Methodological Evaluation and Cost-Effectiveness Analysis of Senegal's Power-Distribution Equipment Using a Difference-in-Differences Model

"background": "The evaluation of power-distribution infrastructure in developing economies often lacks robust, quantitative frameworks for assessing the cost-effectiveness of equipment upgrades and interventions. This creates challenges for evidence-based investment planning and maintenance strategies within the utility sector. ", "purpose and objectives": "This Data Descriptor presents a methodological framework and a novel, curated dataset designed to enable the rigorous evaluation of power-distribution equipment in Senegal. The primary objective is to provide a replicable model for measuring the cost-effectiveness of technical interventions, supporting data-driven decision-making in electrical engineering and utility management. ", "methodology": "The core analytical approach is a difference-in-differences (DiD) model, specified as Y{it = \0 + \1 + \2 + \ (\) +, where Yit is the cost-effectiveness metric for unit i at time t. The dataset comprises panel data on equipment performance, failure rates, maintenance, and capital costs, aggregated from utility records and field surveys. Inference is based on robust standard errors clustered at the feeder level. ", "findings": "The application of the DiD model to the provided dataset reveals a statistically significant positive treatment effect, indicating that targeted equipment upgrades improved cost-effectiveness. A key concrete result is an estimated average treatment effect on the treated (ATT) of 18. 5% reduction in total cost per unit of reliable energy delivered, with a 95% confidence interval of 14. 2%, 22. 8%. ", "conclusion": "The developed methodology and accompanying dataset establish a valid and practical framework for the econometric evaluation of power-distribution equipment investments. The DiD model effectively isolates the causal impact of interventions from underlying trends. ", "recommendations": "Utility engineers and planners should adopt similar panel data structures and quasi-experimental evaluation techniques for capital project appraisal