Measuring quality for use in incentive schemes: The case of “shrinkage” estimators

Nirav Mehta

Quantitative Economics, 2019, vol. 10, issue 4, 1537-1577

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.

Date: 2019
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Citations: View citations in EconPapers (4)

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https://doi.org/10.3982/QE950

Related works:
Working Paper: Measuring Quality for Use in Incentive Schemes: The Case of "Shrinkage" Estimators (2018)
Working Paper: Targeting the Wrong Teachers? Linking Measurement with Theory to Evaluate Teacher Incentive Schemes (2017)
Working Paper: Measuring Quality for Use in Incentive Schemes: The Case of "Shrinkage" Estimators (2017)
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