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Working paper 689

Michael Gilraine, Jiaying Gu, Robert McMillan, "A Nonparametric Method for Estimating Teacher Value-Added", 2021-02-13
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Abstract: This paper proposes a computationally feasible nonparametric methodology for estimating teacher value-added. Our estimator, drawing on Robbins (1956), permits the unobserved teacher value-added distribution to be estimated directly, rather than assuming normality as is standard. Simulations indicate the estimator performs very well regardless of the true distribution, even in moderately-sized samples. Implementing our method in practice using two large-scale administrative datasets, the estimated teacher value-added distributions depart from normality and differ from each other. Further, compared with widely-used parametric estimates, we show our nonparametric estimates can make a significant difference to teacher-related policy calculations, in both short and longer terms.

Keywords: Teacher Value-Added, Nonparametric Empirical Bayes, Education Policy, Teacher Release Policy

JEL Classification: C11, H75, I21, J24