We find that, relative to Republicans residing in more Republican neighborhoods, Republicans in neighborhoods with a higher concentration of Democratic residents report higher levels of anxiety, depression, and loneliness. While Democrats in general report worse mental health than Republicans, Democrats show no sensitivity to partisan context. Using a large-scale national survey and fine-grained data on residential political context, we establish that these patterns persist even when examining partisans who live in the same Zip Codes, and while controlling for other individual and geographic features. The correlation is strongest for the most strongly partisan individuals, suggesting that politics is a significant factor in the relationship. For Republicans, the size of the relationship between partisan context and mental health is comparable to or larger than the correlation between mental health reports and other contextual features of residential areas, such as neighborhood poverty or the way racial minorities respond to changes in the racial or ethnic composition of local geography.

Neighborhoods are frequently cited as impactful for social, economic, political, and health outcomes. Measuring neighborhoods, however, is challenging, as the definition of a neighborhood may change dramatically across places. Researchers lack widespread but locally-sourced data on neighborhoods, and instead often adopt widely available but arbitrary Census geographies as neighborhood proxies. Others invest in the collection of more precise definitions, but these types of data are hard to collect at scale. We address this tension between scale and precision by collecting, cleaning, and providing to researchers a new dataset of city-defined neighborhoods. Our data includes 206 of the largest cities in the United States, covering more than 77 million people. We combine these data with block-level Census demographic data and provide them along with open-source software to aid researchers in their use.

Socioeconomic disadvantage is a major correlate of low political participation. This association is among the most robust findings in political science. However, it is based largely on observational data. The causal effects of early-life disadvantage in particular are even less understood, because long-term data on the political consequences of randomized early-life anti-poverty interventions is nearly nonexistent. We leverage the Moving to Opportunity (MTO) experiment to test the long-term effect of moving out of disadvantaged neighborhoods—and thus out of deep poverty—on turnout. MTO is one of the most ambitious anti-poverty experiments ever implemented in the United States. Although MTO ameliorated children’s poverty long term, we find that, contrary to expectations, the intervention did not increase children’s likelihood of voting later in life. Additional tests show the program did not ameliorate their poverty enough to affect turnout. These findings speak to the complex relationship between neighborhood disadvantage and low political participation.

Significance Differences between Democrats and Republicans in rates of wearing a mask to stop the spread of COVID-19 are associated with the partisan balance of a neighborhood. The difference in rates grew larger as the share of Republicans in a neighborhood increased. This finding appears to be driven by decreased rates of mask wearing by Republicans who live among increasing numbers of Republicans (and not by Democrats in the same neighborhood). Theories about social pressure suggest these findings may be driven by the politicized and publicly observable nature of wearing a mask relative to other COVID-19 mitigation strategies, like vaccination. Neighborhood partisan composition was only weakly related to uptake of the COVID-19 vaccine and unrelated to uptake of flu vaccines.

This paper provides novel evidence on trends in geographic partisan segregation. Using two individual-level panel datasets covering the near universe of the U

Does contact across social groups influence sociopolitical behavior? This question is among the most studied in the social sciences with deep implications for the harmony of diverse societies. Yet, despite a voluminous body of scholarship, evidence around this question is limited to cross-sectional surveys that only measure short-term consequences of contact or to panel surveys with small samples covering short time periods. Using advances in machine learning that enable large-scale linkages across datasets, we examine the long-term determinants of sociopolitical behavior through an unprecedented individual-level analysis linking contemporary political records to the 1940 U.S. Census. These linked data allow us to measure the exact residential context of nearly every person in the United States in 1940 and, for men, connect this with the political behavior of those still alive over 70 years later. We find that, among white Americans, early-life exposure to black neighbors predicts Democratic partisanship over 70 years later.

