This paper explores school-to-work transitions in three new EU Member states, Hungary, Slovenia and Estonia, focusing on the effect of the level of education and social background on the timing of the first significant employment and the match between educational qualifications and occupation among school leavers. So-called cohort effects are a focal point as well, since transition process and outcomes seem to be largely influenced by the dynamics of economic and social changes in the transition economies. To explore the early career developments of job entrants in the three transition countries during the 1990s, the study utilizes the European Union Labour Force Survey ad hoc module on school-to-work transitions, which has been launched in Hungary, Slovenia in 2000 and in Estonia in 2002. Event history methods are applied to explore transition to first significant employment, while the match of educational qualification and occupational attainment is modelled using multinomial logistic regression techniques.

Linkages between the education system and the labour market have received a great deal of attention in the sociological literature. The success of a person in the labour market is seen to be dependent on various resources s/he holds and on whether, if at all, s/he has a chance to convert those resources into a beneficial labour market position (Helemäe and Saar 2000: 85). In all western industrialized countries occupational attainment is shaped by education, which is especially true for new labour market entrants who do not have previous work experience. However, cross-national variation is observed with respect to how and to what extent educational attainment influences occupational outcomes (Maurice et al. 1986; Allmendinger 1989; Kerckhoff 1995, 2000; Shavit and Müller 1998; Ryan 2001; Müller and Gangl 2003).

The human capital approach and the model of status attainment have treated the issue by mainly focusing on the impact of individual resources, above all, education and work experience, as well as ascriptive characteristics like gender and social origin. More recently the emphasis has shifted to the structural settings in which status attainment process occurs (Müller and Shavit 1998; Gangl 2003a,b,c). Findings show that the impact of individual resources, e.g., education, on the occupational outcome depends on the specific institutional settings, i.e. the institutional arrangements of educational systems, the system of labour market and the linkages between the two (Hannan et al. 1997).

While a large body of research exists on the school-to-work transitions and the early careers of youth in EU and other western industrialized countries, little is known about youth transitions in Central and Eastern European (CEE) countries.1 The experience of the post-communist countries seems, however, to be unique since transition economies are undergoing major structural changes in all sectors (education, economy, welfare state) at the same time, which is not comparable with downturns in the business cycle known in western economies. The introduction of the market-based economy, among other things required radical changes in the production system and consequently in the skills of the labour force. Thus the major challenge for school-to-work transition in the transition countries has been to qualify new entrants (in addition to the re-qualifying a large part of the workforce) into the labour market at a time when resources are limited and educational institutions and training providers are themselves being restructured, often at a slower space than the labour market, which complicates the task (Cazes and Nesporova 2003).

This paper aims at describing school-to-work transitions in several CEE countries that joined the European Union in 2004, namely Hungary, Slovenia and Estonia, during the 10-year period after the fall of socialism (1990–2000).2 More specifically, the focus is on the effect of the level of education, social background (and networks connected to it), and interplay between them on the timing of first stable employment3 and the quality of the match4 of the first job with educational qualification among recent school leavers aged 15–35. So-called cohort effects are a focal point as well, since transition process and outcomes seem to be largely influenced by the dynamics of economic and social changes in the transition economies.

This paper, first, relates to the specificity of the situation in the transition economies after the collapse of socialism, further, discussing the background conditions in three CEE countries, Hungary, Slovenia and Estonia. Hypotheses as to the effect of education on the timing of first significant employment and its match with acquired educational qualifications can be found in these sections. The data, variables, and methods are then described in detail. A further section presents results of the event history analysis of entry into the first significant job and of the multinomial regression analysis assessing the match of the first significant job with educational qualification. These results are discussed and summarized in the final section of the paper.

The transformation period in the CEE countries can be divided into two parts: the early transformation period and the stabilization period. The early transformation period in the CEE countries has been marked by extreme disorder and even chaos in youth school-to-work transitions, caused by general economic upheaval in the post-socialist countries (Roberts 1998). It is a time when businessmen could emerge from nowhere and, without appropriate education, rapidly reach high status and amass fortunes or, in other words, entering employment as under-educated was a common reality. The break-down of the normally observed stratification mechanisms was particularly salient as it occurred in sharp contrast to the situation in socialist times when full employment was guaranteed for all people and the link between education and social status of jobs was, due to central planning, even stronger than in Western countries (Solga and Konietzka 1999). At that period the educational system was not able to respond immediately to its changed role in the market economy, while reforms of the educational system were certainly lagging behind the rapid changes in the labour market institutions.

After a period of painful reforms labour market and educational institutions started to stabilize,5 the stabilization, which should contribute to the re-emergence of the traditional stratification processes. In more organized economies the demand for professionals and qualified specialists has increased as western-based or western-linked businesses, privatized companies and the remaining public sector establishments have been recruiting young specialists and skilled labour, while opportunities for quick enrichment and higher status attainment among less educated persons have vanished. As the market began setting demands for school leavers’ qualification the system of post-secondary education has responded with decentralization and expansion, with private institutions flourishing and paid programs in state educational establishments attracting more and more students. A tendency for youth to opt for the role of student has been greeted with enthusiasm as a sign of the increasing importance of education. Lately, however, this enthusiasm has been tempered with some sobering notes, as it has been recognized that this tendency also reflects withdrawal from the labour market in the face of poor labour market prospects (Helemäe and Saar 2000).

The rapid expansion of post-secondary and higher education might lead to the situation when there are more higher education graduates than corresponding jobs, so that some of the school leavers have to accept less attractive employment, often not matching their educational level.6 For developing countries it has been observed that when the development of the educational system and the labour market are not in harmony with each other, i.e. when the output of experts with higher education exceeds the availability of jobs requiring higher education, then the brain-drain from the country increases substantially, meaning also that these countries’ limited resources are being wasted (Bertrand 1995). From the individual point of view, the evidence shows that the satisfaction with the job depends very much on whether a person is able to work in a job matching his/her educational qualifications (Burris 1985; Tsang and Levin 1985; Rose 1994; Saar and Kazjulja 2001). By accepting non-matching, lower-status employment highly educated school leavers might also reduce chances of persons with lower education, making the latter drop further down the queue (Braverman 1977; Borghans and De Grip 2000). Starting from the second half of the 1990s this process might become a reality in the CEE countries.

The expansion of education and its growing role in determining one's occupational outcome and economic status is often regarded as a sign of growing meritocracy. Modernization theories (Treiman 1970; Parsons 1994) argue that in industrial economies individuals are allocated to occupations according to their merit, i.e. educational credentials, and not via social background and networks connected to it. Counter facts, however, show that social background, being mediated through education, still plays an important role in individuals’ occupational outcomes (Blau and Duncan 1967; Marshall et al. 1997; Prandy and Bottero 2000). Comparative research on the effect of family background on children's educational outcomes and early career has shown that people with working class background are more likely to choose vocational education and start their career as blue-collar workers (Müller and Shavit 1998: 40).

The role of social background was clearly a political issue in the former Soviet bloc and numerous attempts were made to reduce class differences through, for instance, providing free education or setting certain quotas for working class children in entering university. Still, research has shown that despite all institutional efforts, children from advantageous family background manage to obtain higher level of education and better occupational outcomes in Eastern European countries (Heyns and Bielecki 1993; Mateju 1993; Szelenyi and Aschaffenburg 1993; Ganzeboom and Nieuwbeerta 1999; Helemae et al. 2000; Iannelli 2002). It is claimed that even if economic factors do not limit persons’ access to education, educational outcomes are dependent on value orientations and educational aspirations which are promoted in the family (Haller and Porter 1973; Sewell and Hauser 1980). In addition, children from higher social class have greater cultural (Bourdieu and Passeron 1977) and social resources (Coleman 1988) at their disposal.

As state has almost ceased to play a role in matching school leavers to vacant positions after the collapse of communism, the significance of social capital might increase in determining occupational outcomes among recent school leavers. Even though the effect of parental education on children's occupational outcomes is observed in most countries, Barbieri et al. (1997) have pointed to the particular importance of the social capital in acquiring a job in countries with a weaker role of the state. Importance of networks in situation where institutional links between educational and labour market system are changing is also confirmed by Clarke (2000), who contends that in Russia where new labour market institutions have managed to replace old ones, the importance of social networks has increased considerably in finding employment.

All in all, we would expect to observe smaller differences between school leavers with different levels of education at entry to first significant employment in the early transformation period. As education was less a determinant of employment chances then, entering the first job as under-educated might be rather pronounced. During the stabilization period education should become a more valuable asset with stronger signalling power for employers, which should increase its importance for securing first significant employment. For less-educated school leavers the chances of obtaining secure employment might, however, decline due to the increased flexibility of labour markets. Labour market uncertainty might lead to the situation when school leavers choose to postpone labour market entry staying longer at school. Together with the educational expansion this might lead to higher probability of young people entering the labour market as over-educated in the later period in CEE countries. With respect to the effect of the social background it is to expect that parental education should play a role in young school leavers’ chances of securing first significant employment and particularly in the match of their educational qualifications to the job characteristics.

Can we expect differences between the three countries in school-to-work transitions? The answer is probably yes, and if so, Estonia is more likely to deviate from the rest of the countries analysed here. While more extensive discussion on cross-country differences can be found in the subsequent sections, in short it should be stressed that although the educational system and the labour market have undergone significant changes in all three countries during the 1990s, the situation in Hungary and Slovenia did differ from the one in Estonia. The former two countries had better starting positions as they had market elements in their central planning already before 1991, and generally their transition from one economic and political system to another could be characterized as more gradual compared to the Estonian case. The Estonian economic reform has been described as one of the most radical among the post-socialist countries, particularly with regard to its highly liberal economic principles and the modest role of the state (Saar and Helemäe 2002).

