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Differences in motivations and academic achievement

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  • Luis Gamboa
  • Mauricio Rodríguez
  • Andrés García

Abstract

This paper provides new evidence on the effect of pupils’ self-motivation on academic achievement in science across countries. By using the OECD´s Programme for International Student Assessment 2006 (PISA 2006) test, we find that self-motivation has a positive effect on students’ performance. Instrumental Variables Quantile Regression is used to analyze the existence of different estimated coefficients over the scores distribution, allowing us to deal with the potential endogeneity of self-motivation. We find that the impact of intrinsic motivation on academic performance depends on the pupil’s score. Our findings support the importance of designing focalized programs for different populations that foster their motivation towards learning.

Suggested Citation

  • Luis Gamboa & Mauricio Rodríguez & Andrés García, 2013. "Differences in motivations and academic achievement," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 78, pages 9-44.
    • Handle: RePEc:lde:journl:y:2013:i:78:p:9-44
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    References listed on IDEAS

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    1. Samer Al-Samarrai, 2006. "Achieving education for all: how much does money matter?," Journal of International Development, John Wiley & Sons, Ltd., vol. 18(2), pages 179-206.
    2. Lee, Jong-Wha & Barro, Robert J, 2001. "Schooling Quality in a Cross-Section of Countries," Economica, London School of Economics and Political Science, vol. 68(272), pages 465-488, November.
    3. Bruce Sacerdote, 2002. "The Nature and Nurture of Economic Outcomes," American Economic Review, American Economic Association, vol. 92(2), pages 344-348, May.
    4. Bishop, John, 2006. "Drinking from the Fountain of Knowledge: Student Incentive to Study and Learn - Externalities, Information Problems and Peer Pressure," Handbook of the Economics of Education, in: Erik Hanushek & F. Welch (ed.), Handbook of the Economics of Education, edition 1, volume 2, chapter 15, pages 909-944, Elsevier.
    5. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    6. Sula, Ozan, 2008. "Demand for International Reserves: A Quantile Regression Approach," MPRA Paper 11680, University Library of Munich, Germany.
    7. Edward P. Lazear, 2001. "Educational Production," The Quarterly Journal of Economics, Oxford University Press, vol. 116(3), pages 777-803.
    8. 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.
    9. Boissiere, M & Knight, J B & Sabot, R H, 1985. "Earnings, Schooling, Ability, and Cognitive Skills," American Economic Review, American Economic Association, vol. 75(5), pages 1016-1030, December.
    10. Eric A. Hanushek, 1998. "Conclusions and controversies about the effectiveness of school resources," Economic Policy Review, Federal Reserve Bank of New York, vol. 4(Mar), pages 11-27.
    11. Ludger Wößmann, 2003. "Schooling Resources, Educational Institutions and Student Performance: the International Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(2), pages 117-170, May.
    12. Caroline Hoxby, 2000. "Peer Effects in the Classroom: Learning from Gender and Race Variation," NBER Working Papers 7867, National Bureau of Economic Research, Inc.
    13. Bishop, John, 1992. "The impact of academic competencies on wages, unemployment, and job performance," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 37(1), pages 127-194, December.
    14. Barrera-Osorio, Felipe & Linden, Leigh L., 2009. "The use and misuse of computers in education : evidence from a randomized experiment in Colombia," Policy Research Working Paper Series 4836, The World Bank.
    15. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    16. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2007. "Inference approaches for instrumental variable quantile regression," Economics Letters, Elsevier, vol. 95(2), pages 272-277, May.
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    Cited by:

    1. José M. Cordero & Víctor Cristóbal & Daniel Santín, 2018. "Causal Inference On Education Policies: A Survey Of Empirical Studies Using Pisa, Timss And Pirls," Journal of Economic Surveys, Wiley Blackwell, vol. 32(3), pages 878-915, July.
    2. World Bank Group, 2016. "Education Sector Public Expenditure Tracking and Service Delivery Survey in Zambia," World Bank Publications - Reports 23884, The World Bank Group.

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    More about this item

    Keywords

    intrinsic motivations; education; ICTs; science;
    All these keywords.

    JEL classification:

    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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