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Teacher Effects on Student Achievement and Height: A Cautionary Tale

Author

Listed:
  • Marianne Bitler
  • Sean Corcoran
  • Thurston Domina
  • Emily Penner

Abstract

Estimates of teacher “value-added” suggest teachers vary substantially in their ability to promote student learning. Prompted by this finding, many states and school districts have adopted value-added measures as indicators of teacher job performance. In this paper, we conduct a new test of the validity of value-added models. Using administrative student data from New York City, we apply commonly estimated value-added models to an outcome teachers cannot plausibly affect: student height. We find the standard deviation of teacher effects on height is nearly as large as that for math and reading achievement, raising obvious questions about validity. Subsequent analysis finds these “effects” are largely spurious variation (noise), rather than bias resulting from sorting on unobserved factors related to achievement. Given the difficulty of differentiating signal from noise in real-world teacher effect estimates, this paper serves as a cautionary tale for their use in practice.

Suggested Citation

  • 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.
    • Handle: RePEc:nbr:nberwo:26480
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    References listed on IDEAS

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    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. Peter Z. Schochet & Hanley S. Chiang, 2013. "What Are Error Rates for Classifying Teacher and School Performance Using Value-Added Models?," Journal of Educational and Behavioral Statistics, , vol. 38(2), pages 142-171, April.
    3. Daniel F. McCaffrey & Tim R. Sass & J. R. Lockwood & Kata Mihaly, 2009. "The Intertemporal Variability of Teacher Effect Estimates," Education Finance and Policy, MIT Press, vol. 4(4), pages 572-606, October.
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    6. Josh Kinsler, 2012. "Assessing Rothstein's critique of teacher value‐added models," Quantitative Economics, Econometric Society, vol. 3(2), pages 333-362, July.
    7. Dale Ballou & William Sanders & Paul Wright, 2004. "Controlling for Student Background in Value-Added Assessment of Teachers," Journal of Educational and Behavioral Statistics, , vol. 29(1), pages 37-65, March.
    8. Cory Koedel & Julian R. Betts, 2011. "Does Student Sorting Invalidate Value-Added Models of Teacher Effectiveness? An Extended Analysis of the Rothstein Critique," Education Finance and Policy, MIT Press, vol. 6(1), pages 18-42, January.
    9. 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.
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    11. Andrew Bacher-Hicks & Mark J. Chin & Thomas J. Kane & Douglas O. Staiger, 2017. "An Evaluation of Bias in Three Measures of Teacher Quality: Value-Added, Classroom Observations, and Student Surveys," NBER Working Papers 23478, National Bureau of Economic Research, Inc.
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    14. Jonah E. Rockoff & Douglas O. Staiger & Thomas J. Kane & Eric S. Taylor, 2012. "Information and Employee Evaluation: Evidence from a Randomized Intervention in Public Schools," American Economic Review, American Economic Association, vol. 102(7), pages 3184-3213, December.
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    18. 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.
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    21. Eric Isenberg & Heinrich Hock, 2011. "Design of Value-Added Models for IMPACT and TEAM in DC Public Schools, 2010-2011 School Year," Mathematica Policy Research Reports 885c9869ad064392aaa24d9e4, Mathematica Policy Research.
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    Citations

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    Cited by:

    1. 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.
    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. Yue-Yi Hwa & Clare Leaver, 2021. "Management in education systems," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(2), pages 367-391.
    4. 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.
    5. Soonwoo Kwon, 2023. "Optimal Shrinkage Estimation of Fixed Effects in Linear Panel Data Models," Papers 2308.12485, arXiv.org, revised Oct 2023.
    6. Pauline Givord & Milena Suarez Castillo, 2021. "What Makes a Good High School? Measuring School Effects beyond the Average," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 528-529, pages 29-45.
    7. Naven, Matthew, 2020. "Within-School Heterogeneity in Quality: Do Schools Provide Equal Value Added to All Students?," MPRA Paper 100123, University Library of Munich, Germany.
    8. John Jerrim & Sam Sims & Rebecca Allen, 2021. "The mental health and wellbeing of teachers in England," DoQSS Working Papers 21-01r, Quantitative Social Science - UCL Social Research Institute, University College London.
    9. Michael J. Andrews & Aaron K. Chatterji & Scott Stern, 2021. "Introduction: Beyond 140 Characters," NBER Chapters, in: The Role of Innovation and Entrepreneurship in Economic Growth, pages 1-28, National Bureau of Economic Research, Inc.
    10. 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.

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    More about this item

    JEL classification:

    • I2 - Health, Education, and Welfare - - Education
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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