STOPPGAPPERS? THE OCCUPATIONAL TRAJECTORIES OF MEN IN FEMALE-

DOMINATED OCCUPATIONS

Margarita Torre, Universidad Carlos III de Madrid1

Abstract

Male participation in female-dominated occupations is very low. Prior research has argued that

men avoid female-dominated jobs because they offer lower pay and social status than male-

dominated occupations. Also, men fear stigmatization. This study contends that female-

dominated occupations are often stopgaps in male occupational trajectories. Thus, men leave the

female-dominated field shortly after entry and perpetuate occupational segregation. Using

Census data and the National Longitudinal Survey of Youth dataset, the study analyzes the job

histories of men employed in female-dominated occupations in the United States between 1979

and 2006, and investigates how they vary with men’s occupational position. The analysis

identifies a group of stopgappers, particularly in low-status occupations. Such men do not

commit to female work; they stay in the female field temporarily and only to move back out,

usually to a more rewarding- non-female job. The study reveals that men’s attrition from female-

dominated occupations is crucial to understanding segregation processes.

Keywords: female-dominated occupations, stopgap, men’s occupational trajectories, segregation,

occupational minorities.

1 E-mail: margarita.torre@uc3m.es. C/Madrid 135. 18.2.D09. 28903. Getafe. Madrid.

Introduction

Compared with the increasing participation of women in male-dominated occupations, the

presence of men in female-dominated occupations remains low (England 2010; Hardie 2015;

Snyder 2008; Williams 2013). Despite their small number, men in such occupations lead their

female counterparts in terms of earnings (Budig 2002; England and Herbert 1993), perceived

workplace support (Kimberly, Ricciardelli and Bartfay 2015; Taylor 2010; Williams 1992, 1995)

and promotion (Williams 1992, 1995). Indeed, male careers in female-dominated occupations

have been defined as a ride on the glass escalator (Williams 1995), in reference to the informal

tracking mechanisms that push men up in the occupational hierarchy. However, not even the

advantages that accrue to men who enter female-dominated occupations have eradicated male

disinterest in women’s work, so high levels of gender segregation persist (Williams 2013).

Several explanations have been proposed for the continuance of gender segregation in female-

dominated occupations. On the one hand, men avoid female-dominated occupations because they

offer lower pay and have a lower social status than male-dominated jobs (England 2010; Jacobs

1993). In addition, men fear stigmatization as a consequence of their association with female

trades (Lupton 2000; Williams 1992, 1995). Nevertheless, prior research has tended to neglect

the relevance of men’s attrition from female jobs in reproducing levels of segregation (Jacobs

1993 1989; Williams and Villemez 1993). How long do men remain employed in female-

dominated occupations? Where do they move after working in a female-dominated job? Are the

experiences of men in high-status occupations comparable to those of men in low-status

occupations? Providing a satisfactory answer to these questions is crucial to understanding the

processes of segregation.

I draw on previous research to argue here that female-dominated occupations are often stopgaps

in male occupational trajectories. In other words, I contend that men work in a female-dominated

occupation temporarily (for example, to avoid unemployment episodes) and leave after a short

period of time, contributing to the continued segregation of such occupations. Furthermore, I

contend that this scenario is particularly likely in low-status occupations for a number of reasons.

On the one hand, gender-egalitarian attitudes are more pronounced among highly educated

people (Cotter et al. 2011). On the other, the ongoing deterioration of low-status occupations

makes men in low-status occupations less likely to ride the glass escalator than those in

advantageous positions(Williams 2015). Moreover, high-status job features are not as heavily

associated with female traits as features in traditional female ghettoes, such as nursery or

elementary teachers. Consequently, stigmatization is presumably greater in low-status than in

other occupations, increasing the cost of being employed in a female domain.

The empirical analysis employs two different data sources. First, census data for 1980, 1990,

and 2000 are used to examine the distribution of workers across sex-typed fields over time.

Second, the National Longitudinal Survey of Youth (NLYS79) is used to examine the work

histories of men employed in the United States between 1979 and 2006. These analyses

contribute to prior research in several ways. First, while prior findings were based primarily on

non-representative interview data, specific institutions or groups of occupations (see Budig 2002;

Taylor 2010 for exceptions), this study is based on a national longitudinal sample and offers

broad, systematic insights into the phenomenon. Second, prior studies were often limited to a

particular point in time, but this study traces the distribution of the working population across

sex-typed occupations in 1980, 1990 and 2000 and discusses how changes in the female-

dominated field (dis)encourage male participation. Third, this study delves deeply into the

dynamics of male entry and exit from female-dominated occupations over their working lives,

exploring how these parameters vary with occupational position.

Findings are consistent with the idea that female-dominated occupations are stopgaps in

occupational trajectories for some men, particularly in the case of low-status occupations. The

study provides new insights on male occupational trajectories in female-dominated occupations

and contributes to the development of a comprehensive theory that accounts for the way the

structure of inequality is reproduced (Hayes 1986; Jacobs 1989, 1993; Williams 2015). These

results highlight the need to design specific approaches to promote sex integration within female-

dominated occupations and reduce the divide between high- and low-status occupations.

Advantages and disadvantages of male occupational minority

Kanter’s theory of tokenism (1977) contends that when people constitute a very small group

within an organization (because of any salient individual characteristics), they are subject to

predictable forms of discrimination. Kanter’s theory has been widely confirmed in the case of

female token workers (Kanter 1977).However, qualitative empirical evidence shows that men do

not experience the negative consequences of tokenism. Rather the opposite, men often benefit

from their numerical rarity. Unlike women employed in male-dominated fields, men report good

relationships with their supervisors, often themselves men (Allan 1993; Kimberly, Ricciardelli

and Bartfay 2015; Williams 1992, 1995), and they perceive their token status as an advantage

with respect to hiring and promotions in occupations with a larger percentage of females (Allan

1993; Evan 1997; Kleinman 2004; Williams 1992). In short, the favorable treatment of male

tokens favors men’s upward mobility, Williams (1992) coined the well-known term glass

escalator.

