JOURNAL ARTICLE
Neoliberal migration states in the Gulf: High-skilled migrants, domestic socio-legal reforms and the centralized migration management in Saudi Arabia and the United Arab Emirates.
Published In: Asian & Pacific Migration Journal (Sage Publications Inc.), 2025, v. 34, n. 1. P. 133 1 of 3
Database: Sociology Source Ultimate 2 of 3
Authored By: Malit Jr, Froilan; AlShanqityi, Yusra 3 of 3
Abstract
This article examines how Gulf Cooperation Council (GCC) states, specifically Saudi Arabia and the United Arab Emirates (UAE), have shifted from a laissez-faire to a centralized migration management model to attract and retain high-skilled migrants. It argues that these Global South host states employ a combination of "hard" legal reforms (such as citizenship and residency by investment schemes) and "soft" socio-cultural and infrastructural reforms to optimize domestic rent-seeking and advance geopolitical interests. Through qualitative case studies, the article highlights how Saudi Arabia’s Premium Residence Scheme and the UAE’s Golden Visa program, alongside urban modernization and social liberalization efforts, serve as strategic tools to compete regionally and globally for skilled human capital essential to economic diversification and development. The study situates these reforms within the broader concept of neoliberal migration states in the Global South, emphasizing their political agency in leveraging migration governance for long-term economic and geopolitical gains.
Additional Information
- Source:Asian & Pacific Migration Journal (Sage Publications Inc.). 2025/03, Vol. 34, Issue 1, p133
- Document Type:Article
- Subject Area:Politics and Government
- Publication Date:2025
- ISSN:0117-1968
- DOI:10.1177/01171968251329562
- Accession Number:185388341
- Copyright Statement:Copyright of Asian & Pacific Migration Journal (Sage Publications Inc.) is the property of Sage Publications Inc. 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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