The Formation of an Organized Working Class

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The formation of an organized working class: Factors influencing working class identity

and trade union support.

Enku Michael C. Ide

2011

University of Kentucky

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Ide, “Enku” MichaelSOC 681

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The formation of an organized working class: Factors influencing working class identity

and trade union support.

Many social theorists, particularly since Marx, have debated the question of economic

class. According to Marx, “objective” class location is based in the relation to the means of

production. Those who own the means of production, the bourgeoisie or capitalist class is said to

rule society while those who must sell their labor power, the proletariat or working class is said

to be dominated and their labor power exploited. These conditions create the structural backdrop

for social conflicts particularly over working conditions, work time, and pay. The working class

only becomes aware of itself as a class through political struggle, whereas the bourgeoisie was

already aware of itself as a class. Weber took up this theme as well, indicating that classes are

only statistical categories until there is a status-based identity and an understanding of shared

issues. For Weber, class was based in the monopolization of market opportunities, either in work

and earnings or in consumption. Further, according to Bourdieu, middle class experiences early

in life generate embodied cultural capital which is carried on throughout the life-course.

Marx indicated that the trend of capitalism, in its ideal form, is to create two “great

classes” and that the middle classes would disappear. History has not borne this out as

government policy and regulations have helped in the formation and sustaining of the middle or

professional class. However, large economic changes impact the class system, with some

authors now speaking of the “proletarianization” of the middle class, as professional jobs change.

Specifically, many middle class jobs are increasingly managed (leading to a decline in worker

autonomy) and pay and job security are in decline. One response to this trend, particularly in

higher education among non-tenured teaching workers, has been unionization.

People of color and whites likely have a different relationship to class identification.

Middle class or professional identity is culturally coded as “white” whereas, particularly for




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Black Americans, the general reference group is working class or lower class people. Although

great strides have been made in the formation of a Black middle class, a much larger portion of

Black males has been incarcerated due to increased law enforcement in Black communities and

increased prosecution of Black offenders. This has had dramatic implications for the entire

Black community. Historically excluded from the higher positions in the private economy,

Black people have historically sought public sector work in higher percentages than whites.

Within this segmented labor market, the public sector has also seen more persistent unionization

providing an economically stabilizing effect for the Black community.

Unions in the US have a complicated relation to class. Before World War II, many

militant union locals were led by communists, socialists, and anarcho-syndicalists who organized

based in their belief in the revolutionary potential of the working class. During the McCarthy

era, “red baiting” drove many radicals out of the labor movement. Unions responded by

supporting business and government (often against the interests of their workers). We live with

this legacy today, with the current rallying-cry of the AFL-CIO being “protecting Americas

middle class” even when representing working-class people. According to this formulation,

class is not based in the relation to the means of production, but rather on salary (and therefore

consumption patterns) and job security. This could be seen as a hegemonic function of big labor

incorporating the ideology of the ruling class into their framing.

Regardless, for those interested in working class issues, labor unions are a key

component. Labor organizing has been in crisis for years, particularly in the private sector.

Today, the largest organized employment segment is the public sector, although these unions are

under political scrutiny and attack in many states. By quantitatively analyzing factors that

influence working class identity and trade union support, we can come to a better understanding




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of the relations between class identity and union organizing today. In order to accomplish this, I

will run binary and multinomial logistic regressions addressing the following hypotheses:

Hypotheses related to factors influencing working class identity:

A.1 Marxist Theory: Relations to the means of production. The probability of self-identifying

as working class will be reduced if the respondent or respondent’s spouse is self -

employed, thereby controlling the means of production, even when controlling for

income.

A.2 Marxist Theory: Class domination. Working class job characteristics will increase the

probability of self-identifying as working class.

A.3 Weberian Theory: Income. Lower income, and therefore exclusion from middle class

consumption, will be positively associated with an increased probability in self-

identifying as working class.

A.4 Weberian Theory: Prestige: According to Weber, class and status are closely related but

distinct hierarchies. As such, higher occupation prestige should be significantly related to

class identification.

A.5 Cultural Theory: Segmented labor market. Being a person of color will increase the

likelihood of self-identifying as working class.

A.6 Bourdieu Theory: Cultural capital. Having a high income and prestige among parents

will decrease the odds of self-identifying as working class.

A.7 Bourdieu Theory: Institutional cultural capital. Increased years of education will

decrease the predicted probability of self-identifying as working class.

Hypotheses related to factors influencing trade union support:

B.1 Marxist Theory: Proletarianization of work. Support for unions will increase among

those with low job security and lower benefits.

B.2 Bourdieu Theory: Social capital. An affinity for other workers (strong bonding capital),

as well as a distrust of management at work (weak bridging capital), will be positively

associated with support for trade unions.

B.3 Cultural Theory: People of color will be more supportive of trade unions.

