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The Intertemporal Variability of Teacher Effect Estimates


  • Daniel F. McCaffrey

    () (RAND Corporation)

  • Tim R. Sass

    () (Department of Economics, Florida State University)

  • J. R. Lockwood

    () (RAND Corporation)

  • Kata Mihaly

    () (RAND Corporation)


The utility of value-added estimates of teachers' effects on student test scores depends on whether they can distinguish between high- and low-productivity teachers and predict future teacher performance. This article studies the year-to-year variability in value-added measures for elementary and middle school mathematics teachers from five large Florida school districts. We find year-to-year correlations in value-added measures in the range of 0.2–0.5 for elementary school and 0.3–0.7 for middle school teachers. Much of the variation in measured teacher performance (roughly 30–60 percent) is due to sampling error from “noise” in student test scores. Persistent teacher effects account for about 50 percent of the variation not due to noise for elementary teachers and about 70 percent for middle school teachers. The remaining variance is due to teacher-level time-varying factors, but little of it is explained by observed teacher characteristics. Averaging estimates from two years greatly improves their ability to predict future performance. © 2009 American Education Finance Association

Suggested Citation

  • 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.
  • Handle: RePEc:tpr:edfpol:v:4:y:2009:i:4:p:572-606

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    References listed on IDEAS

    1. Steven G. Rivkin & Eric A. Hanushek & John F. Kain, 2005. "Teachers, Schools, and Academic Achievement," Econometrica, Econometric Society, vol. 73(2), pages 417-458, March.
    2. Jesse Rothstein, 2010. "Teacher Quality in Educational Production: Tracking, Decay, and Student Achievement," The Quarterly Journal of Economics, Oxford University Press, vol. 125(1), pages 175-214.
    3. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 151-184, February.
    4. 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.
    5. Sass, Tim R. & Semykina, Anastasia & Harris, Douglas N., 2014. "Value-added models and the measurement of teacher productivity," Economics of Education Review, Elsevier, vol. 38(C), pages 9-23.
    6. Daniel Aaronson & Lisa Barrow & William Sander, 2007. "Teachers and Student Achievement in the Chicago Public High Schools," Journal of Labor Economics, University of Chicago Press, vol. 25, pages 95-135.
    7. Donald Boyd & Hamilton Lankford & Susanna Loeb & Jonah Rockoff & James Wyckoff, 2008. "The narrowing gap in New York City teacher qualifications and its implications for student achievement in high-poverty schools," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 793-818.
    8. repec:pri:edures:25ers.pdf is not listed on IDEAS
    9. 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, pages 101-136.
    10. Cory Koedel & Julian Betts, 2007. "Re-Examining the Role of Teacher Quality In the Educational Production Function," Working Papers 0708, Department of Economics, University of Missouri.
    11. Charles T. Clotfelter & Helen F. Ladd & Jacob L. Vigdor, 2006. "Teacher-Student Matching and the Assessment of Teacher Effectiveness," Journal of Human Resources, University of Wisconsin Press, vol. 41(4).
    12. Joseph A. Martineau, 2006. "Distorting Value Added: The Use of Longitudinal, Vertically Scaled Student Achievement Data for Growth-Based, Value-Added Accountability," Journal of Educational and Behavioral Statistics, , vol. 31(1), pages 35-62, March.
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    More about this item


    value-added estimation; teacher effect; student test scores; teacher performance; Florida;

    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education


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