Tracking Public and Private Responses to the COVID-19 Epidemic: Evidence from State and Local Government Actions

By Sumedha Gupta, Thuy Nguyen, Felipe Rojas, Shyam Raman, Byungkyu Lee, A. Bento, Kosali Simon, Coady Wing. 2020

286 citations recorded by the source corpus.

This paper examines the determinants of social distancing during the COVID-19 epidemic. We classify state and local government actions, and we study multiple proxies for social distancing based on data from smart devices. Mobility fell substantially in all states, even ones that have not adopted major distancing mandates. There is little evidence, for example, that stay-at-home mandates induced distancing. In contrast, early and information-focused actions have had bigger effects. Event studies show that first case announcements, emergency declarations, and school closures reduced mobility by 1-5% after 5 days and 7-45% after 20 days. Between March 1 and April 11, average time spent at home grew from 9.1 hours to 13.9 hours. We find, for example, that without state emergency declarations, event study estimates imply that hours at home would have been 11.3 hours in April, suggesting that 55% of the growth comes from emergency declarations and 45% comes from secular (non-policy) trends. State and local government actions induced changes in mobility on top of a large response across all states to the prevailing knowledge of public health risks. Early state policies conveyed information about the epidemic, suggesting that even the policy response mainly operates through a voluntary channel.

Subfields: Clinical Psychology, Economics and Econometrics, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/e025b75f4006b197f2adb422a781eb5af1cde9f7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

R. Groot
M. Pradhan
A. Abajobir
H. Donfouet
E. Sidze
W. Janssens
M. Ravallion

Citations in the corpus

Using Difference-in-Differences to Identify Causal Effects of COVID-19 Policies (2020, unknown): “At least five recent papers use DD methods to show that non-pharmaceutical interventions reduce interactions, infections, or deaths (Dave et al., 2020; Fang et al., 2020; Friedson et al., 2020; Gupta et al., 2020; Hsiang et al., 2020).”
The short-term economic effects of COVID-19 on low-income households in rural Kenya: An analysis using weekly financial household data (2021, background): “In the US, the lockdown policy reduced time spent outside the home (Gupta et al., 2020), contributed to a significant decline in employment (Montenovo, Jiang, Rojas, Schmutte, Simon, & Weinberg, 2020), and caused a large drop in job vacancy postings (Kahn, Lange, & Wiczer, 2020).”
Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020 (2020, unknown): “Papers using employment and jobs data include Forsythe et al. (2020), Rojas et al. (2020), Bartik et al. (2020), Bartik et al. (2020), Gupta et al. (2020a), and Aum et al. (2020).”