Modelling Skilled and Less‐Skilled Interregional Migrations in China, 2000–2005

Millions of Chinese migrants have moved from the countryside to cities to seek job opportunities and a better life. Under the policy shift from “land‐based urbanisation” to “people‐oriented urbanisation,” it is important to understand what determines migrants' settlement intentions. Although previous studies have primarily focused on sociodemographic impacts on settlement intention, the role of city‐level contexts is understudied. Drawing upon data, the 2015 Migrant Dynamic Monitoring Survey in the Yangtze River Delta, this paper addresses this gap by examining the impact of contextual features in host cities, including population size, employment structure, wage levels, and house prices on migrants' settlement intentions. We find that house prices are negatively associated with migrants' decision to settle, and wage levels have a positive effect on migrants with tertiary education. Cities with over 10 million residents or high administrative status are particularly attractive to migrants wishing to settle in urban environments.

Section: Urban Economic (Dis)incentives Education and Migrants' Setsupporting

confidence: 85%

Settlement intention of migrants in the Yangtze River Delta, China: The importance of city‐scale contextual effects

Dang

Chen

Dong

2019

“…The third specification (Equation ) combines these two effects together. In recent studies, more researchers used the third specification to define network weight matrix, and they showed the improvement of considering the competing destination effects and the intervening opportunities effects simultaneously into a network model (e.g., Almeida & Gonçalves, ; Chun, ; Gu et al, ; Guldmann, ; Liu & Shen, ). In this research, we defined a network weight matrix using the third specification.…”

Section: Methodsmentioning

confidence: 99%

“…Using an ESF Poisson GM, Shen () modelled the determinants of interprovincial migration in China from 2005 to 2010, and Shen employed an error analysis based on this model to see which factors of the migration process could make the model more accurate. Extending the ESF Poisson migration model to the ESF negative binomial migration model, Liu and Shen () examined the determinants of skilled and less skilled interprovincial migration in China, and they found that distance, unemployment rate, and foreign direct investment affected skilled worker migration moderately, but wages had a greater effect.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling interprovincial migration in China from 1995 to 2015 based on an eigenvector spatial filtering negative binomial model

Liu

Meng

2019

Interregional migration is a key issue affecting China's future pattern of urbanisation and regional development. In response to the phenomenon of network autocorrelation (NA) commonly found in migration networks, this paper combines eigenvector spatial filtering (ESF) with a negative binomial gravity model based on four‐stage panel data derived from censuses and population sampling surveys, and it analyses the factors that influenced China's interprovincial migration from 1995 to 2015. The results showed that (a) there is a significant spatial spillover effect in the interprovincial migration network. ESF can effectively capture NA in the data to reduce the model's estimation bias. The top 1.4% eigenvectors can extract high NA. (b) There is overdispersion in the interprovincial migration flows. A negative binomial regression gravity model is more appropriate for estimating the driving mechanism for migration than other models. (c) As with the initial variables of the gravity model, population size still exerts a great impact on both outflows and inflows. After considering the influence of NA, the effect of spatial distance is weakening. Additionally, economic, employment, social security, and educational factors are the main forces that are shaping the pattern of interprovincial migration. If the regional unemployment rate and average wages increase by 1%, the outflows and inflows increase by 0.351% and 0.502%, respectively; the coefficient of regional sex ratio at origin is also high, which has a close relationship with the migration motivations and gender differences in the employment market.

“…Recent years have witnessed the combination of the gravity model and the spatial OD model (or ESF) in several empirical studies on determinants of China's interprovincial migration, whereas such spatial models have not been applied in topics on interregional migration intentions. Using the ESF negative binomial regression model, Liu and Shen () examined the determinants of skilled and less skilled interprovincial migration in China and found that skilled migrants were less influenced by factors such as distance and unemployment rate compared with less skilled migrants. Besides, Pu, Han, Ge, and Kong () built the spatial OD model of interprovincial migration flows on the basis of the sixth national population census data of China and concluded that the multilateral effects of the regional economic and social factors through the spatial network system led to the clustering migration flows across interrelated regions.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Liu

Shen

2019

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.