"background": "Municipal infrastructure asset systems in many developing nations face chronic inefficiencies, yet robust, data-driven diagnostic tools for policy evaluation are scarce. Existing assessments often rely on deterministic models that fail to account for spatial heterogeneity and inherent data uncertainty. ", "purpose and objectives": "This policy analysis develops and applies a novel Bayesian hierarchical model to diagnose the technical efficiency of municipal infrastructure systems. It aims to quantify efficiency gains over a multi-year period and identify systemic drivers to inform national asset management policy. ", "methodology": "A Bayesian hierarchical stochastic frontier model is employed, formally specified as y{it = f (xit; \) \ (vit - uit), where uit \ ^+ (, \²) and = Z'it\. This structure explicitly models inefficiency uit as a function of municipal-level covariates Z₈ₓ. Posterior distributions are estimated using Hamiltonian Monte Carlo. ", "findings": "The analysis reveals a positive but spatially uneven trend in aggregate system efficiency, with a mean annual improvement of approximately 1. 7% (95% credible interval: 1. 2% to 2. 3%). Municipalities with integrated digital asset registers demonstrated significantly lower posterior uncertainty in their efficiency estimates. ", "conclusion": "The Bayesian hierarchical framework provides a statistically robust mechanism for benchmarking infrastructure performance, directly quantifying uncertainty for policymakers. The results demonstrate measurable, though variable, progress in system efficiency. ", "recommendations": "National policy should mandate the standardised collection of asset condition and expenditure data to feed periodic efficiency diagnostics. Investment should be prioritised towards building municipal capacity in data management, as this covariate is strongly linked to more reliable performance estimates. ", "key words": "Infrastructure asset management, stochastic frontier analysis, technical efficiency, public policy, uncertainty quantification", "contribution statement": "This paper introduces a novel application of Bayesian hierarchical modelling
Mwangi et al. (Sat,) studied this question.