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Regulatory Fit and Persuasion, p.

Regulatory Fit, Rhetorical Strategy, and Political Persuasion

Christopher D. JohnstonDepartment of Political Science

Duke UniversityDurham, NC 27708

[email protected]

Howard LavineDepartment of Political Science

University of MinnesotaMinneapolis, MN 55455

[email protected]

Paper prepared for the 2012 annual meeting of the Midwest Political Science Association, April 12th-15th, Chicago, IL.

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mailto:[email protected]

mailto:[email protected]


Abstract

We argue that citizen dispositions influence the relative success of different rhetorical strategies

for persuasion, and thus function to constrain the behavior of elite actors in their attempts to win

support for favored outcomes. Drawing on the psychological concept of regulatory fit, we

propose that recent work on psychological dispositions and political preferences can be

understood in terms of functional matches between the regulatory focus of citizens (prevention

or promotion) and the framing of policies in terms of losses and gains (e.g. Cesario et al. 2004;

Higgins 1998). In two experiments that rely on several policy domains, we find that persuasion is

facilitated when a policy is framed so that it matches the chronic regulatory focus of the

individual. We also provide some support for the idea that regulatory fit orientations constrain

the advertising strategies of political campaigns. We conclude by considering the implications of

these results for elite campaign strategy, and for the potential to micro-target persuasive

communications on the basis of personality.

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Fully understanding the political importance of framing requires considering the two-way street of mutual interaction and influence among elites and mass publics. The common understanding of framing as a top-down, elite-driven process should be replaced with a more complex view whereby the beliefs and experiences of citizens affect what will resonate with them.

- Smith (2009)

A spate of recent research has shown that citizens’ stable psychological dispositions (e.g.

traits, goals, needs) exert a substantial influence on their political preferences, identifications,

and ideologies (e.g. Amodio et al. 2007; Carney et al. 2008; Hetherington and Weiler 2009;

Gerber et al. 2010; Jost et al. 2003; Mondak 2010; Settle et al. 2010). In a widely-read theoretical

integration of this literature, Jost, Glaser, Kruglanski and Sulloway (2003) argue that people

embrace political conservatism because it serves a variety of motivational needs, including the

reduction of “fear, anxiety, and uncertainty; [the avoidance of] change, disruption, and

ambiguity; and to explain, order, and justify inequality among groups and individuals” (p. 340).

From this point of view, political conservatism is simply a special case of motivated social

cognition.

In this research, we consider the broader political implications of such influence.

Specifically, we argue that individual psychological dispositions constrain elite behavior by

moderating the relative success of different rhetorical strategies on mass attitude change.

Drawing on the concept of regulatory fit (Higgins 2005), we hypothesize that associations

between psychological dispositions and political preferences emerge as a function of “matches”

between the chronic regulatory focus of the individual (prevention or promotion), and the

framing of policies in terms of losses or gains (Cesario et al. 2004; Higgins 1998). When the

message frame matches the individual’s regulatory focus, persuasion is enhanced. This suggests

the potential for elites to construct issue frames to fit the dispositional characteristics of an

intended audience, or, in other words, to engage in personality-based micro-targeting of political

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messages. Such dynamics also imply a more powerful role for citizens in politics by suggesting

one way in which they may be more than vessels to be filled with elite opinion.

The remainder of the paper proceeds as follows: In the next section, we briefly touch on

recent studies linking psychological dispositions and political preferences, and suggest that this

work can be conceptually understood within the auspices of Higgins’ (1998) regulatory focus

framework. We then outline the concept of regulatory fit, and discuss its implications for

political persuasion. We test this framework with two experiments and aggregate (state-level)

data, and conclude with a discussion of the broader implications of the theory and empirical

findings.

Dispositional Influence on Political Preferences

Research on the role of stable individual differences on political preference draws on

several subfields in psychology, including personality (e.g. Carney et al. 2008; Gerber et al.

2010; Mondak 2010), social cognition (e.g. Hetherington and Weiler 2009; Jost et al. 2003),

cognitive neuroscience (e.g. Amodio et al. 2007; Oxley et al. 2008), and behavioral genetics (e.g.

