A Multilevel Regression Diagnostics Framework for Water Treatment Efficiency Gains in Tanzania, 2000–2026

Persistent inefficiencies in water treatment infrastructure undermine public health and economic development across many regions. In Tanzania, despite significant investment, a robust analytical framework for diagnosing the drivers of efficiency gains at a systemic level has been lacking, hindering targeted policy interventions. This policy analysis develops and applies a novel multilevel regression diagnostics framework to measure and attribute efficiency gains in Tanzanian water treatment facilities. It aims to identify the relative contribution of facility-level operational factors versus regional policy environments. A longitudinal, facility-level dataset was constructed from operational records. The core analytical model is a three-level hierarchical linear model: yᵢⱼₜ = β₀ + β₁Xᵢⱼₜ + uⱼ + vₖ + εᵢⱼₜ, where i, j, and k index facilities, districts, and regions, respectively. Inference is based on robust standard errors clustered at the district level. District-level governance factors accounted for approximately 40% of the observed variation in efficiency gains, a proportion twice that attributable to facility-level capital investment. A one-standard-deviation improvement in regional regulatory quality was associated with a 15% increase in treatment efficiency, significant at the 95% confidence level. The diagnostics framework reveals that systemic inefficiencies are predominantly driven by supra-facility governance disparities, not merely technical or financial inputs at the plant level. Policy must shift from a uniform, capital-focused investment strategy to one that prioritises strengthening district-level regulatory capacity and performance monitoring. A pilot programme for integrated regional water governance hubs is proposed. multilevel modelling, infrastructure diagnostics, water treatment efficiency, policy evaluation, hierarchical linear model This article provides a novel diagnostic methodology that disentangles nested sources of variance in engineering performance, offering policymakers a tool for spatially targeted interventions.