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Supplementary Online Appendix
In this supplementary appendix I provide a series of analyses that are designed to bolster
the main text. In the first section I replicate Table 2 from the main text with data from the
General Social Survey (GSS). The results of these replications are strikingly similar. In
the second section I develop a measure of implicit racism and then test whether it affects
partisanship and vote choice. In this section I also test the effect of immigration attitudes.
I find that racial attitudes exert a strong effect on vote choice and partisanship and this
effect has grown stronger as polarization has increased. The third section is devoted to
assessing the relationship between policy orientations and partisanship. The goal is to
show that policy orientations move partisanship more so than the reverse. I do this by
presenting an analysis of how changes in groups’ policy orientations are connected with
changes in partisanship. The findings demonstrate that groups’ policy orientations are
generally stable while partisanship and vote choice change. The majority of the change in
groups’ positions relative to the overall median can be explained by demographic shifts
altering the distribution of orientations opposed to group members adopting
fundamentally different orientations. The fourth section is devoted to establishing the
comparability of policy orientations over time. Here, I replicate the main text analysis
using estimates of policy orientations derived from a hybrid IRT. In the fifth section I
split the data into South vs. non-South subsamples and then replicate the analysis for
each. I find that the results hold in both the South and non-South. In the sixth and final
section I attempt to disentangle the effect of any coincident trends from the effect of
increasing polarization.
1
1. A Replication Using the GSS Data
The analysis in the main text utilizes the ANES cumulative file to test the determinants of
white partisan change between 1972 and 2012. This analysis is data intensive. I first
developed a measurement model designed to gauge individual’s latent economic and
social policy orientations and then use these latent orientations as independent variables
in a second stage analysis. Given the number of steps involved, one reasonable critique is
that the findings are being driven by some peculiarity in the ANES data, opposed to a
widespread empirical phenomenon. I account for this possibility in the proceeding section
by replicating the analysis using data from the General Social Survey (GSS). The GSS
data has been used by a number of scholars to examine the long-term dynamics of
American public opinion and behavior (see Ura and Ellis 2012 for an analysis of GSS
data that focuses on polarization in particular). Replicating the analysis with the GSS data
provides a thorough test of whether my empirical predictions hold across different
datasets, which employ different question wordings, different sampling and weighting
procedures, and a different pool of respondents.
The GSS cumulative data file spans 1973-2014 and contains questions on a wide
variety of political and social attitudes. I was able to construct a measurement model akin
to the one presented in the main text for the years between 1983-2014.1 Like in the main
text, I factor analyzed a set of questions designed to gauge an individual’s attitudes
towards multiple aspects of government involvement in the economy and moral
traditionalism. The full list of questions used can be found in Table A1-2.
1 A consistent set of questions was not available for the first several years of the survey,
hence, my analysis beings in 1983 opposed to 1973.
2
I present a replication of Table 2 from the main text using GSS data in Table 2A.2
The results of the replication closely match. The interactions between polarization and
policy orientations are statistically significant and in the expected direction. Moreover,
the magnitudes of these interactive relationships are similar. The substantive effects are
shown in Figure A1. The similarities between the analysis using GSS and ANES data
extend beyond just the regression models presented in Table 2 of the main text and Table
2A. Figure 2A displays the distance between the white median vote and the overall
median voter (akin to Figure 4 in the main text). As figure 2A demonstrates, the distance
between the median white position and the overall median has increased. An increasing
proportion of whites are located to the right of the median on the economic dimension
and to the left of the median on the social dimension. Again, the magnitude of these shifts
is similar to those calculated using the ANES data.3
The takeaway is that two different sources of data tell the same story.
Demographic changes have altered the distance between the median white position and
the overall median citizen on both policy dimensions. Moreover, elite polarization has
strengthened the relationship between policy orientations and vote choice/partisanship,
even when controlling for competing explanations. The evidence presented in the main
text combined with the replication using GSS data presented here strongly suggests that
2 The GSS does not ask respondents to place presidential candidates’ positions; therefore
I am only able to replicate models 1 and 3 from Table 2 in the main text.
3 The series is slightly noisier since I am calculating yearly estimates where the samples
are smaller relative to those of the once every four years post-presidential election NES
sample. However, the important thing to note is that the trends are clearly similar in spite
in the disparity in sample sizes between the GSS and NES cross sections.
3
the hypothesized relationships are not the product of some feature endemic to a particular
dataset but rather are reflective of a meaningful empirical phenomenon.
2.1. Constructing a Measure of Implicit Racial Attitudes
In order to conduct a full-fledged test of my argument it is critical that I test potentially
competing explanations. Chief among these is the role that racial attitudes might play in
explaining the white shift towards the Republican Party. Have whites moved toward the
Republican Party as a result of the heightened importance of racial animus? I account for
this possibility by including a measure of implicit racism as an independent variable. To
construct this variable, I created a scale of latent racial attitudes by factor analyzing a
series of responses to four questions pertaining to why minorities are disadvantaged (see
Sears and Henry 2005).4 Including this measure of implicit racial attitudes as an
independent variable serves two key purposes. First, the inclusion of this measure helps
to account for any effect implicit racial attitudes might have on vote choice and
partisanship. Second, and perhaps more importantly, including this measure helps to
account for the possibility that some of the measures used to estimate individuals’ policy
orientations have become increasingly racialized over time (Tessler 2012, 695-700).
Some of the questions used to build these policy orientations scale relate to African
Americans directly (e.g. should the federal government provide more aid to African
Americans). Including this measure of implicit racism helps to account for any indirect
effect racial attitudes might play in shaping policy orientations.
4 The implicit racism scale was built using questions designed to gauge individuals’
levels of latent hostility towards African Americans. Specific details pertaining to the
construction of this measure can be found in the supplementary appendix.
4
One of the challenges involved with testing the relationship between racial
animus and political behaviors is that individuals are reluctant to express overtly racist
attitudes. Beginning in the late 1960s, the proportion of individuals willing to openly
express support for racially based segregation and the like on surveys plummeted
(Ditonto, Lau, and Sears 2013, 488-489). However, this decline in the proportion of
individuals expressing overtly racist opinions should not be taken as evidence that issues
of race or racism have disappeared from the American consciousness. Rather it is
evidence that the nature of racial prejudice has fundamentally changed. With these
changes in mind, scholars have developed alternative approaches to measuring racial
hostility. These alternate approaches focus on measuring implicit racial attitudes that are
less subject self-censoring compared to questions designed to gauge overt feelings of
racial hostility.
Beginning in 1988, the ANES began to ask a battery of questions designed to tap
into these implicit racial attitudes (VCF 9039-VCF9042 in the ANES cumulative file).
