Rebuttals as Deracializing Strategies for African

American and White Candidates

Matthew Tokeshi1

1 Matthew Tokeshi is a Ph.D. candidate in Politics at Princeton University, 130 Corwin Hall,

Princeton, NJ 08540, mtokeshi@princeton.edu

Abstract

The study of American racial politics has long focused on the political conditions that activate

racial animosity. A central line of research demonstrates that campaign messages that highlight

negative stereotypes of African Americans can activate whites’ racial attitudes. However, little is

known about whether this activation can be overcome. I develop a theory of racial deactivation

and test its predictions with four survey experiments conducted from 2011 to 2014. I find that

while white candidates who call out the racial nature of a campaign attack succeed in

neutralizing its effects, African American candidates who use the same rebuttal do not achieve

the same result. However, African American candidates who offer a credible justification of the

attacked action can recover by winning support from racially sympathetic whites. The results

have implications for how race affects campaigns, citizens’ ability to respond to reasons, and

source credibility.

1

In 2006, Democrat Deval Patrick became the second elected African American governor

since Reconstruction when he won the Massachusetts gubernatorial election over Republican

Kerry Healey. Patrick was elected despite facing racially tinged campaign attacks that resembled

the Willie Horton ads used by George Bush to paint former Massachusetts governor Michael

Dukakis as soft on crime in the 1988 presidential election (Mendelberg 2001). One notable

Healey ad began with a white woman walking alone in a dark parking garage and then cut to

footage of Patrick describing a convicted rapist as “thoughtful” and “eloquent.”2 Patrick

responded to this attack by pursuing a number of different strategies: offering a credible reason;

touting his own tough-on-crime record; and accusing his opponent of being soft on crime.

Patrick lead shrank considerably after the racially tinged ads started airing. However, he restored

it after his campaign responded to Healey’s attacks.

Patrick’s successful rebuttal of a racial attack stands in contrast to other African

American and white candidates who lost after failing to respond when faced with similar attacks,

such as Michael Dukakis, the Democratic nominee for president in 1988, and Harold Ford, Jr.,

the African American Democrat who ran for a U.S. Senate seat in 2006.3 Both Dukakis and Ford,

Jr. led in the polls before the racial attacks began airing in their campaigns. Their defeats and

Patrick’s victory suggest that effective rebuttals to racial attacks are critical to the attacked

candidate’s electoral prospects. Yet little is known about whether rebuttals are effective at all,

and if so, which rebuttals are effective, and why. Filling this gap matters for our understanding of

why candidates win or lose. Moreover, knowing whether rebuttals can succeed reveals

2 Patrick had written letters to the state parole board on behalf of the convicted rapist Benjamin LaGuer six years

earlier. Patrick initially supported LaGuer’s release because of jury misconduct during LaGuer’s first trial, but later

backed away from supporting parole after DNA evidence linked LaGuer to the crime.

3 Ford, Jr. was attacked in an ad run by the Republican National Committee that showed a blonde woman who

claimed to have met Ford at a Playboy party. This ad generated national attention during the 2006 midterm elections

and became known as the “Call Me” ad.

2

something important about the quality of public opinion in a democratic society. As Valentino,

Hutchings, and White (2002, 77) write, “implicit racial priming is inherently manipulative, as it

encourages voters to evaluate candidates based on criteria they would likely ignore if they were

aware of the intent of the appeal.” Concern regarding the manipulation of public opinion by

implicit racial priming may be alleviated if we knew that white citizens were receptive to

rebuttals to negative racial messages. If rebuttals that appeal to reason can overcome racial

resentments and stereotypes, this implies that election campaigns can raise the quality of public

opinion. The success of those rebuttals would cast the public as capable of meeting the

democratic requirements of rational self-governance and support for equal political

representation for racial outgroups.

In this paper, I define and test a number of strategies that have been used by African

American and white candidates targeted by racial attacks. The standing prediction in the racial

priming literature is that calling attention to the racial nature of the appeal is the most effective

strategy for countering an implicit racial appeal (Mendelberg 2001). I test this hypothesis and

find mixed evidence: it works for a white candidate who has been targeted by an implicit appeal,

but black candidates do not enjoy the same outcome. Thus, this study adds to a number of other

studies that document bias in the evaluation of African American candidates (Banks 2013;

Berinsky, Hutchings, Mendelberg, Shaker, and Valentino 2010; Kinder and Dale-Riddle 2012;

McDermott 1998; Sigelman, Sigelman, Walkosz, and Nitz 1995; Piston 2010; Terkildsen 1993;

Tesler and Sears 2010).

However, I also find that rebuttals that emphasize nonracial considerations – particularly

credible justifications of the attacked candidate’s actions – improve overall evaluations of black

and white candidates alike. By demonstrating the effectiveness of credible justifications based on

3

relevant information, these findings suggest that white voters are more competent and receptive

to reasonable communication than they are usually portrayed.

This study offers several other contributions to the study of public opinion and

campaigns. First, unlike past studies on the effects of campaign messages (Johnston, Hagan, and

Jamieson 2004; Gerber, Gimpel, Green, and Shaw 2011), I consider not only the effects of

messages, but also how those effects change when pitted against a countermessage. This follows

work by Chong and Druckman (2007) and Sniderman and Theriault (2004), who study elite

communication in this more realistic iterative framework. However, these studies have yet to

consider how countermessages work against messages that operate outside of conscious

awareness, as is done here by testing the effects of rebuttals to implicit racial messages. Second,

this study not only adopts, but also advances Chong and Druckman’s (2007) counterframing

paradigm by addressing the question of message (or “frame”) “strength.” This literature finds

that “strong” counterframes dominate “weak” frames, but it does not say what makes a

counterframe strong or weak. This study tests several counterframes and finds that the strongest

one was characterized by a package of relevant facts that is backed by authoritative actors. Third,

although many scholars have examined the effects of negative campaigning (Ansolabehere and

Iyengar 1995; Geer 2006), few have explored how the findings from that research are affected by

a candidate’s race (although see Krupnikov and Piston 2015). Finally, I compare identical

messages offered by African American and white candidates, thus building on rare studies on the

effects of source cues (Kuklinski and Hurley 1994; Nelson, Sanbonmatsu, and McClerking

2007). These existing studies are informative, but do not consider how source cues operate in an

iterative electoral context, as is done here.

4

The results show that while source effects do constrain black candidates, these candidates

can nevertheless respond as effectively as white candidates against racial attacks by using the

right counterargument. Thus, the role of racial attitudes in campaigns is not determined by

implicit racial attacks alone, but instead by the mix of messages offered by both sides.

Previous Literature on Implicit Appeals and Rebuttals

Implicit appeals remain common in contemporary U.S. politics. In addition to the parking

garage example cited at the beginning of the paper, other examples can be found from Barack

Obama’s 20084 and 20125 presidential campaigns, as well as Cory Booker’s successful campaign

for a New Jersey U.S. Senate seat in 2013.6 McIlwain and Caliendo (2011) argue that messages

such as these are part of a larger pattern of racial attacks in campaigns, particularly against non-

white candidates. They find that racial stereotypes and imagery are common in attack advertising

aimed at non-white candidates in the 1990s and 2000s. Although the cues in these ads can be

subtle, a body of research demonstrates that such cues lead to the activation of racial attitudes

among white voters, making such attitudes a central determinant of citizen’s opposition to the

representation of minorities and their interests (Banks 2013; Banks and Bell 2013; Berinsky et al.

2010; DeSante 2013; Hurwitz and Peffley 2005; Kinder and Dale-Riddle 2012; Kinder and

Sanders 1996; Mendelberg 2001; Tesler 2012; Tesler and Sears 2010; Valentino, Hutchings, and

White 2002; White 2007).

Although many studies have documented the effects of implicit appeals, few have

examined the effects of rebuttals to them. Early studies found that white Democrats in the 1970s

4 Michael Cooper, “McCain says he is proud of ‘Celebrity’ ad,” New York Times, 31 July, 2008.

5 Trip Gabriel, “Romney presses Obama on work in welfare law,” New York Times, 7 August, 2012.

6 David Giambusso and Brent Johnson, “Racially charged Lonegan tweet draws fire from Booker campaign, Newark

leaders,” Newark Star-Ledger, 9 August, 2013.

5

and 1980s did not directly rebut implicit appeals made against them, instead choosing to ignore

them or change the subject (Edsall and Edsall 1991; Glaser 1996). Studies of African American

candidates in the 1980s also found that explicit discussion of race, including directly challenging

implicit appeals as racist, was not a popular strategy (see Perry 1991 on “deracialization”).

The first test of the effect of rebuttals to implicit appeals was conducted by Mendelberg

(2001) using survey data from the 1988 presidential campaign. Using the date of interview from

the 1988 American National Election Study (ANES) as a measure of exposure to the Horton ads,

Mendelberg finds that racial attitudes had the smallest effect on candidate evaluations during the

last phase of the campaign in which the Horton ads were explicitly called anti-black messages.

