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Racial Stereotypes, Racial Context, and the 2008Presidential Election∗
Jason H. Windett†
Assistant ProfessorSaint Louis Universityjwindett@slu.edu
Kevin K. BandaPh.D Candidate
The University of North Carolina at Chapel Hillkbanda@email.unc.edu
Thomas M. CarseyThomas J. Pearsall Distinguished Professor of Political Science
The University of North Carolina at Chapel Hillcarsey@unc.edu
September 4, 2013
Abstract
As the first African-American nominee for President of a major political party,Barack Obama’s campaign and ultimate victory reminded voters, scholars, pundits, andthe press of the centrality of race in American political life. Speculation by observersof all types centered around the potential impact of race as an individual psychologicalprejudice and/or as a geographic/contextual factor. These two themes parallel differentleading scholarly treatments of race and racism in the U.S. Rather than choose onetheme or the other, in this paper we bring both traditions together in a unified analysisof white voter response to Obama. We find strong evidence that the level of prejudicetoward African-Americans held by whites affected their evaluations of Obama as wellas their probability of voting for him. In contrast, we find little evidence that whitesresponded to the racial context of their immediate geographic environment.
∗Forthcoming in Politics, Groups, and Identities.†Please direct all correspondence regarding this submission to jwindett@slu.edu.
1
Race has defined political conflict for much of the United States’ history. It presented
a Constitutional crisis at the country’s birth, nearly tore the country apart through civil
war, and re-emerged beginning in the 1960’s as a fundamental cleavage in American party
politics. As an African American, Barack Obama’s successful campaign for the Presidency
in 2008 against a white opponent, John McCain, renewed the discussion of race in American
politics. It also presents scholars with a historic opportunity to explore the impact of race
in electoral politics as voters faced, for the first time, a choice between an electorally viable
African American candidate and a white candidate in the general election for President.
While several commentators and political pundits argued that the Obama victory marked
the beginning of a “post-racial” society in the United States, early analyses of the impact
of Obama’s race on the 2008 election have provided mixed results. For example, Mas and
Moretti (2009) found little evidence that Obama performed differently due to his race com-
pared to John Kerry in 2004 and Congressional Democrats in 2008. In contrast, Ansolabehere
and Stewart (2009) argued that Obama’s race led to an increase in turnout among the black
population, which contributed to his victory.
More nuanced examinations of voting behavior revealed a backlash against Obama among
white voters driven by their racial attitudes. Using an experimental design, Schaffner (2011)
examined the impact of racial salience on vote choice and found that Obama’s race led to
a three percentage point decline in his share of the white vote. Parker, Sawyer and Towler
(2009) found that symbolic racism contributed to evaluations of Obama, though this finding
is produced from data collected in a single state. Lewis-Beck, Tien and Nadeau (2010)
argued that racial resentment felt towards Obama displaced the effect of economic voting
among some voters, costing Obama what could have been a landslide victory.
In the most expansive analyses of the 2008 election, both Piston (2010) and Tesler and
Sears (2010) examined the direct impact of racial prejudice of white respondents on their
2
vote choice. These works use the ANES questions on racial attitudes towards blacks in
order to evaluate the likelihood of voting for Obama. Using data from 1992 through 2008,
both works conclude that the 2008 election is the only presidential election in which racial
sentiments predicted the behavior of white voters. Tesler and Sears (2010, 6) note that “the
two sides of racialization — that is, the racially resentful opposition to and racially liberal
support for Obama — resulted in a considerably larger influence of racial attitudes on the
presidential vote in 2008 than any other campaign in modern history.” Block Jr. (2011)
supports this argument and shows that race actually affected white voters at greater levels
than black voters. He finds that black voters were considerably less likely to emphasize race
when casting their ballots for Obama compared to white voters who voted against Obama.
These findings follow in a long line of research exploring race and voting behavior in the
United States. While few deny the centrality of race in the American political experience,
there remains a surprising degree of uncertainty over how best to understand it. There are
at least two major approaches in the existing literature. One tackles the problem of race
as an individual-level attitude or opinion held by members of one group toward members
of another. The out-group is defined in collective terms, but the engine for a race-based
response lies within the individual’s internal formation of feelings of racial prejudice. A
number of differences exist among scholars working in this general area, but they share in
common an internal psychological response to race.
A second approach views race as an external contextual factor that triggers a response
among members of a particular group. The notion is that members of one group (e.g. whites)
will behave differently depending on the relative size of the population of another group (e.g
blacks) in their surrounding area. With its origins in Key’s 1949 “racial threat” hypothesis,
the critical causal mechanism in this perspective exists outside the individual in the form of
the racial make-up of someone’s surrounding area.
3
These two perspectives are not absolutes nor unrelated. Certainly the racial threat hy-
pothesis rests on an individual reaction to the surrounding context, while psychological
approaches clearly define racism in terms of how individuals respond to other groups. How-
ever, each perspective points to a different locus for racial politics — internalized racism
or external contextually-defined “threats.” As we will show, there are a number of ways to
conceive of how these two perspectives might be integrated into a single model of candidate
evaluation. We employ direct measures of both racial context and racial attitudes in this
research to predict how survey respondents evaluated the two major party candidates for
President as well who they reported voting for in the 2008 election.
Approaches to Race in American Politics
Individual-level Racial Prejudice
The literature on racism within the United States is vast and well-beyond comprehensive
review here. In this section, we limit our focus to the individual-level phenomenon of racial
prejudice as manifest in the ascription of racial stereotypes to African-Americans by whites.
The use of stereotypes to measure prejudice has a long history in the social sciences.1
We first consider the degree to which racial hostility towards African-Americans as a
group persists among whites in the U.S. Sniderman and Carmines (1997) state, “Every
systematic study of long-term trends in American racial attitudes, without exception, has
concluded that race prejudice has dramatically declined since 1940” (p. 65). Of course, the
decline in racial prejudice does not mean that it has disappeared. Using responses of whites
surveyed as part of the 1991 Race and Politics Survey, Sniderman and Carmines (1997) show
that surprisingly large numbers of whites are unwilling to ascribe positive traits to blacks as
1Interested readers should review the citations provided in Levine, Carmines and Sniderman (1999)regarding the study of stereotypes.
4
a group. They further report that anywhere from 25 to 50 percent of white respondents are
willing to ascribe negative traits such as “lazy,” “boastful,” “irresponsible,” “complaining,”
or “aggressive or violent” to blacks as a group (pp.62-3).
Advocates of the symbolic racism perspective argue that the contemporary political cli-
mate leads respondents to provide socially desirable answers to survey questions rather than
directly express racial prejudice (e.g. Kinder and Kiewiet 1981; Tarman and Sears 2005).
This possibility raises some question as to the accuracy of direct survey-based questions.
However, Sniderman and Carmines (1997) convincingly demonstrate through survey-based
experiments that respondents who express positive or negative views of blacks through stereo-
types are accurately reflecting their underlying beliefs. Furthermore, Levine, Carmines and
Sniderman (1999) report that the willingness of whites to apply positive or negative stereo-
types to African-Americans is largely structured by a single dimension, lending credence to
the idea that such stereotype questions tap into a single underlying racial prejudice dimen-
sion.
To what degree do feelings of racial prejudice shape the political attitudes and evaluations
of whites? Citrin, Green and Sears (1990) argue that racial attitudes affect voters’ evalu-
ations of candidates, explicitly racial policies, and even policies that lack an explicit racial
component. More recently, Sniderman and Carmines (1997) show that racism significantly
predicts the views whites hold on a number of policies designed to benefit African-Americans,
but that the impact of racial prejudice was frequently substantively smaller than they ex-
pected.
