From School to Work: Gender Inequalities and Segregation Following an Apprenticeship in Switzerland

From School to Work: Gender Inequalities and Segregation Following an Apprenticeship in Switzerland | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From School to Work: Gender Inequalities and Segregation Following an Apprenticeship in Switzerland Matteo Lacalamita, Julie Mancini, Matthias Studer, David Glauser This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8137537/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Vocational education and training (VET) is the most common post-compulsory educational pathway in Switzerland, followed by around two thirds of each birth cohort (Cortesi and Imdorf, 2013 ). Upon completion, the majority of apprentices are awarded a Federal VET Diploma (FVETD) following one of its 250 training programmes (Cortesi and Imdorf, 2013 ). By providing ready-to-use occupation-specific skills in a wide range of professions, VET has repeatedly been credited for fostering smooth and linear transitions into employment (Müller and Shavit, 1998 ; Bol, et.al , 2019 ; Kriesi and Schweri, 2019 ). Despite growing political concern, the Swiss VET system nonetheless remains marked by a strong horizontal gender segregation : while men more often undertake technical and manual occupations, women generally cluster in only a few apprenticeships in the health and social care sectors (Leemann and Keck, 2005 ; Becker and Glauser, 2015 ; Kriesi and Imdorf, 2019 ). Given the close link between VET apprenticeships and their subsequent gendered occupational prospects, this horizontal gender segregation may represent a key step in the reproduction of the social inequalities in the Swiss labor market (Kriesi and Imdorf, 2019 ; Grønning and Kriesi, 2022 ). Using sequence analysis and the administrative LABB database (FSO, 2024), this article provides a typology of trajectories of the 2012 FVETD graduate’s cohort over seven years, demonstrating the great diversity of pathways into higher education, employment, reorientation, and NEET status. We then rely on this typology to study the gender disparities in SWT pathways, highlighting that women have less perspectives for further education after VET and are more often limited to experience a fast transition to employment. In a second step, we use multilevel models to estimate how the allocation into one of the 250 specific VET training programmes is related to subsequent SWT. This analysis emphasizes that male-dominated VET programmes offer a substantial protection against more problematic SWT pathways. This methodological approach also allows studying how other key characteristics of VET programmes, such as the number of provided lessons, are related to subsequent pathways. Finally, we look at the consequences of evading the gender norms by enrolling into gender-atypical VET occupations. Our findings reveal that women graduating from male-dominated VET apprenticeships do not benefit from their protection against more problematic pathways and are instead more likely to pursue unstable or NEET trajectories. Vocational Education and Training School-to-work transition Horizontal gender segregation Gendered life-courses Sequence analysis LABB database VET curriculums heterogeneity Figures Figure 1 1. Introduction Despite growing political concern, the Swiss educational system has remained one of the most highly gendered in international comparison over the past decades (Buchmann and Kriesi, 2009; Imdorf and Hupka-Brunner, 2015). This situation is related to a two-step gendered selection effect. First, at the transition from lower- to upper-secondary level, women are more likely to pursue an academic track, whereas men tend to enrol more frequently into initial vocational education and training (VET). Second, within the Swiss VET system, most apprenticeships are gender-typed (Leemann and Keck, 2005; Becker and Glauser, 2015; Kriesi and Imdorf, 2019). As a result, men more often undertake technical and manual training programmes, while women are over-represented in health, social care and service occupations (Becker and Glauser, 2015; Kriesi and Imdorf, 2019). This strong horizontal gender segregation and its consequences for the structuring of gendered pathways following VET education represents the core of the present work. Studying horizontal gender segregation within the VET system is important, as it may represent a key step in the reproduction of the horizontal and vertical occupational segregation in the Swiss labour market for several reasons. First , the Swiss educational system is historically rooted in VET, which represents the most common post-compulsory educational pathway (Cortesi and Imdorf, 2013). Upon completion, the great majority of the VET graduates are awarded a federal VET diploma (FVETD) following one of its 250 training programmes, which is thought to give a direct access to skilled employment (Stalder and Nägele, 2011; Cortesi and Imdorf, 2013). Second , there is a tight linkage between VET occupations and the Swiss labour market. Consequently, as they determine subsequent economic sectors and further education opportunities, VET occupations are strongly associated with specific future gendered occupational prospects (Kriesi and Imdorf, 2019; Grønning and Kriesi, 2022). Third , male-dominated VET occupations tend to be more rewarding than the ones following female-dominated and gender-mixed training programmes (Kriesi and Imdorf, 2019; Korber and Oesch, 2019, Grønning et al. , 2020). This article focuses on the school-to-work transition (SWT) of young FVETD holders and the gendered processes that shape this crucial and vulnerable phase. The SWT refers to the transition period between the end of full-time education and the stable entry into employment (Schoon and Silbereisen, 2009). In this context, VET has often been credited for providing fast, fluid and linear transitions to employment because of its high level of occupational specificity and its close link to the labour market (Kriesi and Schweri, 2019). However, the VET system also hides a great diversity in subsequent SWT trajectories (Babel, 2018), which is expected for at least three reasons. First , SWT has become increasingly diversified, less linear and more de-standardized in most European countries over the last decades (Buchmann and Kriesi, 2011; Brzinsky-Fay and Solga, 2016). Second , many apprentices encounter increasing difficulties in their STW, experiencing NEET ( Not in Education, Employment nor Training ) states or unemployment (Stalder, 2012; Salvisberg and Sacchi, 2014; Babel, 2018). Third , since the early 1990s VET reforms, there is a growing diversity of higher education opportunities following VET education (Stalder and Nägele, 2011; Imdorf et al. , 2017). Hence, for an increasing number of graduates, VET represents only the first step in their educational trajectory before entering tertiary education or reorienting. The diversity in post-FVETD pathways may also stem from the institutional characteristics of the diploma itself (Grønning et al. , 2018). Even if equivalent and integrated at the national level, the FVETD indeed regroups approximately 250 training programmes, which differ in terms of content, teaching, gender concentration, and their educational and labour market prospects. These specific characteristics are further associated with distinct long-term occupational status mobility outcomes (Grønning and Kriesi, 2022). However, variations among individuals from different VET programs are still little known to date. We further aim to fulfil this gap by investigating the extent to which apprentices’ choice of specific FVETD occupations affects their subsequent SWT trajectories over the medium-term. Relying on data from the administrative LABB database (FSO, 2024a), this article analyses the SWT pathways of the 2012 FVETD graduates with a high level of detail, before focusing on the gender disparities characterizing these pathways. Drawing on the key mechanisms outlined above, this work addresses the following research questions. What are the SWT pathways of apprentices over the 7 years following their graduation and what differences exist between men and women in those pathways? To what extent is the heterogeneity of SWT related to the variability between the 250 FVETD occupations? 2.1. Can we relate this heterogeneity with the content of the educational programmes? What are the consequences of horizontal gender segregation on SWT pathways and what are the costs or benefits of undertaking gender atypical training? To address these research questions, we use the following analytical strategy. First, we rely on sequence analysis to create a typology of SWT pathways describing VET graduates’ subsequent professional and educational prospects over the medium-term using monthly data. Second, we use multilevel models to relate the FVETD training programmes, including their organization and gender concentration, with the subsequent SWT outcomes at a detailed level. Finally, we study the importance of horizontal gender segregation, before focusing on the consequences of completing gender-atypical training for subsequent SWT trajectories. This work is structured as follows. Section 2 outlines the characteristics of the Swiss VET system. Section 3 describes the current state of research on gender segregation and on its consequences following VET education. Section 4 presents the data and method used in this article. Section 5 presents the results of our analyses. Section 6 discusses these results, and finally, section 7 draws a conclusion for this contribution. 2. The Swiss Vocational Education and Training System The Swiss education system is characterized by a high degree of vocational specificity (Kerckhoff, 2001 ; Imdorf et al., 2010 ; Blossfeld, et.al., 2016 ). Overall, around two thirds of a school-leaver cohort enter initial vocational education and training (VET) at upper-secondary level after leaving compulsory schooling (SCCRE, 2014). Apprentices can choose from about 250 different school- or company-based VET programmes, which last from two to four years depending on the cognitive demands of the apprenticeship (Stalder and Nägele, 2011 ; SERI, 2024). Although this wide range of training, the 20 most popular occupations account for over 60% of all apprentices, most of whom are enrolled in programmes for commercial employees and retail specialists (Stalder and Nägele, 2011 ). Upon completion, apprentices are awarded a federal VET diploma (FVETD), which is thought to give direct access to skilled employment (Cortesi and Imdorf, 2013 ). There are significant variations across regions in the proportion of learners entering VET education. While in the French- and Italian-speaking regions this proportion is roughly 50%, in the German-speaking area it reaches about two thirds and over 70% in the more rural cantons (Glauser, 2015 ; FSO, 2023; Schmutz, 2023 ). Besides these socio-spatial disparities, the Swiss VET system is also known for its selective enrolment criteria. Together with gender, parental SES and migration background are important predictors of educational pathways, with pupils from a lower social origin more likely to enrol in basic VET programmes (Meyer, 2009 ; Kriesi and Schweri, 2019 ; Zimmermann and Seiler, 2019 ). An important feature of the Swiss VET system relates to the varying institutional characteristics of FVETD programmes (Grønning et.al., 2018 ; Grønning and Kriesi, 2022 ). In the most common dual-track regime , apprentices acquire occupation-specific skills at a host company while attending vocational schools during one or two days per week (Buchmann et al., 2016 ). In contrast, school-based VET programmes are provided for only some apprenticeships and are more common in the French- and Italian-speaking cantons of Switzerland (Imdorf et al., 2016 ). Apprenticeship programmes further differ in many dimensions, such as in the number of lessons attended by apprentices, whether general or occupation-specific lessons. These variations influence the acquired skills, which in turn affect their labour market transition (Grønning, et.al., 2018 ; Grønning and Kriesi, 2022 ). Highly occupation-specific apprenticeships tend to provide smoother transitions to employment and medium-term status stability. Training programmes focusing more on general VET education more often lead to higher education rather than employment, and facilitates medium-term upward status mobility (Grønning and Kriesi, 2022 ). 3. Current State of Research The following chapter reviews the current state of research on gender disparities and segregation in vocational education. Section 3.1 outlines the patterns of gender segregation in upper secondary education and within the Swiss VET system. Section 3.2 focuses on the roots of this gender segregation, while section 3.3 examines its consequences for later trajectories. 3.1 Gender segregation in upper secondary education and the Swiss VET-System Trajectories leading to upper-secondary education and the attended apprenticeship programmes are strongly gendered (Smyth and Steinmetz, 2015; Kriesi and Imdorf, 2019; Hupka-Brunner and Meyer, 2023). On the one hand, trajectories following lower-secondary school reflect gendered selection effects. Young women have a higher propensity to start academic education, but are underrepresented in vocational training (Imdorf and Hupka-Brunner, 2015). In 2011, about 51% of young women attended vocational training, while 29% attended an academic track (Gymnasium, Specialized Middle Schools). Among young men, 71 percent attended vocational training and only 20% an academic track (FSO, 2024b, own calculations). 1 On the other hand, we observe a strong horizontal gender segregation between apprenticeship programmes (Leemann and Keck, 2005; Becker and Glauser, 2015; Kriesi and Imdorf, 2019). While the proportion of young women and men is relatively balanced in a few apprenticeships (e.g., business and administration, wholesale and retail sales, hotel, restaurants and catering, etc.) most occupations are either strongly male-dominated (e.g., building and civil engineering, electricity and energy, motor vehicles, ships and aircraft, etc.) or female-dominated (e.g., social work and counselling, Nursing and midwifery; for details see FSO, 2024b). Moreover, men are trained in a larger number of different apprenticeship programmes, while women cluster in only a few occupations (Becker and Glauser, 2015). 3.2 At the Roots of Gender Segregation In most Swiss cantons, educational choices are made in early adolescence, a life stage which is often marked by the construction and the development of gender identity (Hupka-Brunner and Meyer, 2023). In this context, these choices often serve as a means of affirming an identity and signalling conformity to the established dominant gender norms (Vouillot, 2007). Gendered educational trajectories and vocational choices are therefore rooted in these gendered aspiration patterns (Basler et al ., 2021; Hupka-Brunner and Meyer, 2023). These processes partly explain why gender-atypical occupational choices are rather marginal in Switzerland (Gianettoni, 2011; Gianettoni and Guilley, 2015). According to Schwiter et al . (2014), about two thirds of 15-year-olds aspire to a gender-typical occupation as their future profession. These gendered educational choices are also intertwined in the stereotypes surrounding women’s or men’s skills and the own evaluation of those skills (Jann and Hupka-Brunner, 2020; Combet, 2024). The importance of traditional gender beliefs and past socialisation when evaluating the suitability of an educational choice according to one’s gender is also reflected in the fact that the long-term consequences of an education are often partially unknown (Kriesi and Imdorf, 2019). Other authors focus on the expected utility of an education to explain gendered choices. Women considering gender-atypical occupations anticipate greater difficulties in reconciling work and family responsibilities, making them more likely to avoid male-dominated fields or leave these fields early (Hupka-Brunner and Meyer, 2023). Similarly, young men still expect hurdles in reducing their employment rate to be actively involved in childcare. This is often associated with reduced career prospects in the Swiss labour market, in which about 50 percent of couples adopt the male-breadwinner model (Baumgarten et al., 2016; FSO, 2020; Heiniger and Imdorf, 2018; Schwiter et al., 2014). Furthermore, female-dominated occupations are often associated with a flatter earnings curve and more limited career opportunities, with a higher risk of not maintaining the social status of their parents (Becker and Glauser, 2015, Kriesi and Imdorf, 2019). 