Magnitude and Determinants of Mortalities Related to COVID-19: Evidence from 94 Countries Using Regression Techniques

Abstract

This study attempts to investigate the factors determining COVID-19 deaths during the pandemic across countries by employing a rich dataset sourced from 94 countries updated till 6 February, 2022. For empirical analysis, the study makes use of cross-sectional linear regression technique in the first part and after required diagnostic tests use 2SLS regression technique for correcting possible endogeneity bias in the second part. Findings from the study indicate that factors like total reported cases, population size, population over 70 years of age, extreme poverty, and human development index play significant role in determining COVID-19-related death. Further, to check the robustness of the findings the present study employed LASSO regression. Findings from the study highlight the possibility of government intervention to devise appropriate policies to control COVID-related incidence and death.

Does External Debt Promote Human Longevity in Developing Countries? Evidence from West African Countries

“…Empirically, the study showed that unsustainable, illiquid, and insolvent external debt and macroeconomic volatility shorten longevity in West African countries. Hence, longevity will decline when weak external debt management promotes poverty not just in West Africa, but in other developing countries as reported in empirical studies such as Loko et al 2003 ; Saungweme and Mufandaedga 2013 ; Zaghdoudi and Hakim 2017 ; Ma et al 2022 ; Pradhan et al 2022 ).…”

Untangling the factors influencing finger millet production: Evidence from the Indian states

“…We use this variable to check the impact of the human development indicator on finger millet production. The human development index is the geometric mean of the three indicators, namely life expectancy, educational attainment ratio and per capita income (Pradhan et al, 2022). An improvement in the human development indicator is expected to enhance the standard of living of the people.…”

Islands, remoteness and effective policy making: Aotearoa New Zealand during the COVID‐19 pandemic

“…Several common findings emerge from Table 1. Firstly, areas with older populations and related demographic characteristics (such as high life expectancy) have higher COVID‐19 morbidity and/or mortality than younger ones (Andersen et al, 2021; Cutrini & Salvati, 2021; Ozyilmaz et al, 2022; Pradhan et al, 2022; Teixeira da Silva & Tsigaris, 2020; Valero & Valero‐Gil, 2021; Wang et al, 2021). The same is the case for populations with high risk factors such as obesity, smoking and disabilities (Andersen et al, 2021; Cao et al, 2020; Leffler et al, 2020; Ozyilmaz et al, 2022; Wagschal, 2022; Wang et al, 2021).…”

“…The results are more ambiguous with respect to other commonly considered factors. Several studies suggest that greater population size and density increase COVID‐19 morbidity and mortality (Cao et al, 2020; Cutrini & Salvati, 2021; Hamidi et al, 2020; Pradhan et al, 2022), while others find the effect of population density to be statistically insignificant (Hamidi et al, 2020; Kozlovskyi et al, 2021; Pradhan et al, 2022). Similarly, the effects of various indicators of human development (such as gross domestic product [GDP] per capita, strength of democracy, equity or a composite index such as the Human Development Index [HDI]) are not always clear.…”

“…Studies such as Chang et al (2022) find that some of these factors (e.g., democracy, GDP per capita, life satisfaction) are positively associated with COVID‐19 disease prevalence, while other‐but‐related factors (e.g., education, religious diversity, female leadership, strength of legal system) dampen the impact of the pandemic. Kozlovskyi et al (2021), Pradhan et al (2022) and Wagschal (2022) also find that development (GDP per capita or HDI) assists in mitigating the impact of the pandemic, but Leffler et al (2020) find that disease prevalence is positively correlated with GDP per capita. McCann et al (2022) find that this variable is statistically insignificant.…”