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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 Tijana
  • Dronkers Jaap

    (METEOR)

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.

Suggested Citation

  • Prokic-Breuer Tijana & Dronkers Jaap, 2012. "The high performance of Dutch and Flemish 15-year-old native pupils: Explaining country differences in math scores between highly stratified educational systems," Research Memorandum 039, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  • Handle: RePEc:unm:umamet:2012039
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    References listed on IDEAS

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    1. Eric A. Hanushek & Ludger Wössmann, 2006. "Does Educational Tracking Affect Performance and Inequality? Differences- in-Differences Evidence Across Countries," Economic Journal, Royal Economic Society, vol. 116(510), pages 63-76, March.
    2. Dronkers, Jaap & Avram, S, 2009. "A cross-national analysis of the relations between school choice and effectiveness differences between private-dependent and public schools," MPRA Paper 23911, University Library of Munich, Germany.
    3. Dronkers, Jaap & van der Velden, Rolf & Dunne, Allison, 2011. "Why are migrant students better off in certain types of educational systems or schools than in others?," MPRA Paper 37261, University Library of Munich, Germany.
    4. Dronkers, Jaap, 2010. "Positive but also negative effects of ethnic diversity in schools on educational performance? An empirical test using cross-national PISA data," MPRA Paper 25598, University Library of Munich, Germany.
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    Cited by:

    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. 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.
    3. 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 for the Study of Labor (IZA).

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    Keywords

    education; training and the labour market;

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