negative advertising and voter choice: the role of ads in candidate selection
TRANSCRIPT
This article was downloaded by: [University of Massachusetts, Amherst]On: 01 September 2014, At: 16:53Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
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Negative Advertising and Voter Choice:The Role of Ads in Candidate SelectionYanna Krupnikov aa Political Science , Northwestern UniversityPublished online: 22 Oct 2012.
To cite this article: Yanna Krupnikov (2012) Negative Advertising and Voter Choice: The Role of Ads inCandidate Selection, Political Communication, 29:4, 387-413, DOI: 10.1080/10584609.2012.721868
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Political Communication, 29:387–413, 2012Copyright © Taylor & Francis Group, LLCISSN: 1058-4609 print / 1091-7675 onlineDOI: 10.1080/10584609.2012.721868
Negative Advertising and Voter Choice: The Roleof Ads in Candidate Selection
YANNA KRUPNIKOV
Selecting between two candidates during a campaign is a crucial first step towardpolitical involvement: an individual who does not select a preferred political candi-date is unlikely to take political action. Can negative campaign ads help individualsmake these electoral choices? Empirical evidence on this topic has been mixed. Someargue that negativity can increase the likelihood of choice. Others show that negati-vity will decrease the likelihood of choice by turning individuals away from the polls.Integrating theories from social psychology and political science I argue and showthat under specific conditions, negativity increases the likelihood that an individual willmake a candidate selection. Further, I differentiate between the tone and substance ofads to show that negativity has a unique effect on choice.
[Supplementary material is available for this article. Go to the publisher’s onlineedition of Political Communication for the following free supplemental resource(s):Robustness checks. This supplemental appendix establishes the importance of the choicepoint in individual behavior and considers alternative conceptions of exposure toadvertising.]
Keywords negative advertising, candidate choice
Every campaign season, individuals are bombarded with campaign advertisements.Although surveys suggest that individuals do not always enjoy political advertising, theseads have considerable informational value (Ansolabehere & Iyengar, 1995). As FreedmanFranz, and Goldstein (2004) note, “ultimately, if the political diet of most Americans islacking in crucial information, campaign ads represent the multivitamins of American pol-itics” (p. 725). If ads can transmit new political information during a campaign, what effectdo these “political multivitamins” have on a voter’s likelihood of choosing to support onecandidate over another?
While the informational value of ads may suggest a simple relationship between adsand choice, empirical evidence on this topic has been mixed. Although ads may aid choiceby creating a campaign environment where obtaining political information is neither dif-ficult nor costly, it is less clear which types of ads are most beneficial for individuals asthey make choices during campaigns. Some scholars suggest that negative ads will be morehelpful than positive ads to individuals as they make their decisions (Garramone, Atkin,Pinkleton, & Cole, 1990). Others suggest just the opposite, that rather than helping peoplemake political decisions, negative ads will drive people to ignore politics and turn them
Yanna Krupnikov is Assistant Professor of Political Science at Northwestern University.Address correspondence to Yanna Krupnikov, Northwestern University, 206 Scott Hall, 601
University Place, Evanston, IL 60208, USA. E-mail: [email protected]
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away from the political process before any decision is made (Pinkleton, Um, & Austin,2002). Still other research, however, may lead to a different conclusion: Franz, Freedman,Goldstein, and Ridout (2007) show that while higher exposure to ads in general leads toan increased interest in politics and higher concern about electoral outcomes, “these effectscould not be explained by the tone of the ad” (p. 134). Extrapolating these results suggeststhat perhaps as far as voter decision making is concerned, there is no difference betweennegativity and positivity—it is the sheer presence of ads that matters.
In this article I focus directly on the role of negativity in an individual’s decisionprocess during a campaign. Relying on theories and evidence from social psychologyand marketing psychology, I argue and show that an individual is more likely to make acandidate choice when he or she is exposed to negative ads, rather than positive ads.1
What differentiates this work from previous research on the topic are the empirical andtheoretical approaches. First, while much of the work on the relationship between negativityand choice has relied on experiments, here I use observational data to trace the effect ofnegativity during a campaign setting. Further, I use my data to distinguish between the toneand content of ads, showing a unique relationship between negativity and choice. Finally,I take a conditional approach: Relying on psychologically grounded research, I derive theconditions under which negativity will be particularly likely to aid selection and conditionsunder which it will have little effect on individual choice. As a result, the theory proposedhere not only considers whether negativity has any effect on individual choice, but why andwhen we can expect to see this effect.
I make my case as follows. First, I discuss the importance of analyzing choice. Second,I define negativity. Third, I present four premises on negativity and choice, and use thejoint logic of these premises to derive the main hypotheses. Next, I present the results of anempirical test that relies on survey and ad data to trace an individual’s likelihood of makinga choice during the 2004 presidential campaign. Finally, I consider the potential for variousalternative explanations by distinguishing between the tone (negativity vs. positivity) andsubstance (issue vs. character focus) of ads.
Analyzing Choice
Before I turn to the focus of this article—the analysis of the effect negativity can have onthe likelihood of choice—I briefly pause to consider the importance of my key dependentvariable. In doing so, I pose the question “why study choice?” Broadly speaking, thestudy of voter decision processes focuses on the way individuals evaluate their politicalalternatives, select a preferred alternative, and cast their votes. This approach suggests thatchoice is a key component of future behavior, as some type of candidate selection is madeprior to the individual arriving at the polls. This is not argue that no individual will turn outto vote out of duty, and pick a candidate once at the polls, but merely to suggest that manydo make selections first and then act on these selections with votes. As Lau and Redlawsk(2006) write, “We suspect that in most presidential elections voters make at least someglobal evaluations of the candidates prior to the vote no matter how preordained their votemight be” (p. 42).
While evaluations contribute to choice, they are different concepts. Lau (2003) writesthat “making a choice implies more commitment to the chosen alternative than making ajudgment suggests about the judged entity . . . people make judgments all the time withoutnecessarily ‘putting those judgments into action’ ” (p. 20). This idea of commitment toa course of action is what makes a choice so important for action. Given that making a
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Negative Advertising and Voter Choice 389
choice means a commitment, individuals who have made choices are (all other factors heldconstant) much more likely to act.
Evidence from social psychology speaks directly to this point; scholars show that innumerous decision contexts making a choice significantly increases the likelihood of action(Bagozzi & Dholakia, 2000; Sheeran & Orbell, 1999). To the extent that a political decision“cannot be much different from most other decisions people make in their daily lives”(Lau & Redlawsk, 2006), we can expect the same relationship between choice and actionwhen it comes to voting. In fact, even Downs (1957) suggests such a relationship, notingthat a person who has no clear preference for a candidate has little reason to turn out onelection day.
Further, exposure to new campaign information can change how, when, and if individ-uals make choices, meaning that while previous political characteristics do play a role inchoice, they are not the lone factor. In fact, as Hillygus and Jackman (2003) show, expo-sure to advertising can move individuals toward choices that are different from their initialpreferences. While theoretically a choice is crucial for action, a brief empirical analysisillustrates this point.
Relying on the National Annenberg Election Study (NAES), I estimate a simple voterturnout model for the 2000 presidential election2; this model relies on controls identical toRosenstone and Hansen’s (1993) canonical model of voter turnout. The results show thatmaking a choice has a statistically significant and substantively strong effect on likelihoodof turnout (coefficient for selection is 0.897, significant at p ≤ .05). In fact, making a choiceincreased the likelihood of turnout by 0.17, a change that is significant at p ≤ .05.
Simpler empirical comparisons using the 2000 and 2004 NAES reinforce this point.Individuals who make choices are more likely to turn out and vote. They are also morelikely to discuss the campaign with family and friends, and they are more likely to donatemoney to a candidate. Differences between those who have and have not made a choiceare consistently statistically significant. Further, these comparisons show that increases inparticipation are not simply a function of preexisting political inclinations: The increasein turnout and participation related to choice is consistently significant for populationsthat generally have lower rates of participation such as weak partisans and those with loweducation.3 These empirical patterns reinforce the fact that choice matters for action, andreinforce the need to understand the types of campaign effects that can help individualsmake choices. With this in mind, I now turn to the main focus of this article.
What Is Negativity?
