Early lockdowns save more lives and livelihoods
The COVID-19 pandemic has resulted in huge economic and human costs since its outbreak in early 2020 (Baldwin and Weder di Mauro 2020). While theoretical work on the COVID-19 pandemic is abundant, comparatively little is understood of its economic consequences, partly because the official economic indicators have not been published yet. An important unanswered question for the academic and policy debate is whether the non-pharamteutical interventions (NPIs) implemented in 2020 did indeed reduce mortality and improve economic outcomes just as they did in 1918 (see for example Correia et al. 2020), and how these interventions interact with the supply and demand shocks triggered by the pandemic.
Theoretical work on the potential effects of NPIs on economic activity, though recent, is abundant. Eichenbaum et al. (2020) model the interactions between economic decisions and epidemics and find that the best containment policy can save a large number of lives but may induce a more severe recession. However, Acemoglu et al. (2020) show that targeted policies that are combined with measures that reduce interactions between groups and increase testing and isolation of the infected can minimize both economic losses and deaths. Stay-at-home orders for high risk groups -like the elderly- are one example of such a targeted approach that can be socially beneficial (Brotherhood et al. 2020). Similarly, Aum et al. (2020) calibrate a model to the progression of the pandemic in Korea and the United Kingdom and find that aggressive testing and tracking policies can reduce both the economic and health costs of COVID-19. In this sense, if targeted policies are indeed feasible a lives-livelihoods trade-off in the short can be avoided.
In our recent paper (Demirgüç-Kunt et al., 2020), we estimate the early economic impact of the pandemic by tracking high-frequency proxies of economic activities such as daily measurements of electricity consumption, NO2 emissions, and personal mobility across countries of Europe and Central Asia. Our analysis relates to that of Chen et al. (2020), who look at similar variables across Europe and states within the US.
Figure 1 Electricity consumption by day of the year in Spain
Note: The top-left panel of this graph plots the daily consumption of electricity (grid electricity total load) for weekdays (weekends excluded) in the period between 1 January and 17 April of the year 2019 (dotted line) and 2020 (solid line) in Spain. Values are normalized by electricity consumption on 1 January 1 2020. The top-right panel plots the 30-day running mean NO2 density (tropospheric column) between 1 January and 11 April of the year 2019 (dotted line) and year 2020 (solid line). Values are normalized for each country by NO2 density on 1 January 2020. The bottom-right panel shows these figures for Madrid. The bottom-left panel shows the time spent driving and walking. Values are normalized by time driving and walking on 13 January 2020. Phase I identifies the period with no detected cases of COVID-19; Phase II starts from the day when the first case is reported; Phase III begins at the date of the first death from the disease; Phase IV identifies the period after the peak of daily deaths in the country has been reached.
The NPIs implemented in countries of Europe resulted in closures of businesses, a reduction in production, and restrictions of personal mobility. Figure 1 shows that on the onset of the pandemic in February 2020, the use of electricity in Spain (solid line on the top left panel) was already lower compared to the same period in 2019 (dotted line). The vertical lines on the graph show the four types of NPIs. The introduction of NPIs in early March 2020 resulted in a further decline. Spain consumed about 30% less electricity in April 2020 compared to April 2019. Nitrogen dioxide (NO2) emissions are closely correlated with traffic, construction activities, industry, and coal-fired power plants. The top right panel of Figure 1 shows the decline in the levels of NO2 associated with the introduction of NPIs. The lower left panel of Figure 1 demonstrates a sharp decline in driving and walking when public events were banned in Spain on 10 March 2020. School closings and full lockdown on 14 March further reduced personal movements to almost 90% compared to the pre-pandemic period. Very pronounced weekly mobility patterns seen before the pandemic almost disappeared after the lockdowns. It appears that the population complied with the social distancing measures and that these measures were effectively enforced.
Figure 2 Change in electricity consumption and speed of implementation of national lockdown
Note: This figure plots the relationship between the change in electricity consumption associated with a full national lockdown (vertical axis) and the speed of implementation of the full lockdown (horizontal axis). The first variable is estimated from a country-specific regression of daily electricity consumption (covering the period 2017-2020) on a series of days of the week, week of the year, holidays and temperature dummies, and a dummy variable indicating the implementation of a national lockdown following Cicala (2020). The coefficient of the national lockdown dummy variable is plotted on the vertical axis. The speed of implementation of the full lockdown is calculated as the number of days to the first reported death by COVID-19 from the implementation date (i.e. date of first death – date of the lockdown). A negative value indicates that the full lockdown was implemented after the first death was reported; a positive value indicates that the lockdown was implemented before the first death was reported. The black line plots the linear fit between the change in electricity consumption and the speed of implementation. The size of the bubbles is proportional to the mortality rate per million inhabitants as of 25 April 2020.