Alford, Funk and Hibbing 2005; Settle et al. 2010). Within personality psychology, the strongest

effects on ideology are consistently found for the trait of openness to experience (which refers to

a preference for novelty and stimulation versus the routine and familiar). Within the social

cognitive literature, the need for non-specific cognitive closure (Kruglanski and Webster 1996;

an indicator of aversion to ambiguity and need for certainty), is a strong predictor of conservative

leanings (e.g. Chirumbolo et al. 2004; Thorisdottir and Jost 2011; Van Hiel, Pandelaere and

Duriez 2004). Neuroscientific studies have linked conservatism to both cognitive rigidity (i.e.

deficits in conflict monitoring; Amodio et al. 2007) and sensitivity to threatening visual and

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auditory stimuli (Oxley et al. 2008; Smith et al. 2011). And within behavioral genetics, Settle et

al. (2010) have identified a specific gene associated with novelty seeking, a trait linked to

liberalism (Carney et al. 2008; Mondak 2010).

We believe it may be heuristically useful to subsume these empirical findings within a

single theoretical framework that preserves their commonalities, but offers more straightforward

guidance on the relevance of psychological predispositions to elite strategic behavior.

Specifically, we adopt Higgins’ (1998) regulatory focus theory (hereafter, RFT), which holds

that individuals chronically differ in the strategies by which hedonic principles (seeking pleasure

and avoiding pain) are employed in the self-regulation of affect, cognition, and behavior.

According to RFT, people either prefer a positive strategy focused on achieving gains (i.e., a

promotion focus) or a negative strategy focused on avoiding losses (i.e., a prevention focus).

Promotion-focused individuals attempt to maximize “hits” and minimize errors of omission,

whereas prevention-focused individuals attempt to maximize correct rejections and minimize

errors of commission.1 In this sense, promotion-focused individuals are oriented toward the

achievement of maximal goals such as hopes, wishes, and aspirations (i.e. ideals), while

prevention-focused individuals are oriented toward the achievement of minimal goals such as the

attainment of security and the fulfillment of duties and obligations (i.e. oughts).

The commonalities between regulatory focus and the work reviewed above should be

readily apparent. A promotion-focused strategy entails openness to error and failure, comfort

with risk-reward tradeoffs, and a relatively low need for security. Conversely, a prevention-

focused strategy entails sensitivity to negative outcomes, an aversion to risk and loss, and a

relatively high need for security. The greater concern among prevention-focused (versus 1 It is important to note that regulatory focus is a general psychological principle, and thus operates both as a stable individual difference and, potentially, as a contextually-induced state (Higgins 1999). We see this as an additional advantage of this theoretical language, in contrast to, say, the “Big Five” which focuses on constructs unique to personality psychology.

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promotion-focused) individuals with oughts (duties, obligations) is also consistent with recent

work indicating that conservatives ascribe greater importance than liberals to the moral

principles of in-group loyalty, submission to legitimate authority, and the protection of

culturally-evolved institutions (Haidt 2012; Haidt and Graham 2007).2

Regulatory Fit and Persuasion

Within the persuasion context, regulatory focus at the individual level operates by

moderating the appeal of messages framed in terms of either the gains to be achieved from a

recommended action, or losses to be incurred from not taking the action (see also Kahneman and

Tversky 1979). Promotion-focused individuals are characterized by eagerness for gains and

advancement, while prevention-focused individuals are characterized by their vigilance against

losses. As Higgins (1998: 31) explains, “individuals with a strong promotion focus should be

more motivated by incentives that are relevant to goals of accomplishment, whereas those with a

strong prevention focus should be more motivated by incentives that are relevant to goals of

safety” (p. 31).

Shah, Higgins and Friedman (1998) provided empirical support for RFT by examining

success in solving anagram problems conditional on chronic regulatory focus (promotion or

prevention) and the frame of a monetary incentive. In one condition, participants were informed

that they could earn a dollar (gain frame) if they solved 90 percent of the puzzles, while in a

second condition participants were told they could avoid losing a dollar (loss frame) by not

missing more than 10 percent of the puzzles. The authors found that participants with a chronic 2 Research also suggests that regulatory focus is strongly associated with asymmetrical activation in the frontal cortex (FC). Specifically, prevention-focused individuals show chronically higher levels of activation in the right FC relative to the left, and vice versa (Amodio et al. 2004). Asymmetric activation of the right FC is associated with a preference for the familiar and secure, sensitivity to threatening imagery, behavioral inhibition, and avoidance-related emotions. Asymmetric left FC activation, by contrast, is associated with lesser sensitivity to threatening stimuli, behavioral activation, and approach-related emotions (see Harmon-Jones 2007 for a review).

