The best policies to fight pandemics: Five lessons from the literature so far

Jean-Charles Bricongne, Baptiste Meunier 10 August 2021

a

A

In the context of continuing waves of Covid-19 infections and associated measures to fight the pandemic, the literature is starting to draw lessons from the various policies implemented. In this column, we highlight five lessons, while acknowledging that disentangling the effects of the different factors – among which are lockdown measures – is challenging since they have been at play simultaneously or following the same sequence across countries. 

Lesson 1: A more stringent and earlier lockdown seems more efficient to contain an outbreak, even though the importance of sanitary measures should not be downplayed.

More stringent and earlier lockdowns – when the number of cases is low – are more efficient to curb infections. According to empirical findings in IMF (2020), the number of infections has been significantly lower for countries with early lockdowns. In addition, a more stringent lockdown has an immediate effect on curbing infections; otherwise, the effect is non-significant. Other empirical studies on US (Demirguc-Kunt et al. 2020) or European data (Dave et al. 2021b) confirm these results.

Theoretical models confirm that an early and stringent lockdown reduces the economic impact and death toll of the pandemics. Alvarez et al. (in press) conclude that it is optimal to implement a strict lockdown for only two weeks after the first Covid-19 cases. Other models support this finding both in the medical (Buckman et al. 2020, Vinceti et al. 2020) and economic (Eichenbaum et al. 2020, Farboodi et al. 2021) literatures. However, this might not indicate that returning to a lockdown would be unnecessary thereafter. Caulkins et al. (2021) show that it can be optimal to have two or three distinct lockdown periods, depending on local preferences regarding how to balance health and economic impacts.

Various studies have also highlighted the benefits of sanitary measures based on mass testing, generalized use of masks, and screening. Summers et al. (2020), Shaw et al. (2020) and Yalaman et al. (2021) document the efficiency of Asian countries’ strategies based on: (i) early and massive introduction of borders screening, (ii) stringent process to isolate suspect cases and virus-bearers, (iii) use of new technologies for efficient contact tracing, and (iv) generalised use of masks. Making masks compulsory is assessed to reduce the number of infections between 25% and 40% compared to optional mask-wearing (Mitze et al. 2020, Krishnamachari et al. 2021, Chernozhukov et al. 2021). Masks not only help prevent viral transmission but also reduce exposure to cold environments (Bubbico et al. 2021). These sanitary measures are more efficient, however, if combined with social distancing (Firth et al. 2020, Ando et al. 2021) and if the population’s civic sense is high (Barrios et al. 2021). As regards testing, Atkeson et al. (2020) find that the economic benefits of rapid screening programmes exceed their costs by a ratio of 4 to 15. But while most also point to a positive effect of extensive testing (Brotherhood et al. 2020, Hellmann and Thiele 2020, Su et al. 2021), Acemoglu et al. (2020b) report an ambiguous impact since it might lead the population into reducing its social interactions less, fostering the spread by undetected virus-bearers.

Lesson 2: A cost-benefit analysis across different measures is econometrically complex and might be complicated by heterogeneities, but it tends to show the efficiency of cancelling public events for curbing infections. The negative impact of such measures, notably on inequalities and human capital, can also be highlighted.

Disentangling empirically the marginal impact of each measure is complex as they have generally been implemented simultaneously or following the same sequence (Hsiang et al. 2020) with the low quality of data on infections being an additional challenge (Bonacini et al. 2021). Studies have nevertheless estimated their marginal impact. Among those, Deb et al. (2020) estimate not only the health benefits – i.e. how it slows the spread of the virus – but also the economic costs. They find that workplace closures are efficient in reducing infections but are also the costliest in terms of economic impact. They also report that school and public transport closures have a high economic cost but a limited effect on the outbreak. Finally, the authors find that international travel restrictions and, to a lesser extent, limits on the size of gatherings and cancelling of public events display the greatest benefit-to-cost ratios.

The impact of each measure remains, however, highly debated. Table 1 recapitulates how various studies estimate the health benefits of different measures. It highlights large discrepancies. A consensus seems to emerge, however, on the high impact of cancelling public events, and on the mild impact of public transport and non-essential business closures. On the latter, while Song et al. (2021) estimate that it has indeed been significantly protective for workers in this sector, it however translated into higher unemployment (Sjoquist and Wheeler 2021). Studies point to the benefits of an alternative more targeted closure for only high-contact places such as restaurants, gyms, and pubs (Courtemanche et al. 2020, Chang et al. 2021). 

Studies also bring forward heterogeneities associated with duration, geographical factors, and government efficiency. Li et al. (2021) find evidence that the impact depends on time horizon with for example international travel restrictions efficient after seven days but not after 28 days. Burlig et al. (2021) model a same non-linear impact in time for domestic travel bans. More generally, Bakker and Goncalves (2021) show that the impact of measures on infections declined over time. As regards geographical heterogeneities, Russell et al. (2021) show that international travel restrictions might have little impact on pandemics except in countries with low Covid-19 incidence and large numbers of arrivals from abroad. Bennett (2021) show a significant efficiency of lockdown measures in high-income areas but non-significant in the low-income ones while Becchetti et al. (2020) find them to be more effective in highly polluted areas. Pan et al. (2020) also report heterogeneities associated with racial composition and poverty. Finally, Bakker and Goncalves (2021) find that measures have been more efficient in countries with higher government’s effectiveness. 

