- ‘Late Order’ indicates counties with a stay-at-home order after this point. ‘No Order‘ indicates counties which did not issue a stay-at-home order during this sample period. The dashed vertical line indicates the week starting March 25, 2020. Figure 3: Geographic Variation in Social Distancing and Public Policy Panel A: % Change in SafeGraph Visits (-45,38] (-51,-45] (-56,-51] (-63,-56] [-96,-63] No data Panel B: Shelter-in-Place Order Effective Start Date [12mar,24mar] (24mar,26mar] (26mar,30mar] (30mar,02apr] (02apr,07apr] No identified order Note: This figure shows the U.S. geographic distribution of social distancing and public policy responses.
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- 1. We begin by matching SafeGraph POIs to the counties in which they are located. We use latitude and longitude from SafeGraph’s August 2020 Core POI dataset, along with the 2010 TIGER county shapefile.24 We successfully assign 99.9 percent of the POIs to a county.
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- 2. We then merge the POI-county mapping from (1) onto SafeGraph’s Patterns data using the safegraph-place-id variable. We sum visits by county for a given day, aggregating across POIs. Our SafeGraph series ranges from January 1, 2020 to August 30, 2020.
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- 25Downloaded from https://www.census.gov/programs-surveys/geography/technical-documentation/recordslayout /2010-urban-lists-record-layout.html on June 25, 2020. we assign cases and deaths based on their population share. We also merge data on testing and hospitalizations from the Covid Tracking Project, by state and day.
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- 26In their event-study specification, Dave et al. (2020) aggregate multiple post-treatment periods into a single treatment effect and include other control variables. Appendix Figure A1: Relative Timing of Stay-at-Home Orders 0.0 0.2 0.4 0.6 0.8 1.0 Fraction With a Stay-at-Home Order -20-10 0 10 20 Days Relative to 0.01% of Population Infected Note: Figure plots the population weighted share of counties with a stay-at-home order relative to the point in time in which at least 0.01% of the population has a confirmed case.
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- 3. We then merge onto the output from (2) a dataset of county-level demographic information constructed as follows. We use the Open Census data from SafeGraph, aggregating up the data given at the census block group level to the county level. We combine this with data on county 2016 Presidential votes shares (MIT Election Data and Science Lab 2018). We define the Republican vote share to be the share of votes received by the Republican candidate over the sum of votes across all candidates. We exclude counties without valid vote data, which drops Alaska and two additional counties (FIPS: 15005, 51515). We also merge on the urban population share from the 2010 Census.25 We also use average seasonal temperatures by geography from Wu et al. (2020), which is ultimately sourced from gridMET. Averages for a given county and season are taken across the years 2010-2016.
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- 4. We then merge onto the output from (3) the number of incoming international flights for each US county from December 2019 to February 2020 made available by the OpenSky Network (Schäfer et al. 2014; Olive 2019).
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- 5. We then merge data on Covid-19 health outcomes onto the output from (4). We source confirmed Covid-19 cases and deaths by county and day from The New York Times. We assume zero cases and deaths for the observations not observed in The New York Times data. We drop the four counties which overlap with Kansas City (MO), because The New York Times lists these as geographic exceptions where it either does not assign cases to these counties or excludes cases occurring within the city. For the five boroughs of New York City, 24Downloaded from https://www.census.gov/geo/maps-data/data/cbf/cbf counties.html on July 24, 2018.
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- 7. We then merge on debit card transactions and spending totals from Facteus onto the output from (6). Prior to this merge, Facteus data is aggregated from the zip code to the county level using a 5-digit zip code to county crosswalk downloaded from HUD (https://www.huduser.gov/portal/dataset on April 12, 2020. Facteus data ranges from Jan 1, 2020 to August 27, 2020, with missing data on August 7, 2020.
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- 8. We then merge on employment data from Homebase onto the output from (7). Homebase data is aggregated to the county-day level prior to this merge. This step completes the construction of the dataset used in our county-level analysis. Homebase data ranges from Jan 1, 2020 to August 31, 2020.
