See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/249985491 Male Advantage and the Gender Composition of Jobs: Who Rides the Glass Escalator? Article in Social Problems · May 2002 DOI: 10.1525/sp.2002.49.2.258 CITATIONS READS 275 5,559 1 author: Michelle Budig University of Massachusetts Amherst 62 PUBLICATIONS 4,944 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Differences in Disadvantage View project All content following this page was uploaded by Michelle Budig on 15 January 2014. The user has requested enhancement of the downloaded file. 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 Stable URL: http://www.jstor.org/stable/10.1525/sp.2002.49.2.258 . Accessed: 10/06/2011 14:44 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at . http://www.jstor.org/action/showPublisher?publisherCode=ucal. . Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. University of California Press and Society for the Study of Social Problems are collaborating with JSTOR to digitize, preserve and extend access to Social Problems. http://www.jstor.org 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 nely 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 elds—have generated theories to explain why such women hit the “glass ceiling” (i.e., nd 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 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 ndings regarding male tokens in specic 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 reect 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 reect 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 of cers (Ott 1989), amusement park workers (Yoder and Sinnett 1985), military academy students (Yoder, Adams, and Prince 1983), union of cers (Izraeli 1983), and law students (Spangler, Gordon, and Pipkin 1978). However, other studies nd 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 nd that male tokens suffer from their minority status. Instead, qualitative studies of male tokens imply that men may even bene 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 beneted from their social identication 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 beneted 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 benet 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 t 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 con 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 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 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 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 rst set of hypotheses, 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 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 bene 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 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 stratied national probability sample of 12,686 persons who were aged 14 to 21 when 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 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 xed for years and persons. 5 Person-specic xed effects capture and control for any nonmeasured differences between individuals that do not change over time. The advantage of 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 afuence, 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-xed-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 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 dened 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 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 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 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 signicant. 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 misclassied 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, misclassication could pose problems for the analyses. However, these possible misclassications pose little threat to the analyses because the 80% threshold for de 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. 265 266 BUDIG Table 2 Means and Standard Deviations Dependent variable Hourly pay Job characteristics Female job Male job Balanced job Occupational general education Occupational specic 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 specic 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 specic 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 . 267 268 BUDIG Additional job characteristics in the model include whether the respondent worked in a very small rm (less than twenty employees) to capture the shorter internal ladders and higher volatility of small 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, shing, and mining (the omitted category); construction; manufacturing; public utilities; wholesale and retail trade; nancial 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- 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 rms during three-year period (dummy, changed 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 rms 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 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 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 Coefcients from a Fixed-Effects Model Predicting Wage Levels Coefcient (Std. Err.) Job characteristics Female job Male 3 female job Male job Male 3 male job Occupational general education Occupational specic 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 signicance (P , .10); ** denotes signicance (P , .05); *** denotes signicance (P , .01); two-tailed tests. 269 270 BUDIG each of the two dummy variables representing female and balanced jobs (with male jobs as reference category) will not be signicant. Table 3 reports results from xed-effect regressions in which logged hourly wage is the dependent variable. Interpretation is facilitated by remembering that coef cients in a semilogarithmic specication 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-signicant, as shown in Table 3. Although I cannot measure the effect of gender in the xed-effect model (it is included in the person-xed-effect), interactions with gender can be included in xed-effect models despite the fact that additive effects of gender are in the xed effect. The non-signi 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 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 signicantly differ by gender composition of the respondent’s job. In results not shown, I found that sex interacts signicantly 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 coef 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 ndings 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 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 nancial 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 nding that male advantage does not vary by job gender composition. Turning to the analysis of wage growth, I nd 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 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 coefcient for Male 3 Male Occupation was not signicant, 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 nonsignicant 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 ndings. Table 4 reports results from OLS trimmed models where the difference between logged hourly wage in the third and rst 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 ndings for the theories I am testing? The reader may nd it useful to refer back to Table 1, where predictions of each perspective are reviewed and ndings are summarized. Just like the 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 ndings call into question the predictions of male token advantage drawn from the 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 ndings again refute the interpretation of Kanter, that men in female-dominated jobs will incur penalties from tokenism. And, again, the lack of signicant interactions is not consistent with predictions generated from the case study research on male tokens—the wages of men in female-dominated 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-signi cant interaction coefcient in this model reveals that the effect of being male in male occupations is not statistically different than being male in balanced occupations. 271 272 BUDIG Table 4 Coefcients from OLS Regression Model Predicting Change in Wage Coefcient (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 speci 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 rm and in the same job gender composition category over the three-year period. While the wage bene 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) Coefcient (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 signicance (P , .10); ** denotes signicance (P , .05); *** denotes signicance (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 signicant. 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 signicant, 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 coefcient for the interaction between gender and being in a pre-promotion female job should be positive and signicant. In fact, the interaction is negative. This indicates that the male advantage in promotions is smaller for those in female-dominated jobs. 273 274 BUDIG Table 5 Coefcients 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 Coefcient Odds Ratio Coefcient 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 signicant. All reported coefcients signicant at P , .05, two-tailed test. Control variables in the model include education, experience, seniority, AFQT score, part-time status, occupational general education, occupational specic 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 ndings 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 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 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 reafrms ndings 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 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 ndings 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 ndings 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 bene 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 ndings 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 ndings of this study reafrm 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 suf cient strategy to fully close the gap. The ndings of this article show that within jobs, men are still paid more than are women with comparable qualications. 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 ndings 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. 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