Bayesian Hierarchical Modeling of Multi-Level Policy Diffusion: A Spatiotemporal Calibration Framework for Cross-National Governance Transfer

Abstract

Contemporary governance scholarship increasingly demands methodological precision in tracing how policy instruments migrate across institutional boundaries. This study develops a Bayesian hierarchical calibration framework for modeling spatiotemporal diffusion of regulatory and legislative innovations across multi-level governance architectures. Drawing on panel data spanning 47 democracies (2005–2022), we integrate Markov Chain Monte Carlo (MCMC) estimation with spatial lag decomposition to disentangle vertical intergovernmental transmission from horizontal isomorphic adoption. Our results demonstrate that prior administrative capacity and veto-player configurations serve as primary moderators of diffusion velocity, while supranational conditionality amplifies second-order spillover effects. The framework significantly outperforms conventional event-history and fixed-effects specifications in predictive accuracy. These findings offer actionable calibration benchmarks for comparative governance analysts and international policy transfer practitioners operating under complex institutional interdependence.