Analyzing gender gaps in bicameral legislatures: How asymmetrical institutions affect the supply and demand for female candidates.

  • Published In: Legislative Studies Quarterly, 2025, v. 50, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ono, Yoshikuni; Kasuya, Yuko; Miwa, Hirofumi 3 of 3

Abstract

In bicameral legislatures, gender representation varies significantly between chambers. Historically, Japan's upper house has maintained a proportion of women twice that of the lower house. However, electoral systems alone cannot fully explain this disparity. We argue that seemingly gender‐neutral legislative institutions influence both voting behavior and candidates' willingness to run, contributing to significant disparities in gender representation in bicameral legislatures. To test this argument, we conduct two survey experiments exploring the underlying mechanisms from the perspectives of voters and candidates. Our findings suggest that informing voters about the upper house's subordinate role increases support for female candidates in upper house elections. Furthermore, women display a greater willingness to pursue office when assured of job security in the upper house, while men exhibit less interest when made aware of its limited authority to appoint the prime minister. This study enhances our current understanding of the effects of asymmetrical institutions between chambers from a gender perspective. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Legislative Studies Quarterly. 2025/08, Vol. 50, Issue 3, p1
  • Document Type:Article
  • Subject Area:Politics and Government
  • Publication Date:2025
  • ISSN:0362-9805
  • DOI:10.1111/lsq.12493
  • Accession Number:187616074
  • Copyright Statement:Copyright of Legislative Studies Quarterly 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.