"background": "Municipal infrastructure asset systems in many developing nations face chronic inefficiencies, yet robust, longitudinal methods for quantifying their operational performance are scarce. Existing evaluations often rely on cross-sectional data, failing to capture dynamic efficiency changes and the impact of management practices over time. ", "purpose and objectives": "This study aims to develop and apply a panel-data econometric framework to evaluate the technical efficiency of municipal infrastructure systems. The objective is to measure longitudinal efficiency gains, identify key determinants of performance, and methodologically advance asset management assessment within an engineering context. ", "methodology": "A stochastic frontier analysis (SFA) model was specified for an unbalanced panel of municipal asset systems. The core efficiency estimation model is \ (Output{it) = \ \ (Inputit) + vit - uit, where vit represents random noise and uit \ 0 denotes technical inefficiency. Inefficiency effects were modelled as a function of institutional and maintenance variables. Estimation used maximum likelihood with robust standard errors clustered at the municipal level. ", "findings": "The mean technical efficiency score across the period was estimated at 0. 58 (95% CI: 0. 52, 0. 64), indicating significant room for improvement. A one-standard-deviation increase in the preventative maintenance expenditure ratio was associated with a 7. 3 percentage point increase in efficiency. The null hypothesis of no time-varying efficiency was rejected at the 1% significance level. ", "conclusion": "The application of panel-data SFA provides a rigorous, evidence-based methodology for benchmarking infrastructure asset system performance. Results confirm that efficiency is not static and is significantly influenced by maintenance strategies. ", "recommendations": "Municipal engineers and asset managers should adopt panel-data benchmarking to track performance trends. Policy should prioritise ring-fenced funding for preventative maintenance, as it is a statistically significant driver of sustained efficiency gains. ", "key words": "stochastic frontier analysis, infrastructure asset
Okello et al. (2025) studied this question.