Negativity Bias and Ideology, p.1
Behaviorally Indicated Negativity Bias, Personality, and Political Ideology
Abstract
Recent work suggests that right-wing political preferences are associated with individual differences in negativity bias: a tendency to pay more attention, and give more weight, to negative versus positive stimuli in decision making. This work tends to rely on either self-report measures of personality in large, often-representative samples, or physiological and behavioral indicators of negativity bias in small and unrepresentative samples. Little work has tested hypotheses emerging from this literature with large, diverse samples using non-self-report indicators of negativity bias. In the present study, we attempt to fill this gap. We examine the relationship of negativity bias to political ideology in three large national samples using five distinct behavioral measures of negativity bias. We also examine the association of these measures to four of the most commonly utilized self-report measures of personality. Across a wide range of tests, we find little support for the negativity bias hypothesis.
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Negativity Bias and Ideology, p.2
What factors determine attraction to the political left and right? Theories highlighting stable individual differences in personality now occupy an important place beside classic sociological and economic approaches focused on individual and group interests (e.g., Aarøe,
Petersen, and Arceneaux 2017, Gerber et al. 2010, Hibbing, Smith, and Alford 2014, Jost et al.
2003, Jost 2017, Mondak 2010). While diverse, much of this literature emphasizes traits related to negativity bias—a tendency to pay more attention, and give more weight, to negative (relative to positive) stimuli in decision making. In general, a focus on negative potential outcomes creates a disposition to favor existing arrangements and certain outcomes over mixed gambles
(Kahneman, Knetsch, and Thaler 1991, Tversky and Kahneman 1979). In the political realm, social order and the status quo provide predictable outcomes, while reform and change are risky prospects with the potential for either gain or loss. In turn, negativity bias creates a preference for right-wing policies that promote stability in social and economic arrangements (Jost et al. 2003).
In this view, there is an enduring political conflict between two “primal mindsets”: one focused on order and stability and one on reform and progress (Hibbing, Smith, and Alford 2014, 297).
Evidence for this theory tends to fall into one of two categories. First, many studies examine the association of political ideology to self-reported personality traits related to negativity bias and its implications, such as the need for cognitive closure, threat and disgust sensitivity, and openness to experience (Federico and Malka 2018, Jost 2017). This work often, and increasingly, relies on large samples that are reasonably representative of the broader population of interest (Aarøe, Petersen, and Arceneaux 2017, Federico and Goren 2009, Gerber et al. 2010, Johnston, Lavine, and Federico 2017, Mondak 2010). Yet such studies assume the exogeneity of traits to political ideology, as well as the unbiased reporting of such traits. If traits are instead endogenous to political preferences, or if citizens misrepresent (or systematically Negativity Bias and Ideology, p.3 misperceive) their own traits, the conclusions of such studies would be suspect. And there is reason to be concerned. Recent work suggests that citizens exaggerate the degree to which they possess traits prototypical for their political in-group (Ludeke, Tagar, and DeYoung 2016) and variation in traits over short periods of time may be driven by political variables (Boston et al.
2018). It is thus critical to supplement existing work with research designs with less inferential ambiguity.
And indeed, a growing literature examines the association of ideology to revealed indicators of negativity bias (Hibbing, Smith, and Alford 2014). Such studies include decision making under ambiguity (Shook and Fazio 2009), physiological responses to threatening stimuli
(Oxley et al. 2008, Smith et al. 2011a), and brain imaging studies focused on sensitivity to threat and uncertainty (Ahn et al. 2014, Amodio et al. 2007, Kanai et al. 2011). This work has the virtue of bypassing self-report. In many cases, there is also a strong argument for exogeneity, as indicators of negativity bias are derived from apolitical stimuli and often involve uncontrolled processes (but see Jost et al. 2014). On the downside, the procedures involved, especially in physiological studies, often require substantial resources in time and money, and the physical presence of participants, to employ. This limits the size and representativeness of samples. For example, the seminal study of Oxley et al. (2008) on threat sensitivity surveyed only 48 individuals local to the university.
The upshot is that the literature is densely populated with studies supporting the negativity bias hypothesis, but there is a dearth of research with both of two critical characteristics: (1) large, diverse samples, and (2) revealed, rather than self-reported, traits. The primary aim of the present study is to fill this gap. We examine the negativity bias hypothesis using five distinct behavioral measures in three national samples with over 4,000 respondents. Negativity Bias and Ideology, p.4
Relying on very different operationalizations of negativity bias, we increase the generalizability of our study, and ensure that any conclusions drawn are not specific to a single measurement approach. The large size of our sample also allows for more precise estimates of the relationship of negativity bias to ideology. Whereas small-sample studies tend to focus on statistical significance at the expense of effect size, we can more clearly rule out important subsets of the parameter space.
We also contribute to the literature in three other ways. First, we provide the first examination of the relationship between political ideology and loss aversion, which is perhaps the most intuitive conceptualization of negativity bias, and one that ties the literature to behavioral decision theory. We apply three methods for eliciting individual differences in loss aversion, two for decisions under risk and one for ambiguity (Abdellaoui et al. 2016, Tanaka,
Camerer, and Nguyen 2010, Toubia et al. 2013). Second, we provide the first large-sample examination of the association between behavioral indicators of negativity bias and the most commonly studied self-report personality traits in the literature on ideology: need for closure, openness to experience, conservation versus openness to change, and authoritarian childrearing values. Strong relationships between behavioral measures of negativity bias and self-report measures of personality would provide evidence for a possible pathway through which basic attentional and evaluative dispositions operate, namely, core values and traits (Smith et al.
