The added value of more accurate predictions for school rankings

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.
Original languageEnglish
Pages (from-to)207-215
Number of pages9
JournalEconomics of Education Review
Publication statusPublished - 1 Dec 2018


  • Value-added
  • School rankings
  • Machine learning
  • Monte carlo

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