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<p>This study investigates the non-linear impact of institutional and economic determinants on health outcomes using Artificial Neural Networks. Grounded in the Grossman Health Production Function and political economy frameworks—including Modernization, Gender Stratification, and World-Systems theories—the research focuses on 37 OECD countries (1993–2018). Moving beyond traditional linear estimations, the ANN models prioritize democratic maturity and global integration as primary structural predictors, while treating labor market stability and income growth as socioeconomic controls. Results indicate that the Globalization and Democracy indices are the most decisive predictors of life expectancy and infant mortality, capturing the high sensitivity of vulnerable populations to institutional quality and external shocks. The study demonstrates that machine learning effectively captures complex, non-linear threshold effects that conventional econometric models overlook. These findings suggest that public health resilience in developed economies depends on the synergy between transparent institutions and structural openness rather than aggregate economic growth alone.</p>

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Socio-political determinants of health in OECD countries: a non-linear analysis of globalization, democratization, and economic indicators using artificial neural networks

  • 期刊论文

  • 发表日期：

    2026年7月25日

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  • Ömer Taylan,
  • İsmail Biçer,
  • Yaşar Turna,
  • Cuma Çakmak

摘要

This study investigates the non-linear impact of institutional and economic determinants on health outcomes using Artificial Neural Networks. Grounded in the Grossman Health Production Function and political economy frameworks—including Modernization, Gender Stratification, and World-Systems theories—the research focuses on 37 OECD countries (1993–2018). Moving beyond traditional linear estimations, the ANN models prioritize democratic maturity and global integration as primary structural predictors, while treating labor market stability and income growth as socioeconomic controls. Results indicate that the Globalization and Democracy indices are the most decisive predictors of life expectancy and infant mortality, capturing the high sensitivity of vulnerable populations to institutional quality and external shocks. The study demonstrates that machine learning effectively captures complex, non-linear threshold effects that conventional econometric models overlook. These findings suggest that public health resilience in developed economies depends on the synergy between transparent institutions and structural openness rather than aggregate economic growth alone.