The Bulgarian educational system and gender segregation in the labour market

School-to-work transitions are embedded in the institutional structures of educational systems. There are important institutional features of a country's school structure that are known to impact the allocation of jobs. However, much of the literature investigating the links between schooling and labour market outcomes has been gender-blind (Smyth 2005). Notable exceptions are Buchmann and Charles (1995) who investigate theoretically the effect of general and vocational secondary schooling on gender stratification in employment and Estévez-Abe (2006) who presents empirical evidence linking vocational education to greater horizontal gender segregation in the labour market. Also, Reimer and Steinmetz (2009) and Smyth and Steinmetz (2008) show that the institutional features of the educational and labour market system shape the occupational gender segregation of tertiary graduates.

We contribute to that literature with a case study analysis from Bulgaria. Specifically, we investigate two institutional features through which educational structures have an impact on gender-typical, a-typical, and mixed employment: vocational vs. general education and lower vs. higher education levels. The focus on Bulgaria is in line with the growing research interest on the school-to-work transitions in Central and Eastern European countries (Kogan and Unt 2005; Kogan et al. 2011) and contributes to a few studies on Bulgaria (Popov 2007; Kostova 2008a; Stoilova and Haralampiev 2009; Hofäcker et al. 2013). The former socialist countries and, in particular, Bulgaria provide a unique analytical context. Gender inequality in the Bulgarian labour market has remained low by international standards (European Commission 2009; Hausmann et al. 2010; Eurostat 2013; World Economic Forum 2013). Moreover, Bulgaria provides an opportunity to study the nexus between the educational system and the labour market in the context of rapid transformation involving the decentralisation, liberalisation, and privatisation of educational institutions (Noelke and Müller 2011). Given the recent changes in the labour market and the educational system, Bulgaria offers an illuminative context for the investigation of the educational determinants of gender-typed employment.

Vocational education and training (VET) inclines students to develop a gender-typed career orientation early in their lives and conditions them for gender-typed transitions to work. Similarly, comparative research on higher education has revealed that stratification at the tertiary level can lead to gender segregation in the labour market. According to Smyth (2005), gender tracking at the upper-secondary level and the tertiary level produces gendered labour market entries. Hence, the institutional structures of both the secondary and the tertiary education levels are important determinants of the occupational choices. Unlike much of the literature that has studied those mechanisms separately, we investigate the effect of lower versus higher education and general versus vocational schooling in a unified context allowing us to examine the impact of each mechanism while accounting for the other.

Our analysis uses data from a nationally representative survey in Bulgaria conducted in 2012. We find that the educational system shapes the gendered occupational trajectories for men, but it does not hold the same explanatory power for women. While neither vocational nor higher education has a significant effect for women, men with vocational education are more likely to work in male-typed occupations. In contrast, and in line with the literature, higher education steers men toward gender mixed and a-typical occupations.

The paper proceeds as follows. The next section reviews the relevant literature that links the educational system to horizontal gender segregation in the labour market. Then, we describe the Bulgarian educational system and the level of gender segregation in the labour market. Sections 4 and 5 present our empirical hypotheses, the data, and the methodology. The results are discussed in Section 6 and we conclude with final remarks in Section 7.

Following the seminal work of Maurice et al. (1982) and Allmendinger (1989), the international school-to-work transition research has demonstrated the close relationship between national educational systems and individual youth transitions (see e.g. Müller and Kogan 2010). The main dimensions of the educational system that vary across countries are the degree of stratification and the forms of differentiation in distinct educational pathways. In their comparative study on school-to-work transitions in ten Central and Eastern European (CEE) countries,¹ Kogan et al. (2011) analyse how the educational systems of those countries structure different aspects of the labour market integration, including the duration of the first job search and the quality and the stability of that job. The study highlights the efficiency of VET in the studied countries: vocational graduates have a faster labour market entry compared to general secondary education graduates. Moreover, there is an important difference between Western countries and the CEE countries. Research on Western Europe has identified firm-based vocational training and the close coordination between vocational education providers and employers as key for promoting successful school-to-work transitions. In contrast, the school-to-work transitions in the CEE countries are associated with school-based VET. In addition, the case studies suggest that the more the CEE countries consolidate their labour markets, the more important the role of the educational systems in structuring the transition to work.

