Class Voting Paper

The End of History? Three Models of Class Voting in US Presidential Elections

John Coutley, Department of Sociology, Indiana University

Key Words: Class, Voting, United States

Word Count: 8,236 Tables: 6 Figures: 3

ABSTRACT

Does class matter in electoral politics? This question has been strongly contested among

western democracies in general and in the United States in particular. Central to this debate is

the definition and operationalization of class. Early studies of class voting typically utilized a

manual/nonmanual dichotomous measure of class, while subsequent research opted instead

for a more complex, multi‐categorical occupational class schema that better reflected

postindustrial society. Despite widespread acceptance of this occupational class schema,

especially within sociology, some advocate instead for the use of income as an

operationalization of class, arguing that relative income positions better capture overall life

chances, diverging economic interests, and the effects of economic inequality. This study

develops models of the 2004, 2008, and 2012 presidential elections using each of these

different operationalizations of class. The results show that while the manual/nonmanual

operationalization would indicate an absence of class voting, both the complex occupational

operationalization and the income operationalization indicate the continuing salience of class.

Income, in particular, indicates that traditional class voting persists in the United States

The End of History? Three Models of Class Voting in US Presidential Elections John Coutley

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Does class matter in politics? Certainly, since the financial collapse of 2008 and the

resulting Great Recession class issues have dominated American political discourse. Longer‐

term trends also suggest that class should be increasingly relevant in political decision‐making.

Over the past forty years, economic inequality has increased while social mobility has

decreased (Bartels 2008; Beller and Hout 2006), and the past six years have seen the worst

economic performance in almost eighty years, at least for working and middle‐class individuals.

Class has become an increasingly salient factor in American lives. The question remains of

whether class is a salient factor in political decision‐making. Empirical analyses of class politics

continues to produced mixed results, with arguments for continued traditional class politics,

the decline of class politics, and the emergence of new patterns of class politics all remaining

prevalent in the literature.

Central to the debate over the relationship between class and electoral politics is the

definition and operationalization of class. Early studies of class voting typically utilized a

manual/nonmanual dichotomous measure of class (Goldthorpe 2001). More recent work has

employed a multi‐categorical occupation‐based class schema prevalent in social mobility

studies, originally developed by Erikson, Goldthorpe, and Portocarero (henceforth known as the

“EGP class schema”) (Erikson, Goldthorpe, and Portocarero 1979; Evans 1999; Hout, et al 1995;

Manza and Brooks 1999; Brady; Sosnaud, and Frenk 2009). Finally, others advocate instead for

the use of income as an operationalization of class arguing that relative positions within the

income distribution better capture overall life chances, diverging economic interests, and the

effects of economic inequality (Bartels 2005; 2008; Brewer and Stonecash 2001; Stonecash, et


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al. 2000). These three definitions of class produce consistently different conclusions about the

relationship between class and voting.

While class‐voting researchers argue at length about which measures of class are most

appropriate, few have performed in‐depth empirical comparisons of the different measures of

class (Nieuwbeerta 1996; Stonecash, et al. 2000). Different measures of class imply different

mechanisms by which outcomes such as vote choice are affected. For example, do economic

inequalities have a greater influence on political decision‐making or are the relational

characteristics at the site of production more influential (Brady, et al 2009; Weeden and Grusky

2005)? By comparing different measures of class, this study will not only shed light on the

ongoing debate over whether class voting exists, but how class operates. This paper will add to

the literature by analyzing the 2004, 2008, and 2012 presidential elections, specifying three

different operationalizations of class‐based voting: manual/nonmanual occupational

dichotomy, the EGP class schema, and income categories. While many class‐voting studies

examine the relationship between class and vote cross‐nationally and/or over time, estimating

models of class voting in one country over time, and that vary only in the focal variable, will

allow for a careful and detailed analysis of the differences between measures of class. By

exploring in a critical and comparative manner how the prevailing conceptualizations of class

impact empirical studies of class voting, this paper will clarify how and why decisions about

class operationalizations produce different, sometimes contradictory results.

Literature Review

Before reviewing the developments and debates in class‐voting research, it may be

beneficial to step back and ask what, fundamentally, is meant by “class.” Erik Olin Wright notes


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that Marx himself, whose work deals so closely with class, never provided a concrete definition

(1985), though he notes that Marxist class analysts generally define class as groups sharing

“common structural positions within the social organization of production” (1979:4). Max

Weber, on the other hand defines a class as a group of people that share a similar “specific

causal [economic] component of their life chances” (CSP:181). While there are other

conceptualizations, Marxist and Weberian definitions tend to dominate current sociological

class analysis. Regardless of how class is conceptualized, one basic commonality is that class

describes a “structure of inequality” (Chan 2007). In the case of class‐voting, class is generally

understood as a structure of economic inequality. Thus, much of the debate in class‐voting

research centers on how to operationalize class.

Many have charted the historical course of class voting scholarship over the past sixty

years (Evans 1999; Nieuwbeerta 1996; Hout et al 1995; Goldthorpe 2001). Generally, that

history begins with the era of the Alford index‐ a method for measuring absolute class voting‐

and a broad consensus that the salience of class as a determinant of vote‐choice was declining,

that voting behavior was dealigning from class. A second era of scholarship emerged in the

1980s, criticizing the use of the Alford index and the conclusion of the decline of class salience,

and instead proposed a more complex measure of class and the use of more sophisticated

multivariate statistical techniques (Manza and Brooks 1999; Hout, et al. 1995; Evans 1999;

Goldthorpe 2001). As a result, new conclusions emerged that, at least in the United States,

there has been some type of class realignment‐ that class was still salient, but classes were

forming new allegiances to parties and candidates (Manza and Brooks 1999; Hout, et al. 1995;

Brady, Sosnaud, and Frenk 2009). Recently, these conclusions have been challenged, with


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some research, using income categories to operationalize class and finding that there is

continued traditional strong class voting in many postindustrial western democracies (Bartels

2005; Bartels 2008; Brewer and Stonecash 2001; Stonecash, et al. 2000; van der Waal, et al.

2007).

Class Dealignment and the Alford Index

Goldthorpe (2001:106‐8) identifies four contending reasons often cited for the decline

in class voting found by some studies. One reason is that traditional class divisions have given

way to sectoral divisions, a split between those employed in the public sector versus those

employed in the private sector, and between those who rely on the public sector versus those

who rely on the private sector for the provision of health, housing, education, and transport. A

second reason cited is that social structural locations, however defined, are becoming less

influential, especially among younger generations whose political behavior is shaped more by

belief and value systems. Third, voters are making political decisions based upon rational

assessments of particular issues and policies, rather than voting unthinkingly for the “natural

party” of their class, resulting in volatility from election to election in both the strength of party

support and its social composition.

