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Determinants of Grades in Maths for Students in Economics

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  • Cappellari, Lorenzo

    () (Catholic University of Milan)

  • Lucifora, Claudio

    () (Catholic University of Milan)

  • Pozzoli, Dario

    () (Department of Economics, Aarhus School of Business)

Abstract

This paper investigates the determinants of grades achieved in mathematics by rst-year students in Economics. We use individual administrative data from 1993 to 2005 to t an educational production function. Our main ndings suggest that good secondary school achievements and the type of school attended are signi cantly associated with maths grades. Ceteris paribus, females typically do better than males. Since students can postpone the exam or repeat it when they fail, we also analyze the determinants of the elapsed time to pass the exam using survival analysis. Modeling simultaneously maths grades and the hazard of passing the exam, we nd that the overall hazard rate of passing the exam is higher for those students who get the higher grades. The longer students wait to take the exam, the less likely they are to obtain high grades

Suggested Citation

  • Cappellari, Lorenzo & Lucifora, Claudio & Pozzoli, Dario, 2009. "Determinants of Grades in Maths for Students in Economics," Working Papers 09-17, University of Aarhus, Aarhus School of Business, Department of Economics.
    • Handle: RePEc:hhs:aareco:2009_017
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    References listed on IDEAS

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    1. McIntosh, Steven & Vignoles, Anna, 2001. "Measuring and Assessing the Impact of Basic Skills on Labour Market Outcomes," Oxford Economic Papers, Oxford University Press, vol. 53(3), pages 453-481, July.
    2. Meyer, Bruce D, 1990. "Unemployment Insurance and Unemployment Spells," Econometrica, Econometric Society, vol. 58(4), pages 757-782, July.
    3. Hanushek, Eric A, 1995. "Interpreting Recent Research on Schooling in Developing Countries," World Bank Research Observer, World Bank Group, vol. 10(2), pages 227-246, August.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Lorenzo Cappellari, 2004. "High school types, academic performance and early labour market outcomes," CHILD Working Papers wp03_04, CHILD - Centre for Household, Income, Labour and Demographic economics - ITALY.
    6. Anne Case & Angus Deaton, 1999. "School Inputs and Educational Outcomes in South Africa," The Quarterly Journal of Economics, Oxford University Press, vol. 114(3), pages 1047-1084.
    7. Massimiliano Bratti & Stefano Staffolani, 2013. "Student Time Allocation and Educational Production Functions," Annals of Economics and Statistics, GENES, issue 111-112, pages 103-140.
    8. Daniele Checchi & Giuseppe Bertola, 2001. "Sorting and private education in Italy," Departmental Working Papers 2001-21, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    9. Francisco L. Rivera-Batiz, 1992. "Quantitative Literacy and the Likelihood of Employment among Young Adults in the United States," Journal of Human Resources, University of Wisconsin Press, vol. 27(2), pages 313-328.
    10. Richard Sabot & John Wakeman-Linn, 1991. "Determinants of Performance in Introductory Courses in Economics and Seven Other Disciplines," Williams Project on the Economics of Higher Education DP-7, Department of Economics, Williams College.
    11. Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460 Elsevier.
    12. McNabb, Robert & Pal, Sarmistha & Sloane, Peter, 2002. "Gender Differences in Educational Attainment: The Case of University Students in England and Wales," Economica, London School of Economics and Political Science, vol. 69(275), pages 481-503, August.
    13. 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).
    14. Durden, Garey C & Ellis, Larry V, 1995. "The Effects of Attendance on Student Learning in Principles of Economics," American Economic Review, American Economic Association, vol. 85(2), pages 343-346, May.
    15. Bishop, John Hillman, 1989. "Is the Test Score Decline Responsible for the Productivity Growth Decline?," American Economic Review, American Economic Association, vol. 79(1), pages 178-197, March.
    16. 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 for the Study of Labor (IZA).
    17. Massimiliano BRATTI & Chiara BROCCOLINI & Stefano STAFFOLANI, 2006. "Is '3+2' Equal to 4? University Reform and Student Academic Performance in Italy," Working Papers 251, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
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    Cited by:

    1. Agar Brugiavini & Carlo Carraro & Matija Kovacic, 2014. "Academic Achievements: Grades versus Duration," Working Papers 2014:13, Department of Economics, University of Venice "Ca' Foscari".
    2. Shira Fano & Paolo Pellizzari, 2015. "The Effects of Facebook Discussions on Academic Performance," Working Papers 2015:28, Department of Economics, University of Venice "Ca' Foscari".

    More about this item

    Keywords

    maths grades; quantile regression; survival analysis;

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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