Higher Education: is more better? Gender Differences in Labour Market Returns to Tertiary Education in Israel

Transcription

1 Oxford Review of Education, Vol. 29, No. 4, December 2003 Higher Education: is more better? Gender Differences in Labour Market Returns to Tertiary Education in Israel TALLY KATZ-GERRO & MEIR YAISH ABSTRACT Research on the transition from post-secondary education to the labour market refers mainly to differences between academic and vocational tracks in secondary education. In this paper we analyse Israeli data focusing on the transition from different levels of post secondary degrees and from various fields of study to the labour market. We examine three labour market outcomes: employment status, occupational prestige attainment, and job match. Data are drawn from a supplement to the 1983 Israeli Census, which includes a random sample of Israel s tertiary education degree holders (vocational and academic). Our central finding is that men who work in female-dominated occupations get better returns than women, and women who work in male-dominated occupations get better returns than men. We discuss several explanations of this finding. INTRODUCTION In the past 50 years participation in the education system has increased and the upgrading of the occupational structure as a result of industrialisation processes has created, inter alia, a demand for a skilled, sophisticated and highly educated labour force (Murane & Levy, 1996; Shavit & Müller, 1998). Concurrently, educational qualifications have become important for employment at the bottom of the occupational hierarchy (Collins, 1971, p. 1003). As most individuals in post-industrial societies attain secondary education, the proportion of occupations that require post-secondary education has grown, and is projected to continue growing. Although high-level credentials have become more important, there is relatively little research on the transition from higher education to work. This transition is usually conceptualised as labour market consequences of a very crude classification of vocational versus academic tracks in higher (mainly secondary) education (cf. Shavit & Müller, 1998). Little research has explored labour market consequences using a detailed classification of tertiary education. In this paper, we focus on two important aspects of tertiary education: type of degree attained and field of study. We analyse the transitions from different levels of tertiary education (BA, MA, Ph.D.) and, within each level, from various fields of study (e.g. teaching, engineering) to the labour market in Israel. The increasing demand for a skilled and highly educated labour force resulted, among other things, in policies that aimed to elevate the overall educational level in the population (cf. Shavit & Müller, 1998). An important aspect of the expansion of ISSN print; ISSN online/03/ Taylor & Francis Ltd DOI: /

2 572 Oxford Review of Education education systems is the growing tendency of these systems to become horizontally stratified. For example, the introduction of vocational tracks in secondary education parallel to the existing academic track made it possible for previously excluded groups to attain secondary education (cf. Shavit & Kraus, 1990). More relevant to the current study, though, is the high degree of differentiation and specialisation in tertiary education. It has been previously shown that the distribution of individuals across the different fields of study within tertiary education is uneven. Most notable is the very high and persistent level of sex segregation in tertiary education, where men and women occupy different sex-typical fields of study (cf. Jacobs, 1995; Jonsson, 1999). This is in spite of the equalisation, in most modern societies, between men and women in the overall level of educational attainment (cf. Shavit & Blossfeld, 1993). The high level of sex segregation in tertiary education is then carried over into the labour market. It is well documented in the sociological and economic literatures that the characteristics of sex-typical occupations are distinct. Compared to occupations that are dominated by men, occupations dominated by women tend to have lower rates of pay, are less likely to offer fringe benefits and to afford opportunities for training, promotion or the exercise of authority (cf. Reskin, 1993, p. 242). The literature has also documented men s advantage over women in female-dominated occupations. The gender income gap is higher in female-dominated occupations than in male-dominated occupations (Williams, 1995; Morgan, 1998), while men in female-dominated occupations tend to occupy top managerial positions (Schreiber, 1979; Williams, 1989, 1995). In light of the above discussion, we focus on the following question: in terms of labour market returns, is it equally beneficial for men and women to acquire more education (quantity) or is it the field of study that matters (quality)? To this end we analyse the effect of these aspects of tertiary education (i.e. quantity and quality) in Israel on three labour market outcomes: employment status, occupational prestige attainment, and job match. Employment status is an important labour market outcome since higher education credentials do not always guarantee a job as labour markets may be faced with growing numbers of over-qualified individuals (Collins, 1971). If an increase in society s educational level is not coupled by a sufficient increase in demand for more qualified individuals in the labour market, unemployment rates may be relatively higher among the more (that is over-) qualified graduates (cf. Hughes & O Connel, 1995). Occupational prestige is a common indicator of the translation of educational credentials and qualifications into desirable labour market positions. Occupational prestige is particularly relevant to the issues discussed here because it is sensitive to the distinction between field of study and type of degree. We preferred using prestige over wages because prestige is a measure of the attractiveness of an occupation rather than the attractiveness of an individual. Therefore, labour force experience (and tenure in a particular job) does not affect it. The main problem with wages, as far as our research objectives are concerned, is the time scale of this measure. Measures based on a person s current wages over a month, year, or several years will produce vastly different results because over the life cycle individuals typically experience variations in wages. Finally, we explore whether there is a job match between educational qualifications and the job one holds. An indicator of successful placement in the labour force is individuals ability to occupy positions that are directly related to their educational career and area of expertise. We find marked gender differences in labour market outcomes among individuals