These questions are designed to assess whether African Americans are disadvantaged as a
result of structural factors or whether these disparities are due to the fact that African
Americans are unwilling to work hard and help themselves. The intuition behind asking
these types of questions is that individuals with latent hostility towards minority groups
will be more likely to believe African Americans are socially and economically
marginalized as a result of an unwillingness to work hard opposed to being victims of
social and institutional discrimination. Numerous authors have demonstrated that implicit
or symbolic racism is an important predictor of policy preferences and other political
5
attitudes (e.g. Kinder and Sanders 1996). Therefore, I use individuals’ responses to these
questions to build a scale measuring implicit racism.
My approach to building this implicit racism scale is similar to how I estimate
individuals’ policy orientations. I construct a factor analysis that assess whether
individuals’ responses to these questions correlate with the underlying dimension, which
in this case implicit racism. The factor loadings are displayed in Table A3-1 (the GSS
question wordings are displayed in Table A3-2). The result of the factor analysis
demonstrates that all of these measures correlate with the same underlying dimension. In
other words, these measures are all tapping into the same underlying concept and can be
used to build a scale measuring implicit racism.
2.2 Testing the Effect of Implicit Racism and Immigration Attitudes on Vote Choice
I test the effect of implicit racism and immigration attitudes on vote choice and
partisanship in Table A4. I include the measure of implicit racism in models 1, 2, 4, and 5
and then include an interaction between implicit racism and polarization in models 2 and
5. Including the interaction between implicit racial hostility and polarization accounts for
the possibility that racial animus has become a stronger predictor of behavior as the non-
white proportion of the population has increased and the parties have taken increasingly
divided positions on racial issues. I include a variable that captures whether individuals
favor cutting the number of immigrants entering the country (or keeping the number the
same or expanding it) in models 3 and 6. The other independent variables are specified
identically to the models in the main text. Unfortunately, the questions necessary to
compute a measure of implicit racism is only available after 1984 (1996 excluded), so the
models that include the implicit racism scale are only run on a subset of the data (1988-
6
1992 and 2000-2012). There are similar data problems with the questions pertaining to
immigration attitudes, which are only available from 1992 onward.
What role do racial attitudes play in explaining whites’ shift towards the
Republican Party? How does implicit racism factor into this story of partisan change?
Implicit racism is both a statistically and substantively significant predictor of both vote
choice and partisanship. Moreover, the magnitude of the effect has increased. Figure A3
displays the marginal effect of a one-unit increase in implicit racism. A one-unit increase
in implicit racism is associated with a decreasing likelihood of voting for and identifying
as a Democrat as polarization increases. The substantive conclusion that can be drawn
from this figure is that the white electorate has sort along the lines of implicit racial
attitudes in addition to economic and social policy orientations. Including this variable
also reduces the magnitude of the effect of the interactions between policy orientations
and polarization relative to the models presented in the main text, but it is difficult to
discern whether this is a function of the inclusion of the implicit racism measure or due to
the analysis being run on a truncated time period due to data availability issues. Either
way, the evidence presented here strongly suggests that conflict extension reaches beyond
the confines of policy to racial attitudes as well.
The effect of the immigration attitudes on vote choice and partisanship is
statistically indistinguishable from zero. This finding is likely due to the fact that the
inclusion of the other composite policy orientation measures and the party distance
variable explain vote choice and partisanship more consistently than a single question on
immigration policy.
7
The takeaway here is that while these other proposed explanations can and often
do shape partisanship and vote choice, their effects are limited compared to how policy
orientations, perceptions of the parties’ positions, and racial attitudes (in conjunction with
polarization) shape attitudes and behaviors.
3.1 The Relationship Between Policy Orientations and Partisanship
My claim is that individuals and groups hold meaningful policy orientations and that
these orientations shape partisan identification. Exactly how these orientations are
translated into partisanship, and ultimately voting behavior, is conditional upon the
clarity and distinctiveness of the options presented by the political parties. My
explanation of partisan change conflicts with the Michigan model, which presumes party
ID comes first and policy orientations follow (Campbell et al. 1960). Since the assumed
direction of the relationship between policy orientations and partisanship is critical to my
argument, it is necessary that I assess the plausibility of this assumption empirically. In
this section I present an analysis that is designed to assess the direction of the relationship
between partisanship and policy orientations.5 5 One way that scholars have attempted to untangle this relationship is with panel data
(Goren 2005; Carsey and Layman 2006; Highton and Kam 2011; Chen and Goren 2016;
Goren and Chapp 2017). Repeatedly observing the same individuals allows analysts to
observe if changes in either partisanship or policy orientations cause changes in the other.
A brief summary of this literature based on panel data is that issue positions and
partisanship jointly affect one another, and the magnitude and the direction of this
relationship depends on a number of conditioning factors (political sophistication, clarity
of party cues, polarization, etc.). However, it is important that I note several caveats
about these analyses. Panel data cannot account for either cohort replacement or
8
If partisanship determines policy orientations and more whites have come to
identify as Republicans partisans, their policy orientations should change along with their
partisanship. However, if my argument is correct, a change in partisanship is not
necessarily connected with a change in policy principles, because a group might translate
the same policy orientations into different partisan attachments as the parties adopt
different positions. If a group’s policy orientations remain stable but its partisanship
changes it becomes difficult to make the case that changing partisanship is causing
changes in policy orientations, seeing as how there is no change in policy orientations to
explain.
My position is that groups of voters hold policy orientations that are stable over
long periods of time and are generally reflective of a group’s overall position within
society. Marginalized groups generally favor more government intervention in the
economy in an effort to promote economic equality while more affluent groups prefer
demographic change, both of which are likely a critical part of the story of the changing
relationship between policy orientations and partisanship. Moreover, panel studies, such
as those conducted in conjunction with the ANES, are often limited to a short time frame
(three iterations across a four year span in the case of the ANES), which makes assessing
the types of mass-level changes I am interested in, which unfold over multiple decades,
difficult if not impossible. My analysis is based on cross-sectional data, which accounts
for cohort replacement and demographic changes. Examining cross-sections, which
include a different sample of individuals in each survey year, is better suited for
understanding the types of societal level changes that I explore here (Firebaugh 2008,
201).
9
less. African Americans, and economically marginalized ethnic and racial minorities
generally, prefer government policies designed to increase economic equality. Whites and
other more affluent groups are more opposed to these types of efforts. A similar story can
be told with group attitudes on the social dimension. The religiously adherent favor
policies where the government works to uphold moral traditionalism while those who
identify as non-religious do not. On the group level, these preferences have been quite
stable.
It is my position that partisanship has come to more strongly reflect policy
orientation more than these policy orientations have come to reflect partisanship. Out of
any group, Southern whites’ partisanship has changed the most between 1972-2012. The
question here becomes ‘what has changed, policy orientations, partisanship, or both?’
