No observational data are used or claimed; the analysis characterises the design's power to detect threshold effects under a declared variance structure with an intraclass correlation of 0.64, between-cluster standard deviation of 6.0 and residual standard deviation of 4.5. Power was estimated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design achieves 80 per cent power at 56 clusters for a 5 percentage point effect. The credibility of regression discontinuity estimates in this setting depends less on the fee threshold itself than on the clustering of farmer decisions within wards, and that the intraclass correlation of 0.64 imposes a sample size requirement that most single-city permit evaluations cannot meet. The contribution is a design-based framework for distinguishing threshold effects from ward-level confounding in urban biosolids governance.
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Okot et al. (2026) studied this question.
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