Replication data for: The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation

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 incorrectly assume away. We show that from the perspective of, among other things, bias, efficiency, or power, 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.