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prevention focus performed better when exposed to the loss-focused monetary frame, whereas

those with a chronic promotion focus performed better when exposed to the gains-focused frame.

Cesario et al. (2004) extended RFT to persuasion by framing a message about an after-

school program either in terms of achieving gains (e.g. advancing children’s education) or

preventing losses (preventing more children from failing). They found that attitude change in the

direction of the message was enhanced when the frame of the message matched participants’

chronic regulatory focus. Work on persuasion in the political context similarly supports this

“functional matching” hypothesis (Lavine and Snyder 1996; Lavine et al. 1999). For example,

Lavine et al. (1999) found that citizens who are highly sensitive to threat (i.e. high authoritarians)

were more persuaded by a get out the vote message when it was framed in terms of potential

losses from non-voting than when it was framed in terms of the gains to be achieved by voting.

By contrast, citizens who are low in threat sensitivity (i.e., low authoritarians) responded more

favorably to a GOTV message when it was framed in terms of gains (than losses). In related

work, Kam and Simas (2010) found that dispositionally risk-averse individuals showed greater

loss aversion in a variety of prospect theory games. All of these results are consistent with Jost,

Federico and Napier’s (2009) hypothesis that linkages between stable psychological dispositions

and political preferences occur through a functional match between the motives and goals of the

former and the instrumental and symbolic content of the latter.

The Present Studies

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In the present research, we examine whether the effectiveness of persuasive

communications on policy proposals depends simultaneously on (1) whether the policy frame

highlights gains to be achieved or losses to be avoided; and (2) the chronic regulatory focus of

the individual with respect to promotion or prevention. In particular, we hypothesize that:

Those with a chronic prevention focus will be more persuaded by political communications focusing on potential losses from failure to enact a policy than potential gains from enacting a policy (H1).

Those with a chronic promotion focus will be more persuaded by political communications focusing on potential gains from enacting a policy than potential losses from failure to enact a policy (H2).

If these hypotheses receive support, individual differences in chronic regulatory focus may act to

constraint the capacity of elites to move public opinion in electoral contests. In particular, if such

dispositions cluster in political space, then elite actors have incentives to target their political

advertising strategies to the predominant psychological profile of a given media market. In this

paper, we examine the hypothesis that state-level differences in authoritarianism (Hetherington

and Weiler 2009; Lavine et al. 2002; Stenner 2005) act to create variation in the extent to which

political campaigns utilize positive- vs. negatively-toned advertisements – i.e., those that

highlight gains or losses – in House, Senate, Gubernatorial and Presidential elections. If, as

research amply shows (e.g., Lavine 2002), high authoritarians are more sensitive to threat than

their less authoritarian counterparts (and thus more likely to be chronically focused on losses

than gains), political campaigns should run more negatively-toned (i.e., prevention-focused)

advertising in high authoritarian states, and more positively-toned (i.e., promotion-focused)

advertising in less authoritarian states (H3).

Persuasion Studies

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Two experiments were conducted to test the hypothesis that the effectiveness of

persuasion depends on the conjunction of policy framing and individual differences in regulatory

focus. The main study consists of 907 respondents collected through Amazon.com’s Mechanical

Turk interface (see Appendix A for a description of the experimental procedure and descriptive

statistics). A small college-student replication was conducted with 89 undergraduates at Duke

University. The two studies are identical in design; we thus present them together. In both

studies, respondents were presented with policy statements on eight distinct issues. For each

issue, they were randomly assigned to receive one of three versions of a policy frame: control

(no frame), gains frame, or losses frame (each respondent thus participated in eight separate

experimental manipulations, and could be assigned to any of the three conditions for any issue).

The order of presentation of the issues was randomized across respondents. In a pre-treatment

survey, respondents completed demographic items, those pertaining to general political

knowledge, and a scale of six items measuring chronic regulatory focus.

Policy Treatments and Dependent Variable

Respondents were presented with eight policy statements representing a range of

ideological positions: government aid to help homeowners refinance underwater mortgages, tax

cuts for large businesses, increased spending on infrastructure, greater funding for charter

schools, required electronic verification of the citizenship of potential workers, greater spending

on public services, restrictions on foreign imports, and expiration of the Bush tax cuts for

wealthy individuals. For each issue, respondents were asked the following: “To what degree do

you support or oppose the following policy” (strongly, support somewhat, support a little, oppose

a little, oppose somewhat, or oppose strongly)? As described above, respondents were randomly

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assigned to receive one of three versions for each issue: a gain frame, a loss frame, or no frame.

