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Student Sorting and Bias in Value-Added Estimation: Selection on Observables and Unobservables

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  • Jesse Rothstein

    ()
    (Goldman School of Public Policy, University of California, Berkeley)

Abstract

Nonrandom assignment of students to teachers can bias value-added estimates of teachers' causal effects. Rothstein (2008, 2010) shows that typical value-added models indicate large counterfactual effects of fifth-grade teachers on students' fourth-grade learning, indicating that classroom assignments are far from random. This article quantifies the resulting biases in estimates of fifth-grade teachers' causal effects from several value-added models, under varying assumptions about the assignment process. If assignments are assumed to depend only on observables, the most commonly used specifications are subject to important bias, but other feasible specifications are nearly free of bias. I also consider the case in which assignments depend on unobserved variables. I use the across-classroom variance of observables to calibrate several models of the sorting process. Results indicate that even the best feasible value-added models may be substantially biased, with the magnitude of the bias depending on the amount of information available for use in classroom assignments. © 2009 American Education Finance Association

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File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/edfp.2009.4.4.537
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Bibliographic Info

Article provided by MIT Press in its journal Education Finance and Policy.

Volume (Year): 4 (2009)
Issue (Month): 4 (October)
Pages: 537-571

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Handle: RePEc:tpr:edfpol:v:4:y:2009:i:4:p:537-571

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Keywords: value-added estimation; student sorting; bias; classroom assignments;

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  1. Jesse Rothstein, 2008. "Teacher Quality in Educational Production: Tracking, Decay, and Student Achievement," NBER Working Papers 14442, National Bureau of Economic Research, Inc.
  2. 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, University of Chicago Press, vol. 25, pages 95-135.
  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, University of Chicago Press, vol. 113(1), pages 151-184, February.
  4. Steven G. Rivkin & Eric A. Hanushek & John F. Kain, 2005. "Teachers, Schools, and Academic Achievement," Econometrica, Econometric Society, Econometric Society, vol. 73(2), pages 417-458, 03.
  5. 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, University of Wisconsin Press, vol. 41(4).
  6. Cory Koedel & Julian Betts, 2007. "Re-Examining the Role of Teacher Quality In the Educational Production Function," Working Papers, Department of Economics, University of Missouri 0708, Department of Economics, University of Missouri.
  7. 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, University of Chicago Press, vol. 26, pages 101-136.
  8. 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., John Wiley & Sons, Ltd., vol. 27(4), pages 793-818.
  9. 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.
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