To consider why, how, and when negativity affects choice, I rely on Geer’s (2006) definitionof negativity:
It is simple and straightforward: negativity is any criticism leveled by one can-didate against another during a campaign. Under this definition there is no grayarea. An appeal in a campaign either raises doubts about the opposition (i.e.,negative) or states why the candidate is worthy of your vote (i.e., positive).There is no middle category. (p. 23).
I follow Geer’s tonal definition as the goal of this analysis is to identify whether thereis something unique to negatively toned ads—regardless of their content—that affectsvoter decision making.4 Later in this article—when I address alternative explanations—Ireconsider the substantive content of ads.
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Theory and Hypotheses
It is not difficult to be critical of campaign negativity. Negative ads are designed to paint apolitical opponent in an unflattering light. Often, such ads distort and manipulate politicalinformation, leading to a campaign approach that not only discredits the target of the ad,but also turns individuals away from the political process (Ansolabehere & Iyengar, 1995).Indeed, as Lipsitz, Trost, Grossman and Sideo (2005) note, “majorities believe that ‘nega-tive, attack-oriented campaigning is undermining and damaging our democracy’ ” (p. 338).Yet, as unpleasant as they may be, ads are often the only exposure to political informationthat individuals will receive during the course of a campaign.
Typically, states that are classified as “battleground” by at least one of the campaignsreceive more ads than states the candidates believe to be partisan bases. In addition, withinthese battleground states, campaign ads are usually aired in populous media markets dur-ing the most popular TV shows (Shaw, 2006). As a result, increases in campaign adsincrease the chance of accidental exposure to political information even for individualswho make little effort to follow politics.5 To be sure, not all individuals will pay atten-tion when they accidentally encounter political information during a commercial break,but evidence suggests that many do retain at least some of the information presented incampaign ads. Individuals who live in battleground states, for example, appear to be morepolitically knowledgeable than their counterparts in base states (Gimpel, Kaufmann, &Pearson-Merkowitz, 2007), and increases in exposure to campaign ads do increase over-all knowledge in individuals who previously had little political knowledge (Franz et al.,2007).
Can exposure to certain types of ads increase the likelihood that a person makes aselection? Even more precisely, can this incidental exposure to ads have an even strongereffect on the likelihood of making a choice when the ads in question are negative? Next, Ipresent four premises about the relationship between ads and choice. These premises stemfrom theories in marketing, social psychology, behavioral decision theory, and politicalscience. The joint logic of these premises leads to the key hypotheses of this research.
Negativity and Decision Making: Premises and Hypotheses
Premise 1: In making a candidate selection, the individual’s task is to distinguish betweenalternatives with the goal of selecting one that is sufficiently more attractive than the other.This is a basic premise, and one that sets the foundation for much of the research in behav-ioral decision-theory (Svenson, 1992). Though basic, this task is crucial—if a person cannotsufficiently distinguish between the decision alternatives, no choice, and therefore no com-mitment to a course of action, will be made. Put another way, if an individual is unable todifferentiate between candidates during a campaign, it is unlikely that he or she will make aselection to support one candidate over another. As I discussed earlier, if an individual can-not commit to a course of action during a campaign, he or she is less likely to participate inthe political process.
Svenson (1992) defines the process of distinguishing between decision alternativesas “differentiation.” An abstract concept, differentiation translates to the relative differencebetween the two alternatives in an individual’s mind. The more difference an individual per-ceives between the alternatives, the greater the distance. To complete a selection and forma commitment, an individual must achieve a level of differentiation that is sufficient forhis or her own particular needs.6 Following this approach, an individual may perceive thealternatives to be different (i.e., reach some differentiation), but the distance between these
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Negative Advertising and Voter Choice 391
two alternatives in an individual’s mind may simply not be wide enough to be sufficient fora final selection.
Premise 2: In general, negativity can more helpful than positivity in the task of dis-tinguishing between alternatives. Summarizing research on the ads for various brands andproducts, James and Hensel (1991) write, “the overwhelming preponderance of researchfindings confirms that consumers tend to favor or assign disproportionate value to negativeinformation in the decision-making process” (p. 56). This phenomenon—termed the “nega-tivity bias”—has been shown in a variety of contexts including politics. In fact, Lau (1982)notes “I feel confident in concluding that negativity is a fairly general phenomenon inpolitical perception, as it is in many other areas of human information processing” (p. 371).
Negativity bias stems from the fact that individuals are more likely to focus on informa-tion that provides the greatest diagnostic value—meaning information that is most helpfulin characterizing and distinguishing between available decision alternatives (Lupia et al.,2011; Skowronski & Carlston, 1989). Negative information, Skowronski and Carlston(1989) argue, is more diagnostic than positive information. Lau (1985) underscores thispoint, arguing that negative information can be “perceptually more salient, more easilynoticed, and therefore more readily processed” (p. 121).
There are overlapping reasons for the increased diagnosticity of negativity. Negativityis made more salient and crucial by the discounting principle, the idea that “the role ofa given cause in producing a given effect is discounted if other plausible causes are alsopresent” (Kelley, 1973, p. 113). Experimental evidence suggests that individuals are muchmore likely to believe there are a greater number of causes for positive information than fornegativity—meaning positivity is often discounted as individuals make selections (James &Hensel, 1991). Exacerbating this discounting effect is the general context in which negativ-ity is received. Lau (1985) writes that individuals generally function in a “positive world,”therefore meaning that negative information is more likely to stand out (p. 121).
What is notable about this research on negativity bias is an absence of focus on thesubstance of negativity. In discussing the power of negativity over consumers, for example,James and Hensel (1991) broadly define negative ads as those whose goal is to “impute infe-riority about a competitor’s brand” (p. 56). This research further reinforces my reliance onGeer’s tonal definition, emphasizing that the power of negativity is in its tone or structure:Information is more helpful when it informs individuals of ways in which one alternativeis deficient, rather than when it informs individuals of ways in which the other alternativeis best.
Taken jointly, these two premises suggest that when an individual’s task is to distin-guish alternatives, negativity is more likely to facilitate this task than positivity. This leadsto my first, general, hypothesis.
Hypothesis 1: Increases in exposure to negativity will lead to a higher likelihood ofselection than increases in exposure to positivity.
Next, it is important to consider the conditions under which some forms of negativitywill have an even stronger effect on the likelihood of selection. To do so, I consider the rolean individual’s partisanship may play in his or her decision-making process.
Premise 3: When individuals begin the decision process, they are rarely neutral. Manyindividuals start the decision process with an underlying preference for one of the decisionalternatives (Redlawsk 2008; Svenson, 1996).7 Following Premise 1, however, this initialpreference is usually not enough for an individual to make a final selection, meaning anindividual perceives some difference between the alternatives, but this difference is notsufficient for the individual to fully commit to a final selection.
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The political process reinforces the existence of these initial preferences through par-tisanship. Individuals who have (even slight) partisan inclinations are likely to start theselection process with some preference for a candidate of their own party. While thesepreferences do not always mean that an individual is ready to declare his or her party’scandidate his or her final choice, it is nonetheless important to acknowledge that fewindividuals approach a political selection process from a neutral starting point (Lau &Redlawsk, 2006).8
Premise 4: Individuals may often have an easier time processing congruent informa-tion than incongruent information. As Redlawsk (2008) writes, “information congruentwith expectations is easily assimilated since it requires no effort to accept what onealready knows is true. But incongruent information interrupts normal processing andinstead engages a process where some effort must be expended to make sense of theworld”(p. 1023).9
Considering the joint logic of all four premises suggests that while negativity willbe generally more conducive to decision making, the ease of information processing willamplify the relationship between negativity and choice. In particular, negative ads arelikely to exert an especially strong effect on the likelihood of selection under specificconditions.
Hypothesis 2: Increases in exposure to negativity about a candidate for whom theindividual has a lower initial preference will lead to a higher likelihood of selection thanexposure to positivity about either candidate.
Hypothesis 2 combines the generality of Premise 2 with the information decisioncontext discussed in Premise 3 and the processing specifics discussed in Premise 4. As con-gruent information is easier to process, individuals who have preferences as they begin thedecision process may have an easier time processing negativity about the candidate theyalready do not prefer (congruent information). As a result, increases in exposure to informa-tion that is both more diagnostic (negativity) and easier to process (congruent) will increasethe likelihood of a choice.