Table 1 Relative impact of various measures on containing infections1

Whatever their individual impact, however, most studies converge on their combined efficiency – even though voluntary social distancing also naturally reduces infections. Flaxman et al. (2020) estimate that comprehensive lockdowns (a mix of workplaces and school closures, cancelling of public events, stay-at-home orders, and limits on the size of gatherings) in Europe have reduced the reproduction rate by 80%. Santeramo et al. (2021) for Italy and Ferguson et al. (2020) for the UK and the US reach the same conclusion. Voluntary social distancing taken spontaneously by the population might however explain a share of the reduction in infections. Agrawal et al. (2021) and Berry et al. (2021) fail to find that places that implemented lockdowns earlier or for longer have lower excess deaths, while Singh et al. (2021) find only a modest effect. In the same vein, studies have documented that lockdown measures account for a relatively small share of the change in individuals’ behaviours (Gupta et al. 2020b, Cronin and Evans 2020).

On top of a negative short-term economic impact, stricter measures might entail long-term detrimental effects on inequalities, mental health, and human capital. The impact of lockdowns is disproportionate on vulnerable groups such as low-skilled workers (Cajner et al. 2020), whose jobs are less likely to be able to be performed remotely (Dingel and Nieman 2020). School closures and lack of access to reliable childcare have taken a higher toll on young parents (Papanikolaou and Schmidt 2020), and notably women (Del Boca et al. 2020, Albanesi and Kim 2021). This has even been the case in academia where women, particularly those who have children, report a disproportionate reduction in time dedicated to research relative to others (Deryugina et al. 2021). In the longer run, job losses might have hysteresis effects with workers falling into long-term unemployment. In addition to this immediate destruction of human capital, school closures can also weigh on future generations’ capacity to accumulate human capital (Fuchs-Schündeln et al. 2021). Isolation measures such as stay-at-home orders also affect mental health (Béland et al. 2020b, Sibley et al. 2020). Finally, the aggregate effect on the death toll might be more ambiguous. Mulligan (2020) and Faust et al. (2021) show that the pandemics and associated recession may lead to a significant increase in the number of deaths from suicide, substance abuse, and murder – in particular among disadvantaged populations (Chen et al. 2020b, Krieger et al. 2020). Lin et al. (2021) also show that in low-income countries, recessions coming with lockdowns increase child mortality, leading to an inter-generational trade-off with the Covid-related deaths avoided mostly for seniors. 

Lesson 3: While no consensus emerges on geographical targeting, several models advocate for differentiating restrictions by age and type of jobs. In Europe, studies point out the benefits of a coordinated approach both in implementing and relaxing lockdowns.

Targeting might seem a priori relevant. Studies have documented heterogeneous impacts depending on population density (Dave et al. 2021b), age and dependency ratio which particularly influence the mortality rate (Levin et al. 2020, Bürgi and Gorgulu 2020), and workers categories (Akbarpour et al. 2020).

Numerous theoretical models advocate for targeted measures on seniors and employees whose job can be performed remotely. Acemoglu et al. (2020a), Alon et al. (2020), and Gollier (2020) conclude that applying more stringent measures to those aged 65+ reduces the economic cost while maximizing health benefits.2 Focusing on deaths and ICU (intensive care unit) bed occupancy, Ferguson et al. (2020) estimate that social distancing only for those aged 70+ has two to three times the effect of social distancing for the entire population. In addition, Aum et al. (2020) show that locking-down only employees whose job can be performed remotely reduces by half the economic cost compared to a situation where all workers are required to stay at home – for the same health benefits. Another possibility is the implementation of alternate slots in firms and schools to reduce social interactions (Akbarpour et al. 2020). A counterargument, however, comes from studies such as Checo et al. (2021), who find that targeted measures have a higher macroeconomic cost as they remain in place for longer. Another is given by Singh et al. (2021), who empirically find that only measures targeting the general population have a statistically significant impact.

The literature provides mixed evidence regarding geographical targeting. Li et al. (2020) and Lin and Meissner (2020) conclude that local lockdowns have a limited impact on the spread of the virus. Elenev et al. (2021) also provide evidence of spillovers from stay-at-home orders. Dave et al. (2021c) also show how a ‘super-spreader’ event in a US state with a loose lockdown can impact infections in other states with more stringent measures in place. On the opposite, Fang et al. (2020) empirically show how locking down 63 cities in Hubei had successfully contained the spread across China. On a more theoretical perspective, Fajgelbaum et al.’s (2020) model demonstrates that stringent measures only to selected boroughs in large metropolis could be as efficient as a generalised lockdown while significantly reducing the economic impact. Similarly, in a model separating cities and rest of the state, Bisin and Moro (2021) find that a city-only lockdown does not induce a much larger fraction of infected persons than a general lockdown. Finally, Crucini and O'Flaherty (2020) suggest that local restrictions are optimal in a fiscal union as a national policy would be too restrictive for mildly infected areas, weighing therefore too much on the local economic activity. 

At the European level, some studies show the benefits of a coordinated approach not only when implementing lockdowns but also when relaxing them. Ash (2020) estimates that relaxing together would delay the resurgence of the virus by five weeks. Symmetrically, she shows that a coordinated implementation of lockdowns across Europe has a stronger impact on infections – in line with the findings of Ruktanonchai et al. (2020). This is particularly due to strong health spillovers across Europe (Costa-i-Font 2020).

Lesson 4: Even in the absence of lockdown measures, the spread of the virus affects economic activity due to voluntary social distancing. The negative impact of such measures should therefore not be overvalued.