Thus, Mendelberg argues that calling attention to the racial nature of the appeal is a successful

rebuttal strategy. However, this finding is open to alternative explanations. For example, since

message exposure is measured indirectly (using date of interview as a proxy), it is unclear

whether the message itself or some other factor that occurred at the same time caused candidate

evaluations to change. An experimental test is needed to determine the causal relationship

between message and candidate evaluation. Also, black candidates’ responses have never been

tested.

Rebuttal Strategies

In this study, I test eight rebuttal strategies. Six of them – racial, negative, justify, justify

+ racial, distract, and ignore -- were tested in a preliminary study that used a non-representative

convenience sample with less realistic stimuli (Tokeshi and Mendelberg n.d.). In addition, I test

two strategies that have been offered by candidates facing racial attacks – counterimaging and

counterattack. These rebuttals have been tested in another study of campaign rebuttals (Craig,

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Rippere, and Grayson 2014), but they have not been applied to situations involving racial attacks

or African American candidates. More detail for each rebuttal type is provided below.

1) Racial. The key feature of this strategy is that the attacked candidate calls attention to

the racial nature of the attack. President Obama used this strategy during the 2008 campaign in

response to a John McCain ad that interspersed Obama images with irrelevant images of the

blonde female celebrities Paris Hilton and Britney Spears. Obama replied to the ad by saying that

it was an attempt to remind voters that he “doesn’t look like all those other presidents on the

dollar bills.”7

Mendelberg (2001) argues that this rebuttal will be effective because it brings the racial

content of the message into conscious awareness, triggering the normative rejection of the

message and deactivation of white voters’ racial sentiments. This assumes, however, that citizens

believe the claim that the message was an inappropriate racial attack. Against this notion is the

literature on deracialization, which implies that emphasizing racial considerations might increase

their salience and lead to negative evaluations among those with high levels of anti-black

prejudice, even though mentioning race is intended to alert white respondents to guard against

expressing prejudice (Perry 1991). Peffley and Hurwitz (2007) found that whites exposed to an

anti-death penalty frame that explicitly mentions racial discrimination as a reason to oppose the

death penalty were more pro-death penalty than whites exposed to a non-racial anti-death

penalty frame or no frame at all. Thus, there is reason to believe that racial may evoke a

backlash effect among prejudiced whites.8 However, since the standing claim of the racial

7 Mark Mooney. “Obama aide concedes ‘dollar bill’ remark referred to his race.” ABC News, 1 August, 2008,

http://abcnews.go.com/GMA/Politics/story?id=5495348&page=1.

8 This backlash effect may be particularly strong when the candidate offering the racial rebuttal is black. In the next

section, I offer hypotheses about whether black and white candidates enjoy equal success with each of the rebuttals

tested.

7

priming literature is that racial will boost the targeted candidate’s evaluation, this hypothesis is

tested here.

2) Negative. This strategy also denounces the attack, but uses general, nonracial terms

such as “negative” or “inappropriate.” President Obama used this strategy in April 2011

following the release of his long-form birth certificate. In his attempt to put an end to the

speculation about his birth place, Obama said he is “speaking to the vast majority of the

American people as well as to the press — we do not have time for this kind of silliness”9

(emphasis added).

Predictions for this rebuttal can be derived from the literature on counterframing (Chong

and Druckman 2007). Earlier literature on framing (Nelson, Clawson, and Oxley 1997) found

that exposure to a frame – say, the Ku Klux Klan should be able to hold a rally because it is

entitled to freedom of speech — elevates the importance citizens place on the framed

consideration in political decision-making. However, the counterframing literature argues that

framing effects dissipate when pitted against a counterframe that emphasizes a different

consideration, assuming that the counterframe is a strong argument. For racial attacks, it could be

that rebuttals that emphasize compelling nonracial considerations (such as an opponent’s

negativity) shift focus away from the racial considerations that were primed by the attack,

thereby improving evaluations of the targeted candidate. Thus, the prediction here is that

negative will improve evaluations of the targeted candidate.

3) Justify. This strategy entails offering a credible justification for the attacked action—a

package of relevant facts tied together into a reason-based narrative that is backed by objective

sources or authoritative actors. For example, former Arkansas governor Mike Huckabee

9 Michael D. Shear. “Obama releases long-form birth certificate.” New York Times, 27 April, 2011.

8

employed this strategy in 2009 when he was criticized for pardoning Maurice Clemmons, an

Arkansas convict who murdered four Washington state police officers after Huckabee pardoned

him. Huckabee justified the pardon by saying that he received a recommendation to pardon

Clemmons from the trial judge in the case and the unanimous vote of the parole board.10

Evidence consistent with the notion that such explanations can be persuasive is found in

McGraw (1991), who demonstrated the effective use of politicians’ reason- and fact-based

justifications for their controversial actions. Also, from the counterframing perspective outlined

earlier, justify may improve the targeted candidate’s ratings by shifting attention toward

nonracial considerations evoked by a credible justification.

4) Justify + racial. This rebuttal combines the credible justification with the accusation of

racial intent. McGraw’s theory of credible accounts suggests that this rebuttal will be effective,

but the deracialization perspective predicts failure for this rebuttal (at least among racially

resentful whites) because it continues to make racial considerations salient. This rebuttal

provides a test of these two competing hypotheses.

5) Distract. With this rebuttal, the attacked candidate pivots from the topic on which he is

attacked to a topic more favorable to him. This is the type of strategy implied by deracialization

– talk about issues other than race. For example, Obama usually avoided directly addressing any

attacks or speculation that referenced his race during the 2008 campaign, instead choosing to

focus on the economy. From the counterframing perspective, distract may be effective, as it

focuses attention on nonracial considerations.

6) Counterimaging. This strategy involves targeted candidates reframing the attack in a

way that allows them to talk about their strengths on the very topic on which they are being

10 Amanda Sterling. “Huckabee calls criticisms over clemency ‘disgusting.’” CBS News, 1 December, 2009,

http://www.cbsnews.com/news/huckabee-calls-criticisms-over-clemency-disgusting/.

9

attacked. For example, Deval Patrick responded to the parking garage ad by emphasizing his

experience as a tough-on-crime federal prosecutor. Craig, Rippere, and Grayson (2014) tested

this rebuttal in the context of a nonracial attack on a generic candidate and found that it improved

evaluations of the target. The counterframing perspective suggests that this message shifts

attention to nonracial considerations, but it does so in a specific way – by offering a counter-

stereotypical message. Previous studies find that counter-stereotypical messages can positively

affect white voters’ evaluations of black candidates (Goldman and Mutz 2014). Thus, the

prediction here is that counterimaging will be effective.

7) Counterattack. An example of this is Patrick’s charge that his opponent is the one who

is soft on crime because the crime rate had risen and the number of state police officers had

decreased during her tenure as Lieutenant Governor. The key difference between this strategy

and counterimaging is that counterattack focuses attention on negative aspects of the attacker

while counterimaging portrays the target in a positive light. Craig, Rippere, and Grayson (2014)

found that counterattack also improved overall evaluations of the attacked candidate, but in

response to a nonracial attack on a generic candidate. Counterattack, like counterimaging,

operates as a counter-stereotypical message in response to a racial attack and therefore should be

effective.

8) Ignore. This strategy involves not offering any type of response. One prominent

example of its use was Michael Dukakis, who was criticized for allowing a long stretch of the

1988 presidential campaign to go by without addressing the Horton attacks or issuing any other

message. Since this non-response does not qualify as a strong counterframe, the counterframing

perspective suggests that this strategy will fail.

Black Elite Influence on Public Opinion

10

Thus far, I have not addressed the question of how the candidate’s race influences how

rebuttals are received. Yet, as Kuklinski and Hurley (1994) demonstrate, citizens’ interpretation

of a political message likely varies depending on the race of the messenger. Studies that compare

the influence of white and black elites on public opinion are rare, however. Most of the existing

perspectives on the question of whether black elites enjoy the same influence over public opinion

that white elites do are skeptical of the idea that statements offered by black candidates have

much influence on whites at all. Tesler and Sears (2010) suggest that by virtue of their race

alone, black candidates make racial considerations more salient in political decision making, thus

making it difficult for black candidates to dampen the influence of racial attitudes among whites.

In the case of the 2008 election between Obama and McCain, they argue that simply by being

black, Obama evoked a strong association between racial attitudes and perceptions of him. This

chronic accessibility, they argue, “should make it difficult to either deactivate or prime these

racial predispositions” (Tesler and Sears 2010, 35). However, as mentioned earlier, Mendelberg

(2001) argues that the African American political figure Jesse Jackson successfully bolstered

Dukakis’ support in the 1988 election by calling attention to the racial intent of the Horton ads,

suggesting that black elite rhetoric can influence whites. I seek to clarify these conflicting

expectations by testing a racial disadvantage hypothesis: black candidates’ rebuttals will be less

effective than white candidates’ rebuttals.

Racial disadvantage may apply particularly to racial and justify + racial due to

“discounting,” whereby citizens dismiss the statements of public officials they believe are

speaking from self-interested motives. Nelson, Sanbonmatsu, and McClerking (2007) found that

whites evaluate black elites’ charges of racism as less credible than the same charge made by

11

white elites, perhaps because racism claims are perceived to be made frequently by black leaders

and are therefore discounted.

