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Male Advantage and the Gender Composition of Jobs: Who Rides the Glass
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DOI: 10.1525/sp.2002.49.2.258
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Male Advantage and the Gender Composition of Jobs: Who Rides the Glass Escalator?
Author(s): Michelle J. Budig
Source: Social Problems, Vol. 49, No. 2 (May 2002), pp. 258-277
Published by: University of California Press on behalf of the Society for the Study of Social Problems
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Male Advantage and the Gender
Composition of Jobs: Who Rides
the Glass Escalator?
MICHELLE J. BUDIG, University of Massachusetts
Is the gender gap in pay constant across all jobs, or does the gender composition of the job affect male
advantage? Using data from the NLSY and a Žnely detailed measure of the gender composition of jobs, I investigate gender differences in wages and in wage growth. I show how they differ between female-dominated, maledominated, and balanced jobs. Predictions from Kanter’s theory of tokenism and the Williams and Acker theory
of gendered organizations are tested. Findings indicate that men are advantaged, net of controls, in both pay levels
and wage growth in all jobs, regardless of gender composition. Contrary to predictions generated from Kanter’s
tokenism theory, men do not suffer when they are tokens, in terms of pay. Not only are predictions from Kanter’s
theory untrue for male tokens, they also do not hold for female tokens when it comes to wages. Rather, consistent
with the Williams and Acker theory of gendered organizations, men are no more—and no less—advantaged
when women are tokens; in terms of earnings, men are uniformly advantaged in male-dominated, femaledominated, and balanced jobs. Analyses of promotions data indicate that men are also not additionally advantaged whether they are the numerically dominant or minority gender; in fact, male advantage in promotions is
the smallest when men are tokens.
Women’s increasing participation in paid labor and their experiences in the workplace
have been the subjects of considerable research in recent decades. Past research shows that
women’s career trajectories and earnings are restricted due to occupational gender segregation
(see England 1992), sexism in hiring and promotion (Bielby and Baron 1986), and their
responsibilities for children and the home (Budig and England 2001). Compared with men,
women are disadvantaged in terms of pay either because they are in lower-paying, feminized
occupations, or because they are paid less for the same work. Even women in traditionally
male jobs are disadvantaged (Kanter 1977). The experiences of token women—those in maledominated Želds—have generated theories to explain why such women hit the “glass ceiling”
(i.e., Žnd their upward mobility in organizational hierarchies blocked). Turned on its head,
female disadvantage becomes male advantage.
In this study I ask: Is the magnitude of men’s advantage in earnings the same across three
types of job gender compositions—female-dominated, gender-balanced, and male-dominated—or do men fare differently in each of these groups? Prior theoretical and empirical
work raises the question of relative male advantage in earnings. Some researchers argue that
male advantage at work is hegemonic because the workplace and jobs are gendered in such a
way that traditional male work styles, social networks, and personal lives are favored. Men
are advantaged even when they are the numerical minority in the workplace, as Williams’s
research (1992, 1995) attests. Other research, most notably Kanter’s 1977 study, indicates that
I am indebted to Paula England for comments, criticisms, and encouragement. I also thank Richard Arum, Naomi
Gerstel, Lynn Smith-Lovin, Yvonne Zylan, David Smith, and the anonymous reviewers of this article for Social Problems
for comments on earlier drafts. Direct correspondence to: Michelle Budig, SADRI, W34A Machmer Hall, 240 Hicks Way,
University of Massachusetts, Amherst, MA 01003. E-mail: budig@soc.umass.edu.
SOCIAL PROBLEMS, Vol. 49, No. 2, pages 258–277. ISSN: 0037-7791; online ISSN: 1533-8533
© 2002 by Society for the Study of Social Problems, Inc. All rights reserved.
Send requests for permission to reprint to: Rights and Permissions, University of California Press,
Journals Division, 2000 Center St., Ste. 303, Berkeley, CA 94704-1223.
Male Advantage and the Gender Composition of Jobs
the amount of male advantage may vary by the gender composition of one’s job. Previous
research has not compared the magnitude of male advantage in jobs where men are tokens
with the male advantage where women are tokens or where neither sex predominates. Without making these comparisons, past research on tokens may incorrectly attribute to token
processes the more generic male advantage that is pervasive in all workplaces. In addition to
making these comparisons, this study broadens Kanter’s tokenism theory and the Williams
and Acker theory of men’s workplace advantage by applying these theories to a new variable:
earnings. The motivating question is—does token status affect the amount of male advantage,
in terms of earnings, over and above the existing male advantage in gender-balanced jobs?
Compared with the substantial body of research on token women in male-dominated
work settings, fewer studies examine the experience of men in female-dominated jobs.
Studies that examine men’s experiences using Kanter’s theory typically employ qualitative
methodologies with limited samples, and/or are limited to one or two occupations (Floge and
Merrill 1986; Gans 1987; Heikes 1991; Williams 1995). This research Ž nds that men experience some effects of tokenism, but, contrary to token women, these effects do not negatively
impact the careers of token men. In fact, some authors conclude that men experience extra
advantage due to their minority status in female-dominated jobs, although such studies seldom actually compare men’s advantage in male-dominated to that in female-dominated jobs.
This study addresses the limitations of past research on male tokens in several ways. First,
whereas previous studies gathered data on one or two institutional settings (Floge and Merrill
1986; Heikes 1991) or used snowball sampling (Williams 1995), I use a national probability
sample. Second, instead of limiting analyses to the experiences of male tokens in one femaledominated occupation, I aggregate male tokens across female-dominated occupation/industry
combinations and examine them as a group. This approach reveals whether previous Žndings
regarding male tokens in speciŽc occupations are generalizable. Third, I test whether male status and interactional advantage in work groups explain male wage advantage within all jobs,
as theorized and documented by Williams (1995), or if male advantage depends on the proportional representation of men in jobs, as Kanter (1977) implies. Fourth, I examine men’s
wage growth relative to women’s in both female and male-dominated jobs over three-year
periods. Whereas wage differences between men and women measured at single points in
time are well documented, gender differences in wage growth over time are not. Raises reect
both the employee’s actual performance and how that performance is perceived and valued
by the employer. While wage differences in levels of pay (at one point in time) might reect
either institutional or interactional forces that disadvantage women, differences in wage
change over time may better capture the effects of interactional processes at work, such as
those predicted by Kanter and qualitative studies on tokens. Finally, I provide supplemental
analyses of promotions into more rewarding male-dominated and gender-balanced jobs to
investigate the effects of token status on mobility. The broader scope of this research enables a
more powerful test of Kanter’s theory of tokenism applied to males, and the Williams and
Acker theory of male advantage.
