The recovery in consumer spending in the second half of 2020 is geographically uneven, with heavy weighting in the home counties around outer London and the South. Recovery is strongest in online spending in those commuter belt areas and places with high ownership of second homes.
Over the last few decades, developed countries have experienced inequality in economic growth at the regional level, with some regions not only experiencing less of the economic boom, but also having longer-lasting pain from economic busts. Emerging evidence shows that, in many dimensions, the Covid-19 pandemic has exacerbated existing inequalities, thereby presenting greater challenges to governments seeking to equalise the distribution of economic activity.
In the UK, a recent government aim is to address such disparities through policies to `level-up’ the regions that historically have experienced less benefits arising from globalisation and national economic growth. This is a particularly pertinent issue for the UK, which is among the most geographically unequal developed economies (IFS, 2020).
In new research, we use granular, real-time data on consumer spending to measure the uneven geography of the 2020 recession and recovery across the UK.
How do we measure consumer spending in real time?
A challenge to measuring patterns in consumer spending across geographies and at high frequency is the lack of timely regional level data on spending in the UK. To overcome this challenge, we use transaction data sourced from individual card accounts. The granular data are provided by Fable Data as previously used by Gathergood and Guttman-Kenney, 2020 to analyse the effects of local lockdowns on consumer spending.
That previous work focused on local lockdowns in specific cities and towns in the UK in mid-2020. The new research expands that work to uncover regional variation in consumer spending over the course of the pandemic.
Fable Data record hundreds of millions of transactions on by consumers and small and medium-sized businesses across Europe from 2016 onwards and its real-time structure permits research to inform current policy-making. When aggregated, Fable’s transaction data provide a highly correlated, leading indicator of official statistics (we find correlation coefficients with Bank of England and Office for National Statistics data of 0.91 and 0.87 respectively), but unlike official statistics, Fable’s data are available in real time: our research access to transaction data has a lag of just one working day.
What regional patterns do we see emerging across the UK?
Our headline result is that, while there has been an overall recovery in spending as ‘pent-up’ demand has been realised, there is significant geographical variation in the recovery. Aggregate spending recovered from a low of a year-on-year decline of 29% in April 2020 to year-on-year growth of 12% in October 2020.
Such pent-up demand may be a combination of the lifting of restrictions, consumers becoming more confident of spending given reduced fear of the virus and improved economic prospects, or increased fatigue leading to less compliance with restrictions.
We find three key results relating to the geographical variation in recovery.
North and South
First, as shown in Figures 1 and 2, the recovery in consumer spending has been faster in the South and the home counties surrounding London. In contrast, the Midlands, Wales, the North East and Scotland show the weakest year-on-year growth; in the latter two cases, there is close to no year-on-year growth at all.
Figure 1: October 2020 year-on-year growth in overall credit card spending by NUTS 2 area
Figure 2: October 2020 year-on-year growth in offline credit card (left) and online credit card spending (right) by NUTS 2 area
Moreover, the faster recovery in the South of England and the eastern and western regions is strongly driven by faster growth of online card spending. Notably, within England the fastest year-on-year growth is in the outer west area of London, the South West and Eastern England — areas characterised by highly affluent communities and a high level of ownership of second homes. This suggests that, to a degree, spending growth is strongest in the areas of the UK where many people are working from home or potentially working from their second home.
Cities, towns and countryside
Second, the speed of recovery has been fastest outside large cities, in commuter towns and affluent semi-countryside conurbations. We show that the variation in the speed of recovery can also be characterised as differing by types of urban settlements. In particular, the recovery has been the fastest and strongest in `business, education and heritage centres’ – such areas are popular domestic tourist destinations and thus this is in line with consumers substituting foreign for domestic holidays.
Recovery has been less strong in `countryside living’ – predominantly rural areas but still noticeably stronger than other, more urban, areas. For more urban areas, London has had a steady recovery, whereas ‘affluent England’, ‘services and industrial legacy’ and `urban settlements’ are showing weaker recoveries.
Third, we show that as of the end of November 2020, the point when the second national lockdown was coming to an end to be replaced by the introduction of a revised ‘tier’ system defining levels of restrictions across geographies, the highest tier areas (‘tier 3’) had experienced a much slower recovery in year-on-year spending, compared with the mid-tier areas (‘tier 2’).
We exclude tier 1 from the analysis as only very few, rural, localities have that classification as Tier 1 as of December 2020 (accounting for only 1.3% of the UK adult population). Tier 2 and tier 3 areas exhibit similar year-on-year growth rates in card spending in April and July 2020 (the period before this tier system came into operation).
But by October, this pattern diverges, with a stronger recovering in overall spending in the tier 2 areas compared with the tier 3 areas. This divergence persists through November 2020, with localities facing the UK’s new tighter tier 3 restrictions (mostly the Midlands and northern areas) showing 38.4% lower year-on-year growth in overall spending compared with areas that, until recently, were facing the less restrictive tier 2 (mostly London and the South).
How do these results compare with the broader evidence?
Our results corroborate recent evidence from many economies that the pandemic is ‘levelling down’ economic activity, thereby exacerbating regional inequality. A series of studies demonstrate how consumer behaviour has been radically affected by Covid-19 and government policies to mitigate its effects.
An early study used US ‘fintech’ data (Baker et al, 2020). Following this, Opportunity Insights produced a dashboard using multiple data sources to track regional US consumption behaviour alongside other economic indicators (Chetty et al, 2020).
Beyond the United States, similar exercises have been carried out to understand household consumption in the early stages of the pandemic, showing remarkably consistent results (see the table below for a comprehensive list of studies known to the authors of this article at the time of writing). A broader body of research has sought to measure regional inequality and understand why it arises and what are its effects (including Milanovic, 2005; Glaeser et al, 2008; Chetty et al, 2018; Iammarino et al, 2019; Carnerio et al, 2020).
