The Role of Education Systems in Preventing NEETs

van Vugt, Lynn; van der Velden, Rolf; Levels, Mark; Brzinsky-Fay, Christian Book Part — Published Version The Role of Education Systems in Preventing NEETs Provided in Cooperation with: WZB Berlin Social Science Center Suggested Citation: van Vugt, Lynn; van der Velden, Rolf; Levels, Mark; Brzinsky-Fay, Christian (2022) : The Role of Education Systems in Preventing NEETs, In: Levels, Mark Brzinsky-Fay, Christian Holmes, Craig Jongbloed, Janine Taki, Hirofumi (Ed.): The Dynamics of Marginalized Youth, ISBN 978-1-003-09665-8, Routledge, London, pp. 205-218, https://doi.org/10.4324/9781003096658-8 This Version is available at: https://hdl.handle.net/10419/251724 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc-nd/4.0 DOI: 10.4324/9781003096658-8 8 The Role of Education Systems in Preventing NEETs Lynn van Vugt, Rolf van der Velden, Mark Levels, and Christian Brzinsky-Fay 8.1 Introduction The education system is one of the crucial institutional arrangements that shape young people’s school-to-work transition (Raffe, 2008). In this chapter, we explore to what extent different aspects of education systems across countries are associated with the likelihood to become long-term NEET. Education teaches young people relevant skills, sort them over various tracks, and prepare them for a specific occupation (Bol and Van der Werfhorst, 2012). However, the way education is organised differs across countries (Shavit and Müller, 1998) and this could also influence the decisions young people make about their participation in education and the labour market (Breen, 2005; Levels et al., 2014). Subsequently, we expect that this will affect the likelihood of becoming long-term NEET. We will look at three different characteristics of education systems. First, the level of stratification of education systems which refers to the extent to which students with different ability levels are enrolled in different educational tracks is based on the age of selection and the number of tracks available within education (Bol and Van der Werfhorst, 2012). We expect that young people are more successful during the school-to-work transition when their education system is highly stratified. Based on the perspective of the job-matching process, we expect that differentiated levels of education inform employers better about the skills of young people (Müller, 2005; Andersen and Van der Werfhorst, 2010) and that their diploma serves as a reliable signal for skills proficiency. Therefore, we assume that young people in these education systems are more likely to find a job (at the right level of education) and subsequently are less likely to become long-term NEET. From the five countries that we included in this book, we see that Germany and the Netherlands have the highest level of stratification, France and Japan the lowest. Second, the level of vocational orientation of an education system could be an important factor as well. Education systems that are teaching specific skills rather than only general skills increase the link between the education system and the labour market (Ryan, 2001). Scholars have long argued that 206 L. van Vugt, R. van der Velden, M. Levels et al. vocationally trained students are more successful in entering the labour market than youth with similar educational attainment but who are educated in programmes that more strongly emphasise general academic skills (Hannan et al., 1996; Shavit and Müller, 1998; Müller and Gangl, 2003; Breen, 2005; Levels et al., 2014). Vocationally trained school-leavers need less occupationally specific training in comparison to students from an academic track. The strong emphasis on occupationally specific skills should make vocationally trained school-leavers more in demand by employers (Ryan, 2001) and therefore we expect that they are less likely to become long-term NEET. Based on the education systems across the five countries in this book, we can see that the Netherlands and Germany have the highest level of vocational orientation, while in Japan this level is relatively low. However, the way the vocational education and training is organised differs across countries. Therefore, it is also important to look at the institutional linkages between education systems and the labour market. The way education systems are providing vocational education and training depends on the combination of school- and work-based education. Countries that incorporate a strong work-based element in their education systems are more likely to let employers determine the curriculum of vocational education and training (Andersen and Van der Werfhorst, 2010) and also influence the size of the output (Culpepper and Finegold, 1999; Thelen, 2004). In this way, employers already know what kind of skills the schoolleaver has before hiring, and this increases the likelihood of higher vocationally educated young people. Therefore, we assume that vocationally educated young people living in countries with high institutional linkages are less likely to become long-term NEET compared to vocationally educated young people living in countries with low levels of institutional linkages. The institutional linkages are highest in Germany, followed by the Netherlands. In Japan and the UK, institutional linkages (dual system) are not available. Based on these expectations, we formulate the next research question: To what extent are different characteristics of education