During the socialist period the educational system was highly centralized and state controlled in all three countries. Young people were allocated to the educational system in accordance with the economic and social goals of central planning. The transition from school to work was smooth, as the first workplace was often assigned by state agencies, supported by employers and secured for all school leavers virtually irrespective of the level of education. Organization of the school structure and curricula was based on the dual system model, so the link between the level of education and the future job was clearly defined with status match being, however, more important than skill match (Saar and Helemäe 2002; Róbert and Bukodi 2002). After the launch of economic reforms employers have largely withdrawn from their administrative and financial involvement in education, including a reduced role in paying scholarships to students (OECD 1997). In fact, many economic branches around which vocational education was organized became largely obsolete after the collapse of the socialist system (Bukodi and Róbert 2002).

Another trend in the 1990s is a marked expansion of tertiary education, which is clearly illustrated in Table 1. Actually, the attendance increased faster in the vocational type institutions or fashionable market-oriented colleges (both counting as short-track tertiary education) than in universities. This trend is especially pronounced in Slovenia, where half of school leavers from tertiary education left not from universities but other forms of tertiary education. The expansion of tertiary education is less visible in Estonia according to Table 1. However, there, only within 4 years between 1996 and 1999, did enrolment into universities increase by 51 per cent (Education 1999/2000, 2000). Annus (2000) suggests that an assessment of the results of the expansion in higher education on the labour market in Estonia could be done only in 2002–2003, when people begin to graduate. Since most students do not get any scholarship or other support from the state, universities or enterprises, they are forced to count entirely on their parents’ financial resources or start to work parallel to studying, which considerably extends the length of the schooling period. Hence, the results of the expansion in higher education might not be fully visible yet.

Table 1. 
Level of education of recent school leavers by country and cohort
HungarySloveniaEstonia
LowMediumHighLowMediumHighLowMediumHigh
1990–1991 24.7 64.6 10.7 12.3 80.0 7.7 – – – 
1992–1995 15.2 73.3 11.6 10.4 72.5 17.2 16.9 60.1 23.4 
1996–2000 (2002) 10.8 69.4 19.8 5.6 67.1 27.3 24.0 52.2 23.8 
N 8613    1721   1080  
HungarySloveniaEstonia
LowMediumHighLowMediumHighLowMediumHigh
1990–1991 24.7 64.6 10.7 12.3 80.0 7.7 – – – 
1992–1995 15.2 73.3 11.6 10.4 72.5 17.2 16.9 60.1 23.4 
1996–2000 (2002) 10.8 69.4 19.8 5.6 67.1 27.3 24.0 52.2 23.8 
N 8613    1721   1080  

Source: EULFS 2000 ad hoc module (for Slovenia and Hungary), LFS 2002 ad hoc module (for Estonia).

Unfortunately, simultaneously with the expansion of higher education in Estonia, there is also an increasing share of those with less than secondary education. Moreover, those programs which used to provide vocational training without basic education have been eliminated in vocational schools (Saar and Kazjulja 2001). Overall, while Hungary and Estonia appeared to move slowly in the direction of organizational mobility space,7 in Slovenia educational reforms promoting apprenticeships were launched8 (OECD 1997; Saar and Kazjulja 2001; Róbert and Bukodi 2002).

Full employment and a strong link between the level of education and social status were main features of the command system in socialist countries. The constitutional right to work as well as the principle of full employment shaped both occupational and employment opportunities available to employees and workers (Mach et al. 1994). Still, republics of the former Yugoslavia deviated from this picture, as unemployment was a permanent job characteristic there in the post-WWII period. However, among all Yugoslav republics Slovenia had by far the lowest unemployment rate until 1990, so that this period can safely be labelled as a full employment period (Drobnic and Rus 1995).

Despite similarities, the Slovenian, Hungarian and particularly Estonian economies differed by the early 1990s when socialist rule collapsed. Slovenia, the richest republic in Yugoslavia and more economically advanced than other transition countries in the CEE region, was the country that had already introduced elements of the free market during socialist times. Hungary was another country to introduce market elements into the central planning process, so by the time of the political and economic transition in 1991 a significant part of the economic activity was already nominally private, albeit often in the form of subcontracts from the state-owned sector (OECD 1995). Estonia, on the other hand, being a part of the economy of the Soviet Union, in the beginning of 1990s was closely bound up with the raw material and product markets of the Soviet Union. At the same time, Estonia had quite a special role in the Soviet Union as it was used as a laboratory for economic reforms from the 1950s onwards. This seems to have at least a somewhat positive effect on the economy and entrepreneurship, as this ‘guinea pig’ role brought Soviet Estonia and its enterprise sector slightly closer to the market economy mechanism than other Soviet republics, although not to the same degree as in Slovenia and Hungary (Nove 1992; Liuhto 1996).

In the years immediately after the fall of socialist rule in 1990–91 the transforming economies of Central and Eastern Europe have been overwhelmed by high and lasting unemployment. Koltay (1995) reports that in countries that started carrying out transformation and reforms even earlier, among them Hungary and Slovenia, by 1992 unemployment reached 12.3 and 13.2 per cent, respectively. Developments in the early 1990s were, however, less disruptive in Slovenia than in other CEE countries, so that GDP growth already resumed in the second half of 1993 (OECD 1997). In Estonia the economic situation in 1992 was mostly dreadful with government choosing the path of maximum liberalization (Lauristin and Vihalemm 1998).9

A very important step in the transformation processes in CEE countries was the restitution of ownership rights and privatization. After instituting a small-scale privatization program, in Estonia and in Hungary the government launched a large-scale one, based on international auctions. Thus the share of the private sector grew from 10 per cent in 1990 to 70 per cent in 1998 in Estonia, and from 25 to 85 per cent in Hungary. The respective figures in Slovenia, a slower privatizer as compared to Hungary and Estonia, are 15 per cent in 1990 and only 50 per cent in 1998 (Hunya 2001). The private sector dominates in manufacturing, construction and flourishes in a large part of services and trade, whereas typical public employers are found in administration, education and health. It is in the public sector, hit by serious budget constraints, where new job openings are scarce and fixed-term contracts are more frequent (Róbert and Bukodi 2002).

In the early years of transition the decline in employment in all three countries was mainly due to redundancies in the primary and secondary sectors (Pettai 2001; Bukodi and Robert 2002). Differences are, nevertheless, noticeable between the countries with respect to the structure of employment. Table 2 shows that Hungary has the largest proportion of persons employed in services and the smallest in industry. Figures for Estonia are quite similar (see also Marksoo 2002), whereas in Slovenia industry is still a significant part of the country's economy, as it is obvious from the proportion of people employed in industries.

Table 2. 
Labour market indicators of Estonia, Hungary and Slovenia in 2000
HungarySloveniaEstonia
Population (in million) 9.9 2.0 1.4 
Activity rate (% pop. 15–64) 59.9 67.4 70.0 
Employment rate (% pop. 15–64) 55.9 62.7 60.6 
Employment in services (% total employment) 59.8 52.7 58.3 
Employment in industries (% total employment) 33.8 37.7 34.7 
Purchasing power as% of EU-15 in 1995 46.0 64.0 32.0 
Purchasing power as% of EU-15 in 2000 51.0 71.0 36.0 
Unemployment rate (% of LF 15+) 6.6 6.9 13.2 
Youth unemployment rate (% of LF 15–24) 12.3 16.4 23.7 
HungarySloveniaEstonia
Population (in million) 9.9 2.0 1.4 
Activity rate (% pop. 15–64) 59.9 67.4 70.0 
Employment rate (% pop. 15–64) 55.9 62.7 60.6 
Employment in services (% total employment) 59.8 52.7 58.3 
Employment in industries (% total employment) 33.8 37.7 34.7 
Purchasing power as% of EU-15 in 1995 46.0 64.0 32.0 
Purchasing power as% of EU-15 in 2000 51.0 71.0 36.0 
Unemployment rate (% of LF 15+) 6.6 6.9 13.2 
Youth unemployment rate (% of LF 15–24) 12.3 16.4 23.7 

Source: Kunz, 2002.

At the same time, overall labour force participation rate is lower in Hungary and higher in Slovenia and Estonia. By 2002 unemployment rates fell in Hungary and Slovenia and per capita GDP surpassed the 1990 levels10 (Hunya 2001). In Estonia the situation stabilized in 1996–1998, but since the end of 1998 unemployment has increased further as a result of the economic crisis in Russia. The growth of unemployment stopped only in the beginning of 2000 at the level of 13.2 per cent and encountered a light decrease tendency afterwards.11 The decline in the rate of employment in Estonia is especially dramatic in absolute values: as compared to 1991, the number of working people declined by the year 2000 by an astonishing 25 per cent (or by 203,000 people in a country with 1.4 million of inhabitants).

It was a shock for people when guaranteed employment was replaced by competition for the rapidly reducing number of jobs. Different social groups adapted to the changes in the labour market with various degrees of success, with youth being particularly vulnerable. As is noticeable from Table 2 in all three countries the youth unemployment rate (unemployment rate among 15–24-year-olds), being constantly higher that the average unemployment rate in each of the countries throughout the period between 1990 and 2000, in 2000 is still almost twice as high as the average unemployment rate (Kunz 2002).

An important characteristic of the transformation economies in the CEE countries relevant for school-to-work transition processes is the increased flexibility of labour markets.12 In Slovenia as well as Hungary a significant proportion of newly created jobs are temporary in nature. In Hungary individuals in fixed-term jobs are overrepresented among young people: Indeed, 37 per cent of employees with fixed-term contract are aged 29 or below (Róbert and Bukodi 2002). As far as the socio-economic composition of fixed-term employees is concerned, this type of working arrangement is the most widespread among less educated unskilled workers.13 In Slovenia in order to promote transition from school to work the country's educational system places students in temporary jobs or particular forms of traineeships during their education. The Estonian economy is also characterized as a highly dynamic and flexible one (Cazes and Nesporova 2001). Unfortunately, the negative side of the flexibility in Estonia is that the movement out of employment is much more frequent than the movement into employment, while most job shifts occur not due to the free choices of employees, but as employers’ initiative.