Kanter’s Theory of Tokenism
Kanter’s theory is widely invoked as an explanation of token women’s frustrated progress
and lower earnings in male-dominated organizations. Many studies support her theory
regarding the disadvantages that accrue to token women in various occupations, including
business owners (Jungbauer-Gans and Ziegler 1991), police ofŽ cers (Ott 1989), amusement
park workers (Yoder and Sinnett 1985), military academy students (Yoder, Adams, and Prince
1983), union ofŽ cers (Izraeli 1983), and law students (Spangler, Gordon, and Pipkin 1978).
However, other studies Žnd evidence to reject Kanter’s explanation (Frisbie and Neidert 1979;
South, et al. 1982). Most studies supporting Kanter’s predictions for female tokens do not,
259
260
BUDIG
however, demonstrate that women do worse in work settings where they are tokens than
in settings with higher proportions of females. They simply document female disadvantage in
male-dominated settings. Yet the argument about the unique effects of token status rests precisely on such comparisons. This study extends the literature on female tokens by making
those comparisons.
Kanter’s theory of tokenism posits that people, distinguished by some salient individual
characteristic, who constitute a very small proportion of a group, are faced with unique interactional pressures including higher visibility, contrast, and assimilation. The resolution of
these pressures is critical to one’s career advancement. Although Kanter’s theory was developed to explain the effects of token status on women’s careers in male-dominated jobs, she
concluded that the same processes are likely to occur for all token individuals, no matter what
their distinctive characteristic: white, black, old, young, male, or female.
Many interpret Kanter’s token theory to imply that all tokens will suffer negative outcomes from the unique interactional pressures they face; indeed, Kanter’s (1977) discussion of
Segal’s (1962) study of male nurses indicates she thought men could be disadvantaged by
token status. However, applications of Kanter’s tokenism theory do not Ž nd that male tokens
suffer from their minority status. Instead, qualitative studies of male tokens imply that men
may even beneŽ t from their scarcity in female-dominated jobs.
Empirical Research on Male Tokens
Case studies of and interviews with male tokens in the workplace indicate that men do
not suffer the negative consequences of tokenism. Heikes’s (1991) study of 15 male nurses in
a Texas hospital found that the men experienced heightened visibility, contrast, and assimilation. However, unlike the effects of heightened visibility on women in Kanter’s study, these
men responded by overachieving, not underachieving. Men beneŽted from their social
identiŽcation with other men in the hospital who were mostly doctors and administrators.
Floge and Merrill’s (1986) earlier study came to the same conclusion. Floge and Merrill examined token male nurses and token female doctors in two hospitals. They found that both
token male nurses and token female doctors were affected by their token status in some of the
ways predicted by Kanter, such as heightened visibility and contrast. However, these processes
had opposite effects—negative results for token females and positive effects for token males.
Female doctors felt they were more scrutinized, given less credibility, and were socially isolated by their token status. Conversely, token male nurses beneŽted from their associations
with male doctors and administrators that enhanced their networks. Male nurses were assimilated into stereotypical but advantageous masculine roles including leadership positions.
Token Disadvantage, Token Male Advantage,
or Male Advantage as Usual?
The above studies indicate male tokens fare differently than female tokens: tokenism has
positive outcomes if the token is a man. However, are the positive experiences of token men
in the workplace a product of the special interactional pressures faced by a token? Or are
these positive outcomes just more examples of the well-documented male advantage in the
workplace? Floge and Merrill (1986) and Heikes (1991) conclude that it is the positive resolution of the interactional pressures created by being a token that gives token men their advantage. However, their studies never examined male advantage in settings where men are not
tokens, so their studies shed no light on whether these token men’s male advantage is less or
more than men’s advantage in balanced or male-dominated jobs. Moreover, other theories
offer alternative explanations.
Male Advantage and the Gender Composition of Jobs
Theory of Gendered Organizations
Williams (1992, 1995), drawing heavily from Acker (1990), argues that male tokens
beneŽt from being male in a working world designed to reward stereotypically masculine
attributes. She states that “cultural beliefs about masculinity and femininity are built into the
structure of the work world” and serve to limit women’s and enhance men’s opportunities
(Williams 1995:9). According to the Williams/Acker theory, women are not disadvantaged
simply because they lack work experience, seniority, or other forms of human capital. Instead,
or in addition, women are disadvantaged because the typical woman does not Žt the disembodied category of the ideal worker: one free from non-work (e.g., family) obligations and distractions. Employers statistically discriminate against women when they assume all women
are not “ideal workers” because the average woman has greater non-work obligations to family than does the average man. In effect, then, male advantage is endemic in organizations, no
matter what the gender composition of different jobs. This theoretical approach suggests that
men are advantaged from the start of employment in any job, and their advantage increases
with tenure because they receive promotions and/or raises at a higher rate. Thus, their wage
trajectories are steeper.
Focusing on female-dominated jobs, Williams (1995) asserts that male tokens do not
experience the disadvantages of their minority status. In fact, the token men in nursing, elementary teaching, and librarianship frequently spoke of feeling advantaged at work. Interviewees reported being favored as new hires, for promotions, and as colleagues. Token males
also reported mostly positive treatment from their female peers. The favorable treatment of
male tokens by both their superiors and coworkers prompted Williams to describe the male
token’s career as a ride on the glass escalator:
Often, despite their intentions, they face invisible pressures to move up in their professions. Like
being on a moving escalator, they have to work to stay in place (Williams 1995:87).
Thus, even in female jobs, men are advantaged, sometimes even against their desires, by
being encouraged to assume managerial or administrative positions that are seen as more suitable for their assumed masculine qualities.
Overall, Williams claims that male advantage exists in all organizations and jobs, even
when those jobs are largely female. Her theory makes no particular claims for tokens. 1 In her
discussion, male token advantage in female-dominated jobs becomes just another example
of male advantage, not a unique outcome of the statistical interaction between token status
and gender.
Hypotheses
The arguments presented above make no explicit claims about earnings. However, they
do make claims about how men and women, and tokens and non-tokens, are perceived and
valued on the job. To the extent that tokenism processes and sexism affect employers’ perceptions about employee potential and performance, and to the extent that higher valued
workers are rewarded by employers with higher starting wages, promotions, and raises, one
can reasonably expect earnings to capture some outcomes of tokenism processes and sexism
in the workplace, net of other factors known to affect pay. For example, the higher visibility of
tokens in the workplace brings their job performance to the attention of supervisors more
readily than that of non-tokens. Cultural privileging of men and masculine attributes, embedded in workplace structures as Williams and Acker assert, may lead to systematically more
1. Williams has conŽ rmed this interpretation of her theory in a personal communication.
261
262
BUDIG
Table 1 • Predictions From Theoretical Perspectives and Findings
Male advantage (1), disadvantage
(2), or neither (0) in . . .
Female jobs
Balanced jobs
Male jobs
Male advantage is the same (0),
greater (1), or less (2) in . . .
