Health Spending Efficiency and Workforce Productivity in OECD Economies: Evidence from Explainable Machine Learning

Sworup Kumar Singha
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Sworup Kumar Singha: Research Assistant, GSTU, Bangladesh

Journal of Scientific Reports, 2026, vol. 13, issue 1, 76-92

Abstract: This study examines the relationship between health spending efficiency and workforce productivity using an explainable machine learning framework applied to five advanced economies over the period 2000–2022. Using data from the World Development Indicators, the analysis incorporates health expenditure, health outcomes, demographic, and macroeconomic factors. Ensemble machine learning models are employed to capture nonlinear relationships, with Gradient Boosting exhibiting the strongest predictive performance. Model interpretability is ensured through SHapley Additive exPlanations (SHAP). The results indicate that higher health expenditure and health expenditure per capita are associated with greater workforce productivity, while increased out-of-pocket health spending and adverse health outcomes are negatively associated with workforce productivity. Evidence of nonlinear and interaction effects suggests that productivity gains from health investment depend on efficiency and structural conditions. Overall, the findings highlight the importance of well-structured health investment for sustaining productivity in advanced economies.

Keywords: Health spending efficiency; Workforce productivity; Explainable machine learning; Health expenditure; SHAP; Advanced economies; World Development Indicators (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:aif:report:v:13:y:2026:i:1:p:76-92

DOI: 10.58970/JSR.1174

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