2011b). Finally, we explore the relationship of negativity bias to multiple dimensions of left- right political preferences, including political identity and policy attitudes in both the social and economic domains. We also examine the moderating role of political engagement for these relationships. Recent work suggests variation in the relationship of traits to ideology across different manifestations of left and right and across engagement (Feldman and Johnston 2014, Negativity Bias and Ideology, p.5
Hibbing, Smith, and Alford 2014, Johnston and Wronski 2015, Malka et al. 2014, Terrizzi Jr,
Shook, and Ventis 2010), and we extend this line of research here.
Background Theory and Research
Contemporary theorizing on personality and political ideology begins with the authors of
The Authoritarian Personality (Adorno and Frenkel-Brunswik 1950, Frenkel‐Brunswik 1949), who argued that right-wing preferences are rooted in psychological insecurity and cognitive rigidity. While this work has been heavily criticized (Altemeyer 1981, Brown 2004, Duckitt
1989), the core thesis has been continuously renewed since its publication, albeit with non-
Freudian theoretical foundations (Altemeyer 1988, Duckitt 2001, Feldman and Stenner 1997,
Kruglanski et al. 2006, Wilson 1973). The last two decades have witnessed a surge in interest in these ideas, in large part due to the work of Jost et al. (2003). In their wide-ranging review of the literature, Jost and colleagues theorize that there are at least two aspects of political conflict that transcend time and place: orientations toward change and inequality. They argue that right-wing politics is defined by resistance to the former and acceptance of the latter. In a meta-analysis of over eighty studies, they find these broad orientations are associated with both sensitivity to threat and the aversion to uncertainty and ambiguity that it entails (see Jost 2017 for an updated meta-analysis). As Jost and Hunyady (2005, 262) argue, “There is a good match between needs to reduce uncertainty and threat and system justification, because preserving the status quo allows one to maintain what is familiar and known while rejecting the uncertain prospect of social change. For many people, the devil they know seems less threatening and more legitimate than the devil they don’t.” In this view, threat sensitivity creates a preference for sure-things, and this need is best met by right-wing values, policies, and candidates that preserve status quo Negativity Bias and Ideology, p.6 values and socioeconomic inequalities (e.g., Jost et al. 2017, Landau et al. 2004, Thórisdóttir and
Jost 2011).
This thesis is isomorphic with the claim from behavioral decision theory that greater sensitivity to negative relative to positive outcomes creates a preference for the status quo and certain prospects over mixed gambles, even when the latter substantially exceeds the former in expected value (Kahneman, Knetsch, and Thaler 1991, Köbberling and Wakker 2005, Tversky and Kahneman 1992). When losses loom larger than gains, people will be drawn to options that avoid downside risks, unless the gains are very large and/or very certain. This “negativity bias” hypothesis, as applied to politics, has been stated most explicitly by Hibbing, Smith, and Alford
(2014). They review a growing body of research that suggests right-wing preferences are associated with several psychological and physiological characteristics that evince greater attention and responsiveness to negative than positive stimuli. Most notably, recent work has found associations of political ideology with physiological responsiveness to negative and disgusting stimuli (Ahn et al. 2014, Oxley et al. 2008, Smith et al. 2011a), greater grey matter volume in areas of the brain associated with the fear response (Kanai et al. 2011), attentional biases to negative stimuli (Dodd et al. 2012, McLean et al. 2014, Mills et al. 2014), and a tendency to avoid negative potential outcomes in a decision making task under ambiguity (Shook and Fazio 2009) (for an extensive review, see Federico and Malka 2018).
While acknowledging extensive empirical support for the basic claim of a link between negativity bias and right-wing views, recent work has extended the literature in several ways.
First, several studies demonstrate that the relationship between related traits and political ideology is conditional on political engagement and expertise (Federico, Fisher, and Deason
2011, Federico and Goren 2009, Johnston, Lavine, and Federico 2017, Jost, Federico, and Napier Negativity Bias and Ideology, p.7
2009). Citizens who pay more attention to politics, and know more about it, show stronger relationships between self-reported traits and political attitudes. This is because they are better able to identify the positions that match their dispositions and are more likely to adopt the positions of elite actors who share their broader worldview.
Second, scholars have argued that relationships between traits and ideology vary across ideological dimensions (Federico and Malka 2018, Feldman and Johnston 2014, Johnston,
Lavine, and Federico 2017, Malka et al. 2014). Despite growing constraint over time
(Abramowitz 2010, Freeze and Montgomery 2016), mass belief systems are multidimensional in the sense that they are well-described by models positing distinct, correlated dimensions of values and policy preferences, with social and economic dimensions most prominent (Baldassarri and Gelman 2008, Feldman and Johnston 2014, Layman and Carsey 2002, Lupton, Myers, and
Thornton 2015, Treier and Hillygus 2009). As these two dimensions are correlated, it is possible that negativity bias may be the foundation that links them through time (Jost, Federico, and
Napier 2009). However, it is also possible that negativity bias influences social and economic preferences in distinct ways. Indeed, recent work suggests this is the case. In unconditional models, traits related to negativity bias (e.g., need for closure) are more strongly associated with social than economic preferences (e.g., Feldman and Johnston 2014, Terrizzi, Shook, and
McDaniel 2013, see Federico and Malka 2018 for a review) and occasionally promote left-wing economic views (Arikan and Sekercioglu 2019, Crowson 2009, Johnston 2013). Additionally, in models that condition on political engagement, these traits promote right-wing views among the engaged, but often left-wing views among the unengaged (Jedinger and Burger 2019, Johnston
2018, Johnston, Lavine, and Federico 2017, Malka et al. 2014). Negativity Bias and Ideology, p.8
Finally, scholars have proposed that basic physiological and cognitive differences in negativity bias operate through more proximate psychological constructs such as personality traits and basic values (Smith et al. 2011b, 372). For example, Caprara and Zimbardo (2004) propose a model in which citizens identify with elites who share their core personality dispositions—what they refer to as “dispositional heuristics.” In this view, value priorities (e.g., security; Schwartz 1992) and self-conceptions in terms of traits (e.g., openness to experience;
McCrae and Costa 2003) serve as guides that help citizens navigate a complex political space and choose among diverse alternatives.
