map from Centre for Cities (2020) shows city-level estimates of the share of workers who could work from home, showing that London and the South East have workers who could more easily shift to home working.
Considering that the self-employed in the Midlands and the North are in more vulnerable occupations (Centre for Cities 2019), it is tempting to look at these maps and conclude that London and the South East are better able to ride out the worst of the COVID-19 economic crisis. But London currently has the largest numbers of COVID-19 cases and deaths (ONS 2020). It is also far more reliant on public transport for commutes to work. Depending on how mitigation efforts progress and on what happens as social distancing measures are relaxed, the direct ‘COVID-19 shock’ to London could be larger than other places.
Sectoral composition will also matter. Analysis for the US of area vulnerability based on sectoral shares predicts that some metro areas will be much harder hit than others (Muro et al. 2020) because of their structural composition. The UK is seeing big immediate hits to tourism and leisure and to much of retail, and so areas with high employment shares in those sectors will be struggling. While these demand-side shocks are the most immediately apparent, supply-side shocks will follow as, for example, supply chains react. Differential exposure to imports and exports, as well as decisions by big local employers, will also matter. In the medium term, areas that already have a high percentage of students who do not progress to higher education are likely to be hard hit as youth unemployment rises.
As this brief discussion makes clear, many factors will determine what happens to local areas. In contrast, most of the analysis that will emerge over the next few months will pick one factor and extrapolate from that. A multivariate analysis would need to know the relative importance of each of these factors. If this were a ‘normal’ recession, then past experience could guide the multivariate analysis. But this crisis is anything but ‘normal’. Even if the initial shock could be predicted, long-term responses differ. In a piece for Vox in 2011, entitled “How did London get away with it?”, I made a similar argument about the financial crisis in the UK. If policy had targeted the initial shock in 2008 it would have focused the response on London, which as early as 2011 was already showing signs of bouncing back better and faster than the rest of the UK.
In short, with so many factors at play and given the unusual nature of this economic crisis, it is difficult to work from sectoral shares, share of self-employed, ability to work from home or many other aspects of the local economy to predict which areas will be most seriously affected.
How could we target the worst hit areas?
If this analysis is correct, the impact will be uneven but we do not yet know which places will be worst affected. How, then, can the appropriate short-run policy response be targeted? One feasible option is to focus interventions on key underlying factors that are likely to matter. For example, as discussed above, in the UK, tourism, leisure and retail are going to be particularly hard hit, with implications for areas reliant on those activities. This suggests policy targeting additional support to those sectors would help the most heavily affected areas. For example, are there specific tax holidays that might help (for example, hotel taxes in countries that have them, or business rates relief in the UK)? Normally, economists are cautious about these kinds of ‘sector deals’ because of concerns over lobbying and rent-extraction. But the immediate negative impact on those sectors seem well-evidenced when government has mandated closure for many related activities. That said, if international travel restrictions persist after national restrictions are relaxed, domestic tourism revenues may rebound quite dramatically. Recent figures (Nielson 2020) suggest some parts of retail are also booming.
Given uncertainties over the medium-term sectoral impacts, there is a strong case for avoiding too strong a sectoral emphasis. Instead, policy could focus on the individual characteristics that make people vulnerable and, by extension, increase the vulnerability of communities in which many of these people live. To see how this might work, I return to the question of the share of workers who can work from home. Figure 3, taken from Avdiu and Nayyer (2020) and based on underlying data from Dingel and Neiman (2020), provides a good example. Put simply, the richer you are, the more likely you are to be able to work from home.
Figure 2 Lower-paid jobs are less amenable to home-based work
Source: Avdiu and Nayyer (2020) based on data from Dingel and Neiman (2020).
More generally, evidence from past recessions suggest that poorer, less-educated households are less able to deal with severe economic shocks. This is one regularity that is likely to extend to the current crisis. People with less education and already struggling on low incomes are going to be hit hard, and the communities where those people live are going to be hit hardest. Even if this doesn’t show up in the immediate shock, what is known about local resilience suggests that this will play out in the medium and longer run (Martin et al. 2016). No complicated area analysis underpins this assessment and the conclusion that follows from it: the best way to target the most vulnerable areas will be to focus on existing mechanisms and use them to provide more support to less-educated, lower-income families.
Responding to the local economic impacts of the crisis in the UK
Looking at the UK government response to date, we already see this approach in action. Changes to Universal Credit, the introduction of support for the self-employed and vouchers for free school meals are all policies intended to target support to vulnerable groups and, through this, provide help to the most vulnerable communities. An important next step will be to provide additional support for public services that serve those communities. Local government is playing a vitally important role in dealing with the crisis, against a background of years of budget cuts, and will need more resources.
There are many ways to go further. If the government is worried about the implications of remote schooling on educational outcomes in the most disadvantaged communities, it could consider big increases in the pupil premium that exists to address disadvantage (and extend this to post-16 education). If the government is worried about the long-term scarring impacts for young people entering the labour market at a time of severe recession, it could provide more funding for short remote courses and qualifications, for apprenticeships and to further education colleges. It could also increase additional payments for the hiring of young apprentices and reverse the funding rules that have already contributed to a decline in adult learning (Augar Review 2018). In general, policy should be trying to facilitate more education and training at a time when many will not be able to get jobs. The government also needs to ensure that information on support is clear and accessible and that schemes are operating as intended.
The bonus is that reversing austerity, which has hit northern cities hard, and targeting support to less-educated, lower-income workers is a key part of the policy mix that I argue the government should be following to address long-run area disparities in the UK (Overman 2020). An increasing concentration of more educated workers in certain places and a growing earnings premium for graduates means that the spatial distribution of higher-skilled workers explains up to 90% of area-level disparities in wages in the UK (Gibbons et al 2013).
In less-centralised countries, and in the medium run in the UK, more nuanced place-based responses are possible and over the next months it is important that we work to understand what those might be. But in the short run, immediate support needs to be targeted through existing mechanisms to reach those people and communities that are most vulnerable to the impacts of the current crisis.
Augar Review (2018), Post-18 review of education and funding: independent panel report.
Avdiu, B and G Nayyar (2020), “When face-to-face interactions become an occupational hazard: Jobs in the time of COVID-19”, Brookings, 30 March.
Baldwin, R and B Weder di Mauro (2020) Mitigating the COVID-crisis: Act Fast and Do whatever it takes, a VoxEU.org eBook, CEPR Press.
Centre for Cities (2019), “Self Employment in Cities”.
Centre for Cities (2020), “How will Coronavirus Affect Jobs in Different Parts of the Country?”.
Faggio, G and O Silva (2014), “Self-employment and entrepreneurship in urban and rural labour markets”, Journal of Urban Economics 84: 67-85.
Dingel, J and B Neiman (2020), “How Many Jobs Can be Done at Home?”, COVID Economics: Vetted and Real-Time Papers 1:16-24.
Gibbons, S, H G Overman and P O Pelkonen (2013), “Area disparities in Britain: understanding the contribution of people versus place through variance decompositions”, Oxford Bulletin of Economics and Statistics.
Martin, R, P Sunley, G Gardiner and P Tyler (2016), “How Regions React to Recessions: Resilience and the Role of Economic Structure”, Regional Studies 50(4): 561-585.
Muro, M, R Maxim and J Whiton (2020), “The places a COVID-19 recession will likely hit hardest”, Brookings, 17 March.
Overman, H G (2011), “How did London get away with it?”, VoxEU.org, 29 March.
Overman, H G (2020), “People, places and politics: the challenge of levelling up in the UK”, CEP CentrePiece, Spring.