International comparative research shows that the organisation of education and training impacts not only youth unemployment and job quality, but also gender inequality in the labour market. Countries with established VET programmes are known to have horizontal gender segregation in both their VET systems and labour markets (Estévez-Abe 2006). For example, Buchmann and Charles (1995) argue that young school leavers are forced to make major career choices very early in their lives in the parts of Switzerland where upper-secondary education is dominated by VET. The wide variety of differentiated VET programmes promotes gender-typical career choices precisely at a biographical stage when gender is particularly important in shaping young people's identity. In the case of strong institutional linkages between education and employment such as in Switzerland or Germany, those gendered career choices in early upper-secondary school translate into different jobs for women and men in their adult life (Trappe 2006).

The literature on how higher education shapes gender segregation patterns provides ambiguous theoretical predictions and empirical evidence. Some studies find that higher tertiary participation rates and higher female enrollment increase gender segregation in the labour market, while other scholars present evidence to the contrary. Charles and Bradley (2002) argue that female enrollment in higher education reproduces gender segregation in occupations because female students tend to choose female-typed fields of study. Smyth and Steinmetz (2008) find that higher female tertiary enrollment is associated with more gender-typed work for women and more gender a-typical work for men. They show that both male and female higher education graduates are more likely to work in female-typed jobs rather than in gender mixed jobs and are less likely to work in male-typed jobs rather than gender mixed occupations. These findings are explained as follows: ‘A growth in female representation in higher education appears to be accompanied by a rebalancing of the workforce towards female-type jobs, with more women and men entering these jobs in countries which have experienced such a shift’ (Smyth and Steinmetz 2008: 272). However, these trends do not hold true for those higher education graduates who have selected male-typical fields of study. Indeed, higher tertiary participation rates may lead to an increase of the number of women in traditionally male-dominated fields of study and may, thereby, reduce occupational gender segregation (Bradley and Ramirez 1996). Overall, it appears that the linkage between gendered fields of study and labour market segregation is stronger in some countries than in others (Smyth and Steinmetz 2008).

Hence, if we want to understand the production of horizontal gender segregation by the educational system, we need to rethink institutional characteristics such as stratification, occupational specificity, and the institutional linkages between education and employment using insights from the gender-sensitive research on schooling and occupational choice (Imdorf et al. 2014). This paper aims to do that by identifying the ways in which the institutional features of the educational system influence gender-typed employment by taking a closer look at the case of Bulgaria.

The gender distribution of general versus vocational education at the secondary level in Bulgaria is as follows. Although decreasingly so, young women are still overrepresented in general upper-secondary education (grade 9–12) with 56% in 2013/2014 (Bulgarian National Statistical Institute 2014: 39). In contrast, young men make up a majority in VET with 60% of all students (Eurostat n.d.-a).² Still, the majority of VET graduates can make a ‘second occupational choice’ as there is a significant flexibility of choice at the point of the transition from secondary to tertiary education. That second choice may be less influenced by gender stereotypes in early adulthood than during adolescence.

The post-1989 changes in the educational system have had mixed effects on stratification, promoting some social inequalities while reducing others. A case in point is the massification of higher education: enrollment numbers more than doubled within one decade from 120,000 students in 1990 to 258,000 students in 2000 (Kostova 2008b: 169) and reached 283,294 students in 2013 (Bulgarian National Statistical Institute 2014: 67). However, despite that expansion, Bulgaria is among the countries where the socio-economic inequality in the access to higher education is most salient (Ilieva-Trichkova and Boyadjieva 2014). In 2013/2014, more than half (55%) of all students in Bulgaria's higher education institutions were women (Bulgarian National Statistical Institute 2014: 67). There has been a predominance of women in higher education since the late 1970s when Bulgaria was still under socialism. That educational gap between women and men persisted during the enormous increase in the total enrollment throughout the 1990s (Adnanes 2000). Thus, the educational structures provide a considerable advantage for women compared to men. It is reflected in the positive positioning and the higher upward mobility of girls and women in the Bulgarian educational system (Stoilova 2012).