Female jobs than balanced jobs
Male jobs than balanced jobs
Male jobs than female jobs
The Williams/
Acker Theory
of Gendered
Organizations
Kanter’s
Theory of
Tokens
Male Token
Case Study
Predictions
Findings for
Wage Level
Findings for
Wage
Change
1
1
1
2
NP
1
1
NP
1
1
1
1
1
1
1
0
0
0
2
NP
1
1
1
1
0
0
0
0
0
0
Notes:
NP 5 no prediction is made by the theory.
All predictions apply to both wage level and wage growth.
positive evaluations of the token’s job performance where the token is male, and systematically less positive evaluations where the token is female. Consequently, higher visibility and
sexist job performance evaluation would result in more raises and greater promotions for
male tokens, while less positively reviewed female tokens either do not get these performance
rewards or Ž nd themselves demoted or otherwise penalized.
Job Gender Composition and Earnings
The theoretical arguments generate three sets of competing hypotheses regarding men’s
and women’s relative wages and wage growth in female-dominated, male-dominated, and
“balanced” jobs. All hypotheses concern two different outcomes: wage levels (at a single point
in time), and wage change (over time). Predictions of each theory are summarized in Table 1.
The Ž rst set of hypotheses builds upon the Williams and Acker theory. They posit that
male advantage is endemic to all organizations, such that men are advantaged regardless of
the gender composition of the job. Here, token status should have no special effect on men’s
advantage in either earnings or wage growth. Consequently, at any single point in time,
men’s wages should be higher than women’s, regardless of the gender composition of the job.
Furthermore, if token status does not affect male advantage, men should be equally advantaged in wage growth over time in all types of jobs. Thus,
H1a(b): the effect of being male on wages (wage growth) is positive in female dominated, maledominated, and balanced jobs.
H1c(d): the positive effect of being male on wages (wage growth) is equal in magnitude across
female-dominated, male-dominated, and balanced jobs.
Drawing from the Ž ndings of qualitative studies (Floge and Merrill 1986; Gans 1987; and
Heikes 1991), a second set of hypotheses states that being a token helps men, but hurts
women. In contrast to the Ž rst set of hypotheses, Ž ndings from previous studies indicate that
token men in female-dominated jobs should experience heightened male advantage. If so,
male advantage should be greatest when men are tokens. At the same time, token women in
male-dominated jobs should experience negative outcomes of tokenism. Thus, we should
Male Advantage and the Gender Composition of Jobs
expect male advantage in male-dominated and in female-dominated jobs, both as outcomes of
tokenism, but comparatively less male advantage in balanced jobs.
H2a(b): the positive effect of being male on earnings (wage growth) is greater in female- and in
male-dominated jobs than in balanced jobs.
Finally, the third set of competing hypotheses, derived from Kanter, argue that all tokens
suffer negative outcomes. Accordingly, men should be most disadvantaged when they are
tokens, and most advantaged when women are tokens.
H3a(b): the positive effect of being male on earnings (wage growth) is greater in male-dominated
jobs than in other jobs.
H3c(d): the positive effect of being male on earnings (wage growth) in female-dominated jobs is less
than in balanced or male-dominated jobs.
The second and third sets of hypotheses above have different implications for gender differences in wage at any point in time and gender differences in wage change over time.
Because both theories depend upon the playing out of interactional processes over time,
change in wage over time, rather than a single measurement of wage at any single point in
time, may better reveal the advantage/disadvantage predicted by the theories.
Job Gender Composition and Promotions
The processes described above could also result in male advantage in promotions, in addition to, or instead of, advantage in wages. As Williams (1992, 1995) describes, male advantage
and tokenism processes might cause male tokens to be promoted out of female jobs. Since the
analyses for change in wage are restricted to those respondents who remain with the same
Ž rm and in the same gender composition job category, these analyses could miss detecting
extra male advantage when men are tokens, if this advantage results in promotions out of
female jobs. To control for the possibility that the wage beneŽ ts of tokenism may stem from
promotion out of female jobs, I conduct supplemental analyses of promotions. In analyses of
promotions, a greater likelihood of being promoted into more rewarding balanced or male
jobs indicates advantage, since previous analyses show that female jobs pay least net of controls. Evidence of male token advantage would be found if the probability that men, relative
to that of women, to be promoted into male jobs is greater among those in female jobs prior to
promotion, compared with those in balanced or male jobs prior to promotion. A Ž nding that
men, regardless of pre-promotion job gender compositions, are more likely to be promoted
into male jobs compared to women would further demonstrate the sort of general male
advantage Williams and Acker theorize.
H4a: the positive effect of being male on the likelihood of promotion into male or balanced jobs is
greater in female-dominated jobs than in balanced jobs.
Although the focus of this article is on male advantage in earnings, I include supplementary analyses to test this hypothesis about promotions to provide a broader test of the theories
and token advantage/disadvantage.
Methodology
Data
The panel data for these analyses are drawn from the National Longitudinal Survey of
Youth (NLSY), which is a multi-stage stratiŽed national probability sample of 12,686 persons
who were aged 14 to 21 when Ž rst interviewed in 1979. Subsequent survey waves continued
263
264
BUDIG
annually through 1993. While the relative youth of this sample may be a drawback for examining career trajectories and wage trajectories,2 the NLS data sets are the best national panel
data sets that include measures of interest to this study, such as wage growth with seniority
for a recent large sample of men and women.
The percent female in the respondent’s job is calculated from the percent female in each
detailed occupation/industry cell from 1990 Census data (U.S. Bureau of the Census 1993). 3
NLSY data are coded into 1980 occupation and industry codes starting in 1982, and these
codes were easily mapped onto 1990 occupation and industry codes. Since pre-1982 occupations and industries are coded into 1970 codes, which do not easily map onto 1990 codes, I
limited the sample to the 1982–1993 years.
Unit of Analysis and Statistical Models
For the wage level models, I use Ž xed-effects regression models to analyze NLSY data
arranged in a pooled time-series cross-section with person-year as units of analysis. 4 Effects
are Ž xed for years and persons. 5 Person-speciŽc Žxed effects capture and control for any nonmeasured differences between individuals that do not change over time. The advantage of
Ž xed effects is that the procedure eliminates omitted-variable bias for additive effects of
unchanging, but unmeasured, personal characteristics. Such characteristics include cohort
and socioeconomic background and their effects. They also include unchanging aspects of
intelligence, preferences resulting from early socialization, life cycle plans, tastes for afuence,
goal-setting, and unmeasured human capital. Thus, for example, if those who self-select into
predominantly female jobs are different on unmeasured characteristics such as career ambition that also affect earnings, these characteristics are controlled by the person-Žxed-effect.
