Abstract
Scholars and political observers have suggested that television has “personalized” voting behavior in American presidential elections by encouraging citizens to cast ballots on the basis of candidate image and personality. Though an oft-heard assertion, little solid evidence exists that this is true, and the reinvigoration of partisanship and the persistence of ideological conflict suggest personalization may be less pervasive than supposed. In this paper, I use National Election Studies data to examine whether voters are more concerned with candidates’ personal characteristics now than they were at the outset of the television era. I find, however, that voters are no more likely today to mention candidate personality as a reason for their vote choice than they were in the 1950s and 1960s. Moreover, while personality affects voting behavior, its influence on candidate choice is not significantly larger than it was a half-century ago. The results are not contingent on exposure to television or political awareness and are insensitive to different measures of perceptions of candidate image. The findings are consistent with the resurgence of partisan voting in American elections and suggest that some concerns about TV’s effects on political judgment are exaggerated.
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Notes
For stylistic purposes, I use the both the words “personality” and “image” interchangeably in the paper. In both cases, I am referring to candidates’ personal characteristics, traits, and attributes.
Examples of early presidential ads can be seen at the Archive of the Museum of the Moving Image (http://livingroomcandidate.movingimage.us).
These figures are based on my calculations from Geer’s data, available at http://www.vanderbilt.edu/psci/johngeer/Downloads. I calculated the proportion of all appeals in an ad coded as trait appeals, and averaged those numbers for all advertising in a single election year.
Of course, just because an ad fails to mention a personal characteristic does not mean the images don’t promote candidate personality. But it should give pause to note that personal characteristics have not come to dominate political commercials, as some depictions of campaigns might suggest.
For my purposes, it is not critical whether citizens are updating their party identification to match their issue positions or changing their issue positions to match their party affiliation, which Carsey and Layman (2006) show is a product of individual-level characteristics. What matters is that the relationship between issues and partisanship has grown tighter in recent years.
There are slight variations in the wording in different years. Before 1996, the question asked about “the two candidates” for president rather than the “major” candidates.
The exception is 1972, when respondents had the opportunity to give only three reasons. Because the analyses in this paper focus on proportions of mentions and the comparative assessments of candidates—not raw counts of mentions—the comparability problem is minimal.
An analysis of the voting behavior of “late deciders” also suggests that the likes/dislikes batteries have validity. At the helpful behest of an anonymous reviewer, I investigated whether patterns of personality effects found in my full-sample analyses also appear among those who had not settled on a candidate preference at the time of their NES pre-election interview. The logic is that these individuals’ responses to the likes/dislikes questions could not be rationalizations, since they did not yet possess a choice to rationalize. If the same pattern of effects emerges among these voters, I would have a stronger justification for the use of the open-ended measures. To explore this, I restricted the analysis to individuals who said in the post-election survey that they made up their mind within the last 2 weeks of the campaign, but whose pre-election interview had occurred at least 2 weeks before election day. For each of the 13 election years since 1952, I regressed vote choice (from the post-election survey) on a control for party identification and a measure of evaluations of the candidates’ personal characteristics created from responses to the likes/dislikes questions. Just as with the full-sample analysis, in every case the coefficient for personality mentions was in the expected direction. The effect was statistically significant in just five of the models, but that is not surprising given the small number of individuals (between 70 and 248, depending on the year) in the analysis. Not coincidentally, it would seem, four of the five years in which the coefficient was significant were the four years with the largest number of cases. Moreover, the Pearson correlation between the coefficients for candidate attributes for late deciders and the coefficients in the full-sample models (Table A-3) was 0.84. These results offer evidence that the open-ended measures indeed represent more than post-hoc rationalizations.
In each year, between 5% and 13% of respondents gave no answers to the likes/dislikes batteries. In the vote choice models reported below, I exclude these individuals from the analyses. When they are included in the analyses, the substantive findings are unchanged.
“Other” is omitted from Fig. 1 to ease the data presentation. The proportion of responses in that category has changed little since 1952, fluctuating from 8% (1952) to 21% (1968) to 13% (2004).
Table A-2 displays the distribution of viewers and non-viewers. I use this dichotomous measure (NES Cumulative Data File variable VCF0724) because it is available for every campaign except 1988. Other fine-grained measures of the frequency of TV news consumption are available only in recent elections. As a check of the validity of this strategy, I have also examined the relationship between the mention of personal characteristics and the number of days a respondent reported watching national television news (NES Cumulative Data File variable VCF9035). This measure is available for 1984–2004. Only in 1996 does the proportion of personal characteristics mentions increase with the frequency of national news viewing. Thus, the results are not substantively different than when using the dichotomous TV viewing measure.
