Centrist by Comparison: Extremism and the Expansion of the Political Spectrum


While it is well understood that policy suggestions outside the range of mainstream debate are prevalent in various policy domains of American politics, their effects remain unexplored. In this paper, we suggest that proposing policies far from the political mainstream can re-structure voter perceptions of where alternatives lie in the ideological space. We provide support for this hypothesis using results from six survey experiments. We find that the introduction of extreme alternatives into the public discourse makes mainstream policies on the same side of the spectrum look more centrist in the public eye, thus increasing support for these moderate alternatives.

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Fig. 1


  1. 1.

    Political pundits, on the other hand have suggested that the introduction of extreme policy alternatives into the political discourse is strategic, serving the purpose of shifting the scope of the political debate. This idea is usually attributed to Joseph Overton, who argued that the range of policies or opinions deemed acceptable by the public (the Overton-window) is in a constant flux and can be shifted by introducing and defending ideas not yet “on the table”.

  2. 2.

    To our knowledge, Herne (1997) is the only study reporting on the analysis of context dependent preferences over policies. While that study provides evidence on preference reversals in the context of choices between highly stylized multi-dimensional government programs, represented by numerical values it is unclear how much those results generalize to “simpler” and more salient issues.

  3. 3.

    We illustrate this logic with a simple formal and graphical analysis in Online Appendix A.

  4. 4.

    In the last study we added a further condition with both extreme policies.

  5. 5.

    We conducted multiple smaller studies instead of one large, but better powered experiment in order to increase our confidence that results are not driven by particular features of a particular set of issues or sample.

  6. 6.

    MTurk is now widely used to conduct survey experiments in political behavior (e.g. Huber et al. 2012; Healy and Gabriel 2014). See Berinsky, Huber, and Lenz (2009) for an assessment of MTurk as a data collection platform.

  7. 7.

    Descriptive statistics of the samples in each studies are reported in Table 5 in the Appendix.

  8. 8.

    We also used slightly different survey instruments for measurement in this study as it was programmed using YouGov’s own survey software (the questionnaires for the first and third studies were programmed with Qualtrics).

  9. 9.

    Note that since all subjects were shown these alternatives, we can recover each respondent’s preference with respect to these alternatives. For our analyses we discard information about the absolute rankings because they are not comparable across different choice sets (the distribution of the top-ranked choices across experiments is reported in Fig. S3 in the SI).

  10. 10.

    As Table 6 in the Appendix shows, the randomization was successful in the sense that in each study treatment group assignment was unrelated to measured pre-treatment covariates.

  11. 11.

    We replicated each regression with binary outcomes (such as preference for the moderate liberal policy) using logit models that yielded similar results. We chose to report estimates from the linear model for an ease of interpretation and because the specifications are saturated.

  12. 12.

    In Studies 1 and 2 these indicators are orthogonal to each other (i.e. either a liberal or a conservative extreme policy is introduced). In Study 3, some respondents saw both extreme alternatives, so in this case the estimated effects of these indicators capture the marginal effect of adding a given alternative.

  13. 13.

    We use medians to guard against the effect of outliers which are likely to be due to measurement error (e.g. respondents confusing the endpoint of scales). The results look essentially the same with means as summary statistics.

  14. 14.

    For instance, liberals are overrepresented in the MTurk samples. However, it is also possible that effect sizes differ because the MTurk respondents are more attentive to the survey. Unfortunately it is impossible to disentangle these two explanations (differences in political beliefs vs. survey taking strategies) with our data.

  15. 15.

    A possible problem of adjusting for respondents issue preferences is that they might be affected by the treatment itself, leading to post-treatment bias. At the same time our analyses of range effects presented above showed no evidence of the exposure to extreme alternatives influencing issue preferences. Also, we get similar results when we adjust for predetermined covariates in the pooled model such as partisanship.

  16. 16.

    We restrict our attention here to the first two studies (i.e. first four experiments) where we measured respondents’ own issue preferences (on the same scale as their perceived ideological position of policies). For each respondent, we calculated the perceived spatial distance of the two moderate policy alternatives by taking the absolute value of her rating of a given policy and her own issue preference. We then defined perceived relative proximity to the moderate liberal alternative by taking a respondents’ perceived distance from the moderate liberal policy and subtracting that from her perceived distance from the moderate conservative policy.

  17. 17.