B.4 Political Theory: Being conservative will lead to decreased support for trade unions.

B.5 Union Theory: Unions have long been the main organizing body of the working class.However, current union messaging often relies on a middle class discourse, with AFL-

CIO claiming to be “rebuilding the middle class” and supporting “working families.” As

such, working class identified people are hypothesized to not significantly support trade

unions more than middle class identified people.




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SAMPLE

I draw from the 2010 General Social Survey. Because I am interested in looking at work

characteristics for both of my statistical tests, I have limited my sample to those who are working

full time, working part time, or are temporarily not working. I thus dropped any observations

that were listed as retired, in school, keeping house, laid off or other. Because this research is

focused on factors influencing working class identity in relation to middle class identity,

observations were dropped if coded as upper class or lower class. Further, not all questions were

asked of all respondents. In order to have comparable tests across my models, I then dropped all

observations which were missing data on the dependent variables of interest, resulting in a

sample size of N=860.




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Table 1: Sample Descriptive Characteristics: General Social Survey (2010) (N=860)

Binary Logistic Regression Models Variable

M

(Percent) SD N Minimum Maximum

Dependent Variable

Working class

0.55 0.50 860 0 1 Independent Variables

High job security 0.49 0.50 860 0 1

Work benefits very good 0.38 0.49 860 0 1

Occupation prestige (fam. avg.) 44.80 12.78 860 17 86

Business owner 0.16 0.37 860 0 1

High work autonomy 0.56 0.50 860 0 1

Business owner 0.16 0.37 860 0 1

High work autonomy 0.56 0.50 860 0 1

High income (family of origin) 0.22 0.41 860 0 1

Parental occupational prestige 44.70 12.99 860 17 86

Not low income 0.70 0.46 860 0 1High occupational prestige 0.50 0.50 860 0 1

Interaction Variables:

Not low income*Familyeducation years 10.33 7.05 860 0 20

Family education years * HighOccupation prestige

7.64 7.90 860 0 1

Multinomial Logistic Regression Models

Dependent Variable

Union support 2.43 0.80 860 1 4

Independent Variables

Occupation prestige (fam. avg.) 44.80 12.78 860 17 86

Person of color 0.20 0.40 860 0 1

Work benefits very good 0.38 0.49 860 0 1

Working class 0.55 0.50 860 0 1

Years of education 14.27 2.90 860 2 20

Low income 0.30 0.46 860 0 1

Low job security 0.15 0.35 860 0 1

Co-workers helpful 0.51 0.50 860 0 1

Does not trust management 0.18 0.38 860 0 1

Not conservative 0.67 0.47 860 0 1Low income (family of origin) 0.33 0.47 860 0 1




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Binary Logistic Regression: The likelihood of self-identifying as “Working Class.”

MEASURES

Dependent Variable:

For my first set of models, I was interested in looking at factors that influence subjective

class identity as working class. The GSS 2010 asks respondents their subjective class identity,

with possible categories being lower class, working class, middle class, and upper class. I

generated a dummy variable for Working Class, with working class coded as 1 and middle class

coded as 0, which I used as my dependent variable in my first set of models.

Independent Variables:

To test variables that impact respondents’ likelihood of self -identifying as “working

class,” I ran five models. In the first model, I included independent variables of white, education

years, and business owner. “White” was generated by creating a dummy variable based in the

GSS variable “race,” with possible outcomes of “white,” “black,” and “other.” For the variable

White, the race outcomes of “black” and “other” were coded as 0, while the outcome of “white”

was coded as 1. The GSS 2010 includes a self-report of how many years of education a person

has, ranging from 0 to 20, as well as the number of years of education of the respondents’ spouse

or partner. Because class is often based in the nuclear family and not the individual, I created a

variable, “Education years (fam.)” which was the average of the respondent’s and the

respondent’s spouse’s education years if information was provided for both individuals. When

information was only provided for the respondent, this number was used without modification.

The GSS also includes variables asking if respondents and their spouses are self-employed.

From these variables, I created the dummy variable “Business owner” coded as 1 if a respondent

or spouse is self-employed and coded as 0 if neither is self-employed.




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In Model 2, I added characteristics of the respondent’s employment. The GSS records

“occupational prestige” for respondents and their spouses. Using these variables, I created the

interval-ratio variable “Family occupation prestige” as the mean of the respondent’s occupational

prestige and that of their spouse. If only the respondent’s occupational prestige was listed, this

score was used without alteration. In one model, I used the term “high occupational prestige” in

place of “family occupational prestige,” to allow the creation of an interaction term between

“high occupational prestige” and years of education. To create this variable, I re-coded the

“Family occupation prestige” variable, with a prestige score of 45.5 or above (roughly the

median prestige score) as “high family prestige” and all scores below 45.5 as “not high family

prestige.” The variable “Not low income” was generated from the GSS variable “income06,”

which provided 25 possible income ranges from $1,000 and under to $150,000 and over. I

combined these categories into 3 categories, “lowincome” ranging from under $1,000 to

$34,999, “mid-income” ranging from $35,000 to $74,999, and “high income” ranging from

$75,000 to $150,000 or over, creating dummy variables from each possible categorical outcome.

