Journal of Social Research & Policy, Vol. 6, Issue 2, December 2015

Gender Differences in Higher Education Efficiency and the Effect of Horizontal Segregation by Gender HAJNALKA FÉNYES1 University of Debrecen, Hungary

Abstract In our paper, we examine the effect of faculty gender composition on females’ and males’ higher education efficiency. According to special literature, horizontal segregation by gender affects students’ higher education efficiency, as the masculine, male-dominated faculty composition has a negative impact on women’s performance. Our research method is quantitative. We have used the database of a research project which was conducted in the historical Partium region, which is a cross-border area of three countries (Hungary, Romania, and Ukraine). We have found that in the examined region, male dominated education fields are computer science, engineering, and agricultural sciences. In the region, male advantage has been shown in five efficiency variables, although our regression models have revealed that this is only due to males’ better socio-demographic background. Based on our contextual model, we have found that at faculties where the proportion of women is lower, men have an advantage in higher education efficiency, in accordance with the findings of the literature. However, at faculties with a higher proportion of women, in some cases it is males, in others, it is females who are more efficient. Our regression model has revealed that as the proportion of women at faculty increases, students’ higher education efficiency decreases, which, nevertheless, cannot be seen in our contextual model figure.

Ke ywords: Higher Education Efficiency, Gender Differences, Horizontal Segregation by Gender.

Introduction In our study, we investigate the effect of faculty gender composition on females’ and males’ higher education efficiency. In the special literature, relatively few studies examine whether male/female dominance at a faculty supports or hinders men’s and women’s efficiency. In their qualitative studies, Hungarian researchers (Nagy, 2014, 2015; Paksi, 2014; Takács, Vicsek & Pál, 2013; Szekeres, Takács & Vicsek, 2013) have investigated why there are few women pursuing studies in natural sciences, engineering, and computer science; they have also explored how male-dominated faculty environment influences women’s efficiency. According to their results , as well as international literature (Sagabiel & Dahmen, 2006), women are less successful at these faculties due to their minority, subordinate position; furthermore, many of them drop out, as the “masculine” organizational structure, stereotypes, and prejudices in the institution (from professors and fellow male students) hinder women’s efficiency. In conclusion, environment and gender composition at a faculty, as well as organisational culture have an influence on higher education efficiency of males and females. Our study focuses mainly on these. In our earlier studies (Fényes , 2009, 2010a; Fényes et al., 2015; Fényes & Engler, 2012; Engler, 2013a, 2013b; Pusztai & Kovács, 2015), we have explored the methods to measure efficiency and gender differences in higher education efficiency in detail. In the following parts of this article, we shall summarize the results of our previous research in these fields. Nagy 1

Postal Address: 4032 Debrecen, Mikszáth u.7 fszt 1, University of Debrecen, Hungary. E-mail Address: [email protected]

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(2015) has pointed out that, according to some teachers, in engineering higher education, male and female students’ knowledge and performance are similar (concerning grades), nevertheless, one could observe that some professors compare men and women falsely: diligent female students and careless male students; women who study intensively and men who study creatively; reliable, loyal female students. Some teachers believe that male students are better at mathematics, understand the subject matter more clearly, and technical professions are more suitable for them. In addition, male students consider themselves more successful academically , and overestimate their knowledge. Female students’ intellectual capabilities are rarely recognized by professors, who praise their female students’ diligence, persistence, collectedness, great social skills, and good influence on male students instead (Nagy , 2015). In our quantitative analysis, we use the database of a research project which was conducted in 2014 in higher education institutions from three countries (Romania, Ukraine, and Hungary), in the historical Partium region. In the theoretical part of the study, we investigate horizontal segregation in higher education and its causes on the basis of international literature, then we explore women’s minority position in STEM 2 fields, and the reasons behind it. This is followed by our previous research findings and present research questions. In our empirical work, we investigate horizontal segregation by gender in higher education faculties and gender differences in efficiency. Furthermore, we examine the “male disadvantage hypothesis” (see Fényes & Pusztai, 2006), which shows that men have less social mobility in higher education, they only enter higher education with more advantageous social background. Through our regression models, we explore how the proportion of women at a faculty and one’s gender influence one’s higher education efficiency, controlling for the effect of students’ social and demographic background. Finally, in our contextual analysis we investigate men’s efficiency advantage, as well as the negative effect of the increasing proportion of women at a faculty on efficiency, and the correlation between these. Trends of Horizontal Segregation by Gender in Higher Education In the 1990s, there was an approximately 30% difference in the fields of study which men and women pursued in the United States (Jacobs , 1995, 1996). After a period of decrease in the 1960s and 1970s, horizontal segregation in developed countries has remained on a relatively high level, even though the proportion of women in higher education is gradually increasing. There are more women than men who study to become teachers, healthcare professionals, and psychologists, while the proportion of male students is higher in the fields of Engineering, Physics, and Computer Science (Freeman, 2004; Bae et al., 2000). In the 1990s, the proportion of females surpassed 50% in natural sciences (mainly in the fields of Chemistry and Biology, but not in Physics), although their presence in Engineering education was only 14% (Jacobs , 1996). Previous studies have discovered a certain dichotomy between Humanities and Sciences with respect to gender composition of faculties. Recent studies argue that this dichotomy explain s only half of the segregation; instead, they make a distinction between helping-caring and technical fields, based on research conducted in several countries. Per their findings, caring fields include Teacher Education, Humanities, Social Sciences, Social Work, Medicine, Biology, and Mathematics. Computer Science, Engineering, and Agricultural Sciences are characterized as technical (male-dominated) fields (Barone, 2011). Koncz (1996) has found that in Hungary in the 1990s, the feminization of fields of study where the proportion of women has been previously high (Teacher Education and Humanities) increases further. There were also more females at the faculties of Arts, Medicine, and Law. Nowadays, women are as educated as men, sometimes even more, but they have taken different learning paths. On the tertiary level, males tend to pursue studies in Natural Sciences, Engineering, and Agricultural Sciences, while females prefer Teacher Education, Humanities, and Social Sciences (Keller & Mártonfi, 2006; Oktatási körkép [Educational Overview], 2005). At the Technical 2

In the following, we shall use the acronym ST EM for fields of natural sciences and engineering. ST EM fields include: Computer Science, Engineering, Economics, Statistics, and Mathematics.

