Examines health spending effects on under-five mortality inequality in Indonesia, indicating targeting issues for disadvantaged groups.
While public health expenditure is widely assumed to reduce health inequalities, empirical evidence from low- and middle-income countries remains limited. This study examines whether increases in public health spending in Indonesia between 2010 and 2017 reduced inequalities in under-five mortality (U5MR). We combined two approaches to assess distributional effects of health spending. First, we used a dynamic panel System-GMM model to estimate the impact of public health expenditure on within-province inequality in U5MR, measured by the concentration index across household wealth quintiles. Second, we applied instrumental variable quantile regression to identify heterogeneous treatment effects across provinces with different baseline mortality burdens. In addition, we estimated the average effect of public health expenditure on U5MR levels and found no statistically significant impact. The results show that higher public health spending increased inequality in U5MR across socioeconomic groups, with wealthier households capturing a disproportionate share of the mortality reductions. By contrast, we find no statistically significant heterogeneity in the impact of spending between high- and low-mortality provinces. These results suggest that increases in spending were not well-targeted to disadvantaged groups or high-mortality regions. Our findings demonstrate that raising overall levels of public spending is not sufficient to reduce health inequalities unless resources are explicitly directed toward disadvantaged groups and underserved areas. This study provides a proof of concept for applying distribution-sensitive methods to assess the equity impact of health financing, offering lessons that remain highly relevant for Indonesia’s post-JKN reforms and contributing to global debates on equity-weighted evaluation, benefit incidence, and the distributional consequences of health spending in LMICs.
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Ochoa-Moreno et al. (2026) studied this question.
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