Likewise, men are often channeled into specialties that carry greater rewards and prestige (Allan

1993; England and Herbert 1993; Williams 1995). Recent data (WSR 2013) showed that men

earn more than women in all of the most common female-dominated occupations (while exactly

the opposite holds true for women employed in male-dominated occupations). To be more

specific, the gender median earnings ratio for full-time employers ranges from 2 percent for

“social workers” to 64.3 percent for a “retail salesperson.” The significant advantages that males

experience in terms of remuneration have been interpreted by some scholars as a bonus for being

a token worker (Heikes 1991). However, Budig (2002) refuted this conclusion, showing that

while men’s pay surpasses women’s pay in any sex-typed field, men experience no more or less

advantage when they are tokens than when they are in male-dominated or neutral occupations.

Despite the favorable treatment and economic advantages, male interest in female-dominated

jobs remains scant. This fact is not fortuitous. First, male-dominated jobs offer higher pay, more

fringe benefits, and more promotion opportunities than jobs in female-dominated fields (England

et al. 1994; Glass 1990; Levanon, England, and Allison 2009; Rosenbaum 1985). Therefore,

while access to male-dominated fields appears to be crucial for women’s economic and social

advancement, “men have little reason to choose female-dominated jobs” (Jacobs 1993). Second,

many more fields are male-dominated than female-dominated (Jacobs 1989, 1993). Also, there is

substantial evidence that men working in female jobs suffer negative stereotyping (Allan 1993;

Heickes 1991; Lupton 2000, 2006; Simpson 2005; Williams 1992). The kind of discrimination

and stigmatization that men encounter in female-dominated occupations differs from the

discrimination mechanisms that push women out of male environments. Whereas women in

male-dominated environments are exposed to homophile behavior (McPherson, Smith-Lovin,

and Cook 2001), homo-social reproduction (Moore 1988) and tokenism (Kanter 1977),

discrimination against men in the so-called female professions comes primarily from people

outside of these fields (Kimberly, Ricciardelli and Bartfay 2015; Williams 1992). Qualitative

research has found that male nurses are perceived as deviants, effeminate and homosexual

(Bartfay and Bartfay 2007; Harding 2007; Kimberley et al. 2015) or unable to succeed in higher

status specialties—for example, as doctors (Bradley 2011). Similarly, men employed in

specialties closely associated with children, such as kindergarten or elementary teachers, have

even been branded as sexual predators (Allan 1993; Lupton 2006; Simpson 2005). Some men

react to stigmas by overemphasizing their heterosexuality (Morgan 1992), stressing the more

masculine attributes of the occupations, such as physical strength (Lupton 2000), or even

disassociating themselves from the job when outside the workplace (Williams 1995). In most

cases, however, the social pressures and economic drawbacks of female-dominated fields keep

men away from women’s occupations, to the extreme that some men would rather endure

unemployment than accept a relatively high-paying women’s job because of potential damage to

their identities (Epstein 1989).

Finally, the image of the glass escalator has triggered extensive qualitative research in the last

two decades. While some of these studies have widely confirmed and refined William’s findings

(Evans 1997; Kleinman 2004), other scholars have found that certain kinds of men are excluded

from the glass escalator, for example black men in nursing (Harvey Wingfield 2009). Likewise,

there is no evidence that gay men and transmen ride the fast track (Connell 2012; Schilt 2011).

Moreover, using the National Sample Survey of Registered Nurses, Snyder and Green (2008)

found no conclusive evidence for vertical segregation among nursery school teachers. Giving

credit to recent findings, Williams (2015) herself recognized that the glass escalator is a

privilege of some men but not others (Williams 2015), and that the concept is based on

assumptions about stable employment and career ladders that no longer define many jobs in

today’s market (Kalleberg 2000). In this light, she questioned the relevance of the concept and

called for new research, especially concerning low-status, precarious occupations.

Stopgaps? The dynamics of men’s entry and exit from female-dominated occupations.

Male experiences on entering female-dominated occupations are diverse. Some men maintain an

ambitious work attitude (Evans 1997; Isaacs and Poole 1996) and are willing to seek ‘fast track’

careers (Williams, 1993). Others, however, “settle” into the female field and reject the ideal male

career progression model of steady increases in status and power (Simpson 2005). Despite

significant contributions, prior qualitative and quantitative research has failed to account for men

leaving female-dominated fields. In this study, I delve into the dynamics of men’s entry and exit

from female-dominated occupations, and its relevance for the perpetuation of occupational

segregation.

Empirical evidence addressing withdrawal from female-dominated occupations is limited mostly

to the Williams and Villemez study of the Chicago SMSA 1981 survey (1993). These authors

identified 105 males who had previously worked in a female-dominated occupation and reported

that approximately 75 percent had later moved into a male-dominated occupation. The study,

which was limited to data from the early 1980s, did not explore the men’s reasons for leaving or

analyze the differences between leavers and stayers. Building on previous theoretical and

empirical findings (Jacobs 1993; Oppenheimer 1990; Williams and Villemez 1993), I argue here

that female-dominated occupations are stopgaps in the occupational trajectories of some men,

who use female-dominated occupations (for example, to avoid unemployment episodes) but

leave after a relatively short time. Such men do not commit to female work. Instead, their stay in

the female field is temporary and ends when they find a more rewarding or prestigious non-

female job. If the stopgap notion holds true, we will observe that men transitioning from the non-

female sector are likely to move back out:

H1. Men transitioning from the non-female sector are more likely to move back out than

men formerly employed in a female-dominated occupation.

This pattern of mobility has negative consequences for segregation because stopgappers do not

contribute to the long-term integration of occupations, unlike men riding the glass escalator

(Williams 1993) and settlers (Simpson 20015). Conversely, the continuous exit of men from

female occupations will represent a relevant source of occupational segregation.

The prevalence of stopgappers might, however, be a function of men’s relative position in the

labor market. In addition, I argue that the probability of leaving female-dominated occupations

will vary with men’s occupational positions. Several factors sustain this claim. First, changes in

gender-egalitarian attitudes have been greater among high-status workers than low-status

workers. Empirical evidence showed an increase in liberal attitudes in the 1970s and 1980s for

all workers, followed by a downturn in 1994 and some rebound after 2000. This late rebound has

been stronger among more educated individuals (Cotter et al. 2011). Indeed, the gender

revolution has generated important reductions in vertical gender inequality over the past thirty

years (Charles and Grusky 2004; Weeden 2004), but many occupational ghettoes stubbornly

persist in low-status, female-dominated fields (e.g. secretary, nursery school teacher).

