The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Quantile regression, Box-Cox transformation model, and the U.S. wage structure, 1963–1987
Open accessAbstract
This study examines changes in the increments (‘returns’) to education and experience at different points of the wage distribution. It also examines changes in within-group wage inequality. Newly developed quantile regression techniques are applied to the Box-Cox transformation model using the Current Population Survey data. The increments differ, but they changed in similar pattern at different points of the wage distribution. They are larger at the low end of the distribution for the less skilled workers, and larger at the high end for the more skilled workers. Wage inequality increased differentially for all skill groups. These increases result mainly from a longer tail at the lower end of the wage distributions.
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Citations
Cites
The Jackknife, the Bootstrap and Other Resampling Plans
Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120
Changes in Relative Wages, 1963-1987: Supply and Demand Factors
Katz, L. F., & Murphy, K. M. (1992) · The Quarterly Journal of Economics · doi:10.2307/2118323 · Cited by 4,793
Metadata
- Authors
- Buchinsky, M.
- Venue
- Journal of Econometrics
- Publisher
- Elsevier BV
- Published
- 1995-01
- Volume
- 65
- Issue
- 1
- Pages
- 109-154
- ISSN
- 0304-4076 (ISSN-L)
- OpenAlex
- W2066709835
- Cited by
- 159
- References
- 2
- FWCI
- 3.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Buchinsky, M. (1995). Quantile regression, Box-Cox transformation model, and the U.S. wage structure, 1963–1987. Journal of Econometrics. https://doi.org/10.1016/0304-4076(94)01599-u
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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.