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The school-to-work transition in the Netherlands : an analysis of the time-to-first-job for different educational qualifications

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(Manzara: 2015)

Sociology of Comparative Organizations and Labor Studies Master thesis 2015

University of Amsterdam

Faculty of Social and Behavioral Sciences Milan van der Kuyp (10266313)

milan_manu@hotmail.com Submission date: 2 July 2015 1st supervisor: prof. dr. Beate Völker

2nd supervisor: prof. dr. Herman van de Werfhorst

The school-to-work transition

in the Netherlands

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Table of content

Abstract ... 2

1. Introduction ... 3

2. Theoretical Framework ... 6

2.1 Effect of educational qualifications ... 6

2.2 Dutch landscape ... 7

2.3 Hypothesis... 10

2.4 Previous research ... 11

3. Data and methods and measurements ... 13

3.1 Data ... 13 3.2 Methods... 14 3.3 Measurements ... 15 3.3.1 Dependent variable ... 15 3.3.2 Independent variables ... 19 3.3.3 Control variables ... 25 4. Results ... 28 4.1 Descriptive statistics ... 28 4.2 Explanatory statistics ... 32

5. Conclusion and discussion ... 36

6. Bibliography ... 40

7. Appendix ... 42

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Abstract

In this study the time of finding a job after leaving initial education (time-to-first-job) is studied for young adults aged 15 till 34 in the Netherlands. The research question studied is: How do educational levels and the vocational specificity of the qualifications of young adults in the Netherlands, influence the time-to-first-job? The first hypothesis assumes the higher the educational level the shorter the time-to-first-job and the second hypothesis that people with a qualification which is vocational specified find a job faster than people with a qualification which is general specified. To test both hypotheses, the data of the European Union Labor Force Survey (EU-LFS) of 2009 has been used. All respondents with an answer on the time-to-first-job question which has been found not valid, were not used for the final analysis. Further some limitations of the measurement of vocational specificity were found because Eurostat didn’t made a distinction between people with a higher vocational qualification (HBO) and higher scientific qualification (WO). The results show that the higher the educational level the shorter the time-to-first-job. Further almost no differences are found between people with a general specificity and a vocational specificity. The study shows that educational qualification (mainly educational level) matter for the time young adults need to find a job in the Netherlands. For further studies of the time-to-first-job in the Netherlands this study recommends that the measurement of the first significant jobs should be improved and the educational qualifications should be measured corresponding to the Dutch educational system.

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1. Introduction

The topic of this research is the time to find a job after leaving education for young adults aged 15 till 34 in the Netherlands. The time-to-first-job of young adults is studied for the educational level and vocational specificity of different qualifications in the formal education. Theories of human capital, signaling and credentialism are applied to formulate hypothesis on the influence of educational qualification on the time-to-first-job

The theories of human capital, signaling and credentialism all assume that educational qualifications affect the outcomes in time-to-first-job. Human capital theory focusses on the role of skills and signaling and credentialism theory on the role of formal educational qualifications. Skills and formal qualifications are attained during the initial education. People with a higher education have attained more skills than those with a lower education and those with a vocational qualification have attained more skills which are required at the workplace than those with a general qualification. People with more skills and skills required at the workplace are more productive. Further the high and vocational qualifications provides a signal to the employer to be more productive and their qualifications gives access to restricted jobs for which a high educational level or vocational qualification is required. Employers will always hire the most productive worker and therefore the people higher educated or with a vocational qualification are expected to have a shorter-time-to-first-job than those lower educated or those with a general qualification.

The research questions of this study is:

How do educational levels and the vocational specificity of the qualifications of young adults in the Netherlands, influence the time-to-first-job?

This study has a high societal relevance. First, According to the OECD (2015) labor market statistics, the youth unemployment has strongly increased since the 21st century in the Netherlands (OECD 2015b). In 2000 the youth unemployment was 6,10% and in 2013 this percentage has almost double to 11,01%1. For the total unemployment of people aged 25 till 64 the OECD labor market statistics find differences in the percentage of unemployment between different educational levels (OECD 2015a)2. The lowest unemployment is found for

1 The youth unemployment rate is the number of unemployed 15-24 year-olds expressed as a percentage of the youth labour force. Unemployed people are those who report that they are without work, that they are available for work and that they have taken active steps to find work in the last four weeks.

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people with a tertiary education and the highest is found for people with primary education and lower secondary education.

According to Goos et. all (2009) in the more economically developed country" (MEDC) the occupational structure is changing. Traditional manufacturing occupations are replaced by technological innovations or outsourced to other countries where the cost for labor are cheaper. Further there is an increase in the number of occupations in the service sector (e.g. office clerk and managers). In addition to these changes at the labor market, the Dutch educational structure has changed mostly due the educational expansion of the higher education (Schofer and Meyer 2005) . Between 2000 and 2013 there has been a strong increase of 25-34 year-old with a tertiary education from 27.1% in 2000 to 42.9% in 2013. The changes at the labor market and educational systems has important consequences for the transition youth make into the labor market and needs to be studied. For example, the consequences of the transition into working life for students following an educational program of a job that is disappearing.

At last, using personally experiences as a member of the education committee for Sociology, there has been an increasing emphasis on the role of universities in labor market orientation the bachelor and master program. Students evaluate their program on its labor market orientation and programs which score low are taken responsible and try to improve this. The youth unemployment, the increase and decrease of jobs, educational expansion, and the emphasis on labor market orientation make it highly relevant to study school-to-work transition.

In the last decades, several studies (Shavit and Müller 1998/2000; Corroles-Herrero and Rodriquez-Prado 2004/2012; Nguyen and Taylor 2005; Salas-Velasco 2007; Iannelli and Raffe 2007) have studied the school-to-work transition. The school-to-work transition is the process that defines the mobility of a person exiting the educational system until he reaches a relatively stable working position (Ryan 2001). One of the most influential work in this field is from Shavit and Müller (1998). In their study on the school-to-work transition in 22 countries they find that the structure of the educational system and the labor market strongly influences the transition from school into working life.

2 This indicator shows the unemployment rates of people according to their education levels: below upper secondary, upper secondary non-tertiary, or tertiary. The unemployed are defined as people without work but actively seeking employment and currently available to start work. This indicator measures the percentage of unemployed 25-64 year-olds among 25-64 year-olds in the labour force.

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This study will replicate earlier studies of time-to-first-job looking at the case of the Netherlands in 2009. Recent data of the European Union Labor Force Survey (EU-LFS) of 2009 has been used. The dependent variable is the time-to-first-job and the independent variables are educational level and vocational specificity. Time-to-first-job has been constructed using the date leaving initial education and the date starting the first significant job. Further, educational level and vocational specificity are measured using the different educational qualifications in formal education. In total 11.507 respondents provided an answer on the time-to-first-job questions. Because most answers on time-to-first-job are found not valid, the final analysis used 1.686 respondents. The research question and hypotheses are studied using two multiple regression analyses.