Countries with more stringent lockdowns have experienced sharper GDP contractions. This relation remains valid for other macroeconomic indicators such as households’ consumption (Baker et al. 2020b, Carvalho et al. 2020), employment (Béland et al. 2020a, Schotte et al. 2021) or industrial production (Deb et al. 2020). Theoretical models back such a correlation (e.g. Baqaee and Farhi 2020).

However, even in the absence of lockdown measures, the spread of the virus affects economic activity. Voluntary social distancing has a major impact on activity while heightened uncertainty (Baker et al. 2020a) and deteriorating economic prospects (Baek et al. 2020) also weigh on it. Studies recapitulated in Table 2 show that lockdown measures are estimated to account for around 10 to 60% of the total economic impact of Covid-19. The literature provides vast evidence for an impact of the pandemics in the absence of lockdowns. Chetty et al. (2020) note a contraction of activity before the start of lockdowns in the US. Rojas et al. (2020) and Kahn et al. (2020) observe that the surge in unemployment claims has been homogeneous across the US, notwithstanding local measures. Going forward, Chen et al. (2020a) and Berry et al. (2021) find even no robust empirical evidence for a significant effect of lockdown measures on economic activity.

Table 2 Share of the economic impact attributed to lockdown measures3

Studies regarding the Spanish flu also tend to find no significant effect of lockdowns on economic activity, both in the short and medium run. Some studies have documented an economic impact of the Spanish flu at the local (Dahl et al. 2020) or the global level (Barro et al. 2020) – in contrast however with Velde (2020) attributing the contraction to uncertainty surrounding the end of WWI. On US data, Correia et al. (2020) and Bodenhorn (2020) find no evidence for a significant economic impact of lockdowns in the short term, while Lilley et al. (2020) and Chapelle (2020) reach a similar conclusion regarding medium-term GDP growth. Such results should, however, be taken cautiously given the poor quality of the data and the fact that lockdown measures were far less stringent back then (Beach et al. in press).

Lesson 5: Relaxing the lockdown should be done gradually even during vaccine roll-out as the absence of an epidemic resurgence relies on rigorous sanitary measures. 

Relaxing the lockdown should be done gradually and, where appropriate, differently depending on age and sectors. Gradualness is particularly essential if herd immunity has not been reached (Toda 2020). Dave et al. (2021d) and Singh et al. (2021) have documented anyway the persistence of individuals’ stickiness to pandemic behaviour, suggesting that even rapid and broad-based reopening may have muted impacts on mobility or economic activity. As such, the effects of restrictions and re-openings might be asymmetric depending on the phase of the pandemic (Dave et al. 2021a) with, for example, a smaller role for information shocks and lower demand for mitigation behaviours in the late-pandemic period. In addition, Favero et al. (2020) advocate for relaxing by age groups and by sectors to foster a quicker recovery. Baqaee et al. (2020) and Chang et al. (2021) also call for maintaining restrictions for ‘super-spreaders’ places (e.g. restaurants, gyms, pubs) and large public events. 

Once lockdowns are relaxed, studies show the importance of sanitary measures to limit the spread of the virus even during vaccine roll-out. Renardy et al. (2020) find that delaying the re-opening does not reduce the magnitude of the following infectious wave, but only delays it; on the contrary, reducing levels of social interactions both delays and lowers it. Courtemanche et al. (2021) also document how the re-opening of schools in the midst of high virus spread and with only superficial social distancing rules substantially accelerated the spread of Covid-19 – in line with others putting this finding in perspective with the fact that, if associated with sanitary measures, school reopening has only modest effects on Covid-19 cases (Bravata et al. 2021, Goldhaber et al. 2021). Finally, Cot et al.’s (2021) model finds that vaccinations alone are not enough, and strict social distancing measures are still required until sufficient immunity is achieved. Agarwal et al. (2021) also find that relaxing restrictions during vaccine rollout significantly increase mortality. 

While vaccination curbs infections and severe cases, fighting vaccine hesitancy might be key to achieve herd immunity and might require adequate policies. Broad-based vaccination campaigns have been shown to effectively limit the spread of the virus but above all the emergence of severe cases (Moghadas et al. 2021). However, building confidence in vaccines remains central to achieve a sufficient level of immunity in the population (Dror et al. 2020, Harrison and Wu 2020). General beliefs about vaccination and the own risk of infection can explain variations in this confidence (Wang et al. 2020, Sherman et al. 2021), but there is evidence that vaccine hesitancy is higher among low-income and low-educated people (Khubchandani et al. 2021). For this disadvantaged population, Alsan and Eichmeyer (2021) point to the benefits of relying on non-experts since the lower socioeconomic proximity of experts to them may undermine their trust in the vaccine. In addition, Gans (2021) finds that reducing the costs of access to the vaccine – for example, by providing vaccines at low-priced general merchandise retailers as suggested by Chevalier et al. (2021) – can improve its adoption. He finds, however, that this is not the case with policies that target the utility of un-vaccinated agents – for example, and most notably, vaccine passports – which either lead to equivalent or lower vaccine adoption. Finally, in terms of vaccination strategy, Więcek et al. (2021) show that fractional dosing would substantially reduce infections and mortality if it enables to increase the rate of vaccination.

Authors’ note: Finally, this column reflects the opinions of the authors and does not necessarily express the views of the Banque de France, LEO, LIEPP, or AMSE.

References

Acemoglu, D, V Chernozhukov, I Werning, and M Whinston (2020a), “A multi-risk SIR model with optimally targeted lockdown”, National Bureau of Economic Research Working Paper, No 27102.

Acemoglu, D, A Makhdoumi, A Malekian, and A Ozdaglar (2020b), “Testing, voluntary social distancing and the spread of an infection”, National Bureau of Economic Research Working Paper, No 27483.

