Empathy and Political Preferences
Peter John Loewen∗
Christopher Cochrane†
Gabriel Arsenault‡
January 26, 2017
Abstract
Individuals differ in their capacities for empathy. Some individuals can easily understand and share the feelings of another, while other individuals cannot. We use
three different studies to demonstrate how this ability, empathy, matters for political
preferences. First, using self-reported empathy in an observational survey, we demonstrate that variation in levels of empathy explain variation in partisan identification.
More empathic individuals are more likely to identify with and vote for parties of the
left. Empathy also underwrites differences in the willingness to pay for public spending programs usually associated with the Left. More empathic individuals are more
willing to support such policies, even at a personal cost. We also demonstrate that
partisanship partially mediates the relationship between empathy and policy preferences. Second, we replicate our first findings using a behavioural or revealed measure
of empathy. We also recover distributive preferences using an incentive compatible
task. Here, we find that partisanship again mediates the relationship between empathy and basic political preferences. Third, we use a survey experiment to demonstrate
the relationship between empathy and preferring empathic candidates in the presence
and absence of policy information. Taken together, all of this evidence points to a
relationship in which empathy helps individuals sort over candidates, and that the
resulting partisanship then does some of the work of informing policy preferences.
This work thus contributes to recent work on the relationship between personality
and political behaviour and helps to clarify a longstanding debate on the relationship
between individuals’ partisanship and political preferences.
Word count: approx. 8500
∗
Associate Professor, Department of Political Science, and Director of the School of Public Policy and
Governance, University of Toronto
†
Associate Professor, Department of Political Science, University of Toronto
‡
Assistant Professor of Political Science, Université Moncton
1
Introduction
Individuals differ in their ability to experience the emotions of others. Upon seeing an
individual in a moment of emotional turmoil, some individuals will be able to experience
that same tumult. Likewise, some individuals are able to experience the fear, sadness,
happiness, or joy of others. Relatedly, individuals often experience social situations very
differently. One person at a sporting event or a faculty cocktail party may have trouble
judging whether their conduct is rude or polite, may unwittingly offer insensitive comments
to a colleague, or may struggle to even introduce themselves into a conversation. Another
person, by contrast, may seemingly be at ease, drawing others into conversation and
appearing to know just what others are thinking and feeling. In short, individuals vary
in their ability to put themselves into the shoes of others - sharing their feelings and
understanding their thoughts. This difference has important implications for our abilities
to interact with others, to navigate various social situations, and to comfort others in pain
(Del Barrio, Aluja and Garcı́a, 2004; Goubert et al., 2005). In this paper, we demonstrate
that this ability, empathy, also matters for politics.
We argue that empathy exercises an influence over political preferences in a consistent and theoretically informed way. We present findings indicating that individuals who
have a greater empathic capacity are more likely to identify with parties of the left and
are more likely to indicate support for new spending programs, even at a personal cost.
Moreover, we show that partisanship acts as a partial mediator of the effect of empathy
on policy preferences. Indeed, we demonstrate that empathic individuals not only report
a preference for left-wing parties because they are more likely to favour redistributive and
helping policies, but because they are more likely to offer up candidates who are themselves
empathic.
Our work thus speaks to two important literatures. First, it builds on a recent vein
of research into the influence of personality traits on political behavior. This work has
principally considered the role of Big Five personality traits on political participation
and partisanship (e.g. Gerber et al., 2010; Mondak, 2010). Additional related work has
demonstrated the importance of physiological and genetic differences on various aspects
of political behaviour and preferences (e.g. Fowler, Baker and Dawes, 2008; Hatemi and
McDermott, 2011; Oxley et al., 2008). Our work adds to this by demonstrating the importance of another individual difference for partisanship, vote choice, and policy preferences.
Second, our paper speaks directly to work on the nature and effects of partisanship. As
we show, partisanship varies systematically with the individual difference of empathy.
Moreover, partisanship partially mediates the relationship between this individual differences and policy preferences. This suggests a deep partisanship (Gerber and Huber, 2008;
Green, Palmquist and Schickler, 2002; Greene, 2004; Loewen, 2011), in which a stable
identification with parties is built not only on the foundation of early socialization and
social identity, but also differences between individuals (Settle, Dawes and Fowler, 2009).
Our paper proceeds as follows. First, we review the trait empathy (Section 2). Second,
we forward a likely relationship between empathy and political preferences, and then
outline how this matters for current debates over the relationship between partisanship,
vote choice, and policy preferences (Section 3). We then describe our empirical strategy,
including a brief justification of the use of Canadian data Section 4). We next present
2
data and results from Study 1, an observational study of approximately 2000 subjects. We
highlight the limitations of these data, and then outline a second study, Study 2, which we
undertook to address the shortcomings. We follow this with a further extension in Study
3. Following the description of this study and our results, we conclude by considering the
importance of our findings for the study of personality effects and politics, and for our
understanding of partisanship.
2
Empathy
While empathy is generally understood as an ability to understand and share feelings
of others, recent scholarship distinguishes between two key components: affective (or
emotional) empathy and cognitive (or intellectual) empathy (e.g. Davis, 2006, 443-444).
Affective empathy is conceived as the observer’s capacity to share the target’s emotional
state (or to experience an appropriate emotional response to the target’s). In contrast,
cognitive empathy is defined as the observer’s capacity to discern or understand the target’s emotional state (but without necessarily experiencing it). Although these different
components of empathy are conceptually - and, indeed, neurally - distinct, they are closely
related to one another and operate in conjunction in normal human empathic experience
(e.g. Cox et al., 2012).
Studies 1 and 3 use a measure of individuals’ empathy levels - the Empathy Quotient
(EQ) - that does not distinguish between affective and cognitive empathy (Baron-Cohen
and Wheelwright, 2004; Lawson, Baron-Cohen and Wheelwright, 2004; Wakabayashi et al.,
2006a), whereas Study 2 includes results from both the EQ and a measure of cognitive
empathy (Reading the Mind in the Eyes Test). As we demonstrate in Study 2, scores
across these two measures are significantly correlated.
The extent to which individuals exhibit empathy is certainly influenced by situational factors, such as emotional regulation and familiarity with the situation (Kirman
and Teschl, 2010; Singer and Lamm, 2009). In particular, individuals are generally less
competent at recognizing the facial expressions of members of other cultures (Dailey et al.,
2010) and may feel less empathic toward them (Bloom, 2017; Gutsell and Inzlicht, 2012).
Yet, most empathy scholars agree that robust and stable individual differences exist in
empathic capacities (Baron-Cohen, 2012; Davis, 1983; Knafo et al., 2008). Empathy is
thus construed as a trait (or a disposition), in contrast to a mere state.
The causes of these individual differences are not well-known, although it is increasingly
clear that empathic capacities are genetically informed. For example, correlations on
empathy measures are found to be greater in MZ twins compared to DZ twins (Davis,
Luce and Kraus, 1994); specific genes predict empathy quotient scores (Chakrabarti et al.,
2009) and facial expression recognition abilities (Lin et al., 2012). In particular, Rodrigues
found that people who have two ”G” variants of the oxytocin receptor gene tend to be
substantially more empathic than those with at least one ”A” variant of the oxytocin
receptor (Rodrigues et al., 2009a).
Environmental factors also matter. Although most scholars stress the importance
of very early education for empathy development (Hoffman, 2001, e.g.), a few studies
tentatively suggest that environmental factors might also affect adults’ empathic capacity.
3
Hence, new social media are suspected to account for the decline of self-reported empathy
among American college students in the last few decades (Konrath, O’Brien and Hsing,
2011; Konrath and Grynberg, 2013). Cohort effects are also hypothesized to explain
observed empathy differences across age groups more broadly (Grühn et al., 2008; O’Brien
et al., 2013). Finally, it has recently been suggested that reading a lot of fiction might
increase one’s capacity for empathy (Mar et al., 2006; Mar, Oatley and Peterson, 2009).