Finally, the deracialization and discounting perspectives have implications for whites

with negative views of African Americans. Deracialization proponents are particularly concerned

that these whites disapprove of African American candidates who broach the subject of race. The

discounting perspective also implies that whites with negative views of blacks may be

particularly dismissive of African Americans who charge their opponents of being motivated by

racial animus. Thus, in the final part of the paper, I test the hypothesis that black candidates’ use

of racial or justify + racial will have no effect (or possibly even backfire) among whites with

high levels of anti-black affect.

Sample

To test all of the hypotheses described in the previous sections, I conducted four internet

survey experiments from 2011 to 2014. The first two, conducted in December 2011 and March

2013, used a convenience sample of white respondents from Amazon.com’s Mechanical Turk

(MTurk).11 The second two, conducted in February 2014 and December 2014, used samples

provided by Survey Sampling International (SSI) that were drawn to match the U.S. white adult

population.12 Combining the samples in the four experiments, the total number of white

respondents is 3,482. The combined sample was then weighted to match the U.S. white

registered voter population on age, sex, and educational attainment according to the November

2012 Current Population Survey (CPS). Although the details of the design of each experiment

11 In the March 2013 study, 28 out of 766 respondents (3.7%) were recruited from the Princeton Survey Research

Center’s Mercer County (NJ) panel while the remaining 96.3% were recruited from MTurk.

12 In the February 2014 study, the sample was drawn to match the national white adult population on age, sex, and

region. In the December 2014 study, the sample was drawn to match on age, sex, region, and educational attainment.

12

differ in some respects, the core elements of the design are the same across all four

experiments.13 The design differences do not systematically influence the treatment effects (see

Table 2 in the Supplemental Material for more details).

Method

The experiments simulated an attack-rebuttal episode in a fictitious U.S. Senate

campaign. In the attack stage (the first stage), respondents read a fictitious news story about an

attack ad in a U.S. Senate race.14 The story explains that the target of the ad was attacked by his

opponent for pardoning a former U.S. Representative (the “criminal”) who was convicted of

eight felonies, including assault of a police officer that left the officer hospitalized due to head

injuries.15 After reading the attack news story, respondents provided feeling thermometer

assessments (ranging from 0=negative to 100=positive) of the target and the attacker.

The pardon of a convicted ex-politician was selected as the theme of the ad because

linking candidates to black criminality has been a theme in campaign ads such as the Willie

Horton ad, the anti-Deval Patrick parking garage ad, and an ad from 2008 that tied Obama to

Kwame Kilpatrick, the African American former mayor of Detroit who pleaded guilty to two

counts of obstruction of justice and later resigned. The scenario examined in this study is

intended to be similar to the Kilpatrick attack, as the ex-Representative’s crimes are loosely

13 For an overview of the key design differences across the four experiments, see Table 1 in the Supplemental

Material.

14 The election type (general or primary) and the partisanship of the target and the attacker vary across experiments

(see Table 1 in the Supplemental Material).

15 The target is the sitting governor of the state, which explains how he issued a pardon. In the first experiment, the

assault of a police officer was listed among the ex-Representative’s crimes instead of being emphasized as it was in

the other three experiments. For details on the wording of the attack news stories across experiments, see Figure 1

and Table 3 in the Supplemental Material.

13

Figure 1: Two versions of the attack news story used in Experiment 4, black target/white

criminal (left) and white target/black criminal (right). Larger images of the attack news story

used in Experiment 4 can be seen in the Figure 1 of the Supplemental Material. The text of the

attack news stories used in Experiments 1-3 are shown in Table 3 of the Supplemental Material.

modeled on Kilpatrick’s.16 A side-by-side photo of the target and the criminal is embedded in the

news article (see Figure 1). The target is shown wearing a suit while the criminal is shown in a

mug shot.

The races of both the target and criminal were manipulated to be either white or black.

The target’s race is manipulated in order to test the hypothesis that black candidates’ rebuttals

are less effective than white candidates’ (the racial disadvantage hypothesis). The criminal’s race

is manipulated because it allows for a test of whether the attack “worked” as a racial attack,

meaning that a racial cue (in the form of either a black criminal or black candidate) strengthens

the effect of racial attitudes on target evaluation relative to a white target/white criminal pair.

16 Although Kilpatrick was arrested for assaulting a police officer, the detail about the officer requiring

hospitalization was added. The aim was to make the assault seem severe enough to capture respondents’ attention in

an internet interview mode where attention is scarce.

14

The white and black versions of the target and criminal must be as close to identical as

possible so that differences in ratings between them can be attributed to their skin color and not

some other aspect of their appearance, such as their attractiveness. In order to do this, a

morphing procedure used in recent political science research was used (Bailenson, Iyengar, Yee,

and Collins 2008; Weaver 2012).17 This procedure yields a white face and a black face that are

60% identical, which controls for many extraneous (nonracial) sources of variation while still

allowing realistic variation in skin tone and facial features.18

In the rebuttal stage (the second stage), respondents were randomly assigned to one of

eight rebuttals offered by the target: racial, negative, justify, justify + racial, distract,

counterimaging, counterattack, and ignore.19 Respondents then offered another round of feeling

thermometer evaluations for both candidates.

Table 1 highlights the key features of the eight rebuttal conditions.20 All conditions

(except ignore) contain the same first paragraph, explaining that a U.S. Senate candidate attacked

his opponent for pardoning a convicted former U.S. Representative. The second and third

paragraphs vary for each of the rebuttal conditions:

17 This procedure was used to generate the faces used in Experiments 2-4. In Experiment 1, the faces were created

using FaceGen Modeller 3.2, a 3-D face-generating software program that creates “identical” black and white faces.

These faces, however, lack the realism of the faces created using the morphing procedure. The FaceGen faces used

in Experiment 1 are shown in Figure 2 of the Supplemental Material.

18 For more details on how the faces were created, see Tokeshi and Mendelberg (n.d.). To verify that the white and

black versions of the target and criminal used in Experiments 2-4 were perceived by human subjects as equivalent,

pilot studies were conducted in 2012 on MTurk to assess how people rate the faces along six trait dimensions

identified by psychologists as critical for social judgment: likeable, threatening, dominance, competence,

trustworthiness, and attractiveness (Oosterhof and Todorov 2008). There were no differences at the 0.05 level in

mean rating along any of these dimensions. See Table 4 in the Supplemental Material for ratings of black and white

versions of the target and criminal.

19 Racial and justify + racial were considered to be unrealistic rebuttals for a white target paired with a white

criminal, and so only the other six rebuttals are tested for the white target/white criminal pair.

20 See Figures 3-10 of the Supplemental Material for the full text of each rebuttal.

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Table 1: Description of rebuttal stories

Rebuttal Racial Negative Justify Justify +

racial

Distract Counterimaging Counterattack Ignore

Headline Wells Claims

Ad Had

Racial Intent

Wells

Claims Ad

is a

Negative

Attack

Wells

Claims Ad is

Unfair

Attack

Wells

Claims Ad is

Unfair,

Racial

Attack

Wells

Attacks

Moore on

Economy

Wells Claims Ad

Misrepresents Pro-

Victim Record

Wells Claims

Ad is

Hypocritical

Attack

YouTube

Takes On

Television

First

Paragraph

Wells fired

back against

ad…

Identical

across

conditions

Identical

across

conditions

Identical

across

conditions

Identical

across

conditions

Identical across

conditions

Identical across

conditions

Information

about a non-

political

news story

Second

Paragraph

“…ad is an

attempt to

stir up racial

fears…”

“…ad is an

attempt to

stir up

fears…”

“…ad is a

distortion of

the truth…”

“…ad is a

distortion of

the truth…”

“My

opponent’s

economic

plan will

spell

disaster…”

“…ad is a

misrepresentation

of who I am…”

“…ad is a

hypocritical

attack…”

Information

about a non-

political

news story

Third

Paragraph

“Charges like

this…divide

us—race

against

race…”

“Charges

like this

divide

us…”

Wells

explains

reason for

pardon:

unanimous

vote of

bipartisan

review board

Wells

explains

reason for

pardon, plus:

“Charges

like this

divide us—

race against

race…”

“I fought and

delivered on

behalf of all

working

families…”

“…I have

aggressively fought

for crime victims.

In fact, I have sent

people to jail…”

“My opponent

is trying to look

tough on crime

when his record

shows

otherwise…”

Information

about a non-

political

news story

Photos Candidate

and criminal,

race varying

for each

Identical

across

conditions

Identical

across

conditions

Identical

across

conditions

Identical

across

conditions

Identical across

conditions

Identical across

conditions

Photos that

go with a

non-political

news story

16

In racial, the target says, “This ad is an attempt to stir up racial fears…Charges like this

breed division in our country and our state. They divide us – race against race – so we

blame each other instead of work together” (emphasis added). This statement is meant to

resemble the one offered by then-Governor Bill Clinton’s speech announcing his run for

the presidency in 1991 when he said, “For twelve years, Republicans have tried to divide

us – race against race – so we get mad at each other and not at them” (Mendelberg 2001,

104).