What I show is that Southern whites’ policy orientations have remained stable while their
partisanship and voting behavior has changed a great deal. Southern whites changed their
partisan attachments, not their underlying conservative policy orientations. This is not a
story of Southern whites transitions from liberals to conservatives, but rather Democrats
to Republicans. It is my claim that the majority of partisan change among other groups
has followed a similar process. The vast majority of the changes in groups’ positions
relative to the overall median position are explained by changing demographics opposed
to group members adopting systematically different orientations. Whites have drifted to
the right of the median on economic issues and to the left of the median on social issues
over time as a result of the changing ratio of groups.
I begin my analysis with Figure A4, which displays the median position of 12
social groups in the years 1972, 1988, 2000, and 2012. The intersection of the solid
10
horizontal and vertical lines represents the overall median position. Higher scores
represent more conservative positions. These graphs display two key findings. The first is
that group medians are generally reflective of groups’ behaviors. African Americans
occupy a position well to the left of the median on the economic dimension (a full
standard deviation to the left in most years) and to the right of the median on the social
dimension (in later years especially). Southern whites, on the other hand, are located on
the other side of the space, consistently holding positions to the right of the median on
both dimensions. The non-religious and weekly church attenders hold opposing positions
on the social dimension, with the non-religious holding a liberal position and weekly
church attenders holding a conservative position. Neither group holds a position on the
economic dimension that is markedly different than the overall median. Those in the top
third of the income distribution are more economically conservative and socially liberal
than their counterparts in the bottom third of the income distribution. Jews are
consistently liberal (although exhibit a lot of variation due to the small pool of
respondents in each year). The median Latino is roughly .5 standard deviations to the left
of the median on the economic dimension in most years and indistinguishable from the
overall median on the social dimension. College graduates have been consistently liberal
on the social dimension and have become more conservative on the economic dimension
over time. Figures A5 and A6 contains each group’s estimated median position in each
year as well as the 95 percent confidence around each median.6
6 I used the following formula to construct the standard error of the median:
S . E .=1.253 σ√ N
where σ represents the standard deviation and N represents the
weighted number of observations in each group.
11
These figures demonstrate that while the majority of groups’ positions have
remained stable (i.e. there is no statistically significant change in the group’s median
position), there have been changes in some groups’ median positions that cannot be
explained by random sampling variation. The biggest changes have occurred among
African Americans and Latinos, who have moved closer to the median on the economic
dimension. This shift in the position of African Americans and Latinos has corresponded
with the median white voter moving increasingly to the right of the overall median.
Groups’ positions relative to the median can change for two reasons, 1) there is a
systematic shift in group members’ opinion profiles, whereby group members’ policy
orientations become systematically more liberal or conservative 2) group members’
policy orientations remain consistent but the ratio of groups changes. Increasing the ratio
of economically liberal/socially conservative African Americans and Latinos to
comparatively economically conservative/socially liberal whites will have the effect of
pulling the median towards the median African American/Latino and away from the
median white even though group members’ positions did not change.
Distinguishing between these two possibilities is of considerable importance. If
policy orientations are increasingly driving partisanship, then a group’s policy
orientations should remain relatively unchanged while its partisanship changes. If
partisanship is the causal force, then we should suspect policy orientations to change as a
function of changing partisanship. One way to disentangle these two competing
explanations is to assess the proportion of changes in groups’ positions relative to the
median is a function of compositional changes in the electorate versus group members
adopting systematically different views. Another way of thinking of this issue is “what
12
would group positions look like if the electorate still had the same demographic
composition as it did in 1972 when the time series began?” If groups’ positions remain
constant after I account for the effects of demographic changes than the argument that
changes in partisanship are driving changes in policy orientations become much less
tenable.
Figure A7 displays both whites actual distance from the overall median as well as
the estimated white distance if the demographic composition of the electorate had
remained fixed at 1972 levels. This figure is an estimate of how much of this shift in
white policy orientations away from the overall median is due to demographic change
versus whites adopting more economically conservative/socially liberal views. The figure
demonstrates that once demographic changes are accounted for, there has been very little
change in whites’ orientations—there is no clear trend . The median white position on the
economic dimension actually drifts slightly to the left once demographic changes are
accounted for. Whites did become slightly more socially liberal at the beginning of the
time series but their position has remained highly stable since the 1980s. This stability
suggests that whites’ shift away from the median has been driven by demographic
changes, not by adopting different orientations. The story with other groups’ positions is
similar. African Americans move towards the center on the economic dimension is the
function of the growing proportion of economically liberal non-whites (Latinos and
Asians) opposed to a shift in orientations.
This finding is important. Traditional models of partisanship contend that policy
orientations are ultimately a product of partisan attachments. The claim here is that a
change in partisan attachments should correspond with a shift in policy orientations.
13
However, this model fails to explain the body of evidence I have presented here. There
has been a major shift in whites’ partisan attachments in favor of the Republican Party,
but there has not been a commensurate conservative shift in whites’ policy orientations.
Simply put, there is no evidence that this shift in partisanship has produced a
corresponding shift in policy orientations, at least in the aggregate. The evidence suggests
that whites have become Republicans, not that whites have adopted different policy
orientations. This finding lends credence to my explanation that it is the interaction of
policy orientations with increasingly clear party positions (in conjunction with
demographic changes) that have produced this shift.
This analysis helps to confirm that the assumption that policy orientations are
exogenous to partisanship is reasonable. That said, I should be careful to qualify exactly
what I believe the relationship between party ID and policy orientations actually are. I
suspect that the majority of studies that use panel data and find that party ID moves issue
orientations are likely correct. I think this is true large because people do interpret
information through a partisan lens. Evaluations of current political issues, economic
performance, political scandals, and the like are all strongly shaped by party ID. Issue
positions and policy orientations are not interchangeable concepts. Policy orientations are
a much broader, and I argue, more stable characteristic than positions on individual issues
(there is a considerable amount of evidence that speaks to this fact, see Ansolabehere,
Rodden, and Snyder 2008 or Goren 2013).
This distinction between policy orientations and positions on specific issues helps
to get a question that is too seldom asked. This question is “why do groups hold the
partisan attachments that they do?” We know partisanship is a powerful, and in many
14
instances causal force driving political behavior. What is less frequently asked is why
certain groups identify with certain parties. It is almost taken as a given that African
Americans identify as Democrats while white evangelicals identify as Republicans, but
this was not always the case. I contend that origins of party identification are the nexus of
policy orientations (or core values, defined even more broadly) and how well these
orientations line up with what the parties are offering. In the aggregate, policy
orientations are distributed more or less rationally across groups—economically
marginalized groups favor a more government intervention in the economy while better
off groups favor less. Partisan change occurs when the parties come to represent different
sets of orientations than they previously. My suspicion is that these basic orientations
about what the government should do are slow to change because the basic sociological
conditions that undergird these orientations are slow to change as well. If this is the case,
this implies that changes at the party level, opposed to changes in policy orientations on
the mass level, are what ultimately drive mass-level partisan change.