Hypotheses
Few studies directly test these claims with revealed measures of negativity bias in large, diverse samples. Moreover, there have been no attempts to test intermediate mechanisms linking negativity bias to ideology through personality dispositions, such as values and traits. The present study is intended to fill this gap in the literature. We thus explore the following hypotheses:
1. Indicators of negativity bias will be associated with right-wing preferences in general, but
will be more strongly associated with general left-right identity and social preferences
relative to economic preferences.
2. Indicators of negativity bias will be associated with self-reported personality traits
commonly used to predict right-wing ideology, including: (low) openness to experience,
the prioritization of conservation values over openness to change values, authoritarian
childrearing values, and the need for non-specific cognitive closure.
3. Political engagement will moderate the relationship between indicators of negativity bias
and political ideology. Negativity Bias and Ideology, p.9
The remainder of the paper proceeds as follows. We first describe our data sources and five distinct measures used to operationalize negativity bias. We then turn to our primary results, which are followed by a series of robustness checks. Finally, we discuss the implications of our results for the broader literature.
Data and Methods
Our data consist of three U.S. samples collected during three time periods, one in January of 2015, one in August of 2018, and one in October/November of 2019. The 2015 sample was collected by ClearVoice research through a contract with Qualtrics Panels (see supplemental appendix for further information about all samples). Steps were taken to ensure data quality: respondents were terminated from the survey if they failed either of two attention checks1 or if they failed to complete at least 90% of all survey items in the first half of the survey. The 2018 and 2019 samples were collected through Lucid’s Fulcrum Exchange (now Marketplace) with a restriction of the sample to only non-mobile-device users. We also terminated respondents who failed either of two attention checks in the first part of the survey.2 Respondents completed one
(and only one) of five different measures of negativity bias, which are described in the following
1 The first attention check was a survey item that appeared to be a standard question about self- perceived knowledge in politics but embedded an instruction to select the response option “No knowledge at all.” The second was a multiple-choice item concerning the position currently held by Barack Obama.
2 In the first check, respondents had to accurately select two photos of roads (from a set of six) that contained a stop sign. The second, a multiple-choice question, asked respondents to identify the current President of the United States. Negativity Bias and Ideology, p.10 section. Specifically, all respondents to the 2015 sample completed measure 3; all respondents to the 2019 sample completed measure 4; and respondents to the 2018 sample were randomly assigned to either measure 1, 2, or 5. After describing these measures in detail, we turn to our dependent variables, which are identical across the 2018 and 2019 samples and very similar to the 2015 sample.
Measure 1: Cognitive Accessibility of Threatening Concepts
Our first measure of negativity bias was randomly assigned to a subset of the 2018 Lucid sample and extends previous work on the psychological roots of authoritarianism. This work uses a lexical decision task (LDT) to measure negativity bias (Lavine et al. 2002). Respondents first completed a survey of basic demographics, political interest and knowledge, then the LDT, and then a survey of personality and political attitude and identity measures.
In an LDT, respondents are exposed to a series of trials. In each trial they are asked to decide, as quickly as possible while being accurate, whether a string of letters is a legal English word or a non-word. Speed and accuracy in the legal word trials are, theoretically, measures of the relative accessibility of the concepts represented by the words. That is, concepts which are highly accessible are more quickly activated by exposure to their textual representation and are thus more quickly judged to be words compared to less accessible concepts (Higgins 1996,
Lodge and Taber 2005, Meyer and Schvaneveldt 1971). We use the difference in the average response time to positive relative to negative words as a measure of the relative accessibility of negative concepts, and thus of negativity bias.
Respondents completed a total of 60 trials: 10 each for positive, negative, and neutral words, and 30 for non-words. Words for each trial were randomly sampled without replacement.
Our final measure was calculated similarly to the D-score in research on the implicit association Negativity Bias and Ideology, p.11 test (Greenwald, Nosek, and Banaji 2003). We exclude all responses below 200 milliseconds or above 5000 milliseconds3 and all respondents with less than 80% correct responses.4 We then calculate, for each respondent, the difference between average response time to positive words and average response time to negative words and divide this difference by the respondent- specific standard deviation of latencies to both types of targets combined. The larger the value, the greater the variance-adjusted relative response time to positive words and thus the higher the relative accessibility of negative words. We detail the procedure and calculations in the supplemental appendix. To incentivize effort and reduce measurement error, we informed respondents that the top ten performers in the LDT study would be eligible to receive a $10.00
Amazon.com gift card (winners were paid after all data was collected).
Measure 2: Attentional Biases to Negative Stimuli
Our second measure was randomly assigned to a subset of the 2018 Lucid sample and operationalizes negativity bias as attentional biases to negative relative to positive stimuli.
