IDEAS home Printed from https://ideas.repec.org/a/eaa/eerese/v10y2010i3_8.html
   My bibliography  Save this article

Una Valoracion Del Grado De Segregación Socioeconómica Existente En El Sistema Educativo Español. Un Analisis Por Comunidades Autonómas A Partir De Pisa 2006

Author

Listed:
  • MANCEBÓN TORRUBIA, María Jesús
  • PÉREZ XIMÉNEZ-DE-EMBÚN, Domingo

Abstract

En este artículo se analiza el grado de segregación socioeconómica existente en el sistema educativo español. Para ello se hace uso de los datos suministrados por la tercera evaluación del proyecto PISA llevado a cabo por la OCDE. Se estudia la segregación debida a diferencias entre las características del alumnado que asiste a la enseñanza pública y privada, diferenciando los centros concertados y los no concertados (segregación intersectorial). Asimismo se cuantifica la segregación interna existente en cada uno de los tres sectores educativos (segregación intrasectorial). Los resultados obtenidos apuntan a un grado importante de segregación por motivos socioeconómicos en todas las comunidades autónomas con muestra representativa en PISA 2006. Los alumnos más selectos se concentran mayoritariamente en los centros privados, si bien su reparto entre las diferentes escuelas es bastante heterogéneo, especialmente en la enseñanza privada no concertada. En el sector público, los estudiantes presentan un perfil socioeconómico mucho más homogéneo entre los diversos centros, agrupándose en todos ellos los alumnos pertenecientes a los hogares de menos recursos económicos y culturales y los inmigrantes. This study analyses the degree of socioeconomic segregation in the Spanish educational system. For this purpose, data available from PISA 2006 have been exploited. We identify three different sectors according to ownership and public/private funding: pure private schools, publicly-subsidised private schools and public schools. We examine both inter and intra-sectoral segregation finding empirical evidence that students are not homogenously distributed neither between sectors nor within each sector. Our results point out that more select socioeconomic students are concentrated in private and publicly-subsidised private schools and also account for a very low proportion of immigrants with regard to public schools. Additionally, the comparison of the behaviour between those regions with significant sample sizes in PISA 2006 allows concluding a great homogeneity but some particular segregation patterns in some of these regions.

Suggested Citation

  • MANCEBÓN TORRUBIA, María Jesús & PÉREZ XIMÉNEZ-DE-EMBÚN, Domingo, 2010. "Una Valoracion Del Grado De Segregación Socioeconómica Existente En El Sistema Educativo Español. Un Analisis Por Comunidades Autonómas A Partir De Pisa 2006," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 10(3).
    • Handle: RePEc:eaa:eerese:v:10:y2010:i:3_8
    as

    Download full text from publisher

    File URL: http://www.usc.es/economet/reviews/eers1038.pdf
    Download Restriction: No.
    ---><---