4.1. Ensuring Cross Year Comparability
I ran a separate factor analysis for each year of available data. I ran a separate factor
analysis for each survey year because the set of questions changes from survey to survey.
The key issue with this approach is ensuring that results of the analyses are comparable
across years. This is a common issue in studies that examine change over time. Poole and
Rosenthal’s DW-NOMINATE common-space measure of legislator ideology confronts
this same issue—the set of votes is different in every Congress therefore making
comparisons across legislatures is difficult. Poole and Rosenthal deal with this problem
by using legislators that are common to multiple sessions of Congress as a bridge set
15
which allows the authors to assess year-to-year changes in legislator ideology. The
authors evaluate change in legislator ideology by assuming each legislator’s position
remains fixed across sessions. Therefore, change across years can be measured relative to
the fixed (by assumption) position of these common legislators. Change can only be
measured relative to some constant, therefore I must hold something fixed in order to
evaluate change. Fortunately I am not forced to adopt the assumption that survey
respondents retain fixed positions from year to year. This is because I can utilize
questions that are common to every election as a benchmark to evaluate year-to-year
change, which is the equivalent of legislators voting on the same piece of legislation in
every year.
There are several questions that have been asked in every administration of the
ANES survey since 1972. I utilize one question that loads very highly on the first
dimension as a common benchmark to evaluate the year-to-year consistency of the factor
loadings in order to ensure that the ideological dimensions are comparable.7 The question
that consistently loads the highest on the first dimension is “what role should the
government play in ensuring everyone has a good standard of living.8” I fix the
7 The exact wording of this question is as follows: “Some people feel the government in
Washington should see to it that every person has a job and a good standard of living.
Others think the government should just let each person get ahead on his own.”
8 I could easily rotate the matrix through a different question that loads highly on the first
dimension. The choice is largely arbitrary. The important thing that this rotation achieves
is that it rotates the factor matrix in the same way in each year, making the direct
comparison of one year to another feasible because I am now using the same metric to
evaluate changes.
16
dimensionality of the space by rotating the entire matrix through this common question.
This means that the dimensions that define the policy space are the same in every year
and this facilitates making direct comparisons between the years. These comparisons are
possible because I am now utilizing the same metric to evaluate changes. I present the set
of factor loadings for each year in Table A5. Table A5 demonstrates that questions that
are common to multiple years load consistently, suggesting that the underlying
dimensions remain constant.
4.2 A Replication Using Estimates of Policy Positions Generated from a Hybrid IRT Model
As the previous section noted, the comparability of the policy orientation estimates across
years is of considerable importance. Another way to assess the comparability of policy
orientations is to estimate the policy orientations of all voters in the ANES cumulative
file simultaneously using a Bayesian item response model (IRT). One of the advantages
of IRT models is that they simplify the treatment of missing data, as a result, it is possible
to generate estimates for all citizens simultaneously even though the pool of questions
changes from year to year and some individuals refuse to answer some questions that are
used to build the scale (Treier and Hillygus 2009, 685). The strength of IRT models is
that the estimates for individuals with missing data are simply less precise compared to
individuals where more data is available. This ability to handle missing data is critical,
since it allows for fitting one model to the entire dataset spanning 1972-2012, even
though the set of questions is not identical from year-to-year and not every individual
answers every question. Fitting one model also facilitates direct comparisons across years
without having to worry about issues with comparability, however, as I will show, this
potential concern with the factor analysis derived estimates is in this instance unfounded.
17
I employ a hybrid IRT model (one that combines responses to both binary and
ordinal questions) to generate estimates of individual level policy orientations. Similar to
the estimates generated by the factor analyses, individual scores range from roughly -3 to
3 on both policy dimensions and the mean on both dimensions is zero. The individual
latent trait scores produced by the hybrid IRT model correlate with the individual-level
factor scores at .9 on the economic dimension and .87 on the social dimension. Crucially,
the results of the analyses based on the IRT model reveal the same substantive findings as
the results based on the series of factor analyses. Figure A8 displays the estimated
distance between the median white citizen and the overall median on both dimensions
between 1972 and 2012 (akin to Figure 4 in the main text). On the economic dimension,
the same pattern emerges—the position of the median white citizen has moved farther
and farther away from overall median. Different measurement models reveal the same
finding—an increasing proportion of white citizens are located to the right of the median,
which ultimately has implications for both party positioning and individual-level
partisanship and vote choice. Unlike the economic dimension, the results of the IRT
model show that the median white citizen’s position on the social dimension has
remained indistinguishable from the overall median.
The results of the regression analyses that substitute in the IRT generated
estimates of policy orientations reveal highly similar patterns. Table A6 displays the key
results and Figure A9 displays the marginal effects of the key interaction terms between
orientations and polarization. As the tables and figures make clear, there is still strong
evidence that individuals have sorted as elites have become more polarized (as reflected
by the slope of the interaction terms) and that individual’s perceived proximity to the
18
parties exerts a strong effect. Overall, the evidence presented here demonstrates that
election-by-election factor analysis and a pooled hybrid IRT approach produce
substantively similar results.
5. South vs. Non-South: A Split Sample Analysis
One potential concern with the analysis is that a great deal of white partisan change has
occurred in the South, which raises the possibility that the overall results are being driven
by the behavioral and attitudinal changes of white Southerners alone. I assess this
possibility here, where I present an analysis that replicates the specifications from Table 2
in the main text, only here I divide the sample into Southern vs. non-Southern
subsamples. The goal here is to assess whether the key relationships of interests exist
both inside and outside of the South.
Tables A7 and A8 contain the results of the split samples analyses. As the tables
illustrate, the key variables exert markedly similar effects of partisanship and vote choice
both inside and outside of the South. The terms that interact policy orientations and elite
polarization are negative and exert statistically significant effects on both vote choice and
partisanship (the marginal effects plots for these models are omitted for the sake of
space). One interesting difference is that economic orientations play a bigger role in
explaining white vote choice in the Non-South, while social orientations play a bigger
role in shaping white vote choice and partisanship in the South. Perceived party distances
have a similar effect on voting behavior and partisanship in both the South and non-
South. Overall, the results of these analyses strongly suggest that similar factors shape
white voting behavior and partisanship inside and outside the South.
6. Addressing the Issue of Coincident Trends
19
One of the potential issues with the analysis is the fact that polarization increases
continuously over time, meaning that polarization is correlated with virtually every other
trended variable (e.g. the national debt, global temperatures, etc.). A potential problem
with the model presented in main text is that it is difficult to untangle the effect of
polarization from any of these other similarly trended variables. For example,
polarization is correlated with a linear time trend at .98, making the two trended variables
difficult to distinguish from one another. In this appendix I present an alternative model
specification that deals with this problem empirically.