Specifically, we adapt recent work by McLean et al. (2014) using the flanker task. On each of 30 trials, respondents are presented with a series of three images arrayed horizontally. The middle image is the target and the flanking images to the left and right are distractors. The goal of each trial is to identify, as quickly as possible while being accurate, whether the target image is
3 About 6% of respondents who participated in this task had one or more trials outside of this window, but no one had more than three problematic trials. Less than 1% of all trials were excluded on this basis.
4 About 5% of the sample that participated in this task was excluded on this basis. The median accuracy across respondents was 95%. Negativity Bias and Ideology, p.12 positive or negative. McLean et al. use angry and happy faces; however, we substitute three negative and three positive images drawn from the International Affective Picture System database (IAPS) and the Geneva Affective Picture Database (GAPED). The images in these databases are normed for both valence and arousal and IAPS pictures have been used in previous work on negativity bias and ideology (Ahn et al. 2014). The specific images used in our study were chosen for being extreme in terms of valence. We used three very positive images (flowers, a human baby, and a baby seal) and three very negative images (an aimed gun, a dirty toilet, and a spider). We provide a table with information on all images used in the supplemental appendix.
McLean et al. (2014) test the negativity bias hypothesis by first defining a generic flanker effect and then examining this effect across ideology for negative and positive targets separately.
In their definition, a flanker effect is the difference in average response times comparing trials where all three images are of the same valence (e.g., all three are negative) to trials where the flanking images are conflicting in valence with the target (e.g., a negative image flanked by two positive images). They argue that conflict between target and flanker valence slows response time. However, if negative images strongly capture attention, this flanking effect should be small
(or even reversed) for negative targets and large for positive targets. That is, negativity bias implies a comparative measure such that target-incongruent flankers are more distracting for positive relative to negative targets. A straightforward measure is thus the difference in the flanking effects comparing negative to positive targets. We again exclude all responses below
200 milliseconds or above 5000 milliseconds5 and all respondents with less than 80% correct
5 About 7% of respondents who participated in this task had one or more trials outside this window. Negativity Bias and Ideology, p.13 responses.6 We calculate this measure using a D-score-like procedure, as with measure 1. This is detailed in the supplemental appendix. We again incentivized performance by informing respondents that the top ten performers would be eligible to receive a $10.00 Amazon.com gift card.
Measure 3: Loss Aversion in Decision Making under Risk
Our 2015 Qualtrics study operationalized negativity bias as the loss aversion parameter in prospect theory for decision making under risk (that is, with known outcome probabilities).
Conceptually, loss aversion means that losses loom larger than nominally equivalent gains.
Within cumulative prospect theory (CPT; Tversky and Kahneman 1992), loss aversion is formalized as a weighting parameter (�) on the value function for negative potential outcomes:
� ��� > 0 � � = −�(−� ) ��� < 0
Values above one mean that losses are worth more in terms of absolute utility than nominally equivalent gains, while values between zero and one imply the opposite (i.e., gain-seeking). We utilize the three-parameter version of CPT and estimate value curvature and probability weighting parameters along with loss aversion.
The basic procedure for the study was as follows. Respondents first completed a survey of demographics, political interest and attitudes, and several personality traits through Qualtrics.
They were then redirected to an external website built by Toubia et al. (2013) to complete a choice questionnaire used to measure CPT traits. The entire study took about twenty minutes on average with the bulk of this being consumed by the survey. The questionnaire for eliciting CPT parameters consists of 16 questions (see a screenshot of a question in the supplemental
6 About 10% of respondents who participated in this task were excluded on this basis. Negativity Bias and Ideology, p.14 appendix). For each, the respondent is asked to choose between two prospects of the form
X={x,p,y} where the probability of the outcome x is p and the probability of the outcome y is (1- p). To incentivize truthful and accurate revelation of preferences, respondents were informed prior to completing the task that following data collection four respondents would be randomly selected, each of these four respondents would be given a $20 endowment, and one of their sixteen choices would be selected and played for real stakes. Losses were thus paid out of the endowment and the selected respondents received $20 plus the outcome of their selected gamble.
The universe of prospects is as follows:
� ∈ {$1,30,40,100,1000}
� ∈ {−$20, −15, −10, −5,5,10,30}
� ∈ {0.1,0.3,0.5,0.7,0.9}
The choice questionnaire is adaptive in the sense that the options in choices two through sixteen are selected to minimize the expected uncertainty in the CPT parameters following that choice.
This approach reduces the total number of choices necessary to achieve a given level of precision for each respondent’s parameter estimates. After data collection, estimates are obtained via hierarchical Bayes, which partially pools information across respondents, again generating more efficient estimates. We provide additional details about the questionnaire and estimation process in the supplemental appendix (see also Toubia et al. 2013).
Measure 4: Loss Aversion in Decision Making under Risk
Our 2019 Lucid sample also operationalizes negativity bias as loss aversion under risk but uses a different elicitation procedure adapted from past work in economics (Tanaka,
Camerer, and Nguyen 2010). The procedure consists of three sequential tasks. In each, respondents are presented with a series of choices, each of which is presented on a separate Negativity Bias and Ideology, p.15 screen (see the supplemental appendix for all stimuli). Each choice presents two gambles, A and
B, with B increasing in relative attractiveness as the task proceeds. The goal is to determine the point at which the respondent switches from preferring A to preferring B. We operationalize this as the first choice at which the respondent chooses B (if at all)—that is, once the respondent chooses B, they move to the next task in the sequence. Our construction of the task thus imposes consistency on respondents’ preferences.