3.3 The school-to-work transition(s) after VET and the consequences of gender segregation Overall, VET-programmes provide a safety road to skilled employment (Müller and Shavit, 1998; Blossfeld, et.al ., 2016; Kriesi and Schweri, 2019), even if SWT has become more turbulent in recent years (Salvisberg and Sacchi, 2014). Müller and Schweri (2015) report that about 50% of dual VET graduates stayed in their training firm, while school-based graduates were less likely to be taken over by the company in which they completed their internship (Cahuc and Hervelin, 2024). Furthermore, approximately 90% of VET graduates stayed in the same occupation, while only 10% changed. While entering the labour market is the most common transition after VET, previous research emphasizes various other, less linear SWT. These transition patterns may include not being in education, employment or training (NEET), unstable labour market patterns, but also investments in further professional training or in higher education (Babel, 2018). Gendered educational and vocational choices greatly influence later life trajectories. In education systems with a high degree of vocational specificity, there is a tight link between gender segregation, attained education, the professional career and further training opportunities (Sacchi et al., 2016; Heiniger and Imdorf, 2018). According to Heiniger and Imdorf (2018), the gender concentration in VET is reinforced at the labour market entry in Switzerland, highlighting that initial occupational choices result in increased gender segregation in the subsequent professional career. In general, the type of trajectories followed by women are associated with lower social status attainment and salary, while men are overrepresented in advantageous SWT (Stalder, 2012; Zimmermann and Seiler, 2019). Lorentzen and Vogt (2022) also report highly gendered SWT in Norway, linking gender segregation in education with subsequent career opportunities. These authors further observe an income and unemployment penalty for men in female-dominated education compared to men who completed male-dominated education. Nevertheless, if compared to women in female-dominated occupations, men often reach higher positions (Hall, 2010). Similarly, women in male-dominated education do better with regards to employment and income than women who completed female-dominated education (Schwiter et al , 2014; Lorentzen and Vogt, 2022). Gender-atypical vocational choices are also associated with higher horizontal occupational mobility after graduation (Fritsch et al. , 2020). According to Bessey and Backes-Gellner (2015), male apprentices are more likely to leave a female-dominated training occupation than to upgrade within the occupation, while the authors find no evidence that a lack of social support increases the likelihood for being horizontally mobile in case of women in male-dominated occupations. Besides gendered labour market outcomes, gender differences are also found in further education after VET. Overall, men show a higher likelihood for further training than women, although women have caught up to men in recent years (SCCRE, 2014; FSO, 2024b). However, research on the subject is lacking (Trede and Kriesi, 2016) or relates to older cohorts only (Grossenbacher, 2000). Trajectories leading to further education at tertiary level depend on prior achievement. Access to universities of applied sciences (UAS) is granted to holders of a Federal Vocational Baccalaureate (FVB). Universities further require VET holders to obtain a higher education entrance qualification. As a result, this path is infrequent and highlights the low permeability between the VET system and academic education at tertiary level (Fazekas and Field, 2013; Kost, 2013). Meyer and Sacchi (2020) find that basic VET programmes, with restricted academic requirements, are negatively associated with later training. Sander and Kriesi (2019) report that the probability of starting professional VET (PVET) is higher for those who have completed horizontal undifferentiated VET programmes with a high proportion of school-based learning and highly standardized final examinations. Sander and Kriesi (2021) also find that women are less likely than men to take up professional VET, which is related to the fact that women are over-represented in occupations for which PVET is less common. These findings underline that, firstly, further training after VET is related to the type of apprenticeship completed. Second, that a higher amount of school-based learning, which is often associated with less occupation-specific education, improves the chances of entering higher vocational training. Thirdly, due to selection effects by gender into vocational and academic education at upper secondary level, women are more likely to enter higher education via an academic track, while men are more likely to do so via VET. 4. Data The empirical analyses draw on the LABB longitudinal database, developed in the Longitudinal Analyses in Education project of the Federal Statistical Office (FSO, 2024a). Started in 2011, LABB provides exhaustive and standardized administrative data to study the educational pathways of Swiss residents, from mandatory school to tertiary education. Our analyses focus on the cohort of apprentices who obtained a Federal VET Diploma (FVETD) in 2012 as their first upper-secondary graduation. This is the earliest cohort with complete trajectories. We then reconstructed the trajectories of every 2012’s graduate from September 2012 and followed over a period of 88 months, i.e., until 2019. We excluded the COVID-19 period to avoid the peculiarity of the pandemic. The LABB database has been merged with a complementary dataset produced by Grønning ( et al. , 2018) 2 , which provides detailed information on the specific characteristics of each FVETD educational programme. However, not all training programmes observed in LABB were described by this complimentary database. As a result, around 3’000 individuals (6% of the sample), who were enrolled in these VET trainings, have missing values and were excluded from the multivariate analysis. 3 The final sample is made of 49’899 apprentices (Table 1). Eight variables were selected for the analysis, according to the above discussion. To study the gender disparities in post-FVETD trajectories, sex of apprentices along with horizontal gendered segregation in FVETD programmes represent our main explanatory variables. In line with theory, we consider a training programme to be gendered if the gender concentration exceeds 70%, and mixed otherwise (Deutsch et al., 2005; Gianettoni et al., 2010; Imdorf, Sacchi, and Wohlgemuth, 2014; Imdorf and Hupka-Brunner, 2015). Several control variables at the individual level were included. We control for the age as it accounts for differences in timing, and the linguistic regions as they shape distinct educational and professional prospects (Scharenberg, et.al ., 2017; Babel, 2018). Migration background is also accounted for its strong influence on (lower-) secondary track allocation and thus the subsequent educational and professional perspectives. We further control for the following VET programmes’ characteristics. The teaching modality distinguishes between school-based and dual apprenticeships (Grønning and Kriesi, 2022). The total number of lessons given within a specific VET programme indirectly measure the focus on general education of the program. Table 1 : Descriptive statistics of the 2012 cohort Variables N = 49’899 Sex Men 28’829 (54%) Women 24’345 (46%) Cohort 25 3’608 (6.8%) Region Swiss-German 41’435 (78%) Swiss-French 10’195 (19%) Swiss-Italian 1’544 (2.9%) Migration status Swiss 43’150 (82%) Swiss born abroad 2’781 (5.3%) Foreigners born in Switzerland 3’467 (6.6%) Foreigners born abroad 3’447 (6.5%) (NA’s) 329 Gender segregation Female-dominated 13’834 (26%) Male-dominated 21’583 (41%) Mixed 17’757 (33%) Teaching modality Full-time 3’169 (6.0%) Dual 49’643 (94%) (NA’s) 362 Number of lessons 360-2’880 (range) (NA’s) 3,275 5. Methods We first used sequence analysis to create a typology of ideal-typical post-VET pathways, providing a holistic view on educational pathways—which are understood as a process rather than a later-life single point outcome. The typology is then used in multilevel regression models to study the link between apprentices’ sociodemographic features, VET programme characteristics and SWT outcomes. Eight states monthly characterize apprentices’ pathways. One “Employment” and four educational states, depicting “General secondary,” “Vocational secondary,” “General tertiary” and “Vocational tertiary.” Apprentices who are NEET ( not in education, nor in employment or training ) are distinguished in a “NEET: APG” state - corresponding to compulsory military service for men - and a “NEET” state—regrouping heterogeneous situations including unemployment, and more generally non-working situations without education, working outside of Switzerland, or receiving invalidity insurance benefits. (Babel, 2018 ). The final “other training” state groups all other educational states but is so infrequent that it is de facto invisible in the data. The typology of trajectories was obtained by first comparing all the trajectories to one another using the standard Optimal matching (OM), which is the most widely used distance measure in sequence analysis. OM takes into account the sequencing and duration of spells when comparing trajectories, which are two important characteristics of educational trajectories (Studer and Ritschard, 2016 ). In a second step, we used Partition Around Medoids (PAM) initialized with Ward hierarchical clustering to create the typology. This strategy improves the robustness and quality of clusters, by maximizing a global criterion while overcoming the sensitivity of PAM to the initial medoid selection (Kaufman and Rousseeuw, 1990 ; Studer, 2013 ). Finally, we aim to explain the typology of post-VET trajectories using multilevel regression models, with the specific FVETD programmes representing the grouping variable at level 2 and the explanatory variables described in Table 1 modelled at level 1. This strategy allows us to analyse individual post-VET trajectories while accounting for the residual differences among the 250 FVETD programmes. In these models, the variance of random effects informs us about the residual variability in post-VET pathways according to the specific FVETD programme once accounting for control variables. A high variance reveals that the chances of following a given post-VET path vary strongly between specific FVETD, whereas a low variance suggests weaker associations between post-VET pathways and the specific FVETD obtained. The statistical analyses conducted in this work were performed with R statistical software, using TraMineR (Gabadinho, et.al., 2011 ), WeightedCluster (Studer, 2013 ), fastcluster (Müllner, 2013 ) and lme4 (Bates, et.al., 2015 ) packages. 6. Results As a reminder, this study examines the school-to-work (SWT) trajectories after the obtaining of a federal VET diploma (FVETD) and focuses on the gender disparities that shape this critical process. This section presents our results as follows. First, we describe the typology of post-VET trajectories. In line with our research questions, we present 4 multilevel regression models. We then focus on the gender differences at the individual level, while accounting for the heterogeneity among FVETD occupations. We further examine the influence of educational programmes’ characteristics on SWT. We finally discuss the consequences of horizontal gendered segregation on SWT before addressing the implications of following gender-typical and—atypical FVETD programmes on subsequent trajectory. 6.1 The Typology of Post-VET Trajectories We first describe the SWT pathways of apprentices in Switzerland after completing a FVETD using sequence analysis. Figure 1 below introduces the seven types of post-VET trajectories identified using an Index-plot . In these plots, each individual trajectory is displayed by a coloured line according to the different states it occupies. The first type of trajectories is called “Employment” and describes a direct and stable transition into the labour market. It represents the reference path for the population as it is followed by almost half of all apprentices. This type was expected and clearly identified by the Swiss education system. Indeed, due to its qualifying features, the FVETD is widely recognized as the first diploma giving access to working life and skilled employment (Cortesi and Imdorf, 2013; Laganà and Babel, 2020). Our typology then highlights an “ Early vocational tertiary” and a “ Late vocational tertiary ” types that strongly differ in their timing. These pathways show that the transition to tertiary VET education can take place quickly after graduation, or after an employment spell of up to 4 years. More generally, they emphasize the relevance of a medium-term analysis for understanding educational pathways in Switzerland. In contrast, the “General Tertiary” type embodies the trajectories leading to general tertiary education through the obtaining of a Federal Vocational Baccalaureate (FVB) (shown in blue in Figure 1). The “ Reorientation ” type describes the trajectories of apprentices who pursue their education by staying within the upper-secondary VET, most often to obtain a second FVETD. Further analysis shows that in 80% of cases, this second training is pursued in a different vocational field from the first FVETD (Annexe 2). The last two types of trajectories are potentially the most problematic. The “ NEET ” type encompasses trajectories of young people who make a stable transition into the NEET ( Not in Employment, Education, or Training ) state, potentially leading to social exclusion and long-term adverse consequences. However, as previously mentioned, the NEET state regroups heterogeneous situations, as it overlaps with social insurance records and the absence of individuals from administrative registries. This absence may also include individuals who have left Switzerland without officially reporting their departure. Finally, the “ Unstable pattern” type is characterized by a predominance of employment with frequent back-and-forth between many other situations, particularly NEET states or non-qualifying training programmes as evidently too short. This type illustrates that a significant proportion of trajectories, around 20%, are nonlinear, increasing the complexity of post-VET trajectories overall. 6.2 The gender differences in post-VET transitions The typology outlined in Figure 1 allowed us to visualise and describe ideal-typical post-VET pathways over the 88 months following apprentices’ graduation. We now turn to the study of the gender differences underlying these pathways. Table 2 below shows the results of a multinomial multilevel logistic regression model. In this and the following models, we compare each type of post-VET pathway with the “Employment” type taken as reference. The coefficients are expressed on the log-odds scale and measure the chances of being in a type instead of the “Employment” one. In this first model, age, linguistic region and migration background were included as control variables. Table 2 : Gender differences in post-VET trajectories and the influence of specific FVETD programmes NEET Unstable pattern Reorientation Late vocational Tertiary Early vocational Tertiary General Tertiairy (Intercept) -2.42 *** -0.60 *** -2.20 *** -1.50 *** -1.39 *** -1.89 *** 21 - 25 0.38 *** -0.20 *** -1.21 *** -0.54 *** -0.48 *** -1.03 *** > 25 0.30 *** -0.84 *** -2.87 *** -1.45 *** -1.93 *** -2.85 *** Swiss-French 0.27 *** 0.08 * 0.43 *** -1.29 *** -0.93 *** -0.26 *** Swiss-Italian 0.84 *** 0.50 *** 0.53 *** -1.89 *** -0.47 *** -0.78 *** Swiss born abroad 0.54 *** 0.17 ** -0.52 *** 0.11 0.01 0.15 Foreigners born in Switzerland -0.18 * -0.10 * -0.64 *** -0.34 *** -0.22 ** -0.44 *** Foreigners born abroad 0.14 -0.14 ** -0.92 *** -0.42 *** -0.22 ** -0.39 *** Women -0.07 -0.35 *** -0.13 -0.66 *** -0.51 *** -0.83 *** BIC 16470.15 40305.71 12418.84 18250.66 22448.02 16871.72 Num. obs. 25235 32985 24667 25890 27426 26272 Num. FVETD Prog. 165 172 166 164 162 163 Random Intercept Variance 0.30 0.15 0.56 0.20 0.81 2.27 *** p < 0.001; ** p < 0.01; * p < 0.05 The results confirm that SWT trajectories are significantly different by gender. Women are generally less likely to pursue further education trajectories and more likely to follow the “Employment” type. Furthermore, they follow less often the “Unstable pattern.” More detailed analyses show that this difference can be explained by the military service spells, a predominantly male characteristic which cannot easily be associated with a form of social vulnerability. Interestingly, Swiss apprentices born abroad are found to be the most likely to experience “NEET” trajectories. This relationship aligns with the idea that a part of the NEET trajectories results from individuals leaving Switzerland without officially notifying the authorities. Foreigners cannot do the same as they would not be able to renew their residence permit under these conditions. Some of these gender differences in post-VET pathways are probably linked with the gendered FVETD occupations. As a reminder, there are more than 250 different FVETD occupations, each linked to a specific training programme and rooted in distinct labour market sectors. We account for this heterogeneity by including the specific FVETD occupations as a separate level of analysis and by interpreting the variance of random intercepts. 4 In general, the results highlight that trajectory types linked with pursuing education in the short term depend more closely on the specific FVETD obtained. This is highlighted by the comparatively high variance of the random intercepts, compared with the “Unstable pattern,” “Late Vocational Tertiary,” and “NEET” types. The complementary analyses in Annexe 3 compare the results with and without accounting for the specific FVETD programmes, highlighting that the gender differences for the “NEET” and “Reorientation” patterns are weaker in the former case. This means that the gender differences previously observed (Annexe 3) were in fact partly explained by the differences between specific FVETD occupations. In contrast, gender differences in Table 2 are reinforced when we look at the chances of pursuing tertiary education, meaning that women are even less likely to pursue these paths at equal FVETD obtained. Table 3 below presents the results of our second model, which aims at understanding the influence of these FVETD educational programmes’ characteristics on subsequent SWT trajectories. Indeed, taking the specific FVETD occupations as a level of analysis allows us to include explanatory variables measured at this level, such as the content of specific FVETD occupations (Grønning and Kriesi, 2022). Table 3 : The influence of FVETD programmes’ characteristics on post-VET trajectories NEET Unstable pattern Reorientation Late vocational Tertiary Early vocational Tertiary General Tertiairy (Intercept) -2.29 *** -0.50 *** -2.22 *** -1.46 *** -1.44 *** -1.78 *** Women -0.08 -0.35 *** -0.15 * -0.66 *** -0.50 *** -0.82 *** Full-time 0.83 *** 0.84 *** 0.87 *** 0.74 *** 1.15 *** 1.79 *** Number of lessons 0.12 * 0.13 ** -0.26 ** 0.24 *** 0.35 *** 0.82 *** Length >3 years -0.34 * -0.31 *** -0.13 0.02 0.25 0.03 Control variables: Age, Linguistic region, Migration status BIC 16435.34 40179.66 12411.39 18221.28 22303.65 16472.94 Num. obs. 25235 32985 24667 25890 27426 26272 Num. FVETD Prog. 165 172 166 164 162 163 Random Intercept Variance 0.23 0.10 0.46 0.12 0.55 1.18 *** p < 0.001; ** p < 0.01; * p < 0.05 We observe a marked reduction in the variance of the random intercepts for most types of trajectories, which implies that teaching modalities are important predictors of subsequent SWT trajectories. All else being equal, firm-based (dual) apprenticeships are more tightly linked to employment than full-time school-based apprenticeships, which in contrast lead more often to further education. Apprentices provided with full-time VET education are also more likely to experience a “Reorientation” pathway, probably due to their weaker link to the labour market. Indeed, when reorienting, almost 80% of apprentices undertake a FVETD occupation in another occupational field, which might be easier for apprentices who acquired more general skills. This tight link between the dual system and the labour market also explains why full-time school-based programmes are more strongly associated with the “Unstable pattern” and “NEET” types than with employment. Additionally, the results highlight that the greater the total number of lessons imparted within a training programme, the higher the likelihood of pursuing tertiary education, which is expected. A greater number of lessons also prevents apprentices from reorienting. It is also worth noting that the length of training programmes reduces the chances of experiencing potentially problematic pathways. Interestingly, however, the length of training programmes does not appear to significantly influence the likelihood of pursuing tertiary education trajectories, which are instead more closely associated with full-time school-based education and a higher total number of lessons. Nevertheless, it should be noted that these training programmes’ characteristics do not influence the structure of gender disparities previously described (Table 2). In other words, these characteristics partly explain the observed SWT outcomes, which, however, remain highly gendered. 