Research on sex segregation in the labor market has repeatedly found that women and men are more likely to exit from occupations and firms in which they are the numerical minority and subsequently seek positions that are more represented by their gender. However, this research looked at mobility either across occupations or across firms, leaving unclear how the simultaneous exposure to gender compositions of occupations and firms shapes attrition from gender-atypical positions. We draw on linked employer-employee register data from the German social security insurance system (SIEED, 2012-2018) to highlight that some occupations can be found in firms with various gender compositions, indicating that gender compositions of occupations and firms do not always align and thereby may independently affect mobility. Conditional relative risk ratios for mobility between gender-typed occupations and firms reveal substantial switches from gender-atypical towards more gender-typical positions. This gendered labor market mobility is most pronounced for men across occupations. For women, gender compositions of firms drive not only mobility across firms but also switches out of gender-atypical occupations. Our findings underscore that gender compositions of occupations and firms jointly shape attrition from gender-atypical positions, which ultimately perpetuates labor market segregation.

The sorting of men and women into different occupations and firms is a prominent driver of gender inequalities in the labor market (Penner et al.2022). Although occupational segregation decreased to some extent between 1970 and 1990 due to more women entering previously male-dominated occupations, this process stalled in the 1990s and 2000s (Blau et al.2013; England 2010). Segregation across firms is likewise ubiquitous in many labor markets (Bygren 2013; Bygren and Kumlin 2005; Tomaskovic-Devey et al.2006), despite research showing that workplace gender diversity can increase creativity, innovation, and productivity (DiTomaso et al.2007). Most explanations for persisting segregation focus either on gender differences in occupational aspirations and work preferences driving the individual-level supply for gendered positions (Busch-Heizmann 2015; Cohen 2013) or on employers’ biases against hires into gender-atypical positions (Birkelund et al.2022; Burton and Beckman 2007; Yavorsky 2019). Yet, segregation is not only a consequence of gendered inflows but also maintained by disproportionate gendered outflows out of gender-atypical positions.

A pattern of individuals in gender-atypical positions switching towards labor market positions that are more representative of their gender was already described by Jacobs (1989), who coined the term ‘revolving door’. Gender-atypical positions therein denote an underrepresentation of one's gender, such as men working in a position numerically dominated by women or women working in a position numerically dominated by men. Mobility out of such gender-atypical positions has been linked to negative experiences based on the numeric composition that puts one into the minority (being a ‘token’; Kanter 1977) as well as based on violating gendered expectations that are imprinted into positions in which one gender is overrepresented (Eagly and Karau 2002). Most evidence for this has been based on occupational gender compositions (Busch 2013; Jacobs 1989; Torre 2018; Torre and Jacobs 2021) due to limited measures that capture gender compositions within firms (e.g. Glass et al.2013, p. 726). Workplace scholars have criticized this for inadequately considering how firms' gender compositions shape workplace dynamics (Bygren and Kumlin 2005). Studies that measure gender-atypicality through gender compositions across firms using administrative data from Sweden (Bygren 2013; Folke and Rickne 2022) and Norway (Madsen et al.2021) also substantiate a pattern of individuals transitioning from gender-atypical to more typical firms. However, focusing on firms overlooks the intertwined processes related to occupational gender compositions and stereotypes.

In response, this paper provides a descriptive exploration of mobility across occupations and firms with varying gender compositions to address two questions. First, to what extent do gender compositions of occupations and firms align? If occupations that are numerically overrepresented by one gender are only set in firms with a similar gender composition, a revolving door mobility can be expected to be similar across occupations and firms. By contrast, independent variation would allow to attribute revolving doors to gender compositions of either occupations or firms. Accordingly, the second question asks, whether revolving doors rather manifest across occupations or across firms. We assess men's and women's switches from gender-atypical to more gender-typical occupations and firms in comparison to switches in the opposite direction (from gender-typical to gender-atypical occupations and firms) while simultaneously accounting for employees’ mutual exposure to gender compositions of occupations and firms. Although we only observe mobility patterns as an outcome of interactional and stereotyping processes that are linked to gender compositions, comparing how gender compositions of occupations and firms relate to employees’ attrition from gender-atypical positions indicates where these processes are most pronounced.

Empirically, we draw on linked employer-employee register data from the German social security insurance system (SIEED, 2012–2018) that enables us to estimate gender compositions of occupations and firms. Although recent research has highlighted that jobs (occupation-firm-cells) can account for within-occupation wage inequalities (e.g. Avent-Holt et al.2020; Penner et al.2022), we focus on occupations rather than jobs because widely shared occupational stereotypes of which traits are needed for certain positions rather relate to occupational gender compositions (cf. He et al.2019). This holds especially for the German labor market, which is strongly structured by occupational titles, in which standardized task profiles leave little variation in job-specific tasks across firms. Moreover, the tight linkage between vocational degrees and labor market positions locates Germany near the average of occupational mobility in Europe and its relatively strong employment protection ensures that mobility is less driven by involuntary changes (Bachmann et al.2019). With regard to sex segregation, the German labor market is characterized by internationally comparatively high segregation (though smaller than in the US or Switzerland; Charles and Grusky 2004; Steinmetz 2012), despite average levels of female labor force participation and more women than men holding tertiary degrees (Steinmetz 2012).

