The COVID-19 pandemic threatens to widen gaps in inequality, but a study of consumer spending patterns in the United Kingdom suggests that targeted policies informed by real-time economic data could help level the playing field.
While overall consumption in the UK has increased since the crisis struck, wealthier areas such as London’s commuter belt have recovered faster than less-affluent regions, find University of Nottingham’s John Gathergood, University of Warwick’s Fabian Gunzinger and Neil Stewart, Chicago Booth PhD student Benedict Guttman-Kenney, and University of Oxford’s Edika Quispe-Torreblanca. Moreover, regions that were struggling before the pandemic tended to endure higher virus-transmission rates and more severe targeted lockdowns, potentially pushing them further behind. Such insights could guide policies to help people in areas hurt most during the pandemic, the study concludes.
The researchers used spending data from Fable Data, an information source that records hundreds of millions of credit-card transactions and checking-account flows. These data—tracked by postal code sector and available in real time—are highly correlated with official aggregated statistics from the Bank of England and the Office for National Statistics, which aren’t published until months later. Thus, the earlier data give policy makers precious extra time to make adjustments.
Aggregate consumption contracted by 28 percent in April 2020, and then climbed steadily back through two national lockdowns and a variety of targeted local lockdowns, the researchers observe. (For more on local lockdowns, see “How to contain COVID-19 flare-ups without crushing the economy.”) By the end of November 2020, overall spending—led by online purchases—was up almost 13 percent compared with the same period a year earlier.
But there was wide variation across regions. Spending growth in outer London exceeded 15 percent, but was 11 percent in Northern Ireland, just 6 percent in Wales and 4 percent in southern Scotland. In England, spending was strongest in the more affluent south and east, where many residents were able to work from home, the researchers find.
Various efforts to contain the virus also shaped spending patterns across regions, the researchers find. In October 2020, the UK adopted a three-tiered approach to imposing limitations on social interaction. Tier 1 areas, where transmission rates were relatively low, had softer restrictions, while Tier 3 areas were subject to more severe mandates that included shorter business hours and a ban on indoor gatherings of people from different households. By November, as the virus continued to spread, the UK government ordered a second national lockdown in England—closing all pubs, restaurants, and nonessential shops—before returning to its tiered approach in early December. (A third national lockdown was announced in January 2021, after the researchers’ study had been conducted.)
Tier 2 and Tier 3 areas saw similar spending rates in the spring and summer, but those patterns diverged in October and November, the researchers find. In those months, Tier 2 areas (which tended to be in the south of England) posted monthly spending growth of about 16 percent and 15 percent, respectively, while Tier 3 areas (which tended to be in the north of England)—already weakened by a decline in domestic manufacturing and infrastructure investment—came in at about 8.5 percent and 9 percent. “Thus far there is little evidence such lost output of the Tier 3 areas most affected by COVID-19 would naturally recover, and so these areas may fall even further behind regions that entered the pandemic with stronger regional economies,” the researchers write.
Real-time data could help address those disparities, they argue. Possible maneuvers, they suggest, include tax cuts on commercial rent for offline merchants disproportionally hurt by the lockdowns, lower council taxes on domestic properties to put extra money in homeowners’ pockets, and travel vouchers to bolster hard-hit regions. Armed with real-time indicators, national governments might even make temporary funding available to local governments in proportion to the pandemic’s specific impacts.
“In the past, it may have taken years to work out if a policy worked,” says Guttman-Kenney. “With real-time data, we can work out precisely where to target policy and then track its effectiveness to make a decision in weeks whether to modify the measure.”