The exact wording for all issues and conditions is shown in Table 1. As the table shows, the

frame manipulations are quite subtle, amounting to a change in a few words for each issue. The

dependent variable for all analyses reported below is policy support, which ranges from 1 to 6,

with higher values indicating greater support for the proposed policy.

Table 1. Policy Treatments

Issue Control Promotion Prevention

Refinance

Federal aid to struggling homeowners to refinance their mortgages at the current, lower rates

Federal aid to struggling homeowners to refinance their mortgages at the current, lower rates to help them achieve financial stability

Federal aid to struggling homeowners to refinance their mortgages at the current, lower rates to prevent them from losing their homes

Tax Cuts Additional tax cuts to large businesses

Additional tax cuts to large businesses to promote the hiring of additional workers

Additional tax cuts to large businesses to prevent firing of additional workers

InfrastructureIncreased investments in our country’s infrastructure (e.g. roads, bridges)

Increased investments to improve the quality of our country’s infrastructure (e.g. roads, bridges)

Increased investments to prevent the deterioration of our country’s infrastructure (e.g. roads, bridges)

Charters

Increased funding for charter schools (privately-owned, but publically-funded high schools that would compete with current schools)

Increased funding for charter schools (privately-owned, but publically-funded high schools that would compete with current schools) to incentivize greater achievement in our school systems

Increased funding for charter schools (privately-owned, but publically-funded high schools that would compete with current schools) to prevent further deterioration of our school systems

E-Verify

Requiring all businesses to electronically verify the legality of their workers’ citizenship status

Requiring all businesses to electronically verify the legality of their workers’ citizenship status to promote the hiring of U.S. citizens

Requiring all businesses to electronically verify the legality of their workers’ citizenship status to prevent the hiring of non-U.S. citizens

ServicesIncreased investments in public services such as teachers, cops and firefighters

Increased investments in public services to promote hiring of teachers, cops and firefighters

Increased investments in public services to prevent layoffs of teachers, cops and firefighters

ImportsIncreased restrictions on foreign imports

Increased restrictions on foreign imports to promote domestic manufacturing in global competition

Increased restrictions on foreign imports to protect manufacturing from global competition

Bush CutsAllowing the Bush tax cuts to expire for citizens making more than $250,000 per year

Allowing the Bush tax cuts to expire for citizens making more than $250,000 per year to achieve a smaller budget deficit

Allowing the Bush tax cuts to expire for citizens making more than $250,000 per year to prevent a larger budget deficit

Regulatory Focus

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Respondents’ chronic regulatory focus (RF) was assessed with six items adapted from

Lockwood, Jordan and Kunda (2002). These items appear first in the survey; thus, a gap was

maintained in both time and thought between responses to the RF questions and the presentation

of the policy items. For each RF item, respondents were asked, “Now we will briefly describe

some people. Please indicate for each description whether that person is very much like you, like

you, somewhat like you, a little like you, not like you, or not at all like you.” The six items

included: “In general, I am focused on preventing negative events in my life,” “I am anxious that

I will fall short of my responsibilities and obligations,” “Overall, I am more oriented toward

preventing losses than I am toward achieving gains,” “In general, I am focused on achieving

positive outcomes in my life,” “Overall, I am more oriented toward achieving success than

preventing failure,” and “I frequently imagine how I will achieve my hopes and aspirations.”

Greater agreement with the first three items indicates a relative prevention focus, and greater

agreement with the last three items indicates a relative promotion focus. To remove variance

associated with idiosyncratic use of the common measurement instrument, we calculated

respondent-specific means across the items, and subtracted these from each of the six responses.

We used these mean-deviated responses to construct a composite RF scale (α = .65 for the

MTurk sample and .62 for the student sample). The scale was recoded to range from zero to one

prior to analysis with higher values indicating a greater prevention focus.

Analysis and Results

In each study, we regressed policy support on two dummy variables representing

membership in the two treatment conditions (with the unframed control group as the excluded

category), RF, and the interaction of each treatment dummy with RF, using the OLS estimator.