A prominent concern in the candidate traits literature is that personality assessments may simply be the result of projection, in which voters assign positive attributes to the candidate they already support, rather than reporting an unbiased evaluation (see Bartels 2002b). If that is the case, then evaluations of candidate personality are not valid as predictors of vote choice; they are derivations of that preference. There is evidence in the data, however, that trait assessments represent more than mere projection. Pooling the data across the fourteen election years, I find that 41% of voters had a Personal Characteristics score that indicated they evaluated more favorably the candidate for whom they did not vote than the candidate they supported. The breakdown was similar across party preference: 22% of Republican voters scored the Democratic candidate’s personal traits more favorably, and 19% of Democratic voters rated more favorably the GOP candidate’s personality. Thus, while projection inevitably occurs, it is not so rampant as to eliminate the possibility that personality assessments play an important role in shaping voting behavior.
I note two things about the models. First, only major-party voters are included. Second, because the confidence intervals of estimates generated from different samples are not directly comparable, I have also modeled the relationship among television, personality assessments, and voting by pooling the data across election years and estimating a single equation. In that equation, I include dummies for each election year and interact each dummy with Personal Characteristics to determine whether the impact of personality evaluations has changed over time. The results conform to the patterns in the analyses I present here—no increasing “personalization” of voting behavior.
Pooling the responses across years for Personal Characteristics, 80% fall between −2 and +2. The distributions are similar for all of the net favorability measures.
I use the standard NES 7-point party identification measure. Alternative measures—using dummies for Republican and Democratic identifiers or the 5-point scale with “leaners” coded as partisans—do not change the results of the models. Education and income are categorical variables, age is the precise age of the respondent, race is coded as white (1) and non-white (0), and southern residents are coded as 1, with non-southerners coded 0.
In a separate analysis, I included a control for the respondent’s self-reported ideological placement in the 1968–2004 models, the years for which the measure is available. Those models produce no substantive differences in the effect of candidates’ personal characteristics.
The probabilities are estimated using Clarify (Tomz et al. 2001) in STATA 9.0, with all other variables held at their median values. All simulations presented in the paper are produced by Clarify.
Seventy-one percent of respondents have a Personal Characteristics score between −1 and +1. The range represents individuals who have slightly pro-Democratic and pro-Republican evaluations, respectively, and thus captures a shift that could reasonably occur during a campaign. It would be less useful to examine shifts to the extremes of the distribution, since few people are likely to undergo an attitude transformation of that magnitude. Nevertheless, I have also examined the patterns based on more dramatic shifts—as large as from −6 to +6. While the effects are, of course, larger, the longitudinal patterns are the same as in Fig. 3.
Because of the sizeable increase in the frequency of issue mentions in Fig. 1, I also examined changes in the influence of Issues. Just as with personal characteristics, the effect of a two-unit shift in issue mentions waxes and wanes across the time series, and does not exhibit a dramatic increase.
I have omitted confidence intervals around the estimates for presentation purposes. With confidence intervals included, the graphs, necessarily small because each year requires a separate panel, would be virtually impossible to read. More importantly, the differences between the TV watcher and non-watcher estimates at any point on the x-axis are statistically different only at the values of 1 and 2 in 1960, and at 2 in 1992. In every other case, they overlap. Full results of the models, including figures with confidence intervals, are available from the author on request.
There are several potential explanations for my inability to replicate Keeter’s findings, even for the period from 1952 to 1980. First, his study used questions that asked respondents from which medium they got most of their campaign information and from which they received most of their information about politics and current events. People who answered “television” to these questions—the latter was used only in 1976 and 1980—were identified as “television viewers.” Those who answered “newspapers” were coded as “newspaper readers.” All other respondents were omitted from the analysis. Those questions were not asked in recent surveys. Second, my identification of responses that indicate concern with “personal characteristics” is different than his. Keeter creates a separate category for mentions of “executive capacity and experience” that includes several statements I code as referring to “personal characteristics.” For example, Keeter (1987, p. 347) assigns the statement “He’s a patriot” into the former category, while I regard that as an assessment of the candidate’s personality. Third are differences in modeling techniques. He uses ordinary least squares regression to estimate a model with a dichotomous dependent variable (Keeter 1987, p. 348). Keeter also splits the sample into “newspaper readers” and “television viewers” and then runs two regressions for each election year. He then compares the size of the coefficients in the two groups (Keeter 1987, Table 1). While this is common, it is a less accurate technique than specifying interactive models that capture the relationship between two variables, which does not require separating the sample.
