scholarly journals THE EFFECTS OF ECONOMIC VARIABLES ON HEALTH EXPENDITURE PER CAPITA AND LIFE EXPECTANCY AT BIRTH: PANEL DATA ANALYSIS FOR MIDDLE TOP INCOME COUNTRIES

Author(s):  
Osman ŞENOL ◽  
Durmuş GÖKKAYA ◽  
Ümit ÇIRAKLI
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mwoya Byaro

Abstract Background This commentary assesses critically the published article in the Health Economics Review. 2020; 10 (1), 1–9. It explains the effects of health expenditure on infant mortality in sub-Saharan Africa using a panel data analysis (i.e. random effects) over the year 2000–2015 extracted from the World Bank Development Indicators. The paper is well written and deserve careful attention. Main text The main reasons for inaccurate estimates observed in this paper are due to endogeneity issue with random effects panel estimators. It occurs when two or more variables simultaneously affect/cause each other. In this paper, the presence of endogeneity bias (i.e. education, health, health care expenditures and real GDP per capita variables) and its omitted variable bias leads to inaccurate estimates and conclusion. Random effects model require strict exogeneity of regressors. Moreover, frequentist/classic estimation (i.e. random effects) relies on sampling size and likelihood of the data in a specified model without considering other kinds of uncertainty. Conclusion This comment argues future studies on health expenditures versus health outcomes (i.e. infant, under-five and neonates mortality) to use either dynamic panel (i.e. system Generalized Method of Moments, GMM) to control endogeneity issues among health (infant or neonates mortality), GDP per capita, education and health expenditures variables or adopting Bayesian framework to adjust uncertainty (i.e. confounding, measurement errors and endogeneity of variables) within a range of probability distribution.


2020 ◽  
Author(s):  
Nirajana Banerjee ◽  
Ritojeet Basu ◽  
Ananya De ◽  
Monalisa Poali

Context: Life expectancy best helps to capture the health and well-being of a population. However, wide differences are seen in life expectancies across different countries and at different points of time.Research objective: This study examines trends in global inequalities in Life Expectancy at birth between countries from 1960 to 2017, and studies how income inequality affects inequality in life expectancy.Data and methodology: Life expectancy at birth is the main variable under study. We have also used data on GDP per capita at purchasing power parity, Health Expenditure per capita and Government Expenditure on health per capita. Six measures of inequality have been used, primary being the Gini coefficient. We have divided the countries into four groups according to the World Bank Classification Scheme, and have used graphs, choropleth maps and Moran’s I for statistical analysis. The causal relationship between inequality in Life Expectancy and per capita GDP is examined using reduced form regression models.Findings: The life expectancy at birth shows a steadily rising trend. The choropleth maps indicate considerable spatial variations in life expectancy, which are stable over time. There is a decline in the global inequality in life expectancy over time. Moreover, the decomposition analysis for the Gini coefficient shows that in any year, the between groups inequality is more important than the within groups inequality. This is corroborated by the decomposition of Gini over time. We infer from the devised regression models that there is a positive association between per capita income inequality and inequality in life expectancy. The regressions point out evidence that inequality in lifespan and inequality in per capita income can be explained in terms of inequality in health expenditure.


2017 ◽  
Vol 15 (6) ◽  
pp. 773-783 ◽  
Author(s):  
Davide Golinelli ◽  
Fabrizio Toscano ◽  
Andrea Bucci ◽  
Jacopo Lenzi ◽  
Maria Pia Fantini ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document