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

Permutation inference with a finite number of heterogeneous clusters