Abstract
{ "background": "The expansion and modernisation of power-distribution networks in sub-Saharan Africa require robust, evidence-based frameworks for evaluating equipment investments. Existing life-cycle cost analyses often lack longitudinal rigour, particularly in dynamic operational environments.", "purpose and objectives": "This case study aims to develop and apply a panel-data econometric model to assess the cost-effectiveness of prevalent power-distribution equipment systems, providing a methodological framework for infrastructure investment planning.", "methodology": "A balanced panel dataset was constructed from utility operational and financial records. Cost-effectiveness was modelled using a fixed-effects estimator: $C{it} = \\alphai + \\beta1T{it} + \\beta2F{it} + \\beta3A{it} + \\epsilon_{it}$, where $C$ is total annualised cost per unit, $T$ is technology type, $F$ is failure rate, and $A$ is asset age. Inference is based on cluster-robust standard errors.", "findings": "The analysis indicates a statistically significant cost premium for certain imported equipment systems, with a mean increase of 17.3% in total annualised cost compared to locally serviced alternatives (95% CI: 12.1% to 22.5%). This was not offset by sufficiently lower failure rates over the study period.", "conclusion": "The panel-data approach provides a more nuanced evaluation of long-term cost-effectiveness than static models, revealing that procurement decisions based solely on initial capital cost are suboptimal.", "recommendations": "Utilities should integrate longitudinal cost-tracking into asset management systems. Procurement policies must mandate life-cycle cost analysis using methodologies that account for operational heterogeneity over time.", "key words": "power distribution, panel data, cost-effectiveness, asset management, econometric modelling, infrastructure", "contribution statement": "This study provides a novel application of panel-data econometrics to power infrastructure evaluation in a sub-Saharan context, generating a replicable model that isolates the impact of equipment