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A Nonparametric Method for Estimating Teacher Value-Added

Michael Gilraine, Jiaying Gu and Robert McMillan

Working Papers from University of Toronto, Department of Economics

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 (search for similar items in EconPapers)
JEL-codes: C11 H75 I21 J24 (search for similar items in EconPapers)
Pages: Unknown pages
Date: 2021-02-13
New Economics Papers: this item is included in nep-ecm, nep-lma and nep-ure
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