Private Precaution and Public Restrictions: What Drives Social Distancing and Industry Foot Traffic in the COVID-19 Era?

Abstract

We examine the role of state and local policies to encourage social distancing, including stay at home orders, public school closures, and restrictions on restaurants, entertainment, and large social gatherings. Outcomes come from cell phone records and include foot traffic in six industries (essential and nonessential retail, entertainment, hotel, restaurant, and business services) plus the fraction of cell phones that are home all day. Structural break models show mobility series at the national and state levels start to change dramatically in a short window from March 8-14, well before state or local restrictions of note are in place. In difference-indifference models, declarations of state of emergency reduce foot traffic and increase social distancing. Stay at home restrictions explain a modest fraction of the change in behavior across outcomes. Industry-specific restrictions have large impacts. For example, restrictions on dining in restaurants reduce traffic in restaurants, hotels, and nonessential retail. Private, self-regulating behavior explains more than three-quarters of the decline in foot traffic in most industries. Restrictive regulation explains half the decline in foot traffic in essential retail and 75 percent of the increase in the fraction home all day. In this latter result, public school closings have a substantial effect.

Coronavirus and Social Distancing: Do Non-Pharmaceutical-Interventions Work (at Least) in the Short Run?

“…The mechanism through which NPIs flatten the epi-curve is by reducing mobility and thus lowering the probability of social interactions and thus of contagion. But our data, as well as other studies (Cronin and Evans, 2020), indicate that mobility started to slow down in countries that imposed lockdowns before the implementation of such policies. To measure correctly the impact mobility on public health outcomes, we need to disentangle the fraction of the mobility reduction that is spontaneous, i.e.…”

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.…”

The Impact of the COVID-19 Pandemic and Policy Response on Health Care Utilization: Evidence from County-level Medical Claims and Cellphone data

“…We use the four-digit NAICS code to identify office of physicians (6211) and general medical and surgical hospitals (6221). 3 These data have been commonly used to examine the effect of COVID-19 and COVID-19 related policies on social distancing (Cronin and Evans 2020;Goolsbee and Syverson 2020;Gupta et al 2020;Cook, Newberger, and Smalling 2020;Moreland 2020;Gao et al 2020). To our knowledge, there are only two studies that have used the SafeGraph data to measure utilization of healthcare services, proxied by either visits to hospitals (Jay et al 2020) or abortion clinics (Andersen, Bryan, and Slusky 2020).…”