Mask mandates reduced the spread of covid-19 in the us

Mask mandates and other lockdown policies reduced the spread of COVID-19 in america

Confronted with COVID-19, people rationally and voluntarily react to information on risks, rendering it difficult to distinguish the result of containment policies from that of voluntary behavioural responses. This column examines the result of mandatory mask policies on COVID-19 cases and deaths in america. If the united states had on 1 April 2020 universally mandated that employees of public-facing businesses use masks, there might have already been nearly 40% fewer deaths by the beginning of June. Containment policies had a big impact on the quantity of COVID-19 cases and deaths, directly by reducing transmission rates and indirectly by constraining people’s behaviour, and take into account roughly half the observed change in the growth rates of cases and deaths.


The COVID-19 pandemic has sparked an explosion of research evaluating various policies using observational data (e.g. Courtemanche et al. 2020, Deb et al. 2020, Hsiang et al. 2020, Pei et al. 2020, and Abouk and Heydari 2020), but there is absolutely no consensus on what could have happened to cases and deaths if these policy measures was not implemented.

The evaluation of counterfactual policy effects on the spread of COVID-19 is complicated. People rationally and voluntarily react to information on transmission risks even without the policies. This helps it be difficult to distinguish the result of polices from that of voluntary behavioural responses.

To properly measure the role of policies in accordance with people’s voluntary responses, we develop an empirical framework predicated on the next causal path diagram (Chernozhukov et al. 2020). There are five key components: (1) confounders, such as for example socio-demographic characteristics, (2) information regarding current infection levels and growth rates, (3) policies reacting to the info, and (4) local behaviour of individuals reacting to the info and effective polices, which together determine (5) final outcomes, such as for example case and death growth rates. The machine is dynamic: medical outcomes today become information for another period.

Figure 1 Causal path diagram

Notes: W denotes confounders, I information, P policy, B behaviour, all realised for the reason that sequence by enough time t in state i; Y denotes outcomes realised at future date t+l.

This causal framework explicitly recognises that policies not merely directly affect the spread of COVID-19 (via masks) but also indirectly – by changing people’s behaviour (through restaurant closures, school closures, stay-at-home orders). In addition, it recognises that people respond to new information, such as for example new cases and deaths within their state, and voluntarily adjust their behaviour (social distancing, hand washing, masks) even without the policy set up.

The framework, in conjunction with parametric structural equations, can quantitatively decompose the growth of COVID-19 cases and deaths into three components: direct policy effect, policy effect through behaviour, and direct behaviour effect in response to new information. 1

Lots of the existing papers analyse various areas of the causal diagram. Inside our paper we have a more holistic approach and analyse the complete causal dynamic model, analysing both direct and indirect ramifications of policies on infections and on behaviour as captured by Google Mobility Reports. Using US state-level data, we examine what sort of mask mandate and other lockdown policies have affected the growth rates of cases and deaths by conducting counterfactual experiments within the model.

Mask mandates for employees directly reduced transmissions

Figure 2 illustrates the raw-data fact: the united states states with mask mandates for employees of publicly facing businesses generally have lower case and death growth rates than states without mask mandates because the end of April 2020.

Our causal analysis, which controls for most confounding factors and dynamics, confirms this raw-data finding. The result is mainly direct (without affecting people’s behaviour), suggesting that wearing masks lowers transmission risk per contact.

Figure 2 Evidence from raw US data: The common growth rates in cases and deaths for states without mask mandates (red) and with mask mandates (blue)

Figure 3 shows the consequence of our counterfactual analysis predicated on the estimated causal dynamic model. Nationally implementing mandatory face masks for employees on 1 April could have reduced the weekly growth rate in cases by as much as 10%, which results in a nearly 40% relative decrease in cumulative deaths (90% confidence interval of [17,55]%). Therefore that as much as 17 to 55 thousand lives might have been saved through the start of June; by 27 May 2020, the united states Centers for Disease Control and Prevention reports 99,031 deaths in america.

Figure 3 Relative reduction in deaths induced by nationally mandating masks for employees of publicly facing businesses on 1 April in america

Our finding is corroborated by a different causal methodology predicated on synthetic control using German data in Mitze et al. (2020), 2 which reported a complete 20% decrease in the growth rates. Our finding is further in keeping with the laboratory results in Hou et al. (2020), who show that the nasal cavity could be the principal initial site of infection, thus supporting “the widespread usage of masks to avoid aerosol, large droplet, and/or mechanical contact with the nasal passages”. 3

Could we’ve kept non-essential business open?

In Figure 4, another counterfactual experiment indicates that keeping non-essential businesses open (apart from movie theatres and gyms, and keeping restaurants ‘takeout-only’ mode) could have increased cumulative cases and deaths by 15% (with a 90% confidence interval of [-20, 60]%).

