lw450088 / face_mask_mandate Goto Github PK

README

1. We use both public datasets and purchased datasets for this research. The step-by-step data importing and cleaning steps are shown in the files:

FM_import.do
(Data import file.)
FM_clean.do
(Data cleaning file.)

The data we use include:

(a) New York Times Case Reports
https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv
(b) Safegraph’s dwell time data at the county-level
(c) Safegraph’s point of interest visits by county
(d) County-level weather data
(e)County-level policies
https://ce.naco.org/?dset=COVID-19&ind=Emergency%20Declaration%20Types
And
https://docs.google.com/spreadsheets/d/133Lry-k80-BfdPXhlS0VHsLEUQh5_UutqAt7czZd7ek/edit#gid=0
(f)School closure data from MCH
(g)State-level policy
https://docs.google.com/spreadsheets/d/1zu9qEWI8PsOI_i8nI_S29HDGHlIp2lfVMsGxpQ5tvAQ/edit#gid=973655443

The cleaned dataset is named “FM_data_final.dta”. We only upload the cleaned dataset in google drive used in this study (please contact us for the google drive link). If you are interested in any intermediate datasets:
(a) the links updated on 9/1/2020 for public datasets.
(b) contact us for Safegraph datasets’ information.

2. You can run the master file FM_master.do, which includes 5 separate do files in the analysis:

(a) main mask-mandate policy effects for public and business mandates to the dwell time at home. (FM_main_reg.do)
(b) dynamic analysis for the pre/post-policy. (FM_dynamic.do)
(c) mask-mandate policy effects for public and business mandates to the points of interest visitation. (FM_POI.do)
(d) possible spillover effect analysis from the neighboring counties’ mask mandates (FM_spillover.do)
(e) examine of the fatigue from the stay-at-home policy without mask mandate as robustness checks. (FM_fatigue.do)

3. To start, change the mainpath directory to your local folder. In FM_import.do, the list of folders will be created under the mainpath for figures and table results.