Jacob R. Brown

Jacob R. Brown

PhD Candidate in Government

Harvard University

Jacob R. Brown

I am a PhD Candidate in Government at Harvard University studying political behavior in American politics. In particular, I am interested in how political geography and group identity structure political behavior. My dissertation investigates the behavioral consequences of geographic partisan polarization, with specific focus on how this polarization is self-reinforcing: how living in increasingly homogeneous partisan environments influences voters’ political affiliations. To this end, I develop data on the partisan residential exposure of every voter in the United States over the past decade, leveraging precise information on each voter's residential location, partisan affiliation, and political behavior. With these data I present new evidence on the extent and causes of partisan sorting in the United States and test new theories of how where Democrats and Republicans live in relation to one another influences political behavior.

Job Market Paper

Partisan Conversion Through Neighborhood Influence: How Voters Adopt the Partisanship of their Neighbors and Reinforce Geographic Polarization. (Supporting Information). Revise and Resubmit, American Political Science Review. [Covered by the New York Times].
Abstract Recent studies find that many American neighborhoods have become politically homogeneous, raising concerns about how geographic polarization divides parties and influences voters. What drives this pattern? I argue that voters are influenced by their neighbors' politics, adopting the partisanship of people they live near. I test this using individual-level panel data on over 41 million voters from 2008 to 2020, and an original survey of over 24,000 respondents linked to voterfiles. Focusing on voters who do not move between elections, I find that exposure to partisan neighbors increases the likelihood of switching registration to match neighbors' partisanship. These effects are largest for voters most likely to interact with neighbors: older voters, voters in single-family communities, and voters with more same-race neighbors. Moreover, survey data show that voters accurately perceive neighborhood partisanship, interact more with partisans they live near, and view Democrats or Republicans more favorably when they have more neighbors from that party. These results demonstrate that partisanship is shaped by where voters live, and this conversion reinforces ongoing processes of political segregation.

Selected Working Papers

The Obama Effect? Race, First-time Voting, and Future Participation. (Supporting Information). Revise and Resubmit, Political Behavior.
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. (with Ryan Baxter-King, Ryan Enos, Arash Naeim, and Lynn Vavreck).
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 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.

Measuring and Modeling Neighborhoods. (with Cory McCartan and Kosuke Imai). (PolMeth Poster).
Abstract With the availability of granular geographical data, social scientists are increasingly interested in examining how residential neighborhoods influence attitudes, behavior, politics, socio-economic outcomes, and psychological processes. To facilitate such studies, we develop an easy-to-use online survey instrument that allows respondents to draw their neighborhoods on a map. We then propose a statistical model that can be used to analyze how the characteristics of respondents, those of relevant local areas, and their interactions determine the formation of their neighborhoods. The model also can generate out-of-sample predictions of one's subjective neighborhood given these observed characteristics. We illustrate the proposed methodology by conducting a survey among registered voters in Miami, New York City, and Phoenix. We find that across these cities, voters are more likely to include same-race and co-partisan census blocks into their neighborhoods. We also show that our model provides more accurate out-of-sample predictions than the standard distance-based measures of neighborhoods. Open-source software is available for implementing the proposed methodology.

The Increase in Partisan Segregation in the United States and its Causes. (with Enrico Cantoni, Ryan Enos, Vincent Pons, and Emilie Sartre).
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 in 30 U.S states to construct two key indicators: i) The ratio of Democrats to Democrats and Republicans D/(D+R), which reveals that partisan segregation has increased across geographical units, at 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 further decompose the sources of changes in partisan segregation, calculating the extent to which changes are driven by voter mobility, new voters entering the electorate, and by voters changing their partisanship. The rise in partisan segregation appears mostly driven by generational change in Democrat areas and by the increasing ideological conformity of stayers in Republican areas.

Works in Progress

  • Partisan Segregation and Partisan Activation: How Geographic Polarization Increases Political Engagement.
  • Norms of Intolerance: How Partisan Animosity Spreads in Social Relations. (with Sun Young Park).
  • Who are America’s Homeless Voters? How Shelters Mobilize Housing Insecure Citizens. (with Michael Zoorob).
  • Using Public Camera Feeds to Study Social Distancing and Pro-Health Behaviors in a Pandemic. (with Soubhik Barari, Bryce Dietrich, Ryan Enos, and Melissa Sands).
  • Modeling Turnout. (with Stephen Ansolabehere, Kabir Khanna, and Charles Stewart).
  • Priming Bias Versus Post-Treatment Bias in Experimental Designs. (with Matthew Blackwell, Sophie Hill, Kosuke Imai, and Teppei Yamamoto).

Ongoing Data Collection

  • Started in 2020, the Political Geography Panel Survey is a nationwide annual survey collecting repeated measures of political attitudes that can be connected to changes in geographic context. A feature of this survey is that responses are linked to voterfile data with exact residential address information, allowing for precise tests of the relationship between geography and public opinion. Survey questions cover voters' perceptions of their neighborhoods and residential areas, partisan and racial attitudes, political engagement, and opinions about national and local issues. The survey is run in collaboration with Ryan Enos.

  • The Senior Citizen Survey and Census Linkage Project is a survey targetting voters who were alive in the 1940s and 1950s, collecting contemporary survey data that can be linked to 1940 and 1950 Census data. The central aim of this project is to connect early life experiences to later-in-life political opinions, building off previous work connecting childhood cross-ethnic exposure to downstream partisanship. The survey further collects respondent reflections on over 80 years of American history, offering a unique perspective from a generation of voters that grew up in the wake of World War II and came of age in the 1960s and 1970s. This project is run in collaboration with Ryan Enos, James Feigenbaum, Shom Mazumder, and Dominic Valentino.
  • Other Writing