For the wage change model, the units of analysis are non-overlapping three-year spells of
continuous employment in a job in the same category of job gender composition in the same
Ž rm (male-dominated 5 0–20% female; balanced 5 21–79% female; and female-dominated 5
80–100% female). The leading female occupations where men are most likely to be tokens
include nursing aides, nurses (RNs and LPNs), secretaries, typists, and hairdressers. Note that
one individual can have multiple spells of employment if spells are the unit of analysis. A
three-year spell was chosen because with longer periods of continuous employment within a
given job gender composition category, the number of cases dropped drastically, seriously
decreasing the power of the statistical tests. Both men’s and women’s spells are used. Spells in
which individuals changed jobs such that they moved between gender composition categories
are excluded since the predictions to be tested pertain to individuals who stay in a setting
where they are a token, the other gender is a token, or neither gender is a token.
2. England (1992:12) shows that in the 1980s the gender gap in pay was much smaller among those aged 24 or
less, in comparison with older aged cohorts. For example, in 1988, among full-time workers, women aged 45 to 54
earned 67 cents for every male dollar, whereas women aged 20 to 24 earned 96 cents for every male dollar. This could
be interpreted to mean either that the gender gap in pay is declining over time via a cohort effect, or that within each
cohort, the gender gap in pay increases across the lifespan. Research by Marini and Fan (1997) also documents that the
gender gap in pay is smaller among young people at career entry. Thus, this sample of young people in the 1980s and
1990s may underestimate the gender gap in pay, compared with the entire population of full-time wage earners.
3. Technically speaking, a cell deŽned by detailed 3-digit Census occupation by detailed three-digit Census industry code is not the same as a “job,” which refers to establishment-level titles. However, I use the term “job” for the sake
of brevity. Past research has also used the term “job” to describe an occupation-by-industry cell (see England, Reid, and
Kilbourne 1996 for an example).
4. For further reading on Ž xed-effects regression models, see Baltagi (1995).
5. For the pay level models, the Hausman test was conducted to assess whether random effects models were adequate (see Hausman [1978] for details of the test). In each case, the test indicated a need for Ž xed effects. Hence, only
these models are presented.
Male Advantage and the Gender Composition of Jobs
To examine the effects of token status on promotions, I estimate a multinomial logistic
regression model with maximum likelihood methods to predict promotion into the three job
gender compositional categories. This analysis shows the effects of gender and pre-promotion
job gender category on the likelihood of being promoted into a male-dominated, a balancedgender, or a female-dominated job. Promotion into a female-dominated job is the reference
category.
The mean, standard deviation, and number of observations for each variable used in the
two sets of analyses are given in Table 2. In the following summaries, unless otherwise indicated, all changeable independent variables are measured at time three.
Variables
Dependent Variables. The dependent variable for H1a, H1c, H2a, H3a, and H3c is the natural log of hourly wage at time three (the third interview of the three-year spell). For H1b, H1d,
H2b, H3b, and H3d, change in the natural logarithm of wage from time one to time three is
the dependent variable. Cases with extreme values of wage (less than $.50 and greater than
$75 per hour) were deleted.6 Transforming hourly rate of pay into a logarithm and subtracting
log wage at time three from log wage at time one yield a difference that tells the percent
change in wage from the Ž rst to the third year. This is the dependent variable for tests about
wage change.
In addition to the main models, I conduct supplemental analyses of promotions. The
NLSY data do not provide consistent annual measures of respondents’ promotions. However,
questions about promotions that respondents had received in the past three years while working for the same organization were asked in the 1990 wave of the survey. To assess the impact
of male token advantage accruing through promotions out of female jobs and into more
rewarding male jobs, I use data from the 1990 cross-section. In 1990, 1,407 respondents
reported having been promoted in the past three years.
To control for period effects on real wages and in ation, a linear variable indicating the
last year of the three-year job spell is included in the analyses. Higher order effects of year are
included where signiŽcant.
Job Characteristics. The independent variables of most interest are a dummy variable measuring gender (male51) and two dummies representing a three-category variable derived
from the percent female (in 1990) in the respondent’s detailed census occupation and industry combination, which I term a “job.” A limitation of census data is that individuals may be
misclassiŽed as tokens or non-tokens based on occupational/industrial-level data when the
gender composition of their local work environment differs. Since Kanter’s theory relies upon
the composition of these local environments, misclassiŽcation could pose problems for the
analyses. However, these possible misclassiŽcations pose little threat to the analyses because
the 80% threshold for deŽ ning the job as skewed is so high that it is unlikely that respondents
in occupations over 80% male or female are in jobs in their organization not dominated by
this gender. Excluding real tokens or including non-tokens in the token group will weaken
the effects token status has on earnings or earnings growth.
The data used to compute the percent female in the respondent’s job are from the 1990
Census (U.S. Bureau of the Census 1993). In the NLSY data, respondents’ occupation/industry
combination during 1982-1993 were coded into 1980 detailed Census occupational/industrial
categories. These detailed categories were matched with the 1990 Census detailed occupational categories to obtain the percent female in each category in 1990.
