Does the Ideological Proximity Between Candidates and Voters Affect Voting in U.S. House Elections?

Abstract

Do citizens hold congressional candidates accountable for their policy positions? Recent studies reach different conclusions on this important question. In line with the predictions of spatial voting theory, a number of recent survey-based studies have found reassuring evidence that voters choose the candidate with the most spatially proximate policy positions. In contrast, most electoral studies find that candidates’ ideological moderation has only a small association with vote margins, especially in the modern, polarized Congress. We bring clarity to these discordant findings using the largest dataset to date of voting behavior in congressional elections. We find that the ideological positions of congressional candidates have only a small association with citizens’ voting behavior. Instead, citizens cast their votes “as if” based on proximity to parties rather than individual candidates. The modest degree of candidate-centered spatial voting in recent Congressional elections may help explain the polarization and lack of responsiveness in the contemporary Congress.

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Notes

  1. 1.

    Replication data for this paper is available on the Political Behavior Dataverse at https://doi.org/10.7910/DVN/RRBQAU.

  2. 2.

    Wilkins (2012) finds that “ as polarization substantially increased during the 1990s and 2000s, the penalty for extremism in the 1990s got smaller and in the 2000s, the penalty was no longer significant.”

  3. 3.

    This is especially true given the fact that candidates’ quality and their spatial positioning is often conflated in observational studies. For instance, Canes-Wrone et al. (2002) only control for variation in the quality of incumbents via their campaign spending levels. If other, unobserved aspects of candidates’ quality is correlated with their levels of ideological extremity (e.g., more moderate candidates are higher quality in other respects), this is likely to lead to upwardly biased estimates of the effect of candidate positions on voter margins.

  4. 4.

    Note that our findings do not suggest that legislative candidates can take any position at all. For instance, ideologically extreme candidates that take positions far outside the bounds of their party’s platform may still face electoral consequences (Hall 2015).

  5. 5.

    Tomz and Van Houweling (2008) use survey experiments to adjudicate between theories of spatial and directional voting. They find that spatial voting is four times more common than directional voting.

  6. 6.

    This is one plausible explanation for the findings in Hall (2015) that more extreme candidates do significantly worse in open seat races.

  7. 7.

    In our parametric analysis we will employ both linear and logistic link functions for f.

  8. 8.

    Alternatively, directional voting theory proposes that distance be measured by the product of the absolute value of the distances of the voter and the candidate from some neutral point (Rabinowitz and Macdonald 1989).

  9. 9.

    Supplementary Appendix A shows the full derivation of each model, in which we expand the kernel of \(P(y=R) = f(\cdots )\) for each of the common spatial utility functions, as well as for directional voting.

  10. 10.

    Note that each of these surveys name both the challenger and incumbent candidates in each contest.

  11. 11.

    This choice does not significantly affect the results.

  12. 12.

    See Supplementary Appendix B for more details on both the survey sample and the ideal point measures.

  13. 13.

    Supplementary Appendix B shows all of the questions used in the ideal point model.

  14. 14.

    It is also important to note that the ideological locations between the candidates may be correlated with other differences, such as differences in valence or quality (see Groseclose 2001; Ashworth and De Mesquita 2009). However, it is difficult to measure the valence of challengers. As a result, previous spatial voting studies rarely explicitly control for these differences. We leave it to future work to better understand the role that valence plays in candidate choice.

  15. 15.

    First, in 2010, there is a correlation of only 0.05 between Democratic and Republican candidates’ positions on the National Political Awareness Test (NPAT) conducted by Project Vote Smart (Adams et al. 2016). Similarly, there is only a within-district correlation of .15 in the Campaign Finance (CF) scores of Democrats and Republicans in congressional elections between 2006–2012 (Bonica 2013).

  16. 16.

    A number of recent studies have used the National Political Awareness Test (NPAT) to estimate candidate ideology (see, e.g., Shor and McCarty 2011; Adams et al. 2016; Shor and Rogowski 2016).

  17. 17.

