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Going Maverick: How Candidates Can Use Agenda-Setting to Influence Citizen Motivations and Offset Unpopular Issue Positions

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Abstract

Holding an unpopular position on an issue important to voters can endanger a candidate’s electoral success. What is the candidate’s best agenda-setting strategy? To focus on other issue positions congruent with the same ideological stereotype, shoring up support among like-minded voters? Or to “go maverick” by discussing some issues that signal liberal positions and some that signal conservative positions? Existing voting models suggest the answer depends on voter preferences, since going maverick should have symmetric effects—support among voters who agree with the candidate’s positions will decrease, proportionally, as support increases among voters who disagree. We argue, however, that stereotype incongruence prompts these voters to process information differently, yielding asymmetric effects. We test our expectations experimentally, using a fictional candidate webpage to show how the benefits of going maverick can outweigh the costs.

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Notes

  1. In response to the question “How do you feel about the U.S. war in Iraq?” (N = 12,535), 13% of Republicans and 19% of Democrats said they “somewhat disapproved,” and 9% of Republicans and 65% of Democrats said they “strongly disapproved.” In response to the question “In your vote for U.S. House, how important was the war in Iraq?” (N = 6,350), 30% of Republicans and 40% of Democrats said “extremely important,” and 38% of Republicans and 28% of Democrats said “very important” (Roper Center 2006).

  2. Many people alternatively employ a likability heuristic—associating favorable positions with candidates whom they like and unfavorable positions with candidates whom they dislike (Brady and Sniderman 1985; Sniderman et al. 1993).

  3. We intentionally chose a set of eight issues that provided variation in terms of salience. While the more salient issues, like abortion and capital punishment, are relatively easy for most people to classify in terms of stereotypical ideological positions, others are more nuanced. The issue of prescription drugs is arguably the most challenging, yet even this issue can be ideologically stereotyped. George W. Bush (Miller 2007) and former Senate majority leader Bill Frist (R-TN) (Pear 2004) oppose the practice of reimportation, while in October 2009 (the time our study was run) prominent Democrats such as John Kerry (McCarthy 2004) and Barack Obama (Associated Press 2009) supported the practice. Note that the picture has become cloudier since the time of our study, as many Democrats opposed a December 2009 amendment to the health care reform bill that would have allowed the practice (Welna 2009). In order to confirm that most subjects would reasonably be able to identify the ideological sides of this issue, we administered a pretest in which subjects classified each of the two positions on each of our eight issues (as well as several issues not mentioned by the candidates) as liberal or conservative. In this pretest, 75% of subjects viewed prescription importation as a liberal position, which is lower than the 94% who classified support for gay marriage as liberal and the 87% who classified opposition to the death penalty as liberal, but significantly more than the 50% we would expect from random guessing (\( T_{530} = 15.5).\) Thus, the issue was largely identifiable in terms of ideological alignment.

  4. Table 2, Model 3 (in Appendix B) provides further evidence that the subjects were employing ideological stereotyping. The coefficient associated with the candidate’s conservatism (i.e., a four-point measure of candidate ideology. See Eq. 1 below for more details.) suggests that the probability of a subject placing the candidate as conservative on an issue increases with the candidate’s conservatism. This is strong support because the model includes subjects’ placements on issues that the candidate explicitly mentioned and controls for the stated position on the issue in question.

  5. As this example shows, many subjects saw liberal and conservative issue positions interspersed. The randomization of issue order, of course, caused some subjects to view the liberal (conservative) positions first followed by the conservative (liberal) positions.

  6. We exclude from analysis the 10 subjects who viewed the webpage for less than 10 s. We also exclude from analysis the 105 subjects who participated in a pilot test of the experiment. Finally, we exclude from analysis the 12 subjects who left the experiment early, leaving a total of 531 subjects for our analysis.