Agarwal, N, A Komo, C Patel, P Pathak and M and Ünver (2021), “The Trade-off Between Prioritization and Vaccination Speed Depends on Mitigation Measures”, National Bureau of Economic Research Working Paper, No 28519

Agrawal, V, J Cantor, N Sood and C Whaley (2021), “The Impact of the COVID-19 Pandemic and Policy Responses on Excess Mortality”, National Bureau of Economic Research Working Paper, No 28930

Akbarpour, M, C Cook, A Marzuoli, S Mongey, A Nagaraj, M Saccarola, P Tebaldi, S Vasserman, and H Yang (2020), “Socioeconomic network heterogeneity and pandemic policy response”, National Bureau of Economic Research Working Paper, No 27374.

Albanesi, S and J Kim (2021), “The Gendered Impact of the COVID-19 Recession on the US Labor Market”, National Bureau of Economic Research Working Paper, No 28505

Allcott, H, L Boxell, J Conway, B Ferguson, M Gentzkow, and B Goldman (2020), “What explains temporal and geographic variation in the early US coronavirus pandemic?”, National Bureau of Economic Research Working Paper, No 27965.

Alon, T, M Kim, D Lagakos, and M VanVuren (2020), “How should policy responses to the COVID-19 pandemic differ in the developing world?” National Bureau of Economic Research Working Paper, No 27273.

Alsan, M, and S Eichmeyer (2021), “Experimental Evidence on the Effectiveness of Non-Experts for Improving Vaccine Demand”, National Bureau of Economic Research Working Paper, No 28593

Alvarez F, D Argente, and F Lippi (in press), “A simple planning problem for COVID-19 lockdown , testing, and tracing”, American Economic Review: Insights.

Ando, S, Y Matsuzawa, H Tsurui, T Mizutani, D Hall and Y Kuroda (2021), “Stochastic modelling of the effects of human-mobility restriction and viral infection characteristics on the spread of COVID-19”, Scientific Reports 11: 6856

Andersen, A, E Hansen, N Johannesen, and A Sheridan (2020), “Pandemic, shutdown and consumer spending: Lessons from Scandinavian policy responses to COVID-19”, arXiv preprint. 

Aum, S, S Y Lee, and Y Shin (2020a), “COVID-19 doesn’t need lockdowns to destroy jobs: The effect of local outbreaks in Korea”, National Bureau of Economic Research Working Paper, No 27264.

Aum, S, S Y Lee, and Y Shin (2020b), “Who should work from home during a pandemic? The wage-infection trade-off”, National Bureau of Economic Research Working Paper, No 27908.

Ash, C (2020), “Better relaxing lockdown together”, Science 369(6510): 1443-1445.

Askitas, N, K Tatsiramos, and B Verheyden (2021), “Estimating worldwide effects of non-pharmaceutical interventions on COVID-19 incidence and population mobility patterns using a multiple-event study”, Scientific Reports 11: 1972 

Atkeson, A, M Droste, M Mina and J Stock (2020), “Economic Benefits of COVID-19 Screening Tests”, National Bureau of Economic Research Working Paper, No 28031

Baek, C, P Mc Crory, T Messer, and P Mui (2020), “Unemployment effects of stay-at-home orders: Evidence from high frequency claims data”, Institute for Research on Labor and Employment Working Paper, No 101-20, UC Berkeley.

Baker S, N Bloom, S Davis, and S Terry (2020a), “COVID-induced economic uncertainty”, National Bureau of Economic Research Working Paper, No 26983.

Baker, S, R A Farrokhnia, S Meyer, M Pagel, and C Yannelis (2020b), “How does household spending respond to an epidemic? Consumption during the 2020 COVID-19 pandemic”, National Bureau of Economic Research Working Paper, No 26949.

Bakker, B and C Goncalves (2021), “COVID-19 in Latin America: A High Toll on Lives and Livelihoods”, International Monetary Fund Working Paper, No 2021/168

Baqaee, D and E Farhi (2020), “Nonlinear production networks with an application to the COVID-19 crisis”, National Bureau of Economic Research Working Paper, No 27281.

Baqaee, D, E Farhi, M Mina, and J Stock (2020), “Reopening scenarios”, National Bureau of Economic Research Working Paper, No 27244.

Barrios, J, E Benmelech, Y Hochberg, P Sapienza, and L Zingales (2021), “Civic capital and social distancing during the COVID-19 pandemic”, Journal of Public Economics 193.

Barro, R, J Ursùa, 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”, National Bureau of Economic Research Working Paper, No 26866.

Bartik, A, M Bertrand, F Lin, J Rothstein, and M Unrath (2020), “Measuring the labor market at the onset of the COVID-19 crisis”, National Bureau of Economic Research Working Paper, No 27613.

Beach, B, K Clay, and M Saavedra (in press), “The 1918 influenza pandemic and its lessons for COVID-19”, Journal of Economic Literature.

Becchetti, L, G Conzo, P Conzo and F Salustri (2020), “Understanding the heterogeneity of adverse covid-19 outcomes: the role of poor quality of air and lockdown decisions”, available at SSRN 3572548

Béland, L-P, A Brodeur, and T Wright (2020a), “COVID-19, stay-at-home orders, et employment: Evidence from CPS data”, Institute of Labor Economics (IZA) Discussion Paper, No 13282.

Béland, L-P, A Brodeur, D Mikola, and T Wright (2020b), “The short-term economic consequences of COVID-19: Occupation tasks and mental health in Canada”, Carleton Economics Working Papers, No 20-07.

