Bangladeshi women migrants amidst the COVID‐19 pandemic: Revisiting globalization, dependency and gendered precarity in South–South labour migration.
Published In: Global Networks, 2023, v. 23, n. 1. P. 31 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Ansar, Anas 3 of 3
Abstract
The COVID‐19 pandemic has triggered unprecedented societal disruption and disproportionately affected global mobility dynamics. Within such a troubled and intensifying crisis, the intersection of migration and gender is even more unsettling. Since the pandemic outbreak, Bangladesh witnessed a colossal crisis among millions of Bangladeshi migrants working overseas—a considerable section of them are women. By highlighting the plight of the Bangladeshi women migrants in the Gulf Cooperation Council (GCC) countries, this study expands the emerging literature that addresses the nexus among migration, pandemic fallout and gendered labour. Redrawing our understanding of globalization from below, the study attempts to further advance the theoretical perspectives on the predicaments of globalization and gendered precarity in contract labour migration. The study argues that the focus on the power asymmetry between the host and sending countries remains too limited to provide a comprehensive understanding of how inequalities are reproduced and transformed. Instead, it suggests that the challenges and disadvantages women migrants endure are embedded in the asymmetries of deep‐rooted economic and social structures in tandem with the systemic practice of otherness and exclusion. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Global Networks. 2023/01, Vol. 23, Issue 1, p31
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2023
- ISSN:1470-2266
- DOI:10.1111/glob.12368
- Accession Number:160899833
- Copyright Statement:Copyright of Global Networks 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.