In the next chapter, chapter 2, the theoretical background of this study is presented. The theoretical framework consist of the theories on educational qualifications, an extensive outline of the Dutch landscape and the hypothesis. In the third chapter the data, the methods and the measurement of the key variables are discussed. Results are analyzed in chapter four and in chapter five the conclusive and discussion is presented.

Key words: school-to-work transition; time-to-first-job; educational level; vocational specificity

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2. Theoretical Framework

In this chapter the theoretical framework of this study is presented. This chapter consist of 1) the theories on the effect of educational qualifications 2) an extensive outline of the structure of the educational system and the labor market of the Netherlands 3) hypothesis and 4) discussion of earlier empirical research.

2.1 Effect of educational qualifications

In this section, the classical theories human capital, signaling and credentials are used to explain why those higher educated find a job faster than those lower educated. Theories originally explain differences between incomes but for the purpose of this research they will be used to explain differences in the time-to-first-job. In addition these theories are used to explain why those with a vocational qualification find a job faster than those with a general qualification. The main difference of the theories is that human capital theory focuses on human capital/skills while signaling, and credentialism theory focuses on formal educational qualifications. This study has tested the influence of formal educational qualifications. Although skills were not tested, the human capital theory is one of the most influential theories on the effect of education and will therefore be discussed.

Human capital/skills – According to human capital theories employers prefer higher educated applicants above lower educated applicants because they have more human capital. The more education someone has the more skills or human capital this person has acquired. More human capital makes people more productive and the employer will prefer the most productive applicant. Further some educational programs are more vocational orientated and therefore provide skills required in the workplace. Those with more vocational skills, which are required at the workplace, are more productive. Being more productive improves the employment changes of being preferred by the employer. The human capital theories assume employers have a perfect information of the human capital of their applicants and therefore will always hire the most productive applicant (Becker 1975).

Educational qualifications – According to the signaling and credentialism theory those higher educated find a job faster than those lower educated because those higher educated have a higher formal educational qualification. The signaling theory assumes employers don’t know which applicant is most skilled and productive. To gather information about their applicants they use educational level to screen their applicants. Those higher educated signals to be

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more productive than those lower educated and those with a vocational qualification signals to have more vocational skilles (skills which are required at the workplace) than those with a general qualification (Arrow 1973). They are therefore preferred by the employers. Further according to the credentialism theory, educational qualifications can be seen as the key which give applicants access to restricted jobs. Vocational educational qualifications, irrespective of its level, give access to jobs for which a vocational qualification is required. Therefore those with a vocational qualification are expected to find a job faster (Brown 2001).

In sum, due to formal education, skills and qualifications are acquired. Those higher and vocational educated find a job faster than those lower and general educated because they are more productive, signals to be more productive and have access to more restricted jobs.

2.2 Dutch landscape

In order to study the youth school-to-work transition in the Netherlands a brief description of the Dutch educational system and labor market is provided. Data is gathered from descriptions of other authors (Hannan et al. 1996; De Graaf and Ultee 1998; Shavit and Müller 1998; Levels et al. 2014; Di Stasio 2014) and interpretation of secondary data (e.g. Stateline and OECD). In addition to the theories on educational qualification this section is used to formulate hypothesis. The study argues the Dutch educational system has a high degree of stratification and tracking, standardization and vocational specificity and therefore qualifications structure the labor market entry. Youth problems finding a job is strengthen by the lack of working experience and therefore time-to-first-job is shorter for the ones with a vocational education.

Stratification and tracking – “Stratification refers to the extent and form of tracking at the secondary educational level” (Shavit and Muller 1998: 6). In figure 1 (p. 9), a scheme of the Dutch educational systems is provided. The scheme is classified in different educational levels using the International Standard Classification of Education (ISCED). As one can see, at the lower secondary education there is an early selection into different educational levels. The entry into a higher educational level is conditional on the program students follow at the previous educational level. Further, the higher secondary educational structure is even more stratified because four different levels of higher secondary education follow up the lower secondary vocational education. The movement between educational levels is possible although this will mostly requires additional examinations and implies a delay in study time. Last I find a binary structure of the tertiary education whereby there is a clear distinction

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between the higher vocational education and the higher scientific education (De Graaf and Ultee 1998: 340-343) (Levels et al. 2014: 342-343).

Standardization – “Standardization refers to the degree where the quality of education meets the same standards nationwide” (Shavit and Muller 1998: 6). According to Hannan et al (1996) the Dutch educational system is highly standardized because of the use of several school-leaving examinations at different levels of education. For instance the use of an obligated test at the primary education (CITO3) and a centralized final exam at the different levels lower and higher secondary education. Further there is a high standardization in the way schools are funded and teachers are trained (Di Stasio 2014). For example universities are paid for the numbers of students enter university and the number of students successfully finishing the degree. And also curricula are formed according to the national standards (Levels et al. 2014: 343-344).

Vocational specificity – Describing the links between educational qualifications and labor-market outcomes, Mauire et al. (1982) argue the way qualifications are formed and used by employers leads to system-specific relationships between qualifications and jobs. In the Netherlands there is a close relations between the qualifications and the jobs provided by employers. “A large number of occupational specializations are taught in specific school tracks” (Shavit and Muller 1998: 5). For example to become a hairdresser one requires a qualification for this. In Great Britain this qualification is not required.

In figure 1 a scheme of the Dutch educational system is presented. The scheme uses the International Standard Classification of Education (ISCED) from 1997. ISCED level 2-5 are subdivided in groups of a, b and c based on specificity. Detailed description of these subcategories will be given in the methods chapter (chapter 3). As one can see, the system has a clear distinction between general education (subcategory a) and vocational education (subcategory b and c). Furthermore, at the higher secondary vocational education there is a distinction made between school-based and workplace-based vocational education. In de workplace-based education the student almost solely is educated on the workplace. In the school-based education, accounting for half of all the qualifications in the Dutch system, half of the time of education students attend school and the other time they attend the workplace (De Graaf and Ultee 1998). Employers, the government and unions play an important role in updating the curricula so it provide relevant skills required for the job. The state regulates the

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prerequisites to entry occupations and therefore the qualification are most important to structure the labor market entry (Levels et al. 2014: 344-345).

Figure 1: Scheme of the Dutch educational system

Source: own design based on the scheme of Dynamic Online Tool for Guidance (n.d.)

Labor market – Traditionally the Dutch labor market is associated with relative high wages and a strong employment protection. Policies on wages has set a statutory minimum wage. Further there is a traditional role for the government, trade unions and employer associations in the process of collective wage bargaining per sector. Employer protection are traditionally very strict and are strong influenced by trade unions and work councils. According to De Graaf and Ultee (1998) the strong influences has protected the older and current worker and created a structural youth unemployment. For example having a minimum wage higher the risks for employers when hiring a young un-experienced applicant versus an older more experienced applicant. This risk could even be higher when the young applicant has acquired a general qualification because then the lack of working experience increases (De Graaf and Ultee 1998: 343-346).