Switching points in the first two tasks uniquely determine the prospect theory value and probability weighting parameters. Given values for these parameters for a given respondent, the third task determines a range of possible loss aversion values for that respondent. When the range is not bounded by zero or infinity, we calculate the midpoint of this range as the loss aversion estimate for the respondent. When one bound is zero, we use the upper bound as the estimate. When one bound is infinity, we use the lower bound as the estimate. We discuss the procedure for calculating parameter estimates in detail in the supplemental appendix. To incentivize truthful and accurate revelation of preferences, we informed respondents that five participants would be randomly selected to have one of their choices played out for real stakes with a $21 initial endowment.
Measure 5: Loss Aversion in Decision Making under Ambiguity
Our fifth measure—contained in the 2018 Lucid sample—also draws on CPT but operationalizes negativity bias as loss aversion in contexts of ambiguity—that is, where outcome probabilities are unknown. In practice, however, loss aversion in risky and ambiguous situations appear highly correlated (Abdellaoui et al. 2016). We draw on a recently developed elicitation procedure (Abdellaoui et al. 2016) motivated by the Köbberling and Wakker (2005) definition of loss aversion as the ratio of the left and right derivatives of the prospect theory loss function Negativity Bias and Ideology, p.16 evaluated at the reference point—the differential treatment of losses and gains is represented by a
“kink” at zero. A ratio above one again indicates greater sensitivity to losses, while values between zero and one indicate gain-seeking. The procedure elicits two points on the value function on opposite sides of the zero point, but still very close to zero. The ratio of these points is thus an estimate of the ratio of the derivatives. To minimize the time needed for our study, we stopped the elicitation procedure after acquiring the information needed to estimate loss aversion, and thus have no information on value function curvature or probability weighting parameters. The procedure requires the elicitation of three indifference values and is described in greater depth in the supplemental appendix. This study was not incentivized.
Unfortunately, a large subset of respondents provided non-sensical responses that would result in infinite loss aversion estimates (about 25% of those who participated in the task). There was also a substantial subset of respondents with very large or very small loss aversion estimates, which suggests that many respondents either found the task confusing or did not take it seriously.
For this reason, as well as the fact that this is the only measure that is not incentive-compatible, we do not discuss the results further below or combine this subsample with the others, but these results can be found in the supplemental appendix. It suffices to say that the findings are consistent with conclusions from the other four measures of negativity bias, but we have less confidence in these data.
We recode all measures of negativity bias to have a mean of zero and standard deviation of one prior to analysis.
Dependent Variables and Controls
We examine three measures of political ideology. First, we operationalize right-wing political identity as the average of a seven-point, branching partisanship scale and a seven-point Negativity Bias and Ideology, p.17 ideological identification scale (r=0.64 for the combined sample). Second, we operationalize social conservatism and economic conservatism as factor scores calculated from a two- dimensional principal factors analysis of several political value and policy items. These include:
(a) four items tapping moral traditionalism, (b) three items tapping support for limited government, and (c) ten policy items, including preferences on gay marriage, abortion, immigration, affirmative action for African-Americans, government health insurance, social security privatization, minimum wage, tax rates on wealthy Americans, military spending, and import restrictions. The factor analysis was performed on the combined sample and the factor scores were recoded to range from zero to one. The factor analysis results are provided in the supplemental appendix and support conceptualizing the two factors as social and economic conservatism.
We also consider four personality constructs that have been the focus of previous work on right-wing ideology: openness to experience (e.g., Gerber et al. 2010), the need for non- specific cognitive closure (e.g., Van Hiel, Pandelaere, and Duriez 2004), conservation versus openness to change values (e.g., Malka et al. 2014), and authoritarian childrearing values (e.g.,
Hetherington and Weiler 2009). Our measures are identical in all samples for the latter two constructs but differ for the former two comparing the 2015 sample to the 2018 and 2019 samples. The changes were made to increase the validity and reliability of these two measures.
For each personality construct, we average all available items for each respondent, and then recode the scale from zero to one.
In all models we control for age, gender, race and ethnicity, education, household income, and employment status. We measure political engagement as the average of political Negativity Bias and Ideology, p.18 knowledge (on five objective knowledge questions) and both self-reported political interest and news consumption. All these variables are also coded from zero to one.
All questions used in this study are listed in the supplemental appendix.
Results
To test for an unconditional relationship between negativity bias and right-wing ideology
(Hypothesis 1), we report five sets of estimates: one for each of the first four measures of negativity bias, and one for a dataset that combines the samples for these four measures.7 The estimates for the fifth measure (loss aversion under ambiguity) are provided in the supplemental appendix. The combined dataset thus ignores variation across negativity bias measures, leveraging the gains in efficiency from the larger sample at the expense of conceptual clarity.
Each set of models includes three ordinary least squares regressions—one for each measure of ideology. Each model regresses the respective dependent variable on the respective measure of negativity bias and our set of controls.
Figure 1 displays the estimates for negativity bias in each of the fifteen models (full regression tables are reported in the supplemental appendix). Hypothesis 1 predicts positive and significant coefficients for all three dependent variables, but expects these to be larger for identity and social conservatism relative to economic conservatism. This hypothesis finds no support in our data as none of the coefficients attain statistical significance. Perhaps more importantly, the coefficients for three of the four operationalizations of negativity bias are estimated quite precisely and rule out large, positive effects of negativity bias on all three
7 Sample sizes for the five sets of estimates are as follows: Combined sample with measure 5 excluded = 3413; measure 1 = 989; measure 2 = 964; measure 3 = 381; measure 4 = 1079; measure 5 = 730. Negativity Bias and Ideology, p.19 dependent variables.8 If we ignore conceptual differences in the four operationalizations—and thus pool all the data—the estimate for negativity bias is precisely estimated and of little substantive significance. Thus, our four measures—alone or combined—produce no evidence for a meaningful relationship between negativity bias and right-wing political preferences.