6.3 Horizontal Gender Segregation and Its influence on Post-VET Trajectories We now focus on the consequences of horizontal gendered segregation among FVETD occupations. To this end, we rely on the gender concentration within each FVETD training programme. In line with theory, a specific FVETD training is gendered if the proportion of men or women exceeds 70%, and mixed otherwise (Kriesi and Imdorf, 2019). Our analytical strategy relying on multilevel regression models appropriately account for this dimension, which is measured at the level of the specific FVETD occupations. Table 4 below presents the results of our third estimation. Table 4 : The impact of horizontal gendered segregation on post-VET trajectories NEET Unstable pattern Re-orientation Late vocational tertiary Early vocational tertiary General Tertiairy (Intercept) -2.10 *** -0.34 *** -2.07 *** -1.58 *** -1.76 *** -1.56 *** Full-time 0.82 *** 0.84 *** 0.88 *** 0.74 *** 1.15 *** 1.78 *** Number of lessons 0.08 0.09 * -0.27 ** 0.24 *** 0.37 *** 0.72 *** Length >3 years -0.18 -0.20 * -0.21 0.06 0.32 0.37 Women -0.15 * -0.40 *** -0.11 -0.68 *** -0.51 *** -0.86 *** Female-dominated 0.10 0.03 -0.48 0.26 0.64 ** 0.50 Male-dominated -0.45 *** -0.34 *** -0.06 0.10 0.30 -0.75 ** Control variables: Age, Linguistic region, Migration status BIC 16437.53 40178.01 12426.98 18238.81 22317.55 16471.33 Num. obs. 25235 32985 24667 25890 27426 26272 Num. FVETD Prog. 165 172 166 164 162 163 Random Intercept Variance 0.17 0.07 0.43 0.11 0.50 0.94 *** p < 0.001; ** p < 0.01; * p < 0.05 As expected, these results confirm the tight linkage between male-dominated occupations and labour market access, while controlling for the effect of training programmes’ characteristics. Indeed, they provide a more direct access to employment, while being protected against potentially problematic “NEET” and “Unstable pattern ” types. Furthermore, male-dominated training is less oriented towards the academic “General Tertiary ” path than mixed trainings. In contrast, post-VET trajectories following female-dominated and mixed trainings are generally similar, except for the “Early Vocational Tertiary ” type. In this case, apprentices undertaking female-dominated trainings more often pursue this higher vocational pathway than apprentices coming from mixed trainings. More generally, these results point out a clear distinction between male-dominated occupations on the one hand, and female-dominated and mixed occupations on the other. The next section aims to understand whether the effect of gendered FVETD trainings is the same for men and women. 6.3.1 Post-VET Trajectories Following Gender-Atypical occupations Undertaking gender-typical occupations is the norm within the Swiss educational system (Gianettoni et al , 2010; Kriesi and Imdorf, 2019). In this section, we focus on the specific consequences for the subsequent SWT pathways of evading the gender norms by enrolling in either gender-typical or gender-atypical occupations. Table 5 below presents the results of our last model. Table 5 : The consequences of undertaking gender-typical and gender-atypical trainings on SWT NEET Unstable pattern Reorientation Late vocational tertiary Early vocational tertiary General Tertiairy (Intercept) -2.01 *** -0.28 *** -2.08 *** -1.56 *** -1.71 *** -1.53 *** Women -0.31 *** -0.49 *** -0.10 -0.72 *** -0.61 *** -0.93 *** Female-dominated 0.19 -0.01 0.06 0.14 0.45 0.43 Male-dominated -0.60 *** -0.42 *** -0.07 0.08 0.25 -0.81 ** Women: female-dominated -0.03 0.08 -0.60 * 0.15 0.26 0.12 Women: Male-dominated 0.82 *** 0.42 *** 0.12 0.06 0.18 0.41 ** Control variables: Age, Linguistic region, Migration status, Teaching modality, Number of lessons, Duration of apprenticeships BIC 16420.07 40175.72 12440.34 18258.09 22333.72 16484.76 Num. obs. 25235 32985 24667 25890 27426 26272 Num. FVETD Prog. 165 172 166 164 162 163 Random Intercept Variance 0.16 0.07 0.44 0.11 0.50 0.92 *** p < 0.001; ** p < 0.01; * p < 0.05 In general, the results show that gender differences and their extent depend on gender concentration within FVETD occupations. The differences between men and women following mixed or female-dominated programmes are more pronounced than what we previously observed in Table 4. Indeed, women in these contexts are more likely than men to transition into Employment overall, making them less likely to experience “NEET,” “Unstable pattern” and further education types. The gender differences take another form in male-dominated trainings, where women are more likely than men to experience the “NEET” pattern. Furthermore, no differences are found for the “Unstable pattern” type, even if men were expected to follow it more often because of military service duties. Finally, we observe a significantly lower difference in the chance to follow the “General Tertiary” pathway. While women are still less likely than men to pursue general higher education, these gender differences are smaller in male-dominated occupations than in gender-mixed or in female-dominated trainings. Focusing on gender segregation thereby provides a complimentary view on the interaction. While the main advantage of male-dominated programmes is their protection against the potentially problematic “NEET” and “Unstable pattern” types, this protection does not apply to women, which, in contrast, have better prospects for further general tertiary education than women in the other fields. 7. Discussion We first aimed to describe the SWT pathways following an apprenticeship in Switzerland. The Swiss educational system is characterized by the predominance of initial vocational education at the upper-secondary level, a feature often praised to provide a safe road to skilled employment (Müller and Shavit, 1998 ; Blossfeld et al., 2016 ; Kriesi and Schweri, 2019 ). Overall, our results confirm the successful labour market integration for most apprentices, with around 80% of them into employment seven years and a half after their graduation. However, our typology also emphasizes a great diversity of post-FVETD pathways over the medium term. Previous studies linked this complexity with the growing transition risks for many VET graduates (Stalder, 2012; Salvisberg and Sacchi, 2014 ; Babel, 2018 ). In line with these findings, our study confirms that the trajectories following VET education are far from being simple, smooth, and uniform. A significant proportion of apprentices get trapped in potentially problematic transitions dominated by NEET status, or experience an unstable pattern characterized by frequent back-and-forth between many different statuses, without finding stable jobs. One apprentice out to four ends up in these situations, highlighting the importance of this phenomenon, and thereby the urge for policies that support their labour market integration. The diversity of post-FVETD pathways is also linked to the growing diversity of higher education opportunities for VET holders following the early 1990s reforms of the VET system (Stalder and Nägele, 2011 ; Imdorf et al., 2017 ). While enrolment in tertiary education has historically been low in Switzerland, and especially among apprentices, the proportion of VET graduates pursuing tertiary education has increased to approximately one third in recent years (Babel, 2018 ; Meyer, 2019 ). However, a substantial heterogeneity is also observed among those pursuing higher education. On the one hand, accessing general tertiary education requires a Federal Vocational Baccalaureate, which is a highly selective qualification most often targeted to the VET elite (Cortesi and Imdorf, 2013 ). On the other hand, our typology highlighted two types of pathways into vocational tertiary education differing according to their timing, with a transition that can take place even after 4 years. This further underlines the importance to study SWT as a complex process that occurs over a prolonged period, which is one of the strengths of this work. Furthermore, as already pointed out by Babel ( 2018 ), the post-FVETD transitions are also characterized by the importance of nonlinear trajectories. Our results show that most apprentices experienced at least two status shifts within the seven and a half years following their graduation. This also holds for a significant proportion of apprentices in the “Employment” cluster, which is thought to be the most linear pathway overall. Furthermore, many apprentices experienced a reorientation within the upper-secondary level after obtaining their first FVETD. As a result, only about 20% of the cohort experienced an ideal-typical linear transition to employment. This study points to the importance of gender, showing that women in VET face greater challenges in their SWT than men. Overall, women enter more often into employment, while men are more likely to pursue higher education. Such proximity to employment is not only indicative of a limitation in SWT prospects for women. Indeed, as highlighted by previous research, once in employment, and long before the onset of family formation, a persistent gender wage gap is observed (Combet and Oesch, 2019 ; Zimmermann and Seiler, 2019 , Hupka-Brunner and Meyer, 2023 ). By distinguishing the individual and the group levels of analysis, we highlight that gender differences in SWT are intertwined with the strong horizontal gender segregation characterising the Swiss VET system. The specific FVETD occupation has only a slight influence on the gender differences in SWT trajectories, implying that apprentices’ allocation to a specific FVETD training programme is not the key determinant of the observed gender differences in SWT pathways. .Although evidence regarding the impact of horizontal gender segregation on subsequent SWT are still quite scarce, previous research has highlighted distinct educational and labour market prospects following male-dominated, female-dominated, or gender-mixed occupations (Kriesi and Imdorf, 2019 ; Lorentzen and Vogt, 2022 ; Hupka-Brunner and Meyer, 2023 ). In line with these insights, our work confirms the importance of horizontal gender segregation on medium-term SWT patterns. Male-dominated occupations are more tightly linked with the labour market than female-dominated and mixed occupations, while also being protected against potentially problematic “NEET” and “Unstable pattern” types. In this context, it was argued that women may have greater incentives to enrol in male-dominated trainings to benefit from their better career opportunities (Kriesi and Imdorf, 2019 ). Our analyses suggest that this is only partially true. On the one hand, the protection given by male-dominated trainings against potentially problematic trajectories does not apply to women. As a result, women in male-dominated trainings have less close link to the labour market than men and are potentially more vulnerable. This also suggests the need for a better knowledge on the systematic discrimination in employers’ recruitment practices, which may contribute to the lower likelihood of women to be taken on by a firm (Imdorf, 2012, 2016 ). On the other hand, however, women in male-dominated VET occupations benefit from a closer link with general tertiary education than women graduating in female-dominated or gender mixed occupations. This finding provides more detailed insights on the specific consequences of women undertaking gender-atypical occupations. This work shall also contribute to the existing knowledge on the mechanisms driving social inequalities in Switzerland from a holistic view on educational trajectories. The Swiss labour market is highly segmented, and access to occupations in its subsegments generally depends on prior educational credentials (Sacchi et al, 2016 ). In this context, the relatively high horizontal gender segregation within the Swiss labour market has been linked to the predominance of Swiss VET, in which the pattern of attended apprenticeships is also strongly gendered (Heiniger and Imdorf, 2018 ). By reconstructing apprentices’ SWT over the medium term, this work provides important insights on the path-dependencies between VET education, its strong gender segregation, and later trajectories, which may lead to the very different labour market outcomes substantiated by previous research (Bertschy, 2016; Combet and Oesch, 2019 ; Kriesi and Imdorf, 2019 , Lorentzen and Vogt, 2022 ; Hupka-Brunner and Meyer, 2023 ). Indeed, our analysis suggests that apprentices’ allocation to a gendered occupation acts as an institutional channelling mechanism that sets them on a diversity of SWT paths. This study also explored whether the observed heterogeneity in SWT can be related to the content of the FVETD educational programmes. In line with previous findings, our work shows that accounting for the heterogeneity in the content of educational programmes is important to explain the structuring of subsequent SWT trajectories (Grønning and Kriesi, 2022 ). Our results highlight that apprenticeship organization is a key factor influencing the SWT, with firm-based apprenticeships more tightly linked to the labour market. In contrast, full-time school-based apprenticeships seem to encounter greater difficulties in finding a skilled job, which are comparatively more likely to experience NEET and unstable trajectories. Apprentices in school-based VET programmes only complete a traineeship in a company in the last year of training, which may explain the fact that they are less likely to show a stable labour market pattern. However, apprentices in school-based VET programmes nonetheless attend higher education pathways more often, which might counteract the negative effects of their lower propensity to be taken over by a training firm. Interestingly, although shorter apprenticeships are in general intellectually less demanding (Stalder and Nägele, 2011 ), our results suggest that the length of training programmes does not have a great impact on subsequent SWT. Furthermore, our analyses show that training programmes imparting a high number of lessons are closely related to higher education, which is expected given that more comprehensive programmes are generally more cognitively demanding. These conclusions support the idea that the institutional features of apprenticeship programmes shape the skills acquired by apprentices, which then structure the type of SWT they will experience (Grønning et al., 2018 ; Grønning and Kriesi, 2022 ). However, while previous studies focused on how these variations among VET programs affect subsequent labour market outcomes, such as occupation mobility, skill mismatch or unemployment (Grønning et al., 2018 ; Grønning et al., 2020 ; Grønning and Kriesi, 2022 ), this work explored their association with later SWT pathways , thus not limiting to labour market outcomes per se at a specific time point. 