Wearing a mask-For yourself or for others? Behavioral correlates of mask wearing among COVID-19 frontline workers

Abstract

Human behavior can have effects on oneself and externalities on others. Mask wearing is such a behavior in the current pandemic. What motivates people to wear face masks in public when mask wearing is voluntary or not enforced? Which benefits should the policy makers rather emphasize in information campaigns-the reduced chances of getting the SARS-CoV-2 virus (benefits for oneself) or the reduced chances of transmitting the virus (benefits for others in the society)? In this paper, we link measured risk preferences and other-regarding preferences to mask wearing habits among 840 surveyed employees of two large Swiss hospitals. We find that the leading mask-wearing motivations change with age: While for older people, mask wearing habits are best explained by their self-regarding risk preferences, younger people are also motivated by other-regarding concerns. Our results are robust to different specifications including linear probability models, probit models and Lasso covariate selection models. Our findings thus allow drawing policy implications for effectively communicating public-health recommendations to frontline workers during the COVID-19 pandemic.

Fig 1. Timeline.

(A) Development of COVID-19 in the less-affected and more-affected region relative to data collection periods, according to FOPH (B) Timeline of the study and COVID-19 policy milestones and debates in Switzerland.

Fig 2. Descriptive statistics on mask wearing in percent.

Risk aversion and altruism are binary variables standardized at median. “Younger” refers to up to 44 years of age and “Older” refers to 45 years of age and older.

Fig 3. Motivations behind mask wearing.

In this figure, we visualize the results from regressing mask wearing on the variables of interest risk aversion and altruism. The x-axis shows the estimate for the coefficient on risk aversion and the estimate for the coefficient on altruism first without covariates, then including only situational covariates, then including only individual covariates, and finally including both. The thin line represents the 95% confidence interval and the thick line represents the 90% confidence interval. Risk aversion and altruism are binary variables standardized at median. Situational covariates include having had COVID-19 symptoms, household size, using public transport, having had contact to a COVID-19 infected person at work, having had contact to a COVID-19 infected person outside work, belonging to a COVID-19 risk group, living with a household member who belongs to a COVID-19 risk group, having traveled internationally for at least 2 days since 1st of February 2020. Individual covariates include being a health worker (doctor or nurse), age group, education group (low, medium or high level of education), being native and gender.

Fig 4. Motivations behind mask wearing, split by age.

The presented estimates are from regressions with interaction terms between the variable of interest and being younger, controlling for all situational and individual covariates. Situational covariates include having had COVID-19 symptoms, household size, using public transport, having had contact to a COVID-19 infected person at work, having had contact to a COVID-19 infected person outside work, belonging to a COVID-19 risk group, living with a household member who belongs to a COVID-19 risk group, having traveled internationally for at least 2 days since 1st of February 2020. Individual covariates include being a health worker (doctor or nurse), age group, education group (low, medium or high level of education), being native and gender. See also the comments on Fig 3.