Title: Information and Employee Evaluation: Evidence from a Randomized Intervention in Public Schools
Type Dataset Rockoff, Jonah E., Staiger, Douglas O., Kane, Thomas J., Taylor, Eric S. (2018): Information and Employee Evaluation: Evidence from a Randomized Intervention in Public Schools. Harvard Dataverse. Dataset. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/1OFPOU
Links
- Item record in Abdul Latif Jameel Poverty Action Lab Dataverse
- Digital object URL
Summary
We examine how employers learn about worker productivity in a randomized pilot experiment which provided objective estimates of teacher performance to school principals. We test several hypotheses that support a simple Bayesian learning model with imperfect information. First, the correlation between performance estimates and prior beliefs rises with more precise objective estimates and more precise subjective priors. Second, new information exerts greater influence on posterior beliefs when it is more precise and when priors are less precise. Employer learning affects job separation and productivity in schools, increasing turnover for teachers with low performance estimates and producing small test score improvements.
More information
- DOI: 10.7910/DVN/1OFPOU
Subjects
- Social Sciences
- CESSDA: Educational policy, Labour and employment policy
Dates
- Publication date: 2018
- Submitted: July 03, 2018
- Updated: July 27, 2022
- Collected: 2007-08 to 2009-07
Notes
Datacite resource type: Sample survey data Methods: Restricted: the data can be acquired with approval from the NYC Department of Education. Information on data requests can be found at http://schools.nyc.gov/Accountability/data/DataRequests. Researchers should also feel free to contact Jonah Rockoff at jonah.rockoff@columbia.edu for help with this request.Rights
- info:eu-repo/semantics/openAccess
- http://creativecommons.org/publicdomain/zero/1.0 CC0 1.0
Format
electronic resource
Relateditems
Description | Item type | Relationship | Uri |
---|---|---|---|
IsCitedBy | https://doi.org/10.1257/aer.102.7.3184 |