Problems youth face entering the labor market results in unemployment. To better understand the current youth unemployment situation, in figure 2 (p. 10) an overview of the development in youth unemployment from 1980 till 2013 is presented. Following from the fluctuations in the graph line, there is a repeated increase and decrease of unemployment during these years. The highest unemployment rate of 25.2% is found during the earlier 1980s (second oil crisis) and the lowest rate of 4% around 2001. Most recent increase in unemployment started around the financial crisis of 2008. Noteworthy, youth unemployment has nearly doubled from 2008

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till 2013 and is the highest unemployment since 1995 (12.2%). According to the CBS this position of youth on labor market is still worsening (CBS 2015).

Figure 2: Youth unemployment between the years 1980 - 2013

Note: The youth unemployment rate is the number of unemployed 15-24 year-olds expressed as a percentage of the youth labor force.

Source: OECD (2015)

In addition to the CBS, Keune and Tros (2014) argue youth are in a vicious circle which creates a huge barrier to enter the labor market. Most youth enter the labor market without working experience. Because of the lack of this working experience they have difficulties to find a job. Without a job they won’t learn working experience following that without working experience they won’t find a job. Taking the distinction between vocational educational and general education into account, this problem for the ones with a general education could even be higher.

2.3 Hypothesis

The high degree of stratification and standardization and vocational specificity and the structure of the labor market has important implications for the link between schools and work and the theories earlier discussed. The high degree of stratification and tracking at the secondary education leads to different types of qualifications. These specified qualifications provide detailed signals used by employers. Further the high degree of stratification strengthen the content reliability of the qualification that is obtained. Therefore employers can rely on the qualifications to represent a standard set of skills. As an outcome employers in the Netherlands can more easily compare applicants based on their qualifications. And

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because this educational system has a high vocational specificity, “workers with vocational educations are more likely to be found in skilled, rather than unskilled, occupations” (Shavit and Muller 1998: 5). This implies that not only the level of qualification but mostly the track has a strong link to the occupations. Further following from Keune and Tros (2014), problems of youth unemployment are expected to be the strongest for those without vocational skills.

Considering the strong link between qualifications and occupations and the problems of youth unemployment mentioned by Keune and Tros (2014), differences between higher and lower and vocational and general qualifications are expected to be even higher than assumed using the theories of human capital, signaling and credentialism. The hypothesis tested for this study are:

H1: The higher the educational level the shorter the time-to-first-job

H2: Having a vocational qualification leads to a shorter time-to-first-job than having a general qualification.

2.4 Previous research

Previous research on the Netherlands of De Graaf and Ultee (1998) focuses on the school-to-work transition. The authors use data from the labor market survey 1991 (EBB) and study all respondents that left schools less than ten years before and are not in military service. One of the most important findings, is the differences in employment status between the educational levels. In general the ones with tertiary professional education has a higher rate of

employment compared to the ones with general education. Further people who attended only a primary or a lower post-secondary education (e.g. MBO, HAVO and VWO) have the highest percentage of not in the labor market. For this study it shows the importance of vocational specificity of the educational qualifications.

Previous studies on school-to-work transition focusing on other countries (e.g. Shavit and Muller 1998; Kerckhoff 2000; Lassibille et al. 2001Salas-Velasco 2007), show the importance of educational level. For example in Salas-Velasco (2007) their study on graduates in Spain found that people with tertiary education are shorter unemployed than people with a secondary education. Corrales and Rodriguez (2004) used the European Labor Force Survey data of 2000 (EU LFS) to study the time of finding a job for young people across countries. The most important finding shows that vocational programs lead to a shorter

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time to find a job compared to university students. This study will partly replicate earlier studies and focus on the Netherlands in 2009 and using improved data on time-of-first-job (discussed in the next section). According to Iannelli (2002) the previous data on time-of-first-job EU LFS 2000 suffers from a high measurement error and this has led to a revision in 2009. Using this revision version results of this study is assumed more reliable than previous studies of the 2000 EU LFS.

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3. Data and methods and measurements

This section consists of three parts. The first part provides a detailed explanation of the data. Second the methods used to study the research question is described and third the measurements of the dependent, independent and control variables are given.

3.1 Data

For this study the European Union Labor Force Survey of 2009 (EU-LFS micro data) is used. The research group of the University of Amsterdam gave access to use this data because this study is part of the master thesis. The data is a cross-sectional and longitudinal household sample survey, collected by Eurostat. The target groups are the population of the EU member states with people older than 15 years.

In the Netherlands the survey is called ‘enquête beroepsbevolking” (EBB). Households were selected according to a two-stage selection. In the first stage the towns and the amount of addresses of each town were randomly selected. Thereafter in the second stage for each town twelve or more addresses were selected due a systematic sampling. In total 58,000 addresses were contacted with a response of 37,000 households (N=72,464).

The survey uses a panel research which is repeatedly held every three months. Household who participate are contacted five times in 12 months (each quarter). During the first contact the interviewer visits the home of the household and gives a face-to-face interview with a Computer Assisted Personal Interview (CAPI). The remaining four interviews are done through the telephone with a Computer Assisted Telephone Interview (CATI). The survey consists of big scale of labor market related questions. It uses a “systematic follow-up” form in which answers on previous questions, for examples the date of starting the first job, are used to skip further questions which has the same answer. Further, the survey used for 38% proxy interviews in which one or more people who knows this person very good helped answering the questions when the selected respondent is not possible to answer the questions (e.g. the person who is hearing impaired). Comparing the response rate with the other countries who participated in the survey, the Netherlands has around the lowest non-response rate. This is because other countries didn’t use proxy interviews. The non-response was the lowest for variables referring to the parents and highest educational level and the highest for variables referring to the time-to-first-job and first job duration because the lack of information on the starting months of the first significant job and the months leaving initial education.

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Since 1999 an inherent part of the EU-LFS has been the ‘hoc modules’. The aim of the ad-hoc module is to provide users with statistics on a specific topic concerning the labor market by adding each year a set of variables to supplement the core EU-LFS. The data tracks several changes at the labor market and therefore has great relevance to policymakers, welfare authorities, media, students and researchers and businesses.

In addition to the core EU-LFS this study uses the 2009 ad-hoc module Entry of young people into the labor market. As noted earlier, this module is a revision on the previous ad-hoc module Transition from school to working life from 2000. According to Eurostat improvements were made mostly in methodology to better measure time-to-first-job and new

variables were introduced to better explain this school-to-work transition (EUROSTAT 2012).

Both core and ah-hoc module were combined in a face-to-face interview. The ad-hoc module focused only on young adults aged 15 till 34 (N=25,847) with the aim to study youth school-to-work transitions. This is the target group of this study. Noteworthy is that the total respondents used for this study is quite lower than the respondents aged 15 till 34. Over half of these respondents were not applicable to answer the time-to-first-job questions for reasons of being inactive, still in formal education or never had a significant job. This reduces the total respondents to N=11,507.