Where does this leave positivity? Following Premise 2, positivity is perceived to beless diagnostic than negativity, and thus increases in positivity should have little effect onthe likelihood of selection. Taking this argument further, individuals living in areas wherepositive ads outnumber negative ads will be less likely to make selections than individualsliving in areas where negative ads outnumber positive ads.
Existing Literature
While other scholars have considered the role of negativity in voter decision making, boththe premises presented above, as well as the empirical strategy, offer a new approach tothe topic. First, the premises above take a more conditional tactic to the analysis of therelationship between negativity and choice. More precisely, the approach presented hereaccounts for the fact that many individuals start the decision process with partisan pref-erences. As a result, this research directly considers how and why the effect of negativitymay differ depending on an individual’s initial, underlying preference—something existingresearch has yet to consider directly (see, for example, Garramone et al., 1990).
A second key difference between existing work and this research is the main depen-dent variable. While others have considered how negative ads affect candidate evaluations(Ansolabehere & Iyengar, 1995), my focus is solely on an individual’s ability to make achoice. Even if the selection is “incorrect” in that an individual would have made a differ-ent one if he or she had full information, for the purposes of this analysis all that matters is
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Negative Advertising and Voter Choice 393
that he or she made any selection at all. After all, without this initial selection an individualis unlikely to participate in the political process.
A final difference is in the empirical approach. Much of the work on the rela-tionship between negativity and choice is experimental. While experiments allow forcontrolled analyses, it is important to consider whether the conclusions made throughexperiments hold during a campaign. Following Freedman et al.’s (2004) argument thatmany individuals receive political information through accidental exposure to ads, rely-ing on observational data is a necessary step in forming inferences about the power ofnegativity.
Election 2004
To analyze the effect negativity plays in an individual’s selection process during a cam-paign, I rely on data about the 2004 presidential election. Data about individual behaviorcome from the 2004 National Annenberg Election Study (NAES).10 For this analysis Irely on two data sets within the NAES: (a) the NAES cross-section and (b) the firstwave of the NAES debate panel; in this first wave, respondents were interviewed betweenSeptember 20 and 29, 2004.
I rely on two different data sets for several reasons. First, the cross-section provides alarge sample of interviews conducted over the entire course of the campaign. A drawback tothis data set, however, is its structure: Individuals are interviewed at different points in thecampaign, some close to the beginning, others close to the end. Naturally, those interviewedon later dates would be exposed to more advertising than those interviewed in the earlierwaves of the survey; this means that the timing of interview could confound my results.Although I take numerous precautions to ensure that timing does not muddy the findings,to ensure that my results are robust I replicate my findings with the panel. While the panel isa smaller sample, all individuals were interviewed in a relatively short time period, meaningthat the timing of the interview relative to the duration of the campaign is less likely to be aconfound. These two data sets work together to provide a fuller account of the relationshipbetween negativity and choice.
Campaign ad data come from the Wisconsin Advertising Project (Goldstein & Rivlin,2007).11 This data set was built using advertising tracking data provided by the CampaignMedia Advertising Group (CMAG) and coded for content at the University of Wisconsin.The data provide information about the date the advertisement aired, the media marketin which the advertisement aired, the sponsor of the ad, as well as the number of timeseach advertisement aired. When I merge this data with the NAES, I can pinpoint just howmany ads about the presidential race were aired prior to a respondent’s interview in therespondent’s media market.12
Measures
Dependent Variable. The dependent variable is a respondent’s selection. To measurewhether a respondent has selected a candidate I rely on a two-part question. The first partasks individuals which candidate they prefer; the second part asks them how likely they areto change their mind. Individuals who stated that they are unlikely to change their mindsand will definitely vote for their specified candidate were coded as having made a selection.Individuals who reported that there is a “good chance” that they will change their minds inthe second part of the question were coded as not having made a selection. This measure-ment approach is consistent with the theoretical foundations of selection described above.
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As Lau (2003) writes, a choice is about making a commitment to a course of action. Statingthat one may still change one’s mind suggests a lack of commitment, meaning no choicehas been made. In contrast, an individual who has sufficiently differentiated between alter-natives and made a commitment would express much more certainty in his or her futureaction by stating he or she would not change his or her mind.13
The cross-section sample has, on average, 30.9% undecided voters. The percentageof undecideds, as expected, changes over the course of the campaign, ranging from morethan 35% of the sample in the waves interviewed in the early stages of the campaign (July2004) to 24% in the later days of the campaign (late October 2004). In the small paneldata set, where all respondents were interviewed in 1 week in September 2004, the patternis similar: 27% of respondents have not made a choice.14 This percentage of undecidedvoters is crucial, as elections are typically decided by much smaller margins than these.15
Ad Tone and Exposure. To trace the effect of negativity on an individual’s likelihoodof making a selection, I consider negativity in several different ways (all outlined inTable 1).16
All analyses rely on measures of exposure to negativity. To calculate these levels ofexposure, I follow Franz et al. (2007). Franz et al. (2007) present one of the strongestapproaches to measuring each individual’s potential for exposure to negativity: the numberof ads shown during particular television shows multiplied by the frequency with whichindividuals watch the particular television show. Although the survey data I use do notinclude questions that trace the same detailed viewership behavior as Franz et al. (2007)use in their analysis, I can nonetheless rely on a modified version of this exposure measure:the number of ads in a respondents media market multiplied by the average number of daysa respondent views television. This is similar to Franz et al. (2007), as they too use averagetelevision viewership to account for all television habits not specifically measured. Next,also following Franz et al. (2007), I take the natural log of the obtained value. I calculatemeasures of exposure for different types of ads.
Following Hypothesis 1, I first consider whether increases in exposure to all types ofnegativity increase the likelihood of selection. Here the exposure measure is based on thetotal number of negative ads in a respondent’s media market prior to the interview date; Icall this measure overall negativity.
Next, following Hypothesis 2, I consider whether this effect varies by the target ofnegativity. To do so, I use an individual’s partisanship.17 I rely on partisanship (rather thanthe measure of candidate preference from the initial selection question) as it is a better oper-tionalization of my theoretic premises. Specifically, I theorize that individuals rarely beginthe selection process neutral, and this is why the target of the negativity matters. Were I touse the preferred candidate from the selection question, for individuals who have completedselection this would mean that I would be measuring negativity about the candidate that anindividual chose at the end of the selection phase. While in many cases this final selectionmay be identical to the initial underlying preference at the start of the selection process,this may not be uniformly so. As a result, partisanship may come closer to capturing anindividual’s initial underlying preference. To consider the effect of negativity by target, Icalculate exposure about the candidate of the respondent’s partisanship and the candidatewho is not of the respondent’s partisanship. I call this the targeted negativity variable.
While these are useful measures of ad tone and exposure, they are not the onlyapproach to considering negativity. Finkel and Geer (1998) argue that a relative measure isbest, noting “we believe [a] difference measure is superior, as it captures the relative toneof advertising by balancing positive and negative appeals” (p. 580). Thus, I also analyze
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Tabl
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Neg
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Tabl
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Neg
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diff
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Posi
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re3,
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Tabl
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395
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whether higher levels of negativity relative to positivity lead to an increase in selectionlikelihood. To do so, I calculate the relative difference in the exposure to negative and pos-itive ads in a particular media market. I call this variable the overall ad tone. If individualsare exposed to more positive ads than negative ads, the overall ad tone variable takes on apositive value; if they are exposed to more negative than positive ads, this variable takes ona negative value.
As I stated earlier, increases in exposure to negativity about a candidate who is not ofthe individual’s partisanship will increase the likelihood of selection more than exposure topositivity about either candidate. To compare the effect of various types of ads by target,I calculate the relative difference between exposure to negative ads about the candidatenot of the individual’s partisanship and exposure to positive ads about the candidate ofthe individuals own partisanship. These relative measures serve as additional tests of myhypotheses.
Additional Controls. I also include controls for state-level differences and the traditionaldeterminants of turnout. Candidates view different states (and as a result, media markets)as more or less competitive. As a result, to account for general campaign variations indifferent states, I create a measure of state competitiveness. To construct this measure I useShaw’s (2006) classifications of states during the 2004 campaign as battleground, “possibleleaner,” solidly Republican, or Solidly Democrat.18
Next, in all the models I control for the traditional determinants of voter behavior, fol-lowing Rosenstone and Hansen (1993). These include education, income, partisan strength,efficacy, church attendance, race, gender, age, employment, as well as a control for thepolitical South.19
Aside from the focal ad exposure measures described above, other exposure-basedcontrols are included in the analysis. In addition to their exposure measure, Franz et al.(2007) also include a control for attention to news, which I do as well. I also control forindividual knowledge, following Zaller’s (1992) argument that knowledge represents anindividual’s general level of political awareness.
