Li Haibo, Li Haitao, Guo Shengyu, Fan Xuelong, Liu Feiyue
Department of Economics and Management, Changsha University, Changsha, China.
Bank of Leiyang Sub-Branch, Bank of China, Leiyang, China.
Front Integr Neurosci. 2022 May 3;16:813867. doi: 10.3389/fnint.2022.813867. eCollection 2022.
This 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.
Multilevel 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.
Based 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.
This 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.
There 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.
本论文从个体特征和迁移特征的角度评估和检验农民工城市融入的群体差异，以便为政府出台更有效的帮扶政策提供理论支持和实践指导。
运用多层次综合评价法和熵值法计算农民工的城市融入水平，并采用单因素方差分析和最优尺度回归检验农民工城市融入的群体差异。
基于对中国854名农民工的问卷调查数据，农民工城市融入量表具有良好的信效度。农民工城市融入的总体水平为49.61%，且农民工城市融入存在群体差异。受教育程度、收入水平和迁移时间对农民工城市融入的影响显著为正，而迁移距离对农民工城市融入的影响显著为负。有家庭成员陪同的农民工的城市融入水平高于无家庭成员陪同的农民工。性别、年龄和婚姻状况对农民工城市融入无显著影响。
本研究旨在使用方差分析和最优尺度回归来测量和检验农民工城市融入的群体差异。然而，本研究的不足在于“农民工城市融入”量表的选取以及本研究所用样本的代表性。
不同受教育程度、收入水平、迁移距离、迁移时间以及是否有家庭成员陪同的农民工在城市融入方面存在群体差异。在促进农民工城市融入的政策中，应精准统计农民工特征，更加关注受教育程度低、收入水平低、迁移距离长、迁移时间短且无家人陪同的农民工。
