Methodological Evaluation of Power-Distribution Equipment Systems in Uganda: A Difference-in-Differences Model for Risk Reduction

{ "background": "Power-distribution infrastructure in many developing nations faces significant reliability and safety challenges. There is a pressing need for robust, quantitative methodologies to evaluate the effectiveness of equipment interventions aimed at mitigating technical risks and improving system performance.", "purpose and objectives": "This study aimed to develop and apply a quasi-experimental econometric model to rigorously assess the impact of a nationwide equipment retrofit programme on the frequency of safety-critical faults within Uganda's power-distribution network.", "methodology": "A difference-in-differences (DiD) model was employed, analysing panel data from treated and control substations. The core specification is Yit = \β0 + \β1 + \β2 + \δ ( \× ) + \εit, where Yit is the fault rate. Inference was based on cluster-robust standard errors to account for serial correlation.", "findings": "The retrofit programme caused a statistically significant reduction in the mean fault rate. The DiD estimator, \δ, was -0.18 faults per substation-month (95% CI: -0.24 to -0.12), representing a 22% reduction relative to the control group's pre-intervention mean.", "conclusion": "The methodological application confirms the efficacy of the targeted equipment interventions. The DiD framework provides a credible, transferable model for quantifying risk reduction in infrastructure systems where randomised controlled trials are impractical.", "recommendations": "Utilities should adopt quasi-experimental evaluation designs for capital programme appraisal. Future interventions should prioritise the specific equipment types associated with the largest observed risk reductions.", "key words": "difference-in-differences, power distribution, infrastructure risk, econometric evaluation, quasi-experimental design, fault reduction", "contribution statement": "This paper provides a novel application of the DiD model to evaluate engineering safety