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Excellence for all ? Heterogeneity in high-schools' value-added

Author

Listed:
  • Pauline Givord

    (Institut national de la statistique et des études économiques (INSEE), LIEPP - Laboratoire interdisciplinaire d'évaluation des politiques publiques (Sciences Po) - Sciences Po - Sciences Po)

  • Milena Suarez

    (INSEE)

Abstract

This paper presents a new method that goes beyond the measurement of average value-added of schools by measuring whether schools mitigate or intensify grades dispersion among initially similar students. In practice, school value-added is estimated at different levels of final achieve- ments' distribution by quantile regressions with school specific fixed effects. This method is applied using exhaustive data of the 2015 French high-school diploma and controlling for initial achievements and socio-economic background. Results suggest that almost one-sixth of the high schools significantly reduce, or on the contrary increase, the dispersion in final grades which were expected given the initial characteristics of their intake.

Suggested Citation

  • Pauline Givord & Milena Suarez, 2020. "Excellence for all ? Heterogeneity in high-schools' value-added," Working Papers hal-03389176, HAL.
  • Handle: RePEc:hal:wpaper:hal-03389176
    Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-03389176
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    References listed on IDEAS

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    1. Sacerdote, Bruce, 2011. "Peer Effects in Education: How Might They Work, How Big Are They and How Much Do We Know Thus Far?," Handbook of the Economics of Education, in: Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), Handbook of the Economics of Education, edition 1, volume 3, chapter 4, pages 249-277, Elsevier.
    2. A. Bauer & B. Garbinti & S. Georges-Kot, 2018. "Financial Constraints and Self-Employment in France, 1945-2014," Documents de Travail de l'Insee - INSEE Working Papers g2018-08, Institut National de la Statistique et des Etudes Economiques.
    3. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
    4. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    5. Gabrielle Fack & Julien Grenet & Asma Benhenda, 2014. "L’impact des procédures de sectorisation et d’affectation sur la mixité sociale et scolaire dans les lycées d’Île-de-France," PSE Working Papers hal-02464102, HAL.
    6. Elias Walsh & Eric Isenberg, 2013. "How Does a Value-Added Model Compare to the Colorado Growth Model?," Mathematica Policy Research Reports e703eea3252e43d39fee791e5, Mathematica Policy Research.
    7. repec:mpr:mprres:7949 is not listed on IDEAS
    8. Ryan Yeung & Phuong Nguyen-Hoang, 2016. "Endogenous peer effects: Fact or fiction?," The Journal of Educational Research, Taylor & Francis Journals, vol. 109(1), pages 37-49, January.
    9. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    10. Campagne, Benoît & Poissonnier, Aurélien, 2018. "Structural reforms in DSGE models: Output gains but welfare losses," Economic Modelling, Elsevier, vol. 75(C), pages 397-421.
    11. Guarino, Cassandra M. & Reckase, Mark D. & Stacy, Brian & Wooldridge, Jeffrey M., 2014. "A Comparison of Growth Percentile and Value-Added Models of Teacher Performance," IZA Discussion Papers 7973, Institute of Labor Economics (IZA).
    12. Gadi Barlevy & Derek Neal, 2012. "Pay for Percentile," American Economic Review, American Economic Association, vol. 102(5), pages 1805-1831, August.
    13. repec:ran:wpaper:710-1 is not listed on IDEAS
    14. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    15. Irene Castro-Conde & Jacobo Uña-Álvarez, 2015. "Power, FDR and conservativeness of BB-SGoF method," Computational Statistics, Springer, vol. 30(4), pages 1143-1161, December.
    16. Olivier Bargain & Augustin Vicard, 2014. "Le RMI et son successeur le RSA découragent-ils certains jeunes de travailler ? Une analyse sur les jeunes autour de 25 ans," Économie et Statistique, Programme National Persée, vol. 467(1), pages 61-89.
    17. M. Fort, 2012. "Unconditional and Conditional Quantile Treatment Effect: Identification Strategies and Interpretations," Working Papers wp857, Dipartimento Scienze Economiche, Universita' di Bologna.
    18. Brigham R. Frandsen & Lars J. Lefgren, 2018. "Testing Rank Similarity," The Review of Economics and Statistics, MIT Press, vol. 100(1), pages 86-91, March.
    19. Sean F. Reardon & Stephen W. Raudenbush, 2009. "Assumptions of Value-Added Models for Estimating School Effects," Education Finance and Policy, MIT Press, vol. 4(4), pages 492-519, October.
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    More about this item

    Keywords

    school value-added; quantile regression; Student Growth Percentiles;
    All these keywords.

    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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