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ObjectiveThis manuscript evaluates and tests the group differences in migrant workers’ urban integration from the perspectives of individual characteristics and migration characteristics, so as to provide theoretical support and practical guidance for the government to issue more effective assistance policies.MethodsMultilevel comprehensive evaluation method and Entropy method are used to calculate the urban integration level of migrant workers, and one-way ANOVA and optimal scaling regression are used to test the group differences in migrant workers’ urban integration.ResultsBased on the questionnaire data of 854 migrant workers in China, the scale of migrant workers’ urban integration has good reliability and validity. The overall level of migrant workers’ urban integration is 49.61% and there exist group differences in migrant workers’ urban integration. The impact of education level, income level, and migration time on migrant workers’ urban integration is significantly positive, whereas the impact of migration distance on migrant workers’ urban integration is significantly negative. The urban integration level of migrant workers who have family members accompanying them is higher than that of migrant workers who have no family members accompanying them. Gender, age, and marriage have no significant impact on migrant workers’ urban integration.Strengths and Limitations of This StudyThis study aims to measure and test the group differences in migrant workers’ urban integration using ANOVA and optimal scaling regression. However, the shortcomings of this study are the selection of the “migrant workers’ urban integration” scale and the representativeness of the sample used in this study.ConclusionThere are group differences in migrant workers’ urban integration with different education levels, income levels, migration distances, migration times, and statuses of family members accompanying. In the policy of promoting migrant workers’ urban integration, we should accurately count the characteristics of migrant workers and give more attention to migrant workers with low education levels, low-income levels, long migration distances, short migration times, and no family accompany. |
