"background": "Power distribution networks in many developing nations face significant challenges with equipment reliability and yield losses, leading to frequent outages and economic inefficiencies. There is a pressing need for robust methodological frameworks to evaluate system performance and identify targeted interventions. ", "purpose and objectives": "This study aimed to develop and apply a quasi-experimental design to methodologically evaluate power-distribution equipment systems, with the objective of quantifying yield improvements from a targeted equipment upgrade programme. ", "methodology": "A quasi-experimental, difference-in-differences design was employed, comparing technical losses in treatment and control groups of substations before and after the installation of new generation distribution transformers and associated switchgear. The core statistical model was specified as Y{it = \0 + \1 + \2 + \3 (\) +, where inference was based on cluster-robust standard errors. ", "findings": "The intervention produced a statistically significant reduction in average technical losses. The estimated causal effect of the equipment upgrade was a 4. 7 percentage point decrease in losses (95% CI: 3. 1 to 6. 3), representing a 22% relative improvement from the baseline mean in the treatment group. ", "conclusion": "The methodological approach successfully isolated the impact of equipment modernisation, demonstrating that targeted upgrades are a potent mechanism for improving distribution yield. The quasi-experimental design proved valid for causal inference in a complex field setting. ", "recommendations": "Utilities should adopt similar rigorous evaluation frameworks for capital projects. Investment should be prioritised towards the specific equipment types validated in this study, with rollout strategies informed by the experimental grouping logic. ", "key words": "power distribution, technical losses, quasi-experiment, difference-in-differences, causal inference, field study", "contribution statement": "This paper provides a novel application
Nalwanga et al. (2023) studied this question.