8. Conclusion Drawing on data from the LABB administrative database, this article shed new light on the most recurrent types of post-FVETD trajectories, before focusing on the gendered processes that affect these pathways. Relying on sequence analysis of educational and labour market trajectories, we followed the 2012 VET graduates over more than 7 years, which represents one of the main strengths of this work. It provides detailed knowledge on the school-to-work transition following an apprenticeship in Switzerland. Our analyses show a great diversity in SWT pathways. This diversity highlights that this SWT is not always fast, fluid and linear, as it is often claimed to be. Our results rather point to the frequency of unstable patterns, NEET trajectories and vocational reorientations for the leavers of the VET system. In sum, our results further highlight that for many apprentices the SWT is still not achieved 7 years following their graduation. Gender feature among the key factors affecting the post-FVETD trajectories, highlighting distinct transitional patterns for men and women. These gender differences are only partly due to the specific FVETD occupations undertaken. Thus, our results show that men and women experience distinct subsequent SWT trajectories at equal FVETD obtained. We further studied the consequences of horizontal gender segregation on SWT. While male-dominated occupations benefit from a closer link with the labour market and prevent them from experiencing potentially problematic SWT patterns, this does not hold for women in these sectors. As a result, the consequences of enrolling in gender-atypical occupations are very different for men and women. This article faces at least two limitations. First, we were not able to account for two potential confounders: social origin, which is typically measured by parents’ highest educational level, and the lower-secondary track attended by apprentices. Integrating social origin using the highest educational level of parents would have reduced the sample size to about 5’000 apprentices. Such reduced sample size would have made it impossible to study the specific FVETD occupation at a detailed level. Lower-secondary track allocation is not available for the 2021 cohort. Second, we focused on labour market and educational statuses. However, further studies are required to evaluate the quality of these statuses, for instance by looking at wages or employment rates. Although our cohort of apprentices is followed at a very precise level over more than seven years, the data does not enable us to directly link the gender segregation in VET to more specific labour market outcomes. Declarations Availability of data and materials The data supporting the findings of this study is available from the Federal Statistical Office. The data is not publicly available, but available on request on a per project basis from the Federal Statistical Office. The code is available from the authors on request. Competing interests The authors declare no competing interests. Funding and acknowledgements The authors gracefully acknowledge the support received from the Swiss National Science Foundation (project “Strengthening Sequence Analysis”, grant No.: 10001A_204740). Authors’ contributions ML and JM took care of the statistical analyses, including data management and coding, sequence analysis and multilevel regressions. ML wrote the first draft of the article, based on previous work by JM. MS supervised the study, designed the methodological framework, and contributed to the writing of the article. DG contributed to the state-of-the-art section and reviewed the whole article. Use of Large Language Models (LLMs) This work benefited from the use of Large Language Models (LLMs) for two purposes. Firstly, during the data handling process, LLMs were used to identify solutions associated with R software error messages. Secondly, LLMs were integrated into the writing process to improve the clarity of the text and to translate words in context. Although LLMs have been helpful for these purposes, it should be noted that their use in this work was kept under severe control for several reasons. 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(2014) Why Are Male Care Workers and Female Electricians still Rare? Gender Segregation in the Educational and Vocational Pathways of Young Adults in Switzerland. Swiss Journal of Sociology 40(3): 401-428. DOI: 10.5167/uzh-101134. SERI [State Secretariat for Education, Research and Innovation] (2024) Swiss VPET System – A Brief Guide. Smyth E and Steinmetz S (2015) Vocational Training and Gender Segregation Across Europe. Gender Segregation in Vocational Education . Emerald Group Publishing Limited, pp.53-81. DOI: 10.1108/S0195-631020150000031003. Stalder BE (2012) School-to-work transitions in apprenticeship-based VET systems. The swiss approach. In: Billett S, Johnson G, Thomas S, et al. (eds) Experience of school transitions. Policies, practice and participants . New York: Springer, pp.123-139. Stalder BE and Nägele C (2011) Formation professionnelle en Suisse: Organisation, développement et défis pour le futur. In: Bergman, Manfred Max; Hupka-Brunner, Sandra; Keller, Anita; Meyer, Thomas; Stalder, Barbara E. (éds.) Transitionen im Jugendalter. Ergebnisse der Schweizer Längsschnittstudie TREE = Transitions juvéniles en Suisse. Resultats de l’étude longitudinale TREE = Youth transitions in Switzerland. Results from the TREE panel study Vol. 1 Zürich: Seismo. URL: https://boris.unibe.ch/131094/, pp. 18-39. Studer M (2013) WeightedCluster Library Manual: A practical guide to creating typologies of trajectories in the social sciences with R. LIVES Working Papers 24. DOI: 10.12682/lives.2296-1658.2013.24. Studer M and Ritschard G (2016) What matters in differences between life trajectories: A comparative review of sequence dissimilarity measures. Journal of the Royal Statistical Society Series A: Statistics in Society 179(2): 481–511. Trede I and Kriesi I (2016) Übergang in die höhere Berufsbildung im Gesundheitsbereich: Die Rolle von Geschlecht und Migrationshintergrund. In: Kriesi I, Liebig B, Horwath I, et al. (eds) Gender und Migration in der tertiären Hochschulbildung . Münster: Westfälisches Dampfboot, pp.102–122. Vouillot, F (2007) L'orientation aux prises avec le genre. Travail, genre et sociétés, 18(2), 87-108. Zimmermann B and Seiler S (2019) The Relationship between Educational Pathways and Occupational Outcomes at the Intersection of Gender and Social Origin. Social Inclusion 7(3): 79-94. DOI: 10.17645/si.v7i3.2035. Footnotes There are also significant differences between the language regions regarding the proportion of school leavers in VET and academic tracks (Glauser, 2015 ; Leemann et al., 2022 ). Hupka-Brunner (2003) shows for the first TREE cohort that the proportion of school leavers who commence an academic track is considerably higher in the Italian- and French-speaking cantons than in the German-speaking cantons. As in the case of VET, there is substantial cantonal variation of the proportion of persons attaining a higher education entrance qualification. In the French- and Italian-speaking part of Switzerland, the proportion was around 43 percent in 2013, while in the German-speaking cantons it was around a third (see FSO, 2024b). We sincerely thank the authors for providing this valuable database that enriched this work. However, all the analyses were also estimated on the full sample without the additional information without any noticeable difference in the results (see Annexe 4). In our multilevel models, the specific FVETD occupations are treated as random effects and constitute the level 2 of analysis. A random intercept is thus estimated for each of the FVETD occupations included. See Gelman and Hill (2007) for a comprehensive understanding of multilevel modelling. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":1011737,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTypology of post-VET trajectories\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8137537/v1/f79c330f4cc0fdf95e3f701b.jpeg"},{"id":97135592,"identity":"83e7f051-56f5-4666-b315-cf476bbd8b6a","added_by":"auto","created_at":"2025-12-01 09:51:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2366779,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8137537/v1/4f672368-59fe-4e9d-b7e1-1a7caba054e7.pdf"},{"id":96925359,"identity":"9a73330a-6a94-4a1f-a4bf-80ff5278ba60","added_by":"auto","created_at":"2025-11-27 14:24:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":352157,"visible":true,"origin":"","legend":"","description":"","filename":"Annex.docx","url":"https://assets-eu.researchsquare.com/files/rs-8137537/v1/c830ccca9e7a52fa77662eda.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"From School to Work: Gender Inequalities and Segregation Following an Apprenticeship in Switzerland","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDespite growing political concern, the Swiss educational system has remained one of the most highly gendered in international comparison over the past decades (Buchmann and Kriesi, 2009; Imdorf and Hupka-Brunner, 2015). This situation is related to a two-step gendered selection effect. First, at the transition from lower- to upper-secondary level, women are more likely to pursue an academic track, whereas men tend to enrol more frequently into initial vocational education and training (VET). Second, within the Swiss VET system, most apprenticeships are gender-typed (Leemann and Keck, 2005; Becker and Glauser, 2015; Kriesi and Imdorf, 2019). As a result, men more often undertake technical and manual training programmes, while women are over-represented in health, social care and service occupations (Becker and Glauser, 2015; Kriesi and Imdorf, 2019). This strong \u003cem\u003ehorizontal gender segregation\u0026nbsp;\u003c/em\u003eand its consequences for the structuring of gendered pathways following VET education represents the core of the present work.\u003c/p\u003e\n\u003cp\u003eStudying horizontal gender segregation within the VET system is important, as it may represent a key step in the reproduction of the horizontal and vertical occupational segregation in the Swiss labour market for several reasons. \u003cem\u003eFirst\u003c/em\u003e, the Swiss educational system is historically rooted in VET, which represents the most common post-compulsory educational pathway (Cortesi and Imdorf, 2013). Upon completion, the great majority of the VET graduates are awarded a federal VET diploma (FVETD) following one of its 250 training programmes, which is thought to give a direct access to skilled employment (Stalder and N\u0026auml;gele, 2011; Cortesi and Imdorf, 2013). \u003cem\u003eSecond\u003c/em\u003e, there is a tight linkage between VET occupations and the Swiss labour market. Consequently, as they determine subsequent economic sectors and further education opportunities, VET occupations are strongly associated with specific future gendered occupational prospects (Kriesi and Imdorf, 2019; Gr\u0026oslash;nning and Kriesi, 2022). \u003cem\u003eThird\u003c/em\u003e, male-dominated VET occupations tend to be more rewarding than the ones following female-dominated and gender-mixed training programmes (Kriesi and Imdorf, 2019; Korber and Oesch, 2019, Gr\u0026oslash;nning \u003cem\u003eet al.\u003c/em\u003e, 2020).\u003c/p\u003e\n\u003cp\u003eThis article focuses on the school-to-work transition (SWT) of young FVETD holders and the gendered processes that shape this crucial and vulnerable phase. The SWT refers to the transition period between the end of full-time education and the stable entry into employment (Schoon and Silbereisen, 2009). In this context, VET has often been credited for providing fast, fluid and linear transitions to employment because of its high level of occupational specificity and its close link to the labour market (Kriesi and Schweri, 2019). However, the VET system also hides a great diversity in subsequent SWT trajectories (Babel, 2018), which is expected for at least three reasons. \u003cem\u003eFirst\u003c/em\u003e, SWT has become increasingly diversified, less linear and more de-standardized in most European countries over the last decades (Buchmann and Kriesi, 2011; Brzinsky-Fay and Solga, 2016). \u003cem\u003eSecond\u003c/em\u003e, many apprentices encounter increasing difficulties in their STW, experiencing NEET (\u003cem\u003eNot in Education, Employment nor Training\u003c/em\u003e) states or unemployment (Stalder, 2012; Salvisberg and Sacchi, 2014; Babel, 2018). \u003cem\u003eThird\u003c/em\u003e, since the early 1990s VET reforms, there is a growing diversity of higher education opportunities following VET education (Stalder and N\u0026auml;gele, 2011; Imdorf \u003cem\u003eet al.\u003c/em\u003e, 2017). Hence, for an increasing number of graduates, VET represents only the first step in their educational trajectory before entering tertiary education or reorienting.\u003c/p\u003e\n\u003cp\u003eThe diversity in post-FVETD pathways may also stem from the institutional characteristics of the diploma itself (Gr\u0026oslash;nning \u003cem\u003eet al.\u003c/em\u003e, 2018). Even if equivalent and integrated at the national level, the FVETD indeed regroups approximately 250 training programmes, which differ in terms of content, teaching, gender concentration, and their educational and labour market prospects. These specific characteristics are further associated with distinct long-term occupational status mobility outcomes (Gr\u0026oslash;nning and Kriesi, 2022). However, variations among individuals from different VET programs are still little known to date. We further aim to fulfil this gap by investigating the extent to which apprentices\u0026rsquo; choice of specific FVETD occupations affects their subsequent SWT trajectories over the medium-term.\u003c/p\u003e\n\u003cp\u003eRelying on data from the administrative LABB database (FSO, 2024a), this article analyses the SWT pathways of the 2012 FVETD graduates with a high level of detail, before focusing on the gender disparities characterizing these pathways. Drawing on the key mechanisms outlined above, this work addresses the following research questions.\u003c/p\u003e\n\u003col class=\"decimal_type\"\u003e\n \u003cli\u003eWhat are the SWT pathways of apprentices over the 7 years following their graduation and what differences exist between men and women in those pathways?\u003c/li\u003e\n \u003cli\u003eTo what extent is the heterogeneity of SWT related to the variability between the 250 FVETD occupations?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e2.1. Can we relate this heterogeneity with the content of the educational programmes?\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003eWhat are the consequences of horizontal gender segregation on SWT pathways and what are the costs or benefits of undertaking gender atypical training?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo address these research questions, we use the following analytical strategy. First, we rely on sequence analysis to create a typology of SWT pathways describing VET graduates\u0026rsquo; subsequent professional and educational prospects over the medium-term using monthly data. Second, we use multilevel models to relate the FVETD training programmes, including their organization and gender concentration, with the subsequent SWT outcomes at a detailed level. Finally, we study the importance of horizontal gender segregation, before focusing on the consequences of completing gender-atypical training for subsequent SWT trajectories.\u003c/p\u003e\n\u003cp\u003eThis work is structured as follows. Section\u0026nbsp;2 outlines the characteristics of the Swiss VET system. Section\u0026nbsp;3 describes the current state of research on gender segregation and on its consequences following VET education. Section\u0026nbsp;4 presents the data and method used in this article. Section\u0026nbsp;5 presents the results of our analyses. Section\u0026nbsp;6 discusses these results, and finally, section\u0026nbsp;7 draws a conclusion for this contribution.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"2. The Swiss Vocational Education and Training System","content":"\u003cp\u003eThe Swiss education system is characterized by a high degree of vocational specificity (Kerckhoff, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Imdorf et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Blossfeld, et.al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Overall, around two thirds of a school-leaver cohort enter initial vocational education and training (VET) at upper-secondary level after leaving compulsory schooling (SCCRE, 2014). Apprentices can choose from about 250 different school- or company-based VET programmes, which last from two to four years depending on the cognitive demands of the apprenticeship (Stalder and N\u0026auml;gele, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; SERI, 2024). Although this wide range of training, the 20 most popular occupations account for over 60% of all apprentices, most of whom are enrolled in programmes for commercial employees and retail specialists (Stalder and N\u0026auml;gele, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Upon completion, apprentices are awarded a federal VET diploma (FVETD), which is thought to give direct access to skilled employment (Cortesi and Imdorf, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThere are significant variations across regions in the proportion of learners entering VET education. While in the French- and Italian-speaking regions this proportion is roughly 50%, in the German-speaking area it reaches about two thirds and over 70% in the more rural cantons (Glauser, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; FSO, 2023; Schmutz, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Besides these socio-spatial disparities, the Swiss VET system is also known for its selective enrolment criteria. Together with gender, parental SES and migration background are important predictors of educational pathways, with pupils from a lower social origin more likely to enrol in basic VET programmes (Meyer, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kriesi and Schweri, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zimmermann and Seiler, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAn important feature of the Swiss VET system relates to the varying institutional characteristics of FVETD programmes (Gr\u0026oslash;nning et.al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gr\u0026oslash;nning and Kriesi, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the most common \u003cem\u003edual-track regime\u003c/em\u003e, apprentices acquire occupation-specific skills at a host company while attending vocational schools during one or two days per week (Buchmann et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In contrast, \u003cem\u003eschool-based VET programmes\u003c/em\u003e are provided for only some apprenticeships and are more common in the French- and Italian-speaking cantons of Switzerland (Imdorf et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Apprenticeship programmes further differ in many dimensions, such as in the \u003cem\u003enumber of lessons attended\u003c/em\u003e by apprentices, whether general or occupation-specific lessons. These variations influence the acquired skills, which in turn affect their labour market transition (Gr\u0026oslash;nning, et.al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gr\u0026oslash;nning and Kriesi, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Highly occupation-specific apprenticeships tend to provide smoother transitions to employment and medium-term status stability. Training programmes focusing more on general VET education more often lead to higher education rather than employment, and facilitates medium-term upward status mobility (Gr\u0026oslash;nning and Kriesi, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. Current State of Research","content":"\u003cp\u003eThe following chapter reviews the current state of research on gender disparities and segregation in vocational education. Section\u0026nbsp;3.1 outlines the patterns of gender segregation in upper secondary education and within the Swiss VET system. Section\u0026nbsp;3.2 focuses on the roots of this gender segregation, while section\u0026nbsp;3.3 examines its consequences for later trajectories.