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Cited by 379

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

The Primary Reason for Women's Underrepresentation? Reevaluating the Conventional Wisdom

Open access
Lawless, Jennifer L., Pearson, Kathryn·2008·The Journal of Politics

Abstract

When women run in general elections for the U.S. House of Representatives, they win at approximately the same rates as their male counterparts. With the exception of studies of selected congressional districts in particular years, however, scholars have virtually ignored the gender dynamics of the congressional primary process. In this paper, we fill this void, analyzing data from 1958 to 2004 to test hypotheses about women's victory rates and levels of primary competition. Our analysis results in an additional explanation for women's underrepresentation: the congressional primary process. Although women generally do not win primaries at lower rates than their male counterparts, women in both parties face more primary competition than do men. Gender neutral victory rates, then, are not the result of a gender neutral primary process. Women have to be “better” than their male counterparts in order to fare equally well.

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Metadata

Authors
Lawless, J. L., & Pearson, K.
Venue
The Journal of Politics
Published
2008-01
Volume
70
Issue
1
Pages
67-82
ISSN
  • 0022-3816 (ISSN-L)
  • 1468-2508
OpenAlex
W171876484
Cited by
379
References
15
FWCI
56.00
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: The Primary Reason for Women's Underrepresentation? Reevaluating the Conventional Wisdom. https://app.cochat.ai/papers/10.1017/s002238160708005x
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Lawless, J. L., & Pearson, K. (2008). The Primary Reason for Women's Underrepresentation? Reevaluating the Conventional Wisdom. The Journal of Politics. https://doi.org/10.1017/s002238160708005x

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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.