Race as a Contextual Factor
Key’s 1949 classic study of Southern politics gave birth to the contextual study of race
in American politics both in the South (e.g. Black and Black 1987) and across the country
5
as a whole (e.g. Huckfeldt and Kohfeld 1989). Key’s central contribution in this field has
been termed the “racial threat” hypothesis. Key found that the willingness of whites to
engage in political actions viewed as hostile to the interests of blacks depended on the size
or density of the black population in the surrounding area. The racial threat hypothesis
assumes that whites have political interests that differ from blacks and that larger black
populations represented a political threat to what would otherwise be white dominance of
politics. Thus, the real threat depended on how government would be expected to respond
if African-Americans achieved meaningful political influence. In this regard, Key essentially
argued that whites viewed the potential political threat of blacks as a numbers game.
Key’s original thesis has received substantial support both in and out of the South (e.g.
Wright 1977; Huckfeldt and Kohfeld 1989). Glaser (2003), for example, reports evidence from
experiments showing whites as less supportive of policy benefits being distributed evenly
among residents of an area as the proportion of the population in the area that is black
increases.
Others, however, have found a positive rather than negative response among white voters
to increases in black population densities. For example, Carsey (1995) finds that white
support for black mayoral candidates in New York and Chicago increased as the size of the
black population increased in a precinct. Liu (2003) finds a deracialization effect among
white voters living in majority black areas. Others debate the true effect of racial context on
how whites respond (see for example Giles and Buckner 1993, 1996; Voss 1996), or present
mixed results (e.g. Voss and Lublin 2001). Campbell, Wong and Citrin (2006), for example,
find only limited support for racial contextual effects on white voter behavior regarding three
racially charged ballot initiatives in California.
Oliver and Mendelberg (2000) offer a detailed explanation of the contextual determinants
of racial attitudes and conclude that these attitudes are conditional on a number of variables.
6
First, they observe the overly simplified nature of the earlier analyses on the racial threat
hypothesis. They argue that the contextual effects of race are deeply intertwined with
socioeconomic status. Second, they conclude that the level of analysis is also an important
consideration when analyzing contextual effects. They show no relationship between racial
attitudes and racial composition at the zip code level, but when they expand the scope of
geography to a larger geographic space, racial antagonism is moderately related to the size
of the black population. Hopkins (2010) offers a similar conditional argument for the affect
of the size of nearby immigrant populations on attitudes regarding immigration policy.2
Carsey and Windett (2013) suggest that these conditional—or what might be better
called mixed—findings may result from differences in the expectations whites hold regarding
a perceived “threat” from black political power compared to whites’ actual experiences with
black political leaders. The absence of real experience may lead voters to rely on race as
a low information cue that might shape how they vote (Williams 1990; Terkildsen 1993;
McDermott 1998). Hajnal (2001) argues that actual experience with black city officials led
whites to be more likely to vote for their re-election. However, Carsey and Windett (2013)
find little evidence that prior experience with a black elected official influenced the voting
behavior of whites in five statewide elections held in 2006 that involved an African-American
candidate.
A Unified Approach to Race and Racism
The brief reviews provided in the previous two sections highlight three features of our
current understanding of racial politics in the U.S. First, there exists evidence of a nega-
tive response among whites toward African-Americans at both the individual-level and the
2Blalock (1967) offers a slightly different, though essentially conditional, argument, suggesting that theimpact of the size of a minority population on the views held by others might be non-linear. We exploredthat possibility in this analysis and found no evidence to support it.
7
contextual-level. Whether or not prejudice is on the decline, it remains a potentially pow-
erful force in American politics. Second, empirical results are mixed, particularly regarding
the contextual effect of black population density on the behavior of whites. A multitude
of explanations have been offered for these mixed findings (see Carsey and Windett 2013
for a review), but the more general conclusion is that we do not yet understand how racial
contexts become politically meaningful for individuals immersed in them. Third, vast liter-
atures exist in these two areas of research, but comparatively less has been done to examine
both individual and contextual factors together.
Missing from our discussion thus far is how race becomes politicized in a particular cir-
cumstance. Mendelberg (2001) rightly notes that how, and to what degree, race is activated
as a political issue for a given electoral contest fundamentally shapes how voters will respond.
She finds that subtle appeals to racial animosities might stimulate a negative reaction among
white voters to black interests, but that explicit racist appeals may actually generate sym-
pathy rather than fear among whites (but see Hutchings, Walton Jr. and Benjamin 2010).
Sears et al. (1997) also suggest that the manner and extent to which the underlying racial
prejudices held by many whites emerge depends on whether and how they are primed. Simi-
larly, Hopkins (2010) finds that the impact of proximity to immigrant populations on citizen
attitudes toward immigration depend on the combination of a sudden change in the size
of the proximate immigrant population and whether national political rhetoric regarding
immigration is particularly sharp and prominent.
Black candidates themselves rarely seek to inject race as an issue in their electoral cam-
paigns (Lupia and McCubbins 1998). As a result, the importance of race to white voters
may not differ substantially when a black candidate is running compared to when one is not
(e.g. Citrin, Green and Sears 1990). However, the emergence of race was largely unavoidable
in the first general election contest for president involving an African-American candidate
with a real chance to win. Race was firmly injected into the Presidential contest leading up
8
to the South Carolina Democratic primary, but press coverage of the primary and general
election campaigns likely made race unavoidably salient. Bell (1997) believes that white
negative backlash is unavoidable for black candidates, but Hajnal (2001) argues that white
voters learn from experience with black candidates and elected officials in a way that reduces
their negative fears of black political leaders.
We also consider the possibility that individual racial prejudice may be shaped by the
racial context within which individuals live. Key’s racial threat hypothesis may be broadened
to predict that negative views of blacks more generally will emerge among whites who live
in areas with large black populations. In contrast, Allport (1954) suggests that greater
interaction with individuals from another group would serve to erode prejudice and foster
the development of a shared identity with common goals. Carsey (1995) suggests such a
mechanism may be at work in explaining his finding of an increased likelihood of white
voters voting for a black candidate for mayor as the percentage of the population in a white
respondent’s neighborhood increased. Finally, Kinder and Mendelberg (1995) find that racial
context conditions the impact of racial prejudice on attitudes regarding race-based policies,
with greater proximity to racial minority populations lowered the impact of prejudice on
policy attitudes.
In sum, the existing literature points to a number of different models that might explain
how both racial context and racial prejudice might contribute to explaining how people
evaluate candidates for a given election. The simplest model is a basic additive model, such
as the one represented in Equation 1, where candidate evaluation (CE) is expressed as a
simple linear additive function of racial context (C) and racial Prejudice (P):3
CE = β0 + β1(C) + β2(P ) + ε (1)
3Again, recall that Blalock (1967) would suggest including both C and C2.
9
The theoretical motivation for a model like Equation 1 assumes that the separate arguments
made regarding the impact of racial prejudice and the impact of racial context are essentially
independent of each other. Correlation between racial attitudes and racial context might
exist, of course, but correlation among independent variables is a normal common feature of
regression models.
