Abstract
{ "background": "The performance of transport maintenance depot systems is critical for preserving road infrastructure and mitigating risks of asset failure in developing economies. However, rigorous quantitative evaluation of such systems' impact on infrastructure risk remains methodologically underdeveloped.", "purpose and objectives": "This working paper develops and applies a quasi-experimental econometric model to quantify the causal effect of a reformed maintenance depot system on reducing engineering risk in Tanzania's transport infrastructure network.", "methodology": "A difference-in-differences (DiD) model is employed, using panel data from treated and control road sections before and after depot system intervention. The core model is $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\cdot \\text{Post}t) + \\epsilon{it}$, where $Y{it}$ is a composite risk index. Inference is based on cluster-robust standard errors at the depot catchment level.", "findings": "The analysis indicates a statistically significant reduction in the composite risk index for road sections serviced by the reformed depot system. The average treatment effect, $\\delta$, is estimated at -0.18 (95% CI: -0.27 to -0.09), representing an approximate 15% reduction in measured infrastructure risk relative to control sections.", "conclusion": "The reformed depot system demonstrates a measurable, positive effect on reducing transport infrastructure risk. The DiD approach provides a robust methodological framework for evaluating engineering maintenance interventions where randomised controlled trials are not feasible.", "recommendations": "Implement the DiD model as a standard evaluation tool for future infrastructure maintenance programmes. Policy should prioritise the scaling of depot system reforms to regions with higher baseline risk profiles, informed by the model's catchment-level analysis.", "key words": "infrastructure maintenance, difference-in-differences, risk reduction, transport engineering, quasi-experimental design, asset management",