The target in negative issues the same rebuttal as the target in racial, except he leaves out

racial’s two racial references – “racial fears” and “divide us – race against race” – and

instead uses “fears” and “divide us.”

The target in justify offers a credible justification for his action. He explains that the trial

judge in the case recommended commuting the sentence and that the convicted

Representative received a 5-0 vote recommending parole from the state’s bipartisan

parole board. He concludes by saying that he does not condone the Representative’s

actions, but he relied on the good judgment of the trial judge and parole board. This

reasoning was similar to the real-life justification offered by Mike Huckabee described

earlier.

Justify + racial combines the credible justification of justify with racial’s claim that

charges like this divide us “race against race.”

In distract, the target avoids addressing the ad and instead criticizes the attacker’s

economic policy.

17

In counterimaging, the target argues that he is the real tough-on-crime candidate because

he sent people to jail when he was a federal prosecutor and that he has dedicated his life

to working on criminal justice issues.

The target’s counterattack charges the attacker with voting six times to limit employer

access to criminal background checks on workers and that the attacker’s proposed budget

would cut funds for the state police. Both the counterimaging and counterattack rebuttals

were modeled after rebuttals offered by Deval Patrick.

Finally, the target does not offer a rebuttal in ignore. Instead, respondents read an

irrelevant article about YouTube that is similar in length and formatting to the other

rebuttal conditions.

Racial and justify were each tested in all four experiments. Negative, distract, and ignore

were tested in three out of four. Justify + racial was tested in two out of four, while

counterimaging and counterattack were each tested once.21

The dependent variable I use to test the overall effectiveness of a rebuttal is the change

between the first round (post-attack) and second round (post-rebuttal) of candidate feeling

thermometer ratings. It is measured by 1) calculating the target-attacker feeling thermometer

difference after the attack; 2) calculating the same difference after the rebuttal; and 3) calculating

the difference between the first quantity and the second quantity. This score is the measure of the

improvement in the evaluation of the target caused by the rebuttal.22 For simplicity, it sometimes

21 Some rebuttals were tested more often than others because I added and dropped rebuttals for each experiment.

Specifically, the original set of five rebuttals tested in Experiment 1 was racial, negative, justify, distract, and

ignore. I added a sixth rebuttal, justify + racial, to Experiments 2 and 3. For Experiment 4, I kept racial and justify,

dropped all other rebuttals, and replaced them with counterimaging and counterattack. I kept racial and justify

because they were consistently effective (at least for some race pairs) in Experiments 1-3 and I wanted to replicate

their effects in a fourth test.

22 Although the theoretical range of this variable is -200 to 200, the actual range for the sample was -163 to 145,

with most of the data clustering between -1 and 15 (the interquartile range).

18

will be referred to as the “rebuttal effect.” The target-minus-attacker difference is used instead of

simply looking at the target’s ratings in order to account for the possibility that the attacker might

suffer a penalty for issuing a negative attack (Lau and Rovner 2009).23

Results

Before examining the effects of the eight rebuttals, I verify that the attack ad news story

worked as a racial attack. In other words, I want to establish that a racial cue, in the form of a

black target and/or black criminal, increases the effect of racial attitudes on evaluation of the

target compared to a white target paired with a white criminal. Following existing studies, this

was tested by comparing the effect of racial resentment (controlling for party identification and

ideology) on target evaluation after the attack (but before the rebuttal) for each of the four race

pairs separately, pooling across the four experiments.24 Racial resentment is an average score

(ranging from 0=low to 1=high) on a four-item battery designed to measure “symbolic racism,” a

concept developed to measure anti-black affect by asking respondents to agree or disagree with

statements such as, “Over the past few years, blacks have gotten less than they deserve” or “It’s

really a matter of not trying hard enough; if blacks would only try harder they could be just as

well off as whites” (Kinder and Sanders 1996). If the news story primed racial attitudes, the

effect of racial resentment will be more negative for the three pairs that include a black

target or criminal compared to the white/white pair.25 The coefficients plotted in Figure 12 in the

Supplemental Material confirm this expectation: racial resentment’s impact is more negative for

23 I ran a robustness check using an alternative dependent variable of post-rebuttal target – attacker difference score

controlling for pre-rebuttal target – attacker difference score and found similar results. See Figure 11 of the

Supplemental Material.

24 Heterogeneity across experiments is captured using a fixed effect term for each experiment.

25 Target/criminal pairs will be referenced using the target’s race first and the criminal’s race second. For example,

black/white refers to a black target paired with a white criminal.

19

the three pairs that include a black target or black criminal (p<0.01 for the black/black and

black/white to white/white comparisons; p=0.06 for the white/black to white/white comparison,

all two-tailed tests). This racialization is brought about by the implicit cue of the black target or

black criminal because the identical news story does not racialize the white/white pair. Thus, the

news stories worked as negative racial messages.26

Next, I turn to the analysis of the rebuttal effects. These effects are measured by pooling

observations across all four experiments.27 Rebuttal effects are tested in six ways: 1) is the effect

positive and significantly different from zero? Tests 2-5 directly test whether rebuttals work

differently depending on the target and criminal’s race. 2) Does the race of the target matter for

the effect of a given rebuttal? This test, which directly tests the racial disadvantage hypothesis,

requires two comparisons: black/black vs. white/black (i.e., is a black candidate’s rebuttal less

effective than a white candidate’s when the criminal is black?), and black/white vs. white/white

(i.e., is a black candidate’s rebuttal less effective when the criminal is white?). 3) Does the race

of the criminal matter? This also requires two comparisons: black/black vs. black/white (i.e.,

does the race of the criminal hurt a black target?) and white/black vs. white/white (i.e., does the

race of the criminal hurt a white target?). 4) How does the rebuttal of the target in the most

racialized pair (black/black) compare to the same rebuttal offered by the target in the least

racialized pair (white/white)? This is an additional test of racial disadvantage since it tests the

26 For complete results of the regression model, see Table 5 in the Supplemental Material.

27 Although there is heterogeneity in the rebuttal effect across experiments for 5 out of the 22 rebuttal/race pair

combinations that are tested in more than one experiment, pooling the observations and treating them as one sample

is still the best way to represent the average effect. For a full justification of pooling all respondents across

experiments, see Table 6 in the Supplemental Material. For the full table of rebuttal effects in each experiment, see

Table 7 in the Supplemental Material. In addition to showing the average effect pooling across all experiments, I

show the fraction of all experiments in which that rebuttal was tested that show a positive significant effect in Table

2. This is a check against finding a large average effect that is really driven by one outlier experiment.

20

effect of a black target’s rebuttal (when race is at the height of its salience) against a white

target’s rebuttal (in a scenario where race is not salient at all). 5) How do the rebuttals of the two

pairs that are partially racialized – black/white and white/black – compare? This is another test of

racial disadvantage as it tests a black target’s rebuttal (when not additionally racialized) against a

white target who is racialized. A finding of racial disadvantage according to this test would

indicate that target race strongly influences the rebuttal’s effect. After I conduct tests 2-5 for each

rebuttal, in the following section I conduct test 6: which rebuttal is most effective for each race

pair? This provides another test of racial disadvantage since some race pairs may have more

rebuttals available to them than others. Tests 2-6 jointly cover all possible paired comparisons.

Tests 2-6 require a large number of pairwise comparisons, which increases the

probability of incorrectly finding significant effects when the null hypothesis of no effect cannot

be rejected (Type I error). To guard against the possibility of finding significant effects simply as

a result of conducting multiple comparisons, the “False Discovery Rate” (FDR) method is

employed for these pairwise comparisons (Benjamini and Yekutieli 2001). Applying the FDR

correction requires adjusting the alpha level based on the number of tests conducted. Based on

the 141 tests conducted in this study, the FDR-adjusted alpha level for pairwise comparisons in

this study is 0.018 instead of the usual 0.05.28 All tests are two-tailed.

Figure 2 conducts each test by displaying all comparisons to zero (test 1) and all

comparisons between pairs (tests 2-6). Each dot represents the rebuttal effect for each race pair

with its 95% confidence interval relative to the null hypothesis of zero change and its 90%

confidence interval, which is consistent with a two-tailed test of overlap at the FDR-corrected

28 The alpha level accounts for 141 tests conducted (42 within-rebuttal comparisons (e.g., black-black racial vs.

white/black racial) and 99 within-pair comparisons (e.g., black-black racial vs. black-black justify). For calculation

details, see Appendix B of Larson-Hall (2009).

21

Figure 2: Rebuttal effects, all experiments pooled; positive values indicate rebuttal helped the

target (solid lines indicate 95% CI; brackets indicate 90% CI, which is consistent with test of

overlap at p=0.018, the FDR-corrected alpha level). The “rebuttal effect” is the average change

in target-attacker rating from post-attack to post-rebuttal for each rebuttal.

p=0.018. In other words, a test of any two rebuttals can be conducted by seeing if their 90%

confidence intervals overlap.

