Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/180879
Authors: 
Mehta, Nirav
Year of Publication: 
2017
Series/Report no.: 
CHCP Working Paper 2017-1
Abstract: 
Measurement is crucial to the implementation of output-based incentive schemes. This paper uses models to study the performance of teacher quality estimators that enter teacher incentive schemes. I model an administrator tasked with (i) categorizing teachers with respect to a cutoff, (ii) retaining teachers in a hidden type environment, and (iii) compensating teachers in a hidden action environment. The preferred estimator would be the same in each model and depends on the relationship between teacher quality and class size. I use data from Los Angeles to show that simple fixed effects would almost always outperform more popular empirical Bayes.
Subjects: 
teacher incentive pay
teacher quality
Document Type: 
Working Paper
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