Fourth, Clark and Lipset ([1991]2001), perhaps the most well‐known of the class‐

dealignment camp, argue that class salience in the political sphere is a function of the extent of

polarization of hierarchical divisions in the labor market and society (Clark and Lipset

[1991]2001). In the second half of the twentieth century, there has been a diversification of

occupational structures, moving away from regimented industrial jobs and towards a more

diverse array of service and information jobs, characterized by more middle‐management and


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more specialized knowledge jobs, decreasing hierarchical relationships and decreasing shared

labor market experiences (Clark, Lipset, and Rempel [1993]2001:83). At the same time, the

development of welfare states have alleviated the boom‐bust cycles, improving the condition of

the working class and diffusing class conflict (Clark, et al. [1993]2001:83). Consequently, the

fragmentation of the working class and the rise of the middle class have rendered traditional

conceptions of class obsolete (Clark, et al. [1993]2001). Further, traditional political divisions

are being redefined. There are now “two lefts”‐ one representing a traditional working‐class

constituency and another representing a socially liberal professional class. As society becomes

wealthier, people begin to take basic material necessities for granted and grow more concerned

with lifestyle issues, a trend that is most pronounced among young, more affluent persons

(Clark and Lipset [1991]2001 47).

Hechter comes to a similar conclusion as Clark and Lipset‐ cultural concerns have grown

in importance, supplanting class identity as a significant influence on vote‐choice. Hechter

claims that two trends have resulted in status, and hence culture, becoming more salient than

class (2004). On the one hand, class boundaries have become more permeable, and class

organizational capacity and class consciousness have declined. At the same time, on the other

hand, status boundaries have become less permeable, status‐group organizational capacity has

not declined, and status‐group consciousness has grown (2004:408‐9,412‐3). He credits claims

that the rise of direct rule in the form of the welfare state, which increased social welfare

benefits and negated many of the incentives for class‐based organizing. This development

provided opportunities for status‐groups to organize and demand minority‐group benefits,

resulting in the rise of cultural politics and the decline of class politics (Hechter 2004:429‐30).


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The classic measure upon which many of the class‐dealignment arguments rest is the

Alford index (Alford 1967, Clark and Lipset [1991]2001, Clark, et al [1993]2001). The Alford

index uses a 2x2 cross‐tabulation of class, measured as manual or nonmanual occupation, by

party, measured as left or non‐left party. The index is calculated by taking the difference

between the percentage of manual workers that vote for the left party and the percentage of

nonmanual workers that vote for the left party. Thus, if 60 percent of manual workers vote for

the left party and 40 percent of nonmanual workers vote for the left party, then the Alford

index is 20; a low or negative score means that there is little or no traditional class voting

(Alford 1967).

Class Realignment and the EGP Class Schema

While the Alford Index was the standard measure of class voting for many years, it has

been widely challenged on theoretical and methodological grounds (Nieuwbeerta 1996; Evans

1999; Weakliem 1995; Hout, et al 1995; Goldthorpe 2001). The manual/nonmanual dichotomy

has been criticized in that it can obscure variations within the classes, thus showing changes in

class voting that in reality are changes in class composition (Evans 8). Further, the rise of the

postindustrial era and the growth of low wage white‐collar and retail employment render the

manual/nonmanual distinction less theoretically salient.

As a result, many favor a more complex, multi‐categorical class schema (Hout, et al.

1995; Manza and Brooks 1999:Chap. 3; Brady, et al. 2009, Evans, Nieuwbeerta 1996,

Goldthorpe 2001). Many sociologists studying class voting have adopted some version of the

EGP class‐schema (Erikson, et al 1979; Evans 1999; Manza and Brooks 1999; Hout, et al 1995;


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Brooks, Manza, and Bolzendahl 2003; Nieuwbeerta1 1996). Manza and Brooks (1999:57) argue

for a well‐designed relational class scheme that distinguishes groups that differ in educational

credentials, trusted salaried employees vs. restricted wage employees, and those who possess

organizational assets in their employment situations vs. those who do not possess such assets.

They contend that this model best captures long‐term differences in life chances that current

income may miss. Brady, et al. (2009) make similar arguments in favor of their similar

operational definition.

By and large, research utilizing a type of the EGP class schema tends to conclude that,

while class remains a salient factor in American electoral politics, some type of realignment has

occurred beginning around 1970 (Manza and Brooks 1999; Hout, et al. 1995; Brady; Sosnaud,

and Frenk 2009; Evans 2000). Manza and Brooks find that while there is overall stability in the

class cleavage, traditional class‐party alignments are shifting. Between 1972 and 1992

professionals trended more Democratic, while in 1980 self‐employed and unskilled workers

experienced an abrupt shift towards the Republican Party (Manza and Brooks 1999:75; Hout,

Brooks, and Manza 1995:825). Increasingly liberal views on social issues are credited with the

shift of professionals towards the Democratic Party, while high levels of economic

dissatisfaction under Democratic administrations are identified as the causal factors for the

rightward shift of the self‐employed and unskilled workers (Manza and Brooks 1999:78). Brady,

et al. (2009:129) find that after 1992, working‐class men and women begin to differ sharply,

1 Nieuwbeerta’s expansive cross‐national study actually finds that most of the 20 western democracies included in his study indicate a general declining trend in class voting. However, the United States is an exception that shows not a decline, but rather a trendless fluctuation (370)


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with men strongly supporting Republicans and women tending to support the Democratic

Party.

Income and Traditional Class Voting Alignments

Some researchers find occupationally based class definitions like the EGP class schema

to be problematic, and instead argue that income is a more useful indicator of class (Bartels

2005; Bartels 2008; Brewer and Stonecash 2001; Stonecash 2006; Stonecash, et al. 2000; van

der Waal, et al. 2007). “Income reflects the resources individuals have at their disposal, which

significantly affects their access to opportunities and quality of life” (Brewer and Stonecash

2001:137). Income is associated with quality of schools, chances of attending college, and

whether a person has health insurance or pensions (Stonecash, et al. 2000:738). Other

measures, such as the EGP class schema, capture situations that are not directly related to a

person’s economic position, which can confuse as class effects those effects that might be

better understood as cultural or status driven (Brewer and Stonecash 2001; van der Waal, et al.

2007; Chan 2007). Using relative income position offers an objective, clear‐cut measure of class

that focuses on the effects of material economic differences upon voting behavior and political

affiliation (Bartels 2008:71).

