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How are inequality of opportunity and mean student performance related? A quantile regression approach using PISA data

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  • Hermann, Z.
  • Horn, D.

Abstract

Previous research provided ambiguous results on the association between average student performance and inequality of opportunity measured by the effect of family background on student achievement. In this paper we explore this association distinguishing between inequality of opportunity at the bottom and the top of the score distribution using a two step method. In the first step, we use quantile regression models to estimate the family background effect at different points of the distribution within each country in PISA 2000-2009. In the second step, we analyse the association between these estimates and the mean achievement of countries. Both cross-section and country fixed-effect estimates indicate that while there is no clear pattern for the bottom of the distribution, lower inequality of opportunity at the top of the distribution goes strongly together with higher mean achievement. In other words, countries where family background has a weaker impact on achievement among the most able students tend to perform better. In short, there is indeed a positive association between equality of opportunity and mean student performance, at least for some groups of students.

Suggested Citation

  • Hermann, Z. & Horn, D., 2011. "How are inequality of opportunity and mean student performance related? A quantile regression approach using PISA data," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 11(3).
  • Handle: RePEc:eaa:eerese:v:11:y2011:i:3_2
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    References listed on IDEAS

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    1. James Heckman, 2011. "Policies to foster human capital," Educational Studies, Higher School of Economics, issue 3, pages 73-137.
    2. Gabriela Schütz & Heinrich W. Ursprung & Ludger Wößmann, 2008. "Education Policy and Equality of Opportunity," Kyklos, Wiley Blackwell, vol. 61(2), pages 279-308, May.
    3. Thomas Fuchs & Ludger Wößmann, 2008. "What accounts for international differences in student prformance? A re-examination using PISA data," Studies in Empirical Economics, in: Christian Dustmann & Bernd Fitzenberger & Stephen Machin (ed.), The Economics of Education and Training, pages 209-240, Springer.
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    5. Ludger Wößmann, 2008. "Efficiency and equity of European education and training policies," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 15(2), pages 199-230, April.
    6. Daniel Aaronson & Lisa Barrow & William Sander, 2007. "Teachers and Student Achievement in the Chicago Public High Schools," Journal of Labor Economics, University of Chicago Press, vol. 25, pages 95-135.
    7. Cecilia Garcia-Penalosa & Eve Caroli & Philippe Aghion, 1999. "Inequality and Economic Growth: The Perspective of the New Growth Theories," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1615-1660, December.
    8. Costas Meghir & Mårten Palme, 2005. "Educational Reform, Ability, and Family Background," American Economic Review, American Economic Association, vol. 95(1), pages 414-424, March.
    9. Zoltan Hermann & Daniel Horn, 2011. "How inequality of opportunity and mean student performance are related? - A quantile regression approach using PISA data," CERS-IE WORKING PAPERS 1124, Institute of Economics, Centre for Economic and Regional Studies.
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    Cited by:

    1. John Jerrim & Álvaro Choi, 2013. "The mathematics skills of school children: how does England compare to the high performing east Asian jurisdictions?," Working Papers 2013/12, Institut d'Economia de Barcelona (IEB).
    2. John Jerrim & John Micklewright, 2012. "Socioeconomic gradients in children's cognitive skills: Are cross-country comparisons robust to who reports family background?," DoQSS Working Papers 12-06, Quantitative Social Science - UCL Social Research Institute, University College London.
    3. John Jerrim & Alvaro Choi, 2013. "The mathematics skills of school children: How does England compare to the high performing East Asian jurisdictions?," DoQSS Working Papers 13-03, Quantitative Social Science - UCL Social Research Institute, University College London.
    4. Gabriel Machlica, 2017. "Enhancing skills to boost growth in Hungary," OECD Economics Department Working Papers 1364, OECD Publishing.
    5. Jerrim, J. & John Micklewright, 2013. "GINI DP 65: Socioeconomic gradients in children’s cognitive skills: are cross-country comparisons robust to who reports family background?," GINI Discussion Papers 65, AIAS, Amsterdam Institute for Advanced Labour Studies.
    6. Zlata Bruckauf & Yekaterina Chzhen & UNICEF Innocenti Research Centre, 2016. "Education for All? Measuring inequality of educational outcomes among 15-year-olds across 39 industrialized nations," Papers inwopa843, Innocenti Working Papers.

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    More about this item

    Keywords

    equality of opportunity; educational performance; quantile regression; PISA;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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