How demographic representation of women and men shapes social interactions and experiences at work has been prominently described by Kanter's (1977) work on the concept of tokenism. It denotes employees in gender-atypical positions, in which they are in the gender-wise numerical minority, as ‘tokens’. A skewed gender composition puts tokens ‘under a magnifying glass’ (Ott 1989, p. 42), with this heightened visibility creating performance pressures on them. Additionally, a numerical imbalance reduces the likelihood and intensity of social interactions between genders because those belonging to the majority perceive each other as similar, more trustworthy, and reliable, while members of the minority are perceived as atypical (McPherson et al.2001). A skewed gender composition also increases the salience of gender identities (Randel 2002). The presence of tokens strengthens the dominant gender majority group's consciousness of their own gender identity, which intensifies in-group favoritism and leads to social exclusion and feelings of alienation of tokens (Makarova et al.2016). Thereby, numerical imbalances can induce physiological stress for men and women in gender-atypical positions (Taylor 2016).

Gender stereotypes likewise play a central role in explaining employees’ experiences in gender-atypical positions. Role expectations theory highlights how social interactions are shaped by associations of women and men with widely shared cultural beliefs about their character traits and competencies (Ridgeway 2011). Beyond effects within individual-level social interactions, such associations are also ‘imprinted’ as gendered expectations into labor market positions with skewed gender compositions, which are perceived as typical and more suitable for the dominant gender (Burton and Beckman 2007; Doering and Thébaud 2017). Thereby, essentialist stereotypes of women and men being more suited for specific roles are attached to labor market positions and perpetuated via cultural depictions and observations of women and men in different positions and carrying out divergent tasks. Moreover, diffuse status beliefs ascribe men a general superiority in the labor market (Ridgeway 2011) and greater capabilities of achieving organizational goals (Thébaud 2015). Any employee is subject to these gendered expectations, with individuals in gender-atypical positions facing a role incongruity between their gender and gendered expectations about their labor market positions. Such role incongruity undermines the perception of individuals in gender-atypical positions as being suitable or competent for the labor market position (Eagly and Karau 2002).

Empirical evidence backs up the theoretical arguments for largely negative experiences of individuals in gender-atypical positions derived from tokenism and role expectations. For instance, tokens are more vulnerable to different forms of gender and sexual harassment (Folke and Rickne 2022; Stainback et al.2011), including women being sexualized and perceived as potential sex partners instead of colleagues (Burgess and Borgida 1997). Tokens are not only subject to gendered treatment of direct co-workers; clients also evaluate the work of individuals in gender-atypical positions against higher standards (Doering and Thébaud 2017; Tak et al.2019). Furthermore, clients, family members, and acquaintances call out role incongruities by, for instance, questioning men's sexuality when they work in occupations that are perceived as unsuitable for men, such as nursing or childcare (Evans and Frank 2003; Sargent 2004). Simultaneously, the diffuse status beliefs of men's superiority in the labor market have been invoked to explain a male advantage in gender-atypical occupations for promotions into supervisory positions. This ‘glass escalator’ (Williams 1992) aligns their employment role with gender status beliefs. By contrast, women working in gender-atypical roles that entail agency and masculinity have been shown to violate prescriptions of feminine niceness (Eagly and Karau 2002; Heilman and Wallen 2010) and report more often than men sexual harassment or intimidation at the firm (Stojmenovska 2023). Acker (1990) furthermore describes how firms are constituted by masculinized workplace cultures and working arrangements centering on men, with male-dominated work environments at times creating hostile and isolating atmospheres for women in gender-atypical positions (Makarem and Wang 2020).

Experiences related to tokenism and role incongruities not only drive exits from gender-atypical positions but also prompt employees to seek subsequent positions that are more typical for one's gender. Therein, the cumulative evidence on overwhelmingly negative experiences of women and men in gender-atypical working positions has been invoked as the underlying mechanisms that explain the revolving doors of individuals switching from gender-atypical to more gender-typical positions (Busch 2013; Madsen et al.2021; Torre 2017; Torre 2018; Torre and Jacobs 2021). Notably, such mobility occurs after similar attrition from gender-atypical training in vocational (Beckmann 2023) and higher education (Meyer and Mantinger 2021), with only selective individuals entering the labor markt in gender-atypical positions in the first place.