I then generated a dummy variable “not low income” as the inverse of “low income” to more

easily compare the effects of having a lower income compared to medium or high income. The

GSS includes a variable concerning work security, based in the statement that their job security

is good, with possible outcomes of “very true,” “somewhat true,” “not too true,” and “not at all

true.” From these outcomes I created the dummy variable “high work security,” coded as 1

indicating a respondent reported “very true.”

The GSS also includes a variable concerning freedom at work, based in the statement that

a respondent has “a lot of freedom to decide how to do [their] job.” Possible ordinal outcomes

are “very true,” “somewhat true,” “not too true,” and “not at all true.” Work autonomy is central




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in the literature concerning the “proletarianization” of previously middle class jobs, with

autonomy seen as a central characteristic of middle class jobs and a lack of autonomy as marking

working class jobs. As such, I created a dummy variable, “high work autonomy” from this

variable indicating that the respondent reported to this question as “very true.” Benefits

(including health insurance) are also seen as a hallmark of middle class jobs. The GSS asked

respondents about their satisfaction with their work benefits, asking respondents to reply to the

statement, “[my] fringe benefits are very good.” I recoded these responses into the dummy

variable “Benefits very good” with “very true” coded as 1 and other possible responses

(“somewhat true,” “not too true,” and “not true at all”) coded as 0. The GSS includes variables

indicating the occupational prestige of respondents and their spouses. The mean of these

occupational prestige scores was averaged in order to generate the variable “Family occupational

prestige.” When prestige scores were only given for the respondent, these were included without

alteration.

In Model 3, I added terms to address the respondent’s family of origin. I created the

variable “Parents’ occupation prestige” as the mean of the respondent’s fathers’ occupational

prestige and the respondent’s mothers’ occupational prestige. As there were no observations

missing either of these variables, the mean of the two scores was valid for all observations. To

further investigate the effects that the family of origin has on current class identification, I used

the GSS variable “incom16” which measured the respondent’s family income when the

respondent was 16 years old. Possible outcomes were “far below average,” “below average,”

“average,” “above average,” and “far above average.” If respondent’s answered “far above

average” or “above average,” this was coded as a dummy variable “High income (fam. of

origin).




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In Models 4 and 5, I included interaction terms. Model 4 included an interaction term

between “not low income” and “education years.” Model 5 introduces the interaction variable

“white*high occupational prestige.” In order to create the Model 5 interaction term, I had to

generate a categorical variable from the interval ratio “Family prestige” variable, which ranges

from 17 to 86. Scores of 45 and greater, roughly the highest 45th percentile, were coded as a 1 in

the dummy variable “High family prestige.”

Model specification: Tests of model fit

Across my three base models (without interaction terms), LRχ

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test of nested models

found that each subsequent model improves the likelihood over the previous model. An LRχ 2

test between model 2 and model 3 (LRχ 2=16.62; p<0.001) indicates that the full model improves

the likelihood of the model relative to the restricted model. An LRχ 2 test between model 1 and

Model 2 (LRχ 2=41.27; p<0.001) indicates that the full model improves the likelihood of the

model relative to the restricted model. BIC’ tests also provide strong support for each

subsequent model relative to the restricted model, with a BIC’ difference of 7.49 providing

strong support for Model 2 in relation to Model1 and a BIC’ providing positive support for

Model 3 in relation to Model 2. An LRχ 2 test further indicates that Model 4, with the interaction

term improves the likelihood of the previous model (LRχ 2=12.09; p<0.001). A BIC’ test also

provides weak support for Model 5, including the second interaction term, over Model 3, with a

difference in BIC’ of 0.16. Tests for multicolinearity were administered to each model,

indicating that these models do not violate the multicolinearity assumption, with R2 not going

above 0.36 and 1.57 as the largest variance inflation factor across each model.




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ANALYSIS AND SIGNIFICANT FINDINGS

Table 2: Binary Logistic Regression, Odds Ratios of self-identifying as “working class.”

General Social Survey (N=860)Model 1: Model 2: Model 3:

Demographics Jobcharacteristics

Family of origin

Variables Odds Ratio Odds Ratio Odds Ratio

White 0.52*** 0.60* 0.63*

Education years (fam.) 0.69*** 0.74*** 0.76***

Business owner 0.62* 0.63* 0.63*

Not low income — 0.46*** 0.44***

High work security — 0.76 0.73

High work autonomy — 0.77 0.75

Benefits very good — 0.81 0.80

High income (fam. of origin) — — 0.52**

Parents occupation prestige — — 0.99

Family occupation prestige — 0.99 0.99

High occupation prestige — — — Interaction Variables

— — — Not low income* Education

Education * High occupationalprestige

— — —

LRX 2

171.52*** 212.79*** 229.41*** Adjusted Count R

2 0.35 0.38 0.39***p<0.001; **p<0.01; *p<0.05

Omitted Categories: POC, non-business owner, low income, low work security, low work

autonomy, benefits not very good, not high income (fam. of origin), not high occupation prestige.