3 Gender Differences in Tertiary Education in Israel 573 with equal education levels but different fields of study. A discussion of these differences in an attempt to explain them concludes the paper. THE COMPARATIVE CONTEXT A considerable body of comparative research has been carried out on school to work transitions in Canada and Britain (Ashton & Lowe, 1991), England and Germany (Bynner & Roberts, 1991), and other cross-national comparisons (Heinz, 1999). Several conclusions that stem from the literature provide the basis for the questions addressed in this paper. First, employment growth in Ireland and England over the 1980s and 1990s has been primarily concentrated in higher professional, managerial and associate professional occupations and in service employment (Sexton et al., 1998; Wilson, 2000). Formal qualifications are becoming increasingly important for a growing proportion of nonmanual occupations. In France as well, as labour markets have tightened, the jobs which have disappeared are the unskilled entry-level jobs (Bagnall, 2000). Second, while young people are now remaining in education in much greater numbers than in the past, they are still deeply attached to the idea of getting a job (Canny, 2001). Growing enrolments as well as labour market problems in many industrialised countries fuel the debate on the relationship between higher education and work. In spite of controversial assessments of graduate employment and work, most experts tend to agree that a growing diversification of the educational system is an appropriate response (Teichler, 1999). This paper addresses the issue of diversification through analysis of field of study and level of study. Different labour market outcomes also receive attention in the literature. Debates over the differential outcomes afforded by a university education focus on the tension between cultural and instrumental values, or the tension between liberal and vocational education (Zeng, 2001). In Japan, an increase in the unemployment rate at the time of labour market entry reduces the probability of gaining full-time regular employment and, more important, increases the future probability of workers of leaving employers by lowering the quality of job matches (Genda & Kurosawa, 2001). University graduates in Canada have been significantly stratified by fields of study in terms of income, unemployment rates, work prospects, and job satisfaction (Zeng, 2001). Vocational high school education in the USA inhibits the likelihood of finding employment in the professions and managerial occupations but also reduces the risk of unemployment and increases chances of employment as skilled workers (Arum & Shavit, 1995). In the Netherlands, different field-related educational resources have been shown to have varying impact on a number of labour market outcomes such as occupational status, economic status and cultural status (van de Werfhorst & Kraaykamp, 2001). In this paper, we address three labour market outcomes: employment status, occupational prestige and job match. THE NATIONAL CONTEXT Higher-education-to-work transitions are best understood in their specific national and historical contexts. In this paper we employ data on Israeli Jewish society that represents an interesting case study for two reasons. First, the higher education sector in Israel has expanded dramatically over recent decades. As a result, a long-term growth in the enrolment of men and women in higher education is apparent. Second, and

4 574 Oxford Review of Education FIG. 1. Jewish Israeli tertiary degree holder aged 25 44, by type of degree. related to the previous point, Israel has a very high percentage of enrolment in post-secondary education. Figure 1 shows that in the period the percentage of those holding a post-secondary degree has increased from 2.7% to 11.7%, and the percentage of those holding an academic degree has increased from 6.4% to 20.6%. Note that a large part of this increase occurs in the 1970s and 1980s (as we shall discuss below, our data are from 1984). The other side of the transition from higher education to work is the labour market. In what follows we present the major features of the Israeli labour market and its economy. As a result of massive immigration waves in the early years of statehood, from 1951 through to 1953, the newly created state suffered from severe unemployment. In 1954, mainly due to reparation money received from Germany, the economy entered a period of rapid economic growth that continued until By the end of this period the reparation payments had ended (Aharoni, 1991), and in 1966, there was widespread unemployment and a significant drop in GDP (Aharoni, 1991, p. 79). Economic growth began again only after the Six-Day War of 1967, and the period from 1967 to 1972 is characterised by an exceptionally high and rapid economic growth (Remba, 1971). From 1974 through to 1982, economic growth came to a standstill, and then through to 1988, it improved only marginally. Towards the end of 1988, another recession gripped the economy, coupled with a relatively high rate of unemployment. Israel s economic growth has been accompanied by marked structural changes in its various economic segments. The proportion of the population actively engaged in agriculture declined, industrial growth slowed down in the late 1970s, and services expanded substantially, especially in the public sector (Kraus, 1992). Israel has a highly centralised, state regulated economy. Ownership of economic resources is divided between the government, the Histadrut (the General Federation of Labour), and the

