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How Bad is Bad?: Dispositional Negativity in Political Judgment

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Abstract

When citizens approach political decision-making tasks, they carry with them differing values and preferences, yielding heterogeneity in their assessments. This study explores one source of variation, the negativity bias, a response tendency in which individuals characteristically react more strongly to highly-salient negative information than to comparable positive information. In political science, most prior research on the negativity bias has been of two forms, either treating the bias as a universal, or seeking to identify correlates of individual-level variation. We advance a third track, one in which the individual-level negativity bias is viewed as a source of heterogeneity in the outcome of judgmental processes. If individual-level variation, labeled here as dispositional negativity, manifests itself in political decision-making, then variation in dispositional negativity may prompt otherwise similar citizens exposed to identical information to produce disparate responses. To explore this, we present a conceptual framework that clarifies the potential role of dispositional negativity in political judgment. Expectations arising from this framework are tested with data from vignette experiments included on two surveys: one with a national sample of Costa Ricans, and a second with respondents from three U.S. states.

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Notes

  1. In subsequent work, Ashare et al. (2013) identify genetic sources of individual-level variation in negativity, findings that underpin the claims of temporal and trans-situational consistency.

  2. Data for Study One and Study Two are available at https://doi.org/10.7910/DVN/FXTGF0.

  3. For a previous use of ANEW, see Lodge and Taber (2005). We added four words to diversify the battery: alegria (joy), besar (kiss), disfrutar (enjoy), and reir (laugh). For the eight terms, values on our survey and those reported in Hinojosa et al. (2016) are correlated at r = 0.76.

  4. We use the mean provided that the respondent rated at least two terms; 716 respondents rated all four. Our negativity measure is intentionally brief, as we are aware of the frequent need for truncated batteries on surveys. But our measure still must work. In its preliminary defense, we would note (1) it has high reliability, and (2) prior research has shown that even a single item can provide a satisfactory representation of both the negativity bias and the positivity offset (Larsen et al., 2009). We will see momentarily if use of the measure generates theory-consistent findings.

  5. There is nothing uniquely important about a linguistic measure. The word-based scale provides a rating of the intensity of responses to strong negative stimuli, much the same as does our rating of the mayor among subjects in the negative treatment condition. If dispositional negativity is a trait, it should operate trans-situationally. Evidence of that will be seen in the relationship between our linguistic measure and the dependent variable: how intensely individuals reacted to negative stimuli in one context should be predictive of how intensively they react to a different negative stimulus in a different context.

  6. Hibbing et al., (2014a, 2014b) find that negativity is correlated with ideological conservatism, whereas Fournier et al.’s (2020) cross-national test shows no consistent evidence of that link. Although our focus is not on correlates of negativity, we examined the relationship with ideology, both in bivariate form and with controls for age, sex, and education. Both tests show effects near zero (r = 0.02, b = 0.01, both n.s.).

  7. Our central test occurs in the third model, making the contrast between the first and third models important. This test is sufficiently powered (0.88 for a p < .05 threshold); a very similar power estimate (0.87) is obtained below in Study Two. Because dispositional negativity is observed rather than being experimentally manipulated, we ran alternate specifications of the models in Table 1 with controls included for age, education, gender, race and ethnicity, and ideology. Inclusion of these controls produced no noteworthy changes from the estimates in Table 1.

  8. In panel C, we include lines only for the positive and negative scenarios for the sake of visual clarity. Coefficient estimates in the second model in Table 1 show that slopes across dispositional negativity are statistically insignificant and substantively slight for all but the negative information treatment.

  9. A reader suggested we explore whether the effects of dispositional negativity might be nonlinear. Our theoretical account specifies the presence and direction of expected effects, but not their form. Prompted by the reader, we ran tests with a limited number of nonlinear specifications. No clear evidence emerged to suggest that any nonlinear treatment outperformed the linear specifications we report.

  10. Rather than dispositional negativity, might the results stem from a mechanism such as response extremity, neuroticism, or negative affect? As noted above, research in psychology (e.g., Ito and Cacioppo 2005) rules these out. Our findings support that view. The logic of response extremity is that some people react intensely as a matter of course, whether to positive or negative stimuli. It would follow that our erstwhile measure of dispositional negativity taps a general response tendency. In this scenario, we would have seen a significant interaction between dispositional negativity and the positive treatment, but no such interaction is observed. Also, respondents’ ratings of positive words should have been strongly negatively correlated with ratings of negative words. The correlations are negative, but the average correlation for a positive–negative pair is only − 0.23, versus 0.54 for negative-negative pairs and 0.51 for positive-positive pairs. The logic of neuroticism or negative affectivity is that some people worry about everything (neuroticism) or view everything pessimistically (negative affect). If our would-be dispositional negativity measure reflected one of these, we would have seen a significant main effect for dispositional negativity, and insignificant interactions with all treatments. That pattern is absent in Table 1. Lastly, a reviewer noted that three of the four terms could be interpreted as involving political violence, evoking negative political memories among some respondents, leading to negative effects in our models. This seems unlikely in Costa Rica, a nation with no history of terrorism or political violence. Also, this merely would be a political variant of negative affect, meaning dispositional negativity should suppress responses in all treatment conditions. It does not.

