Methodological Evaluation and Yield Improvement Diagnostics for Kenyan Water Treatment Facilities: A Difference-in-Differences Model

{ "background": "Water treatment facilities in Kenya face persistent challenges in operational efficiency and yield. A systematic, quantitative methodology for evaluating the impact of specific interventions on plant performance is lacking, hindering evidence-based engineering management and investment.", "purpose and objectives": "This data descriptor presents a methodological framework and a novel, curated dataset designed to enable the rigorous evaluation of yield improvement interventions in water treatment facilities. The primary objective is to provide a robust analytical model and associated data structure for performance diagnostics.", "methodology": "A quasi-experimental difference-in-differences (DiD) model is proposed, formalised as $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\cdot \\text{Post}t) + \\epsilon{it}$, where $Y{it}$ is the yield for facility $i$ at time $t$. The dataset comprises panel data on key performance indicators, including raw and treated water volumes, chemical usage, and energy consumption, from multiple facilities before and after targeted interventions. Inference is based on cluster-robust standard errors.", "findings": "The application of the DiD model to the provided dataset isolates the causal effect of interventions from secular trends. A diagnostic analysis of a representative subset indicates that interventions targeting coagulation-flocculation processes were associated with a yield improvement of approximately 7.3 percentage points (95% CI: 5.1, 9.5).", "conclusion": "The developed methodology and dataset offer a novel, evidence-based tool for engineering performance evaluation. The DiD framework effectively controls for confounding temporal variations, providing a clearer attribution of yield changes to specific management or technical actions.", "recommendations": "Adoption of this DiD modelling approach is recommended for facility audits and regulatory assessments. Future data collection should standardise metrics according to this schema to facilitate broader comparative analysis and benchmarking across the sector.", "key