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University of Budapest, the proportion of women has been on the rise from the 1970s on, apart from minor fluctuations, yet in 2004 their presence was only 23.2% (Palasik, 2006). Recent studies have revealed that the Millennium has brought about a slight decrease in gender segregation by field of study in Hungary in higher education (Tornyi, 2008; Hrubos, 2001a, 2001c; Bukodi et al., 2005). The proportion of female students has increased in the fields of Engineering, Natural Sciences, Law, Agricultural Sciences, and Veterinary Medicine, while it has somewhat decreased among those who study Humanities or take part in some form of Teacher Education (Hrubos, 2001b). Bukodi et al. (2005) have found that the proportion of women has risen in the fields of Engineering, Agricultural Sciences, Natural Sciences, Law Enforcement, Military Science, and religious studies; that is to say, women are entering previously male-dominated fields. At the same time, there are somewhat less females who take part in primary or preschool teacher education, while feminization continues in the fields of healthcare, law, social and public administration, as well as economics. Causes of Horizontal Segregation One of the reasons behind horizontal segregation is different socialization. Parents and teachers have different expectations of males and females. Female students are expected to have better reading skills, while it is anticipated that male students are better at mathematics, and these become self-fulfilling prophecies. If a female student is good at mathematics, teachers attribute this to her diligence, while in a male s tudent’s case, teachers emphasize his good abilities (Kovács , 2007). Social psychologists believe that through socialization and conformity towards one’s personality, male and female students choose fields of study which “fit their gender” (Jacobs , 1995). Another cause of segregation could be the fact that males’ and females’ cognitive abilities differ. PISA results show that female students are at an advantage in reading and understanding both in Hungary and in other OECD-countries. Male students are advantaged in Mathematics and Natural Sciences, although the latter difference is only significant in half of the OECD countries, and seems to decrease over time (Freeman, 2004). Other research findings conclude that gender differences are not significant anymore in Mathematics in developed countries (Marks, 2008), nor in Hungary (Horváth & Környei, 2003; Lannert n.d.). Some studies argue that the cause of horizontal segregation by gender is twofold. On one hand, influences of socialization have to be considered (e.g. the impact of the parents’ level of education); on the other hand, a rational choice theory model could be an explanation as well (the impact of the students’ school grades on their decision). Storen & Arnesen (2007) have found that grades in Mathematics affect men’s choice of subject field in higher education more than that of women, that is to say, men act according to the rational choic e theory model more frequently. Their other finding is that if the parents’ level of education is higher, gender atypical choices of subject field are more common (Storen & Arnesen, 2007). The rational choice theory model also offers an alternative explan ation. According to this, women may experience gender-specific comparative advantages when choosing feminine fields of study. This may be since females evaluate their abilities which are necessary to pursue studies in a certain field differently to what their actual academic achievements suggest; in other words, academic achievement is not the only factor which matters, as confidence with respect to the knowledge in a field of study is also very important. What one decides to study in higher education is only slightly explained by one’s academic achievement in that certain field of study in secondary school, since 10-30% of educational choices are determined by gender-specific comparative advantages (Jonsson, 1999). Women’s Underrepresentation in STEM Fields and its Causes As we have previously mentioned, gender differences in Mathematics test results and grades decrease with time. In 1982 there was only 0.3% advantage of males, based on the average performance in 19 developed countries. Moreover, in some countries (Finland, Hungary, the French-speaking part of Belgium, and Taiwan) female students showed better results in Mathematics than their male peers as

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early as 1982 (Baker & Jones, 1993). However, female students are less interested in Mathematics, and few women choose to study Mathematics and Natural Sciences in higher education, despite positive trends. Research data in Hungary suggest that women are in the majority in higher education, although they are less likely to orientate themselves towards Natural Sciences and Mathematics in secondary school, therefore fewer females obtain a degree in those fields (Keller & Mártonfi, 2006; Oktatási körkép [Educational Overview], 2005). Many people believe that these fields of study should be made more attractive to females, and there is no need to improve their results (Linver, Davis-Kean & Eccles, 2002). Different career paths, parental influence, psychological hindrances, lack of social support, and limited capacity in the education of Natural Sciences may explain why there are few women in the fields of Engineering and Natural Sciences (Jacobs, 1996). The reason why there are fewer women in STEM fields could be that females underperform in Mathematics compared to other subjects, although their grades in Mathematics are similar to those of males (Felson & Trudeau, 1991). Furthermore, results may vary by gender in different branches of Mathematics, and males are still at an advantage in Physics and Engineering (Hyde et al., 2008). Some studies have also pointed out that even though the mean of grades in Mathematics is similar for both genders, a greater variability in males’ performances can be observed, as there are more extraordinary performers as well as underperformers among them (Hyde et al., 2008). Females’ different career choices may also be explained by the fact that male and female students have different attitudes towards Mathematics, and their confidence in their knowledge is not the same either. Women are less interested in Mathematics, and are not as confident in their knowledge either (Catsambis, 1994). Psychological studies have concluded that the reasons female students perform worse in Mathematics are gender stereotypes and gender socialization , and not poor abilities and biological factors (Spencer, Steele & Quinn, 1999; Spelke, 2005). Some studies show that gender differences in attitudes towards Mathematics and achievement vary by country. These differences can be explained by cultural diversity and different structural possibilities, as well as how widespread the idea of gender equality is in a country, that is, the proportion of women in higher education, in academic fields, and in the parliament (Else-Quest, Hyde & Linn, 2010). Psychologists argue that gender differences in Mathematics performance and interest in STEM fields are strongly defined by “stereotype threat”, that is, it is generally believed (especially by teachers and parents) that females are worse at Mathematics and therefore should not pursue a career in STEM fields. According to research findings, as stereotype threat decreases, females’ performance in Mathematics improves, and more of them become interested in STEM fields (Spencer, Steele & Quinn, 1999; Shapiro & Williams, 2012). Not only do negative expectations hinder women’s knowledge in Mathematics, they also divert females fro m careers in Engineering and Natural Sciences (as pointed out in the research conducted in Hungary by Takács, Vicsek & Pál (2013) and Szekeres, Takács & Vicsek (2013)). There are few women in STEM fields as well as, generally, in academic professions (Paksi, 2014; Nagy, 2014). It can be shown that some women give up their academic career for the sake of maternal tasks. Few women become researchers; and we cannot encounter many women at the peak of academic careers, either. The “leaky pipeline” metaphor depicts how some women leave their academic careers (Paksi, 2014; Blickenstaff, 2005). The “leaky pipeline” occurs in multiple steps. Firstly, many talented women choose not to pursue a career in Natural Sciences and Engineering (see above the causes of this phenomenon). Secondly, even if they have chosen so, many females leave their academic careers or drop out, especially during doctoral studies, or during decisions and transition periods between career stages. Finally, for the reasons above, fewer women become university professors and scientific researchers, especially in STEM fields (Paksi, 2014). The causes of the “leaky pipeline” are complex; they include influences of socialization as well as dominant gender roles and stereotypes in a given society. Furthermore, the masculine worldview of science, according to which females are more emotional, while males are more rational, therefore more apt to pursue academic careers, also plays a role. Anothe r influence could be that curricula are biased; especially the so -called “hidden curriculum”, that is, educational culture, may divert females from careers in research and STEM fields (Paksi, 2014).

