Risk attitudes and human mobility during the COVID-19 pandemic

Behavioural responses to pandemics are less shaped by actual mortality or hospitalisation risks than they are by risk attitudes. We explore human mobility patterns as a measure of behavioural responses during the COVID-19 pandemic. Our results indicate that risk-taking attitudes are a critical factor in predicting reductions in human mobility and social confinement around the globe. We find that the sharp decline in mobility after the WHO (World Health Organization) declared COVID-19 to be a pandemic can be attributed to risk attitudes. Our results suggest that regions with risk-averse attitudes are more likely to adjust their behavioural activity in response to the declaration of a pandemic even before official government lockdowns. Further understanding of the basis of responses to epidemics, e.g., precautionary behaviour, will help improve the containment of the spread of the virus.


Robustness Checks.
This section presents the checks for robustness of our results, which are shown in Table S7 to S11 for the six sets of regressions conducted in the main text, respectively. The first two checks concern including regions with censored mobility value in the sample of the analysis.
We impose two restrictions on sample inclusion 1) regions with at least one censored value for the outcome mobility measures are excluded from the corresponding regression and 2) a more restrictive criteria with regions at least one censored value for any of the outcome mobility measures are excluded from the analysis. The first criteria excluded number of regions ranging from 54 (Workplace) to 217 (Residential), depending on the outcome mobility measure used while the second criteria reduce the number of regions to 484. The third check concerns if estimates are sensitive to whether government response is general by recoding indicators as no measures taken if the movement restrictions (or recommendation of restrictions) were not applied countrywide. In general, our main findings are robust to all three checks.
For the overall risk-mobility relationship (comparing estimates from Table S7 to  [-1.213;0.252], P=0.199). The coefficient estimates for our main control variables (pandemic declaration and weekends) are also close to those found in the main results, apart from % population 65+, where the negative effects on mobility change to non-residential places are more prominent in the restricted samples (checks 1 and 2).
For the declaration moderator effect on the risk-mobility relationship, the results (coefficients of the declaration x risk preference term) remain highly robust except for residential, where statistically significance is drop when regions with censored values were . This suggests that the tendency to further reduce mobility on the weekends than during the week for low risk-tolerance regions (as compared to high risk-tolerance regions) is evident before pandemic declaration. Moreover, we see that the results with triple interactions between risk preference, weekend, and pandemic declaration resembles to that in the main text, albeit for regions with very high risk preference, the preand post-declaration difference in the weekend reduction in mobility is less precisely estimated in the second sample restriction, in particular for retail and recreation, grocery and pharmacy, and parks.
Lastly, we found some of the estimates of the risk preference-risk pool interaction terms is similar to that in the main analysis. For retail & recreation, the first exclusion rule s.e.=0.185, Fig. S1 | Average marginal effects of weekends on mobility changes over risk attitudes, before and after pandemic declaration.    Figure 1 in the main text. Random-effects GLS regression estimates. Standard errors (clustered at regional level) in parentheses. † p < .10; * p < .05; ** p < .01; *** p < .001. Reference categories are: Before WHO declares COVID-19 as pandemic, Weekdays and No measures taken for all government response indicators.  Figure 2 in the main text. Random-effects GLS regression estimates. Standard errors (clustered at regional level) in parentheses. † p < .10; * p < .05; ** p < .01; *** p < .001. Reference categories are: Before WHO declares COVID-19 as pandemic, Weekdays and No measures taken for all government response indicators.  Figure 3 in the main text. Random-effects GLS regression estimates. Standard errors (clustered at regional level) in parentheses. † p < .10; * p < .05; ** p < .01; *** p < .001. Reference categories are: Before WHO declares COVID-19 as pandemic, Weekdays and No measures taken for all government response indicators.  Figure 4 in the main text and Supplementary Figures S1 and S2. Random-effects GLS regression estimates. Standard errors (clustered at regional level) in parentheses. † p < .10; * p < .05; ** p < .01; *** p < .001. Reference categories are: Before WHO declares COVID-19 as pandemic, Weekdays and No measures taken for all government response indicators.  Figure 5 in the main text. Random-effects GLS regression estimates. Standard errors (clustered at regional level) in parentheses. † p < .10; * p < .05; ** p < .01; *** p < .001. Reference categories are: Before WHO declares COVID-19 as pandemic, Weekdays and No measures taken for all government response indicators. Robust 1 = regions with at least one censored values on the outcome mobility measures excluded. Robust 2 = regions with at least one censored values on any mobility measures excluded. Robust 3 = government response indicators recoded as no measures taken if policy is not applied countrywide. Random-effects GLS regression estimates. Standard errors (clustered at regional level) in parentheses. † p < .10; * p < .05; ** p < .01; *** p < .001. We controlled for number of confirmed cases (in logs), population density, and the set of government response indicators in each regression. Reference categories are: Before WHO declares COVID-19 as pandemic, Weekdays and No measures taken. Robust 1 = regions with at least one censored values on the outcome mobility measures excluded. Robust 2 = regions with at least one censored values on any mobility measures excluded. Robust 3 = government response indicators recoded as no measures taken if policy is not applied countrywide. Random-effects GLS regression estimates. Standard errors (clustered at regional level) in parentheses. † p < .10; * p < .05; ** p < .01; *** p < .001. We controlled for weekend dummy, share of population over 65, day since first confirmed death, number of confirmed cases (in logs), population density, and the set of government response indicators in each regression. Reference categories are: Before WHO declares COVID-19 as pandemic, Weekdays and No measures taken. Robust 1 = regions with at least one censored values on the outcome mobility measures excluded. Robust 2 = regions with at least one censored values on any mobility measures excluded. Robust 3 = government response indicators recoded as no measures taken if policy is not applied countrywide. Random-effects GLS regression estimates. Standard errors (clustered at regional level) in parentheses. † p < .10; * p < .05; ** p < .01; *** p < .001. We controlled for pandemic declaration dummy, day since first confirmed death, share of population over 65, number of confirmed cases (in logs), population density, and the set of government response indicators in each regression. Reference categories are: Before WHO declares COVID-19 as pandemic, Weekdays and No measures taken. Robust 1 = regions with at least one censored values on the outcome mobility measures excluded. Robust 2 = regions with at least one censored values on any mobility measures excluded. Robust 3 = government response indicators recoded as no measures taken if policy is not applied countrywide. Random-effects GLS regression estimates. Standard errors (clustered at regional level) in parentheses. † p < .10; * p < .05; ** p < .01; *** p < .001. We controlled for the day since first confirmed death, share of population over 65, number of confirmed cases (in logs), population density, and the set of government response indicators in each regression. Reference categories are: Before WHO declares COVID-19 as pandemic, Weekdays and No measures taken. 768 Notes: Robust 1 = regions with at least one censored values on the outcome mobility measures excluded. Robust 2 = regions with at least one censored values on any mobility measures excluded. Robust 3 = government response indicators recoded as no measures taken if policy is not applied countrywide. Random-effects GLS regression estimates. Standard errors (clustered at regional level) in parentheses. † p < .10; * p < .05; ** p < .01; *** p < .001. We controlled for weekend dummy, pandemic declaration dummy, days since first confirmed death, number of confirmed cases (in logs), population density, and the set of government response indicators in each regression. Reference categories are: Before WHO declares COVID-19 as pandemic, Weekdays and No measures taken.