Latent Regimes in Sustainable Development Performance: The Roles of Digital Divides and Governance Quality

Global progress towards the 2030 Sustainable Development Goals (SDGs) remains critically off-track, with current trends suggesting that only 17% of targets will be met by the 2030 deadline. This study investigates whether observed divergence reflects temporary setbacks or persistent structural regimes characterised by distinct configurations. Using panel data from over 160 countries (2019–2024), we employ annual latent class analysis to identify hidden structures in SDG performance across 15 goals, introducing intertemporal volatility as a dimension of development dynamics. We complement this with ordered logistic regression to examine structural determinants of regime membership, including governance quality, digital infrastructure, health investment, and macroeconomic indicators. Our analysis identifies three temporally stable development regimes —lagging, transitional, and leading — with fewer than 15% of countries transitioning between classes over the observation period. ANOVA results reveal that internet access and government effectiveness exhibit the most considerable between-regime differences. Ordered logit models indicate that governance quality and digital connectivity are the primary predictors of regime membership, with marginal effects of 18–19 percentage points in regime probability. In contrast, short-term GDP growth exerts a negligible influence. These findings challenge linear convergence assumptions and suggest that achieving the SDGs requires addressing deep structural constraints, particularly digital divides and institutional quality, rather than relying solely on incremental policy adjustments or economic growth.