Efficiency of Health Systems in Sub-Sahara Africa: A Comparative Analysis of Time Varying Stochastic Frontier Models
Keywords:
Efficiency, Health systems, health expenditure, Sub-Saharan Africa, Stochastic Frontier Analysis, 'True' random effectAbstract
The purpose of the current study was to estimate efficiency of health systems in sub-Sahara Africa (SSA) and to compare efficiency estimates from various time-varying frontier models. The study used data for 45 countries in SSA from 2005 to 2011 sourced from the Word Bank World Development Indicators. Parametric time-varying stochastic frontier models were used in the analysis. Infant survival rate was used as the outcome variable, while per-capita health expenditure was used as main controllable input. The results show some variations in efficiency estimates among the various models. Estimates from the ‘true’ random effect model were however preferable after controlling for unobserved heterogeneity which was captured in the inefficiency terms of the other frontier models. The results also suggest a wide variation in the efficiency of health systems in sub-Sahara Africa. On average health system efficiency was estimated to be approximately 0.80 which implies resource wastage of about 0.20. Cape Verde, Mauritius and Tanzania were estimated to be relatively efficient while Angola, Equatorial Guinea and Sierra Leone were among the least performers in terms of health system efficiency. The findings suggest that the omission of unobserved heterogeneity may lead to bias in estimated inefficiency. The ‘true’ random effect model was identified to address the problem of unobserved heterogeneity. The findings also suggest a generally poor performance of health systems in terms of efficiency in the use of resources. While resource commitment to the health sector is critical, it is important to also ensure the efficient use of these resources. Improving the performance of institutions in the health sector may go a long way in improving the general health status of the African population.
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