A final set of controls works to ensure that the timing of interview does not serve as aconfound in the analyses that rely on the cross-section. As individuals in the cross-sectionwere interviewed on different dates and as, naturally, those interviewed on later dates wouldbe exposed to more advertising than those interviewed in the earlier waves of the survey,I take several precautions to ensure that the timing of the interview does not muddy theresults. First, I control for the days remaining until the election for each individual basedon his or her interview date. Second, I control for the individual’s reported attention tothe progress of the campaign. Next, I control for the total number of all ads and the totalnumber of ads specific to the particular model that aired in the individual’s media marketover the duration of the entire campaign. Finally, I control for the percentage of differenttypes of ads prior to the individual’s interview in each media market. As this descriptionsuggests, the ad controls depend on the model at hand—the more ad types included, themore ad controls. These controls help to ensure that the observed findings are, indeed, afunction of increases in certain types of ads, rather than the result of interview timing.
Relying on these variables, I specify and estimate five sets of models. Each set includesthe same model estimated using the cross-section and the panel. Model sets 1 and 2 testHypothesis 1 and consider general positivity and negativity. Model sets 3, 4, and 5 testHypothesis 2 and focus on targeted positivity and negativity.20 All models rely on pro-bit with clustered standard errors to account for any media market-level variation that
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Negative Advertising and Voter Choice 397
is not explicitly controlled for in the model. An overview of key variables, models, andexpectations is presented in Table 1.
A Check on Ad Distribution. In order to ensure that any results I obtain here are a functionof tone, rather than simple changes in ad distribution across media markets, my first step isto compare media markets. To do so, I use Shaw’s (2006) state classifications, and comparethe amount of positivity and negativity in states by group. I do this to ensure that the propor-tion of negativity will not be serving as a proxy for state competitiveness. After all, if statesthat are more competitive tend to have more negativity than states that are less competitive,then an increased likelihood of selection may well be the result of state competitivenessrather than negativity.
First, I compare the average number of ads shown by competitiveness category. Theseresults (shown in Figure 1) reinforce Shaw’s classifications of the states, with the averagenumber of ads decreasing as competitiveness decreases. If the same effect is observed fornegativity, then it will be difficult to discern whether changes in likelihood of selection area function of tone or competitiveness. As Figure 1 shows, however, this is not the case.While the distribution of ad totals follows a clear pattern, the proportion of negativity doesnot. Thus, while individuals in more competitive states were, in fact, seeing more ads, onecannot conclude that they were seeing more negativity. This conclusion is directly in linewith previous work. Looking at presidential elections 2000 and 2004, Franz et al. (2007)conclude that there is no relationship between tone and media market competitiveness, not-ing that ”negativity for presidential candidates tends to be part of a more national strategy,as opposed to a market-by-market phenomenon” (p. 58).
4500
1: Battleground 5: Partisan base2 3 4
4000
3500
3000
2500
2000
1500
1000
500
0
1
0.9
0.8
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Figure 1. Comparison of ad number and tone by state competitiveness.
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In sum, my controls for state competitiveness and use of clustered errors—along withthe comparison shown in Figure 1—ensure that any observed relationships speak to theimportance of tone, rather than to changes in the relative competitiveness of a media market.
Results
In examining the results of the tests, I discuss the models that rely on the cross-section andthe models that rely on the panel data simultaneously. I do so as there is little substantivevariation in the results. Although there are some slight variations in the results on the controlvariables,21 the results on my key independent variable, exposure to negativity, are similarin both direction and significance across the two types of models. In considering the results,I present figures that trace the substantive effect of negativity; the key coefficient estimatesare discussed in the text and the full model estimates are shown in Appendix A.
Hypothesis 1. In the first set of results, the coefficients for overall negativity are positive andstatistically significant for both the cross-sectional data (coefficient = 0.024, p ≤ .05) andthe panel data (coefficient = 0.059, p ≤ .05). In turn, this means that increases in exposureto negativity lead to increases in the likelihood of selection, which are shown in Figure 2A(cross-sectional) and Figure 2B (panel). Further, each of the shifts in likelihood of selectionis significant.
A second set of models considers how the relationship between negativity and positiv-ity affects the likelihood of selection. The key independent variable in this analysis, overallad tone, is the relative relationship between exposure to negativity and exposure to positiv-ity in a respondent’s media market. If there is a higher percentage of positivity in a mediamarket, then overall ad tone takes on a positive value; if there is more negativity, then ittakes on a negative value. The coefficients for both the cross-sectional data (coefficient =−0.034, p ≤ .1) and the panel data (coefficient = −0.302, p ≤ .1) are negative and sig-nificant, meaning that individuals are less likely to have made selections at the time of theNAES interview if they live in media markets with more positive than negative ads. Turningto substantive results, shifting from an environment with equal positivity and negativity toone with more negativity produces an average increase in likelihood of choice of 0.05 forthe cross-sectional data and 0.06 for the panel data; these changes in likelihood are signif-icant at p ≤ .05.22 Relying on both data sets, the results offer support for Hypothesis 1:Exposure to negativity increases the likelihood of choice.
Hypothesis 2. Turning now to the targeted models, the results once again follow predictions.The results are substantively similar across data sets. First, increases in negativity aboutthe candidate not of the individual’s partisanship have a significant positive relationshipto selection for both the cross-sectional data (coefficient = 0.019, p ≤ .01) and the paneldata (coefficient = 0.094, p ≤ .01). The substantive effect is shown in Figure 3A (cross-sectional) and Figure 3B (panel) and follows from Hypothesis 2—increases in ads thatare congruent (i.e., negative ads about the other party’s candidate) lead to an increase inthe likelihood of selection. Next, I compare exposure to negativity about the candidatenot of the individual’s partisanship with other types of ads—including positivity about thecandidate of the individual’s partisanship. In both the cross-section and the panel data onlynegativity about the candidate not of the individual’s partisanship leads to an increase inselection likelihood. This is shown in Figure 4, which directly compares the average changein likelihood based on a standard deviation increase in exposure to both negativity aboutthe candidate of the other party and positivity about the candidate of the individual’s own
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Negative Advertising and Voter Choice 399
B: Panel Data0.9
0.8
0.7
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0.5No
Exposure
Negativity Exposure
AverageExposure
MaximumExposure
0.68
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0.9A: Cross-Sectional Data
0.8
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oo
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ice
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ice
0.5No
ExposureAverage
Exposure
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MaximumExposure
0.77
0.72
0.66
Figure 2. The effect of increases in exposure to negativity on likelihood of choice. All covariateswere set to either mean (continuous) or mode (categorical). Estimates were obtained using Clarify(Tomz, Wittenberg, & King, 2003). Results are based on Table A1: Model set 1.
party. As shown, while an increase in negativity about the other party leads to a statisticallysignificant increase in the likelihood of choice, an increase in exposure to positivity has aneffect that cannot be statistically distinguished from 0. Indeed, in the cross-sectional data,an increase in positivity about a candidate of the same party as the individual leads to adecrease in the likelihood of choice.