\u003c/p\u003e\n\u003ch2\u003e3.1 Gender segregation in upper secondary education and the Swiss VET-System\u003c/h2\u003e\n\u003cp\u003eTrajectories leading to upper-secondary education and the attended apprenticeship programmes are strongly gendered (Smyth and Steinmetz, 2015; Kriesi and Imdorf, 2019; Hupka-Brunner and Meyer, 2023).\u003c/p\u003e\n\u003cp\u003eOn the one hand, trajectories following lower-secondary school reflect gendered selection effects. Young women have a higher propensity to start academic education, but are underrepresented in vocational training (Imdorf and Hupka-Brunner, 2015). In 2011, about 51% of young women attended vocational training, while 29% attended an academic track (Gymnasium, Specialized Middle Schools). Among young men, 71 percent attended vocational training and only 20% an academic track (FSO, 2024b, own calculations).\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOn the other hand, we observe a strong horizontal gender segregation between apprenticeship programmes (Leemann and Keck, 2005; Becker and Glauser, 2015; Kriesi and Imdorf, 2019). While the proportion of young women and men is relatively balanced in a few apprenticeships (e.g., business and administration, wholesale and retail sales, hotel, restaurants and catering, etc.) most occupations are either strongly male-dominated (e.g., building and civil engineering, electricity and energy, motor vehicles, ships and aircraft, etc.) or female-dominated (e.g., social work and counselling, Nursing and midwifery; for details see FSO, 2024b). Moreover, men are trained in a larger number of different apprenticeship programmes, while women cluster in only a few occupations (Becker and Glauser, 2015).\u003c/p\u003e\n\u003ch2\u003e3.2 At the Roots of Gender Segregation\u003c/h2\u003e\n\u003cp\u003eIn most Swiss cantons, educational choices are made in early adolescence, a life stage which is often marked by the construction and the development of gender identity (Hupka-Brunner and Meyer, 2023). In this context, these choices often serve as a means of affirming an identity and signalling conformity to the established dominant gender norms (Vouillot, 2007). Gendered educational trajectories and vocational choices are therefore rooted in these gendered aspiration patterns (Basler \u003cem\u003eet al\u003c/em\u003e., 2021; Hupka-Brunner and Meyer, 2023). These processes partly explain why gender-atypical occupational choices are rather marginal in Switzerland (Gianettoni, 2011; Gianettoni and Guilley, 2015). According to Schwiter \u003cem\u003eet al\u003c/em\u003e. (2014), about two thirds of 15-year-olds aspire to a gender-typical occupation as their future profession. These gendered educational choices are also intertwined in the stereotypes surrounding women\u0026rsquo;s or men\u0026rsquo;s skills and the own evaluation of those skills (Jann and Hupka-Brunner, 2020; Combet, 2024). The importance of traditional gender beliefs and past socialisation when evaluating the suitability of an educational choice according to one\u0026rsquo;s gender is also reflected in the fact that the long-term consequences of an education are often partially unknown (Kriesi and Imdorf, 2019).\u003c/p\u003e\n\u003cp\u003eOther authors focus on the expected utility of an education to explain gendered choices. Women considering gender-atypical occupations anticipate greater difficulties in reconciling work and family responsibilities, making them more likely to avoid male-dominated fields or leave these fields early (Hupka-Brunner and Meyer, 2023). Similarly, young men still expect hurdles in reducing their employment rate to be actively involved in childcare. This is often associated with reduced career prospects in the Swiss labour market, in which about 50 percent of couples adopt the male-breadwinner model (Baumgarten \u003cem\u003eet al.,\u003c/em\u003e 2016; FSO, 2020; Heiniger and Imdorf, 2018; Schwiter \u003cem\u003eet al.,\u003c/em\u003e 2014). Furthermore, female-dominated occupations are often associated with a flatter earnings curve and more limited career opportunities, with a higher risk of not maintaining the social status of their parents (Becker and Glauser, 2015, Kriesi and Imdorf, 2019).\u003c/p\u003e\n\u003ch2 id=\"_Toc187754951\"\u003e3.3 The school-to-work transition(s) after VET and the consequences of gender segregation\u003c/h2\u003e\n\u003cp\u003eOverall, VET-programmes provide a safety road to skilled employment (M\u0026uuml;ller and Shavit, 1998; Blossfeld, \u003cem\u003eet.al\u003c/em\u003e., 2016; Kriesi and Schweri, 2019), even if SWT has become more turbulent in recent years (Salvisberg and Sacchi, 2014). M\u0026uuml;ller and Schweri (2015) report that about 50% of dual VET graduates stayed in their training firm, while school-based graduates were less likely to be taken over by the company in which they completed their internship (Cahuc and Hervelin, 2024). Furthermore, approximately 90% of VET graduates stayed in the same occupation, while only 10% changed. While entering the labour market is the most common transition after VET, previous research emphasizes various other, less linear SWT. These transition patterns may include not being in education, employment or training (NEET), unstable labour market patterns, but also investments in further professional training or in higher education (Babel, 2018).\u003c/p\u003e\n\u003cp\u003eGendered educational and vocational choices greatly influence later life trajectories. In education systems with a high degree of vocational specificity, there is a tight link between gender segregation, attained education, the professional career and further training opportunities (Sacchi \u003cem\u003eet al.,\u003c/em\u003e 2016; Heiniger and Imdorf, 2018). According to Heiniger and Imdorf (2018), the gender concentration in VET is reinforced at the labour market entry in Switzerland, highlighting that initial occupational choices result in increased gender segregation in the subsequent professional career. In general, the type of trajectories followed by women are associated with lower social status attainment and salary, while men are overrepresented in advantageous SWT (Stalder, 2012; Zimmermann and Seiler, 2019). Lorentzen and Vogt (2022) also report highly gendered SWT in Norway, linking gender segregation in education with subsequent career opportunities. These authors further observe an income and unemployment penalty for men in female-dominated education compared to men who completed male-dominated education. Nevertheless, if compared to women in female-dominated occupations, men often reach higher positions (Hall, 2010). Similarly, women in male-dominated education do better with regards to employment and income than women who completed female-dominated education (Schwiter \u003cem\u003eet al\u003c/em\u003e, 2014; Lorentzen and Vogt, 2022).\u003c/p\u003e\n\u003cp\u003eGender-atypical vocational choices are also associated with higher horizontal occupational mobility after graduation (Fritsch \u003cem\u003eet al.\u003c/em\u003e, 2020). According to Bessey and Backes-Gellner (2015), male apprentices are more likely to leave a female-dominated training occupation than to upgrade within the occupation, while the authors find no evidence that a lack of social support increases the likelihood for being horizontally mobile in case of women in male-dominated occupations.\u003c/p\u003e\n\u003cp\u003eBesides gendered labour market outcomes, gender differences are also found in further education after VET. Overall, men show a higher likelihood for further training than women, although women have caught up to men in recent years (SCCRE, 2014; FSO, 2024b). However, research on the subject is lacking (Trede and Kriesi, 2016) or relates to older cohorts only (Grossenbacher, 2000). Trajectories leading to further education at tertiary level depend on prior achievement. Access to universities of applied sciences (UAS) is granted to holders of a Federal Vocational Baccalaureate (FVB). Universities further require VET holders to obtain a higher education entrance qualification. As a result, this path is infrequent and highlights the low permeability between the VET system and academic education at tertiary level (Fazekas and Field, 2013; Kost, 2013). Meyer and Sacchi (2020) find that basic VET programmes, with restricted academic requirements, are negatively associated with later training. Sander and Kriesi (2019) report that the probability of starting professional VET (PVET) is higher for those who have completed horizontal undifferentiated VET programmes with a high proportion of school-based learning and highly standardized final examinations. Sander and Kriesi (2021) also find that women are less likely than men to take up professional VET, which is related to the fact that women are over-represented in occupations for which PVET is less common. These findings underline that, firstly, further training after VET is related to the type of apprenticeship completed. Second, that a higher amount of school-based learning, which is often associated with less occupation-specific education, improves the chances of entering higher vocational training. Thirdly, due to selection effects by gender into vocational and academic education at upper secondary level, women are more likely to enter higher education via an academic track, while men are more likely to do so via VET.\u003c/p\u003e"},{"header":"4. Data","content":"\u003cp\u003eThe empirical analyses draw on the LABB longitudinal database, developed in the \u003cem\u003eLongitudinal Analyses in Education project\u003c/em\u003e of the Federal Statistical Office (FSO, 2024a). Started in 2011, LABB provides exhaustive and standardized administrative data to study the educational pathways of Swiss residents, from mandatory school to tertiary education.\u003c/p\u003e\n\u003cp\u003eOur analyses focus on the cohort of apprentices who obtained a Federal VET Diploma (FVETD) in 2012 as their first upper-secondary graduation. This is the earliest cohort with complete trajectories. We then reconstructed the trajectories of every 2012\u0026rsquo;s graduate from September 2012 and followed over a period of 88 months, i.e., until 2019. We excluded the COVID-19 period to avoid the peculiarity of the pandemic. The LABB database has been merged with a complementary dataset produced by Gr\u0026oslash;nning (\u003cem\u003eet al.\u003c/em\u003e, 2018)\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e, which provides detailed information on the specific characteristics of each FVETD educational programme. However, not all training programmes observed in LABB were described by this complimentary database. As a result, around 3\u0026rsquo;000 individuals (6% of the sample), who were enrolled in these VET trainings, have missing values and were excluded from the multivariate analysis.\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e3\u003c/sup\u003e The final sample is made of 49\u0026rsquo;899 apprentices (Table 1).\u003c/p\u003e\n\u003cp\u003eEight variables were selected for the analysis, according to the above discussion. To study the gender disparities in post-FVETD trajectories, \u003cem\u003esex\u0026nbsp;\u003c/em\u003eof apprentices along with \u003cem\u003ehorizontal gendered segregation\u0026nbsp;\u003c/em\u003ein FVETD programmes represent our main explanatory variables. In line with theory, we consider a training programme to be gendered if the gender concentration exceeds 70%, and mixed otherwise (Deutsch \u003cem\u003eet al.,\u003c/em\u003e 2005; Gianettoni \u003cem\u003eet al.,\u003c/em\u003e 2010; Imdorf, Sacchi, and Wohlgemuth, 2014; Imdorf and Hupka-Brunner, 2015). Several control variables at the individual level were included. We control for the \u003cem\u003eage\u0026nbsp;\u003c/em\u003eas it accounts for differences in timing, and the \u003cem\u003elinguistic regions\u003c/em\u003e as they shape distinct educational and professional prospects (Scharenberg, \u003cem\u003eet.al\u003c/em\u003e., 2017; Babel, 2018). \u003cem\u003eMigration background\u003c/em\u003e is also accounted for its strong influence on (lower-) secondary track allocation and thus the subsequent educational and professional perspectives. We further control for the following VET programmes\u0026rsquo; characteristics. The \u003cem\u003eteaching modality\u003c/em\u003e distinguishes between school-based and dual apprenticeships (Gr\u0026oslash;nning and Kriesi, 2022). The \u003cem\u003etotal number of lessons\u003c/em\u003e given within a specific VET programme indirectly measure the focus on general education of the program.\u003c/p\u003e\n\u003cp id=\"_Toc187713455\"\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e: Descriptive statistics of the 2012 cohort\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"385\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 49\u0026rsquo;899\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eMen\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e28\u0026rsquo;829 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eWomen\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e24\u0026rsquo;345 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;20\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e40\u0026rsquo;342 (76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003e21-25\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e9\u0026rsquo;224 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026gt;25\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e3\u0026rsquo;608 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eSwiss-German\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e41\u0026rsquo;435 (78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eSwiss-French\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e10\u0026rsquo;195 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eSwiss-Italian\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e1\u0026rsquo;544 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMigration status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eSwiss\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e43\u0026rsquo;150 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eSwiss born abroad\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e2\u0026rsquo;781 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eForeigners born in Switzerland\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e3\u0026rsquo;467 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eForeigners born abroad\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e3\u0026rsquo;447 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003e(NA\u0026rsquo;s)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender segregation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eFemale-dominated\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e13\u0026rsquo;834 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eMale-dominated\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e21\u0026rsquo;583 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eMixed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e17\u0026rsquo;757 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeaching modality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eFull-time\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e3\u0026rsquo;169 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e49\u0026rsquo;643 (94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003e(NA\u0026rsquo;s)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e362\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of lessons\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e360-2\u0026rsquo;880 (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62.8571%;\"\u003e\n \u003cp\u003e\u003cem\u003e(NA\u0026rsquo;s)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1429%;\"\u003e\n \u003cp\u003e3,275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"5. Methods","content":"\u003cp\u003eWe first used sequence analysis to create a typology of ideal-typical post-VET pathways, providing a holistic view on educational pathways\u0026mdash;which are understood as a process rather than a later-life single point outcome. The typology is then used in multilevel regression models to study the link between apprentices\u0026rsquo; sociodemographic features, VET programme characteristics and SWT outcomes.\u003c/p\u003e\u003cp\u003eEight states monthly characterize apprentices\u0026rsquo; pathways. One \u0026ldquo;Employment\u0026rdquo; and four educational states, depicting \u0026ldquo;General secondary,\u0026rdquo; \u0026ldquo;Vocational secondary,\u0026rdquo; \u0026ldquo;General tertiary\u0026rdquo; and \u0026ldquo;Vocational tertiary.\u0026rdquo; Apprentices who are NEET (\u003cem\u003enot in education, nor in employment or training\u003c/em\u003e) are distinguished in a \u0026ldquo;NEET: APG\u0026rdquo; state - corresponding to compulsory military service for men - and a \u0026ldquo;NEET\u0026rdquo; state\u0026mdash;regrouping heterogeneous situations including unemployment, and more generally non-working situations without education, working outside of Switzerland, or receiving invalidity insurance benefits. (Babel, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The final \u0026ldquo;other training\u0026rdquo; state groups all other educational states but is so infrequent that it is \u003cem\u003ede facto\u003c/em\u003e invisible in the data.