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Table 2 continued: Binary Logistic Regression, Odds Ratios of self-

identifying as “working class.” General Social Survey (N=860)

Model 4: Model 2:

InteractionTerm1

Interaction Term2

Variables Odds Ratio Odds Ratio

White 0.60* 0.61*

Education years (fam.) 0.91 0.83***

Business owner 0.65* 0.66

Not low income 16.93** 0.45***

High work security 0.75 0.73

High work autonomy 0.74 0.74

Benefits very good 0.81 0.82

Hi income (fam. of origin) 0.50*** 0.51***Parents occupation prestige 0.99 0.99 Family occupation prestige 0.99 — High occupation prestige — 7.22

Interaction Variables 0.77*** — Not low income* Education

Education * High occupationalprestige

— 0.85*

LRX 2

241.50*** 236.33***

Adjusted Count R2 0.42 0.40

In order to test variables that influenced respondents’ self -identification as working class,

I ran five binary logistic models, with “working class” as my outcome of inter est compared to

“middle class.” In Model 1, I found that race (coded White), average family education years,

and whether or not the respondent and/or spouse was self-employed to be significant. On

average, the odds of self-identifying as working class are decreased by a factor of 0.52 for whites

relative to people of color, holding covariates constant (p<0.01). The odds of self-identifying as

working class are estimated to decrease by 31% for every additional year of education, averaged

between respondent and spouse, all else equal (p<0.001). Being a self-employed or having a

self-employed spouse is estimated to decrease the odds of self-identifying as working class by a




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factor of 0.62, holding covariates constant (p<.05). An LRχ 2 test suggests that the model

provides improvement in fit over a model with no covariates (LRχ 2=171.52; p<0.001). Further,

the Pseudo-R2 indicates that our covariates improve the likelihood of the model by 15%.

In Model 2, I added characteristics of employment to Model 1, including work security,

autonomy, income, occupational prestige and benefits. All variables from Model 1 retained

significance. On average, the odds of self-identifying as working class are decreased by a factor

of 0.60 for whites relative to people of color, holding covariates constant (p<0.05). The odds of

self-identifying as working class are estimated to decrease by 26% for every additional year of

education, averaged between respondent and spouse, all else equal (p<0.001). Being a self-

employed or having a self-employed spouse is estimated to decrease the odds of self-identifying

as working class by a factor of 0.63, holding covariates constant (p<.05). Not having a low

income (under $35,000/year) is estimated to decrease the odds of self-identifying as working

class by a factor of 0.46 (p<0.001), all else equal. An LRχ 2 test suggests that the model provides

improvement in fit over a model with no covariates (LRχ 2=212.79; p<0.001). Further, the

Pseudo-R2 indicates that our covariates improve the likelihood of the model by 18%.

In Model 3, I also added variables concerning respondents’ family of origin. In this

model, I found that on average, the odds of self-identifying as working class are decreased by a

factor of 0.63 for whites relative to people of color, holding covariates constant (p<0.05). The

odds of self-identifying as working class are estimated to decrease by 24% for every additional

year of education, averaged between respondent and spouse, all else equal (p<0.001). Being a

self-employed or having a self-employed spouse is estimated to decrease the odds of self-

identifying as working class by a factor of 0.63, holding covariates constant (p<.05). Not having

a low income (under $35,000/year) is estimated to decrease the odds of self-identifying as




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working class by a factor of 0.44 (p<0.001), all else equal. Coming from a family of origin with

a high income is estimated to decrease the odds of self-identifying as working class by a factor of

0.52 (p<0.01), all else equal. An LRχ 2 test suggests that the model provides improvement in fit

over a model with no covariates (LRχ 2=229.41; p<0.001). Further, the Pseudo-R

2indicates that

our covariates improve the likelihood of the model by 19%.

In models 4 and 5, I also added interaction terms to better understand the moderated

impact of my independent variables. Across these interaction models, non-constitutive

independent variables were found to vary only slightly. In Model 4, an interaction model

suggests that the effects of additional years of education differ by whether or not a respondent

has a low income (OR=0.77; p<0.001). Specifically, for those without a low income, each

additional year of education is associated with a 30% decrease in odds of self-identifying as

working class, holding covariates constant. In addition, each additional year of education is

associated with a decrease in odds of 9% for those with low income, holding covariates constant.

This relationship is shown in Graph 1.