    References listed on IDEAS

    as
    1. D. N. Figlio & J. A. Stone, "undated". "School Choice and Student Performance: Are Private Schools Really Better?," Institute for Research on Poverty Discussion Papers 1141-97, University of Wisconsin Institute for Research on Poverty.
    2. Mª Jesús Mancebón Torrubia & Domingo Pérez-Ximénez de Embún, 2007. "Conciertos educativos y selección académica y social del alumnado," Hacienda Pública Española / Review of Public Economics, IEF, vol. 180(1), pages 77-108, March.
    3. Eric A. Hanushek & Ludger Wössmann, 2006. "Does Educational Tracking Affect Performance and Inequality? Differences- in-Differences Evidence Across Countries," Economic Journal, Royal Economic Society, vol. 116(510), pages 63-76, March.
    4. Ganzeboom, H.B.G. & de Graaf, P.M. & Treiman, D.J. & de Leeuw, J., 1992. "A standard international socio-economic index of occupational status," WORC Paper 92.01.001/1, Tilburg University, Work and Organization Research Centre.
    5. Dee, Thomas S. & Fu, Helen, 2004. "Do charter schools skim students or drain resources?," Economics of Education Review, Elsevier, vol. 23(3), pages 259-271, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Krüger, N., 2011. "The Segmentation of the Argentine Education System: Evidence from PISA 2009," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 11(3).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gabriela Schütz & Heinrich W. Ursprung & Ludger Wößmann, 2008. "Education Policy and Equality of Opportunity," Kyklos, Wiley Blackwell, vol. 61(2), pages 279-308, May.
    2. Parker, Philip D. & Jerrim, John & Schoon, Ingrid & Marsh, Herbert W., 2016. "A multination study of socioeconomic inequality in expectations for progression to higher education: the role of between-school tracking and ability stratification," EconStor Open Access Articles, ZBW - Leibniz Information Centre for Economics, pages 6-32.
    3. M J Mancebón & M A Muñiz, 2008. "Private versus public high schools in Spain: disentangling managerial and programme efficiencies," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(7), pages 892-901, July.
    4. Antonio Di Paolo, 2012. "Parental Education And Family Characteristics: Educational Opportunities Across Cohorts In Italy And Spain," Revista de Economia Aplicada, Universidad de Zaragoza, Departamento de Estructura Economica y Economia Publica, vol. 20(1), pages 119-146, Spring.
    5. Jaap Dronkers & Rolf van der Velden & Allison Dunne, 2012. "Why are migrant students better off in certain types of educational systems or schools than in others? On the effects of educational systems, school composition, track level, parental background, and ," CReAM Discussion Paper Series 1215, Centre for Research and Analysis of Migration (CReAM), Department of Economics, University College London.
    6. Elke Lüdemann, 2011. "Schooling and the Formation of Cognitive and Non-cognitive Outcomes," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 39.
    7. John Jerrim, 2014. "The link between family background and later lifetime income: how does the UK compare to other countries?," DoQSS Working Papers 14-02, Quantitative Social Science - UCL Social Research Institute, University College London.
    8. Giorgio Brunello & Daniele Checchi, 2007. "Does school tracking affect equality of opportunity? New international evidence," Economic Policy, CEPR;CES;MSH, vol. 22, pages 781-861, October.
    9. Dronkers, J. & van der Velden, R.K.W. & Dunne, A., 2011. "The effects of educational systems, school-composition, track-level, parental background and immigrants' origin on the achievement of 15-years old native and immigrant students : a reanalysis of PISA ," ROA Research Memorandum 006, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    10. Gottschling, J. & Hahn, E. & Beam, C.R. & Spinath, F.M. & Carroll, S. & Turkheimer, E., 2019. "Socioeconomic status amplifies genetic effects in middle childhood in a large German twin sample," Intelligence, Elsevier, vol. 72(C), pages 20-27.
    11. Mancebón-Torrubia, María Jesús & Ximénez-de-Embún, Domingo Pérez, 2009. "Spanish publicly-subsidised private schools and equality of school choice," MPRA Paper 21164, University Library of Munich, Germany.
    12. repec:oup:ecpoli:v:22:y:2007:i::p:781-861 is not listed on IDEAS
    13. Maciej Jakubowski, 2015. "Latent variables and propensity score matching: a simulation study with application to data from the Programme for International Student Assessment in Poland," Empirical Economics, Springer, vol. 48(3), pages 1287-1325, May.
    14. Ariga, Kenn & Brunello, Giorgio, 2007. "Does Secondary School Tracking Affect Performance? Evidence from IALS," IZA Discussion Papers 2643, Institute of Labor Economics (IZA).
    15. Emanuela di Gropello, 2006. "Meeting the Challenges of Secondary Education in Latin America and East Asia : Improving Efficiency and Resource Mobilization," World Bank Publications, The World Bank, number 7173, June.
    16. Jörn-Steffen Pischke & Alan Manning, 2006. "Comprehensive versus Selective Schooling in England in Wales: What Do We Know?," NBER Working Papers 12176, National Bureau of Economic Research, Inc.
    17. Clara H. Mulder & Michael Wagner, 2001. "The Connections between Family Formation and First-time Home Ownership in the Context of West Germany and the Netherlands," European Journal of Population, Springer;European Association for Population Studies, vol. 17(2), pages 137-164, June.
    18. Martin Schlotter & Guido Schwerdt & Ludger Woessmann, 2011. "Econometric methods for causal evaluation of education policies and practices: a non-technical guide," Education Economics, Taylor & Francis Journals, vol. 19(2), pages 109-137.
    19. Paul Anand & Jere R. Behrman & Hai-Anh H. Dang & Sam Jones, 2018. "Inequality of opportunity in education: Accounting for the contributions of Sibs, schools and sorting across East Africa," Working Papers 480, ECINEQ, Society for the Study of Economic Inequality.
    20. Mahmut Ozer & Matjaž Perc, 2020. "Dreams and realities of school tracking and vocational education," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-7, December.
    21. van Elk, Roel & van der Steeg, Marc & Webbink, Dinand, 2011. "Does the timing of tracking affect higher education completion?," Economics of Education Review, Elsevier, vol. 30(5), pages 1009-1021, October.

    More about this item

    Keywords

    socioeconomic segregation; private versus public and publicly subsidised private schools; autonomous communities; PISA; equity. segregación socioeconómica; escuelas públicas; privadas y concertadas; rendimiento escolar; comunidades autónomas; PISA; equidad.;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I29 - Health, Education, and Welfare - - Education - - - Other
    • R19 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Other

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eaa:eerese:v:10:y2010:i:3_8. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (M. Carmen Guisan). General contact details of provider: http://www.usc.es/economet/eaa.htm .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.