  11. The selection of these states was made by the survey’s administrators. We see no reason for dispositional negativity or its effects to differ across contexts, possibly making the location of the survey inconsequential. That said, we are mindful of Fournier et al.’s (2020) finding that the political correlates of negativity are not consistent cross-nationally. The fielding of our studies in Costa Rica and the U.S. provides leverage on this.

  12. We chose words from Bradley and Lang’s (1999) ANEW list, where valence is rated on a 1 to 9 scale. The ANEW project requests that the specific words and their ANEW scorings not be published. Negativity is measured with words 605, 514, 723, and 995 from Bradley and Lang (1999). We also included one neutral word (1008) and one positive word (420). For the six words, the correlation between the mean ratings we obtained and those in ANEW is 0.98.

  13. As in Costa Rica, dispositional negativity is unrelated to ideological conservatism in both bivariate (r = − 0.02) and multivariate (b = − 0.02) tests.

  14. An alternate specification included indicator variables to differentiate among the survey’s three states in case local circumstances shaped how subjects viewed the hypothetical governors. Inclusion of the state variables did not alter coefficient estimates for the other variables, and thus they are omitted. However, a significant negative effect emerged for the Kentucky indicator, the state where incumbent governor Matt Bevin went on to be defeated in his 2019 reelection bid. We also ran specifications of the third model controlling for education, age, race and ethnicity, gender, and ideology, plus another specification with neuroticism. These additions produced no noteworthy changes to the coefficients in Table 2.

  15. A reader suggested subjects might ignore gains and losses, and instead consider whether the final numbers were good or bad. We doubt this. First, we are skeptical that, absent contextual information, many people know whether a given crime or drop-out rate is good or bad. Second, the logic of retrospective voting is that voters consider whether conditions have improved or worsened during an official’s term. In any case, this would be irrelevant for our hypotheses because a bad trajectory and a bad absolute showing both would signal poor performance, thus potentially activating dispositional negativity. However, it would mean that the neutral and negative treatments would be indistinguishable because they have similar end points for crime and drop-outs. Coefficients in Table 2 allow us to reject this alternate. As we move from positive treatment to control to neutral to negative, the largest jump in coefficient size is for the last step, a shift of 1.23 points (Model 1) on the 0–6 dependent variable. Subjects strongly differentiated between the neutral and negative treatments, suggesting that they followed the logic of retrospective voting and assessed the change in policy performance over the governor’s term.

  16. We again can rule out the possibility that results stem not from dispositional negativity, but instead from response extremity, neuroticism, or negative affect. Each of those would have produced significant effects in multiple treatment conditions, not effects exclusive to the negative information cell. Additionally, for response extremity, the ratings for the one positive word respondents assessed were correlated at an average of only − 0.26 with the four negative terms, while the average inter-item correlation for those four was 0.48, indicating there is something distinctive about intense reactions to negative stimuli. For neuroticism, the survey included a brief Big Five battery. The correlation between neuroticism and dispositional negativity is a meager − 0.08, and it is incorrectly signed (individuals scoring high in neuroticism responded less intensely to the negative words than did individuals scoring low in neuroticism). The dispositional negativity scale is not an inadvertent measure of neuroticism.

  17. Although we are advocates of using the Big Five taxonomy as a starting point in research on personality and politics, we acknowledge that the Big Five approach does not capture the entirety of personality trait structure. When, as with the negativity bias, there is an important trait that is not captured by the Big Five, that trait warrants independent attention. Prior research does not establish strong links between dispositional negativity and the Big Five. Likewise, using data from our three-state survey, an R2 of only 0.04 was obtained when we regressed dispositional negativity on the Big Five.

References

  • Ashare, R. L., Norris, C. J., Wileyto, E. P., Cacioppo, J. T., & Strasser, A. A. (2013). Individual differences in positivity offset and negativity bias: Gender-specific associations with two serotonin receptor genes. Personality and Individual Differences, 55, 469–473.

    Article  Google Scholar 

  • Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of General Psychology, 5, 323–370.

    Article  Google Scholar 

  • Bloom, H. S., & Price, H. D. (1975). Voter response to short-term economic conditions: The asymmetric effects of prosperity and recession. American Political Science Review, 69, 1240–1254.

