Replication and Methodological Evaluation of Water Treatment Facility Adoption in Kenya: A Difference-in-Differences Analysis

{ "background": "The original study employed a difference-in-differences (DiD) model to assess the adoption rates of community-scale water treatment facilities following a national infrastructure programme. Its methodological approach has been influential in engineering policy evaluation, yet its robustness to alternative specifications and data handling assumptions remains untested.", "purpose and objectives": "This replication study aims to methodologically evaluate the original DiD analysis, verifying the robustness of its core findings and examining the sensitivity of its estimated adoption effects to alternative modelling choices and data construction protocols.", "methodology": "We conducted a computational reproducibility check and a series of robustness tests. The core DiD model is specified as $Y{it} = \\beta0 + \\beta1 (Treati \\times Postt) + \\gammai + \\deltat + \\epsilon{it}$, where $Y_{it}$ is the adoption rate. We re-estimated the model using cluster-robust standard errors at the sub-county level and tested sensitivity to alternative fixed effects structures and sample definitions.", "findings": "The replication confirmed the original study's primary finding of a positive, statistically significant programme effect. However, the effect magnitude was sensitive to specification: the point estimate of the adoption rate increase varied from 15 to 22 percentage points depending on the fixed effects specification, with the original estimate lying at the upper end of this range. Statistical significance was robust across all sensitivity checks.", "conclusion": "While the original conclusion of a positive causal effect is supported, the estimated magnitude is less definitive. This underscores the importance of methodological transparency and robustness checks in engineering impact evaluations.", "recommendations": "Future engineering policy evaluations using DiD should pre-register analysis plans, explicitly report multiple specifications to demonstrate robustness, and justify the chosen level for clustering standard errors. Sensitivity analyses should be a standard component of reporting.", "key words": "replication study, difference-in-differences, robustness, water treatment, infrastructure adoption, Kenya, engineering policy", "cont