5 Gender Differences in Tertiary Education in Israel 575 private sector, with a growing tendency towards privatisation of government and Histadrut enterprises. Up to the early 1990s, the proportion of organised labour in Israel was amongst the highest in western societies about 90% of the work force. The Histadrut, which is, inter alia, a labour union, negotiates collective arrangements with the government and the private sector that cover most aspects of employment relations, including pay rises. Institutional arrangements in the Israeli labour market, particularly regarding women s employment, are also apparent in legislation. At the heart of this legislation is the state s desire to prevent employers from discriminating against employees on the basis of gender. In line with this objective, moreover, many of Israel s social policy are designed to provide women with the opportunity to take part in the paid economy. Women s participation rate in the Israeli paid economy is similar to that of women in other western societies (Ben-Porath & Gronau, 1985); 45% of Israeli women are active in the paid economy, and a majority of them (more than 60%) are employed in full-time jobs (Stier, 1998). As in most modern societies, gender segregation in the Israeli labour force is high. Most Israeli women work in a few female-dominated occupations (Cohen et al., 1987): in 1972, three-quarters of working Israeli women were employed in female-dominated occupations, and nearly one-third of them were crowded in only seven (three-digit) occupations (Izraeli, 1979). In 1983, the proportion of women in female-dominated occupations was 77.2% (Cohen et al., 1987). Most Israeli women work in white-collar occupations, and thus enjoy a relatively high level of occupational prestige (Semyonov & Kraus, 1993). At the same time, women s occupations pay less than men s (Izraeli & Gaier, 1979). DATA AND VARIABLES The Sample Data are drawn from the 1984 higher education survey in Israel. This survey is a supplement to the 1983 Israeli census, which includes a random sample of 10,914 individuals who represent Israel s tertiary education (vocational or academic) degree holders. The analyses employ a subset of Jews aged who were educated in Israel (N 5100) [1]. Concentrating on this relatively narrow age range ensures that we study individuals who were part of education systems that were similarly situated in historical and social contexts [2]. We exclude Arabs from the analysis since it is well grounded in the literature that Arabs and Jews in Israel do not share similar patterns of educational and occupational attainment (Shavit, 1984). The process of modernisation among Arabs in Israel was rapid and extensive but it was put off by different factors related to the characteristics of the Arab population and its special status in Israel. Discrimination against the Arab population results in gaps between Jews and Arabs in several areas: education, welfare services, infrastructure and more (Haidar, 1987). Ethnic stratification in Israel results in a glass ceiling that limits their advancement. The educational gap between Jews and Arabs has decreased over the years but it is still significant: in 1988 the average years of education was 8.6 among the Arab population and 11.8 among the Jewish population (Al-Haj, 1996). Higher educated Arabs have very few labour market positions open to them. There is another, more specific, reason for the exclusion of the Arab sub-population. This paper focuses on gender differences in labour market returns. However, the

6 576 Oxford Review of Education majority of Arab women do not participate in the paid economy: in 1989, for example, more than 90% of Arab women did not participate in the paid economy (CBS, 1991, p. 327, table 12.8). Thus a comparison of labour market returns between men and women amongst the Arab population is problematic. We also restrict the analyses to younger cohorts (ages 25 44) in order to minimise the effect of career mobility. This allows us to examine the direct link between education and work. Put another way, we want to minimise the effects of experience and negotiation power on the transition into work, while focusing on individuals who were part of education systems that were similarly situated in historical and social contexts. At the same time, we have to allow for enough time for our respondents to complete military service, obtain a Ph.D. and start working. Our post-secondary vocational degree holders cannot accomplish that earlier than the age of 25, while our doctoral degree holders can reasonably accomplish that by the age of 44. Finally, we carry out the analysis separately for men and women to examine whether schooling has different labour market sequences for the two sexes. Variables For the purposes of the current study it is important to focus on jobs held or entered after the completion of the last and highest level of tertiary education. At the same time, though, we do not wish to allow for a long period of time to pass between graduation and entrance to the labour market. In other words, we wish to measure the direct association between higher education and work, and to control for any disturbances, mainly those connected with career mobility. Since the study is based on cross sectional data, we cannot satisfy these criteria completely. We need, therefore, to organise our data in such way that an acceptable linkage between higher education and work is achieved. First, we are looking at young cohorts only (ages 25 44). Second, the respondents were asked about their last that is their highest educational qualification, and the year they attained it. They were also asked to give information about their current labour force activities (i.e. in 1984), their employment status and occupation title, and the year in which they started working. From these pieces of information we are able to classify the data in a more refined way and construct the dependent variables. Twenty-two per cent of the respondents started their current job five years or more after they achieved their last educational qualification (see Table I). Since we do not wish to confuse the higher education to work transition with other factors, we are inclined to exclude this group from the analyses [3]. Therefore, the analyses will concentrate on Jews educated in Israel aged who started their current job less than five years after graduation. Having established that, we can now explain our dependent variables. The first dependent variable is employment status: whether or not respondents were employed at the time of the survey (1984). Here we want to assess whether the (log) odds of being employed decrease or increase depending on education levels so we can test the over-qualification argument (de la Fuente et al., 1995) and on a specific field of study (controlling for education levels). In Table I we can see that the level of employment in our sub-sample is high (about 90%), and that women s employment rate is somewhat lower than that of men. The second dependent variable is occupational prestige. We use the Kraus prestige scale (Kraus, 1976), which is based on the 1972 three-digit standard occupational

7 Gender Differences in Tertiary Education in Israel 577 TABLE I. Descriptive statistics (proportions, means, and standard deviations) by gender, Jews aged Variable All Men Women Father s Education Primary or less Sec. Vocational Sec. Academic Post-secondary University Degree Ethnicity (Ashkenazim) Gender (Men) 57.8 Religious High-School Secondary School Academic Vocational Other external Degree Post-Secondary BA MA Ph.D Belong to 1984 Labour Force Economic Sector Government Histadrut Private (non-profit) Private Educational Unknown Matching Occupation and Field of Study Beginning 1984 Job relative to graduation More than five years before Four to one year before Up to five years after [More than 6 years after][a] [22.0] [19.1] [25.7] Occupational Prestige (18.92) (19.62) (17.70) Age (4.97) (4.83) (5.06) Year of Graduation (4.75) (4.50) (5.06) N (min.) [a] Omitted from the analysis (see text). codes in Israel and ranges from We chose to study status and not income attainment since we believe that prestige is a more adequate measure that reflects structural elements in the labour market, while income attainment is associated with the individual experience, negotiating skills, etc. Table I shows that men attain, on average, a higher level of occupational prestige than women. The third dependent variable is a job match variable that we constructed. We assign each respondent to one of eleven academic or five post-secondary (mainly vocational) fields of study, based on information about the respondent s last (representative) field of study (see Appendix 1). We then construct occupational groups based on the three