Occupations, firms, and their intersection

Considering that both – working in a gender-atypical occupation or firm – predict a greater likelihood of switching to more gender-typical positions, the question arises, whether this applies equally to mobility across occupations and firms. Previous research has highlighted harassment as a key reason for female tokens to leave male-dominated positions (Folke and Rickne 2022), with the immediate work context in one's firm being a stronger predictor for negative interactional experiences and harassment than the gender composition of their occupation (Cortina and Areguin 2021; Gruber 1998). Moreover, women in gender-atypical firms often face androcentric working norms that they might be unable to fulfill due to competing obligations outside of work, which can result in exits due to a lack of fit (Cech and Blair-Loy 2019; Madsen et al.2021; Makarem and Wang 2020). By contrast, qualitative evidence highlights that the primary source of prejudices for men in gender-atypical occupations derives from outside of one's firm, such as clients or family members (Manzi 2019; Williams 1992), and they face even more pronounced stereotypes about gender-appropriate occupations than women (DiDonato and Strough 2013). At the same time, female co-workers and supervisors privileging men (Moskos 2020; Simpson 2009) lets some men to ride on the glass-escalator from gender-atypical occupations into authority positions (Williams 1992) while staying in the same, potentially gender-atypical, firm. These arguments suggest that the gender composition of firms is relatively more important for women's switches to more gender-typical firms, whereas the occupational gender composition is relatively more important for men's gendered labor market mobility.

As previous research overwhelmingly investigated gendered labor market mobility across occupations or firms separately, the comparison of mobility across studies cannot attribute experiences of being a gender-wise minority to gender compositions of either occupations or firms. Similarities in the gender composition of occupations and firms may confound the comparison of switches out of gender-atypical positions for each. Adjusting for the gender composition of occupations and firms simultaneously allows to assess their independent influences and their relative importance in driving switches from gender-atypical towards more gender-typical positions.

Finally, the importance of the gender composition of occupations can also depend on the gender composition within firms and vice versa (cf. Kim 2018). While role expectation theory highlights that role incongruities between occupational roles and one's gender undermine employees’ perceived competencies, greater visibility of tokens in firms could amplify the resulting performance pressure. A perspective of social differentiation also suggests that working in a gender-atypical role in one domain (e.g. occupation or firm) is tolerated as long as social differentiation between genders is pertained via staying in gendered boundaries in other domains (Reskin 1988). Backlash would occur when differentiation is undermined by individuals who work in a position of a double minority status (working in a gender-atypical occupation and a gender-atypical firm). By contrast, Turco (2010) highlights that tokens may not only deviate from general gendered expectations but that the extent of role incongruity depends on local cultures that are specific to immediate working contexts. Therein, the deviation of working in a gender-atypical role can lead to stronger feelings of role incongruence in contexts that are otherwise dominated by one's gender. For instance, men in gender-atypical occupations might be harassed by male co-workers for violating stereotypically male attributes, while among female co-workers they might benefit from the diffuse status beliefs about male superiority; or women in gender-atypical firms could suffer more from sexual harassment when working in gender-typical occupations, which align stereotypes about their subordinate status, instead of working in gender-atypical occupations, in which their occupational role conflicts with perceptions of femininity. However, due to the limited research that investigated gender compositions across occupations and firms simultaneously, evidence for either mechanism – exacerbated gendered mobility for a double minority status of working in a gender-atypical occupation in a gender-atypical firm or for ‘gender-atypicalness’ becoming more salient in contexts in which one's gender is in the majority – is limited.

To address the simultaneous exposure of individuals to gender compositions of occupations and firms, we first evaluate the extent to which the gender composition of one's occupation mirrors the gender composition of one's firm. While we expect some similarity, a degree of independence implies that the gender composition of occupations and firms can have separate effects on switches from gender-atypical to more gender-typical positions. Second, we explore mobility across consecutive years between occupations and firms that are dominated by one gender. Separately for male and female employees, we estimate relative risk ratios (RRR)1 of switching from gender-atypical towards more gender-typical occupations (firms) compared to the opposite direction (switching from gender-typical towards more gender-atypical positions). The ratio of the RRRs across occupations and firms allows to assess the relative importance of occupations’ and firms’ gender compositions for each gender's mobility. The RRRs are subsequently adjusted for socio-demographic characteristics to account for potential confounding and importantly adjusted for the gender composition of occupations and firms, respectively, to obtain estimates of how each gender composition independently shapes gendered labor market mobility. In a final step, we include interaction terms between gender-typed occupations and firms to assess how gender compositions of occupations and firms interact in prompting gendered labor market mobility.

Data

We draw on the administrative register Sample of Integrated Employer-Employee Data (SIEED; Berge et al.2020). The SIEED is a 1.5 percent sample of firm establishments in Germany covered in the social security system at the reference date of June 30th each year. It contains social security notifications of all employees within these establishments, as well as their entire employment history, including firm-level information about previous and subsequent employers. The linkage between employee- and firm-level data allows us to analyze gender compositions and gendered labor market mobility across occupations and firms.