\u003c/p\u003e\u003cp\u003eThe typology of trajectories was obtained by first comparing all the trajectories to one another using the standard \u003cem\u003eOptimal matching\u003c/em\u003e (OM), which is the most widely used distance measure in sequence analysis. OM takes into account the sequencing and duration of spells when comparing trajectories, which are two important characteristics of educational trajectories (Studer and Ritschard, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In a second step, we used \u003cem\u003ePartition Around Medoids\u003c/em\u003e (PAM) initialized with \u003cem\u003eWard\u003c/em\u003e hierarchical clustering to create the typology. This strategy improves the robustness and quality of clusters, by maximizing a global criterion while overcoming the sensitivity of PAM to the initial medoid selection (Kaufman and Rousseeuw, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Studer, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFinally, we aim to explain the typology of post-VET trajectories using multilevel regression models, with the specific FVETD programmes representing the grouping variable at level 2 and the explanatory variables described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e modelled at level 1. This strategy allows us to analyse individual post-VET trajectories while accounting for the residual differences among the 250 FVETD programmes. In these models, the variance of random effects informs us about the residual variability in post-VET pathways according to the specific FVETD programme once accounting for control variables. A high variance reveals that the chances of following a given post-VET path vary strongly between specific FVETD, whereas a low variance suggests weaker associations between post-VET pathways and the specific FVETD obtained.\u003c/p\u003e\u003cp\u003eThe statistical analyses conducted in this work were performed with \u003cem\u003eR\u003c/em\u003e statistical software, using \u003cem\u003eTraMineR\u003c/em\u003e (Gabadinho, et.al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), \u003cem\u003eWeightedCluster\u003c/em\u003e (Studer, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), \u003cem\u003efastcluster\u003c/em\u003e (M\u0026uuml;llner, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and \u003cem\u003elme4\u003c/em\u003e (Bates, et.al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) packages.\u003c/p\u003e"},{"header":"6. Results","content":"\u003cp\u003eAs a reminder, this study examines the school-to-work (SWT) trajectories after the obtaining of a federal VET diploma (FVETD) and focuses on the gender disparities that shape this critical process. This section presents our results as follows. First, we describe the typology of post-VET trajectories. In line with our research questions, we present 4 multilevel regression models. We then focus on the gender differences at the individual level, while accounting for the heterogeneity among FVETD occupations. We further examine the influence of educational programmes\u0026rsquo; characteristics on SWT. We finally discuss the consequences of \u003cem\u003ehorizontal gendered segregation\u003c/em\u003e on SWT before addressing the implications of following gender-typical and\u0026mdash;atypical FVETD programmes on subsequent trajectory.\u003c/p\u003e\n\u003ch2 id=\"_Toc187754955\"\u003e6.1 The Typology of Post-VET Trajectories\u003c/h2\u003e\n\u003cp\u003eWe first describe the SWT pathways of apprentices in Switzerland after completing a FVETD using sequence analysis. Figure 1 below introduces the seven types of post-VET trajectories identified using an \u003cem\u003eIndex-plot\u003c/em\u003e. In these plots, each individual trajectory is displayed by a coloured line according to the different states it occupies.\u003c/p\u003e\n\u003cp\u003eThe first type of trajectories is called \u0026ldquo;Employment\u0026rdquo; and describes a direct and stable transition into the labour market. It represents the reference path for the population as it is followed by almost half of all apprentices. This type was expected and clearly identified by the Swiss education system. Indeed, due to its qualifying features, the FVETD is widely recognized as the first diploma giving access to working life and skilled employment (Cortesi and Imdorf, 2013; Lagan\u0026agrave; and Babel, 2020).\u003c/p\u003e\n\u003cp\u003eOur typology then highlights an \u0026ldquo;\u003cem\u003eEarly vocational tertiary\u0026rdquo;\u0026nbsp;\u003c/em\u003eand a \u0026ldquo;\u003cem\u003eLate vocational tertiary\u003c/em\u003e\u0026rdquo; types that strongly differ in their timing. These pathways show that the transition to tertiary VET education can take place quickly after graduation, or after an employment spell of up to 4 years. More generally, they emphasize the relevance of a medium-term analysis for understanding educational pathways in Switzerland. In contrast, the \u0026ldquo;General Tertiary\u0026rdquo; type embodies the trajectories leading to general tertiary education through the obtaining of a Federal Vocational Baccalaureate (FVB) (shown in blue in Figure\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003eThe \u0026ldquo;\u003cem\u003eReorientation\u003c/em\u003e\u0026rdquo; type describes the trajectories of apprentices who pursue their education by staying within the upper-secondary VET, most often to obtain a second FVETD. Further analysis shows that in 80% of cases, this second training is pursued in a different vocational field from the first FVETD (Annexe\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003eThe last two types of trajectories are potentially the most problematic. The \u0026ldquo;\u003cem\u003eNEET\u003c/em\u003e\u0026rdquo; type encompasses trajectories of young people who make a stable transition into the NEET (\u003cem\u003eNot in Employment, Education, or Training\u003c/em\u003e) state, potentially leading to social exclusion and long-term adverse consequences. However, as previously mentioned, the NEET state regroups heterogeneous situations, as it overlaps with social insurance records and the absence of individuals from administrative registries. This absence may also include individuals who have left Switzerland without officially reporting their departure. Finally, the \u0026ldquo;\u003cem\u003eUnstable pattern\u0026rdquo;\u003c/em\u003e type is characterized by a predominance of employment with frequent back-and-forth between many other situations, particularly NEET states or non-qualifying training programmes as evidently too short. This type illustrates that a significant proportion of trajectories, around 20%, are nonlinear, increasing the complexity of post-VET trajectories overall.\u003c/p\u003e\n\u003ch2 id=\"_Toc187754956\"\u003e6.2 The gender differences in post-VET transitions\u003c/h2\u003e\n\u003cp\u003eThe typology outlined in Figure\u0026nbsp;1 allowed us to visualise and describe ideal-typical post-VET pathways over the 88 months following apprentices\u0026rsquo; graduation. We now turn to the study of the gender differences underlying these pathways.\u003c/p\u003e\n\u003cp\u003eTable 2 below shows the results of a multinomial multilevel logistic regression model. In this and the following models, we compare each type of post-VET pathway with the \u0026ldquo;Employment\u0026rdquo; type taken as reference. The coefficients are expressed on the log-odds scale and measure the chances of being in a type instead of the \u0026ldquo;Employment\u0026rdquo; one. In this first model, age, linguistic region and migration background were included as control variables.\u003c/p\u003e\n\u003cp id=\"_Toc187713456\"\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Gender differences in post-VET trajectories and the influence of specific FVETD programmes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"575\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNEET\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstable pattern\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReorientation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLate vocational Tertiary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEarly vocational Tertiary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral Tertiairy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-2.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.60\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-2.20\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-1.50\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-1.39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.89\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e21 - 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.20\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-1.21\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.54\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.48\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.03\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026gt; 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.30\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.84\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-2.87\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-1.45\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-1.93\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-2.85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSwiss-French\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.27\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.08\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.43\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-1.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.93\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.26\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSwiss-Italian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.84\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.50\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.53\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-1.89\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.78\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSwiss born abroad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.54\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.17\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-0.52\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eForeigners born in Switzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.18\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.10\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-0.64\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.22\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eForeigners born abroad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-0.92\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.22\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.35\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.66\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.83\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e16470.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e40305.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e12418.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e18250.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e22448.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e16871.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNum. obs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e25235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e32985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e24667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e25890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e27426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e26272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNum. FVETD Prog.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eRandom Intercept Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 575px;\"\u003e\n \u003cp\u003e\u003csup\u003e***\u003c/sup\u003ep \u0026lt; 0.001; \u003csup\u003e**\u003c/sup\u003ep \u0026lt; 0.01; \u003csup\u003e*\u003c/sup\u003ep \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe results confirm that SWT trajectories are significantly different by gender. Women are generally less likely to pursue further education trajectories and more likely to follow the \u0026ldquo;Employment\u0026rdquo; type. Furthermore, they follow less often the \u0026ldquo;Unstable pattern.\u0026rdquo; More detailed analyses show that this difference can be explained by the military service spells, a predominantly male characteristic which cannot easily be associated with a form of social vulnerability. Interestingly, Swiss apprentices born abroad are found to be the most likely to experience \u0026ldquo;NEET\u0026rdquo; trajectories. This relationship aligns with the idea that a part of the NEET trajectories results from individuals leaving Switzerland without officially notifying the authorities. Foreigners cannot do the same as they would not be able to renew their residence permit under these conditions.\u003c/p\u003e\n\u003cp\u003eSome of these gender differences in post-VET pathways are probably linked with the gendered FVETD occupations. As a reminder, there are more than 250 different FVETD occupations, each linked to a specific training programme and rooted in distinct labour market sectors. We account for this heterogeneity by including the specific FVETD occupations as a separate level of analysis and by interpreting the variance of random intercepts.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e4\u003c/sup\u003e In general, the results highlight that trajectory types linked with pursuing education in the short term depend more closely on the specific FVETD obtained. This is highlighted by the comparatively high variance of the random intercepts, compared with the \u0026ldquo;Unstable pattern,\u0026rdquo; \u0026ldquo;Late Vocational Tertiary,\u0026rdquo; and \u0026ldquo;NEET\u0026rdquo; types.\u003c/p\u003e\n\u003cp\u003eThe complementary analyses in Annexe\u0026nbsp;3 compare the results with and without accounting for the specific FVETD programmes, highlighting that the gender differences for the \u0026ldquo;NEET\u0026rdquo; and \u0026ldquo;Reorientation\u0026rdquo; patterns are weaker in the former case. This means that the gender differences previously observed (Annexe\u0026nbsp;3) were in fact partly explained by the differences between specific FVETD occupations. In contrast, gender differences in Table 2 are reinforced when we look at the chances of pursuing tertiary education, meaning that women are even less likely to pursue these paths at equal FVETD obtained.\u003c/p\u003e\n\u003cp\u003eTable 3 below presents the results of our second model, which aims at understanding the influence of these FVETD educational programmes\u0026rsquo; characteristics on subsequent SWT trajectories. Indeed, taking the specific FVETD occupations as a level of analysis allows us to include explanatory variables measured at this level, such as the content of specific FVETD occupations (Gr\u0026oslash;nning and Kriesi, 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc187713457\"\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e: The influence of FVETD programmes\u0026rsquo; characteristics on post-VET trajectories\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"616\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNEET\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstable pattern\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReorientation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLate vocational Tertiary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEarly vocational Tertiary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral Tertiairy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-2.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.50\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-2.22\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-1.46\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-1.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-1.78\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.35\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.15\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.66\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.50\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eFull-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.83\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.84\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.87\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.74\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.15\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.79\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eNumber of lessons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.12\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.13\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.26\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.24\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.35\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eLength \u0026gt;3 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.34\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.31\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 616px;\"\u003e\n \u003cp\u003eControl variables: Age, Linguistic region, Migration status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e16435.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e40179.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e12411.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e18221.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e22303.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e16472.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eNum. obs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e25235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e32985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e24667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e25890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e27426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e26272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eNum. FVETD Prog.