In order to explore the early career developments of school leavers in Hungary and Slovenia, a dataset made available by Eurostat14 is used, the ‘European Union Labour Force Survey (EULFS) 2000 ad hoc module on transition from school to working life’ (see Eurostat 1999). For Estonia, the similar ad hoc module was carried out in 2002 as an additional module of the general LFS. In addition to the data's linkage to the general EULFS (Eurostat 1998), the ad hoc module on school-to-work transitions provides a core set of substantively important variables on labour market entry.15

A central concept within the EULFS ad hoc module is the so-called ‘first significant job’, defined by Eurostat as non-marginal employment of at least 20 hours per week that has lasted at least six months and started after the employee's leaving continuous education. The LFS ad hoc module contains information on the dates (month and year) of leaving continuous education and of entry into the job, enabling a dynamic approach to the analyses of the first job entry.16 In Hungary the actual sample size of the target population, i.e. young people aged 15–35 who left education within the previous ten years (between 1990 and 2000), is 8614, in Slovenia 1750, and in Estonia 1091.

The dependent variable in the analysis of transition to first significant employment is the limit of conditional probability that a person enters first significant employment within a particular month (since the time is measured on the month scale in the present study), assuming that this person did not do so until that time. The dependent variable in the analysis of the quality of the match of education and occupational status of first significant employment is the probability of finding matching employment, entering employment over-educated, or entering employment under-educated.

It should be noted that defining somebody over- or undereducated is a social construct and depends on the particular context. In this article, the match between the education and occupational status of first significant job is defined in reference to the situation of total labour force in each country. If the labour market entrants’ returns to education in terms of occupational attainment are on average the same as in the total labour force, then they are considered to have matching job. If the status of first job deviates considerably from average status of respective educational level in the total population, then they have considered having mismatching job. More precisely, if the prestige of the job (measured on ISEI scale) is at least one standard deviation below the medium prestige score of the respective educational group in the total population aged 16–64, then the person holding it is considered to be more educated compared to the status group in the total population or in other words over-educated. If the prestige of the job is at least one standard deviation above the medium prestige score of the respective educational group, then the person is considered under-educated, i.e. less educated compared to the status group in the total population. And finally if the prestige of the job falls in the range of the medium prestige score plus–minus one standard deviation, the job is considered matching. Information on the medium prestige scores and standard deviations of the total population are taken from Luxembourg Employment Survey (LES)17 and from the Estonian LFS 1995 and 1999.

Independent variables used in the multivariate analyses are summarized in Table 3.

Table 3. 
Description of the independent variables in the multivariate analyses
Independent variableDescription
Age at leaving education Age minus time since leaving education (in years) 
Gender Men (reference category), women 
Level of education when leaving school for the first time23 A group of dummy coded variables: 
 
  1. Low – ISCED 0–2 (reference category)

  2. Medium – ISCED 3–4

  3. High – ISCED 5–6

 
Parental highest level of education A group of dummy coded variables: 
 
  1. Low – ISCED 0–2 (reference category)

  2. Medium – ISCED 3–4

  3. High – ISCED 5–6

 
Cohort A group of dummy coded variables: 
 
  1. Cohort 1990–1991 (not for Estonia)

  2. Cohort 1992–1995

  3. Cohort 1996–2000 (for Hungary and Slovenia), 1996–2002 (for Estonia) – reference category

 
Timing of the job entry A group of dummy coded variables: 
 
  1. Prior to leaving education (only in the multinomial regression analysis)

  2. 0–5 months after leaving education – reference category

  3. 6–24 months after leaving education

  4. 25–60 months after leaving education

  5. >60 months after leaving education

 
Independent variableDescription
Age at leaving education Age minus time since leaving education (in years) 
Gender Men (reference category), women 
Level of education when leaving school for the first time23 A group of dummy coded variables: 
 
  1. Low – ISCED 0–2 (reference category)

  2. Medium – ISCED 3–4

  3. High – ISCED 5–6

 
Parental highest level of education A group of dummy coded variables: 
 
  1. Low – ISCED 0–2 (reference category)

  2. Medium – ISCED 3–4

  3. High – ISCED 5–6

 
Cohort A group of dummy coded variables: 
 
  1. Cohort 1990–1991 (not for Estonia)

  2. Cohort 1992–1995

  3. Cohort 1996–2000 (for Hungary and Slovenia), 1996–2002 (for Estonia) – reference category

 
Timing of the job entry A group of dummy coded variables: 
 
  1. Prior to leaving education (only in the multinomial regression analysis)

  2. 0–5 months after leaving education – reference category

  3. 6–24 months after leaving education

  4. 25–60 months after leaving education

  5. >60 months after leaving education

 

The empirical analysis focuses on the transition from education to first significant jobs, approached from an event history perspective. In such models one estimates the time-dependent hazard rate or ‘risk’ r(t) of an event, defined by (e.g., Blossfeld and Rohwer 1995: 28):
i.e. the limit (as t* approaches t) of the conditional probability (Pr) that the event occurs (at time T) between time points t and t*, given that it has not occurred until t, divided by the length of the interval between t* and t.

The starting time of an episode (t=0) is defined as the time of leaving education. An event occurs when an individual takes a first significant job. In this case the point in time at which the job is begun defines the ending time of an episode. Hence, the duration of the search, measured in months, equals the period between leaving continuous education and starting the first significant employment. Those individuals (episodes) who did not enter a first significant job by the time of the interview are treated as right censored. In such cases, the duration of the job search is defined as the period of time between leaving continuous education and the date of the interview.

Piecewise constant exponential models (see Blossfeld and Rohwer 1995: 110–9) are run to approximate the shape of the hazard functions and to estimate the impact of independent variables. This model postulates that the transition rate is given by:
i.e. the time axis is divided into L intervals, and an interval-specific constant al is estimated for each interval . Furthermore, for all covariates x1,…,xm, interval-independent parameters a1,…, am are estimated. As the piecewise-constant model is a proportional hazards model, the exponents of these parameters can be interpreted as hazard ratios.
To assess the match of the first significant employment and educational qualification, multinomial logistic regression analysis is conducted. The probability that a person with characteristics x finds a specific type of employment (Ej) can be written:
where 1, 2, and 3 represent three employment options: (1) matching employment, (2) over-education, (3) under-education. bj is a set of logic parameters corresponding to each of the options. Since the probabilities of ending up in each of the job options must sum to 1, only (j–1) independent sets of parameters can be estimated. Thus, option 1, finding matching employment, is set to 0 and serves as the base for comparison.

Descriptive characteristics

To get an idea of the speed of entry to first significant employment it is advisable to look at the survivor functions18 estimated using the Kaplan–Meier method (product-limit estimator). In our case the survivor function can be interpreted as the proportion of young people who still did not find a first significant job at time t after leaving education. The results of the analyses for three educational levels and two cohorts of school leavers are shown in Figure 1.19
Figure 1. 

Survival curves of the transition to first significant employment by country, cohort and level of education in Hungary, Slovenia and Estonia. Source: EULFS 2000 ad hoc module (for Slovenia and Hungary), LFS 2002 ad hoc module (for Estonia).

Figure 1. 

Survival curves of the transition to first significant employment by country, cohort and level of education in Hungary, Slovenia and Estonia. Source: EULFS 2000 ad hoc module (for Slovenia and Hungary), LFS 2002 ad hoc module (for Estonia).

Close modal

An almost identical picture is apparent for Hungary and Slovenia. While for the earlier cohort disparities between school leavers with different levels of education in the speed of the entry to first significant employment are quite small, especially when it comes to individuals with secondary and tertiary education, these widen in the later cohort of school leavers. School leavers with low and even with secondary education have much slower entry to their first employment than people with higher education in the later cohort. Particular difficulties are noticeable for school leavers with lower, i.e. less than secondary education. Among them only 20 per cent in 1996–2000 were able to enter significant employment 2 years after leaving education. As for the 1990–1995 year school leavers’ cohort the corresponding figure is about 60 per cent in Hungary and 40 per cent in Slovenia. Even among school leavers with secondary education the process of entry to first significant employment has slowed down significantly in 1996–2000. No such trend is observed among tertiary educated school leavers in either Hungary or Slovenia. In Estonia, even though growing disparities between more educated and less educated school leavers at the entry to first significant employment are visible over time (cohorts), these are of much lower magnitude. In fact, in the earlier cohort the survival curves for different educational levels hardly differ,20 or in other words, education plays no significant role in the speed of entry to first significant employment. In the later cohort, the differences between levels of education are increasing, but still not to the same degree as occurred in Hungary and Slovenia. All in all, educational stratification has increased in all three CEE countries when comparing earlier and later cohorts of school leavers, although the change is more modest in Estonia than in Hungary or Slovenia.

To get an impression whether the link between occupational attainment and education has changed during the transition process, occupation by educational level and cohort is presented in Table 4. The educational level of persons starting their career as managers or professionals has been very high through the 1990s in Estonia and Hungary, especially in the latter, where over 80 per cent of them had higher education. Slovenia deviates from the picture, as over the half of managers and professionals who started their job during the reform years in 1990–1991 did not have higher education.21 This was probably due to the creation of small companies. However, later on the picture changed completely as already in 1992–1995 about 81 per cent and in 1996–2000 92 per cent of school leavers entered managerial and professional jobs having higher education in Slovenia.

