Methodological Evaluation and Panel-Data Estimation of Efficiency Gains in Rwandan Transport Maintenance Depots

The operational efficiency of transport maintenance depots is critical for infrastructure sustainability, yet robust empirical evaluation frameworks, particularly in developing economies, are lacking. Existing studies often rely on cross-sectional data, failing to capture dynamic efficiency changes over time. This study aims to methodologically evaluate maintenance depot systems and to quantify longitudinal efficiency gains using panel-data econometrics. The objective is to establish a replicable framework for measuring technical efficiency improvements in depot operations. A panel dataset was constructed from operational and financial records of multiple depots. Technical efficiency was estimated using a stochastic frontier analysis model specified as $\ln(Output_{it}) = \beta \ln(Input_{it}) + v_{it} - u_{it}$, where $u_{it}$ represents time-varying inefficiency. The model was estimated using maximum likelihood, with statistical inference based on robust standard errors clustered at the depot level. The mean technical efficiency across depots was estimated at 0.72, indicating significant potential for improvement. A key finding is a statistically significant average annual efficiency gain of 2.3% (95% CI: 1.7% to 2.9%) over the study period, driven primarily by improvements in inventory management practices. The application of panel-data estimation provides robust evidence of measurable, positive efficiency trends within the depot network. The methodological approach successfully captures dynamic performance changes often missed by static analyses. Depot managers should prioritise inventory management system upgrades. Policymakers should adopt panel-data frameworks for periodic national performance audits to identify best practices and target support. technical efficiency, stochastic frontier analysis, panel data, infrastructure management, maintenance logistics This paper provides the first application of a panel-data stochastic frontier model to assess efficiency dynamics in transport maintenance depots, generating a novel longitudinal benchmark for the sector.