Those that utilize income as an indicator of class tend to find a continuation of

traditional class politics, with low‐income voters more likely to support the Democratic Party

and middle‐ and upper‐income voters showing stronger support for the Republican Party

(Bartels 2005; Bartels 2008; Brewer and Stonecash 2001; Stonecash, et al. 2000; van der Waal,

et al 2007; Gelman 2010). van der Waal, et al (2007) argue that rather than the zero‐sum game

that Hechter (2004:430) suggests, class voting has in fact become stronger in the United States,


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but is being overwhelmed by crosscutting cultural voting “driven by a cultural dynamic that is

rooted in educational differences” (van der Waal, et al.2007:417). They contend that using

occupation to operationalize class essentially confuses class voting, based on economic

position, with cultural voting, based on cultural capital, leading to the flawed conclusion that

class voting has declined, when in fact it has increased, just not as much as cultural voting (van

der Waal, et al. 2007:409).

Bartels (2006; 2008) finds that rather than being eclipsed by cultural concerns, as

Hechter (2004) concludes, or overwhelmed by cultural voting, as van der Waal, et al (2007)

conclude, traditional class voting has actually strengthened with the gap in the share of the

Democratic vote between low‐income and high‐income voters growing over the past forty

years (Bartels 2008:73). Brewer and Stonecash find that class, as opposed to race, accounts for

declining Southern support for the Democratic Party, as the middle and upper class moved

more toward the Republican Party (2001). However, these declines are offset with steadily

increasing working‐class support outside of the South between the 1950s and 1990s, and large

increases in Democratic support amnd middle‐income groups in the 1990s (Stonecash, et al.

2000).

Andrew Gelman presents findings that complicate both Bartels’ and van der Waal, et

al.’s conclusions (2010).2 Gelman seeks to explain the red state/blue state phenomenon that

leads many in the media to conflate low‐income red state support for Republicans with low‐

income individual support for the Republicans. He finds that while income predicts vote choice

2 Andrew Gelman doesn’t specifically engage around the concept of “class‐voting,” in the same vein as this discussion.


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in every state, with high‐income voters more likely than low‐income voters to vote Republican,

this relationship is much more pronounced in poor red states than wealthy blue states. In

other words, in the rich blue states, high‐income voters are only slightly more likely to vote

Republican than low‐income voters, but in poor red states that difference is much greater.

Rather than a general continuation of traditional class voting (Bartels 2008; Stonecash, et al.

2000; Brewer and Stonecash 2001; Stonecash 2006) or class voting being overwhelmed by

cultural voting altogether (van der Waal, et al. 2007), Gelman’s results suggest that while low‐

income voters continue traditional class voting, high income votes are split by cultural and

religious beliefs and values.

The evolution of class voting research is one of competing claims resulting from

competing operationalizations of class. It is clear that class‐voting studies based on occupation

yield different results and different conclusions than studies based on income. What isn’t clear

is the reason for these differences. Many of these studies assume that their preferred variable

better captures the same underlying phenomenon more completely than the other. But

perhaps they are in fact capturing different aspects of that phenomenon. Occupation‐based

class voting models may be capturing the effects of different authority relations,

responsibilities, educational requirements, or other job‐related differences more so than

economic inequalities or differences in overall life chances. Income‐based class‐voting models,

on the other hand, are almost exclusively capturing economic differences that are closely

associated with many determinants of life chances (future earnings, benefits, education, etc…).

Further, an income‐based class map generates a few class categories of relative‐income

positions, categories with clearly different economic interests. Complex occupational class


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maps, on the other hand, can’t necessarily clearly differentiated in any meaningful way the way

relative‐income can or the way that manual/nonmanual class served as a proxy for owners and

workers in earlier generations. Therefore, an income‐based class model may be better

positioned to capture class antagonisms that result and the impact they have on vote choice.

What follows is an empirical analysis of class voting in the 2004, 2008, and 2012 US

presidential election that use three different operationalizations of class: manual/nonmanual

occupation, EGP occupation, and income.3 By varying the focal independent variable, this

research design will allow for a systematic analysis of the differences that class

operationalization makes in our understanding of class politics.

Data and Methods

This paper uses data from the 2004, 2008, and 2012 American National Election Study

(ANES) Time Series surveys. During each election cycle, the ANES conducts face‐to‐face pre‐

and post‐election surveys. In 2004 and 2008, the surveys used multistage random sampling to

identify the sample.4 5 The preliminary release of the 2012 survey is unique in that it includes

both a face‐to‐face and online component. The online component recruited respondents from

3 While some have used education as a measure of class (e.g. defining the “working class” as those without a college degree), there is good reason not to operationalize class by level of education. Bartels (2008:69‐71) notes that, among other reasons, there is a lack of correspondence between education and concrete economic circumstances, which makes using education itself as a measure of class problematic. 4 In 2004, the sample was derived in four stages: primary sampling of metropolitan statistical areas (MSA), New England statistical areas, and non‐MSA counties; secondary sampling of area segments; tertiary sampling of households; and random selection of an eligible respondent from selected households. 5 The 2008 American National Election Survey (2008 ANES) utilized a complex survey design with a purposive oversample of African American and Latino respondents. The target population was all English and Spanish‐speaking US citizen residing in the continental United States that were 18 or older as of October 31, 2008. The first three stages of sample selection were hierarchically clustered into counties, census tracts, and census block groups. Stage 4 selected a sample of residential households from a mailing list for the selected census block group. At the fifth and final stage, the interviewer selected an eligible respondent. Two sets of weight variable were included in the dataset to adjust for the oversample (Lupia, Krosnick, Luevano, DeBell, and Donakowski 2009:7‐8).


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members of the KnowledgePanel, a panel of regular respondents administered by GfK (5). The

panel is recruited through address‐based and random‐digit dialing probability sampling, from

which a sample of respondents for the 2012 ANES was drawn (21). The online component of

the sample is appealing in this case because it includes pre‐coded data on occupation, whereas

the data for the open‐ended question on occupation for the face‐to‐face sample has yet to be

released. Because of the complex sampling methods utilized in all three surveys, each includes

weights to correct for the unequal probability of selection (DeBell 2010:15). All regressions are

estimated using the appropriate weights with the “svy” commands in Stata 11.2.

Following the lead of previous studies, in addition to the key occupational and income

independent variables, eight independent variables are included as possible predictors: age,

education, gender, ideological self‐identification, race, region, religion, and household union

membership. These variables are included as controls since they are often theorized to impact

vote choice (Hout, et al. 1995:810; Manza and Brooks 1999; Brady, et al. 2009: 123; Winders

1999:837‐8 for succinct review of the research). Table 1 shows the descriptive statistics of all

variables.

