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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 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.

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  • Nirav Mehta, 2018. "Measuring Quality for Use in Incentive Schemes: The Case of "Shrinkage" Estimators," CESifo Working Paper Series 7163, CESifo.
    • Handle: RePEc:ces:ceswps:_7163
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    2. Michael Gilraine & Jiaying Gu & Robert McMillan, 2020. "A New Method for Estimating Teacher Value-Added," NBER Working Papers 27094, National Bureau of Economic Research, Inc.
    3. Johannes S. Kunz & Carol Propper & Kevin E. Staub & Rainer Winkelmann, 2023. "Assessing the Quality of Public Services: For-profits, Chains, and Concentration in the Hospital Market," Papers 2023-01, Centre for Health Economics, Monash University.

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    More about this item

    Keywords

    economics of education; empirical contracts; teacher incentive schemes; teacher quality;
    All these keywords.

    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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