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District-based elections and class-based representation: evidence from the California Voting Rights Act

Published online by Cambridge University Press:  26 February 2026

Michaela Cushing-Daniels
Affiliation:
School of Public and International Affairs, University of Pittsburgh, Pittsburgh, PA, USA
Daniel Jones*
Affiliation:
School of Public and International Affairs, University of Pittsburgh, Pittsburgh, PA, USA
Brooke Shannon
Affiliation:
Department of Political Science, University of Memphis, Memphis, TN, USA
*
Corresponding author: Daniel Jones; Email: dbj10@pitt.edu
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Abstract

District-based elections are a central feature of local governance throughout the United States. Prior work has explored whether district-based elections impact racial/ethnic descriptive representation in local office; much less is known about the impacts of local district-based elections on other dimensions of representation. We consider another such dimension: socioeconomic class. To explore how district-based elections shape the composition of locally elected officials on class dimensions, we focus on city councils and study the dramatic shift towards district-based elections in California in the 2010s. We construct a statewide mapping of newly drawn council districts; we also draw on rich and partially hand-collected data on council candidates and members. We find that district-based elections increase the share of candidates and council members from lower-income and higher renter share neighborhoods, and lead to fewer members with business backgrounds.

Information

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2026. Published by Cambridge University Press on behalf of EPS Academic Ltd.
Figure 0

Figure 1. Cities by at-large vs. district-based elections during the sample period (2008–2020).

Note: “Switched” refers to cities that switched from at-large to district-based city council elections between 2008 and 2020.
Figure 1

Table 1. Average district characteristics, split by racial composition

Figure 2

Figure 2. Difference-in-difference analysis: how individual characteristics of candidates and winners change under district-based elections.

Note: Each point in the figure is from a distinct regression. The regression plots the “Post-Districting” coefficient from the difference-in-difference specification. Also displayed are 90% and 95% confidence intervals, with standard errors clustered at the city level.
Figure 3

Figure 3. Difference-in-difference analysis: how neighborhood characteristics of candidates and winners change under district-based elections.

Note: Each point in the figure is from a distinct regression. The regression plots the “Post-Districting” coefficient from the difference-in-difference specification. Also displayed are 90% and 95% confidence intervals, with standard errors clustered at the city level.
Figure 4

Figure 4. Event study: district-based elections and share of Hispanic winning candidates.

Note: Each panel in the figure is from a distinct event study regression. Estimates are depicted by the solid line. Also reported are 90% (dark shaded area) and 95% (light shaded area) confidence intervals, with standard errors clustered at the city level.
Figure 5

Figure 5. Difference-in-difference analysis: distinct effects of district-based elections in more and less income segregated cities.

Note: Each point in the figure is from a distinct regression. The regression plots the “Post-Districting” coefficient from the difference-in-difference specification. Also displayed are 90% and 95% confidence intervals, with standard errors clustered at the city level. “High (Low) BG Inc. Seg.” refers to cities that are above (below) median in the dissimilarity index, which captures residential segregation of higher and lower income individuals.
Figure 6

Figure 6. Difference-in-difference analysis: winning candidate characteristics interacted with race/eth.

Note: Each point in the figure is from a distinct regression. The regression plots the “Post-Districting” coefficient from the difference-in-difference specification. Also displayed are 90% and 95% confidence intervals, with standard errors clustered at the city level. All regressions are at the city-by-year level.
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