How traits like empathy are linked to political preferences is a vexing question. In
the study of political preferences, the effects of core beliefs or values, especially on party
identification, has been hard to disentangle. This is in part because the concept of values’ has not been properly conceptualized and measured, and in part because political
science surveys and experiments tend to focus, for understandable reasons, on questions
about highly politicized subjects (Goren, 2005). Opinions about family values, abortion,
homosexuality, taxes and spending, war and peace, and so on, might well tap the underlying latent values of respondents, but they are also often the subjects of intense partisan
controversy. As a result, it is difficult to parcel out the extent to which opinions about
these issues reflect enduring party loyalties rather than enduring core beliefs or values. An
alternative strategy to uncover the causes of political preferences is to look at traits and to
measure them with batteries of indicators which are far removed from the objects of partisan contestation. We follow this strategy by examining the effect on political preferences
of a single trait, empathy, measured via a battery of questions which are not remotely connected to partisan political disagreement. In other words, our strategy involves measuring
an underlying trait without using questions about politically relevant topics, and then
testing whether that underlying trait has an effect on political preferences. If variation
on an apolitical measure of empathy is associated with distinctive patterns of political
preferences, then we will have uncovered evidence which points to a role for traits in our
understanding of political preferences. We then engage a series of further experiments to
attempt to discern the precise nature and ordering of the relationship between empathy,
partisanship, and political preferences.
What is the likely relationship between empathy and political preferences? Thus far,
work on the relationship between personality and political preferences has mostly focussed
on the “Big Five” traits. Those traits – openness, conscientiousness, extraversion, agreeableness, neuroticism - and the effect of these traits on policy preferences and partisanship
has recently come under the scrutiny of political science research (e.g. Gerber et al., 2010;
Mondak, 2010). Although it may be characterised as a “personality disposition” (Johnson, 1990), empathy is distinct from those five personality traits. While it is closest to
and most strongly correlated with “agreeableness” (Del Barrio, Aluja and Garcı́a, 2004),
empathy and agreeableness are not merely synonyms for the same construct. Being able
to recognize and feel others emotions does not necessarily mean that one is “acquiescent”
or “trusting”; similarly, one could generally be considered “good-natured” (e.g McCrae
and Costa, 2003, 4) in spite of finding it difficult to discern others’ emotional states. As
with “personality traits” likes the Big Five, variation in empathy levels likely precedes
politics.
4
3
Empathy and Political Preferences
How should we expect empathy to affect political preferences? The literature on empathy
suggests that high levels of empathy should make individuals adopt left-wing political
preferences regarding the economic dimension of the left-right cleavage (i.e. related to
taxation and public spending). This is so for two distinct but related reasons.
First, one’s level of empathy might influence one’s distributional preferences (Hoffman
(2001), p 230, although seeVan Lange (2008) for disconfirming evidence of this). Hoffman
suggests that highly empathic individuals are more likely than others to prefer distribution
according to “need” and “equality” and less likely than others to prefer distribution according to “merit”, understood as competence and productivity. Such emphasis on equality
and caring for the “needy” is traditionally associated with parties of the left (Noel and
Therien, 2008). Second, relatively empathic individuals might be drawn to leftist parties
because leftist parties emphasize “care” to a greater extent in their discourse - independently of policy cues. Hence, Lakoff (2002) argues that liberals views the government as
an empathic “nurturant parent” taking care of its citizens qua children through generous
social programs, whereas conservatives view the government as a strict father and the
“nanny state” as an overindulgent mother, lacking in discipline. Empirically, Democrats
have indeed been shown to use a more nurturant parent language than Republicans, who
use a more “strict father” language (Ahrens, 2011; Cienki, 2005; Deason and Gonzales,
2012) - but see Ahrens (2009). More generally, leaders of the Left are usually perceived
as more empathic and caring (Goren, 2005; Hayes, 2005). Graham and Haidt’s moral
foundations theory (Graham, Haidt and Nosek, 2009) similarly contends that “caring” is
more central to the moral system of the left than that of the right.” Taken together, then,
we expect empathy to be positively associated with more left wing positions, whether with
partisanship, or policy preferences. However, to the extent that these two types of political preferences are related, it bears making clear our priors concerning the relationship
between these measures and then articulating the order of influence of empathy on these
measures.
The effect of individual differences on political preferences and behaviors does not operate in a vacuum. Researchers consistently find that personality and social environments
interact with each other to influence preferences and behaviors (Caspi et al., 2002; Gerber
et al., 2010). Among the most important properties of the political environment are the
established lines of political competition between parties (Sniderman, 2000). The concept
of partisanship, a psychological attachment to a political party, is an important caveat
to bottom-up understanding of politics. Political sociologists in the 1950s and 60s, for
example, contended that party policy was but a reflection of the underlying structural
cleavages in society (Lipset, 1960). Similarly, important economic models were built on
the assumption that political elites catered strategically to the policy preferences of voters
in order to maximize their party’s appeal in elections (Downs, 1957). From both of these
perspectives, party policy was but a reflection of the distribution of voter preferences in
the electorate. The American Voter (1960) turned these arguments upside down. In this
seminal work, Cambell et al. (1960) found that “the influence of party identification on
attitudes toward the perceived elements of politics has been far more important than the
influence of these attitudes on party identification itself” (135). Party identification, in
5
other words, shapes the policy preferences of voters. Indeed, partisanship tended to precede in time, and outlast, the attitudes of citizens toward specific political objects. A large
body of subsequent research supports the key empirical finding that party loyalties drive
political opinions at least as much, if not more, than the other way around (Bartels, 2002;
Goren, 2005; Green, Palmquist and Schickler, 2002). Although scholars have attempted
to re-interpret the stability of party identification from the standpoint of core beliefs and
values (Feldman, 1988; Killian and Wilcox, 2008), Bayesian learning (Achen, 1992), and
rational shortcuts (Lupia and McCubbins, 1998), the balance of evidence suggests that
partisanship is something more than a “running tally” (Fiorina, 1981, 89) or a heuristic
(Sniderman, Brody and Tetlock, 1991) for the projection of exogenous preferences (Bartels, 2002). Partisanship, for at least some voters much of the time, and many voters some
of the time, shapes policy preferences.
We are left then with three closely-related but ultimately separable explanations for
why more empathic individuals might prefer parties of the left and prefer policies which
are at once redistributive and aimed at helping other citizens. First, more empathic
individuals may be attracted to more expansive government programs because they are
more redistributive and generative of more equal outcomes. Second, they may prefer
more expansive government programs not because they are more redistributive per se, but
because they are framed in a language of helping and care. Third, they may be attracted
to parties of the left not for their policy positions (at least principally), but because those
parties appeal to the voter’s basic values and dispositions. In this case, parties of the
left may present more empathic candidates, leading more empathic individuals to identify
with these parties. Policy preferences may then follow from that identification.
Cross-sectional survey data cannot disentangle empirically the direction of causality
between partisanship and preferences; however, we expect for theoretical reasons that
partisanship plays a role in mediating the effects of core personality traits, such as empathy, on policy preferences. Empathy may shape issue positions directly, but it also
may shape how people orient themselves to established political parties and thereby exert
an additional influence on political preferences through partisanship. There is nothing
intrinsically or exclusively political about empathy. Indeed, measuring empathy means
moving beyond analyses that are confined exclusively to political attitudes and behaviors
by gauging people’s orientations and behaviors in other domains of life, such as casual
social situations. Nonetheless, more empathic individuals may find themselves drawn to
left-wing parties because of their distributional preferences or because the leaders of these
left-wing parties display a more caring attitude towards those in need than members of
right-wing parties. As they come to identify with left-wing parties, moreover, the influence of partisanship means that empathic individuals are likely to adopt more of their
party’s policy stances on a broad range of issues. In such a scenario, an apolitical trait,
empathy, is politicized by the established lines of party competition, and thereby shapes
one’s political preferences and values.