As a final check, I compare exposure to negativity about the other party’s candidate toexposure to positivity about the same-party candidate. I follow the same approach here asin the overall ad tone variable and consider whether in a particular media market an individ-ual is more likely to see positive ads about the same-party candidate or negative ads aboutthe different-party candidate. Just as in the overall ad tone variable, when positive ads out-number negative ads, the variable takes on a positive value; when negative ads outnumber
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NoExposure
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ExposureAverage
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A: Cross-Sectional Data
Figure 3. The effect of increases in exposure to negativity about a candidate of a different partyon likelihood of choice. All covariates were set to either mean (continuous) or mode (categorical).Estimates were obtained using Clarify (Tomz, Wittenberg, & King, 2003). Results are based on TableA2: Model set 3.
positive ads, it takes on a negative value. As a result, a negative coefficient would meanindividuals with higher exposure to negative ads about the different-party candidate thanto positive ads about the same-party candidate are more likely to make selections. This iswhat we observe; for both the cross-sectional (coefficient = −0.268, p ≤ .05) and the paneldata (coefficient = −0.120, p ≤ .001), the coefficients for the relative positivity-negativityvariable are significant and negative, reinforcing the results shown in Figure 4.23
In all five tests, the rest of the controls perform as may be expected, with increasesin attention to news and partisan strength, for example, being significant predictors of
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Negative Advertising and Voter Choice 401
Figure 4. The effect of increases in exposure to negativity about a candidate of a different party ver-sus increases in exposure to positivity about a candidate of the same party. ∗∗∗p ≤ .01. All covariateswere set to either mean (continuous) or mode (categorical). Estimates were obtained using Clarify(Tomz, Wittenberg, & King, 2003). Confidence intervals are 95%. Change is a function of a standarddeviation increase from the mean. Results are based on Table A2: Model set 4.
selection. While the fact that education fails to reach significance may initially seemsurprising—as education is commonly one of the strongest predictors of behaviors suchas voter turnout—this outcome is not unexpected. First, it is not clear exactly how one mayexpect education to affect selection. One argument would suggest that those with higherlevels of education may know which candidate they prefer sooner, and another argumentcould suggest just the opposite: that those with higher levels of education are more awareof the complexities of the political process and would therefore take longer to synthesizecampaign information and make a selection. Furthermore, previous research on selectiontiming has shown that education has little effect on when an individual makes a selection(McAllister, 2004). Indeed, much of the recent work that focuses directly on voter selec-tion does not include education in the full model, as it is not expected to be a significantpredictor (for example, Hillygus & Jackman, 2003).
Robustness. In order to ensure that these results are robust to different model specificationsand operationalizations of key variables, I conduct several additional tests using both thepanel and cross-sectional data. First, I consider whether the recency of negativity has anyeffect on the likelihood of choice. Specifically, given that the effect of an ad may be rel-atively short (Ansolabehere & Iyengar, 1995), I consider whether ads aired closer to theNAES interview had a stronger effect on choice than those aired earlier in the campaign.To do so, I follow Krasno and Green (2008) and measure exposure by including only ads
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aired 3 weeks before the NAES interview. Relying on this measure does not change theresults; negativity still has a positive effect on likelihood of choice and is particularly likelyto have this effect when it is about the other party’s candidate.24
As I discussed earlier, measuring exposure is crucial for this analysis. As a result, Iconsider an additional approach to exposure. While the Franz et al. (2007) measure multi-plies ad numbers by TV viewership, as a second approach I use an interaction term betweenthese two measures. This interaction will enable me to observe the effects of both ads andTV viewership, and ensure that all effects are the product of exposure to negativity. Thisis an important point. First, my theoretical premises are based on the idea that individualsactually observe the ads. Second, it is important to determine that the results are a functionof ad exposure, rather than simple increases in general TV viewing. Specifically, followingmy theoretical predictions, this interaction should show that increases in both negative adsand TV viewership jointly work to produce exposure and increase the likelihood of choice.I consider this interactive effect using my main model of overall negativity. The results areconsistent with my theoretical predictions and other increased support for the psychologicalmechanisms described in the theoretical premises. Considering these relationships throughmarginal effects (Brambor, Clark, & Golder, 2006) shows that increases in negativity havea significant marginal effect on choice as TV viewership increases. In turn, as negativity ina media market increases, higher TV viewership has a stronger marginal effect on choice.Importantly, increases in TV viewership do not have a significant marginal effect on choicewhen there is little or no negativity in a media market.25 In short, these tests reinforce themain results and speak to the individual-level mechanisms.
Is It Negativity?
Thus far the results have shown that exposure to negative ads increases the likelihood thatan individual will make a selection. Is this effect due to negativity as a tonal factor, or isthere an underlying alternative explanation? Indeed, scholars have shown that negative adsgenerally contain more political information than positive ads (Franz et al., 2007; Geer,2006). Could the effect observed be the result of increased levels of political information(as proxied by different types of ads), rather than a process unique to negativity? If such analternative explanation holds, increasing the availability of politically useful informationof any tone should lead to an increased likelihood of selection. The next step, then, is toconsider such an alternative explanation for my results.
I can use the substantive content of ads aired during 2004 to consider whether it is anyincrease in politically useful information, rather than negativity, that leads to an increasein the likelihood of selection. To do so I differentiate between ads that focus on issues andads that focus on character. Issue ads focus on various political positions candidates havetaken as well as previous political behavior; character ads focus on a candidate’s biography.Scholars suggest that issue ads are more likely than character ads to contain informationthat is useful for decision making such as issue positions, ideology, and previous politicalbehavior. Indeed, as evidence to the informational power of ads, Franz et al. (2007) dis-tinguished between ads that “focused on policy matters” and those that “focused solely onpersonal characteristics” (p. 69), and others also reinforce the argument that issue ads aremore politically informative (Iyengar & Simon, 2000; Valentino, Hutchingse & Williams,2004). Koch (2008) summarizes the relationship between tone and content as follows:
Scholars maintain that at least in presidential campaigns negative ads are char-acterized by a higher level of issue content than positive ads (Geer, 2005;
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Negative Advertising and Voter Choice 403
Mayer, 1996). Positive ads frequently aim to promote the personal qualitiesof the candidate and thus are viewed as fluff pieces that fail to provide citizenswith information necessary for influencing the direction of government policy.(p. 610)
If negative ads are more likely to contain useful political information and if it is increasesin political information (rather than negative tone) within a media market that are leadingto a higher likelihood of selection, then including issue ads in the model should lead to aresult similar to the one obtained with negativity.
Relying on the University of Wisconsin coding team’s classification of ads as eitherissue or character, I use my base model to estimate the effect of issue ads on selection.26
I consider this in two parts. First, I consider whether simple increases in issue ads lead toa higher likelihood of selection. Second, I consider if differently toned issue ads have adifferent effect on selection; here I consider the differing effects of negative issue ads andnegative character ads, as well as positive issue ads and positive character ads. Again, I relyon both the cross-section and the panel data; as a result, I present the models in sets.
First, I estimate a model that considers the effect of issue ads on choice. The coeffi-cients in both the cross-sectional and the panel data do not reach significance, showing thatissue ads do not have a significant effect on likelihood of selection and suggesting that thereis something unique to tone, aside from informational content.
Next, I consider the differences in positive and negative issue ads. Again, if it isthe basic increase in information that matters, both types of issue ads should lead to anincreased likelihood of selection. The results show differently (Figure 5). In fact, only neg-ative issue ads have a significant, positive effect on selection. Moreover, both negativeissue and negative character ads have a significant relationship to selection. As shown inFigure 5, a single standard deviation increase in either type of negativity leads to a signif-icant increase in the likelihood of choice. Indeed, these increases in choice as a functionof negative issue and character ads are even similar in size. In short, the higher likelihoodof selection is not simply a function of the higher informational content of negative ads, asads that are generally believed to have the highest informational content, issue ads, do noton their own lead to increases in likelihood of selection.27
Discussion
The above results suggest a unique power to negativity. This power follows from a phe-nomenon psychologists have termed “negativity bias”—an individual’s tendency to focuson negative information at the expense of positive information when making decisions.In a campaign, negativity bias manifests itself through the key role negative ads play inan individual’s selection process. As the empirical evidence suggests, it is increases innegativity—rather than positivity—that leave individuals more likely to make selections.
Increases in negativity consistently lead to a higher likelihood of selection. Movingfrom minimum ad exposure (an individual who either did not report watching televisionat all or one who lived in an area with no ads) to maximal exposure (an individual whoreported watching television every day and lived in a media market with numerous ads)increased the likelihood of selection by 0.10. Even for individuals who reported watchingtelevision on average only once a week, increases in numbers of negative ads in a mediamarket led to statistically significant increases in likelihood of selection: Just 100 more adsin a media market increased their likelihood of choice by 0.05.28
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0.10*
–0.08
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NegativeCharacter
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A: Cross-Sectional Data
Figure 5. Effect of issue and character ads on choice ∗p ≤ .1, ∗∗p ≤ .05, ∗∗∗p ≤ .01. All covariateswere set to either mean (continuous) or mode (categorical). Estimates were obtained using Clarify(Tomz, Wittenberg, & King, 2003). Change is a function of a standard deviation increase from themean. Results are based on Table A3: Model set 2.
