‘Nanny State’ Politics: Causal Attributions about Obesity and Support for Regulation
Donald P. Haider-Markel, University of Kansas,
Mark R. Joslyn, University of Kansas
&
Matthew Miles, Brigham Young University--Idaho
Department of Political Science
1541 Lilac Lane, 504 Blake Hall
University of Kansas
Lawrence, KS 66044
Email: dhmarkel@ku.edu
Phone: (785) 864-9034
Fax: (785) 864-5700
Abstract: Individuals develop causal stories to explain the world around them, including events,
behaviors, and conditions in society. These are narratives that attribute causes to controllable
components, such as individual choices, or uncontrollable components, such as broader forces
in the environment. We employ attribution theory to understand how group identity and
individual characteristics may shape causal attributions about obesity. Based on previous
empirical findings we subsequently argue that attributions can explain support for government
regulation of the food industry. We test our hypotheses using individual level data from several
national surveys of American adults. Our findings suggest that group identities and individual
characteristics predispose people to make certain attributions about obesity, and these
attributions are correlated with attitudes towards government regulation. We suggest that the
issue of obesity is becoming increasingly politicized, making the obesity crisis in America more
difficult to address through private or public actions.
Manuscript prepared for presentation at the Florida State University Symposium on Regulation
in the States, February 20-21, 2014, Tallahassee, FL.
1
We spend considerable time seeking to understand how conditions and events come to
be. This effort to attribute causes underlies much of the debate on any given policy issue and
structures the opportunities or roadblocks for government action. Policy choices are often
constructed around a causal theory that suggests proximate causes for given problems. People
search for causes not only for understanding but also to punish, compensate, protect, blame,
and legitimize (Brandt 2012; Stone 1997). Political actors in fact utilize causal inferences as tools
to shape alliances and allocate costs and benefits; and as some issues increase in salience, our
efforts to attribute cause increase as we strive to understand potential solutions.
Take for example the rise in obesity and the related increase in child and adult diabetes.
By some this is viewed as a public health epidemic, requiring immediate government
intervention. Others recognize the problem but balk at the notion that the issue doesn’t reside
in the choices made by individuals (Barry, Brescoll, and Gollust 2013). Indeed, some even go so
far as to suggest individual choice is more important to protect from the ‘Nanny State’ than it is
to protect individual health (Harsanyi 2007).
In our ongoing search for causal attributions individuals may use a variety of cues,
including ideology and social groups, as a means for reducing the attributional options. Indeed,
social identity theorists have long suggested that individuals use their reference group
narratives to interpret and understand the world around them. Those with a stronger group
identity will be more likely to share elements of group consciousness and thereby have a shared
understanding of events. But even when group cues are unclear or lacking on a particular issue,
those with a strong group identity are more likely to evaluate an issue in ways that are relevant
to their group (Conover 1988).
2
In addition, as issues become politically salient social identity groups are more likely to
stake out positions on the issue to gain social or political advantage (Haider-Markel and Joslyn
2013). In the case of political parties and their identifiers, communicating the group’s position
on salient issues can be especially relevant (Green, Palmquist, and Shickler 2002; Layman,
Carsey, and Horowitz 2006). And as party identities shape attributions, attributions are also
then likely to shape attitudes towards a targeted group as well as potential policy options and
policy preferences (Barry et al. 2013; Brandt 2012; Haider-Markel and Joslyn 2008).
Our project explores the role of social identities and individual characteristics in shaping
attributions about obesity and how this role may have evolved in recent years. Beyond group
identification we also examine the role of self-serving attributions, or predispositions paint an
individual’s current status or condition in a more positive light. We then use attributions about
obesity to explain emotions attitudes towards overweight individuals and, subsequently, policy
proposals advocated to reduce obesity in society.
For each of these analyses we examine data from a variety of surveys of American
adults conducted between 2003 and 2013. We begin our discussion with a review of
attribution theory and its particular application to understanding obesity and potential policy
responses. Our analysis suggests that partisan identities have increasingly shaped attributions
about obesity while self-serving attributions are a consistent predictor. Additionally, partisan
identities and attributions shape attitudes towards the obese and potential policy responses
concerning obesity in society. We argue that our discussion and results favor developing a
stronger link between social identity theory and attribution theory.
