The economic effects of COVID-19 containment measures

Pragyan Deb, Davide Furceri, Jonathan D. Ostry, Nour Tawk 17 June 2020

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Stringent containment measures and non-pharmaceutical interventions were effective in containing the spread of the coronavirus disease (COVID-19) and limiting fatalities, ensuring that the medical systems around the world were not overwhelmed (Deb et al. 2020a). While the lives saved have laid the foundation for a resumption of growth in the medium term (Barro et al. 2020), the Great Lockdown resulted in large short-term economic losses and a decline in global economic activity not seen since the Great Depression (Baldwin and Weder di Mauro 2020, Baker et al. 2020).

Quantifying these short-term economic effects and whether they vary across types of containment measure is of paramount importance for policymakers facing a painful short-term trade-off between normalising economic activity and minimising health risks.

Our paper (Deb et al. 2020b) aims to quantify the average economic effect – across countries and measures – of containment measures. Second, it examines whether fiscal and monetary measures implemented by many governments and central banks around the world have been effective in mitigating some of the negative effects of containment measures. Finally, we examine which types of containment measure have resulted in larger economic costs and trade-offs between health risks and economic losses.

Economic effects of containment measures

The existing literature on the economic effects of COVID-19 either relies on past epidemics (Barro et al. 2020 and Ma et al. 2020, for example), survey data (Coibion et al. 2020), or theoretical models (Eichenbaum et al. 2020 and references therein). We use high-frequency indicators of economic activity (World Bank 2020, Kumar and Muhuri 2019, and Cerdeiro et al. 2020) to monitor the impact of COVID-19 containment measures.

We use a variety of indicators such as international and domestic flights, energy consumption, maritime trade, and retail mobility indices. Our main variable of interest, however, is nitrogen dioxide (NO2) emissions, and this choice reflects three reasons:

1. NO2 emissions are strongly correlated to lower-frequency economic variables such as industrial production;

2. Emission levels can be directly linked to overall economic activity, and are not indicative of activity in specific sectors (as flights would be for tourism, for instance);

3. Data are available on a daily frequency, covering a relatively large sample of 57 countries.

Figure 1 presents the pattern of NO2 emissions (left scale) together with the evolution of the stringency indicator (right scale) in a few key cities. It shows that emissions declined significantly after containment measures were put in place.

Figure 1 Evolution of NO2 emissions in Wuhan and Rome

A) NO2 emssions, Wuhan (parts per billion (ppb))

B) NO2 emssions, Rome (parts per billion (ppb))

Notes: Levels of emissions are smoothed with a five-day moving average to remove excess volatility. Data sourced from Air Quality Open Data Platform for NO2 emissions and OxCGRT Stringency Index for containment measures. See Deb et al. (2020b) for details.

Our empirical analysis follows an approach similar to our earlier work (Deb et al. 2020a). We establish causality by exploiting lags in the implementation of the containment measures and by controlling for the change in the number of infected cases and deaths the day before implementation, as well as for lagged changes in daily economic indicators. The analysis also controls for unobserved country-specific characteristics and a time trend.

Results presented in Figure 2 show that containment measures significantly reduced NO2 emissions: in countries that implemented stringent containment measures, the amount of NO2 emissions cumulatively fell by almost 99% by 30 days after measures were implemented, relative to the underlying country-specific path in the absence of intervention. Translating the estimated effect on NO2 to economic variables, containment measures may have led to a 15% decline (month-on-month) of industrial production.

Figure 2 Effect of containment measures on total NO2 emissions

Notes: Impulse response functions are estimated for a sample of 57 countries using daily data from the start of the outbreak. The analysis is restricted to countries with a significant outbreak that has lasted at least 30 days. The graph shows the response and confidence bands at 95% to an index capturing the stringency of containment and mitigation measures. The horizontal axis shows the response x days after the introduction of containment measures. The regressions include time-varying control variables and country-specific time trend. Results are based on May 26 data. The figure displays log-difference changes whereas the text translates these into percent changes. See Deb et al. (2020b) for details.

Results based on other sector-specific indicators of economic activity suggest that the impact of containment measures has been overwhelmingly adverse across all sectors, and especially on tourism. Containment measures have reduced the total number of international and domestic flights by more than 99% in the 30-day period following the implementation of containment measures; total energy consumed has declined by more than 95%; maritime imports and exports have been reduced by over 40%, with a more pronounced impact on exports; and retail and transit mobility have been reduced by more than 400% relative to country-specific paths in the absence of intervention.