In addition, work transformation in recent decades has considerably worsened the quality and

pay of jobs in the low-status sector of the economy (Kalleberg 2012; Williams 2015). Relative to

high-status jobs, the insecurity and precarious nature of low-status occupations have risen to the

point that some authors claim that class inequality has exploded (Cobble 2007; McCall 2007;

Williams 2015). As a consequence, men in high-status positions will be likely to ride the glass

escalator, while men employed in low-status occupations will often be trapped in low-paying

dead-end jobs and excluded from the advantages of being employed in a female-dominated

occupation (Williams 2015).

Finally, I expect stigmatization to be higher in low-status occupations. Women are culturally

devalued (England 1992), as are things associated with women. As much of the literature is

based on the study of the most populated and traditional female occupations (i.e. nursery and

elementary school teachers), the literature has tended to assume that all female-dominated

occupations share the attributes of those occupations. However, some female-dominated

occupations are not as heavily associated with feminine attributes, such as caring, as other

traditional female professions studied in the literature. This statement is especially true now, as

the female-dominated sector has expanded due to the increased presence of women in the labor

market. To be more specific, according to the 2000 Census data (Census Bureau 2000),

approximately 40 percent of the men employed in female-dominated high-status positions

worked in occupations that were non-female-dominated by 1980. Examples of these occupations

are “Managers, service organizations, n.e.c.,” “Managers in Health,” “Optical good workers,”

“Legal assistant” and educational specialties other than elementary teacher, such as “Counselors,

educational and vocational Supervisors.” Managers, and to a lesser degree professionals, by

definition exercise authority over others in the workplace (Cohen & Huffman 2007; Wright

1997). According to O*Net occupational information, the occupations mentioned above involve

the tasks of coordinating, training, supervising and managing the activities of others to

accomplish goals. In addition, all of these occupations score quite high in specific vocational

preparation, namely 8-9 out of ten in the case of managerial occupations and 6 and above for

professions. Therefore, it seems reasonable to expect that the negative stereotypes and

discrimination associated with male work might be significantly lower for these occupations than

for other traditional female professions, such as nursery school teachers. Overall, I anticipate the

following:

H2. The probability of using female-dominated occupations as stopgaps is higher

among workers in low-status occupations than among workers in high-status

occupations.

The exit of token female workers from non-traditional occupations was well documented by

Jacobs (1989). Using the image of revolving doors, the author illustrated the continued departure

of women from male-dominated occupations and the concomitant perpetuation of segregation

despite women’s ability to enter male-dominated occupations. Jacobs (1989) claimed that

women are subject to a lifelong system of social control which continually channels and

rechannels them into female-dominated fields. The lifelong social control perspective argues that

women are not only discriminated against at the point of hiring but also continue to face

numerous impediments to effective job performance. The exit of men from female-dominated

occupations, although apparently similar, might differ significantly from this process. First,

while women often leave male-dominated fields as a result of exclusionary processes (Jacobs

1989; Kanter 1977; Moore 1988), men feel high levels of support (Taylor 2010). In addition,

men are welcomed by their female colleges, who believe that recruiting men will raise the status

and pay of the profession (Williams 1995). Second, women who leave male-dominated jobs are

much more likely to experience downward social mobility than men who leave female-

dominated fields (Jacobs 1993; Williams and Villemez 1993). Finally, male leavers are likely to

show higher rates of job satisfaction than males who remain in female-dominated occupations

(Williams and Villemez 1993). Here, I account for this potential asymmetry and examine

whether men’s entries and exits from the male-dominated field can be explained in economic

terms or whether men experience downward mobility when leaving female-dominated fields.

DATA and METHODS

Data

This paper draws on two different data sources. First, Census data for 1980, 1990, and 2000 are

used to explore variations in sex-typed fields over time. Second, the National Longitudinal

Survey of Youth (NLSY79) is used to examine the entry and exit patterns of men in female-

dominated occupations. The survey consists of data concerning a nationally representative

sample of 3,108 young women and 3,003 young men in the civilian population who were born in

the 1950s or 1960s. Individuals were first surveyed in 1979, and the period analyzed here runs

until 2006. In line with prior research (Harvey and Myles 2014, Author 2014), I decided to end

the analysis in 2006 to avoid noise derived from the economic crisis. Additionally, I use the

period variable to control for changes in the occupational structure occurring in the early 1990s.

The NLSY79 offers detailed information about employment status, current occupation, job

tenure, hours worked, and earnings. In addition, respondents are asked about educational

attainment, training, and marital and fertility histories. Altogether, this survey is particularly

suitable for the aim of this paper. Appended to the NLSY79 is the sex composition of three-digit

census occupations. Occupational codes are standardized and expressed as the 1990 three-digit

occupational codes to make them comparable over time.

Dependent Variables

The multivariate analyses in this paper examine the occupational trajectories of men in female-

dominated occupations. In a first step, I estimate the likelihood of changing occupations, which

allows me to observe whether high-status workers display different rates of mobility than low-

status workers. The dependent variable occupational change is scored 1 if a man employed in a

female occupation changes jobs and 0 otherwise. In a second step, I run separate regressions for

high-status workers (managers and professionals) and low-status workers (service, clerical,

service and blue-collar workers) and estimate the likelihood of transitioning from a female-

dominated occupation to a non-female occupation. The dependent variable exit from female-

dominated occupations is coded 1 if a man moves from a female to a non-female occupation and

0 if he changes occupations within the female-dominated field. Occupations are defined as

female-dominated if women’s representation is 66.6 percent or above and as male-dominated if

the female presence in the occupation is below 33.3 percent. All other occupations are gender-

neutral2. Additionally, I investigate whether entries and exits from female-dominated

occupations are driven by economic reasons or whether exits might occur as a result of

discrimination against men. To achieve this goal, I estimate the probability of receiving a wage

promotion greater than 15 percent3 when changing occupations.

Covariates

The analyses include both time-dependent and non time-dependent covariates. The main interest

of this study lies in the variable last occupation, which indicates the type of occupation that the

individual held at moment t-1. Specifically, this variable distinguishes whether a man arrived at

his current female occupation from a male-dominated occupation, a neutral occupation, another

female-dominated occupation, or whether he was unemployed. This variable is meant to capture

whether the probability of exit from female-dominated occupations is higher among those who

arrived recently than among insiders. Similarly, occupation of destination identifies the type of

occupation that the individual holds at t+1, after changing occupation. This variable allows an

examination of the effect of a particular occupational move on the probability of wage

promotion.