Specifying this effect of negativity even further, research on decision processing sug-gests that individuals will generally have an easier time with information that is congruent.Following this logic, it is exposure to negativity about the other party’s candidate thatshould have the strongest effect on choice. This is, indeed, the case. Holding the other typesof negativity constant, moving from minimum negative ad exposure to maximal exposureto negative ads about the other party’s candidate increases the likelihood of selection by0.17. Focusing on those individuals who reported watching little television, just 100 morenegative ads about the opposing party’s candidate lead to a 0.12 increase in selection.29
Notably, negativity about a candidate of a different party has a stronger effect on selec-tion than positivity about a candidate of the same party. Specifically, a shift from minimalto maximal exposure in positivity about the candidate of ones own party actually decreasedthe likelihood of selection by 0.02. This change in selection did not reach conventionallevels of statistical significance.30
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Negative Advertising and Voter Choice 405
This effect is a function of tone, rather than the informational power of negativity.If increases in negativity increased likelihood of selection as a result of increasing theinformational content of ads, then issue ads—which have the most informational content—should have the same effect as negative ads. This is not the case. Indeed, when issue andcharacter ads are split by tone, it is negative ads, rather than issue ads, that have an effecton selection. This is not to argue that the informational content of advertising does not mat-ter, but merely to suggest that negativity is uniquely powerful in helping individuals makeselections.31
Conclusions
Although the process of making a selection may appear to be the simplest—and arguably,least costly—part of the campaign process, the selection is a crucial first step toward politi-cal participation. Individuals who do not (or cannot) select which candidate they prefer areunlikely to participate in the political process. Furthermore, the earlier in the campaign anindividual determines his or her preferred candidate, the higher the likelihood of politicalinvolvement. Only an individual who supports one candidate over another has reason todonate money to a campaign, volunteer, or participate in other ways. To the extent that webelieve the democratic process would benefit from greater individual involvement, then, itis important that we understand when and why individuals come to make candidate selec-tions. In short, it is not only important to consider why individuals pick one candidate overanother and what leads an individual to support a particular candidate for office, but it isjust as important to understand what types of campaign conditions can help an individualmake any choice at all.
This focus on selection naturally leads to a focus on campaign tactics, and few cam-paign tactics are as ubiquitous and accessible as ads. As scholars have suggested, to theextent that some individuals obtain any information during a campaign, it is throughaccidental exposure to advertising. It is then an important conclusion that certain adsleave individuals better able to make choices. And it is further notable that it is negativeads—rather than positive ads—that increase this likelihood of selection. At a time whennumerous policy organizations, pundits, politicians, and journalists continue to argue thatnegativity is detrimental to the political process, it is important to note that increases innegativity lead to increases in likelihood of selection. In short, individuals who saw morenegative ads were more likely to take an initial, necessary step toward political involvement:making a choice.
Negative ads are doubtlessly unpleasant. They are critical and, at times, seek to shametheir target. At the same time, evidence suggests that individuals focus on negativity andthat negative information is, in general, a crucial part of a decision context. As a result,the role of negativity in the campaign process is distinctly more murky. In short, simplybecause negative ads are unpleasant does not logically determine that negative ads will beharmful under all conditions.
Notes
1. I use the terms “selection”, “choice,” and “decision” interchangeably.2. I use Multiple Re-Interview Panel B as it includes post-election turnout measures. For
predicted probabilities controls were set at mean for continuous variables and mode for othervariables.
3. See supplemental material: Robustness checks.
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4. There are other definitions of negative ads. Freedman, Wood, and Lawton (1999) distinguishbetween “personal attacks” or “issue” appeals. Jamieson, Waldman, and Sheer (2000) argue for adistinction between “attack” and “contrast” appeals. In relying on Geer’s definition, I make a trade-off: A tonal definition cannot discern which substantive elements of negativity have the greatest effecton choice.
5. Prior (2005) calls this “chance encounters.” The idea of chance encounters is founded on thepremise that individuals who are not seeking to encounter serious political information may still doso—and may even retain this information—when they encounter it accidentally.
6. Different individuals have different differentiation thresholds to complete a selection.7. Unless, of course, an individual has never encountered such a decision context before or has
absolutely no understanding of his or her choice set.8. A series of long-form interviews conducted during the 2008 presidential election with a group
of adults (N = 15, all residents of a midwestern state) highlight that while individuals do feel apreference for candidates of their own party, they also want to know more information before makinga final selection.
9. Individuals who are motivated by accuracy may have an easier time processing incongru-ent information, though Kunda (1990) shows that an accuracy motivation is not always enough toovercome bias.
10. The NAES is a study commissioned by the Annenberg School for Communication andthe Annenberg Public Policy Center of the University of Pennsylvania (Romer, Kenski, Waldman,Adasiewicz, & Jamieson, 2004).
11. Use of the Wisconsin Advertising Project data requires the following acknowledgment: “Thedata were obtained from a joint project of the Brennan Center for Justice at New York UniversitySchool of Law and Professor Kenneth Goldstein of the University of Wisconsin-Madison, andincludes media tracking data from the Campaign Media Analysis Group in Washington, D.C. TheBrennan Center-Wisconsin project was sponsored by a grant from the Pew Charitable Trusts. Theopinions expressed in this article are those of the author(s) and do not necessarily reflect the views ofthe Brennan Center, Professor Goldstein, or the Pew Charitable Trusts.”
12. As the control variables in the models that follow control for behaviors, attitudes, andmost importantly choice during the presidential election, ads are those that focus on the presiden-tial contest. Notably, other political races have lower visibility than the presidential race; this lowervisibility would affect the likelihood of exposure to ads without affecting the underlying decisionmechanism, which stems from general theories of decision making. Further, while in one single cam-paign period there are ads from a number of different national contests, the theory here proposes thatinformation about specific alternatives affects the choice made between these alternatives, suggest-ing that individuals will be able to differentiate between races and the ads that are specific to eachrace.
13. This is a particularly useful measure. First, questions that simply ask whether or not a choicehas been made lead to problematic outcomes: They may incentivize stating a slight candidate pref-erence as a fully made choice (Martin, Traugott, & Kennedy, 2005). Second, leaving room open forchanging one’s mind expressly signifies a lack of commitment (Gilbert, & Ebert, 2002).
14. These numbers are comparable to other 2004 surveys and data; for example, the percentageof respondents in the 2004 American National Election Survey who have not made a choice is 28.9%.
15. To consider robustness, I reestimate every model discussed below using only the first part ofthe two-part choice question. The results are robust to this change.
16. All ads were coded for negativity, positivity, and content by a team of coders at the Universityof Wisconsin.
17. For those who are true independents (i.e., do not lean toward either party), I do rely on thestated preference in the selection question. These individuals comprise 4.17% of my sample. I relyon the preferred candidate in order to include these individuals in the analysis—as excluding themwould lower the sample size and, further, the exclusion would not be random. As a check, I estimatemodels where I do exclude the independents and obtain substantively similar results. In models wherethe independents are included, I rely on a control variable to account for the difference.
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Negative Advertising and Voter Choice 407
18. Using these classifications, states that both campaigns classified as battleground are codedas 1; states that one of the campaigns classifies as battleground are coded as 0.75; states that bothcampaigns classified as “possible leaners” are coded as 0.5; and states that one of the campaignsclassified as “possible leaners” are coded as 0.25; and states that both campaigns agree are solidlyone party or the other are coded as 0.
19. Political South is defined following the ANES: Alabama, Arkansas, Florida, Georgia,Mississippi, North Carolina, South Carolina, Texas, and Virginia.
20. The N for the cross-section model will be smaller than the full cross-section. This is dueto three reasons: (a) The analysis includes respondents interviewed after the start of the generalelection; (b) my ad data did not track all media markets, meaning I did not have ad information for allrespondents; and (c) the NAES had multiple question batteries for different waves of the cross-section,which means that respondents were excluded in order to maintain consistent question wording andinclude key controls.