Moreover, Bulgaria has one of the lowest field-specific gender segregation rates in Europe. The female representation in computer science and other traditionally male technical fields is higher in Bulgaria compared to other EU countries (Charles and Bradley 2009; Eurostat n.d.-a).³ Although Bulgaria has a relatively high share of women in the so-called STEM (sciences, technology, engineering, and math) subjects in international comparison, the share of women still remains limited and is well below 50% (see Table 1). We also observe changes towards a greater number of male and female students enrolled in female-typed professional fields (Boyadjieva 2012). Hence, higher education in Bulgaria seems to be geared towards female-typical programmes, whereas male students and male-typical programmes are overrepresented in the VET system.

Bulgaria has greater gender equality in terms of education and employment opportunities compared to many other countries (Kovacheva 2008). Moreover, occupational gender segregation has remained surprisingly small until recently, at least in comparison to countries that are well-known for high gender inequalities in the labour market (van Langen et al. 2006; Hausmann et al. 2010). Table 1 shows how the relatively high share of Bulgarian women educated in STEM professions is reflected in their employment.

For example, the share of women employed in Research & Development is 35% in the EU and in Bulgaria it is 52% (Eurostat 2013: 40). According to The Global Gender Gap Report 2013, Bulgaria tops the global ranking of the gender ratio among professional and technical workers (with 63% females vs. 37% males, see World Economic Forum 2013). These positive statistics might be the heritage of the former socialist regime where occupational sex segregation did not reach the levels found in capitalist countries and where female labour force participation was among the highest in the world (Glass 2008). However, the country's female economic activity has declined in the past decade. Bulgaria's female labour force participation rate stood at 56% in 2002 and dropped to 49% in 2012 which is a decline from 86% to 81% when expressed as a percent of the male labour force participation (UNDP 2004: 228; UNDP 2014: 172). Some of those figures may come under additional pressure due to the decreasing age of Bulgarian women at marriage and first birth (Kovacheva 2008) as well as the growing adherence of young Bulgarian parents to traditional gender roles in the division of care responsibilities (Stoilova and Slavova 2006; Hofäcker et al. 2013). The combination of raising children, trying to establish oneself in a precarious youth labour market, and the weakened social welfare state may create important challenges to the school-to-work transitions of young parents (Kovacheva 2008).⁴

We investigate how two institutional features of the educational system – the enrollment in vocational vs. general track education and the completion of higher education – condition gender-typed, a-typical, and mixed employment in Bulgaria. In addition, we study whether cohort specific differences exist in terms of gender-typed employment patterns. With regard to vocational education, based on the previous literature (Buchmann and Charles 1995; Estévez-Abe 2006; Trappe 2006), we expect that vocational training increases the likelihood of men to work in male-typed occupations and of women to work in female-typed occupations:

In terms of higher education, we can expect different effects for men and women (Smyth and Steinmetz 2008). For male higher education graduates, we can expect a higher chance to work in a mixed or a gender a-typical occupation when female tertiary enrollment is high and when female-typed fields of study are prominently represented at the higher education institutions. As was explained in the preceding section and summarised in Table 1, those are indeed existing conditions in Bulgaria. Hence, based on the literature (Charles and Bradley 2002; Smyth and Steinmetz 2008) and the particularities of the Bulgarian higher education landscape, we expect that:

For women the effect is unclear. According to some studies (Charles and Bradley 2002; Smyth and Steinmetz 2008), greater female enrollment in higher education tends to reproduce female-typed employment for women. However, higher tertiary enrollment can also direct women into male-typed or gender mixed employment, especially when women enroll in traditionally male-typed fields of study (Bradley and Ramirez 1996; Smyth and Steinmetz 2008). Similarly, Bulgaria's contextual factors point in opposing directions. On the one hand, we can expect that Bulgaria's female higher education graduates are more likely to find themselves in female-typed jobs given the high female enrollment rates in Bulgaria's tertiary sector, coupled with the growing importance of female-typed fields of study. On the other hand, Bulgarian women are relatively well represented in male-typed fields of study compared to other countries and, therefore, we would expect that they are less likely to find themselves in female-typed jobs. These opposing factors may imply a nil net effect. Therefore, we predict that:

