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Licensed Unlicensed Requires Authentication Published by De Gruyter April 19, 2021

Protecting Lives and Livelihoods with Early and Tight Lockdowns

  • Francesca Caselli EMAIL logo , Francesco Grigoli and Damiano Sandri
From the journal The B.E. Journal of Macroeconomics
https://doi.org/10.1515/bejm-2020-0266

Abstract

Using high-frequency proxies for economic activity over a large sample of countries, we show that the economic crisis during the first seven months of the COVID-19 pandemic was only partly due to government lockdowns. Economic activity also contracted severely because of voluntary social distancing in response to higher infections. Furthermore, we show that lockdowns substantially reduced COVID-19 cases, especially if they were introduced early in a country’s epidemic. This implies that, despite involving short-term economic costs, lockdowns may pave the way to a faster recovery by containing the spread of the virus and reducing voluntary social distancing. Finally, we document that lockdowns entail decreasing marginal economic costs but increasing marginal benefits in reducing infections. This suggests that tight short-lived lockdowns are preferable to mild prolonged measures.

Keywords: COVID-19; infections; lockdown; mobility; voluntary social distancing
JEL Classification: E1; I1; H0
Corresponding author: Francesca Caselli, IMF, Washington, USA, E-mail:
The views expressed in this working paper are those of the authors and do not necessarily represent those of the IMF, its Executive Board, or its management. Working papers describe research in progress by the authors and are published to elicit comments and to encourage debate. We thank Jörg Decressin, Gabriel Di Bella, Gian Maria Milesi Ferretti, Gita Gopinath, Yuriy Gorodnichenko, Òscar Jordà, Malhar Nabar, Mikkel Plagborg-Møller, and Antonio Spilimbergo for insightful comments.

Appendix A: Data Sources and Country Coverage

Table A.1 lists the data sources used in the analysis. The country coverage for the different sections of the analysis is reported in Table A.2, with the selection of countries being driven by data availability. For the analysis relying on high-frequency indicators, the sample includes 22 countries when job postings are used and 128 countries when mobility is used. When we employ sub-national data on mobility, the sample consists of 422 units for 15 G20 countries. Finally, the analysis of infections is based on a sample of 89 countries for which information on temperature, humidity, testing, and contact tracing is available. At the sub-national level, the sample consists of 373 units for G20 15 countries.

Table A.1:

Data sources.

Indicator Source
Contact tracing Oxford COVID-19 Government Response Tracker
COVID-19 cases Oxford COVID-19 Government Response Tracker
Humidity Air Quality Open Data Platform
Lockdown stringency index Oxford COVID-19 Government Response Tracker
Mobility Google Community Mobility Reports, Baidu for China
Stock of job postings Indeed
Temperature Air Quality Open Data Platform
Testing Oxford COVID-19 Government Response Tracker
Table A.2:

Country coverage.