Table 4. 
Occupation of the first job by educational level and cohort
Hungary  Low Medium High 
1990–1991 Managers and professionals 3.3 14.3 82.4 91 
Technicians 6.7 79.0 14.3 119 
Low-white collar 7.1 89.4 3.5 255 
Blue-collar 30.1 67.2 2.7 625 
1992–1995 Managers and professionals – 19.2 80.8 380 
Technicians 1.9 85.6 12.5 416 
Low-white collar 5.6 91.7 2.7 961 
Blue-collar 17.8 78.7 3.5 1754 
1996–2000 Managers and professionals 0.2 17.5 82.3 458 
Technicians 0.5 66.9 32.6 384 
Low-white collar 3.1 90.5 6.4 745 
Blue-collar 9.1 86.3 4.6 949 
Slovenia  Low Medium High N 
1990–1991 Managers and professionals – 56.3 43.7 16 
Technicians – 90.9 9.1 22 
Low-white collar 10.5 86.9 2.6 38 
Blue-collar 22.2 77.8 – 45 
1992–1995 Managers and professionals – 19.1 80.9 89 
Technicians 1.1 73.9 25.0 88 
Low-white collar 9.0 88.4 2.6 155 
Blue-collar 8.3 90.7 1.0 204 
1996–2000 Managers and professionals 1.3 6.4 92.3 156 
Technicians – 47.1 52.9 119 
Low-white collar 1.0 89.9 9.1 208 
Blue-collar 3.9 93.0 3.1 257 
Estonia  Low Medium High N 
1992–1995 Managers and professionals – 34.3 65.7 64 
Technicians 2.9 47.1 50.0 68 
Low-white collar 7.7 79.2 12.1 77 
Blue-collar 22.1 72.7 5.2 172 
1996–2000 Managers and professionals 3.4 27.1 69.5 59 
Technicians 4.1 43.8 52.1 48 
Low-white collar 13.3 71.7 15.0 60 
Blue-collar 34.1 60.0 5.9 135 
2001–2002 Managers and professionals – 21.2 78.8 33 
Technicians – 30.0 70.0 20 
Low-white collar – 83.9 16.1 31 
Blue-collar 31.3 58.2 10.5 67 
Hungary  Low Medium High 
1990–1991 Managers and professionals 3.3 14.3 82.4 91 
Technicians 6.7 79.0 14.3 119 
Low-white collar 7.1 89.4 3.5 255 
Blue-collar 30.1 67.2 2.7 625 
1992–1995 Managers and professionals – 19.2 80.8 380 
Technicians 1.9 85.6 12.5 416 
Low-white collar 5.6 91.7 2.7 961 
Blue-collar 17.8 78.7 3.5 1754 
1996–2000 Managers and professionals 0.2 17.5 82.3 458 
Technicians 0.5 66.9 32.6 384 
Low-white collar 3.1 90.5 6.4 745 
Blue-collar 9.1 86.3 4.6 949 
Slovenia  Low Medium High N 
1990–1991 Managers and professionals – 56.3 43.7 16 
Technicians – 90.9 9.1 22 
Low-white collar 10.5 86.9 2.6 38 
Blue-collar 22.2 77.8 – 45 
1992–1995 Managers and professionals – 19.1 80.9 89 
Technicians 1.1 73.9 25.0 88 
Low-white collar 9.0 88.4 2.6 155 
Blue-collar 8.3 90.7 1.0 204 
1996–2000 Managers and professionals 1.3 6.4 92.3 156 
Technicians – 47.1 52.9 119 
Low-white collar 1.0 89.9 9.1 208 
Blue-collar 3.9 93.0 3.1 257 
Estonia  Low Medium High N 
1992–1995 Managers and professionals – 34.3 65.7 64 
Technicians 2.9 47.1 50.0 68 
Low-white collar 7.7 79.2 12.1 77 
Blue-collar 22.1 72.7 5.2 172 
1996–2000 Managers and professionals 3.4 27.1 69.5 59 
Technicians 4.1 43.8 52.1 48 
Low-white collar 13.3 71.7 15.0 60 
Blue-collar 34.1 60.0 5.9 135 
2001–2002 Managers and professionals – 21.2 78.8 33 
Technicians – 30.0 70.0 20 
Low-white collar – 83.9 16.1 31 
Blue-collar 31.3 58.2 10.5 67 

Source: EULFS 2000 ad hoc module (for Slovenia and Hungary), LFS 2002 ad hoc module (for Estonia).

The educational level of technicians has increased considerably during the last 10 years in all three countries. In Hungary, among school leavers who entered the labour market in 1990–1991 and started to work as technicians, 7 per cent had lower education and 14 per cent higher education. For the later cohort those youth who entered employment as technicians were more educated: 33 per cent of the 1996–2000 cohort had higher education and people with lower education were ‘pushed out’. A similar trend is observable in Slovenia and Estonia. In Slovenia, only one-tenth of people starting as technicians had higher education in the early 1990s, while in the second half of 1990s, already over a half of them had higher education.

The educational level of blue-collar workers has also increased in all three countries. Hungary and Slovenia are quite similar in this respect, as the share of low educated people among blue-collar workers has dropped from 30 per cent to 9 per cent in Hungary and from 22 per cent to 4 per cent in Slovenia, comparing the 1990–1991 and 1996–2000 cohorts. Additionally, the proportion of people working in blue-collar occupations while possessing higher education has increased. Estonia differs from other countries partly as the share of school leavers with low education entering blue-collar employment has increased, although similarly, the proportion of people with high education entering blue collar employment has increased, albeit slightly.

It is clear from Table 4 that educational level of school leavers who entered employment increased throughout the decade. This is obviously due to the massive educational expansion that occurred at that period. The extent of the benefits that education may offer depends, however, on the availability of jobs in the labour market. If the number of workers with a certain educational level exceeds the demand, then the process of downwards replacement of workers begins and some of school leavers have to enter employment as over-educated. We will discuss the issue of the match of educational qualifications and occupations using multivariate models later in the paper.

Timing of the entry to first significant employment

To answer the question how education and education-related variables affect the timing of entry to first significant employment after leaving school in three countries a piecewise constant exponential model, which successively includes covariates pertaining to person's level of education and cohort (Model 1), parental education (Model 2) and interaction of education with the cohort (Model 3) was run. The results are reported in Table 5. While positive estimates indicate a positive influence, i.e. increased probability of finding first significant employment after leaving education, negative coefficients signify an opposite effect.