6. Less than one percent of the sample had hourly earnings greater than $75 per hour.
265
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BUDIG
Table 2 • Means and Standard Deviations
Dependent variable
Hourly pay
Job characteristics
Female job
Male job
Balanced job
Occupational general education
Occupational speciŽc vocational training
Occupational complexity w/data
Occupational complexity w/people
Occupational complexity w/things
Occupational average reported
work effort
Authority associated w/occupation
Professional or managerial occupation
Small firm (,20 employees)
Irregular shifts
Industrial sector
Construction
Manufacturing
Public utilities
Wholesale and retail trade
Financial services
Business and repair services
Personal services
Professional services
Entertainment and recreation services
Public administration
Agriculture, mining, forestry
Wage Change
N 5 19774
Mean
(Std. Deviation)
Wage Levels
N 5 73364
Mean
(Std. Deviation)
9.553 (5.581)
8.024 (6.316)
0.200 (0.400)
0.410 (0.492)
0.391 (0.488)
3.624 (0.827)
5.232 (1.537)
2.724 (1.425)
1.754 (1.416)
2.817 (1.919)
0.237 (0.426)
0.302 (0.459)
0.461 (0.498)
3.501 (0.821)
4.910 (1.545)
2.556 (1.409)
1.680 (1.351)
2.628 (1.871)
0.544 (0.059)
0.058 (0.235)
0.173 (0.378)
0.375 (0.484)
0.133 (0.340)
0.547 (0.056)
0.062 (0.241)
0.166 (0.372)
0.356 (0.479)
0.142 (0.349)
0.106 (0.307)
0.171 (0.377)
0.071 (0.257)
0.179 (0.384)
0.058 (0.234)
0.084 (0.278)
0.022 (0.145)
0.193 (0.394)
0.021 (0.143)
0.049 (0.217)
0.040 (0.195)
0.066 (0.249)
0.187 (0.390)
0.063 (0.243)
0.213 (0.410)
0.065 (0.247)
0.089 (0.285)
0.026 (0.159)
0.185 (0.389)
0.021 (0.144)
0.048 (0.214)
0.035 (0.190)
(Continued)
Control Variables. A potential problem arising from the coding of the gender composition
variables is that men and women respondents who are in the same gender composition category may be in different occupations. For example, childcare providers and registered nurses
are in the same gender composition category, but a childcare provider would be expected to
earn less than a registered nurse because of differences in the education, type of skill, and
other demands of these two jobs, regardless of the gender of the respondent. Thus, without
controlling for the type of skill, training, education, authority, and effort demanded by the
speciŽc occupation, between-occupation differences in earnings within the gender composition categories might erroneously be attributed to gender. To control for the varying attributes
of occupations within the gender composition categories, multiple occupational and industrial
sector variables are included. All of these variables pertain to the occupation held at time one
in both the wage and wage change equations. Authority is a dummy variable coded 1 for census detailed occupational categories with titles containing the words “management,” “supervi-
Male Advantage and the Gender Composition of Jobs
Table 2 • (continued)
Wage Change
N 5 19774
Mean
(Std. Deviation)
Human capital and labor supply
Education (highest grade completed)
Change in education t1–t2
AFQT
Enrolled in school
Experience (years)
Seniority (years)
Seniority at t2
Weeks worked
Part-time
Became part-time t1–t2
Became full-time t1–t2
Wage Levels
N 5 73364
Mean
(Std. Deviation)
12.639 (2.384)
0.143 (0.495)
43.362 (28.522)
0.088 (0.283)
6.638 (3.002)
2.697 (1.728)
3.066 (2.086)
47.283 (9.939)
0.329 (0.470)
0.124 (0.330)
0.173 (0.378)
12.643 (2.352)
Family characteristics
Married
Became married t1–t2
Became divorced t1–t2
Preschooler
Acquired preschooler t1–t2
Lost preschooler t1–t2
0.437 (0.496)
0.110 (0.312)
0.043 (0.203)
0.345 (0.476)
0.094 (0.292)
0.076 (0.265)
0.301 (0.459)
Demographic characteristics
South
Urban residence
Moved to urban residence t1–t2
Moved to rural residence t1–t2
Changed counties t1–t2
Local county unemployment rate
0.403 (0.490)
0.793 (0.405)
0.023 (0.149)
0.022 (0.147)
0.687 (0.464)
3.037 (1.073)
0.385 (0.487)
0.777 (0.416)
0.593 (0.491)
0.231 (0.422)
0.174 (0.379)
27.648 (3.595)
0.522 (0.500)
0.247 (0.431)
0.168 (0.374)
26.044 (3.999)
Personal characteristics
Gender (male 5 1)
African American
Hispanic
Age
0.101 (0.302)
4.952 (3.264)
2.574 (2.842)
44.850 (12.985)
0.389 (0.487)
0.336 (0.472)
3.015 (1.052)
sor,” or “foreman” (England 1992:137–139). I measure cognitive skill demanded by an
occupation with a scale created by England (1992:134–135). The scale was created from a factor
analysis of numerous items, most taken from the Dictionary of Occupational Titles (U.S. Department of Labor 1977). The scale score was merged with NLSY respondents’ records according
to their detailed (1990) census occupational category. Measures of speciŽc vocational preparation, the general educational level, and levels of skills with people, data, and things are occupational averages of variables taken from the Dictionary of Occupational Titles and are merged
with the data according to the NLSY respondents’ detailed occupation. One variable, created
from the 1977 Quality of Employment Survey (Quinn and Staines 1979), is included as a continuous variable measuring how much “effort they put into their occupations” scaled to the amount
of effort respondents said it takes to watch television (Bielby and Bielby 1988). A dummy variable coded 1 if the respondent is in a professional or managerial occupation is also included .
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BUDIG
Additional job characteristics in the model include whether the respondent worked in a
very small Ž rm (less than twenty employees) to capture the shorter internal ladders and
higher volatility of small Ž rms as they may affect pay. A dummy measure indicating whether
a respondent worked an irregular shift is included and is coded 1 if the respondent worked
rotating, evening, or night shifts.
To control for industrial sector, I use eleven dummy variables: agriculture, Ž shing, and
mining (the omitted category); construction; manufacturing; public utilities; wholesale and
retail trade; Žnancial services; business and repair services; personal services; professional services; entertainment and recreation services; and public administration.
Additional control variables include demographic, family, human capital, and labor supply variables. Demographic variables include residence in a rural vs. urban area (one dummy,
urban 5 1); region of residence (one dummy coding south 5 1); and change in county of residence from time one to time three (change in county 5 1). In non-Ž xed effects models, race
(dummies for African-American and Hispanic, with non-Hispanic White as the reference category) and age are included. I control for these variables because previous research indicates
that being non-white, young, and living in rural and southern regions lead to lower wages. I
also control for change in county of residence, rural vs. urban residence, and the region of residence from time one to time three, because such moves may increase or decrease wages.
Family variables are marital status (married 5 1, other 5 0), presence of children under
three years old in the household at time one, and change in these variables over the threeyear spells used in the wage change equation. Statistical interactions between marriage and
gender and between children and gender examine whether marriage and children affect the
size of the male advantage in pay.
Human capital variables include years of employment experience, years of seniority
(experience with one’s current employer), part-time status (dummy, part-time 5 1), years of
education, AFQT score (standardized test of cognitive skills and knowledge), and change in
Ž rms during three-year period (dummy, changed Ž rms 5 1). Years of experience, seniority,
and education should positively affect wage, and possibly wage growth. The Armed Forces
Qualifying Test is a measure of work-related skills and should be positively correlated with
wage growth. A statistical interaction between changing Žrms and gender is included because
it is expected that men have larger networks and greater opportunities to increase their wages
by taking positions with different Ž rms.
Labor supply variables include number of weeks worked per year, current enrollment in
school, and part-time status. Part-time status and enrollment in school are expected to negatively impact wage levels and wage growth, while number of weeks worked should have a
positive impact.
Analyses
I use Ž xed-effect regression to test the three sets of competing hypotheses for wage levels,
and ordinary least squares (OLS) regression to test hypotheses for wage change. For analyses
of promotions, I use a multinomial logistic regression model with maximum likelihood estimation. Because sample weights are a function of independent and dependent variables
included in the models, regression analyses were not weighted (although weighted means are
presented), following Winship and Radbill (1994).