The analytic sample is limited to employees aged 18–65, omits spells of vocational training, internships, student trainees, and military services. Additionally, it is restricted to the period from 2012 to 2018, for which we observe a coherent occupational classification. Person-year observations are obtained from employment characteristics at the reference date of the firm-level data. We select the employment with the highest daily wages for co-occurring employment spells.2 This provides 4,381,989 person-year observations for 1,217,808 women and 5,332,022 person-year observations for 1,424,541 men.

Variables

Gender compositions are operationalized as the share of female employees in each occupation and in each firm based on pooling all observations across the analytical period. We calculate the share of female employees across 846,037 distinct firms irrespective of the employment status and age of their employees.3 For occupations, we draw on the German classification of occupations from 2010 (KldB2010), with 698 occupations on the 4-digit level observed in the SIEED data. Applying the same sample restrictions as above, we calculate the share of female employees across occupations.4

To capture gendered labor market mobility, we first categorize the share of female employees for occupations and firms and second compare these across consecutive years. The categorization as gender-typed occupations and gender-typed firms highlights changes in gender compositions of labor market positions at the margins instead of mean changes in the share of female employees. We use cut-off points of less than 25 percent female employees to denote male-dominated occupations and firms that are gender-atypical for women, more than 75 percent female employees to denote female-dominated occupations and firms that are gender-atypical for men, and 25–75 percent to denote integrated firms or occupations. As the choice of a cut-off point is to some extent arbitrary and previous research has used different cut-offs (Anker 1998, pp. 82–84), we ensured the robustness of our results against 20 and 80 percent as well as 33.3 and 66.6 percent cut-offs (Figure S.1 in the Supplementary Materials).5 By comparing for each employee their gender-typed occupation (firm) in a given year t with the gender-typed occupation (firm) in the following year t+1, we construct an indicator of switches between gender-typed occupations (firms). In contrast to analyzing exits from gender-typed positions disregarding the destination, our indicator of switches appropriately captures the gendered labor market mobility underlying persistent sex segregation.

We adjust for fixed effects of year, age, labor market experience, German citizenship, educational attainment, a part-time indicator, and a fixed-term contract indicator at the individual-level and for firm-size fixed effects at the firm-level. Additionally, we include an indicator that denotes employment gaps of at least four weeks between two consecutive person-years. Table S.1 in the Supplementary Materials provides a descriptive overview of the sample. All analyses include clustered standard errors at the firm level to account for clustered sampling. A replication package for data preparation and for reproducing all analyses is provided in an OSF-repository (doi.org/10.17605/OSF.IO/A8N4R).

Note that the SIEED data is limited to social security-relevant employment, which could miss some variation. Especially the exclusion of self-employment could affect estimates of gender compositions of firms when, for instance, a male self-employed owner of a dentist's office is not included in the data. Still, with larger firm-size the omission of self-employment becomes less relevant, and the data covers 80 percent of the working population in Germany, including private and public sector employment. The register data is also limited in the variety of characteristics provided, which does not allow us to directly test for negative experiences or workplace dynamics described by the concepts of tokenism and role incongruities. Nevertheless, our endeavor – similar to other research (e.g. Torre and Jacobs 2021) – focuses on the description of gendered labor market mobility as an outcome of the proposed mechanisms, which is enabled by SIEED's large number of observations and its possibility to observe gender compositions across occupations and firms.

Gender compositions of occupations and firms

To what extent does the gender composition of one's occupation align with the gender composition of one's firm? Figure 1 depicts for each occupation the share of female employees on the x-axis and on the y-axis the mean (dots) and the mid-80 percent (vertical lines) of the share of female employees across firms in the year 2018. A general association between the gender compositions of occupations and firms is evident, with larger means of the firms’ share of female employees being observed for occupations with greater shares of female employees. The corresponding employee-level correlation of the gender composition of occupations and firms is strong (0.549 for men and 0.488 for women), but far from perfect.
Figure 1. 

Spread of men's and women's occupations across firms with various gender compositions. Note: Dots denote occupations’ mean share of female employees and line its mid-80 percent. Only occupations shown that spread across at least 200 firms for each gender in 2018. Source: SIEED7518, own calculations.

Figure 1. 

Spread of men's and women's occupations across firms with various gender compositions. Note: Dots denote occupations’ mean share of female employees and line its mid-80 percent. Only occupations shown that spread across at least 200 firms for each gender in 2018. Source: SIEED7518, own calculations.

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Accordingly, male-dominated occupations with fewer than 25 percent female employees are not necessarily observed within firms with a similar gender composition, and female-dominated occupations with more than 75 percent female employees are not necessarily observed within similarly composed firms; rather some occupations can be found across firms with various gender compositions. This is illustrated in Figure 1 via occupations with the largest (red) and smallest (blue) variation of the share of female employees across firms. For instance, women working in the heavily female-dominated occupation of ‘midwives’ overwhelmingly work in firms that are also heavily composed of women (on average, firms with 76.7 percent female employees), while female ‘customer information clerks’ can be found in firms with various gender compositions (on average they work in firms with 59 percent female employees, with a standard deviation of 26.1). Men in the male-dominated occupation ‘smelters’ can be found nearly exclusively in male-dominated firms, while those working in the similarly male-dominated occupation ‘road and tunnel inspectors’ work in firms with gender compositions that spread from 3.4 percent to 69 percent women in their firm. Occupations with more balanced gender compositions (‘surgeons’ and ‘building cleaners’ for men or ‘anesthetists’ and ‘physical securities’ for women) also display varying spreads of the gender compositions of the firms in which they are observed. Thus, for some occupations the gender compositions of one's occupation and firm are similar, but for other occupations, they are not, which can lead to independent effects of the gender compositions of occupations and firms on gendered labor market mobility.

