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Interpreting Tests of School VAM Validity

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  • Joshua Angrist
  • Peter Hull
  • Parag Pathak
  • Christopher Walters

Abstract

We develop over-identification tests that use admissions lotteries to assess the predictive value of regression-based value-added models (VAMs). These tests have degrees of freedom equal to the number of quasi-experiments available to estimate school effects. By contrast, previously implemented VAM validation strategies look at a single restriction only, sometimes said to measure forecast bias. Tests of forecast bias may be misleading when the test statistic is constructed from many lotteries or quasi-experiments, some of which have weak first stage effects on school attendance. The theory developed here is applied to data from the Charlotte-Mecklenberg School district analyzed by Deming (2014).

Suggested Citation

  • Joshua Angrist & Peter Hull & Parag Pathak & Christopher Walters, 2016. "Interpreting Tests of School VAM Validity," American Economic Review, American Economic Association, vol. 106(5), pages 388-392, May.
    • Handle: RePEc:aea:aecrev:v:106:y:2016:i:5:p:388-92
    Note: DOI: 10.1257/aer.p20161080
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    References listed on IDEAS

    as
    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. White, Halbert, 1982. "Instrumental Variables Regression with Independent Observations," Econometrica, Econometric Society, vol. 50(2), pages 483-499, March.
    5. 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.
    6. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    7. K. Newey, Whitney, 1985. "Generalized method of moments specification testing," Journal of Econometrics, Elsevier, vol. 29(3), pages 229-256, September.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Joshua Angrist & Peter Hull & Parag A. Pathak & Christopher R. Walters, 2020. "Simple and Credible Value-Added Estimation Using Centralized School Assignment," NBER Working Papers 28241, National Bureau of Economic Research, Inc.
    2. Ebrahim Azimi & Jane Friesen & Simon Woodcock, 2023. "Private Schools and Student Achievement," Education Finance and Policy, MIT Press, vol. 18(4), pages 623-653, Fall.
    3. Bar, M.; & Bakx, P.; & Wouterse, B.; & van Doorslaer, Eddy.;, 2022. "Estimating the health value added by nursing homes," Health, Econometrics and Data Group (HEDG) Working Papers 22/12, HEDG, c/o Department of Economics, University of York.
    4. Atila Abdulkadiroğlu & Parag A. Pathak & Jonathan Schellenberg & Christopher R. Walters, 2020. "Do Parents Value School Effectiveness?," American Economic Review, American Economic Association, vol. 110(5), pages 1502-1539, May.
    5. Jason Abaluck & Mauricio Caceres Bravo & Peter Hull: & Amanda Starc, 2021. "Mortality Effects and Choice Across Private Health Insurance Plans," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(3), pages 1557-1610.
    6. Lars J. Kirkebøen, 2021. "School value-added and longterm student outcomes," Discussion Papers 970, Statistics Norway, Research Department.
    7. Bär, Marlies & Bakx, Pieter & Wouterse, Bram & van Doorslaer, Eddy, 2022. "Estimating the health value added by nursing homes," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 1-23.
    8. Max Gross & Menbere Shiferaw & Jonah Deutsch & Brian Gill, "undated". "Using Promotion Power to Identify the Effectiveness of Public High Schools in the District of Columbia," Mathematica Policy Research Reports 1c010e51ed7e478aa1f3f8304, Mathematica Policy Research.
    9. Susanna Loeb & Michael S. Christian & Heather Hough & Robert H. Meyer & Andrew B. Rice & Martin R. West, 2019. "School Differences in Social–Emotional Learning Gains: Findings From the First Large-Scale Panel Survey of Students," Journal of Educational and Behavioral Statistics, , vol. 44(5), pages 507-542, October.

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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

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