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To account for autocorrelation (inherent to within-subject designs), we clustered standard errors

by respondent.3 We expected to observe a negatively-signed interaction between RF and the

gains frame dummy (indicating a greater persuasive effect of gains frames for promotion- than

prevention-focused respondents), and a positively-signed interaction between RF and the loss

frame dummy (indicating a greater persuasive effect of loss frames for prevention- than

promotion-focused respondents).

The statistical results for both studies are shown in Appendix B. Graphical depictions are

shown in Figure 2a (MTurk Sample) and Figure 2b (undergraduate sample). The bars in each

figure represent the estimated level of policy support for each experimental condition, separately

for promotion- and prevention-focused respondents. The spikes represent 95% confidence

bounds on these estimates.

Results of MTurk Study. As Figure 2a shows, our hypotheses receive strong support in the

MTurk study. Among respondents with a promotion focus (i.e., those who scored at the 10th

percentile of the RF distribution), averaged policy support is highest in the frame highlighting

gains to be achieved from acting on the proposed policies (M=4.34, vs. 4.12 and 4.23 in the

control and loss conditions, respectively). By contrast, among respondents with a prevention

focus (i.e., those scoring at the 90th percentile of the RF distribution), averaged policy support is

highest in the frame highlighting losses to be avoided from enacting the proposed policies

(M=4.56, vs. 4.22 and 4.36 for the control and gain conditions, respectively).

The pattern of mean difference tests corresponds with expectations. For promotion-

focused respondents, there was a significant effect for the gains treatment (vs. control; D=.21,

SE=.10, p<.05), but not for the loss treatment (vs. control; D=.11, SE=.10, n.s.). Conversely, for

3 We replicated this analysis via restricted maximum likelihood, modeling the intercept as a function of individual-level characteristics and a respondent-specific, normally distributed disturbance term. The results were largely identical. The findings also remain identical with or without clustering the standard errors.

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prevention-focused respondents, there was a significant effect for the loss treatment (vs. control;

D=.34, SE=.10, p<.05), but not the gain treatment (vs. control; D=.14, SE=.10, n.s.). In

substantive terms, framing a policy statement in terms of potential gains significantly heightened

persuasion (i.e., policy agreement) among promotion- but not prevention-focused respondents,

while framing a policy in terms of preventing potential losses significantly heightened persuasion

among prevention- but not promotion-focused respondents. This is exactly the pattern predicted

by theory.

Results of Replication Study. We turn now to the results from the undergraduate study,

which are shown in Figure 2b. As the figure shows, these results strongly replicate the main

study’s findings. Among promotion-focused respondents, policy support was highest in the gains

frame (M=4.41, vs. 3.90 and 4.11 in the control and loss conditions, respectively). By contrast,

among prevention-focused respondents, policy support was highest in the loss frame (M=4.39,

vs. 3.90 and 4.06 in the control and gain conditions, respectively). Once again, the pattern of

mean difference tests corresponds with expectations. For promotion-focused respondents, the

mean difference in policy support between the gains frame and the control condition is

statistically significant (D=.52, SE=.23, p<.05), while the difference between the loss frame and

the control condition is not (D=.21, SE=.24, n.s.). For prevention-focused respondents, the mean

difference in policy support between the loss frame and the control condition is statistically

significant (D=.47, SE=.23, p<.05), while the difference between the gain frame and the control

condition is not (D=.16, SE=.23, n.s.).

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3.5

44.

55

Pol

icy

Sup

port

Promote Control Prevent Promote Control Prevent

Promotion Focus (10%) Prevention Focus (90%)

Notes: Data from undergrad sample. Bars are estimated levels of policy support. Spikes are 95% CIs.

Figure 2A. Policy Support by Condition and Regulatory Focus

3.5

44.

55

Pol

icy

Sup

port

Promote Control Prevent Promote Control Prevent

Promotion Focus (10%) Prevention Focus (90%)

Notes: Data from MTurk sample. Bars represent estimated policy support. Spikes are 95% CIs.

