Assessing food insecurity under global risks using a clustered pooled quantile framework for sustainable development goal 2

Abstract Achieving Sustainable Development Goal 2 remains a global priority, yet progress is increasingly constrained by interconnected political, demographic, economic, and environmental risks. Most studies estimate average effects, leaving limited evidence on how these systemic risks are associated with food insecurity across different vulnerability contexts. This study examines the heterogeneous associations between multidimensional risk factors and food insecurity, measured by the prevalence of undernourishment, across 120 countries from 2001 to 2022. Composite indices are constructed to capture institutional, demographic, environmental, and economic risks. A two-stage empirical strategy combines K-means clustering to classify countries into three food insecurity profiles—high, moderate, and low—with pooled quantile regression to estimate heterogeneous associations and assess the relative contribution of each risk dimension within clusters. Environmental risk emerges as the most consistent factor associated with undernourishment across all clusters. Demographic risk is positively associated with food insecurity in highly and moderately insecure countries but negatively associated with food insecurity in more secure contexts, while institutional quality is associated with lower food insecurity in moderate- and low-risk clusters. Economic risk is significant only at the upper quantile of the most vulnerable group, revealing asymmetric, quantile-specific associations not captured by mean regression and calling for severity-specific rather than uniform policy interventions.