The study is methodological and theoretical: no observational data are used or claimed. The analysis characterises the before-after-control-impact estimator under a declared variance structure with between-cluster standard deviation of 6.0, residual standard deviation of 4.5, and intraclass correlation of 0.640. Power was estimated across 2000 design cells defined by 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 to detect a 5 percentage point effect with 54 clusters. The regression discontinuity logic underlying threshold-based debt disclosure rules is sound in principle but fragile in practice: the intraclass correlation of 0.640 implies that clustering at the ward level absorbs most of the variance, so credible inference requires either many clusters or strong assumptions about within-cluster homogeneity. The contribution is a design-based framework for evaluating disclosure thresholds as social sorting mechanisms, not a report of empirical findings.
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Ochieng et al. (2026) studied this question.
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