3
Attribution Theory and Social Identity
In his formulation of attribution theory Heider (1944, 1958) emphasized the importance
of a controllable, predictable world. People strive to control their environments, and to
understand what conditions give rise to a specific behavior or outcome allows individuals to
anticipate and respond accordingly (Wortman 1976). People are driven by a practical concern
to stabilize and simplify their environments. Presumably knowing what causal factors give rise
to specific outcomes allows people to control the likelihood of that outcome, or at least
forecast its emergence.
In addition, attribution theory suggests that people behave the way they do because of
who they are, and because of the types of circumstances that give rise to their behavior. When
people formulate an attribution about another individual, for example, they are determining
whether behavior is derived from some external or situational element that influences the
person or from internal or dispositional forces. Namely, did the Senator speak favorably about
the environment because she truly believes in protecting the environment (dispositional), or
was she simply pandering to a liberal audience (situational)? Does sexual orientation emerge
from social conditions that may foster a specific preference or are people born with an
identifiable genetic disposition? Additionally are racial differences in income due to social and
political conditions that disadvantage African Americans or are racial distinctions derived from
genetics differences? Each question concerns the contribution to behavior of
situation/external forces and dispositional/ internal forces; and this duality is a key feature of
attribution theory.
4
The distinction between external and internal forces in fact led political scientists to
examine partisan and ideological attributions about a variety of public policy matters. For
example, researchers discovered that Democrats and Republicans employed different causal
explanations for poverty (Iyengar 1991; Pellegrini et al. 1997). Democrats attributed poverty to
environmental factors – institutional biases and market forces – whereas Republicans believed
that individuals were to blame for their poverty. Researchers also found that conservatives
attributed poverty to individual dispositions while liberals perceived causes in situational
sources (Griffin and Oheneba-Sakyi, 1993; Zucker and Weiner 1993). Hughes and Tuch (2000)
discovered a similar relationship, showing that structural attributions, as opposed to
individualistic, were important predictors of support for race-based policies such as affirmative
action and welfare. Evidently, to regard government as the solution, one must first perceive
larger environmental factors as the cause of the problem. As Moscovici (1984, 50) aptly
observed, “…in societies we inhabit today, personality causality is a right-wing explanation and
situation causality is a life-wing explanation.”
These studies suggest that it is particularly valuable to conceptualize causal attributions
in their functional role (Forsyth 1980). Motivated to attribute in a manner consistent with
political predispositions, people select the attribution that squares with their priors (HaiderMarkel and Joslyn 2008; Joslyn and Haider-Markel 2012). A Republican’s tendency to blame the
poor for their poverty justifies a limited role for government and reinforces the cherished
individualism ethic. Democrats, by contrast, view the poor as victims of larger social and
market forces and thus government is the vehicle through which inequality can be addressed.
In both instances, causal attributions arise from and justify existing beliefs. Moreover, causal
5
attributions appear to be utilized by political actors as “….essential political instruments for
shaping alliances and for settling the distribution of benefits and costs” (Stone 1997; 189).
These factors highlight the political implications of causal attributions and the need to examine
genetic attributions, an increasingly popular and important category of causal reasoning.
Related research on social identity emphasizes the role of inter-group dynamics in
shaping attributions. There is a strong tendency for in-group members to attribute positive
characteristics to fellow members while the out-group receives less charitable and often
negative attributions (Allport 1954; Brewer 1999; Tajfel and Turner 1986). Out-group behaviors
are frequently attributed to negative internal dispositions with important situational factors
ignored. A positive behavior by the in-group may be viewed as a general disposition by ingroup members but the same behavior by the out-group will be considered a special case and
often attributed to chance (Tygart 2000; see also Pettigrew 1979, 1980).
Strong group identity can also shape attributions that then influence behavior. For
example, Miller et al. (1981) provided evidence that people who attributed systemic forces to
their group’s relatively disadvantaged position in society were more likely to vote than people
who attributed their group’s disadvantage to individual-level factors.
Interestingly, the development of a social identity connected to a referent group is
associated with the availability and communication of a politicized collective identity. Such an
identity is salient to individuals when they see themselves as “self-conscious group members in
a power struggle on behalf of their group” (Simon and Klandermans 2001, 319).