Dependent and Key Independent Variables

The presidential vote‐choice variable was recoded into an indicator variable (1: voted

for the Democrat; 0: did not vote for the Democrat). A summary variable of household income

combines data for both household and individual income6. In keeping with the previous

6 For household income, respondents are asked, “Please look at the booklet and tell me the letter of the income group that includes the income of all members of your family living here in [the prior calendar year] before taxes. This figure should include salaries, wages, pensions, dividends, interest, and all other income.” For individual income, respondents are asked, “Now we are interested in the income that you yourself received in [the prior calendar year] , not including any of the income received by (your spouse and) the rest of your family. Please look at this page and tell me the income you yourself had in [the prior calendar year] before taxes. This figure should


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practice of class‐voting research that operationalizes class as household income, this study

recoded the variable into four income categories7 (Bartels 2008; Brewer and Stonecash 2001).

The survey includes detailed information on both the respondent’s current and former

occupation.8 Whether or not a respondent is currently or was recently an owner was also

captured. The 2004 data coded occupation into the 2000 Standard Occupation Classification

(SOC) 25 major groups; the 2008 data were coded into the 2010 SOC 97 minor groups; and the

2012 data were coded into the 2010 SOC 23 major occupation groups9. These responses were

coded into two different occupation variables. The first is a close approximation of the EGP

schema utilized by Manza and Brooks (1999): professionals, managers and administrators,

routine white‐collar workers, skilled manual labor, and unskilled manual labor.10 The 2004 and

2012 data were recoded from the SOC major groups, while the 2008 data was first collapse

from the 97 SOC minor groups into the major codes before finally being recoded into the EGP

class schema. Then, respondents that were self‐employed were recoded as owners and

proprietors. Finally, the second occupation variable was coded from the EGP variable into a

manual/nonmanual indicator variable (1=Nonmanual).11

Control Variables

include salaries, wages, pensions, dividends, interest, and all other income.” The variable is categorical, with 25 income categories ranging from $0‐2,999 to $150,000 or more. 7 1= $0‐34,999; 2=$35,000‐74,999; 3=$75,000 to $124,999; 4=$125,000+. 8 Respondents were asked “What is (was) your main occupation? (What kind of work do (did) you do? What are (were) your most important activities or duties?)” 9 The 2010 SOC major codes were revised and combined business occupations with finance specialist and construction trades with extraction trades. Neither change resulted in differences in final codes. 10 Manza and Brooks included a category for non‐fulltime labor‐force participation. However, giving the available data on past occupations, these respondents were coded as their most recent occupation rather than a residual category. 11 Manual= Skilled and unskilled manual labor. Nonmanual= Professionals, managers, owners, and routine white‐collar workers.


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Age, gender, race, region, and race were all determined using a pre‐interview household

screener tool. In the 2004 and 2008 data, age was quantitative and recoded into four age

categories; the 2012 data was already restricted to age categories and recoded to match the

2004 and 2008 coding.12 Gender was recoded into an indicator variable (1=female). Region was

coded as an indicator variable (1=South) to control for the South’s often unique post‐Civil War

voting trends (Bartels 2008; Brewer and Stonecash 2001).13 Race was recoded into an indicator

variable (white=1, nonwhite=0).14 Race marks the one point of departure between the models

to be estimated. In the 2008 sample, only 33 nonwhite respondents reported voting for

someone other than Barack Obama. This lack of variation is probably indicative of the historic

nature of Barack Obama’s candidacy. Because of this, race is dropped as a control in 2008

Education was derived from two questions about high school and postsecondary

education and combined into a single variable.15 Religion and union membership were both

recoded into indicator variables as well. 16 17 Ideological identification was coded on a seven‐

point likert scale from extreme liberal to extreme conservative; because of small cell sizes on

the extremes, extreme liberal was combined with liberal and extreme conservatives were

combined with conservatives to create a five point scale.

There were significant missing data across all three surveys for presidential vote choice,

in 2004 and 2012 for occupation, and in 2004 and 2008 on income, and is shown in Table A.

12 17‐29 year‐olds, 30‐49, 50‐64, 65 and up. 13 Originally coded into four census regions, 1=northeast, 2=north central, 3=south, and 4=west 14 Originally coded as 1=white, 2=black/African America, 4=other Race, 5=white and another race, 6=black and another race, and 7=white, black, and another race 15 1= Less than high school, 2= High school diploma, 3= Some college/Associate’s degree, 4= Bachelor's degree, and 5= Advanced Degree. 16 Respondents were asked “Do you consider religion to be an important part of your life, or not?” 17 Respondents were asked “Do you or anyone else in this household belong to a labor union?”


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Most of the missing data on presidential vote choice were nonvoters or did not participate in

the post‐election survey, and listwise deletion was deemed appropriate. Missing income data

were problematic in the 2004 and 2008 data. Analysis indicated that missing data was

significantly correlated with education. As such, the conditional median income by education

was imputed. After estimating the models using both the imputed data and listwise deletion, I

found that there was no significant difference in the statistical significance, size, or direction of

the income coefficients, and therefore listwise deletion is preferred. Finally, missing occupation

data is most extreme for the 2012 data. Since the preliminary release of the 2012 data

provides no information concerning the nature of the missing data (it is simply assigned a

generic “missing” code), imputation was not attempted and listwise deletion was used. The

final analytic samples for each year are: 2004 N=647; 2008 N=1,319; and 2012 N= 2,214.

Results

Four unconditional and full models were estimated for each election, found on Tables 2,

3, and 4: manual/nonmanual class, EGP class, income, and a model including both EGP class and

income; models that include both income and race also include an interaction term. In addition

to the logistic regression, the Alford index was calculated for the manual/nonmanual models.

Assessment of the overall goodness of fit of models that correct for complex sampling are

limited. An F‐adjusted mean residual test was performed using the command “svylogitgof” to

assess model fit; a significant result indicates poor model fit (Archer and Lemeshow 2006).

Adjusted Count R2 statistics were also calculated for each model. Predicted probabilities of

voting Democratic were calculated with all control variables set at their mean; they are

presented in Table 5 and Figures 1, 2, and 3.


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Manual/Nonmanual Class Voting.

