Abstract
Recent studies contend that negative advertising benefits voters. However, these studies only measure the volume of negativity in campaigns, often relying on survey data on voter behavior coupled with estimates of negative ad exposure. Theories of information processing indicate that the proportion of negativity may yield influences spanning a range of judgments related to candidate construction and voting behavior, yielding effects that are different from the influence of sheer volume. Thus, I argue that the proportion of negativity also has an influence, and that it is likely more often to be detrimental. I examine this claim using survey data and conclude that prevailing accounts of the effects of negative advertising campaigns are underspecified and, as a result, potentially overly optimistic.
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Notes
Proportion effects are not ignored entirely, as Table 1 below illustrates, but they are examined at the aggregate level or, in experiments, left implicit, and they are seen as interchangeable with volume effects.
My conception of negative advertising, like the data I use in this paper, owes much to the Wisconsin Advertising Project. The definition of negative advertising in their coding instrument is advertising whose primary purpose is “to attack a candidate (‘In his long years in Washington, Senator Jones has raised your taxes over and over. We can’t afford 6 more years of Senator Jones’).” Thus negative advertising refers to the content of the ad; it does not convey any evaluative meaning.
Kahn and Kenney argue that individuals view negative advertising as a legitimate aspect of campaigning, and thus respond positively to it, unless it descends into mudslinging.
Indeed, Martin (2004, pp. 545–546) claims, “Evidence supporting the idea that negative campaigning discourages voter turnout comes primarily through experimental research, whereas evidence supporting the idea that negative campaigning encourages voter turnout comes from survey research,” although Lau et al. find that experiments are as likely as surveys to find that negative advertising stimulates turnout (1999, pp. 858–859; 2007, p. 1185).
Table 1 includes studies of actual or intended turnout. Lau et al. sometimes seem to include findings under turnout that concern likelihood to vote for a particular candidate; I did not. Studies using recall of ads, or perceptions of campaign tone, are classified as “Not applicable” in Table 1: their basis in the volume or proportion of negative advertising is unknown.
Lau et al. note an impact “of the realism of the candidates”, however, on the unadjusted effect sizes on turnout, “with real candidates … more likely to produce positive effects” and “artificial candidates … more likely to produce demobilizing effects.” They also find “more volatile outcomes” of experiments—stronger negative effects and all the positive effects—on vote choice (p. 1183), which is not a part of this study.
They also have an additional code for “mudslinging” campaigns, based on campaign managers’ characterizations of their own and their opponent’s ads. Managers’ perceptions may be influenced by the proportion of negative ads in the campaign.
This is conventional wisdom. For a counterargument, see Huber and Arceneaux (2007).
It is also central to Lau (1985). He argues that the cost-orientation account is particularly useful in explaining negativity effects in presidential elections, where the stakes are highest.
The timing of exposure to messages may also be important. However, time presents an additional layer of complexity that I have left for another paper.
While positive information tends to have less impact, a more positive balance of information should weaken the negative affect attached to memories.
Indeed this is an integral aspect of Marcus’s theory. Individuals sometimes have aversive reactions to negative information, such as responses of disgust and depression whose impact is paralysis and withdrawal rather than arousal.
I do not mean to discount the possibility that election-specific responses, unusually intense interest in a presidential election for example, could also influence perceptions of the system.
Thanks to Freedman et al. for sharing their data. While their article is not part of the emerging consensus about negative advertising—they look at exposure to advertising in general—it is congruent with the growing optimism about advertisements, which they compare to “political vitamins.” Moreover, CMAG data have been front and center in the revisionist literature.
For a more detailed discussion of the calculation of individual exposure using CMAG data and a description of the measures used in this paper (available as supplementary material), see Freedman et al.’s (2004) Appendix B.
There are sceptics toward this approach to estimation and modelling, however, in particular those arguing that it does not account for the possibility of selective exposure. Some individuals may be more likely to watch political ads precisely because they are politically knowledgeable or more likely to vote. This remains an area of debate, especially in terms of its impact on findings. I return to this discussion below.
The correlation between proportion of exposure to negative presidential advertising and living in a competitive state is .27 (i.e., positive but not overwhelming). I also looked at the correlation between proportion of negative advertising and time of interview (days before the election). It was −.09.
However, when volume and proportion are included separately in the same models both have positive and statistically significant relationships with turnout. See the discussion below.
I examined the indirect effects further by multiplying the maximum effects of volume or proportion of negative advertising exposure on perceptions of closeness, external efficacy, and attention to the campaign, based on the estimates in Table 4, by the maximum effects of perceptions of closeness, external efficacy, and attention to the campaign on turnout (through identical simulations to those described previously). For example, the maximum effect of the proportion of exposure to advertising (i.e., the difference in external efficacy between the lowest proportion, zero, and the highest proportion of exposure) is to move a respondent 10% along the scale, while the maximum effect of external efficacy on turnout is to change the probability of voting by 16.6%, according to these simulations. Multiplying these two estimates together, the maximum effect of proportion of exposure via external efficacy is to reduce turnout by about 1.7%. By the same logic, the maximum effect of volume of exposure to negative advertising via perceptions of closeness is to boost turnout by approximately 1%; via enhanced attention to the campaign it is to boost turnout by about 2.5%. In other words, a focus purely on volume effects would conclude that exposure to negative advertising potentially increases turnout by about three-and-a-half points. This echoes the notion of ads as the vitamins of electoral politics. However, the maximum impact of proportion of exposure to negative advertising, through diminished external efficacy and interest in the campaign, is to reduce turnout by approximately 1.7% and 1.5% respectively. This implies a net increase in turnout of about 0.3%—essentially no overall impact.
Readers may also be interested in potential interaction effects between volume and proportion. I therefore re-estimated all the models in Table 3 and 4 with an additional interaction term. There was one statistically significant interaction at p < .10, for the ability to place the candidates ideologically. From ten models this is within the bounds of chance.
These estimates are available from the author on request.
But there is a trade-off: multicollinearity introduced by the inclusion of both terms may also hide a real impact. When included separately volume and proportion are positively related to turnout, which is one of the two models where the measures of volume and proportion are correlated most highly. But three-stage least squares models (see footnote 25) again suggest no impact. Estimates of turnout effects are among the most unstable in this paper.
With that caveat in mind, I estimated three-stage least squares models for all the dependent variables in Tables 3 and 4 that attempted to account for any endogeneity in the volume and proportion of exposure. I used the same independent variables. The models are interesting in two main respects. First, exposure to advertising appears to be more a function of local news watching and the competitiveness of campaigns in a respondent’s locale than of political sophistication. Second, the results are largely consistent with those presented, with volume most clearly affecting indicators of attention to the campaign and proportion (negatively) perceptions of the system. The results for information are less stable. These models also indicate no impact of volume and proportion on turnout, as in Table 4. Thus the evidence for an independent effect of proportion of exposure appears robust to different specifications. Nevertheless, it is clearly necessary to continue to explore potential selection bias effects and to adopt research designs beyond surveys and experiments in this field.
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Acknowledgements
Thanks to Barbara Allen, Paul Kellstedt, George Marcus, and Jack Vowles for helpful comments and suggestions and to Ken Goldstein for sharing his data.
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Stevens, D. Elements of Negativity: Volume and Proportion in Exposure to Negative Advertising. Polit Behav 31, 429–454 (2009). https://doi.org/10.1007/s11109-008-9082-9
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DOI: https://doi.org/10.1007/s11109-008-9082-9