Methodological Evaluation and Panel-Data Estimation for Cost-Effectiveness in Nigerian Power-Distribution Systems

Persistent inefficiencies and high capital costs in power-distribution networks hinder reliable electricity supply. A rigorous, data-driven framework for evaluating the cost-effectiveness of equipment systems is required to inform infrastructure investment. This paper aims to develop and apply a panel-data econometric methodology to assess the cost-effectiveness of different power-distribution equipment systems within the operational context of Nigeria, identifying the most economically efficient configurations. A fixed-effects panel-data model is estimated using operational and financial data from multiple distribution companies. The core specification is $C_{it} = \alpha_i + \beta_1 T_{it} + \beta_2 V_{it} + \beta_3 (T_{it} \times V_{it}) + \epsilon_{it}$, where $C$ is normalised maintenance cost, $T$ is transformer type, and $V$ is voltage level. Robust standard errors are clustered at the company level. Estimates indicate that composite conductor systems with polymer insulators are associated with a 17.5% reduction in annualised maintenance costs compared to traditional bare conductor with pin-type insulator systems (95% CI: 12.1% to 22.9%). The interaction between transformer type and voltage level was statistically significant. The methodological approach provides a robust empirical basis for comparing distribution equipment. The results demonstrate significant variations in the life-cycle cost performance of different technical systems. Utilities should prioritise investment in the identified higher-performance equipment systems. Regulatory frameworks should incorporate panel-data cost-effectiveness analyses into technical standards and capital approval processes. power distribution, cost-effectiveness, panel data, fixed effects, infrastructure, Nigeria This paper provides a novel application of panel-data econometrics to power-distribution equipment evaluation in Nigeria, generating specific, evidence-based rankings for engineering decision-making.