The coefficient for the manual/nonmanual variable is not significant in 2004 and 2012,

but it is, somewhat surprisingly, significant in 2008. The Alford index also indicates significant

class voting in 2008; whereas the Alford index in 2004 and 2012 is ‐0.74 and 2.87 (among the

lowest levels since 1972), respectively, in 2008 the index is 11.96 (Clark, et al 2001:100). The

predicted probabilities in Table 5 and Figure 1 show that the cause of this 2008 divergence is

the significant variation election‐to‐election among manual workers; their predicted probability

of voting Democratic dramatically increases between 2004 and 2008, from 0.5 to 0.69, only to

return to a lower level of 0.48 in 2012. Nonmanual workers show a similar, though much more

attenuated pattern. In 2004 the odds of a nonmanual worker voting Democratic were 6.6%

lower than manual workers, whereas in 2012, that difference grew to 19.1%.

EGP Class Voting

In the EGP models, each occupational category was included as separate indicator

variables with managers as the reference category. In 2004 the coefficient for professionals is

significant; in 2008 the coefficients for routine white‐collar, skilled, and unskilled workers are

significant; and in 2012, the coefficients for professionals, routine white‐collar workers, and

skilled workers are significant. Examining the predicted probabilities in Table 5 and illustrated

in Figure 2, we see several patterns. Skilled, unskilled, and routine white‐collar works were

fairly diverse in 2004, ranging in probabilities of voting Democratic from a low of 0.48 for skilled

workers to a high of 0.67 for unskilled workers. In 2008 the probabilities for skilled and routine

white‐collar workers are significantly greater, 0.68 and 0.67 respectively, and much closer to

skilled workers’ 0.72 probability of voting Democratic. At the same time the opposite trend


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occurs among professionals and owners/proprietors, whose relatively high probabilities of

voting Democratic in 2004 trends downwards to align closer to managers’ consistently low

probability of voting Democratic. In 2012, skilled workers’ and professionals’ maintain their

relatively high and middling probabilities, respectively, while unskilled workers, routine white‐

collar workers, and owners converged with professionals, all with predicted probabilities

between 0.5 and 0.54. In other words, while there was considerable variation between

occupations’ probability of voting Democratic in 2004 and 2008, in 2012 four out of the six

occupational predicted probabilities are close to fifty percent‐ they were as likely to vote

Democratic as not. The only significant difference in 2012 is between skilled workers and

managers with probabilities of 0.64 and 0.42, respectively.

Income Class Voting

The income models were estimated using separate indicator variables with very high

income (those making over $125,000 per year) as the reference category. All of the coefficients

are significant except for low and high income in 2004. The predicted probabilities in Table 5

and Figure 3 show some trends contrary to expectations. In 2004 the predicted probabilities of

voting Democratic for low and middle income respondents are above 60%, with middle‐income

earners actually more likely to vote Democratic than low‐income voter. The probabilities of

voting Democratic for high and very high income respondents are much lower at 0.33 and 0.23,

respectively. The probability for middle‐income earners drops significantly from its high in 2004

(when they had the highest probability of any group at 0.78) to a low in 2012 of 0.51, slightly

lower than high‐income earners. The probabilities of both high‐ and very high‐income earners

voting Democratic rise significantly in 2008. High‐income earners maintain this increased


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probability in 2012 but the probability of very high‐income earners voting Dem0cratic falls to a

new low of 0.18. The probability of low‐income earners voting Democratic increases both in

2008 and 2012 to a high of 0.75.

EGP and Income Class Voting

A final model was estimated that included both EGP occupations and income categories

as controls for each other. The coefficients for skilled workers and high‐income earners in 2008

and professionals in 2012 are no longer significant. Table 6 shows the absolute and

proportional differences in odd ratios between the respective individual models and the

combined models for significant key variables. For the most part, when controlling for both

occupation and income, each odds ratio decreases between two and twenty‐five percent.

There are three notable differences. In 2008 the odds ratio for unskilled workers is halved when

controlling for income, and the odds ratio for middle‐income workers 1.38 times greater when

controlling for occupation. Likewise, in 2012, when controlling for occupation, the odds ratio of

high‐income earners is 1.17 times larger.

Model Fit

Many of the more common measures of model fit are not available when logistic

regressions are fitted using the ‘svy’ command to adjust for complex sample designs. However,

the adjusted count R2 and an F‐adjusted mean residual test are provided for each model

(Archer and Lemeshow 2006). A significant F‐test indicates poor model fit. The adjusted count

R2 indicates how much better the model is at predicted the outcome than simply predicting the

modal outcome for all cases. For example, in 2004 the income model generated correct

predictions for 65.6 percent more cases than a null model, while the EGP class model generated


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correct predictions for 64.4 percent more cases than the null model. Note that the low R2

statistics for all of the 2008 models are due to the very large row margin for those that voted

for Barack Obama‐ in other words, simply predicting that every case voted for Barack Obama

will by itself correctly predict the outcome for 65% of the cases. That said, within each year,

there is little difference between the models on either measure, though the p‐values for the F‐

tests of income in 2004 and 2008 indicates that those models are only marginally well‐fitted.

Discussion and Conclusion

Since the end of World War II, there have been three waves of class voting research that

have attempted to answer the question: does class matter in electoral politics. Each wave of

research has produced differing answers‐ class politics is dead, class politics is realigning, or

class politics persists‐ and each wave of research has utilized different measures of class. Yet

few class‐voting scholars have systematically compared different measures of class in the same

electoral context, instead arguing that either occupation (measured as a manual/nonmanual

dichotomy or as the EGP class schema) or income are better measures of the same underlying

concept that we call “class.” But in modern postindustrial economies, it is reasonable to ask

whether or not there is‐ at least in the political realm‐ one underlying phenomenon of class. In

order to address these issues empirically, this paper has estimated four models of class voting

for the 2004, 2008, and 2012 presidential elections: a manual/nonmanual class model, an EGP

class model, an income class model, and a model that included both income and EGP class

measures.

The manual/non‐manual coefficients were not significant in 2004 and 2012, but did

produce a significant result in 2008. In 2004 and 2012 manual workers are slightly less likely to


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vote Democratic, while in 2008 manual workers have a significantly higher probability of voting

Democratic than non‐manual workers (0.69 and 0.56, respectively). Likewise, relying on the

Alford index would lead to the same conclusion that class voting was nonexistent in the 2004

and 2012 elections, but present in 2008. These results suggest that logistic regression, with its

ability to control for changes in the sizes of the different classes and to control for other

variables, doesn’t produce significantly different results from the Alford Index.