Segregation across social groups is an enduring feature of nearly all human societies and is associated with numerous social maladies. In many countries, reports of growing geographic political polarization raise concerns about the stability of democratic governance. Here, using advances in spatial data computation, we measure individual partisan segregation by calculating the local residential segregation of every registered voter in the United States, creating a spatially weighted measure for more than 180 million individuals. With these data, we present evidence of extensive partisan segregation in the country. A large proportion of voters live with virtually no exposure to voters from the other party in their residential environment. Such high levels of partisan isolation can be found across a range of places and densities and are distinct from racial and ethnic segregation. Moreover, Democrats and Republicans living in the same city, or even the same neighbourhood, are segregated by party.

and Social versity politics. on how interactions with and decisions made by the administrative state in fl uence individuals ’ political behavior and attitudes. The Social Geography and Politics and the of the the International Society of Political Psychology. on partisan segregation in the United States and the long ‐ term de terminants of political behavior. Pro fessor Government, of and African American Studies, at racial and ethnic dynamics, mostly in the United States, the politics surrounding genomic science. Genomic Politics: How the Revolution in Genomic Science American

Objective A growing literature argues that national issues and partisanship structure local-level conflict in the United States. This argument contrasts starkly with the traditional view of local politics as fundamentally nonpartisan and nonideological. We reconsider these diverging arguments. Methods We use a large-scale survey of municipal officials to examine elite preferences on a series of policy tradeoffs. We identify latent dimensions that underlie elite preferences across cities and on a common scale. We examine correlates of these dimensions including individual- and city-level demographics and electoral support from constituent groups. Results Our results demonstrate that elite preferences in local politics-unlike in national politics-are multidimensional, with two underlying cleavages: one based on partisanship, the other a market orientation to the provision of local services. These latent dimensions align with indicators of constituent group support, suggesting that each dimension reflects substantively meaningful features of conflict in local electoral politics. Conclusions These findings suggest that local politics are not completely structured by national-level partisan politics. Future research should aim to measure and understand local conflict along both partisan and market-orientation dimensions.

Violent protests are dramatic political events, yet we know little about the effect of these events on political behavior. While scholars typically treat violent protests as deliberate acts undertaken in pursuit of specificgoals, due toa lackof appropriatedataanddifficulty in causal identification, there is scant evidence of whether riots can actually increase support for these goals. Using geocoded data, we analyzemeasures ofpolicy support beforeandafter the 1992LosAngeles riot—oneof themost high-profile events of political violence in recent American history—that occurred just prior to an election. Contrary to some expectations from the academic literature and the popular press, we find that the riot caused amarked liberal shift in policy support at the polls. Investigating the sources of this shift, we find that it was likely the result of increasedmobilizationof bothAfricanAmerican andwhite voters.Remarkably, thismobilization endures over a decade later.

Do judges telegraph their preferences during oral arguments? Using the U.S. Supreme Court as our example, we demonstrate that Justices implicitly reveal their leanings during oral arguments, even before arguments and deliberations have concluded. Specifically, we extract the emotional content of over 3,000 hours of audio recordings spanning 30 years of oral arguments before the Court. We then use the level of emotional arousal, as measured by vocal pitch, in each of the Justices' voices during these arguments to accurately predict many of their eventual votes on these cases. Our approach yields predictions that are statistically and practically significant and robust to including a range of controls; in turn, this suggests that subconscious vocal inflections carry information that legal, political, and textual information do not.

Violent protests are dramatic political events, yet we know little about the effect of these events on political behavior. While scholars typically treat violent protests as deliberate acts undertaken in pursuit of specific goals, due to a lack of appropriate data and difficulty in causal identification, there is scant evidence of whether riots can actually increase support for these goals. Using geocoded data, we analyze measures of policy support before and after the 1992 Los Angeles riot—one of the most high-profile events of political violence in recent American history—that occurred just prior to an election. Contrary to some expectations from the academic literature and the popular press, we find that the riot caused a marked liberal shift in policy support at the polls. Investigating the sources of this shift, we find that it was likely the result of increased mobilization of both African American and white voters. Remarkably, this mobilization endures over a decade later.