2Like those reported in previous studies, these cut-off points are arbitrary. The results are

consistent when using alternative frames (40-20-40). To account for the shifting sex composition

of occupations, I updated the classification every 10 years, using census data from the closest

decade. For each employment period, the sex composition of the occupation is kept constant.

Thus, the most appropriate occupational data are assigned to each employment experience, but

the occupational sex composition remains the same for each experience.

3Analyses are replicated for promotions of 10 percent and 20 percent. The results do not vary

significantly.

Models include relevant work-related variables, such as full-time work (vs. part-time), number of

unemployment episodes, years of job tenure, number of job spells, and years of experience in the

labor market. These variables allow us to control for early episodes in female jobs before men

settle into a career (Oppenheimer 1990). Controls for major occupational groups are added to

capture possible inter-group differences. Thus, for high-status workers, I distinguish between

top-managers, managers and professionals. Top managers and managers differ in their

individual hourly rates of pay relative to the average hourly rate of pay in their occupation

(weighted by both year and job tenure). Men whose hourly rate of pay is in the 75 percentile and

above are classified as high-paid managers. The remaining men are defined as managers. For

low-status workers, I differentiate between service, clerical and sales, and blue-collar workers.

Finally, socio-demographic variables control for differences in educational attainment (college

or more vs. less than college), the sex-composition of the field of study (scored 1 for those men

who studied a male-dominated major (male presence of 66 percent or higher) and 0 otherwise),

changes in marital status (getting married and marriage dissolution), and fertility history (first

born and second (or posterior) born).

Table 1 summarizes the main descriptive statistics for the variables included in the analyses. The

table provides values for men and women in female-dominated occupations and for men in non-

female dominated occupations, allowing an assessment of the extent to which men in female-

dominated occupations have a different profile.

Table I. Variables included in the analyses.

Women Men

in female-dominated

occupations

in female-

dominated

occupations

in non female-

dominated

occupations

Socio-demographic characteristics

Age 31.85 30.86 32.43

High school or more 0.46 0.55 0.38

Male-dominated major 0.03 0.18 0.17

One child 0.30 0.23 0.27

Two or more children 0.16 0.13 0.14

Married 0.58 0.45 0.53

Occupational Category

High- status managers <0.01 0.01 0.05

Managers 0.01 0.02 0.10

Professionals 0.20 0.15 0.13

Service, clerical, sales workers 0.76 0.81 0.14

Blue-collar workers 0.03 0.02 0.55

Work experience

Full-time employment 0.71 0.86 0.90

Job duration 4.49 4.03 4.66

Job spells 5.88 5.97 6.71

Unemployment episodes 0.14 0.08 0.11

Age of entry in the labor market 20.70 21.19 20.63

Analytical strategy

The empirical part of the paper is divided into three sections. First, I trace the distribution of

workers across sex-typed occupations in 1980, 1990, and 2000 and discuss the changes in

occupational composition that have occurred within female-dominated fields. Second, I examine

the fluxes of entry into and exit from male-dominated occupations from 1979 to 2006, paying

attention to the occupations of origin and destination. Third, a discrete-time hazard model is used

to model career experiences. Specifically, the analyses estimate the conditional probability (Pit)

that individual i will experience an event at time t, given that the individual has not already

undergone such an occurrence in the past (Allison 1984). In a first step, I estimate the risk of

changing occupations. In a second step, I split the sample and estimate the risk of exit from the

female field for both high- and low-status workers. Finally, I run supplemental regressions to

estimate the probability of wage increase among occupational changers, depending on their

occupation of destination.

In all regressions, the probability Pit defined above is related to the covariate vectors by a logistic

regression equation, which can be specified as follows:

(1)

LogPi t

1Pi t

Xi tZi tWi t

Respondents who were unemployed or working in a neutral- or male-dominated occupation in

the first wave could enter the risk set in subsequent years. In the case of repeated events, the

clock is reset to 0 each time the individual enters the risk set, and the intervals between events

are treated as distinct observations (Allison 1995). Using this approach, two observations will be

created for a man who took a job in a female-dominated occupation twice during the observation

period. This approach provides more statistical power, which is clearly an advantage, but it also

raises the likelihood of dependence between observations. To correct for dependence derived

from repeated events, I calculated robust standard errors (Allison 1995). As the cases

contributing to the pooled data set may vary each year, detailed person-year figures are presented

in Table 2. The sum of person-year data in the pooled data set from 1979 to 2006 was 6,317.

Table II. Person-year data, 1979-2006.

Year Person

1979 68

1980 98

1981 141

1982 207

1983 272

1984 179

1985 303

1986 275

1987 304

1988 303

1989 276

1990 299

1991 308

1992 290

1993 310

1994 298

1995 280

1996 269

1997 249

1998 243

1999 231

2000 229

2001 167

2002 142

2003 152

2004 138

2005 142

2006 144

Total 6317

Findings

The distribution of men and women across sex-typed occupations in 1980, 1990 and 2000.

Figure 1 below shows the distribution of workers across sex-typed occupations in the 1980, 1990

and 2000 Censuses. In 1980, male-dominated fields harbored approximately 45 percent of the

working population, 22 percent worked in neutral jobs and the rest, approximately 33 percent,

were employed in female-dominated occupations. In 2000, these percentages shifted to 36, 33

and 31 percent, respectively, primarily due to women’s increasing ability to enter previously

male-dominated occupations (Cotter et al. 2004; England 2010; Jacobs 1989).

The plot on the right shows male labor force diversion between 1980 and 2000. As observed, the

proportion of men in male-dominated occupations dropped moderately from 71 percent in 1980

to 58 percent in 2000, with an increase from 20 to 31 percent in neutral occupations during the

same period. Interestingly, the presence of men in female occupations remained low and largely

constant over the whole period. In fact, the representation of men in female-dominated jobs rose

by barely 1.5 percentage points, from 8 percent in 1980 to 9.5 percent in 2000.

Figure 1. Distribution of workers across sex-typed occupations. Census 1980, 1990 and 2000.

