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The added value of more accurate predictions for school rankings

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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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    1. Jesse Rothstein, 2009. "Student Sorting and Bias in Value-Added Estimation: Selection on Observables and Unobservables," Education Finance and Policy, MIT Press, vol. 4(4), pages 537-571, October.
    2. Bruce Sacerdote, 2014. "Experimental and Quasi-Experimental Analysis of Peer Effects: Two Steps Forward?," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 253-272, August.
    3. Joshua D. Angrist & Peter D. Hull & Parag A. Pathak & Christopher R. Walters, 2017. "Erratum to “Leveraging Lotteries for School Value-Added: Testing and Estimation”," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(4), pages 2061-2062.
    4. Nunes, Luis C. & Reis, Ana Balcão & Seabra, Carmo, 2015. "The publication of school rankings: A step toward increased accountability?," Economics of Education Review, Elsevier, vol. 49(C), pages 15-23.
    5. Bertoni, Marco & Brunello, Giorgio & Rocco, Lorenzo, 2013. "When the cat is near, the mice won't play: The effect of external examiners in Italian schools," Journal of Public Economics, Elsevier, vol. 104(C), pages 65-77.
    6. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2014. "Measuring the Impacts of Teachers II: Teacher Value-Added and Student Outcomes in Adulthood," American Economic Review, American Economic Association, vol. 104(9), pages 2633-2679, September.
    7. David J. Deming, 2014. "Using School Choice Lotteries to Test Measures of School Effectiveness," American Economic Review, American Economic Association, vol. 104(5), pages 406-411, May.
    8. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    9. Joshua D. Angrist & Peter D. Hull & Parag A. Pathak & Christopher R. Walters, 2017. "Leveraging Lotteries for School Value-Added: Testing and Estimation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(2), pages 871-919.
    10. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    11. Petra E. Todd & Kenneth I. Wolpin, 2003. "On The Specification and Estimation of The Production Function for Cognitive Achievement," Economic Journal, Royal Economic Society, vol. 113(485), pages 3-33, February.
    12. De Simone, Gianfranco, 2013. "Render unto primary the things which are primary's: Inherited and fresh learning divides in Italian lower secondary education," Economics of Education Review, Elsevier, vol. 35(C), pages 12-23.
    13. Gregory F. Branch & Eric A. Hanushek & Steven G. Rivkin, 2012. "Estimating the Effect of Leaders on Public Sector Productivity: The Case of School Principals," NBER Working Papers 17803, National Bureau of Economic Research, Inc.
    14. Cassandra M. Guarino & Michelle Maxfield & Mark D. Reckase & Paul N. Thompson & Jeffrey M. Wooldridge, 2015. "An Evaluation of Empirical Bayes’s Estimation of Value-Added Teacher Performance Measures," Journal of Educational and Behavioral Statistics, , vol. 40(2), pages 190-222, April.
    15. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    16. Backes, Ben & Cowan, James & Goldhaber, Dan & Koedel, Cory & Miller, Luke C. & Xu, Zeyu, 2018. "The common core conundrum: To what extent should we worry that changes to assessments will affect test-based measures of teacher performance?," Economics of Education Review, Elsevier, vol. 62(C), pages 48-65.
    17. Eric A. Hanushek & Steven G. Rivkin, 2010. "Generalizations about Using Value-Added Measures of Teacher Quality," American Economic Review, American Economic Association, vol. 100(2), pages 267-271, May.
    18. Jason M. Fletcher & Leora I. Horwitz & Elizabeth Bradley, 2014. "Estimating the Value Added of Attending Physicians on Patient Outcomes," NBER Working Papers 20534, National Bureau of Economic Research, Inc.
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    Cited by:

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    2. 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.
    3. 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.

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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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