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Simple and Credible Value-Added Estimation Using Centralized School Assignment

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Listed:
  • Joshua Angrist
  • Peter Hull
  • Parag A. Pathak
  • Christopher R. Walters

Abstract

Many large urban school districts match students to schools using algorithms that incorporate an element of random assignment. We introduce two simple empirical strategies to harness this randomization for value-added models (VAMs) measuring the causal effects of individual schools. The first estimator controls for the probability of being offered admission to different schools, treating the take-up decision as independent of potential outcomes. Randomness in school assignments is used to test this key conditional independence assumption. The second estimator uses randomness in offers to generate instrumental variables (IVs) for school enrollment. This procedure uses a low-dimensional model of school quality mediators to solve the under-identification challenge arising from the fact that some schools are under-subscribed. Both approaches relax the assumptions of conventional value-added models while obviating the need for elaborate nonlinear estimators. In applications to data from Denver and New York City, we find that models controlling for both assignment risk and lagged achievement yield highly reliable VAM estimates. Estimates from models with fewer controls and older lagged score controls are improved markedly by IV.

Suggested Citation

  • 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.
    • Handle: RePEc:nbr:nberwo:28241
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    References listed on IDEAS

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    2. 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.
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    10. 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.
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    Cited by:

    1. Klein, Thilo & Aue, Robert & Ortega, Josué, 2024. "School choice with independent versus consolidated districts," Games and Economic Behavior, Elsevier, vol. 147(C), pages 170-205.
    2. Lars J. Kirkebøen, 2021. "School value-added and longterm student outcomes," Discussion Papers 970, Statistics Norway, Research Department.
    3. Parag A. Pathak & Kevin Ren & Camille Terrier, 2021. "From immediate acceptance to deferred acceptance: effects on school admissions and achievement in England," CEP Discussion Papers dp1815, Centre for Economic Performance, LSE.
    4. Christine Mulhern & Isaac M. Opper, 2021. "Measuring and Summarizing the Multiple Dimensions of Teacher Effectiveness," CESifo Working Paper Series 9263, CESifo.
    5. Jiafeng Chen, 2021. "Nonparametric Treatment Effect Identification in School Choice," Papers 2112.03872, arXiv.org, revised Oct 2023.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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