In addition to testing the average magnitude of each rebuttal effect (Figure 2), I also test

the consistency of finding a significant effect across multiple studies. The reason for providing

this robustness check is to make sure that a large average magnitude is not driven by the effect

22

Table 2: Consistency of rebuttal effects (fraction of all experiments that show a significant

positive rebuttal effect at 0.05 level, two-tailed).

Black target/

Black criminal

White target/

Black criminal

Black target/

White criminal

White target/

White criminal

Racial 0 / 4 2 / 4 0 / 4 N/A

Negative 0 / 3 0 / 3 1 / 3 0 / 3

Justify 3 / 4 3 / 4 4 / 4 4 / 4

Justify + racial 2 / 2 2 / 2 2 / 2 N/A

Distract 1 / 3 2 / 3 0 / 3 1 / 3

Counterimaging 1 / 1 1 / 1 0 / 1 1 / 1

Counterattack 1 / 1 1 / 1 1 / 1 1 / 1

Ignore 0 / 3 0 / 3 0 / 3 0 / 3

found in only one study. Table 2 shows the fraction of tests against a zero null (test 1) for which

I find a statistically significant effect at the 0.05 level, two-tailed.29

I start with racial. Looking at the average magnitudes compared to the zero null (test 1),

Mendelberg’s hypothesis that this strategy works is confirmed, but only for the racialized white

target.30 The same rebuttal fails test 1 for a black target regardless of the criminal’s race. To

directly test the racial disadvantage hypothesis, test 2 shows that a white target fares better than a

black target when both are paired with a black criminal, as demonstrated by the lack of overlap

of their 90% confidence intervals. Test 3 shows that criminal race makes no difference for a

black target – his rebuttals are equally ineffective regardless of whether he is tied to a black or

white criminal.31 Finally, test 5 reveals further evidence of racial disadvantage: even when a

white target is paired with a black criminal, his rebuttal is still more effective than the rebuttal of

29 For the two rebuttals that were only tested once (counterimaging and counterattack), the magnitudes come from

only one study. Thus, the results should be interpreted with caution.

30 “Racialized” is used to describe a target paired with a black criminal. Thus, a “racialized white target” is a white

target paired with a black criminal.

31 Test 4 (black/black vs. white/white) is impossible for racial because it was not tested for a white/white pair.

23

a black target paired with a white criminal. In sum, the effectiveness of racial is conditional on

the target being white, which supports the racial disadvantage hypothesis.

For negative, Figure 2 shows that the rebuttal is effective relative to zero only for the

black/white pair (test 1). However, Table 2 shows that a significant effect was only found in 1

out of 3 tests for that pair. This suggests that the magnitude was influenced by the result of one

experiment, and so some uncertainty remains regarding the effect. The racial disadvantage

hypothesis is evaluated by test 2. Black targets are not worse off than white targets when tied to a

black criminal, but black targets are actually better off than white targets when paired with a

white criminal, although again, the effect for the black/white pair remains uncertain. Tests 3, 4,

and 5 reveal no other differences between pairs. In all, this rebuttal is ineffective for all but the

possible exception of the black/white pair. Thus, we do not have strong evidence that negative

works, which disconfirms the counterframing prediction that emphasizing the opponent’s

negativity works.

Turning to justify, Figure 2 shows that this rebuttal has positive and large effects (test 1),

confirming McGraw’s prediction that credible justifications work. Also, there is no support for

the racial disadvantage hypothesis: test 2 yields null results for both comparisons. No differences

between pairs were found for tests 3, 4, and 5. Justify, then, is effective for all pairs regardless of

the race of the target and criminal.

The same holds true for justify + racial as all pairs pass test 1, confirming McGraw’s

credible justification prediction over the deracialization hypothesis, as a mention of race does not

drag down the rebuttal’s effect. Furthermore, there are no differences between any two pairs

24

according to tests 2, 3, and 5.32 This suggests that the rebuttal is equally effective for black and

white targets, which disconfirms the racial disadvantage hypothesis.

For distract, it appears that the rebuttal’s effect clears zero for black/black and

white/black, but Table 2 shows that only white/black’s effect was found in more than one

experiment. Thus, only this pair passes test 1, providing some support for the prediction that this

rebuttal serves as an effective counterframe. Test 2 shows no evidence of racial disadvantage, as

black and white targets show no differences regardless of whether each was tied to a black

criminal or a white criminal. The race of the criminal, however, does seem to make a difference

for the white target, as determined by test 3. This is the only difference between pairs, as Tests 4

and 5 show no differences. In all, distract seems to work for the white target paired with a black

criminal, although we cannot be sure that it works better for this pair than for any of the other

pairs.

Although it was only tested in one experiment, counterimaging has large and statistically

significant effects compared to zero (test 1) for all but the black/white pair, supporting the

counterframing prediction. There is evidence of racial disadvantage (test 2) for the black target

tied to a white criminal, as his rebuttal is less effective than a rebuttal offered by a white target

tied to the same white criminal. No other differences between pairs were found (tests 3-5). In

sum, counterimaging is effective for all, but with some uncertainty for the black target paired

with a white criminal, for whom it does not meet statistical significance and who may be

penalized compared to a white target in the same circumstances.

32 Test (4) could not be conducted for justify + racial because it was not tested for the white/white pair.

25

Counterattack also appears to be a promising strategy, as it has positive effects for all

four pairs (test 1). This confirms the counterframing prediction. There is no evidence of racial

disadvantage or any other differences between pairs (tests 2-5).

Finally, ignore is ineffective according to test 1 for all pairs and actually backfires for the

black/white and white/white pairs. Its ineffectiveness supports the counterframing prediction.

Examining overlap in Figure 2, we see that test 2 provides no evidence of racial disadvantage.

However, test 3 shows that the criminal’s race matters for the white target: the white/black pair

actually fares better than white/white. Test 4 shows that white/black is also better off than

black/white, supporting the racial disadvantage prediction. Test 5 shows no difference between

the most and least racialized pairs (black/black vs. white/white). In sum, ignore is ineffective for

all with some evidence of a backlash effect for any target paired with a white criminal.

To summarize the findings so far, justify and justify + racial were highly and equally

effective strategies for all race pairs. Racial and distract were effective only for the racialized

white target, with racial in particular providing strong evidence of racial disadvantage for black

targets. Counterimaging and counterattack appear equally effective for almost all pairs, although

caution is warned since their effects have yet to be replicated. Finally, negative and ignore were

largely ineffective, with ignore generating backlash for some pairs.

Next, I turn to test (6): which rebuttal works best for each race pair? I conduct this test by

taking all of the effective rebuttals (relative to zero) for each race pair and testing them against

all of the other rebuttals for that pair.

Starting with the black/black pair, the four successful rebuttals relative to zero are justify,

justify + racial, counterimaging, and counterattack. These four rebuttals are statistically

indistinguishable from one another. All four are more effective than racial and ignore. Based on

26

this test, it appears that the effective set of strategies for a black/black pair are the two credible

justification rebuttals (justify and justify + racial) and the two counter-stereotypical rebuttals

(counterimaging and counterattack). At the same time, this target should avoid racial and

ignore.

For the white/black pair, the six successful rebuttals relative to zero are the same four for

the black/black pair plus racial and distract. Justify (the most successful rebuttal) works better

than racial and distract, but is indistinguishable from the other three. The set of four strategies

that cannot be dominated by any other strategy, then, are the two credible justifications (justify

and justify + racial) and the two counter-stereotypical rebuttals (counterimaging and

counterattack).

For the black/white pair, the three successful rebuttals relative to zero are the two

credible justifications (justify and justify + racial), plus counterattack. These three are

indistinguishable from each other, and all three are significantly more effective than racial,

distract, and ignore. The set of effective and ineffective strategies is clear: use either of the

credible justifications or counterattack and avoid racial, distract, and ignore.

Finally, for the nonracialized white/white pair, justify, counterimaging, and counterattack

are the only successful rebuttals compared to zero. They are indistinguishable from each other

and more successful than the other three rebuttals: negative, distract, and ignore. Thus, the

conclusion is clear: use any of the rebuttals that are successful relative to zero and avoid all the

others. In sum, across all four race pairs, credible justifications (with or without a racial mention)

and the counter-stereotypical rebuttals are generally the most effective strategies.

In the final section, I return to the hypotheses regarding how racially resentful whites

respond to rebuttals. The deracialization and discounting perspectives imply that racial and

27

Figure 3: Rebuttal effects among high racial resentment respondents only (top third of the

sample, corresponding with racial resentment >=0.75 on a 0-1 scale)

justify + racial will be ineffective among racially resentful whites when used by a black target.

Figure 3 shows the average magnitude of each rebuttal’s effect among high racial resentment

respondents only (those in the top third of the sample, consisting of those who scored at or above

0.75 on the 0-1 racial resentment scale). As with the overall results, I also present the fraction of

tests for which I find a statistically significant result at the 0.05 level in Table 3 as a robustness

check.

