Effect of Healthcare Financing on Poverty Levels Among East Africa Member Countries Caroline

Abstract

The relationship between healthcare financing and poverty levels can be viewed as a dynamic interplay between two fundamental components of societal well-being. Adequate healthcare financing acts as both a safeguard against the financial pitfalls of illness and a catalyst for economic productivity. When individuals have access to affordable healthcare, the risk of incurring substantial medical expenses that could lead to poverty diminishes. Conversely, impoverished communities often grapple with limited access to healthcare, creating a scenario where untreated illnesses can deepen economic hardship. This research objective was to determine the effect of healthcare financing on poverty levels among East Africa member countries. Grounded in the health capital model and supported by the social determinants of health theory and health production function theory, the research adopted a descriptive research design. The target population comprised the seven East Africa member countries, and secondary data spanning a 10-year period (2013 to 2022) was sourced from the Central banks of the member countries and the World Bank. Descriptive, correlation, and regression analyses are employed in analyzing the data, with a focus on the natural logarithm of annual government expenditure on health (healthcare financing), GDP growth rate, unemployment rate, and inflation rate as predictor variables, and the Multidimensional Poverty Index as the response variable. The correlation analysis indicates a negative correlation between poverty levels and healthcare financing (r = - .511, p = .000) and GDP growth rate (r = -.658, p = .000), emphasizing the pivotal role of these variables in poverty reduction. Conversely, the unemployment rate and inflation rate show no statistically significant correlations with poverty levels. The fixed-effects regression model further confirms the significance of healthcare financing (coefficient = -0.0775, p = 0.001) and GDP growth rate (coefficient = -0.0190, p = 0.001) in influencing poverty levels, while the unemployment rate and inflation rate exhibit non-significant coefficients. The overall fit of the model is robust, with an Rsquared value of 0.5740, indicating that approximately 57.40% of the variance in poverty levels is explained by the included variables. In conclusion, this study underscores the critical importance of healthcare financing and economic growth in shaping poverty dynamics within East Africa. Policymakers are encouraged to prioritize sustained investments in healthcare infrastructure and stimulate economic development as primary strategies for poverty alleviation. For further research, this study suggests adopting a longitudinal approach to capture temporal dynamics, exploring subnational variations to understand regional disparities, and incorporating qualitative methods to gain deeper insights.