060

20

40

%

1980 1990 2000year

All workers

020

40

60

80

1 1.5 2 2.5 3year

Male-dominated Neutral

Female-dominated

Men

Figure 2 displays the distribution of workers within female-dominated occupations by

occupational group. The dot-dashed line represents both females and males, and the dashed line

represents only men.

Figure 2. Distribution of workers in female-dominated occupations by occupational category.

Census 1980, 1990 and 2000.

The upper part of the panel shows a substantial rise in the relative size of both managerial (1.6

percent increase) and professional occupations (13 percent increase). Relative to 1980, men were

slightly overrepresented among managerial workers and equally represented in professional

occupations in 2000. The opposite trend was observed for low-status occupations. While the

low-status sector continues to account for most of the working population, it has tended to shrink

in recent decades. Interestingly, the male presence in non-professional occupations has lessened

to women’s levels, although men continue to be slightly overrepresented in blue-collar

occupations.

01

23

4

1980 1990 2000year

Managers

20

30

25

15

1980 20001990year

Professionals

60

58

56

62

54

1980 1990 2000year

Service, Sales and Clerical workers

46

810

12

1980 1990 2000year

All workers Male

Blue-collar workers

Mobility fluxes, 1979-2006

Turning to men’s mobility patterns, Table 3 below reflects the flows of men into and out of

female-dominated jobs between 1979 and 2006. The upper part of the table refers to men

employed in high-status positions. The data show that only 28 percent of men moving into a

female-dominated occupation had previously worked in another female-dominated occupation

(insiders), while over 64 percent arrived from other fields (newcomers). Among the last, almost

40 percent were previously employed in a male-dominated occupation, 25 percent were

previously employed in a gender-neutral occupation, and the remaining 6 percent were

unemployed. The difference between insiders and outsiders is even more noticeable in low-status

occupations. In this case, roughly 15 percent of the male workers were already employed in a

female-dominated occupation. The majority of low-status workers, 52 percent, moved from a

male-dominated occupation, and approximately 24 percent moved from a neutral occupation.

The remaining 7.5 percent were unemployed at the time of entering the female field.

The proportion of leavers is also considerably higher in low-status occupations than in high-

status occupations. When switching from a low-status occupation, only 18.5 percent stay in the

female-dominated field. More than half of men (52.4 percent) move to a male-dominated

occupation, and another 24 percent move to a gender-neutral occupation. For high-status

workers, the proportion of stayers increases to 33.5 percent, while 37.39 percent and 27 percent

move to a male-dominated or a neutral occupation, respectively. Finally, 5 percent of low-status

workers and 2 percent of high-status workers become unemployed.

Interestingly, switching to the male field often involves a change in occupational sector and an

upgrade in a man’s current position. To be more specific, approximately 38 percent of men in

low-status occupations move to high-status occupations. Of these men, 49 percent of men

coming from non-professional occupations enter “Managers and administrators, n.e.c.,” and an

additional 31 percent become “Managers, marketing, advertising and publicity.” The proportion

of men moving from professional to non-professional occupations is considerably lower— 4.7

percent become “Truck drivers” after changing occupations. The next section takes a more

detailed look at the determinants of mobility in and across gender boundaries.

Table III. Occupation of origin and destination when entering female-dominated occupations

Managerial and Professional workers

Type of occupation t-1 t t+1

Female-dominated 28.66 100 33.53

Neutral 25.61 27.30

Male-dominated 39.43 37.39

Unemployed 6.30 1.78

Sales, Clerical, Service and Blue-collar workers

t-1 t t+1

Female-dominated 15.75 100 18.56

Neutral 24.36 23.87

Male-dominated 52.42 52.38

Unemployed 7.47 5.20

Multivariate analyses.

In the following analyses, I calculate the probability of changing occupations (Column 1) and the

probability of exiting female-dominated occupations (Colum 2) based on previous occupational

trajectories, occupation-related attributes and individual attributes. Next, I split the sample into

high- and low-status occupations in Column 3 and Column 4, respectively. Table 4 displays the

estimates of the regressions, and Figure 3 charts the effect of the previous occupation on men’s

exit from female-dominated occupations.

The results in Column 1 show that the probability of changing occupations does not vary with

men’s occupational group, so differences in mobility patterns cannot be attributed to differences

in the mobility of one or another group of workers. Meanwhile, the coefficients in Column 2

indicate that the probability of exiting from female-dominated occupations is lower for high-

status than low-status occupations. Significantly, given the aims of this paper, the risk of leaving

was found to be significantly lower among workers who had previously worked in the female-

dominated field (insiders) than for workers who had previously worked in the male-dominated

field or who were unemployed, confirming H1. After splitting the sample, men’s exits continued

to be higher among newcomers, particularly if they arrived from a male-dominated occupation.

Figure 3 represents the effect of a worker’s last occupation on the probability of exit, for both

high- and low-status workers4. First, according to H1, it is newcomers who are most likely to exit

the occupation, while those changing occupations within the female field are least likely to leave.

Second, as predicted in H2, the gap is larger in low-status occupations than in high-status

occupations. In particular, the difference between those who were previously employed in male-

and female-dominated occupations rises from 7 percentage points in the case of high-status

occupations to 15 percentage points in the case of low-status occupations5.

Table IV. Probability of changing occupations and probability of exit from a female-dominated

occupation.

Change

occupations Exit

All workers

High-status

occup.

Low-status

occup.

High-status occupation (r.c.:low-status) 0.881 0.710***

(0.071) (0.080)

Top-managers (r.c.: professionals) 0.899

(0.288)

Managers (r.c.: professionals) 1.156

(0.323)

Blue-collar (r.c.: service, sales, clerical workers) 1.043

(0.191)

Last occupation: (rc: female)

Male 1.026 2.031*** 1.868*** 2.041***

(0.070) (0.191) (0.426) (0.200)

Neutral 0.954 1.653*** 1.607† 1.637***

(0.078) (0.191) (0.410) (0.170)

Unemployed 0.858 1.426** 1.269 1.425†

(0.156) (0.249) (0.666) (0.270)

Work-experience

Tenure (years) 0.237*** 0.329*** 1.059 1.109

(0.011) (0.014) (0.298) (0.119)

Tenure 2 (years) 1.066*** 1.050*** 0.889*** 0.908***

4 Figure 3 shows the lowest predicted probability of a switch to a non-female-dominated

occupation. 5 It could be argued that the stopgap effect is present in all sex-typed occupations. To test this

possibility, all regressions have been calculated for men in male-dominated occupations. The

number of men making the transition from male- to female-dominated occupations represented

less than 3.5 percent of the total male mobility rate. Additionally, no significant differences were

found between newcomers and insiders in terms of the probability of exit, or between high- low-

status occupations. Results available on request.