6
4
Empirical approach and summary of results
Following from this discussion, our empirical approach is the following. In Study 1, we
demonstrate that individuals who have higher levels of empathy are more likely to identify
with parties of the left. We also show that they are more likely to support policies
which are redistributive in nature and which are framed as caring and helpful. Finally,
we show in this study that the link between empathy and these policy preferences is
partially mediated by partisanship. Because Study 1 presents policies which are both
redistributive and helping, it does not allow us to understand if the direct relationship
between empathy and policy preferences is a result of empathic individuals having more
redistributive preferences, preferences for helping policies, or both.
In Study 2, we present three advances. First, we demonstrate that a behavioural
measure of empathy also explains policy and partisan preferences. Second, we use an experiment to demonstrate that empathic individuals are more likely to choose candidates
who signal empathy. This suggests that at least some of the relationship between empathy and partisanship is driven by the characteristics of candidates rather than their policy
positions. Third, we demonstrate that when presented with a choice over distributional
schemes that is stripped of helping language, there is no direct relationship between empathy and distributional preferences. There is, however, a mediated relationship between
empathy and distributional preferences via partisanship. This suggests that empathy has
an effect on distributional preferences only via partisanship.
In Study 3, we expand on our candidate choice findings by exploring the potential for
empathy signalling by candidates to affect the preferences of empathic respondents in the
presence or absence of policy information.
Taken together, our results suggest that empathic individuals exhibit left wing preferences because of both the nature of left policies (in particular their helping nature; less so
their redistributive elements) and because of the characteristics of left-wing politicians.
4.1
Canada as a test case
To demonstrate the relationship between empathy and political preferences, we rely on
Canadian data. Canada is a suitable case for at least two reasons. First, in Canada outside
Quebec, the three main parties earning 94% of the vote in the election in question can be
clearly distinguished in terms of the economic dimension of the left/right cleavage1 . In
a comparative perspective, the left-right gap between the Conservative Party (right) the
Liberal Party (center-left) and the New Democratic Party (left) is particularly significant
(Benoit and Laver, 2006; Cochrane, 2010). Second, the effect of traits on political preferences has thus been mainly studied in the United States (Gerber et al., 2010). To confirm
that traits influence political preferences beyond America, it is useful to conduct studies
in different polities.
1
In contrast, the leading party in Quebec at the time we collected our data for Study 1, the Bloc
Québécois,is mainly defined by its stance on identity and ethnic issues.
7
5
Study 1
5.1
Data
To test these expectations, we first make use of an online survey of nearly 4000 subjects,
conducted in May 2007. The study was conducted by a commercial polling firm. The
firm uses a self-selected sample which, while more broadly constituted than a student
convenience sample, is not a representative sample. Subjects are required to login to the
survey using a unique identification. Subjects all answered questions concerning their
partisan identification. All subjects also completed an eight-item empathy measure. All
subjects also answered a series of standard attitudinal questions and a series of questions
on past charitable giving, and a completed set of four dictator games. Finally, subjects
completing the survey in the second half of the month were exposed to four questions
regarding their willingness to pay for public spending programs.
As is conventional with much survey research in Canadian political behaviour, we
retain only those subjects residing outside of Quebec. We exclude those subjects who do
not report their income or education levels. This leaves us with effective samples of 3700
subjects for our analysis of party identification and 1985 subjects for policy preferences.2
5.2
Subjects
Our subjects3 are evenly balanced between males (50.5%) and females (49.5%). The
average age of our subjects was 50.5 years old (S.D. 13.4). The majority of subjects
report at least some university education (59.6%), while a quarter report at least some
post-high school training (27.9%). The remaining subjects report a high school education
or less (12.6%). Approximately one-in-five subjects report a household income of $40,000
or less per year (21.0%). A similar proportion report an income between forty- and
sixty-thousand per year (19.6%) or an income between sixty- and eighty-thousand per
year (19.0%). The remaining subjects (40.4%) report a household income of greater than
$80,000 per annum.
5.3
Measuring empathy
The eight-item version of the Empathy Quotient was derived from Wakabayashi et al.
(2006b)’s analysis of the full forty-item EQ. They presented a principal components factor
analysis of a 60-item scale (Wakabayashi et al., 2006b, Table 2). Using their results, the
four affirmative EQ questions with the highest principal component factor loadings and
the four reversal items (marked by (R)) with the highest factor loading were chosen for
the eight-item version. Responses were forced so that the survey could not be completed
without answering all eight questions. Question order was randomized for each respondent.
The items are:
1. I find it easy to put myself in somebody else’s shoes.
2
Policy preferences were only queried among those who responded to the survey in the last two weeks
of the month.
3
In reporting subject statistics, we report all subjects who are included in at least one of the analyses.
8
2. I am good at predicting how someone will feel.
3. I am quick to spot when someone in a group is feeling awkward or uncomfortable.
4. Other people tell me I am good at understanding how they are feeling and what
they are thinking.
5. I find it hard to know what to do in a social situation. (R)
6. I often find it hard to judge if something is rude or polite. (R)
7. It is hard for me to see why some things upset people so much. (R)
8. Other people often say that I am insensitive, though I don’t always see why. (R)
Following Wakabayashi et al. (2006b), respondents indicate whether they strongly
agree, agree, disagree, or strongly disagree with the statement. A respondent scores two
points if they exhibit a empathizing trait strongly, 1 if they exhibit it slightly, and 0 in
the other two response categories. Scores are then aggregated over all items, with each
being equally weighted, producing an overall empathy quotient. All eight questions exhibit a respectable internal consistency (α = .76) and load heavily on a single dimension.4
Moreover, as should be expected from prior work (Baron-Cohen, 2004), women exhibit a
significantly higher empathy score than men (t = 14.9, p = .00, two-tailed). We provide an
additional test of construct validity in Section 9. For interpretive ease in the regressions
presented below, we transform the EQ8 into a 0 to 1 score, where 0 indicates the lowest
possible empathic capacity and 1 the highest.
5.4
Dependent Variables
Study 1 uses two dependent variables: a standard partisan identification instrument5 and
a unique scale measuring respondents’ willingness to pay more in taxes or time to support
new public spending programs.
5.4.1
Partisan identification
For partisanship, we use a standard series of questions from the Canadian Election Study.
Respondents are asked “Thinking about federal politics in Canada, generally speaking, do
you usually think of yourself as a Liberal, a Conservative, a New Democrat, or none of
these?” Respondents are then asked how strongly they think of themselves as a partisan
of the identified party. We only consider as partisan identifiers those who indicate a fairly
strong or very strong identification. From these questions, we generate a categorical variable that indicates whether an individual has a Conservative, Liberal or New Democratic
identification. No identification acts as the reference category. Forty-seven percent of our
4
A principal components factors analysis indicates a first factor eigenvalue of 3.08, with a mean loading
of .62 and a minimum loading of .50.
5
We note that we have also run analysis using vote choice as a dependent variable. The returned results
are substantively the same as those for partisanship: the likelihood of voting for the Conservative party
decreases as empathy increases, while the likelihood voting for the New Democrats and Liberals increases
9
respondents indicate no identification. Of those identifying with a party, Conservatives
(20.2%) and Liberals (21.6%) are evenly split, while approximately one-in-ten respondents
identify as New Democrats (10.9%). These results are presented in section 5.5.1.
