IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/27094.html
   My bibliography  Save this paper

A New Method for Estimating Teacher Value-Added

Author

Listed:
  • Michael Gilraine
  • Jiaying Gu
  • Robert McMillan

Abstract

This paper proposes a new methodology for estimating teacher value-added. Rather than imposing a normality assumption on unobserved teacher quality (as in the standard empirical Bayes approach), our nonparametric estimator permits the underlying distribution to be estimated directly and in a computationally feasible way. The resulting estimates fit the unobserved distribution very well regardless of the form it takes, as we show in Monte Carlo simulations. Implementing the nonparametric approach in practice using two separate large-scale administrative data sets, we find the estimated teacher value-added distributions depart from normality and differ from each other. To draw out the policy implications of our method, we first consider a widely-discussed policy to release teachers at the bottom of the value-added distribution, comparing predicted test score gains under our nonparametric approach with those using parametric empirical Bayes. Here the parametric method predicts similar policy gains in one data set while overestimating those in the other by a substantial margin. We also show the predicted gains from teacher retention policies can be underestimated significantly based on the parametric method. In general, the results highlight the benefit of our nonparametric empirical Bayes approach, given that the unobserved distribution of value-added is likely to be context-specific.

Suggested Citation

  • 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.
  • Handle: RePEc:nbr:nberwo:27094
    Note: ED LS PE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w27094.pdf
    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. Kane, Thomas J. & Rockoff, Jonah E. & Staiger, Douglas O., 2008. "What does certification tell us about teacher effectiveness? Evidence from New York City," Economics of Education Review, Elsevier, vol. 27(6), pages 615-631, December.
    3. 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.
    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. 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.
    6. Andrew Bacher-Hicks & Thomas J. Kane & Douglas O. Staiger, 2014. "Validating Teacher Effect Estimates Using Changes in Teacher Assignments in Los Angeles," NBER Working Papers 20657, National Bureau of Economic Research, Inc.
    7. 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.
    8. Brian A. Jacob & Lars Lefgren, 2008. "Can Principals Identify Effective Teachers? Evidence on Subjective Performance Evaluation in Education," Journal of Labor Economics, University of Chicago Press, vol. 26(1), pages 101-136.
    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. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Koenker, Roger & Mizera, Ivan, 2014. "Convex Optimization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i05).
    17. Marianne Bitler & Sean Corcoran & Thurston Domina & Emily Penner, 2019. "Teacher Effects on Student Achievement and Height: A Cautionary Tale," NBER Working Papers 26480, 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. Dominic Coey & Kenneth Hung, 2022. "Empirical Bayes Selection for Value Maximization," Papers 2210.03905, arXiv.org, revised Jan 2023.
    2. Brunello, Giorgio & Yamamura, Eiji, 2023. "Desperately Seeking a Japanese Yokozuna," IZA Discussion Papers 16536, Institute of Labor Economics (IZA).
    3. Tom Ahn & Esteban Aucejo & Jonathan James, 2021. "The Importance of Matching Effects for Labor Productivity: Evidence from Teacher-Student Interactions," Working Papers 2106, California Polytechnic State University, Department of Economics.
    4. Jiaying Gu & Roger Koenker, 2023. "Invidious Comparisons: Ranking and Selection as Compound Decisions," Econometrica, Econometric Society, vol. 91(1), pages 1-41, January.
    5. Joan Martinez, 2022. "The Long-Term Effects of Teachers' Gender Stereotypes," Papers 2212.08220, arXiv.org, revised Jul 2023.
    6. Antoine Deeb, 2021. "A Framework for Using Value-Added in Regressions," Papers 2109.01741, arXiv.org, revised Oct 2021.
    7. Soonwoo Kwon, 2023. "Optimal Shrinkage Estimation of Fixed Effects in Linear Panel Data Models," Papers 2308.12485, arXiv.org, revised Oct 2023.
    8. Duque, Valentina & Gilraine, Michael, 2022. "Coal use, air pollution, and student performance," Journal of Public Economics, Elsevier, vol. 213(C).
    9. Christine Mulhern & Isaac M. Opper, 2021. "Measuring and Summarizing the Multiple Dimensions of Teacher Effectiveness," CESifo Working Paper Series 9263, CESifo.
    10. Jiaying Gu & Roger Koenker, 2020. "Invidious Comparisons: Ranking and Selection as Compound Decisions," Papers 2012.12550, arXiv.org, revised Sep 2021.

    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. Mike Gilraine & Jiaying Gu & Robert McMillan, 2022. "A Nonparametric Approach for Studying Teacher Impacts," Working Papers tecipa-716, University of Toronto, Department of Economics.
    3. 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.
    4. 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.
    5. 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.
    6. Ingo E. Isphording & Ulf Zölitz, 2020. "The value of a peer," ECON - Working Papers 342, Department of Economics - University of Zurich.
    7. 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.
    8. Timothy B. Armstrong & Michal Kolesár & Mikkel Plagborg‐Møller, 2022. "Robust Empirical Bayes Confidence Intervals," Econometrica, Econometric Society, vol. 90(6), pages 2567-2602, November.
    9. 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).
    10. 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.
    11. Eric Parsons & Cory Koedel & Li Tan, 2019. "Accounting for Student Disadvantage in Value-Added Models," Journal of Educational and Behavioral Statistics, , vol. 44(2), pages 144-179, April.
    12. 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.
    13. 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).
    14. Evan Riehl & Meredith Welch, 2023. "Accountability, Test Prep Incentives, and the Design of Math and English Exams," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(1), pages 60-96, January.
    15. Gershenson, Seth, 2021. "Identifying and Producing Effective Teachers," IZA Discussion Papers 14096, Institute of Labor Economics (IZA).
    16. 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.
    17. Allison Atteberry & Susanna Loeb & James Wyckoff, 2013. "Do First Impressions Matter? Improvement in Early Career Teacher Effectiveness," NBER Working Papers 19096, National Bureau of Economic Research, Inc.
    18. 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.
    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

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: 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
    • J45 - Labor and Demographic Economics - - Particular Labor Markets - - - Public Sector Labor Markets

    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:nbr:nberwo:27094. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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.