    In the terminology of framing theory, our stimuli can be considered repeated “weak frames”, which have shown to have very limited impact on attitudes (Chong and Druckman 2007).

  18. 18.

    For instance, we would not expect any treatment effects for a sample in which all subjects have extremely liberal or conservative issue preferences.

  19. 19.

    We provide more detail about this survey in the SI.


  1. Abramowitz, A.I. (1978). The impact of a presidential debate on voter rationality. American Journal of Political Science, 22(3), 680–690.

    Article  Google Scholar 

  2. Bailey, M. A. Mummolo, J., & Hoel, N. (2012). Tea Party influence: A story of activists and elites. American Politics Research, 769-804.

  3. Bakker, R., Jolly, S., Polk, J., & Poole, K. (2014). The European common space: Extending the use of anchoring vignettes. The Journal of Politics, 76(4), 1089–1101.

    Article  Google Scholar 

  4. Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research: Amazon.com’s Mechanical Turk. Political Analysis, 20, 251–68.

    Article  Google Scholar 

  5. Broockman, D. E. (2016). Approaches to studying representation. Legislative Studies Quarterly, 41(1), 181–215.

    Article  Google Scholar 

  6. Bullock, J. G. (2011). Elite influence on public opinion in an informed electorate. American Political Science Review, 105(3), 496–515.

    Article  Google Scholar 

  7. Callander, S. & Wilson, C. H. (2006). Context-dependent voting. Quarterly Journal of Political Science, 1(3), 227–254.

    Article  Google Scholar 

  8. Chong, D. & Druckman, J. N. (2007). Dynamic public opinion: Communication effects over time. American Political Science Review, 104(4), 663–680.

    Article  Google Scholar 

  9. Chong, D. & Druckman, J. N. (2007). Framing theory. Annual Review of Politcal Science, 10, 103–126.

    Article  Google Scholar 

  10. Claassen, R. L. & Nicholson, S. P. (2013). Extreme voices: Interest groups and the misrepresentation of issue publics. Public Opinion Quarterly, 77(4), 861–887.

    Article  Google Scholar 

  11. Daily, K. (2006). Why the Right-Wing Gets It-and Why Dems Don’t. http://www.dailykos.com/story/2006/05/09/208784/-Why-the-Right-Wing-Gets-It-and-Why-Dems-Don-t-UPDATED

  12. Downs, A. (1957). An economic theory of political action in a democracy. The Journal of Political Economy, 135–150.

  13. Druckman, J. N. & Leeper, T. J. (2012). Learning more from political communication experiments: Pretreatment and its effects. American Journal of Political Science, 56(4), 875–896.

    Article  Google Scholar 

  14. Egan, P.J. (2014). Do something politics and double-peaked policy preferences. The Journal of Politics, 76(2), 333–349.

    Article  Google Scholar 

  15. Ezrow, L., Homola, J., & Tavits, M. (2014). When extremism pays: Policy positions, voter certainty, and party support in postcommunist Europe. The Journal of Politics, 76(2), 535–547.

    Article  Google Scholar 

  16. Ezrow, L., Tavits, M., & Homola, J. (2014). Voter polarization, strength of partisanship, and support for extremist parties. Comparative Political Studies, 47(11), 1558–1583.

    Article  Google Scholar 

  17. Goodman, C., Grimmer, J., Parker, D., and Zlotnick, F. (2015). Creating and Destroying Party Brands. Unpublished Working Paper, URL http://stanford.edu/ jgrimmer/destroy. pdf.

  18. Grose, C. R., Malhotra, N., & Parks Van Houweling, R. (2015). Explaining explanations: How legislators explain their policy positions and how citizens react. American Journal of Political Science, 59(3), 724–743.

    Article  Google Scholar 

  19. Healy, A. & Lenz, G. S. (2014). Substituting the end for the whole: Why voters respond primarily to the election year economy. American Journal of Political Science, 58(1), 31–47.

    Article  Google Scholar 

  20. Herne, K. (1997). Decoy alternatives in policy choices: Asymmetric domination and compromise effects. European Journal of Political Economy, 13(3), 575–589.

    Article  Google Scholar 

  21. Huber, G. A., Hill, S. J., & Lenz, G. S. (2012). Sources of bias in retrospective decision making: Experimental evidence on voters’ limitations in controlling incumbents. American Political Science Review, 106(4), 720–741.

