{ "background": "The systematic evaluation of transport maintenance depot performance in developing economies is hindered by a lack of robust, data-driven methodologies. Current assessments often rely on cross-sectional data, which fails to account for unobserved heterogeneity and temporal dynamics, leading to potentially biased estimates of cost-effectiveness.", "purpose and objectives": "This article presents a novel methodological framework designed to measure the cost-effectiveness of transport maintenance depots. Its primary objective is to provide a replicable analytical procedure using panel-data econometrics to isolate true efficiency gains from observed cost and output data.", "methodology": "The framework employs a fixed-effects panel-data model to control for time-invariant unobserved depot characteristics. Cost-effectiveness is modelled as a function of operational outputs, input prices, and technological change. The core estimation equation is $C{it} = \\alphai + \\beta'X{it} + \\deltat + \\epsilon{it}$, where $C{it}$ is cost, $\\alphai$ are depot-specific fixed effects, $X{it}$ is a vector of outputs and inputs, and $\\delta_t$ are time dummies. Inference is based on cluster-robust standard errors to account for serial correlation.", "findings": "As a methodology article, this paper presents analytical findings rather than empirical results. The framework demonstrates that omitting depot-specific fixed effects leads to a substantial overestimation of the impact of scale on cost reduction, by approximately 22%, when applied to simulated data reflecting typical depot operations. The direction of this bias underscores the necessity of the panel approach.", "conclusion": "The proposed panel-data estimation framework provides a statistically rigorous and operationally relevant method for assessing the cost-effectiveness of maintenance depots. It successfully addresses key limitations of prior cross-sectional approaches by controlling for unobserved heterogeneity.", "recommendations": "Researchers and practitioners should adopt panel-data designs for infrastructure performance analysis. Future applications should seek to compile longitudinal datasets encompassing financial, operational, and asset condition variables to fully leverage the framework.",