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Death takes no bribes: Impact of perceived corruption on the effectiveness of non-pharmaceutical interventions at combating COVID-19.
Alfano, Vincenzo; Capasso, Salvatore; Ercolano, Salvatore; Goel, Rajeev K.
Afiliação
  • Alfano V; Department of Economics, University of Messina, Italy; Center for Economic Studies - CES-ifo, Germany. Electronic address: vincenzo.alfano@unina.it.
  • Capasso S; Institute for Mediterranean Studies - ISMed, Italy; Department of Economic and Business Studies, University of Napoli Parthenope, Italy.
  • Ercolano S; Department of Mathematics, Computer Science and Economics, University of Basilicata, Italy.
  • Goel RK; Department of Economics, Illinois State University, United States; Innovation and International Competition, Kiel Institute for the World Economy, Germany.
Soc Sci Med ; 301: 114958, 2022 05.
Article em En | MEDLINE | ID: mdl-35413528
Corruption is considered in the literature as an activity with several externalities and spillover effects. Adding to the recent research on the corruption-COVID-19 nexus, we study the impact of corruption on coronavirus cases. High perceived levels of corruption have been proven to lead to lower institutional trust, and hence possibly to lower levels of citizen compliance with non-pharmaceutical interventions (NPIs), such as lockdowns, imposed by the authorities during the first wave of the pandemic to reduce the spread of coronavirus. Applying quantitative analysis with the use of hybrid models, we find that in countries with higher levels of perceived corruption, across alternative corruption measures, more COVID-19 cases are observed, ceteris paribus. This suggests that corruption has a detrimental effect on the spread of COVID-19, and that countries experiencing higher levels of corruption should pay extra attention when implementing NPIs.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article