Role of macro policy responses in mitigating the fallout in economic activity

Governments and central banks around the world have implemented unprecedented economic measures in response to the COVID-19 pandemic. Using IMF Policy Tracker data on discretionary fiscal and monetary measures taken in response to COVID-19, we find that the measures were effective in mitigating some of the economic costs of containment.

Consistent with Ma et al. (2020), containment measures have had a much larger adverse impact on economic activity in countries with relatively small fiscal packages – equivalent to a 22% decline in industrial production (Figure 3, panel a). Similarly, the adverse impact of containment measures was mitigated in countries with large cuts in policy rates (Figure 3, panel b).

Figure 3 Interaction with fiscal and monetary policy (deviation from baseline, log-difference*100)

A) Interaction with fiscal policy

B) Interaction with monetary policy

Note: Impulse response functions are estimated for a sample of 57 countries using daily data from the start of the outbreak. The analysis is restricted to countries with a significant outbreak that has lasted at least 30 days. The graph shows the response and confidence bands at 95% to an index capturing the stringency of containment and mitigation measures, interacted with the magnitude of fiscal and monetary policy measures in response to COVID-19. The horizontal axis shows the response x days after the introduction of containment measures. The regressions include time-varying control variables and country-specific time trend. Results are based on May 26 data. The figure displays log-difference changes whereas the text translates these into percent changes. See Deb et al. (2020b) for details.

Cost-effectiveness of different containment measures

An analysis of the short-term trade-offs between minimising health risks and economic losses is necessary to inform the discussion of how countries should open-up their economies as well as how best they can respond to any second wave of infections. We therefore analyse the effects on economic activity, infections, and deaths, of different containment measures.

We find that among different types of containment measure, workplace closures and stay-at-home orders are the most effective in flattening COVID-19-related infections and deaths, but they are also the costliest in terms of their impact on economic activity. Less costly containment measures, such as school closures and restrictions on gathering size, are successful in reducing COVID-19 infections, but less effective in curbing fatalities (Figure 4).

Figure 4 Cost-benefit analysis of individual measures (deviation from baseline 30 days after introduction of containment measures)

Notes: The bars denote the 30-day cumulative local projection response to NO2 emissions and confirmed deaths, to each type of containment measure. Impulse response functions are estimated for a sample of 57 countries using daily data from the start of the outbreak. The analysis is restricted to countries with a significant outbreak that has lasted at least 30 days. The regressions include time-varying control variables and country-specific time trends. Results are based on May 26 data. Darker bars indicate responses that are statistically significant at the 95% level. See Deb et al. (2020b) for details.

Authors' note: The views expressed in this column should not be ascribed to the institutions with which the authors are affiliated.

References

Baldwin, R, and B Weder di Mauro (2020), Mitigating the COVID economic crisis: Act fast and do whatever it takes, London: CEPR Press.

Baker, S, N Bloom, S Davis and S Terry (2020), “COVID-induced economic uncertainty and its consequences”, VoxEU.org, 13 April.

Barro, R J, J F Ursúa and J Weng (2020), “The coronavirus and the Great Influenza Pandemic: Lessons from the ‘Spanish Flu’ for the coronavirus’s potential effects on mortality and economic activity”, NBER Working Paper 26866.

Cerdeiro D, A Komaromi, Y Lui and M Saeed (2020), “World seaborne trade in real time: A proof of concept for building AIS-based nowcasts from scratch”, IMF Working Paper, forthcoming.

Coibion, O, Y Gorodnichenko and M Weber (2020), “The cost of the COVID-19 crisis: Lockdowns, macroeconomic expectations, and consumer spending”, NBER Working Paper 27141.

Deb, P, D Furceri, J Ostry and N Tawk (2020a), “The effects of containment measures on the COVID-19 pandemic”, Covid Economics: Vetted and Real-Time Papers 19: 53–86.

Deb, P, D Furceri, J Ostry and N Tawk (2020b), “The economic effects of COVID-19 containment measures”, Covid Economics: Vetted and Real-Time Papers 24: 32–75.

Eichenbaum, M, S Rebelo and M Trabandt (2020), “The macroeconomics of epidemics”, NBER Working Paper 26882.

Kumar, S, and M Pranab (2019), “A novel GDP prediction technique based on transfer learning using CO2 emission dataset”, Applied Energy 253: 113476.

Ma, C, J Rogers and S Zhou (2020), “Modern pandemics: Recession and recovery”, working paper.

World Bank (2020), The economy in the time of COVID-19, LAC Semiannual Report, April.

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Topics:  Covid-19

Tags:  COVID-19, coronavirus, containment, lockdown

Economist, IMF

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