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

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Campbell, Angus, Phillip Converse, Warren E Miller & Donald Stokes. 1960. The

American Voter. Chicago, IL: University of Chicago Press.

Carsey, Thomas & Geoffrey Layman. 2006. Changing Sides of Changing Minds? Party

Identification and Policy Preferences in the American Electorate, American

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Dynamic Model of Individual-Level Change in Partisan and Ideological

Predispositions. Political Research Quarterly, 69(4): 703-715.

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Comparing the Power of Explicit and Implicit Racism Measures in 2008, Political

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University Press.

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22

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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

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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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