The measurement of partisan sorting for 180 million voters
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.
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A potential hurdle is homogenous networks, given residential partisan sorting, media partisan sorting, and patterns of canvassing.
“A potential hurdle, given residential partisan sorting (Brown & Enos, 2021), media partisan sorting (Broockman & Kalla, 2023b), and patterns of canvassing (Nall et al., 2018) is homogenous networks.”
From How to study democratic backsliding (2024) · cites this paper as Brown & Enos, 2021
Many partisans in the USA appear to live in areas where they have little exposure to out-party members.
“In any case, many partisans in the USA appear to live in areas where they have little exposure to out-party members211,330.”
From Cognitive–motivational mechanisms of political polarization in social-communicative contexts (2022) · cites this paper as [211]
The ideological homogeneity of social networks is enhanced by structural dynamics such as geographic sorting, the growing politicization of the rural-urban divide, and 'lifestyle' differences that are correlated with political orientation.
“The ideological homogeneity of social networks is enhanced by structural dynamics such as geographic sorting209–211, the growing politicization of the rural–urban divide212,213, and ‘lifestyle’ differences that are correlated with political orientation107,214–216”
From Cognitive–motivational mechanisms of political polarization in social-communicative contexts (2022) · cites this paper as [211]
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Self-citationDoes residential sorting explain geographic polarization?
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How digital media drive affective polarization through partisan sorting
Törnberg, P. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2207159119 · Cited by 288 · see citing works
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Self-citation
- Authors
- Brown, J. R., & Enos, R. D.
- Affiliations
- Harvard University (US)
- Corresponding author
- Brown, Jacob R.
- Enos, Ryan D.
- Venue
- Nature Human Behaviour
- Published
- 2021-03-08
- Volume
- 5
- Issue
- 8
- Pages
- 998-1008
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W3135902915
- Cited by
- 330
- References
- 6
- Citations by year
- 2026: 69 · 2025: 71 · 2024: 64 · 2023: 51 · 2022: 40 · 2021: 29 · 2020: 5 · 2018: 1
- FWCI
- 152.87
- Type
- journal article
- Language
- en
- Update notices
- correction (2025-01-28)
- Open access
- closed
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Social Sciences › Sociology and Political Science › Urban, Neighborhood, and Segregation Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Keywords
- residential segregation, political polarization, spatial segregation
- MeSH terms
- Social Segregation, Humans, Politics, Residence Characteristics, United States, Spatial Analysis
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