JOURNAL ARTICLE

Older Working Persons and the Gender Pay Gap: Estimations Using Gender Norm Variables in Peru.

  • Published In: Social Politics: International Studies in Gender, State & Society, 2023, v. 30, n. 4. P. 1186 1 of 3

  • Database: Sociology Source Ultimate 2 of 3

  • Authored By: Saco, María Amparo Cruz; Gil, Mirian; Vergaray, Valeria 3 of 3

Abstract

This article examines the gender pay gap among older working persons (aged 60 and above) in Peru, finding an average national gap of 68 percent, which is substantially larger than that observed among younger workers. Using a Mincer-type labor income model and Oaxaca–Blinder decomposition on household survey data from 2004 to 2021, the study incorporates three gender norm variables—being head of household, receiving private family transfers, and political participation—that significantly increase the explained portion of the pay gap, highlighting the role of cultural and social gender norms in perpetuating income disparities. The gender pay gap is highest in urban areas (around 80 percent) and varies regionally, with the model explaining more of the gap in rural and non-Metropolitan Lima areas than in the capital's modern districts. The authors recommend expanding social pension coverage and adopting gender-sensitive labor policies to address persistent inequities, while suggesting further research on intersecting identities and the impact of COVID-19 on the pay gap.

Additional Information

  • Source:Social Politics: International Studies in Gender, State & Society. 2023/12, Vol. 30, Issue 4, p1186
  • Document Type:Article
  • Subject Area:Women's Studies and Feminism
  • Publication Date:2023
  • ISSN:1072-4745
  • DOI:10.1093/sp/jxad022
  • Accession Number:174292043
  • 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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