TY - JOUR
T1 - Trust predicts compliance with COVID-19 containment policies
T2 - Evidence from ten countries using big data
AU - Sarracino, Francesco
AU - Greyling, Talita
AU - O'Connor, Kelsey J.
AU - Peroni, Chiara
AU - Rossouw, Stephanie
N1 - Publisher Copyright:
© 2024
PY - 2024/8
Y1 - 2024/8
N2 - We use Twitter, Google mobility, and Oxford policy data to study the relationship between trust and compliance over the period March 2020 to January 2021 in ten, mostly European, countries. Trust has been shown to be an important correlate of compliance with COVID-19 containment policies. However, the previous findings depend upon two assumptions: first, that compliance is time invariant, and second, that compliance can be measured using self reports or mobility measures alone. We relax these assumptions by calculating a new time-varying measure of compliance as the association between containment policies and people's mobility behavior. Additionally, we develop measures of trust in others and national institutions by applying emotion analysis to Twitter data. Results from various panel estimation techniques demonstrate that compliance changes over time and that increasing (decreasing) trust in others predicts increasing (decreasing) compliance. This evidence indicates that compliance changes over time, and further confirms the importance of cultivating trust in others.
AB - We use Twitter, Google mobility, and Oxford policy data to study the relationship between trust and compliance over the period March 2020 to January 2021 in ten, mostly European, countries. Trust has been shown to be an important correlate of compliance with COVID-19 containment policies. However, the previous findings depend upon two assumptions: first, that compliance is time invariant, and second, that compliance can be measured using self reports or mobility measures alone. We relax these assumptions by calculating a new time-varying measure of compliance as the association between containment policies and people's mobility behavior. Additionally, we develop measures of trust in others and national institutions by applying emotion analysis to Twitter data. Results from various panel estimation techniques demonstrate that compliance changes over time and that increasing (decreasing) trust in others predicts increasing (decreasing) compliance. This evidence indicates that compliance changes over time, and further confirms the importance of cultivating trust in others.
KW - Big Data
KW - COVID-19
KW - Twitter
KW - compliance
KW - trust
UR - http://www.scopus.com/inward/record.url?scp=85199297933&partnerID=8YFLogxK
U2 - 10.1016/j.ehb.2024.101412
DO - 10.1016/j.ehb.2024.101412
M3 - Article
AN - SCOPUS:85199297933
SN - 1570-677X
VL - 54
JO - Economics and Human Biology
JF - Economics and Human Biology
M1 - 101412
ER -