21. These differences can be explained by the different data samples and slight differences inquestion wording.
22. Likelihood estimates were obtained using Clarify (Tomz, Wittenberg, & King, 2003).23. Full model estimations are shown in Table A2, model set 5.24. See supplemental material: Robustness checks.25. Moving from 0 to the mean level of negativity and moving one standard deviation from the
mean level of negativity have a significant marginal effect on the likelihood of choice, holding TVviewership constant. The marginal effect of a standard deviation increase from the mean in the paneldata is 0.045 (significant at p ≤ .05) and in the cross-section is 0.082 (significant at p ≤ .05). Themarginal effect of TV viewership increases as the number of negative ads increases. In the paneldata, the marginal effect of more TV viewership is 0.001 (not significant) when there are no ads; thiseffect is 0.030 (p ≤ .1) when ad numbers are at mean, and 0.056 (p ≤ .05) when ads are one standarddeviation above the mean. In the cross-sectional data, the marginal effect of more TV viewership is0.003 (not significant) when there are no ads; this effect is 0.037 (p ≤ .05) when ad numbers are atmean, and 0.052 (p ≤ .05) when ads are one standard deviation above the mean.
26. Some of the ads were coded as both issue and character (15.23% of all ads), and others asneither issue nor character (0.74% of all ads). To consider the ads that were coded both as issue andcharacter, I follow Koch’s (2008) logic and deal with this issue in several ways: (a) I include the adsthat were coded as both in the issue total and in the character totals, (b) I include the ads coded asboth as a separate control, and (c) I exclude these ads. All three ways lead to substantively similarresults. The models shown use the approach second.
27. Full model estimates are shown in Table A3.28. Results are based on panel data. Controls were set to mean for continuous variables; for
binary or categorical variables controls were set to mode. Changes in selection likelihood in this caseare significant at p ≤ .1. Results from the cross-section data reflect the same pattern.
29. Changes in selection likelihood are significant at p ≤ .05.30. Changes in selection likelihood are significant at p ≤ .05.31. To consider robustness, I reestimate these results using the same set of robustness checks as
were used with the main negativity models. Specifically, I reestimate these models using only the firstpart of the choice question and reconsider the results using only ads aired 3 weeks prior to the NAESinterview. The results are robust to these different specifications.
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Appendix: Full Model Estimates
Table A1Effect of negativity on likelihood of selection: Cross-section (N = 5760) and panel
(N = 552)
Model 1:cross-section Model 1: panel
Model 2:cross-section Model 2: panel
Coef. SE Coef. SE Coef. SE Coef. SE
Ad ToneOverall neg. 0.024∗∗ (0.012) 0.059∗∗ (0.027) – – – –Overall ad tone – – – – −0.034∗ (0.020) −0.302∗ (0.169)
DemographicsEducation −0.009 (0.011) −0.047 (0.077) −0.012 (0.009) −0.005 (0.040)Income 0.002 (0.014) 0.049 (0.043) 0.004 (0.012) 0.023 (0.042)Age 0.002 (0.002) 0.006 (0.005) 0.002 (0.002) 0.003 (0.005)Gender 0.051 (0.050) 0.425∗∗∗ (0.137) 0.055 (0.046) 0.370∗∗∗ (0.131)Married −0.051 (0.051) −0.028 (0.149) −0.039 (0.043) −0.039 (0.146)Employed −0.009 (0.059) −0.314∗∗ (0.150) −0.015 (0.049) −0.339∗ (0.148)Church −0.012 (0.015) −0.074∗ 0.043 −0.014 (0.015) −0.074∗ (0.042)Years in home −0.003 (0.002) −0.117∗∗ (0.057) −0.018 (0.021) −0.008 (0.006)South 0.006 (0.039) 0.083 (0.143) 0.027 (0.047) 0.174 (0.126)Black −0.019 (0.098) 0.696∗∗ (0.262) 0.009 (0.082) 0.516∗ (0.291)Hispanic −0.112 (0.090) −0.349 (0.324) −0.152 (0.098) −0.364∗ (0.307)
Political determinantsEfficacy 0.054∗ (0.028) 0.074 (0.071) 0.025 (0.028) 0.059 (0.085)Interest −0.045 (0.035) −0.412∗∗∗ (0.092) −0.072∗∗ (0.030) −0.380∗∗∗ (0.091)Strength of PID 0.245∗∗∗ (0.056) 0.302∗∗∗ (0.080) 0.223∗∗∗ (0.048) 0.318∗∗∗ (0.246)
Cross-section controlsDays until
election0.014∗∗∗ (0.001) – – 0.014∗∗∗ (0.001) – –
Follow campaign 0.180∗∗∗ (0.035) – – 0.188∗∗∗ (0.029) – –
ExposureKnowledge −0.025 (0.026) −0.084 (0.075) −0.019 (0.023) −0.080 (0.079)News 0.031∗∗∗ (0.008) 0.051∗∗ (0.026) 0.029∗∗∗ (0.008) 0.023 (0.074)
Campaign conditionsCompetitiveness 0.019 (0.053) −0.301 (0.252) 0.035 (0.051) −0.096 (0.604)Cross-Sec/Vol. 1 0.000∗ (0.000) 0.492∗ (0.242) 0.040 (0.102) 0.039 (0.189)Cross-Sec/Vol. 2 −0.000∗∗ (0.000) −0.365∗ (0.219) −0.000∗∗ (0.000) 0.000 (0.000)Cross-Sec −0.009 (0.092) – – 0.000∗∗ (0.000) – –
∗p ≤ .1; ∗∗p ≤ .05; ∗∗∗p ≤ .01.
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Tabl
eA
2E
ffec
tof
nega
tivity
onlik
elih
ood
ofse
lect
ion,
targ
eted
:Cro
ss-s
ectio
n(N
=57
60)
and
pane
l(N
=55
2).
Mod
el3:
cros
s-se
ctio
nM
odel
3:pa
nel
Mod
el4:
cros
s-se
ctio
n.M
odel
4:pa
nel
Mod
el5:
cros
s-se
ctio
n.M
odel
5:pa
nel
Coe
f.SE
Coe
f.SE
Coe
f.SE
Coe
f.SE
Coe
f.SE
Coe
f.SE
Ad
Tone
Neg
:dif
f.PI
D0.
019∗∗
∗(0
.007
)0.
094∗∗
∗(0
.036
)0.
056∗∗
(0.0
28)
0.14
6∗∗∗
(0.0
42)
––
––
Neg
:sam
ePI
D–
––
–0.
060
(0.0
57)
−0.0
65(0
.045
)–
––
–
Pos:
diff
.PID
––
––
−0.0
25(0
.064
)−0
.047
(0.0
56)
––
––
Pos:
sam
ePI
D–
––
–−0
.086
(0.0
76)
0.00
9(0
.038
)–
––
–O
vera
llto
ne–
––
––
––
–−0
.268
∗∗(0
.115
)−0
.120
∗∗∗
(0.0
45)
Dem
ogra
phic
sE
duca
tion
−0.0
09(0
.009
)−0
.017
(0.0
39)
−0.0
10(0
.010
)−0
.014
(0.0
40)
−0.0
08(0
.009
)−0
.014
(0.0
40)
Inco
me
0.00
9(0
.012
)0.
041
(0.0
40)
0.00
4(0
.016
)0.
042
(0.0
40)
0.01
0(0
.012
)0.
041
(0.0
41)
Age
0.00
1(0
.002
)0.
005
(0.0
05)
0.00
1(0
.002
)0.
005
(0.0
05)
0.00
2(0
.002
)0.
005
(0.0
05)
Gen
der
0.01
4(0
.047
)0.
333∗∗
∗(0
.124
)0.
006
(0.0
51)
0.35
0∗∗∗
(0.1
23)
0.01
8(0
.046
)0.
369∗∗
(0.1
27)
Mar
ried
0.03
5(0
.043
)0.
013
(0.1
42)
0.04
0(0
.048
)−0
.010
(0.1
42)
0.04
1(0
.043
)−0
.033
(0.1
43)
Em
ploy
ed0.
006
(0.0
48)
−0.3
37∗∗
(0.1
58)
0.03
4(0
.058
)−0
.315
∗(0
.164
)−0
.008
(0.0
50)
−0.3
21∗∗
(0.1
60)
Chu
rch
0.00
9(0
.015
)0.
074∗
(0.0
44)
0.01
1(0
.017
)0.
081∗
(0.0
44)
0.01
4(0
.015
)−0
.075
∗(0
.043
)Y
ears
inH
ome
−0.0
02(0
.021
)−0
.009
(0.0
06)
−0.0
24(0
.025
)−0
.008
(0.0
06)
−0.0
21(0
.021
)−0
.008
(0.0
06)
Sout
h0.