28

Table 3: Consistency of rebuttal effects (fraction of all experiments that show a significant

positive rebuttal effect at 0.05 level, two-tailed). High racial resentment respondents only (top

third of the sample, corresponding with racial resentment >=0.75 on a 0-1 scale).

Black target/

Black criminal

White target/

Black criminal

Black target/

White criminal

White target/

White criminal

Racial 0 / 4 1 / 4 0 / 4 N/A

Negative 0 / 3 0 / 3 1 / 3 0 / 3

Justify 1 / 4 1 / 4 3 / 4 1 / 4

Justify + racial 1 / 2 0 / 2 1 / 2 N/A

Distract 0 / 3 1 / 3 0 / 3 0 / 3

Counterimaging 1 / 1 0 / 1 0 / 1 1 / 1

Counterattack 1 / 1 0 / 1 1 / 1 0 / 1

Ignore 0 / 3 0 / 3 0 / 3 1 / 3

In the first panel of Figure 3, racial shows the same pattern as the overall results: null

effects for both black targets, but a positive and significant effect for the racialized white target.33

Thus, mirroring the results for the overall sample, the deracialization and discounting predictions

are confirmed: racial has no effect among high racial resentment respondents when used by a

black target.

Turning to justify + racial in the fourth panel of Figure 3, none of the effects for the three

pairs reaches a significant magnitude. This is consistent with the deracialization and discounting

prediction of no effect when used by a black target. This also differs from the overall findings,

which showed that this rebuttal was effective relative to zero for all race pairs.

Looking at the overall pattern of results among high resentment respondents in Figure 3

and Table 3, there is strong evidence of a positive rebuttal effect (as measured by a positive,

significant magnitude and replication in more than experiment) for only one pair: black/white

justify. This suggests that it is difficult under almost any circumstances to move the opinions of

the racially resentful after a damaging racial attack has taken place.

33 In terms of consistency (Table 3), however, the positive effect of the rebuttal is only found in one out of four

experiments for this pair.

29

Figure 4: Rebuttal effects among low racial resentment respondents only (bottom third of the

sample, corresponding with racial resentment <=0.5 on 0-1 scale)

Much of the overall success of the rebuttals shown in Figure 2 may be driven by people

with lower levels of racial resentment. To confirm this, Figure 4 shows the magnitudes and Table

4 shows the consistency for low racial resentment respondents (those in the bottom third of the

sample, consisting of those who scored at or below 0.5 on the 0-1 scale). Among these

30

Table 4: Consistency of rebuttal effects (fraction of all experiments that show a significant

positive rebuttal effect at 0.05 level, two-tailed). Low racial resentment respondents only (bottom

third of the sample, corresponding with racial resentment <=0.5 on a 0-1 scale).

Black target/

Black criminal

White target/

Black criminal

Black target/

White criminal

White target/

White criminal

Racial 0 / 4 0 / 4 1 / 4 N/A

Negative 0 / 3 1 / 3 0 / 3 0 / 3

Justify 3 / 4 3 / 4 3 / 4 2 / 4

Justify + racial 2 / 2 2 / 2 2 / 2 N/A

Distract 0 / 3 1 / 3 0 / 3 0 / 3

Counterimaging 1 / 1 0 / 1 0 / 1 0 / 1

Counterattack 1 / 1 0 / 1 1 / 1 0 / 1

Ignore 0 / 3 0 / 3 0 / 3 0 / 3

respondents, there is strong evidence that justify and justify + racial are effective regardless of

the target or criminal’s race. For these rebuttals, then, black and white targets appear to draw on

support from racially sympathetic whites.

Discussion

Despite numerous examples of racial attacks in recent campaigns, little is known about

whether rebuttals are effective and if so, which ones. In this paper, I define and systematically

test eight rebuttals used by African American and white candidates. Answering this question is

important not only for understanding electoral outcomes, but also for understanding the limits of

racial campaigns and the public’s capacity to respond to reasonable communication. I find that

calling attention to the racial nature of the attack works well for a white candidate, but not for an

African American candidate, contrary to the standing prediction in the racial priming literature.

However, offering a credible justification for the attacked action proved to be an effective

rebuttal for black and white candidates alike.

The finding that whites reward a white target that uses the racial rebuttal, but not a black

target who uses the same rebuttal suggests an aversion to racial grievance expressed by African

Americans among whites. This point seems well understood by recent African American

31

candidates, many of whom carefully manage their public image in a way that avoids any

suggestion of racial grievance.34 How can this be reconciled with the advice offered by

Mendelberg (2001) to defuse implicit racial appeals by unmasking their racial intent? This

advice assumes that the content of the message itself determines how it is understood without

considering how the meaning of the message may be influenced by other important factors such

as who is delivering the message. As Kuklinski and Hurley (1994, 748) write: “When political

statements are made in a social context and concern real-world issues, people focus on the

pragmatic meaning of the statements, that is, why they were said and not their semantic meaning

alone. And the characteristics of the political source influence the pragmatic meaning that people

assign.” With respect to accusations of racism, whites assign a different (negative) pragmatic

meaning to the statement when the messenger is black.

Although black candidates did not have much success with the racial rebuttal, the results

suggest that they can succeed with credible justifications and possibly with counter-stereotypical

messages. The success of these strategies points to a more optimistic conclusion regarding the

ability of black elites to shape white opinion than the one offered by some racial politics

scholars. The findings presented in this study are in line with studies that portray white voters as

receptive to reasonable information, such as Goldman and Mutz’s (2014) finding that white

racial prejudice declined over the course of the 2008 presidential campaign as a result of the

frequency of counter-stereotypical cues provided by the Obama family, and with Hajnal’s (2007)

finding that white prejudice declined in cities following their first experience of black mayoral

leadership.

34 For a description of how Obama did this in 2008, see Kinder and Dale-Riddle (2012, 84-85).

32

Finally, a few limitations of the results are considered. First, only one attack-rebuttal

sequence is tested and thus the question of how multiple iterations of attack-rebuttal sequences

influence candidate evaluation cannot be addressed. Second, the experiments employed fictitious

candidates running in fictitious elections, and thus confirmation of these dynamics in a real

election involving real candidates is necessary. Third, this study does not consider rebuttals

offered by a candidate’s surrogates. Finally, counterimaging and counterattack were effective for

most race pairs in their one trial, but further tests are needed to be sure of their effect.

This study goes beyond the existing literature on racial campaigns to show that although

implicit racial attacks are powerful, they do not necessarily determine the role of race in

campaigns. Instead, black and white candidates can counter the negative effects of these attacks

depending on how they respond.

33

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37

Supplemental Material

Table 1: Overview of key design features of each experiment

Experiment Date Subject pool n Attack details Election

type

Candidate party

labels

Rebuttals tested Realistic

images?

1

Dec. 2011

and Feb.

2012

MTurk 670

Extortion, tax

evasion, appointment

of friends and

relatives to public

positions, and assault

of a police officer

General No party labels

given

1. Racial (n=114)

2. Negative

(n=140)

3. Justify (n=152)

4. Distract (n=149)

5. Ignore (n=115)

No

2

March

2013 and

July 2013

MTurk (97%)

and Princeton

Survey Research

Center’s Mercer

County (NJ)

Panel (3%)

766

Emphasize assault on

police officer; list

other crimes

(extortion, tax

evasion, etc.)

Primary

Both candidates’

party matched to

respondent’s

1. Racial (n=101)

2. Negative

(n=144)

3. Justify (n=144)

4. Justify + racial

(n=96)

5. Distract (n=138)

6. Ignore (n=143)

Yes

3 February

2014 SSI 1,035

Emphasize assault on

police officer; list

others (extortion, tax

evasion, appointment

of friends and

relatives)

Primary

Both candidates’

party matched to

respondent’s

1. Racial (n=140)

2. Negative

(n=217)

3. Justify (n=220)

4. Justify + racial

(n=147)

5. Distract (n=209)

6. Ignore (n=222)

Yes

4 December

2014 SSI 897

Emphasize assault on

police officer; list

others (extortion, tax

evasion, appointment

of friends and

relatives)

General

Targeted

candidate is a

Democrat and

attacking

candidate is a

Republican

1. Racial (n=180)

2. Justify (n=239)

3. Counterimaging

(n=250)

4. Counterattack

(n=228)

Yes

38

Table 2: Features that vary across experiments do not influence treatment effects

I created three dummy variables to represent the features that vary across experiments:

1) SSI/MTurk (1=sample was provided by SSI (Exp. 3-4); 0=sample was provided by

MTurk (Exp. 1-2))

2) Realistic faces/severe attack (1=faces used in news articles were realistic versions created

by morphing process and attack emphasized assault on a police officer (Exp. 1); 0=faces

used in news articles were less realistic versions created by FaceGen and attack listed

several crimes, mostly white collar (Exp. 2-4)

3) General election/PID unspecified or Democrat/weak attack (1=general election setting

involving two candidates without party label or target was a Democrat and attacker was

Republican and attack did not generate severely anti-target ratings (Exp. 1 and 4);

0=primary election settings involving two candidates whose party ID matched

respondent’s party ID and attack generated stronger anti-target ratings (Exp. 2 and 3)

Next, I regressed rebuttal effect (the DV) on one of the dummies, each rebuttal, and the

interaction between the dummy and the rebuttal. Column 1 shows the results for the SSI dummy.

