The DOI matches its Crossref record.
Job Matching and On-the-Job Training
Abstract
Conventional analysis predicts that workers pay part of their on-the-job training costs by accepting a lower starting wage and subsequently realize a return to this investment in the form of greater wage growth. Missing from the conventional treatment of on-the-job training is a discussion of the process by which heterogeneous workers are matched to jobs requiring varying amounts of training. This matching process constitutes a key feature of the on-the-job training model presented in this article and tested with a unique data set containing extensive information concerning on-the-job training, employer search, wages, and wage and productivity growth.
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Citations
Cited by
The Effects of Algorithmic Labor Market Recommendations: Evidence from a Field Experiment
Horton, J. J. (2017) · Journal of Labor Economics · doi:10.1086/689213 · Cited by 203
Metadata
- Authors
- Barron, J. M., Black, D. A., & Loewenstein, M. A.
- Venue
- Journal of Labor Economics
- Publisher
- University of Chicago Press
- Published
- 1989-01
- Volume
- 7
- Issue
- 1
- Pages
- 1-19
- ISSN
- 0734-306X (ISSN-L)
- 1537-5307
- OpenAlex
- W1968563816
- Cited by
- 331
- References
- 0
- FWCI
- 26.34
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