3.2 Methods

The study tests the effects of educational qualifications on the time to find a job of young adults aged 15 till 34 in the Netherlands and answer the research question. The statistics obtained with the analysis are examined only on the 1,686 respondents who were found valid on their measure of the time-to-first-job.4 The relation between the time-to-first-job and educational level and vocational specificity are studied using a multiple linear regression analysis.

This method is applicable to study this relation because the time-to-first job variable has a ratio measuring level with a minimum of zero. As noted earlier, the time-to-first-job display the duration in months to find a job after leaving initial education. Respondents having a zero

4 The detailed explanation for selection the 1,686 respondents for the analysis is given in the next section 3.3.1 dependent variable.

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found a job in less than one months after leaving initial education and the higher the score the more months needed to find the first job.

To test the hypothesis two regression analyses were used. In the first analysis the effect of educational level is studied and in the second analysis the effect of vocational specificity. In both analysis main effects are controlled by including control variables. If the main effects are in line with the theoretical model, the hypothesis will be confirmed.

3.3 Measurements

3.3.1 Dependent variable

Time-to-first-job –– Noteworthy is that the dependent variable of time-to-first-job did not exist in the original data and needed to be constructed. The construction of this variable has been one of the most important aspects of this study. Therefore table 2 (p.17) table 3 (p. 18) and figure 3 (p. 18) are used to give a detailed explanation how the variable is constructed and why it is constructed this way. As one can see in table 3, three different calculations were used to construct the variable time-to-first-job. The first constructed variable (calculation 1) is the one used for the results.

In all calculations the date starting the first significant job (JOBSTART) and the date leaving initial education for the last time (STOPDATE) are used to construct time-to-first-job with the basic calculation (step a) of subtracting JOBSTART with STOPDATE. The calculations differ in time span (see figure 3) and step b) and c) used to construct the time-to-first-job. The first significant job is operationalized as the first employment consist of self-employed, family work or employee of three months or more after leaving initial education for the last time and initial education are measured by the last educational programs with a full-time content. At last, for respondents starting the first significant job before leaving initial education the real date of JOBSTART< STOPDATE was used.

(1) –– The first calculation tries to construct the time span between date of leaving initial education and finding the first significant job (figure 3), which is drawn as the ‘single line’. First the basic formula is used in which the JOBSTART is subtracted by the STOPDATE. Second, all respondents who found the first significant job before leaving initial education are coded as missing because over 70% of this groups states they found a job before leaving education. This methodological choice will be further explained with the second formula.

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In table 3 the descriptive statistics of the constructed variables are presented. The statistics of the first constructed variable are most important for the results because these are the respondents this study uses. As you can see the variable consist of 1686 respondents5. The mean is 8.13 which means the time to find the first job after leaving initial education is around 8 months for young adults aged 15 till 35 in the Netherlands. The shortest time is found for 0 months and the longest for 172 months and the standard error is 16.27.

(2) –– The second calculation is almost identical to the previous formula but differs in time span. In contrast to the first constructed variable, respondents who found the first significant job before leaving initial education are included in this variable. These respondents left education and directly had a job and are therefore coded as zero months to find a job. The formula therefore constructs the time span of all respondents starting the first job before leaving initial education and after leaving initial education, which is drawn as the ‘double line’ in figure 3.

Over 70% of the respondents states their first significant job was found before they left initial education. Based on common knowledge this percentage was found too high. This study therefore expects the question on the first significant job didn’t measure the first significant job. The time-to-first-job represents as unrealistic low score and therefore the second constructed variables was not used for the results.

(3) –– As noted earlier, the survey has a systematic form in which answers on previous questions are used to skip further questions which ask for the same answers. At the question of JOBSTART (what is the date starting the first significant job?) this systematic form was used. Previous questions of the core module on the current job were used to provide answers on the JOBSTART. This questions asked for the months working at the current job (STARTIME). All respondent whose current job is the first significant job, and already answered the previous questions of the current job have therefore only answered that the first significant job is their current job.

Information of the date starting the current job is gathered using the answers on the previous questions of the current job. For all the respondents with this answer (GROUP2) the time-to-first-job is constructed in a different way. First the reference year of the survey (2009) is used

5 As noted earlier, the total respondents applicable to answer time-of-first-job is N= 11,507. All respondents stating their current job is the first significant job (5,833) and all respondents whose first significant job was found before leaving initial education (3,988) were not included because these measures were found unreliable. The sum of 11,507 minus 5,833 and 3,988 gives a total number of respondents of 1,686.

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to establish the date of the current job (CURRENTJOB).. Second this date was subtracted with the months working at the current job (STARTIME) to find the date starting the first significant job (JOBSTART). Thereafter the sum of CURRENTJOB – STARTIME was subtracted by the date of leaving initial education for the last the (STOPDATE). All respondent answering the date of starting the first significant job were used for GROUP1 with the basic formula of subtracting JOBSTART with STOPDATE. In the last step all respondents of both groups were combined to construct the variable time-to-first-job.

Based on the descriptive statistics of table 3 there has been decided to not use this variable because its measurement was found not valid. Respondents of GROUP2 gave on average extremely low answers on the months working for the current job. The differences between the date of leaving initial education and finding the first job is therefore too high as we find a mean of 27.17 months and 46.54 months for GROUP2 (whereby the current job questions of the core module were used).

Table 2: Three different formulas used to construct the variable time-to-first-job

Variable Formula

(1) Time-to-first-job (used for results) a) JOBSTART – STOPDATE

b) JOBSTART < STOPDATE= MISSING

(2) Time-to-first-job a) JOBSTART – STOPDATE

b) JOBSTART < STOPDATE = 0 MONTHS

(3) Time-to-first-job a) JOBSTART – STOPDATE = GROUP1

b) (CURRENTJOB- STARTIME) - STOPDATE = GROUP2 c) JOBSTART < STOPDATE= MISSING

d) GROUP1 + GROUP2

JOBSTART Date starting first significant job

STOPDATE Date leaving initial education for the last time

CURRENTJOB Current job is the first significant job (coded as the survey year)

STARTIME Months working for current job

Note: Only (1) was used for this study. Both other variables had an unreliable measures of the months needed to find a job. For example around 70% of the respondents of (2) states the first significant job was found before leaving initial education and at (3) the mean time-to-first-job is around 64 months

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Table 3: Descriptive statistics of time-to-first-job

Variables N mean sd range

1 Time-to-first-job (GROUP1) 1686 8.13 16.27 0-172

2 Time-to-first-job (1) 5674 2.41 9.61 0-172

(GROUP2) 1658 46.54 42.84 0-209

3 Time-to-first-job (GROUP1+GROUP2) 3344 27.17 37.58 0-209

Note: (1) 70.29% of the respondents has found the first significant job before leaving initial education for the last time (JOBSTART < STOPDATE) and is thus coded as zero months of time-to-first-job.