Column 2 shows the results for the Realistic faces/severe attack dummy. Column 3 shows the

results for the General election/PID unspecified or Dem/weak attack dummy. There are no

significant interaction effects at the p=0.05 level between rebuttal and dummy, which suggests

that none of the features that vary across experiments influences rebuttal effects.

Finally, I estimated two more models. Column 4 shows results for regressing rebuttal

effect on the SSI dummy, the Realistic faces and severe attack dummy, each rebuttal, and the

interaction between each of the two dummies and each rebuttal. Column 5 shows results for

regressing rebuttal effect on the SSI dummy, the General election/PID unspecified or Dem/weak

39

attack dummy, each rebuttal, and the interaction between each of the two dummies and each

rebuttal. As in Columns 1-3, there are no significant interaction effects at the p=0.05 level

between rebuttal and dummy.

A model that includes each rebuttal, all three dummies, and interactions between each

rebuttal and the three dummies is impossible because Realistic faces/severe attack and General

election/PID unspecified or Dem/weak attack are collinear for negative, distract, and ignore.

SSI Realistic

faces and

severe

attack

General

election/

PID

unspecified or

Dem/weak

attack

SSI and

Realistic

faces and

severe

attack

SSI and General

election/PID

unspecified or

Dem/weak attack

SSI -1.1 -0.2 -0.2

Bb racial*SSI 3.2 1.3 2.2

wb racial*SSI 5.7 0.7 4.8

Bw racial*SSI 0.5 0.1 -0.7

Bb negative*SSI -0.4 -1.2 -1.2

wb negative*SSI -1.0 3.9 3.9

Bw negative*SSI 8.2 9.6 9.6

ww negative*SSI 2.2 3.2 3.2

Bb justify*SSI 2.3 2.0 1.3

wb justify*SSI 2.8 1.2 0.8

Bw justify*SSI -4.6 -6.4 -5.6

ww justify*SSI -0.5 0.2 -1.4

Bb justify +

racial*SSI

-

12.8^

-13.8^ -13.8^

wb justify +

racial*SSI

-5.3 -6.2 -6.2

Bw justify +

racial*SSI

-8.1 -9.0 -9.0

Bb distract*SSI -6.1 -1.9 -1.9

wb distract*SSI 0.8 -5.2 -5.2

Bw distract*SSI -0.9 -2.2 -2.2

ww distract*SSI -

10.1^

-6.7 -6.7

Bb ignore*SSI -0.1 0.0 0.0

wb ignore*SSI 2.1 1.4 1.4

Bw ignore*SSI -0.5 -0.4 -0.4

40

Realistic faces and

severe attack

-2.7 -2.6

Bb racial* Realistic

faces and severe

attack

5.3 4.2

wb racial* Realistic

faces and severe

attack

10.2 9.6

Bw racial*

Realistic faces and

severe attack

1.5 1.5

Bb negative*

Realistic faces and

severe attack

1.5 2.3

wb negative*

Realistic faces and

severe attack

-5.4 -8.3

Bw negative*

Realistic faces and

severe attack

5.1 -2.2

ww negative*

Realistic faces and

severe attack

0.2 -2.0

Bb justify*

Realistic faces and

severe attack

3.2 1.5

wb justify*

Realistic faces and

severe attack

4.9 4.0

Bw justify*

Realistic faces and

severe attack

-1.2 4.2

ww justify*

Realistic faces and

severe attack

-1.2 -1.3

Bb distract*

Realistic faces and

severe attack

-7.1 -5.7

wb distract*

Realistic faces and

severe attack

7.2 10.9

Bw distract*

Realistic faces and

severe attack

1.6 3.3

41

ww distract*

Realistic faces and

severe attack

-12.4^ -7.9

Bb ignore*

Realistic faces and

severe attack

0.1 0.0

wb ignore*

Realistic faces and

severe attack

3.2 2.1

Bw ignore*

Realistic faces and

severe attack

0.2 0.5

General

election/PID

unspecified or

Dem/weak attack

2.7 2.6

Bb racial* General

election/PID

unspecified or

Dem/weak attack

0.7 0.8

wb racial* General

election/PID

unspecified or

Dem/weak attack

-3.1 -2.9

Bw racial* General

election/PID

unspecified or

Dem/weak attack

6.4 6.6

Bb negative*

General

election/PID

unspecified or

Dem/weak attack

-1.5 -2.3

wb negative*

General

election/PID

unspecified or

Dem/weak attack

5.4 8.3

Bw negative*

General

election/PID

unspecified or

Dem/weak attack

-5.1 2.2

ww negative*

General

election/PID

-0.2 2.0

42

unspecified or

Dem/weak attack

Bb justify* General

election/PID

unspecified or

Dem/weak attack

-3.9 -3.8

wb justify* General

election/PID

unspecified or

Dem/weak attack

8.6 8.7

Bw justify*

General

election/PID

unspecified or

Dem/weak attack

-1.3 -0.8

ww justify*

General

election/PID

unspecified or

Dem/weak attack

-3.8 -3.6

Bb distract*

General

election/PID

unspecified or

Dem/weak attack

7.1 5.7

wb distract*

General

election/PID

unspecified or

Dem/weak attack

-7.2 -10.9

Bw distract*

General

election/PID

unspecified or

Dem/weak attack

-1.6 -3.3

ww distract*

General

election/PID

unspecified or

Dem/weak attack

12.4^ 7.9

Bb ignore* General

election/PID

unspecified or

Dem/weak attack

-0.1 0.0

wb ignore* General

election/PID

-3.2 -2.1

43

unspecified or

Dem/weak attack

Bw ignore*

General

election/PID

unspecified or

Dem/weak attack

-0.2 -0.5

^ p<0.10

44

Figure 1: Attack news story (black target/black criminal, Experiment 4)

45

Table 3: Text of attack news stories used in Experiments 1-3. The formatting of the articles is

identical to that of Experiment 4. In Experiments 2 and 3, respondents are asked for their party

identification and are assigned to read about the party primary that matches their own

partisanship. Independent respondents were randomly assigned to a Democratic or Republican

primary.

Experiment 1 Experiments 2 and 3

Ad Links Wells, Jones

The campaign of U.S. Senate candidate Peter

Moore launched an ad against rival U.S.

Senate candidate Michael Wells on Monday,

criticizing Wells’ pardon of convicted former

state representative David Jones.

Wells is the current governor and is running

for an open seat in the U.S. Senate. He is a

48-year-old businessman who is married with

two children. He has served as governor for

two terms. As governor, Wells passed a tax

relief plan for middle-class families.

The 30-second ad begins with a description of

Jones and the eight felonies he was convicted

of, which include extortion, tax evasion,

improper use of state funds, including the

appointment of 29 of his closest friends and

relatives to public positions, and assault of a

police officer. The ad ends with text that

reads, “Michael Wells: He Can’t Be Trusted”

Ad Links Wells, Jones

The campaign of [Democratic/Republican]

U.S. Senate candidate Peter Moore launched

an ad against rival [Democratic/Republican]

U.S. Senate candidate Michael Wells on

Monday, criticizing Wells’ pardon of

convicted former state representative David

Jones.

Wells is the current governor and is running

for an open seat in the U.S. Senate. He is a

48-year-old businessman who is married with

two children. He has served as governor for

two terms. As governor, Wells passed a tax

relief plan for middle-class families.

The 30-second ad begins with a description of

Jones and the eight felonies he was convicted

of, which include the assault of a police

officer. The officer was hospitalized for

injuries suffered to his head. Jones was also

convicted of appointing 29 of his closest

friends and relatives to public positions,

extortion, tax evasion, and improper use of

state funds. The ad ends with text that reads,

“Michael Wells: He Can’t Be Trusted”

46

Figure 2: These faces were created using FaceGen Modeller 3.2 and used in Experiment 1

47

Table 4: Pilot study (2012) ratings of candidates and criminals, 7-point scale for all traits

Black candidate White candidate p

mean sd mean sd

Likeable 5.88 1.41 Likeable 5.33 1.59 0.31

Threatening 3.59 1.66 Threatening 3.93 2.15 0.61

Dominance 5.71 1.49 Dominance 5.13 1.46 0.28

Trustworthiness 5.59 0.94 Trustworthiness 4.93 2.05 0.25

Competence 6.18 1.33 Competence 6.33 1.35 0.74

Attractiveness 5.06 1.56 Attractiveness 4.53 2.07 0.42

N=17 N=15

Black criminal White criminal

mean sd mean sd

Likeable 3.62 1.45 Likeable 4.06 1.83 0.48

Threatening 5.85 1.34 Threatening 6.06 1.43 0.68

Dominance 6.00 1.41 Dominance 6.72 1.32 0.16

Trustworthiness 3.69 1.25 Trustworthiness 4.17 1.47 0.35

Competence 4.38 1.26 Competence 4.72 1.71 0.55

Attractiveness 3.08 1.44 Attractiveness 4.06 1.83 0.12

N=13 N=18

48

Figure 3: Racial (black target/black criminal)

49

Figure 4: Negative (black target/black criminal)

50

Figure 5: Justify (black target/black criminal)

51

Figure 6: Justify + racial (black target/black criminal)

52

Figure 7: Distract (black target/black criminal)

53

Figure 8: Counterimaging (black target/black criminal)

54

Figure 9: Counterattack (black target/black criminal)

55

Figure 10: Ignore (all race pairs)

56

Figure 11: Rebuttal effects using alternative dependent variable of post-rebuttal target – attacker

rating controlling for pre-rebuttal target – attacker rating, all experiments pooled; positive values

indicate rebuttal helped target (solid lines indicate 95% CI; brackets indicate 90% CI, which is

consistent with test of overlap at p=0.018 (which is FDR-corrected alpha level).