051
(0.0
45)
0.13
5(0
.140
)0.
063
(0.0
43)
−0.1
18(0
.143
)0.
028
(0.0
48)
0.12
8(0
.138
)B
lack
−0.0
49(0
.086
)0.
511∗
(0.2
91)
−0.1
85∗
(0.1
01)
0.48
9∗(0
.289
)−0
.085
(0.0
88)
−0.5
42∗
(0.2
88)
His
pani
c−0
.128
(0.0
89)
−0.3
38(0
.317
)−0
.173
∗(0
.091
)−0
.348
(0.3
26)
−0.1
52(0
.091
)−0
.317
(0.3
18)
Polit
ical
dete
rmin
ants
Effi
cacy
0.01
4(0
.028
)0.
045
(0.0
68)
0.02
6(0
.031
)0.
045
(0.0
68)
0.02
4(0
.027
)0.
050
(0.0
69)
PID
stre
ngth
0.25
1∗∗∗
(0.0
51)
0.21
6∗∗(0
.097
)0.
415∗∗
∗(0
.042
)0.
268∗∗
∗(0
.086
)0.
256∗∗
∗(0
.050
)0.
264∗∗
∗(0
.090
)
(Con
tinu
ed)
411
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Tabl
eA
2(C
ontin
ued)
Mod
el3:
cros
s-se
ctio
nM
odel
3:pa
nel
Mod
el4:
cros
s-se
ctio
n.M
odel
4:pa
nel
Mod
el5:
cros
s-se
ctio
n.M
odel
5:pa
nel
Coe
f.SE
Coe
f.SE
Coe
f.SE
Coe
f.SE
Coe
f.SE
Coe
f.SE
Inte
rest
−0.0
73∗∗
0.02
9−0
.385
∗∗∗
(0.0
90)
−0.0
40(0
.032
)−0
.378
∗∗∗
(0.0
89)
−0.0
77∗∗
(0.0
30)
−0.3
92∗∗
∗(0
.090
)In
d.co
ntro
l−0
.712
∗∗∗
(0.0
87)
−0.2
62(0
.203
)−0
.772
∗∗∗
(0.0
91)
−0.2
35(0
.202
)−0
.680
∗∗∗
(0.0
87)
−0.2
52(0
.198
)C
ross
-sec
tion
cont
rols
Day
sto
elec
tion
0.01
6∗∗∗
(0.0
01)
––
0.01
7∗∗∗
(0.0
01)
––
0.01
4∗∗∗
(0.0
01)
––
Follo
w0.
176∗∗
∗(0
.030
)–
–0.
173∗∗
∗(0
.034
)–
–0.
177∗∗
∗(0
.030
)–
–
Exp
osur
eK
now
ledg
e−0
.016
(0.0
22)
−0.0
88(0
.074
)−0
.011
(0.0
25)
−0.0
82(0
.073
)-0.
020
(0.0
22)
-0.
084
(0.0
73)
New
s−0
.028
∗∗∗
(0.0
08)
0.04
7∗(0
.027
)−0
.026
∗∗∗
(0.0
09)
0.05
0∗(0
.027
)0.
027∗∗
∗(0
.008
)0.
051∗
(0.0
27)
Cam
paig
nco
nditi
ons
Com
petit
iven
ess
−0.0
10(0
.052
)−0
.218
(0.3
42)
−0.0
52(0
.066
)−0
.171
(0.3
36)
−0.0
43(0
.051
)0.
141
(0.3
19)
Cro
ss-
Sec/
Vol
.1−0
.606
∗∗∗
(0.1
41)
−0.0
50(0
.039
)−0
.637
∗(0
.386
)−0
.454
∗∗(0
.137
)−0
.005
(0.0
36)
Cro
ss-
Sec/
Vol
.2−0
.000
∗(0
.000
)0.
000
(0.0
00)
−0.0
00∗∗
(0.0
00)
0.00
0∗∗(0
.000
)−0
.000
(0.0
00)
0.00
0(0
.000
)
Cro
ss-S
ec0.
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(0.0
00)
––
0.19
9(0
.288
)–
–0.
536∗∗
(0.1
56)
––
Cro
ss-S
ec–
––
–0.
000∗
(0.0
00)
––
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0∗(0
.000
)–
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ross
-Sec
––
––
0.00
0∗(0
.000
)–
–0.
000
(0.0
00)
––
∗ p≤
.1;∗∗
p≤
.05;
∗∗∗ p
≤.0
1.
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Negative Advertising and Voter Choice 413
Table A3Effect of issue ads on likelihood of selection: Cross-section (N = 5760) and panel
(N = 552).Model 1:
cross-section Model 1: panelModel 2:
cross-section Model 2: panel
Coef. SE Coef. SE Coef. SE Coef. SE
Ad contentOverall issue ads −0.002 (0.016) 0.046 (0.039) – – – –Pos. issue ads – – – – 0.008 (0.020) −0.073 (0.048)Pos. char. ads – – – – −0.022 (0.014) −0.034 (0.028)Neg. issue ads – – – – 0.030∗∗∗ (0.011) 0.134∗∗ (0.068)Neg. char. ads – – – – 0.026∗ (0.015) 0.061∗∗ (0.029)Other ads 0.017 (0.020) −0.025 (0.029) 0.001 (0.011) −0.068 (0.041)
DemographicsEducation −0.010 (0.009) −0.046 (0.079) −0.014 (0.009) −0.054 (0.079)Income 0.008 (0.012) 0.048 (0.044) 0.003 (0.012) 0.044 (0.044)Age 0.002 (0.002) 0.006 (0.005) 0.002 (0.002) 0.008 (0.005)Gender 0.051 (0.047) 0.418 (0.144) 0.061 (0.045) 0.453∗∗∗ (0.149)Married 0.023 (0.043) 0.027 (0.173) 0.018 (0.041) 0.028 (0.178)Employed −0.009 (0.051) −0.384 (0.407) −0.003 (0.049) −0.369 (0.411)Church 0.011 (0.015) 0.085∗ (0.049) 0.012 (0.015) 0.091∗ (0.050)Years in home −0.019 (0.021) −0.008 (0.006) −0.002 (0.002) −0.008 (0.006)South 0.052 (0.044) 0.0171 (0.147) 0.047 (0.046) 0.054 (0.157)Black 0.002 (0.079) 0.520 (0.378) 0.002 (0.079) 0.0527 (0.385)Hispanic −0.123 (0.093) −0.530∗ (0.290) −0.113 (0.081) −0.574∗ (0.294)
Political determinantsEfficacy 0.025 (0.028) 0.062 (0.069) 0.043∗ (0.025) 0.058 (0.069)Interest −0.075∗∗ (0.030) −0.363∗∗∗ 0.097 −0.075∗∗∗ (0.029) 0.368∗∗∗ (0.095)Strength of PID 0.227∗∗∗ (0.049) 0.469∗∗∗ (0.148) 0.217∗∗∗ (0.047) 0.464∗∗∗ (0.149)
Cross-section controlsDays until election 0.015∗∗∗ (0.001) – – 0.016∗∗∗ (0.001) – –Follow campaign 0.189∗∗∗ (0.029) – – 0.181∗∗∗ (0.028) – –
ExposureKnowledge −0.020 (0.022) −0.073 (0.078) −0.017 (0.023) −0.084 (0.080)News −0.030∗∗∗ (0.009) −0.059∗∗ (0.026) −0.020∗∗ (0.009) 0.052∗∗ (0.026)
Campaign conditionsState
competitiveness0.062 (0.058) −0.374 (0.253) 0.057 (0.050) −0.473∗ (0.247)
Cross-Sect/Vol. 1 0.135 (0.124) −0.007 (0.226) −0.046 (0.097) 0.057∗ (0.034)Cross-Sect/Vol. 2 −0.000 (0.000) 0.000 (0.000) 0.000 (0.000) −0.000 (0.000)Cross-Sec 0.000 (0.000) – – −0.000∗∗ (0.000) – –Cross-Sec – – – – −0.000∗ (0.000) – –Cross-Sec – – – – −0.338 (0.296) – –Cross-Sec – – – – −0.000∗ (0.000) – –
∗p ≤ .1; ∗∗p ≤ .05; ∗∗∗p ≤ .01.
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