Bennett M (2021), “All things equal? Heterogeneity in policy effectiveness against COVID-19 spread in Chile”, World Development 137.

Bisin, A and A Moro (2021), “Spatial-SIR with Network Structure and Behavior: Lockdown Rules and the Lucas Critique”, National Bureau of Economic Research Working Paper, No 28932

Bodenhorn, H (2020), “Business at the time of the Spanish influenza”, National Bureau of Economic Research Working Paper, No 27495.

Bonacini, L, G Gallo, and F Patriarca (2021), “Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures”, Journal of Population Economics 34: 275-301.

Born, B, A Dietrich, and G Müller (2020), “Do lockdowns work? A counterfactual for Sweden”, Covid Economics: Vetted and Real-Time Papers, Centre for Economic Policy Research, 16: 1-22.

Bravata, D, J Cantor, N Sood and C Whaley (2021), “Back to School: The Effect of School Visits During COVID-19 on COVID-19 Transmission”, National Bureau of Economic Research Working Paper, No 28645

Bricco, J, F Misch, K Sakr, and A Solovyeva (2020), “Sweden: Will COVID-19 economics be different?” IMF Country Focus. 

Brotherhood, L, P Kircher, C Santos, and M Tertilt (2020), “An economic model of the Covid-19 epidemic: The importance of testing and age-specific policies”, Centre for Economic Policy Research Discussion Paper, No 14695.

Bubbico, L, G Mastrangelo, F Larese-Filon et al. (2021), “Community Use of Face Masks against the Spread of COVID-19”, International journal of environmental research and public health 18(6).

Buckman, S, R Glick R., Lansing K., Petrosky-Nadeau N., and Seitelman L. (2020). “Replicating and projecting the path of COVID-19 with a model-implied reproduction number”, Infectious Disease Modelling 5: 635-651

Bürgi, C. and Gorgulu N. (2020). “Social Distancing and the Economic Impact of COVID-19 in the United States”, CESifo Working Paper Series, No 8577

Burlig, F, A Sudarshan and G Schlauch (2021), “The Impact of Domestic Travel Bans on COVID-19 is Nonlinear in Their Duration”, National Bureau of Economic Research Working Paper, No 28699

Cajner, T, L Crane, R Decker, J Grigsby, A Hamins-Puertolas, E Hurst, C Kurz, and A Yildirmaz (2020), “The US labor market during the beginning of the pandemic recession”, National Bureau of Economic Research Working Paper, No 27159.

Carvalho ,V, S Hansen, A Ortiz, J Ramón García, T Rodrigo, S Rodriguez Mora, and J Ruiz (2020), “Tracking the COVID-19 crisis with high-resolution transaction data”, Centre for Economic Policy Research Discussion Paper, No 14642.

Caulkins J, D Grass, G Feichtinger, R Hartl, P Kort, A Prskawetz, A Seidl, and S Wrzaczek (2021), “The optimal lockdown intensity for COVID-19”, Journal of Mathematical Economics, Advance online publication.

Chang, S, E Pierson, P W Koh, J Gerardin, B Redbird, D Grusky, and J Leskovec (2021), “Mobility network models of COVID-19 explain inequities and inform reopening”, Nature, 589: 82–87.

Chapelle, G (2020), “The medium run impact of non-pharmaceutical interventions. Evidence from the 1918 influenza in US cities”, Sciences Po publications, No 112.

Checo, A, F Grigoli and J Mota (2021), “Assessing Targeted Containment Policies to Fight COVID-19”, The B.E. Journal of Macroeconomics, pre-published online.

Chen, S, D Igan, N Pierri, and A Presbitero (2020), “Tracking the economic impact of COVID-19 and mitigation policies in Europe and the United States”, Covid Economics: Vetted and Real-Time Papers, Centre for Economic Policy Research, 36: 1-24.

Chen, Y-H, M Glymour, R Catalano, A Fernandez, T Nguyen, M Kushel and K Bibbins-Domingo (2020b). “Excess Mortality in California During the Coronavirus Disease 2019 Pandemic, March to August 2020”, Journal of American Medical Association Internal Medicine 181(5): 705-707

Chernozhukov, V, H Kasahara, and P Schrimpf (2021), “Causal impact of masks, policies, behavior on early COVID-19 pandemic in the US”, Journal of Econometrics 220(1): 23-62.

Chetty, R, J Friedman, N Hendren, M Stepner, and the Opportunity Insights Team (2020), “How did COVID-19 and stabilization policies affect spending and employment? A new real-time economic tracker based on private sector data.” National Bureau of Economic Research Working Paper, No 27431.

Chevalier, J, J Schwartz, Y Su and K Williams (2021), “Distributional Impacts of Retail Vaccine Availability”, National Bureau of Economic Research Working Paper, No 28835.

Coibion, O, Y Gorodnichenko, and M Weber (2020), “The cost of the COVID-19 crisis: Lockdowns, macroeconomic expectations, and consumer spending”, National Bureau of Economic Research Working Paper, No 27141.

Correia, S, S Luck, and E Verner (2020), “Pandemics depress the economy, public health interventions do not: Evidence from the 1918 flu”, SSRN.

Costa-i-Font, J (2020), “The EU needs an independent public health authority to fight pandemics such as the COVID-19 crises”, in Bénassy-Quéré A and B Weder di Mauro (eds), Europe in the time of Covid-19, a VoxEU.org eBook, Centre for Economic Policy Research Press.

