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Cited by 576

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Estimating the Effect of Personality on Male and Female Earnings

Mueller, Gerrit, Plug, Erik·2006·ILR Review

Abstract

The authors adopt the Five-Factor Model of personality structure to explore how personality affected the earnings of a large group of men and women who graduated from Wisconsin high schools in 1957 and were re-interviewed in 1992. All five basic traits—extroversion, agreeableness, conscientiousness, neuroticism, and openness to experience—had statistically significant positive or negative earnings effects, and together they appear to have had effects comparable to those commonly found for cognitive ability. Among men, substantial earnings advantages were associated with antagonism (the obverse of agreeableness), emotional stability (the obverse of neuroticism), and openness to experience; among women, with conscientiousness and openness to experience. Of the five traits, the evidence indicates that agreeableness had the greatest influence on gender differences in earnings: men were considerably more antagonistic (non-agreeable) than women, on average, and men alone were rewarded for that trait.

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Crossref · 10.1177/001979390606000101

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Metadata

Authors
Mueller, G., & Plug, E.
Venue
ILR Review
Published
2006-10
Volume
60
Issue
1
Pages
3-22
ISSN
  • 0019-7939 (ISSN-L)
  • 2162-271X
OpenAlex
W3124049559
Cited by
576
References
13
FWCI
25.48
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Estimating the Effect of Personality on Male and Female Earnings. https://app.cochat.ai/papers/10.1177/001979390606000101
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Mueller, G., & Plug, E. (2006). Estimating the Effect of Personality on Male and Female Earnings. ILR Review. https://doi.org/10.1177/001979390606000101

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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.