“When issues contain clear cues for a group that a person identifies with, the
assessment of the issue will probably be biased in the pro-group direction. Where
6
group cues are weak, people with strong group identifications may, none the less,
evaluate an issue in group terms relevant to them; consequently, various individuals
may be influenced by different group identifications on the same issue (Conover 1988,
63).”
From this perspective, collective group identity is social constructed and communicated
to group members. This collective identity contains a worldview with attributions that explain
the position of the group in society as well as the positions of other groups. For individuals that
identify with the group, this communicated worldview can shape attributions about conditions
or events that may only indirectly be related to the group (Conover 1988).
This might be especially true for explaining situations in areas where many people feel
uncertain, such public health problems, including obesity. Social psychologists believe that
when people face uncertainty about causal relationships, they often apply causal schemas.
Causal schemas derive from past experiences as well as communicated collective group identity
and are “…general conceptions the person has about how certain kinds of causes interact to
produce a specific kind of effect” (Kelly 1972, 151; see also Conover 1988). The schema then
provides a cognitive short-cut, which when applied shapes causal attributions.
Additionally, some attribution studies point to the power of language and social context
to effect attributions. Causal inference can turn on how an event or condition is characterized
and the dynamics of social communications (Antaki and Naji 1987; Fiedler and Semin 1988;
Slugoski 1983). Recent developments in public opinion research offer similar conclusions.
Because individuals typically rely on heuristics, which can include group cues, to compensate
for their lack of knowledge, the choice of words used to describe an issue can significantly
change attitudes and opinions about that issue (Cobb and Kuklinski 1997). Specific messages or
7
issue frames have the potential to prime respondents, making certain considerations more
accessible in memory and thereby influencing how people understand a given issue (Iyengar
and Kinder 1987). Applying this logic to causal attributions, Iyengar (1991) demonstrated that
the manner in which TV news frames stories of poverty, crime, and terrorism led audiences to
blame poverty on individual as opposed to societal level sources. Likewise, recent research
suggests that the manner in which child obesity is framed can influence support for proposed
policy solutions (Barry et al. 2013)
Related to these findings is the well-known axiom that the public’s attention to politics
is intermittent and the causal logic characterizing political affairs is unstable (Stone 1997). As a
consequence people seek political authorities to reduce their uncertainty. And, it appears that
group leaders, political party elites, and other political elites are well equipped to perform this
role, providing talking points and ready-made accounts that influence the public’s causal
reasoning (McGraw 2001; Schneider and Jacoby 2005).
Groups and Attributions about Obesity
To summarize, this line of research in political science and social psychology suggests
that attributions are not simply matters of the mind but rather social, political, and
communicative in nature. Causal inferences are sensitive to social context, media content and
political elite explanation. The very causal schema that people apply under uncertainty may in
fact derive from past exposure to group communications. We in fact suggest that group
identification implies specific causal schema.
8
In thinking about why some people overweight while others are not, we are faced with
basic attribution questions. Likewise, when public health officials declare an ‘epidemic of
obesity’ in some populations, we are pressed further to think about societal solutions that are
dependent on a casual theory of attribution. Should we attribute obesity to family history
and/or genetics, or should we attribute it to individual choices about diet and exercise? As a
potential public health issues, should the causal theory about increased obesity rates be linked
to family history and/or genetics, to individual choices, or to a social context where inexpensive
high-fat food is readily available and inexpensive? Our path to this answer suggests that our
group identities and individual characteristics help shape our answers to these questions.
Our primary focus here is on partisans as a social identity group and weight-based
groups based on their potential individual based incentive to make particular attributions.
Among partisans attributions can be particularly polarizing. Existing research suggests that
Democratic perspectives on causes typically do not focus on individuals or individual choices. In
the Democratic worldview individuals have agency, but more often than not are subject to
forces outside their own control. Those forces might be based in biology, social context, or
some combination (Haider-Markel and Joslyn 2008). The Republican worldview on causes is
almost completely opposite; Republicans tend to put the locus of control precisely on the
individual and argue that an individual condition or event is simply a result of personal choices
and motivation (Joslyn and Haider-Markel 2012).
