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Trust predicts compliance to Covid-19 containment policies: evidence from ten countries using big data

Francesco Sarracino, Talita Greyling (), Kelsey O'Connor (), Chiara Peroni () and Stephanie Rossouw ()

Department of Economics University of Siena from Department of Economics, University of Siena

Abstract: Previous evidence indicates that trust is an important correlate of compliance with Covid-19 containment policies. However, this conclusion hinges on two crucial assumptions: first, that compliance does not change over time, and second, that mobility and self-reported measures are good proxies for compliance. We demonstrate that compliance changes over the period March 2020 to January 2021, in ten mostly European countries, and that increasing (decreasing) trust in others predicts increasing (decreasing) compliance. We develop the first time-varying measure of compliance, which is calculated as the association between containment policies and people’s mobility behavior using data from Oxford Policy Tracker and Google. We also develop new measures of both trust in others and national institutions by applying sentiment analysis to Twitter data. We test the predictive role of trust using a variety of dynamic panel regression techniques. This evidence indicates compliance should not be taken for granted and confirms the importance of cultivating social trust.

Keywords: compliance; covid-19; trust; big data; Twitter. (search for similar items in EconPapers)
JEL-codes: D91 H12 I18 (search for similar items in EconPapers)
Date: 2021-06
New Economics Papers: this item is included in nep-big and nep-hea
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