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The high performance of Dutch and Flemish 15-year-old native pupils: explaining country differences in math scores between highly stratified educational systems

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  • Prokic-Breuer, T.
  • Dronkers, J.

    (Macro, International & Labour Economics, Externe publicaties SBE)

Abstract

This paper aims to explain the high scores of 15-year-old native pupils in the Netherlands andFlanders by comparing them with the scores of pupils in countries with the same highly stratifiededucational system. Therefore, we compare only the educational performance of 15-year-old pupilsfrom the following regions: the Netherlands, Flanders, Wallonia, the German Länder, the SwissGerman cantons, and Austria. We use the data from the general Program for International PupilAssessment (PISA) 2006 together with the specific PISA data of Germany and Switzerland also from2006. We apply a multilevel model that takes into account the individual-, curriculum-, andsystem-level features in these highly stratified educational systems. The high scores of the Dutchpupils can be explained by the size of the Netherlands’ vocational sector. The high Flemish scorescan be only partly explained by the high curriculum mobility (as indicated by the lowest level ofentrance selection). Central exit exams are not a good explanation of the high Dutch scores.Despite being limited to highly stratified systems, we still find educational policies andarrangements to have significant effects on the educational performance of pupils.
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Suggested Citation

  • Prokic-Breuer, T. & Dronkers, J., 2012. "The high performance of Dutch and Flemish 15-year-old native pupils: explaining country differences in math scores between highly stratified educational systems," ROA Research Memorandum 008, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    • Handle: RePEc:unm:umaror:2012008
    DOI: 10.26481/umaror.2012008
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    1. Dronkers, J., 2014. "Parental background, early scholastic ability, the allocation into secondary school tracks and language skills at the age of 15 years in a highly differentiated system: a test of the contradictions be," ROA Technical Report 001, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    2. Gert-Jan Veerman & Jaap Dronkers, 2013. "Ethnic composition of schools and school performances in secondary education of Turkish migrant students in 7 countries and 19 European educational systems," RF Berlin - CReAM Discussion Paper Series 1314, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).
    3. Ã lvaro Choi & John Jerrim, 2015. "The use (and misuse) of PISA in guiding policy reform: the case of Spain?," DoQSS Working Papers 15-04, Quantitative Social Science - UCL Social Research Institute, University College London.
    4. Álvaro Choi & John Jerrim, 2015. "The use (and misuse) of Pisa in guiding policy reform: the case of Spain," Working Papers 2015/6, Institut d'Economia de Barcelona (IEB).
    5. Verhagen, Mark D., 2021. "Prediction in Educational Research: An Application to the Study of Teacher Bias," SocArXiv y6mnb, Center for Open Science.
    6. Stijn Baert & Frank W. Heiland & Sanders Korenman, 2016. "Native-Immigrant Gaps in Educational and School-to-Work Transitions in the 2nd Generation: The Role of Gender and Ethnicity," De Economist, Springer, vol. 164(2), pages 159-186, June.
    7. Baert, Stijn & Heiland, Frank & Korenman, Sanders, 2014. "Native-Immigrant Gaps in Educational and School-to-Work Transitions in the Second Generation: The Role of Gender and Ethnicity," IZA Discussion Papers 8752, Institute of Labor Economics (IZA).

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