(0.004) (0.003) (0.031) (0.014)

Full-time worker 1067 1117 0.936 0.930**

(0.093) (0.110) (0.059) (0.032)

Number of prior jobs 0.863*** 0.907*** 1.021 0.944***

(0.012) (0.013) (0.041) (0.015)

Unemployment episodes 0.894*** 0.932** 0.410*** 0.309***

(0.026) (0.030) (0.040) (0.016)

Total years of work experience 0.958*** 0.957*** 1.032*** 1.056***

(0.014) (0.015) (0.007) (0.005)

Socio-demographic controls 1.662**

College 1182 (0.346) 1.898† 1.068

(0.334) 1.160† (0.623) (0.325)

Field of study: male-dominated 0.947 (0.094) 1.324 1.153

(0.068) 1.323*** (0.245) (0.103)

Married 1.307*** (0.108) 0.950 1.411***

(0.092) 1.463*** (0.190) (0.122)

Divorced or separated 1.339*** (0.165) 1.302 1.512***

(0.147) 1058 (0.475) (0.178)

First-order birth 1125 (0.124) 1.142 1.056

(0.128) 1061 (0.293) (0.137)

Second-order birth (or posterior) 0.994 (0.158) 1.242 1.046

(0.147) 1035 (0.431) (0.174)

Period 1.327*** (0.098) 2.055*** 0.963

(0.123) (0.503) (0.099)

Constant 108.446*** 19.369*** 1.692 28.213***

-36504 (7.222) (2.025) (10.770)

N 5,421 5,252 1,009 4,243

Chi2 1753,883 1170,45 254.8 995.3

Cluster 1306 1293 199 1218

Exponentiated coefficients; Numbers in parentheses are robust standard errors. Individuals are clustered.

***p < .01 **p < .05 †p<.01

Figure 3. Probability of exit from a female-dominated occupation.

The central and bottom part of the table shows the coefficients for other relevant occupational

mobility variables. Not surprisingly, the probability of changing occupations varies with job

tenure, rising gradually from low levels after the first few years in a job. The opposite is

observed with respect to exits. The probability of moving out of the female field is higher in the

first few years and tends to decrease with time. Being employed full-time significantly reduces

the probability of leaving low-status occupations, where part-time work is concentrated (Glauber

2011). In addition, increases in the number of previous jobs and the years of unemployment

attenuate the probability of exiting, while years of labor market experience increase the risk of

leaving. Finally, mobility does not vary substantially with individual attributes. Changes in

marital status increase the probability of men leaving low-status occupations, but no particular

differences were observed regarding level of education, field of study, or changes in paternity

status.

The analyses described so far did not allow any assessment of whether men’s choice to stay or

leave might be economically driven. To this end, the regression presented in Table 5 tests the

.05

.1.1

5.2

.25

.3

Pr(

exit)

Male-dom Neutral Fem-dom Unemp

Last occupation

.3.3

5.4

.45

.5

Male-dom Neutral Fem-dom Unemp

Last occupation

probability of receiving a wage increase of 15 percent or higher when changing occupations, as a

function of the occupation of origin (Column 1 and Column 3) and the occupation of destination

(Column 2 and Column 4).

Table V. Probability of 15 percent of wage increase when changing occupations from a female-

dominated occupation.

High-status occupations Low-status occupations

Entry Exit Entry Exit

Top-manager (r.c.: professionals) 0.735 0.844

(0.228) (0.466)

Managers (r.c.: professionals) 0.752 0.568

(0.270) (0.312) 0.991

Blue-collar (r.c.: service, sales, clerical workers) 1.759† (0.321)

(0.513)

Last occupation (r.c.: female)

Male 1019 1104

(0.182) (0.129)

Neutral 1056 1166

(0.177) (0.150)

Occupation of destination (r.c.: female)

Male 1.900** 1.465***

(0.596) (0.174)

Neutral 1.276 1.277†

(0.384) (0.170)

Work-experience

Tenure (years) 0.841*** 0.961 1009 1028

(0.049) (0.222) (0.054) (0.089)

Tenure 2 (years) 1.009*** 1.023 0.999 0.995

(0.003) (0.026) (0.004) (0.009)

Full-time worker 1086 1.062 1152 0.796†

(0.262) (0.365) (0.183) (0.103)

Number of prior jobs 0.937** 1.059 0.916*** 0.976

(0.029) (0.048) (0.018) (0.019)

Unemployment episodes 0.966 1.137 1028 1.107**

(0.055) (0.102) (0.036) (0.051)

Total years of work experience 0.977 1.013 0.989 0.979

(0.034) (0.048) (0.021) (0.020)

Socio-demographic controls

College 1230 1.318 0.574 1320

(0.399) (0.488) (0.197) (0.594)

Field of study: male-dominated 1049 0.905 1115 1208

(0.188) (0.241) (0.130) (0.146)

Married 0.815 0.761 0.893 0.847

(0.134) (0.198) (0.097) (0.089)

Divorced or separated 1207 1.012 1152 1058

(0.391) (0.480) (0.236) (0.174)

First-order birht 1119 0.923 1270 0.956

(0.233) (0.297) (0.199) (0.142)

Second-order birth (or posterior) 0.963 1.478 0.998 1164

(0.270) (0.594) (0.204) (0.216)

Period 0.944 0.767 0.571*** 0.864

(0.242) (0.271) (0.086) (0.113)

Constant 1222 0.274 0.696 1035

(1.018) (0.333) (0.337) (0.487)

N 976 300 2,255 2,227

Chi2 32.69 15.90 133.1 43.07

Clusters 194 181 688 1152

Exponentiated coefficients; Numbers in parentheses are robust standard errors. Individuals are clustered.

***p < .01 **p < .05 †p<.01

The last occupation coefficients are not statistically significant, showing that newcomers are just

as likely as insiders to obtain a wage promotion when entering a female-dominated occupation.