Table 5. 
Unstandardized coefficients and standard errors (in parentheses) of the piecewise constant exponential model of the transition to first significant employment in Hungary, Slovenia and Estonia
Model 1Model 2Model 3
Hungary 
Gender (men – ref.) −0.04 (0.03) −0.04 (0.03) −0.04 (0.03) 
Age at leaving education 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 
Education (low – ref.) 
 Medium 0.36** (0.05) 0.36** (0.05) 0.99** (0.10) 
 High 0.34** (0.08) 0.35** (0.08) 0.97** (0.13) 
Parental education (low – ref.) 
 Medium   −0.01 (0.03)   
 High   −0.04 (0.05)   
Cohort (1996–2000 – ref.) 
 1990–1991 0.32** (0.04) 0.31** (0.04) 1.15** (0.12) 
 1992–1995 0.29** (0.03) 0.29** (0.03) 1.04** (0.11) 
Interaction (education – cohort) 
 Medium * 1990–1991     −0.91** (0.13) 
 High*1990–1991     −0.88** (0.17) 
 Medium * 1992–1995     −0.79** (0.12) 
 High* 1992–1995     −0.84** (0.13) 
Time periods 
 6–24 months −2.00** (0.03) −2.00** (0.03) −2.00** (0.03) 
 25–60 months −3.28** (0.04) −3.28** (0.04) −3.27** (0.04) 
 >60 months −4.55** (0.07) −4.55** (0.07) −4.61** (0.07) 
Constant −1.44** (0.13) −1.44** (0.13) −2.05** (0.15) 
Log likelihood −8324.19  −8323.79  −8290.76  
Slovenia 
Gender (men – ref.) −0.07 (0.06) −0.07 (0.06) −0.08 (0.06) 
Age at leaving education −0.00 (0.02) −0.00 (0.02) −0.00 (0.02) 
Education (low – ref.) 
 Medium 0.45** (0.16) 0.46** (0.16) 1.01** (0.30) 
 High 0.54** (0.23) 0.56** (0.23) 1.23** (0.35) 
Parental education (low – ref.) 
 Medium   −0.06 (0.06)   
 High   −0.13 (0.10)   
Cohort (1996–2000 – ref.) 
 1990–1991 0.47** (0.10) 0.47** (0.10) 1.53** (0.41) 
 1992–1995 0.35** (0.06) 0.35** (0.06) 1.11** (0.35) 
Interaction (education – cohort) 
 Medium*1990–1991     −1.08* (0.42) 
 High* 1990–1991     −1.19* (0.56) 
 Medium* 1992–1995     −0.70* (0.35) 
 High* 1992–1995     −1.06** (0.35) 
Time periods 
 6–24 months −2.08** (0.07) −2.07** (0.07) −2.09** (0.07) 
 25–60 months −3.41** (0.09) −3.40** (0.09) −3.39** (0.09) 
 >60 months −4.63** (0.14) −4.62** (0.14) −4.71** (0.14) 
Constant −1.19** (0.33) −1.21** (0.36) −1.78** (0.43) 
Log likelihood −1551.06  −1550.07  −1544.30  
 Model 1 Model 2 Model 3 
Estonia 
Gender (men – ref.) −0.21** (0.08) −0.21** (0.08) −0.22** (0.08) 
Age at leaving education 0.02 (0.02) 0.02 (0.02) 0.02 (0.02) 
Education (low – ref.) 
 Medium 0.18° (0.12) 0.18° (0.13) 0.11 (0.15) 
 High 0.28° (0.19) 0.30° (0.19) 0.23 (0.22) 
Parental education (low – ref.) 
 Medium   0.05 (0.14)   
 High   −0.07 (0.15)   
Cohort (1996–2002 – ref.) 
 1992–1995 0.54** (0.09) 0.54** (0.09) 0.38* (0.21) 
Interaction (education – cohort) 
 Medium* 1992–1995     0.20 (0.22) 
 High * 1992–1995     0.19 (0.22) 
Time periods 
 6–24 months −2.07** (0.10) −2.06** (0.10 −2.07** (0.10) 
 25–60 months −3.26** (0.12) −3.27** (0.12) −3.27** (0.12) 
 >60 months −4.35** (0.14) −4.36** (0.14) −4.34** (0.15) 
Constant −1.54** (0.41) −1.62** (0.44) −1.47** (0.42) 
Log likelihood −908.37  −907.34  −907.92  
Model 1Model 2Model 3
Hungary 
Gender (men – ref.) −0.04 (0.03) −0.04 (0.03) −0.04 (0.03) 
Age at leaving education 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 
Education (low – ref.) 
 Medium 0.36** (0.05) 0.36** (0.05) 0.99** (0.10) 
 High 0.34** (0.08) 0.35** (0.08) 0.97** (0.13) 
Parental education (low – ref.) 
 Medium   −0.01 (0.03)   
 High   −0.04 (0.05)   
Cohort (1996–2000 – ref.) 
 1990–1991 0.32** (0.04) 0.31** (0.04) 1.15** (0.12) 
 1992–1995 0.29** (0.03) 0.29** (0.03) 1.04** (0.11) 
Interaction (education – cohort) 
 Medium * 1990–1991     −0.91** (0.13) 
 High*1990–1991     −0.88** (0.17) 
 Medium * 1992–1995     −0.79** (0.12) 
 High* 1992–1995     −0.84** (0.13) 
Time periods 
 6–24 months −2.00** (0.03) −2.00** (0.03) −2.00** (0.03) 
 25–60 months −3.28** (0.04) −3.28** (0.04) −3.27** (0.04) 
 >60 months −4.55** (0.07) −4.55** (0.07) −4.61** (0.07) 
Constant −1.44** (0.13) −1.44** (0.13) −2.05** (0.15) 
Log likelihood −8324.19  −8323.79  −8290.76  
Slovenia 
Gender (men – ref.) −0.07 (0.06) −0.07 (0.06) −0.08 (0.06) 
Age at leaving education −0.00 (0.02) −0.00 (0.02) −0.00 (0.02) 
Education (low – ref.) 
 Medium 0.45** (0.16) 0.46** (0.16) 1.01** (0.30) 
 High 0.54** (0.23) 0.56** (0.23) 1.23** (0.35) 
Parental education (low – ref.) 
 Medium   −0.06 (0.06)   
 High   −0.13 (0.10)   
Cohort (1996–2000 – ref.) 
 1990–1991 0.47** (0.10) 0.47** (0.10) 1.53** (0.41) 
 1992–1995 0.35** (0.06) 0.35** (0.06) 1.11** (0.35) 
Interaction (education – cohort) 
 Medium*1990–1991     −1.08* (0.42) 
 High* 1990–1991     −1.19* (0.56) 
 Medium* 1992–1995     −0.70* (0.35) 
 High* 1992–1995     −1.06** (0.35) 
Time periods 
 6–24 months −2.08** (0.07) −2.07** (0.07) −2.09** (0.07) 
 25–60 months −3.41** (0.09) −3.40** (0.09) −3.39** (0.09) 
 >60 months −4.63** (0.14) −4.62** (0.14) −4.71** (0.14) 
Constant −1.19** (0.33) −1.21** (0.36) −1.78** (0.43) 
Log likelihood −1551.06  −1550.07  −1544.30  
 Model 1 Model 2 Model 3 
Estonia 
Gender (men – ref.) −0.21** (0.08) −0.21** (0.08) −0.22** (0.08) 
Age at leaving education 0.02 (0.02) 0.02 (0.02) 0.02 (0.02) 
Education (low – ref.) 
 Medium 0.18° (0.12) 0.18° (0.13) 0.11 (0.15) 
 High 0.28° (0.19) 0.30° (0.19) 0.23 (0.22) 
Parental education (low – ref.) 
 Medium   0.05 (0.14)   
 High   −0.07 (0.15)   
Cohort (1996–2002 – ref.) 
 1992–1995 0.54** (0.09) 0.54** (0.09) 0.38* (0.21) 
Interaction (education – cohort) 
 Medium* 1992–1995     0.20 (0.22) 
 High * 1992–1995     0.19 (0.22) 
Time periods 
 6–24 months −2.07** (0.10) −2.06** (0.10 −2.07** (0.10) 
 25–60 months −3.26** (0.12) −3.27** (0.12) −3.27** (0.12) 
 >60 months −4.35** (0.14) −4.36** (0.14) −4.34** (0.15) 
Constant −1.54** (0.41) −1.62** (0.44) −1.47** (0.42) 
Log likelihood −908.37  −907.34  −907.92  

Source: EULFS 2000 ad hoc module (for Slovenia and Hungary), LFS 2002 ad hoc module (for Estonia).

Notes: °P<0.10; *P<0.05; **P<0.01.

In Hungary having either medium or high education increases the probability of obtaining first significant employment (to a similar degree for both levels of education), controlling both for age at leaving education and gender (see Model 1). In Slovenia the same is true: effects for tertiary educated school leavers and those with upper secondary education are higher than for low-educated youth but are not statistically different from each other. In Estonia, the educational level seems to play a lesser role, although weak evidence suggests that education still determines the speed of the entry to first significant employment. In all three countries there exists, however, a significant cohort effect, implying that the chances of entering first significant employment is higher for cohorts that left education prior to 1996–2000. For Hungary and Slovenia, where differentiation between 1990–1991 and 1992–1995 cohorts is made, it is noticeable that the conditional probability of finding first significant employment is somewhat higher in the earlier pre-reform cohort, ceteris paribus.22

To examine the role social background and related networks possibly play in the first job search Model 2 includes parental education. Results show that in none of the countries does parental education influence the speed of the job search process. Since no effect of parental education is observed, in Model 3, this variable is omitted, while the interaction of education and cohort is included to examine if the effect of education has changed during the decade after the fall of socialist rule. Results show that in Hungary and Slovenia the effect of education on the chances of first significant job entry has changed during the decade. It appears, and the findings are similar for both countries, that for earlier cohorts of school leavers education was less a determinant of first job entry, or in other words, the differences between school leavers with less than secondary education and those with secondary education or higher were much smaller. The disparities between lower educated school leavers and those with secondary education and higher grew substantially for those who left education between 1996 and 2000, in the so-called stabilization period, which confirms our expectations. Estonia is the only country in which this hypothesis has not been confirmed. There, on the other hand, unlike in other countries a significant gender effect is evident, suggesting that women have significantly lower probability of entering first secure employment after leaving education than men do, other things being equal.

Match of education and first significant employment

As mentioned earlier, not only speed but also match of education and first significant employment is an important indicator of the success of school-to-work transition and this is explored further using multinomial logistic regression techniques for those persons who found their first significant employment. Results for entry to first significant employment as over-educated or under-educated (compared to the total population) versus entry to matching employment are reported in Table 6 for the three countries. Independent variables, as in the earlier analysis, are education of a school leaver, his/ her parental education, and cohort, while control variable include gender, age at leaving education and timing of entry to first significant employment. For an easier interpretation we plot predicted probabilities (see Figure 2) for low and highly educated school leavers to enter employment as over- or under-educated dependent on parental educational level and school-leaving cohort (all other variables are fixed at their sample means). In the box to the right in Figure 2, descriptive information on the proportion of school leavers employed in matching and non-matching employment is reported.
Figure 2. 

Predicted probabilities of being over- or under-educated for first significant employment by parental level of education and school-leaving cohort.

Figure 2. 

Predicted probabilities of being over- or under-educated for first significant employment by parental level of education and school-leaving cohort.