Here I replicate my main analysis, only replacing the absolute level polarization
with the change in polarization from time t-1 to time t. While polarization increases
steadily over time, there is considerable variation in year-to-year change. This year-to-
year change is much less highly correlated with a linear time trend (r = .312). Some years
experience much larger increases in polarization than others (the increases in the periods
between 1992 and 1996 and between 2008 and 2012 were especially pronounced). The
logic here is that if individuals are responding to increasing polarization, larger increases
in polarization should be associated with larger shifts in attitudes in behaviors. I present
the results of these models in table A9. Substituting changes in polarization in place of
the absolute level of polarization produces substantively similar results. Figure A10
displays the marginal effects of the key interaction terms—the interpretation of these
marginal effects are similar to the marginal effects presented in the main text.
Individual’s policy orientations become a stronger predictor of voting behavior and
partisanship in response to a change in polarization.
20
These finding help to corroborate the findings in the main text and demonstrate
that increasing polarization, and not other collinear trends, is responsible for the observed
changes. The similarity of these finding should help to confirm that the relationship
between polarization, policy orientations, and partisan support is not spurious.
21
References
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Issues: Using Multiple Measures to Gauge Preference Stability, Ideological
Constraint, and Issue Voting. American Political Science Review, 102(2): 215-232
Campbell, Angus, Phillip Converse, Warren E Miller & Donald Stokes. 1960. The
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23
Table A1-1: ANES Items used to Build Policy Orientations Measures
Variable Question Scores Coding YearsVCF0806 Favor or oppose government
supplied health insurance1-7 1=Government should provide universal
coverage7=Totally private
1972-1976, 1984-2012
VCF0809 Should the government ensure everyone has a job and a certain standard of living
1-7 1=Government see to job and good standard of living 7=Government let each person get ahead on his own
1972-2012
VCF0817 Is school bussing necessary to achieve integration
1-7 1=Bus to achieve integration 7=Keep children in neighborhood schools.
1976-1984
VCF9037 Should the government ensure fair jobs for blacks
1, 5 1=Yes, the government should 5=No, it is not the government’s job
1972, 1988-2012
VCF0832 To what extent do we need to protect the rights of the accused when stopping crime
1-7 1=Protect rights of accused 7=Stop crime regardless of rights of accused
1972
VCF0602 Do you approve of civil disobedience to protest unjust laws
1-3 1=Disapprove2=Depends3=Approve
1972-1976
VCF9050 Should we increase spending on programs that aid blacks
1-3 1=Increased2=Same3=Decreased
1972-2012
VCF0829 Is the government in Washington too strong
1,2 1=No2=Yes
1976-1988
VCF0828 Should we cut military spending 1,2 1=Yes2=No
1976, 1980
VCF0838 When should abortion be allowed
1-4 1=Never Permitted4=Never Prohibited
1972-2012
VCF0834 What should women’s role in society be
1-7 1=Women should have an equal role7=Women’s place is in the home
1972-2008
VCF0815 Are you in favor of desegregation, strict segregation, or something in between
1-3 1=Desegregation2=In Between3=Strict Segregation
1972, 1976
VCF0814 Civil Rights Pushes Too Fast or Not Fast Enough
1-3 1=Too Slowly2=About Right3=Too Fast
1972-1984
VCF0833 Favor or oppose equal rights amendment
1,5 1=Favor5=Oppose
1976-1980
VCF9051 When should school prayer be allowed
1,5 1=Schools should be allowed to start with prayer5=Religion does not belong in school
1980-1984
VCF0845 Authority of the bible 1-4 1=The bible is God’s literal word4=The bible was written by men and is of little worth today
1980-2012
VCF9047 Should the federal government spend more money to protect the environment
1-3 1=Increased2=Same3=Decreased
1984-1988, 2004-2012
VCF9046 Should the federal government 1-3 1=Increased 1984-
24
spend more money on food stamp programs
2=Same3=Decreased
1988
VCF0839 Should the government reduce services in order to reduce spending
1-7 1=Many fewer services7=Many more services
1984-1988
VCF9049 Should the government spend more money on social security programs
1-3 1=Increased2=Same3=Decreased
1984-2012
VCF0890 Should the government spend more money on public schools
1-3 1=Increased2=Same3=Decreased
1984-2012
VCF0853 Should we place more emphasis on traditional values
1-5 1=Agree strongly5=Disagree strongly
1988-2008
VCF0876 Do you favor or oppose laws protecting homosexuals from discrimination
1,5 1=Favor5=Oppose
1988-2008
VCF0851 Newer lifestyles contribute to societal breakdown
1-5 1=Agree strongly5=Disagree strongly
1988-2012
VCF9131 Is it better for the government to do less or the government to do more
1,2 1=Less is better2=The government should do more
1992, 2000-2012
VCF0894 Should the government spend more money on welfare programs
1-3 1=Increased2=Same3=Decreased
1992-2012
VCF0886 Should the government spend more money on aid to the poor
1-3 1=Increased2=Same3=Decreased
1992-2012
VCF0816 Should the government ensure school integration
1,2 1=Yes2=No
1992, 2000
VCF0878 Should gays and lesbians be able to adopt children
1,5 1=Yes5=No
1992, 2000-2008
VCF0877a Should gays be allowed to serve in the military
1-5 1=Strongly support5=Strongly oppose
1992-2008
VCF0879a Should the number of immigrants entering the U.S. increase or decrease
1-3 1=Increased3=Decreased
1996-2012
VCF0892 Should the government spend more money of foreign aid
1-3 1=Increased2=Same3=Decreased
1996-2004
25
Table A1-2: GSS Items used to Build Policy Orientations Measures(Note: Each Question was asked in all years included in the analysis)
Variable Question Scores CodingHELPPOOR Should government improve standard of
living?1-5 1= Government should do
everything possible to help the poor5= People should help themselves
HELPNOT Should government do more or less to help individuals or should we leave it up to businesses?
1-5 1= Government should do more5= Government is doing too much
HELPSICK Should the government help pay for medical care?
1-5 1= Government should do more5= People should help themselves
HELPBLK Should the government aid blacks? 1-5 1= Government should do more5= Black people should help themselves
NATFARE Are we spending too much of welfare 1-5 1= too little3= about right5= too much
NATRACE Are we spending too much on improving the conditions of blacks
1-5 1= too little3= about right5= too much
EQWLTH Should the government reduce income differences?
1-7 1= Government should do everything possible7= Government should do nothing
CAPPUN Favor or oppose death penalty for murder? 1-2 1= Favor2= Oppose
ABANY Should a woman be able to get an abortion for any reason?
1-2 1= Yes2= No
ABRAPE Should a woman be able to get an abortion if she got pregnant as a result of rape?