Graph 1: Interaction effect on probability of self-identifying as

working class: Income catagories by education years.




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As we can see, the impact of education on the probability of self-identifying as working

class is significantly different depending on whether or not the respondent has a low income

(under $35,000/ year). Specifically, for those with low income, the effect of education is three

times less than for those with medium or high income.

In Model 5, an interaction model suggests that the effects of additional years of education

differ by whether or not a respondent has a high occupational prestige (OR=0.85; p<0.05).

Specifically, for those with high occupational prestige, each additional year of education is

associated with a 29% decrease in odds of self-identifying as working class, holding covariates

constant. In addition, each additional year of education is associated with a decrease in odds of

8% for those without high occupational prestige, holding covariates constant. This relationship

is shown in Graph 2.

Graph 2: Interaction effect on probability of self-identifying as working class: Occupational prestige byeducation years.

0

. 2

. 4

. 6

. 8

1

0 5 10 15 20family years of education

Not low income Low income




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As we can see, the impact of education on the probability of self-identifying as working class is

significantly different depending on whether or not the respondent has a high occupational

prestige. Specifically, for those with high occupational prestige, the effect of education is

greater than for those without high occupational prestige.

Multinomial Logistic Regression: Probability of attitudes toward trade unions.

MEASURES

Dependent Variable:

The GSS 2010 includes the question “Workers need strong unions,” with responses

including “1 strongly agree” “2 agree” “3 disagree” and “4 strongly disagree.” In order to test

respondents’ view of trade unions, I reverse-coded this variable as “Union support” with the

following possible outcomes: “1 strongly disagree,” “2 disagree,” “3 agree,” and “4 strongly

0

. 5

1

1 . 5

0 5 10 15 20family years of education

High occupational prestige Not high occupational prestige




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agree.” With this scale as my dependent variable, I used multinomial regression to find

characteristics that influence respondents’ views on the importance of trade unions.

Independent Variables:

To test respondents’ likelihood of attitudes toward trade unions, I incorporated

demographic variables and work-related variables. The first variable was “POC” (Person of

Color” which was the inverse of the above-mentioned variable “white.” I also used “Education

years,” an interval-ratio GSS variable indicating the number of years of education of the

respondent, ranging from 2 to 20. The independent variable “Low income (Family of origin)”

was generated in the same manner as “High income (Family of origin)” used in the binary

logistic regression, but was coded as 1 if the respondent indicated that their family income at age

16 was “below average” or “very below average.” I also created the dummy variable “Working

Class” which is coded as 1 if the respondent’s subjective class identification was “working

class.” The GSS includes a variable concerning work security, based in the statement that their

job security is good, with possible outcomes of “very true,” “somewhat true,” “not too true,” and

“not at all true.” From these outcomes I created the dummy variable “low work security,” coded

as 1 indicating that a respondent reported “not at all true.”

I was also interested in analyzing characteristics of respondents’ employment, benefits,

and income. “Low Income (Current)” was coded as the inverse of “Not low income (Current)”

described in the above binary logistic regression. “Benefits very good” is the same independent

variable of the same name used in the above binary logistic regression. The variable “Low work

security” was generated as a dummy variable coded as 1 if the respondent answered “not too

true” or “not at all true” to the statement “job security is good.”




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The GSS offered the statement “coworkers can be relied on when r [the respondent]

needs help.” If this was answered as “very true” (as opposed to “somewhat true,” “not too true,”

or “not at all true”), I coded the dummy variable “Co-workers helpful” as a 1. Further, the GSS

asked about respondents’ relation to management at work. If respondents answered that they

“strongly disagree” or “disagree” that they trust management, then this was coded as a 1 in the

dummy variable “Does not trust management.” In this model, I also included “Family

occupational prestige,” which was also used in the binary logistic above (see above for details

regarding variable generation and coding). I also added a variable based in the GSS variable

“polviews” which asked respondents to rank their political views in a seven-category range from

“extremely liberal” to “extremely conservative.” If respondents listed themselves as “extremely

liberal” to “moderate,” this was coded as a 1 in the dummy variable “Not conservative.”

Model specification: Tests of model fit:

I originally ran ordinal regressions using the above-described dependent and independent

variables. However, the Brant Test indicated that one of my variables violated the parallel

regression assumption. While using ordinal logistic regression, I originally designed the test as a

series of nested models. However, in running the multinomial regression I am only reporting the

full model, which includes demographic information, work characteristics and political views. .

An LRχ 2

test (LRχ 2

=119.85; p<0.001) indicates that this model improves the likelihood of the

model relative to a model with no covariates. Further, the Pseudo-R2

indicates that our

covariates improve the likelihood of the model by 6%. Also, there were several variables of




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interest which had to be dropped due to small N per cell, including those dealing with

experiences of discrimination on the job.

