IDEAS home Printed from https://ideas.repec.org/p/tor/tecipa/tecipa-716.html
   My bibliography  Save this paper

A Nonparametric Approach for Studying Teacher Impacts

Author

Listed:
  • Mike Gilraine
  • Jiaying Gu
  • Robert McMillan

Abstract

We propose a nonparametric approach for studying the impacts of teachers, built around the distribution of unobserved teacher value-added. Rather than assuming this distribution is normal (as standard), we show it is nonparametrically identified and can be feasibly estimated. The distribution is central to a new nonparametric estimator for individual teacher value-added that we present, and allows us to compute new metrics for assessing teacher-related policies. Simulations indicate our nonparametric approach performs very well, even in moderately-sized samples. We also show applying our approach in practice can make a significant difference to teacher-relevant policy calculations, compared with widely-used parametric estimates.

Suggested Citation

  • Mike Gilraine & Jiaying Gu & Robert McMillan, 2022. "A Nonparametric Approach for Studying Teacher Impacts," Working Papers tecipa-716, University of Toronto, Department of Economics.
    • Handle: RePEc:tor:tecipa:tecipa-716
    as

    Download full text from publisher

    File URL: https://www.economics.utoronto.ca/public/workingPapers/tecipa-716.pdf
    File Function: Main Text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Heckman, James & Singer, Burton, 1984. "A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data," Econometrica, Econometric Society, vol. 52(2), pages 271-320, March.
    2. Alberto Abadie & Maximilian Kasy, 2019. "Choosing Among Regularized Estimators in Empirical Economics: The Risk of Machine Learning," The Review of Economics and Statistics, MIT Press, vol. 101(5), pages 743-762, December.
    3. Editors, 2016. "16 and all that," Stata Journal, StataCorp LP, vol. 16(1), pages 3-4, March.
    4. Jonah E. Rockoff, 2004. "The Impact of Individual Teachers on Student Achievement: Evidence from Panel Data," American Economic Review, American Economic Association, vol. 94(2), pages 247-252, May.
    5. Thomas J. Kane & Douglas O. Staiger, 2008. "Estimating Teacher Impacts on Student Achievement: An Experimental Evaluation," NBER Working Papers 14607, National Bureau of Economic Research, Inc.
    6. Amitabh Chandra & Amy Finkelstein & Adam Sacarny & Chad Syverson, 2016. "Health Care Exceptionalism? Performance and Allocation in the US Health Care Sector," American Economic Review, American Economic Association, vol. 106(8), pages 2110-2144, August.
    7. Koenker, Roger & Mizera, Ivan, 2014. "Convex Optimization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i05).
    8. Li, Tong & Vuong, Quang, 1998. "Nonparametric Estimation of the Measurement Error Model Using Multiple Indicators," Journal of Multivariate Analysis, Elsevier, vol. 65(2), pages 139-165, May.
    9. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    10. Lee H. Dicker & Sihai D. Zhao, 2016. "High-dimensional classification via nonparametric empirical Bayes and maximum likelihood inference," Biometrika, Biometrika Trust, vol. 103(1), pages 21-34.
    11. C. Kirabo Jackson, 2018. "What Do Test Scores Miss? The Importance of Teacher Effects on Non–Test Score Outcomes," Journal of Political Economy, University of Chicago Press, vol. 126(5), pages 2072-2107.
    12. Roger Koenker & Ivan Mizera, 2014. "Convex Optimization, Shape Constraints, Compound Decisions, and Empirical Bayes Rules," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 674-685, June.
    Full references (including those not matched with items on IDEAS)

    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. Michael Gilraine & Jiaying Gu & Robert McMillan, 2021. "A Nonparametric Method for Estimating Teacher Value-Added," Working Papers tecipa-689, University of Toronto, Department of Economics.
    2. Michael Gilraine & Jiaying Gu & Robert McMillan, 2020. "A New Method for Estimating Teacher Value-Added," NBER Working Papers 27094, National Bureau of Economic Research, Inc.
    3. Stéphane Bonhomme & Martin Weidner, 2019. "Posterior average effects," CeMMAP working papers CWP43/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Timothy B. Armstrong & Michal Kolesár & Mikkel Plagborg‐Møller, 2022. "Robust Empirical Bayes Confidence Intervals," Econometrica, Econometric Society, vol. 90(6), pages 2567-2602, November.
    5. Gershenson, Seth, 2021. "Identifying and Producing Effective Teachers," IZA Discussion Papers 14096, Institute of Labor Economics (IZA).
    6. Timothy B. Armstrong & Michal Koles'ar & Mikkel Plagborg-M{o}ller, 2020. "Robust Empirical Bayes Confidence Intervals," Papers 2004.03448, arXiv.org, revised May 2022.
    7. Matthew A. Kraft & John P. Papay & Olivia L. Chi, 2020. "Teacher Skill Development: Evidence from Performance Ratings by Principals," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 315-347, March.
    8. Ingo E. Isphording & Ulf Zölitz, 2020. "The value of a peer," ECON - Working Papers 342, Department of Economics - University of Zurich.
    9. Goldhaber, Dan & Krieg, John & Theobald, Roddy, 2020. "Effective like me? Does having a more productive mentor improve the productivity of mentees?," Labour Economics, Elsevier, vol. 63(C).
    10. Michael Dinerstein & Isaac M. Opper, 2022. "Screening with Multitasking," CESifo Working Paper Series 9869, CESifo.
    11. Christopher Walters, 2024. "Empirical Bayes Methods in Labor Economics," RF Berlin - CReAM Discussion Paper Series 2422, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).
    12. Feng, Long & Dicker, Lee H., 2018. "Approximate nonparametric maximum likelihood for mixture models: A convex optimization approach to fitting arbitrary multivariate mixing distributions," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 80-91.
    13. Dinand Webbink & José María Cabrera, 2016. "Do higher salaries yield better teachers and better student outcomes?," Documentos de Trabajo/Working Papers 1604, Facultad de Ciencias Empresariales y Economia. Universidad de Montevideo..
    14. 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.
    15. 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.
    16. Tanaka, Ryuichi & Bessho, Shun-ichiro & Kawamura, Akira & Noguchi, Haruko & Ushijima, Koichi, 2020. "Determinants of Teacher Value-Added in Public Primary Schools: Evidence from Administrative Panel Data," IZA Discussion Papers 13146, Institute of Labor Economics (IZA).
    17. Dan Goldhaber & Cyrus Grout & Nick Huntington-Klein, 2017. "Screen Twice, Cut Once: Assessing the Predictive Validity of Applicant Selection Tools," Education Finance and Policy, MIT Press, vol. 12(2), pages 197-223, Spring.
    18. Zheng, Lei & Qi, Xiang & Zhang, Chongjiu, 2023. "Can improvements in teacher quality reduce the cognitive gap between urban and rural students in China?," International Journal of Educational Development, Elsevier, vol. 100(C).
    19. Jean-William Laliberté, "undated". "Long-term Contextual Effects in Education: Schools and Neighborhoods," Working Papers 2019-01, Department of Economics, University of Calgary.
    20. Jiaying Gu & Roger Koenker, 2014. "Unobserved heterogeneity in income dynamics: an empirical Bayes perspective," CeMMAP working papers 43/14, Institute for Fiscal Studies.

    More about this item

    Keywords

    Teacher Impacts; Teacher Value-Added; Value-Added Distribution; Nonparametric Estimation; Empirical Bayes; Education Policy; Teacher Release Policy; False Discovery Rate;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:tor:tecipa:tecipa-716. 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: RePEc Maintainer (email available below). General contact details of provider: .

    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.