  We introduce a new Stata package called summclust that summarizes the cluster
structure of the dataset for linear regression models with clustered
disturbances. The key unit of observation for such a model is the cluster. We
therefore propose cluster-level measures of leverage, partial leverage, and
influence and show how to compute them quickly in most cases. The measures of
leverage and partial leverage can be used as diagnostic tools to identify
datasets and regression designs in which cluster-robust inference is likely to
be challenging. The measures of influence can provide valuable information
about how the results depend on the data in the various clusters. We also show
how to calculate two jackknife variance matrix estimators efficiently as a
byproduct of our other computations. These estimators, which are already
available in Stata, are generally more conservative than conventional variance
matrix estimators. The summclust package computes all the quantities that we
discuss.