If context has a direct effect on candidate evaluation, but also impacts racial prejudice,
then the resulting 2-equation model would captured by Equations 2 and 3:
P = α0 + α1(C) + ε1 (2)
CE = β0 + β1(C) + β2(P ) + ε2 (3)
This model rests on the assumption that racial context is an exogenous variable, but that
racial attitudes are endogenous to context as well as being a potentially intervening variable
between context and candidate evaluation. If the sole reason that racial context impacts
candidate evaluation is because racial context drives racial prejudice, and that racial prej-
udice affects candidate evaluation, then we would expect α1 to be statistically significantly
different from zero in Equation 2, β2 to be statistically significantly different from zero in
Equation 3, but β1 in Equation 3 to not be statistically significantly different from zero.4
A third model suggested by the literature is that racial context and racial prejudice inter-
act with each other to impact how citizens evaluate candidates. That implies the following
model, represented in Equation 4:
CE = β0 + β1(C) + β2(P ) + β3(C × P ) + ε (4)
Such a model is consistent with Hopkins (2010) and Kinder and Mendelberg (1995), who
4Whether α1 and β2 would be positive or negative would, of course, depend on how the various variablesin the model were coded.
10
argue that the impact of attitudes about racial or ethnic groups on some evaluation or opinion
is conditioned by context. Making statements about exactly what the coefficient estimates
would look like in a model that includes interactions is unwise because that depends on how
each variable is measured, but we can say that we should observe an improvement in model
fit moving from Equation 1 to Equation 4, and that we should see a meaningful change in
the marginal effect of racial prejudice on candidate evaluations as racial context changes. Of
course, we should see a similar meaningful change in the marginal effect of racial context on
candidate evaluations across the range of racial prejudice if Equation 4 was the appropriate
model.
In short, there is some theoretical foundation and at least partial empirical support for
each of these models in the existing literature. The bulk of the literature focuses on just the
impact of racial context or the impact of racial prejudice on candidate evaluations, policy
preferences, or some other attitude or behavior. In that sense, the majority of existing
studies do not even fully specify the model represented by Equation 1. Some studies have
examined limited versions of the other two models, but, to the best of our knowledge, the
literature has not previously made each of these competing models explicit and then tested
them against observed data.
In this paper, we argue that racial context and individually held racial stereotypes must
be brought together to understand how race might affect the views people hold regarding
candidates in a given election. We suspect that both might have direct effects on how citizens
respond when race is made salient in a particular election, which leads us to focus on analyses
based on Equation 1. However, we also explore whether contextual factors shape the racial
views people hold, as implied by Equation 2. We find no evidence for the model described by
Equation 4, so we do not discuss that model further.5 We evaluate these models using two
different data sets based on voter surveys conducted during the 2008 Presidential election.
5We also find no empirical support for including both our context measure and context squared.
11
Data and Methods
We explore the impact of individual-level racial prejudice and racial context on white
citizens in the context of the 2008 Presidential election using two nationally representative
surveys. Our data come from the 2008 Cooperative Congressional Elections Study (CCES)
and the 2008 American National Election Survey (ANES). The CCES surveyed about 33,000
respondents nationally as part of a multi-team effort. The survey itself was administered
online to a sample of opt-in respondents who were chosen to be representative of the national
population using a matching algorithm.6 The specific sample from which we run our analyses
consists of a subsample of 1,000 respondents who answered a series of unique questions. The
ANES survey contains over 2,100 nationally representative respondents.7
Individual Level Measures of Racial Prejudice
One of our primary explanatory variables of interest is our measure of the prejudicial
attitudes that individuals hold towards African-Americans as a group. We use three separate
but related measures of racial prejudice. Two of the measures are the degree to which
respondents reported believing that blacks are “hardworkers.”8 This is measured on a seven
point scale in the ANES data and a four point scale in the CCES data. Higher values
indicate more positive views towards blacks in both data sets. The mean and standard
deviation in the ANES data are 3.9 and 1.3. In the CCES data, these values are 2.9 and
0.8. Accounting for the different scales, this suggests that whites in the CCES sample on
average held more positive views towards blacks than did white ANES respondents. While
6The CCES collected data between October 8 and November 3 for the preelection battery and fromNovember 5 to December 1 for the postelection battery. The within-panel response rate for the 2008 CCESwas 47.1%.
7ANES data was collected from September 7 through November 3 for the preelection battery and betweenNovember 5 and December 21 for the postelection battery. The within-panel response rate was 66.3% andthe minimum response rate was 59.5%.
8Interested readers can find the question wording for these instruments in the Appendix.
12
the range of these scales differ from one another, the measures themselves tap into the same
underlying concept. This allows us to compare results across data sets.9
We construct a third and more conceptually rich measure of racial prejudice through the
use of factor analysis on four separate indicators of attitudes towards blacks included in our
CCES data. Using a subset of stereotypes like those employed by Sniderman and Carmines
(1997), we asked respondents the degree to which they agreed that African-Americans are
“hardworking,” “trustworthy,” “violent,” and “complainers”10. Our resulting factor score
ranges from -2.26 to 1.18; lower scores indicate that respondents hold more negative stereo-
typical views of blacks while higher scores suggest that respondents hold more positive views
of blacks. This measure has a mean score of -0.47 and a standard deviation of 0.773. Be-
cause lower values on each score are associated with more negative views towards blacks
while higher values are associated with more positive views of blacks, the literature cited
above would predict that our measures would be positively related to a higher likelihood of
voting for Obama and an increased likelihood of holding positive views toward him.
Racial Context
We measure racial context as the black population density of each white respondent’s
environment by matching each respondent’s five digit FIPS county code to the percentage
of residents in the county who were black as reported in the 2000 Census. We measure
racial context at the county level for two reasons. First, the county level provides a unit
of geography that is small enough to capture the potential for interaction between whites
and blacks while also being large enough to extend well beyond direct interpersonal contact.
The racial make-up of a county is something residents are likely to be casually aware of, but
counties are large enough to allow for social segregation within the county and a possible
9This particular measure of racial attitudes is the only one that is included in both data sets.10See the Appendix for wording and coding of these questions.
13
negative response. Measuring racial context at a smaller level would begin to mix simple
racial proximity with inter-personal interaction and self-selection into integrated neighbor-
hoods. Our second reason is pragmatic – the county-level is the smallest unit of geography
available to us in both data sets we employ in this study.11
This measure of racial context ranges from 0.1 to 72.3 with a mean of 10.68 and a standard
deviation of 11.75 among CCES respondents. It ranges 0.5 to 66.3 with a mean of 11.51 and
standard deviation of 12.09 among ANES respondents. As outlined above, the racial threat
hypothesis predicts that white voters will be less likely to vote for Obama and would be
more likely to have negative views of him as the size of the black population in their county
increases. Of course, we noted above that several scholars have reported positive rather than
negative effects, and still others have reported mixed findings.
Dependent Variables
We examine several dependent variables to test the effect of racial prejudice and racial
context on whites’ behaviors and attitudes during the 2008 Presidential election. One de-
pendent variable is the presidential vote choice reported by respondents. We measure this
variable as a dummy variable coded 1 for respondents who reported having voted for Obama
and 0 for those reporting having voted for McCain. Just over 45% of the white respondents
in our CCES sample and about 44% of white ANES respondents reported voting for Obama.
We also examine several measures of how respondents evaluated the two major party
nominees. The CCES asked respondents whether or not they thought each candidates was
“honest,” “knowledgeable,” and “experienced.” For each trait, we code the variable equal
to 1 if the respondent feels the candidate has the trait in question and 0 if they do not.
11Respondents’ zip codes are available for CCES respondents, but not for ANES respondents. The sameanalyses reported below was conducted at the zip code level with the results — direction of coefficients andlevels of significance — being unaffected.
