id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-172814-rywu0xp5	Chen, M. Keith	Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission	2020-05-11		.txt	text/plain	3790	177	47	To mitigate the severity of the ongoing COVID pandemic, governments have launched a range of social distancing policies including, by early April, mandatory stay-at-home orders (SHOs) in forty-five U.S. states and the District of Columbia. Utilizing device-level geolocation data for 10 million U.S. smartphones to measure individual movement, combined with precinct-level election outcomes and block-group level demographics, we estimate the causal effect of SHOs on daily movement-and what drives non-compliance. Examining correlations between aggregate distancing behavior and political make-up can misestimate partisan responses to stay-at-home orders because Democrat-leaning counties account for the overwhelming majority of COVID cases, at all phases of the epidemic (Fig. S5 ). By regressing new COVID diagnoses on observed daily (lagged) movement, we estimate that for every 10% decrease in meters traveled by residents, the local transmission rate decreases by 4.3% (column 3). Regression 2 then estimates the effect of movement on local transmission (i.e., within an infected individual's county) controlling for any social distancing policies.	./cache/cord-172814-rywu0xp5.txt	./txt/cord-172814-rywu0xp5.txt