Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S.
Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S.
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DOI：
10.1016/j.jeconom.2020.09.003
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发表时间：
2021-01
影响因子：
6.3
通讯作者：
Schrimpf P
中科院分区：
文献类型：
--
作者：
Chernozhukov V;Kasahara H;Schrimpf P
本文在一个因果结构模型框架中评估了美国各州采取的各种政策对新冠肺炎确诊病例和死亡增长率以及谷歌移动报告衡量的社会疏远行为的动态影响，其中我们在因果结构模型框架中考虑了人们对传播风险新信息的自愿行为反应。我们的分析发现，政策和有关传播风险的信息都是新冠肺炎病例和死亡的重要决定因素，并表明政策的变化可以解释很大一部分观察到的社交疏远行为的变化。我们的主要反事实实验表明，在疫情大流行初期，在全国范围内强制员工佩戴口罩，可能会使4月底的病例和死亡人数每周增长率降低10个百分点以上，到5月底，全国的死亡人数可能会减少19%到47%，这大致相当于挽救了1.9万到4.7万人的生命。我们还发现，如果没有居家订单，案件将增加6%至63%，如果没有企业关闭，案件将增加17%至78%。我们发现，由于缺乏横向差异，学校关闭的影响存在相当大的不确定性；我们不能有力地排除大的或小的影响。总体而言，增长率的大幅下降可归因于私人行为反应，但政策也起到了重要作用。我们还进行了敏感性分析，以找到模型下结果稳健的社区：面具政策的结果似乎比企业关闭和在家订单的结果稳健得多。最后，我们强调，我们的研究是观察性的，因此应该非常谨慎地解释。从完全不可知的角度来看，我们的发现揭示了观察到的政策和行为变化对未来健康结果的预测效果(关联)，控制了信息和其他混杂的变量。
The paper evaluates the dynamic impact of various policies adopted by US states on the growth rates of confirmed Covid-19 cases and deaths as well as social distancing behavior measured by Google Mobility Reports, where we take into consideration people’s voluntarily behavioral response to new information of transmission risks in a causal structural model framework. Our analysis finds that both policies and information on transmission risks are important determinants of Covid-19 cases and deaths and shows that a change in policies explains a large fraction of observed changes in social distancing behavior. Our main counterfactual experiments suggest that nationally mandating face masks for employees early in the pandemic could have reduced the weekly growth rate of cases and deaths by more than 10 percentage points in late April and could have led to as much as 19 to 47 percent less deaths nationally by the end of May, which roughly translates into 19 to 47 thousand saved lives. We also find that, without stay-at-home orders, cases would have been larger by 6 to 63 percent and without business closures, cases would have been larger by 17 to 78 percent. We find considerable uncertainty over the effects of school closures due to lack of cross-sectional variation; we could not robustly rule out either large or small effects. Overall, substantial declines in growth rates are attributable to private behavioral response, but policies played an important role as well. We also carry out sensitivity analyses to find neighborhoods of the models under which the results hold robustly: the results on mask policies appear to be much more robust than the results on business closures and stay-at-home orders. Finally, we stress that our study is observational and therefore should be interpreted with great caution. From a completely agnostic point of view, our findings uncover predictive effects (association) of observed policies and behavioral changes on future health outcomes, controlling for informational and other confounding variables.
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影响因子：
7.6
作者：
BARON, RM;KENNY, DA
通讯作者：
KENNY, DA
影响因子：
1.9
作者：
Cho, Sang-Wook (stanley)
通讯作者：
Cho, Sang-Wook (stanley)
影响因子：
9.7
作者：
Courtemanche, Charles;Garuccio, Joseph;Yelowitz, Aaron
通讯作者：
Yelowitz, Aaron
DOI：
10.1177/1179548420964759
发表时间：
2020
期刊：
Clinical medicine insights. Circulatory, respiratory and pulmonary medicine
影响因子：
--
作者：
Fernández-Trujillo L;Bolaños JE;Álvarez C;Giraldo J;Velásquez M;Zúñiga-Restrepo V;Pérez B;Sua LF
通讯作者：
Sua LF
影响因子：
13.7
作者：
Adda, Jerome
通讯作者：
Adda, Jerome