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Transition from secondary to higher education : a multilevel model for students graduating from technical and vocational secondary education

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  • Mike Smet

Abstract

Mainstream secondary education in Flanders (i.e. the Dutch speaking part of Belgium) is divided into four major education forms : general education, technical education, vocational education and arts education. The focus of this paper is on pupils graduation from technical and vocational education. Although technical education is more oriented towards higher education and vocational education is more labor market oriented, both degrees allow access to higher education and should also prepare students to start working. Despite the distinction between technical and vocational education, a number of similar study fields coexist both in the technical and the vocational form. A first aim of this paper is to investigate whether students from similar study fields in technical and vocational education do have different transition probabilities from secondary to higher education. In addition we will quantify the impact of individual, school and local (labor market) characteristics on the probability of continuing their educational career after having obtained a degree in secondary education. International literature has been examining the impact of determinants of the transition from secondary to higher education. Four main categories of determinants have been distinguished. First, individual characteristics e.g. gender, age, ability and nationality are found to significantly influence the choice of field of study (Ayalon and Yogev, 2005, Benito and Alegre, 2012). Second, the transition choice is found to be highly influenced by family background characteristics such as type of family, number of siblings, education of the parents and family income (Van de Werfhorst et al., 2001, Van de Werfhorst et al., 2003, Ayalon and Yogev, 2005, Nguyen and Taylor, 2003). Third, Nguyen and Taylor (2003) and Benito and Alegre (2012) found the impact of certain secondary school characteristics (e.g. percentage of students from families with a low educational level and school type) to have a significant impact on the transition choices after secondary education. Finally, regional characteristics such as geographic location have been found to play a part in educational achievement and the transition from secondary to tertiary education. For example, higher unemployment levels in the region you live can make you choose for programmes that lead to higher job security (Ayalon and Yogev, 2005, Nguyen and Taylor, 2003, Kauppinen, 2008). Methodologically, the most frequently used techniques to investigate the impact of student, family and school characteristics on transition probabilities are the estimation of (multinomial) probit or logit models (Breen and Jonsson 2000; Lucas 2001; Ayalon and Yogev 2005; Benito and Alegre 2012). Since pupils are nested in schools, the multilevel structure of the data should be accounted for. Therefore a multilevel logistic regression will be used in the empirical part of this paper. The results of various multilevel logistic regressions clearly indicate differences in transition probabilities between students graduating from vocational secondary education versus students graduating from technical secondary education. In addition, a number of individual characteristics (e.g. grade retention an problematic non-attendances) also have a significant impact on transition probabilities. Evidence of the impact of school characteristics and regional characteristics (e.g. local unemployment rate or an index of urbanization) is mixed.

Suggested Citation

  • Mike Smet, 2016. "Transition from secondary to higher education : a multilevel model for students graduating from technical and vocational secondary education," EcoMod2016 9256, EcoMod.
  • Handle: RePEc:ekd:009007:9256
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    References listed on IDEAS

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    1. Anh Ngoc Nguyen & Jim Taylor, 2003. "Post-high school choices: New evidence from a multinomial logit model," Journal of Population Economics, Springer;European Society for Population Economics, vol. 16(2), pages 287-306, May.
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    Keywords

    Belgium; Labor market issues; Labor market issues;

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