Wild Cluster Bootstrap (Standard Errors)

Clustered standard errors rely on asymptotic theory and require a large number of independent clusters (typically 42+) to be reliable. In this exercise you will test what happens when you cluster at a level with relatively few clusters, and then correct using the Wild Cluster Bootstrap.

• Using Michler_JEEM.dta (maize only), run the pooled IV regression clustering at the ward level (ward_id) instead of the household level. Use ivregress 2sls rather than ivreg2 for compatibility with boottest.
• Now apply the Wild Cluster Bootstrap via post-estimation to get a more reliable p-value: boottest CA

1. How many unique wards are there in the maize sample? Is this above or below the commonly cited minimum of 40 clusters?
2. Compare the analytical cluster-robust p-value with the wild cluster bootstrap p-value. Does the bootstrap make you more or less confident in the significance of the CA effect?

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