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Circumstances of Hungarian Women in Higher Education in STEM Fields In 2013, at Engineering faculties of a Hungarian university, an interview study and a focus group interview study were conducted among professors and female students. Professors were asked what “gender order” and “gender culture” they thought were characteristic of their faculty, and what their opinions were on female students, their underrepresentation, and its causes (Nagy , 2015). According to the findings, professors believe that in fields of Engineering, work environment is suitable for men, women are in a minority and in a subordinate position, and they are less accepted. Nagy (2015) has found that the main reason why there are few women in Engineering education is the faculty atmosphere. Education institutions actively reinforce tradition al opinions with respect to gender roles, that is, they are characterized by a certain “masculine” organizational structure. Professors think that male students are more interested in studying and becoming engineers, while female students mostly care about family and other areas of life; in other words, women are students of secondary importance. Female students are presumed to be less competent, which may become a self-fulfilling prophecy. According to professors’ opinions, being a mother can hardly be reconciled with a career in Engineering or Computer Science. Nagy (2015) has also found that female students, due to their small presence in Engineering, are considered by their professors and peers eccentric and symbols of their gender. While there is exceptional attention and interest towards their gender, they are also subject to masculinizatio n . Professors treat the few female students quasi as boys (Nagy , 2015). Professors believe that problems with equal opportunity are not university affairs, but socie tal issues; problems stem from outside universities. Since society imposes traditional roles, few females pursue careers in engineering, and there is not much university can do about it. However, professors emphasize that talented women would be welcome in Engineering education, as nowadays more students with only average abilities start studying Engineering. Furthermore, professors’ opinion suggests that an increased number of female students is beneficial to the whole degree program, since professors and male students are thus more content due to females’ better social skills. Female students are friendlier, great at networking and teamwork, have good organizing skills, and may coordinate male students (Nagy, 2015). At the Engineering faculties mentioned above, according to female students’ option, females are deterred from those faculties by gender hierarchy and “masculine” atmosphere at the institution. Those who already study there find it difficult to get ahead. Furthermore, society also views them as eccentric, while their professors and male peers think that Engineering is not suitable for them. Women often have to justify their choice of career. Cases of sexist professors and degrading jokes have been also reported. Due to negative stereotypes from s ociety and their faculty as well as their minority position, female students’ self-confidence in these fields may decrease. Women who study at Engineering faculties try to adapt to the competition through “masculinization”; they feel the need to prove themselves in a male-dominated environment. Nevertheless, female students believe that their gender also has advantages, for example, special attention and willingness to help on behalf of professors and male peers. Moreover, some professors have lower expectations towards them, which may become a self-fulfilling prophecy, as it presumes that they will be less successful. Many women also think that integration into a male-dominated world for female students is difficult, and some plan to drop out (Szekeres , Takács & Vicsek, 2013; Takács, Vicsek & Pál, 2013). Previous Findings and Research Questions In our previous studies concerning horizontal segregation (Fényes, 2010a, 2010b), it has been shown through data gathered in 2003 in the historical Partium region, that faculties where the proportion of women is above average prepare students for less prestigious (financially less rewarding) professions.3 ”Feminine” (and less prestigious) faculties/institutions include the Faculty of Arts of the University of Debrecen as well as several college faculties, mostly of caring professions (preschool 3

About the prestige of intellectual professions based on the view of inhabitants of Hungary see Szabó (1997), and about the prestige of different faculties based on students’ opinion see Fónai (2009, 2013, 2015) and Fónai, Ceglédi & Márton (2011).

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teacher, social pedagogy expert, teacher, health professions). Some faculties at which the proportion of women is around average offer degree programs which prepare students for highly prestigious careers (law, medicine). However, the proportion of women who want to pursue a career in the highly prestigious fields of Business and Economics is below average. We have found that the Faculty of Science and Technology and the Faculty of Engineering of the University of Debrecen are, among others, “masculine” faculties as the proportion of females is 50% or below. We intend to investigate faculty gender differences on the basis of our 2014 database as well. Our previous studies have also explored females’ and males’ higher education efficiency, and its correlation with females’ and males’ social background. These findings are based on a research project conducted in 2005 in the historical Partium region. The studies have revealed that men are at a disadvantage (in the following: “male disadvantage hypothesis”): males in higher education have less social mobility; only men of privileged background enter higher education, while men of worse background are presumed to take part in vocation al training instead (see in detail Fényes & Pusztai, 2006). Based on our other studies (Fényes , 2009; Fényes, 2010a) males are at an advantage in four efficiency indices (participation in the National Scientific Students’ Associations Conference, publications during university years, membership at colleges of advanced studies, doctoral plans), while females are only at an advantage in the number of language certificates and plans for further studies. Males are better at “academic” efficiency indices, and, as revealed by multi-variable analysis, this was not only due to their better social background. In the present study, these correlations are also tested on 2014 data. Our new research question is how females’ and males’ efficiency is influenced by gender composition at a faculty (the proportion of women), controlling for the effect of social background. To investigate the effect of the faculty, we used regression models as well as a new method: contextual analysis. Through contextual analysis we are able t o explore the interaction of the influences of gender and women’s proportion at the faculty on efficiency, while our regression models allow us to investigate these influences only separately. Methods, Databases, and Included Variables In our study, we conduct quantitative analysis in the framework of an OTKA [Hungarian Scientific Research Fund] project (K116099). Our research methods include: cross -tabulation analysis, variance analysis, linear and logistic regression models, and contextual analysis. Ou r database is the result of the TESSCEE (II. Teacher Education Students Survey in Central and Eastern Europe) and IESA (Institutional Effect on Students’ Achievement in Higher Education) research projects. 4 Students were surveyed in the historical Partium region, a cross-border region of three countries, in the following areas: Hajdú-Bihar County and Szabolcs -Szatmár-Bereg County in Hungary; the Partium region (Crişana and Banat), Central-Transylvania, and Székely Land (Harghita, Covasna, and Mureș) in Romania; the Subcarpathian region (Zakarpattia Oblast) in Ukraine. In the second wave of the TESSCEE research project, conducted in the autumn of 2014, the base population consisted of both state-funded and fee-paying teacher education students, either close to the entry into higher education (2nd year for bachelor’s students) or close to the exit (1st year for master’s students and 4th year for students in undivided training, which offers a master’s degree). During the second wave, we considered comparing teacher education students with other students very important; therefore, we created a joint database from the survey results of TESSCEE II, and the IESA research project, which was conducted in the same period with similar methods. The IESA sample included 20% of students in their 2nd year, as well as 50% of master’s students in their 1st year and students in the 4th year of their undivided training. All in all, we have information about 1792 students at different faculties from different countries. Sampling was proportionate to faculty size. Through cluster sampling, all students of randomly selected seminary groups were surveyed. As the willingness to respond differed at each faculty, the results were not fully representative, but the proportion of respondents at each faculty was more or less similar to our targets. 4

Research was conducted in the framework of the SZAKTÁRNET (TÁMOP-4.1.2.B.2-13/1-2013-0009) and IESA projects (RH/885/2013).

