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Who Do You Trust? The Consequences of Partisanship and Trust for Public Responsiveness to COVID-19 Orders
Published online by Cambridge University Press: 16 April 2021
Abstract
Non-uniform compliance with public policy by citizens can undermine the effectiveness of government, particularly during crises. Mitigation policies intended to combat the novel coronavirus offer a real-world measure of citizen compliance, allowing us to examine the determinants of asymmetrical responsiveness. Analyzing county-level cellphone data, we leverage staggered roll-out to estimate the causal effect of stay-at-home orders on mobility using a difference-in-differences strategy. We find movement is significantly curtailed, and examination of descriptive heterogeneous effects suggests the key roles that partisanship and trust play in producing irregular compliance. We find that Republican-leaning counties comply less than Democratic-leaning ones, which we argue underlines the importance of trust in science and acceptance of large-scale government policies for compliance. However, this partisan compliance gap shrinks when directives are given by Republican leaders, suggesting citizens are more trusting of co-partisan leaders. Furthermore, we find that higher levels of social trust increase compliance; yet these gains attenuate or intensify depending upon community-level partisan sentiments. Our study provides a real-world, behavioral measure that demonstrates the influence of partisanship, social trust, and their interaction on citizen welfare. Finally, we argue that our results speak to how trust in government may impact successful containment of the COVID-19 pandemic.
- Type
- Special Issue Articles: Pandemic Politics
- Information
- Perspectives on Politics , Volume 20 , Issue 2: Special Issue: Pandemic Politics , June 2022 , pp. 412 - 438
- Copyright
- © The Author(s), 2021. Published by Cambridge University Press on behalf of the American Political Science Association
Footnotes
A list of permanent links to Supplemental Materials provided by the authors precedes the References section.
Data replication sets are available in Harvard Dataverse at: https://doi.org/10.7910/DVN/CEJAHP
References
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