1-2 1= Yes2= No
GRASS Should marijuana be made legal 1-2 1= Should2= Should not
BIBLE Is the bible the actual word of God? 1-3 1= Actual Word2= Inspired Word3= Ancient Book
FEFAM Is it much better if the man is the achiever outside the home and the woman takes care of the home and family
1-4 1= Strongly Agree4= Strongly Disagree
HOMOSEX Are sexual relations between two adults of the same sex always wrong?
1-4 1= Always Wrong4= Not at all Wrong
PREMARSX Is sex before marriage always wrong? 1-4 1= Always Wrong4= Not at all Wrong
PRAYER Do you approve of the Supreme Court’s ruling banning prayer in school?
1-2 1= Approve2= Disapprove
LETDIE1 Should a person be allowed to end their own life when they have a terminal illness?
1-2 1= Yes2= No
26
Table A2: The Conditioning Effect of Polarization on the Relationship Between Policy Orientations and White Vote Choice/Partisanship using GSS Data
VARIABLES Vote Choice 1
Vote Choice 2
Vote Choice 3
Party ID 1
Party ID 2
Party ID 3
Econ Orientations 0.02 0.16 0.03 -0.11 0.14 -0.02(0.12) (0.15) (0.16) (0.11) (0.15) (0.16)
Soc Orientations 0.56* 0.62* 0.57* 0.58* 0.66* 0.61*(0.11) (0.15) (0.16) (0.11) (0.16) (0.16)
White Southerner -0.05 -0.02 -0.02 0.07 0.05 0.05(0.04) (0.06) (0.05) (0.04) (0.06) (0.06)
Female 0.08 0.13* 0.14* 0.11* 0.17* 0.17*(0.04) (0.05) (0.05) (0.04) (0.05) (0.05)
Income -0.04* -0.05* -0.05* -0.03* -0.04* -0.04*(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
Age 0.01* 0.01* 0.01* 0.01* 0.01* 0.01*(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
Weekly Church -0.19* -0.15* -0.15* -0.18* -0.16* -0.15*(0.05) (0.06) (0.06) (0.05) (0.06) (0.06)
Catholic 0.10* 0.06 0.05 0.22* 0.17* 0.17*(0.05) (0.06) (0.06) (0.05) (0.06) (0.06)
Jew 0.36* 0.37* 0.36* 0.77* 0.84* 0.83*(0.11) (0.13) (0.13) (0.12) (0.14) (0.14)
Union 0.34* 0.33* 0.32* 0.38* 0.34* 0.34*(0.05) (0.06) (0.06) (0.06) (0.07) (0.07)
Education -0.01 -0.02* -0.02* -0.04* -0.04* -0.04*(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
Polarization -0.56* -0.65* 1.10 -0.08 0.14 2.27*(0.22) (0.27) (0.80) (0.21) (0.26) (0.79)
Polarization*Econ -0.78* -0.90* -0.74* -0.80* -1.06* -0.88*(0.14) (0.18) (0.19) (0.13) (0.17) (0.18)
Polarization*Soc -1.08* -1.26* -1.20* -1.07* -1.24* -1.18*(0.13) (0.18) (0.19) (0.12) (0.18) (0.19)
Policy Mood 0.01 0.03* 0.03* -0.01 -0.00 -0.00(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
Dem Vote% 3.43* 3.87* 3.86* -0.80 -0.96 -0.95(0.47) (0.57) (0.57) (0.45) (0.56) (0.56)
Implicit Racism -0.11 0.52 -0.12* 0.66*(0.06) (0.27) (0.06) (0.29)
Polarization*IR -0.75* -0.91*(0.32) (0.33)
Constant -2.11* -2.68* -4.12* 4.34* 4.16* 2.32*(0.73) (0.87) (1.07) (0.69) (0.87) (1.08)
Observations 6,089 4,029 4,029 9,551 6,226 6,226Pseudo R2/R2 0.24 0.24 0.24 0.22 0.22 0.22
Robust Standard errors in parentheses * p<0.05
27
Table A3-1: Factor Loadings for Implicit Racism Scale (ANES)
Question Factor Loading UniquenessBlacks have gotten less than they deserved .72 .48Blacks must try harder to succeed .72 .49Blacks should not have special favors .72 .48Conditions make it difficult for blacks to succeed .68 .54
Table A3-2: Factor Loadings for Implicit Racism Scale (GSS)
28
Table A4: The Implicit Racial Attitudes and Immigration Attitudes on White Vote Choice/Partisanship
VARIABLES Vote Choice Vote Choice Vote Choice PID PID PID
Econ Orientations -0.13 -0.32 -0.32 -0.39* -0.45*
-0.51*
(0.19) (0.20) (0.22) (0.11) (0.13) (0.13)Soc Orientations 0.07 -0.03 0.06 0.12 0.08 0.25
(0.17) (0.17) (0.21) (0.11) (0.12) (0.14)White Southerner 0.01 0.00 -0.05 0.14* 0.14* 0.06
(0.08) (0.08) (0.08) (0.05) (0.05) (0.05)Female -0.24* -0.23* -0.25* -0.07 -0.07 -0.04
(0.07) (0.07) (0.07) (0.05) (0.05) (0.05)Income -0.05 -0.05 -0.07 -0.06* -
0.06*-0.06*
(0.04) (0.04) (0.04) (0.02) (0.02) (0.02)Age 0.01* 0.01* 0.01* 0.01* 0.01* 0.01*
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)Weekly Church -0.04 -0.04 -0.08 -0.01 -0.02 -0.02
(0.08) (0.08) (0.08) (0.06) (0.06) (0.06)Catholic 0.05 0.04 0.05 0.20* 0.20* 0.20*
(0.08) (0.08) (0.08) (0.06) (0.06) (0.06)Jew 0.27 0.25 0.34 0.52* 0.51* 0.57*
(0.35) (0.35) (0.40) (0.12) (0.12) (0.12)Union 0.15 0.14 0.14 0.25* 0.25* 0.25*
(0.10) (0.10) (0.11) (0.07) (0.07) (0.07)Education -0.07* -0.07* -0.05 -0.08* -
0.08*-0.07*
(0.03) (0.03) (0.03) (0.02) (0.02) (0.02)Polarization -0.33 2.02* -0.59* 0.00 0.64 0.15
(0.29) (0.79) (0.25) (0.20) (0.51) (0.17)Polarization*Econ -0.38 -0.17 -0.24 0.04 0.12 0.16
(0.22) (0.24) (0.25) (0.13) (0.14) (0.15)Polarization*Soc -0.41* -0.30 -0.39 -0.30* -0.25 -0.39*
29
Question Factor Loading UniquenessBlacks poorer as a result of discrimination .56 .70Blacks have less ability to learn .50 .75Black poverty is a result of lack of educational opportunities .55 .70Blacks lack motivation to succeed .80 .36Blacks should not have special favors .65 .58
(0.19) (0.20) (0.24) (0.12) (0.13) (0.15)Policy Mood -0.00 -0.00 -0.02 -0.02 -0.02 -0.01
(0.02) (0.02) (0.01) (0.02) (0.02) (0.01)Dem Vote% 2.01* 1.98* 2.46* 1.12* 1.13* 2.22*