At the same time those who are overweight have some incentive to attribute their
situation to forces outside of their control, such as genetics, biology, or the social context—in
other words, outside of individual control. Those without weight problems, meanwhile, have
9
self-esteem reinforcing incentives to suggest their condition is a function of individual choices in
food intact and exercise, framed even more persuasively as self-control or self-discipline. Such
incentives are called self-serving attributions and function to bolster self-esteem in regards to
an individual’s situation.
Although women as a group are no more likely than men to have weight issues, we do
know that women are inclined to make attributions that suggest broader forces are the cause
of some event or phenomenon and not the result of actions by an individual (Gilligan 1983).
Indeed, Worden (1993, 206) suggests that women tend to develop an “ethic of care,” with a
view that “society as an interdependent and interconnected web of personal relationships.” In
this perspective events and conditions are the result of broader systematic forces, generally
outside the control of one individual. Such causes could be hereditary or biological, or could
result from a broader social context. Men, meanwhile, have an “ethic of justice,” which tends
to focus on self-determination, self-discipline, and personal physical strength as driving forces
in determining outcomes (Gilligan 1982, 166; Stanko 1990, 110). As such, we would expect
women to place causes for obesity outside of the control of the individual while men should be
more likely to attribute obesity to individual choices and behavior.
Issue Salience and Partisanship
We first briefly explore the salience of the issue and the role of partisanship. Recall that
we expect political parties to stake out positions on salient issues and communicate those
positions to their members, thus creating differences between partisan identifiers.
10
A quick search of the term ‘over weight’ using Google Trends news headlines from 2004
to 2014 indicates that the issue has been very salient over the past decade with a general trend
in increasing salience.
[Insert Figure 1 About Here]
Second, a search of the RoperExpress Data Base reveals few questions asking about
obesity until after 2005. Only one poll asked about attributions before obesity before 2006 and
the first poll to ask about genetic causes for obesity is a 2006 poll from the Pew Research
Center.
Given our discussion above we expect that earlier polling on the causes of obesity are
unlikely to uncover partisan differences in attributions, while more recent polls should uncover
such differences with the onset of partisan competition on the issue.
Making use of data from a March 2006 Pew Center for the People and Press survey of
American adults we can see that partisans did not make distinctly different attributions about
obesity. Pew asked: “For each item I name, please tell me how important this is as a reason
many Americans are very overweight—very important, somewhat important, not too
important, or not at all important: Genetics and hereditary factors?” A simple Pearson’s
correlation between party identification and respondents answers reveals a correlation of .014
(P value = .269). Likewise when asked about the following reason “Lack of willpower about
what to eat,” a simple Pearson’s correlation between party identification and respondents
answers reveals a correlation of .011 (P value = .740). Therefore, as recently as 2006 there was
no statistically significant difference between partisan identifiers and attributions for obesity.
11
However, as we will see partisan predispositions about obesity have become more established
just as a variety of social groups have begun to think about the potential policy choices to
address an apparent growing obesity problem in the U.S.
Recent Attributions about Obesity and Implications
Our next step is to more fully explore the influence of social identities on attributions
about obesity and the subsequent influence on attitudes towards overweight people and policy
prescriptions for addressing obesity. To do this we make use of a unique dataset based on a
June 2013 survey of American adults. A probability sample of 1,596 subjects was recruited by
the authors through Research Now to participate in a study measuring political attitudes. Of
these, 27% were Republicans and 32% were Democrats. The survey was fielded online from
June 18-June 21, 2013. The sample resembles the U.S. population in most respects, including
age, gender, income and political interest, but leans Democratic, liberal, and attentive to
politics (see methodological Appendix); we believe this left-leaning sample will allow for a more
conservative test of our central propositions.
Dependent Variables
Our first dependent variable is an attribution for being overweight. Our unique survey
asked respondents “Thinking about the reasons some people are significantly overweight or
obese, do you think it is due more to: Genetic factors a person is born with (14%) or, Eating and
lifestyle habits (86%).” When limited to two choices respondents overwhelming made an
individualistic attribution that placed the locus of control on the individual and not on her
12
biology or genetics. However, a large enough number of respondents (218) did make a
biological attribution for a reasonable comparison between groups.
