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

Abstract

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.