ANALYSIS AND SIGNIFICANT FINDINGS

Relative risk ratios and predicted probabilities were found in relation to the likelihood of

responding to the phrase “workers need strong unions” with possible outcomes being “strongly

agree,” “agree,” “disagree,” and “strongly disagree.”

Relative Risk Ratios and Predicted Probabilities

Table 3a: Multinomial Logistic Regression Relative Risk Ratios of responsesto the statement, “Workers need strong trade unions.” Base outcome category:Strongly Disagree. General Social Survey (N=860)

Disagree Agree Strongly Agree

Variable

Relative Risk

Ratio

Relative Risk

Ratio

Relative Risk

Ratio

Person of color 1.04 2.51* 3.68**

Education years 1.06 1.04 1.06Working class 2.09** 1.85* 2.08*

Coworkers helpful 0.98 1.03 1.86

Low income (fam. of origin)

1.37 1.39 1.50

Low income (current) 1.71 1.87 2.30*

Benefits very good 1.14 0.90 1.80




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Does not trustmanagement

1.10 1.51 3.47**

Family occupationalprestige

1.00 1.00 1.00

Low work security 0.88 1.10 1.30

Non-conservative 2.25*** 3.03*** 3.60*** LRX

2 119.85***

Adjusted Count R2 0.10

***p<0.001; **p<0.01; *p<0.05

Omitted Categories: White, Middle Class, Coworkers not helpful, Not low income (fam. of

origin), Not low income (current), Benefits not very good, Trusts management, Not low work

security, Conservative

Table 3b: Multinomial Logistic Regression Relative Risk Ratios of responsesto the statement, “Workers need strong trade unions.” Base outcome category:Disagree. General Social Survey (N=860)

Strongly Disagree Agree Strongly Agree

Variable

Relative Risk

Ratio

Relative Risk

Ratio

Relative Risk

Ratio

Person of color 0.96 2.42*** 3.54***

Education years 0.95 0.99 1.01

Working class 0.48** 0.88 1.00

Coworkers helpful 1.02 1.05 1.90*

Low income (fam. of origin)

0.58 0.81 0.88

Low income (current) 0.73 1.37 1.68

Benefits very good 0.87 0.79 1.57

Does not trustmanagement

0.91 1.38 3.16***

Family occupational 1.00 1.00 1.00






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prestige


Non-conservative 0..33*** 0.74 1.19

LRX 119.85***


***p<0.001; **p<0.01; *p<0.05Omitted Categories: White, Middle Class, Coworkers not helpful, Not low income (fam. of



Table 3d: Multinomial Logistic Regression Relative Risk Ratios of responsesto the statement, “Workers need strong trade unions.” Base outcome category:Strongly Agree. General Social Survey (N=860)

StronglyDisagree

Disagree Agree

Variable

Relative Risk

Ratio

Relative Risk

Ratio

Relative Risk

Ratio

Person of color 0.27** 0.28*** 0.68

Education years 0.94 0.99 0.98

Working class 0.48* 1.00 0.89Coworkers helpful 0.54 0.53* 0.55*

Low income (fam. of origin) 0.67 1.14 0.93

Low income (current) 0.43* 0.59 0.81

Benefits very good 0.56 0.64 0.50*

Does not trust management 0.29** 0.32*** 0.44**

Family occupational prestige 1.00 1.00 1.00





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Non-conservative 0.28*** 0.63 0.84

LRX 2 119.85***


***p<0.001; **p<0.01; *p<0.05

Omitted Categories: White, Middle Class, Coworkers not helpful, Not low income (fam. of



Being a person of color, compared to being white, is associated with a 151% (p<0.05)

increase in the odds of agreeing and a 268% (p<0.01) increase in the odds of strongly agreeing

that workers need strong unions relative to strongly disagreeing, all else equal. Being working

class, compared to being middle class, is estimated to increase the odds of disagreeing, agreeing

and strongly agreeing that workers need strong unions, relative to strongly disagreeing, by 109%

(p<0.01), 85%(p<0.05), and 108%(p<0.05) respectively, holding covariates constant. Having a

low income, compared to having a higher income, is estimated to increase the odds of strongly

agreeing that workers need strong unions, relative to strongly disagreeing, by 130%, all else

equal (p<0.05). Compared to those who trust management at work, those who do not trust

management are estimated to have a 347% increase in the odds of strongly agreeing that workers

need strong unions, relative to strongly disagreeing, all else equal (p<0.01). On average,

compared to being non-conservative, being conservative is estimated to increase the odds of

disagreeing, agreeing and strongly agreeing that workers need strong unions, compared to

strongly disagreeing, by 125% (p<0.001), 203%(p<0.001) and 260% (p,0.001), respectively,

holding covariates constant.

