What Influences Family Migration Decision of China’s New Generation Rural-urban Migrants? A Multilevel Logistic Regression Analysis

The massive scale of new-generation rural-urban migrants in China has attracted extensive scholarly attention in recent years. While previous studies on China’s rural migrant workers focus on migrants’ individual settlement intentions, migrant’s family migration decision-making and the intergenerational differences between the old-generation migrants and new-generation migrants are underexplored. Based on the data of 2017 China Migrants Dynamic Survey, this paper adopts a multilevel logistic regression approach to explore family and destination factors influencing family migration decision of China’s new generation rural migrant workers. The empirical results reveal that both the migrants’ family and destination attributes significantly influence their family migration decision. The demographic and socioeconomic characteristics of family have been pivotal factors underlying family migration decision of China’s new generation rural-urban migrants, while 16.9% of the chances is explained by between-destination differences. Self-employed migrants with housing properties in host cities, long migration duration and high-income level are more likely to migrate with their family members. Yet, the possibility of family migration is found to be significantly and negatively correlated with the age, education level, number of children and inter-provincial mobility of the new generation migrant workers. In addition, new generation rural-urban migrants’ family migration is more likely to be found in cities with service-oriented industry structure, better environmental quality, and higher hukou barriers which is possibly related to more job opportunities. These research findings not only complement the existing literature on China’s new generation rural-urban migrants, but also have important policy implications for reforming hukou system and enhancing social integration of rural-to-urban migrant population.