Results
Wage Levels. The hypotheses center on whether there is a male advantage (female disadvantage) in each of male, female, and balanced jobs, and whether any such advantage or disadvantage is of equal magnitude (in percentage terms) across the three types of jobs. A
constant advantage across the three types of jobs implies that the interactions of gender with
Male Advantage and the Gender Composition of Jobs
Table 3 • CoefŽcients from a Fixed-Effects Model Predicting Wage Levels
CoefŽcient
(Std. Err.)
Job characteristics
Female job
Male 3 female job
Male job
Male 3 male job
Occupational general education
Occupational speciŽc vocational training
Occupational complexity w/data
Occupational complexity w/people
Occupational complexity w/things
Occupational average reported work effort
Authority associated w/occupation
Professional or managerial occupation
Small firm (,20 employees)
Irregular shifts
20.039 (0.005)***
0.005 (0.011)
0.035 (0.010)***
20.003 (0.011)
0.027 (0.006)***
0.015 (0.003)***
20.001 (0.003)
20.013 (0.002)***
20.002 (0.001)
20.000 (0.030)
0.013 (0.006)**
0.033 (0.005)***
20.040 (0.003)***
20.013 (0.004)**
Industrial sector
Construction
Manufacturing
Public utilities
Wholesale and retail trade
Financial services
Business and repair services
Personal services
Professional services
Entertainment and recreation services
Public administration
0.098 (0.011)***
0.070 (0.010)***
0.118 (0.011)***
20.044 (0.010)***
0.052 (0.012)***
20.020 (0.010)**
20.138 (0.013)***
0.002 (0.010)
0.017 (0.014)
0.072 (0.012)***
Human capital and labor supply characteristics
Education (highest grade)
Currently enrolled in school
Experience (years)
Seniority (years)
Weeks worked per year
Part-time worker (dummy)
0.056 (0.002)***
20.127 (0.005)***
0.068 (0.001)***
0.009 (0.001)***
0.002 (0.000)***
20.005 (0.003)
Family characteristics
Married
Preschooler
Demographic characteristics
South
Urban residence
Local unemployment rate (county)
Intercept
R-squared
0.019 (0.005)***
0.005 (0.005)
20.055 (0.009)***
0.050 (0.007)***
20.012 (0.002)***
20.639 (0.481)
0.030
Note:
* Denotes signiŽcance (P , .10); ** denotes signiŽcance (P , .05); *** denotes signiŽcance (P , .01); two-tailed tests.
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270
BUDIG
each of the two dummy variables representing female and balanced jobs (with male jobs as
reference category) will not be signiŽcant.
Table 3 reports results from Žxed-effect regressions in which logged hourly wage is the
dependent variable. Interpretation is facilitated by remembering that coefŽ cients in a semilogarithmic speciŽcation such as this (when multiplied by 100), can be interpreted, roughly
speaking, as the percentage change in wage associated with a one-unit change in the independent variable.
In models predicting wage level, interactions between gender and gender composition
category were non-signiŽcant, as shown in Table 3. Although I cannot measure the effect of
gender in the Ž xed-effect model (it is included in the person-Žxed-effect), interactions with
gender can be included in Ž xed-effect models despite the fact that additive effects of gender
are in the Žxed effect. The non-signiŽ cance of the interactions shows that the effect of being
male on wage level, net of the control variables, is of the same magnitude (in percentage
terms) in male, female, and balanced jobs. 7 In results not shown, I estimated the size of the
male wage advantage using an OLS model. This obtains an estimate of the magnitude of men’s
advantage, since a gender effect cannot be estimated from the Ž xed-effects model, as
explained above. In this OLS model, men enjoy an 11% higher wage than do women, despite
the controls for human capital, occupational characteristics, and family variables. Even in OLS
results, this male advantage does not signiŽcantly differ by gender composition of the respondent’s job.
In results not shown, I found that sex interacts signiŽcantly with marital status, preschool
child, and urban residence. Among those who are not married, do not live in an urban area,
nor have a preschool child, the male wage advantage is 7%. The male advantage increases by
another 7% (shown by the coefŽ cient of .072 on the interaction of Male and Married), to
14% for married persons. Similarly male advantage increases 4% if there is a preschool child
in the home, and 2% for persons living in urban areas. Thus, the male advantage is largest
among urban, married respondents with a preschool child—20%—even after adjusting for
any sex differences in experience, seniority, and the other controls. These Žndings provide
support for Williams’s/Acker’s argument that job categories are designed for ideal workers
with no non-work (family) obligations. Given the typical gendered division of labor within the
family, wherein women shoulder the responsibilities for children and housework while men
meet Ž nancial responsibilities as the family breadwinners, marriage and children send differ ent signals to employers depending on the gender of the employee. For women, marriage and
children signal non-work obligations and distractions. Husbands and fathers, however, are
expected to have heightened work commitment to meet their family Žnancial obligations.
Indeed, previous research documents an earnings penalty for children for women that is
greatest for married women (Budig and England 2001), while men receive an earnings premium for fatherhood (Hersch 1985). The inclusion of statistical interactions between these
family status variables and gender did not alter the substantive Ž nding that male advantage
does not vary by job gender composition.
Turning to the analysis of wage growth, I Žnd the male wage advantage is constant across
the three categories of gender composition. While there is no evidence of special token advantage or disadvantage in any of the job types, the male advantage is built into male-dominated
jobs: the pay is the highest of all job gender compositions, regardless of the gender of the job7. Since the Ž rst model in Table 3 took Balanced Occupations as the reference category for the occupation gender
composition dummy variables, the interactions (Male 3 Female Occupation and Male 3 Male Occupation) test whether
the male advantage in male occupations is of the same magnitude (in percentage terms) as in balanced occupations, and
whether this equivalence holds when comparing female and male occupations. To test for the one remaining comparison, whether the male advantage differs between female and male occupations, in results not shown, I changed the
omitted category to female occupations. The coefŽcient for Male 3 Male Occupation was not signiŽcant, supporting the
conclusion that the average male advantage is the same (in percentage terms) in all three categories.
Male Advantage and the Gender Composition of Jobs
holder. The wage penalty for being in a female job is consistent with past research on comparable worth (England 1992; Kilbourne, et al. 1994), which shows female-dominated occupations are devalued simply because the incumbents are primarily women.
What is important for the hypotheses is that there is male wage advantage in all three
types of jobs (male, balanced, and female) and it is of the same magnitude for each of these
three types of jobs. The fact of a male advantage in all job types supports the Williams and
Acker theory of gendered organizations. The lack of interaction contradicts the predictions of
Kanter’s theory and the predictions generated by case studies on male tokens. Kanter’s theory
predicts that tokens will suffer for their token status, or, as applied to these data, that they will
incur a penalty for working in a female-dominated job. While both women and men earn less,
overall, in female jobs, men are not especially disadvantaged by their token status, as the nonsigniŽcant interaction shows. At the same time, token men in female jobs do not experience
an extra male advantage, as the case studies research implies. Token processes apparently do
not affect relative wage levels of men and women.