14
We use these measures to construct an additive measure of overall candidate evaluation for
both Barack Obama and John McCain on a scale of zero to three. If a respondent felt
Obama had all three of these qualities, she would be given a three for this measure. For
these measures, higher values indicate high evaluations of the candidate in question. We
then analyze each trait for each candidate separately using logistic regression. This allows
us to consider a more precise analysis of how prejudice and/or racial context affected the
way in which whites viewed both Obama and McCain.12
Control Variables
Our models also include a number of control variables. First, we control for partisanship
using two dichotomous variables indicating whether or not a respondent is a Republican or a
Democrat, respectively. We also use the respondent’s self-reported ideology on the standard
seven point scale where the lowest value indicates that the respondent identifies as being
extremely liberal while the highest value indicates that the respondent identifies as being
extremely conservative.
We include a control variable for sex coded one for women and zero for men. Categorical
measures of individual-level education and income are also included in our models. Education
levels range from one to six, with one indicating no high school diploma to six indicating
that the respondent has a post graduate degree for CCES respondents. The median level of
education is “some college.” Education among ANES respondents is measured more directly
as the number of formal years of education experienced by a respondent. The mean number
of years of education for white ANES respondents was approximately 13.7. Income varies
on a 15 point scale on which one indicates that the respondent reported earning “less than
10,000 dollars” and 15 indicates that they reported earning “greater than 150,000 dollars” in
12The ANES lacked comparable measures, so our analysis of citizens’ perceptions of the candidates’ traitsis limited to the CCES data.
15
the CCES data. The median level of income in these data “50,000 to 60,000 dollars.” In the
ANES data, income is measured on a 25 point scale with a mean of just under 17. Finally,
we control for born again Christian status as a religious preference. Individuals who classify
themselves as “born again” are coded one while those who do not are coded zero.
Analysis and Results
As a first step, we examine how racial prejudice and racial context might be related to each
other among white voters. We argue it is possible that racial stereotypes might be driven in
part by the racial context in which respondents live, as illustrated by Equation 2. We regress
each of our three measures of racial prejudice on the percent black in a county, controlling for
party identification, ideology, income, gender, education, status as a born again Christian,
and median county income. The results of these models are presented in Table 1. The
results show that there is not a statistically significant (p ≤ .05) relationship between the
racial context in which a white respondent lives and her level of racial prejudice among
CCES respondents. The coefficient for the ANES model appears to be differ significantly
from zero, though the cumulative probabilities plotted in Figure 1 suggest that this effect
is not substantively large. Overall, these findings suggest that the attitudes held by white
respondents in these two surveys regarding blacks as a group are not strongly driven by the
racial context in which they live.13
The results in Table 1 suggest that the racial stereotypes about blacks expressed by white
citizens are consistently related to their ideological dispositions across models and data sets.
Whites who are relatively more conservative tend to hold more negative views of blacks
compared to whites who are relatively more liberal. The attitudes of CCES respondents
13Including the race of the interviewer as a control variable in the models using ANES data does notalter the substance of our results. The CCES is administered online and thus is not subject to potentialinterviewer effects.
16
were responsive to the level of education attained by whites. Not surprisingly, those with
higher levels of education tend to report more positive views of blacks as a group than do
those with lower levels of education. Racial stereotypes, then, appear to be in part a function
of political ideology and education.
[Table 1 about here.]
[Figure 1 about here.]
Next, we predict white respondents’ vote choices in a number of models, each of which
contains one of our measures of racial stereotypes.14 Tables 2 contains the results of two
models, both of which make use of either the four-point or the seven-point hardworking
racial stereotypes measure to examine the influence of racial prejudice and context on the
decision to vote for Barack Obama in the 2008 presidential election. These models allow
us to compare the results produced by similar measures using two different survey samples
taken during the 2008 election.
The results of both models reported in Table 2 yield similar findings — the degree to
which whites believe that blacks are hardworking is a powerful predictor of reported vote
choice. As respondents view blacks as being more hardworking, they are increasingly likely
to report having voted for Obama. The models differ on the effect of racial context on
white citizens’ vote choice; the results of the ANES model suggest that as the percentage of
county residents who are black increases, the likelihood that whites report voting for Obama
decreases. The estimated coefficient for county percent black in the CCES model, on the
other hand, fails to achieve a traditional level of significance and has a positive rather than
14Some might question whether a full analysis of both racial stereotypes and racial context should allowfor a possible interaction between the two. Such a model was illustrated above in Equation 4. We estimatedsuch models using racial context indicators taken at both the zip code and county level. None of theseanalyses produced substantively different results from those presented here. This suggests the effect of racialstereotypes on vote choice is not conditional on the environment in which citizens live.
17
a negative sign. As expected, Republicans and those who identify as more conservative are
less likely to vote for Obama in 2008 while Democrats were more likely to report having
voted for him.
To evaluate the magnitude of the effects reported in Table 2, we plot the predicted
probabilities of voting for Obama drawn from both models across all values of the racial
stereotypes and racial context variables in Figure 2. Panels (a) and (b) represent the feeling
that blacks are hardworking. As evident in both the plot for the CCES and ANES, there is
a positive and significant relationship showing that as respondents report feeling that blacks
are more hardworking, their predicted probability of voting for Obama increases. In the
CCES plot, there are four categories on the feeling thermometer that correspond with a
predicted probability of voting for Obama ranging from .5 up to .8. In the ANES plot, the
hardworking scale has seven values ranging from a low predicted probability of voting for
Obama at .21 to a high of .6.
As evident in panel (c) of Figure 2, the predicted probability of voting for Obama remains
relatively constant across the entire spectrum of the percent black in each county among
CCES respondents. In the ANES plot in panel (d), however, there is a sharp decrease in the
predicted probability of voting for Obama as the percent black in the respondent’s county
increases. White respondents in counties with the lowest black population had a predicted
probability of voting for Obama of just under .6, while whites living in counties with the
highest black population had a predicted probability of voting for Obama of only .1. The
results produced by the ANES model support the traditional racial threat hypothesis.
[Table 2 about here.]
[Figure 2 about here.]
Table 3 contains the results of a model predicting vote choice for Obama using the CCES
data. Here we employ our more complex measure of racial stereotypes based on the factor
18
score outlined above. As indicated by the estimated coefficients and their associated standard
errors, as individuals’ feelings about blacks as a group become more positive, the probability
of voting for Obama increases in a statistically significant manner (p ≤ .05). Much like
the previous models, party identification and ideology are also important predictors of vote
choice while racial context fails to achieve a significant effect on the likelihood of voting for
Obama.
Figure 3 plots the predicted probability of voting for Obama based on the coefficients
in Table 3. Panel (a) of Figure 3 shows that the predicted probability of voting for Obama
increases as attitudes about blacks become more positive. In this figure, moving from re-
spondents with the most negative views towards blacks to those with the most positive views
increased their predicted probability of voting for Obama by almost 30 percentage points.
This is certainly a large substantive effect comparing across individuals. However, another
way to evaluate the impact of stereotypes on the election is to access how many votes this
might have cost Obama on Election Day.
In order to evaluate the impact of whites holding negative racial stereotypes on the votes
they reported casting, we conducted two simulations. Each simulation is based on the results
of our logistic regression model presented in Table 3. We explored two hypothetical scenarios.
