The DOI matches its Crossref record.
The Nonprofit Worker in a For-Profit World
Abstract
This article investigates the experience of white-collar workers in nonprofit firms. The theoretical model of the nonprofit labor market suggests that workers supply labor to nonprofit organizations at lower than market wages in return for the opportunity to provide goods with positive social externalities. Average nonprofit wage differentials of approximately -0.20 and -0.05 are estimated for managers and professionals and for clerical and sales workers, respectively, in two national worker data sets. Further research is needed for more conclusive evidence concerning the possibility that the estimated differential may reflect low-quality workers selecting work in the nonprofit sector.
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Citations
Cited by
Economic Perspectives on Corporate Social Responsibility
Kitzmueller, M., & Shimshack, J. (2012) · Journal of Economic Literature · doi:10.1257/jel.50.1.51 · Cited by 1,116
Spinning gold: The financial returns to stakeholder engagement
Henisz, W. J., Dorobantu, S., & Nartey, L. J. (2014) · Strategic Management Journal · doi:10.1002/smj.2180 · Cited by 546
Metadata
- Authors
- Preston, A. E.
- Venue
- Journal of Labor Economics
- Publisher
- University of Chicago Press
- Published
- 1989-10
- Volume
- 7
- Issue
- 4
- Pages
- 438-463
- ISSN
- 0734-306X (ISSN-L)
- 1537-5307
- OpenAlex
- W2005102024
- Cited by
- 431
- References
- 0
- FWCI
- 16.96
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: The Nonprofit Worker in a For-Profit World. https://app.cochat.ai/papers/10.1086/298216
- The paper
- Preston, A. E. (1989). The Nonprofit Worker in a For-Profit World. Journal of Labor Economics. https://doi.org/10.1086/298216
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.
