Too Much Information: When Does Additional Testing Benefit Schools?

Vanitha Virudachalam (), Sergei Savin () and Matthew P. Steinberg ()
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Vanitha Virudachalam: Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, Illinois 61820
Sergei Savin: The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Matthew P. Steinberg: Accelerate, Brooklyn, New York 11217

Management Science, 2024, vol. 70, issue 9, 6220-6233

Abstract: U.S. K–12 school districts that traditionally utilized ongoing “formative” assessments of student progress increasingly rely on additional “interim” assessments to predict student performance on standardized tests. Moreover, some districts are experimenting with merit-based teacher bonuses tied to standardized test scores. We examine the relationship between interim assessments and teacher bonuses using a two-period principal–agent model. The school district (principal), operating under a limited budget, decides whether to implement interim assessments and how much merit pay to offer, and teachers (agents) choose how much effort to exert in each period. We use two-state (proficient versus not proficient) Markovian dynamics to describe the evolution of student test readiness, in which the transition probability in a given period depends on both teachers’ effort decisions and the starting state. Our results indicate that, despite the popularity of interim assessments, their usefulness is far from guaranteed. In particular, the accuracy promised by these assessments is a double-edged sword: positive midyear results can make it easier to incentivize second period teacher effort, but negative results can have a demotivating effect. Moreover, even when an interim assessment does result in a higher probability of the school ending the year in the proficient state, the resulting higher expected costs of merit-based bonuses for the district may exceed the available budget. Thus, even a free interim assessment might be too expensive for the school district.

Keywords: education systems; performance-based incentives; dynamic programming; applications (search for similar items in EconPapers)
Date: 2024
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