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Las Tutorías Entre Pares y sus efectos en el desempeño de los estudiantes

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  • Maximiliano Machado

    (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía)

Abstract

Tutorías Entre Pares(TEP) is a program employed with the objective of improving the introduction of freshmen on the university system, without focusing on academic topics. Through several data sets and controlling by demographic and academic background characteristics, propensity score matching methods are employed to investigate if the TEP has effects over academic and non-academic dimensions. The results show that such a program reduces the probability of desertion, a dimension over which the program is expected to act. On the other hand, results over academic variables are found, so that the students enrolled in TEP experience a greater number of courses passed and better grades when compared to similar students that did not enrolled. These results are robust to several matching criteria and to OLS estimations, as well as to different populations. According to these findings, mentoring programs that are meant to improve social dimensions of college students can have positive externalities on academic results, which may be an important point to begin the design of educational policies at college level.

Suggested Citation

  • Maximiliano Machado, 2020. "Las Tutorías Entre Pares y sus efectos en el desempeño de los estudiantes," Documentos de Trabajo (working papers) 20-16, Instituto de Economía - IECON.
  • Handle: RePEc:ulr:wpaper:dt-16-20
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    File URL: https://hdl.handle.net/20.500.12008/26894
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    References listed on IDEAS

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    1. Alberto Abadie & Guido W. Imbens, 2016. "Matching on the Estimated Propensity Score," Econometrica, Econometric Society, vol. 84, pages 781-807, March.
    2. Vincent Tinto, 1997. "Classrooms as Communities," The Journal of Higher Education, Taylor & Francis Journals, vol. 68(6), pages 599-623, November.
    3. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    4. Joshua D. Angrist, 1998. "Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants," Econometrica, Econometric Society, vol. 66(2), pages 249-288, March.
    5. Richard Blundell & Lorraine Dearden & Barbara Sianesi, 2003. "Evaluating the impact of education on earnings in the UK: Models, methods and results from the NCDS," IFS Working Papers W03/20, Institute for Fiscal Studies.
    6. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    7. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
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    More about this item

    Keywords

    College Education; Educational Policy; Mentoring; Propensity Score Matching;
    All these keywords.

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other

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