Spatial pattern and determinants of migrant workers' interprovincial hukou transfer intention in China: Evidence from a National Migrant Population Dynamic Monitoring Survey in 2016

Abstract

Using 2016 data from the National Migrant Population Dynamic Monitoring, this article establishes a relationship network to describe migrant workers' interprovincial hukou transfer intention in China. Spatial analysis methods and the eigenvector spatial filtering gravity model are employed to examine the spatial pattern and determinants of the hukou transfer intention network. The results show that (a) most interprovincial migrant workers in China are less educated, middle aged, with middle‐ or low‐income levels, and job oriented, and their average interprovincial hukou transfer intention is 0.361; (b) there is significant network autocorrelation in the intention network, which presents a clustered and unbalanced spatial pattern where higher ranking intention flows from relatively less developed regions to more developed megacities; (c) provinces' hukou attractiveness to migrant workers demonstrates a random spatial pattern, but the hukou exclusion patterns are spatially concentrated: The north‐east provinces are hotspots, whereas several central and north‐east provinces are cold spots; (d) among geographical factors, distance exerts a negative influence on migrant workers' hukou transfer intention, whereas population size does not matter at origin or destination. Socio‐economic factors, especially disposable income, play the most significant role in impacting migrant workers' hukou transfer intention; and (e) as for individual factors, migrant workers' interprovincial migration is an economic decision based on family development: Those with more children, higher ratios of income to cost, or higher education levels are reluctant to transfer their hukous. Besides, the job and housing conditions of migrant workers are also closely related to their hukou transfer intentions.

Beyond neighbouring: Migrants' place attachment to their host cities in China

“…This sampling method enables migrants in each city to be selected randomly, but it cannot guarantee randomness when considering the eight cities together as a whole sampling frame. Despite the above drawback in sampling method, this provides so far one of the most reliable and comprehensive datasets for understanding migrant social integration in China, and it has been used widely in different studies (Gu, Liu, & Shen, 2020; Lin et al, 2016; Zheng, Song, & Sun, 2020). In the survey, migrants were defined as individuals whose hukou was not registered in the host city, but who had resided in the city for at least 1 month and whose age was between 15 and 59 at the time of the survey.…”

Unveiling various spatial patterns of determinants of hukou transfer intentions in China: A multi‐scale geographically weighted regression approach

“…Second, temporary migrants can enjoy the benefits of both origin and destination places, instead of giving up benefits from original hukou (such as contracted farmland and rural homestead) (Fan, 2011). Third, the residence intention is a comprehensive consideration based on the evaluation of economic benefit‐cost and payment capacity, which is more influenced by competence factors including human capital, socio‐economic status, and social integration level (Gu, Liu, & Shen, 2020); while the hukou transfer intention is a decision process based on the social benefit‐cost measurement with diverse expected return forms, which is more affected by pull forces of migration, such as the abundance of public services (including employment, welfare, education) in destination cities, as well as the family demands of bringing children to the destination cities to live and accept formal education (Lin & Zhu, 2016).…”

Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression

“…The results show that the Gini index of the distribution of researchers is 0.746, which indicates that financially the spatial pattern of Chinese researchers is highly uneven. This could be related to the uneven distribution of job opportunities, urban public services, and other resources inherent to the administrative spatial units of the city (Gu, Jie, et al., 2020; Gu, Liu, et al., 2020).…”