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Predicted Future Earnings and Choice of College Major
Abstract
Using Data from the National Longitudinal Survey of Young Men, the author of this paper examines the relationship between predicted future earnings for five broad fields of study and college students' choice of major. Conditional logit models of major choice that incorporate alternative predicted earnings variables are specified and estimated. The results indicate that, holding family background characteristics constant, individuals are likely to choose majors offering greater streams of future earnings rather than, as some have argued, majors with higher beginning earnings at the time of the choice. It is also found that earnings profiles corrected for self-selection bias have flattened for more recent graduates in business, liberal arts, and education. The life-cycle earnings in these disciplines appear to be more severely depressed than those in science and engineering.
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Citations
Cited by
Wang, M.-T., & Degol, J. (2013) · Developmental Review · doi:10.1016/j.dr.2013.08.001 · Cited by 837
Ability sorting and the returns to college major
Arcidiacono, P. (2004) · Journal of Econometrics · doi:10.1016/j.jeconom.2003.10.010 · Cited by 833
Torche, F. (2011) · American Journal of Sociology · doi:10.1086/661904 · Cited by 587
Determinants of College Major Choice: Identification using an Information Experiment
Wiswall, M., & Zafar, B. (2015) · The Review of Economic Studies · doi:10.1093/restud/rdu044 · Cited by 533
Horizontal Stratification in Postsecondary Education: Forms, Explanations, and Implications
Gerber, T. P., & Cheung, S. Y. (2008) · Annual Review of Sociology · doi:10.1146/annurev.soc.34.040507.134604 · Cited by 511
Metadata
- Authors
- Berger, M. C.
- Venue
- ILR Review
- Publisher
- SAGE Publications
- Published
- 1988-04
- Volume
- 41
- Issue
- 3
- Pages
- 418-429
- ISSN
- 0019-7939 (ISSN-L)
- 2162-271X
- OpenAlex
- W2122191627
- Cited by
- 358
- References
- 0
- FWCI
- 2.67
- Type
- journal article
- Language
- en
- Open access
- closed
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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.