In the first scenario, we examined what the predicted voting behavior of white respondents
would be if all those who had a negative score on our racial stereotype measure actually had
a neutral score of zero. In the second scenario, we examined the predicted voting behavior
of white respondents if every respondent held the most positive view of blacks we observed
on our measure. We left all other values for each variable at their observed levels for the
respondents. We then generated predicted probabilities of voting for Obama under these two
scenarios using our estimates from Table 3 and compared them to the predicted probabilities
of voting for Obama based on the actual data.
19
Based on the actual data, the mean of the predicted probability of voting for Obama
is estimated to be 0.456. When we changed every negative score on the racial stereotype
measure that respondents held to a zero, the mean predicted probability of voting for Obama
increased by 1.4 percentage points. Looking more closely, our results reveal that 10 of the
455 respondents saw their predicted probability of voting for Obama switch from being
less than 0.5 to greater than 0.5 based on this simulation. When we re-ran the simulation
giving all respondents the highest observed value on our racial stereotype measure, the
average predicted probability of voting for Obama increased by 4.9 percentage points, and
26 respondents saw their predicted probability of voting for Obama switch from less than
0.5 to greater than 0.5.
At first glance, the results of these simulations suggest a much more muted impact
of racial stereotypes on the behavior of whites than the 30 percentage point swing our
cross-sectional analysis reported in Figure 3 suggests. That would be a misreading of these
results. Many respondents already had very high or very low probabilities of voting for
Obama based on other powerful predictors like party identification. That many of those
individuals became more or less supportive, but never switched sides, due to their views
regarding blacks as a group does not deny the power of racial stereotypes to affect such
individuals. Additionally, we would never expect to observe a 30 percentage point vote
swing in the aggregate based on any variable. The aggregation of individual preferences into
election outcomes concentrates attention on those voters nearest the .50/.50 dividing line.
Given that nearly 100 million white voters cast ballots in the 2008 Presidential election, our
simulations predict that anywhere from 2.2 to 5.7 million white voters would have shifted
from being just below the .50/.50 threshold of voting for Obama to just above it. We consider
this a substantial number.15
15We also conducted a third simulation in which we examined a hypothetical scenario in which all respon-dents who had a positive score on our racial stereotype measure instead had a neutral score. In other words,we wished to observe how many votes Obama would have lost due to the disappearance of positive racial
20
In contrast, panel (b) of Figure 3 shows that the percent black in a white respondent’s
county has no meaningful effect on the predicted probability of voting for Obama. The
coefficient estimate, however is positive and the predicted probability line shows a slight
positive trend, which runs counter to Key’s (1949) racial threat hypothesis. This effect is
not, however, significantly different than a null effect.
[Table 3 about here.]
[Figure 3 about here.]
Next we examine the effects of racial stereotypes and racial context on the respondents’
evaluations of both presidential candidates in 2008. Table 4 reports the results of ordered
logistic models predicting respondents’ evaluations of both Obama and McCain. The depen-
dent variable is an additive score of three components of candidate quality – whether or not
the candidate is knowledgeable, honest, and experienced.16 The measure ranges from zero
to three, with higher scores indicating a higher perception of candidate quality.
As evidenced by the results reported in Table 4, the same variables that predicted vote
choice remain key predictors of how white respondents view Obama as a candidate. Racial
stereotypes are a significant (p ≤ .05) predictor of how whites view Obama as a candi-
date. We again find that the coefficient estimate operating on the percent black in the
county does not approach a traditional level of statistical significance. Much like our vote
choice model, party identification and ideology are significant predictors of evaluations of
attitudes among white citizens. The results of our simulation shows that the predicted probability of votingfor Obama switched from greater than 0.5 to less than 0.5 for 6 of 455 respondents. Our simulation thuspredicts that neutralizing white citizens’ positive racial attitudes would lead to Obama receiving approxi-mately 1.3 million fewer votes. Thus, while Obama may have received votes from sympathetic whites whomight not have supported him otherwise, such support was more than off-set by the loss of votes amongthose holding negative views toward blacks as a group.
16We report models estimating respondents’ views on whether or not Obama and McCain possessed eachof these traits in the appendix. The models suggest that the relationships we observe in Table 4 are notdriven primarily by any single component of our index.
21
Obama; Democrats feel substantially more positive towards Obama compared to indepen-
dents, though Republicans were not significantly more negative of their evaluations of Obama
than were independents. Whites reported relatively more negative evaluations of Obama as
they became more ideologically conservative.
Evaluations of McCain, on the other hand, appear to be responsive primarily to party
identification, ideology, and born again Christian identification. Republicans, born again
Christians, and conservatives all evaluated McCain more positively. Unlike evaluations of
Obama, evaluations of McCain do not appear to be affected by racial stereotypes or racial
context. Given that McCain was a white candidate, it may not be surprising that racial
attitudes and context did not affect evaluations of him, but demonstrating that this was the
case is important because it indicates that citizens use different information and attitudes to
form attitudes about black and white candidates, even when those black and white candidates
are running against each other.17
[Table 4 about here.]
[Figure 4 about here.]
We plot the cumulative probabilities of expressing each level of affect towards both Obama
and McCain across the values of our racial stereotypes measure in Figure 4. The results in
Figure 4 show that the more positive a white respondent’s views towards blacks was, the
warmer she felt towards Obama. The cumulative probabilities plotted in Panel (a) of Figure
4 suggest that those who held the most negative views of blacks were likely to express lower
17We also ran models using the ANES data. In these models, the dependent variables were affect felttowards each of the candidates as measured by feeling thermometers and the primary independent variablesof interest were our simple measure of racial stereotypes — the degree to which respondents agreed with thestatement that blacks are hardworking — and racial context at the county level. These models producedsubstantively identical results in the case of affect felt towards McCain. This additional model estimat-ing affect towards Obama produced similar results to those reported in Table 4 for our racial stereotypesindicator, but the county level percent black variable produced an estimated coefficient that was negativeand statistically significantly (p ≤ .05) different than zero, indicating that white respondents who lived incounties with higher black populations reported liking Obama less.
22
levels of affect toward Obama while those who held more positive views towards blacks
were more likely to express higher levels of affect towards Obama. The flat cumulative
probabilities plotted in Panel (b) suggest that racial attitudes towards blacks do not predict
affect towards McCain very well when controlling for the other covariates in the model.
Conclusions
We have argued that a unified approach to analyzing the impact of individual-level prej-
udice and context on political attitudes is the most appropriate way to evaluate how white
voters respond to black political candidates. Previous scholarship focusing on one theoretical
motivation or another often misses or underestimates the complex role of race in electoral
politics. The evidence presented in our paper fits with decades of research that offers mixed
findings. In 2008, there is overwhelming evidence that racial attitudes were a driving influ-
ence in both vote choice and candidate evaluations. There is limited evidence, on the other
hand, that racial context influenced these factors.
We found strong and consistent evidence of a direct effect of racial prejudice on how
whites viewed Obama and their likelihood of voting for him. Even after controlling for
robust predictors such as party identification and self-placed ideology, whites who held more
negative views of blacks as a group were significantly less likely to view Obama as honest,
knowledgeable, or experienced. Such voters were also significantly less likely to report having
voted for Obama. Interestingly, these same prejudicial feelings did not inform how voters
evaluated Obama’s white opponent, John McCain. Negative views of blacks as a group
transferred directly to evaluations of the black candidate, but did not transfer beyond that
to his opponent. While Obama’s evaluations appear to have suffered due to white prejudice,
McCain’s evaluations did not appear to have been affected by prejudicial attitudes held by
white citizens.
