Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/217174 
Authors: 
Year of Publication: 
2019
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 10 [Issue:] 4 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2019 [Pages:] 1537-1577
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
Researchers commonly "shrink" raw quality measures based on statistical criteria. This paper studies when and how this transformation's statistical properties would confer economic benefits to a utility-maximizing decision-maker across common asymmetric information environments. I develop the results for an application measuring teacher quality. The presence of a systematic relationship between teacher quality and class size could cause the data transformation to do either worse or better than the untransformed data. I use data from Los Angeles to confirm the presence of such a relationship and show that the simpler raw measure would outperform the one most commonly used in teacher incentive schemes.
Subjects: 
Economics of education
empirical contracts
teacher incentive schemes
teacher quality
JEL: 
D81
I21
I28
J01
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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