Partisan Alignment Increases Voter Turnout: Evidence from Redistricting

Abstract

Partisan gerrymandering and polarization have created an electoral landscape where Americans increasingly reside in congressional districts dominated by one party. Are individuals more likely to vote when their partisanship aligns with the partisan composition of the district? Leveraging nationwide voter file data and the redistricting process, we present causal evidence on this question via a longitudinal analysis of individual-level political participation. Tracking turnout before and after a redistricting cycle, where the boundaries of congressional districts change, we observe what happens when registrants experience a shock to the partisan composition of their district. We find turnout increases for individuals assigned to districts aligned with their partisanship as compared to individuals in misaligned districts, consistent with voters deriving expressive benefits from voting for the winning party. By demonstrating how districting influences political participation, our findings suggest a new implication of partisan gerrymandering that may clash with other democratic goals.

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Notes

  1. 1.

    Benisek v. Lamone, 348 F. Supp. 3d 493, 501 (D. Md. 2018).

  2. 2.

    https://ballotpedia.org/Congressional_elections_decided_by_10_percent_or_less,_2018.

  3. 3.

    In instrumental models of voting, the size of the expressive benefit to voting is not a function of which candidate wins. In subsequent sections of the paper, we will use the term “expressive voting” to refer to our narrower expressive voting hypothesis.

  4. 4.

    Alternatively, we may consider “threat” to be greatest when the risk of loss is greatest, as in highly competitive districts. In this case, turnout should be highest in the most competitive districts, and less high in both heavily aligned and heavily misaligned districts.

  5. 5.

    Online Appendix Section A.4 provides context for analyzing differences in turnout in presidential versus mid-term election years.

  6. 6.

    See here for basic information on Catalist: http://www.catalist.us/data/.

  7. 7.

    See Online Appendix Section A.6 for the list of party-registration states in the sample.

  8. 8.

    Specifically, we use an average of the de-meaned presidential vote from the 2004 and 2008 presidential elections to measure the district partisan composition (Moskowitz and Schneer 2019). We use this same measure based on the 2004 and 2008 presidential election returns to measure district partisanship for pre-2012 district boundaries and post-redistricting boundaries.

  9. 9.

    We use mean deviations here to address the issue of partisan tides; doing so facilitates direct comparisons within districts across years.

  10. 10.

    Using a binary variable in this context allows us to focus on the comparisons between aligned and misaligned districts rather than changes within each category. Second, and crucially, it allows us to match exactly on similar voters who do and do not experience changes in partisan alignment. Third, we examine an alternate threshold of \(\pm 10\) in the Online Appendix and it does not appear to make a meaningful difference to our results.

  11. 11.

    When estimating the effect of moving from a competitive to a misaligned district, we conceptualize the binary variable as \(\text {Partisan Misalignment}_{ist}\) with a value of 1 when party registration is opposite district partisanship and 0 otherwise.

  12. 12.

    As noted above, the sample includes only stationary individuals.

  13. 13.

    This approach is based on the framework provided in Sekhon and Titiunik (2012).

  14. 14.

    For a sense of the extent of changes in partisan composition experienced by voters in the sample, see the histograms in Figures A.5–A.7.

  15. 15.

    The new regression equation takes the form \(E(\text {Turnout}_{ist}) = \gamma _i + \lambda _{st} + \delta \cdot \text {Partisan Alignment}_{ist} + \zeta \cdot \text {Marginal Voter}_{ist} + \pi \cdot \text {Partisan Alignment}_{ist} \times \text {Marginal Voter}_{ist}\).

  16. 16.

    Online Appendix A.8 outlines some other possible explanations for this phenomenon.

  17. 17.

    As in our analyses using voter file data, we restrict to respondents who did not move throughout the period of the panel. This question is only asked to panel respondents (i.e., it is not included in the CCES Common Content for 2012 or 2014).

  18. 18.

    Moving from an R+10 to a D+10 district is associated with about a 0.4 increase in perceived district partisanship (which is measured on a scale from -1 to 1).

  19. 19.

    While we focus on perceived district partisanship in 2012, results are extremely similar for perceptions of district partisanship in 2014. See Figure A.9 and Table A.31 in the Appendix.

  20. 20.

    For technical details on measuring voter awareness of the party’s candidate, see Online Appendix Section A.11.

  21. 21.

    Results are based on candidate evaluations made in 2010 and 2014. We use 2010 and 2014 because the 2010–2014 CCES Panel does not have candidate evaluations for 2008.

  22. 22.

    While we show the relationship between partisan alignment and ideology for 2012, the patterns are generally similar for other characteristics and other years.

  23. 23.

    Respondents are asked: “Did a candidate or political campaign organization contact you during the [INSERT YEAR] election?” If “yes,” they are asked: “How did these candidates or campaigns contact you...[in person / phone call / email or text message / letter or post card.”

  24. 24.

    We show this relationship for 2010, 2012, and 2014 in Figure A.10 in the Appendix.

  25. 25.

    The loess curve begins to increase again in the extreme ends of both aligned and misaligned districts, but very few CCES respondents are in these parts of the distribution.

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Acknowledgements

We are grateful for comments from Stephen Ansolabehere, Brad Gomez, Jon Rogowski and Jim Snyder, participants in the Harvard Kennedy School DPI Junior Faculty Workshop, as well as audiences at MPSA 2017 and the MIT Election Sciences workshop. Schneer thanks Florida State University for research support for this project in the form of the First-Year Assistant Professor Grant.

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Fraga, B.L., Moskowitz, D.J. & Schneer, B. Partisan Alignment Increases Voter Turnout: Evidence from Redistricting. Polit Behav (2021). https://doi.org/10.1007/s11109-021-09685-y

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Keywords

  • Redistricting
  • Partisanship
  • Voter turnout
  • Voter participation