23
In contrast, we show mixed evidence that race had any contextual effect on white voters
in this election. Our measure of racial context, the percent black in each respondent’s
county, produced a statistically significant coefficient estimate only in the model estimating
vote choice using the ANES data. The coefficient operating on our contextual measure
never differs significantly from zero in any of the models using the CCES data. There is
a weak tendency toward positive coefficients for this variable in the CCES as evidenced by
the models predicting evaluations of Obama. However, even if there is a weak direct effect
that is positive, this weak pattern is offset by the weak negative effect racial density had on
holding negative racial stereotypes.
Our fundamental conclusion is not a surprising one — race matters. The exact mechanism
regarding how race matters, and to what degree, remains uncertain and conditional. In 2008,
implicit racial cues could very well have triggered racial prejudice in white voters. Nobody
had to cue the public that Obama was black. The South Carolina primary and some of the
language surrounding the “birther” movement (which argued Obama was born in Kenya)
may have also raised the salience of racial attitudes as well. Still, the mere presence of
a black candidate could very easily cue uncertainty based on Obama’s perceived policy
preferences. As Kinder and Sears (1981, 415) argue, “the magnitude of racial threat to an
individual white should be the product of affect about some end state and expectancy that it
may materialize...” Having no previous major party black presidential candidates may have
induced uncertainty about the manner in which Obama would govern and prioritize various
groups of citizens.
Although we have little evidence that demographic contextual effects influence candidate
evaluations and vote choice in the 2008 presidential election, this is not to say they should be
dismissed. Most of the evidence of social-learning and positive responses to black candidates
occur in metropolitan elections (e.g. Liu 2003; Carsey 1995). The negative findings and
null findings in terms of contextual influences are almost all tied to elections at the state or
24
national level (Wright 1977; Huckfeldt and Kohfeld 1989; Carsey and Windett 2013, e.g.).
These mixed results may also be due to the unit of geographic analysis–with the metropolitian
election analyses relying on census block and tract data, while our analysis and others use
more broad measures of geographic proximity. Future studies should aim for a more nuanced
analysis
The electoral victory of Barack Obama marks another milestone in the history of racial
politics in the U.S. Many commentators speculated about the impact of race on this election,
and some have concluded that Obama’s victory is the death knell for racialized politics in
the U.S. Our analysis clearly suggests otherwise, as individual racial prejudice played an
important role in shaping the attitudes and behaviors of white voters. We have shown that
negative racial attitudes among white voters in 2008 may have reduced Obama’s vote total
by 2.2 to 5.7 million votes. The group of voters who did not vote for Obama due to his race
were not numerous enough to alter the outcome of the 2008 presidential election, but our
results imply that in more competitive contests, racial attitudes and their effects on both
public opinion and voting behavior may become a more important consideration for potential
candidates to take into account when deciding whether or not to enter a race and, if they
do, what kind of strategy to pursue. Continued research in this area should strive to further
explore the root causes of this racialized behavior, all the while noting the challenging nature
of this intricate process.
25
Appendix
Racial Stereotype Question Wording: CCES
In this appendix, we provide greater detail on our measures of racial stereotypes. [Note
to Editor/Reviewers: if the paper is accepted for publication, we are happy to post this
appendix online or include it as part of the published article.] The following is the wording
for the racial stereotype questions in the 2008 CCES. Note that the coding scheme was
reversed for our analyses.
UNC40 should always be listed first. UNC41, UNC42, and UNC43 should be randomized:
“Below are several characteristics often used to describe groups of people. Of course no
characteristic applies to all people in a group, but thinking in general terms, do you agree
or disagree that each of the following characteristics applies to the majority of African-
Americans?”
Columns:
1 Agree Strongly
2 Agree Somewhat
3 Disagree Somewhat
4 Disagree Strongly
Rows:
UNC40 TRUSTWORTHY FIXED
UNC41 VIOLENT
UNC42 COMPLAINERS
UNC43 HARDWORKING
26
Hardworking Stereotype Question Wording: ANES
Now I have some questions about different groups in our society. I’m going to show you
a seven-point scale on which the characteristics of the people in a group can be rated. In the
first statement score of one means that you think almost all of the people in that group tend
to be “hard-working.” A score of seven means that you think most people in the group are
“lazy.” A score of four means that you think that most people in the group are not closer
to one end or the other, and of course, you may choose any number in between.
Factor Analysis to Compute CCES Racial Stereotype Measure
Below are the results of an exploratory factor analysis. This shows a single significant
factor is returned from our 4 stereotype questions. We have a theoretical motivation that
these are all indicators of racial stereotypes. In the factor score construction, a single sig-
nificant factor is the equivalent of forcing a single factor in a confirmatory factor analysis
approach. We have done both and the results in our models remain unchanged.
[Table 5 about here.]
[Table 6 about here.]
Analysis of the Components of our Additive Measure of Affect
Table 7 reports the results of estimating separate logistic regression models for each
component of our additive measures of candidate evaluation. These results indicate that
racial stereotypes are an important predictors of perceptions of Obama’s honesty, knowledge,
and experience. In each case, more positive evaluations of blacks as a group on average lead
to a higher likelihood of attributing each trait to Obama. Like our models predicting vote
27
choice using the CCES data set, we again observe no statistically significant effect of racial
context on how whites viewed Obama as a candidate. In each case, the estimated coefficients
are indistinguishable from zero.
[Table 7 about here.]
Our results differ for respondents’ evaluations of McCain. In no instance does our in-
dicator of racial attitudes towards blacks influence white citizens’ evaluations of McCain’s
qualities as a candidate. Thus there does not appear to be any evidence that whites formed
their opinions of McCain in response to their attitudes toward blacks as a group even though
McCain was running against a black opponent. If Obama was hurt by negative stereotypes
held by some white voters, McCain did not benefit directly in terms of garnering a more
positive evaluation from such voters. At best, he only benefited indirectly because voters
who held negative attitudes towards blacks were less likely to vote for Obama ceteris paribus
and were thus more likely to vote for McCain. Like Obama, evaluations of McCain appear to
also be largely independent of the racial context within which white voters find themselves.
The estimated effect was positive in all three models, but not significantly different from
zero in any of them.
We plot predicted probabilities for attributing each of the three traits for both candidates
across all values of the racial stereotypes measure in Figure 5. The top row of plots were pro-
duced from the models estimating evaluations of Obama and the bottom row were produced
by the models predicting evaluations of McCain. Each plot shows the predicted probability
that each candidate is viewed as being honest, knowledgeable, or experienced. As evident in
the top row, the predicted probability that respondents attribute each of the three traits to
Obama increases as racial attitudes towards blacks become more favorable. The evaluations
of Obama’s honesty and experience show the sharpest increases; the predicted probability of
respondents viewing Obama as holding these traits increases more than 40 percentage points
28
from the least to the most favorable views of blacks.
[Figure 5 about here.]
The bottom row of plots in Figure 5 provide further evidence that attitudes towards
blacks do not affect the attribution of these qualities to McCain by white respondents. Each
of the predicted probability plots is essentially flat across all values of the racial stereotypes
factor score.