This means that men are not attracted into female-dominated occupations by the offer of higher

earnings, as there is no premium associated with arriving from male or neutral occupations. Aas

shown in Columns 2 and 4, however, workers moving from a female- to a male-dominated

occupation are significantly more likely to receive a wage promotion than workers changing

occupations within the female field. Interestingly, wage increases are largely unrelated to job and

individual attributes (the only exceptions being possession of a male-field degree and years’

experience among low-status workers), and the occupation of destination isw the main

explanatory factor. It could be argued, then, that these results reflect the fact that male-dominated

occupations are on average better remunerated than female-dominated occupations. To control

for this possibility, I provide supplemental analyses for women’s wage increases (see Appendix

I). The findings reveal a rather different situation in the case of high-status female leavers. In

contrast to men, women exiting the female-dominated sector are not any more likely to gain a

wage increase. In fact, women benefit more from moving into neutral occupations than moving

into male-dominated occupations. For women in low-status occupations, however, the

coefficients of moving to a male-dominated occupation and moving to a neutral occupation are

highly significant, but the probability of wage promotion is still lower than observed for men.

These differences suggest that the findings are, at least in part, related to individuals, rather than

related only to occupations.

Altogether, these findings support the idea that some males use female-dominated occupations as

a stopgap, contributing to the continued segregation of those occupations.

Discussion

Compared with the increasing entry of women into male-dominated occupations, the numbers of

men in female-dominated occupations remains very low. The data in this study show that the

male presence in female-dominated occupations continues at the levels observed in 1980. More

specifically, it barely rose from 8 to 9.5 in this period. Building on leading explanations, this

study has argued that high levels of segregation persist at least in part because men transitioning

from non-female occupations are likely to move back out. In other words, female-dominate

occupation are merely stopgaps in male-occupational trajectories.

The study findings are consistent with a stopgap scenario, in which men leave the female-

dominated field shortly after entry. In addition to men riding the glass escalator (Williams 1993)

and those settling away from traditional male careers (Simpson 2004), I have thus been able to

identify a group of men who use female-occupations in a very instrumental way, possibly to

avoid unemployment or to overcome some slowdown in their careers. This kind of mobility has

serious consequences for segregation, as stopgappers, unlike glass escalator riders and settlers,

contribute to the perpetuation of segregation levels. Moreover, men, unlike women, are not likely

to experience downward mobility when leaving sex-atypical occupations. In fact, the opposite is

true in financial terms—men leaving the female field are on average better off than those who

stay in female occupations. These results are consistent with the findings reported by Williams

and Villemez (1993) that male job satisfaction was higher among leavers than among men

staying in the female-dominated field.

These findings also help to envision future trends in the segregation of these occupations.

Egalitarian attitudes, better working conditions and lower rates of stigmatization have slowly

increased the presence of males in high-status female-dominated occupations relative to low-

status occupations. This trend will presumably contribute to the gender neutralization of some

occupations, thereby raising their status and initiating a cycle. In contrast, it seems that low-

status occupations will continue to be highly segregated and suffer from a continuous process of

devaluation. In this scenario, the inequality between high- and low-status occupations will

increase with time.

Because the mechanisms that contribute to the perpetuation of segregation in female occupations

are different from those involved in male-dominated occupations, specific actions are needed to

promote integration. As argued by Williams (1995), affirmative action makes little sense for

attracting men to female-dominated occupations. Rather, we need measures designed to reduce

the social sanctions applied to men who do women’s work and eradicate the negative stereotypes

of female traits. In addition, we must improve the economic conditions of female occupations by

instituting a comparable worth policy that helps to raise both men’s and women’s interest in

female-dominated occupations.

Inevitably, this study raises questions that will need to be addressed in future research. The

analyses in this paper do not allow us to discern whether men who stay in the female field are

there willingly or whether they are trapped. The results thus suggest the need for further research

to examine the extent to which periods in the female field hinder men’s ability to exit back to the

male-dominated field. Related to this issue, recent research (Author 2014) revealed that time

spent in female-dominated occupations has a negative impact on women’s careers within male-

dominated jobs. More research is needed to determine whether time spent in female-dominated

occupations might likewise damage the future careers of men in male-dominated occupations.

Only by revealing and eradicating the disincentives to work in female-dominated occupations

will it be possible to reduce gender inequalities in the labor market.

References

Allan, Jim. 1993. "Male elementary teachers. Experiences and perspectives." Pp. 113-127 in

Doing "women's work": Men in nontraditional occupations., edited by C.L. Williams.

Newbury Park, CA: Sage.

Allison, Paul D. 1984. Event History Analysis: Regression for Longitudinal Event Data. Iowa

City: Sage Publications

Author. 2014.

------. 1995. Survival Analysis using SAS: A Practical Guide. Cary, NC: SAS Institute.

Bartfay, Wally J. and Emma Bartfay. 2007. "Canadian View of Men in Nursing Explored." Men

in Nursing, 2:32-37.

Bradley, James. 2011. "Lessons from My Life's Work." Narrative Inquiry in Bioethics 1:135-

137.

Budig, Michelle J. 2002. "Male Advantage and the Gender Composition of Jobs: Who Rides the

Glass Escalator?" Social Problems 49(2):258-277.

Charles, Maria and David B. Grusky. 2004. Occupational Ghettos. The World Wide Segregation

of Women and Men. Stanford University Press.

Clow, Kimberly A., Rosemary Ricciardelli and Wally J. Bartfay. 2015. "Are You Man enough to

be a Nurse? the Impact of Ambivalent Sexism and Role Congruity on Perception of Men

and Women in Nursing Advertisements." Sex Roles 72:363-376.

Cobble, Dorothy S. 2007. The Sex of Class: Women Transforming American Labor. Ithaca, NY:

Cornell University Press.

Cohen, Phillip. N. and Matt L. Huffman. 2007. "Working for the Woman? Female Managers and

the Gender Wage Gap." American Sociological Review 72(5): 681-704.

Connell, Catherine. 2012. "Dangerous Disclosures." Sexuality Research and Social Policy 9(2):

168-177.

Cotter, David A., Joan M. Hermsen and Reeve Vanneman. 2011. "The End of the Gender

Revolution? Gender Role Attitudes from 1977 to 2008." The American Journal of Sociology

117(July):259-589.

