  I introduce a generic method for inference about a scalar parameter in
research designs with a finite number of heterogeneous clusters where only a
single cluster received treatment. This situation is commonplace in
difference-in-differences estimation but the test developed here applies more
generally. I show that the test controls size and has power under asymptotics
where the number of observations within each cluster is large but the number of
clusters is fixed. The test combines weighted, approximately Gaussian parameter
estimates with a rearrangement procedure to obtain its critical values. The
weights needed for most empirically relevant situations are tabulated in the
paper. Calculation of the critical values is computationally simple and does
not require simulation or resampling. The rearrangement test is highly robust
to situations where some clusters are much more variable than others. Examples
and an empirical application are provided.
