Rich Country, Poor Chances? How Institutions and Resources Shape School-to-Work Transitions of Disadvantaged Students in Germany
Thursday, 19 July 2018: 18:30
Location: 716B (MTCC SOUTH BUILDING)
Germany is a rich country with a pronounced educational expansion over the past decades. Yet, educational inequality in Germany remains among the highest in industrialized countries. About 8% of a student cohort leaves general schooling without any educational degree. This group is particularly vulnerable at the transition to vocational training and the labor market. Students without any degree come from the lowest track of the stratified regular school system and from special-needs schools. The assignment to these school types differs by student ability, but assignments also vary by region, proximity to schools, and administrative practices. As a result, students with very similar (low) basic competencies are found in both school types. Depending on these institutional contexts, students experience differential support in school and especially in their transitions into the vocational training system and the labor market. In our paper, we analyze the pathways of students with similar cognitive ability: Do comparable school leavers face the same disadvantages at labor market entry, or do students from special-needs schools suffer from additional stigmatization? How effective is the support program for special need students to overcome their low chances of a successful labor market entry? What is the role of social, cognitive and motivational resources (“agency”) besides institutional constraints and support for labor market integration?
We use data from the German National Educational Panel Study (NEPS). Due to an oversampling of low-achieving students, the data sets offer the unique possibility to compare students from special-needs schools with similar students from regular schools. In our analyses, we first employ sequence analysis to illustrate pathways to labor market integration for all low-achieving students. In a second step, we match students from different school types and model their employment status at the end of the observation frame, controlling for the differential support they received.