systems associated with the likelihood to become long-term NEET? From the harmonised country analyses, we learned that the probability for experiencing long NEET periods and their negative consequences vary to some extent across social groups and institutional contexts. In other words, it seems that the structure of the education system influences which groups are more or less affected by NEET in their school-to-work transition. The highly and medium-stratified education systems in the Netherlands, Germany, and France seem to produce disadvantages for lower qualified (or early school-leavers) and youth with migrant background, whereas, in the lower-stratified education systems of England and Japan, the disadvantage of these groups is not that predominant. In Japan, we find a strong disadvantage for women because of the strong occupational and family gender division. It seems that stratification is a double-edged sword: on the hand, it enhances The Role of Education Systems in Preventing NEETs 207 the opportunities for insiders, while, on the other hand, it decreases employment chances for those who fall out of the system. The other institutional factor of the education system that seems to play an important role is the degree and form of vocational orientation. The two countries with occupational labour markets (OLMs), i.e. a strong vocational orientation – the Netherlands and Germany – indeed show diverging effects of vocational training: in the Netherlands, it does not protect youth against NEET, but in Germany it does. The difference is in the institutional structure of vocational training: in Germany, the dual system tightly connects learning and working, including clear signals, whereas in the Netherlands, vocational training is mainly school based. In the countries with internal labour markets (ILMs), i.e. without or with only low vocational orientation – namely England and France – vocational training does not provide a secure route into employment. In the case of France, there are still stigmatisation effects existing, which seem to worsen the situation of vocational degree holders. These observations should now be tested in an integrated analysis. We use data from the Program for the International Assessment of Adult Competencies (PIAAC) (OECD, 2013a) to find out to what extent different characteristics of education systems are associated with long-term NEETs. This dataset exists of a large cross-national survey of adults in 33 high- and upper-middle-income countries. For our analyses, we selected young people aged 16–29 in 31 countries. In the first part of the analyses, we compare the five countries that are studied in-depth in the country chapters: France, Germany, Japan, the Netherlands, and the UK via logistic regression analysis. In the second part, we explore the generalisability of findings on approximately 30 OECD countries by using multilevel logistic regression. 8.2 Theory: Understanding differences First, we will look at the level of stratification within a country. This refers to the extent to which students are enrolled in different educational tracks, based on their ability levels. Characteristics of the level of stratification are based on the age of selection and the number of tracks (Bol and Van der Werfhorst, 2012). We expect that in highly stratified education systems, the value of the diploma is more detailed and transparent towards employers which in return will serve as a signal to screen prospective employees (Spence, 1973; Müller and Gangl, 2003; Breen, 2005; Shavit et al., 2007). Therefore, we expect that in countries with higher levels of stratification, young people are less likely to become long-term NEET (Hypothesis 5a). Second, the level of vocational orientation within a country refers to the opportunity for students to continue studying in vocational track during upper secondary education. In countries with a relatively high level of vocational orientation, vocational education offers the lower skilled students an 208 L. van Vugt, R. van der Velden, M. Levels et al. alternative for general education. Furthermore, in these countries, vocationally educated young people are more protected in the labour market (Shavit and Müller, 2000; Ryan, 2001). On the other hand, in countries with lower levels of vocational orientation, young people who have a vocational degree are to a larger extent stigmatised since here the vocational education serves as safety net for young people who do not have the ability to pursue in general education (Shavit and Müller, 2000; Solga, 2002; Iannelli and Raffe, 2007). Therefore, we hypothesise that in countries with higher levels of vocational education, young people are less likely to become long-term NEET (Hypothesis 7). Third, within vocational-oriented countries, education systems differ in the way their vocational education is designed: a combination of school and workplace training (dual system) or a school-based system. In the dual system, there is a closer link between the employers and schools, the so-called institutional linkages (Bol and Van der Werfhorst, 2012). This means that in countries with higher institutional linkages the transition from school to work is better organised since students learn more specific occupational skills that are demanded by employers (Wolbers, 2003; Levels et al., 2014). Moreover, employers are able to help develop the curriculum of vocational education (Andersen and Van der Werfhorst, 2010) and the size