Source: EU-LFS 2009

Figure 3: Timeline of the construction of time-to-first-job

Source: Own design

Implications –– Both constructed variable 2 and 3 were not used for the results of this study because the measure was found not valid. Based on the results of table 3, this study is very critical about the way these measures were done. First, the variable JOBSTART didn’t measure the first job after leaving initial education because over 70% of the total applicable respondents (N=11,507) on the question “what is the date of first significant job of three or more months after leaving initial education?” indicated a date before leaving education. It is plausible a quarter of the respondents found the first significant job before leaving education but 70% of them seem not plausible. An explanation might be that respondents interpreted this question wrong. Perhaps their answer relies on their first job ever for example, a part time student job (e.g. working in the super market). The youth school-to-work transition is not measured with the time of finding the first sideline job and therefore this measurement error restricts the results of this study.

Further this study assumes a measurement error in measuring the “the current job” and “time working at current job” because all respondents stating “their current job is their first significant job” score extremely high on time-to-first-job namely an average of 46.54 (see

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table 3). Probably the current job isn’t the first significant job but the second or third job. The date calculated for the first significant job (CURRENTJOB – STARTIME) is therefore too high.

As a result a high percentage of total respondents were not included for the final analysis. This applies to all respondents who stated that their current job is the first significant job (5,833) and to all respondents whose first significant job was found before leaving initial education (3,988). For maintaining the credibility, transparency and (particular) the reproducibility of this study’s methodological choices, statistics of the target group (N = 1.686) as well as the respondents applicable to answer the time-to-first-job (N = 11.507) are provided.

3.3.2 Independent variables

The independent variables consist of educational level and vocational specificity. First the measurement of educational level is discussed and second the measurement of vocational specificity. Educational level is constructed using the highest completed qualification. Vocational specificity is constructed using the highest completed qualification in formal education and the orientation of this qualification. Two variables were used to test vocational specificity, one ordinal and one nominal

Educational level –– The highest completed qualifications in formal education is measured based on the ISCED classification of UNESCO from 1997. ISCED is the International Standard Classification of Education. The variable has an ordinal measuring scale of seven main groups of educational levels (ISCED 0-6). The numbers of the classification are ranked according to the sequence in which the programs are followed. As noted earlier, ISCED levels 2-5 are subdivided in groups of a, b and c based on specificity, follow-up level and entrance of the labor market. The subcategory “a” correspond to the educational programs which prepare students for tertiary education (ISCED 5/6). Further the subcategory “b” is used for preparing the student for vocational education and “c” is used to prepare students for the labor market.

The first twelve years students go the primary education (ISCED 0-1). Thereafter students are tracked in four different programs at the secondary education. The ISCED 2c and 3c prepares students for the labor market. ISCED 2a and 3a (HAVO and VWO) both prepare students for the higher education namely higher vocational education (HBO) and higher scientific

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education (WO). Further with the highest secondary vocational education qualification (MBO level 4) one gets access to the higher vocational education. For the Doctor of Philosophy program one needs a qualification of the higher scientific education (WO).

Educational level is measured using the highest completed qualification in formal education. At each level (ISCED0-6) all the subcategories “a”, “b” and “c” are merged into the level of its qualification (e.g. ISCED2c and ISCED2a are merged into ISCED2). The variable has a ordinal measuring scale. The educational levels are: primary education (ISCED0/1), lower secondary education (ISCED2), higher secondary education (ISCED3), post-secondary education (ISCED4), tertiary education (ISCED5) and Doctor of Philosophy ISCED6).

In table 6 the descriptive statistics of educational level is presented. None of the respondents used for the analysis had an educational level of the preprimary education (ISCED0) and therefore only the level 1 till 6 are shown. The highest percentage of respondents was found for those with a higher secondary education (HAVO/VWO class 4-6 + MBO level 1-4) and tertiary education (HBO short and HBO/WO) and the lowest for those with the post-secondary education and Ph.D. program.

Table 6: Descriptive statistics of educational level

Model 1 Model 2

N % N %

Educational level

ISCED1(Elementary) 62 3.68 322 2.80

ISCED2 (Lower secondary education) 371 22.02 2.149 18.68

ISCED3 (Higher secondary education) 633 37.57 5.106 44.38

ISCED4 (Post –secondary education) 17 1.01 225 1.96

ISCED5 (Tertiary education) 595 35.31 3.642 31.66

ISCED6 (Ph.D.) 7 0.42 60 0.52

Source: EU-LFS 2009

Vocational specificity (1) –– For the orientation of the highest completed qualification five different categories classified by Eurostat were used. The categories are 1) general education, 2) school-based vocational education, 3) workplace-based vocational education 4) combination school-based and workplace-based vocational education and 5) vocational education, with no distinction possible between 2, 3 and 4. The answer categories 2, 3 and 4 didn’t correspond with the Dutch educational system. Therefore “general specificity” is made up by all respondents answering the orientation of their qualification was the first category (1.

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general education) and “vocational specificity” was made up answering the orientation of their qualification was the fifth category (5. Vocational education, with no distinction possible).

First all the categories of the highest completed qualifications were used. All the categories are ISCED0/1 (primary education), ISCED2 (VMBO/HAVO/VWO), ISCED3c (MBO level 1-3), ISCED3ab (HAVO/VWO/MBO level 4), ISCED4c (professional specialization education), ISCED5b (HBO 2 years or shorter), ISCED5a (HBO/WO) and ISCED6 (Doctor of Philosophy). Second subcategories “a” and “b” were added to ISCED2 and ISCED3 using the orientation of the highest completed qualification variable for each category (see table 4). For ISCED2 lower secondary education the subcategory ISCED2a (HAVO/VWO class 1-3) and ISCED2b (VMBO) were added. At ISCED3 the higher secondary education the subcategory ISCED3a (HAVO/VWO class 4-6) and ISCED3b (MBO level 4) was created.

All the qualifications with the subcategory “a” (ISCED2a, ISCED3a and ISCED5a) are studied as having a general specificity. In addition, also the ISCED0/1 primary education and the ISCED6 Doctor of Philosophy (Ph.D.) are studied as having a general specificity. The primary education has a general specificity because here the mostly general skills of literacy, numeracy and problem solving are learned. Further the access to the Ph.D. program is conditional on having a higher scientific education (WO) which is general specified. Thus both have a general specificity. All other qualification with the subcategory “b” or “c” (ISCED2b, ISCED3b, ISCED3c, ISCED4c, ISCED5b) were studied as having a vocational specificity.

Vocational specificity (2) –– The second variable of vocational specificity uses the exact same classification of general and vocational specificity. All qualifications ISCED0/1 ISCED2a, ISCED3a, ISCED5a and ISCED6 were merged into the category general specificity and all qualifications ISCED2b, ISCED3b, ISCED3c, ISCED4c and ISCED5b were merged into the category vocational specificity.

