A Difference-in-Differences Modelling Framework for Evaluating Power-Distribution Equipment Adoption in Kenya, 2000–2026

{ "background": "Evaluating the impact of infrastructure programmes on the adoption of new engineering technologies, such as advanced power-distribution equipment, requires robust quasi-experimental designs. Many existing methods struggle to isolate causal effects from concurrent policy changes and regional development variations.", "purpose and objectives": "This article presents a methodological framework for quantifying the causal effect of a national electrification programme on the adoption rates of modern distribution transformers and switchgear. The objective is to provide a replicable model for engineers and planners to assess technology uptake.", "methodology": "A difference-in-differences (DiD) modelling framework is specified. The core statistical model is $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, where $Y{it}$ is the adoption rate in county $i$ at time $t$. Inference relies on cluster-robust standard errors at the county level to account for serial correlation.", "findings": "As this is a methodology article, no empirical results from the application are reported. The framework's application to simulated data indicates that the model can detect a statistically significant average treatment effect on the treated (ATT) of 15–20 percentage points in adoption rates when key identifying assumptions are met.", "conclusion": "The proposed DiD framework provides a rigorous, transparent methodology for evaluating the efficacy of engineering and policy interventions aimed at accelerating the deployment of critical power-grid assets.", "recommendations": "Practitioners applying this method must rigorously test the parallel trends assumption using pre-intervention data and consider staggered adoption designs. Future work should integrate spatial econometric techniques to account for network interdependencies.", "key words": "difference-in-differences, causal inference, power distribution, technology adoption, quasi-experimental design, infrastructure evaluation", "contribution statement": "This paper provides a novel