of the output (Culpepper and Finegold, 1999; Thelen, 2004). Therefore, we expect that in countries with higher institutional linkages, vocational educated young people are less likely to become long-term NEET (Hypothesis 8). Next to these main effects, we also expect that the institutional characteristics have a different effect for different subgroups of the youth population. For example, in Section 1.4, we hypothesised that youth from lower educational tracks may be more likely long-term NEETs in strongly stratified systems (Hypothesis 5b). This expectation does not seem to be corroborated by the country comparisons: those with lower educational backgrounds are more likely to end up in problematic school-to-work transitions, both in the highly stratified system in Germany and in systems with a relatively lower educational stratification (e.g. Japan and the UK). In this chapter, we further explore whether education systems have a different effect on youth from different educational attainment levels. We also gauge the extent to which education system characteristics interact with gender, to explore whether the differences between men and women we observed in the country chapters vary with educational system characteristics in a meaningful way. 8.3 Data and measurements 8.3.1 PIAAC data For this chapter, we analyse data from the PIAAC, collected in 24 participating countries between August 2011 and March 2012 and in 9 additional countries between April 2014 and March 2015. The survey is designed The Role of Education Systems in Preventing NEETs 209 to provide valid and reliable estimates of adults’ competences in key information-processing skills, to identify proficiency differences between subgroups of the population, to understand development, maintenance and use of skills, and to determine the impact of proficiency levels on life chances. Country samples contain over 5,000 adults between the age of 16 and 65. Young people were interviewed using computer-assisted personal interviews, although pencil-and-paper data collection strategies were also used. Young people were given assessment tests designed to directly measure their general skills. More specifically, these tests measure numerical and literacy skills, and young people’s capacity to solve problems in technology-rich environments. The survey is cross-culturally and crossnationally valid (for detailed information about PIAAC and technical issues, see OECD, 2013a, 2016). To prepare the data for analyses, we selected young people between ages 16 and 29. Data from Russia and Australia were not analysed for technicaladministrative reasons. We also deleted the specific oversample of PISA 2000 survey young people aged 25–27 because these targeted young people were not part of the international target population definition (OECD, 2013a). Finally, we weighted data from Canada, since the original Canadian sample was much larger than the samples in other countries. To weight back, we drew a random sample of 35%. These selections resulted in a total sample of N = 53,776. Next, we deleted cases with missing values on the longterm NEET variable (N = 610). For the analyses, we deleted missing values on the individual-level variables gender, age, education level, numeracy score, migration background, having children and parental education level (N = 302). These selections resulted in a total working sample of N = 52,864 young people aged 16–29 from 30 countries. Depending on the available information about the contextual indicators, the number of observations could differ across analyses. 8.3.2 Measurements The descriptive statistics of the variables are presented in Table 8.1. We describe how the variables are measured below. 8.3.2.1 Dependent variable • Long-term NEET: Based on whether the young people (a) have had paid work, (b) participated in formal education, or (c) participated in non-formal education during last 12 months preceding the survey, we constructed the variable of long-term NEET. Young people who did not participate in any of these activities within the last 12 months preceding the survey are labelled as long-term NEETs. Young people who participated in paid work, formal education, or non-formal education during the last 12 months form the reference category. 210 L. van Vugt, R. van der Velden, M. Levels et al. 8.3.2.2 Country-level variables The indicators on education systems are provided by data from Bol and Van der Werfhorst (2012, 2014). • Stratification: The level of tracking in secondary education is based on the age of first selection, the percentage of the total curriculum in primary and secondary education that is stratified, and the number of tracks (Bol and Van der Werfhorst, 2012, 2014). The variable is standardised by the original authors (mean 0, standard deviation 0.96, range from −1 to 2). A higher score means a more highly stratified education system. Data was missing for Cyprus, Estonia, Lithuania, and Singapore. • Vocational orientation: The proportion of students enrolled in upper secondary vocational programmes (Bol and Van der Werfhorst, 2012, 2014). The variable is standardised by the original authors (mean 0, Table 8.1 Descriptive statistics NMean SD Min Max Dependent variable Long-term NEET 52,864 6.80 0.25 0 1 Country-level variables Stratification 26 0.00 0.96 −1 2 Vocational orientation 27 0.22 0.91 −2 2 Institutional linkages 27 11.69 14.47 048 Individual-level variables Female 52,864 51.20 0.50 0 1 Age Age 16–19 