Cot, C, G Cacciapaglia, A S Islind, M Óskarsdóttir and F Sannino (2021), “Impact of US vaccination strategy on COVID-19 wave dynamics”, Scientific Reports 11

Courtemanche, C, J Garuccio, A Le, J Pinkston and A Yelowitz (2020), “Strong social distancing measures in the United States reduced the COVID-19 growth rate”, Health Affairs 39: 1237-1246

Courtemanche, C, A Le, A Yelowitz and R Zimmer (2021), “School Reopenings, Mobility, and COVID-19 Spread: Evidence from Texas”, National Bureau of Economic Research Working Paper, No 28753

Cronin, C and W Evans (2020), “Private precaution and public restrictions: what drives social distancing and industry foot traffic in the COVID-19 era?” National Bureau of Economic Research Working Paper, No 27531

Crucini, M and O O'Flaherty (2020), “Stay-at-Home Orders in a Fiscal Union”, National Bureau of Economic Research Working Paper, No 28182.

Dahl, C, C Hansen, and P Jensen (2020), “The 1918 epidemic and a V-shaped recession: Evidence from municipal income data”, Covid Economics: Vetted and Real-Time Papers, Centre for Economic Policy Research, 6: 137-162.

Dave, D, A Friedson, K Matsuzawa, D McNichols, and J Sabia (2020), “Did the Wisconsin Supreme Court restart a COVID-19 epidemic? Evidence from a natural experiment”, National Bureau of Economic Research Working Paper, No 27322.

Dave, D, A Friedson, K Matsuzawa, and J Sabia (2021a), “When do shelter-in-place orders fight COVID-19 best? Policy heterogeneity across states and adoption time”, Economic Inquiry, 59(1): 29-52.

Dave, D, A Friedson, D McNichols, and J Sabia (2021b), “The contagion externality of a superspreading event: The Sturgis motorcycle rally and COVID-19”, Southern Economic Journal, 87(3): 769-807.

Dave, D, J Sabia and S Safford (2021c), “The Limits of Reopening Policy to Alter Economic Behavior: New Evidence from Texas”, National Bureau of Economic Research Working Paper, No 28804

Deb, P, D Furceri, J Ostry, and N Tawk (2020), “The economic effects of COVID-19 containment measures”, Centre for Economic Policy Research Discussion Paper, No 15087.

Del Boca, D, N Oggero, P Profeta, and M-C Rossi (2020), “Women’s work, housework and childcare, before and during COVID-19”, Review of Economics of the Household, 18: 1001-1017.

Demirguc-Kunt, A, M Lokshin, and I Torre (2020), “The sooner, the better: The early economic impact of non-pharmaceutical interventions during the COVID-19 pandemic”, World Bank Policy Research Working Paper, No 9257.

Deryugina, T, O Shurchkov and J Stearns (2021), “COVID-19 Disruptions Disproportionately Affect Female Academics”, National Bureau of Economic Research Working Paper, No 28360.

Dingel, J and B Neiman (2020), “How many jobs can be done at home?” Journal of Public Economics, 189.

Dror, A, N Eisenbach, S Taiber et al. (2020), “Vaccine hesitancy: The next challenge in the fight against COVID-19”, European Journal of Epidemiology 35(8): 775-779

Eichenbaum, M, S Rebelo, and M Trabandt (2020), “The Macroeconomics of Epidemics”, National Bureau of Economic Research Working Paper, No 26882.

Elenev, V, L Quintero A Rebucci and E Simeonova (2021), “Direct and Spillover Effects from Staggered Adoption of Health Policies: Evidence from COVID-19 Stay-at-Home Orders”, National Bureau of Economic Research Working Paper, No 29088

Fang, H, L Wang, and Y Yang (2020), “Human mobility restrictions and the spread of the novel coronavirus (2019-nCoV) in China”, Journal of Public Economics, 191.

Farboodi, M, G Jarosch and R Shimer (2020), “Internal and External Effects of Social Distancing in a Pandemic”, National Bureau of Economic Research Working Paper, No 27059.

Faust, J, C Du, K Mayes, S-X Li, Z Lin, M Barnett and H Krumholz (2021), “Mortality From Drug Overdoses, Homicides, Unintentional Injuries, Motor Vehicle Crashes, and Suicides During the Pandemic, March-August 2020”, Journal of American Medical Association 326(1): 84-86

Favero, C, A Ichino, and A Rustichini (2020), “Restarting the economy while saving lives under COVID-19”, Centre for Economic Policy Research Discussion Paper, No 14664.

Ferguson, N, D Laydon, G Nedjati Gilani et al. (2020). “Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand”, Imperial College London COVID-19 Response Team

Firth, J, J Hellewell, P Klepac, S Kissler, CMMID COVID-19 Working Group, A Kucharski, and L Spurgin (2020), “Using a real-world network to model localized COVID-19 control strategies”, Nature Medicine, 26: 1616-1622.

Flaxman, S, S Mishra, A Gandy, J Unwin, T Mellan, H Coupland, C Whittaker, H Zhu, T Berah, J Eaton, M Monod, Imperial College COVID-19 Response Team, A Ghani, C Donnelly, S Riley, M Vollmer, N Ferguson, L Okell, and S Bhatt (2020). “Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe”, Nature, 584: 257-261 

Fuchs-Schündeln, N, D Krueger, A Ludwig, and I Popova (2020), “The long-term distributional and welfare effects of Covid-19 school closures”, National Bureau of Economic Research Working Paper, No 27773.