29
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Table 1: Racial Stereotypes as a Function of Racial Context
ANES CCES CCEShardworking hardworking stereotypes
County percent black -0.017* -0.005 -0.003(0.01) (0.01) (0.00)
Democrat -0.402 0.056 -0.083(0.36) (0.24) (0.10)
Republican -0.346 0.253 0.082(0.35) (0.23) (0.10)
Ideology: 7 point -0.145* -0.294* -0.126*(0.08) (0.07) (0.03)
County median income 0.000 -0.000* -0.000*(0.00) (0.00) (0.00)
Born again 0.417* -0.256 -0.055(0.19) (0.20) (0.09)
Female 0.003 0.075 0.025(0.18) (0.17) (0.07)
Level of education 0.025 0.216* 0.076*(0.05) (0.06) (0.03)
Age in years 0.007 0.008 0.003(0.01) (0.01) (0.00)
Income -0.028 -0.017 0.008(0.02) (0.03) (0.01)
Intercept 0.381(0.27)
Cut 1 -4.526* -3.925*(0.90) (0.66)
Cut 2 -2.868* -2.007*(0.85) (0.64)
Cut 3 -1.586* 0.133(0.85) (0.63)
Cut 4 0.208(0.85)
Cut 5 1.326(0.84)
Cut 6 2.520*(0.87)
BIC 1,417.397 1,203.106 1,196.912N 415 488 488
Note: Estimated ordered logistic regression coefficients are reportedfor the first and second models while OLS coefficients are reportedfor the third model. Standard errors are reported in parentheses.* = p ≤ .05 (one tailed)
34
Table 2: Vote Choice in the 2008 Presiden-tial Election: The Effect of Beliefs About theWork Ethics of Blacks
ANES CCESBlacks are hardworking 0.272* 0.538*
(0.15) (0.23)County percent black -0.035* 0.013
(0.02) (0.02)Democrat 1.882* 0.711*
(0.59) (0.44)Republican -1.282* -1.574*
(0.61) (0.49)Ideology: 7 point -0.555* -1.640*
(0.17) (0.21)County median income -0.000 0.000
(0.00) (0.00)Born again -0.719* -0.697*
(0.39) (0.40)Female -0.442 -0.644*
(0.39) (0.38)Level of education -0.119 0.045
(0.10) (0.14)Age in years -0.013 -0.006
(0.01) (0.01)Income 0.003 0.127*
(0.05) (0.06)Intercept 4.096* 4.951*
(1.85) (1.50)BIC 267.962 290.237N 324 455
Note: Estimated logistic regression coeffi-cients are reported. Standard errors are shownin parentheses.* = p ≤ .05 (one tailed)
35
Table 3: Racial Stereotypes and Vote Choice inthe 2008 Presidential Election
Estimated coefficientRacial stereotypes 0.503*
(0.24)County percent black 0.014
(0.02)Democrat 0.756*
(0.44)Republican -1.572*
(0.49)Ideology: 7 point -1.614*
(0.21)County median income 0.000
(0.00)Born again -0.726*
(0.40)Female -0.627*
(0.37)Level of education 0.054
(0.14)Age in years -0.005
(0.01)Income 0.120*
(0.06)Intercept 6.405*
(1.39)
BIC 291.456N 455
Note: Estimated logit coefficients are reported.Standard errors are shown in parentheses. Datawas drawn from the 2008 CCES.* = p ≤ .05 (one tailed)
36
Table 4: Additive Measure of Affect Felt To-wards Obama and McCain in 2008
Obama McCainRacial stereotypes 0.695* -0.065
(0.15) (0.15)County percent black -0.007 0.012
(0.01) (0.01)Democrat 0.900* -0.309
(0.32) (0.27)Republican -0.239 0.855*
(0.29) (0.34)Ideology: 7 point -0.964* 0.581*
(0.11) (0.09)County median income 0.000 0.000
(0.00) (0.00)Born again -0.359 0.659*
(0.25) (0.29)Female -0.320 0.381*
(0.23) (0.23)Level of education 0.099 0.007
(0.08) (0.08)Age in years -0.006 0.003
(0.01) (0.01)Income 0.036 -0.019
(0.04) (0.04)Cut 1 -5.181* -1.482*
(0.92) (0.82)Cut 2 -3.869* 0.816
(0.90) (0.78)Cut 3 -3.257* 2.294*
(0.88) (0.79)BIC 768.798 777.574N 428 417
Note: Estimated coefficients are reported.Standard errors are shown in parentheses.Data was drawn from the 2008 CCES.* = p ≤ .05 (one tailed)
37
Table 5: Factor Loadings For Racial StereotypeMeasure
Variable Factor1 Factor2 UniquenessTrustworthy 0.7939 -0.1957 0.3315Hard workers 0.7733 -0.2169 0.3549Complainers 0.7201 0.2331 0.4271Violent 0.7591 0.2045 0.3819
38
Table 6: Eigenvalues For CCES Racial Stereotype Factor Anal-ysis
Factor Eigenvalue Difference Proportion CumulativeFactor 1 2.32314 2.14165 1.0657 1.0657Factor 2 0.18149 0.33429 0.0833 1.1489Factor 3 -0.15280 0.01902 -0.0701 1.0788Factor 4 -0.17182 . -0.0788 1.0000
39
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epublica
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.768
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700*
-0.0
410.
439
-1.1
70*
0.07
3(0
.42)
(0.3
4)(0
.30)
(0.4
1)(0
.42)
(0.7
2)Id
eolo
gy:
7p
oint
-1.4
40*
0.67
4*-0
.814
*0.
467*
-0.8
93*
0.47
4*(0
.17)
(0.1
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.12)
(0.1
1)(0
.13)
(0.1
9)C
ounty
med
ian
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me
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0.00
0-0
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0.00
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000
(0.0
0)(0
.00)
(0.0
0)(0
.00)
(0.0
0)(0
.00)
Bor
nag
ain
-0.2
250.
470
-0.4
130.
399
-0.3
232.
126*
(0.3
6)(0
.30)
(0.2
7)(0
.34)
(0.3
3)(1
.06)
Fem
ale
-0.2
660.
314
0.08
50.
344
-0.5
69*
-0.9
94*
(0.3
2)(0
.25)
(0.2
5)(0
.26)
(0.3
0)(0
.46)
Lev
elof
educa
tion
0.07
00.
044
0.14
8-0
.010
0.14
60.
073
(0.1
2)(0
.09)
(0.0
9)(0
.09)
(0.1
1)(0
.16)
Age
inye
ars
-0.0
200.
014
-0.0
10-0
.006
-0.0
06-0
.006
(0.0
1)(0
.01)
(0.0
1)(0
.01)
(0.0
1)(0
.02)
Inco
me
0.03
50.
028
0.00
1-0
.041
0.02
2-0
.029
(0.0
6)(0
.04)
(0.0
4)(0
.04)
(0.0
5)(0
.08)
Inte
rcep
t6.
946*
-3.4
11*
4.32
9*0.
256
2.83
3*0.
608
(1.3
2)(0
.91)
(1.0
1)(0
.92)
(1.0
7)(1
.56)
BIC
340.
362
495.
103
483.
097
471.
113
399.
212
236.
353
N45
444
946
245
346
647
3
Not
e:E
stim
ated
logi
tco
effici
ents
are
rep
orte
d.
Sta
ndar
der
rors
are
show
nin
par
enth
eses
.D
ata
isdra
wn
from
the
2008
CC
ES.
*=
p≤
.05
(one
tailed
)
40
County Percent Black
Cum
ulat
ive
Pro
babi
lity
of T
hink
ing
Bla
cks
are
Incr
easi
ngly
Har
dork
ing
0 10 20 30 40 50 60
00.
20.
40.
60.