Gendered labor market mobility

Do women and men more often switch from gender-atypical to more gender-typical occupations or firms than in the opposite direction? The upper panel of Table 1 provides a descriptive answer by showing for men's and women's gender-typed occupations in a given year the distribution of employees across gender-typed occupations in the following year. The very large percentages on the diagonal indicate that most employees stay within two years in the gender-type of their occupation, which is unsurprising, as occupational mobility is generally low in Germany. But men also stay more often in male-dominated occupations that are gender-typical for them, than in female-dominated occupations that are gender-atypical for them (98.49 percent compared to 94.94 percent). While this could indicate a generally higher attrition from female-dominated occupations (e.g. due to less employment security), we observe the opposite for women, who are less likely to stay in male-dominated occupations that are gender-atypical for them rather than in female-dominated occupations that are gender-typical for them (95.66 percent compared to 98.15). Thus, for both genders, we observe a disproportionate mobility towards occupations that are more representative of their own gender. For men, this can be described with an RRR of (10.9494)/(10.9849)=3.36, meaning that the risk for switching from a gender-atypical to a more gender-typical occupation is 236 percent larger compared to the risk of switching from a gender-typical towards a more gender-atypical occupation. For women, the corresponding RRR is (10.9566)/(10.9815)=2.34 (cf. unadjusted coefficients in Figure 2).
Figure 2. 

Men's and women's relative risk ratios of switching from gender-atypical vs. from gender-typical occupations and firms. Note: Coefficients for the fully adjusted model are provided in Table S.3 in the Supplementary Materials. RRRs shown with 95%-confidence intervals based on clustered standard errors on the firm-level. Fixed effects for year, age, labor market, educational attainment, German citizenship, part-time, type of contract, employment gaps, and firm size. Source: SIEED7518, own calculations.

Figure 2. 

Men's and women's relative risk ratios of switching from gender-atypical vs. from gender-typical occupations and firms. Note: Coefficients for the fully adjusted model are provided in Table S.3 in the Supplementary Materials. RRRs shown with 95%-confidence intervals based on clustered standard errors on the firm-level. Fixed effects for year, age, labor market, educational attainment, German citizenship, part-time, type of contract, employment gaps, and firm size. Source: SIEED7518, own calculations.

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Table 1. 
Gendered labor market mobility across occupations and firms by sex.
Occupational type at tOccupational type at t + 1
Male-dominatedIntegratedFemale-dom.
Men 
 Male-dominated 98.49 1.30 0.21 
 Integrated 2.56 96.75 0.68 
 Female-dominated 1.99 3.07 94.94 
Women 
 Male-dominated 95.66 2.74 1.61 
 Integrated 0.69 97.15 2.16 
 Female-dominated 0.29 1.56 98.15 
Firm-type at t Firm-type at t + 1 
Male-dominated Integrated Female-dom. 
Men 
 Male-dominated 97.53 2.36 0.10 
 Integrated 3.17 96.29 0.54 
 Female-dominated 1.12 4.89 94.01 
Women 
 Male-dominated 95.13 4.04 0.84 
 Integrated 0.79 97.10 2.11 
 Female-dominated 0.21 2.65 97.14 
Occupational type at tOccupational type at t + 1
Male-dominatedIntegratedFemale-dom.
Men 
 Male-dominated 98.49 1.30 0.21 
 Integrated 2.56 96.75 0.68 
 Female-dominated 1.99 3.07 94.94 
Women 
 Male-dominated 95.66 2.74 1.61 
 Integrated 0.69 97.15 2.16 
 Female-dominated 0.29 1.56 98.15 
Firm-type at t Firm-type at t + 1 
Male-dominated Integrated Female-dom. 
Men 
 Male-dominated 97.53 2.36 0.10 
 Integrated 3.17 96.29 0.54 
 Female-dominated 1.12 4.89 94.01 
Women 
 Male-dominated 95.13 4.04 0.84 
 Integrated 0.79 97.10 2.11 
 Female-dominated 0.21 2.65 97.14 

Note: Row percentages shown. Table S.2 in the Supplementary Materials also denotes mobility out of social security-relevant employment. Source: SIEED7518, own calculations.