Compared to being white, being a person of color is estimated to increase the odds of

agreeing that workers need strong unions by 142% (p<0.001) and strongly agreeing that workers

need strong unions by 254% (p<0.001), relative to disagreeing, all things equal. Those who do

not trust management, compared to those who do trust management, are estimated to have an




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increased odds of 216% of strongly agreeing that workers need strong trade unions compared to

disagreeing (p<0.001), holding covariates constant. Those who are certain that their coworkers

are helpful, compared to those who are not certain, are estimated to have an increased odds of

9% of strongly agreeing that workers need strong trade unions, relative to disagreeing that

workers need strong trade unions, all else equal (p<0.05).

Those who see their coworkers as helpful, compared to those who do not see their

coworkers as helpful, are estimated to have an increased odds of 8% of strongly agreeing that

workers need strong unions relative to agreeing, all things equal (p<0.05). Having very good

benefits, compared to not having very good benefits, is associated with a 100% increase in

strongly agreeing that workers need strong trade unions relative to agreeing that workers need

strong trade unions, holding covariates constant (p<0.05). Compared to those who trust

management, those who do not trust management are estimated to have an increase of 129% in

odds of strongly agreeing that workers need strong trade unions relative to agreeing that workers

need strong trade unions, all things equal (p<0.01).

Table 4. Predicted Probabilities of responses to the statement, “Workers need strong tradeunions.”*General Social Survey (N=860)

Ideal Type Strongly Agree Agree Disagree Strongly Disagree

Holding all covariates constant**

Person of Color 0.13 0.51 0.30 0.06

Working Class 0.08 0.38 0.47 0.07

Low Income 0.10 0.43 0.41 0.07

Does not Trust Management 0.16 0.40 0.37 0.07

Not Conservative 0.08 0.41 0.44 0.07

Person of Color

Working Class 0.14 0.50 0.31 0.05

Middle Class 0.12 0.51 0.28 0.08

Low Income 0.17 0.54 0.26 0.04

Not Low Income 0.12 0.49 0.32 0.07




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Trusts Management 0.12 0.50 0.32 0.06


Conservative 0.10 0.45 0.33 0.11

White

Working Class 0.07 0.34 0.52 0.08Middle Class 0.06 0.34 0.46 0.14

Low Income 0.08 0.39 0.45 0.08

Not Low Income 0.06 0.32 0.51 0.12




Conservative 0.05 0.28 0.49 0.18

Not Conservative

Working Class 0.09 0.40 0.46 0.05

Middle Class 0.08 0.41 0.41 0.10

Low Income 0.11 0.45 0.39 0.05Not Low Income 0.08 0.39 0.46 0.08



Conservative

Working Class 0.06 0.32 0.49 0.13

Middle Class 0.05 0.31 0.42 0.22

Low Income 0.07 0.37 0.43 0.13

Not Low Income 0.05 0.29 0.47 0.19


Trusts Management 0.05 0.31 0.47 0.17*In all categories, variables not included are held constant: poc, educ, working class, coworkers helpful, low incomefamily of origin, low income current, job benefits very good, no trust management, family occupational prestige,low work security, non-conservative**Omitted categories: white, middle class, not low income, trusts management, conservative Note: Presented values are predicted probabilities

Of the ideal types listed in Table 4, People of Color who do not trust management have

the highest predicted probability of strongly agreeing that workers need strong unions, with a

predicted probability of 0.26. The lowest predicted probability of strongly agreeing that workers

need strong unions, 0.05, is shared amongst conservatives who trust management, conservatives

that do not have a low income, middle class conservatives, white conservatives, and whites who

trust management. Those with the highest predicted probability of agreeing that workers need

strong unions, with a predicted probability of 0.54, are low income people of color. The lowest




26

predicted probability of agreeing that workers need strong unions is found among white

conservatives, with 0.28. Those with the highest predicted probability of disagreeing that

workers need strong unions are white people who self-identify as working class, with a predicted

probability of 0.52. Those with the lowest predicted probability of disagreeing that workers need

strong unions are low income people of color, with a predicted probability of 0.28. Those with

the highest predicted probability of strongly disagreeing that workers need strong unions are

middle class conservatives, with a predicted probability of 0.22. Those with the lowest predicted

probability of believing that workers need strong unions are low income people of color and

people of color who do not trust management, with a predicted probability of 0.04.

Being a person of color, compared to being white, is associated with an increase of 0.16

and 0.07 in the predicted probability of agreeing or strongly agreeing, respectively, that workers

need strong unions, and a decreased predicted probabilities of 0.19 and 0.04 in disagreeing or

strongly disagreeing that workers need strong unions, all else equal. Being a working class,

compared to being middle class, is associated with a decrease of 0.001 in predicted probability of

agreeing that workers need strong unions and an increase of 0.05 and 0.01 in predicted

probability of disagreeing and strongly agreeing, respectively, that workers need strong unions,

all things equal. Being non-conservative, compared to being conservative, is associated with a

decrease of 0.10 and 0.02 in predicted probability of strongly disagreeing and disagreeing,

respectively, that workers need strong unions and an increase in 0.03 and 0.09 in agreeing and

strongly agreeing, respectively, that workers need strong unions, all else constant.