Subcategories “a” and “b” were added to ISCED2 and ISCED3 using the cross table of the highest completed qualification and the orientation of the highest completed qualification. The distribution results are presented in table 4. To see if distribution results are not caused due the selection of the 1.686 respondents the distribution of the 10.507 respondents (applicable to answer the time-to-first-job) is also showed. As one can see, the

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orientation/specificity variable has a high non-response (of around 40%)6 and this non-response is found for the qualification no presented namely primary education (ISCED1), tertiary education (ISCED5) and Doctor of Philosophy (ISCED6). The respondents with this qualification have stated this question was not applicable. Thus the classifications used by Eurostat for the orientation of the qualifications didn’t correspond to most of the qualifications used in the Dutch educational system.

Table 4: Cross table of educational qualifications ISCED and vocational specificity Educational qualification ISCEC

Specificity 2 3c 3a 4 Total General 159 - 114 - 273 Vocational 212 201 318 17 748 Total 317 201 432 17 General 865 - 738 - 1.603 Vocational 1.284 1.938 2.430 223 5.875 Total 2.149 1.938 3.169 225

Note: ISCED2 = Lower secondary education ISCED3c = Practical education

ISCED3a = Higher secondary education ISCED4 = Post-secondary education Source: Calculations on EU-LFS 20097

The distribution of the first and second variable of vocational specificity is shown in table 5. Also here the descriptive statistics of the 1.686 respondents and the 10.507 respondents are shown. The first variable consist of the highest completed qualifications in formal education measured by the ISCED 97 classifications plus the added subcategories “a” and “b”. The highest percentage of respondent is found for those with a HBO/WO qualification and a MBO (level 1-3 and level 4) qualification. This is in line with what is expected to be the biggest group. Further a small percentage is found for those with a MBO professional specialization qualification, post-secondary general qualification, HBO of 2 years or shorter qualification and Ph.D. qualification. According to the CBS, the programs of MBO professional and HBO of 2 years shorter are rarely held in the Netherlands and therefore small percentages are found. Further only a small group gets access to the Ph.D. program. For the post-secondary general qualification only two respondents are found in the second model. This is not surprising because this educational program doesn’t even exist in the scheme of

6 Non-response is obtained with the sum of 1 – (1,021 / 1,686)

7 This research almost solely uses the EU-LFS 2009. For the rest of this study, if the source is not provided the EU-LFS 2009 was used

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the Dutch educational system (figure 1). The second variable is merged from the subcategories “a” “b” and “c”. As one can see, most of the respondents have a qualification with a vocational specificity. This is in line with what is expected to be the biggest group.

Table 5: Descriptive statistics of highest educational level and vocational specificity

Model 1 Model 2

N % N %

Vocational specificity (1)

ISCED1 (Elementary) 62 3.68 322 2.80

ISCED2c (VMBO class 1-4) 212 12.58 1,284 11.16

ISCED2a (HAVO/VWO level 1-3) 159 9.44 865 7.52

ISCED3c (MBO level 1-3) 201 11.93 1,938 16.85

ISCED3b (MBO level 4) 318 18.87 2,430 21.12

ISCED3a (HAVO/VWO class 4-6) 114 6.77 738 6.42

ISCED4c (MBO vocational specialization) 17 1.01 223 1.94

ISCED4a (Post-secondary general education) - - 2 0.02

ISCED5b (HBO 2 years or shorter) 36 2.14 215 1.87

ISCED5a (HBO/WO) 559 33.18 3,427 29.79

ISCED6 (Ph.D.) 7 0.42 60 0.52

Vocational specificity (2)

General specificity 342 30.37 1.987 24.60

Vocational specificity 784 69.63 6.090 75.40

Note: (1) 5a were excluded on this variable. Source: EU-LFS 2009

Implications –– These classifications used for this study overlap each other and this has some implications for the results. To start, in the Netherlands there is a clear distinction between vocational and general education. Combining 5a higher vocational education four year bachelor and master (HBO) and 5a university bachelor and master (WO) for the results on educational level makes to broad categories. Although both degrees are determined as the same the university bachelor and in particular the university master qualification is perceived different than the higher vocational education bachelor and master. The university educates students to be an academic and the higher vocational education professional knowledge is gathered for the higher management position in businesses and government. These differences in knowledge is even used to restricted applicants to apply for a job whereby for example an academic thinking ability is required. Contrary to this classification other classification (e.g. the Standard Educational Classification of 2001 or 2006 ) makes a distinction between the academic bachelors and vocational bachelors. With the ISCED 97 no

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statements could be made about the differences in time-to-first-job between both groups. The classification of the ISCED 1997 is thereby found as a restriction of the results of this study. At last, due the selection of the 1.686 for the final analysis the effect for having a Ph.D. qualification is tested with the time-to-first-job of only seven respondents. Results of this effect should therefore interpreted nuanced.

Field of study –– In addition to the vocational specificity and educational level, the field of study is measured. This variable was measured by asking for the field of the highest completed qualification in formal education. The fields are categorized according to the CSO Standard Field of Education Classification of Eurostat and UNESCO based on the previous classification of ISCED 1997. The classification consists of nine main groups defining the field of study and coded from 0 to 8 (see table 6 p.25). These main groups are subdivided in one and two digits (e.g. 3 = Social sciences, Business and Law, 31 = Social and behavioral science and 312 = Sociology and cultural studies)8. Around 440 respondents, which is around 26%, didn’t answer to this question because the field of study was unknown or they were not applicable to answer this question. Further small response rates were found at the categories: foreign languages (N = 10), life sciences (N = 8), physical sciences (N = 16), mathematics (N = 5), computer sciences (N = 24) and computer use (N = 1). All one and two digits were computed to the main group (e.g. 222 was coded as 2). Results of this computing are presented in table 6. Because most of the groups were still small and had relatively high standard errors the main groups were computed in two groups namely 1) beta sciences and 2 gamma and humanities sciences. Based on both distribution of model 1 and model 2 the selection of model 1 is assumed to not influence the distribution of the field of study.

