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Academic achievement in sciences: the role of preferences and educative assets

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  • Luis Fernando Gamboa
  • Mauricio Rodríguez-Acosta
  • Andrés Felipe García-Suaza

Abstract

This paper provides new evidence on the effect of pupil´s self-motivation andacademic assets allocation on the academic achievement in sciences acrosscountries. By using the Programme for International Student Assessment 2006 (PISA2006) test we find that both explanatory variables have a positive effect onstudent´s performance. Self-motivation is measured through an instrumentthat allows us to avoid possible endogeneity problems. Quantile regression isused for analyzing the existence of different estimated coefficients over thedistribution. It is found that both variables have different effect on academicperformance depending on the pupil´s score. These findings support theimportance of designing focalized programs for different populations,especially in terms of access to information and communication technologiessuch as internet.

Suggested Citation

  • Luis Fernando Gamboa & Mauricio Rodríguez-Acosta & Andrés Felipe García-Suaza, 2010. "Academic achievement in sciences: the role of preferences and educative assets," Documentos de Trabajo 006701, Universidad del Rosario.
    • Handle: RePEc:col:000092:006701
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    File URL: http://repository.urosario.edu.co/bitstream/handle/10336/11008/6701.pdf
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    References listed on IDEAS

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    Cited by:

    1. Paul Rodríguez-Lesmes & José D. Trujillo & Daniel Valderrama, 2015. "Are Public Libraries Improving Quality of Education? When the Provision of Public Goods is not Enough," Revista Desarrollo y Sociedad, Universidad de los Andes – Facultad de Economía – CEDE, December.
    2. Paul Rodríguez Lesmes & José Trujillo & Daniel Valderrama, 2013. "Más allá de la infraestructura: el impacto de las bibliotecas públicas en la calidad de la educación," Documentos de Trabajo 010499, Universidad del Rosario.

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    More about this item

    Keywords

    PISA; self-motivation; academic assets; academic achievement; Quantile regression;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy

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