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Can Partisan Cues Diminish Democratic Accountability?

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Abstract

When evaluating political candidates, citizens can draw on partisan stereotypes and use partisan cues to make inferences about the candidates’ issue positions without undertaking a costly information search. As long as candidates adopt policy positions that are congruent with partisan stereotypes, partisan cues can help citizens make an accurate voting decision with limited information. However, if candidates take counter-stereotypical positions, it is incumbent upon citizens to recognize it and adjust their evaluations accordingly. Using the dual-processing framework, I hypothesize about the conditions under which individuals reduce their reliance on partisan cues and scrutinize counter-stereotypical messages, and test these hypotheses with experimental data collected from a nationally representative sample of adults. The findings show that whether individuals punish a candidate from their party for taking a counter-stereotypical position is contingent on the salience of the issue and the political awareness of the message recipient. The article concludes with a discussion of the theoretical and normative implications of these findings.

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Notes

  1. Petty and Cacioppo (1986) call systematic processing, central processing and heuristic processing, peripheral processing. The elaboration likelihood model that they develop is similar to Chaiken, Liberman, and Eagly’s framework, but differs in several key respects. Nevertheless, both theories generate the same observable implications about the effects of counter-stereotypical messages in this research setting. For the sake of expositional clarity, I use the heuristic-systematic terminology found in Chaiken et al. (1989).

  2. An additional 142 subjects were assigned to a control group. These individuals were asked to state their preferences on the issues raised in the treatment conditions, but because they did not read any articles about the candidates, they were not asked to evaluate them. The treatments did not affect the subjects’ issue attitudes, and thus, are not discussed in this study. These results are available from the author upon request. The experiment was conducted between January 6 and 12, 2005; the completion rate was 72.4% and the AAPOR-standard response rate 3 was 41.4%. Because it was drawn from a nationally representative panel, the sample is quite diverse. (See Table A1 in the appendix for a summary of demographic characteristics.)

  3. At the end of the study, subjects were informed that the news story was not real.

  4. As a randomization check, a joint-test of statistical significance shows that treatment assignment does not systematically covary with subjects’ demographic and attitudinal characteristics (age, gender, education, income, ethnicity, martial status, urbanity, region, home ownership, party identification, ideology, and knowledge about politics) (χ2[189] = 184.22, p = 0.585).

  5. Subjects who responded “don’t know” to these items were placed at the center of the scale. The results generated by this approach do not differ substantively from the alternative strategy in which these subjects are excluded from the analysis.

  6. KN measured subjects’ educational attainment prior to the study, and so it was unnecessary to include it in the post-treatment survey. The results are not affected substantively by excluding education from the scale or randomly assigning don’t know responses to correct and incorrect responses to address the possibility that particular individuals are more likely to answer “don’t know” on knowledge questions even when they know the correct answer (cf. Mondak 2000).

  7. I calculated the first differences and their standard errors with Monte Carlo simulations using the Clarify program for Stata (Tomz et al. 2003).

  8. These p-values were calculated by subtracting the counter-stereotypical effect for all Democrats from the counter-stereotypical effect for liberal Democrats, estimating the standard error of the difference, and calculating the resulting t-statistic. All p-values are one-tailed.

  9. The values reported in Figs. 1 and 2 are first differences calculated in the same fashion as the ones in Table 3, except the political awareness of the subject is taken into account. For instance, the expected counter-stereotypical effect for a Democrat in the high-salience issue condition is estimated as follows:

    $$ {\rm E}(Y|RC = 0,CP = 1,D = 1,HS = 1,PA = x) - {\rm E}(Y|RC = 0,CP = 0,D = 1,HS = 1,PA = x), $$

    where Y = candidate evaluation, RC = Republican candidate, CP = conservative position, D = Democratic subject, HS = high-salience issue, PA = political awareness, and x = an arbitrary value on the PA scale. These quantities and their 95% confidence intervals were estimated with the Clarify program (Tomz et al. 2003).

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Acknowledgments

This research was made possible with the help of the Time-sharing Experiments for the Social Sciences project, which is funded by National Science Foundation Grant 0094964, Diana C. Mutz and Arthur Lupia, Principal Investigators. I am also deeply indebted to Jamie Druckman, Don Green, Martin Johnson, Megan Mullin, seminar participants at the University of California-Riverside, and the anonymous referees for their thoughtful comments at various phases in the project. As always, though, any errors remain my own.

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Authors and Affiliations

  1. Department of Political Science, Institute for Public Affairs,, Temple University, 453 Gladfelter Hall, 1115 West Berks St, Philadelphia, PA, 19122, USA

    Kevin Arceneaux

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  1. Kevin Arceneaux
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Correspondence to Kevin Arceneaux.

Appendix: Survey Question Wording

Appendix: Survey Question Wording

  • Q1A. [Subjects in abortion condition and control group] What statement best describes your opinion on abortion?

    I strongly believe that abortion should rarely be legal

    1

    2

    3

    4

    5

    I strongly believe that abortion should be legal most of the time

  • Q1B. [Subjects in federalism condition and control group] Which level of government should have the most responsibility when it comes to protecting the environment?

    I strongly believe that the federal government should have the most responsibility

    1

    2

    3

    4

    5

    I strongly believe that the state and local government should have the most responsibility

  • Q2. Based on what you know now, how much would you like to see Kirk Watson win the congressional race?

    I would very much like to see Kirk Watson win

    1

    2

    3

    4

    5

    I would not like to see Kirk Watson win at all

  • Q3. Based on what you know now, how well do you think Kirk Watson would represent you?

    I strongly believe Kirk Watson WOULD represent me well

    1

    2

    3

    4

    5

    I strongly believe Kirk Watson would NOT represent me well

  • Q4. Generally speaking, when it comes to politics would you consider yourself...

    • (1) A Democrat, (2) A Republican, (3) An Independent, (4) Or something else? (8) Don’t Know

  • Q5. Who is Bill Frist?

    • (1) Secretary of the Treasury, (2) U.S. Senate Majority Leader, (3) Prime Minister of Canada, (8) Don’t Know

  • Q6. Whose responsibility is it to determine if a law is constitutional or not?

    • (1) President, (2) Congress, (3) Supreme Court, (8) Don’t Know

  • Q7. How much of a majority is required for the U.S. Senate and House to override a presidential veto?

    • (1) Unanimous, (2) Two-thirds, (3) One-fifth, (4) Simple majority, (8) Don’t Know

Table A1 Demographic characteristics of knowledge networks sample
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Model Estimates

Below are the model estimates that generated the substantive effects reported in the analysis section of the article. The effects reported in Table 3 are derived from the models reported in Table A2, and the effects reported in Figs. 1 and 2 are derived from the models displayed in Table A3.

Table A2 Interactive model estimates for the effect of issue position, issue salience, and partisanship on candidate evaluations
Full size table
Table A3 Interactive model estimates for the effect of issue position, issue salience, partisanship, and political awareness on candidate evaluations
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Arceneaux, K. Can Partisan Cues Diminish Democratic Accountability?. Polit Behav 30, 139–160 (2008). https://doi.org/10.1007/s11109-007-9044-7

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