    Article  Google Scholar 

  • Bradley, M.M., Lang, P. J. (1999) Affective norms for English words (ANEW): Instruction manual and affective ratings. Technical Report C-1, The Center for Research in Psychophysiology, University of Florida.

  • Cacioppo, J. T., & Bernston, G. G. (1994). Relationship between attitudes and evaluative space: A critical review, with emphasis on the separability of positive and negative substrates. Psychological Bulletin, 115, 401–423.

    Article  Google Scholar 

  • Cacioppo, J. T., Cacioppo, S., & Golian, J. K. (2014). The negativity bias: Conceptualization, quantification, and individual differences. Behavioral and Brain Sciences, 37, 309–310.

    Article  Google Scholar 

  • Coe, C. M., Canelo, K. S., Vue, K., Hibbing, M. V., & Nicholson, S. P. (2017). The physiology of framing effects: Threat sensitivity and the persuasiveness of political arguments. Journal of Politics, 79, 1465–1468.

    Article  Google Scholar 

  • Fournier, P., Soroka, S., & Nir, L. (2020). Negativity biases and political ideology: A comparative test across 17 Countries. American Political Science Review, 114, 775–791.

    Article  Google Scholar 

  • Fridkin, K. L., & Kenney, P. J. (2011). Variability in citizens’ reactions to different types of negative campaigns. American Journal of Political Science, 55, 307–325.

    Article  Google Scholar 

  • Grosskopf, A., & Mondak, J. J. (1998). Do attitudes toward specific Supreme Court decisions matter? The impact of Webster and Texas v. Johnson on public confidence in the Supreme Court. Political Research Quarterly, 51, 633–654.

    Article  Google Scholar 

  • Hajcak, G., Moser, J. S., Holroyd, C. B., & Simons, R. F. (2007). It’s worse than you thought: The feedback negativity and violations of reward prediction in gambling tasks. Psychophysiology, 44, 905–912.

    Article  Google Scholar 

  • Hibbing, J. R., Smith, K. B., & Alford, J. R. (2014a). Differences in negativity bias underlie variations in political ideology. Behavior and Brain Sciences, 37, 297–307.

    Article  Google Scholar 

  • Hibbing, J. R., Smith, K. B., & Alford, J. R. (2014b). Predisposed: Liberals, conservatives, and the biology of political differences. Routledge.

    Google Scholar 

  • Hinojosa, J. A., Martínez-García, N., Fernández-Folgueiras, U., Sánchez-Carmona, A., Pozo, M. A., & Montoro, P. R. (2016). Affective norms of 875 Spanish words for five discrete emotional categories and two emotional dimensions. Behavior Research Methods, 48, 272–284.

    Article  Google Scholar 

  • Ito, T. A., & Cacioppo, J. T. (2005). Variations on a human universal: individual differences in positivity offset and negativity bias. Cognition and Emotion, 19, 1–26.

    Article  Google Scholar 

  • Keene, J. R., Shoenberger, H., Berke, C. K., & Bollis, P. D. (2017). The biological roots of political extremism: Negativity bias, political ideology, and preferences for political news. Politics and the Life Sciences, 36, 37–48.

    Article  Google Scholar 

  • Kernell, S. (1977). Presidential popularity and negative voting: An alternative explanation of the midterm congressional decline of the president’s party. American Political Science Review, 71, 44–66.

    Article  Google Scholar 

  • Larsen, J. T., Norris, C. J., McGraw, A. P., Hawkley, L. C., & Cacioppo, J. T. (2009). The evaluative space grid: A single-item measure of positivity and negativity. Cognition and Emotion, 23, 453–480.

    Article  Google Scholar 

  • Lau, R. R. (1982). Negativity in political perception. Political Behavior, 4, 353–377.

    Article  Google Scholar 

  • Lau, R. R. (1985). Two explanations for negativity effects in political behavior. American Journal of Political Science, 29, 119–138.

    Article  Google Scholar 

  • Lodge, M., & Taber, C. S. (2005). The automaticity of affect for political leaders, groups, and issues: An experimental test of the hot cognition hypothesis. Political Psychology, 26, 455–482.

    Article  Google Scholar 

  • Mondak, J. J. (2010). Personality and the foundations of political behavior. Cambridge University Press.

    Book  Google Scholar 

  • Mondak, J. J., Hibbing, M. V., Canache, D., Seligson, M. A., & Anderson, M. R. (2010). Personality and civic engagement: An integrative framework for the study of trait effects on political behavior. American Political Science Review, 104, 85–110.