Figure 2B. Policy Support by Condition and Regulatory Focus

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Dispositional Clustering and Elite Behavior

The foregoing studies indicate that the effectiveness of a persuasive appeal depends on

the functional match between the chronic regulatory focus of the individual and the frame of a

message in terms of losses or gains. In this last empirical section, we extend our analysis to

examine whether these findings have any import for the political advertising strategies of elites

in Congressional, Gubernatorial, and Presidential elections. Specifically, if traits (e.g., RF,

authoritarianism) are both geographically clustered and perceived to moderate the effectiveness

of different types of ads (positive vs. negative tone), campaigns may choose to run a greater

proportion of negatively-toned ads in states where levels of a prevention (vs. a promotion) focus

(consciously or unconsciously understood) are perceived to be relatively high.

State-Level Authoritarianism

To obtain measures of state-level regulatory focus, we rely on four items used to measure

authoritarianism (Feldman and Stenner 1997; Hetherington and Weiler 2009; Stenner 2005).

These items are included in the 2000, 2004, and 2008 ANES studies, giving us enough data to

generate reasonably efficient estimates of the state-level parameters. Our method of estimation is

multilevel-regression and post-stratification (MRP; Gelman and Hill 2007; Lax and Phillips

2009; Park, Gelman and Bafumi 2006). MRP proceeds in two basic stages. First, individual-level

responses to the variable of interest (in this case, authoritarianism) are modeled as a function of

both individual and state-level predictors. The estimates from this model are then used to

generate predicted values of the dependent variable for every possible combination of predictors

(e.g., a white female between the ages of 18 and 29 with a high school degree residing in

Oklahoma). These predicted values can then be utilized to estimate state-level opinion by

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weighting the predicted value for each combination of predictors by the actual percentage of

people residing in a state with those characteristics, and then summing across these weighted

estimates (i.e. the post-stratification in MRP). This is the basic procedure we use to generate

state-level authoritarianism estimates.

First, we pooled the data from each of the ANES studies. Our individual-level predictors

included age (recoded into 4 categories, 18-29, 30-44, 45-64, and 65-up), the intersection of race

and gender (six categories corresponding to the intersection of male/female with black, Hispanic

and other), and education (four categories: high school degree or less, some college, college

degree, post-graduate degree). At the state level, we included the following predictors: per capita

income, proportion of the state’s vote that went to George W. Bush in 2004, a scale measuring

the multiculturalism of the state (XXXX), the % of the state’s population living in urban areas,

the state’ region, and the % of the state’s population with a high school diploma. The overall

model was estimated as follows (Y=Authoritarianism):

y i=β+α j [i ]Age+α k [i ]

Race ,Gender+αl [i ]Educ+α m [i]

State+αn [i ]Year,

α j [i ]Age N (0 , σ Age

2 ), for j = 1 to 4,

α k [i ]Race ,Gender N (0 , σ Race, Gender

2 ), for k = 1 to 6,

α l [i ]Educ N (0 , σ Educ

2 ), for l = 1 to 4,

α n [i ]Year N (0 , σ Year

2 ), for j = 1 to 3,

α m [i ]State N (α p [ m]

Region+∑i=1

5

γi∗x i m , σState2 ), where xi = State-Level Predictor ‘i’

α p [m ]Region N (0 , σ Region

2 ), for p = 1 to 5

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To post-stratify, we utilize the 2000 Census’ 1% Public Use Micro-Data Sample, which gives

estimates of the proportion of each citizen “type” within each state. The final state-level

estimates of authoritarianism are calculated as:

(1) ∑c=1

4704

N c∗θc /∑c=1

4704

N c , where Nc is the number of individuals in cell “c” within a given state,

and θc is the authoritarianism estimate for cell “c.”

State-level authoritarianism estimates are plotted in Figure X (the authoritarianism scale ranges

from zero to one).

WA

AK UT ORMACODC NH VT HI

WYMTCT CA MN IA ME WI ID NV AZ MDNE NJ KS

VA MI NY IL DENDNMOH

SD OK PA MO RI INFL TX NC

TNGA

WV KY SC ALAR

MSLA

.5.5

5.6

.65

.7.7

5.8

Est

imat

ed A

utho

ritar

iani

sm

Figure X. State-Level Authoritarianism Estimates

As a “sanity-check” on these estimates, we examined the association between state-level

authoritarianism and a recent measure of state-level “racial animus” derived from analyses of

Google searches for racially charged words during the 2008 Presidential Election (Stephens-

Davidowitz 2011). As Figure X indicates, there is a strong correspondence between the two

state-level variables, suggesting that our measure of aggregate authoritarianism is not without a

modicum of construct validity.