Close modal
Table 6. 
Unstandardized coefficients and standard errors (in parentheses) of the multinomial regression predicting the match of education and first significant job
Hungary: Entering 1st job asSlovenia: Entering 1st job asEstonia: Entering 1st job as
over-educatedunder-educatedover-educatedunder-educatedover-educatedunder-educated
vs. entering 1st matching jobvs. entering 1st matching jobvs. entering 1st matching job
Gender (men – ref.) −1.44** −0.17 −0.12 0.56** 0.48° 0.70** 
 (0.06) (0.12) (0.21) (0.18) (0.27) (0.21) 
Age at leaving education −0.18** 0.12** −0.09 0.21** −0.15** 0.19** 
 (0.02) (0.03) (0.06) (0.04) (0.06) (0.05) 
Education (low – ref.): 1.79** −1.74** 0.88 −1.31** −0.67° −0.06 
Medium (0.12) (0.19) (0.71) (0.39) (0.43) (0.33) 
 High 1.69** −1.57** 1.16 −2.49** 1.83** −2.60** 
 (0.20) (0.30) (0.89) (0.58) (0.55) (0.55) 
Parental education (low – ref.): −0.46** 0.89** −0.35 0.37° −0.29 0.21 
Medium (0.07) (0.21) (0.22) (0.21) (0.44) (0.40) 
 High −1.04** 1.08** −0.19 0.96** −0.43° 1.01** 
 (0.11) (0.25) (0.34) (0.27) (0.46) (0.41) 
Cohort (1996–2000 – ref.): −0.26** −0.38 −0.97 0.93**   
1990–1991 (0.09) (0.22) (0.54) (0.30)   
 1992–1995 −0.15* −0.05 −0.22 0.52* 0.25 0.41° 
 (0.06) (0.14) (0.23) (0.21) (0.27) (0.22) 
Timing of the job entry (0–6 months – ref.)       
Prior to leaving education −0.20 −0.09 0.43 −0.46 0.34 0.13 
 (0.14) (0.25) (0.35) (0.35) (0.34) (0.31) 
 6–24 months after leaving school −0.18* 0.09 0.04 0.28 0.20 −0.00 
 (0.07) (0.16) (0.26) (0.23) (0.36) (0.30) 
 25–60 months after leaving school −0.20* 0.20 0.33 0.40 0.40 0.64° 
 (0.08) (0.19) (0.31) (0.27) (0.44) (0.30) 
 >60 months after leaving school −0.41** 0.32 0.10 1.04** 0.28 0.39 
 (0.13) (0.25) (0.63) (0.35) (0.46) (0.40) 
Constant 2.52** −3.96** −1.19 −5.72** 0.52 −6.33** 
 (0.30) (0.51) (1.20) (0.84) (1.32) (1.04) 
Log likelihood −4903.74 −797.53 −524.05    
Pseudo R2 0.13 0.05 0.12    
N 6744 1337 801    
Hungary: Entering 1st job asSlovenia: Entering 1st job asEstonia: Entering 1st job as
over-educatedunder-educatedover-educatedunder-educatedover-educatedunder-educated
vs. entering 1st matching jobvs. entering 1st matching jobvs. entering 1st matching job
Gender (men – ref.) −1.44** −0.17 −0.12 0.56** 0.48° 0.70** 
 (0.06) (0.12) (0.21) (0.18) (0.27) (0.21) 
Age at leaving education −0.18** 0.12** −0.09 0.21** −0.15** 0.19** 
 (0.02) (0.03) (0.06) (0.04) (0.06) (0.05) 
Education (low – ref.): 1.79** −1.74** 0.88 −1.31** −0.67° −0.06 
Medium (0.12) (0.19) (0.71) (0.39) (0.43) (0.33) 
 High 1.69** −1.57** 1.16 −2.49** 1.83** −2.60** 
 (0.20) (0.30) (0.89) (0.58) (0.55) (0.55) 
Parental education (low – ref.): −0.46** 0.89** −0.35 0.37° −0.29 0.21 
Medium (0.07) (0.21) (0.22) (0.21) (0.44) (0.40) 
 High −1.04** 1.08** −0.19 0.96** −0.43° 1.01** 
 (0.11) (0.25) (0.34) (0.27) (0.46) (0.41) 
Cohort (1996–2000 – ref.): −0.26** −0.38 −0.97 0.93**   
1990–1991 (0.09) (0.22) (0.54) (0.30)   
 1992–1995 −0.15* −0.05 −0.22 0.52* 0.25 0.41° 
 (0.06) (0.14) (0.23) (0.21) (0.27) (0.22) 
Timing of the job entry (0–6 months – ref.)       
Prior to leaving education −0.20 −0.09 0.43 −0.46 0.34 0.13 
 (0.14) (0.25) (0.35) (0.35) (0.34) (0.31) 
 6–24 months after leaving school −0.18* 0.09 0.04 0.28 0.20 −0.00 
 (0.07) (0.16) (0.26) (0.23) (0.36) (0.30) 
 25–60 months after leaving school −0.20* 0.20 0.33 0.40 0.40 0.64° 
 (0.08) (0.19) (0.31) (0.27) (0.44) (0.30) 
 >60 months after leaving school −0.41** 0.32 0.10 1.04** 0.28 0.39 
 (0.13) (0.25) (0.63) (0.35) (0.46) (0.40) 
Constant 2.52** −3.96** −1.19 −5.72** 0.52 −6.33** 
 (0.30) (0.51) (1.20) (0.84) (1.32) (1.04) 
Log likelihood −4903.74 −797.53 −524.05    
Pseudo R2 0.13 0.05 0.12    
N 6744 1337 801    

Source: EULFS 2000 ad hoc module (for Slovenia and Hungary), LFS 2002 ad hoc module (for Estonia).

Notes: °P<0.10; *P<0.05; **P<0.01.

For all three countries results clearly show that parental education plays a significant role in determining the match of education and job for recent school leavers. Individuals with tertiary educated parents have a lower probability of entering a job as over-educated compared to persons with lower-educated parents. In other words, evidence shows that highly educated youth with tertiary educated parents are clearly protected from entering employment of significantly lower occupational status that it is in the total population. Differences in the predicted probabilities for entering employment as over-educated are particularly pronounced for Hungary, being quite marginal in Slovenia.

Entering employment for which the person is under-educated compared to the standards applied for the total population is more probable for individuals with highly educated parents rather than those whose parents are low educated. This holds for all countries under discussion and is particularly evident among young people who left schooling with low-secondary education only. It looks like highly educated parents of less educated offspring help their children to get employment of somewhat higher status than is normally found among the lower educated group in the population.

Significant cohort effects are evident and these are reflected in varying gradients for curves of predicted probabilities plotted for the countries discussed in the study. It appears that the risk of over-education has somewhat increased during the decade in Hungary and Slovenia, but remained unchanged or even slightly decreased in Estonia. This is particularly evident from the lower part of the figure plotting predicted probabilities for the highly educated school leavers, who are overall more likely to enter employment as over-educated. On the other hand, under-education decreased during the decade in Slovenia (quite drastically) and Estonia, albeit slightly increasing in Hungary. This is particularly evident from the upper part of the figure plotting predicted probabilities for low educated school leavers, potentially more likely to enter employment as undereducated.

While full regression results can be found in Table 6, we would like to draw attention to the gender effect in Estonia. It appears that women there are not only disadvantaged with respect to the speed of entry to the first significant employment (see Table 5), they also significantly more likely to enter employment not matching their educational credentials, being either over- or under-educated, other things being equal.

In this paper we examined school-to-work transition processes during the 10-year period after the fall of socialism (1990–2000(02)) in Hungary, Slovenia and Estonia, three former socialist countries that entered the European Union in May 2004. A major challenge of school-to-work transition in the post-communist countries has been to qualify new labour market entrants into the labour market at a time when resources are limited and educational institutions are themselves being restructured. This task was especially complicated to achieve in the early transformation period immediately after the collapse of the socialist system, when the chaos and breakdown in economy found its reflection in the school-to-work transitions. Together with the labour market stabilization, reforms and expansion of the educational system traditional stratification mechanisms seem to start re-emerging in the transitional economies (see also Svejnar 1999). The paper assesses how the role of education has changed during the transition period with respect to the speed of the entry to first significant employment and the match of education and employment. It also ascertains the effect of the parental education on the allocation processes among young school leavers in post-communist countries.

Results show that when it comes to entry to first significant employment the effect of education has grown in all three countries under discussion: modestly in Estonia, and more profoundly in Hungary and Slovenia. As it was expected, there were smaller differences between school leavers with different levels of education at the entry to first significant employment in the early transformation period. Entering the job with education less than required in the total population was more common in Estonia and in Slovenia during the reform years, but not so in Hungary. Consequently, during the stabilization period, the importance of education in securing first significant employment has increased in Hungary and Slovenia. Estonia deviates from the picture, as there the importance of education has only slightly changed across the whole period between 1992 and 2002. Results of the multinomial regression analyses show that over-education became more common in Hungary and Slovenia (albeit to a slightly lesser degree) during the stabilization years. In Estonia, entering a job with more education than is normally required in total population did not appear to be as pronounced as expected since no significant cohort effect was found. It might be that there is a time lag between the expansion of higher education and its effects on the labour market, as only limited number of students has graduated from the expanded tertiary education system in Estonia. It also may well be that the expanding service sector has been managing to accommodate many of tertiary education graduates.

Even though parental education does not seem to play any role in the speed of entry to first significant employment, its influence in determining the match of education and job for recent labour market entrants is noteworthy. In all three countries, the probability of entering first significant employment as under-educated among lower educated school leavers is higher for those whose parents have tertiary education as compared to children of less educated parents. On the other hand, highly educated parents in Hungary and Estonia24 avert the ‘danger’ of over-education among their children particularly when the latter possess tertiary education. These findings point to the importance of social background and networks in the CEE countries particularly when it comes to the occupational status or match of educational qualifications an occupation (Iannelli 2002).

Overall, recent school leavers of the later cohorts encounter growing difficulties when entering the labour market. This is particularly true for lower educated individuals who are no longer able to secure significant employment as quickly as earlier. The process of the downgrading of educational credentials and even the downward replacement of workers, being a by-product of the educational expansion taking place in the CEE countries, seems to be at least partially responsible for this trend. Education has become a valuable market asset and young people seem to realize that without higher education their opportunities to succeed in highly competitive labour markets are poor (Saar and Kazjulja 2001). Moreover, the possibility cannot be excluded that labour market flexibility has contributed to the situation in which lower educated school leavers appear to have slimmer chances for secure, long-term employment relationship, particularly if compared to the earlier period when stable employment was ensured by the state.

The results remain rather descriptive and mechanisms of stratification processes in youth transitions are still not fully clear, since institutional packages in the CEE countries are yet under formation. Furthermore it is necessary to acknowledge the limited scope of the study not the least due to limitations of the 2000 ad hoc module on school-to-work transitions, among them a small number of CEE countries with reliable information and a modest range of variables covered by the survey. Moreover, the exploratory nature of the study results from the lack of a systematic comparative framework for the analyses of school-to-work transition processes in the CEE countries. This framework, as well as comparative studies to examine newly emerging stratification processes in a larger number of former socialist countries and to compare them to the processes observed in other EU member states, should become important in light of the expansion of the European Union eastwards.

*

Earlier draft of this paper was presented at the Transitions in Youth Conference ‘Competencies and careers‘, 4–6 September 2003, Funchal, Madeira, Portugal, and at the CHANGEQUAL meeting ‘Comparative Research on Inequality and Quality of Life: Integrated or Diverse Measures for East and West’, 4–7 December 2003, Tartu, Estonia.

1.

This is not to disregard existing country studies, for example on Hungary (Bukodi 2002; Bukodi and Róbert 2002; Róbert and Bukodi 2002) and Estonia (Saar and Helemäe 2002; Katus et al. 2002) and comparative undertakings (e.g., Roberts 1998).

2.

The original idea was to cover as many CEE countries as possible. Overall 6 CEE countries took part in the EULFS 2000 ad hoc module: Hungary, Romania, Slovakia, Slovenia, Latvia, and Lithuania. Estonia conducted the survey in 2002. However, quality of the data for the type of analyses pursued in the study, i.e. focusing on the timing of entry to first significant employment, was satisfactory only for three countries, Hungary, Slovenia and Estonia. All three share the similarity of being among the most successful CEE countries to enter the EU in 2004. At the same time, the countries do differ with respect to a number of aspects central for our research question (see below), which makes the study worthwhile.