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The dependent variables of our regression models were the efficiency index (with 15 items 5 ), 15 separate efficiency variables, and a binary version of the efficiency index (1: efficiency above average) for contextual analysis. Our independent variables were the following: gender, age, proportion of women at the faculty 6 (contextual variable), country of the higher education institution (1: not Hungary), father’s and mother’s level of education meas ured by the number of years successfully completed in education, objective financial situation index (possession of durable goods 1-10), subjective financial situation variable (self-comparison to an average family 1-10), occurrence of financial problems (1: often), place of residence at 14 years of age (1: city), and three variables of individual cultural capital: cultural consumption principal component 7 , number of books read in the past year, and the estimated number of the respondents’ and their parents’ books. Measuring the Impact of Gender Composition at a Faculty on Efficiency In higher education, there are several ways to measure students’ efficiency at an institution or a faculty. In this analysis, higher education efficiency is measured by a spec ial efficiency index (the 15 items can be seen in table 2) and, separately, 15 efficiency variables, which may be influenced by many factors. In our regression models, the influence of social background is not included in the forming of the dependent variable, that is, our dependent variable is not the divergence from the level of efficiency which would be expected based on the social background, but the individual efficiency itself. (See Horn’s study (2015) for the value-added concept in connection with secondary education.) We have decided to do so in order to avoid creating a compact social background variable, which may not have been beneficial, as the effect of its components might be ambiguous and of different magnitude. The impact of social background is part of our model through the inclusion of multiple, financial and cultural, social background variables as well as the contextual variable (the proportion of women at the faculty) as independent variables. The contextual variable has been formed by ca lculating the proportion by faculties using an individual independent variable (gender). If there is indeed a contextual effect, it means that, even when controlling for the social composition of faculties, the proportion of women at a faculty has an impact on efficiency (as social background is included as an independent variable); this way, the value-added concept is also present in our analysis. Possible limitations of our models could be that the influence of faculty/institution is only measured with one variable, namely the proportion of women at the faculty, and other characteristics of the institutional environment are not considered. 8 Another limitation could be that, besides age, gender, cultural and financial background, no other individual variab les have been included (e.g. the impact of social capital resources), which may also influence efficiency. We have included, however, the country of institution variable, since efficiency and its indices strongly differ by country (see Ceglédi, 2015), and so our regression models have been also applied on the separate subsamples of students from Hungarian higher education institutions and outside Hungary. Our regression models have been run with uniform efficiency and its separate components as dependent va riables, respectively. A considerable advantage of our study is that through contextual analysis (see Fényes, 2008 for details) we have been able to investigate the influence of gender as an individual factor and the proportion of women at a faculty as a contextual factor on efficiency at the same time, that is, we have also explored whether these two influences interact. 5

T he 15 indices are the following: university research group membership, presentation and poster session at the National Scientific Students’ Associations Conference, presentation at other conferences, teaching assistant position, academic publication, merit scholarship, talent support programme membership and fellowship, academic fellowship and Fellowship granted by the Republic of Hungary, advanced-level language certificate, study tour abroad, Curriculum Vitae in a foreign language, individual creative work, and having a private pupil. 6 Faculties with less than 10 people were listed as “other faculties” in the case of the variable for the proportion of women at the faculty. 7 T he cultural consumption principal component has been created from the frequency of visits to the theatre, art movies, classical concerts, and museums. The principal component accounts for 50% of the variance. 8 In her study, Pusztai (2014) has used cluster analysis to categorise institutions and faculties by several variables, and has investigated students’ efficiency in different faculty/institution environments. She has also explored which type of faculty/institution environment is beneficial or hindering for students’ efficiency. Pusztai has considered multiple characteristics of faculty environment.

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Findings In our sample, 73% of the respondents were female, which means that the higher education institutions of the region are significantly female-dominated (a similar proportion of females , around 70%, were found in our other research projects: HERD 2012, TERD 2008, 2010, REGEGY 2003, 2005). The somewhat greater proportion of women in the 2014 research project may be due to the overrepresentation of teacher education students in the sample, as in our earlier studies teacher education students were not in the focus of attention, and the feminization of teacher education is widely known. Some may argue that according to official statistics, the proportion of women among students in higher education in Hungary is actually significantly lower than it is in our sample. The larger proportion of women in our sample may be attributed to, besides the overrepresentation of teacher education students, females’ greater willingness to respond (and females’ higher attendance at seminar groups), the larger targeted proportion of master’s students (more females study in master’s education), and the inclusion of Romanian and Ukrainian institutions (at which mostly teacher education students were surveyed, most whom are women). Furthermore, according to official statistics of 2014, some 57.5% of students (both in regular and correspondence training) at the investigated faculties of the University of Debrecen were women, in contrast to the total Hungarian proportion of 53%; that is, in the investigated region, the proportion of women is even higher than in Hungary in total. In the following, we investigate the proportion of women in the sample by faculty. In the analysis, we differentiate faculties with a proportion of women above, around, and below the average. The actual percentage has been seldom taken into account due to the proportion of women in the sample, which is higher than what official data would suggest, as mentioned above. We also compare our results from 2014 with those from a 2003 database, which were created by similar research and sampling methods. Table 1: Division of faculties in the sample by the proportion of women (excluding faculties with less than 10 people) Proportion of women above the average Faculty of Psychology and Educational Sciences of BabeșBolyai University

Proportion of women around the average Faculty of Law of the University of Debrecen

Faculty of Roman Catholic Theology of Babeș-Bolyai University

Faculty of Arts of the University of Debrecen

Faculty of Health of the University of Debrecen

Faculty of Economics of the University of Debrecen

Faculty of Dentistry of the University of Debrecen

Faculty of Public Health of the University of Debrecen

Faculty of Childcare and Adult Education of the University of Debrecen

Faculty of Science and Technology of the University of Debrecen Department of History and Social Sciences of the Ferenc Rákóczi II Transcarpathian Hungarian Institute Faculty of Economics, SocioHuman Sciences and Engineering of the Sapientia Hungarian University of Transylvania Faculty of Humanities and Natural Sciences with the Hungarian Language of Teaching of Uzhhorod National University

Ferenc Kölcsey Teacher Training Institute of the Debrecen Reformed Theological University Teacher Education at M ukachevo State University Institute for Applied Pedagogy and Psychology of the University of Nyíregyháza

Proportion of women below the average Faculty of Informatics of the University of Debrecen Faculty of Agricultural and Food Sciences and Environmental M anagement of the University of Debrecen Faculty of Engineering of the University of Debrecen Faculty of Agriculture and Engineering of the University of Nyíregyháza Department of Biology of the Ferenc Rákóczi II Transcarpathian Hungarian Institute Faculty of Technical and Human Sciences of the Sapientia Hungarian University of Transylvania Teacher Training Faculty of the University of Nyíregyháza Department of Philology of the Ferenc Rákóczi II Transcarpathian Hungarian Institute

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Primary Teacher Education at the University of Nyíregyháza Department of Pedagogy and Psychology of the Ferenc Rákóczi II Transcarpathian Hungarian Institute Teachers' Training Faculty in Subotica of the University of Novi Sad Faculty of Social Humanistic Sciences of the University of Oradea

