JUE Insight: How much does COVID-19 increase with mobility? Evidence from New York and four other U.S. cities

Abstract

How effective are restrictions on mobility in limiting COVID-19 spread? Using zip code data across five U.S. cities, we estimate that total cases per capita decrease by 19% for every ten percentage point fall in mobility. Addressing endogeneity concerns, we instrument for travel by residential teleworkable and essential shares and find a 25% decline in cases per capita. Using panel data for NYC with week and zip code fixed effects, we estimate a decline of 30%. We find substantial spatial and temporal heterogeneity; east coast cities have stronger effects, with the largest for NYC in the pandemic’s early stages.

The propagation effect of commuting to work in the spatial transmission of COVID-19

“…Importantly, we also find that the estimated spatial commuting channel (different from spatial dependence of general form) breaks down once mobility restrictions during the first lockdown are in place, which points to the effectiveness of such strict measures in mitigating spatial disease transmission. Our estimated effect size is comparable to recent other studies, such as for US cities reported in Glaeser et al (2020).…”

Simulating the Impacts of Interregional Mobility Restriction on the Spatial Spread of COVID-19 in Japan

“…Surprisingly, in prefectures with large cities that attract outside workers (such as Tokyo and Osaka), the number of infections increased after restricting the interregional mobility. Although restricting mobility has reduced the total number of COVID-19 cases per capita in some U.S. cities, 20 our simulation results from the spatial SEIR model suggest that the interregional mobility restriction has heterogeneous impacts on the infection expansion across regions. For example, an influx of uninfected persons from outside, and an outflux of infectious persons from regions with many infections, such as Tokyo and Osaka, will reduce the infection risk in the daytime in those regions.…”

“…[9][10][11][12][13][14][15][16] Effective control measures that prevent spatial spread of SARS-CoV-2 are urgently demanded, and how NPIs such as travel restrictions and social distancing mitigate the epidemic must be investigated. [17][18][19][20][21][22][23][24][25][26][27] The present study aims to provide meaningful implications for combating the COVID-19 pandemic through interregional mobility restrictions.…”

How to go viral: A COVID-19 model with endogenously time-varying parameters

“…In general, one would expect a greater level of social distancing when cases increase (e.g. Glaeser et al (2020) ) due to an endogenous response from both households and governments. On the other hand, a higher number of cases mechanically increases the positive test rate if the number of tests remains constant.…”

“…In contrast, we estimate the dependence of parameters on these predictors jointly with the rest of the model. Finally, numerous papers have used microeconometric methods that make use of differences across localities (e.g., Almagro and Orane-Hutchinson, 2020 , Desmet and Wacziarg, 2020 , Glaeser et al, 2020 ). By incorporating the panel structure, we similarly utilize variation across both states and time to determine how social distancing and testing affect the path of the virus.…”