Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Using baseline data, IPA calculates an intra-cluster correlation coefficient (ICC) on key variables of interest (total income, durable asset value, 30-day consumption) ranging from less than 0.001 to 0.0015 within the subdivided barangays. Conservatively using 0.0015, we estimate that we have 80% power to detect a minimum detectable effect size (MDE) of 0.16 standard deviations between any treatment group and the control group when we randomize by sub-barangay clusters, assuming full compliance and no attrition. Power improves for pooled comparisons: testing any group coaching (n = 1,200) vs. any individual coaching (n = 600) or any group livelihood (n = 600) vs. any individual livelihood (n = 1,200) yields estimated MDEs of 0.14 standard deviations. In Banerjee et al. (2016), treatment assignment, covariates, and stratification FE explain between 5% and 60% of total variation, depending on the outcome of interest. In the case of consumption, for example, the previously estimated R2 of 0.46 would provide 80% power to detect an MDE of 0.12 standard deviations between treatment and control, and an MDE of 0.10 standard deviations for the pooled comparisons described above .