Social distancing causally impacts the spread of SARS-CoV-2: a U.S. nationwide event study

BMC Infectious Diseases

Table 5 Placebo tests

Coefficient	Mean	Min	p1	p5	p10	p25	p50	p75	p90	p95	p99	Max
Panel A: Random protest onset date and and counties where protests took place
\(Post_{FP}\)	− 1.18	− 4.82	− 3.46	− 2.82	− 2.48	− 1.87	− 1.20	− 0.49	0.15	0.57	1.42	2.99
t-statistic	− 1.21	− 5.77	− 3.70	− 2.99	− 2.57	− 1.93	− 1.19	− 0.44	0.13	0.44	1.01	2.59
Panel B: Estimates from Table 4
\(Post_{FP}\)	1.34
t-statistic	2.32

This table reports results from a Monte Carlo simulation of the impact of the protests on the SARS-CoV-2 infection rate across the U.S. In each iteration of this simulation, we assign 541 counties randomly to the potential treatment group and the remaining 2077 counties to the potential control group. We then implement our propensity score matching process to create a balanced sample of treated and control counties. Next, we assign a [− 30, + 30]-day event period to each treated county randomly with start dates ranging between March 1, 2020, and May 8, 2020. Then, we create the \(Post_{FPi,j,t}\) indicator variable. Finally, we estimate our staggered DID regression specification on the simulated sample and collect the \(\beta _{1}\) coefficient estimate, along with its county-cluster robust t-statistic [26]. We implement this process 5000 times to produce the simulated distribution of \(\beta _{1}\) coefficients and their associated t-statistics. We describe this process in greater detail in “Placebo test” section. In Panel A, we report the simulated distribution of the \(\beta _{1}\) coefficients, along with the distribution of their t-statistics. In Panel B, we report the \(\beta _{1}\) estimate from Table 4 to facilitate comparisons

ISSN: 1471-2334