Finally, we expect cohort specific patterns reflecting the educational careers before and after the end of the socialist regime in 1989: workers younger than 45 years old are expected to work more often in gender-typed jobs compared to employees aged 45 or older. Under socialism, the educational decisions were guided by the state and the access to employment was more or less guaranteed. As Kogan (2008) finds, young people in the CEE countries were often assigned to their first workplace. In that context, the female labour force participation rate was very high and women were well represented in male-typed vocational training, fields of study, and male-typed work. The transition and related reforms gave way to more uncertainty in the labour market. At the same time, it created new freedoms for the individuals and allowed the pupils and their families to make their own educational and vocational choices (Popov 2007). These changes could lead to new inequalities and opportunities to make gendered decisions. These cohort effects, however, may be relatively weak due to the time passed and the significant economic structural changes in the country. Most notably, the decline in manufacturing and the rise in the service industry created more female-typed jobs (Castells 2002). Many older workers had to change their occupations and find employment in the newly expanding sectors. Hence, the current labour market situation may be only weakly tied to the particularities of the socialist educational system. In addition, we may observe age specific effects. Specifically, young men may find employment in the male-typed construction and transportation industries due to the physical requirements that come with those jobs. Therefore, we expect that the difference between the younger and the older male workers is more pronounced than the difference between the younger and the older female workers.

We propose the following relationship:

We use data from a nationally representative survey conducted by an established national polling agency based in Sofia, Bulgaria. The respondents were contacted via telephone by trained professional interviewers following a detailed interview script. The interviews were conducted during February 2012. The ‘Sociological Survey of Labour Market Aspects in Bulgaria’ is based on a sample of 1006 respondents aged 18 and older that were selected through random dialing. The standard sampling size for a nationally representative survey in Bulgaria (population 7.5 million) is usually about 1000. The cooperation rate was 30%, that is, of those contacted, 30% completed the interview (N = 1006), 64% refused to participate in the survey, and 6% interrupted the interview for different reasons. The respondents provided answers to ten closed-ended labour market questions. After the exclusion of all missing values and limiting our sample to people under the working age of 65, the total number of respondents included in this analysis is 783.

We use the following survey question to construct our dependent variable: ‘Is your current (or last if currently not employed) occupation mostly exercised by men or by women?’ Respondents could choose to answer: almost exclusively by women; mostly by women; equally by women and men; mostly by men; almost exclusively by men; or inapplicable (has never been employed). The dependent variable Gender Typed Job takes the value 1 for female/male respondents whose occupation is almost exclusively or mostly exercised by women/men; 0 for respondents in gender neutral occupations; and −1 for female respondents in male-typed jobs and male respondents in female-typed jobs.

The respondents’ perceptions of their professional environment are largely in line with macro level data. According to data from the 2007 European Labour Force survey report, 33% of Bulgarians are in female-dominated occupations, 36% are in predominantly male occupations, and 31% are in mixed occupations (European Commission 2009: 93).⁵

Similarly, according to the Bulgarian Census and Labour Market data, 35% of the adult Bulgarian population are employed in occupations that are female-typed, 33% are in male occupations, and 32% work in mixed occupations (these calculations follow Hakim's measure of gender segregation and use pooled Bulgarian Census and Labour Market data 2006–2010; N = 3,051,335). According to our survey's subjective measurement, 63% of the respondents work in gender-typed occupations, 25% are in mixed occupations, and 12% work in gender a-typical occupations. Put differently, 42% of the respondents report working in a male-typed occupation, while 33% work in a female-typed occupation. Thus, our measurement closely matches macro statistics for female-typed employment, slightly overestimates male-typed employment, and slightly underestimates the mixed category.

