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PISA : what makes the difference?

  • Andreas Ammermüller

    ()

    (Centre for European Economic Research)

The huge difference in the level and variance of student performance in the 2000 PISA study between Finland and Germany motivates this paper. It analyses why Finnish students performed so much better by estimating educational production functions for both countries. The difference in the reading proficiency scores is assigned to different effects, using Oaxaca-Blinder and Juhn-Murphy-Pierce decomposition techniques. The analysis shows that German students have on average a more favorable background except for the lowest deciles, but experience much lower returns to these background characteristics in terms of test scores than Finnish students. The results imply that early streaming in Germany penalizes students in lower school types and leads to a greater inequality of educational achievement. It remains unclear, however, if this can be attributed to the effect of school types per se or student background and innate ability that determine the allocation process of students into school types. Overall, the variation in test scores can be explained much better by the observable characteristics in Germany than in Finland.

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Paper provided by Research Group Heterogeneous Labor, University of Konstanz/ZEW Mannheim in its series Working Papers of the Research Group Heterogenous Labor with number 04-07.

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Length: 32 pages
Date of creation: 08 Mar 2004
Date of revision:
Handle: RePEc:knz:hetero:0407
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  1. Bishop, John H. & Wößmann, Ludger, 2004. "Institutional effects in a simple model of educational production," Munich Reprints in Economics 20279, University of Munich, Department of Economics.
  2. Martin R. West & Ludger Wößmann, 2003. "Which School Systems Sort Weaker Students into Smaller Classes? International Evidence," Kiel Working Papers 1145, Kiel Institute for the World Economy.
  3. Blau, Francine D & Kahn, Lawrence M, 1992. "The Gender Earnings Gap: Learning from International Comparisons," American Economic Review, American Economic Association, vol. 82(2), pages 533-38, May.
  4. Petra E. Todd & Kenneth I. Wolpin, 2003. "On The Specification and Estimation of The Production Function for Cognitive Achievement," Economic Journal, Royal Economic Society, vol. 113(485), pages F3-F33, February.
  5. Hanushek, Eric A. & Luque, Javier A., 2003. "Efficiency and equity in schools around the world," Economics of Education Review, Elsevier, vol. 22(5), pages 481-502, October.
  6. Alan S. Blinder, 1973. "Wage Discrimination: Reduced Form and Structural Estimates," Journal of Human Resources, University of Wisconsin Press, vol. 8(4), pages 436-455.
  7. Lauer, Charlotte, 2000. "Gender wage gap in West Germany: how far do gender differences in human capital matter?," ZEW Discussion Papers 00-07, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  8. Oaxaca, Ronald, 1973. "Male-Female Wage Differentials in Urban Labor Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(3), pages 693-709, October.
  9. Juhn, Chinhui & Murphy, Kevin M & Pierce, Brooks, 1993. "Wage Inequality and the Rise in Returns to Skill," Journal of Political Economy, University of Chicago Press, vol. 101(3), pages 410-42, June.
  10. Ben Jann, 2005. "Standard Errors for the Blinder-Oaxaca Decomposition," German Stata Users' Group Meetings 2005 03, Stata Users Group.
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