Figure 1: Negativity Bias and Right-Wing Political Preferences
Political Identity Social preferences Economic preferences 0.10 0.10 0.10 0.05 0.05 0.05
●
● ● ● ● ● ● ● ● ● 0.00 0.00 ● ● 0.00 ● ● ● − 0.05 − 0.05 − 0.05 Marginal effect of negativity bias of negativity effect Marginal bias of negativity effect Marginal bias of negativity effect Marginal − 0.10 − 0.10 − 0.10
All Lex Flank Toubia Tanaka All Lex Flank Toubia Tanaka All Lex Flank Toubia Tanaka
Notes: “All” = combined sample; “Lex” = lexical decision task; “Flank” = flanker task; “Toubia”
= loss aversion under risk estimated using Toubia et al. (2013) procedure; “Tanaka” = loss aversion under risk estimated using Tanaka, Camerer, and Nguyen (2010) procedure.
We test Hypothesis 2 in a similar fashion. We again estimate five sets of models: one set each for the four negativity bias operationalizations and one for the combined data. Each set consists of four ordinary least squares regressions, one each for the four personality measures in our study. Figure 2 displays the coefficient estimates and confidence bounds for these models.
8 Recall that the coefficients represent the percentage point change in right-wing ideology for a one standard deviation increase in negativity bias. Negativity Bias and Ideology, p.20
Recall that all personality measures are coded so that higher values correspond with
“conservative” traits. Thus, Hypothesis 2 predicts a positive relationship in all cases but finds little support in our data. Only five of twenty coefficients are statistically significant and two are in the opposite direction predicted by theory. Interestingly, the Toubia et al. (2013) measure of loss aversion under risk produces all three of the significant coefficients that are also in the expected direction. This suggests that additional work with this measure may be useful.
Alternatively, given that these tests are not independent (the measures are correlated), the result may be due to chance. It is also important to note that the results for this measure are less clear when we add controls for the other prospect theory parameters (value curvature and probability weighting) as the three are highly correlated with one another (see supplemental appendix). The more important point, perhaps, is that we rule out large, positive relationships in all cases. Even with the Toubia et al. measure the relationship is unlikely to exceed 0.10 for a two standard deviation change in loss aversion.
Finally, we re-estimate all models from Figure 1 with an added interaction between negativity bias and political engagement. Figure 3 displays these interaction coefficient estimates and confidence bounds. As previous work has found a positive interaction between self-reported traits related to negativity bias and engagement, Hypothesis 3 predicts positive coefficients in all cases. We find no significant interactions and thus no support for this hypothesis.
Negativity Bias and Ideology, p.21
Figure 2: Negativity Bias and Personality
(Low) Openness to experience Need for closure 0.10 0.10
0.05 0.05 ● ●
● ● ● ● ● 0.00 ● ● 0.00 ● − 0.05 − 0.05 Marginal effect of negativity bias of negativity effect Marginal bias of negativity effect Marginal − 0.10 − 0.10
All Lex Flank Toubia Tanaka All Lex Flank Toubia Tanaka
Conservation v. openness Authoritarian childrearing values 0.10 0.10 0.05 0.05
●
● ● ● ● ● 0.00 0.00 ● ● ● ● − 0.05 − 0.05 Marginal effect of negativity bias of negativity effect Marginal bias of negativity effect Marginal − 0.10 − 0.10
All Lex Flank Toubia Tanaka All Lex Flank Toubia Tanaka
Notes: “All” = combined sample; “Lex” = lexical decision task; “Flank” = flanker task; “Toubia”
= loss aversion under risk estimated using Toubia et al. (2013) procedure; “Tanaka” = loss aversion under risk estimated using Tanaka, Camerer, and Nguyen (2010) procedure.
Negativity Bias and Ideology, p.22
Figure 3: Interaction of Negativity Bias and Political Engagement
Political Identity Social preferences Economic preferences 0.2 0.2 0.2 0.1 0.1 0.1
● ● ● ● ● ● ● ● ● ● 0.0 0.0 ● 0.0 ● ● ●
● − 0.1 − 0.1 − 0.1 Coefficient for engagement interaction for engagement Coefficient interaction for engagement Coefficient interaction for engagement Coefficient − 0.2 − 0.2 − 0.2
All Lex Flank Toubia Tanaka All Lex Flank Toubia Tanaka All Lex Flank Toubia Tanaka
Notes: “All” = combined sample; “Lex” = lexical decision task; “Flank” = flanker task; “Toubia”
= loss aversion under risk estimated using Toubia et al. (2013) procedure; “Tanaka” = loss aversion under risk estimated using Tanaka, Camerer, and Nguyen (2010) procedure.
Data Quality and Robustness Checks
In sum, we find little to no support for the hypotheses extracted from previous work.
There is no reliable relationship of any of our four measures of negativity bias with any of our political dependent variables and there is no evidence for relationships conditional on engagement. Further, we find evidence for a relationship with personality for only one operationalization of negativity bias. Even when significant, estimated relationships are substantively small and our confidence intervals rule out large, positive relationships between negativity bias and ideology/personality. Given a set of null results, conclusions rest on the quality of our data and research design. We thus undertake a series of data quality checks and model robustness checks. These are reported in the supplemental appendix and we summarize the results here. Negativity Bias and Ideology, p.23
First, we reproduce well-established correlations in the American public opinion and political psychology literatures. Most relevant to the present paper, we find significant and substantial relationships between our four self-report personality indicators and both right-wing political identity and social conservatism. The magnitude of these effects is comparable to or exceeds other standard predictors of political orientation, such as income, education, race, and ethnicity. As found in past work, these personality associations are smaller and, in two cases, insignificant for economic conservatism. These results suggest that there is nothing intrinsically problematic with the sampling frame of Lucid or Clearvoice relative to past work on personality and political ideology. Indeed, Hypothesis 1 is strongly confirmed when we use self-report measures of traits.