Despite the sizeable probabilities of staying within the same gender-type across two years, the disproportionate switches towards more gender-typical occupations accumulate over time, thereby maintaining occupational segregation. To illustrate this, we calculate the Dissimilarity Index6 (Duncan and Duncan 1955) for the segregation of women and men across gender-typed occupations as observed in the sample and multiply it ten times (denoting mobility over ten years) with the observed transition matrix of the upper panel of Table 1. This hardly changes the Dissimilarity Index (from 0.534 to 0.530), indicating how the revolving door maintains occupational segregation. By contrast, an alternative transition matrix corresponding to the sample average of switching probabilities applied equally to all gender-typed occupations (0.976 on the diagonal and even probabilities of 0.012 for entering either alternative gender-type) reduces occupational segregation by 30.7 percent over ten years.

Turning to firms in the lower panel of Table 1, we also observe that fewer men and women stay in gender-atypical firms. The respective RRRs of switching out of gender-atypical compared to gender-typical firms are 2.43 for men and 1.70 for women (cf. unadjusted coefficients in Figure 2), meaning men are 143 percent more likely and women are 70 percent more likely to switch from a gender-atypical towards a more gender-typical firm than the opposite. In accordance with our expectation of greater importance of occupational gender compositions for men's gendered labor market mobility, men's RRR is lower for mobility across firms than across occupations. However, for women, the descriptive RRRs contradict our expectation: the gender composition of occupations seems likewise more important than the gender composition of firms because relatively more women switch from gender-atypical towards more typical occupations than firms. This conclusion remains when we adjust for year, age, labor market experience, German citizenship, educational attainment, part-time, type of contract, and firm size-fixed-effects, as well as an indicator for intermediate employment gaps. The corresponding RRR of switches across occupations is for men 1.29 and for women 1.14 times larger than the RRR of switches across firms (Figure 2, diamond-shaped markers).7

As we observed that some occupations are predominantly found in firms with similar gender compositions, it is important to analyze the gender-type of occupations and firms simultaneously to tease out the independent effect of being a gender-wise minority in an occupation or firm. Doing so does not substantially alter men's RRRs of switching gender-typed positions, nor women's RRR of switching gender-typed firms (Figure 2, squared markers). By contrast, the RRRs for women's mobility across occupations decreases from 2.14 to 1.90. Thus, women's disproportionate switches from gender-atypical towards more gender-typical occupations are partly based on the fact that they occur more likely in gender-atypical firms. The decrease of the RRR at the occupational level results in a balanced ratio of the RRRs of occupations compared to firms of 1.01. Thus, after accounting for similarities in the gender compositions of occupations and firms, the gender composition of firms turns out to be more important for women's gendered labor market mobility than the occupational level gender composition as we not only observe a balanced ratio, but moreover women's gendered mobility across occupations is partly attributable to the effect of the gender compositions in their firms. In sum, we find robust evidence on revolving doors of disproportionate switches from gender-atypical towards more gender-typical positions that are for men especially driven by occupational gender compositions whereas for women more strongly shaped by gender compositions of firms.

We explore the heterogeneity of this finding along several characteristics to assess its robustness. First, the gender compositions of firms might, especially in larger firms, miss variation in gender compositions of the immediate work surroundings. Accordingly, supplementary analyses reveal more pronounced patterns of gendered labor market mobility in smaller firms, yet, substantial gendered mobility patterns also persist in firms with more than 100 employees (Figure S.2 in the Supplementary Materials). Second, we observe that mobility towards positions that are more represented by one's gender is especially large within the first two years after entering a firm and slightly attenuates with increasing tenure, which suggests a form of habituation. Yet, we still observe switches out of gender-atypical towards more gender-typical positions for individuals with tenure of at least 10 years (Figure S.3 in the Supplementary Materials). Third, our finding of a greater relevance of gender compositions of occupations for men and of gender compositions of firms for women in shaping mobility towards more gender-typical positions is much more pronounced for blue-collar occupations than for white-collar occupations (Figure S.4 in the Supplementary Materials), which mirrors variation by occupational strata indicated by Torre and Jacobs (2021). Finally, note that the extensive mobility of men towards more gender-typical occupations is not explained by a glass escalator: adjusting for promotions8 leads to a significant reduction of the RRR of men's switches out of gender-atypical occupations – underlining promotions are an advantageous mechanism for men's switches out of female-dominated occupations – but the reduction is only weak in size (Figure S.5 in the Supplementary Materials).

Finally, we explore how the gender compositions of occupations and firms in conjunction shape gendered labor market mobility by interacting the gender-type of one's positions. For switches across occupations (upper panel of Figure 3), we observe that women and men in a double majority status – working in a gender-typical occupation in a gender-typical firm – have the lowest probability of switching their occupational gender-type. However, the highest exit rates are not observed for positions of a double minority status. For gender-typical occupations, we rather observe that those in gender-atypical firms are relatively more likely to switch their occupational gender-type, while for gender-atypical occupations, the opposite holds: those that work in gender-typical firms are relatively more likely to switch their occupational gender-type. This pattern supports the local context perspective, which predicts the highest mobility for positions when the ‘gender-atypicalness’ of one domain (occupations or firms) is more visible in contexts in which their own gender is otherwise in the majority. This does not seem to be an artifact of gender-atypical occupations constituting an occupational minority that fulfills secondary roles in gender-typical firms as that the patterns hold when adjusting for occupational prevalence across firms (Figure S.6 in the Supplementary Materials). For mobility across firms, the patterns are less pronounced (lower panel of Figure 3): While we still observe the lowest probabilities for men and women in a double majority status, switches out of gender-atypical firms when working in gender-typical occupations are only slightly larger compared to when working in gender-atypical occupations.
Figure 3. 

