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Effects of corruption and income inequality on the reported number of COVID-19 cases and deaths: Evidence from a time series cross-sectional data analysis
Corruption-income inequality nexus is likely to affect the healthcare services, which in turn affect a country’s ability to suppress an epidemic. Widespread corruption in public sectors may influence the data inventory practices to control the recording and sharing of official statistics to avoid po...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10022113/ https://www.ncbi.nlm.nih.gov/pubmed/36962690 http://dx.doi.org/10.1371/journal.pgph.0001157 |
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author | Khan, Atikur R. Abedin, Sumaiya Rahman, Md. Mosiur Khan, Saleheen |
author_facet | Khan, Atikur R. Abedin, Sumaiya Rahman, Md. Mosiur Khan, Saleheen |
author_sort | Khan, Atikur R. |
collection | PubMed |
description | Corruption-income inequality nexus is likely to affect the healthcare services, which in turn affect a country’s ability to suppress an epidemic. Widespread corruption in public sectors may influence the data inventory practices to control the recording and sharing of official statistics to avoid political disturbance or social problems caused by an epidemic. This empirical study examines the effects of income inequality, data inventory, and universal healthcare coverage on cross-country variation in reported numbers of COVID-19 cases and deaths in the presence of corruption in public sectors. Daily numbers of COVID-19 cases and deaths of selected 29 countries are integrated for the first 120 days of the epidemic in each country. COVID-19 dataset is then integrated with a dataset of different indices. Fixed effect panel model is applied to explore the effects of corruption perception, income inequality, open data inventory practice, and universal health coverage on the daily numbers of COVID-19 cases and deaths per million. Income inequality, corruption perception and open data inventory are found to significantly affect the number of confirmed cases and deaths. Countries with alarming income inequality are found to report 39.89 more COVID-19 cases per million, on average. Under a lower level of corruption, countries with lower level of open data inventory are expected to report 74.31 more COVID-19 cases but 1.43 less deaths per million. Given a higher level of corruption, countries with lower level of open data inventory are expected to report lower number of COVID-19 cases and deaths. Corruption demonstrates a significant influence on the size of the epidemic in terms of the number of COVID-19 cases and deaths. A country with higher level of corruption in public sector along with lower levels of open data inventory is expected to report lower number of COVID-19 cases and deaths. |
format | Online Article Text |
id | pubmed-10022113 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-100221132023-03-17 Effects of corruption and income inequality on the reported number of COVID-19 cases and deaths: Evidence from a time series cross-sectional data analysis Khan, Atikur R. Abedin, Sumaiya Rahman, Md. Mosiur Khan, Saleheen PLOS Glob Public Health Research Article Corruption-income inequality nexus is likely to affect the healthcare services, which in turn affect a country’s ability to suppress an epidemic. Widespread corruption in public sectors may influence the data inventory practices to control the recording and sharing of official statistics to avoid political disturbance or social problems caused by an epidemic. This empirical study examines the effects of income inequality, data inventory, and universal healthcare coverage on cross-country variation in reported numbers of COVID-19 cases and deaths in the presence of corruption in public sectors. Daily numbers of COVID-19 cases and deaths of selected 29 countries are integrated for the first 120 days of the epidemic in each country. COVID-19 dataset is then integrated with a dataset of different indices. Fixed effect panel model is applied to explore the effects of corruption perception, income inequality, open data inventory practice, and universal health coverage on the daily numbers of COVID-19 cases and deaths per million. Income inequality, corruption perception and open data inventory are found to significantly affect the number of confirmed cases and deaths. Countries with alarming income inequality are found to report 39.89 more COVID-19 cases per million, on average. Under a lower level of corruption, countries with lower level of open data inventory are expected to report 74.31 more COVID-19 cases but 1.43 less deaths per million. Given a higher level of corruption, countries with lower level of open data inventory are expected to report lower number of COVID-19 cases and deaths. Corruption demonstrates a significant influence on the size of the epidemic in terms of the number of COVID-19 cases and deaths. A country with higher level of corruption in public sector along with lower levels of open data inventory is expected to report lower number of COVID-19 cases and deaths. Public Library of Science 2022-11-15 /pmc/articles/PMC10022113/ /pubmed/36962690 http://dx.doi.org/10.1371/journal.pgph.0001157 Text en © 2022 Khan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Khan, Atikur R. Abedin, Sumaiya Rahman, Md. Mosiur Khan, Saleheen Effects of corruption and income inequality on the reported number of COVID-19 cases and deaths: Evidence from a time series cross-sectional data analysis |
title | Effects of corruption and income inequality on the reported number of COVID-19 cases and deaths: Evidence from a time series cross-sectional data analysis |
title_full | Effects of corruption and income inequality on the reported number of COVID-19 cases and deaths: Evidence from a time series cross-sectional data analysis |
title_fullStr | Effects of corruption and income inequality on the reported number of COVID-19 cases and deaths: Evidence from a time series cross-sectional data analysis |
title_full_unstemmed | Effects of corruption and income inequality on the reported number of COVID-19 cases and deaths: Evidence from a time series cross-sectional data analysis |
title_short | Effects of corruption and income inequality on the reported number of COVID-19 cases and deaths: Evidence from a time series cross-sectional data analysis |
title_sort | effects of corruption and income inequality on the reported number of covid-19 cases and deaths: evidence from a time series cross-sectional data analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10022113/ https://www.ncbi.nlm.nih.gov/pubmed/36962690 http://dx.doi.org/10.1371/journal.pgph.0001157 |
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