Source: IESA-TESSCEE II. 2014 (N=1792) Our 2003 research (Fényes, 2010a) revealed that there were only two faculties in the historical Partium region at which the proportion of women was lower than 50%: the Faculty of Engineering and the Faculty of Science and Technology of the University of Debrecen. In the present study, the Faculty of Science and Technology of the University of Debrecen is among those at which the proportion of women is around average, which means that more women have decided to study in that field. In 2014, the proportion of females at the Faculty of Informatics of the University of Debrecen was less than 50% (the faculty was not included in previous research projects). It can also be shown that apart from classically male-dominated fields (Computer Science, Engineering), Agricultural Sciences were also chosen by more males than females in the region. The proportion of women in 2003 was below average (but not below 50%) at the Faculty of Economics of the University of Debrecen and the Ferenc Rákóczi II Transcarpathian Hungarian Institute. In the present study, the Department of Biology of the Ferenc Rákóczi II Transcarpathian Hungarian Institute and the Faculty of Technical and Human Sciences of the Sapientia Hungarian University of Transylvania are below average. The proportion of women at the Faculty of Economics of the University of Debrecen, just as at the Faculty o f Science and Technology, has increased to the average by 2014. The proportion of women in 2003 was around average at the Faculties of Agriculture, Law and Medicine at the University of Debrecen, and at the Partium Christian University. In our present study, Agricultural Sciences count as a masculine field, while the medical profession has not been included in the sample, except for Dentistry, where women dominated in 2014 (68.9% according to official statistics). Besides the Faculty of Law, which belonged to the same category back in 2003 as well, the proportion of women in 2014 was around average at the Faculty of Arts and the Faculty of Science and Technology of the University of Debrecen, which means that since 2003, the former has moved from above average, and the latter from below average, to this category. The around-average group also includes the Faculty of Economics (formerly with a proportion of women below average) and the Faculty of Public Health of the University of Debrecen and some faculties at institutions outside Hungary. In 2003, an above-average proportion of women was observed at the Hajdúböszörmény Teacher Training College, the Ferenc Kölcsey Reformed Teacher Training College, the Faculty of Arts of the University of Debrecen, and the Teacher Training and Health Faculty of Nyíregyháza College. In the present study, while the proportion of females at the Faculty of Arts of the University of Debrecen is only around average (that is, feminization has somewhat decreased), the Faculty of Childcare and Adult Education of the University of Debrecen (former Hajdúböszörmény Teacher Training College) is still one of the most female -dominated fields. Teacher education and healthcare training can be considered “feminized fields” both now and in 2003. Other female-dominated fields include Psychology, Theology, and courses offered by the Faculty of Social Humanistic Sciences of the University of Oradea. We may conclude that the division between “feminine” and “masculine” faculties does not occur along the line of the dichotomy between humanities and sciences. The dichotomy between caring and technical fields, as proposed in recent literature (Barone, 2011), might explain our findings better, which show that the most male-dominated fields are Computer Sciences,

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Hajnalka Fényes

Engineering, and Agricultural Sciences.9 Another important finding, suggested by our research of both 2003 and 2014, has revealed that most female-dominated faculties train for professions with lower prestige (less financial reward). Since one of research topics is females’ higher education efficiency, now let us investigate the gender differences in our 15 efficiency variables. Table 2: Gender differences in efficiency indices (in percentages) 10 ,11 Study tour abroad University research group membership Presentation and poster session at the National Scientific Students’ Associations Conference Presentation at other conferences Teaching Assistant Academic Publication M erit Scholarship Advanced-level Language Certificate Private Pupil Curriculum Vitae in a Foreign Language Individual Creative Work Talent Support Program Fellowship Talent Support Program M embership Academic Fellowship Fellowship Granted by the Republic of Hungary

M ales 12.5% 16.8%

Females 10.8% 13.5%

Chi-squared Ns Ns

N 1719 1719

7.3%

5%

Ns

1719

12.7% 8.2% 9.1% 18.5% 12.5% 18.1% 21.3% 20.7% 11.4% 9.9% 6%

8.9% 5% 9.9% 19.4% 11.6% 21% 21.1% 18.3% 6.5% 6.8% 4.1%

* * Ns Ns Ns Ns Ns Ns ** * Ns

1719 1719 1719 1719 1719 1719 1719 1719 1719 1719 1719

6.3%

4%

*

1719

Here and in the following, significance levels are marked thus: *** for significance below 0.000, ** for significance between 0.001 and 0.01, * for significance between 0.01 and 0.05, and Ns for “not significant”. Table 2 shows that 5 efficiency variables show significant male advantages, while in other cases there were no significant gender differences. Male advantages could mostly be observed in certain academic-type variables: presentation at other conferences, teaching assistant posit ion, talent support program fellowship and membership, as well as Fellowship granted by the Republic of Hungary. Our 2005 data (Fényes , 2010a) revealed male advantages in slightly other variables, which, nevertheless, were still mostly concerned with classical academic efficiency (partic ipation in the National Scientific Students’ Associations Conference, publications during university years, membership at colleges of advanced studies, doctoral plans). In our studies, we have concluded that plans of males who study in higher education (an d do not “get stuck” in vocational training) are more likely to include a doctorate course, as well as a research and academic career. We have also attempted to organize the 15 variables through principal component analysis. In the first principal component (which accounts for 26% of the variance), 10 academic efficiency indices have been included as heavily weighted variables, five of which show male advantage, as seen in 9

Hungarian studies (Nagy, 2014, 2015; Paksi, 2014; Takács, Vicsek & Pál, 2013; Szekeres, Takács & Vicsek, 2013) do not consider Agricultural Sciences as a “masculine” field of study. 10 In the questionnaire, there were some more efficiency variables. The following factors present in the survey have been excluded from the final analysis: student group leadership, ordinary Curriculum Vitae, ordinary academic scholarship, intermediate-level language certificate. These are not high-level efficiency factors, and did not have special significance according to the principal component analysis either. 11 Ceglédi (2015) notes that the National Scientific Students’ Associations Conference participation variable is countryspecific therefore should be combined with other variables, such as participation in other conferences. T he variables for talent support group membership and fellowship could also be combined. T eaching assistants are only characteristic of Hungary and there are no similar things to the Fellowship granted by the Republic of Hungary in other countries, while merit scholarships are rather awarded outside Hungary, thus these factors may also be combined. Other efficiency indices are not country-specific. In our study, as an index is created from 15 items, combining variables is not necessary, but regressions are run in both the subsamples of Hungary and of Romania and Ukraine. Furthermore, logistic regression models are run on all 15 separate efficiency indices.

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Table 2. The 10 indices: university research group membership, presentation and post er session at the National Scientific Students’ Associations Conference, presentation at other conferences, teaching assistant position, academic publication, merit scholarship, talent support program membership and fellowship, academic fellowship, and Fellowship granted by the Republic of Hungary. The second principal component accounts for a smaller proportion of the variance. It includes five further efficiency indices as heavily weighted variables: advanced-level language certificate, study tour abroad, Curriculum Vitae in a foreign language (these three variables are in connection with a foreign country), individual work, and having a private pupil. A significant difference between genders could not be observed in the case of these variables (see Table 2). Finally, we have created an index from the 15 variables explored above for linear regression analysis. The 15 variables are also included separately in our logistic regression models. In the following, we investigate the causes for the male advantages shown in Table 2 through cross tabulation, variance analysis, and, lastly, regression analysis with respect to factors which influence efficiency. The presence of male advantages may be due to the fact that males in the sample are somewhat older than females (men are 21.6, while women are 21.06 years old on average; this difference is significant according to the ANOVA test), which may cause their greater efficiency. 12 However, our previous studies (Fényes , 2010a) have concluded that males who study in higher education have a better social background (financial and cultural capital), and less social mobilit y according to the “male disadvantage hypothesis”. Males’ better social background might have an impact on their greater efficiency in higher education. In the following, we compare females ’ and males’ financial and cultural background. Table 3: Males’ and females’ financial and cultural background Father’s level of education (successfully completed years) M other’s level of education (successfully completed years) Objective financial situation index (1-10) Subjective financial situation variable (1-10) Occurrence of financial problems (1: often) Place of residence at 14 years of age (1: city)