57

Figure 12: Priming effects of racial resentment for each race pair, controlling for party ID and

ideology (black lines indicate 84% confidence intervals, which is consistent with a two-tailed test

at the p=0.05 level when examining overlap between the lines.)

58

Table 5: Did the attack prime racial resentment? Predicting target-minus-attacker difference

score (post-attack)

Black target/black criminal 0.19

(0.04)

White target/black criminal 0.10

(0.04)

Black target/white criminal 0.18

(0.04)

Racial resentment

(0=min, 1=max)

-0.07

(0.05)

Party identification

(0=strong Dem, 1=strong GOP)

-0.07

(0.05)

Ideology

(0=strong liberal, 1=strong conservative)

-0.07

(0.06)

Black target/black criminal

x Racial resentment

-0.24

(0.08)

White target/black criminal

x Racial resentment

-0.17

(0.07)

Black target/white criminal

x Racial resentment

-0.36

(0.07)

Black target/black criminal

x Party identification

0.14

(0.06)

White target/black criminal

x Party identification

0.05

(0.06)

Black target/white criminal

x Party identification

0.05

(0.07)

Black target/black criminal

x Ideology

-0.18

(0.08)

White target/black criminal

x Ideology

-0.06

(0.08)

Black target/white criminal

x Ideology

0.02

(0.08)

Study 2 -0.05

(0.02)

Study 3 0.00

(0.02)

Study 4 0.04

(0.02)

(Intercept) -0.03

(0.03)

N 3221

Adjusted R2 0.09 Entries are ordinary least-squares regression coefficients.

Standard errors are in parentheses. The dependent variable is a

-1 to 1 scale ranging from strong opposition to the target to strong

59

support of the target. Standard errors are displayed in parentheses.

Party identification and ideology were both measured on a 7-point

scale collapsed to the 0-1 interval. The purpose of including

these controls is to verify that racial cues have a specifically

racial effect and are not the spurious effect of nonracial

predispositions such as party identification and ideology.

If the racial cues have a specifically racial effect, then the interactions between racial

resentment and each dummy variable will be negative (anti-target) and significant even after

accounting for the interactions between the dummy variables and party identification and

ideology. Table 5 confirms our expectation: racial resentment has a negative effect on target

evaluation among those who saw a black target and/or black criminal, confirming that the news

stories worked as negative racial messages.

60

Table 6: Why pooling across four experiments is the best way to represent the average effect of

each rebuttal (cell entries are ANOVA p-values)

Black target/

Black criminal

White target/

Black criminal

Black target/

White criminal

White target/

White criminal

Racial 0.50 0.34 0.02 N/A

Negative 0.94 0.25 0.13 0.48

Justify 0.97 0.02 0.46 0.87

Justify + racial 0.03 0.39 0.10 N/A

Distract 0.02 0.25 0.89 <0.01

Ignore 0.79 0.82 0.51 0.70 Note: Counterimaging and counterattack are not included in the table because they were only tested in

one experiment.

Why pooling across four experiments is the best way to represent the average effect of each

rebuttal:

As a first test to see if pooling respondents across the four experiments and treating them

as one sample is justified, I conducted an ANOVA for each rebuttal/race pair combination to see

if the effects vary by experiment. If the ANOVA shows that there are no differences across

experiments, then we can feel confident that pooling the responses together across all

experiments and treating them as one sample is justified. For 17 out of the 22 rebuttal/race pair

combinations that were tested more than once, we see no significant differences in rebuttal

effects across experiments at the p=0.05 level.

For the five rebuttal/race pair combinations that show differences across experiments,

pooling the observations and treating them as one sample is still the best way to represent the

average effect. For these rebuttals, we know that the observations are being generated by more

than one process. Pooling respondents across experiments and taking the mean in this context

means that equal weight is given to all the different processes that generated the data. Without

any principled way to assign weight to different processes, weighting them equally (in other

words, taking the average) is the most sensible solution.

61

Also, from Table 7 in the Supplemental Material, which shows the effect of every

rebuttal/race pair combination for each experiment, it does not appear that there is one

experiment that is producing unusually small or large effects. It also does not appear that there is

one rebuttal type or race pair that consistently produces results that vary by experiment. Thus,

the “experiment effects” do not appear to conform to a pattern that can be corrected in a

principled way.

Finally, in the body of the paper, I show the fraction of tests that show a positive

significant rebuttal effect in Table 2. This is a check against finding a large average effect that is

really driven by one outlier experiment.

62

Table 7: Rebuttal effect for each rebuttal in each experiment (standard errors are displayed in parentheses)

Black/black White/black Black/white White/white

Exp.

1

Exp.

2

Exp.

3

Exp.

4

Exp.

1

Exp.

2

Exp.

3

Exp.

4

Exp.

1

Exp.

2

Exp.

3

Exp.

4

Exp.

1

Exp.

2

Exp.

3

Exp.

4

Racial

-2.1

(3.2)

n=40

-0.4

(3.1)

n=36

-2.6

(3.9)

n=41

3.0

(2.5)

n=59

0.1

(3.9)

n=39

7.2

(3.7)

n=30

6.3

(2.9)

n=50

8.9

(2.9)

n=61

1.2

(3.0)

n=35

0.1

(3.7)

n=35

-7.0

(2.8)

n=49

5.4

(2.8)

n=60

N/A

N/A

N/A

N/A

Negative

3.0

(4.0)

n=34

2.8

(2.9)

n=27

1.4

(3.9)

n=46

N/A

10.4

(5.1)

n=32

-0.5

(2.8)

n=29

3.3

(3.4)

n=51

N/A

5.6

(3.7)

n=35

0.8

(4.1)

n=31

10.3

(2.8)

n=51

N/A

2.6

(3.4)

n=39

-1.9

(2.8)

n=57

1.2

(1.9)

n=69

N/A

Justify

11.3

(3.4)

n=40

10.2

(6.1)

n=28

13.3

(3.6)

n=47

11.1

(4.4)

n=54

12.1

(3.7)

n=37

13.5

(4.6)

n=36

5.1

(3.6)

n=52

21.4

(3.8)

n=63

20.2

(4.4)

n=40

21.8

(6.0)

n=35

13.1

(3.0)

n=46

16.4

(2.9)

n=62

15.8

(4.8)

n=35

11.9

(3.4)

n=45

13.2

(2.4)

n=75

10.6

(3.8)

n=60

Justify +

racial

N/A

22.0

(4.9)

n=33

8.1

(3.7)

n=45

N/A

N/A

18.9

(5.5)

n=31

12.5

(3.5)

n=46

N/A

N/A

20.8

(4.8)

n=32

11.6

(3.2)

n=50

N/A

N/A

N/A

N/A

N/A

Distract

10.5

(3.8)

n=41

2.2

(3.0)

n=27

0.2

(2.2)

n=48

N/A

3.1

(3.3)

n=37

11.4

(4.0)

n=27

6.0

(2.5)

n=41

N/A

3.8

(4.5)

n=36

4.5

(4.8)

n=34

2.1

(2.7)

n=49

N/A

11.4

(4.3)

n=35

0.9

(2.6)

n=50

-5.9

(3.1)

n=71

N/A

Counter-

imaging

N/A

N/A

N/A

9.0

(2.2)

n=61

N/A

N/A

N/A

7.9

(3.1)

n=64

N/A

N/A

N/A

4.6

(3.4)

n=62

N/A N/A N/A 14.9

(2.7)

n=63

Counter-

attack

N/A

N/A

N/A

10.7

(3.2)

n=59

N/A

N/A

N/A

12.6

(4.8)

n=57

N/A

N/A

N/A

18.8

(3.7)

n=54

N/A

N/A

N/A

11.2

(2.4)

n=58

Ignore

1.2

(2.1)

n=30

-1.3

(1.6)

n=38

-1.5

(3.0)

n=48

N/A

0.5

(0.7)

n=32

0.1

(2.7)

n=33

1.3

(1.3)

n=51

N/A

-0.9

(0.8)

n=26

-2.9

(1.9)

n=28

-3.5

(1.4)

n=50

N/A

-1.7

(2.3)

n=27

-4.2

(2.4)

n=44

-4.4

(1.6)

n=77

N/A