IDEAS home Printed from https://ideas.repec.org/a/ces/ifodre/v26y2019i03p09-13.html
   My bibliography  Save this article

Wodurch lässt sich der Stadt-Land-Unterschied in den Übergangsquoten auf das Gymnasium erklären?

Author

Listed:
  • Julia Sonnenburg

Abstract

Der Anteil der Schüler, der im Anschluss an die Grundschule auf ein Gymnasium wechselt, fällt für den ländlichen Raum weiterhin deutlich geringer aus als in städtischen Regionen. Basierend auf detaillierten Daten zur Bildungsempfehlung von Grundschülern der vierten Klasse untersuche ich für sächsische Gemeinden, welche Einflussfaktoren diesen Stadt-Land-Unterschied erklären können. Die Ergebnisse verdeutlichen, dass insb. die wirtschaftlichen Rahmenbedingungen, in denen Bildungsprozesse stattfinden, zu diesem Unterschied beitragen könnten. Darüber hinaus scheinen schulische Faktoren, wie das Alter der Lehrpersonen, eine wichtige Rolle zu spielen.

Suggested Citation

  • Julia Sonnenburg, 2019. "Wodurch lässt sich der Stadt-Land-Unterschied in den Übergangsquoten auf das Gymnasium erklären?," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 26(03), pages 09-13, June.
  • Handle: RePEc:ces:ifodre:v:26:y:2019:i:03:p:09-13
    as

    Download full text from publisher

    File URL: https://www.ifo.de/DocDL/ifoDD_19-03_09-13_Sonnenburg.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christian Dustmann & Patrick A. Puhani & Uta Schönberg, 2017. "The Long‐term Effects of Early Track Choice," Economic Journal, Royal Economic Society, vol. 127(603), pages 1348-1380, August.
    2. Eide, Eric & Showalter, Mark H., 1998. "The effect of school quality on student performance: A quantile regression approach," Economics Letters, Elsevier, vol. 58(3), pages 345-350, March.
    3. Caroline M. Hoxby, 2000. "The Effects of Class Size on Student Achievement: New Evidence from Population Variation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(4), pages 1239-1285.
    4. Hanushek, Eric A., 2002. "Publicly provided education," Handbook of Public Economics, in: A. J. Auerbach & M. Feldstein (ed.), Handbook of Public Economics, edition 1, volume 4, chapter 30, pages 2045-2141, Elsevier.
    Full references (including those not matched with items on IDEAS)

    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. Ludger Wößmann, 2003. "European education production functions: what makes a difference for student achievement in Europe?," European Economy - Economic Papers 2008 - 2015 190, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    2. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
    3. Corak, Miles & Lauzon, Darren, 2009. "Differences in the distribution of high school achievement: The role of class-size and time-in-term," Economics of Education Review, Elsevier, vol. 28(2), pages 189-198, April.
    4. Ludger Wößmann, 2005. "Educational Production in East Asia: The Impact of Family Background and Schooling Policies on Student Performance," German Economic Review, Verein für Socialpolitik, vol. 6(3), pages 331-353, August.
    5. Cohen-Zada, Danny & Gradstein, Mark & Reuven, Ehud, 2013. "Allocation of students in public schools: Theory and new evidence," Economics of Education Review, Elsevier, vol. 34(C), pages 96-106.
    6. Gerald Eisenkopf & Pascal A. Sulser, 2016. "Randomized controlled trial of teaching methods: Do classroom experiments improve economic education in high schools?," The Journal of Economic Education, Taylor & Francis Journals, vol. 47(3), pages 211-225, July.
    7. Jürges Hendrik & Schneider Kerstin, 2004. "International Differences in Student Achievement: An Economic Perspective," German Economic Review, De Gruyter, vol. 5(3), pages 357-380, August.
    8. Maria De Paola & Michela Ponzo & Vincenzo Scoppa, 2013. "Class size effects on student achievement: heterogeneity across abilities and fields," Education Economics, Taylor & Francis Journals, vol. 21(2), pages 135-153, March.
    9. Fertig, Michael, 2003. "Educational Production, Endogenous Peer Group Formation and Class Composition - Evidence From the PISA 2000 Study," RWI Discussion Papers 2, RWI - Leibniz-Institut für Wirtschaftsforschung.
    10. Argaw, Bethlehem A. & Puhani, Patrick A., 2018. "Does class size matter for school tracking outcomes after elementary school? Quasi-experimental evidence using administrative panel data from Germany," Economics of Education Review, Elsevier, vol. 65(C), pages 48-57.
    11. Graham McKee & Katharine Sims & Steven Rivkin, 2015. "Disruption, learning, and the heterogeneous benefits of smaller classes," Empirical Economics, Springer, vol. 48(3), pages 1267-1286, May.
    12. Ludger Woessmann, 2016. "The Importance of School Systems: Evidence from International Differences in Student Achievement," Journal of Economic Perspectives, American Economic Association, vol. 30(3), pages 3-32, Summer.
    13. Barra, Cristian & Boccia, Marinella, 2019. "“The determinants of students' achievement: a difference between OECD and not OECD countries”," MPRA Paper 92561, University Library of Munich, Germany.
    14. Eric A. Hanushek, 2004. "What if there are no ‘best practices’?," Scottish Journal of Political Economy, Scottish Economic Society, vol. 51(2), pages 156-172, May.
    15. Marcotte, Dave E., 2007. "Schooling and test scores: A mother-natural experiment," Economics of Education Review, Elsevier, vol. 26(5), pages 629-640, October.
    16. Paul Bingley & Vibeke Myrup Jensen & Ian Walker, 2007. "The Effect of School Class Size on Post-Compulsory Education: Some Cost Benefit Analysis," Working Papers 200717, Geary Institute, University College Dublin.
    17. Cristian Barra & Marinella Boccia, 2022. "What matters in educational performance? Evidence from OECD and non-OECD countries," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4335-4394, December.
    18. Lingjie Ma & Roger Koenker, 2004. "Quantile regression methods for recursive structural equation models," CeMMAP working papers 01/04, Institute for Fiscal Studies.
    19. Victor Lavy, 2009. "Performance Pay and Teachers' Effort, Productivity, and Grading Ethics," American Economic Review, American Economic Association, vol. 99(5), pages 1979-2011, December.
    20. Dinand Webbink, 2005. "Causal Effects in Education," Journal of Economic Surveys, Wiley Blackwell, vol. 19(4), pages 535-560, September.

    More about this item

    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:ces:ifodre:v:26:y:2019:i:03:p:09-13. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/ifooode.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.