    Article  Google Scholar 

  • Norris, C. J., Larsen, J. T., Crawford, L. E., & Cacioppo, J. T. (2011). Better (or Worse) for some than for others: individual differences in the positivity offset and negativity bias. Journal of Research in Personality, 45, 100–111.

    Article  Google Scholar 

  • Settle, J. E., Dawes, C. T., Loewen, P. J., & Panagopoulos, C. (2017). Negative affectivity, political contention, and turnout: A genopolitics field experiment. Political Psychology, 38, 1065–1082.

    Article  Google Scholar 

  • Skowronski, J. J., & Carlston, D. E. (1989). Negativity and extremity biases in impression formation: A review of explanations. Psychological Bulletin, 106, 131–142.

    Article  Google Scholar 

  • Soroka, S. N. (2014). Negativity in democratic politics: Causes and consequences. Cambridge University Press.

    Book  Google Scholar 

  • Soroka, S. N., Fournier, P., & Nir, L. (2019). Cross-national evidence of a negativity bias in psychophysiological reactions to news. PNAS, 116, 18888–18892.

    Article  Google Scholar 

  • Vohs, K. D., & Luce, M. F. (2010). Judgment and decision making. In R. F. Baumeister & E. J. Finkel (Eds.), Advanced social psychology: The State of the Science. Oxford University Press.

    Google Scholar 

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Acknowledgements

Much of the research for this project was completed while the first two authors were senior fellows at the Center for the Study of Democratic Institutions at Vanderbilt University. Helpful recommendations regarding this project were provided by Matt Powers. Question designed for Study One were included on a survey fielded by the Latin American Public Opinion Project at Vanderbilt University. Questions designed for Study Two were included on a survey fielded by Professor Joel Turner and the Political Behavior Lab in the Department of Political Science at Western Kentucky University. Helpful feedback on earlier versions of this paper was received during presentations at the 2018 annual meeting of the International Political Science Association, the 2018 annual meeting of the American Political Science Association, and at the American Politics Workshop at the University of Wisconsin. Lastly, instructive feedback also was received from this journal’s editors and six reviewers.

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Authors and Affiliations

  1. Department of Political Science, University of Illinois, 420 David Kinley Hall, Urbana, IL, 61801, USA

    Damarys Canache & Jeffery J. Mondak

  2. Department of Political Science, Vanderbilt University, Commons Center, Nashville, TN, 37203, USA

    Mitchell A. Seligson & Bryce Tuggle

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  1. Damarys Canache
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  2. Jeffery J. Mondak
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Correspondence to Jeffery J. Mondak.

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Appendix: Wording for Experimental Vignettes

Appendix: Wording for Experimental Vignettes

Costa Rica

Suppose that the mayor of your community proposed several reforms in development projects.

Positive frame::

Suppose he invested 10 million colones in a successful development project and the Municipality doubled the money.

Negative frame::

Suppose he invested 10 million colones in a development project that failed and the municipality lost all the money.

Neutral frame::

Suppose that he invested 10 million colones in a development project that was neither successful nor failed, and the municipality neither gained nor lost money.

Proposal frame (baseline)::

No added text.

On a scale of 0 to 10 where 0 is "Very bad" and 10 is "Very good," how would you evaluate the mayor who did this?

Three-State Survey

Suppose that you live in a state in which the governor is running for reelection. When first running for office, the future governor promised to focus on two issues: reducing the high school dropout rate, and decreasing the violent crime rate.

Positive frame::

During the governor’s time in office, the high school dropout rate has decreased from 12.1 to 9.5%, and the violent crime rate, measured in violent crimes per 100,000 people, has decreased from 371.8 to 366.1. Please indicate how likely or unlikely you would be to vote to reelect this governor.

Negative frame::

During the governor’s time in office, the high school dropout rate has increased from 9.5 to 12.1%, and the violent crime rate, measured in violent crimes per 100,000 people, increased from 366.1 to 371.8. Please indicate how likely or unlikely you would be to vote to reelect this governor.

Neutral frame::

During the governor’s time in office, the high school dropout rate has decreased, from 12.1% to 11.9%, and the violent crime rate, measured in violent crimes per 100,000 people, has increased, from 371.7 to 371.9. Please indicate how likely or unlikely you would be to vote to reelect this governor.

Agenda frame (baseline)::

The governor again is focusing on these same two issues. Please indicate how likely or unlikely you would be to vote to reelect this governor.

The evaluative scale ranges from 1 (very unlikely) to 7 (very likely).

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Canache, D., Mondak, J.J., Seligson, M.A. et al. How Bad is Bad?: Dispositional Negativity in Political Judgment. Polit Behav 44, 915–935 (2022). https://doi.org/10.1007/s11109-021-09757-z

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