IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp2491.html
   My bibliography  Save this paper

Which Factors Determine the Grades of Undergraduate Students in Economics? Some Evidence from Spain

Author

Listed:
  • Dolado, Juan J.

    (Universidad Carlos III de Madrid)

  • Morales, Eduardo

    (Harvard University)

Abstract

This paper analyses the determinants of grades achieved in three core subjects by first-year Economics undergraduate students at Universidad Carlos III de Madrid, over the period 2001-2005. Gender, nationality, type of school, specialization track at high school and the grades at the university entry exam are the key factors we examine. Our main findings are that those students who did a technical track at high school tend to do better in mathematics than those who followed a social sciences degree and, that the latter do not perform significantly better than the former in subjects with less degree of formalism and more economic content. Moreover, students from public schools are predominant in the lower (with social sciences or humanities tracks) and upper (with a technical track) parts of the grade distribution, and females tend to perform better than males.

Suggested Citation

  • Dolado, Juan J. & Morales, Eduardo, 2006. "Which Factors Determine the Grades of Undergraduate Students in Economics? Some Evidence from Spain," IZA Discussion Papers 2491, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp2491
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp2491.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Anne Case & Angus Deaton, 1999. "School Inputs and Educational Outcomes in South Africa," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(3), pages 1047-1084.
    2. Jesse Levin, 2001. "For whom the reductions count: A quantile regression analysis of class size and peer effects on scholastic achievement," Empirical Economics, Springer, vol. 26(1), pages 221-246.
    3. 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.
    4. Hanushek, Eric A, 1995. "Interpreting Recent Research on Schooling in Developing Countries," The World Bank Research Observer, World Bank, vol. 10(2), pages 227-246, August.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    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. Lorenzo Cappellari & Claudio Lucifora & Dario Pozzoli, 2012. "Determinants of grades in maths for students in economics," Education Economics, Taylor & Francis Journals, vol. 20(1), pages 1-17, February.

    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. Juan José Dolado & E. Morales, 2007. "Which Factors Determine Academic Performance of Undergraduate Students in Economics?: Some Spanish Evidence," Working Papers 2007-23, FEDEA.
    2. Giambona, Francesca & Porcu, Mariano, 2015. "Student background determinants of reading achievement in Italy. A quantile regression analysis," International Journal of Educational Development, Elsevier, vol. 44(C), pages 95-107.
    3. 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.
    4. Juan J. Dolado & Eduardo Morales, 2009. "Which factors determine academic performance of Economics freshers? Some Spanish evidence," Investigaciones Economicas, Fundación SEPI, vol. 33(2), pages 179-210, May.
    5. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
    6. Chowdhury, Biplob & Jeyasreedharan, Nagaratnam & Dungey, Mardi, 2018. "Quantile relationships between standard, diffusion and jump betas across Japanese banks," Journal of Asian Economics, Elsevier, vol. 59(C), pages 29-47.
    7. Oka, Tatsushi & Qu, Zhongjun, 2011. "Estimating structural changes in regression quantiles," Journal of Econometrics, Elsevier, vol. 162(2), pages 248-267, June.
    8. Woessmann, Ludger, 2004. "How Equal Are Educational Opportunities? Family Background and Student Achievement in Europe and the United States," IZA Discussion Papers 1284, Institute of Labor Economics (IZA).
    9. Billger, Sherrilyn M., 2007. "Principals as Agents? Investigating Accountability in the Compensation and Performance of School Principals," IZA Discussion Papers 2662, Institute of Labor Economics (IZA).
    10. Fertig, Michael, 2003. "Who's to Blame? The Determinants of German Students' Achievement in the PISA 2000 Study," RWI Discussion Papers 4, RWI - Leibniz-Institut für Wirtschaftsforschung.
    11. Ludger Wossmann, 2005. "The effect heterogeneity of central examinations: evidence from TIMSS, TIMSS-Repeat and PISA," Education Economics, Taylor & Francis Journals, vol. 13(2), pages 143-169.
    12. Carlos Arias & Javier Valbuena & Jose Manuel Garcia, 2021. "The Impact of Secondary Education Choices on Mathematical Performance in University: The Role of Non-Cognitive Skills," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    13. Sheng-Tung Chen & Hsiao-I. Kuo & Chi-Chung Chen, 2012. "Estimating the extreme behaviors of students performance using quantile regression -- evidences from Taiwan," Education Economics, Taylor & Francis Journals, vol. 20(1), pages 93-113, December.
    14. Meligkotsidou, Loukia & Vrontos, Ioannis D. & Vrontos, Spyridon D., 2009. "Quantile regression analysis of hedge fund strategies," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 264-279, March.
    15. Lorenzo Cappellari & Claudio Lucifora & Dario Pozzoli, 2012. "Determinants of grades in maths for students in economics," Education Economics, Taylor & Francis Journals, vol. 20(1), pages 1-17, February.
    16. Liu, Sezhu & Hite, Diane, 2013. "Measuring the Effect of Green Space on Property Value: An Application of the Hedonic Spatial Quantile Regression," 2013 Annual Meeting, February 2-5, 2013, Orlando, Florida 143045, Southern Agricultural Economics Association.
    17. Bouhlila, Donia Smaali, 2015. "The Heyneman–Loxley effect revisited in the Middle East and North Africa: Analysis using TIMSS 2007 database," International Journal of Educational Development, Elsevier, vol. 42(C), pages 85-95.
    18. Juliana Guimarães & Breno Sampaio, 2007. "The Influence Of Family Background And Individual Characteristics On Entrance Tests Scores Of Brazilian University Students," Anais do XXXV Encontro Nacional de Economia [Proceedings of the 35th Brazilian Economics Meeting] 092, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    19. Kristen Capogrossi & Wen You, 2013. "Academic Performance and Childhood Misnourishment: A Quantile Approach," Journal of Family and Economic Issues, Springer, vol. 34(2), pages 141-156, June.
    20. Ludger Woessmann, 2004. "The Effect Heterogeneity of Central Exams: Evidence from TIMSS, TIMSS-Repeat and PISA," CESifo Working Paper Series 1330, CESifo.

    More about this item

    Keywords

    multinomial logit; gender; school type; grade achievement; quantile regressions;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I29 - Health, Education, and Welfare - - Education - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:iza:izadps:dp2491. 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: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.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.