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Using Lagged Outcomes to Evaluate Bias in Value-Added Models

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  • Raj Chetty
  • John N. Friedman
  • Jonah Rockoff

Abstract

Value-added (VA) models measure agents' productivity based on the outcomes they produce. The utility of VA models for performance evaluation depends on the extent to which VA estimates are biased by selection. One common method of evaluating bias in VA is to test for balance in lagged values of the outcome. We show that such balance tests do not yield robust information about bias in value-added models using Monte Carlo simulations. Even unbiased VA estimates can be correlated with lagged outcomes. More generally, tests using lagged outcomes are uninformative about the degree of bias in misspecified VA models. The source of these results is that VA is itself estimated using historical data, leading to non-transparent correlations between VA and lagged outcomes.

Suggested Citation

  • Raj Chetty & John N. Friedman & Jonah Rockoff, 2016. "Using Lagged Outcomes to Evaluate Bias in Value-Added Models," American Economic Review, American Economic Association, vol. 106(5), pages 393-399, May.
    • Handle: RePEc:aea:aecrev:v:106:y:2016:i:5:p:393-99
    Note: DOI: 10.1257/aer.p20161081
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    References listed on IDEAS

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    1. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2014. "Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates," American Economic Review, American Economic Association, vol. 104(9), pages 2593-2632, September.
    2. Joshua D. Angrist & Peter D. Hull & Parag A. Pathak & Christopher R. Walters, 2017. "Leveraging Lotteries for School Value-Added: Testing and Estimation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(2), pages 871-919.
    3. Jesse Rothstein, 2010. "Teacher Quality in Educational Production: Tracking, Decay, and Student Achievement," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(1), pages 175-214.
    4. David J. Deming, 2014. "Using School Choice Lotteries to Test Measures of School Effectiveness," American Economic Review, American Economic Association, vol. 104(5), pages 406-411, May.
    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.
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    Citations

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    Cited by:

    1. Hanushek, Eric A. & Rivkin, Steven G. & Schiman, Jeffrey C., 2016. "Dynamic effects of teacher turnover on the quality of instruction," Economics of Education Review, Elsevier, vol. 55(C), pages 132-148.
    2. Dan Goldhaber, 2018. "Impact and Your Death Bed: Playing the Long Game," Education Finance and Policy, MIT Press, vol. 13(1), pages 1-18, Winter.
    3. Xiaopeng Wu & Tianshu Xu & Jincheng Zhou, 2022. "Sustainability of Evaluation: The Origin and Development of Value-Added Evaluation from the Global Perspective," Sustainability, MDPI, vol. 14(23), pages 1-13, November.
    4. Brian Gill & Christine Ross & Erin Dillon & Ann Li & Patricia Troppe & Eric Isenberg & Anthony Milanowski & Roberta Garrison-Mogren & Louis Rizzo, "undated". "The Transition to ESSA: State and District Approaches to Implementing Title I and Title II-A in 2017–18," Mathematica Policy Research Reports b081b481f0ae4dcca46c338f9, Mathematica Policy Research.
    5. Sam Jones, 2020. "Testing the Technology of Human Capital Production: A General‐to‐Restricted Framework," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(6), pages 1429-1455, December.
    6. 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).
    7. Cook, Jason B. & Mansfield, Richard K., 2016. "Task-specific experience and task-specific talent: Decomposing the productivity of high school teachers," Journal of Public Economics, Elsevier, vol. 140(C), pages 51-72.
    8. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2017. "Measuring the Impacts of Teachers: Reply," American Economic Review, American Economic Association, vol. 107(6), pages 1685-1717, June.
    9. Marco Ovidi, 2021. "Parents know better: primary school choice and student achievement in London," Working Papers 919, Queen Mary University of London, School of Economics and Finance.
    10. 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.
    11. Marco Ovidi, 2022. "Parents Know Better: Sorting on Match Effects in Primary School," DISCE - Working Papers del Dipartimento di Economia e Finanza def121, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).

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    More about this item

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • 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

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