Science Skepticism Reduces Compliance with COVID-19 Shelter-in-Place Policies

Name: Science Skepticism Reduces Compliance with COVID-19 Shelter-in-Place Policies
Published: 2021-07-12
License: https://opensource.org/licenses/MIT

Principal Investigator(s): View help for Principal Investigator(s) Austin L. Wright, University of Chicago. Harris School of Public Policy; Valentin Kecht, Bocconi University; Adam Brzezinski, University of Oxford; David Van Dijcke, University of Michigan. College of Literature, Science, and the Arts

Version: View help for Version V1

Name	File Type	Size	Last Modified
openICPSR_12072021.zip	application/zip	1 GB	07/12/2021 05:20:AM

Project Citation:

Wright, Austin L., Kecht, Valentin , Brzezinski, Adam, and Van Dijcke, David. Science Skepticism Reduces Compliance with COVID-19 Shelter-in-Place Policies. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-07-12. https://doi.org/10.3886/E144861V1

Project Description

Summary: Physical distancing reduces transmission risks and slows the spread of COVID-19. Yet compliance with shelter-in-place policies issued by local and regional governments in the United States is uneven and may be influenced by science skepticism and attitudes towards topics of scientific consensus. Using county-day measures of physical distancing derived from cellphone location data, we demonstrate that the proportion of people who stay at home after shelter-in-place policies go into effect is significantly lower in counties with a high concentration of science skeptics. These results are robust to controlling for other potential drivers of differential physical distancing, such as political partisanship, income, education and COVID severity. Our findings suggest public health interventions that take local attitudes toward science into account in their messaging may be more effective.

Scope of Project

Subject Terms: economics; political economy; political science; social sciences; science

Geographic Coverage: United States of America

Time Period(s): 3/1/2020 – 4/19/2020

Collection Date(s): 5/1/2020 – 7/1/2020

Universe: Mobile devices of individuals across the United States, as collected by Safegraph https://www.safegraph.com/, a data company that aggregates anonymized location data from numerous

Data Type(s): aggregate data; census/enumeration data; geographic information system (GIS) data; program source code; survey data

Methodology

Data Source: SafeGraph (https://safegraph.com), various government sources, additional open data.

Weights: County population.

Unit(s) of Observation: United States county

Geographic Unit: United States county

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V1 [2021-07-12]

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