E.J. Ourso College of Business Administration, Louisiana State University, Baton Rouge, Louisiana, United States of America.
PLoS One. 2020 Sep 22;15(9):e0239572. doi: 10.1371/journal.pone.0239572. eCollection 2020.
Social distancing, a non-pharmaceutical tactic aimed at reducing the spread of COVID-19, can arise because individuals voluntarily distance from others to avoid contracting the disease. Alternatively, it can arise because of jurisdictional restrictions imposed by local authorities. We run reduced form models of social distancing as a function of county-level exogenous demographic variables and jurisdictional fixed effects for 49 states to assess the relative contributions of demographic and jurisdictional effects in explaining social distancing behavior. To allow for possible spatial aspects 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. We run our models weekly and examine the evolution of the estimated coefficients over time since the onset of the COVID-19 pandemic in the United States. These estimated coefficients express the revealed preferences of individuals who were able to and chose to stay at home to avoid the disease. Stay-at-home behavior measured using cell phone tracking data exhibits considerable cross-sectional variation, increasing over nine-fold from the end of January 2020 to the end of March 2020, and then decreasing by about 50% through mid-June 2020. Our estimation results show that demographic exogenous variables explain substantially more of this variation than predictions from jurisdictional fixed effects. Moreover, the explanations from demographic exogenous variables and jurisdictional fixed effects show an evolving correlation over the sample period, initially partially offsetting, and eventually reinforcing each other. Furthermore, the predicted social distance from demographic exogenous variables shows substantial spatial autoregressive dependence, indicating clustering in social distancing behavior. The increased variance of stay-at-home 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 spread and mitigation.
社交距离是一种旨在减少 COVID-19 传播的非药物策略，可以由个人自愿与他人保持距离以避免感染疾病而产生，也可以由地方当局实施的管辖限制而产生。我们以县级外生人口统计学变量和管辖固定效应为函数，对 49 个州进行社交距离简化模型的回归，以评估人口统计学和管辖因素在解释社交距离行为方面的相对贡献。为了考虑传染病的可能的空间方面，我们还对周边县的人口统计学变量的溢出效应进行建模，并允许依赖周边县的干扰项。我们每周运行模型，并检查自美国 COVID-19 大流行开始以来，随着时间的推移，估计系数的演变。这些估计系数表达了那些能够选择并选择待在家里以避免疾病的个人的显示偏好。使用手机跟踪数据衡量的居家行为存在相当大的横截面差异，从 2020 年 1 月底到 2020 年 3 月底增加了九倍多，然后到 2020 年 6 月中旬减少了约 50%。我们的估计结果表明，人口统计学外生变量比管辖固定效应的预测解释了更多的这种变化。此外，人口统计学外生变量和管辖固定效应的解释在样本期间表现出不断变化的相关性，最初部分抵消，最终相互加强。此外，人口统计学外生变量预测的社交距离显示出相当大的空间自回归依赖关系，表明社交距离行为存在聚类。居家行为的方差增加加上高度的空间依赖性可能导致社交距离的热点和冷点相对集中，这对疾病的传播和缓解有影响。