Country Samples Country Samples Country Samples
Afghanistan Mn, In Iraq Mn, In Guatemala Mn, In
Algeria In Ireland Mn, In, Jp Guinea In
Angola Mn Israel Mn, In Haiti Mn
Argentina Mn, Ms, In, Is Italy Mn, Ms, In, Is, Jp Honduras Mn
Aruba Mn Jamaica Mn Hong Kong SAR Mn, In, Jp
Australia Mn, Ms, In, Is, Jp Japan Mn, Ms, In, Is, Jp Hungary Mn, In
Austria Mn, In, Jp Jordan Mn, In Iceland In
Bahrain Mn, In Kazakhstan Mn, In India Mn, Ms, In, Is
Bangladesh Mn, In Kenya Mn Indonesia Mn, Ms, In, Is
Barbados Mn Korea Mn, In Iran In
Belarus Mn Kosovo In Puerto Rico Mn
Belgium Mn, In, Jp Kuwait Mn, In Qatar Mn
Belize Mn Kyrgyz Republic Mn, In Romania Mn, In
Benin Mn Lao P.D.R. Mn, In Russia Mn, In
Bolivia Mn, In Latvia Mn Rwanda Mn
Bosnia and Herzegovina Mn, In Lebanon Mn Saudi Arabia Mn, Ms, In, Is
Botswana Mn Libya Mn Senegal Mn
Brazil Mn, Ms, In, Is, Jp Lithuania Mn, In Serbia Mn, In
Bulgaria Mn, In Luxembourg Mn Singapore Mn, In, Jp
Burkina Faso Mn Macao SAR In Slovak Republic Mn, In
Cambodia Mn Malaysia Mn, In Slovenia Mn
Cameroon Mn Mali Mn, In South Africa Mn, Ms, In, Is
Canada Mn, Ms, In, Is, Jp Mauritius Mn Spain Mn, In, Jp
Chile Mn, In Mexico Mn, Ms, In, Is, Jp Sri Lanka Mn, In
China Mn, Ms, In, Is Moldova Mn Sweden Mn, In, Jp
Colombia Mn, In Mongolia Mn, In Switzerland Mn, In, Jp
Costa Rica Mn, In Morocco Mn Taiwan Province of China Mn
Croatia Mn, In Mozambique Mn Tajikistan Mn, In
Czech Republic Mn, In Myanmar Mn, In Tanzania Mn
Côte d’Ivoire Mn, In Namibia Mn Thailand Mn, In
Cyprus In Nepal Mn, In Togo Mn
Denmark Mn, In Netherlands Mn, In, Jp Trinidad and Tobago Mn
Dominican Republic Mn New Zealand Mn, In, Jp Turkey Mn, In
Ecuador Mn, In Nicaragua Mn Uganda Mn, In
Egypt Mn Niger Mn Ukraine Mn, In
El Salvador Mn, In Nigeria Mn United Arab Emirates Mn, In, Jp
Estonia Mn, In Norway Mn, In United Kingdom Mn, Ms, In, Is, Jp
Ethiopia In Oman Mn United States Mn, In, Jp
Fiji Mn Pakistan Mn, In Uruguay Mn
Finland Mn, In Panama Mn Uzbekistan In
France Mn, Ms, In, Is, Jp Papua New Guinea Mn Venezuela Mn
Gabon Mn Paraguay Mn Vietnam Mn, In
Georgia Mn, In Peru Mn, In Yemen Mn
Germany Mn, Ms, In, Is, Jp Philippines Mn, In Zambia Mn
Ghana Mn, In Poland Mn, In, Jp Zimbabwe Mn
Greece Mn, In Portugal Mn, In

  1. Mn, national-level regressions of mobility; Ms, subnational-level regressions of mobility; In, national-level regressions of infections; Is, subnational-level regressions of infections; Jp, job postings.

Appendix B: Lockdown Stringency Dynamics

When interpreting the results from local projections, it is important to consider that the estimation controls for present and past levels of lockdowns and infections but not for future levels. Figure B.1 illustrates the predicted future path of lockdowns implied by the local projections. Panel B.1a shows that a lockdown tightening at time t tends to persist in the future while gradually easing. Panel B.1b shows that a doubling of infections leads to a modest subsequent lockdown tightening, of about two points out of a scale of 100.

Figure B.1: Lockdown dynamics (index). The x-axes denote the number of days, the lines denote the point estimates, and the shaded areas correspond to 90% confidence intervals computed with standard errors clustered at the country level.
Figure B.1:

Lockdown dynamics (index).

The x-axes denote the number of days, the lines denote the point estimates, and the shaded areas correspond to 90% confidence intervals computed with standard errors clustered at the country level.