    See Adams et al. (2016, pp. 4–6) for more details on their methodology for bridging these latent positions. They state that “Project Vote Smart data provide information on both major-party candidates’ policy positions in 288 districts. [M]any of these questions–15 in all–matched (or nearly matched) the text of questions that appeared on the CCES, which allowed us to generate joint estimates of operational ideology for both citizens and candidates in a common space using the estimation procedure described above.”

  18. 18.

    The DW-DIME measure from Bonica (forthcoming) has not yet been subjected to the same scrutiny as previous measures. It shows promise, however, in overcoming critiques of previous measures (e.g., it displays a very high contemporaneous within-party correlation with the DW-Nominate scores of incumbents).

  19. 19.

    All of the analyses that follow focus on contested races, but the results are similar if we analyze all races.

  20. 20.

    All of the curves are weighted using respondents’ survey weights.

  21. 21.

    Of course, it is always possible that voters are capable of using a proximity voting rule, but that the use of such voting rules is not prevalent enough to matter. It is also possible that they use a proximity voting rule, but with respect to some orthogonal unmeasured policy or consideration.

  22. 22.

    Note that 67% of Democrats are in the liberal tercile.

  23. 23.

    Note that we use standardized measures of both voter and legislator ideology in all the regression analyses in Table 4.

  24. 24.

    As shown below, logistic regression models yield similar results. Also, all of the regression models are weighted using respondents’ survey weights. In addition, the standard errors in all the regression models are clustered at the state-year level.

  25. 25.

    For these analyses, we matched the data on candidates’ ideal points in the replication data of Adams et al. (2016) and Bonica (forthcoming) with our master dataset on voters’ preferences and voting behavior.

  26. 26.

    These models interact all coefficients with voters’ party identification.

  27. 27.

    The graphs are on a logistic regression of the model in Table 4 where voters’ party ID is interacted with the other terms in the model.

  28. 28.

    It is important to note, however, that the task of estimating voter positions in the space of legislators is a difficult one. It requires assuming equivalence between some set of behaviors that are driven by policy position: for instance, that casting roll call votes in a legislature can be considered equivalent to answering survey questions about roll call votes, or that campaign contributions are given to more spatially proximate candidates. Lewis and Tausanovitch (2013) and Jessee (2016) find that jointly scaling voters and legislators in the same space requires very strong modeling assumptions. Moreover, Lewis and Tausanovitch (2013) show that the data often do not support these assumptions.

  29. 29.

    We find substantively similar results with logistic regression models.

  30. 30.

    We use a logistic regression form of these models, which is more difficult to interpret but more appropriate for modeling a binary vote choice.

  31. 31.

    In contrast, most previous survey-based studies of spatial voting suggest much larger effects of candidate moderation on vote share. These large effects are inconsistent with the results in electoral studies.

  32. 32.

    Our results leave open the possibility that highly salient individual votes, such as the one on the Affordable Care Act, could have larger effects on election results than the aggregate measures of candidates’ ideological positions that we examine here (Brady et al. 2011; Nyhan et al. 2012).

  33. 33.

    Of course, it is possible that spatial voting for candidates may have been more important in earlier eras when the parties were less polarized.

  34. 34.

    However, it is important to note that this theory is observationally equivalent to several others. It may be the case the voters attempt to vote on the basis of candidate positions, but do so with extremely low acuity. Alternatively, the strength of affective party attachments may determine both policy positions and votes. Future work should seek to distinguish between these potential theoretical mechanisms.

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Acknowledgements

We are grateful to Devin Caughey, Robert Erikson, Anthony Fowler, Justin Grimmer, Seth Hill, Stephen Jessee, Jeffrey B. Lewis, Howard Rosenthal, and seminar participants at MIT’s American Politics Conference, Princeton University, the University of California-Berkeley, UCLA, and UCSD for feedback on previous versions of this manuscript.

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Correspondence to Chris Tausanovitch.

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This paper was previously circulated under the name “Electoral Accountability and Representation in the U.S. House: 2004–2012.”

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Tausanovitch, C., Warshaw, C. Does the Ideological Proximity Between Candidates and Voters Affect Voting in U.S. House Elections?. Polit Behav 40, 223–245 (2018). https://doi.org/10.1007/s11109-017-9437-1

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Keywords

  • Spatial voting
  • Electoral accountability
  • Congress
  • Representation