The added value of more accurate predictions for school rankings

Fritz Schiltz (fritz.schiltz@kuleuven.be), Paolo Sestito (paolo.sestito@bancaditalia.it), Tommaso Agasisti (tommaso.agasisti@polimi.it) and Kristof De Witte
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Fritz Schiltz: University of Leuven
Paolo Sestito: Bank of Italy

No 1209, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

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 a more flexible random forest (RF), rooted in the machine learning literature, to minimize prediction errors and to improve school rankings. Monte Carlo simulations demonstrate the advantages of this approach. Applying the proposed method to data on Italian middle schools 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 better policies.

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: 2019-02
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: The added value of more accurate predictions for school rankings (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:bdi:wptemi:td_1209_19

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