Impact of opponents' race, gender, and party on U.S. congressional fundraising.

  • Published In: Social Science Quarterly (Wiley-Blackwell), 2024, v. 105, n. 3. P. 544 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Halcoussis, Dennis 3 of 3

Abstract

Objective: A donation for a candidate can be motivated by support for that candidate or by opposition to the candidate's opponent. This study tests the impact that race, gender, and party affiliation of the candidate and the candidate's opponent have on the candidate's fundraising. Methods: This study uses data from the 2016, 2018, and 2020 U.S. congressional elections to estimate a regression model where the dependent variable is funds raised by each mainstream party candidate, with party, race, and gender of the candidate and the candidate's opponent accounted for in the model, as well as district competitiveness, district economic and demographic characteristics, and whether the seat is open. Results: Female Democrats and non‐white male Democrats have a fundraising advantage when running against a white male Republican. Female Republicans or non‐white male Republicans do not have this advantage when running against white male Democrats. Conclusion: The interaction effects of gender and race on fundraising for a candidate and opponent are different depending on party affiliation, and the characteristics of both the candidate and the candidate's opponent must be considered for these effects to be visible. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Social Science Quarterly (Wiley-Blackwell). 2024/05, Vol. 105, Issue 3, p544
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2024
  • ISSN:0038-4941
  • DOI:10.1111/ssqu.13369
  • Accession Number:177532328
  • Copyright Statement:Copyright of Social Science Quarterly (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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