Purpose: The purpose of this paper is to explore the drivers of economic growth in South Asia region for the period of 1975–2016 using the World Bank data.Design/methodology/approach: Panel corrected standard error (static estimation) approach and one-step system generalised method of moments (dynamic estimation) approach are used.Findings: Both the static and dynamic estimations indicate that energy use, gross capital formation and remittances are the main drivers of economic growth in South Asian countries. The effects of all these variables are positive and significant. The extent of the effect of energy use is much higher than that of other two variables on the economic growth. A 1 per cent increase in the growth of energy consumption can expedite the gross domestic product growth by approximately 3 per cent in South Asia. However, the key variables, such as trade, government expenditure and foreign direct investment demonstrate no significant effect.Originality/value: The current research is original in the sense that it investigated the issue with a new data set using improved econometric techniques. Moreover, in South Asia as a whole, this kind of study is totally absent, particularly with panel data of a large number of years. Furthermore, this study has taken into account the problem of heterogeneity and the biases created by cross-section dependence, which were mostly absent in previous studies. Therefore, the findings of this research are new contributions to the existing literature.

This paper provides empirical evidence on how the relationship between health expenditure and health outcomes varies across countries at different income levels. Heterogeneity and cross-section dependence were controlled for in the panel data which consist of 161 countries over the period 1995–2014. Infant, under-five and maternal mortality along with life expectancy at birth were selected as health outcome measures. Cross-sectional augmented IPS unit root, panel autoregressive distributed lag, Dumitrescu-Hurlin and Toda-Yamamoto approach to Granger causality tests were used to investigate the relationship across four income groups. An impulse response function modelled the impact on health outcomes of negative shocks to health expenditure. The results indicate that the health expenditure and health outcome link is stronger for low-income compared to high-income countries. Moreover, rising health expenditure can reduce child mortality but has an insignificant relationship with maternal mortality at all income levels. Lower-income countries are more at risk of adverse impact on health because of negative shocks to health expenditure. Variations in child mortality are better explained by rising health expenditure than maternal mortality. However, the estimated results showed dissimilarity when different assumptions and methods were used. The influence of health expenditure on health outcome varies significantly across different income levels except for maternal health. Policymakers should recognize that increasing spending has a minute potential to improve maternal health. Lastly, the results vary significantly due to income level, choice of assumptions (homogeneity, cross-section independence) and estimation techniques used. Therefore, findings of the cross-country panel studies should be interpreted with cautions.

Background: This study aimed to examine the healthcare-seeking (hospital, primary and preventive care) and healthcare utilisation behaviour of patients with private health insurance (PHI) in Australia. It also aimed to examine the socioeconomic, demographic and lifestyle factors that influence the choice of hospital care in Australia. Method: A logistic regression model with repeated measure t-test and Pearson’s Chi-square test were used to identify the factors that affect the choice of care. Data from waves 9 (2009) and 13 (2013) of the nationally-representative Household, Income and Labour Dynamics in Australia (HILDA) survey were used in the analysis. Results: Patients with PHI had a higher number of hospital nights’ stay despite having a lower number of hospital admissions than those without private cover. Significant disparities were identified in preventive and specialist care use between patients with cover and without cover. No significant variations were observed in healthcare utilisation for PHI patients before and after dropping PHI. One in four patients chose to use public hospitals despite holding PHI cover. Moreover, those insured and from lower socioeconomic backgrounds and those who were younger and without long-term health conditions showed a higher probability of selecting public rather than private care. Conclusions: It is evident that PHI cover encourages people to use private care. However, a considerable number of PHI patients are using public care, even though eligible for private care may indicate consumer information asymmetry.