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Higher Education Value Added Using Multiple Outcomes

Author

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  • Joniada MILLA
  • Ernesto SAN MARTIN
  • Sébastien VAN BELLEGEM

Abstract

We build a multidimensional value added model to analyze jointly the test scores on several outcomes. Using a unique Colombian data set on higher education within a seemingly unrelated regression equations (SURE) framework we estimate school outcome specific value added indicators. These are used to measure the relative contribution of the school on a certain outcome, which may serve as an internal accountability measure. Apart from the evident estimation efficiency gains, a joint value added analysis is preferable to the unidimensional one. First, unless modeled in a multidimensional framework, the comparison of value added estimates for different outcomes within a school is not well defined; our model circumvents this issue. Second, even in the case of a separate major field of study analysis there still exists unobserved heterogeneity due to institutional diversity. This makes it more compelling to employ a rich set of outcomes in computing value added indicators. In the end, we aggregate the outcome-specific value added estimates to produce a composite value added index that reflects the combined value added contribution of all the subjects for each school.
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Suggested Citation

  • Joniada MILLA & Ernesto SAN MARTIN & Sébastien VAN BELLEGEM, 2016. "Higher Education Value Added Using Multiple Outcomes," LIDAM Reprints CORE 2757, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:2757
    Note: In : Journal of Educational Measurement, 53, 368-400, 2016
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    References listed on IDEAS

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    1. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2014. "Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates," American Economic Review, American Economic Association, vol. 104(9), pages 2593-2632, September.
    2. Michael David Bates & Katherine E. Castellano & Sophia Rabe-Hesketh & Anders Skrondal, 2014. "Handling Correlations Between Covariates and Random Slopes in Multilevel Models," Journal of Educational and Behavioral Statistics, , vol. 39(6), pages 524-549, December.
    3. Andrew Bacher-Hicks & Thomas J. Kane & Douglas O. Staiger, 2014. "Validating Teacher Effect Estimates Using Changes in Teacher Assignments in Los Angeles," NBER Working Papers 20657, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Garritt L. Page & Ernesto San Martín & Javiera Orellana & Jorge González, 2017. "Exploring complete school effectiveness via quantile value added," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 315-340, January.
    2. Cláudia Lessa & Arnaldo Coelho, 2024. "Building Trust in Higher Education Institutions: Using Congruence to Overcome Scepticism and Increase Credibility, Reputation, and Student Employability Through CSR," Corporate Reputation Review, Palgrave Macmillan, vol. 27(1), pages 18-32, February.

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    More about this item

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • A22 - General Economics and Teaching - - Economic Education and Teaching of Economics - - - Undergraduate
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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