Changes in Wage
It is possible that token advantage/disadvantage appears over time as the individual
works for one employer. Certainly Kanter’s theory depends upon time for tokenism processes
to be played out. To get at this, the second set of analyses examines change in wage for tokens
over time. Table 4 presents the Žndings.
Table 4 reports results from OLS trimmed models where the difference between logged
hourly wage in the third and Žrst year of the spell is the dependent variable. Contrary to the
hypotheses based on Kanter, the same pattern of uniform male advantage across gender composition of jobs holds when I look at male advantage in wage change in the three types of jobs
(male, female, and balanced), supporting predictions drawn from the Williams and Acker
notion of gendered organizations.8 Overall, men’s wages grew three percent faster than
women’s wages over the three-year time spans.
What are the implications of these Ž ndings for the theories I am testing? The reader may
Ž nd it useful to refer back to Table 1, where predictions of each perspective are reviewed and
Ž ndings are summarized. Just like the Ž ndings for wage levels, male advantage in wage
growth is uniform across job gender composition categories. Interestingly, wages grew more
slowly for all workers in male-dominated jobs, compared with balanced and female jobs,
although wages are consistently the highest in male jobs at any point in time (as evidenced in
Table 3). Perhaps this is because industrial restructuring more adversely affected male jobs.
These Žndings call into question the predictions of male token advantage drawn from the
Ž ndings of Heikes (1991), Gans (1987), and Floge and Merrill (1986). Male advantage in
wage growth is no larger in the female jobs where men are tokens than in balanced or male
jobs where they are not. The Žndings again refute the interpretation of Kanter, that men in
female-dominated jobs will incur penalties from tokenism. And, again, the lack of signiŽcant
interactions is not consistent with predictions generated from the case study research on male
tokens—the wages of men in female-dominated Ž elds are not increased by an extra token
advantage. The Williams and Acker notion of gendered organizations is supported by the persistent male advantage in wage growth across all types of jobs.
8. Actually, Table 4 tests whether male advantage differs between male and balanced occupations, and balanced
and female occupations. In results not shown, I tested whether it is different in male and balanced occupations by
changing the omitted category for gender composition to balanced occupations. The non-signiŽ cant interaction
coefŽcient in this model reveals that the effect of being male in male occupations is not statistically different than being
male in balanced occupations.
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BUDIG
Table 4 • CoefŽcients from OLS Regression Model Predicting Change in Wage
CoefŽcient (Std. Err.)
Gender (male 5 1)
Occupational and occupational characteristics
Female occupation/industry category
Male 3 female occupation/industry category
Male occupation/industry category
Male 3 male occupation/industry category
Occupational general education
Occupational speciŽ c vocational training
Occupational complexity with data
Occupational complexity with people
Occupational complexity with things
Occupational average reported work effort
Authority associated with occupation
Professional or managerial occupation
Small firm (,20 employees)
Irregular shift
0.025 (0.012)**
0.032 (0.024)
20.046 (0.035)
0.019 (0.032)
20.019 (0.026)
20.003 (0.012)
0.012 (0.006)**
0.010 (0.006)*
20.004 (0.004)
20.005 (0.002)**
20.016 (0.060)
20.003 (0.014)
20.000 (0.011)
20.017 (0.007)**
0.011 (0.010)
Industrial sector
Construction
Manufacturing
Public utilities
Wholesale and retail trade
Financial services
Business and repair services
Personal services
Professional services
Entertainment and recreation services
Public administration
0.000 (0.017)
0.007 (0.016)
0.037 (0.019)**
0.002 (0.017)
0.017 (0.021)
0.021 (0.018)
20.008 (0.026)
0.020 (0.018)
0.025 (0.028)
0.049 (0.020)**
Human capital and labor supply
Education at t1
Change in education (t1–t2)
Enrolled in school at t1
0.003 (0.002)
20.003 (0.009)
0.008 (0.015)
(Continued)
Male Advantage, Earnings, and Promotions
Perhaps the absence of extra male advantage in earnings for male tokens results from the
promotion of token men out of female jobs. The wage change analysis is restricted to those
respondents who remained with the same Ž rm and in the same job gender composition category over the three-year period. While the wage beneŽ ts of promotions for those workers
who were promoted within job gender categories are captured in these analyses, it is possible
that the advantages incurred by male tokens in female jobs push them out of female jobs and
into balanced or male-dominated jobs. This would, perhaps falsely, de ate extra advantage
conferred on men by their token status.
Table 5 presents results from a multinomial logistic regression using maximum likelihood
estimation predicting promotion into one of the three job gender compositional categories.
This analysis shows the effects of gender and pre-promotion job gender category on the likelihood of being promoted into a male-dominated, a balanced gender, or a female-dominated
job. Promotion into a female-dominated job is the reference category.
Male Advantage and the Gender Composition of Jobs
Table 4 • (continued)
CoefŽcient (Std. Err.)
Experience at t1 (years)
AFQT
Seniority at t1 (years)
Seniority at t2 (years)
Weeks worked per year
Part-time at t1
Full-time at t1 and part-time at t2
Part-time at t1 and full-time at t2
20.007 (0.002)***
0.000 (0.000)
20.022 (0.007)***
0.017 (0.007)***
20.000 (0.000)
20.045 (0.012)***
20.006 (0.010)
0.066 (0.012)***
Family characteristics
Married at t1
Not married at t1 and married at t2
Married at t1 and divorced at t2
Preschooler at t1
No preschooler at t1 and preschooler at t2
Preschooler at t1 and no preschooler at t2
20.008 (0.008)
20.016 (0.011)
20.012 (0.016)
20.002 (0.008)
20.001 (0.011)
0.008 (0.012)
Demographic characteristics
South at t1
Urban at t1
Rural at t1 and urban at t2
Urban at t1 and rural at t2
Changed counties t1 to t2
Unemployment rate in local county at t1
20.015 (0.007)**
0.001 (0.009)
0.086 (0.024)***
20.038 (0.023)*
0.016 (0.019)
20.010 (0.004)***
Individual controls
Black
Latino
Age at t1
Age at t1 squared
Year interviewed
Year interviewed squared
Intercept
20.014 (0.009)
20.004 (0.009)
20.049 (0.014)***
0.001 (0.000)***
0.018 (0.006)***
20.002 (0.000)***
20.639 (0.481)
R-squared
0.442
Note:
* Denotes signiŽcance (P , .10); ** denotes signiŽcance (P , .05); *** denotes signiŽcance (P , .01); two-tailed tests.