Probabilities of switching the gender-type of one's occupation and firm by occupation-firm-combinations and sex. Note: Probabilities are predicted from linear probability models and shown with 95%-confidence intervals. Adjusted for year-, age-, labor market-, educational attainment-, part-time-, type of contract-, employment gaps, and firm size-fixed-effects with clustered standard errors on the firm-level. Source: SIEED7518, own calculations.

Figure 3. 

Probabilities of switching the gender-type of one's occupation and firm by occupation-firm-combinations and sex. Note: Probabilities are predicted from linear probability models and shown with 95%-confidence intervals. Adjusted for year-, age-, labor market-, educational attainment-, part-time-, type of contract-, employment gaps, and firm size-fixed-effects with clustered standard errors on the firm-level. Source: SIEED7518, own calculations.

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Segregation in the labor market endures not only due to the sorting of women and men into gender-typical occupations and firms but is also upheld by disproportionate mobility from gender-atypical towards more gender-typical positions. Drawing on theories of tokenism and role expectations, previous research has highlighted a revolving door as either disproportionate switches from gender-atypical towards more gender-typical occupations or as disproportionate switches from gender-atypical towards more gender-typical firms. Thus, the mutual exposure of individuals to the gender compositions of occupations and firms has been neglected. In response, we use linked employer-employee data from the German social security insurance system (SIEED, 2012-2018) that allowed us to describe similarities in gender compositions of occupations and firms and to explore how both are associated with men's and women's mobility from gender-atypical towards more gender-typical positions. Overall, our results make three contributions.

First, descriptive evidence on occupations’ and firms’ gender compositions elucidated that some occupations cluster in firms with similar gender compositions, whereas other occupations can be found in firms with diverging and varying gender compositions. This is likely due to the content of an occupation being linked to more or less specialized firms and due to firms employing varying numbers of different occupations. For instance, healthcare-related occupations, such as nurses or midwives, clustered strongly in firms with a high share of women, as these are presumably hospitals in which employees in these female-dominated occupations are supervised by few employees in more integrated occupations, such as surgeons or chief physicians. By contrast, information and office clerks or building cleaners are often supplementary tasks in firms across various sectors and thereby find themselves in firms with various gender compositions (cf. Figure 1). This partial independence of occupations’ and firms’ gender compositions might change in the future, as research in the US indicates that low-paying occupations increasingly cluster in low-paying firms (Wilmers and Aeppli 2021), and this reinforcement of occupational and firm inequalities could also affect the prospective sorting of women and men across more or less well-paying positions. Nevertheless, our finding for the current German labor market of highly segregated occupations spreading across more or less integrated firms highlights an opportunity for future research: Leveraging incidences where the gender composition in one's firm diverges from the gender composition in one's occupation (or vice versa) allows to tease out how being gender-atypical in one's occupation or firm carry distinct mechanisms for gendered workplace processes (cf. Yoder 1991).

Building on this, we showed secondly that revolving doors occur across occupations and firms and the gender composition of each shapes women's and men's mobility from gender-atypical towards more gender-typical positions. However, a gender difference emerged therein: For women, we observed not only a similarly pronounced revolving door across firms and occupations but also that gender compositions of firms affected their occupational mobility. Hence, for women's mobility, facing a majority of male co-workers in their firm seems relatively more important than occupational gender compositions. This mirrors previous research that indicated the importance of gender compositions of firms for women (Gruber 1998) and that attributed women's negative experiences in male-dominated firms to barriers towards the compatibility of work-life demands, a masculinized workplace culture (Acker 1990; Cech and Blair-Loy 2021; Madsen et al.2019) and sexual harassment (Cortina and Areguin 2021; Folke and Rickne 2022; Stainback et al.2011). We observed an even more pronounced mobility from gender-atypical towards more gender-typical positions for men across occupations. This aligns with research that suggests that men face more pronounced stereotypes about gender-appropriate occupations than women (DiDonato and Strough 2013; Yavorsky 2019). Importantly, men's mobility towards more gender-atypical occupations occurs despite shrinking employment opportunities in male-dominated production sectors and expanding female-dominated health and social service sectors (Esping-Andersen 2009). The finding of the most pronounced revolving door for men across occupations, thus, demands increased efforts to encourage and keep men in gender-atypical occupations to tackle occupational gender segregation (Hamjediers 2023).

