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

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 10 ; Year: 2019 ; Issue: 4 ; Pages: 1537-1577 ; New Haven, CT: The Econometric Society

Classification and Topics

• Classification: Wirtschaft ; Criteria for Decision-Making under Risk and Uncertainty ; Analysis of Education ; Education: Government Policy ; Labor Economics: General
• Subject: Economics of education ; empirical contracts ; teacher incentive schemes ; teacher quality

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Mehta, Nirav
• Event: Veröffentlichung
• (who): The Econometric Society
• (where): New Haven, CT
• (when): 2019

Further information

• DOI: doi:10.3982/QE950
• Handle: http://hdl.handle.net/10419/217174
• Last update: 10.03.2025, 11:43 AM CET