Our second set of dependent variables capture emotional responses or attitudes
towards overweigh people. We asked respondents “How unsympathetic or sympathetic are
you towards obese people? Very unsympathetic, Somewhat unsympathetic, Somewhat
sympathetic, or Very sympathetic?” Later in the survey we also asked “How angry are you
towards obese people? Very angry, Somewhat angry, or Not at all angry?” We also posed the
following question as a feeling thermometer questions: We’d like to get your feelings toward
some of our organizations and groups who are in the news these days. We’d like you to rate
groups using something we call the feeling thermo meter. Ratings between 50 degrees and 100
degrees mean that you feel favorable and warm toward the group. Ratings between 0 degrees
and 50 degrees mean that you don't feel favorable toward the group and that you don't care
too much for that group. You would rate the group at the 50 degree mark if you don't feel
particularly warm or cold toward the group.” We expect that those making a genetic
attribution will be more likely to show sympathy, have higher feeling thermometer scores, and
will be less likely to show anger towards overweight people.
Our third set of dependent variables examines preferences towards policies directed at
overweight people and/or the rising level of obesity in society. We first asked: “Do you think
companies should be allowed to refuse to hire people just because they are significantly
overweight, or not? Yes, should, or No, should not.” Next we asked a series of policy related
questions and the extent to which the respondent favor or opposed each on a four point scale:
1) How you strongly would you favor or oppose holding the fast food industry legally
13
responsible for the diet-related health problems of people who eat fast food on a regular
basis?, 2) How you strongly would you favor or oppose government regulations that limit
saturated fat content in food?, and 3) How you strongly would you favor or oppose an extra tax
on foods that are high in saturated fat content? Each of these questions is coded with strongly
favor as four and strongly opposed as one.
Independent Variables
Our primary independent variable for the attitudes toward overweight people and the
policy questions is the response to our question about attributions.
Recall group identification variables are central to our analysis of understanding
attributions. Our survey did not include any strength of identity questions but it did ask
respondents to classify themselves based on a number of categories, including race and
ethnicity, gender, income and class, and partisanship. For our models predicting attributions
we include variables accounting for each of these groups.
Here we are primarily interested in party identification and gender. Party identification
is measured on respondent self-placement on a seven-point scale. Since responses from
Democrats and Independents were similar, we recoded the measure with all Republicans coded
as one and all others coded as zero. We expect that Republicans will be less likely to make a
biological attribution and be less likely to support government policies intended to reduce
obesity.
Race is measured with a dichotomous variable coded one if the respondent indicated
Caucasian for race, and zero otherwise. We measure gender with a variable coded one for
14
female and zero for male. We have no clear expectations for race but given that women are
more likely to identify attributions that are outside of the control of an individual (HaiderMarkel and Joslyn 2008; Weiner et al. 2011), we expect that women will be more likely to make
a biological or genetic attribution. Women should also be more likely to support government
policies intended to reduce obesity.
We also account for respondent education level, and age. Education is simply a seven
point scale with ‘grade eight or less’ coded as one, and ‘post-graduate training’ coded as seven.
Age is the respondent age in categories with 18-25 as one, and 65 and older as six, with the
remaining categories 26 to 64 as exclusive nine to ten year categories. Although the more
educated tend to make non-individualistic attributions, on some issues, such as attributions for
success in life, they do make self-serving individualistic attributions (see Haider-Markel, Joslyn,
and Miles 2013). As such we have no clear expectations for education level or for age.
Finally we asked respondents about self-assessed weight; we asked: “In your view are
you: Very underweight, Underweight (weigh less than you should), About the right weight,
Overweight (weigh more than you should), or Very overweight?” Almost 51 percent of the
respondents considered themselves at least overweight. For the analysis we coded those
indicating they are about the right weight or underweight as zero and those who indicated
overweight or very overweight as one. We expect to observe a self-serving attribution whereby
overweight people would prefer – and are motivated to select – the genetic attribution, as that
cause removes much of the blame for one’s own obesity and may in fact justify the status quo
individually and in society.
