{"id":8109,"date":"2020-10-08T23:01:00","date_gmt":"2020-10-08T22:01:00","guid":{"rendered":"http:\/\/www.economicsobservatory.com\/question\/how-can-authorities-control-coronavirus-without-killing-economy"},"modified":"2021-01-15T09:50:57","modified_gmt":"2021-01-15T09:50:57","slug":"how-can-authorities-control-coronavirus-without-killing-economy","status":"publish","type":"question","link":"https:\/\/www.economicsobservatory.com\/test\/how-can-authorities-control-coronavirus-without-killing-economy","title":{"rendered":"How can authorities control coronavirus without killing the economy?"},"content":{"rendered":"

It is widely recognised that the crude \u2018health versus the economy\u2019 dichotomy imposed by strong forms of lockdown is far from being the best approach to controlling virus transmission (DELVE, 2020<\/a>). Governments around the world are looking for the most efficient measures to control the virus, with studies offering a wide variety of proposals for the optimal combination of distancing measures between households, in schools and universities, and in the production and consumption of good and services (Glover et al, 2020<\/a>; Miles et al, 2020<\/a>; Toxvaerd, 2020<\/a>).<\/p>\n

The tools available to limit transmission of the virus are likely to have different economic effects on different groups at different periods of time. For example, school closures may have modest short-term effects, but evidence from previous epidemics indicates large and irreversible long-term effects on children\u2019s lives (Bandiera, 2020<\/a>). Understanding the effects of different types of lockdown restrictions applied to different groups is therefore of first order concern for policy-makers.<\/p>\n

Newly available forms of data make it possible to address these issues. To get a better understanding of the trade-offs involved in the introduction of different forms of lockdowns, a number of studies have started using newly available forms of transaction data sourced from financial services providers including payment processors, banks and financial aggregator apps (see, for example, Bounie, 2020<\/a>; Bourquin, 2020<\/a>; Chetty et al, 2020<\/a>; and Chronopoulos et al, 2020<\/a>).<\/p>\n

What are the effects of local lockdowns on consumption?<\/h3>\n

The initial Covid-19 outbreak and nationwide lockdown of schools, businesses and household activities resulted in widespread and deep declines in consumption spending in the UK. Our research analyses the more recent experience of localised lockdowns on consumer spending behaviour.<\/p>\n

A notable feature of UK local lockdowns introduced since late May 2020 is that they focus on limiting contact between households in and around the home (such as within houses, within gardens or in local parks), while placing few or limited restrictions on trading activity of businesses including in the hospitality sector.<\/p>\n

A premise of this type of intervention is that households mixing in Covid-19-secure settings \u2013 such as hospitality venues that enforce social distancing, hygiene and mask wearing \u2013 is low risk. This form of intervention allows consumption to continue (and may even encourage consumption away from the home as it complements social mixing) while severely limiting socialisation between households in home settings.<\/p>\n

To analyse the effects of local lockdowns, we use newly available transaction data sourced from UK banks and credit card lenders (Gathergood and Guttman-Kenney, 2020<\/a>). To do so, we work with data provided by Fable Data, who source anonymised, real-time, highly disaggregated spending data covering the whole of the UK. Data from this source closely match official Bank of England data on credit card spending (0.91 correlation 2018-2020) but are available far faster (a few days, compared with months for Bank of England data).<\/p>\n

The data contain individual spending transactions tagged to postcodes and are available the next weekday. We use these data to measure credit card spending (excluding online transactions) within each UK local authority. We combine these data on card spending with data of Covid-19 cases to examine how local lockdowns relate to infections and spending in locked-down areas, such as Leicester and Manchester.<\/p>\n

The analysis shows that local lockdowns slowed infections but inflicted little (if any) decreases in spending. The figures show the first three local lockdowns as examples: Leicester (Figure 1), Manchester (Figure 2) and Preston (Figure 3). Each figure compares infections and spending in the locked-down city with that in a nearby control city (respectively, Coventry, Liverpool and Sheffield).<\/p>\n

Figure 1: Leicester versus Coventry<\/h4>\n

\"Figure\"Covid-19<\/p>\n

Figure 2: Manchester versus Liverpool<\/h4>\n

\"Figure\"Figure<\/p>\n

Figure 3: Preston versus Sheffield<\/h4>\n

\"Figure\"Figure<\/p>\n

Source:\u00a0Fable Data (offline credit card spending) and\u00a0Public Health England (Covid-19 cases)<\/h5>\n

In each case, the local lockdown intervention leads to a turning point for daily infections, which decrease with the introduction of lockdown. At the same time, the effects on card spending are very small, with the pattern of card spending in the local lockdown city very similar to that in the comparable control city.<\/p>\n

There is no evidence of local lockdowns causing large decreases in spending. Where we observe differences, they appear to be temporary and small relative to the declines from a national lockdown in March, where spending decreased 40%.<\/p>\n

How should local lockdowns be implemented?<\/h3>\n

This evidence from local lockdowns suggests that it is possible to control the virus without resorting to extreme measures that damage the economy. Isolating, testing and tracing cases are key to the effectiveness of keeping lockdowns local and preventing the need for additional measures.<\/p>\n

The introduction of stronger measures \u2013 such as closing pubs and restaurants or mandating working from home \u2013 would restrict the ability of consumers to spend, and undoubtedly have negative effects on the economy similar to those experienced earlier in 2020.<\/p>\n

The government\u2019s top priority should be preventing a return to such measures by containing the virus using targeted local lockdowns with intensive testing and tracing. The introduction of national measures is likely to result in economic harm, and miss the opportunity to control the virus using local lockdowns.<\/p>\n

What further research is going on?<\/h3>\n