SummaryBackground We assessed aspects of Seguro Popular, a programme aimed to deliver health insurance, regular and preventive medical care, medicines, and health facilities to 50 million uninsured Mexicans.

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 classroomseven 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.

Social divisions between American partisans are growing, with Republicans and Democrats exhibiting partisan homophily in a range of seemingly non-political domains. It has been widely claimed that this partisan social divide extends to Americans' decisions about where to live. In two original survey experiments, we show that Democrats are, indeed, more likely than Republicans to prefer living in dense, racially diverse, more Democratic places. Improving on previous studies, we test respondents' stated preferences against their actual moving behavior, showing that even as partisans differ in their residential preferences in ways that should lead to sorting, on average they are not migrating to more politically different zip codes. Using zip-code-level Census and partisanship data on the places where respondents live, we provide a likely explanation for this null effect: by prioritizing common concerns when deciding where to live, Americans forgo the opportunity to move to more politically compatible communities.

Significance Recent political events show that members of extreme political groups support partisan violence, and survey evidence supposedly shows widespread public support. We show, however, that, after accounting for survey-based measurement error, support for partisan violence is far more limited. Prior estimates overstate support for political violence because of random responding by disengaged respondents and because of a reliance on hypothetical questions about violence in general instead of questions on specific acts of political violence. These same issues also cause the magnitude of the relationship between previously identified correlates and partisan violence to be overstated. As policy makers consider interventions designed to dampen support for violence, our results provide critical information about the magnitude of the problem.