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

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  • Nirav Mehta

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.

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  • Nirav Mehta, 2019. "Measuring quality for use in incentive schemes: The case of “shrinkage” estimators," Quantitative Economics, Econometric Society, vol. 10(4), pages 1537-1577, November.
    • Handle: RePEc:wly:quante:v:10:y:2019:i:4:p:1537-1577
    DOI: 10.3982/QE950
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    JEL classification:

    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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