3.

The transition from initial education to work is rarely a linear process but rather a sequence of transitions, which start at the point when educational pathways first diverge and end at the point (not clearly defined) when people's positions in the labour market become relatively stable (Wolbers 2001). Moreover, such transitions often do not follow a ‘normal’ sequence of school and then work, but represent a combination of learning and working as well as other dual statuses. In empirical research, often because of the lack of data, transition period from school to work is frequently defined as a time period between leaving the education and finding a first job. Similar approach is adopted in this paper.

4.

Occupational mismatch is central topic of several empirical undertakings with most of the research addressing the issue of over-education (Freeman 1976; Clogg and Shockey 1984; Smith 1986; Halaby 1994; Borghans and De Grip 2000).

5.

Stabilization means slowing of the growth in unemployment and an eventual trend to unemployment reduction. It should be remembered, however, that overall difficulties of school-to-work transitions might have increased in the market economies due to the end of state planning and sponsoring of youth transition processes (see below).

6.

The argument stems from queuing theory (Thurow 1975), which asserts the existence of two kinds of queues, a worker queue and a vacancy queue. Since the two might not necessarily match, persons with higher education, who are ranked higher in the worker hierarchy, will not necessarily get higher status jobs. This idea is also echoed in job matching theory (Sattinger 1993), which explains the incidence of job mismatches by differences in the shares of vacant jobs of a given level and available workers with adequate educational qualifications.

7.

This is characterized by a general curriculum in the educational institutions and weaker match between the type of qualification and the type of job.

8.

Unfortunately the data do not allow differentiating between general and vocational education, so hypotheses as to the link between education and job in the light of ILM-OLM dichotomy are not discussed.

9.

In Estonia falling living standards bottomed in 1993; they began to rise again only in 1994. These were the first signs of growing GDP and of steadily decreasing inflation.

10.

In fact, per capita GDP as a percentage of the EU-15 average in Slovenia is similar to the indicator in Greece in 2000. The GDP per capita remains lower in Hungary and particular in Estonia.

11.

In 2001 unemployment rate in Estonia was already 12.6%, about the same as in Spain, country with the highest unemployment rate in the European Union (Labour Force 2001).

12.

Flexibility has been defined as the high circulation of labour, including movements inside the labour market, out of employment and return to the labour market.

13.

Even in the well-educated service class more than 20 per cent of the employees have no permanent job contract.

14.

Of course Eurostat is not responsible for the use of the data, the interpretations drawn, or the views held by the authors.

15.

It has to be acknowledged that the data set lacks a number of variables important for deeper understanding of school-to-work transition process, e.g., unemployment incidence prior to first significant employment, income, job changing, aspirations.

16.

For Hungary and Slovenia a missing month for leaving education was substituted by ‘June’ if the year of the event was present. To minimize mistakes in calculation of the duration variables, similar imputations were made in those cases in which the month was missing for the start of the first significant job.

17.

LES database includes several LFSs, including 1993 and 1999 Hungarian LFS, and 1994 and 1999 Slovenian LFS.

23

ISCED 0-2 pertains to education level below low secondary education; ISCED 3-4 covers upper secondary and post-secondary non-tertiary education; and ISCED 5-6 refers to tertiary education.

18.

The survivor function is defined by G(t) = Pr(T > t).

19.

Survival functions illustrate the first 5 years (60 months) after leaving education for the first time.

20.

According to the Wilcoxon test differences between the curves pertaining to tertiary educated and those with secondary education are not statistically different for 1992-1995, but for later cohort 1996-2002, the differences are already statistically significant (p<0.05).

21.

We urge some caution in interpreting results for Slovenia due to a smaller N for the 1990-1991 cohort.

22.

To note, in Hungary differences between 1990-1991 and 1992-1995 cohorts are not statistically significant.

24.

A weak effect is found for Slovenia.