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eRandom Intercept Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 616px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e***\u003c/sup\u003ep \u0026lt; 0.001; \u003csup\u003e**\u003c/sup\u003ep \u0026lt; 0.01; \u003csup\u003e*\u003c/sup\u003ep \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe observe a marked reduction in the variance of the random intercepts for most types of trajectories, which implies that teaching modalities are important predictors of subsequent SWT trajectories. All else being equal, firm-based (dual) apprenticeships are more tightly linked to employment than full-time school-based apprenticeships, which in contrast lead more often to further education. Apprentices provided with full-time VET education are also more likely to experience a \u0026ldquo;Reorientation\u0026rdquo; pathway, probably due to their weaker link to the labour market. Indeed, when reorienting, almost 80% of apprentices undertake a FVETD occupation in another occupational field, which might be easier for apprentices who acquired more general skills. This tight link between the dual system and the labour market also explains why full-time school-based programmes are more strongly associated with the \u0026ldquo;Unstable pattern\u0026rdquo; and \u0026ldquo;NEET\u0026rdquo; types than with employment.\u003c/p\u003e\n\u003cp\u003eAdditionally, the results highlight that the greater the total number of lessons imparted within a training programme, the higher the likelihood of pursuing tertiary education, which is expected. A greater number of lessons also prevents apprentices from reorienting. It is also worth noting that the length of training programmes reduces the chances of experiencing potentially problematic pathways. Interestingly, however, the length of training programmes does not appear to significantly influence the likelihood of pursuing tertiary education trajectories, which are instead more closely associated with full-time school-based education and a higher total number of lessons.\u003c/p\u003e\n\u003cp\u003eNevertheless, it should be noted that these training programmes\u0026rsquo; characteristics do not influence the structure of gender disparities previously described (Table\u0026nbsp;2). In other words, these characteristics partly explain the observed SWT outcomes, which, however, remain highly gendered.\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;6.3 Horizontal Gender Segregation and Its influence on Post-VET Trajectories\u003c/h2\u003e\n\u003cp\u003eWe now focus on the consequences of horizontal gendered segregation among FVETD occupations. To this end, we rely on the \u003cem\u003egender concentration\u0026nbsp;\u003c/em\u003ewithin each FVETD training programme. In line with theory, a specific FVETD training is gendered if the proportion of men or women exceeds 70%, and mixed otherwise (Kriesi and Imdorf, 2019). Our analytical strategy relying on multilevel regression models appropriately account for this dimension, which is measured at the level of the specific FVETD occupations. Table 4 below presents the results of our third estimation.\u003c/p\u003e\n\u003cp id=\"_Toc187713458\"\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The impact of horizontal gendered segregation on post-VET trajectories\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNEET\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUnstable pattern\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRe-orientation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLate vocational tertiary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEarly vocational tertiary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral Tertiairy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.58\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.76\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.56\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFull-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.84\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.88\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.15\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.78\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber of lessons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.27\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.24\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.37\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLength \u0026gt;3 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.20\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.15\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.40\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.68\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.86\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale-dominated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.64\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale-dominated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.45\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.75\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003eControl variables: Age, Linguistic region, Migration status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16437.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40178.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12426.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18238.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22317.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16471.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNum. obs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNum. FVETD Prog.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom Intercept Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003csup\u003e***\u003c/sup\u003ep \u0026lt; 0.001; \u003csup\u003e**\u003c/sup\u003ep \u0026lt; 0.01; \u003csup\u003e*\u003c/sup\u003ep \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs expected, these results confirm the tight linkage between male-dominated occupations and labour market access, while controlling for the effect of training programmes\u0026rsquo; characteristics. Indeed, they provide a more direct access to employment, while being protected against potentially problematic \u0026ldquo;NEET\u0026rdquo;\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u0026ldquo;Unstable pattern\u003cem\u003e\u0026rdquo;\u003c/em\u003e types. Furthermore, male-dominated training is less oriented towards the academic \u0026ldquo;General Tertiary\u003cem\u003e\u0026rdquo;\u003c/em\u003e path than mixed trainings. In contrast, post-VET trajectories following female-dominated and mixed trainings are generally similar, except for the \u0026ldquo;Early Vocational Tertiary\u003cem\u003e\u0026rdquo;\u003c/em\u003e type. In this case, apprentices undertaking female-dominated trainings more often pursue this higher vocational pathway than apprentices coming from mixed trainings.\u003c/p\u003e\n\u003cp\u003eMore generally, these results point out a clear distinction between male-dominated occupations on the one hand, and female-dominated and mixed occupations on the other. The next section aims to understand whether the effect of gendered FVETD trainings is the same for men and women.\u003c/p\u003e\n\u003ch3 id=\"_Toc187754958\"\u003e\u003cstrong\u003e6.3.1 Post-VET Trajectories Following Gender-Atypical\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eoccupations\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eUndertaking gender-typical occupations is the norm within the Swiss educational system (Gianettoni \u003cem\u003eet al\u003c/em\u003e, 2010; Kriesi and Imdorf, 2019). In this section, we focus on the specific consequences for the subsequent SWT pathways of evading the gender norms by enrolling in either gender-typical or gender-atypical occupations. Table 5 below presents the results of our last model.\u003c/p\u003e\n\u003cp id=\"_Toc187713459\"\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The consequences of undertaking gender-typical and gender-atypical trainings on SWT\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNEET\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUnstable pattern\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eReorientation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLate vocational tertiary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEarly vocational tertiary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral Tertiairy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.01\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.08\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.56\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.71\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.53\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.49\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.72\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.61\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.93\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale-dominated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale-dominated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.60\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.81\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen: female-dominated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.60\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen: Male-dominated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.41\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003eControl variables: Age, Linguistic region, Migration status, Teaching modality, Number of lessons, Duration of apprenticeships\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16420.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40175.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12440.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18258.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22333.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16484.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNum. obs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNum. FVETD Prog.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom Intercept Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003e\u003csup\u003e***\u003c/sup\u003ep \u0026lt; 0.001; \u003csup\u003e**\u003c/sup\u003ep \u0026lt; 0.01; \u003csup\u003e*\u003c/sup\u003ep \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn general, the results show that gender differences and their extent depend on gender concentration within FVETD occupations. The differences between men and women following mixed or female-dominated programmes are more pronounced than what we previously observed in Table\u0026nbsp;4. Indeed, women in these contexts are more likely than men to transition into Employment overall, making them less likely to experience \u0026ldquo;NEET,\u0026rdquo; \u0026ldquo;Unstable pattern\u0026rdquo; and further education types.\u003c/p\u003e\n\u003cp\u003eThe gender differences take another form in male-dominated trainings, where women are more likely than men to experience the \u0026ldquo;NEET\u0026rdquo; pattern. Furthermore, no differences are found for the \u0026ldquo;Unstable pattern\u0026rdquo; type, even if men were expected to follow it more often because of military service duties. Finally, we observe a significantly lower difference in the chance to follow the \u0026ldquo;General Tertiary\u0026rdquo; pathway. While women are still less likely than men to pursue general higher education, these gender differences are smaller in male-dominated occupations than in gender-mixed or in female-dominated trainings.\u003c/p\u003e\n\u003cp\u003eFocusing on gender segregation thereby provides a complimentary view on the interaction. While the main advantage of male-dominated programmes is their protection against the potentially problematic \u0026ldquo;NEET\u0026rdquo; and \u0026ldquo;Unstable pattern\u0026rdquo; types, this protection does not apply to women, which, in contrast, have better prospects for further general tertiary education than women in the other fields.\u003c/p\u003e"},{"header":"7. Discussion","content":"\u003cp\u003eWe first aimed to describe the SWT pathways following an apprenticeship in Switzerland. The Swiss educational system is characterized by the predominance of initial vocational education at the upper-secondary level, a feature often praised to provide a safe road to skilled employment (M\u0026uuml;ller and Shavit, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Blossfeld et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kriesi and Schweri, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Overall, our results confirm the successful labour market integration for most apprentices, with around 80% of them into employment seven years and a half after their graduation.\u003c/p\u003e\u003cp\u003eHowever, our typology also emphasizes a great diversity of post-FVETD pathways over the medium term. Previous studies linked this complexity with the growing transition risks for many VET graduates (Stalder, 2012; Salvisberg and Sacchi, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Babel, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In line with these findings, our study confirms that the trajectories following VET education are far from being simple, smooth, and uniform. A significant proportion of apprentices get trapped in potentially problematic transitions dominated by NEET status, or experience an unstable pattern characterized by frequent back-and-forth between many different statuses, without finding stable jobs. One apprentice out to four ends up in these situations, highlighting the importance of this phenomenon, and thereby the urge for policies that support their labour market integration. The diversity of post-FVETD pathways is also linked to the growing diversity of higher education opportunities for VET holders following the early 1990s reforms of the VET system (Stalder and N\u0026auml;gele, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Imdorf et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While enrolment in tertiary education has historically been low in Switzerland, and especially among apprentices, the proportion of VET graduates pursuing tertiary education has increased to approximately one third in recent years (Babel, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Meyer, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, a substantial heterogeneity is also observed among those pursuing higher education. On the one hand, accessing general tertiary education requires a Federal Vocational Baccalaureate, which is a highly selective qualification most often targeted to the VET elite (Cortesi and Imdorf, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). On the other hand, our typology highlighted two types of pathways into vocational tertiary education differing according to their timing, with a transition that can take place even after 4 years. This further underlines the importance to study SWT as a complex process that occurs over a prolonged period, which is one of the strengths of this work.\u003c/p\u003e\u003cp\u003eFurthermore, as already pointed out by Babel (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the post-FVETD transitions are also characterized by the importance of nonlinear trajectories. Our results show that most apprentices experienced at least two status shifts within the seven and a half years following their graduation. This also holds for a significant proportion of apprentices in the \u0026ldquo;Employment\u0026rdquo; cluster, which is thought to be the most linear pathway overall. Furthermore, many apprentices experienced a reorientation within the upper-secondary level after obtaining their first FVETD. As a result, only about 20% of the cohort experienced an ideal-typical linear transition to employment.\u003c/p\u003e\u003cp\u003eThis study points to the importance of gender, showing that women in VET face greater challenges in their SWT than men. Overall, women enter more often into employment, while men are more likely to pursue higher education. Such proximity to employment is not only indicative of a limitation in SWT prospects for women. Indeed, as highlighted by previous research, once in employment, and long before the onset of family formation, a persistent gender wage gap is observed (Combet and Oesch, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zimmermann and Seiler, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Hupka-Brunner and Meyer, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBy distinguishing the individual and the group levels of analysis, we highlight that gender differences in SWT are intertwined with the strong horizontal gender segregation characterising the Swiss VET system. The specific FVETD occupation has only a slight influence on the gender differences in SWT trajectories, implying that apprentices\u0026rsquo; allocation to a specific FVETD training programme is not the key determinant of the observed gender differences in SWT pathways. .Although evidence regarding the impact of horizontal gender segregation on subsequent SWT are still quite scarce, previous research has highlighted distinct educational and labour market prospects following male-dominated, female-dominated, or gender-mixed occupations (Kriesi and Imdorf, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lorentzen and Vogt, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hupka-Brunner and Meyer, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In line with these insights, our work confirms the importance of horizontal gender segregation on medium-term SWT patterns. Male-dominated occupations are more tightly linked with the labour market than female-dominated and mixed occupations, while also being protected against potentially problematic \u0026ldquo;NEET\u0026rdquo; and \u0026ldquo;Unstable pattern\u0026rdquo; types.