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Re-testing PISA Students One Year Later: On School Value Added Estimation Using OECD-PISA

Author

Listed:
  • Bratti, Massimiliano

    () (University of Milan)

  • Checchi, Daniele

    () (University of Milan)

Abstract

Thanks to the effort of two local educational authorities, in two regions of North Italy (Valle d'Aosta and the autonomous province of Trento) the PISA 2009 test was re-administered to the same students one year later. This paper is the first to analyse in the OECD-PISA context the potential advantages of re-testing the same students in order to provide better measures of schools' contributions to student achievement. We show that while cross-sectional measures of school value added based on PISA student literacy, which measures "knowledge for life", tend to be very volatile over time whenever there is a high year-to-year attrition in the student population, longitudinal measures of school value added are very robust to student attrition (even without controlling for sample selection). Moreover, persistence in individual test scores tends to be higher in highly "selective" (i.e. high drop-out) school environments.

Suggested Citation

  • Bratti, Massimiliano & Checchi, Daniele, 2013. "Re-testing PISA Students One Year Later: On School Value Added Estimation Using OECD-PISA," IZA Discussion Papers 7722, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp7722
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    References listed on IDEAS

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    1. Angrist, Joshua D., 1997. "Conditional independence in sample selection models," Economics Letters, Elsevier, vol. 54(2), pages 103-112, February.
    2. Jesse Rothstein, 2009. "Student Sorting and Bias in Value-Added Estimation: Selection on Observables and Unobservables," Education Finance and Policy, MIT Press, vol. 4(4), pages 537-571, October.
    3. Bratti, Massimiliano & Checchi, Daniele & Filippin, Antonio, 2007. "Territorial Differences in Italian Students’ Mathematical Competencies: Evidence from PISA 2003," IZA Discussion Papers 2603, Institute for the Study of Labor (IZA).
    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 3-33, February.
    5. Puhani, Patrick A, 2000. " The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
    6. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    7. Roland Bénabou, 1996. "Equity and Efficiency in Human Capital Investment: The Local Connection," Review of Economic Studies, Oxford University Press, vol. 63(2), pages 237-264.
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    Cited by:

    1. Richard B. Freeman & Martina Viarengo, 2014. "School and family effects on educational outcomes across countries," Economic Policy, CEPR;CES;MSH, vol. 29(79), pages 395-446, July.
    2. Enrico Conti & Silvia Duranti & Carla Rampichini & Nicola Sciclone, 2015. "Quanto conta l’effetto scuola nel ciclo primario? L’efficacia delle istituzioni scolastiche in Toscana," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2015(3), pages 59-84.

    More about this item

    Keywords

    Italy; OECD-PISA; school value added; student attrition;

    JEL classification:

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

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