Gans, J (2021), “Vaccine Hesitancy, Passports and the Demand for Vaccination”, National Bureau of Economic Research Working Paper, No 29075

Goldhaber, D, S Imberman, K Strunk, B Hopkins, N Brown, E Harbatkin and T Kilbride (2021), “To What Extent Does In-Person Schooling Contribute to the Spread of COVID19? Evidence from Michigan and Washington”, American Institute for Research Working Paper, No 247-2020-2

Gollier, C (2020), “Pandemic economics: Optimal dynamic confinement under uncertainty and learning”, The Geneva Risk and Insurance Review, 45: 80-93.

Goolsbee, A and C Syverson (2021), “Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline”, Journal of Public Economics, 193.

Gupta, S, L Montenovo, T Nguyen, F Lozano Rojas, I Schmutte, K Simon, B Weinberg, and C Wing (2020), “Effects of social distancing policy on labor market outcomes”, National Bureau of Economic Research Working Paper, No 27280.

Gutkowski, V (2021), “Lockdown Responses to COVID-19”, Federal Reserve Bank of St. Louis Review, Second Quarter: 127-151

Harrison, E and J Wu (2020), “Vaccine confidence in the time of COVID-19”, European Journal of Epidemiology 35(4): 325-330

Haug, N, L Geyrhofer, A Londei et al. (2020), “Ranking the effectiveness of worldwide COVID-19 government interventions”, Nature Human Behaviour 4: 1303–1312 

Hellmann, T and V Thiele (2020), “A theory of voluntary testing and self-isolation in an ongoing pandemic”, National Bureau of Economic Research Working Paper, No 27941.

Hsiang, S, D Allen, S Annan-Phan et al. (2020), “The effect of large-scale anti-contagion policies on the COVID-19 pandemic”, Nature 584: 262-267

IMF – International Monetary Fund (2020), “The Great Lockdown: dissecting the economic effects”, World Economic Outlook, Chapter 2, October.

Jamison, J, D Bundy, D Jamison, J Spitz, and S Verguet (2020), “Comparing the impact on COVID-19 mortality of self-imposed behavior change and of government regulations: An observational analysis of 13 countries”, SSRN.

Jones, C, T Philippon and V Venkateswaran (2020), “Optimal Mitigation Policies in a Pandemic: Social Distancing and Working from Home”, National Bureau of Economic Research Working Paper, No 2698

Kahn, L, F Lange, and D Wiczer (2020), “Labor demand in the time of COVID-19: Evidence from vacancy postings and UI claims”, Journal of Public Economics, 189.

Khubchandani, J, S Sharma, J H Price, M Wiblishauser, M Sharma and F Webb (2021), “COVID-19 Vaccination Hesitancy in the United States: A Rapid National Assessment”, Journal of Community Health 46: 270–277.

Krieger, N, P Waterman and J Chen (2020), “COVID-19 and Overall Mortality Inequities in the Surge in Death Rates by Zip Code Characteristics: Massachusetts, January 1 to May 19, 2020”, American Journal of Public Health 110(12): 1850-1852.

Krishnamachari, B, A Morris, D Zastrow, A Dsida, B Harper, and A Santella (2021), “The role of mask mandates, stay at home orders and school closure in curbing the COVID-19 pandemic prior to vaccination”, American Journal of Infection Control, Advance online publication.

Levin, A, W Hanage, N Owusu-Boaitey, K Cochran, S Walsh and G Meyerowitz-Katz (2020), “Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications”, European Journal of Epidemiology 35: 1123-1138

Li, Y, E Undurraga, and J R Zubizarreta (2020), “Effectiveness of localized lockdowns in the SARS-CoV-2 pandemic”, medRXiv preprint.

Li Y, H Campbell, D Kulkarni, A Harpur, M Nundy, X Wang, and H Nair (2021), “The temporal association of introducing and lifting non-pharmaceutical interventions with the time-varying reproduction number (R) of SARS-CoV-2: a modelling study across 131 countries”, The Lancet – infectious diseases, 21(2): 193-202.

Lilley, A, M Lilley, and G Rinaldi (2020), “Public health interventions and economic growth: Revisiting the Spanish Flu evidence”, SSRN.

Lin, Z and C Meissner (2020), “Health vs. wealth? Public health policies and the economy during Covid-19”, National Bureau of Economic Research Working Paper, No 27099.

Liu, X, X Xu, G Li, X Xu, Y Sun, F Wang, X Shi, X Li, G Xie, and L Zhang (2020), “Differential impact of non-pharmaceutical public health interventions on COVID-19 epidemics in the United States”, Research square.

Maloney, W and T Taskin (2020), “Determinants of social distancing and economic activity during COVID-19: A global view”, World Bank Policy Research Working Paper, No 9242.

Mitze T, R Kosfeld, J Rode, and K Wälde (2020), “Face masks considerably reduce COVID-19 cases in Germany: A synthetic control method approach”, Covid Economics: Vetted and Real-Time Papers, Centre for Economic Policy Research, 27: 74-103.

Moghadas, S, T Vilches, K Zhang et al. (2021), “The impact of vaccination on COVID-19 outbreaks in the United States”, medRXiv preprint, https://doi.org/10.1101/2020.11.27.20240051 

Mulligan, C (2020), “Deaths of Despair and the Incidence of Excess Mortality in 2020”, National Bureau of Economic Research Working Paper, No 28303

Pan, W, S Tyrovolas, I Vazquez, R Raj, D Fernandez, B Zaitchik,  P Lantos, and C Woods (2020), “COVID-19: Effectiveness of non-pharmaceutical interventions in the United States before phased removal of social distancing protections varies by region”, medRXiv preprint. 