(0.69) (0.69) (0.94) (0.49) (0.49) (0.59)Party Distance -0.45* -0.46* -0.48* -0.45* -
0.45*-0.46*
(0.02) (0.02) (0.02) (0.01) (0.01) (0.01)IMP Score -0.19* 0.33* -0.03 0.12
(0.04) (0.16) (0.03) (0.12)Polarization* IMP Score
-0.59* -0.17
(0.19) (0.13)Immigrants Scale -0.02 0.00
(0.07) (0.04)Constant 0.40 -1.63 0.54 4.58* 4.05* 3.51*
(1.61) (1.73) (0.90) (1.04) (1.09) (0.59)
Observations 3,528 3,528 3,553 4,540 4,540 4,647Pseudo R2/R2 0.59 0.59 0.60 0.54 0.54 0.56
Robust standard errors in parentheses * p<0.05
30
Table A5: Factor Loadings Across Common Questions 1972-2012
31
1972 1976
1980 1984
1988 1992 1996
2000 2004
2008 2012
Economic DimensionGovernment Insurance
.45 .38 ~~ .44 .50 .45 .45 .36 .47 .47 .61
Government S.L. .61 .52 .67 .60 .65 .59 .67 .53 .64 .71 .71Aid to Blacks .67 .72 .69 .5 .63 .5 .61 .53 .62 .62 .61Social DimensionAbortion .53 .52 .66 .58 .59 .62 .62 .62 .64 .65 .61Women’s Role .44 .60 .51 .45 .48 .52 .48 .58 .48 .49 ~~Authority of the Bible ~~ ~~ .66 .69 .55 .62 .64 .58 .66 .61 .80
Table A6: The Conditioning Effect of Polarization on the Relationship Between Policy Orientations and White Vote Choice/Partisanship using Estimates of Partisan
Orientations Derived from a Hybrid Item Response Model
VARIABLES Vote Choice
Vote Choice PID PID
Econ Orientations -0.13 -0.07 -0.22* -0.11(0.08) (0.12) (0.07) (0.08)
Soc Orientations 0.36* 0.20 0.48* 0.22*(0.09) (0.12) (0.08) (0.08)
White Southerner -0.02 0.02 0.32* 0.34*(0.04) (0.05) (0.04) (0.04)
Female 0.04 -0.11* 0.03 -0.02(0.03) (0.05) (0.03) (0.04)
Income -0.13* -0.08* -0.15* -0.10*(0.02) (0.02) (0.02) (0.02)
Age 0.00* 0.00* 0.01* 0.01*(0.00) (0.00) (0.00) (0.00)
Weekly Church -0.15* -0.02 -0.14* 0.01(0.04) (0.05) (0.04) (0.04)
Catholic 0.19* 0.12* 0.48* 0.37*(0.04) (0.05) (0.04) (0.04)
Jew 0.61* 0.39* 1.04* 0.70*(0.15) (0.19) (0.09) (0.09)
Union 0.35* 0.29* 0.56* 0.42*(0.04) (0.06) (0.04) (0.04)
Education -0.06* -0.05* -0.14* -0.10*(0.01) (0.02) (0.01) (0.01)
Polarization -0.74* -0.38* -0.72* 0.01(0.14) (0.19) (0.14) (0.14)
Polarization*Econ -0.83* -0.54* -0.64* -0.20*(0.12) (0.17) (0.10) (0.10)
Polarization*Soc -1.13* -0.64* -1.27* -0.48*(0.12) (0.17) (0.10) (0.10)
Policy Mood -0.01* -0.00 -0.02* -0.00(0.00) (0.01) (0.00) (0.00)
Dem Vote% 3.06* 2.77* 0.81* -0.72*(0.27) (0.38) (0.28) (0.29)
Party Distance -0.46* -0.46*(0.01) (0.01)
Constant 0.04 -0.88* 5.57* 4.79*(0.25) (0.35) (0.26) (0.27)
Observations 8,785 6,849 12,359
9,002
Pseudo R2/R2 0.26 0.50 0.23 0.45Robust standard errors in parentheses * p<0.05
32
Table A7: The Conditioning Effect of Polarization on the Relationship Between Policy Orientations and White Vote Choice in the South and Non-South
VARIABLES South South Non-South
Non-South
Econ Orientations 0.15 0.26 0.15 0.09(0.13) (0.18) (0.08) (0.12)
Soc Orientations 0.46* 0.40* 0.10 0.01(0.11) (0.16) (0.08) (0.10)
Female 0.02 -0.21* 0.05 -0.07(0.06) (0.09) (0.04) (0.05)
Income -0.07*
0.00 -0.15* -0.12*
(0.03) (0.05) (0.02) (0.03)Age 0.01* 0.00 0.00 0.00
(0.00) (0.00) (0.00) (0.00)Weekly Church -
0.21*0.01 -0.19* -0.07
(0.07) (0.10) (0.04) (0.06)Catholic 0.13 0.15 0.17* 0.08
(0.09) (0.11) (0.04) (0.06)Jew 0.84* 0.87* 0.61* 0.33
(0.26) (0.39) (0.15) (0.19)Union 0.26* 0.06 0.39* 0.36*
(0.09) (0.14) (0.05) (0.07)Education -
0.07*-0.04 -0.06* -0.06*
(0.02) (0.03) (0.01) (0.02)Polarization -
1.10*-0.58 -0.29 -0.10
(0.25) (0.35) (0.17) (0.22)Polarization*Econ -
1.15*-0.95* -1.18* -0.71*
(0.19) (0.26) (0.12) (0.17)Polarization*Soc -
1.16*-0.91* -0.63* -0.27
(0.15) (0.21) (0.11) (0.15)Policy Mood -0.01 -0.00 -0.00 0.01
(0.01) (0.01) (0.00) (0.01)Dem Vote% 3.65* 3.22* 3.17* 2.86*
(0.52) (0.73) (0.32) (0.44)Party Distance -0.43* -0.47*
(0.03) (0.02)Constant -0.64 -1.03 -0.75* -1.55*
(0.45) (0.64) (0.30) (0.41)
Observations 2,536 1,947 6,249 4,902Pseudo R-squared 0.27 0.50 0.26 0.50
Robust standard errors in parentheses * p<0.05
33
34
Table A8: The Conditioning Effect of Polarization on the Relationship Between Policy Orientations and White Partisanship in the South and Non-South
VARIABLES South South Non-South
Non-South
Econ Orientations 0.26* 0.14 -0.01 -0.13(0.12) (0.13) (0.07) (0.07)
Soc Orientations 0.58* 0.42* 0.25* 0.14(0.11) (0.12) (0.07) (0.07)
Female 0.03 -0.02 0.01 -0.03(0.06) (0.07) (0.04) (0.04)
Income -0.14*
-0.10* -0.14* -0.09*
(0.03) (0.03) (0.02) (0.02)Age 0.01* 0.01* 0.00* 0.00*
(0.00) (0.00) (0.00) (0.00)Weekly Church -0.13 0.04 -0.22* -0.05
(0.07) (0.08) (0.05) (0.05)Catholic 0.35* 0.21* 0.50* 0.42*
(0.09) (0.09) (0.05) (0.05)Jew 0.44 0.13 1.21* 0.83*
(0.25) (0.22) (0.10) (0.09)Union 0.53* 0.35* 0.55* 0.42*
(0.10) (0.10) (0.05) (0.05)Education -
0.12*-0.08* -0.14* -0.11*
(0.02) (0.03) (0.01) (0.02)Polarization -
1.51*-0.53* 0.01 0.37*
(0.24) (0.24) (0.15) (0.15)Polarization*Econ -
1.15*-0.49* -0.99* -0.22*
(0.15) (0.15) (0.10) (0.10)Polarization*Soc -
1.18*-0.65* -0.82* -0.30*
(0.13) (0.14) (0.10) (0.10)Policy Mood 0.01 0.01 -0.01* -0.00
(0.01) (0.01) (0.00) (0.00)Dem Vote% 1.23* -1.18* 0.98* -0.38
(0.53) (0.57) (0.33) (0.33)Party Distance -0.42* -0.46*
