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.
JCBh9 on January 20th, 2021 at 04:12 UTC »
Have any of you looked at the math in this article? I swear I can't help but think they're trying to be as confusing as humanely possible
because I'm trying to stick with all of the variables but they never show any data for how they got it
" Structural Equations Model (SEM). Let i∈{1,…,N} denote the observational unit, t be the time periods, and ℓ be the time delay. (1) For each i and t≤−ℓ, the confounder, information, behavior, and policy variables Wit,Iit,Bit,Pit are determined outside of the model, and the outcome variable Yi,t+ℓ is determined by factors outside of the model for t≤0. (2) For each i and t≥−ℓ, confounders Wit are determined by factors outside of the model, and information variables Iit are determined by (I); policy variables Pit are determined by setting ι=Iit in (P) with a realized stochastic shock εitp that obeys the exogeneity condition (E); behavior variables Bit are determined by setting ι=Iit and p=Pit in (SO) with a shock εitb that obeys (E); finally, the outcome Yi,t+ℓ is realized by setting ι=Iit, p=Pit, and b=Bit in (SO) with a shock εity that obeys (E). "
I mean .. does that mean anything?
FurretsOotersMinks on January 20th, 2021 at 04:06 UTC »
Seems hindsight is 2020
MathSandwich on January 20th, 2021 at 02:02 UTC »
How do the authors account for the idiots-who-can’t-keep-their-masks-over-their-noses variable?