Do you feel accepted? Perceived acceptance and its spatially varying determinants of migrant workers among Chinese cities. (June 2022)

• Record Type: Journal Article
• Authors: Gu, Hengyu; Lin, Yuhao; Shen, Tiyan
• Is Part Of: Cities. Volume 125(2022)
• Publication Date: 2022-06
• Subjects: Perceived acceptance -- Migrant workers -- Spatial patterns -- Spatially varying determinants -- Multiscale geographically weighted regression (MGWR) -- Chinese cities; City planning -- Periodicals; Urban policy -- Periodicals; 711.4
• DOI: 10.1016/j.cities.2022.103626
• Languages: English
• ISSNs: 0264-2751

Abstract

Abstract: Attention to and investigation of the perceived acceptance of rural-urban migrants are crucial for understanding their subjective wellbeing, social integration, and living status in receiving cities in China and have pronounced academic and policy significance in the post-reform rapid-urbanisation era. Supported by a nationwide survey on the floating population, the article elaborates on the geography of migrant workers' perceived acceptance and its spatially varying determinants. It reveals that migrant workers in wealthier, southeastern coastal cities perceive a higher degree of non-acceptance. Guided by a conceptual framework incorporating factors of individual characteristics, migration status, and external incentives, a multiscale geographically weighted regression (MGWR) approach is applied, unveiling gradient differentiation patterns of some determinants (educational attainment, employment industry, interprovincial migration, dialect diversity, GDP per capita, hometown location), and clustered spatial patterns of others (average age, marital status, migrating with families). Three latent mechanisms are responsible for the heterogeneity of perceived acceptance: socioeconomic externalities, cultural and custom differences, and group differences of migrants. Further, the influencing scales for individual characteristic variables and some migration status variables are smaller, while those for external incentive variables and other migration status variables are larger. Our findings provide references to formulate targeted governance policies for migrant workers. Highlights: Migrant workers in wealthier, southeastern coastal cities perceive a higher degree of non-acceptance. A conceptual framework incorporating factors of individual characteristics, migration status, and external incentives is proposed. A multiscale geographically weighted regression (MGWR) approach is applied. Three latent mechanisms are responsible for the heterogeneity of perceived acceptance. The influencing scales for variables of individual characteristics, migration status, and external incentives are heterogeneous.