  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 the resulting efficiency loss, some studies pair similar
clusters and randomize treatment within pairs. However, many other studies
avoid pairing, in part because of claims in the literature, echoed by clinical
trials standards organizations, that this matched-pair, cluster-randomization
design has serious problems. We argue that all such claims are unfounded. We
also prove that the estimator recommended for this design in the literature is
unbiased only in situations when matching is unnecessary; its standard error is
also invalid. To overcome this problem without modeling assumptions, we develop
a simple design-based estimator with much improved statistical properties. We
also propose a model-based approach that includes some of the benefits of our
design-based estimator as well as the estimator in the literature. Our methods
also address individual-level noncompliance, which is common in applications
but not allowed for in most existing methods. We show that from the perspective
of bias, efficiency, power, robustness or research costs, and in large or small
samples, 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.
