JUE Insight: Were urban cowboys enough to control COVID-19? Local shelter-in-place orders and coronavirus case growth

Abstract

One of the most common policy prescriptions to reduce the spread of COVID-19 has been to legally enforce social distancing through shelter-in-place orders (SIPOs). This study examines the role of localized urban SIPO policy in curbing COVID-19 cases. Specifically, we explore (i) the comparative effectiveness of county-level SIPOs in urbanized as compared to non-urbanized areas, (ii) the mechanisms through which SIPO adoption in urban counties yields COVID-related health benefits, and (iii) whether late adoption of a statewide SIPO yields health benefits beyond those achieved from early adopting counties. We exploit the unique laboratory of Texas, a state in which the early adoption of local SIPOs by densely populated counties covered almost two-thirds of the state's population prior to adoption of a statewide SIPO on April 2, 2020. Using an event study framework, we document that countywide SIPO adoption is associated with an 8 percent increase in the percent of residents who remain at home full-time and between a 13 to 19 percent decrease in foot-traffic at venues that may contribute to the spread of COVID-19 such as restaurants, bars, hotels, and entertainment venues. These social distancing effects are largest in urbanized and densely populated counties. Then, we find that in early adopting urban counties, COVID-19 case growth fell by 21 to 26 percentage points two-and-a-half weeks following adoption of a SIPO, a result robust to controls for county-level heterogeneity in COVID-19 outbreak timing, coronavirus testing, the age distribution, and political preferences. We find that approximately 90 percent of the curbed growth in COVID-19 cases in Texas came from the early adoption of SIPOs by urbanized counties, suggesting that the later statewide shelter-in-place mandate yielded relatively few health benefits.

The contagion externality of a superspreading event: The Sturgis Motorcycle Rally and COVID‐19

“…Despite evidence that a large share of variation in mitigation behaviors is driven by voluntary behaviors (Cronin and Evans, 2020; Sears et al ., 2020), there is mounting evidence that statewide policy interventions such as shelter‐in‐place orders (Friedson et al ., 2020; Courtemanche et al ., 2020a; Dave et al ., 2020a; 2020b; 2020c) and mask wearing mandates (Lyu and Wehby, 2020) may play an important role in preventing the spread of COVID‐19. In Table 5, we explore whether the nationwide spread of COVID‐19 cases from the Sturgis Rally was moderated by state policies designed to encourage mitigation.…”

“…SafeGraph provides census‐block‐group‐level data from 45 million anonymized cell phones that allow us to measure the residence of individuals and the jurisdictions to which they travel. These data have been used widely by scholars examining the impact of COVID‐19 mitigation policies (Abouk and Heydari, 2020; Friedson et al ., 2020; Lasry et al ., 2020; Dave et al ., 2020a; 2020b; 2020c) and large gatherings (Dave et al . 2020d,e) on stay‐at‐home behavior.…”

“…For our analyses of Meade County and its environs, we exclude from the donor pool (a) counties from within the state of South Dakota or from border states, and (b) counties with urbanicity rates above 64.5% or below 59.5% (creating a band of counterfactuals that envelope Meade County's urbanicity rate of 62.0) and counties with a population‐weighted density greater than 650 persons per square mile or below 350 persons per square mile (creating a band of donor counties that envelope Meade County's population weighted density of approximately 500 persons per square mile). These restrictions help to select on factors that may be important to COVID‐19 spread (Friedson et al ., 2020; Dave et al ., 2020b, 2020c).…”

“…(2020) also find evidence of substantial spatial diffusion and spillovers, with cases crossing county lines and the diffusion being impacted by lockdown policies in bordering states. Some prior work (Dave et al ., 2020b, 2020c) further finds important heterogeneity in the effects of certain policies such as shelter‐in‐place orders, which implies a role for more nuanced, targeted, and localized approach to policymaking in such cases.…”

Political Violence, Risk Aversion, and Non-Localized Disease Spread: Evidence from the U.S. Capitol Riot

“…The heat map reflects the intensity of inflows into the protests. Following Dave et al (2020f), we explore five categories of inflows: High, Moderate,Low,and None. 11 As illustrated, the events of January 6 (including the protest and certification vote) garnered attendees from all over the United States.…”