The respondents were asked if they obtained a general education or a vocational education: ‘On a scale from 1 to 3 please indicate to what extent your education trained you for a specific profession.’ The three possible answers were as follows: (1) I completed a general education which did not train me for a specific profession; (2) I completed an education which somewhat trained me for a specific profession; (3) I completed a vocational or professional education which trained me for a specific profession.⁶ A dummy variable was created for vocational education (VET) equal to 1 if respondents selected (3) and 0 for all others.

The level of education was measured with the following survey question: ‘What is the highest level of education you have attained?’ We created a dummy variable for higher education (HigherEdu) which equals 1 if the respondents obtained tertiary education by selecting either ‘First stage of tertiary education (not leading directly to an advanced research qualification)’ or ‘Second stage of tertiary education (leading to an advanced research qualification).’

The respondents also reported their age in years. We restricted our sample to respondents aged 18–65 and created a dummy variable Plus45 that equals 1 for respondents aged 45 and older and 0 otherwise. That variable distinguishes between individuals who completed their education in the socialist pre-1989 educational system and the younger cohorts who gained their qualification more recently.⁷

In addition, we include two control variables: LowSocialStanding and Bulgarian. Controlling for social status is important since both the educational careers and the gender-typed career decisions could be confounded by the job holders’ social origin. Social status was measured with the following survey question: ‘Where would you place yourself on the following scale if the top box indicated high social standing in the country and the bottom box indicated low social standing.’⁸ The answers ranked on a scale from 1 to 5 with 1 indicating the lowest social standing and 5 the highest social standing. Our variable LowSocialStanding equals 1 for the respondents reporting 1 or 2 on the social standing scale and zero for all other respondents. The respondents also reported their ethnicity (Bulgarian, Turkish, Pomak, Roma or other).⁹ The variable Bulgarian is coded as 1 for respondents that identify themselves as Bulgarian and all other categories are coded as 0. The inclusion of ethnicity is important because of the relevant linkages between ethnicity, educational achievement, and labour market outcomes. In particular, the minorities of Roma and Turkish descent have significantly lower enrollment rates in higher education and are more likely to experience unemployment or precarious work (Stoilova and Haralampiev 2009).

The descriptive statistics are presented in the  Appendix, and Table 2 provides an overview of the key variables.

Sixty-three percent of all survey respondents report working in a gender-typical occupation. However, we can see that this percentage differs by gender. While 55% of women work in a gender-typical occupation, 72% of men report doing so. Respectively, 29% of the female respondents work in a mixed occupation compared to 20% of the male respondents. Gender a-typical work also appears more common for women (16%) than for men (8%). Aside from the gender differences, Table 2 also shows that the respondents with higher education are less likely to work in a gender-typical occupation (58%) compared to the respondents with vocational education (64%). Of the respondents aged 45 and older, 14% report working in a gender a-typical occupation, 23% in a mixed job, and 63% in a gender-typical job. In contrast, of the respondents aged 44 and younger, 28% are in mixed employment and only 10% report working in a gender a-typical job.

Table 3 presents the estimation results from an ordered probit model where the dependent variable Gender Typed Job takes the value 1 for gender-typical occupations, 0 for gender mixed occupations, and -1 for gender a-typical occupations.

The table shows the coefficient estimates of the independent variables as well as the cut parameters from the ordered probit estimations that we use later to calculate predicted probabilities. The estimations are carried out separately for male and female respondents using robust standard errors to reduce the influence of outliers on the estimation results. In unreported estimations, we also performed analyses with a combined sample of female and male respondents, adding interaction variables of gender with vocational education and higher education. The results from these models reveal the same effects as reported in the paper. However, as Mood (2010) points out, interpreting interaction variables in non-linear models is not straightforward and can yield incorrect and misleading conclusions. Therefore, we opted to report a simpler version of the estimations using split samples, but we can make all other estimations available on request.¹⁰

None of the coefficient estimates in the sample of female respondents in Table 3 is statistically significant at accepted significance levels. In contrast, almost all of the independent variables for male respondents are statistically significant. For men, vocational education increases the likelihood of participating in gender-typical occupations whereas higher education and being above 45 years of age have the opposite effect, that is, they reduce the likelihood of gender-typical occupations. Of the two additional control variables, Bulgarian ethnicity does not have a statistically significant effect whereas low social status is associated with a greater likelihood of working in a gender-typical occupation.

