A matter of trust? Political trust and the COVID-19 pandemic

Abstract

There is significant cross-country variation in COVID-19 fatalities worldwide. In this study, we analyze the relationship between political trust and fatalities from the COVID-1s9 pandemic. By performing a cross-country analysis and controlling for other determinants, we find that government trust is negatively associated with the excess mortality rate during the pandemic.

Keywords:

• COVID-19
• pandemic
• trust
• lockdown
• cross-country regression
• voluntary compliance

Acknowledgments

We appreciate the helpful comments and suggestions of Markus Hadler (Editor), two anonymous reviewers, Sven Fischer and Jhoana Ocampo.

Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on request.

Notes

1 One recent notable study is Robinson et al. (2021) which focuses on the role of confidence in government and support for relevant self-protective actions and policies during the COVID-19 pandemic in the case of the United States. The authors use a nationwide survey of U.S. adults. Their descriptive and bivariate analyses show that trust is a critical factor in reducing the human costs of COVID-19 “as it is related to the intention to adopt personal protective actions and support for key public policies.”

2 This is based on data from World Value Survey, Wave 7, 2017–2020 and is the share of respondents who replied: a great deal of confidence or quite a lot of confidence in the government.

3 The data can be accessed under the following link: https://github.com/dkobak/excess-mortality/blob/main/excess-mortality.csv

4 A few recent studies have also investigated the role of populism in COVID-19 pandemic. For examples, see the studies of Bayerlein et al. (2021) and Cepaluni, Dorsch, and Dzebo (2021a).

5 Interquartile range (IQR) = Q3 − Q1. The IQR for the significant independent variables in Table 2 slightly differs from the shown IQR in Table 1 due to a smaller amount of observations.