5.4.2
Willingness of Pay for Public Spending
Near the end of the survey, a subset of respondents were asked about four hypothetical
public spending programs usually associated with the Left. For half of the policies, respondents were asked whether they would pay a high price for the good. If they agreed,
they proceeded to the next question. If they disagreed, then they were asked about their
willingness to pay a lower price. This continued until they agreed to pay some price, or
until the lowest price was reached. For the remaining half of the policies, the expressed
price began low and then increased until subjects expressed an unwillingness to pay or
reached the highest price. The question order was randomized for each respondent.
These questions have two important features. First, each policy has a distributive
consequence, in that the respondent is asked how much they would be willing to pay to
provide more of a service or good to another individual. Second, the policies are framed
in a helping fashion, in that some problem of potentially great distress for another citizen
is solved by the adoption of the policy. Apart then from standard self-interest motivations
for supporting one policy or another, these items should recover support for public policy
on redistributive and/or caring grounds.
The exact question wordings are:
• One proposed solution to fight climate change and decrease air pollution is to impose
carbon taxes. Supporters of these environmental policies say such taxes would result
in cleaner air and better health for everyone. Would you support carbon taxes if
you knew it would cost you $2000 ($1500, $1000, $500, $100) more per year to heat
your home, ride the bus, and drive a car?
• Some politicians and policy groups propose making the first four years of university
free for all qualified students, just like high school. This will result in greater accessibility to university education. Would you support the elimination of tuition fees if
it cost you $100 ($250, $500, $1000, $2000) more per year in taxes?
• Provincial health care programs often do not cover the cost of drugs for those with
cancer. This can make fighting cancer financially taxing for cancer sufferers and
their families. Would you support covering the cost of cancer drugs if you knew it
would increase average emergency wait times for non-critical injuries (such as ear
infections, the flu, or small cuts) by one hour (90 minutes, two hours, three hours,
five hours, ten hours)?
• Wait times for many medical procedures (such as cataract surgery and hip and knee
replacements) are currently longer than recommended by doctors. If tax dollars
were guaranteed to go to these priority areas and to reduce wait times, would you
be willing to pay $2000 ($1500, $1000, $500, $100) more per year in taxes?
10
We convert the willingness to pay for each policy to a 0 to 1 scale, where 0 indicates
an unwillingness to pay even the lowest cost, while 1 indicates a willingness to pay the
highest stated cost. We then average out these scores in a single scale. When all four items
are scaled together, they have reasonable internal consistency (α = .62). Conceptually,
the scale represents a respondent’s general willingness to incur personal costs for the
provision of public spending programs generally associated with the Left. The results for
this dependent variable are presented in section 5.5.2.
5.5
5.5.1
Study 1 Results
Partisanship Results
We begin by examining the relationship between expressed levels of empathy and partisan
identification. We specify a multinomial logit with four outcomes: no identification (our
reference category), Conservative identification, Liberal identification, or New Democratic
identification. We also include a standard schedule of controls, including gender, age,
dummies for region, and a variable measuring income category. Our results, in the form
of logit coefficients and their associated standard errors, are presented in Table 1. All
variables are scaled from 0 to 1 for ease of interpretation.
We also present our results graphically in Figure 1. As these estimates demonstrate, the
probability of identification with the Conservative party (versus no identification) declines
as empathy increases. Indeed, across the range of empathy values, the probability of
identifying with the Conservative party decreases by approximately 9 percentage points
from 25 percent to 16 percent, or more than a full third of the starting value. By contrast,
the probability of identifying with the Liberal party over no identification increases from 17
percent to 24 percent, an increase of 7 percentage points, or more than 40 percent. Finally,
the probability of identifying with the New Democratic party experiences the steepest rise,
increasing from 7 percentage among those with the lowest empathy level to 13 percent
among those with the highest levels, almost a completely doubling of the likelihood. We
note further that compared to Conservative identification, subjects are more likely to
identify with the Liberal party (p = .00) or the NDP (p = .00) as empathy increases.
There is no difference, however, in the probability of identifying with the NDP over the
Liberal party (p = .34). Taken together, these results suggest that empathy underwrites
large differences in the probability of identifying with Canada’s principal parties and does
so in a fashion consistent with a traditional left-right understanding of politics.
5.5.2
Policy Preference Results
We next consider the effect of empathy on policy preferences. Table 2 presents our first
results. Once again, we employ the same series of controls, but add in controls for each
partisanship. Empathy again exercises an independent and substantively impressive effect.
According to the estimates in Table 2, over the range of empathy support for public
spending increases by nearly 50%, with 31% at the lowest level of empathy supporting
all policies at their highest price to 45% at the highest level of empathy supporting all
policies at their highest price. As we demonstrate in Section 9.2, this result also holds
across all four components of our scale measured independently.
11
Table 1: Empathy and Partisan Identification (multinomial logit)
Variable
Coefficient
(Std. Err.)
NDP partisan identification vs. no identification
Empathy
0.733∗∗
(0.263)
Female
0.216†
(0.118)
Age
-0.106∗
(0.042)
∗∗
Income
-0.256
(0.049)
College
-0.381∗
(0.185)
University
-0.038
(0.168)
Alberta
-0.696∗∗
(0.250)
Prairies
-0.051
(0.209)
Ontario
-0.299∗
(0.135)
Atlantic Canada -0.515∗
(0.204)
Intercept
-0.808∗
(0.354)
Conservative partisan identification vs. no identification
Empathy
-0.306
(0.207)
∗∗
Female
-0.486
(0.094)
Age
0.122∗∗
(0.035)
†
Income
0.068
(0.040)
College
-0.122
(0.141)
University
-0.514∗∗
(0.135)
Alberta
0.394∗
(0.162)
Prairies
0.029
(0.184)
Ontario
0.013
(0.111)
†
Atlantic Canada -0.337
(0.173)
Intercept
-0.368
(0.293)
Liberal partisan identification vs. no identification
Empathy
0.450∗
(0.203)
Female
0.100
(0.090)
Age
0.074∗
(0.034)
Income
0.095∗
(0.039)
College
0.049
(0.156)
University
0.094
(0.146)
Alberta
0.113
(0.183)
Prairies
-0.138
(0.204)
Ontario
0.442∗∗
(0.115)
Atlantic Canada 0.183
(0.164)
Intercept
-2.124∗∗
(0.299)
N
Log-likelihood
Significance levels :
3799
.
† : 10%
∗ : 5%
12
∗∗ : 1%
LIB
NDP
.3
.2
.1
0
Probability of identification
.4
CON
0
.5
1
0
.5
1
0
.5
1
EMPATHY
Graphs by Party
Figure 1: These graphs estimate the probability that an individual identifies with the
Conservative, Liberal or New Democratic party respectively as empathy increases. The
gray lines represent 95% confidence intervals. All estimates are generated using Clarify
(Tomz, Wittenberg and King, 2001).
To this point, we have demonstrated that empathy is associated with both partisanship and policy preferences. Moreover, our estimate of the effect of empathy on policy
preferences controls for partisanship. This points us towards a potential mediated relationship in which partisanship (partially) mediates the relationship between empathy
and policy preferences. Following Baron and Kenny (1986), some variable M, in our case
partisanship, mediates the relationship between X, in our case empathy, and Y, or policy
preferences, if X predicts M and M predicts Y controlling for X. There are multiple ways
to estimate the parameters of such a mediated relationship, most frequently the protocol
of Baron and Kenny (1986). However, recent research (Green, Ha and Bullock, 2010) has
noted the potential inferential challenges of traditional mediation tests when neither the
causal variable or the mediator are randomly assigned. While this potentially limits the
confidence with which we can make inferences from mediation tests, recent advances at
least allow us to estimate the robustness of any mediation effects to omitted variables.