Third, our exploration of how gender compositions of occupations and firms jointly shape mobility between gendered positions provides evidence for ‘gender-atypicalness’ being more pronounced in local contexts that are dominated by one's own gender (cf. Turco 2010). One could expect that a double minority status – working in a gender-atypical occupation in a gender-atypical firm – would come with exacerbated switches towards more gender-typical positions. Yet we rather observed more switches from gender-atypical towards more gender-typical occupations in gender-typical firms. Potential underlying processes could be that male co-workers may penalize men in gender-atypical occupations for violating stereotypically male attributes more than female co-workers in female-dominated firms (Heilman and Wallen 2010), or that women in gender-atypical occupations, such as supervising positions, suffer from female co-workers in female-dominated firms unconsciously restoring threatened feminine values by showing little trust and less support in high-status women (Srivastava and Sherman 2015).

However, these are only indicative ideas that could account for the observed patterns, and it is important to acknowledge that we had no information on workplace interactions or negative experiences related to being in a gender-wise minority, nor were we able to distinguish whether observed occupational or firm-level switches were voluntary or involuntary. Thus, we cannot attribute the observed mobility patterns to distinct mechanisms or distinguish between the role of employees’ and employers’ behavior in explaining them. Future research should, therefore, leverage broader sets of characteristics to open the ‘black box’ (Lawrence 1997, p. 2) of underlying mechanisms that undermine women's and men's persistence in gender-atypical positions.

Our findings of occupational and firm-level gender compositions each affecting attrition from gender-atypical positions not only indicates potentially distinct mechanisms rooted in the theoretical accounts of tokenism and role incongruity, but additionally, the observed interdependence between occupational and firm-level gender compositions provides a descriptive starting-point for advancing theoretical arguments that elucidate their joint influence on employees’ experiences. Adequate theoretical explanations for revolving doors is not only important to address the shown perpetuating effect for sex segregation in the labor market, but also because mobility out of gender-atypical positions has detrimental economic consequences for individuals and employers: individuals lose vocational and firm-specific human capital (McLaughlin et al.2017), and firms must search for, hire, and train new workers.

We thank Laura Lükemann, Maximilian Sprengholz, the participants of the workshop Longitudinal Research of Income Inequality at Haifa University, and three anonymous reviewers for feedback and advice on this article.

No potential conflict of interest was reported by the author(s).

1

RRRs are calculated as non-linear combinations of the predicted probabilities for switching the gender-type occupation (firm) from seemingly unrelated linear probability models. The results do not change substantially when predicting the probabilities via logistic regressions or obtaining the RRRs from log-binomial regressions.

2

Daily wages compromise the product of hourly wages and daily working hours; randomly selecting one of the co-occurring spells leads to the same results.

3

When calculating the share of female employees, we could only apply the sample restrictions for the primary sampled firms, because the necessary information is unavailable for firms where employees work prior or posterior. However, as the correlation between the restricted sample- and unrestricted sample-based share of female employees across primary sampled firms is 0.959, this should not affect our results.

4

While the 5th percentile across occupations denotes a cell-size of 59 observations per occupation and the median entails 699 observations, we observe fewer than 10 observations for six occupations; nevertheless, due to their small sample size, they marginally affect our estimations.

5

Only for men, the RRRs of switches from gender-atypical compared to gender-typical occupations are substantially bigger for the 33.3/66.7 percent and smaller for the 20/80 percent cut-offs, which is based on mobility between gender-typical and integrated occupations across occupations with 20 to 33.3 percent female employees; men's risk of switching from gender-atypical towards more typical occupations stays rather constant across cut-offs.

6

Although the Dissimilarity Index has been critiqued for insufficiently accounting for marginal changes (changing gender gaps in labor force participation or (dis-)appearing occupations; e.g., Charles and Grusky 2004), our illustration does not invoke any assumptions about marginal changes.

7

Notably, integrated positions are positioned in between the extremes, with the risks for switches out of integrated occupations and firms being also greater than switches out of gender-typical positions (Figure S.1 in the Supplementary Materials), indicating that women and men in positions of mixed gender compositions are still seeking positions that are even more represented by their gender (see also mobility out of integrated occupations in Table 1).

8

Promotions are operationalized as switches from not working in a supervisory position in a given year t, but doing so in the following year t+1. Supervisory positions are identified via the fourth digit of the KldB2010-codes (supervisory positions task within an occupational group) and the codes 7110 (Managing directors and executive board members), 7121 (Legislators), and 7122 (Senior officials of special interest organizations) among those with high complex tasks (cf. Eisenmenger et al.2014). Although this measure of promotions in registry data comes with measurement error that seems to be larger for men (Collischon 2023), it is similarly to the measure of occupational gender compositions based on the KldB-4-digits occupational codes and thus most directly speaks to promotions as a mechanism for men's change of their occupational gender-type.

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Author notes

Edited by Patrick Präg

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14616696.2023.2290254.

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