Papanikolaou, D and L Schmidt (2020), “Working remotely and the supply-side impact of COVID-19”, National Bureau of Economic Research Working Paper, No 27330.

Proietti, C, M Santini, P Probst, A Annunziato, T De Groeve and C Fonio (2021), “Uplifting of COVID-19 containment measures in Europe”, JRC Technical Reports, Publications Office of the European Union.

Renardy, M, M Eisenberg and D Kirschner (2020). “Predicting the second wave of COVID-19 in Washtenaw County, MI”, Journal of Theoretical Biology 507

Rojas, F, X Jiang, L Montenovo, K Simon, B Weinberg, and C Wing (2020), “Is the cure worse than the problem itself? Immediate labor market effects of COVID-19 case rates and school closures in the U.S.”, National Bureau of Economic Research Working Paper, No 27127.

Ruktanonchai, N, R Floyd, S Lai, C Ruktanonchai, A Sadilek, P Rente-Lourenco, X Ben, A Carioli, J Gwinn, J E Steele, O Prosper, A Schneider, A Oplinger, P Eastham, and J Tatem (2020), “Assessing the impact of coordinated COVID-19 exit strategies across Europe”, Science 369(6510): 1465-1470.

Russell, T, J Wu, S Clifford, J Edmunds, A Kucharski and M Jit (2021). “Effect of internationally imported cases on internal spread of COVID-19: a mathematical modelling study”, The Lancet Public Health 6(1): e12-20

Santeramo, F, M Tappi and E Lamonaca (2021), “On the management of COVID-19 pandemic in Italy”, Health Policy 125(8): 995-1001

Schotte, S, M Danquah, R Darko Osei, and K Sen(2021), “The labour market impact of COVID-19 lockdowns: Evidence from Ghana”, World Institute for Development Economic Research (WIDER) Working Paper Series, No 2021-27.

Shaw, R, Y-K Kim, and J Hua (2020), “Governance, technology and citizen behavior in pandemic: Lessons from COVID-19 in East Asia”, Progress in Disaster Science 6: 1-11.

Sibley, C, L Greaves, N Satherley, M Wilson, N Overall, C Lee, and F Barlow (2020), “Effects of the COVID-19 pandemic and nationwide lockdown on trust, attitudes toward government, and well-being”, American Psychologist, 75(5): 618-630.

Singh, S, M Shaikh, K Hauck and M Miraldo (2021), “Impacts of Introducing and Lifting Nonpharmaceutical Interventions on COVID-19 Daily Growth Rate and Compliance in the United States”, Proceedings of the National Academy of Sciences of the United States of America 118(12)

Sjoquist, D and L Wheeler (in press), “Unemployment insurance claims and COVID-19”, Journal of Economics and Business.

Song, H, R McKenna, A Chen, G David and A Smith-McLallen (2021), “The Impact of the Non-essential Business Closure Policy on Covid-19 Infection Rates”, National Bureau of Economic Research Working Paper, No 28374

Su, E C-Y, C-H Hsiao, Y-T Chen and S-H Yu (2021), “An Examination of COVID-19 Mitigation Efficiency among 23 Countries”, Healthcare 9(6): 755.

Summers, J, H-Y Cheng, H-H Lin, L Barnard, A Kvalsvig, N Wilson, and M Baker (2020), “Potential lessons from the Taiwan and New Zealand health responses to the COVID-19 pandemic”, The Lancet Regional Health - Western Pacific, 4.

Toda, A (2020), “Susceptible-infected-recovered (SIR) dynamics of COVID-19 and economic impact”, Covid Economics: Vetted and Real-Time Papers, Centre for Economic Policy Research, 1: 43-63.

Velde, F (2020), “What happened to the US economy during the 1918 influenza pandemic? A view through high-frequency data”, Federal Reserve of Chicago Working Papers, No. 2020-11.

Vinceti, M, T Filippini, K Rothman, F Ferrari, A Goffi, G Maffeis, and N Orsini (2020), “Lockdown timing and efficacy in controlling COVID-19 using mobile phone tracking”, E-Clinical Medecine, 25:1-8.

Wang, J, R Jing, X Lai, H Zhang, Y Lyu, M Knoll and H Fang (2020), “Acceptance of COVID-19 Vaccination during the COVID-19 Pandemic in China”, Vaccines 8(3): 482

Więcek, W, A Ahuja, M Kremer et al. (2021), “Could Vaccine Dose Stretching Reduce COVID-19 Deaths?”, National Bureau of Economic Research Working Paper, No 29018

Yalaman, A, G Basbug, C Elgin and A Galvani (2021). “Cross-country evidence on the association between contact tracing and COVID-19 case fatality rates”, Scientific Reports 11.

Endnotes

1 The relative impacts are interpolated by this column’s authors. We put our best efforts into retrieving the information with as much fidelity as possible with respects to the original studies.

2 As noted in the preamble, it should however be acknowledged that these studies do not tackle potential feasibility issues, notably on the legal ground. Though, as regards legality, Gutkowski (2021) concludes that heterogeneities in political rights and civil liberties do not explain the differences in lockdowns across countries.

3 Discrepancies can stem from differences in geographical coverage and variables under consideration. In addition, econometric identification remains complex due to potential endogeneity issues or omitted third variables. The shares are sometimes interpolated by this column’s authors. We put our best efforts to retrieve the information with as much fidelity as possible with respects to the original studies.

a

A

Topics:  Covid-19

Tags:  COVID-19, lockdowns, pandemics, Covid-19 measures

Deputy-Director, Banque de France

Economist, Banque de France

Events

CEPR Policy Research