Figure 4 Aftereffect of leaving non-essential businesses open on COVID-19 cases in america

These findings are consistent with other analyses that discovered that closing non-essential businesses hardly affected social-distancing behaviour (Maloney and Taskin 2020). This shows that universal masks policies could have compensated for keeping substantial elements of the economy open.

Stay-at-home (or shelter-in-place) orders

Figure 5 demonstrates, without stay-at-home orders, there might have already been an 80% upsurge in total cases by the beginning of June (90% confidence interval of [25,170]%). Therefore that 0.5-3.4 million more Americans might have been infected without stay-at-home orders, providing suggestive evidence that reopening via removal of stay-at-home orders may lead to a substantial upsurge in cases and deaths. 4

Figure 5 Aftereffect of not implementing stay-at-home order on COVID-19 cases in america

Policy or private behavioural response?

Hsiang et al. (2020) and Chernozhukov et al. (2020) find that the containment policies have substantially reduced the COVID-19 growth rates in america, with policies roughly explaining one-third to two-thirds of the observed decline in growth rates of confirmed cases and deaths.

We further find that both policies and information on past cases and deaths determine people’s social-distancing behaviour, where policy effects explain about 50% of the observed decline in Google mobility variables and behaviour effects take into account the spouse. 5 The estimates claim that there are both large policy effects and large behavioural effects on the growth of cases and deaths. Aside from mandatory masks, the containment policies affect cases and deaths indirectly through their effect on behaviour.

There are many strong claims in social media that containment policies didn’t matter and that the observed decline in COVID-19 growth rates and distancing behaviour is nearly entirely because of private responses to information. We believe these claims are generated by misinterpretations of statistical analyses.

One reason behind this, for example, may be the difficulty of identifying school-closure effects. As shown in Figure 6 and 7 , the timing of school closures (Figure 7) predates or nearly coincides with a considerable drop in mobility as reported by Google reports (Figure 6).

Figure 6 Intensity of visits to ‘workplaces’

Notes: The figure shows the dynamics of the intensity of visits to ‘workplaces’ from Google Mobility Reports, which ultimately shows the sudden drop within the last fourteen days of March.

Figure 7 School closures in america

Notes: The figure shows the part of states with closed schools, which slightly leads the mobility drop in Figure 6.

The upsurge in school closures also coincides with the large upsurge in total COVID-19 cases, an information variable. Insufficient cross-sectional variation in school closures helps it be difficult to distinguish the info influence on behaviour from the policy influence on behaviour. It really is clear that school closures potentially affect behaviour by imposing very difficult constraints on parents’ mobility, however the exact quantification of the policy effect requires richer data. 6

Economic relevance

The economic evaluation of varied policies is underway in lots of new papers (e.g. Chetty et al. 2020, Coibion et al. 2020). Understanding the result of containment policies on fatalities and cases is vital to comprehend the trade-off between health insurance and economic wellbeing since fatalities entail direct economic losses however, many COVID-19 survivors may have problems with long-term health complications (see e.g. Alvarez et al. 2020, Baqaee et al. 2020, Fernández-Villaverde and Jones 2020, Acemoglu et al. 2020, Keppo et al. 2020, McAdams 2020).

As the economic aftereffect of some containment policies is hard to judge immediately, the result of mask policies is clear and may be gauged from a straightforward calculation: 40,000 saved lives, in April and could, times the statistical value of life, e.g. $5 million, means a $200 billion economic impact – an order of magnitude bigger compared to the cost of implementing the universal mask policy.


Abaluck, J, J A Chevalier, N A Christakis, H P Forman, E H Kaplan, A Ko and S H Vermund (2020), “The case for universal cloth mask adoption and policies to improve way to obtain medical masks for health workers”, Covid Economics 5.

Acemoglu, D, V Chernozhukov, I Werning and M D Whinston (2020), “Optimally targeted lockdowns in a multi-group SIR model”, NBER 27102.

Alvarez, F E, D Argente and F Lippi (2020), “A straightforward planning problem for COVID-19 lockdown”, Covid Economics 14.

Baqaee, D, E Farhi, M J Mina, and J H Stock (2020), “Reopening scenarios”, NBER 27244.

Baron, R M, and D A Kenny (1986), “The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations”, Journal of Personality and Social Psychology 51: 1173-82.

Chetty, R, J N Friedman, N Hendren and M Stepner (2020), “Real-time economics: A fresh platform to track the impacts of COVID-19 on people, businesses, and communities using private sector data”, mimeo.

Coibion, O, Y Gorodnichenko and M Weber (2020), “Labor markets through the COVID-19 crisis: An initial view”, Covid Economics 21.

Courtemanche, C, J Garuccio, A Le, J Pinkston and A Yelowitz (2020), “Strong social distancing measures in america reduced the COVID-19 growth rate”, Health Affairs 39(7).

