JOURNAL ARTICLE
Do Men Care about Childcare? Women's Relative Resources and Men's Preferences for Work–Family Reconciliation Policies.
Published In: Social Politics: International Studies in Gender, State & Society, 2024, v. 31, n. 2. P. 321 1 of 3
Database: Sociology Source Ultimate 2 of 3
Authored By: Estévez-Abe, Margarita; Lim, Tae Hyun 3 of 3
Abstract
This article examines how women's relative resources within households influence men's preferences for work–family reconciliation policies, focusing on subsidized childcare and the gender-egalitarian splitting of paid parental leave. Using regression analysis of survey data from twenty OECD countries in the International Social Survey Program (ISSP) 2012 module on Family and Changing Gender Roles IV, the study finds that men in dual-earner and college-educated homogamous households are more likely to support subsidized childcare, while men's support for splitting parental leave is significantly associated with women's educational attainment rather than their relative income. The findings suggest that men's policy preferences are shaped not only by economic considerations but also by women's education, reflecting household bargaining dynamics and possibly assortative mating. The study highlights the importance of considering male preferences and household contexts in the politics of reconciliation policies and notes limitations related to data on partners and couple types.
Additional Information
- Source:Social Politics: International Studies in Gender, State & Society. 2024/06, Vol. 31, Issue 2, p321
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2024
- ISSN:1072-4745
- DOI:10.1093/sp/jxae002
- Accession Number:177948149
- 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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