8 578 Oxford Review of Education digit-occupational codes. We start with 13 occupational groups that are similar to the fields of study we have in our file (see Appendix 2). These are our matched jobs. The remaining occupations, which could not be matched, are assigned to a modified version of the 11 major occupational groups in Israel (cf. Kraus & Hodge, 1990: Appendix C, pp ) [4]. Thus, altogether we created 13 occupational groups that we consider to be qualification-related occupational groups (matched job), and 6 occupational groups that could not be matched. Based on this we construct a dummy variable (1 matched, 0 otherwise). Table I shows that nearly two thirds of the Israeli respondents have a matched job occupation, with very little gender difference. The labour market outcomes mentioned above (i.e. the dependent variables) are determined by three main factors: social origin, educational history, and labour force activities. We represent these factors by a series of independent variables. Social origin measures are represented by three variables: Father s Education includes five categories: primary education or less, secondary vocational education, secondary academic education, post-secondary education, and university degree. We construct five dummy variables from these categories and contrast the first four with the last one. We also use father s education as a proxy for father s occupational prestige since the latter is not available. Ethnicity is measured on the basis of the respondents and their fathers country of birth: European-American origin and Israeli origin (Ashkenazi) contrasted with Asian-African origin (Mizrahi). We can see in Table I that the majority of respondents are from Ashkenazi origin (90%). It is well documented that in Israel, Mizrahi Jews attain less education compared to Ashkenazi Jews (Kraus & Hodge, 1990). Religiosity is measured on the basis of attendance at a religious high school contrasted with a non-religious high school. Respondent s educational history was measured by three variables: Secondary School is a measure with three categories: Academic secondary school, Vocational secondary school, and External secondary school. The latter refers to individuals who obtained their secondary certificate (matriculation diploma) in alternative systems [5]. Table II shows that men are represented over three times more than women in the vocational track. These vocational post-secondary studies, however, lead to the matriculation diploma and are not to be confused with vocational studies that prevent one from obtaining a post-secondary degree (cf. Shavit, 1984). Degree includes four categories, representing the highest degree obtained: Post Secondary (mostly vocational), BA, MA, and Ph.D. Here we can also see a disparity between the sexes; women are over-represented in post-secondary degrees and under-represented in academic degrees, whereas for men the opposite is true. Field of Study is a measure with nearly 80 specific fields of study. From this information we construct 16 categories (see Appendix 2). Figure 2 presents the distribution of fields of study by gender. It shows that women are over-represented in the Humanities, Arts, Social Sciences, Teaching and Nursing School, while men are over represented in Economics, Law, Medical School and Engineering. Similar results were reported in the USA (cf. Jacobs, 1995, table 2, p. 88). The labour force measures are represented by three variables: the Job Match variable was mentioned above, and we made use of it both as a dependent and independent variable. Economic Sector is a measure with two broad categories: Public sector and Private sector. The former was separated into three categories: Government, Histadrut (labour union), and Educational sectors. Private sector was divided into two categories: non-profit Private sector, and the remaining of the Private sectors. We construct five dummy variables from these categories and contrast the first four with the last one. Table I shows that women are over-represented in the educational sector and under-

9 Gender Differences in Tertiary Education in Israel 579 FIG. 2. Jewish Israeli tertiary degree holder aged 25 44, by gender and field of study. represented in the government and private sectors. We introduce this variable in the analyses to control for differences in outcomes that may be related to the structure of the Israeli labour market and not to the individual. Time span between graduation and 1984 job is a measure with four categories. Those who began to work before graduation were assigned to the first two categories: the first category represents individuals who began their job more than four years before graduation; the second category represents individuals who began their job one to four years before graduation. The third and fourth categories consist of those who started to work after they graduated. To remind the reader, however, we excluded from the analyses those who started to work more than five years after graduation (i.e. the fourth category). We believe that the second category consists of individuals who worked in occupations that reflect their field of study they started to work soon after they began to study. The third category is likely to consist of individuals in their first job after graduation. To control for these differences the three categories were included in the analysis as dummy variables, contrasting the first and the second categories with the third. In addition, we employ two variables measuring time to control for changes in the opportunity structure of the Israeli labour market: Age at the time of interview, and Year of graduation. RESULTS Labour Force Participation We begin our substantive examination with the following question: what is the association between higher education and employment status? Turning to our first point of interest level of education we present in Table II the net effect of each level of degree (relative to a BA degree) on the likelihood of being out of employment in 1984, for men and women separately[6]. Apart from the ordinary b coefficients of these contrasts we present in Table II the quasi-variance of each b coefficient[7]. The advantage of the quasi-variances is great when interpreting the effects of dummy variables. The standard interpretation of regression coefficients that refer to a set of dummy variables is of a pair-wise comparison. The researcher sets one of the coefficients