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WA

AKUT ORMACO DC NH VTHI WYMT

CTCAMN IA ME WIID NVAZ MDNE NJKSVA MINYILDENDNM OHSD OK PAMORIIN FLTX NC

TNGA

WVKYSCALAR

MS LA

R2 = .56

.45

.5.5

5.6

.65

.7.7

5.8

Sta

te-L

evel

Aut

horit

aria

nism

20 40 60 80 100State-Level Prejudice

Figure X. Relationship between State-Level Authoritarianism and Prejudice

Negativity of Campaign Advertising across States

To measure strategic elite behavior related to rhetorical strategy, we utilize data from the

Wisconsin Advertising Project at the University of Wisconsin (http://wiscadproject.wisc.edu/).

We were able to obtain data for 2000, 2002 and 2008.4 For each year, the project codes every ad

for House, Senate, Gubernatorial, and Presidential campaign with respect to “tone” (i.e., each ad

is coded as negative, positive, or “contrast”). To estimate the overall negativity of campaign ads

in each state (in each year), we divided the number of negative ads across all campaigns by the

total number of ads for that year. Our measure of state-level campaign negativity is thus the

proportion of all ads in a given state (within a given year) that were negative.5

4 The Wisconsin Advertising Project sent us a data disk for 2004, but it was unreadable. We have not yet received a replacement.5 Not all states have data for every year, and thus our analyses are restricted to those with available ads.

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http://wiscadproject.wisc.edu/


Analysis

To evaluate our hypothesis, we present scatter plots of advertising negativity against

state-level authoritarianism for each year. We include a loess curve for each plot, which gives a

locally-weighted picture of the relationship between the two variables. As the panels in Figure X

indicate, the results are mixed. In 2000, there appears to be a meaningful relationship between

state-level authoritarianism and the proportion of negative ads in each state. The loess line slopes

upward from about 15% negative ads to about 40% negative ads across the range of

authoritarianism. This visual association is reinforced by a significant and substantively strong

bivariate correlation (r=.42, p<.01). In 2002, the results are less clear, although there appears to

be some suggestion of a positive relationship (r=.23, p<.15). In 2008, however, we find no clear

evidence of any relationship between these variables (r=-.10, p>.45).

0.1

.2.3

.4.5

Pro

porti

on o

f Neg

ativ

e A

ds

.5 .55 .6 .65 .7 .75State-Level Authoritarianism

Figure XX. Authoritarianism and Ad Negativity, 2000 Election

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0.2

.4.6

Pro

porti

on o

f Neg

ativ

e A

ds



0.2

.4.6

.8P

ropo

rtion

of N

egat

ive

Ads



Discussion and Conclusions

As psychologists have long noted, rationally equivalent descriptions of reality can be

framed in terms of either gains or losses (e.g., 10% unemployment vs. 90% employment;

Quattrone and Tversky 1982). Extensive work on prospect theory has shown that people are

generally more sensitive to losses than gains, and that social judgments are often responsive to

such framing effects (see Kahneman0 and Tversky 1979). Regulatory focus theory (RFT;

Higgins 1998, 1999) builds on prospect theory by proposing that stable individual differences

exist in the extent to which a person’s affect, cognition and behavior is motivated more by

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achieving gains (a promotion focus) or avoiding losses (a prevention focus). In the present

research, we relied on RFT to examine two questions about mass and elite politics: (1) Are

individual differences in regulatory focus systematically related to the effectiveness of different

types of policy frames? (2) Does geographic clustering in regulatory focus orientation constrain

elite behavior in terms of campaign advertisement strategy?

In two experiments that relied on eight different issues, we found that functional matches

between individual dispositions and the framing of policy statements produce more persuasion

than functional mismatches. Specifically, promotion-focused individuals were persuaded by the

potential gains to be achieved from changes in public policy, whereas prevention-focused

individuals were not. Equally, prevention-focused individuals were persuaded by the potential

losses to be avoided by enacting a proposal, whereas promotion-focused were indifferent to such

communications. Moreover, we found some evidence that aggregated clusters of RF orientations

at the state level are linked to the degree of negativity in political advertising. In two of three

years of elections examined, “prevention-oriented” states received a greater proportion of

negative ads than “promotion-oriented” states.

