Australian Parental Leave Policy, Employers' Cognitive Bias, and Mothers' Wages: Penalty or Premium?
Published In: Gender, Work & Organization, 2025, v. 32, n. 5. P. 1771 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Lee, Dongju; Craig, Lyn 3 of 3
Abstract
This paper utilizes a natural experiment created by Australia's first national mandate for paid parental leave in 2011, to investigate employer‐side mechanisms in motherhood wage penalties. Drawing on literature on cognitive bias, we hypothesize that maternity leave taking behaviors could trigger employer discrimination. We test this proposition by comparing whether and how four types of leave‐taking behaviors affect the wage prospects of working mothers. Using fixed effect models with lagged dependent variables and nationally representative panel data, the Household Income and Labor Dynamics in Australia (HILDA) for the period 2005–2019, this study reveals that before the mandate, mothers who had to use unpaid leave due to ineligibility for employer‐funded leave suffered pay penalties. After the mandate, mothers who forwent paid leave received pay premiums. Our study contributes to debates about parental leave policy and gender discrimination in the labor market by indicating that employers interpret contrasting leave taking behaviors differently, and reward employees in accordance with what they believe maternity leave behaviors imply about working mothers' conformity to the "ideal worker" norm. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Gender, Work & Organization. 2025/09, Vol. 32, Issue 5, p1771
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2025
- ISSN:0968-6673
- DOI:10.1111/gwao.13216
- Accession Number:187236828
- Copyright Statement:Copyright of Gender, Work & Organization 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.