Deb, P, D Furceri, J D Ostry and n Tawk (2020), “The consequences of containment measures on the COVID-19 pandemic”,, 5 June.

Fernández-Villaverde, J, and C I Jones (2020), “Estimating and simulating a SIRD style of COVID-19 for most countries, states, and cities”, NBER 27128.

Gitmez, A, K Sonin and A L Wright (2020), “Political economy of crisis response”, University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2020-68.

Greenland, S, J Pearl and J M Robins (1999), “Causal diagrams for epidemiologic research”, Epidemiology January: 37-48.

Haavelmo, T (1944), “The probability approach in econometrics”, Econometrica: Journal of the Econometric Society 12: iii-115.

Hernán, M A, and J M Robins (2020), Causal inference: Imagine if, Chapman & Hall/CRC.

Hines, O, S Vansteelandt and K Diaz-Ordaz (2020), “Robust inference for mediated effects in partially linear models”, arXiv:2007.00725.

Hou, Y J, K Okuda, C E Edwards et al. (2020), “SARS-CoV-2 reverse genetics reveals a variable infection gradient in the respiratory system”, Cell 182: 1-18.

Howard, J, A Huang, Z Li et al. (2020), “Face masks against COVID-19: An evidence review”, Preprints 2020.

Jones, T C, B Mühlemann, T Veith et al. (2020), “An analysis of SARS-CoV-2 viral load by patient age”, medRxiv 2020.06.08.20125484, preprint.

Keppo, J, E Quercioli, M Kudlyak, L Smith, and A Wilson (2020), “The behavioral SIR model, with applications to the Swine Flu and COVID-19 pandemics”, in Virtual Macro Seminar.

L’Huillier, A G, G Torriani, F Pigny, L Kaiser and I Eckerle (2002), “Shedding of infectious SARS-CoV-2 in symptomatic neonates, children and adolescents”, medRxiv 2020.04.27.20076778, preprint.

Maloney, W F, and T Taskin (2020), “Determinants of social distancing and economic activity during COVID-19: A worldwide view”, Covid Economics 13.

McAdams, D (2020), “Nash SIR: An economic-epidemiological style of strategic behavior throughout a viral epidemic”, Covid Economics 16.

Mitze, T, R Kosfeld, J Rode and K Walde (2020), “Face masks considerably reduce COVID-19 cases in Germany”, Covid Economics 27.

Peters, J, D Janzing, and S Bernhard (2017), Components of causal inference: Foundations and learning algorithms, Cambridge MA: MIT Press.

Wright, A L, K Sonin, J Driscoll and J Wilson (2020), “Poverty and economic dislocation reduce compliance with COVID-19 shelter-in-place protocols”, SSRN.

Wright, P G (1928), Tariff on animal and vegetable oils, NY: Macmillan Company.

Wright, S (1923), “The idea of path coefficients an answer to Niles’s criticism”, Genetics 8(3): 239.


1 The causal model is framed using the language of structural equations models and causal diagrams of econometrics (P Wright 1928, Haavelmo 1944) and genetics (S Wright 1922). See Greenland et al. (1999), Peters et al. (2017), and Hernán and Robins (2020) for modern developments, especially in computer science and epidemiology. This causal diagram has several ‘mediation’ components, where variables affect outcomes directly and indirectly through other variables called mediators; these structures return back at least to S Wright (1922); see e.g. Baron and Kenny (1986) and Hines et al. (2020) for modern treatments. The daddy and son, P Wright and S Wright, closely collaborated to build up structural equation models and causal path diagrams; P Wright’s key work represented supply-demand system as a directed acyclical graph and established its identification using exclusion restrictions on instrumental variables.

2 Our study was initially released in ArXiv on 28 May 2020; Mitze (2020) premiered at SSRN on 8 June 2020.

3 Our findings also align well with the medical observational evidence reviewed in Howard et al. (2020), who declare that “mask wearing reduces the transmissibility per contact by reducing transmission of infected droplets in both laboratory and clinical contexts.” Abaluck et al. (2020) find the association between growth rates of cases and of deaths with mask-wearing norms: countries with pre-existing norms where sick people wear masks are lower by 8-10% than those rates in countries without pre-existing mask norms.

4 This finding is consistent with those implied by Courtemanche et al. (2020), who attribute a 6% absolute decrease in growth rates to these orders, using US county-level data.

5 That is consistent with theoretical study by Gitmez et al. (2020) that investigates the role of private behaviour and negative external effects for individual decisions over policy compliance and information acquisition during pandemics.

6 There is another reason school closures could have played a job in transmission reduction: emerging evidence shows that children infected with COVID-19 transmit the virus (L’Huillier et al. 2020, Jones et al. 2020), though to a smaller extent than adults.

Leave a Reply

Your email address will not be published. Required fields are marked *