81
Note: Cumulative probabilities were generated from the results reported in the first column of Table 1. These
are the cumulative probabilities of evaluating blacks as being various levels of hardworking across all values
of county percent black for white male independents who did not report that they were born again and who
identified as ideological moderates. The rest of the variables in the models were held at their means. From
top to bottom, each line represents an increasingly high evaluation.
Figure 1: Cumulative Probabilities of Believing Blacks are Hardworking
41
Bla
cks
are
Har
dwor
king
Predicted Probability of Voting for Obama
12
34
00.20.40.60.81
(a)
CC
ES
har
dw
ork
ing
Bla
cks
are
Har
dwor
king
Predicted Probability of Voting for Obama
12
34
56
7
00.20.40.60.81
(b)
AN
ES
hard
work
ing
Cou
nty
Per
cent
Bla
ck
Predicted Probability of Voting for Obama
010
2030
4050
6070
00.20.40.60.81
(c)
CC
ES
raci
alco
nte
xt
Cou
nty
Per
cent
Bla
ck
Predicted Probability of Voting for Obama
010
2030
4050
60
00.20.40.60.81
(d)
AN
ES
raci
al
conte
xt
Note
:P
redic
ted
pro
babi
liti
esw
ere
gen
erate
dfr
om
the
resu
lts
repo
rted
inT
abl
e2.
Thes
eare
the
pre
dic
ted
pro
babi
liti
esof
voti
ng
for
Oba
ma
acr
oss
all
valu
esof
the
dep
enden
tva
riabl
esof
inte
rest
for
whit
em
ale
indep
enden
tsw
ho
did
not
repo
rtth
at
they
wer
ebo
rnaga
inan
dw
ho
iden
tifi
edas
ideo
logi
cal
mod
era
tes.
The
rest
of
the
vari
abl
esin
the
mod
els
wer
ehel
dat
thei
rm
ean
s.
Fig
ure
2:W
hit
es’
Vie
ws
onB
lack
s’W
ork
Eth
ics,
Rac
ial
Con
text,
and
Vot
ing
for
Obam
a
42
Rac
ial S
tere
otyp
es
Predicted Probability of Voting for Obama
−2
−1.
5−
1−
0.5
00.
51
00.20.40.60.81
(a)
Rac
ial
ster
eoty
pes
Cou
nty
Per
cent
Bla
ck
Predicted Probability of Voting for Obama
010
2030
4050
6070
00.20.40.60.81
(b)
Raci
al
conte
xt
Note
:P
redic
ted
pro
babi
liti
esw
ere
gen
erate
dfr
om
the
resu
lts
repo
rted
inT
abl
e3.
Thes
eare
the
pre
dic
ted
pro
babi
liti
esof
voti
ng
for
Oba
ma
acr
oss
all
valu
esof
the
raci
al
ster
eoty
pes
vari
abl
efo
rw
hit
em
ale
indep
enden
tsw
ho
did
not
repo
rtth
at
they
wer
ebo
rnaga
inan
dw
ho
iden
tifi
ed
as
ideo
logi
cal
mod
erate
s.T
he
rest
of
the
vari
abl
esin
the
mod
els
wer
ehel
dat
thei
rm
ean
s.
Fig
ure
3:R
acia
lSte
reot
yp
esan
dth
eP
redic
ted
Pro
bab
ilit
yof
Vot
ing
for
Obam
a
43
Rac
ial S
tere
otyp
es F
acto
r S
core
Cumulative Probability of Feeling Increasingly Positive Towards Obama
−2
−1
01
00.20.40.60.81
(a)
Ob
ama
Ad
dit
ive
Aff
ect
Sco
re
Rac
ial S
tere
otyp
es F
acto
r S
core
Cumulative Probability of Feeling Increasingly Positive Towards McCain
−2
−1
01
00.20.40.60.81
(b)
McC
ain
Ad
dit
ive
Aff
ect
Sco
re
Note
:C
um
ula
tive
pro
babi
liti
esw
ere
gen
erate
dfr
om
the
resu
lts
repo
rted
inT
abl
e4.
Thes
eare
the
cum
ula
tive
pro
babi
liti
esof
feel
ing
vari
ou
s
leve
lsof
aff
ect
tow
ard
sO
bam
aan
dM
cCain
acr
oss
all
valu
esof
the
raci
al
ster
eoty
pes
mea
sure
for
whit
em
ale
Rep
ubl
ican
sw
ho
did
no
tre
port
that
they
wer
ebo
rnaga
inan
dw
ho
iden
tifi
edas
ideo
logi
cal
mod
erate
s.T
he
rest
of
the
vari
abl
esin
the
mod
els
wer
ehel
dat
thei
rm
ean
s.F
rom
the
bott
om
of
each
figu
reto
the
top,
each
lin
ere
pre
sen
tth
ecu
mu
lati
vepro
babi
lity
that
indiv
idu
als
felt
at
least
that
leve
l(z
ero
thro
ugh
thre
e)of
aff
ect
tow
ard
sth
eca
ndid
ate
s.
Fig
ure
4:R
acia
lSte
reot
yp
esan
dA
ffec
tF
elt
Tow
ards
Pre
siden
tial
Can
did
ates
44
Rac
ial S
tere
otyp
es
Predicted Probability of Believing Obama is Honest
−2
−1.
5−
1−
0.5
00.
51
00.20.40.60.81
Rac
ial S
tere
otyp
es
Predicted Probability of Believing Obama is Knowledgeable
−2
−1.
5−
1−
0.5
00.
51
00.20.40.60.81
Rac
ial S
tere
otyp
es
Predicted Probability of Believing Obama is Experienced
−2
−1.
5−
1−
0.5
00.
51
00.20.40.60.81
Rac
ial S
tere
otyp
es
Predicted Probability of Believing McCain is Honest
−2
−1.
5−
1−
0.5
00.
51
00.20.40.60.81
(a)
Hon
est
Rac
ial S
tere
otyp
es
Predicted Probability of Believing McCain is Knowledgeable
−2
−1.
5−
1−
0.5
00.
51
00.20.40.60.81
(b)
Kn
owle
dgea
ble
Rac
ial S
tere
otyp
es
Predicted Probability of Believing McCain is Experienced
−2
−1.
5−
1−
0.5
00.
51
00.20.40.60.81
(c)
Exp
erie
nce
d
Note
:P
redic
ted
pro
babi
liti
esw
ere
gen
erate
dfr
om
the
resu
lts
repo
rted
inT
abl
e7.
Thes
eare
the
pre
dic
ted
pro
babi
liti
esof
thin
kin
gth
at
Oba
ma
an
dM
cCain
are
hon
est,
know
ledge
abl
e,an
dex
peri
ence
dacr
oss
all
valu
esof
the
raci
al
ster
eoty
pes
mea
sure
for
whit
em
ale
Rep
ubl
ican
sw
ho
did
not
repo
rtth
at
they
wer
ebo
rnaga
inan
dw
ho
iden
tifi
edas
ideo
logi
cal
mod
erate
s.T
he
rest
of
the
vari
abl
esin
the
mod
els
wer
ehel
dat
thei
r
mea
ns.
The
top
row
of
plo
tsare
evalu
ati
on
sof
Oba
ma
whil
eth
eplo
tsin
the
bott
om
row
are
evalu
ati
on
sof
McC
ain
.
Fig
ure
5:P
redic
ted
Pro
bab
ilit
yof
Att
ributi
ng
Char
acte
rist
ics
toO
bam
aan
dM
cCai
n
45
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