But when we examine the predicted probabilities of the EGP model in Figure 2, we find

that the inconsistent results of the manual/non‐manual models are not due to a substantive

decline in class politics, but due to changes among occupational groups within those two big

classes. In 2004 and 2012, the probabilities of the two components of the manual class, skilled

and unskilled workers, are farther apart, aligned more with managers in the case of skilled

works and aligned more with professionals in the case of unskilled workers. Likewise in 2012,

skilled and unskilled workers are still far apart, albeit in different configurations, skilled workers

are no longer aligned with anyone in their greatly increased support for Democrats, whereas

unskilled workers much decreased support for Democrats aligns them more with professionals,

routine white‐collar workers, and owners. In other words, rather than an increasingly complex

occupational structure leading to class being “less inescapably polarizing… [and thus] less

subjectively salient and less politically influential,” such results demonstrate one of the major

shortcomings in operationalizing class in this manner: significant changes in political alignments

among different types of workers classified as “manual laborers” are obscured and lead one,

wrongly, to conclude that class is no longer a significant political force (2001:101). But, when

taken in the context of the other models estimated here, the more realistic conclusion is that,


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because of the increasing complexity in occupational structure, the manual/nonmanual class

model is no longer a relevant distinction and is therefore producing inaccurate class‐voting

models.

It is exactly this failure that led many sociologists to favor the EGP model, which is

essentially a decomposition of the manual/nonmanual model (Nieuwbeerta 1996; Evans 1999;

Weakliem 1995; Hout, et al 1995; Goldthorpe 2001). Aside from the insights into the failure of

the dichotomous occupational measure, the results of the EGP model are most surprising in

that they diverge from previous research in at least two significant ways (Manza and Brooks

1999; Hout, et al. 1995). While Manza and Brooks detected strong support for the Democratic

candidate among professionals (1999:65), the predicted probabilities of professionals voting

Democratic declined significantly after 2004, such that, in 2012, their probability of voting

Democratic is indistinguishable from routine white‐collar works, unskilled works, and owners.

Manza and Brooks also found that unskilled workers’ support of the Democrats decreased

dramatically beginning in 1980‐ they are the so‐called “Reagan Democrats.” These results show

strong support for Democratic candidate in 2004 and 2008. And while that support did decline

in 2012, they are still more likely to support the Democratic candidate than the Republican

candidate. Further, this research confirms Manza and Brooks’ research that shows skilled

workers more strongly supporting the Democratic candidate (1999:65).

Overall, operationalizing class using income categories resulted in voting that one would

expect from traditional class politics and previous class‐voting studies (Bartels 2008:73; van der

Waal, et al. 2007:415), with low‐income individuals supporting the left political party, the

middle‐ and high‐income individuals less supportive of the left political party (with the


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exception of the middle‐income earners’ very high support of Democrats in 2004), and very

high‐income individuals supporting the right political party. This is a clear relationship that one

would expect in a capitalist democracy with very high wealth and income inequalities. But,

rather than a traditional rational‐choice explanation of class voting (that is, vote‐choice based

on a rational expectation of optimal economic outcome), or thinking of income as simply a

proxy measure of class (Brady, et al. 2009), another explanation is possible. As income and

wealth inequality grow and class mobility declines (Beller and Hout 2006), the shared

experiences of individuals from different economic backgrounds also declines—as do the life

chances of those with lower incomes. More so than in the past, income captures more than

simply an individual’s earning potential at that moment in time. It has become much more

predictive of an individual’s overall life‐chances, the life‐chances of their children, and their

social relations in general (Stonecash, et al. 2000). In other words, income may play a much

greater role in class formation and the creation of class antagonisms that go beyond the

immediate pocket‐book effects that are typically theorized to be at work in income‐based

voting patterns.

The conclusions drawn here are limited insofar as they are based on only three

elections, two of which occurred in unique historical circumstances. The election in 2008 saw

the first African American from a major party to appear on the general election ballot. At the

same time, the United States was just beginning to experience the most severe economic crisis

in eighty years. Further, in 2012, the economic recovery had been experienced primarily by

only the most well‐to‐do Americans, while the working and middle classes suffered prolonged

unemployment or stagnant earnings. Even under ideal circumstances, it is difficult to


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differentiate meaningful trends from trendless fluctuation; even more‐so given the

circumstances described above. These limitations highlight the need for future research that

takes into account a larger range of elections.

Nevertheless, this study provides strong evidence of the continuing influence of class on

voting behavior. We have seen that rather understanding occupation and income as two

competing measures of the same underlying concept of “class,” they are capturing different

conceptualizations of class. The complex occupational class map that is appropriate for

postindustrial economies best captures relational differences in authority, autonomy, and

responsibility, but can obscure material economic differences. Income, on the other hand, is

more and more reflective of deep economic divisions that are forming classes that are more

and more isolated from each other in society more generally. In either case, claims that class

no longer matters in politics or has been supplanted by cultural issues in prosperous

postindustrial societies are clearly unwarranted.


Coutley 24

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Bartels, Larry M. 2006. “What’s the Matter with What’s the Matter with Kansas?” Presentation

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Brewer, Mark and Jeffrey Stonecash. 2001. “Class, Race Issues, and Democratic White Support

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Chan, Tak Wing and John H. Goldthorpe. 2007. “Class and Status: The Conceptual Distinction

and its Empirical Relevance.” American Sociological Review 72:512‐32.

Clark, Terry Nichols and Seymour Martin Lipset. 2001. “Are Social Classes Dying?” Pp. 39‐54 in

The Breakdown of Class Politics: A Debate on Post‐Industrial Stratification, edited by

Terry Nichols Clark and Seymour Martin Lipset. Baltimore: The John Hopkins University

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Industrial Stratification, edited by Terry Nichols Clark and Seymour Martin Lipset.

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Mobility in Three Western European Societies: England, France and Sweden.” The British

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Evans, Geoffrey. 2000. “The Continued Significance of Class Voting.” Annual Review of Political

Science 3:401‐17.

‐‐‐‐‐‐‐‐‐. 1999. The End of Class Politics? Class Voting in Comparative Context. Oxford: Oxford

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Way They Do. Princeton, NJ: Princeton University Press.

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The John Hopkins University Press.

Hechter, Michael. 2004. “From Class to Culture.” The American Journal of Sociology 110:400‐45.

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Nieuwbeerta, Paul. 1996. “The Democratic Class Struggle in Postwar Societies: Class Voting in

Twenty Countries, 1945‐1990.” Acta Sociologica 39:345‐83.

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Politics: State and Society in Global Perspective [forthcoming].

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Stonecash, Jeffrey, Mark D. Brewer, R. Eric Petersen, Mary P. McGuire, Lori Beth Way. 2000.