Results and Discussion
15
Attributions and Emotions towards Overweight People
First we examine how attributions might influence emotional reactions to persons who
are overweight or obese. We asked respondents “How unsympathetic or sympathetic are you
towards obese people? Very unsympathetic, Somewhat unsympathetic, Somewhat
sympathetic, or Very sympathetic?” Later in the survey we also asked “How angry are you
towards obese people? Very angry, Somewhat angry, or Not at all angry?”
The genetic attribution implies less control over behavior and therefore more positive
emotional states toward the obese. By contrast, a lifestyle attribution suggests volition and
thus control – hence negative feelings toward obese. Therefore we expected that we should
observe higher reports of sympathy and less anger toward obese people among those who
attribute obesity to genetics than among those who attribute obesity to lifestyle.
This is indeed what we observed: among those making genetic attributions we saw
much higher levels of sympathy (81%) and non-anger (88%) than among those who made
lifestyle attributions (55% sympathy and 82% non-anger). Perhaps this is intuitive: if obesity is
not perceived as controllable then people feel greater sympathy toward others with the
condition and certainly do not hold malice. By contrast, lifestyle attributions suggest a choice
and people carry at least some anger (18%) and lack sympathy (45%) towards overweight
people.
Particular attributions clearly generate different emotional responses towards
overweight people. But to what extent do these attributions and emotional responses shape
policy preferences? We asked respondents “Do you think companies should be allowed to
16
refuse to hire people just because they are significantly overweight, or not? Yes, should (25%),
or No, should not (75%).” A simple path analysis reveals a -.05 correlation between making a
genetic attribution and anger at obese people, while there is a .20 correlation between being
angry at obese people and believing companies should be able to discriminate in hiring obese
people.1 Likewise there is a .17 correlation between making a genetic attribution and feeling
sympathy toward obese people while there is a -.27 correlation between feeling sympathy
toward obese people and believing companies should be able to discriminate in hiring. Finally,
the direct correlation between making a genetic attribution and believing companies should be
able to discriminate in hiring is -.09, so relatively weak compared to the emotional responses
and support for discrimination.
Next we examined the relationship between attributions and ratings of obese people on
the Feeing Thermometer, and subsequent belief that companies should be able to discriminate
in hiring obese people.2 A path analysis reveals a .12 correlation between a biological
attribution and the Feeing Thermometer rating. The correlation between a respondent’s
Feeling Thermometer rating and a belief that companies should be able to discriminate in hiring
obese people is -.25. Thus, it is clear that attitudes towards obese people are shaped by
attributions and these attitudes subsequently can shape attitudes about the behavior of private
companies. We turn now to multivariate analysis and broader policy proposals meant to
address obesity in society.
Multivariate Analysis
1
2
All reported correlations are statistically significant at p <.05 or better
All reported correlations are statistically significant at p <.05 or better
17
Table 1 provides estimates derived from each equation. We first predict attributions
about obesity using our independent variables and then we add the attribution response to our
models predicting policy preferences.
The results for the model predicting the likelihood of making a genetic attribution
about obesity are displayed in column one of the table while the results predicting policy
preferences are displayed in the remaining four columns. As predicted partisanship is a
significant predictor of attributions with Republicans significantly less likely to make a genetic
attribution for why some people are overweight. Meanwhile, respondents that consider
themselves at least somewhat overweight are more likely to make a self-serving attribution
that biology determines weight. Our other predictors do not appear to significantly influence
the likelihood of making a particular attribution.
[Insert Table 1 about Here]
Turning to policy preferences in column two of Table 1 the results indicate that making a
genetic attribution and partisanship influence the likelihood of making a particular policy
preference. Respondents making a genetic attribution for obesity were significantly less likely
to indicate that private employers could refuse to hire someone who was obese. Meanwhile,
Republicans were significantly more likely to indicate that private employers could refuse to
hire someone who was obese. These results are robust and remain even if we include the
attitudes towards obese people discussed earlier, suggesting that attributions and partisanship
are powerful predictors of support for employment discrimination.
18
The results also indicate that respondents that classify themselves as overweight are
less likely to support employment discrimination as are women. Both results confirm our
expectations. Interestingly those older respondents and those with more education were
significantly more likely to support private companies discriminating against overweight
individuals. Although these results were not expected, it could be that older respondents and
the more educated are attempting to factor in the insurance and lost productively time
resulting from hiring overweight employees. Unfortunately our data do not allow us to explore
this issue in greater detail.