Once a fixture of research in the social and behavioral sciences, volunteer subjects are now only rarely used in human subjects research. Yet volunteers are a potentially valuable resource, especially for research conducted online. We argue that online volunteer laboratories are able to produce high-quality data comparable to that from other online pools. The scalability of volunteer labs means that they can produce large volumes of high-quality data for multiple researchers, while imposing little or no financial burden. Using a range of original tests, we show that volunteer and paid respondents have different motivations for participating in research, but have similar descriptive compositions. Furthermore, volunteer samples are able to replicate classic and contemporary social science findings, and produce high levels of overall response quality comparable to paid subjects. Our results suggest that online volunteer labs represent a potentially significant untapped source of human subjects data.

Hill et al.’s study (1) of the relationship between Hispanic population growth and voters’ support for Donald Trump contributes to our understanding of the larger puzzle of diversity and politics. The relationship between diversity and reactionary politics should be considered one of the most important sociopolitical issues facing the world today—it is a near certainty that almost every developed country and many developing countries will be more diverse a generation from now than they are today (2). And, thus, if increasing diversity affects political outcomes, the relationship can point in two consequentially different directions: toward increased diversity liberalizing politics or toward increased diversity causing a reactionary backlash. The different possible directions of this effect may point to profoundly divergent paths for the harmony of future societies. If demographic change causes a reactionary backlash, this has grave implications for the long-term success of diverse places: If people are compelled to “hunker down” in the face of demographic change (2) and cannot achieve political and economic cooperation with diverse peers (3, 4), then societies face a choice of trading short-term economic and political success for the long-term social, economic, and ethical benefits of increasing diversity (5). Indeed, the relationship between ethnic diversity and a host of socially undesirable outcomes, including poor economic growth (6), unstable politics (7), and even violence (8), paints a grim picture for the future of a diversifying world. The election of Trump in 2016 is, by some accounts, a demonstration of this phenomenon: Following on his antiimmigrant campaign rhetoric, Trump has pursued the xenophobic policies that have slowed the flow of immigration into the United States—thus a reactionary backlash to demographic change has damaged the potential human-capital benefits that come with future diversity. And the antiimmigrant sentiment that brought Trump victory is, by some accounts, part of … [↵][1]1Email: renos{at}gov.harvard.edu. [1]: #xref-corresp-1-1

AbstractInter-ethnic residential segregation is correlated with intergroup bias and conflict, poorly functioning states and civil societies, weak economic development, and ethnocentric political behavior. As such, segregation has been a subject of long-standing interest. However, segregation has not been assigned in randomized controlled trials, so the observed correlations may be spurious and the mechanism behind these correlations is poorly understood. In two experiments, we randomly assign segregation in a laboratory and demonstrate that segregation affects perceptions of other people and causes intergroup bias in costly decision-making. Rather than segregation merely inhibiting intergroup contact, we demonstrate that segregation directly affects perception and thus can affect intergroup relations even when holding contact constant.

The central role of partisanship in shaping political behavior and attitudes is wellestablished in research on political behavior in the United States. Partisan identification is highly stable, even in the face of changes to context and individual traits, after adolescence. But what shapes partisanship before it becomes stable? Can early life experiences produce enduring political affiliations? We examine the long-term determinants of partisan identification through an unprecedented individual-level dataset that links contemporary voterfiles to the 1940 U.S. Census. Linking these two datasets allows for novel analysis of the connection between early life experiences and presentday political behavior, providing new insights on classic questions about the nature of early-life socialization and partisanship. We investigate the effect of childhood exposure to people of different races and incomes, exploiting the ordering of households on the 1940 U.S. Census enumeration sheets to construct individual-level measures of racial and inequality exposure. We find that, among whites, early-life exposure to Black neighbors predicts Democratic partisanship over 70 years later. On the other hand, early life exposure to inequality, in the form of a neighbor of a greatly different income, has no long-term effect on partisanship. We discuss the implications of these findings and potential for the broader research paradigm to yield additional insights. ∗The project is generously supported by the Russell Sage Foundation and Harvard’s Inequality in America Competitive Research Fund. †Department of Government, Harvard University, jrbrown@g.harvard.edu ‡Department of Government, Harvard University, renos@gov.harvard.edu §Department of Economics, Boston University and National Bureau of Economic Research, jamesf@bu.edu ¶Department of Government, Harvard University, smazumder@g.harvard.edu 1