A basic feature of many field experiments is that investigators are only able to randomize
clusters of individuals—such as households, communities, firms, medical practices, schools,
or classrooms—even when the individual is the unit of interest. To recoup some of the
resulting efficiency loss, many studies pair similar clusters and randomize treatment within
pairs. Other studies (including almost all published political science field experiments) avoid
pairing, in part because some prominent methodological articles claim to have identified serious
problems with this "matched-pair cluster-randomized" design. We prove that all such
claims about problems with this design are unfounded. We then show that the estimator
for matched-pair designs favored in the literature is appropriate only in situations where
matching is not needed. To address this problem without modeling assumptions, we generalize
Neyman’s (1923) approach and propose a simple new estimator with much improved
statistical properties. We also introduce methods to cope with individual-level noncompliance,
which most existing approaches assume away. We show that from the perspective of,
among other things, bias, efficiency, power, or robustness, and in large samples or small,
pairing should be used in cluster-randomized experiments whenever feasible; failing to do so is equivalent to discarding a considerable fraction of one’s data. We develop these techniques
in the context of a randomized evaluation we are conducting of the Mexican Universal Health
Insurance Program.