What policy options are available to governments?
The challenge to government policy posed by Covid-19 is that the effects of the pandemic exacerbate existing inequalities and imbalances in the economy. Addressing regional inequalities that have persisted in the UK and other countries for decades is a hard problem with no quick fixes.
The ability to measure regional economic data in real time, such as using Fable data, offers exciting potential to inform when, where and how to target regional policy interventions for evidence-based policy-making. This is a more nimble strategy than traditional government approaches of typically applying policies at a national level with limited abilities to assess their impact.
The regional inequalities shown indicate that there is a rationale for trialling short-term interventions to address the levelling down that has occurred in 2020 as a result of Covid-19. These could occur in 2021 once the virus outbreaks are under control and vaccinations have been more broadly rolled out.
But targeted and temporary regional measures in 2021 may be able to rebalance the 2020 inequalities before they become entrenched. What form could such measures take? Given the clear effects of online spending on offline retailers in particular locations, providing relief to such businesses can help to sustain them and enable them to pass through savings to increase demand for their business. This could take the form of business rate relief or VAT cuts based on store location.
Given some regions have recovered rather well, providing incentives to encourage residents in those locations to visit harder-hit parts of the UK could be an effective way to generate spending in depressed areas. One method for doing so would be providing vouchers for discounted travel and to spend in particular destinations, potentially through a lottery system to enable some individuals to have large incentives to do so. Such initiatives may also yield broader, longer-term benefits for consumers living in different regions. They may make the labour market more geographically mobile and repair cultural divides.
A less centralised approach would be for national governments to make temporary funding available to local governments. This could be done in proportion to how adversely such regions have been affected by the crisis and adjust the duration and amount of funding to local authorities in response to real-time indicators.
Other areas may consider forms of spending to be more efficient, such as funding local events or services or initiatives targeted at providing relief to socio-economic groups of people most adversely affected by the pandemic.
The new availability of real-time regional data such as used in our research offers policy-makers the ability to target regional interventions finely and then to evaluate their effectiveness rapidly to decide whether to expand, modify or remove such measures.
Where can I find out more?
Since the pandemic began, there have many studies conducted around the world of the impact on household finances, consumption, savings and balance of spending between online and offline. Below is a list of studies by country:
- Levelling Down and the COVID-19 Lockdowns: Uneven Regional Recovery in UK Consumer Spending: New study by John Gathergood, Fabian Gunzinger, Benedict Guttman-Kenney, Edika Quispe-Torreblanca and Neil Stewart
- Consumption in the time of Covid-19: Evidence from UK transaction data – Surico et al (2020)
- The distributional impact of the pandemic – Hacioglu et al (2020)
- Consumer spending responses to the COVID-19 pandemic: An assessment of Great Britain – Chronopoulos et al (2020)
- Income protection policy during COVID-19: evidence from bank account data – Delestre et al (2020)
- The effects of coronavirus on household finances and financial distress – Bourquin et al (2020)
- How does household spending respond to an epidemic? Consumption during the 2020 Covid-19 pandemic – Baker et al (2020a)
- Income, liquidity, and the consumption response to the 2020 economic stimulus payments – Baker et al (2020b)
- Do stay-at-home orders cause people to stay at home? Effects of stay-at-home orders on consumer behavior – Alexander and Karger (2020)
- How did COVID-19 and stabilization policies affect spending and employment? A new real-time economic tracker based on private sector data – Chetty et al (2020)
- The COVID-19 shock and consumer credit: Evidence from credit card data – Horvath et al (2020)
- Initial impacts of the pandemic on consumer behavior: Evidence from linked income, spending, and savings data – Bachas et al (2020)
- Measuring the effects of the COVID-19 pandemic on consumer spending using card transaction data – Dunn et al (2020)
- Income and poverty in the COVID-19 pandemic – Han et al (2020)
- Tracking the Covid-19 crisis with high-resolution transaction data – Carvalho et al (2020)
- Consumption in Spain during the state of alert: An analysis based on payment card spending – González Mínguez et al (2020)
- Consumer responses to the COVID-19 crisis: Evidence from bank account transaction data – Andersen et al (2020a)
- Pandemic, shutdown and consumer spending: Lessons from Scandinavian policy responses to COVID-19 – Andersen et al (2020b)
- Consumers’ mobility, expenditure and online-offline substitution response to COVID-19: Evidence from French transaction data – Bounie et al (2020)
- The impact of the COVID-19 pandemic on consumption: Learning from high frequency transaction data – Chen et al (2020)
- Online consumption during the COVID-19 Crisis: Evidence from Japan – Watanabe and Omori (2020)
- COVID-19 and the demand for online food shopping services: Empirical evidence from Taiwan – Chang and Meyerhoefer (2020)
- Weighting bias and inflation in the time of Covid-19: Evidence from Swiss transaction data – Seiler (2020)
- Consumption and geographic mobility in pandemic times: Evidence from Mexico – Campos-Vazquez and Esquivel (2020)
- What and how did people buy during the Great Lockdown? Evidence from electronic payments – Carvalho et al (2020)
- Emergency loans and consumption: Evidence from COVID-19 in Iran – Hoseini and Beck (2020) and Hoseini and Valizadeh (2020)
Who are UK experts on this question?
- John Gathergood, Professor at University of Nottingham
- Benedict Guttman-Kenney, University of Chicago Booth School of Business
- Neil Stewart, Professor of Behavioural Science at Warwick Business School, University of Warwick
- Paolo Surico, Professor at London Business School