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Autor:innen: 
Adamecz-Völgyi, Anna
Szabó-Morvai, Ágnes
Datum: 
2021
Schriftenreihe/Nr.: 
GLO Discussion Paper No. 861
Verlag: 
Global Labor Organization (GLO), Essen
Zusammenfassung: 
This paper investigates the relative importance of confidence in public institutions to explain cross-country differences in the severity of the COVID-19 pandemic. We extend the related literature by employing regression and machine learning methods to identify the most critical predictors of deaths attributed to the pandemic. We find that a one standard deviation increase (e.g., the actual difference between the US and Finland) in confidence is associated with 350.9 fewer predicted deaths per million inhabitants. Confidence in public institutions is one of the most important predictors of deaths attributed to COVID-19, compared to country-level measures of individual health risks, the health system, demographics, economic and political development, and social capital. Our results suggest that effective policy implementation requires citizens to cooperate with their governments, and willingness to cooperate relies on confidence in public institutions.
Schlagwörter: 
COVID-19
death rate
confidence in public institutions
machine learning
JEL: 
I18
P16
Dokumentart: 
Working Paper

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