The question of male token advantage hinges on the relative size of the male advantage in
being promoted into male and balanced jobs, depending upon the gender composition of the
pre-promotion job. Table 5 shows that the interaction between gender and being in a maledominated pre-promotion job is not signiŽcant. This indicates that men are equally advantaged
in being promoted into male or balanced jobs when they are in either male or balanced jobs
prior to promotion. The interaction between gender and being in a female-dominated pre-promotion job is signiŽcant, however, as Table 5 shows. The direction of this interaction is contrary
to the hypothesis that token men use female jobs as springboards into more rewarding male and
balanced jobs. If male advantage (in terms of promotion) was greater among those in female
jobs, the coefŽcient for the interaction between gender and being in a pre-promotion female job
should be positive and signiŽcant. In fact, the interaction is negative. This indicates that the male
advantage in promotions is smaller for those in female-dominated jobs.
273
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BUDIG
Table 5 • CoefŽcients from a Multinomial Logistic Regression Model Predicting the Likelihood of
Promotion into Male and into Balanced Jobs Relative to Female Jobs: Selected Results
Promotion into Male Job
Gender (male 5 1)
Pre-promotion male job
Pre-promotion female job
Gender 3 pre-promotion male job
Gender 3 pre-promotion female job
Promotion into Balanced Job
CoefŽcient
Odds
Ratio
CoefŽcient
Odds
Ratio
3.827
0.799
21.715
NS
22.1752
45.929
2.223
0.180
NS
0.114
3.031
20.794
21.972
NS
21.800
20.716
0.452
0.139
NS
0.165
Notes:
N 5 1,407. NS 5 not signiŽcant. All reported coefŽcients signiŽcant at P , .05, two-tailed test. Control variables in
the model include education, experience, seniority, AFQT score, part-time status, occupational general education,
occupational speciŽc vocational training, occupational complexity with data, occupational complexity with people,
occupational complexity with things, authority associated with occupation, marital status, presence of preschooler
in the home, southern residence, urban residence, and age of respondent.
The results of the analysis of promotions demonstrate that the lack of Žndings for male
token advantage in terms of earnings is not a result of men being disproportionately promoted
out of female-dominated jobs into more rewarding male and balanced-gender jobs. In fact,
results are contrary to this claim. The male advantage in promotions is smallest among those
working in female-dominated jobs.
Discussion
Overall, the Ž ndings for earnings tell a straightforward story of the fate of male tokens in
female-dominated jobs and of female tokens in male jobs. There is no special advantage to
male tokens, nor special disadvantage to either male or female tokens in terms of wage levels
or wage growth. The consistent Ž nding of uniform male advantage in wages and wage growth
across categories of job gender composition supports the Williams/Acker theory of gendered
organizations. Within each of male-dominated, female-dominated, and balanced job category,
at any single point in time, and over time, men are equally advantaged over women in terms
of pay.9 Although men in female jobs are paid less than men in male and balanced jobs, this is
true for women too, and men maintain their male advantage in pay over women in jobs of
any gender composition. In statistical terms, both gender and gender composition of jobs have
additive effects. Men experience no more or less advantage when they are tokens than when
women are tokens or the gender ratio is balanced. Token status appears to be irrelevant to
both wage levels and wage trajectories.
The analysis of promotions reafŽrms Žndings from the earnings analyses. Men are more
likely than women to be promoted into rewarding male and balanced jobs, regardless of the
gender composition of the job held prior to promotion. Contrary to predictions derived from
Kanter that female tokens should be disadvantaged in terms of promotions compared with their
male colleagues, there is no extra penalty for token women in terms of promotions into male or
gender-balanced jobs. Like all men, male tokens in female-dominated jobs are more likely to be
promoted into male or gender-mixed jobs than are their female colleagues; however, their com9. These analyses of wage levels use wage at time three. In separate analyses not shown, at starting wage (time
one), men are also equally advantaged, within each job gender category, over women.
Male Advantage and the Gender Composition of Jobs
parative advantage is actually smaller than that experienced by men in male and gender-mixed
jobs. This is completely contrary to the idea of extra male advantage when men are tokens.
These Ž ndings underscore the importance of making the right comparisons. While earlier
studies found that male tokens in female-dominated jobs were advantaged as men, they did
not compare the magnitude of this advantage in jobs where men weren’t tokens. Thus, they
may incorrectly attribute to token processes what was actually explained by more generic
male advantage. Similar problems with not comparing across settings that vary token status
apply to the literature on female tokens. No special claim of extra advantage generated by
their token status can be supported by my results. Furthermore, the evidence does not even
support any extra disadvantage female tokens suffer over female non-tokens by virtue of their
token status. While Kanter’s theory is not disproved, since the interactional pressures on
tokens may take place, any interpretation of her theory that makes predictions about pay or
wage growth for tokens vs. non-tokens is not supported here.
What do these Žndings say about men riding the glass escalator? Williams (1995) coined this
term to talk about token men in female jobs. But her theory was really about the male advantage
built into all work settings. Extending that theory, I argue that men earn more and have faster
wage growth than women, perhaps a result of a “glass escalator.” If so, it is their status as men
that puts them on the automatic stairway. The Žndings here suggest that it is not only token men
in female jobs riding the glass escalator, but men in male and balanced jobs as well. Thus, token
status seems to give men no disadvantage, but also no special advantage, in terms of earnings.
The earnings of all workers in male-dominated jobs beneŽ t equally from the fact that the
job is associated with the more valued gender. The wages of men and women in balanced jobs
average less than those in male jobs; and the wages of men and women in female-dominated
jobs are the lowest. Despite this hierarchy of earnings, the relative distance (in percentage
terms) between men’s and women’s earnings within each category is equivalent.
The implications of these Žndings for gender earnings inequality overall are notable. Occupational sex segregation arguments claim that the central reason behind the gender gap in pay is
that men and women are concentrated in different jobs, and that men’s jobs pay more than
women’s jobs comparable in their demands for skill, education, and other occupational characteristics. The Žndings of this study reafŽrm this argument, that male jobs pay the highest wages.
However, while the integration of women into men’s, and men into women’s, jobs would
decrease the gender pay gap, it would not be a sufŽ cient strategy to fully close the gap. The
Žndings of this article show that within jobs, men are still paid more than are women with comparable qualiŽcations. This indicates that men’s economic advantages may persist despite occupational integration. As the work of Acker (1990) and Williams (1992, 1995) suggests, the very
structure of jobs and organizations advantage men. The Žndings that marriage and children negatively affect women’s earnings while positively affecting men’s earnings are telling. Women’s
greater responsibilities for children and housework make them poor candidates for the ideal
worker category that rewards those with few non-work obligations. At the same time, the typical
gender division of labor in the home both comparatively frees men from non-work distractions
and signals their heightened work commitment as the family breadwinners. Until we also challenge the structure of work that currently rewards stereotypical masculine attributes, as well as
the traditional gender division of labor in the home, gender inequalities in earnings will persist.
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