Second, we examine our reaction time measures of negativity bias in greater depth. The lexical decision task and flanker task were modeled on two studies that were conducted in a controlled laboratory setting. Our studies, in contrast, were conducted over the internet, and we thus have less control over the environment in which participants complete the task. While we provided a monetary incentive to take the task seriously, this may have been insufficient to ensure reliable data.
Reassuringly, accuracy rates are very high for both studies and we remove the small percentage of respondents with less than 80% accuracy (i.e., those who may be guessing on most trials). Reaction times also fall within a reasonable range and we remove the small number of trials that were extremely slow or fast. Most importantly, however, we find theoretically expected relationships between reaction times and non-political variables. For the lexical decision task, we find a very strong relationship between mean reaction times for words and word frequency, which is considered the most important variable in the broader literature on Negativity Bias and Ideology, p.24 word recognition (Brysbaert et al. 2011, Brysbaert and New 2009). Indeed, a simple model with a linear and a quadratic term for frequency explains 77% of the variance in mean reaction times across words (Figure 4). Thus, there does not seem to be anything intrinsically problematic about conducting this task over the internet and our reaction time data is not just noise. To the contrary, we clearly replicate a well-established finding in non-political lexical decision studies.
Figure 4: LDT Reaction Time by Word Frequency
Turning to the flanker task, we examine the flanker effect—that is, the difference in mean reaction times comparing target-incongruent to target-congruent flanker trials—for both positive and negative targets separately (as in McLean et al. 2014, Table 1). As expected, we find a large and statistically significant flanker effect for positive targets: when these targets (e.g., a seal pup) are flanked by negative images (e.g., a spider), respondents are 18 milliseconds slower to Negativity Bias and Ideology, p.25 respond than when they are flanked by positive images.9 In contrast, when targets are negative, we find a large and statistically significant flanker effect, but in the opposite direction: when negative targets are flanked by similarly-negative images, respondents are 34 milliseconds slower to respond than when they are flanked by positive images. For both sets of targets— positive and negative—negative flankers capture attention and slow response times relative to positive flankers, which suggests a general negativity bias in our sample. Thus, our data is reliable enough to detect flanker effects comparable in size to past research and we find a negativity bias in the sample as a whole—we simply do not find a gradient in the bias across political ideology.
Third, we estimate alternative models for each operationalization of negativity bias to ensure that certain choices are not critical to the null results. For both the lexical and the flanker task, we re-estimate all models using only correct responses, but the results do not support
Hypotheses 1-3. For the flanker task, we also estimate models identical to McLean et al. (2014), which examine the relationship between ideology and flanker effects for positive and negative targets separately. Again, we find no significant relationships. For all three loss aversion measures, we re-estimate all models using alternative restrictions. Specifically, we exclude those
9 We label this effect size “large” based on McLean et al. (2014) who find a 10 millisecond flanker effect and note, “Though a 10-millisecond difference may seem inconsequential, it is consistent with typical findings in the literature on attentional cueing.” Statistical significance was assessed by constructing 95% percentile intervals based on 10,000 bootstrap samples. We sampled, with replacement, at the respondent level to account for clustering in responses by respondent. Negativity Bias and Ideology, p.26 with loss aversion coefficients less than one-third or greater than three. This change produces similar results and no additional support for Hypotheses 1-3. Finally, for loss aversion under risk, we also re-estimate all models including the other two prospect theory parameters (i.e., value function and probability weighting parameters). Inclusion of these parameters, however, does not produce additional support for Hypotheses 1-3 and reduces support for Hypothesis 2 in the 2015
Qualtrics study.
Discussion and Conclusions
In three U.S. datasets with over 4,000 respondents, and with four distinct behavioral measures of negativity bias, we fail to find evidence for prominent hypotheses in the literature on individual differences and political ideology. Specifically, we find no consistent evidence that negativity bias (1) promotes right-wing ideology in terms of identity, social, or economic preferences; (2) promotes “closed” values or personality traits, such as need for closure or (low) openness to experience; or (3) interacts with political engagement. While we find a few statistically significant coefficients in the expected direction, these are not replicable across operationalizations, and are about what would be expected by chance alone. The more important implication of our results is that we rule out substantively large positive coefficients. That is, even if the predicted relationships exist in terms of direction, they are unlikely to be large in terms of magnitude. To support these null results, we conduct a series of data quality and robustness checks that suggest our data is comparable in quality to that of previous work and our results do not hinge on particular operationalizations or modeling choices. Indeed, our results are consistent with several recent studies that fail to replicate associations between physiological indicators of threat sensitivity and political ideology (Bakker et al. 2019, Knoll, O’Daniel, and Negativity Bias and Ideology, p.27
Cusato 2015, Osmundsen et al. 2019). Taken together, these studies span a wide range of plausible operationalizations of the concept of negativity bias and come to similar conclusions.
This presents a puzzle. The relationship between self-reported personality dispositions theoretically related to negativity bias (e.g., low openness to experience, disgust sensitivity) and right-wing ideology is replicable and substantively meaningful. Our data also strongly support these findings. It is thus quite surprising that seemingly straightforward behavioral measures of negativity bias are unrelated to ideology (and self-reported personality). In the remainder of the paper we consider possible reasons for the conflict between results based on self-report and results using behavioral and physiological measures of negativity bias.