“Class and Party: Secular Realignment and the Survival of the Democrats Outside the

South.” Political Research Quarterly 53:731‐52.

van der Waal, Jeroen, Peter Achterberg, and Dick Houtman. 2007. “Class Is Not Dead It Has

Been Buried Alive: Class Voting and Cultural Voting In Postwar Western Societies (1956‐

1990).” Politics & Society 35:403‐26.

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Appendix: Missing Data

Table A: Missing Data

2004 2008 2012

Presidential Vote Choice 20% 23.2% 16.9%

(243) (552) (652)

Income 9.9% 6.8% 2%

(120) (159) (76)

Occupation 8.0% 6.8% 22.8%

(120) (158) (881)

N 1,066 2,102 3,581

Source: American National Election Survey 2004, 2008, 2012 Time Series Studies


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Table 1: Descriptive Statistics

2004 2008 2012

Presidential Vote Choice

Voted for Democratic candidate 0.50 0.65 0.52

Income

$0‐34,999 0.29 0.39 0.29

$35‐74,999 0.34 0.36 0.32

$75,000‐$124,999 0.26 0.17 0.24

<$125,000 0.11 0.09 0.16

EGP Occupation

Professionals 0.27 0.232 0.31

Managers 0.11 0.13 0.12

Owners 0.15 0.12 0.2

Routine white‐collar workers 0.26 0.24 0.19

Skilled workers 0.12 0.16 0.08

Unskilled workers 0.1 0.13 0.1

Manual/Nonmanual Occupation

Manual 0.21 0.29 0.18

Nonmanual 0.79 0.71 0.82

Age

18‐29 0.17 0.16 0.09

30‐49 0.35 0.39 0.27

50‐64 0.31 0.28 0.35

65 and up 0.17 0.17 0.29

Education

Less than high school 0.05 0.08 0.05

High school diploma 0.24 0.3 0.2

Some college/Associate’s degree 0.33 0.35 0.32

Bachelor’s degree 0.22 0.19 0.24

Advanced degree 0.16 0.08 0.19

Gender

Female 0.5 0.56 0.45

Ideological Identification

Liberal 0.25 0.29 0.25

Slightly liberal 0.14 0.1 0.12

Moderate 0.06 0.1 0

Slightly conservative 0.15 0.12 0.15

Conservative 0.4 0.38 0.48

Political Party Self‐Identification

Republican 0.45 0.32 0.4


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Table 1: Descriptive Statistics

2004 2008 2012

Democrat 0.5 0.61 0.5

Independent 0.05 0.07 0.1

Race White 0.77 0.64 0.7

Region South 0.3 0.46 0.36

Religion R says religion is important 0.77 0.76 0.67

Union Household with union member 0.19 0.14 0.19

N 674 1,316 2,214Source: American National Election Studies 2004, 2008, 2012 Time Series Studies

Standard Deviation in Parenthesis


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Table 2: Logistic Regression Predicting 2004 Democratic Presidential Vote

Manual EGP Income Income and EGP

Professionals 1.01** 0.94* 0.94** 0.78* (0.30) (0.35) (0.30) (0.37)Owners 0.41 0.55 0.29 0.40 (0.30) (0.42) (0.31) (0.43)Routine W‐C 0.46 0.61 0.19 0.41 (0.29) (0.35) (0.27) (0.32)Skilled 0.42 0.26 0.22 0.11 (0.34) (0.51) (0.36) (0.50)Unskilled 1.02** 1.04 0.66 0.68 (0.35) (0.51) (0.33) (0.43)Non‐manual ‐0.11 ‐0.07 (0.18) (0.37) Low Income 1.00* 1.77 1.03** 1.52 (0.39) (0.99) (0.36) (1.11)Middle Income 0.71** 2.48** 0.74** 2.30* (0.25) (0.81) (0.25) (0.87)High Income 0.38 0.49 0.37 0.34 (0.28) (0.80) (0.29) (0.91)Age 0.04 0.06 0.04 0.06 (0.10) (0.10) (0.11) (0.12)Education ‐0.19 ‐0.25* ‐0.03 ‐0.11 (0.10) (0.12) (0.09) (0.13)Gender 0.58* 0.44* 0.42* 0.32 (0.21) (0.21) (0.17) (0.20)Ideological ID ‐0.88*** ‐0.89*** ‐0.90*** ‐0.90*** (0.08) (0.08) (0.07) (0.08)Race (White) ‐2.00*** ‐2.06*** ‐1.33 ‐1.51 (0.39) (0.38) (0.75) (0.85)Race*Low Income ‐0.49 ‐0.35 (1.14) (1.22)Race*Mid Income ‐1.58 ‐1.40 (0.99) (1.02) Race*High Income ‐0.09 0.08 (0.97) (1.04)Region (South) 0.22 0.14 0.20 0.14 (0.26) (0.27) (0.26) (0.27)Religiosity 0.00 0.01 ‐0.05 ‐0.04 (0.28) (0.29) (0.27) (0.27)Union household 0.86** 0.85** 1.03** 1.00** (0.29) (0.30) (0.33) (0.32)Constant 0.08 5.55*** ‐0.59* 5.18*** ‐0.62* 3.74*** ‐1.05** 3.78** (0.17) (0.63) (0.28) (0.86) (0.28) (0.93) (0.33) (1.13)F‐Adjusted Goodness of Fit p=0.74 p<0.01 p=0.8 p=0.18 p=0.982 p=0.067 p=0.99 p=0.04 Adjusted count R2 0.6346 0.6439 0.6563 0.6594N 647 647 647 647 647 647 647 647