10 580 Oxford Review of Education TABLE II. Net effect (log odds) of educational levels on being out of employment for Israeli Jews aged in 1984, by gender Educational level Men b Q.Var. a Q-s.e. b Women b Q.Var. Q-s.e. Vocation degree (p.s.) BA Set to zero Set to zero MA 0.504* Ph.D * Notes: *p 0.05 a Quasi Variance b Quasi Standard Error (omitted category BA) (commonly known as the omitted category) to zero, and interprets the other coefficient as a measure of deviation from this benchmark. However, it is not possible to infer from this analysis the existence of (statistically significant) differences between the effects of pair-wise comparisons that do not include this omitted category. In order to know if there are some statistically significant differences of that sort, one has to change the omitted category and to present all possible pair-wise comparisons. The use of quasivariances allows the researcher to calculate all possible pair-wise differences and to test whether or not these differences are statistically significant without re-analysing the data[8]. It is clear from Table II that marked gender differences exist in the effect of education on employment status. For women the effect of educational level on employment status is not statistically significant in the three comparisons presented (for the contrasts with BA degree)[9]. For men, on the other hand, an MA degree and a Ph.D. degree (compared to a BA degree) increase the likelihood of being employed in 1984[10]. The general pattern we find for men suggests that the higher the degree the more likely are men to be employed. These findings would appear to run contrary to the overqualification hypothesis. That is, the more qualified graduates, compared with the less qualified graduates, do not have a higher risk of being out of employment. Furthermore, in the case of men the evidence presented above suggests the opposite. Our next analysis addresses the qualitative property of education. That is, we would like to see if there are gender differences in employment status with respect to fields of study (controlling for level of education). To this end we include the dummy variables for fields of study in our model. However, instead of presenting the net effect of the field-of-study dummies, we present, for each field of study, the average predicted likelihood of being out of employment when all other variables in the equation are held constant. We present these averages for men and women separately, and to assess whether or not these averages differ (statistically) between men and women we carry out a t-test between these averages (for independent samples). Table III presents these averages and their standard deviations for men and women in each field of study. The entries in the table show that women with a degree in economics, business, medicine, or mathematics have either a higher (in the case of economics) or equal (in the other three fields of study) propensity as men to be employed. Among those who studied the other eleven professions men have a higher propensity to be in employment than women do. If we consider the distribution of the genders across our 16 fields of study, which is shown in Figure 2, an interesting pattern becomes apparent: women in male-dominated fields of study are more or equally as likely as men to be employed in

11 Gender Differences in Tertiary Education in Israel 581 TABLE III. Mean estimated likelihood (and standard deviation) of being employed, for Israeli Jews aged in 1984, by field of study and gender Men Women Field of Study Mean s.d. Mean s.d. Female dominated field of studies Humanities* Teaching credential* Arts* Social sciences Para-medical* Teaching (p.s)* Nursing School (p.s)* Clerical (p.s)* Male dominated field of studies Economics and Business* Law* Medical School Mathematics Natural Sciences* Engineering* Technicians (p.s)* Other (p.s)* * Difference in mean average between the genders is statistically different from zero (p 0.05) At the same time, men in female-dominated fields of study are more likely to be employed in 1984 than women. Further results from this analysis (not shown here) also revealed that current (1984) employment status is affected by age: older individuals, men and women, have a higher propensity to be employed. This, in turn, may imply that job opportunities in the Israeli labour market become scarce with time, or that older people acquired higher education while working, and continued working in the same place. Since we have studied cross-sectional data we can only speculate on the mechanism that produces this result. For women, moreover, current (1984) employment status is affected by year of graduation: veteran graduates are more likely to be out of employment, compared with recent graduates. This, too, may reflect the lack of opportunities for highly educated women in the Israeli labour market in the past. However, one has to bear in mind that opportunities for highly educated women are constantly created over time. This finding fits very well with the long-term trend, in Israel and in other post-industrial societies, of the increase in women s education and the dramatic increase in their labour force participation. Occupational Prestige The next question we address is the following: Is there a gender gap in occupational prestige attained among different educational levels and different fields of study? As in the previous section, we shall examine two types of educational effects on occupational prestige attainment: a quantitative effect and a qualitative effect. Table IV presents the net effect of the level of degree on occupational prestige, for men and women

