Income-Level Heterogeneity in the Sustainable Development–Human Development Nexus: Evidence from Machine Learning

Human development is increasingly expected to reflect progress in health, education, living conditions, and sustainability. Yet evidence on how specific Sustainable Development Indicators (SDIs) relate to such progress remains limited, especially in studies that jointly consider cross-income heterogeneity, high-dimensional indicators, and nonlinear relationships. This study examines the SDI–HDI relationship across low-, lower-middle-, upper-middle-, and high-income countries using 408 World Bank SDG indicators and UNDP HDI series for 1990–2020. An interpretable Random Forest framework, combined with SHAP rankings and Partial Dependence Plots, identifies the most influential predictors and marginal associations with HDI. The model shows strong predictive performance across income groups and marked heterogeneity in the predictors associated with HDI. In low-income countries, HDI is mainly associated with early-life health conditions and human capital; in lower-middle-income countries, electrification and service access become more prominent; and in upper-middle- and high-income groups, digital connectivity, higher education, and institutional factors gain importance. Mortality-related indicators are consistently associated with lower predicted HDI, whereas literacy, electricity access, and internet use are associated with higher HDI. These results highlight how AI-based analytical tools can support sustainable economic development by identifying income-specific development priorities and structural constraints. They also suggest that disparities in health, education, infrastructure, and digital connectivity may influence the conditions under which entrepreneurial opportunities emerge or remain constrained across development stages. Overall, the SDI–HDI relationship is nonlinear and income-specific, supporting more differentiated, data-driven development strategies.