Accordingly, following the procedure of Imai et al. (2011), we first estimate a series of
logistic regression between three binary variables measuring partisan identifications (Liberal, Conservative, and NDP), and empathy, with a series of controls. We then estimate
a model of policy preferences on the same partisan identification variables, empathy, and
13
the same series of controls. We report three relevant quantities in Table 3: the Average
Causal Mediation Effect (ACME), the percent of the direct effect of empathy mediated by
the partisanship in question, captured in the coefficient on empathy in Table 20 in Section
10, and ρ, which reports the minimum level of correlation between the error terms of our
first and second regressions required to reduce the ACME to 0 Imai et al. (2011). Full
results for the mediation analysis are available in Section 10.
Table 2: Empathy and policy preferences (OLS)
Variable
Empathy
Conservative ID
Liberal ID
New Democratic ID
Female
Age
Alberta
Prairies
Ontario
Atlantic Canada
Income
College
University
Intercept
Coefficient
0.143∗∗
-0.091∗∗
0.072∗∗
0.106∗∗
-0.003
-0.003
-0.053∗
-0.021
-0.023
-0.002
0.016∗∗
0.026
0.080∗∗
0.240∗∗
N
Log-likelihood
Significance levels :
(Std. Err.)
(0.026)
(0.019)
(0.021)
(0.026)
(0.011)
(0.004)
(0.022)
(0.023)
(0.014)
(0.020)
(0.005)
(0.018)
(0.017)
(0.036)
1985
.
† : 10%
∗ : 5%
∗∗ : 1%
The results reported in Table 3 demonstrate that the partisanship routinely if modestly
mediates the relationship between empathy and policy preferences. Three percent of the
relationship between empathy and policy preferences is mediated by Liberal partisanship.
This increases to a more impressive 5% of mediation attributable to New Democratic
identification and 4% to Conservative identification. As our sensitivity analysis estimate
(ρ) suggests, the nullification of these results would require an omitted variable which was
strongly related to both a subject’s underlying levels of empathy and their partisanship.
Given the relatively low amount of variance already explained by variables known to
be related to partisanship, this seems empirically unlikely. Moreover, given the strong
evidence on the early sources of individual difference in empathy (Baron-Cohen, 2004;
Plomin et al., 1993; Rodrigues et al., 2009b), it seems unlikely that some lurking variable
would completely obviate our results.
14
Table 3: Mediation statistics for influence of empathy on policy preferences through partisanship
Partisanship
Conservative
Liberal
New Democrat
5.6
ACME
0.01
0.01
0.01
% Total Effect Mediated (95% ci)
4.0 (3.0-5.9)
3.2 (2.4-4.9)
5.1 (3.8-7.7)
ρ
-0.30
0.30
0.30
Study 1 Limitations
Study 1 has two clear limitations which we seek to address in Study 2 and 3. First, while we
find a statistically-clear mediated relationship between empathy, partisan identification,
and policy preferences, the causal ordering of this relationship is still the subject of debate.
Relatedly, we have not specified the mechanism linking empathy to leftist (party or policy)
preferences. Second, our measure of empathy is self-reported, and thus subject to social
desirability. While the instrument we use has been validated elsewhere, it is still possible
that respondents on the left may be more likely to feel that they are supposed to exhibit
caring and empathic characteristics. Accordingly, they may exaggerate their own empathy.
Such social desirability could lead to a spurious relationship between empathy and left wing
identification and policy preferences. In Study 2, we address both of these limitations.
(Study 3 expands on the results of Study 2).
6
Study 2
To replicate and extend the findings of Study 1, we conducted a second study (Study 2)
in the Fall of 2012. Study 2 goes beyond Study 1 in two manners. First, although our
measure of empathy in Study 1 is arguably pre-political, it is possible that individuals’
self-reports of empathy could be influenced by their partisanship. In Study 2, we therefore
employ a behavioural measure of empathy using the “Reading the Mind in the Eyes” task.
This method requires respondents to reveal rather than report their empathic capacity.
Second, Study 1 does not determine the mechanism through which empathy influences
political preferences. Study 2 tests for the two mechanisms alluded to in section 3: distributional preferences and identification with candidates displaying empathy. Through a
“sorting experiment” and a “distributional game”, we find that empathy influences political preferences not by informing distributional preferences but by drawing high-empathy
individuals to political candidates displaying high levels of empathy (independently of
partisan or policy cues). In turn, we show that partisan identifications link empathy to
abstract distributional preferences, helping to clarify both the direction of relationships
and the mechanisms of the effects estimated in Study 1.
6.1
Data
Study 2 relies on data collected from a relatively representative sample of Canadians. This
sample is drawn from a panel of more than 100,000 respondents, most of who are recruited
15
to a commercial panel through a voting advice application called Vote Compass. After
joining the panel, participants are occasionally invited to complete surveys in exchange
for entry in a draw. The survey was hosted on the Qualtrics platform.
6.1.1
Subjects
Our Study 2 sample includes N = 434 respondents. Respondents had an average age of
53.4 (S.D. 15.4). Subjects were 64% male.
6.2
Behavioural measures of empathy
The first innovation of Study 2 is the use of a behavioural measure of empathy. Rather
than relying on a self-report, we use a behavioural measure of empathy. We began by
telling individuals that they would complete a task and that if their name was drawn we
would pay them according to their performance and the payment system they chose. We
use this to measure distributive preferences, described the Section 6.3.2. Our behavioural
measure of task is the “Reading the Mind in the Eyes” paradigm. In this task, individuals
are shown a set of eyes, and then asked to choose from four words the one which best
describes the emotion displayed. Subjects completed this for four sets of eyes. We find
that this behavioural measure is significantly correlated with the EQ measure of Study 1
(r=.22, p <.00).
6.3
Dependent variables
Study 2 relies on two separate dependent variables. To further explore the relationship
between empathy and preferences over candidates and parties, we designed a simple empathy cuing and partisan sorting experiment, described in Section 6.3.1. To understand the
relationship between empathy and distributive preferences, we developed a unique task,
designed to elicit basic distributive preferences. This is described in Section 6.3.2.
6.3.1
A test of partisan sorting
We wished to understand how empathy influences candidate or party choice absent policy
cues. If we can demonstrate that empathy helps voters sort into parties according to their
values and independently of their policies, then this can help explain how partisanship
links empathy to policy preferences. Accordingly, we designed a simple experiment to
explore whether empathy helps individuals sort over parties.
The experiment was conducted in the following manner. First, we told respondents to
Study 2 the following:
In the next question, we’d like to tell you about two hypothetical politicians.
These individuals don’t exist, but they are meant to reflect real politicians in
the world. We will tell you something about each of these politicians. Then,
we’d like to know which candidate you think you’d be more likely to vote for
in an election.
16
We then presented them with two candidates. While the experiment manipulated
the gender and occupation of the respondents, we here only focus on a manipulation of
the degree of empathic language expressed by the second politician. For example, some
respondents would be presented with the two following vignettes, where more empathic
substitutes are indicated in parentheses.
John Smith is a businessperson and is 42 years old. He is entering public life
because he believes he can be a good representative of his constituents in Ottawa, because he has a longstanding interest in public policy, and because he
is concerned about the future of the country.
Frank Cochrane is a teacher and is 47 years old. He is entering public life
because he (is concerned/cares deeply) about people, because he (thinks/feels)
that many citizens are (falling behind/suffering) and government is (not doing
enough/not meeting their needs), and because he thinks government should
(lookout/care) for its citizens.
The only difference between treatments is that the degree of empathic language used by
Frank Cochrane varies between that which is low in care (e.g. thinks that many citizens)
and high in care (e.g. feels that many citizens). Neither candidate indicates any policy
preferences or intentions, and no party labels are used.
