A Difference-in-Differences Model for the Cost-Effectiveness Evaluation of Transport Maintenance Depot Systems in Rwanda

{ "background": "Evaluating the cost-effectiveness of transport maintenance depot systems in low-resource settings is a persistent challenge for infrastructure asset management. Traditional before-and-after comparisons are often confounded by external factors, leading to unreliable estimates of intervention impact.", "purpose and objectives": "This article presents a methodological framework for the rigorous evaluation of transport maintenance depot systems. The primary objective is to detail the application of a difference-in-differences (DiD) model to isolate the causal effect of system upgrades on maintenance costs and vehicle availability.", "methodology": "The methodology constructs a quasi-experimental design using panel data from treatment and control groups of depots. The core statistical model is specified as $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, where $Y{it}$ is the outcome (e.g., cost per kilometre), and $\\delta$ is the average treatment effect. Inference relies on cluster-robust standard errors to account for serial correlation.", "findings": "As a methodology article, this paper presents no empirical results from a completed study. However, the framework demonstrates, through a simulated application, that the DiD estimator can detect a hypothesised 15% reduction in unit maintenance costs, with the 95% confidence interval for $\\delta$ excluding zero under plausible assumptions of within-depot correlation.", "conclusion": "The proposed DiD model provides a robust, quasi-experimental methodology for evaluating infrastructure management interventions, effectively controlling for time-invariant unobserved heterogeneity and common temporal trends across depots.", "recommendations": "Practitioners should adopt this DiD framework for ex-post evaluation of depot system upgrades, ensuring data collection is designed to establish suitable control groups and to capture key cost and operational metrics over a sufficient time horizon.", "key words": "Difference-in