%0 Report
        %A Nie, Peng
%A Wang, Lu
%A Dragone, Davide
%A Lu, Haiyang
%A Sousa-Poza, Alfonso
%A Ziebarth, Nicolas R.
        %T "The Better You Feel, the Harder You Fall": Health Perception Biases and Mental Health among Chinese Adults during the COVID-19 Pandemic
        %D 2021
        %8 2021 Dec
        %I Institute of Labor Economics (IZA)
        %C Bonn
        %7 IZA Discussion Paper
        %N 14905
        %U https://www.iza.org/index.php/publications/dp14905
        %X The health risks of the current COVID-19 pandemic, together with the drastic mitigation measures taken in many affected nations, pose an obvious threat to public mental health. The social science literature has already established a clear link between mental health and sociodemographic as well as economic factors, and a growing number of studies investigate the role of biased risk perceptions. To assess this role in the context of COVID-19, this study first implements survey-based measures of over- and underconfidence in the health self-perceptions among Chinese adults during the pandemic. Then, it analyzes their relation to three mental health outcomes: life satisfaction, happiness, and depression (as measured by the CES−D). 
We show that the health overconfidence displayed by approximately 30% of the survey respondents is a clear risk factor for mental health problems; it is a statistically significant predictor of depression and low levels of happiness and life satisfaction. We also document that these effects are stronger in regions that experienced higher numbers of confirmed COVID-19 cases and deaths. Recent research has shown that health overconfidence can influence risky behaviors such as smoking and excessive alcohol consumption, which may be particularly detrimental during a pandemic. Our results also offer clear guidelines for the implementation of effective interventions to temper overconfidence, particularly in uncontrollable situations like the COVID-19 pandemic.
        %K China
%K mental health
%K health perception bias
%K overconfidence
%K underconfidence
%K COVID-19