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The added value of more accurate predictions for school rankings

Author

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  • Schiltz, Fritz
  • Sestito, Paolo
  • Agasisti, Tommaso
  • De Witte, Kristof

Abstract

School rankings based on value-added (VA) estimates are subject to prediction errors, since VA is defined as the difference between predicted and actual performance. We introduce the use of random forest (RF), rooted in the machine learning literature, as a more flexible approach to minimize prediction errors and to improve school rankings. Monte Carlo simulations demonstrate the advantages of this approach. Applying the proposed method to Italian middle school data indicates that school rankings are sensitive to prediction errors, even when extensive controls are added. RF estimates provide a low-cost way to increase the accuracy of predictions, resulting in more informative rankings, and more impact of policy decisions.

Suggested Citation

  • Schiltz, Fritz & Sestito, Paolo & Agasisti, Tommaso & De Witte, Kristof, 2018. "The added value of more accurate predictions for school rankings," Economics of Education Review, Elsevier, vol. 67(C), pages 207-215.
    • Handle: RePEc:eee:ecoedu:v:67:y:2018:i:c:p:207-215
    DOI: 10.1016/j.econedurev.2018.10.011
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    Cited by:

    1. Xiaopeng Wu & Tianshu Xu & Jincheng Zhou, 2022. "Sustainability of Evaluation: The Origin and Development of Value-Added Evaluation from the Global Perspective," Sustainability, MDPI, vol. 14(23), pages 1-13, November.
    2. Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
    3. Carmen Aina & Massimiliano Bratti & Enrico Lippo, 2021. "Ranking high schools using university student performance in Italy," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 38(1), pages 293-321, April.

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    More about this item

    Keywords

    Value-added; School rankings; Machine learning; Monte carlo;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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