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Peer Heterogeneity, Parental Background and Tracking: Evidence from PISA 2006



The empirical literature using large international students’ assessments tends to neglect the role of school composition variables in order not to incur in a misidentification of peer effects. However, this leads to an error of higher logical type since the learning environment crucially depends on peers’ family background and on peer heterogeneity. In this paper, using PISA 2006, we show how peer heterogeneity is a key determinant of student attainment and of opportunity equalization. Interestingly, the effect of school compositional variables differs depending on the country tracking policy: peer heterogeneity reduces efficiency in comprehensive systems whereas it has a non-linear impact in early-tracking ones. In turn, linear peer effects are larger in early-tracking systems. Besides, higher heterogeneity tends to equalize student differences related to family background. Results do not change in school- and student-level regressions suggesting that the impact of heterogeneity is correctly identified. Results are also robust when we add school-level dummies and several controls correlated with the school choice to alleviate the selectivity bias of linear peer effects.

Suggested Citation

  • Michele Raitano & Francesco Vona, 2010. "Peer Heterogeneity, Parental Background and Tracking: Evidence from PISA 2006," Documents de Travail de l'OFCE 2010-23, Observatoire Francais des Conjonctures Economiques (OFCE).
  • Handle: RePEc:fce:doctra:1023

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    Cited by:

    1. Marchionni, Mariana & Vazquez, Emmanuel & Pinto, Florencia, 2012. "Desigualdad educativa en la Argentina. Análisis en base a los datos PISA 2009
      [Education Inequality in Argentina. An analysis based on PISA 2009 data]
      ," MPRA Paper 56420, University Library of Munich, Germany.
    2. Francesco Vona, 2011. "Does the Expansion of Higher Education Reduce Educational Inequality? Evidence from 12 European Countries," Documents de Travail de l'OFCE 2011-12, Observatoire Francais des Conjonctures Economiques (OFCE).
    3. Marchionni, Mariana & Pinto, Florencia & Vazquez, Emmanuel, 2013. "Determinantes de la desigualdad en el desempeño educativo en la Argentina
      [Determinants of the inequality in PISA test scores in Argentina]
      ," MPRA Paper 56421, University Library of Munich, Germany.
    4. Laura Cavalli & Alessandro Bucciol & Paolo Pertile & Veronica Polin & Nicola Sartor & Alessandro Sommacal, 2012. "Modelling life-course decisions for the analysis of interpersonal and intrapersonal redistribution," Working Papers 25/2012, University of Verona, Department of Economics.
    5. Marina Murat, 2011. "Do immigrant students succeed? Evidence from Italy and France based on PISA 2006," Department of Economics 0670, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    6. Agasisti, Tommaso & Cordero-Ferrera, Jose M., 2013. "Educational disparities across regions: A multilevel analysis for Italy and Spain," Journal of Policy Modeling, Elsevier, vol. 35(6), pages 1079-1102.
    7. Marina Murat & Davide Ferrari & Patrizio Frederic, 2012. "Immigrant students and educational systems. Cross-country evidence from PISA 2006," Department of Economics 0683, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    8. Murat Marina, 2012. "Do Immigrant Students Succeed? Evidence from Italy and France," Global Economy Journal, De Gruyter, vol. 12(3), pages 1-22, September.

    More about this item


    peer heterogeneity; peer effects; schooling tracking; educational production function; equality of opportunities.;

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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