England, Paula. 1992. Comparable Worth: Theories and Evidence. New York: Adline the

Gruyter.

England, Paula and Melissa S. Herbert. 1993. "The pay of men in female occupations. Is

comparable worth only for women?" Pp. 28-48 in Doing women's work: Men in non-

traditional occupations., edited by C.L. Williams. Newbury Park, CA: Sage.

England, P., M. S. Herbert, B. S. Kilbourne, L. L. Reid and L. McCreary. 1994. "The Gendered

Valuation of Occupations and Skills: Earnings in 1980 Census Occupations." Social Forces

73(1):65-100.

England, Paula. 2010. "The Gender Revolution: Uneven and Stalled." Gender and Society

24(2):149-66.

Epstein, Cynthia F. 1989. "Workplace Boundaries: Conceptions and Creations." Social Research

56:571-590.

Evans, Joan. 1997. "Men in Nursing: Issues of Gender Segregation and Hidden Advantage."

Journal of Advance Nursing 26:226-231.

Floge, Liliane and Deborah M. Merrill. 1986. "Tokenism Reconsidered: Male Nurses and

Female Physicians in a Hospital Setting." Social Forces 64:925-947.

Glass, J. 1990. "The Effect of Occupational Segregation on Working Conditions." Social Forces

68(3): 779-796.

Glauber, Rebecca. 2011. "Limited Access: Gender, Occupational Composition, and Flexible

Work Scheduling." The Sociological Quarterly 52:427-494.

Hardie, Jessica H. 2015. "Women's Work? Predictors of Young Men's Aspiration for Entering

Traditionally Female-Dominated Occupations." Sex Roles, 23:5-26.

Harding, Thomas. 2007. "The Construction of Men Who are Nurses as Gay." Journal of Advance

Nursing, 60:636-644.

Harvey Wingfield, Adia and Ranell L. Myles. 2014. "Still a Man's World? Revisiting Men Who

do Women's Work." Sociology Compass 8(10): 1206-1215.

Hayes, Rader. 1989. "Men in Female-Concentrated Occupations." Journal of Organizational

Behavior 10(3): 201-212.

Heickes, E. J. 1991. "When Men are the Minority: The Case of Men in Nursing." The

Sociological Quarterly 32(3): 389-401.

Isaacs, Dallas and Marilyn Poole. 1996. "Being and Man and Becoming and Nurse: Three Men's

Stories." Journal of Gender Studies 5(1): 3-47.

Jacobs, Jerry A. 1989. Revolving Doors. Sex Segregation and Women's Careers. Stanford,

California: Stanford University Press.

------. 1993. "Men in female-dominated fields. Trends and turnover." Pp. 49-63 in Doing

women's work: Men in non-traditional occupations., edited by C.L. Williams. Newbury

Park, CA: Sage.

Kalleberg, Arne L. 2000. "Nonstandard Employment Relations." Annual Review of Sociology

26:341-365.

------. 2009. "Precarious Work, Insecure Workers: Employment Relations in Transition."

American Sociological Review 74:1-22.

Kanter, Rosabeth M. 1977. Men and Women of the Corporation. New York: Basic Books.

Kleinman, Carol S. 2004. "Understanding and Capitalizing on Men's Advantage in Nursing."

Journal of Nursing and Administration 34(2):78-82.

Levanon, Asaf, Paula England and Paul D. Allison. 2009. "Occupational Feminization and Pay:

Assessing Causal Dynamics using 1950-2000 Census Data." Social Forces 88:497-517.

Lupton, Ben. 2000. "Maintaining Masculinity: Men Who do "Women's Work"." British Journal

of Management, 11:33-48.

------. 2006. "Explaining Men's Entry into Female-Concentrated Occupations: Issues of

Masculinity and Social Class." Gender, Work and Organization 3(2):103-128.

McCall, Leslie. 2005. "The Complexity of Intersectionality." Signs 30:1771-1800.

------. 2007. "Increasing class disparities among women and the politics of gender equity." in The

sex of class: women transforming American labor., edited by D.S. Cobble. Ithaca, NY:

Cornell University Press.

McPherson, Miller, Lynn Smith-Lovin and James Cook. 2001. "Birds of a Feather: Homophile in

Social Networks." Annual Review of Sociology 27:415-444.

Moore, Gwen. 1988. "Women in Elite Positions: Outsiders Or Insiders?" Sociological Forum

3(4): 566-585.

Morgan, David H. J. 1992. Discovering Men. London: Routledge.

Oppenheimer, V. K. 1990. "Life-cycle jobs and the transition to adulthood." Unpublished works.

Department of Sociology, UCLA.

Rosenbaum, James E. 1985. "Jobs, Job Status and Women's Gains from Affirmative Action:

Implications for Comparable Worth." Pp. 116-136 in Comparable Worth: New Directions

for Research., edited by H.I. Hartmann. Washington D.C.: National Academy Press.

Schilt, Kristen. 2011. Just One of the Guys? Chicago: University of Chicago Press.

Simpson, Ruth. 2005. "Men in Non-Traditional Occupations: Career Entry, Career Orientation

and Experience of Role Strain." Gender, Work and Organization 12(4):363-380.

Snyder, Karrie A. and Adam I. Green. 2008. "Revisiting the Glass Escalator: The Case of

Gender Segregation in a Female Dominated Occupations." Social Problems 55(2):271-299.

Taylor, Catherine J. 2010. "Occupational Sex Composition and the Gendered Availability of

Workplace Support." Gender and Society 24:189-212.

Williams, Christine L. "The Glass Escalator: Hidden Advantages for Men in the "Female"

Professions." Social Problems 39:253-267.

------. 1993. "The Glass Escalator, Revisited: Gender Inequality in Neoliberal Times." Gender

and Society 27:609-629.

------. 1995. Still a man´s World: Men Who do "Women's Work". Berkeley: University of

California Press.

------. 2015. "Crossing Over: Interdisciplinary Research on "Men Who do Women's Work"." Sex

Roles 72:390-395.

Williams, Christine L. and Wayne J. Villemez. 1993. "Seekers and finders: Male entry and exit

in female-dominated jobs." Pp. 64-90 in Doing women's work: Men in non-traditional

occupations., edited by C.L. Williams. Newbury Park, CA: Sage.

Wright, Erik O. 1997. Class Count. Verso.