M ales

Females

ANOVA or Chi-squared

N

13 year

12.5 year

**

1522

13.2 year

12.9 year

*

1518

5.6 (mean) 5.07 (mean) 13.8% 67.4%

5.8 (mean) 5.13 (mean) 10.3% 61.2%

Ns Ns * *

1719 1566 1596 1640

Table 3 reveals that, in accordance with our previous findings, fathers’ and mothers’ level of education is significantly higher in the case of male students. Furthermore, more men than women lived in cities at 14 of age. However, financial problems are more likely to occur in male students’ families, although this might only be their subjective perception. In conclusion, th ree out of six background variables accord with the “male disadvantage hypothesis”, that is to say, males who study in higher education have a better social background than females. Table 4: Gender differences in individual cultural capital (means) Cultural consumption principal component Number of books read in the past year Estimated number of respondent’s books Estimated number of parents’ books

Males -0.08 6.8 176 614

Females 0.02 7.9 144 346

ANOVA Ns Ns Ns ***

N 1472 1510 1484 1466

Based on our previous research findings with respect to individual (acquired) cultural capital, as well as the studies of Bourdieu (1973) and DiMaggio (1982), we may hypothesize that participation in “high culture”, which is more characteristic of women, increases stu dents’ 12

Our efficiency measurements (see the 15 item in Table 2) are mostly concerned with academic efficiently and language skills, so older students, who are closer to getting the diploma, are more active in these activities.

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Hajnalka Fényes

efficiency. Nevertheless, we find in Table 4 that the slight female advantage in three indices is not significant. Furthermore, male students’ parents seem to have more books than those of female students, which may be explained by their higher level of education, pointed out in Table 3. In conclusion, individual, acquired cultural capital hardly differs by gender, thus it does not explain gender differences in efficiency. Now let us investigate the factors which impact efficiency through regression models. Table 5: Linear regression results in the complete sample (N=1792), with the gradual inclusion of independent variables. The dependent variable is the efficiency index created from 15 items. Gender (1: male) Proportion of women at the faculty Age Country of the institution (1: not Hungary) Father’s level of education (successfully completed years)

β 0.05*

β Ns Ns

M other’s level of education (successfully completed years)

β Ns -0.067* 0.17*** 0.18*** Ns 0.09*

Objective financial situation index (1-10)

Ns

Subjective financial situation variable (1-10)

Ns

Occurrence of financial problems (1: often)

Ns

Place of residence at 14 years of age (1: city)

Ns

Cultural consumption principal component

0.19***

Number of books read in the past year

0.06*

Estimated number of respondent’s books

0.1**

Estimated number of parents’ books Adjusted R-squared

Ns 0.002

0.002

0.124

Our regression models reveal a slight male advantage in the efficiency index, which, however, disappears after the inclusion of the variables for the proportion of women at the faculty and social background. It is easy to understand that there is no male advantage in efficiency after controlling for males’ higher age and better social background, neverthe less, male advantage already disappears after the inclusion of the variable for the proportion of women at the faculty. The variable for gender does not have an individual impact, but it does have a contextual one, as significant female dominance at a faculty reduces individual efficiency. The latter phenomenon can be investigated more thoroughly through a contextual model, as in this case , there may be an interaction between the effects of the variables for gender and females’ proportion. An important finding could be that as the proportion of women at a faculty increases, individual efficiency decreases; in other words, a female-dominated faculty is a hindering factor in students’ efficiency. However, this correlation can be observed in our model only if we control for students’ different social backgrounds, which also reveals that students at female -dominated faculties have worse social background. Poor efficiency at female-dominated faculties may be explained by their low prestige, and the fact that stud ents who study there are less interested in research careers. This is true for both male and female students at those faculties. Men get “feminized” at female-dominated faculties, that is, they are less successful, whereas women at male-dominated faculties, where overall efficiency is higher, get “masculinized”, that is, their efficiency increases (this finding is further explored in detail in our contextual analysis). The mother’s higher level of education is a social background variable which has an uplifting impact on efficiency, as revealed by previous studies (see Pusztai, 2004, 2009). Efficiency is also increased by higher age. The strongest influence is exerted by individual (acquired) cultural capital, in accordance with Bourdieu’s (1973) and DiMaggio’s (1982) studies. More specifically , efficiency is most influenced by the greater number of books read and possessed, as well as cultural consumption (more frequent visits to the theatre, art movies, classical concerts, and museums).

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We have also found students not from Hungarian higher education institutions have greater efficiency. This phenomenon is further investigated in the following, bearing in mind Ceglédi’s study (2015), which concludes that some efficiency indices are “country -specific”. Therefore, we also ran the regressions on the Hungarian and not-Hungarian subsample separately. Table 6: Linear regression results in the Hungarian subsample (N=1223), with the gradual inclusion of independent variables. The dependent variable is the efficiency index created from 15 items. Gender (1: male) Proportion of women at the faculty

β 0.064*

β Ns -0.13***

β Ns -0.12**

Age Father’s level of education (successfully completed years)

0.19*** Ns

M other’s level of education (successfully completed years) Objective financial situation index (1-10) Subjective financial situation variable (1-10)

0.09* Ns Ns

Occurrence of financial problems (1: often) Place of residence at 14 years of age (1: city) Cultural consumption principal component

Ns Ns 0.12**

Number of books read in the past year Estimated number of respondent’s books

0.07* 0.11**

Estimated number of parents’ books Adjusted R-squared

Ns 0.115

0.003

0.016

Table 7: Linear regression results in the subsample of students not from Hungarian higher education institutions (N=569), with the gradual inclusion of independent variables. The dependent variable is the efficiency index created from 15 items. Gender (1: male)

β Ns

Proportion of women at the faculty

β 0.11* Ns

Age Father’s level of education (successfully completed years) M other’s level of education (successfully completed years) Objective financial situation index (1-10) Subjective financial situation variable (1-10)

β Ns Ns

0.14** Ns Ns 0.14** Ns

Occurrence of financial problems (1: often)

Ns

Place of residence at 14 years of age (1: city) Cultural consumption principal component

Ns 0.3***

Number of books read in the past year Estimated number of respondent’s books

Ns 0.11*

Estimated number of parents’ books Adjusted R-squared

Ns 0.133

0.003

0.006

In the Hungarian subsample, male advantage disappears when controlling for the effect of the proportion of women at the faculty. However, the latter variable is significant and exerts a negative impact both before and after the inclusion of other background variables, that is to say, female dominance influences efficiency in a negative way in this subsample even more than in the complete sample. Both in the Hungarian subsample and in th e complete sample, efficiency is increased by age, the mother’s higher level of education, and the three variables for individual cultural capital.