Standard errors in parentheses*** p<0.001, ** p<0.01, * p<0.05


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Professionals 0.42 0.51 0.34 0.39 (0.24) (0.30) (0.23) (0.31)Owners 0.23 0.19 0.14 0.09 (0.35) (0.39) (0.35) (0.39)Routine W‐C 0.82** 1.00** 0.56 0.79* (0.30) (0.37) (0.31) (0.37)Skilled 0.77* 1.04* 0.49 0.80 (0.30) (0.41) (0.31) (0.40)Unskilled 1.15*** 1.23** 0.79* 0.95* (0.31) (0.43) (0.32) (0.43)Non‐manual ‐0.49** ‐0.57** (0.18) (0.21) Low Income 1.48*** 1.55*** 1.28*** 1.33*** (0.25) (0.31) (0.27) (0.33)Middle Income 1.06*** 1.02*** 0.93*** 0.89** (0.22) (0.25) (0.22) (0.27)High Income 0.71** 0.63* 0.62* 0.50 (0.26) (0.29) (0.27) (0.31)Age ‐0.16* ‐0.13 ‐0.17* ‐0.14 (0.08) (0.09) (0.08) (0.08)Education ‐0.27*** ‐0.22** ‐0.18* ‐0.10 (0.07) (0.08) (0.07) (0.08)Gender 0.36** 0.30* 0.12 0.19 (0.14) (0.14) (0.15) (0.15)Ideological ID ‐0.72*** ‐0.73*** ‐0.71*** ‐0.73*** (0.06) (0.06) (0.06) (0.06)Race (White) Race*Low Income Race*Mid Income Race*High Income Region (South) ‐0.47 ‐0.49 ‐0.48 ‐0.49 (0.25) (0.26) (0.25) (0.25)Religiosity ‐0.13 ‐0.16 ‐0.21 ‐0.21 (0.20) (0.21) (0.20) (0.21)Union household 0.09 0.07 0.30 0.21 (0.23) (0.23) (0.25) (0.24) Constant 0.50** 5.05*** ‐0.43 3.87*** ‐0.86*** 3.55*** ‐1.12*** 2.90*** (0.17) (0.51) (0.24) (0.62) (0.24) (0.55) (0.25) (0.63)F‐Adjusted Goodness of Fit p=0.98 p<0.01 p=1.0 p=0.58 p=0.98 p=0.07 p=0.89 p=0.49 Adjusted count R2 0.18 0.1887 0.2104 0.2191 N 1,316 1,316 1,316 1,316 1,316 1,316 1,316 1,316



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Professionals 0.49* 0.48* 0.50** 0.41 (0.19) (0.23) (0.19) (0.23)Owners 0.26 0.42 0.19 0.34 (0.21) (0.25) (0.21) (0.26)Routine W‐C 0.55** 0.59* 0.49* 0.51* (0.21) (0.25) (0.21) (0.25)Skilled 0.53* 0.86** 0.48 0.85* (0.26) (0.32) (0.26) (0.33)Unskilled 0.22 0.48 0.13 0.35 (0.24) (0.29) (0.25) (0.30)Non‐manual ‐0.03 0.21 (0.15) (0.20) Low Income 0.32 2.66*** 0.32 2.61*** (0.17) (0.51) (0.18) (0.51)Middle Income ‐0.08 1.55*** ‐0.11 1.53** (0.17) (0.47) (0.17) (0.46)High Income ‐0.03 1.59** ‐0.04 1.57** (0.18) (0.50) (0.18) (0.51)Age 0.11 0.12 0.13 0.14 (0.07) (0.07) (0.07) (0.07)Education 0.06 0.08 0.09 0.12 (0.07) (0.07) (0.06) (0.07)Gender 0.59*** 0.55*** 0.51*** 0.51*** (0.14) (0.15) (0.14) (0.15)Ideological ID ‐0.57*** ‐0.57*** ‐0.58*** ‐0.58*** (0.04) (0.04) (0.04) (0.04)Race (White) ‐1.75*** ‐1.75*** 0.08 0.08 (0.18) (0.18) (0.43) (0.43)Race*Low Income ‐2.66*** ‐2.67*** (0.56) (0.55)Race*Mid Income ‐1.78*** ‐1.81*** (0.51) (0.51)Race*High Income ‐1.67** ‐1.66** (0.55) (0.56)Region (South) 0.07 0.07 0.03 0.04 (0.15) (0.15) (0.15) (0.15)Religiosity ‐1.19*** ‐1.20*** ‐1.23*** ‐1.23*** (0.15) (0.15) (0.14) (0.14)Union household ‐0.34 ‐0.32 ‐0.43* ‐0.38* (0.19) (0.19) (0.19) (0.19) Constant 0.04 4.27*** ‐0.37* 4.00*** ‐0.05 2.95*** ‐0.38 2.38*** (0.18) (0.64) (0.16) (0.58) (0.14) (0.65) (0.20) (0.68)F‐Adjusted GOF p‐values p=1 P=0.85 p=1 p=0.34 p=0.61 p=0.91 p=0.89 p=0.93 Adjusted count R2 0.4929 0.49 0.5014 0.4986 N 2,214 2,214 2,214 2,214 2,214 2,214 2,214 2,214



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Table 5: Odds Ratios and Predicted Probabilities

2004 2008 2012Variables Odds Ratios Probabilities Odds Ratios Probabilities Odds Ratios Probabilities

Professionals 2.56 0.65 1.67 0.55 1.62 0.54Managers ‐ 0.42 ‐ 0.43 ‐ 0.42Owners 1.73 0.56 1.21 0.47 1.52 0.53Routine W‐C 1.83 0.57 2.73 0.67 1.81 0.5Skilled 1.3 0.48 2.84 0.68 2.37 0.64Unskilled 2.82 0.67 3.43 0.72 1.61 0.54Manual ‐ 0.5 ‐ 0.69 ‐ 0.48Nonmanual 0.93 0.52 0.57 0.56 0.81 0.53Low 5.88 0.64 4.73 0.72 14.26 0.75Middle 11.91 0.78 2.76 0.59 4.73 0.5High 1.63 0.33 1.89 0.5 4.9 0.51Very High ‐ 0.23 ‐ 0.35 ‐ 0.18

Odds ratios and predicted probabilities from full models

Table 6: Differences in Odds Ratios between Individual and Combined Models

Individual Model Combined

ModelAbsolute

Difference Proportional Difference

2004 Professional 2.56 2.18 ‐0.38 0.85 Middle income 11.94 9.97 ‐1.97 0.84

2008 Routine white‐collar 2.72 2.2 ‐0.52 0.81 Skilled workers* 2.83 2.22 ‐0.61 0.78 Unskilled workers 3.43 2.59 ‐0.84 0.76 Low income 4.73 2.6 ‐2.13 0.55 Middle income 2.76 3.8 1.04 1.38 High income* 1.89 1.64 ‐0.25 0.87

2012 Professional* 1.62 1.51 ‐0.11 0.93 Routine white‐collar 1.81 1.66 ‐0.15 0.92 Skilled workers 2.37 2.33 ‐0.04 0.98 Low income 14.26 13.56 ‐0.7 0.95 Middle income 4.73 4.6 ‐0.13 0.97 High income 4.09 4.8 0.71 1.17

*Insignificant in the combined model


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0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

2004 2008 2012

Probability of voting Dem

ocratic

Figure 1: Manual/Nonmanual Class Voting

Manual Workers Nonmanual Workers


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0

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0.4

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2004 2008 2012


ocratic

Figure 2: EGP Class Voting

Professionals Managers Owners Routine W‐C Skilled Unskilled


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2004 2008 2012


ocratic

Figure 3: Income Class Voting

Low Middle High Very High

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