While these results provide information about the direction of the relationships between the independent variables and the gender type of occupations, the actual sizes of the effects remain unclear. Specifically, by what percent does vocational or higher education heighten or lower the likelihood of being in a male/female or gender mixed occupation? To gain insight into that question, in Table 4 we report predicted probabilities based on the ordered probit model presented above. Specifically, we calculated the predicted probability of gender-typical, mixed and gender a-typical occupations for several subsamples defined on the basis of our two variables of primary interest: vocational education and higher education. To calculate the predicted probabilities, we assign all independent variables their mean value and change only the variables for vocational education or higher education. That allows us to compare the predicted likelihoods by varying one variable at a time.

As can be expected from the statistically insignificant coefficient estimates in the sample of female respondents, there is very little difference in terms of predicted probabilities between female respondents with vocational education and general education. For example, the likelihood of working in a gender-typical occupation for female respondents with general education is 54% and the same likelihood for female respondents with vocational education is 55%. Similarly, the probability of gender-typical work for female respondents with higher education is 56% and without higher education it is 54%. The same pattern of very similar probabilities for female respondents is observed for mixed and gender a-typical occupations.

For men, the differences in the predicted probabilities are greater and in line with the estimation results reported in Table 3. Men with general education have a 67% likelihood of being in a gender-typical occupation compared to 74% for men with vocational education. Similarly, vocational training lowers the likelihood of being in a gender a-typical occupation from 10% to 7% and the likelihood of being in a gender mixed occupation from 23% to 19%. In other words, vocational education shifts men from gender a-typical work to mixed and gender-typical jobs as well as from gender mixed jobs to gender-typical jobs. Higher education has an even more pronounced effect. Men with less than higher education have a 75% likelihood of being in a gender-typical occupation. Higher education lowers that likelihood to 63%. Higher education also doubles the probability of gender a-typical occupations from 6% to 12%. Thus, the effect of educational structures on the gender type of occupations for men is not only statistically significant but also of substantial size.

In this paper, we ask how vocational vs. general education and lower vs. higher education levels impact the gender type of occupations in the Bulgarian labour market. Our regression results point to differential processes for men and women. While the educational system appears to be salient in shaping men's entry or non-entry into gender-typed jobs, it does not hold the same explanatory power for women. As expected, VET steers men towards male-typed work. Following Buchmann and Charles (1995), we attribute these findings to the early biographical timing (around age 15/16) of the VET programme choice when gender is particularly important in shaping young people's identity. Moreover, gender-typed VET programmes tend to reinforce gender-typical career choices and future employment through gender socialisation (Busch-Heizmann 2014). This reinforcement process may be particularly strong for men as male-typed VET programmes foster the construction of hegemonic masculine identities (Connell 2005).

In contrast, men seem more willing to leave male-typical pathways and to pursue non- or a-typical educational careers when they choose a higher education programme. This effect of higher education can be understood in the context of growing female tertiary enrollment rates and an increasing number of female-typed professional fields (Smyth and Steinmetz 2008) in Bulgaria's higher education since 1989. These processes promote men's entry into mixed and typically female jobs once they have completed higher education.

While we find that vocational education and higher education are relevant factors in explaining gender-typical or a-typical employment for men, the same cannot be concluded for women. Neither vocational nor higher education significantly predicts women's placement in gender-typical, a-typical or mixed professions. The missing link between vocational education and female-typed jobs for women may be due to their growing underrepresentation in the VET system. The finding that higher education leads men but not women into female-typed employment can also be expected given the Bulgarian higher education landscape. As Table 1 shows, Bulgarian women have a higher enrollment in STEM and other traditionally male-typed higher education programmes compared to other EU countries. This, in turn, could explain why women with higher education are not steered toward female-typed jobs in Bulgaria (Bradley and Ramirez 1996; Smyth and Steinmetz 2008). Overall, the differential impacts of the institutional variables on gender-typed employment for men and women point to the importance of assessing these dynamics separately for men and women.

