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Which factors determine academic performance of Economics freshers? Some Spanish evidence

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Author Info

  • Juan J. Dolado

    (Universidad Carlos III & CEPR & IZA)

  • Eduardo Morales

    (Harvard University)

Abstract

This paper analyses the impact of several factors potentially affecting academic performance of first-year undergraduates (freshers) in Economics at Universidad Carlos III de Madrid during 2002-2005. Outcomes are the grades obtained in three core subjects which differ substantially in their requirements of math skills. Our main finding is that those students who completed a technical track at high school tend to perform much better in math-intensive subjects than those who followed a social sciences track and that the latter do not perform better in subjects where prior training in economics is bound to be helpful.

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Bibliographic Info

Article provided by Fundación SEPI in its journal Investigaciones Económicas.

Volume (Year): 33 (2009)
Issue (Month): 2 (May)
Pages: 179-210

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Handle: RePEc:iec:inveco:v:33:y:2009:i:2:p:179-210

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Postal: Investigaciones Economicas Fundación SEPI Quintana, 2 (planta 3) 28008 Madrid Spain
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Related research

Keywords: Academic performance; pre-university determinants; selection biases; quantile regressions.;

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References

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  1. Glewwe, Paul & Kremer, Michael, 2006. "Schools, Teachers, and Education Outcomes in Developing Countries," Handbook of the Economics of Education, Elsevier.
  2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  3. Juan José Dolado & E. Morales, 2007. "Which Factors Determine Academic Performance of Undergraduate Students in Economics?: Some Spanish Evidence," Working Papers 2007-23, FEDEA.
  4. Anne Case & Angus Deaton, 1999. "School Inputs And Educational Outcomes In South Africa," The Quarterly Journal of Economics, MIT Press, vol. 114(3), pages 1047-1084, August.
  5. Lorraine Dearden & Javier Ferri & Costas Meghir, 2002. "The Effect Of School Quality On Educational Attainment And Wages," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 1-20, February.
  6. Petra E. Todd & Kenneth I. Wolpin, 2003. "On The Specification and Estimation of The Production Function for Cognitive Achievement," Economic Journal, Royal Economic Society, vol. 113(485), pages F3-F33, February.
  7. Lagerlöf, Johan N.M. & Seltzer, Andrew J, 2008. "The Effects of Remedial Mathematics on the Learning of Economics: Evidence from a Natural Experiment," CEPR Discussion Papers 6895, C.E.P.R. Discussion Papers.
  8. 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.
  9. Smith, Jeremy & Naylor, Robin, 2001. " Determinants of Degree Performance in UK Universities: A Statistical Analysis of the 1993 Student Cohort," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 63(1), pages 29-60, February.
  10. Hanushek, Eric A, 1995. "Interpreting Recent Research on Schooling in Developing Countries," World Bank Research Observer, World Bank Group, vol. 10(2), pages 227-46, August.
  11. Hanushek, Eric A, 1986. "The Economics of Schooling: Production and Efficiency in Public Schools," Journal of Economic Literature, American Economic Association, vol. 24(3), pages 1141-77, September.
  12. Stéphane Bonhomme & Ulrich Sauder, 2009. "Accounting For Unobservables In Comparing Selective And Comprehensive Schooling," Working Papers wp2009_0906, CEMFI.
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Cited by:
  1. Cristina Lopez-Mayan, 2013. "Performance in Post-compulsory Education: Evidence from Vocational and Academic Tracks," Working Papers wpdea1302, Department of Applied Economics at Universitat Autonoma of Barcelona.

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