Sabina Ampon – Wireko is a distinguish researcher and lecturer, where her research focuses on Health care expenditure, health outcome and economic development in the emerging economies. She has published several papers in reputed Journals. She is currently pursuing a Ph.D studies at Jiangsu University, School of Management.  



The global industrialization has experienced an unimaginable increase in life expectancy rate. Yet, precise study of impacts of health insurance on life expectancy rate remains at the edge of extant studies. Though several researchers have struggled to reveal the forces that drive the increase in Life Expectancy Rate among the emerging countries. Most of these studies tends to concentrate on interplay between health insurance, perception and accessibility of health care and utilized ordinary econometric models such as vector autoregression which for some parts prompts, however, inconsistent results and conclusions. Hence, there is an exigent need for a precise study of health insurance effect on life expectancy rate whilst applying robust econometric approaches and including all the important variables in the health care literature. This help to provide robust results and advances the debate for appropriate policy formulation and guidelines to improve health care accessibility especially in emerging countries. This study, therefore, seeks to investigate the causal effect of social health insurance on life expectancy rate among emerging economies using a robust and recent econometric approach such as Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Squares (DOLS) and Dumitrescu-Hurlin Granger causality. The study empanel and test an ensemble of a group of vital variables predominant in recent studies on health insurance-life expectancy interplay,  economic growth, physician to population ratio, health care expenditure, and literacy rate. The study results showed that Dumitrescu-Hurlin Granger and Fully Modified Ordinary Least Square (FMOLS) techniques provide accurate statistical inference regarding the direction of the causality among the variables than the conventional method such as OLS and vector error correction (VECM) Granger Causality predominantly used in the literature as it stronger and provide accurate statistical inference.