Appendix C: Main Regression Results

Table C.1:

Regressions of mobility.

h = 0 h = 5 h = 10 h = 15 h = 20 h = 25 h = 30
(1) (2) (3) (4) (5) (6) (7)
Stringency index −0.026*** −0.214*** −0.223*** −0.161*** −0.103*** −0.050** −0.028
(0.004) (0.018) (0.022) (0.022) (0.025) (0.024) (0.021)
Ln of daily COVID-19 cases −0.128*** −1.561*** −2.744*** −2.992*** −3.124*** −3.106*** −2.682***
(0.030) (0.201) (0.313) (0.379) (0.405) (0.463) (0.409)
Number of countries 128 128 128 128 128 128 128
Observations 17,995 17,686 17,139 16,519 15,880 15,240 14,600
R 2 0.998 0.954 0.903 0.872 0.850 0.845 0.856

  1. Source: Authors’ calculations. h denotes the horizon of the dependent variable. All specifications include seven lags of the dependent variable and any other regressor, and country and time fixed effects. Clustered standard errors at the country level are reported in parentheses. ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively.

Table C.2:

Regressions of COVID-19 infections.

h = 0 h = 5 h = 10 h = 15 h = 20 h = 25 h = 30
(1) (2) (3) (4) (5) (6) (7)
Stringency index −0.000* −0.000 −0.001 −0.001 −0.002 −0.004** −0.005**
(0.000) (0.001) (0.001) (0.001) (0.002) (0.002) (0.002)
Temperature 0.000 0.000 −0.001 −0.003** −0.005** −0.006** −0.007**
(0.000) (0.001) (0.001) (0.002) (0.002) (0.003) (0.003)
Humidity −0.000 0.000 −0.000 −0.000 −0.001* −0.001* −0.001
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001)
Public information campaigns 0.006 0.032 0.049 0.184 0.407 0.534 0.571
(0.009) (0.099) (0.148) (0.177) (0.271) (0.334) (0.409)
Testing 0.002 0.021 0.052 0.135* 0.161* 0.168 0.156
(0.002) (0.016) (0.034) (0.068) (0.094) (0.112) (0.114)
Contact tracing −0.003 −0.015 −0.030 −0.044 −0.057 −0.059 −0.031
(0.004) (0.020) (0.032) (0.050) (0.068) (0.083) (0.093)
Number of countries 89 89 89 89 89 89 89
Observations 10,832 10,793 10,571 10,204 9,763 9,318 8,873
R 2 0.914 0.881 0.859 0.848 0.843 0.841 0.844

  1. Source: Authors’ calculations. h denotes the horizon of the dependent variable. All specifications include seven lags of the dependent variable and any other regressor, a linear and a quadratic trend, and country and time fixed effects. Seven lags of each variables are included in the estimations. Clustered standard errors at the country level are reported in parentheses. ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively.

Appendix D: Robustness Results

Figure D.1: Impact of a full lockdown on mobility (percent). The x-axes denote the number of days, the lines denote the point estimates, and the shaded areas correspond to 90% confidence intervals computed with standard errors clustered at the country level.
Figure D.1:

Impact of a full lockdown on mobility (percent).

The x-axes denote the number of days, the lines denote the point estimates, and the shaded areas correspond to 90% confidence intervals computed with standard errors clustered at the country level.

Figure D.2: Impact of voluntary social distancing on mobility (impact of a doubling in daily COVID-19 cases, percent). The x-axes denote the number of days, the lines denote the point estimates, and the shaded areas correspond to 90% confidence intervals computed with standard errors clustered at the country level.
Figure D.2:

Impact of voluntary social distancing on mobility (impact of a doubling in daily COVID-19 cases, percent).

The x-axes denote the number of days, the lines denote the point estimates, and the shaded areas correspond to 90% confidence intervals computed with standard errors clustered at the country level.

Figure D.3: Heterogeneous response of mobility (percent). The x-axes denote the number of days, the lines denote the point estimates, and the shaded areas correspond to 90% confidence intervals computed with standard errors clustered at the country level.
Figure D.3:

Heterogeneous response of mobility (percent).

The x-axes denote the number of days, the lines denote the point estimates, and the shaded areas correspond to 90% confidence intervals computed with standard errors clustered at the country level.

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Received: 2020-12-11
Accepted: 2021-02-08
Published Online: 2021-04-19

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