IDEAS home Printed from https://ideas.repec.org/a/eee/ecoedu/v67y2018icp207-215.html
   My bibliography  Save this article

The added value of more accurate predictions for school rankings

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0272775718301286
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econedurev.2018.10.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    17. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Naven, Matthew, 2019. "Human-Capital Formation During Childhood and Adolescence: Evidence from School Quality and Postsecondary Success in California," MPRA Paper 97716, University Library of Munich, Germany.
    2. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    3. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    4. Nirav Mehta, 2019. "Measuring quality for use in incentive schemes: The case of “shrinkage” estimators," Quantitative Economics, Econometric Society, vol. 10(4), pages 1537-1577, November.
    5. David Blazar, 2018. "Validating Teacher Effects on Students’ Attitudes and Behaviors: Evidence from Random Assignment of Teachers to Students," Education Finance and Policy, MIT Press, vol. 13(3), pages 281-309, Summer.
    6. Andrew McEachin & Allison Atteberry, 2017. "The Impact of Summer Learning Loss on Measures of School Performance," Education Finance and Policy, MIT Press, vol. 12(4), pages 468-491, Fall.
    7. Araujo P., Maria Daniela & Quis, Johanna Sophie, 2021. "Parents can tell! Evidence on classroom quality differences in German primary schools," BERG Working Paper Series 172, Bamberg University, Bamberg Economic Research Group.
    8. Araujo P., María Daniela & Quis, Johanna Sophie, 2021. "Teacher Effects in Germany: Evidence from Elementary School," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242457, Verein für Socialpolitik / German Economic Association.
    9. Ingo E. Isphording & Ulf Zölitz, 2020. "The value of a peer," ECON - Working Papers 342, Department of Economics - University of Zurich.
    10. Canales, Andrea & Maldonado, Luis, 2018. "Teacher quality and student achievement in Chile: Linking teachers' contribution and observable characteristics," International Journal of Educational Development, Elsevier, vol. 60(C), pages 33-50.
    11. Hinnerich, Björn Tyrefors & Vlachos, Jonas, 2017. "The impact of upper-secondary voucher school attendance on student achievement. Swedish evidence using external and internal evaluations," Labour Economics, Elsevier, vol. 47(C), pages 1-14.
    12. Bruhn, Jesse & Imberman, Scott & Winters, Marcus, 2022. "Regulatory arbitrage in teacher hiring and retention: Evidence from Massachusetts Charter Schools," Journal of Public Economics, Elsevier, vol. 215(C).
    13. Christian Posso & Jorge Tamayo & Arlen Guarin & Estefania Saravia, 2024. "Luck of the Draw: The Causal Effect of Physicians on Birth Outcomes," Borradores de Economia 1269, Banco de la Republica de Colombia.
    14. Julie Berry Cullen & Cory Koedel & Eric Parsons, 2021. "The Compositional Effect of Rigorous Teacher Evaluation on Workforce Quality," Education Finance and Policy, MIT Press, vol. 16(1), pages 7-41, Winter.
    15. Araujo P., Maria Daniela & Heineck, Guido & Cruz Aguayo, Yyannú, 2020. "Does test-based teacher recruitment work in the developing world? Experimental evidence from Ecuador," BERG Working Paper Series 165, Bamberg University, Bamberg Economic Research Group.
    16. Koedel Cory & Leatherman Rebecca & Parsons Eric, 2012. "Test Measurement Error and Inference from Value-Added Models," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 12(1), pages 1-37, November.
    17. Stacy, Brian, 2014. "Ranking Teachers when Teacher Value-Added is Heterogeneous Across Students," EconStor Preprints 104743, ZBW - Leibniz Information Centre for Economics.
    18. Goel, Deepti & Barooah, Bidisha, 2018. "Drivers of Student Performance: Evidence from Higher Secondary Public Schools in Delhi," GLO Discussion Paper Series 231, Global Labor Organization (GLO).
    19. Stacy, Brian & Guarino, Cassandra & Wooldridge, Jeffrey, 2018. "Does the precision and stability of value-added estimates of teacher performance depend on the types of students they serve?," Economics of Education Review, Elsevier, vol. 64(C), pages 50-74.
    20. Nirav Mehta, 2019. "Measuring quality for use in incentive schemes: The case of “shrinkage” estimators," Quantitative Economics, Econometric Society, vol. 10(4), pages 1537-1577, November.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecoedu:v:67:y:2018:i:c:p:207-215. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/econedurev .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.