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The Estimation of a Joint Wage-Hours Labor Supply Model
Abstract
In this paper the standard cross-sectional static model of labor supply is modified to make the wage endogenous, and a joint wage-hours model is estimated. The econometric technique addresses the non-linearity of the budget constraint by approximating the constraint by a series of discrete points. The results show that the budget constraint is indeed nonlinear, that hours affect the wage quadratically, that true wage elasticities are lower as a result, and that the model fits the hours distribution much better than the standard Tobit model.
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Citations
Cited by
Structural Models of Family Labor Supply: A Discrete Choice Approach
van Soest, A. (1995) · The Journal of Human Resources · doi:10.2307/146191 · Cited by 649
Chapter 2 Female labor supply: A survey
Killingsworth, M. R., & Heckman, J. J. (1986) · Handbook of Labor Economics · doi:10.1016/s1573-4463(86)01005-2 · Cited by 481
Labor Supply Preferences, Hours Constraints, and Hours-Wage Trade-offs
Altonji, J. G., & Paxson, C. H. (1988) · Journal of Labor Economics · doi:10.1086/298183 · Cited by 249
Why Do Women Wait? Matching, Wage Inequality, and the Incentives for Fertility Delay
Caucutt, E. M., Guner, N., & Knowles, J. (2002) · Review of Economic Dynamics · doi:10.1006/redy.2002.0190 · Cited by 198
New Developments in Econometric Methods for Labor Market Analysis
(1999) · Handbook of Labor Economics · doi:10.1016/s1573-4463(99)03005-9 · Cited by 152
Self-citation
Metadata
- Authors
- Moffitt, R.
- Venue
- Journal of Labor Economics
- Publisher
- University of Chicago Press
- Published
- 1984-10
- Volume
- 2
- Issue
- 4
- Pages
- 550-566
- ISSN
- 0734-306X (ISSN-L)
- 1537-5307
- OpenAlex
- W2047742453
- Cited by
- 190
- References
- 0
- FWCI
- 6.28
- Type
- journal article
- Language
- en
- Open access
- closed
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- Moffitt, R. (1984). The Estimation of a Joint Wage-Hours Labor Supply Model. Journal of Labor Economics. https://doi.org/10.1086/298047
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
