  I introduce a simple permutation procedure to test conventional (non-sharp)
hypotheses about the effect of a binary treatment in the presence of a finite
number of large, heterogeneous clusters when the treatment effect is identified
by comparisons across clusters. The procedure asymptotically controls size by
applying a level-adjusted permutation test to a suitable statistic. The
adjustments needed for most empirically relevant situations are tabulated in
the paper. The adjusted permutation test is easy to implement in practice and
performs well at conventional levels of significance with at least four treated
clusters and a similar number of control clusters. It is particularly robust to
situations where some clusters are much more variable than others. Examples and
an empirical application are provided.
