Early school-leaving in the Netherlands: the role of family resources, school composition and background characteristics in early school-leaving in lower secondary education

Abstract

This study explains early school-leaving in lower secondary education in the Netherlands, taking into account background characteristics, family resources and school composition factors at the same time. We distinguish four groups of school-leavers: ‘dropouts’ (those without any qualification), those who leave school with a diploma in lower secondary education (‘low qualified’), those who complete apprentice-based tracks (‘apprentices’) and those who continue education and receive a full upper secondary qualification (‘full qualification’). The breakdown into these four groups reveals clear differences in the effects of different factors on the risk of early school-leaving.

Keywords:

• early school-leaving
• human capital theory
• random coefficient model

Notes

1. In 2000, 15.5% of all Dutch 18-to-24-year-olds were considered early school-leavers; by 2006 this rate had decreased to 12.9% (Ministry of Education 2006). This is lower than the average of 25% in all 25 EU countries, but higher than for example in Finland (7.9%) or Sweden (8.6%). In Ireland, the percentage of early school leavers was 12.3% in 2006.

2. Apart from individual and environmental factors, other factors such as the economic climate can also affect early school-leaving, pushing and/or pulling young people from school into the labour market. However we do not discuss these factors in this contribution.

3. Although the separation of some of the factors mentioned is quite artificial in some cases, we used this structure for the sake of clarity.

4. However, this effect of gender is not found in all studies (Barrington and Hendricks 1989).

5. The data collection for VOCL is largely based on registry data of enrolment in subsidised education. Therefore, panel attrition and measurement error are not of major concern.

6. At the school level, analyses were carried out on the number of students in the first year, the size of the municipality, region and denomination. Large schools (over 206 students in year one) were underrepresented in the sample while schools with 56–65 students were slightly overrepresented. The largest municipalities were also underrepresented, as well as the Amsterdam region and the whole of the province of North Holland. This was caused by the relatively large proportion of schools in Amsterdam that refused to participate in the study.

7. At the individual level, analyses were carried out on the representativeness of the sample based on the educational tracks provided in the school, gender, school recommendation, availability of parental data from the parental questionnaire, availability of data on ethnicity, the number of students with special needs, the educational and occupational level of the parents and the participation in school performance tests. These analyses showed that the total number of students in pre-vocational secondary education (VMBO) in the sample was consistent with the total population in the school year 1999/2000. Students with missing data on ethnicity, parental occupation and parental education were shown to have lower scores on the school recommendation test and the scale for school perception, although these differences were not significant.

8. As all private education is excluded from the VOCL data collection, students may in fact have attained an upper secondary level qualification while this was not included in our data. There are reliable figures on the number of students in private education in the Netherlands, although van der Meer and van der Ploeg (2008) estimated that there were about 34 private schools in the Netherlands with about 1000 students overall, including primary education. Therefore we do not consider this shortcoming of our data design to cause bias in our results.

9. This finding is very much in line with school effectiveness research showing a multiplier effect where students in schools with a high concentration of lower track or lower performing students experience an additional negative effect across a range of outcomes.

10. These findings are in line with previous results found in Serbia (Baucal, Pavlovic-Babic, and Willms 2006).