The objective of the experiment then is to determine whether more (less) empathic
individuals are more (less) likely to support candidate 2 when empathic language is
used. Since high-empathy candidates are more likely to have left-wing political preferences (Goren, 2005; Hayes, 2005), this suggests that high-empathy individuals may adopt
left-wing political preferences partly because they are drawn to high-empathy left-wing
political candidates in the first place.
6.3.2
Distributional preferences
We recovered subjects’ preferences for income distributions in an incentive-compatible
task. Subjects were told that they would be completing a task of which they had no
knowledge. They were told that three participants would be randomly drawn. These
participants would be paid according to their performance in the task relative to the two
other participants and according to one of five distribution schemes chosen by one of the
study’s participants. Choices ranged from a very fair system, in which the income of the
bottom third was $3.20 and the top third was $3.50 to a very unequal system, in which
those in the bottom third received $0 and those in the top received $9. Table 4 shows the
complete schemes, as well as the percentage of subjects preferring each scheme.
This design has three notable features. First, better performance is always rewarded, so
subjects are choosing a tradeoff between better pay for better performance and a decreasing
minimum income. Second, as subjects did not know the task they could not know with
certainty how well they would perform. Third, the scheme isolates distributive preferences
from utilitarian preferences, as the total amount of money available at the tasks end does
not vary across schemes.
17
Income distribution system
Payment by rank
Bottom third Middle third Top third
3.20
3.30
3.50
2.50
3.00
4.50
1.50
2.00
6.50
0.50
1.50
8.00
0.00
1.00
9.00
Percentage preferring
32%
47%
15%
3%
3%
Table 4: Preferences for income distribution in unknown task. The first three columns report the
number of dollars or pounds paid to players depending on their rank in the unknown task. The
remaining column reports the percentage preferring each distribution scheme.
Table 4 reports the distribution of preferred payment systems. While most subjects
prefer relatively equal systems, the modal choice does allow for the best performer to take
45% of the game income, suggesting some preferences for mild inequality. In Section 6.4
we demonstrate how empathy is related to these preferences.
6.4
Study 2 results
Having described our three instruments, we now turn to a discussion of our results. We
begin by presenting simple results for our sorting experiment and then turn to a full
estimation of the link between empathy, partisanship, and policy preferences.
6.5
Linking empathy to partisan identification
Table 5 presents the frequencies of support for candidate 2 (Frank Cochrane) in two conditions (no empathy signal and empathy signal) for individuals below and above the median
of observed empathy. These results suggest that when candidates do signal empathy, more
empathic individuals increase their support for such candidates. Those with low empathy
sort in the opposite direction.
We confirm the significance of these results in Table 10. This table estimates a simple
logit regression of choice of candidate 2 regressed on empathy level, the treatment and
their interaction. We also include controls for age and income in an effort to tighten our
standard errors. The results clearly suggest that empathic respondents are significantly
more likely to choose candidate 2 when empathy is signalled.
6.6
Linking empathy to distributional preferences
Our final test in Study 2 involves specifying, as in Study 1, a mediation model of the
effects of empathy on policy preferences, via partisanship. In this case, we take for policy
preferences the distributional preferences captured in 6.3.2. For partisan identification,
we create a variable reading -1 for Conservative identifiers, 0 for non-identifiers, and 1 for
18
Low empathy
High empathy
No signal
59%
53%
Empathy signal
49%
60%
Table 5: Percentage of low and high empathy voters support candidate 2 when empathy is not
and is signalled. This table shows that support for candidate 2 increases (decreases) for those with
high (low) empathy when empathy is signalled by the candidate
Table 6: Empathy signals and candidate sorting (logistic regression)
Variable
Empathy signal
High empathy
Empathy signal*High empathy
Income
Age group
Coefficient
-0.483
-0.327
0.911∗
-0.332∗∗
0.014∗
N
Log-likelihood
χ2(5)
Significance levels :
(Std. Err.)
(0.329)
(0.285)
(0.415)
(0.102)
(0.007)
434
-287.546
19.314
† : 10%
∗ : 5%
∗∗ : 1%
those who identify with the either of the centre-left Liberals or NDP. Tables 7-9 present
the results for each stage of the mediation test.
As shown the first stage (Table 7), there is no direct relationship between empathy and
distributive preferences. However, as Table 8 demonstrates, there is a link between high
empathy and partisanship (as in Study 1). Finally, Table 9 demonstrates that partisanship
clearly influences preferences over distribution, such that those who are on the left are more
likely to prefer more egalitarian income schemes.
In sum, what we find is that while empathy does not link to distributive preferences
directly, it does underwrite partisanship, which in turn influences distributive preferences. An analysis of mediation statistics suggests this relationship may be significant
(ACME=.01 (95% CI -.002, .022), % direct effect mediated=22%, ρ = .27).6
The importance of these results is the following: when we consider the relationship
between empathy and abstracted policy preferences, i.e. those stripped of the language of
care or concern but retaining their distributional features, we find no relationship. However, we do find that preferences over such policies are shaped by partisanship, which is
itself a product of empathy. Accordingly, our replication suggests that to the extent that
empathy matters for policy preferences, it is especially through its concurrent influence on
partisanship. These results, combined with our sorting experiment, suggest that partisanship serves as in important link between empathy and policy preferences. Perhaps, over
6
We also note that if we drop controls for gender, the significance of our mediation results increases
substantially.
19
time, empathic individuals learn about the political world and the policy preferences they
should hold via the signals and proposals of politicians with whom they identify. Such a
mechanism seems consistent with the kind of story told in the moral tastes framework, in
which individuals come to understand over time how different policy choices can be linked
to their own moral and psychological taste (Haidt, 2013).
Table 7: Redistribution preferences and behavioural empathy measure, with gender (ordered logit)
Variable
High empathy
Age category
income
Female
Coefficient
0.038
-0.003
-0.185∗
0.540∗∗
N
Log-likelihood
χ2(4)
Significance levels :
(Std. Err.)
(0.188)
(0.006)
(0.092)
(0.188)
434
-517.044
13.78
† : 10%
∗ : 5%
∗∗ : 1%
Table 8: Partisanship and behavioural empathy measure, with gender (ordered logit)
Variable
High empathy
Age category
Income
Female
Coefficient
0.365†
0.000
-0.149
0.787∗∗
N
Log-likelihood
χ2(4)
Significance levels :
7
(Std. Err.)
(0.209)
(0.007)
(0.103)
(0.226)
422
-359.287
20.409
† : 10%
∗ : 5%
∗∗ : 1%
Study 3
Our partisan sorting experiment in Study 2 demonstrates that individuals who demonstrate greater empathy are more likely to select a candidate when that candidate in turn
signals more empathic behaviour. This provides some evidence that it is the characteristics
of candidates, rather than their policy positions, which helps voters sort on to parties. It is
possible, however, that such a pattern of responses could still be generated by differential
policy positions among candidates. Suppose in the real world that candidates who display
more empathy are also more likely to take more progressive policy positions. If this is
20
Table 9: Redistributive preferences and behavioural empathy measure, with partisanship
and gender (ordered logit)
Variable
Partisanship
High empathy
Age category
Income
Female
Coefficient
0.638∗∗
-0.053
-0.003
-0.171†
0.413∗
N
Log-likelihood
χ2(5)
Significance levels :
(Std. Err.)
(0.119)
(0.192)
(0.006)
(0.095)
(0.193)
422
-487.16
43.456
† : 10%
∗ : 5%
∗∗ : 1%
true, then choosing more empathic candidates may be a learned behavioural response of
empathic individuals not because they value empathy per se, but because they prefer more
redistributive and helping policies, and they know these are more likely to be advanced
by more empathic candidates.
