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Separating uncertainty from heterogeneity in life cycle earnings
Open accessAbstract
This paper develops and applies a method for decomposing cross section variability of earnings into components that are forecastable at the time students decide to go to college (heterogeneity) and components that are unforecastable. About 60% of variability in returns to schooling is forecastable. This has important implications for using measured variability to price risk and predict college attendance.
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Citations
Cited by
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Heckman, J. J., Stixrud, J., & Urzua, S. (2006) · Journal of Labor Economics · doi:10.1086/504455 · Cited by 4,427
Self-citationStructural Equations, Treatment Effects, and Econometric Policy Evaluation1
Heckman, J. J., & Vytlacil, E. (2005) · Econometrica · doi:10.1111/j.1468-0262.2005.00594.x · Cited by 1,220
Personality Psychology and Economics
Almlund, M., Duckworth, A. L., Heckman, J., & Kautz, T. (2011) · Handbook of the Economics of Education · doi:10.1016/b978-0-444-53444-6.00001-8 · Cited by 1,204
The Economics of Human Development and Social Mobility
Heckman, J. J., & Mosso, S. (2014) · Annual Review of Economics · doi:10.1146/annurev-economics-080213-040753 · Cited by 940
Understanding Instrumental Variables in Models with Essential Heterogeneity
Heckman, J. J., Urzua, S., & Vytlacil, E. (2006) · Review of Economics and Statistics · doi:10.1162/rest.88.3.389 · Cited by 874
Metadata
- Authors
- Cunha, F.
- Venue
- Oxford Economic Papers
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-12-10
- Volume
- 57
- Issue
- 2
- Pages
- 191-261
- ISSN
- 0030-7653 (ISSN-L)
- 1464-3812
- OpenAlex
- W2978648229
- Cited by
- 516
- References
- 0
- FWCI
- 54.52
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Cunha, F. (2004). Separating uncertainty from heterogeneity in life cycle earnings. Oxford Economic Papers. https://doi.org/10.1093/oep/gpi019
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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.