8 The classifications of the CSO can be found at

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Table 6: Descriptive statistics of field of study eight main groups and beta, gamma and humanities sciences Model 1 Model 2 N % N % Field of Study General program 67 5.38 443 4.93 Education 69 5.54 615 6.85

Humanities and Arts 62 4.98 357 3.98

Social sciences, Businesses and Law 431 34.59 2,665 29.68

Science, Mathematics and Computing 54 4.33 364 4.05

Engineering, Manufacturing and Construction 40 3.21 1,573 17.52

Agriculture and Veterinary 180 14.45 340 3.79

Health and Welfare 143 11.48 1,589 17.69

Services 67 5.38 1,034 11.51

Field of study

Gamma and humanities sciences 952 76.40 6.703 74.64

Beta sciences 294 23.60 2.277 25.36

Source: EU-LFS 2009

3.3.3 Control variables

Previous research (Chuang 1999; Betts et al 2000; Biggeri et all 2001; Lassibille et al. 2001; Corrales and Rodriquez 2004; Nguyen and Taylor 2005; Levels et al. 2014; Di Stasio 2014) provides results and arguments for other variables which influence the time-to-first-job. All variables present in the data are therefore used to control the effect of educational level and vocational specificity. The control variables are: age, sex, nationality, and previous working experience. The descriptive statistics of all the control variables are provided in table 7 (p.27). In addition, the control variables are also used for a correlation with the time-to-first-job which will be presented in the next chapter.

Age –– Following from the signaling theory, employers use cheap signal to gather information about their applicants. A relative younger applicant signal to be successful in his previous (educational) career while the relative older applicant signal to have a delay. The employer will try the choice the most competent worker and choices the younger applicant because he signal to be successful (Salas-Velasco 2006: 336). Previous research of Chuang (1999) confirms the signaling theory and finds that older workers are longer unemployed in relation to their younger peers.

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During the survey respondents are asked for their present age. To anonymize the respondents, in this micro data all ages are aggregated in five year age bands, hierarchically, whereby the median is used to code this band (e.g. 15-19, coded as 17). The target group of this study exist of four age bands (see table 7).

Sex & country of birth –– In previous research of Lassibilie et al. (2001) and Salas-Velasco (2006) researchers found that young woman are longer unemployed than young man after leaving initial education. Irrespective of their educational level they receive fewer invitation than man for a job interview. In addition, a recent field experiment of the Dutch research institute find that non-western immigrants significantly have a lower change on getting invited for a job interview. Both young female and immigrants are expected to be discriminated on the labor market and therefore have a longer time-to-first-job.

For sex the interviewer note if the respondent is a male or female. The country of birth is measured with the “country classification scheme of Eurostat 2004”9. This scheme consist of 15 categories and provides information if the respondent is native (category 1) , from a country of the Member States of the European Union (category 2-4) or from another continent (5-15). Respondents could exclusively give answer for one nationality. Because categories 2-15 all consist of a small percentage of the total respondents all respondents of these categories were classified as foreigners.

Working experience –– Following from the human capital theory skills are not only acquired at school but also taught at the workplace. The more working experiences someone has the more productive this person is in doing the job. Regardless of whether this person is more productive the employer prefers the applicant with the most previous working experiences. Previous research (Salas-Velasco 2006) on the association between working experience and first-job show that having previous working experience leads to a shorter time-to-first-job. Following for a comparative study of the labor market entry of young people of Salas-Velasco (2006), Dutch graduates are found to have a shorter time-of-first-job partly because almost all of them work during their studies.

Previous work experience is measured in the ad-hoc module by asking of the respondent has worked before leaving initial education. The question was asked to respondents who finished initial education as well as to the current students. To avoid respondents answering a minor

9

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job “work” is defined as a job for pay or profit, of a minimal duration of about 1 month per year, on average: this can be expressed as approximatively 4 weeks full-time, 8 weeks part-time or 150 hours within a year (not as 1 month adding up all employment spells for the whole period of studies). All respondents who never worked more than 1 moths per year were classified as having “no working experience”. All respondents who worked more than 1 month per year, as part of their educational program, outside their educational program or a combination of both were classified as having “previous working experience”.

Table 7: Descriptive statistics of the control variables

Model 1 Model 2 N % N % Age 17 (15-19) 34 2.02 374 3.25 22 (20-24) 326 19.34 2.620 22.77 27 (25-29) 585 34.70 4.153 36.09 32 (30-34) 741 43.95 4360 37.89 Sex Male 887 52.61 5.791 50.33 Female 799 47.39 5.716 49.67 Country of birth Native 1.655 98.16 11.222 97.55 Foreign 31 1.84 282 2.45

Previous working experience

No working experience 515 30.55 3.156 27.58

Working experience 1.171 69.45 8.290 72.42

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4. Results

In this chapter first the descriptive statistics are discussed and second the explanatory statistics. The descriptive statistics consist of two analysis. The first analysis uses a cross table of the independent variables vocational specificity, educational level and field of study on the descriptive statistics of time-to-first-job. The descriptive statistics provide the distribution of the time-to-first-job for each category of the independent variable and results gives insight in the differences in time-to-first-job. For the second analysis a correlation analysis is used on time-to-first-job using the independent and control variables. The importance of the results is based on the strength of the correlation and the significance. At last, for the explanatory statistics the regression analysis will be discussed which provide an answer on the hypothesis and the research question.

4.1 Descriptive statistics

In table 8 (p.30) the descriptive statistics of educational level, vocational specificity and field of study on time-to-first-job are shown. The first column consist of the number of respondents having this qualification and the second the mean time-to-first-job for the respondents with this qualification. In the third column the standard deviation of this mean is given and in the fourth the range of time-to-first-job of the respondents. The mean is used to compare the differences in time-to-first-job between different qualifications and the standard deviation and range is used to see how the group of respondents of each qualification are distributed.

Educational level –– To start, the highest time-to-first-job of 14.73 was founded for the respondents having the lowest educational level and the lowest time-to-first-job of 3.42 was founded for the respondents with the highest educational level. In other words, respondents with only an elementary qualification (ISCED 1) needed on average around fifteen months to find a job after leaving initial education while for respondents with a Ph.D. qualification (ISCED6) this time was only around three and a half month. As one can see, the higher de educational level the shorter the average time-to-first-job. These descriptive statistics are in line with the first hypothesis which expect that having a higher educational level leads to a shorter time-of-first-job.

Vocational specificity (1) –– Further at vocational specificity, the first variable of vocational specificity shows the time-to-first-job for the different educational qualification of the Dutch educational system. For the educational qualifications of the elementary education (ISCED1),

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the MBO professional education (ISCED4c) and Ph.D. program the descriptive statistics are the same as for educational level. The highest and the lowest average time-to-first-job were found for respondents with a general qualification namely the elementary and Ph.D. qualification. Finding the highest and lowest time-to-first-job thus doesn’t imply that people with a general qualification have a shorter-time-to-first-job than people with a vocational qualification. Therefore the averages in time-to-first-job are compared between the qualifications with an “a” subcategory (the general qualifications) and the qualifications with a “b” and “c” subcategory (the vocational qualifications) at the different educational levels.