Allmendinger
,
J
, (
1989
). ‘
Educational systems and labour market outcomes
’,
European Sociological Review
5
((
1989
)), pp.
231
50
.
Annus
,
T
, (
2000
).
Ülevaade Eesti haridussüsteemist.
Tallinn
. (
2000
).
Barbieri
,
P
,
Paugam
,
S
, and
Russell
,
H
, (
1997
). “‘Social capital and exits from unemployment’”. In:
Welfare Regimes and the Experience of Unemployment in Europe.
Oxford
. (
1997
). pp.
200
17
, in D. Gallie and S. Paugam (eds).
Bertrand
,
O
, (
1995
). “
‘Education and Work’
”. In:
Education for the Twenty-first Century, UNESCO Publishing.
(
1995
). pp.
157
192
, in J. Delors (ed.).
Blau
,
P
, and
Duncan
,
OD
, (
1967
).
The American Occupational Structure.
New York
. (
1967
).
Blossfeld
,
HP
, and
Rohwer
,
G
, (
1995
).
Techniques of Event History Modelling: New Approaches to Causal Analysis.
Mahwah, NJ
. (
1995
).
Borghans
,
L
, and
De Grip
,
A
, (
2000
).
The Overeducated Worker? The Economics of Underutilization of Skills.
Cheltenham
. (
2000
), (eds).
Bourdieu
,
P
, and
Passeron
,
J-C
, (
1977
).
Reproduction: IN Education, Society and Culture.
London
. (
1977
).
Braverman
,
H
, (
1977
).
Die Arbeit im modernen Produktionprozess.
Frankfurt
. (
1977
).
Bukodi
,
E
, (
2002
). “
The Career Entry, Human Capital, and Partnership Formation in Hungary
”. (
2002
), Paper presented on the 10th Workshop of the European Research Network on Transitions in Youth, Florence.
Bukodi
,
E
, and
Róbert
,
P
, (
2002
). “
Men's Career Mobility in Hungary during the 1990s
”. (
2002
), Globalife: Life Courses in the Globalization Process, Working Paper (38).
Burris
,
V
, (
1985
). ‘
The Social and Political Consequences of Overeducation
’,
American Sociological Review
48
((
1985
)), pp.
454
67
.
Cazes
,
S
, and
Nesporova
,
A
, (
2001
).
Towards Excessive Job Insecurity in Transition Economies?.
Geneva
. (
2001
).
Cazes
,
S
, and
Nesporova
,
A
, (
2003
).
Labour Markets in Transition: Balancing flexibility and security in Central and Eastern Europe.
Geneva
. (
2003
).
Clarke
,
S
, (
2000
). ‘
The closure of the Russian Labour Market
’,
European Societies
2
((
2000
)), pp.
483
504
.
Clogg
,
C
, and
Shockey
,
J
, (
1984
). ‘
Mismatch between occupation and schooling. A prevalence measure, recent trends and demographic analysis
’,
Demography
21
((
1984
)), pp.
235
57
.
Coleman
,
J
, (
1988
). ‘
Social capital in the creation of human capital
’,
American Journal of Sociology
94
((
1988
)), pp.
95
120
.
Drobnic
,
S
, and
Rus
,
V
, (
1995
). “‘Unemployment in transition economies: The case of Slovenia’”. In:
Unemployment and Evolving Labor Markets in Central and Eastern Europe.
Aldershot
. (
1995
). pp.
93
122
, in M. Jackson, J. Koltay and W. Biesbrouck (eds).
(
2000
).
ESA.
Tallinn
. (
2000
).
(
1998
).
The European Union Labour Force Survey. Methods and definitions 1998.
Luxembourg
. (
1998
).
Freeman
,
R
, (
1976
).
The Overeducated American.
New York
. (
1976
).
Gangl
,
M
, (
2003a
). “‘The structure of labour market entry in Europe: a typological analysis’”. In:
Transitions from Education to Work in Europe: The Integration of Youth into EU Labour Markets.
Oxford
. (
2003a
). pp.
107
28
, in W. Müller and M. Gangl (eds).
Gangl
,
M
, (
2003b
). “‘Returns to education in context: individual education and transition outcomes in European labour markets’”. In:
Transitions from Education to Work in Europe: The Integration of Youth into EU Labour Markets.
Oxford
. (
2003b
). pp.
156
85
, in W. Müller and M. Gangl (eds).
Gangl
,
M
, (
2003c
). ‘
The only way is up? Employment protection and job mobility among recent entrants to European labour markets
’,
European Sociological Review
19
((5)) ((
2003c
)), pp.
429
49
.
Ganzeboom
,
H
, and
Nieuwbeerta
,
P
, (
1999
). ‘
Access to education in six Eastern European countries between 1940 and 1985: Results from a cross-national survey
’,
Communist and Post-Communist Studies
32
((
1999
)), pp.
339
57
.
Hannan
,
DF
,
Raffe
,
D
, and
Smyth
,
E
, (
1997
). “‘Cross-national research on school to work transitions: An analytical framework’”. In:
Youth Transitions in Europe: Theories and Evidence.
Marseille
. (
1997
). pp.
409
42
, in R. Werquin, J. Breen and J. Plans (eds).
Halaby
,
C
, (
1994
). ‘
Overeducation and skill mismatch
’,
Sociology of Education
67
((
1994
)), pp.
47
59
.
Haller
,
A
, and
Porter
,
A
, (
1973
). ‘
Status attainment processes
’,
Sociology of Education
46
((
1973
)), pp.
51
99
.
Helemäe
,
J
, and
Saar
,
E
, (
2000
). “‘Linkages between the education system and labour market: Estonia against the backdrop of EU countries’, Estonian Human Development Report”. In:
TPU RASI.
Tallinn
. (
2000
).
Helemäe
,
J
,
Saar
,
E
, and
Vöörmann
,
R
, (
2000
).
Kas haridusse tasus investeerida? Hariduse selekteerivast ja stratifitseerivast rollist kahe põlvkonna kogemuse alusel.
Tallinn
. (
2000
).
Heyns
,
B
, and
Bielecki
,
I
, (
1993
). “‘Educational inequalities in postwar Poland’”. In:
Persistent Inequality. Changing Educational Attainment in Thirteen Countries.
Boulder, CO
. (
1993
). pp.
303
35
, in Y. Shavit and H. P. Blossfeld (eds).
Hunya
,
G
, (
2001
). “‘International competitiveness: Impacts of foreign direct investment in Hungary and other Central and East European countries’”. In:
Transformations in Hungary.
Heidelberg
. (
2001
). pp.
125
156
, in P. Meusburger and H. Jöns (eds).
Iannelli
,
C
, (
2002
). (
2002
),
‘Parental education and young people's educational and labour market outcomes: A comparison across Europe’, Working Paper, MZES (45).
Katus
,
K
,
Puur
,
A
, and
Sakkeus
,
L
, (
2002
). “
‘Transition to adulthood in Estonia: evidence from the FFS’
”. In:
Globalife: Life Courses in the Globalization Process.
(
2002
), Working Paper (27).
Kerckhoff
,
A
, (
1995
). ‘
Institutional arrangements and stratification processes in industrial societies
’,
Annual Review of Sociology
15
((
1995
)), pp.
323
47
.
Kerckhoff
,
A
, (
2000
). “‘Transition from school to work in comparative perspective’”. In:
Handbook of the sociology of education.
New York/Boston
. (
2000
). pp.
453
474
, in M. Hallinan (ed.).
Koltay
,
J
, (
1995
). “‘Unemployment and employment policy in Central and Eastern Europe: Similarities and differences’”. In:
Unemployment and Evolving Labor Markets in Central and Eastern Europe.
Aldershot
. (
1995
). pp.
1
30
, in M. Jackson, J. Koltay and W. Biesbrouck (eds).
Kunz
,
J
, (
2002
).
Labour Mobility and EU Enlargement – A Review of Current Trends and Debates.
DWP Brussels
. (
2002
).
(
2002
).
Statistical Office of Estonia.
Tallinn
. (
2002
).
Lauristin
,
M
, and
Vihalemm
,
P
, (
1998
).
Return to the Western World: Cultural and Political Perspectives on the Estonian Post-Communist Transition.
Tartu
. (
1998
).
Liuhto
,
K
, (
1996
). ‘
Entrepreneurial transition in Post-Soviet Republics: The Estonian path
’,
Europe-Asia Studies
48
((
1996
)), pp.
121
40
.
Mach
,
BW
,
Mayer
,
KU
, and
Pohoski
,
M
, (
1994
). ‘
Job changes in the Federal Republic of Germany and Poland: A longitudinal assessment of the impact of welfare-capitalist and state-socialist labour-market segementation
’,
European Sociological Review
10
((
1994
)), pp.
1
28
.
Marshall
,
G
,
Swift
,
A
, and
Roberts
,
S
, (
1997
).
Against the Odds? Social Class and Social Justice in Industrial Societies.
Oxford
. (
1997
).
Marksoo
,
Ü.
, (
2002
). “‘Changing labour market in Estonia, 1994–1999’”. In:
Living Conditions in Estonia Five Year Later.
Tartu
. (
2002
). pp.
86
108
, in D. Kutsar (ed.).
Mateju
,
P
, (
1993
). “‘Who won and who lost in a Socialist redistribution in Czechoslovakia’”. In:
Persistent Inequality. Changing Educational Attainment in Thirteen Countries.
Boulder, CO
. (
1993
). pp.
251
271
, in Y. Shavit and H. P. Blossfeld (eds).
Maurice
,
M
,
Sellier
,
F
, and
Silvestre
,
J-J
, (
1986
).
The Social Foundations of Industrial Power: A Comparison of France and Germany.
Cambridge, MA
. (
1986
).
Müller
,
W
, and
Gangl
,
M
, (
2003
).
Transitions from Education to Work in Europe: The Integration of Youth into EU Labour Markets.
Oxford
. (
2003
), (eds.).
Müller
,
W
, and
Shavit
,
Y
, (
1998
). “‘The institutional embeddedness of the stratification process’”. In:
From School to Work: A Comparative Study of Educational Qualifications and Occupational Destinations.
Oxford
. (
1998
). pp.
1
48
, in Y. Shavit and W. Müller (eds).
Nove
,
A
, (
1992
).
An Economic History of USSR.
London
. (
1992
).
OECD
, (
1995
).
Social and Labour Market Policies in Hungary.
Paris
. (
1995
).
Oecd
, (
1997
).
Labour Market Policies in Slovenia.
Paris
. (
1997
).
Parsons
,
T
, (
1994
). “‘Equality and inequality in modern society, or social stratification revisited’”. In:
Social Stratification. Class, Race and Gender in Sociological Perspective.
Boulder, CO
. (
1994
). pp.
670
85
, in D. Grusky (ed.).
Pettai
,
Ü.
, (
2001
). “‘Labour market’”. In:
Social Trends 2.
Tallinn
. (
2001
). pp.
35
58
, in R. Vöörmann (ed.).
Prandy
,
K
, and
Bottero
,
W
, (
2000
). ‘
Social reproduction and mobility in Britain and Ireland in the nineteenth and early twentieth centuries
’,
Sociology
34
((2)) ((
2000
)), pp.
265
81
.
Roberts
,
K
, (
1998
). ‘
School-to-work transitions in former communist countries
’,
Journal of Education and Work
11
((3)) ((
1998
)), pp.
221
38
.
Robert
,
P
, and
Bukodi
,
E
, (
2002
). “
The Effects of Globalization Process on the Transition to Adulthood in Hungary
”. (
2002
), Globalife: Life Courses in the Globalization Process, Working Paper (27).
Rose
,
M
, (
1994
). “‘Job satisfaction, job skills, and personal skills’”. In:
Skill and Occupational Change.
Oxford
. (
1994
). pp.
244
80
, in R. Penn, M. Rose and J. Rubery (eds).
Ryan
,
P
, (
2001
). ‘
The school-to-work transition: A cross-national perspective
’,
Journal of Economic Literature
39
((
2001
)), pp.
34
92
.
Saar
,
E
, and
Helemäe
,
J
, (
2002
). “
‘Employment careers of men in Estonia’
”. In:
Globalife: Life Courses in the Globalization Process.
(
2002
), Working Paper (39).
Saar
,
E
, and
Kazjulja
,
M
, (
2001
). “‘Utilizing educational potential on the Estonian labour market’”. In:
Estonian Human Development Report.
Tallinn
. (
2001
). pp.
54
9
, in.
Sattinger
,
M
, (
1993
). ‘
Assignment models of the distribution of earnings
’,
Journal of Economic Literature
31
((
1993
)), pp.
851
80
.
Sewell
,
W
, and
Hauser
,
R
, (
1980
). ‘
The Wisconsin Longitudinal Study of Social and Psychological Factors in Aspirations and Achievements
’,
Research in Sociology of Education and Socialization
1
((
1980
)), pp.
59
99
.
Shavit
,
Y
, and
Müller
,
W
, (
1998
).
From School to Work: A Comparative Study of Educational Qualifications and Occupational Destinations.
Oxford
. (
1998
), (eds).
Smith
,
HL
, (
1986
). ‘
Overeducation and underemployment: An agnostic review
’,
Sociology of Education
59
((2)) ((
1986
)), pp.
85
99
.
Solga
,
H
, and
Konietzka
,
D
, (
1999
). ‘
Occupational matching and social stratification. Theoretical insights and empirical observations taken from a German–German comparison
’,
European Sociological Review
15
((
1999
)), pp.
25
47
.
Svejnar
,
J
, (
1999
). “‘Labor markets in the transitional Central and European economies’”. In:
Handbook of Labor Economics.
Amsterdam
. (
1999
). pp.
2809
57
, in Ashenfelter Orley and David Card (eds).
Szelényi
,
I
, and
Aschaffenburg
,
K
, (
1993
). “‘Inequalities in educational opportunity in Hungary’”. In:
Persistent Inequality. Changing Educational Attainment in Thirteen Countries.
Boulder, CO
. (
1993
). pp.
273
302
, in Y. Shavit and H. P. Blossfeld (eds).
Thurow
,
LC
, (
1975
).
Generating Inequality. Mechanisms of Distribution in the U.S. Economy.
New York
. (
1975
).
Treiman
,
DJ
, (
1970
). “‘Industrialization and social stratification’”. In:
Social Stratification: Research and Theory for the 1970s.
Indianapolis, IN
. (
1970
). pp.
207
34
, in E.O. Laumann (ed.).
Tsang
,
M
, and
Levin
,
H
, (
1985
). ‘
The economics of overeducation
’,
Economics of Education Review
4
((
1985
)), pp.
93
104
.
Wolbers
,
MHJ
, (
2001
). “
Learning and working: Double statuses in youth transitions within the European Union
”. (
2001
), Working Paper, MZES (26).

Irena Kogan is a researcher at the Mannheim Centre for European Social Research (MZES), University of Mannheim, Germany. She participated in the project ‘Evaluation and Analyses of the EULFS 2000 Ad Hoc Module’ funded by the Eurostat and together with Walter Müller co-edited a book School-to-Work Transitions in Europe: Analyses of the EU LFS 2000 Ad Hoc Module, which resulted from this project. Her main research interests include immigration and ethnicity, transitions in youth, social stratification and inequality in comparative perspective.

Marge Unt is a researcher at the Institute of International and Social Studies, Tallinn University, Estonia. She is currently involved in a project ‘Life Plans and Life Courses in the Post-Socialist Estonia’ and in an ESRC-funded project looking at long-run changes in the significance of social stratification in Britain. Her research interests lie in social stratification and class analysis, methods of data analysis, labor markets, occupations and careers in comparative perspective.

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the use is non-commercial and the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by-nc/4.0/legalcode.