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


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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  1. 1.

    Replication data for this paper is available on the Political Behavior Dataverse at

  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.


  1. Adams, J., Engstrom, E., Joesten, D. A., Stone, W. J., Rogowski, J., & Shor, B. (2016). Do moderate voters weigh candidates’ ideologies? Voters’ decision rules in the 2010 congressional elections. Political Behavior, 39(1), 205–227.

    Article  Google Scholar 

  2. Ansolabehere, S., Snyder, J. M., Jr., & Stewart III. C. (2001). Candidate positioning in US house elections. American Journal of Political Science, 45(1), 136–159.

  3. Ansolabehere, S., & Jones, P. E. (2010). Constituents’ responses to congressional roll-call voting. American Journal of Political Science, 54(3), 583–597.

    Article  Google Scholar 

  4. Ashworth, S., & Mesquita, E. B. D. (2009). Elections with platform and valence competition. Games and Economic Behavior, 67(1), 191–216.

    Article  Google Scholar 

  5. Black, D. (1948). On the rationale of group decision-making. The Journal of Political Economy, 56(1), 23–34.

    Article  Google Scholar 

  6. Bonica, A. (2013). Ideology and interests in the political marketplace. American Journal of Political Science, 57(2), 294–311.

    Article  Google Scholar 

  7. Bonica, A. (forthcoming). Inferring roll call scores from campaign contributions using supervised machine learning. American Journal of Political Science.

  8. Bonica, A., & Cox, G. W. (2017). Ideological extremists in the US congress: Out of Step but still in office. Available at:

  9. Brady, D. W., Fiorina, M. P., & Wilkins, A. S. (2011). The 2010 elections: Why did political science forecasts go awry? PS: Political Science & Politics, 44(02), 247–250.

  10. Canes-Wrone, B., Brady, D. W., & Cogan, J. F. (2002). Out of step, out of office: Electoral accountability and house members’ voting. American Political Science Review, 96(1), 127–140.

    Article  Google Scholar 

  11. Clinton, J. D. (2006). Representation in congress: Constituents and roll calls in the 106th house. Journal of Politics, 68(2), 397–409.

    Article  Google Scholar 

  12. Clinton, J., Jackman, S., & Rivers, D. (2004). The statistical analysis of roll call data. American Political Science Review, 98(2), 355–370.

    Article  Google Scholar 

  13. Dancey, L., & Sheagley, G. (2013). Heuristics behaving badly: Party cues and voter knowledge. American Journal of Political Science, 57(2), 312–325.

    Article  Google Scholar 

  14. Downs, A. (1957). An economic theory of democracy. New York: Harper and Row.

    Google Scholar 

  15. Enelow, J. M., & Hinich, M. J. (1984). The spatial theory of voting: An introduction. Cambridge University Press.

  16. Fiorina, M. P., & Abrams, S. J. (2008). Political polarization in the American public. Annual Review of Political Science, 11(1), 563–588.

    Article  Google Scholar 

  17. Fowler, A., & Hall, A. B. (2016). The elusive quest for convergence. Quarterly Journal of Political Science, 11(1), 131–149. URL

  18. Green, D., Palmquist, B., & Schickler, E. (2002). Partisan hearts and minds: Political parties and the social identities of voters. Yale University Press.

  19. Groseclose, T. (2001). A model of candidate location when one candidate has a valence advantage. American Journal of Political Science, 45(4), 862–886.

    Article  Google Scholar 

  20. Hall, A. B. (2015). What happens when extremists win primaries? American Political Science Review, 109(01), 18–42.

    Article  Google Scholar 

  21. Hall, A. B., & Snyder, J. M., Jr. (2013). Candidate ideology and electoral success. Available at:

  22. Hill, S. J., & Huber, G. A. (2015). Representativeness and motivations of the contemporary donorate: Results from merged survey and administrative records. Political Behavior, 1–27.

  23. Hirano, S., Lenz, G. S., Pinkovskiy, M., & Snyder, J. M. (2015). Voter learning in state primary elections. American Journal of Political Science, 59(1), 91–108.

    Article  Google Scholar 

  24. Hopkins, D. J. (2018). The increasingly United States: Why American political behavior nationalized. University of Chicago Press.