  7. The omitted baseline category contains subjects for whom the candidate did not discuss their MIP issue.

  8. The standard error of the marginal effect is equal to, \( s.e.(\frac{{\partial \hat{y}}}{\partial x}) = \sqrt { V(\hat{\beta }_{x} ) + z^{2} *V(\hat{\beta }_{xz} ) + 2*z*Cov(\hat{\beta }_{x } ,\hat{\beta }_{xz} )} \)

    Hence, for subjects with stereotype-incongruent candidates,

    \( s.e.\left( {\frac{{\partial \widehat{support}}}{\partial MIP Share}} \right) = 4.46^{2} + 6.03^{2} + 2* - 19.93 = \sqrt {16.4 } = 4.05 \).

  9. \( s.e.\left( {\frac{{\partial \widehat{support}}}{\partial MIP oppose}} \right) \)

    \( \begin{gathered} = \sqrt {V(\hat{\beta }_{MIP oppose} ) + Inconsistent^{2} *V(\hat{\beta }_{MIP oppose*Inconsistent} ) + 2*Inconsistent*Cov(\hat{\beta }_{MIP oppose} ,\hat{\beta }_{MIP oppose*Inconsistent} )} = \sqrt {17.54 + 33.65 - 35.02 } \hfill \\ = 4.02 \hfill \\ \end{gathered} \).

  10. Indicator variable coded one if subject correctly identified candidate’s stated position and zero otherwise.

  11. Indicator variable coded one if subject placed candidate as conservative on the issue and zero otherwise.

  12. We employ the Zelig package (Imai et al. 2008, 2009) in R to create the estimates by using the model parameters and associated uncertainty to generate 10,000 simulations of the relevant contrast. We compute the effect size and confidence interval from the distribution of these simulations with the median draw corresponding to the effect. The parameters we use to predict support are from Table 1 and the remaining estimates come from models presented in Table 2 that apply the model in equation 1 to the remaining three response variables.

  13. The covariate profile for all bars in Fig. 2 is a moderate subject in the Liberal treatment, with a candidate who is moderate overall. The estimates use the average intercept size and average coefficient for subject conservatism. The view time model includes a control for the subject’s average response latency (Fazio 1990). The predictions use the average of this measure.

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Authors and Affiliations

  1. Department of Political Science, University of California, Davis, 469 Kerr Hall, Davis, CA, 95616, USA

    Matthew T. Pietryka & Amber E. Boydstun

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  1. Matthew T. Pietryka
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  2. Amber E. Boydstun
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Correspondence to Matthew T. Pietryka.

Additional information

We thank Robert Huckfeldt and Walter J. Stone for helpful comments; Scott Sigmund Gartner and Carl L. Palmer for help coordinating the experimental session; and Douglas M. Gibler for providing a face for our fictional candidate.

Appendices

Appendix A

Fig. 3
figure 3

Example candidate webpage from experiment

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All subjects viewed the above webpage with the same first and third paragraphs; only the second paragraph varied. Subjects in the two Stereotype-Congruent treatments (Conservative and Liberal) viewed a webpage in which the second paragraph listed “Ted’s” ideologically-congruent policy positions (all conservative or all liberal) on five issues drawn randomly from the eight we employed in total. Subjects in the two Stereotype-Incongruent treatments viewed instead a second paragraph listing ideologically-incongruent policy positions on five randomly-drawn issues: either three liberal and two conservative, or three conservative and two liberal, but not necessarily clustered according to ideology. The example shown here is from one of the Stereotype-Incongruent (Conservative) treatments, offering three stereotypically-conservative positions (on abortion, banking, and capital punishment) and two stereotypically-liberal positions (on prescription drugs and gay marriage). In all cases, issue order was randomized.

Appendix B

Table 2 Measures of campaign engagement by MIP position and candidate stereotype congruence
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Pietryka, M.T., Boydstun, A.E. Going Maverick: How Candidates Can Use Agenda-Setting to Influence Citizen Motivations and Offset Unpopular Issue Positions. Polit Behav 34, 737–763 (2012). https://doi.org/10.1007/s11109-011-9180-y

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