We believe that regulatory focus can be understood as an umbrella concept in personality

with the potential to subsume a variety of dispositional factors that have been linked to political

orientation. As Jost et al. (2003) explain:

The promotion goals of accomplishment and advancement should naturally introduce a preference for change over stability, insofar as advancement requires change. The prevention goals of safety and security, on the other hand, should favor stability over change, to the extent that stability entails predictability and hence psychological security and control…To the extent that political conservatism is motivated, at least in part, by the desire for security and stability and the avoidance of threat and change, situations inducing a prevention-oriented regulatory focus might also induce a conservative shift in the general population.

What is appealing about regulatory focus is its emphasis on the abstract concepts of approach

and avoidance and pleasure and pain. Few principles in psychology are more basic than these

21


distinctions, and they are critical to understanding human behavior in a variety of domains,

including (as we have shown here), political persuasion. The fundamental nature of the

distinction between gain and loss has also pervaded other subfields of political science, including

the emphasis in political economy on risk aversion and political preference (e.g. Iversen 2005;

Moene and Wallerstein 2001). The RF framework thus may prove to provide an integration point

for two seemingly disparate literatures: the biological/psychological and the economic.

That RF orientations moderate the effectiveness of different types of persuasion strategies

has interesting implications for the strategies pursued by elites in policy and electoral contests. If

dispositions cluster in political space, whether concrete (e.g., geographical) or contrived (e.g.

opt-in networks such as TV and blogs), the opportunity for the microtargeting of political

messages exists, and the present results suggest advantages in aggregate persuasion to such

strategies. Future work should explore in greater depth the potential for elite micro-targeting on

the basis of personality.

Finally, our results speak to the literature on framing effects in political science. As

indicated by the epigraph to this paper, the framing literature has largely ignored individual-level

heterogeneity in the effectiveness of different types of frames. However, as citizens differ with

respect to the types of goals – promotion vs. prevention – that are both accessible and important

to them, frames focusing on gains vs. losses should be likely to attract differential levels of

cognitive scrutiny and positive evaluation (Avnet and Higgins 2006; Freitas and Higgins 2002;

Higgins 2005). A deeper understanding of the conditionalities of framing effects is therefore

critical, and while recent work has begun to address this question (Druckman 2001; 2004; Kam

and Simas 2010), much remains to be done. More generally, we hope that research on the joint

effects of individual differences in abstract goal priorities and message framing leads to a greater

22


understanding of the ways in which elites and citizens both constrain, and are constrained by,

one another.

23


APPENDIX A. DETAILS FOR MECHANICAL TURK EXPERIMENT

Mechanical Turk is a service that Amazon.com provides to those who are interested in the performance of a large number of small tasks that require human judgment. Individuals performing the tasks create a user profile on the site and are paid a fee for their services that varies depending on the time, effort, and expertise involved in the task. Researchers post a human intelligence task (HIT) to the Amazon site and offer payment rate per HIT. A link to our survey was posted on this site, and advertised under the keywords “politics,” “survey,” “public policy,” and “2012 election.” The description of the task read, “You will be asked to answer several questions about yourself and watch and respond to a campaign ad.” Valid completions of the survey were compensated $1.00. The effective hourly rate was estimated to be $7.26. Summary statistics for the respondents are shown below.

Central Tendencies and %s

Median Age 28

Male 60%

Black 4%

White 81%

Mean Political Interest (1-5) 3.11

Mean Subjective Political Knowledge (1-5) 3.08

Mean Partisanship (1-7, higher=Rep) 3.31

Mean Ideology (1-7, higher=Con) 3.27

Mean Importance of Religion to Self (1-5) 2.11

Median Income 20K-30K

Mean Income 30K-40K

Median Education Some College

Unemployed or Temporarily Laid Off 13%

Student 24%

Working Now 53%

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APPENDIX B. OLS REGRESSION ESTIMATES (MTurk and Undergraduate Studies)

Variable MTurkUndergrad

.Promotion Condition .58 (.31) .22 (.13)Prevention Condition .16 (.34) .07 (.14)Promote X RF -.53 (.64) -.11 (.28)Prevent X RF .41 (.64) .35 (.28)Regulatory Focus .02 (.41) .14 (.21)Constant 3.86 (.30) 3.88 (.11)

Subjects 87 907Observations 694 7247R2 .01 .01F(7,86) and F(7,686) 2.48 8.03

25

Regulatory Fit and Persuasion, p. 26


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