The DOI matches its Crossref record.
Learning-by-Doing as a Propagation Mechanism
Open accessAbstract
This paper suggests that skill accumulation through past work experience, or “learning-by-doing” (LBD), can provide an important propagation mechanism in a dynamic stochastic general-equilibrium model, as the current labor supply affects future productivity. Our econometric analysis uses a Bayesian approach to combine micro-level panel data with aggregate time series. Formal model evaluation shows that the introduction of the LBD mechanism improves the model's ability to fit the dynamics of aggregate output and hours.
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Citations
Cited by
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Coad, A., Segarra, A., & Teruel, M. (2013) · Structural Change and Economic Dynamics · doi:10.1016/j.strueco.2012.07.002 · Cited by 420
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Deming, D. (2021) · doi:10.3386/w28733 · Cited by 52
Metadata
- Authors
- Chang, Y., Gomes, J. F., & Schorfheide, F.
- Venue
- American Economic Review
- Publisher
- American Economic Association
- Published
- 2002-11-01
- Volume
- 92
- Issue
- 5
- Pages
- 1498-1520
- ISSN
- 0002-8282 (ISSN-L)
- OpenAlex
- W2167669534
- Cited by
- 219
- References
- 0
- FWCI
- 13.56
- Type
- journal article
- Language
- en
- Open access
- green
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- Chang, Y., Gomes, J. F., & Schorfheide, F. (2002). Learning-by-Doing as a Propagation Mechanism. American Economic Review. https://doi.org/10.1257/000282802762024601
Data sources
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.