\u003c/p\u003e\u003cp\u003eIn this context, it was argued that women may have greater incentives to enrol in male-dominated trainings to benefit from their better career opportunities (Kriesi and Imdorf, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our analyses suggest that this is only partially true. On the one hand, the protection given by male-dominated trainings against potentially problematic trajectories does not apply to women. As a result, women in male-dominated trainings have less close link to the labour market than men and are potentially more vulnerable. This also suggests the need for a better knowledge on the systematic discrimination in employers\u0026rsquo; recruitment practices, which may contribute to the lower likelihood of women to be taken on by a firm (Imdorf, 2012, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). On the other hand, however, women in male-dominated VET occupations benefit from a closer link with general tertiary education than women graduating in female-dominated or gender mixed occupations. This finding provides more detailed insights on the specific consequences of women undertaking gender-atypical occupations.\u003c/p\u003e\u003cp\u003eThis work shall also contribute to the existing knowledge on the mechanisms driving social inequalities in Switzerland from a holistic view on educational trajectories. The Swiss labour market is highly segmented, and access to occupations in its subsegments generally depends on prior educational credentials (Sacchi et al, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this context, the relatively high horizontal gender segregation within the Swiss labour market has been linked to the predominance of Swiss VET, in which the pattern of attended apprenticeships is also strongly gendered (Heiniger and Imdorf, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). By reconstructing apprentices\u0026rsquo; SWT over the medium term, this work provides important insights on the path-dependencies between VET education, its strong gender segregation, and later trajectories, which may lead to the very different labour market outcomes substantiated by previous research (Bertschy, 2016; Combet and Oesch, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kriesi and Imdorf, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Lorentzen and Vogt, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hupka-Brunner and Meyer, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Indeed, our analysis suggests that apprentices\u0026rsquo; allocation to a gendered occupation acts as an institutional channelling mechanism that sets them on a diversity of SWT paths.\u003c/p\u003e\u003cp\u003eThis study also explored whether the observed heterogeneity in SWT can be related to the content of the FVETD educational programmes. In line with previous findings, our work shows that accounting for the heterogeneity in the content of educational programmes is important to explain the structuring of subsequent SWT trajectories (Gr\u0026oslash;nning and Kriesi, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our results highlight that apprenticeship organization is a key factor influencing the SWT, with firm-based apprenticeships more tightly linked to the labour market. In contrast, full-time school-based apprenticeships seem to encounter greater difficulties in finding a skilled job, which are comparatively more likely to experience NEET and unstable trajectories. Apprentices in school-based VET programmes only complete a traineeship in a company in the last year of training, which may explain the fact that they are less likely to show a stable labour market pattern.\u003c/p\u003e\u003cp\u003eHowever, apprentices in school-based VET programmes nonetheless attend higher education pathways more often, which might counteract the negative effects of their lower propensity to be taken over by a training firm. Interestingly, although shorter apprenticeships are in general intellectually less demanding (Stalder and N\u0026auml;gele, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), our results suggest that the length of training programmes does not have a great impact on subsequent SWT. Furthermore, our analyses show that training programmes imparting a high number of lessons are closely related to higher education, which is expected given that more comprehensive programmes are generally more cognitively demanding. These conclusions support the idea that the institutional features of apprenticeship programmes shape the skills acquired by apprentices, which then structure the type of SWT they will experience (Gr\u0026oslash;nning et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gr\u0026oslash;nning and Kriesi, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, while previous studies focused on how these variations among VET programs affect subsequent labour market outcomes, such as occupation mobility, skill mismatch or unemployment (Gr\u0026oslash;nning et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gr\u0026oslash;nning et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gr\u0026oslash;nning and Kriesi, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), this work explored their association with later SWT \u003cem\u003epathways\u003c/em\u003e, thus not limiting to labour market outcomes \u003cem\u003eper se\u003c/em\u003e at a specific time point.\u003c/p\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eDrawing on data from the LABB administrative database, this article shed new light on the most recurrent types of post-FVETD trajectories, before focusing on the gendered processes that affect these pathways. Relying on sequence analysis of educational and labour market trajectories, we followed the 2012 VET graduates over more than 7 years, which represents one of the main strengths of this work. It provides detailed knowledge on the school-to-work transition following an apprenticeship in Switzerland.\u003c/p\u003e\u003cp\u003eOur analyses show a great diversity in SWT pathways. This diversity highlights that this SWT is not always fast, fluid and linear, as it is often claimed to be. Our results rather point to the frequency of unstable patterns, NEET trajectories and vocational reorientations for the leavers of the VET system. In sum, our results further highlight that for many apprentices the SWT is still not achieved 7 years following their graduation.\u003c/p\u003e\u003cp\u003eGender feature among the key factors affecting the post-FVETD trajectories, highlighting distinct transitional patterns for men and women. These gender differences are only partly due to the specific FVETD occupations undertaken. Thus, our results show that men and women experience distinct subsequent SWT trajectories at equal FVETD obtained.\u003c/p\u003e\u003cp\u003eWe further studied the consequences of horizontal gender segregation on SWT. While male-dominated occupations benefit from a closer link with the labour market and prevent them from experiencing potentially problematic SWT patterns, this does not hold for women in these sectors. As a result, the consequences of enrolling in gender-atypical occupations are very different for men and women.\u003c/p\u003e\u003cp\u003eThis article faces at least two limitations. First, we were not able to account for two potential confounders: social origin, which is typically measured by parents\u0026rsquo; highest educational level, and the lower-secondary track attended by apprentices. Integrating social origin using the highest educational level of parents would have reduced the sample size to about 5\u0026rsquo;000 apprentices. Such reduced sample size would have made it impossible to study the specific FVETD occupation at a detailed level. Lower-secondary track allocation is not available for the 2021 cohort. Second, we focused on labour market and educational statuses. However, further studies are required to evaluate the quality of these statuses, for instance by looking at wages or employment rates. Although our cohort of apprentices is followed at a very precise level over more than seven years, the data does not enable us to directly link the gender segregation in VET to more specific labour market outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study is available from the Federal Statistical Office. The data is not publicly available, but available on request on a per project basis from the Federal Statistical Office. The code is available from the authors on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding and acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gracefully acknowledge the support received from the Swiss National Science Foundation (project \u0026ldquo;Strengthening Sequence Analysis\u0026rdquo;, grant No.: 10001A_204740).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eML and JM took care of the statistical analyses, including data management and coding, sequence analysis and multilevel regressions. ML wrote the first draft of the article, based on previous work by JM. MS supervised the study, designed the methodological framework, and contributed to the writing of the article. DG contributed to the state-of-the-art section and reviewed the whole article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse of Large Language Models (LLMs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work benefited from the use of Large Language Models (LLMs) for two purposes. Firstly, during the data handling process, LLMs were used to identify solutions associated with \u003cem\u003eR software\u003c/em\u003e error messages. Secondly, LLMs were integrated into the writing process to improve the clarity of the text and to translate words in context. Although LLMs have been helpful for these purposes, it should be noted that their use in this work was kept under severe control for several reasons. Firstly, regarding the high degree of confidentiality of our data. Under no circumstances were these data shared with LLMs. Secondly, LLMs can make mistakes at any time, and this is exacerbated by the fact that LLMs always generate an answer, which can sometimes be invented. It is therefore always necessary to be critical of the content generated by LLMs. Finally, we consider it important to make explicit the relationship between the researcher and LLMs; the latter is subordinate to the former. This condition is fundamental, as researchers are professionally and ethically responsible for their work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBabel J (2018) Transitions apr\u0026egrave;s un titre du degr\u0026eacute; secondaire II et int\u0026eacute;gration sur le march\u0026eacute; du travail\u0026mdash;Edition 2018\u0026mdash;\u003cem\u003eAnalyses longitudinales dans le domaine de la formation \u003c/em\u003e| Publication. 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DOI: 10.1093/sf/sov099.\u003c/li\u003e\n\u003cli\u003eSacchi S, Kriesi I and Buchmann M (2016) Occupational mobility chains and the role of job opportunities for upward, lateral and downward mobility in Switzerland. \u003cem\u003eResearch in Social Stratification and Mobility\u003c/em\u003e 44: 10-21. DOI: http://dx.doi.org/10.1016/j.rssm.2015.12.001.\u003c/li\u003e\n\u003cli\u003eSalvisberg A and Sacchi S (2014) Labour market prospects of Swiss career entrants after completion of vocational education and training. \u003cem\u003eEuropean Societies\u003c/em\u003e 16(2): 255-274. DOI: 10.1080/14616696.2013.821623.\u003c/li\u003e\n\u003cli\u003eSander F and Kriesi I (2019) Medium and Long-Term Returns to Professional Education in Switzerland: Explaining Differences between Occupational Fields. \u003cem\u003eSocial Inclusion\u003c/em\u003e 7(3): 136-153. 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DOI: 10.12682/lives.2296-1658.2013.24.\u003c/li\u003e\n\u003cli\u003eStuder M and Ritschard G (2016) What matters in differences between life trajectories: A comparative review of sequence dissimilarity measures. \u003cem\u003eJournal of the Royal Statistical Society Series A: Statistics in Society\u003c/em\u003e 179(2): 481\u0026ndash;511. \u003c/li\u003e\n\u003cli\u003eTrede I and Kriesi I (2016) \u0026Uuml;bergang in die h\u0026ouml;here Berufsbildung im Gesundheitsbereich: Die Rolle von Geschlecht und Migrationshintergrund. In: Kriesi I, Liebig B, Horwath I, et al. (eds) \u003cem\u003eGender und Migration in der terti\u0026auml;ren Hochschulbildung\u003c/em\u003e. M\u0026uuml;nster: Westf\u0026auml;lisches Dampfboot, pp.102\u0026ndash;122.\u003c/li\u003e\n\u003cli\u003eVouillot, F (2007) L\u0026apos;orientation aux prises avec le genre. Travail, genre et soci\u0026eacute;t\u0026eacute;s, 18(2), 87-108.\u003c/li\u003e\n\u003cli\u003eZimmermann B and Seiler S (2019) The Relationship between Educational Pathways and Occupational Outcomes at the Intersection of Gender and Social Origin. \u003cem\u003eSocial Inclusion\u003c/em\u003e 7(3): 79-94. DOI: 10.17645/si.v7i3.2035. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e There are also significant differences between the language regions regarding the proportion of school leavers in VET and academic tracks (Glauser, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Leemann et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hupka-Brunner (2003) shows for the first TREE cohort that the proportion of school leavers who commence an academic track is considerably higher in the Italian- and French-speaking cantons than in the German-speaking cantons. As in the case of VET, there is substantial cantonal variation of the proportion of persons attaining a higher education entrance qualification. In the French- and Italian-speaking part of Switzerland, the proportion was around 43 percent in 2013, while in the German-speaking cantons it was around a third (see FSO, 2024b).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e We sincerely thank the authors for providing this valuable database that enriched this work.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e However, all the analyses were also estimated on the full sample without the additional information without any noticeable difference in the results (see Annexe 4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e In our multilevel models, the specific FVETD occupations are treated as \u003cem\u003erandom effects\u003c/em\u003e and constitute the level 2 of analysis. A random intercept is thus estimated for each of the FVETD occupations included. See Gelman and Hill (2007) for a comprehensive understanding of multilevel modelling.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vocational Education and Training, School-to-work transition, Horizontal gender segregation, Gendered life-courses, Sequence analysis, LABB database, VET curriculums heterogeneity","lastPublishedDoi":"10.21203/rs.3.rs-8137537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8137537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVocational education and training (VET) is the most common post-compulsory educational pathway in Switzerland, followed by around two thirds of each birth cohort (Cortesi and Imdorf, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Upon completion, the majority of apprentices are awarded a Federal VET Diploma (FVETD) following one of its 250 training programmes (Cortesi and Imdorf, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). By providing ready-to-use occupation-specific skills in a wide range of professions, VET has repeatedly been credited for fostering smooth and linear transitions into employment (M\u0026uuml;ller and Shavit, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Bol, \u003cem\u003eet.al\u003c/em\u003e, 2019 ; Kriesi and Schweri, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite growing political concern, the Swiss VET system nonetheless remains marked by a strong \u003cem\u003ehorizontal gender segregation\u003c/em\u003e : while men more often undertake technical and manual occupations, women generally cluster in only a few apprenticeships in the health and social care sectors (Leemann and Keck, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Becker and Glauser, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kriesi and Imdorf, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given the close link between VET apprenticeships and their subsequent gendered occupational prospects, this horizontal gender segregation may represent a key step in the reproduction of the social inequalities in the Swiss labor market (Kriesi and Imdorf, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gr\u0026oslash;nning and Kriesi, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Using sequence analysis and the administrative LABB database (FSO, 2024), this article provides a typology of trajectories of the 2012 FVETD graduate\u0026rsquo;s cohort over seven years, demonstrating the great diversity of pathways into higher education, employment, reorientation, and NEET status. We then rely on this typology to study the gender disparities in SWT pathways, highlighting that women have less perspectives for further education after VET and are more often limited to experience a fast transition to employment. In a second step, we use multilevel models to estimate how the allocation into one of the 250 specific VET training programmes is related to subsequent SWT. This analysis emphasizes that male-dominated VET programmes offer a substantial protection against more problematic SWT pathways. This methodological approach also allows studying how other key characteristics of VET programmes, such as the number of provided lessons, are related to subsequent pathways. Finally, we look at the consequences of evading the gender norms by enrolling into gender-atypical VET occupations. Our findings reveal that women graduating from male-dominated VET apprenticeships do not benefit from their protection against more problematic pathways and are instead more likely to pursue unstable or NEET trajectories.\u003c/p\u003e","manuscriptTitle":"From School to Work: Gender Inequalities and Segregation Following an Apprenticeship in Switzerland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 14:15:53","doi":"10.21203/rs.3.rs-8137537/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf5cc097-c075-4ad2-b4f8-80ed77c07364","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T13:39:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-27 14:15:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8137537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8137537","identity":"rs-8137537","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}