The central problem is that concealment is a strategic response to a threshold, not a continuous behaviour, and conventional regression approaches cannot identify threshold-induced bunching when the running variable is unobserved. The paper formalises a bunching estimator for notice-period thresholds, specifies the data architecture required for its implementation, and characterises the identification conditions under which the estimator recovers concealment behaviour. A design-based power analysis, using a before-after-control-impact structure with an intraclass correlation of 0.64, between-cluster SD of 6.0 and residual SD of 4.5, indicates that 54 clusters are required to detect a 5 percentage-point effect at 80% power. The framework contributes a threshold-sensitive measurement strategy for urban sociological research on informal water governance, and identifies the boundary conditions under which bunching estimation is informative about concealment rather than about notice-period administration.
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Otieno Omondi Oyugi (2026) studied this question.
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