To do so, I trichotomized the five-point political information variable by dividing it into thirds, based on the distribution. Thus, roughly one-third of respondents are defined as having low awareness, one-third are moderate, and one-third are high.
I pool the observations for three reasons. First, because the data in Figs. 4 and 5 reveal no consistent time trend in the effects of TV watching or political awareness, aggregating the data for the three-way interaction does not obscure longitudinal change. Second, because the number of non-TV watchers in each election year is relatively small, the three-way interaction in any single election produces estimates that are fairly imprecise. Third, pooling the data simplifies the presentation of the results. I have also run the analysis separately for each election year and obtained the same pattern of null findings.
As noted earlier, political communication scholars have argued that television is not the only medium that personalizes the news (Bennett 2009). If political coverage as a whole has become increasingly personalized, then heavy news consumers—not just television viewers—would be more likely to weigh candidate attributes more heavily than citizens who are exposed to less political news. I have also explored whether heavy news consumers are more likely to base their vote choice on personal characteristics than those who pay less attention to the news. Using an NES measure that queries respondents about their use of a variety of media (NES Cumulative Data File variable VCF0728), I specified vote choice models with an interaction between the media exposure count and Personal Characteristics. Just as with the analysis of television viewers, there is no evidence that an increase in media consumption contributes to personality-based voting. I have also run the same analysis using a measure of a respondent’s level of education, and again find no differences, confirming the results of Glass’ (1985) work.
The issue measure is a scale constructed from five questions in the NES tapping a respondent’s beliefs about the government’s responsibility to provide health care, jobs, and aid to blacks; whether government spending should be increased or decreased; and whether defense spending should be increased or decrease. The measure ranges from −15 to +15, with the higher scores indicating more conservative attitudes.
Roughly 40% of respondents have a trait score between −2 and +2. Just as with the open-ended likes/dislikes analysis, I use this range because it represents a shift that could conceivably occur in a real campaign. Analysis of larger shifts produces no change in the longitudinal trend presented in Fig. A-1.
According to my calculations from Geer’s data, which stop in 1996, the proportion of trait-focused appeals in 1988 was 35%. The figure was 19% in 1984, 24% in 1992, and 20% in 1996.
Even if the overall influence of traits has not grown over time, it is possible that particular traits may be more conducive to TV-based priming. To determine whether this hypothesis has merit, I ran a separate series of analyses in which I examined the effect on vote choice of respondents’ perceptions of the four traits that have been asked consistently since 1984. The effects of candidate leadership, empathy (“cares about people like me”), and morality, have largely remained flat. The one exception is knowledge, which was insignificant as a predictor of vote choice in 1998, 1992, and 1996, but was significant in 2000 and 2004. One reason may be the questions raised in 2000 about George W. Bush’s intelligence (Jamieson and Waldman 2003), in particular as contrasted with Al Gore’s wonkishness. (Unfortunately, the NES did not ask about the candidates’ honesty, a characteristic that may have had unusual influence in 2000 (Johnston et al. 2004).) But this small finding does nothing to challenge the basic results discussed in the text. It does not appear that candidate traits—even when disaggregated—have become significantly more important in presidential voting.
I have also run interactive models with the closed-ended trait batteries to discern whether media usage, political awareness, or education has any influence on the use of candidate attributes as a criterion for judgment. As in the models using the open-ended likes/dislikes categories, I find no significant interactive relationships. These results are available from the author.
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Acknowledgments
A previous version of this paper was presented at the 2007 meeting of the Midwest Political Science Association. I thank Brian Arbour, Ben Bishin, Noah Kaplan, John Sides, Mary Slosar, Jeff Stonecash, Jeremy Teigen, three anonymous reviewers, and the editors for helpful comments and suggestions.
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Appendix
Appendix
The effect of candidate trait assessments on the probability of voting Republican, 1984–2004, closed-ended trait measures. Note: Each data point represents the difference in the probability of a Republican vote between a respondent with a score of −2 and +2 on the composite closed-ended trait measure. Simulations are generated from the probit models shown in Table A-4, calculated using Clarify (Tomz et al. 2001). Shaded lines are 95% confidence intervals around the estimates
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Hayes, D. Has Television Personalized Voting Behavior?. Polit Behav 31, 231–260 (2009). https://doi.org/10.1007/s11109-008-9070-0
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DOI: https://doi.org/10.1007/s11109-008-9070-0