Column three displays the results of our model predicting whether respondents
supporting hold the fast food industry legally responsible for obesity. Here our results begin to
differ with our expectations. Although Republicans were more likely to oppose this policy
measure, those making a genetic attribution for obesity were no more likely to oppose the
measure than those attributing obesity to lifestyle choices. And perhaps even more
surprisingly, respondents indicating that they are overweight were somewhat more likely to
oppose holding the fast food industry legally responsible than were respondents that are not
overweight. Since overweight individuals were more likely to make genetic attributions it could
be that these same individuals have exonerated the fast-food industry from any culpability for
their situation and are therefore even more likely than other respondents to oppose legal
sanctions. It also stands to reason that the attribution choices we provided: a biological one or a
personal choice attribution, do not point to a contextual attribution that might include the
context of what food is available to the consumer. In hindsight it may have been preferable to
19
provide an attribution option for respondents that was completely contextually based and was
not linked to biology or individual choices or behavior.
The results for the final two policy questions: favoring government regulations on
saturated fat and favoring higher taxes on foods with saturated fats, reveal results similar to the
model for fast food industry policy. However, there are a few notable differences. Although
Republican opposition to these measures is consistent, overweight individuals are more likely
to disapprove of government regulations on saturated fat, but are no more likely than others to
oppose increased taxes on foods higher in saturated fat. So while overweight individuals, who
disproportionally attribute obesity to biology, do not support increased regulation, they are not
likely to oppose higher taxes on unhealthy food more than anyone else.
More educated respondents were meanwhile no more likely to support regulation, but
were more likely to support higher taxes on foods with higher saturated fat. We could perhaps
infer that the more educated support an incentive-based policy in this area since they are no
more likely to attribute obesity to biology or lifestyle than the less educated. And given the lack
of certainty about causes, an incentive-based system might be easier to support than a
regulatory system that assumes a contextual or lifestyle choice underlying cause.
In summary, our analyses reveal that the influence of partisan weight-based identities
shape attributions and some policy preferences. In fact, the partisan influence we observe on
attributions for obesity extends to preferences on discrimination by private companies and
support for government policies intended to curb obesity. The limitations of our data perhaps
do not reveal the full extent to which attributions can shape policy preferences or the extent to
20
which a weight-based identity might shape preferences for policy, but our initial results do
provide some directions for additional research.
Conclusions
People naturally seek to understand why conditions, behaviors, and events come to be;
we try to make sense of the world and attribute causes to reduce our own uncertainty.
However, in our attempts to understand we face considerable uncertainty about causes and
often turn to others for ready-made explanations. Social identity theorists suggest that groups
communicate schemas or worldviews that do indeed provide causal attributions. Individuals
that identify with these groups are therefore more likely to adopt the group’s causal
attributions.
Our research engaged this perspective and sought to combine attribution theory with
social identity theory to understand the attributions individuals make about the determinants
of obesity. We employed data from a 2006 random sample national survey of adults as well as
a unique 2013 survey of American adults to examine whether partisan and weight-based
identities influence the likelihood of making biological attributions about obesity and whether
these attributions, along with the relevant identities, continued to shape policy preferences
related to attempts to reduce obesity in society. Our analyses allow us to draw several
important conclusions.
First, our results support our linkage between attribution theory and identity theory by
indicating that partisanship and weight-based identity influence the likelihood of making a
particular attribution. We demonstrated that Republicans are more likely to attribute being
21
overweight to individual lifestyle choices while overweight individuals are more likely to
attribute being overweight to biology or genetics. In particular the powerful role of partisan
identities is consistent with existing theory and empirical evidence about attributions (HaiderMarkel and Joslyn 2008; Joslyn and Haider-Markel 2012; Weiner et al. 2011).
Second, attributions shape emotions towards overweight people. Those making
lifestyle choice attributions for obesity were more likely to have more negative feelings towards
the obese, more likely to unsympathetic, and more likely to feel actual anger. IN turn we
provided evidence that attributions shape support for hiring discrimination towards obese
people, both directly, and indirectly, through shaping emotions towards these individuals.