    Article  Google Scholar 

  22. Hutchinson, J. W. (1983). On the locus of range effects in judgment and choice. In R. P. Bagozzi & A. M. Tybout (Eds.), NA - Advances in Consumer Research (Vol. 10, pp. 305–308). Ann Abor: Association for Consumer Research.

    Google Scholar 

  23. King, G. & Wand, J. (2007). Comparing incomparable survey responses: Evaluating and selecting anchoring vignettes. Political Analysis, 15(1), 46–66.

    Article  Google Scholar 

  24. Lenz, G. S. (2009). Learning and opinion change, not priming: Reconsidering the priming hypothesis. American Journal of Political Science, 53(4), 821–837.

    Article  Google Scholar 

  25. Lenz, G. S. (2012). Follow the leader? How voters respond to politicians’ performance and policies. Chicago: University of Chicago Press.

    Google Scholar 

  26. McCarty, N., Poole, K. T., & Rosenthal, H. (2006). Polarized America: The dance of ideology and unequal riches. Cambridge: MIT Press.

    Google Scholar 

  27. Mebane, W. R. Jr. and Waismel-Manor, I.S. (2005). Does it help or hurt Kerry if Nader is on the ballot?” Paper prepared for presentation at the annual meeting of the Midwest Political Science Association. April 6. http://citeseerx.ist.psu.edu/viewdoc/download?doi=

  28. Pan, Y., O’Curry, S., & Pitts, R. (1995). The attraction effect and political choice in two elections. Journal of Consumer Psychology, 4(1), 85–101.

    Article  Google Scholar 

  29. Parducci, A. (1965). Category judgment: A range-frequency model. Psychological Review, 72(6), 407–418.

    Article  Google Scholar 

  30. Parducci, A. (1968). The relativism of absolute judgment. Scientific American, 219(6), 84–90.

    Article  Google Scholar 

  31. Peterson, E. & Simonovits, G. (Forthcoming). Costly values: Values- based justifications exacerbate the consequences of policy disagreements for politician support? Journal of Experimental Political Science

  32. Rivers, D. (2006). Sample matching: representative sampling from internet samples. Polimetrix White Paper Series. http://www.websm.org/uploadi/editor/1368187057Rivers_2006_Sample_matching_Representative_sampling_from_Internet_panels.pdf

  33. Rotter, G. S. & Rotter, N. G. (1966). The influence of anchors in the choice of political candidates. The Journal of Social Psychology, 70(2), 275–280.

    Article  Google Scholar 

  34. Sniderman, P. M. & Bullock, J. (2004). A consistency theory of public opinion and political choice: The hypothesis of menu dependence (pp. 337–357). Attitudes, nonattitudes, measurement error, and change: Studies in public opinion.

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

    Google Scholar 

  36. Tourangeau, R. & Rasinski, K. A. (1988). Cognitive processes underlying context effects in attitude measurement. Psychological Bulletin, 103(3), 299–314.

    Article  Google Scholar 

  37. Tversky, A. & Simonson, I. (1993). Context-dependent preferences. Management Science, 39(10), 1179–1189.

    Article  Google Scholar 

  38. Yeung, C. W. M. & Soman, D. (2005). Attribute evaluability and the range effect. Journal of Consumer Research, 32(3), 363–369.

    Article  Google Scholar 

  39. Zaller, J. (1992). The nature and origins of mass opinion. Cambridge: Cambridge University Press.

    Google Scholar 

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Corresponding author

Correspondence to Gabor Simonovits.

Additional information

Data for Study 2 was collected by the Omnibus Survey of the Laboratory for the Study of American Values (LSAV) at Stanford University. The author thanks Steve Callander, Pat Egan, Simon Ejdemyr, Neil Malhotra, Julia McLean, Erik Peterson, Itamar Simonson, Paul Sniderman and Mike Tomz for helpful comments. All remaining errors are mine. The data and code necessary to replicate the numerical results is available at the Journals Dataverse at http://dx.doi.org/10.7910/DVN/EXVUEA.

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Supplementary material 1 (pdf 302 KB)



See Tables 5 and 6.

Table 5 Demographics
Table 6 Randmization check

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Simonovits, G. Centrist by Comparison: Extremism and the Expansion of the Political Spectrum. Polit Behav 39, 157–175 (2017). https://doi.org/10.1007/s11109-016-9351-y

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  • Public opinion
  • Extremism
  • Political psychology