First, differences are found in the mean time-to-first-job for those with a HAVO/VWO class 1-3 qualification (subcategory a) and a VMBO qualification (subcategory b). On average the respondents with a HAVO/VWO class 1-3 qualification need around eight months to find a job while the respondents with a VMBO qualification needed on average over eleven months to find a job. Further at the higher secondary education (ISCED3) the highest and lowest time-to-first-job is found for respondents with a vocational qualification namely MBO level 1-3 and MBO level 4. The average of the respondents of both qualification were merged together and an average time-to-first-job of 8.80 was found.10 The average time-to-first-job for the respondents with the HAVO/VWO class 4-6 qualification is higher (namely 9.27) and therefore at the higher secondary education the respondents with vocational qualification have a faster time-to-first-job. At last, when comparing the differences time-to-first-job between the MBO professional qualification (subcategory c), the HBO qualification of 2 years or shorter (subcategory b) and the HBO/WO qualification (subcategory a) the shortest qualification was found for the respondents with general qualification namely the HBO/WO qualification. In the final analysis the effects of the general and vocational qualification will be tested to see if the differences are significant.

Vocational specificity (2) –– At the second variable of vocational specificity all qualification are merged into two categories namely general specificity and vocational specificity. General specificity has a mean of 9.5 and vocational specificity a mean of 9.27. The average months to find a job is shorter for respondents with a vocational qualification compared to respondents with a general qualification although this differences is very small. The variable is used for the correlation analysis to see if this differences is significant.

10 The average time-to-first-job for both qualifications is calculated by ((201 * 9.51) + (318 * 8.35)) / (201 + 318)

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Field of study –– For field of study, the gamma and humanities studies have a mean of 7.13 and beta sciences a mean of 7.91. Respondents of both fields thus have around the same average time-to-first-job and also here a high standard deviation and range was found. Based on these results there is assumed to find an insignificant and weak correlation for the field of study. With the correlation analysis the assumptions for vocational specificity and field of study will be further studied.

Table 8: Descriptive statistics of time-of-first-job per educational qualification, vocational specificity and field of study Time-of-first job (months of finding a job after leaving initial education)

N mean sd range

Educational level N=1685

ISCED1(Elementary) 62 14.73 22.48 0-124

ISCED2 (Lower secondary education) 371 9.75 16.70 0-119

ISCED3 (Higher secondary education) 663 8.88 19.35 0-172

ISCED4 (Post –secondary education) 17 6 8.63 0-35

ISCED5 (Tertiary education) 595 5.670 10.44 0-100

ISCED6 (Ph.D.) 7 3.43 2.64 1-8

Vocational specificity (1) N=1685

ISCED1 (Elementary) 62 14.73 22.48 0-124

ISCED2b (VMBO class 1-4) 212 11.14 19.70 0-119

ISCED2a (HAVO/VWO class 1-3) 159 7.90 11.41 0-64

ISCED3c (MBO level 1-3) 201 9.51 18.50 0-88

ISCED3b (MBO level 4) 318 8.35 20.75 0-172

ISCED3a (HAVO/VWO class 4-6) 114 9.27 16.71 0-103

ISCED4c(MBO professional) 17 6 8.63 0-35

ISCED5b (HBO 2years or shorter) 36 6.67 9.73 0-41

ISCED5a (HBO/WO) 559 5.60 10.5 0-100 ISCED6 (Ph.D.) 7 3.43 2.64 1-8 Vocational specificity (2) N=1126 General specificity 342 9.50 15.82 0-124 Vocational specificity 784 9.27 19.32 0-172 Field of study (N=1246)

Gamma and humanities sciences 952 7.13 14.41 0-124

Beta sciences 294 7.91 19.18 0-172

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In table 9 the correlation results of the independent and the control variables on the time-to-first-job is presented. To start, a significant correlation of r = -0.127, p = 0.000 is found for educational level and time-to-first-job. The negative correlation shows that the higher the educational level the shorter the time-to-first-job. This is in line with the results of the previous table and indicates that the first hypothesis will be confirmed.

Further an insignificant correlation of almost zero (r = 0.006, p = 0.850) is found for the second variable vocational specificity. In the previous table the differences between respondents with a general and vocational qualification were small and therefore these results were not surprising. For field of study an insignificant correlation of r = 0.022, p = 0.445 was found. So people with a qualification of a gamma and humanity sciences do not differ in their time-of-first-job compared to those with a qualification of a beta sciences.

Looking at the control variables significant correlations were found for age, foreigner and working experience. For age the positive correlation of r = 0.081, p = 0.001 means that being older leads to a longer time of finding a job after leaving initial education. Further the foreigners are found to have a longer time-to-first-job compared to inhibiters and previous working experiences lead to a shorter time-to-first-job.

Table 9: Correlation analysis on time-of-first-job for educational level, vocational specificity, age, sex, country of birth and working experience

Time-of-first-job Correlation r N (P-value) Educational level -0.127*** 1685 (0.000) Vocation specificity (1) -0.006NS 1126 (0.850) Beta studies (2) 0.022NS 1246 (0.445) Age 0.081*** 1686 (0.001) Male (3) -0.013NS 1686 (0.597) Foreigner (4) 0.109*** 1684 (0.000) Working experience (5) -0.119*** 1686 (0.000)

Note: All reference categories are (1) general specificity (2) gamma and humanity sciences (3 )female (4 ) native (5) no working experiences

Source: EU-LFS 2009

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4.2 Explanatory statistics

The final regression consist of two main analysis. In the first analysis the main effects of the educational level on the time-to-first-job is examined and in the second analysis the main effects of vocational specificity. The effect of the educational level is tested using the ISCED level 1-6 (table 10) and vocational specificity is tested using the subcategories a, b and c of the ISCED levels (table 11).

Further the second variables of vocational specificity, consisting of general specificity and vocational specificity, is not used for the model because the previous results showed no differences on the average time to find a job and no correlation with time-to-first-job was found. The inclusion of the field of study has omitted the effect of the educational qualifications ISCED2a, ISCED2b and ISCED6. This study couldn’t find a statistical or theoretical explanations why they were omitted. Because the previous results also showed no differences in average or correlation for the field of study, there has been decided to not use this variable for the last analysis.

Educational level –– To start with the first model, a constant of 15.32 was found. The constant predicts the time-to-first-job for the reference category namely having a qualification of the elementary school (ISCED1). The main effects predict the effect of educational level on time-to-first-job compared to the reference category. As one can see, all main effects were found significant and negative. Having an ISCED1 is therefore predicted to have the longest time-to-first-job of over fifteen months. The highest effects were found for those with a ISCED level of 4 till 6 and the lowest effect were found for those with a ISCED level 2 till 3.

To see if the effects of the educational levels significantly differ from each other, the same regression has been performed using different educational levels as the reference category. With qualifications of the tertiary education (ISCED5) as the references category, new significant differences were found with a positive effect of around four months from the lower secondary education (ISCED3) and a positive effect of around three months from the higher secondary education (ISCED4). Another noteworthy difference found is its effect -2.32 for those with a Ph.D. qualification (ISCED6) compared to the reference category ISCED5. Having a Ph.D. qualification therefore is predicted to have shortest time to find a job although the exact months of time-to-first-job will differ from the predicted effect because the effect was found highly insignificant.

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