JOURNAL ARTICLE

Tradeoff between Gender and Class Equality: An Analysis of Tax Policy in Japan.

  • Published In: Social Politics: International Studies in Gender, State & Society, 2024, v. 31, n. 2. P. 376 1 of 3

  • Database: Sociology Source Ultimate 2 of 3

  • Authored By: Toyofuku, Miki 3 of 3

Abstract

This article examines the role of political parties' preferences in shaping economic gender inequality in Japan, focusing on the expansion of the spouse deduction in income tax policy. Contrary to the common view that conservative parties' preference for traditional gender roles drives policies that suppress women's labor force participation, the study finds that both conservative and leftist parties' shared preference for class equality—defined as reducing economic disparities between social classes while assuming families provide welfare—has contributed to tax policies that inadvertently increase gender inequality. Using text data from parliamentary statements and regression analyses of income tax reforms from 1961 to 2022, the research shows that parties' advocacy for class equality positively influenced the promotion of the spouse deduction, which supports lower-income families but discourages women's full labor participation. The findings suggest that achieving higher economic gender equality in conservative welfare regimes like Japan may require rethinking the tradeoff between class equality and gender equality, particularly by addressing the familialistic assumption that families are primarily responsible for welfare provision.

Additional Information

  • Source:Social Politics: International Studies in Gender, State & Society. 2024/06, Vol. 31, Issue 2, p376
  • Document Type:Article
  • Subject Area:Women's Studies and Feminism
  • Publication Date:2024
  • ISSN:1072-4745
  • DOI:10.1093/sp/jxad036
  • Accession Number:177948147
  • Copyright Statement:Copyright of Social Politics: International Studies in Gender, State & Society is the property of Oxford University Press / USA 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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