Figure 2 plots the change in electricity consumption associated with national lockdowns against the speed of implementation of the full lockdown, defined as the number of days between the implementation and the first reported death by COVID-19. The size of the bubbles corresponds to the mortality rate per million inhabitants as of 25 April 2020. Countries that implemented a lockdown at earlier stages of the pandemic have seen lower overall drops in electricity consumption and so far, also lower mortality rates. Hence, the combined human and economic costs seem to have been lower for countries that acted faster. Further, we show in the paper that countries that acted faster were also able to control the pandemic with less strict interventions.
Estimating the economic costs of NPIs
Social distancing measures reduce aggregate supply by forcing workers to stay home and decrease aggregate demand by negatively affecting the consumption of services, particularly those that involve direct contact with customers or clients. The pandemic also directly affects labor supply by reducing the number of workers due to sickness and lowering the productivity of sick workers. The fears and uncertainties associated with the progression of the pandemic resulted in a sharp increase in grocery spending and in a dramatic drop in expenditures on restaurants, retail, travel, and public transportation that translates into reduced energy demand (Baker at al. 2020, Chronopoulos et al. 2020).
To estimate these effects we rely on an econometric model that relates electricity consumption and levels of NO2 in a country, as a proxy of economic activity, to the implementation of NPIs, the daily number of death per million due to COVID-19, and a range of controls. In our specification, we account for seasonality and weekly patterns in electricity consumption, as well as the changes in electricity demand during the national holidays. We also control for differences in electricity consumption and NO2 emission related to heating and cooling degrees due to temperature changes. To address concerns about endogeneity due to unobservables, we instrument the daily number of deaths with daily predictions from a standard SIR epidemiological model that assumes an unmitigated spread of the disease (no NPIs implemented), and where the cross-country variation comes only from pre-pandemic characteristics like the demographic profile of the country, the number of ICU beds and an initial rate of contagion (estimates available here).
To assess the magnitude of the NPIs’ impact on a country’s economy, we use the estimated elasticities between the proxy measures of economic activity and actual economic indicators. For electricity consumption, this elasticity is assumed to be close to 1 in the very short term (Cicala 2020); for NO2 emissions, the elasticity ranges between 0.32 and 1, with a midpoint at 0.66.
It has been argued that these NPIs, while useful to “flatten the curve” of health costs, may come at high economic costs. The results of our econometric model suggest that NPIs, and specifically national lockdowns, are associated with a decline in economic activity of around 10% across regions. The decline is similar independent of using electricity or emission data as a measure of economic activity. Our analysis also shows that the spread of the disease itself has an economic impact that is distinct from the one of NPIs: at the peak of the outbreak the drop in activity associated with the spread of the disease — be it by incapacitation of workers or by the precautionary reaction of consumers and investors — can be as strong as the shock triggered by lockdown measures. The average country in our sample may have seen a decrease of 11% in economic activity that is solely due to the spread of the disease at the peak of the pandemic.
The overall effect of national lockdowns on economic activity is therefore conditional on when they are implemented: a country that implemented a lockdown one week before the first death by COVID-19 was reported saw a decrease in economic activity that was about 2% smaller than a country that implemented a lockdown on the day of the first death. On the other hand, a country that implemented the lockdown only one week after the first death by COVID-19 experienced a 2% larger decrease in economic activity. Each day of delay is estimated to be associated with a 0.3% additional decrease in activity. This smaller economic fallout of speedier interventions can be partly explained by their effectiveness in containing the spread of the disease and, therefore, limiting the economic damage of the pandemic itself. In fact, in countries that implemented a national lockdown before any death by COVID-19 was reported — about 19 countries in Europe and Central Asia–, the eventual peak number of deaths on a single day was about 0.81 per million. Within those countries that implemented a national lockdown only after at least one death was reported — about 24 countries in Europe and Central Asia –, the peak daily deaths stood at 6.29 per million – more than six times higher. These initial NPIs also provided a much-needed breathing space for developing testing and contact tracing capacity in many countries, which can be put to use in designing a better and faster response to the next wave of infections. In this sense, our results suggest that the sooner NPIs are implemented, the better are both the economic and the health outcomes of a country.
At a time when countries are grappling with ways of relaxing lockdown measures, our results suggest that policymakers should be cautious in reopening their economies too fast. The drop in economic activity observed when lockdowns are in place is not solely explained by the lockdown restrictions themselves but is also associated with the behavioral response to the spread of the disease. Therefore, a fast reopening that generates a rebound in the spread of the disease can be damaging not only in human terms but also in economic ones. An unexpected increase in the infection rates or the number of deaths after opening up might slow down or even reverse positive economic trends.
The authors are at the World Bank. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
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