Believing your coworkers are helpful, compared to not believing your coworkers are

helpful, is associated with an increase of 0.04 in predicted probability of strongly agreeing that

workers need strong unions and a decrease in predicted probability of 0.03 of disagreeing that




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workers need strong unions, all else equal. Not trusting management, compared to trusting

management, is associated with an increase of 0.09 and 0.03 in predicted probability of strongly

agreeing and agreeing that workers need strong unions, all else equal. Not trusting management

is associated with a decrease of 0.10 and 0.03 in disagreeing and strongly disagreeing that

workers need strong unions, holding covariates constant.

Having a low income, compared to not having a low income, is associated with a 0.04

increase in strongly believing that workers need strong unions and a 0.04 decrease in strongly

disagreeing that workers need strong unions, holding covariates constant. Having very good

benefits, compared to not having very good benefits, is associated with an increase of 0.04 of

predicted probability of strongly agreeing that workers need strong unions, all else equal.

Graph 3: Discrete Change Plot: Multinomial Logistic Regression of responses to thestatement, “Workers need strong trade unions.” General Social Survey (N=860)




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The effects of being a person of color are the largest, increasing the predicted probability

of agreeing that workers need strong unions and decreasing the probability of disagreeing that

workers need strong unions, relative to whites. The effects of not trusting management are also

large, with those who do not trust management being more likely to strongly agree less likely to

disagree that workers need strong unions, relative to those who do trust management. Also,

being non-conservative has an impact, with non-conservatives being more likely to agree and

less likely to strongly disagree that workers need strong unions, compared to conservatives.

DISCUSSION

Note: (-)=Strongly Disagree, (D)=Disagree, (A)=Agree, (+)=Strongly Agree

Change in Predicted Probability for unionsupport

-.19 -.15 -.1 -.06 -.02 .03 .07 .12 .16

D A+-

D A +-

D A+-

DA+-

D A+-

DA +-

D A +-

person of color-0/1

does not trust management-0/1

not conservative-0/1

working class-0/1

low income-0/1

benefits very good-0/1

Co-workers helpful-0/1




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In relation to class identity, the data support the Marxist relations to the means of

production hypothesis (A.1) but not the Marxist class domination hypothesis (A.2). While self-

employment was consistently negatively associated with working class identity, the data show

that middle class job characteristics of security, high benefits, and autonomy do not significantly

increase the odds of working class self-identification. The data support the Weberian income

hypothesis (A.3) that class is subjectively understood as a relation to market opportunities, with

income consistently being found as the largest predictor of class identity. However, the data do

not support the Weberian prestige hypothesis (A.4), with occupational prestige having no

significant impact on class identity. The data do support the Cultural segmented labor market

hypothesis (A.5), with people of color being consistently more likely to self-identify as working

class than whites. The data also support both the Bourdieu cultural capital and institutional

capital hypotheses (A.6, A.7). A high-income family of origin and increased education were

both consistently found to decrease the odds of self-identifying as working class.

Interaction terms did indicate that education impacted one’s likelihood of self -identifying as

working class differently among those with high occupational prestige and not high occupational

prestige, as well as between those with low income and those with medium to high income.

Specifically, this institutionalized cultural capital has substantial benefits for those with medium

to high income and those with high occupational prestige, increasing the likelihood that these

individuals will self-identify as middle class. Among those with low income or without high

occupational prestige, the institutional cultural capital of education does not as greatly increase

the odds of self-identifying as working class.

In relation to union support, the data do not support the Marxist proletarianization hypothesis

(B.1), with job insecurity, high benefits and low income being generally insignificant in




predicting attitudes toward unions. Low income was found to increase strongly agreeing that

workers need unions relative to strongly disagreeing, but had no impact on more moderate views.

The data do support the Bourdieu social capital theory (B.2), with distrust of management (weak

bridging capital) consistently improving the likelihood of strongly agreeing that workers need

unions relative to all outcome categories. Seeing one’s coworkers as helpful (bonding capital) is

positively associated with strongly agreeing that workers need strong unions, compared to both

agreeing and disagreeing. The data support the cultural theory hypothesis (B.3) and the political

hypothesis (B.4), with people of color and non-conservatives consistently more likely to support

trade unions. The data also support the class hypothesis (B.5), with working class identity

having a significant yet ambiguous relationship to union support.

Dropping observations of the unemployed and upper-class and lower-class identified

people may mask some factors related to union support. Further, many aspects that may

influence trade union support could not be tested with the available data, including the presence

of a union organizer or advocate.

Documents

The Formation of an Organized Working Class