Early Evidence on Social Distancing in Response to COVID-19 in the United States
By Martin S. Andersen. Social Science Research Network, 2020
223 citations recorded by the source corpus.
The COVID-19 pandemic continues to grow in the United States and, in the absence of a vaccine or effective treatment, social distancing measures are essential to slow the spread of this disease. Using cellular mobility data from 2019 and 2020, I demonstrate that there have been substantial increases in social distancing since the start of the pandemic. Rates of voluntary, as opposed to mandatory, social distancing varies by county characteristics, including partisanship, media consumption, and racial and ethnic composition. Mandatory measures to increase social distancing appear to be effective, most notably stay at home orders which increase the share of devices at home by 2 percentage points. Social distancing orders also appear to have substantial informational content and, in the case of mask mandates, the informational content appears to be greater than the gross effect of mask mandates on behavior. These results provide insight into the importance of communicating the threat posed by COVID-19, since most changes in social distancing appear to be voluntary, plausibly reflecting beliefs about disease risk.
Subfields: Clinical Psychology, Economics and Econometrics, Modeling and Simulation
View Paper: https://www.semanticscholar.org/paper/841892856ec9877a238a38c41ed2eb3c905fc98f
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In Economics and Econometrics
Citations in the corpus
- Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic (2020, unknown): “…Concern over Spread of Coronavirus B) Behavior Change from Coronavirus 1 Coverage in the media and some studies examine partisan heterogeneity in response to COVID-19 with no or few controls for differential risk exposure or costs of social distancing (e.g., Economist, 2020; Andersen, 2020).”
- Mapping county-level mobility pattern changes in the United States in response to COVID-19 (2020, background): “have utilized various sources (e.g., Google mobility reports) of mobile location big data to quantify mobility change patterns and its relation to COVID-19 spread, air pollution, and economic impacts [3, 7, 22, 10, 13, 9]. In the following, we first introduce the data sources and the design principle”