We also find that men aged 45 and above are significantly more likely to work in female-typed professions compared to younger men. These cohort effects can be understood in terms of the differences in schooling. Those aged 45 and older are people who have completed their education in the socialist educational system. The educational selections were determined less by individual choice and more based on state planning and (pseudo) egalitarian criteria. Therefore, more men entered gender mixed or female-typed professions prior to the transition. Age reflects respondents’ differential school experiences in the socialist and the post-socialist contexts and, therefore, further illustrates how the educational system conditions people's labour market placement. Similarly, changes in the labour market structure may be relevant with respect to these cohort dynamics. Specifically, the rise of the service sector and the decline of manufacturing in Bulgaria mirror a post-industrial transition towards more female-typed service work (Castells 2002). Also, the dynamics seen in our analysis may be interpreted as age rather than cohort effects: older men might be selected less often for male-dominated occupations because of the physical requirements which are predominant in male-typed jobs such as in the transportation, construction, or heavy industry.

To assess the extent and to understand the mechanisms of horizontal gender segregation in the labour market, one must consider the educational institutional factors in mitigating or reinforcing gender-typical career paths for both men and women. We should keep in mind that the Bulgarian educational system has two institutional features which (albeit not intended) might facilitate the lateral occupational mobility for men and women at the early stage of their careers: there is no straightforward vocational linkage between upper-secondary and tertiary educational levels and only weak linkages between the educational system and the labour market. Overall, Bulgaria appears to have lost some of its previously achieved higher gender equality compared to other Western European countries. Still, the Bulgarian case shows the role that higher education could play in decreasing the occupational gender segregation in the labour market. Similarly, the case shows that vocational education does not necessarily have to lead to highly gendered labour market outcomes, especially for women.

Additional information on the survey and data analysed in this paper can be found here: http://www.schooltowork.bg

No potential conflict of interest was reported by the authors.

Franziska Bieri received her Ph.D. in Sociology from Emory University, USA. She is a senior researcher at the University of Basel and a faculty member of the University of Maryland University College. Her areas of research include civil society organisations, global governance, and comparative labour markets. She has written on public-private partnerships in the diamond industry, on the institutional contexts of corporate social responsibility, and on the role of social trust in civic engagement.

Christian Imdorf is currently a research professor at the University of Bern. His research projects focus on education systems and gendered school-to-work transitions, on new organisational and institutional forms of vocational training, on social inequality in higher education, and on labour market integration of disadvantaged young workers. He is a member of the Transitions from Education to Employment (TREE) panel survey management team and a coordinator of the Sociology of Education Research Network of the Swiss Sociological Association.

Rumiana Stoilova is Professor and Director of the Institute for the Study of Societies and Knowledge at BAS. Her research interests and expertise are focused on the international comparative research on transitions from education to work in Europe; integration of youth into the labour market; impact of education and gender on stratification; social trust and mechanisms for obtaining equal access to educational opportunities; and the impact of the welfare state on groups in risk of poverty and exclusion. At present, Professor Stoilova is a team leader of the project funded under Horizon 2020 entitled ‘Negotiating early job-insecurity and labour market exclusion in Europe’. Rumiana Stoilova is an expert to the EU Commission and member of the Editorial Board of the Journal ‘European Policy Analysis'.

Pepka Boyadjieva is Professor at the Institute for the Study of Societies and Knowledge at BAS. She is Honorary Professor of Sociology of Education at the University of Nottingham and member of the Editorial Board of the ISA's SSIS series Sage Studies in International Sociology Books and of the International Journal of Lifelong Education. Her research interests are in the field of education with emphasis on higher education and social justice, educational inequalities, lifelong learning and school-to-work transitions. Currently, Boyadjieva directs the project ‘The Culture of Giving in Education', leads the Bulgarian team of ‘Youth in Transition Countries' international project and the Encouraging Lifelong Learning for an Inclusive and Vibrant Europe (ENLIVEN) project.