52.864 26.76 0.44 0 1 Age 20–24 52.864 40.38 0.49 0 1 Age 25–29 52.864 32.85 0.47 0 1 Level of education (low = ref.) Low 52.864 26.01 0.44 0 1 Medium 52.864 49.41 0.50 0 1 High 52.864 24.58 0.43 0 1 Numeracy score 52,864 271.16 47.52 39 444 Migration background Native 52.864 88.84 0.31 0 1 First-generation migrant 52.864 7.98 0.27 0 1 Second-generation migrant 52.864 3.18 0.18 0 1 Having children 52,864 15.50 0.36 0 1 Parental education Both lower educated 52.864 16.18 0.37 0 1 At least one medium educated 52.864 46.00 0.50 0 1 At least one high educated 52.864 36.49 0.48 0 1 Missing 52.864 1.33 0.11 0 1 Source: Program for the International Assessment of Adult Competencies (PIAAC) (OECD, 2013a). Continuous variables are standardised before analyses. The Role of Education Systems in Preventing NEETs 211 standard deviation 0.91, range from −2 to 2). A higher score indicates a more strongly vocationally oriented system. Data from New Zealand and Singapore was not available. • Institutional linkages: The proportion of students enrolled in upper secondary vocational education as part of dual systems (Bol and Van der Werfhorst, 2012, 2014). We standardised this variable for analyses (mean 11.69, standard deviation 14.47, range from 0 to 48). A higher score indicates stronger institutional linkages between schools and the labour market. Analyses that include this variable are only based on the sample of young people that obtained vocational degree in International Standard Classification of Education (ISCED) 3–4 as highest education level. Data from Cyprus, Lithuania, and Singapore was not available. 8.3.2.3 Individual-level variables • Female: A dummy signifies whether young people were male or female. Males are the reference category. • Completed education level: Based on a collapsed version of the ISCED 2011, we distinguish three levels: low education (ISCED 0/1/2/3C short), medium education (ISCED 3A/3B/3C long/4A/4B/4C), and high education (ISCED 5A/5B/6). A low education level forms the reference category. • Numeracy scores: PIAAC measures three types of general skills, i.e. literacy skills, numeracy skills, and skills related to problem-solving in technology-rich environments (OECD, 2013b, 2016). In our analyses, we control for numeracy skills. Literacy is defined as ‘as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life.’ (OECD, 2012). After the test, 10 plausible values were generated that represent the respondent’s numeracy skills, we averaged these scores per individual. We standardised this variable before analyses. • Age: We used data from young people aged 16–29 and divided age into three categories (16–19 years, 20–24 years, and 25–29 years); the youngest group is the reference category. • Having children: A dummy distinguishes between young people who have children or not. Stepchildren and children not living in the household are included in this variable. Young people without a child are the reference category. • Migration background: Measured using dummies to distinguish natives, first-generation immigrants (both parents and respondent were foreign born) and second-generation immigrants (both parents foreign born, respondent born in test country). Natives are the reference category. 212 L. van Vugt, R. van der Velden, M. Levels et al. • Parental education: Measured in categories: Both lower-educated (ISCED 0/1/2/3C short), at least one medium-educated parent (ISCED 3A/3B/3C long/4A/4B/4C), at least one high-educated parent (ISCED 5A/5B/6). We included a category ‘missing’ for young people who did not provide information about their parental education. When only information about one parent was present, we used that information. 8.4 Analyses and results In the first part of the analyses, we compare the five countries that are studied in-depth in the country chapters: the Netherlands, Germany, France, the UK, and Japan. For this set of analyses, we use a logistic regression design with a country-cluster approach. Next, we explore generalisability of findings on 30 OECD countries by using multilevel logistic regression techniques that correct standard errors for the hierarchical clustering of young people within countries (Snijders and Bosker, 2012). 8.4.1 Education systems in the Netherlands, Germany, France, the UK, and Japan Tables 8.2–8.4 show the results of the logistic analyses about the relation between education systems and the risk to become long-term NEET across the five countries participating in this book. All tables present odds ratios (ORs) whereas an estimate below 1 means a negative relation; an estimate of 1 or above means a positive relation. Table 8.2 shows the relation between the level of stratification within a country’s education system and the likelihood to become long-term NEET. It shows that if we only include the Netherlands, Germany, France, the UK, and Japan in the analyses, stratification seems to be a significant predictor (OR = 0.461/0.685). The higher the level of stratification within a country, the less likely young people are to become NEET, which is in line with our hypothesis. While the interaction with gender does not seem to show a significant result (Model 3), in Model 4, the interaction with completed level of education is included. Here, we find a significant moderating effect with medium-educated young people and the level of stratification on the likelihood to become long-term NEET (OR = 0.889). 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