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Social divisions between American partisans are growing, with Republicans and Democrats exhibiting homophily in a range of seemingly nonpolitical 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 confirm that Democrats are, in fact, more likely than Republicans to prefer living in more Democratic, dense, and racially diverse places. However, improving on previous studies, we test respondents’ stated preferences against their actual moving behavior. While partisans differ in their residential preferences, on average they are not migrating to more politically distinct communities. Using zip-code-level census and partisanship data on the places where respondents live, we provide one explanation for this contradiction: by prioritizing common concerns when deciding where to live, Americans forgo the opportunity to move to more politically compatible communities.

In the postwar era, Democratic voters have become increasingly more likely than Republican voters to live in urban counties. Public policies that shape geographic space have been a major contributor to this geographic polarization. This article examines the effect of the Interstate Highway System, the largest public works project in American history, on this phenomenon. Drawing on a database of US highway construction since the passage of 1956 highway legislation, it shows that suburban Interstate highways made suburban counties less Democratic, especially in the South and where highways were built earlier. Metropolitan areas with denser Interstate networks also became more polarized. Analysis of the Youth-Parent Socialization Panel Study (1965–97) reveals individual-level mechanisms underlying these changes: Interstates drew more white and affluent residents, who tended to be Republican, to the suburbs.

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. We randomly assigned treatment within 74 matched pairs of health clusters—ie, health facility catchment areas—representing 118 569 households in seven Mexican states, and measured outcomes in a 2005 baseline survey (August, 2005, to September, 2005) and follow-up survey 10 months later (July, 2006, to August, 2006) in 50 pairs (n=32 515). The treatment consisted of encouragement to enrol in a health-insurance programme and upgraded medical facilities. Participant states also received funds to improve health facilities and to provide medications for services in treated clusters. We estimated intention to treat and complier average causal effects non-parametrically. Intention-to-treat estimates indicated a 23% reduction from baseline in catastrophic expenditures (1·9% points; 95% CI 0·14–3·66). The effect in poor households was 3·0% points (0·46–5·54) and in experimental compliers was 6·5% points (1·65–11·28), 30% and 59% reductions, respectively. The intention-to-treat effect on health spending in poor households was 426 pesos (39–812), and the complier average causal effect was 915 pesos (147–1684). Contrary to expectations and previous observational research, we found no effects on medication spending, health outcomes, or utilisation. Programme resources reached the poor. However, the programme did not show some other effects, possibly due to the short duration of treatment (10 months). Although Seguro Popular seems to be successful at this early stage, further experiments and follow-up studies, with longer assessment periods, are needed to ascertain the long-term effects of the programme. Mexican Ministry of Health, the National Institute of Public Health of Mexico, and Harvard University Institute for Quantitative Social Science.

Political scientists, pundits, and citizens worry that America is entering a new period of violent partisan conflict. Provocative survey data show that a large share of Americans (between 8% and 40%) support politically motivated violence. Yet, despite media attention, political violence is rare, amounting to a little more than 1% of violent hate crimes in the United States. We reconcile these seemingly conflicting facts with four large survey experiments (n = 4,904), demonstrating that self-reported attitudes on political violence are biased upward because of respondent disengagement and survey questions that allow multiple interpretations of political violence. Addressing question wording and respondent disengagement, we find that the median of existing estimates of support for partisan violence is nearly 6 times larger than the median of our estimates (18.5% versus 2.9%). Critically, we show the prior estimates overstate support for political violence because of random responding by disengaged respondents. Respondent disengagement also inflates the relationship between support for violence and previously identified correlates by a factor of 4. Partial identification bounds imply that, under generous assumptions, support for violence among engaged and disengaged respondents is, at most, 6.86%. Finally, nearly all respondents support criminally charging suspects who commit acts of political violence. These findings suggest that, although recent acts of political violence dominate the news, they do not portend a new era of violent conflict.

Why does the relationship between income and partisanship vary across U.S. regions? Some answers to this question have focused on economic context (in poorer environments, economics is more salient), whereas others have focused on racial context (in racially diverse areas, richer voters oppose the party favoring redistribution). Using 73 million geocoded registration records and 185,000 geocoded precinct returns, we examine income-based voting across local areas. We show that the political geography of income-based voting is inextricably tied to racial context, and only marginally explained by economic context. Within homogeneously nonblack localities, contextual income has minimal bearing on the income-party relationship. The correlation between income and partisanship is strong in heavily black areas of the Old South and other areas with a history of racialized poverty, but weaker elsewhere, including in urbanized areas of the South. The results demonstrate that the geography of income-based voting is inseparable from racial context.

Widespread concern that voter identification laws suppress turnout among racial and ethnic minorities has made empirical evaluations of these laws crucial. But problems with administrative records and survey data impede such evaluations. We replicate and extend Hajnal, Lajevardi, and Nielson’s 2017 article, which concludes that voter ID laws decrease turnout among minorities, using validated turnout data from five national surveys conducted between 2006 and 2014. We show that the results of their article are a product of data inaccuracies, the presented evidence does not support the stated conclusion, and alternative model specifications produce highly variable results. When errors are corrected, one can recover positive, negative, or null estimates of the effect of voter ID laws on turnout, precluding firm conclusions. We highlight more general problems with available data for research on election administration, and we identify more appropriate data sources for research on state voting laws’ effects.

Why is housing supply so severely restricted in US cities and suburbs? Urban economists offer two primary hypotheses: homeowner self-interest and political fragmentation. Homeowners, who outnumber and have organizational advantages over renters, are said to lobby against development to protect their property values. The fragmentation hypothesis emphasizes that development's negative externalities are borne locally while most of the benefits accrue regionally or nationally, leading localities to block housing. This paper offers another explanation: ordinary people simply do not believe that adding more housing to the regional stock would reduce housing prices. Across three original surveys of urban and suburban residents, only a minority of respondents say that a large, positive, regional housing supply shock would reduce prices or rents. These beliefs are weakly held and unstable (suggesting people have given the issue little thought), but respondents do have stable views about who is to blame for high housing prices: developers and landlords. Large, bipartisan supermajorities support price controls, demand subsidies, and restrictions on putative bad actors, policies which they believe would be more effective than supply liberalization for widespread affordability. We discuss the implications of these findings for efforts to expand the supply of housing.

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.