(0.02) (0.01)Constant 4.63* 4.85* 4.85* 4.37*
(0.48) (0.51) (0.30) (0.31)
Observations 3,747 2,638 8,612 6,364 R-squared 0.23 0.42 0.25 0.47
Robust standard errors in parentheses * p<0.05
35
36
Table A9: The Conditioning Effect of Changes in Polarization on the Relationship Between Policy Orientations and Vote Choice/Partisanship
VARIABLES Vote Choice PID
Economic Orientations -0.39* -0.35*(0.08) (0.05)
Social Orientations 1.69* -0.18(0.57) (0.39)
Latino -0.82 -0.42(0.61) (0.34)
Black -0.18* 0.01(0.07) (0.04)
White Southerner -0.74 -0.75*(0.51) (0.33)
Female 0.39* 0.68*(0.11) (0.07)
Income 1.12* 0.96*(0.12) (0.06)
Age -0.03 0.16*(0.06) (0.05)
Weekly Church -0.06 0.03(0.05) (0.04)
Catholic -0.11* -0.10*(0.03) (0.02)
Jew 0.00* 0.01*(0.00) (0.00)
Union -0.03 -0.05(0.06) (0.04)
Education (6-category) 0.12 0.24*(0.06) (0.05)
Δ Polarization 0.54* 0.72*(0.17) (0.12)
Δ Polarization*Economic
-0.40* -0.38*
(0.06) (0.05)Δ Polarization*Social -0.09* -0.06*
(0.02) (0.01)Policy Mood -0.00 -0.02*
(0.01) (0.00)Democratic Vote Share 2.34* 1.44*
(0.37) (0.25)Party Distance -0.46* -0.44*
(0.02) (0.01)Constant -0.06
(0.37)5.24*(0.26)
Observations 2,259 6,961R-squared 0.53Robust standard errors in parentheses * p<0.05
37
Table A10: Models Regressing Perceived Party Distance on Economic and Social Orientations
VARIABLES 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012
Economic Orientations 0.99* 0.57* 1.02* 1.10* 1.07* 1.01* 1.24* 1.06* 1.58* 1.40* 1.87*(0.06) (0.06) (0.07
)(0.06) (0.07) (0.05) (0.07
)(0.10) (0.08) (0.07) (0.05)
Social Orientations 0.39* 0.35* 0.55* 0.48* 0.62* 0.56* 0.70* 0.87* 0.87* 0.95* 0.91*(0.06) (0.06) (0.07
)(0.06) (0.06) (0.06) (0.06
)(0.11) (0.08) (0.07) (0.05)
Constant 0.61* 0.07 -0.04 -0.15* 0.47* -0.04 0.25* 0.15 0.12 -0.07 0.16*(0.06) (0.06) (0.07
)(0.06) (0.06) (0.05) (0.06
)(0.10) (0.08) (0.09) (0.05)
Observations 1,358 1,286 669 953 897 1,394 1,115 444 740 1,020 2,542R-squared 0.24 0.13 0.30 0.30 0.31 0.32 0.37 0.34 0.47 0.45 0.53
Robust standard errors in parentheses * p<0.05
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Figure A1: The Marginal Effect of a One-Unit Increase in Policy Orientations across a Range of Values of PolarizationY-Axis values reflects the change in the probability of voting for/identifying as Democrats associated with a 1-point increase
along the policy orientations scale using GSS Data(Generated using the coefficients from vote choice model 1 and partisanship model 1 in Table A2)
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Figure A2: The Gap Between the Overall Median Economic and Social Orientations and the Median White Orientations with Line of Linear Best Fit Calculated using
GSS Data
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Figure A3: The Marginal Effect of a One-Unit Increase in Implicit Racism across a Range of Values of Polarization (years 1988-1992, 2000-2012)
Y-Axis values reflects the change in the probability of voting for/identifying as Democrats associated with a 1-point increase along the policy orientations scale
(Generated using the coefficients from vote choice model 1 and partisanship model 1 in Table A4)
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Figure A4: Median Group Positions in 1972, 1988, 2000, and 2012
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Figure A5: Median Group Positions and 95% Confidence Intervals on the Economic Dimension (Negative scores reflect more liberal positions)
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Figure A6: Median Group Positions and 95% Confidence Intervals on the Social Dimension (Negative scores reflect more liberal positions)
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Figure A7: White Distance from the Median on the Economic and Social Dimensions—Actual and Weighted to Reflect 1972 Demographics
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Figure A8: The Gap Between the Overall Median Economic and Social Orientations and the Median White Orientations with Line of Linear Best Fit using IRT Generated Estimates of Policy Orientations
46
Figure A9: The Marginal Effect of a One-Unit Increase in Policy Orientations across a Range of Values of Polarization
Y-Axis values reflects the change in the probability of voting for/identifying as Democrats
associated with a 1- point increase along the policy orientations scale derived from a Hybrid IRT Model
(Generated using the coefficients from vote choice model 1 and
partisanship model 1 in Table A4)
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Figure A10: The Marginal Effect of a One- Unit Increase in
Policy Orientations across a Range of Values of
Change in Polarization. Y-Axis values reflects the
change in the probability of voting for/identifying
as Democrats associated with a 1-point increase along the policy orientations
scale(Generated using the coefficients from vote
choice model 1 and partisanship model 1 in
Table A2)
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