Abstract
BackgroundThis 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 textThe 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.ConclusionThis 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.
Highlights
The main reasons for inaccurate estimates observed in this paper are due to endogeneity issue with random effects panel estimators
To address the aforementioned weakness encountered in Kiross et al [4], the use of panel dynamic system Generalized method of moments (GMM) would be preferred to overcome endogeneity and its omitted variable bias present among health, real Gross domestic product (GDP) per capita, education and health expenditures variables
The use of Bayesian framework would be important for capturing the uncertainty of health expenditures on infant mortality in SubSahara Africa (See, [7])
Summary
Economics Review, 10 (5) by Kiross et al [4], using macro data, relying on frequentist/classic methods. In other words; education, GDP per capita, health (i.e. infant or neonates mortality) and health expenditures variables in regression models may be correlated with the error term This endogeneity bias can cause inconsistent estimates leading to misleading conclusion. Based on the outlined facts above, it is clear that Kiross et al [4] conclusion that, increasing government’s health care financing over the years will be crucial in reducing mortality and improving health outcomes in sub-Sahara Africa still falls under uncertainty This uncertainty may occur due to the failure of random effects models to control endogeneity and other omitted variables bias
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