Measuring and Modeling Neighborhoods. (with Cory McCartan and Kosuke Imai). (Survey tool). Conditionally Accepted, American Political Science Review.

Abstract

Granular geographic data present new opportunities to understand how neighborhoods are formed, and how they influence politics. At the same time, the inherent subjectivity of neighborhoods creates methodological challenges in measuring and modeling them. We develop an open-source survey instrument that allows respondents to draw their neighborhoods on a map. We also propose a statistical model to analyze how the characteristics of respondents and local areas determine subjective neighborhoods. We conduct two surveys: collecting subjective neighborhoods from voters in Miami, New York City, and Phoenix, and asking New York City residents to draw a community of interest for inclusion in their city council district. Our analysis shows that, holding other factors constant, White respondents include census blocks with more White residents in their neighborhoods. Similarly, Democrats and Republicans are more likely to include co-partisan areas. Furthermore, our model provides more accurate out-of-sample predictions than standard neighborhood measures.

The Obama Effect? Race, First-time Voting, and Future Participation. 2023. Political Science Research and Methods. (Supporting Information; Replication Data; Cite).

Abstract

Did the 2008 United States election produce stronger future mobilization for Blacks than non-Blacks? First-time voting influences long-term political behavior, but do minority voters see the most powerful effects when the formative election is tied to their group’s political empowerment? I test this hypothesis in the context of the election of the first Black president in United States history, using voting eligibility discontinuities to identify the effect of voting in 2008 on future voting for Blacks, Hispanics, and Whites. Voting in 2008 caused a greater increase in the likelihood of voting in 2010 for Blacks than for other new voters, but there is no evidence of a sustained mobilizing advantage in subsequent elections. Furthermore, 2008 was not a unique formative voting experience for new Black voters, but rather produced similar effects on future voting as other presidential elections. These results signal that group political empowerment does not influence the effect of first-time voting experiences on future political participation.

How Local Partisan Context Conditions Pro-social Behaviors: Mask-Wearing During COVID-19. 2022. (with Ryan Baxter-King, Ryan Enos, Arash Naeim, and Lynn Vavreck). Proceedings of the National Academy of Sciences.(Supporting Information; Replication Data; Cite).

Abstract

Does local partisan context influence compliance with public health recommendations? Using a nationwide survey of 60,000 adults and geographic data on over 180 million registered voters, we investigate whether neighborhood partisan composition affects a publicly observable and politicized behavior: wearing a mask. We find that Republicans are less likely to comply with mask-wearing as the share of Republicans in their Zip Codes increases. Democratic mask-wearing, however, is unaffected by local partisan context. Consequentially, the partisan gap in mask-wearing is largest in Republican neighborhoods, and less apparent in Democratic areas. These effects are distinct from other contextual effects such as variations in neighborhood race, income, or education. Additionally, partisan context does not influence unobservable public health recommendations like COVID-19 vaccination, or non-politicized behaviors like flu vaccination, suggesting that mask-wearing reflects the publicly observable and politicized nature of the behavior instead of underlying differences in dispositions toward medical care.

Locked Out of College: When Admissions Bureaucrats Do and Do Not Discriminate. 2022. (with Hanno Hilbig). British Journal of Political Science. (Supporting Information; Replication Data; Cite).

Abstract

How does an individual's criminal record shape interactions with the state and society? This article presents evidence from a nationwide field experiment in the United States, which shows that prospective applicants with criminal records are about 5 percentage points less likely to receive information from college admission offices. However, this bias does not extend to race: there is no difference in response rates to Black and White applicants. The authors further show that bias is all but absent in public bureaucracies, as discrimination against formerly incarcerated applicants is driven by private schools. Examining why bias is stronger for private colleges, the study demonstrates that the private–public difference persists even after accounting for college selectivity, socio-economic composition and school finances. Moving beyond the measurement of bias, an intervention designed to reduce discrimination is evaluated: whether an email from an advocate mitigates bias associated with a criminal record. No evidence is found that advocate endorsements decrease bureaucratic bias.

Resisting Broken Windows: The Effect of Neighborhood Disorder on Political Behavior. 2022. (with Michael Zoorob). Political Behavior. (Ungated; Supporting Information; Replication Data; Cite).

Abstract

Concurrent housing and opioid crises have increased exposure to street-crime, homelessness and addiction in American cities. What are the political consequences of this increased neighborhood disorder? We examine a change in social context following the relocation of homelessness and drug treatment services in Boston. In 2014, an unexpected bridge closing forced nearly 1000 people receiving emergency shelter or addiction treatment to relocate from an island in the Boston Harbor to mainland Boston, causing sustained increases in drug-use, loitering, and other features of neighborhood disorder. Residents near the relocation facilities mobilized to maintain order in their community. In the subsequent Mayoral election, their turnout grew 9% points while participation in state and national elections was unchanged. However, increased turnout favored the incumbent Mayor, consistent with voter learning about candidate quality following local shocks. Voters responded to neighborhood changes at the relevant electoral scale and rewarded responsive politicians.

Childhood Cross-ethnic Exposure Predicts Political Behavior Seven Decades Later: Evidence from Linked Administrative Data. 2021. (with Ryan Enos, James Feigenbaum, and Shom Mazumder). Science Advances. (Supporting Information; Replication Data; Cite). [Covered by the Los Angeles Times, CNN, the Daily Mail, and the Harvard Gazette].

