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Fig 1.

Effect of state-level mask mandates on COVID-19 outcomes.

Shaded area represents 95% confidence interval, with standard errors clustered at the state level. Red vertical line represents the start of the mask mandate, and the red horizontal line represents the line of zero treatment effect. We observe the treatment effect to start near zero when mask mandates are introduced (red vertical line) and to gradually increase (i.e. the treatment effect becomes more negative) reaching, 40 days after the introduction of mask mandates, -0.49 standard deviations (95% confidence interval is [-0.61,-0.37], adjusted R2 is 0.632 and p < 0.001) for new cases (a), -2.38 percentage points ([-2.99,-1.78], adjusted R2 is 0.698 and p < 0.001) for the proportion of daily hospitalization admissions (b) due to COVID-19, and 0.52 standard deviations (95% confidence interval is [-0.69,-0.35], adjusted R2 is 0.537 and p < 0.001) for deaths (c). These effects are large, corresponding to 14% of the highest recorded number of cases, 13% of deaths, and 7% of admission proportion.

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Fig 2.

Effect of different types of mask mandates.

Robustness check of main event study estimation where, here, we only consider mask mandates that require business employees (right) vs. the public (left) to wear masks. The trajectory and magnitude of the treatment effect is consistent with our previous main result, and do not differ significantly between each other, which demonstrates that our main result is not sensitive to our definition of mask mandate start dates. Full regression results are shown in S5 and S6 Tables in S1 File.

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Fig 3.

Effect of mask mandates on earlier-hit or later-hit states.

Robustness check of main event study results (section Fig 1) where we compare the effect of mask mandates on cases, deaths and hospitalization for earlier wave states only (i.e. states who had early spikes of COVID-19 peaks in the pandemic, namely, NY, NJ, MA, CT, MI, DC, RI, IL, WA, PA, GA, VT, MD, FL and LA) vs later wave states. Masks mandates took more time to have an effect on earlier states whereas they had a faster effect on later states. Full regression results are shown in S9 Table in S1 File.

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Fig 4.

Effect of mask mandates on mask-adherence.

Treatment effect of mask mandates on mask adherence in the only four states (Hawaii, Iowa, North Dakota and New Hampshire) which had late mask mandates (Nov 14, 16, and 20) during the period we have daily state-level mask adherence data. We find that there is a flat pre-treatment trend up to 13 days before the introduction of a mask mandate followed by a 23.4 [12.0,34.8] percentage points increase in mask adherence following introduction (adjusted R2 is 0.925 and p < 0.001).

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Fig 5.

Effect of lifting mask mandates on adherence.

We observe a flat pre-trend before the lifting of state-level mask mandates (red vertical line at zero) followed by a statistically significant decrease. Full regression results in S15 Table in S1 File.

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Fig 6.

Effect of lifting mask mandates on COVID-19 outcomes.

We observe a significant increase in daily new cases, but no effect on hospitalizations and deaths. Full regression results in S12 Table in S1 File.

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Fig 7.

Impact of community mask adherence and attitudes.

Using a novel survey-based dataset of 847 thousand online Facebook survey respondents in 51 languages from 69 countries, we estimate that a 1% increase in community mask adherence is associated with a decrease of -0.61 [-0.82,-0.39] cases per 100K and a decrease of -0.035 [-0.040,-0.029] deaths per 100K. Similarly, we find that a 1% increase in community attitude about the importance of wearing masks leads to a decrease of -0.56 [-0.80,-0.32] cases per 100K and a decrease of -0.036 [-0.042,-0.030] deaths per 100K. For reference, the current COVID-19 outcomes in the world as of December 14, 2020 is 10 daily new cases per 100K and 0.17 daily deaths per 100K, which suggest that mask adherence and attitudes are associated with a strong positive impact on COVID-19 outcomes worldwide. Samples are expected to be representative because we use a unique weight for each sample which corrects for a variety of biases.

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