Methodological Evaluation and Panel-Data Estimation for Yield Improvement in Rwanda's Power-Distribution Infrastructure, 2000–2026

{ "background": "Power-distribution infrastructure in many developing nations faces persistent challenges with technical losses and reliability, constraining economic development. A rigorous, data-driven methodology for evaluating equipment performance and forecasting yield improvements is required for effective infrastructure investment.", "purpose and objectives": "This study aims to develop and apply a panel-data econometric model to evaluate methodological approaches for assessing power-distribution equipment and to estimate the yield improvement trajectory of the national infrastructure.", "methodology": "A balanced panel dataset of technical and operational parameters from primary substations and medium-voltage feeders was constructed. The core analysis employs a fixed-effects model: Yit = \ + \β Xit + \ + \εit, where Yit is the technical loss rate, Xit is a vector of time-varying covariates including load factor and equipment age, \ denotes entity-specific effects, and \δₜ represents time effects. Estimation uses robust standard errors clustered at the feeder level.", "findings": "The model indicates a statistically significant negative relationship between targeted infrastructure investment and technical losses, with a coefficient of -0.15 (95% CI: -0.21 to -0.09). This translates to a projected aggregate yield improvement of approximately 8.7 percentage points over the evaluation period, driven predominantly by transformer upgrades and conductor replacement programmes.", "conclusion": "The panel-data approach provides a robust methodological framework for evaluating distribution infrastructure, confirming that systematic capital investment is a principal driver of yield enhancement. The model offers a reliable tool for long-term planning.", "recommendations": "Utilities should adopt panel-data methodologies for asset-performance tracking. Planning should prioritise investment in ageing transformer fleets and high-loss feeders, as identified by the model, to maximise yield gains.", "key words": "power distribution, technical losses, panel data, fixed-effects model, infrastructure yield, asset management", "contribution statement": "This paper provides a novel application