12 582 Oxford Review of Education TABLE IV. Net effect of educational levels on current occupational prestige for Israeli Jews aged 25 44, by gender Educational level Men b Q.Var. a Q-s.e. b Women b Q.Var. Q-s.e. Vocational degree * * BA Set to zero Set to zero MA * * Ph.D * * Notes: * a Quasi Variance b Quasi Standard Error (omitted category BA) separately [11]. The general pattern that emerges from this analysis is that higher educational levels are positively associated with occupational prestige. That is, the higher the degree the higher the occupational prestige attained. Furthermore, the effect of educational level on occupational prestige is nearly monotonic for both men and women: the lowest effect is for vocational post-secondary degree, followed by BA and it is the strongest for MA and Ph.D. degrees [12]. We can conclude based on this analysis, that in the Israeli context, more education is better. Our next analysis addresses the qualitative property of education; namely, does a subject choice (i.e. field of study) result in further differentiation in the level of occupational prestige attained, controlling for levels of education? To answer this question we include the dummy variables for fields of study in our multivariate regression analysis. As in the previous section, instead of presenting the net effect of the field-of-study dummies, we present, for each field of study, the average predicted occupational prestige (when all other variables are held constant). We present these averages for men and women separately, and to assess whether or not these averages differ (statistically) between men and women we carry out a t-test between these averages (for independent samples). Table V presents these averages and their standard deviations for men and women in each field of study, and indicates if the differences between the genders are statistically significant. The figures in Table V show that women with a degree in male dominated fields of study have either a higher or an equal average prestige score compared to men. Thus, for example, women with a degree in economics, business and natural sciences, and technical degree holders, have a higher average prestige score than men. Women with a degree in other male dominated subjects, such as a degree from law school or medical school, and engineering degree holders, have a (statistically) similar average prestige score to men. At the same time, among the female dominated fields of study, such as the humanities, teaching, arts, and para-medical, men attain higher prestige scores than women. It is important to note that a similar pattern was found when employment status was examined. We would like to postpone our explanation of this pattern to the discussion [13]. Job Match Our final analysis will concentrate on differences between men and women in occupational prestige returns for a job match, and on the likelihood of obtaining a job that matches one s education. We introduced to the status attainment model a job match

13 Gender Differences in Tertiary Education in Israel 583 TABLE V. Mean estimated current occupational prestige (and standard deviation) for Israeli Jews aged 25 44, by field of study and gender Men Women Field of Study Mean s.d. Mean s.d. Female dominated field of studies Humanities* Teaching credential* Arts* Social sciences* Para-medical* Teaching (p.s) Nursing School (p.s) Clerical (p.s)* Male dominated field of studies Economics and Business* Law Medical School Mathematics* Natural Sciences* Engineering Technicians (p.s)* Other (p.s) * Difference in mean average between the genders is statistically different from zero (p 0.05) variable as an independent variable. This analysis revealed that the returns in prestige for job match are positive and similar for men and women: both genders gain about 15 prestige points if they work in an occupation related to their field of study. Furthermore, the predicted prestige score by gender, for each field of study, that we reported earlier (in Table V) does not change. That is, men attain higher prestige in female dominated fields of study, and vice versa. Now that we have seen that having a job match occupation is beneficial in terms of occupational prestige we would like to estimate the likelihood of having such an occupation in the Israeli setting. Thus, we fit a logistic regression model that estimates the likelihood of being in a job that is not related to one s field of study relative to being in a job that is [14]. TABLE VI. Net effect (log odds) of educational levels on having a non-related current occupation, for Israeli Jews aged 25 44, by gender Educational level Men b Q.Var. a Q-s.e. b Women b Q.Var. Q-s.e. Vocation degree (p.s.) 0.679* * BA Set to zero Set to zero MA 1.032* * Ph.D * * Notes: *p 0.05 a Quasi Variance b Quasi Standard Error (omitted category BA).

14 584 Oxford Review of Education TABLE VII. mean estimated likelihood (and standard deviation) of having a matched job in current occupation, for Israeli Jews aged 25 44, by field of study and gender Men Women Field of Study Mean s.d. Mean s.d. Female dominated field of studies Humanities* Teaching credential Arts* Social sciences* Para-medical* Teaching (p.s)* Nursing School (p.s)* Clerical (p.s)* Male dominated field of studies Economics and Business* Law* Medical School Mathematics* Natural Sciences Engineering* Technicians (p.s) Other (p.s) * Difference in mean average between the genders is statistically different from zero (p 0.05) In Table VI we see that net effect of level of education on the likelihood of having a job match occupation confirms the general pattern that emerged with the previous labour market outcomes, for both men and women. High education is positively associated with having a job match occupation (which, as we have shown earlier, is also beneficial in terms of occupational prestige attainment). However, we find that both men and women with a post-secondary vocational degree have a somewhat higher propensity to have a job match occupation, particularly when compared with BA degree holders [15]. This may be explained by the fact that most post-secondary education provides individuals with specific qualifications, such as nursing and teaching training. Table VII presents the mean predicted probabilities of having a non-matched job by fields of study, for men and women separately. This table reveals that the propensity of having a job match occupation follows a similar pattern as that of our earlier observations. That is, men who studied in female-dominated fields of study humanities, arts have a higher propensity, compared to women, to hold a job match occupation. Females, on the other hand, who studied in male-dominated fields of study are not discriminated against (in life and physical sciences, medicine, and technicians) and even have higher propensity than men to hold a job match occupation (in economics and business, law and mathematics). Further results from this analysis revealed that socio-economic and demographic indicators are not significant in explaining the (log) odds of being in non-matched jobs. And, the level of education appears to be positively correlated with the odds of being in a matched job: the higher the educational level one has the higher the propensity for being in a job that relates to one s field of study.

