Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/180846
Authors: 
Mehta, Nirav
Year of Publication: 
2018
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 decisionmaker 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: 
empirical contracts
teacher incentive schemes
teacher quality
economics of education
JEL: 
J01
I21
I28
D81
Document Type: 
Working Paper

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