The aim of Study 3 is to demonstrate that this is not the case. We achieve this by
extending our sorting experiment, by introducing into it a policy positions stated by the
candidates. Our results suggest that more empathic individuals are more likely to sort onto
candidates who signal more empathy. However, in the absence of an empathy signal, they
are not more likely to sort onto those who indicate support for redistributive or helping
policies. Likewise, in the presence of an empathy signal, indicating policy positions does
not change the distribution of preferences among more empathic individuals.
7.1
Data
Study 3 was conducted in November of 2014. A sample of 1033 individuals was provided
by Survey Sample International (SSI). Our study was conducted on the Qualtrics platform.
The results presented below are from questions placed in the second half of the survey.
The first half included a series of unrelated foreign policy questions (see Soroka et al.
(2016)).
7.1.1
Subjects
We exclude subjects who do not indicate their age group, gender, household income, or
province of residence. We likewise exclude those who did not complete our empathy
measure or our sorting experiment. This leaves 1012 respondents. Subjects display an
even balance on gender (52.1% male, 47.9% female) and age group (18 to 24: 9.7%, 25 to
34: 17.4%; 35 to 44: 18.7%; 45 to 54: 16.1%, 55 to 64: 19.4%; 65+, 18.7%). Respondents
are likewise well-balanced on income category and province of residence.
21
7.1.2
Empathy
As in Studies 1 and 2, empathy was recovered using the eight-item version of the Empathy
Quotient. As in Study 1, the scale demonstrates respectable internal consistency (α = .75).
The items all load heavily on a single dimension.7 As in Study 1, women display a higher
mean score than mean (t=6.1, p = .00, two-tailed).
7.2
Experiment
As in Study 2, respondents were presented with two fictional candidates and asked for
which they would be more likely to vote. Once again, the second candidate was randomized
to a description that included cognitive language or empathic language. The innovation
of this experiment is to include a second treatment in which the description of the second
candidate is also randomized to include a description of a redistributive, helping policy
position.
The exact text of the experiment is as follows, with the randomizations for the first
experiment included in parentheses and the randomization for the second experiment
included in bold text:
John Smith is a businessperson and is 42 years old. He is entering public life
because he believes he can be a good representative of his constituents in Ottawa, because he has a longstanding interest in public policy, and because he
is concerned about the future of the country.
Frank Martin is a teacher and is 47 years old. He is entering public life because
he (is concerned/cares deeply) about people and because he (thinks/feels) that
he can make their lives better. He thinks tuition fees are too high and
public schools are under-funded. He supports raising taxes in order to decrease university tuition and increase public school funding.
Which candidate would you be more likely to vote for?
7.3
Study 3 results
As articulated in Section 7, there are two relevant tests to demonstrate that the link between empathy and candidate choice can occur independently of policy positions. First,
to demonstrate that empathic individuals are more likely to sort onto more empathic
candidates even in the presence of a policy position. This would demonstrate that the
additional information present in a policy position does not eliminate the effect of empathic signalling. Second, to demonstrate that absent an empathic signal, signalling a
policy position has no additional effect on the likelihood that empathic individuals select
a candidate.
To begin, Table 10 shows the frequency of preferences for the left wing candidate
according to their empathy signal and the empathy of respondents. Once again, more
7
A principal components factor analysis indicates a first factor eigenvalue of 2.28, with a mean loading
of .52 and a minimum losing of .47.
22
empathic individuals appear more likely to sort onto the more empathic candidate. Compared to Study 2 (table 5), there is less sorting away from the empathic candidate among
those low in empathy. However, the general pattern appears consistent.
Low empathy
High empathy
No signal
35%
44%
Empathy signal
34%
52%
Table 10: Percentage of low and high empathy voters support candidate 2 when empathy is not
and is signalled. This table shows that support for candidate 2 increases (decreases) for those with
high (low) empathy when empathy is signalled by the candidate
Tables 11 and 12 present results related to our two more critical tests. Two sets of
regressions are estimated. In the first regression, we estimate a model of support for
the left candidate as a function of an empathy signal conditional on being empathic and
conditional upon knowing the policy position of the left candidate. This is the first test.
The results suggest that a candidate who sends an empathic signal is no more likely to win
the votes of individuals above the median on empathy than those below the median. In the
second regression, we estimate a model of support for the left candidate as a function of a
policy signal conditional on the respondent being empathic and the candidate not signally
empathy. Here, as expected, we find no effect for the policy signal. Empathic individuals
do not appear to sort on to candidates who propose more redistributive, helping policies
in the absence of empathic language.
Taken together, this provides some evidence that the effect of candidates sending
empathy signals to empathic voters is not simply as a stand in for policy positions. Indeed,
while we do not find evidence of that an empathy signal has an independent effect in the
presence of a policy position, nor do we find evidence that a policy position alone in the
absence of an empathy signal moves empathic voters.
Table 11: Empathy signalling and support for left candidates, conditional on policy information (logistic regression)
Variable
Empathy signal
Female
Household income
Intercept
Coefficient
0.050
0.088
0.003
0.310∗
N
R2
F (3,220)
Significance levels :
(Std. Err.)
(0.067)
(0.068)
(0.011)
(0.133)
224
0.01
.731
† : 10%
23
∗ : 5%
∗∗ : 1%
Table 12: Policy signalling and support for left candidates, conditional on no empathy
signalling (logistic regression)
Variable
Policy signal
Female
Household income
Intercept
Coefficient
0.166
0.483†
-0.107∗
-0.561
N
Log-likelihood
χ2(3)
Significance levels :
8
(Std. Err.)
(0.266)
(0.269)
(0.045)
(0.508)
243
-161.964
9.935
† : 10%
∗ : 5%
∗∗ : 1%
Discussion and Conclusion
Individual traits matter for politics. This paper suggests that one individual trait, empathy, has unsuspected political implications. When parents or day-care workers teach
young children how to attend and respond to others’ emotions, we do not usually view
them as engaging in a political activity. Yet, even without mentioning political parties,
political candidates, or political issues, they may be shaping the children’s future political preferences. Our results suggest that those who exhibit strong empathic capacities
will be more likely than others to identify with left-wing parties as well as to support
higher spending programs, even at a cost to themselves. Much of this support for greater
government programs comes not as a result of the programs themselves, but because of
empathy’s influence on the political identifications these individuals develop. We have
demonstrated this first through an observational study relying principally on self-report
measures. The results from this study are largely replicated by the behavioural measures
employed in Study 2. The link is further clarified in Study 3.
These results add to our understanding of the relationship between personality and
political behavior. As with notable previous work (e.g. Gerber et al., 2010; Johnston, 1992;
Lenz, 2013; Mondak, 2010), we demonstrate that an individual difference in a key trait
matters for a large schedule of political preferences. Moreover, it does so in a manner which
is theoretically informed by our understanding of the properties of this trait. Empathic
individuals are better able to imagine and feel the pain of others and to exhibit care for
others, so it makes sense that they would demonstrate greater preference for candidates
who exhibit care.
A focus on empathy also helps us gain a deeper understanding of both the causes
and the effects of partisanship. First, we argued that partisanship is informed by one’s
empathic capacity; second, we showed that partisanship mediates part of the effect of
empathy on policy preferences. Given that individual traits tend to be stable among
adults (Knafo et al., 2008), these findings support the characterization of partisanship as
an “unmoved mover” (Johnston, 2006). It also helps us in understanding how partisanship
precedes other political preferences and values (Goren, 2005; Lenz, 2013).
24
Our work has several limitations. First, it is conducted in a single country. Given the
likely conditional effect of the political environment on personality traits (Mondak et al.,
2010), replication of these results in countries with a different array of political choices
would help us understand how consistently empathy is related to political preferences.
Second, our work has considered only a limited schedule of policy preferences, particularly
those related to social spending and, in Study 2, more basic questions of distribution.
Our questions thus tap into the economic dimension of the left-right cleavage. We have
not theoretically entertained or empirically measured the relationship between empathy
and preferences for social and moral policy and issues (Feldman and Johnston, 2014).
