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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    7. 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.
    8. 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.
    9. Editors, 2016. "16 and all that," Stata Journal, StataCorp LP, vol. 16(1), pages 3-4, March.
    10. 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.
    11. 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.
    12. Koenker, Roger & Mizera, Ivan, 2014. "Convex Optimization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i05).
    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, 2020. "A New Method for Estimating Teacher Value-Added," NBER Working Papers 27094, National Bureau of Economic Research, Inc.
    2. 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.
    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. 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.
    10. 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).
    11. Jiaying Gu & Roger Koenker, 2014. "Unobserved heterogeneity in income dynamics: an empirical Bayes perspective," CeMMAP working papers 43/14, Institute for Fiscal Studies.
    12. Andrew Agopsowicz & Chris Robinson & Ralph Stinebrickner & Todd Stinebrickner, 2020. "Careers and Mismatch for College Graduates: College and Noncollege Jobs," Journal of Human Resources, University of Wisconsin Press, vol. 55(4), pages 1194-1221.
    13. Jose Maria Cabrera & Dinand Webbink, 2020. "Do Higher Salaries Yield Better Teachers and Better Student Outcomes?," Journal of Human Resources, University of Wisconsin Press, vol. 55(4), pages 1222-1257.
    14. Jiaying Gu & Roger Koenker, 2017. "Rebayes: an R package for empirical bayes mixture methods," CeMMAP working papers CWP37/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Jiaying Gu & Roger Koenker & Stanislav Volgushev, 2017. "Testing for homogeneity in mixture models," CeMMAP working papers CWP39/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Jacob, Brian A. & Rockoff, Jonah E. & Taylor, Eric S. & Lindy, Benjamin & Rosen, Rachel, 2018. "Teacher applicant hiring and teacher performance: Evidence from DC public schools," Journal of Public Economics, Elsevier, vol. 166(C), pages 81-97.
    17. 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.
    18. 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.
    19. 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).
    20. Michael Dinerstein & Isaac M. Opper, 2022. "Screening with Multitasking," CESifo Working Paper Series 9869, CESifo.

    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.