First, it is possible that common behavioral and physiological operationalizations of negativity bias are too unreliable to pick up on these relationships. In this view, the virtue of such measures—that they bypass self-report—is inextricably linked to their major drawback—their low level of measurement reliability. Osmundsen et al. (2019), for example, find very low levels of reliability for a common measure of negativity bias based on electrodermal responses to threatening images. This could explain the better performance of self-report measures, which are typically based on multi-items scales, and which increase in predictive power as the number of items increases (Bakker and Lelkes 2018).10
Reliability may be a particular problem for the lexical decision and flanker tasks, both of which rely on reaction time data to measure negativity bias. Moreover, since these were conducted over the internet, there may be an additional concern that respondents were inattentive or distracted while completing the tasks. Data quality checks, however, suggest that both
10 Though correcting for unreliability does not produce supportive results in Osmundsen et al. (2019) Negativity Bias and Ideology, p.28 measures reproduce well-established, non-political findings in the relevant literatures. Turning to our first measure of loss aversion under risk, previous work suggests that response error on the choice task is moderate: the average proportion of respondent choices inconsistent with their estimated preferences is between 20% and 30% (Toubia et al. 2013, 621). Research also suggests that the measure has out-of-sample predictive validity in both absolute terms and relative to a standard alternative measure of prospect theory preferences (Toubia et al. 2013, 632-633).
Similarly, our second measure of loss aversion under risk shows predictive validity in a field study concerning real economic decisions (Tanaka et al. 2010). One possible concern with this latter measure is that a substantial proportion of our sample displayed high levels of gain-seeking by choosing option B in the first paired lottery of the third sequential choice task (35%). This is reflected in a median loss aversion of slightly less than 1. While we agree that further work with loss aversion would be useful, we note that a large (though smaller) percentage of respondents also made this choice in the original work using this measure which studied a much poorer population of respondents (20%; Tanaka, Camerer, and Nguyen 2010). Further, excluding respondents with very high levels of gain-seeking does not change our results.
Another possibility is that the relationship between self-reported personality traits and political ideology is due to the influence of the latter on the former. That is, people adjust their self-reported traits to match the prototypes of their political identities. For example, a liberal may sort geographically and socially to be around liberal people and subsequently adopt the broader norms of that group, many of which are related to personality. There is some evidence for this.
Ludeke, Tagar, and DeYoung (2016), for example, find that liberals and conservatives report traits closer to their in-group’s prototype relative to criterion measures of these same traits (e.g., objective tests or third-party reports of traits). For example, liberals self-report higher levels of Negativity Bias and Ideology, p.29 intelligence compared to an objective intelligence measure. Other work suggests that traits may move as a function of political events (Boston et al. 2018) and that core moral dispositions exert little causal effect on political ideology over time (Smith et al. 2017) and may even be endogenous to ideology and partisanship (Hatemi, Crabtree, and Smith 2019, Ciuk 2018).
It is unlikely, however, that reverse causality can account for the entire association between self-reported traits and ideology. Indeed, this would raise questions about where such norms come from in the first place: if personality has no causal effect on political attitudes, why do personality-related partisan stereotypes exist at all? It is more likely that conformity to the political in-group strengthens the causal relationship than explains it away: people who are higher (lower) than average in openness gravitate toward liberalism (conservatism) and the social connections formed through politics reinforce this disposition and extend it to new domains.
Another possibility is that it is differences in beliefs rather than preferences that divides the left from the right. That is, political groups may be similar in their attentiveness and reaction to negative outcomes, but may differ in their beliefs about the likelihood of these outcomes. In a model of decision making, there would be no difference in loss aversion. Rather, right-wing
“negativity bias” would operate through the subjective probabilities assigned to negative and positive outcomes. Consistent with this idea, a substantial literature demonstrates that right-wing citizens are more likely to see the world as a threatening and dangerous place (see Jost et al.
2017 for a review). Since beliefs about the probability of negative outcomes are theoretically related to the same self-report traits that robustly predict ideology, this is a worthwhile path for future research. Further, if differences in belief are important, left-wing citizens may be characterized by a “negativity bias” on at least some issues. For example, Jost et al. (2017) find Negativity Bias and Ideology, p.30 that liberal identifiers self-report greater fear of climate change and overpopulation, while conservatives report greater fear of illegal immigration and terrorist attacks.
Another possibility is that the search for a single individual difference variable that explains self-reported personality traits at the level of basic attentional and physiological processes (e.g., negativity bias) is a misguided endeavor. It is a well-supported finding in behavioral genetics that complex traits are shaped by “many genes of small effect” (Plomin et al.
2016, 6). Analogously, complex personality dispositions, like need for closure, may indeed be causally related to ideology, but they are shaped by a diverse array of only weakly-correlated antecedent factors, each of which has a small marginal effect on the overall trait level. Given the large number of diverse characteristics studied under the label “negativity bias” (Hibbing, Smith, and Alford 2014), this seems like a viable hypothesis. The implication is that there may be a level of analysis, such as core values, beyond which it is no longer useful to theorize for the purposes of studying political behavior, because the causal factors become too weak and too numerous. While potentially dispiriting, this could also be fruitful by forcing the literature to focus on theoretical development at a single level of analysis.
These are merely speculations on the origin of the apparent disconnect between behavioral and physiological measures of negativity bias and self-report measures of theoretically related personality dispositions. More research is needed to shed light on these issues.
Negativity Bias and Ideology, p.31
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