Even with these limitations, we have demonstrated that another important individual
difference matters for political preferences. Political science should continue to explore
the importance of such heterogeneities among various populations.
25
9
Appendix A: Additional Results
In this section, we present additional results to demonstrate the construct validity of our
measure of altruism, to demonstrate the robustness of our policy results over each item,
and to present additional components from our mediation analysis.
9.1
Construct validity
The survey contains a unique behavioural measure by which we can test the validity of
our measure of empathy, in addition to the check on gender presented in Section 5.3.
We assume, based on the Empathy-Altruism hypothesis (Batson et al., 2002), that those
who exhibit higher empathy will also reveal greater preferences for altruism To test this,
we rely on an anonymous recipient dictator game embedded in the survey. Respondents
were given the opportunity to share any amount of a $100 prize with an anonymous
recipient. Subjects were told that they would be entered in a draw and that, if they
won, their allocation decision in the dictator game would determine their share of the
prize. Subjects were thus making a decision over expected money. A bivariate regression
suggests that empathy is strongly and positively related to dictator game allocations
(b = 9.40, p = .00, N = 3765).
9.2
Additional Results
The following tables (13-16) demonstrate that our policy preference results hold for each
individual policy item.
26
Table 13: Empathy and prefrences for carbon taxes (OLS)
Variable
Empathy
New Democratic identification
Conservative identification
Liberal identification
Female
Age
Alberta
Prairies
Ontario
Atlantic Canada
Income
College
University
Intercept
Coefficient
0.096†
0.181∗∗
-0.219∗∗
0.152∗∗
-0.003
-0.003
-0.088∗
-0.016
-0.039
0.006
0.023∗
0.050
0.193∗∗
0.231∗∗
N
R2
F (13,1435)
Significance levels :
(Std. Err.)
(0.049)
(0.051)
(0.035)
(0.039)
(0.022)
(0.008)
(0.041)
(0.044)
(0.027)
(0.039)
(0.009)
(0.035)
(0.033)
(0.067)
1449
0.126
15.915
† : 10%
∗ : 5%
27
∗∗ : 1%
Table 14: Empathy and prefrences for free university (OLS)
Variable
Empathy
New Democratic identification
Conservative identification
Liberal identification
Female
Age
Alberta
Prairies
Ontario
Atlantic Canada
Income
College
University
Intercept
Coefficient
0.200∗∗
0.145∗∗
-0.074∗∗
0.049†
0.010
-0.028∗∗
-0.061∗
-0.049
-0.002
-0.015
0.013∗
0.028
0.051∗
0.229∗∗
N
R2
F (13,1864)
Significance levels :
(Std. Err.)
(0.033)
(0.033)
(0.024)
(0.027)
(0.015)
(0.005)
(0.028)
(0.030)
(0.018)
(0.026)
(0.006)
(0.024)
(0.022)
(0.045)
1878
0.082
12.773
† : 10%
∗ : 5%
28
∗∗ : 1%
Table 15: Empathy and prefrences for cancer drugs (OLS)
Variable
Empathy
New Democratic identification
Conservative identification
Liberal identification
Female
Age
Alberta
Prairies
Ontario
Atlantic Canada
Income
College
University
Intercept
Coefficient
0.106∗
0.184∗∗
-0.064∗
0.064†
-0.041∗
0.022∗∗
-0.047
-0.005
-0.066∗∗
-0.020
0.031∗∗
0.042
0.107∗∗
0.148∗
N
R2
F (13,1390)
Significance levels :
(Std. Err.)
(0.045)
(0.046)
(0.033)
(0.036)
(0.020)
(0.008)
(0.037)
(0.043)
(0.025)
(0.036)
(0.009)
(0.032)
(0.030)
(0.063)
1404
0.062
7.095
† : 10%
∗ : 5%
29
∗∗ : 1%
Table 16: Empathy and prefrences for reduced wait times (OLS)
Variable
Empathy
New Democratic identification
Conservative identification
Liberal identification
Female
Age
Alberta
Prairies
Ontario
Atlantic Canada
Income
College
University
Intercept
Coefficient
0.122∗∗
0.031
-0.019
0.070∗
0.010
0.006
-0.019
-0.034
-0.001
0.003
0.005
0.037
0.031
0.291∗∗
N
R2
F (13,1789)
Significance levels :
(Std. Err.)
(0.037)
(0.037)
(0.028)
(0.030)
(0.017)
(0.006)
(0.032)
(0.034)
(0.020)
(0.029)
(0.007)
(0.027)
(0.025)
(0.052)
1803
0.016
2.25
† : 10%
∗ : 5%
30
∗∗ : 1%
10
Appendix B: Mediation Results
The following tables demonstrate logit regressions of each partisanship on empathy and
the schedule of control variables in Table 2.
Table 17: Empathy and Conservative Partisanship (logit)
Variable
Empathy
Female
Age
Income
College
University
Alberta
Prairies
Ontario
Atlantic
Intercept
Coefficient
-0.517∗∗
-0.540∗∗
0.117∗∗
0.078∗
-0.083
-0.531∗∗
0.453∗∗
0.063
-0.061
-0.309†
-0.660∗
N
Log-likelihood
χ2(10)
Significance levels :
(Std. Err.)
(0.195)
(0.089)
(0.033)
(0.038)
(0.131)
(0.126)
(0.152)
(0.174)
(0.105)
(0.165)
(0.275)
3799
-1843.546
132.092
† : 10%
31
∗ : 5%
∗∗ : 1%
Table 18: Empathy and Liberal Partisanship (logit)
Variable
Empathy
Female
Age
Income
College
University
Alberta
Prairies
Ontario
Atlantic
Intercept
Coefficient
0.433∗
0.195∗
0.060†
0.117∗∗
0.135
0.233†
0.082
-0.136
0.482∗∗
0.331∗
-2.905∗∗
N
Log-likelihood
χ2(10)
Significance levels :
(Std. Err.)
(0.191)
(0.085)
(0.032)
(0.037)
(0.146)
(0.137)
(0.172)
(0.194)
(0.108)
(0.156)
(0.283)
3799
-1943.842
72.852
† : 10%
∗ : 5%
∗∗ : 1%
Table 19: Empathy and New Democratic Partisanship (logit)
Variable
Empathy
Female
Age
Income
College
Education
University
Alberta
Prairies
Ontario
Atlantic
Intercept
Coefficient
0.696∗∗
0.296∗∗
-0.148∗∗
-0.292∗∗
0.000
-0.356∗
0.055
-0.813∗∗
-0.029
-0.407∗∗
-0.491∗
-1.266∗∗
N
Log-likelihood
χ2(10)
Significance levels :
(Std. Err.)
(0.253)
(0.113)
(0.040)
(0.047)
(0.000)
(0.177)
(0.160)
(0.242)
(0.200)
(0.129)
(0.197)
(0.338)
3799
-1251.44
109.431
† : 10%
32
∗ : 5%
∗∗ : 1%
This table demonstrates the effects of empathy on policy preferences absent controls for
partisanship.
Table 20: Empathy and policy preferences without partisanship (OLS)
Variable
Empathy
Age
Income
Alberta
Prairies
Ontario
Atlantic Canada
College
University
Intercept
Coefficient
0.159∗∗
-0.006
0.014∗∗
-0.074∗∗
-0.027
-0.025†
-0.005
0.024
0.088∗∗
0.247∗∗
N
R2
F (9,1975)
Significance levels :
(Std. Err.)
(0.025)
(0.004)
(0.005)
(0.022)
(0.024)
(0.014)
(0.020)
(0.019)
(0.017)
(0.031)
1985
0.059
13.848
† : 10%
33
∗ : 5%
∗∗ : 1%
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