Measuring Quality for Use in Incentive Schemes: The Case of "Shrinkage" Estimators

Nirav Mehta

No 7163, CESifo Working Paper Series from CESifo

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.

Keywords: economics of education; empirical contracts; teacher incentive schemes; teacher quality (search for similar items in EconPapers)
JEL-codes: D81 I21 I28 J01 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-lab
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Related works:
Journal Article: Measuring quality for use in incentive schemes: The case of “shrinkage” estimators (2019)
Working Paper: Measuring Quality for Use in Incentive Schemes: The Case of “Shrinkage” Estimators (2018)
Working Paper: Measuring Quality for Use in Incentive Schemes: The Case of "Shrinkage" Estimators (2017)
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