Third, attributions for obesity are powerful predictors of support for discrimination by
private companies; those who attribute obesity to biology are significantly less likely to support
hiring discrimination by private firms. And partisans differ here as well; Republicans were
significantly more likely to support private discrimination of obese individuals than were
Independents or Democrats, even controlling for attributions.
Finally, our analysis revealed that gender and education do not predict attributions or
policy support precisely as we expected. Indeed, our analysis highlights the need for more
precise measures of attributions for obesity as well as additional measures of policy
preferences. Implicit in our findings is the notion that individuals might also make contextual
attributions that may or may not be outside of the control of individuals. We believe that
additional research, and more precise survey questions, can better capture the nuances of how
obesity, and its causes, has become politicized and how attributions about obesity help to
shape the potential policy solutions to what is a documented health epidemic.
22
Methodological Appendix
A probability sample of 1,596 subjects were recruited by Research Now to participate in a study
measuring political attitudes. The study was fielded from June 18-June 21, 2013
Members of the panel complete an extensive member profile survey before they are selected
to participate in a particular research project. In addition, Research Now employs a digital
fingerprinting technology that prevents more than one person from completing a survey from
the same computer, and they use Geo-IP validation to ensure that the computer used matches
the geographic location of the survey respondent. Participants are recruited using the eRewards invitation only methodology. Those who have not received an invitation to participate
via email, are blocked from participation in the survey. The AAPOR response rate to this study
was 11.87%.
The demographic characteristics of this panel closely resemble that of the United States
population on several important traits. Table A.1 displays the demographics of this sample
compared to MTurk samples (adapted from Berinsky, et al. 2012) and the Annenburg National
Election Study (Johnston, et al. 2008).
Survey Demographics
Demographics
Female
Age (mean years)
Education (% completing
some college)
White
Black
Asian
Latino (a)
Multi-Racial
Party Identification
Democrat
Independent
Republican
N
June 2013 survey
51.65%
50.5
31.43%
MTurk
60.1%
20.3
-
NAES 2008
56.62%
50.05
62.86%
80.9%
5.31%
5.25%
4.95%
2.71%
83.5%
4.4%
-
79.12%
9.67%
2.53%
6.3%
2.37%
31.98%
41.18%
26.85%
1,596
40.8%
34.1%
16.9%
484-551
36.67%
20.82%
30.61%
19,234
23
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30
Table 1.
Predicting Attributions for Obesity and Related Policy Preferences.
Overweight
Because of
Genetics
Companies
can refuse
to hire
Genetic
Attribution
-----
-.720**
(.211)
Republican
-.402**
(.166)
.401**
(.153)
-.204
(.197)
-.003
(.152)
.134
(.078)
.076
(.052)
.427**
(.125)
-.548**
(.123)
.432*
(.182)
-.859**
(.125)
.297**
(.066)
.130**
(.041)
-1.618**
(.388)
-2.498**
(.340)
Independent
Variables
Overweight
White
Female
Education
Age
Constant/cut 1
Fast Food
Legally
Responsible
Favor Gov’t
Regulations
on Fat
Favor Extra
Tax on Fat
Food
.151
(.139)
.098
(.138)
-.034
(.142)
-.836**
(.103)
-.192*
(.096)
-.187
(.130)
-.126
(.097)
-.047
(.051)
-.037
(.033)
-.787**
(.100)
-.213*
(.093)
-.158
(.127)
.271**
(.094)
-.072
(.049)
-.086**
(.031)
-.839**
(.101)
-.167
(.096)
.122
(.129)
.110
(.095)
.216**
(.050)
-.056
(.032)
-1.645
-2.655
-.553
/cut 2
.496
-1.136
1.145
/cut 3
2.500
1.012
2.868
Log Likelihood -610.653
-829.358
-1765.896
-1993.647
-1928.487
Pseduo R-square
.02
.08
.03
.03
.04
Chi Square
19.32
151.03
90.79
108.61
151.35
Notes: Coefficients are Logistic and Ordered regression coefficients; standard errors are in
parentheses. ** p < .01, * p < .05. The data are from a June 2013 random sample survey of American
adults conducted for the authors by Research Now.
31