  25. Jessee, S. (2016). (How) can we estimate the ideology of citizens and political elites on the same scale? American Journal of Political Science, 60(4), 1108–1124.

    Article  Google Scholar 

  26. Jessee, S. A. (2009). Spatial voting in the 2004 presidential election. American Political Science Review, 103(1), 59–81.

    Article  Google Scholar 

  27. Jessee, S. A. (2012). Ideology and spatial voting in American elections. New York, NY: Cambridge University Press.

    Google Scholar 

  28. Joesten, D. A., & Stone, W. J. (2014). Reassessing proximity voting: Expertise, party, and choice in congressional elections. The Journal of Politics, 76(3), 740–753.

    Article  Google Scholar 

  29. Jones, P. E. (2011). Which buck stops here? Accountability for policy positions and policy outcomes in congress. Journal of Politics, 73(3), 764–782.

    Article  Google Scholar 

  30. Lee, D. S., Moretti, E., & Butler, M. J. (2004). Do voters affect or elect policies? Evidence from the US house. The Quarterly Journal of Economics, 119(3), 807–859.

    Article  Google Scholar 

  31. Lenz, G. S. (2013). Follow the leader? How voters respond to politicians’ policies and performance. University of Chicago Press.

  32. Levitt, S. D. (1996). How do senators vote? Disentangling the role of voter preferences, party affiliation, and senator ideology. The American Economic Review, 86(3), 425–441.

    Google Scholar 

  33. Lewis, J., & Tausanovitch, C. (2013). Has joint scaling solved the achen objection to miller and stokes? Presented at the Vanderbilt Miller-Stokes Conference on Representation. Available at:

  34. Montagnes, B. P., & Rogowski, J. C. (2015). Testing core predictions of spatial models: Platform moderation and challenger success. Political Science Research and Methods, 3(03), 619–640.

    Article  Google Scholar 

  35. Nyhan, B., McGhee, E., Sides, J., Masket, S., & Greene, S. (2012). One vote out of step? The effects of salient roll call votes in the 2010 election. American Politics Research, 40(5), 844–879.

    Article  Google Scholar 

  36. Poole, K. T., & Rosenthal, H. (2000). Congress: A political-economic history of roll call voting. New York, NY: Oxford University Press.

    Google Scholar 

  37. Rabinowitz, G., & Macdonald, S. E. (1989). A directional theory of issue voting. The American Political Science Review, 83(1), 93–121.

    Article  Google Scholar 

  38. Shor, B., & Rogowski, J. C. (2016). Ideology and the congressional vote. Political Science Research and Methods .

  39. Shor, B., & McCarty, N. (2011). The ideological mapping of American legislatures. American Political Science Review, 105(03), 530–551.

    Article  Google Scholar 

  40. Simas, E. N. (2013). Proximity voting in the 2010 US House elections. Electoral Studies, 32(4), 708–717.

    Article  Google Scholar 

  41. Sniderman, P. M. & Stiglitz, E. H. (2012). The reputational premium: A theory of party identification and policy reasoning. Princeton University Press.

  42. Stone, W. J., & Simas, E. N. (2010). Candidate valence and ideological positions in US House elections. American Journal of Political Science, 54(2), 371–388.

    Article  Google Scholar 

  43. Tausanovitch, C., & Warshaw, C. (2013). Measuring constituent policy preferences in congress, state legislatures and cities. Journal of Politics, 75(2), 330–342.

    Article  Google Scholar 

  44. Tausanovitch, C., & Warshaw, C. (2017). Estimating candidates’ political orientation in a polarized congress. Political Analysis, 25(2), 167–187.

    Article  Google Scholar 

  45. Tomz, M., & Van Houweling, R. P. (2008). Candidate positioning and voter choice. American Political Science Review, 102(3), 303–318.

    Article  Google Scholar 

  46. Wilkins, A. S. (2012). The effect of extreme incumbent roll-call voting records on U.S. House elections, 1900–2010. Prepared for presentation at the annual meeting of the American Political Science Association, New Orleans.

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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).

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  • Spatial voting
  • Electoral accountability
  • Congress
  • Representation