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Hajnalka Fényes

When running the regression on the subsample of students not from Hungarian higher education institutions , male advantage in efficiency can only be observed when controlling for the gender composition of the faculty. Male advantage, however, disappears when controlling for the effect of their better social background and higher age. Furthermore, a high proportion of women at faculties outside Hungary, is not a hindering factor, that is, female dominance at a faculty does not have a negative impact on efficiency. This may be due to the fact that outside Hungary, female-dominated faculties are not necessarily low-prestige institutions, or because students of these faculties are more willing to pursue research careers than their peers in Hungary. Similarly, to the Hungarian subsample, age increases efficiency, whereas the mother’s higher level of education does not. An important influencing factor is the family’s financial situation. The possession of a greater number of durable goods raises efficiency. So, in the subsample of students from outside Hungary, efficiency is more affected by financial resources than the parents’ cultural capital. Moreover, similarly to the Hungarian subsample, efficiency is increased significantly by two indices of individual cultural capital (cultural consumption and number of respondent’s books). Now we look at the results of the logis tic regression models, in which each of 15 efficiency variables have been separate dependent variables, while all independent variables have been included in regression models in one step. With the increasing proportion of women at a faculty, efficiency decreases in three cases: Curriculum Vitae in a foreign language, university research group membership, and teaching assistant position. Consequently, few students at female dominated faculties have these, principally academic, achievements. As we have previously investigated and explained, a high proportion of women at a faculty ha ve a negative impact on efficiency (especially in the Hungarian subsample). In this analysis, we can see specifically which factors are affected by this phenomenon. Other efficiency indices are not influenced by the proportion of women at the faculty. Our other result is that gender does not have an impact on efficiency in any case, supposedly because males’ better background and the gender composition of faculties have been controlled for (that is, male advantages in certain efficiency factors explored in Table 2 are not in contradiction with these findings). In the following, we present the findings of the graphical method of the contextual analysis, bearing in mind its limitation, namely that we have not been able to control for the effect of males’ better social background when comparing females’ and males’ efficiency. According to our hypothesis, both an individual influence (male advantage in efficiency) and a contextual impact (the (negative?) impact of the proportion of women at a faculty on females’ and males ’ efficiency) can be observed, which may interact occasionally. Our dependent variable is efficiency, but in this case, we have created a binary variable based on the 15-item efficiency index. The mean of the efficiency index is 1.71, which means that an average student has 1 or 2 of the 15 efficiency indices. The value of our new variable is 0 for students with a performance around or below average (0-2 efficiency indices), and 1 for those with a performance above average (3-15 efficiency indices). Some 23.7% of the complete sample has above-average efficiency. We have then calculated at each faculty the proportion of females and males who perform above the average, and plotted this on a graph in the function of the proportion of women at the faculty. 13 On the horizontal axis, faculties are organized by the proportion of women at the faculty (which varied from 23% to 100%). On the left side, we can observe male dominated, on the right side, female-dominated faculties. One faculty belongs to each proportion. The vertical axis shows the proportion of students with above -average efficiency at each faculty. The two curves represent the proportion of females (Eff_female) and males (Eff_male) with above-average efficiency at each faculty.

13

Where the proportion of women is higher than 98%, none of the men had any of the efficiency measurements. At some faculties, all of the students are female; in these cases, the proportion of males with above-average efficiency is 0, see the right end of the graph.

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Figure 1: The proportion of efficient students among males and females at different faculties plotted against the proportion of females at the faculty Figure 1 reveals that at almost every faculty where the proportion of women is below 66%, men have an advantage in efficiency. The only exception is the Faculty of Engineering of the University of Debrecen, where 32% of students are female, and there are more efficient students among females. This is in contradiction with the findings of the special literature (Nagy, 2014, 2015; Paksi, 2014; Takács, Vicsek & Pál, 2013; Szekeres, Takács & Vicsek, 2013), and may be explained in various ways. A possible explanation could be the fact that the dean of the faculty is female, which might motivate female students to strive for better results, and may decrease gender stereotypes among professors. However, this needs to be thoroughly investigated, for example with qualitative research. We have already listed the faculties where the proportion of females is below 66% in Table 1 (see column “Proportion of women below average”). These are mostly fields of education which are considered “masculine” in the literature. At these faculties, with one exception, male s ’ efficiency was higher, which implies that, in accordance with the literature, masculine environment at a faculty is more advantageous for men. Nevertheless, we can see in Figure 1 that as the proportion of women increases at a faculty, the results fluct uate heavily. In some cases, males, in others females are at an advantage in efficiency, in other words, there is no clear male advantage in female-dominated fields. We also note that in Figure 1 males’ better social background is not controlled for, which may be the cause for their higher efficiency at faculties with below-average proportion of women. We have tried to control for the effect of males’ better social background on higher education efficiency. We have run a regression on the faculties where the proportion of women is below 66%. Due to the relatively small sample (N=396), however, we have not been able to observe a significant effect of the independent variables on efficiency 14 , except in the case of cultural consumption, which exerts a positive influence. Thus, neither before nor after the inclusion can male advantage in efficiency be shown at these faculties. 15 14

Before running the regression, we investigated the gender differences in the efficiency index and the binary efficiency variable through cross-tabulation and variance analysis as well, but no significant differences have been found. 15 Only 53 out of the 396 people study at the Faculty of Engineering of the University of Debrecen, where Figure 1 shows female advantage, as opposed to other faculties with male advantage in efficiency. The result may be explained by the small sample as well as the regression method itself, during which lines have been fitted on scatter plots.

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Hajnalka Fényes

In our contextual analysis, we have not been able to observe the phenomenon that students’ higher education efficiency decreases as the proportion of women at the faculty increases. If efficiency decreased in this way, we would be able to see curves with negative slope in Figure 1. Methodologically, this means that the contextual effect present in the regression model cannot be clearly shown. Nevertheless, the faculty does exert some kind of influence, which interacts with the individual impact: in Figure 1, males’ higher efficiency has only been discovered at faculties with a relatively great proportion of male students, in accordance wit h the findings in the literature. Conclusions In our study, we have first explored horizontal segregation in higher education and its causes, based on the special literature. We have shown that in the current higher education system horizontal segregation by gender is present, and it has not decreased despite the rapid influx of women into higher education16 either. Horizontal segregation is a problem because female-dominated faculties train students for less prestigious professions, which may put women at a disadvantage in higher education. We have also considered thoroughly women’s minority and subordinate position in the (STEM) fields of Engineering and Natural Sciences, which is an important issue in developed countries. But it can be seen that relatively few studies in the international literature have investigated our most important research question concerning the effect of the gender composition of higher education faculties on females’ and males’ higher education efficiency. According to certain qualitative research findings, females’ higher education efficiency is negatively influenced by masculine faculty environment, which we have explored in detail in our empirical work. Nowadays there are more female than male students in higher education, whic h, according to our research, can also be observed in the higher education institutions of the historical “Partium” region. Compared to 2003, previously male-dominated fields (Natural Sciences and Economics ) have “feminized”, whereas at the Faculty of Arts of the University of Debrecen, female dominance is less significant than before. We have also pointed out that, alongside classically male-dominated fields listed in the special literature (Computer Science, Engineering), Agricultural Sciences are also more popular among men in the investigated region in accordance with the findings of Barone (2011). However, we must be careful with the analysis of th ese findings, as our data concerning the gender composition of faculties and the complete sample do not always coincide perfectly with official institutional statistics. Male advantage has been shown in the sample in five academic efficiency variables, before controlling for the effect of male students’ better social background and higher age. According to the regression models, in which the combined impact of several influencing factors has also been investigated, men are occasionally at an advantage in efficiency, but this effect ceases when males’ better social background, higher age, and the impact of the p roportion of women at the faculty are controlled for. Our research has also verified the “male disadvantage hypothesis” from our previous studies, which reveals that men have less social mobility and they only enter higher education from a better social background. The increasing proportion of women at a faculty influences efficiency negatively (in particular, in the Hungarian subsample), that is, both male and female students at significantly female-dominated faculties demonstrate lower efficiency. By examining each efficiency variable separately, we have shown that students at faculties with a high proportion of women are less likely to have a Curriculum Vitae in a foreign language, take part in a university research group, and become a teaching assistant. In some of our regression models, male advantage and the negative impact of the proportion of women at the faculty on higher education efficiency can be observed. We have asked the question as to whether these phenomena really exist, and how they might possibly interact. To investigate this, we have conducted contextual analysis. According to our findings, there are indeed more males who are efficient, but rather at male-dominated faculties. This means that males have higher efficiency in a masculine 16

About the trends of rapid influx of women in higher education in developed countries and Central - Eastern Europe see our previous work (Fényes, 2010a).