Abstract

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.

The Measurement of Partisan Sorting for 180 Million Voters. 2021. (with Ryan Enos). Nature Human Behaviour. (Supporting Information; Replication Data; Cite). [Covered by the New York Times, The Atlantic, Fast Company, The Harvard Gazette, The Harvard Crimson , The Academic Times, and the Raw Story We've Got Issues podcast].

Abstract

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.

Weakening Strong Black Political Empowerment: Implications from Atlanta’s 2009 Mayoral Election. 2014. (with Michael Leo Owens). Journal of Urban Affairs, 36:4, 663-681. (Cite).

Abstract

Atlanta is perhaps the city with the greatest degree of black political empowerment (BPE) in the United States. Yet in 2009 a relatively weak white mayoral candidate nearly won the general and runoff elections over a field of stronger black candidates. Why? Treating Atlanta as a prototypical case, the article examines factors that undermine the capacity of blacks to retain control of mayoralties in strong BPE cities, with an emphasis on disruptions to black electorates, discontent among black citizens, and reinvestment in electoral politics by whites at the local level.

Partisan Conversion Through Neighborhood Influence: How Voters Adopt the Partisanship of their Neighbors. (Supporting Information). [Covered by the New York Times]. Under Review.

Abstract

Recent studies show that American neighborhoods have become politically homogeneous, raising concerns about how geographic polarization divides parties and influences voters. But it remains unclear how voters are influenced by the politics of their neighbors. I argue that voters are influenced by local norms when defining their own partisan affiliations, adopting local partisanship. Panel data on 41 million voters from 2008-2020 and an original survey of 24,623 respondents demonstrate that exposure to partisan neighbors increases party switching. These effects are largest for older voters, voters in single-family communities, and voters with more same-race neighbors. Survey data support mechanisms of social influence: voters accurately perceive local partisanship, interact more with partisans they live near, and are more comfortable when their partisanship matches neighbors' political affiliations. Partisanship is thus shaped by where voters live and who they live close to, demonstrating the behavioral consequences of geographic polarization.

Partisan Segregation and Partisan Activation: How Geographic Polarization Increases Political Engagement.

Abstract

Do voters participate more in politics when they live around neighbors who share their party? I argue that voters are more likely to be socialized into politics when they live near more co-partisans. Using administrative data on every registered voter across 30 U.S. states from 2012-2021 and an original survey of 45,139 voters, I measure the effect of partisan neighborhoods on voting and other political engagement. Focusing on voters who do not change residences between elections, I find that increased exposure to co-partisan neighbors makes voters more likely to turnout, volunteer for campaigns, attend protests, participate in local political meetings, and to publicly express their partisanship through lawn signs, clothing, or bumper stickers. Survey data further show that voters respond to in-group exposure by becoming more comfortable in their neighborhood and more likely to politically engage with neighbors, consistent with social influence driving participatory effects.

The Increase in Partisan Segregation in the United States and its Causes. (with Enrico Cantoni, Ryan Enos, Vincent Pons, and Emilie Sartre). [Covered by the New York Times].

Abstract

This paper provides novel evidence on trends in geographic partisan segregation. Using two individual-level panel datasets covering the near universe of the U.S. population between 2008 and 2020, we leverage information on individuals’ party affiliation to construct two key indicators: i) the fraction of Democrats among voters affiliated with either major party, which reveals that partisan segregation has increased across geographical units, at the tract, county, and congressional district levels; ii) The dissimilarity index, which measures differences in the partisan mix across distinct sub-units and highlights that partisan segregation has also increased within geographical units. Tracking individuals across election years, we decompose changes in partisan segregation into different sources: voter migration, generational change, older voters entering the electorate, and voters changing their partisanship or their registration status. The rise in partisan segregation is mostly driven by generational change, in Democratic-leaning areas, and by the increasing ideological conformity of stayers, in Republican-leaning areas.

Priming Bias versus Post-treament Bias in Experimental Designs. (with Matthew Blackwell, Sophie Hill, Kosuke Imai, and Teppei Yamamoto).

Abstract

Conditioning on variables affected by treatment can induce post-treatment bias when estimating causal effects. Although this suggests that researchers should measure potential moderators before administering the treatment in a survey experiment, doing so may also bias causal effect estimation if the covariate measurement primes respondents to react differently to the treatment. This paper formally analyzes this trade-off between post-treatment and priming biases in three experimental designs that vary when moderators are measured: pre-treatment, post-treatment, or a randomized choice between the two. We derive nonparametric bounds for interactions between the treatment and the moderator in each design and show how to use substantive assumptions to narrow these bounds. These bounds allow researchers to assess the sensitivity of their empirical findings to either source of bias. We extend the basic framework in two ways. First, we apply the framework to the case of post-treatment attention checks and bound how much inattentive respondents can attenuate estimated treatment effects. Second, we develop a parametric Bayesian approach to incorporate pre-treatment covariates in the analysis to sharpen our inferences and quantify estimation uncertainty. We apply these methods to a survey experiment on national identity. We conclude with practical recommendations for scholars designing experiments.