The added value of more accurate predictions for school rankings

Fritz Schiltz, Paolo Sestito, Tommaso Agasisti () and Kristof De Witte

Economics of Education Review, 2018, vol. 67, issue C, 207-215

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.

Keywords: Value-added; School rankings; Machine learning; Monte carlo (search for similar items in EconPapers)
JEL-codes: C50 I21 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: The added value of more accurate predictions for school rankings (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecoedu:v:67:y:2018:i:c:p:207-215

DOI: 10.1016/j.econedurev.2018.10.011

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