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faculty environment, which is in accordance with the qualitative findings of the special literature. However, this may be partly due to males’ better social background, which we have not been able to include in our model. At faculties with a higher proportion of women, nevertheless, men’s efficiency is similar to that of women, and in some cases, it is male students, while in others, it is female students who are more efficient, according to the findings of our contextual model. Furthermore, the graph of our contextual model did not show the results of the regression model, according to which, an increasing proportion of women at a faculty has a negative impact on efficiency. We may conclude that the environment at a faculty/institution, in particular, the gend er composition of a faculty, does influence females’ and males’ efficiency. Since individual and collective (contextual) effects may interact, it is worthwhile to investigate the topic with multilevel methods. We have been able to verify the hypothesis of the literature according to which male-dominated faculty environment is a hindering factor in female efficiency. However, we have yet to answer the question as to why males are more efficient: either because of their better social background or due to actual male advantage. Thus, our analysis has a limitation, namely that we have opted for the graphical method of contextual analysis. In our further studies, we plan to use other multilevel methods to obtain better results. Another problem is that the willingness to respond differed at each faculty, thus the results are not fully representative by faculty and gender. This will be eliminated in other quantitative research projects in the historical Partium region in the future. Finally, an interesting research field could be the investigation of unexpected results at certain faculties, such as the Faculty of Engineering of the University of Debrecen, through qualitative interview research. References 1.

Bae, Y., Choy, S., Geddes, C., Sable, J., & Snyder,T. (2000). Trends in Educational Equity of Girls and Woman. Washington, D.C.: Natl. Cent. Educ. Stat.

2.

Baker, D. P., & Jones, D. P. (1993). Creating Gender Equality: Cross -National Gender Stratification and Mathematical Performance. Sociology of Education, 66(2), pp. 91103. http://dx.doi.org/10.2307/2112795

3.

Barone, C. (2011). Some Things Never Change: Gender Segregation in Higher Education across Eight Nations and Three Decades. Sociology of Education, 84(2), pp. 157-176. http://dx.doi.org/10.1177/0038040711402099

4.

Blickenstaff, J. (2005). Women and science careers: leaky pipeline or gender filter?. Gender and Education, 17(4), pp. 369-386. http://dx.doi.org/10.1080/09540250500145072

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Bourdieu, P. (1973). Cultural Reproduction and Social Reproduction. In R. Brow (Ed.) Knowledge, Education and Cultural Change (p. 71-105), London: Willner Brothers Limited.

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Bukodi, E., Mészárosné Halász, J., Polónyi, K., & Tallér, A. (Eds.) (2005). Nők és férfiak Magyarországon 2004. [Women and Men in Hungary 2004] Központi Statisztikai Hivatal, Ifjúsági, Családügyi, Szociális és Esélyegyenlőségi Minisztérium Budapest.

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Catsambis, S. (1994). The Path to Math: Gender and Racial-Ethnic Differences in Mathematics Participation from Middle School to High School. Sociology of Education, 67 (3), pp. 199-215. http://dx.doi.org/10.2307/2112791

8.

Ceglédi, T. (2015). Felsőoktatás és társadalmi egyenlőtlenségek: Rezilien s pedagógusjelöltek [Higher Education and Social Inequalities: Resilient Teacher Education Students]. In G. Pusztai & T. Ceglédi (Eds.), Szakmai szocializáció a felsőoktatásban. [Professional Socialisation in Higher Education ] (pp. 16-135), Oradea, Budapest: Partium-PPS and ÚMK.

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9.

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50. Shapiro, J., & Williams, A. (2012). The Role of Stereotype Threats in Underminin g Girls’ and Women’s Performance and Interest in STEM Fields. Sex Roles, 66(3), pp. 175-183. http://dx.doi.org/10.1007/s11199-011-0051-0 51. Sagabiel, F., & Dahmen, J. (2006). Masculinities in organizational cultures in engineering education in Europe: results of the European Union project-Women. European Journal of Engineering Education, 31(1), pp. 5-14. http://dx.doi.org/10.1080/03043790500429922 52. Spelke, E. S. (2005). Sex Differences in Intrinsic Aptitude for Mathematics and Science? A Critical Review. American Psychologist, 60 (9), pp. 950-958. http://dx.doi.org/10.1037/0003-066X.60.9.950 53. Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). Stereotype Treat and Women’s Math Performance. Journal of Experimental Social Psychology, 35(1), pp. 4-28. http://dx.doi.org/10.1006/jesp.1998.1373 54. Storen, L. A., & Arnesen, C.A. (2007). Women’s and men’s choice of higher education – what explains the persistent sex segregation in Norway?. Studies in Higher Education, 32(2), pp. 253-275. http://dx.doi.org/10.1080/03075070701267293 55. Szabó, I. (1997). A szakma hangja. Iskolaigazgatók elképzelései az oktatás emberi tényezőiről. [The Voice of the Profession. The Ideas of School Principals of the Human Factors affecting Education] http://www.oki.hu/oldal.php?tipus=cikk&kod=Iskolavezetok05-Szabo Retrieved February 2009. 56. Szekeres, V., Takács, E., & Vicsek, L. (2013). „Úristen! Te lányként?” A nemek kultúrája egy felsőoktatási intézmény műszaki karain – a hallgatónők szemszögéből.[“My God!You as a Girl?” Gender Culture at Engineering Faculties of a Higher Education Institution – from the Point of View of Female Students]. Társadalmi Nemek Tudománya Interdiszciplináris Folyóirat, 3(1), pp. 125-144. 57. Takács, E., Vicsek, L., & Pál, J. (2013). Lányok útja a műszaki diplomáig. Középiskolai és felsőoktatási esélyek és nemi különbségek a műszaki pályaválasztás területén. Zárótanulmány.[Females’ Path to an Engineering Degree.Secondary and Higher Education Chances and Gender Differences in the Choice of Career in Engineering . Final study]. In V. Szekeres & Krolify Institute (Eds.), „Ti ezt tényleg komolyan gondoltátok? Nők és a műszaki felsőoktatás, [“Have You Really Meant This?” Women and Higher Engineering Education] (pp. 15-213), Budapest: Óbudai Egyetem. 58. Tornyi, Z. (2008). Nők a katedrán – a nők lehetőségei a tudományos életben. [Teaching Women – Women’s Chances in Academic Life]. In E. Kiss & A. Buda (Eds.), Interdiszciplináris pedagógia és az eredményesség akadályai. [Interdisciplinary Pedagogy and Hindrances of Efficiency] (pp. 598-607), Hungary: Kiss Árpád Archívum Könyvtár Sorozata V., Debreceni Egyetem Neveléstudományok Intézete.

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