15 Gender Differences in Tertiary Education in Israel 585 DISCUSSION The main findings can be summarised as follows. First, level of education has no effect on the chances of women to be employed, whereas for men the more education they have the higher their chances of being employed. At the same time, men and women who did enter the labour market, and who have a higher level of education, get better labour market returns. This finding refutes the over-qualification argument (Hughes & O Connel, 1995), which suggests that unemployment rates may be relatively higher among individuals who attained higher education and might be over-qualified for certain jobs (see also Wielers & Glebbeek, 1995). Second, a striking symmetry between men and women would appear to exist in the general pattern of the transition from higher education to the labour market in Israel. We find that for men and women alike, graduating from the opposite sex-typical field of study is not associated with penalties in labour market outcomes. That is, neither men nor women experience discrimination in the labour market if they have graduated from an opposite (to their own) sex-typical field of study. Moreover, in some instances, particularly when occupational prestige attainment was examined, our results show that women who studied in male-dominated fields have better labour market outcomes than their male counterparts, and men who studied in female-dominated fields have better labour market outcomes than their women counterparts. How can we explain this pattern, and especially the success of women in male professions? The patriarchy approach argues that male domination of labour markets has serious consequences for women: men deny women access to the more desirable and privileged positions in the labour market. This being the case, it is expected that men and women will not have similar occupations and that men will have better labour market outcomes when compared to women (Acker, 1990; Raskin & Padvic, 1994). Indeed, research has shown that occupations held primarily by women are much less rewarding than those held by men, even when comparing workers with equal resources (cf. England, 1981; Jacobs, 1989). A process of filtering may explain the finding that women who studied in male-dominated fields have better labour market outcomes than men do. In this filtering process, women who penetrate fields of study that are traditionally associated with men may put more effort than men into their educational attainment and have higher ability than men. Thus, women s achievements in schools may exceed those of men. The human capital theory would then anticipate that employers would value these qualities. The finding that men in female-dominated fields of study do better than women is then explained by the fact that men control the labour market (cf. Williams, 1989, 1995). We would like to build on the patriarchy and the filtering explanations and offer a more symmetric explanation. We assume, as indeed most advocates of the patriarchy approach do, that men dominate labour markets and hence women are more likely to experience discrimination. We also assume, however, that for men and women alike the decision to study in the other gender-dominated field of study involves a special commitment to that field of study (or even to the profession). The standard human capital model asserts that employers should value this commitment. While in many job-matching processes employers may have some difficulty in identifying these committed individuals, in the examples we explore in this paper, we would argue, this identification problem is less pronounced [16]. Recruiting Spence s (1974) signalling theory, we argue that since men and women who study in the other sex-dominated environment are more committed individuals, sex becomes a signal to the employer of

16 586 Oxford Review of Education this higher commitment. Sex is a very strong signal because it is very difficult to fake. By choosing women who studied in male-dominated fields of study the employer is in fact selecting highly committed individuals. It is clear from this signalling model that the same explanation may hold for men s advantaged position in a female-dominated environment. One has to acknowledge, however, that discrimination against women in labour markets (that is, men s domination of labour markets) may offset part of the expected positive effects of this signalling process. As we have shown in this paper, a deviation from an apparent existing norm in choosing field of study, particularly among women, does not always produce positive effects as expected by this model. Nonetheless, this deviation, and the signal it provides, would appear as we have shown in the analysis to protect women from discrimination in the labour market. Additional evidence from other data sets and societies is required in order to establish that we have indeed identified a regularity that requires a sociological explanation. Nonetheless, a signalling mechanism is a very plausible explanation of the pattern that was identified in this paper. Thus, we would encourage further research on this topic. In particular, we would encourage students of education and labour market processes to look into the way signalling commitment and credentials of certain fields and levels of education intertwine to affect employers recruitment decisions. In conclusion, a decomposition of different aspects of higher education and the way they are associated with labour market positions and rewards sheds new light on the transition from higher education to work. We have highlighted the advantages that are related to investments in specific fields of study versus those that accompany different degrees. We have also highlighted the importance of the distribution of gender along occupations, and revealed some of the least negative consequences of the gender segregation in the labour market for both men and women. ACKNOWLEDGEMENTS We wish to thank the editor and two anonymous reviewers for their valuable comments and suggestions. A version of this paper was presented at the ECSR conference in Giens, France, in September We would like to thank Richard Breen, Diego Gambetta, David Firth, Phillip Fucella, Sam Lucas, Markus Mübius, Walter Müller, and the participants of these conferences for their helpful comments. The usual disclaimers apply. The authors are listed alphabetically. NOTES [1] The 1984 higher education survey provides the most recent data on tertiary education in Israel. Nevertheless, some critics may still argue that these data are too old and even outdated for dealing with the questions we address here. Our reply to such a critique would be that the data we analyse are relevant for studying labour market consequences of the expansion of tertiary education through to the 1980s. What is more, we would argue that from a historical perspective, this study offers an examination of the initial expansion of tertiary education and their social consequences in Israel. [2] The Israeli education system, like most other education systems, underwent a major reform in the early 1970s. Most of this reform was aimed at equalising educational opportunities in the secondary educational level (for more on this reform, see Kraus et al., 1998, and the citations herein). A major advantage of

