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

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  • Jesse Rothstein

    (Princeton University)

Abstract

Non-random assignments of students to teachers can bias value added estimates of teachers' causal effects. Rothstein (2008) shows that typical value added models indicate large counter-factual effects of 5th grade teachers on students' 4th grade learning, implying that assignments do not satisfy the imposed assumptions. This paper quantifies the resulting biases in estimates of 5th grade teachers' causal effects from several value added models, under varying assumprions about the assignment process. Under selection on observables, models for gain scores without controls or with only a single lagged score control are subject important bias, but models with controls for the full test score history are nearly free of bias. I consider several scenarios for selection on unobservables, using the across-classroom variance of observed variables to calibrate each. Results indicate that even well-controlled models may be substantially biased, with the magnitude of the bias depending on the amount of information available for use in classroom assignments.

Suggested Citation

  • Jesse Rothstein, 2008. "Student Sorting and Bias in Value Added Estimation: Selection on Observables and Unobservables," Working Papers 1059, Princeton University, Woodrow Wilson School of Public and International Affairs, Education Research Section..
  • Handle: RePEc:pri:edures:26
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    References listed on IDEAS

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    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. repec:pri:edures:25ers.pdf is not listed on IDEAS
    11. 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.
    12. 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.
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    More about this item

    JEL classification:

    • 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
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • J33 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Compensation Packages; Payment Methods
    • J45 - Labor and Demographic Economics - - Particular Labor Markets - - - Public Sector Labor Markets

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