Free to Distance

Social distancing depends upon the prevalence of COVID-19 and the spread of COVID-19 depends upon social distancing. Therefore, social distancing and the spread of COVID-19 are simultaneously determined which complicates estimation. To avoid the issues of simultaneity and measurement error, we focus on reduced form modeling of social distancing as gauged by the change in the log of the proportion of individuals staying at home each week using cell phone tracking data. The reduced form involves a regression of the dependent variable as a function of county-level exogenous demographic variables (population density, age, education, race, and income) as well as jurisdictional fixed effects for 49 states. The evolving parameter estimates reflect stay at home choices by individuals (subject to their capacity to stay at home). Because of the spatial nature of a contagious disease, we also model the spillovers associated with demographic variables in surrounding counties as well as allow for disturbances that depend upon those in surrounding counties. Over the course of the data, the cross-sectional variation in social distance behavior increased over seven-fold from 0.0052 to 0.0375. The estimation results show that predicted social distance from demographic exogenous variables explain substantially more variation (46.4%) of individual stay at home behavior than predictions from jurisdictional fixed effects (13.2%) with a low correlation between the jurisdictional and demographic predictive components (0.006). The predicted social distance from demographic exogenous variables shows substantial spatial autoregressive dependence (lambda=0.847) which indicates that social distances will cluster. The increased variance of the behavior coupled with the high level of spatial dependence can result in relatively intense hotspots and coldspots of social distance which has implications for disease effects and mitigation.