JOURNAL ARTICLE
The impacts of CAFTA-DR on gender inequality and apparel trade in El Salvador.
Published In: International Journal of Sustainable Fashion & Textiles, 2025, v. 4, n. 2. P. 179 1 of 3
Database: Textile Technology Complete 2 of 3
Authored By: Robertson, Cydni Meredith; Ha-Brookshire, Jung 3 of 3
Abstract
This article examines the impact of the Dominican Republic–Central America Free Trade Agreement (CAFTA-DR), implemented in 2006, on gender inequality and apparel trade in El Salvador, the leading apparel manufacturing employer in Central America. Using a mixed-methods approach combining multivariate regression analysis of United Nations Development Programme Gender Inequality Index (GII) indicators—maternal mortality rate, adolescent birth rate, secondary education attainment, women in parliament seats, and labor force participation—with in-depth interviews of Salvadoran women working in apparel factories, the study finds that CAFTA-DR and associated apparel export and import growth have positively influenced four of the five GII indicators over time. Qualitative data reveal themes of maternal responsibility, prioritization of education and career before childbearing, realization of educational aspirations, and the importance of women's networking in the apparel workforce. The findings suggest that international trade policies like CAFTA-DR can contribute to economic growth and gender equality, while highlighting the need for gender-specific labor protections and supportive programs within the global apparel supply chain.
Additional Information
- Source:International Journal of Sustainable Fashion & Textiles. 2025/11, Vol. 4, Issue 2, p179
- Document Type:Article
- Subject Area:Geography and Cartography
- Publication Date:2025
- ISSN:2754-026X
- DOI:10.1386/sft_00063_1
- Accession Number:190475799
- Copyright Statement:Copyright of International Journal of Sustainable Fashion & Textiles is the property of Intellect Ltd. 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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