Reintegration Barriers and Prospects on Emotional and Psychological Well-Being of Gulf Returnees in Kerala
Abstract
This study examines the reintegration barriers and prospects affecting the emotional and psychological well-being of Gulf returnees in Kerala. The research analyses the difficulties encountered by return migrants across five key dimensions: financial constraints, limited government support (GS), family reintegration challenges, societal reintegration barriers, and career transition difficulties. Primary data were collected from 768 return migrants across selected districts in Kerala identified through the Kerala Migration Survey (2018). Using Structural Equation Modeling, the study identifies the most significant factors affecting the reintegration process. The findings reveal that economic difficulties—particularly debt, declining income, and reduced savings—and societal reintegration challenges represent the strongest barriers. Societal barriers mainly include difficulty rebuilding social networks, perceived social discrimination, and limited participation in local community organizations. Government program access also emerged as a major concern due to lack of awareness and bureaucratic constraints. In contrast, family reintegration and career transition factors showed comparatively weaker influence on overall reintegration challenges. The demographic analysis indicates that most returnees are middle-aged Muslim men who worked in low- to medium-skilled occupations in Gulf countries for more than a decade. The study highlights how economic insecurity and social marginalization indirectly affect the emotional and psychological well-being of return migrants. These findings underscore the need for targeted policy interventions, including improved financial assistance, accessible GS programs, and community-based reintegration initiatives to facilitate sustainable socio-economic and psychological adjustment for Gulf returnees in Kerala.
© 2026 Nithin Ninan Thomas, KM Anu, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.