17 Gender Differences in Tertiary Education in Israel 587 using the relatively old 1984 higher education survey is that most men and women in this survey entered secondary schools under the pre-reform rules. [3] In a separate analysis (not shown here) we examined the effect of excluding this group, and found that: (i) in all fields of study women are less likely to be in the excluded category whereas men are more likely to be in this category; (ii) the older a person is and the later in time that person graduated, the lower the chances they will be found in the excluded category. The implications are that we exclude, ceteris paribus, men and young individuals. Women are more likely than men to enter the labour market immediately after graduation and tend to stay in the same job for a longer time. Young individuals approach their occupational maturity in their early thirties (Goldthorpe et al., 1987), and until that stage they are more likely to change jobs and occupations. The full results are available from the authors on request. [4] First we combine sales and proprietors, and the skilled, semi-skilled, and unskilled workers. Second, we combine professionals and scientific professionals since most of them are assigned to the matched occupations. Finally, the clerical workers category, which is part of the 11 major occupational groups in Israel, is assigned to the matched job groups. [5] Students who drop out from high school, for all kinds of reasons, may obtain their matriculation diploma through so-called external schools. About 5% of the students who drop out from high school later re-enter the system through external schools (CBS, 1991; see also Table I). [6] The detailed analysis is available from the authors on request. [7] The quasi-variances reported in this paper were generated by a web based QV calculator: firth/qvcalc/web.html. For a full documentation and explanation of this methodology see Firth (2000). [8] For example, if one is interested to calculate the contrast MA/PhD for men, the following steps are required. First, the difference of the b coefficients between MA and PhD is calculated: ( 0.504)-( 1.964) This equals to setting the MA coefficient to zero, and the new b coefficient (1.46) means that men with an MA degree are over four times (e ) more likely to be out of employment compared with men with a Ph.D. degree. We then calculate the Q-s.e. for this new coefficient as follows: Q s.e. ( ) The t-value of the product of these two values (1.46/ ) indicates that there is no statistically significant difference (when p 0.05) between the effect of an MA degree and a Ph.D. degree on employment status. [9] Calculating all possible pairs reveals that in none of these contrasts women with a lower level of education are more likely to be employed when compared with women with a higher level of education. The only statistically significant contrast suggests that women with an MA degree are nearly two times more likely to be in employment compared to women with a post-secondary vocational degree. [10] Calculating all possible pair-wise contrasts reveals that the effects for the contrasts BA/P.S., MA/P.S., and MA/Ph.D. are not statistically significant. However, men with a Ph.D. degree are more likely to be employed when compared with both post-secondary degree and BA degree holders, and MA degree holders are more likely to be employed compared to BA degree holders.

18 588 Oxford Review of Education [11] The detailed regression analysis on occupational prestige is available from the authors on request. [12] Calculating all possible pairs reveals that for both men and women the contrast MA/Ph.D. is not statistically significant. This implies that the net payoff of a PhD in terms of occupational prestige is similar to that of an MA degree. [13] Further results from this analysis (not shown here) revealed that after controlling for fields of study, the effect of educational levels, although smaller, is still positively associated with occupational prestige. That this, the quality, as well as the quantity of education, is important for occupational prestige attainment. This finding holds for men and women alike. [14] The results from the full model are available from the authors on request. [15] Thus, for example, women with a post secondary degree are more likely to have a job match occupation compared with women with an MA or a BA degree. At the same time, women with an MA degree or a Ph.D. degree are more likely to have a job match occupation than women with a BA degree. Similarly, men with a post secondary vocational degree are more likely to have a job match occupation compared with men with a BA degree, while men with an MA degree are more likely than men with a BA degree to have a job match occupation. [16] In a job-matching process employees and employers try to fit their preferences for one another. REFERENCES ACKER, J.(1990) Hierarchies, jobs, bodies: a theory of gendered organisation, Gender and Society, 4,pp AHARONI, Y.(1991) The Israeli Economy: dreams and realities (New York, Routledge). AL-HAJ, M.(1996) Education among Arabs in Israel: control and social change (Jerusalem, Magnes) (in Hebrew). ARUM, R.& SHAVIT, Y.(1995) Secondary vocational education and the transition from school to work, Sociology of Education, 68, 3, pp ASHTON, D.N. & LOWE, G.(1991) Making Their Way: education, training and the labour market in Canada and Britain (Milton Keynes, Open University Press). BAGNALL, N.(2000) The balance between vocational secondary and general secondary schooling in France and Australia, Comparative Education, 36, 4, pp BEN-PORATH, Y.& GRONAU, R.(1985) Jewish mother goes to work: trends in the labour force participation of women in Israel, , Journal of Labour Economics, 3,1,pp BYNNER, J.& ROBERTS, K.(1991) Youth and Work: transition to employment in England and Germany (London, Anglo-German Foundation). CANNY, A.(2001) The transition from school to work: an Irish and English comparison, Journal of Youth Studies, 4, 2, pp CENTRAL BUREAU OF STATISTICS (CBS) (1991) Statistical Abstract of Israel, 42 (Jerusalem, CBS). CENTRAL BUREAU OF STATISTICS (CBS) (1995) Statistical Abstract of Israel, 46 (Jerusalem, CBS). COHEN, Y., BECHAR, S.& RAIJMAN, R.(1987) Occupational sex segregation in Israel, , Israel Social Science Research, 5, 1/2, pp

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