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- 9. For analysis at the level of a CSA, order date, and day, we then aggregate the output from (8) to this level. We use a county to CSA crosswalk downloaded on May 29, 2020 from the NBER (http://data.nber.org/cbsa-csa-fips-county-crosswalk/cbsa2fipsxw.csv), which uses delineation files originally sourced from the Census. We sum countable variables such as POI visits or Covid-19 cases. For other variables, we take a population-weighted average across counties in a CSA-day with the same social distancing policy start date. In our CSA-level analysis, we exclude counties not assigned to a CSA.
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- Abatzoglou, John T. 2011. Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology. http://www.climatologylab.org/gridmet.html Alexander, Diane and Ezra Karger. 2020. Do stay-at-home orders cause people to stay at home? Effects of stay-at-home orders on consumer behavior. Working Paper.
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Allcott, Hunt, Levi Boxell, Jacob Conway, Matthew Gentzkow, Michael Thaler, and David Yang. 2020. Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic. Journal of Public Economics. Forthcoming.
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- Appendix Table A3: Variance Decomposition for Differences in Log Contact Rates Cross-CSA Variance of Log Contact Rate 1.514 Share of variance explained by: Social Distancing 0.008 Policy 0.000 Timing of Virus 0.001 Observed Covariates 0.264 Population 0.182 Climate 0.000 Transport 0.000 Race 0.032 Partisanship 0.031 College Degrees 0.000 Age Demographics 0.000 Note: Table reports the cross-CSA variance of the average log contact rate log(βit) between March 15 and April 30, 2020 in the first row. We calculate the cross-CSA variance of each explanatory variable and report the share of the log contact rate variance accounted for by the variation in each set of explanatory variable using the estimated coefficients from our lasso model in Section 5.2.
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Dave, Dhaval M., Andrew I. Friedson, Kyutaro Matsuzawa, and Joseph J. Sabia. 2020. When do shelter-in-place orders fight COVID-19 best? Policy heterogeneity across states and adoption time. Economic Inquiry.
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- Overall Difference 1.228 2.171 3.565 Share of difference explained by: Social Distancing-0.009-0.010-0.023 Policy-0.006-0.007-0.007 Timing of Virus-0.000-0.003-0.003 Observed Covariates 0.548 0.508 0.424 Population 0.482 0.423 0.336 Climate 0.000 0.000 0.000 Transport 0.000 0.000 0.000 Race-0.036-0.015-0.023 Partisanship 0.103 0.100 0.110 College Degrees 0.000 0.000 0.000 Age Demographics 0.000 0.000 0.000 Note: Table reports the difference in the average log contact rate log(βit) between March 15 and April 30, 2020 for each group of CSAs. It also reports the counterfactual share of the overall difference explained by each set of determinants as outlined in Section 5.2. A Appendix See the replication code for exact details on data construction and estimation. A.1 Data Construction Procedures We construct the datasets used in our analysis as follows.
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- We use the county “shelter date” and “work date” from Hikma Health in our construction of stay-at-home and business closure policies respectively. Given that none of the sources have entirely overlapping policy data, we define both our stay-at-home and business closure policies by sequentially assigning enforcement dates when data is available in the order: NYT, Keystone, Stanford/Virginia crowdsource, and Hikma Health. Once a state enacts a policy, the counties inherit the policy of the state. We then merge on a dataset of reopening dates at the state level collected by the NYT (Mervosh et al. 2020b) and curated by Rearc.
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- Week Normalized Log(New Cases) Timing Early Order Late Order No Order Note: Figure reports trends in average mobility and health by county order timing and week. Panel A plots the log of daily average POI visits, normalizing relative to the week starting January 29, 2020. Panel B plots the log of daily new COVID-19 cases, normalized to the week starting March 25, 2020. In both panels, averages are weighted by population and taken across counties and days prior to taking logs or normalization. ‘Early Order’ indicates counties with a stay-at-home order on or before March 25, 2020.
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