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Effect of Stay-at-Home Orders
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\backslash
begin{centering}
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\backslash
subfloat[Panel A:
 Effect on Mobility]{
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label{sfig:a}
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includegraphics[scale=.35]{../input/event_studies/csa/log_visits_sip.pdf}} 
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vspace{3mm}
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\backslash
subfloat[Panel B:
 Effect on Consumer Spending]{
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label{sfig:a}
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includegraphics[scale=.35]{../input/event_studies/csa/log_total_spend_sip.pdf}} 
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\backslash
subfloat[Panel C:
 Effect on Employment]{
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label{sfig:a}
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includegraphics[scale=.35]{../input/event_studies/csa/log_count_hours_pos_sip.pdf}} 
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vspace{3mm}
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\backslash
subfloat[Panel D:
 Effect on Log New Cases,
 Model Free]{
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label{sfig:a}
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includegraphics[scale=.35]{../input/event_studies/csa/log_new_cases_absorb_date_0.pdf}} 
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\backslash
subfloat[Panel E:
 Effect on Log Contact Rate,
 $
\backslash
gamma = 1/6$]{
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label{sfig:a}
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includegraphics[scale=.35]{../input/event_studies/csa/log_new_cases_absorb_date_6.pdf}} 
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Note:
 Figure plots estimated treatment effects 
\begin_inset Formula $\omega_{k}$
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 of stay-at-home orders on different outcomes,
 using the event-study specification at the CSA-day level outlined in equation (
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) for the mobility and economic outcomes and equation (
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) for the health outcomes.
 Panel A shows the effect on mobility,
 using the log of total POI visits in the SafeGraph data.
 Panel B shows the effect on consumer spending,
 using the log of total spending in Facteus' debit card sample.
 Panel C shows the effect on employment,
 using the log number of individuals with positive work hours from the Homebase sample.
 Panel D examines log new cases as the outcome and sets 
\begin_inset Formula $\gamma=0$
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 which implies 
\begin_inset Formula $\log(I_{it})=\log(C_{it})$
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.
 Panel E is the same as Panel D,
 except that it sets 
\begin_inset Formula $\gamma=1/6$
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.
 All regressions include date fixed effects 
\begin_inset Formula $\delta_{t}$
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.
 Panels A-C include CSA fixed effects 
\begin_inset Formula $\mu_{i}$
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;
 Panels D-E include the event window indicator 
\begin_inset Formula $\xi_{it}$
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.
 CSAs are weighted by population in the regression.
 Standard errors are clustered at the CSA level irrespective of order timing.
 
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Temporal Variation Explained by Policy
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Panel A:
 Share of Temporal Variation in Social Distancing and Employment Outcomes
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	scale 40

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Panel B:
 Share of Temporal Variation in Contact Rate 
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Panel C:
 Counterfactual Case Growth
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Note:
 Figure plots the share of the total change in each outcome that is attributable to a given policy 
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 following Section 
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.
 Panel A reports estimates using stay-at-home orders and business closure orders for POI visits from SafeGraph,
 total wages from Homebase,
 and employment from Homebase.
 For each policy treatment,
 we restrict attention to the CSAs treated by the given policy.
 Panel B reports estimates of the policy-induced change in the contact rate 
\begin_inset Formula $\beta_{t}$
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 from stay-at-home orders,
 varying the assumed basic reproduction number 
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.
 In both panels,
 the bars depict 95 percent confidence intervals.
 See Table 
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 for additional details on Panels A and B.
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 for our sample of cumulative statistical areas (CSAs) using 
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.
 Panel C reports the observed number of cases along with the estimated number of cases if a uniform stay-at-home order was implemented on March 17 with proportional effect on social distancing behaviors,
 a uniform stay-at-home order was implemented on March 17 that caused social distancing behaviors to fall to a fixed level of 35 percent of March 1 levels,
 and the estimated number of cases if all areas followed the same social distancing behavior as the San Francisco Bay Area (defined to be the counties in the San Francisco CSA that were first to implement a stay-at-home order).
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Determinants of Geographic Variation in Log Contact Rates
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Panel A:
 Univariate Regression of 
\emph default

\begin_inset Formula $\hat{\theta}_{i}$
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\emph on
 on Covariates
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Panel B:
 Controlling for Log Population
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Note:
 Figure plots the coefficients of regressing the CSA log contact rate fixed effect estimates 
\begin_inset Formula $\hat{\theta}_{i}$
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 from equation (
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) on CSA-level determinants.
 The 
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 and all covariates have been standardized to have a mean 0 and a standard deviation of 1.
 Panel A plots the standardized coefficients and 95 percent confidence intervals from univariate regressions.
 Panel B repeats Panel A but the regressions also control for the log of population.
 Population weights are not used.
 Robust standard errors are used to compute the confidence intervals.
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