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

Abstract

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.

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

“…People intend to move to big cities that can offer them better‐living conditions, while social benefits are tied to what kinds of hukou they have (Gu, Liu, Shen, & Meng, 2019). It is the “screening and moderating effect” of China's hukou system that excludes the floating population from civil rights and benefits (Chan, 2009; Huang, 2010; Liu, 2005; Nielsen, Smyth, & Liu, 2011; Whalley & Zhang, 2004).…”

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

“…However, the residuals from the GWR and the MGWR models do not depict any significant spatial autocorrelation. Through these tests, it can be concluded that even though GWR and MGWR models are primarily employed to tackle the spatial heterogeneity within variables, the issues concerning spatial autocorrelation have also been addressed (Gu, Liu, et al., 2019). The Moran's I statistic of the residuals from the MGWR model is furthermore seen to be lower than that of the residuals from the GWR model, indicating that the MGWR model specification is more potent in filtering spatial autocorrelation.…”

Modelling skilled and less‐skilled internal migrations in China, 2010–2015: Application of an eigenvector spatial filtering hurdle gravity approach

“…An essential assumption of the gravity model is the independence of the observations, which might be violated when there is significant network autocorrelation in the data (Chun, 2008; Gu, Liu, et al, 2019; Gu, Shen, et al, 2019). Network autocorrelation can be interpreted that one origin–destination (OD) flow is correlated with its nearby OD flows.…”

“…Eigenvector spatial filtering (ESF) is considered the representative of all the spatial filtering models (Griffith, 2003), where eigenvectors can serve as proxies for distinct network autocorrelation patterns; thus, by entering selected eigenvectors into gravity models, we can split the network autocorrelation from the residuals and alleviate the issue of network autocorrelation (Griffith, 2003). In comparison with other methods for network autocorrelation, the flexibility of ESF enables its adaption to different types of gravity models (Chun, 2008; Chun & Griffith, 2011; Gu, Liu, et al, 2019; Gu, Shen, et al, 2019; Liu & Shen, 2017).…”

“…From 1985 to 2015, China's interprovincial migration scale increased tremendously from 11.03 million to 53.28 million. Using data from population censuses and sample surveys, researchers have given increasing attention to the spatial pattern and determinants of China's internal migration (Fan, 2005; Gu, Liu, et al, 2019; Gu, Shen, et al, 2019; Liu & Shen, 2014a, 2014b, 2017; Pu et al, 2019; Shen, 2012, 2013, 2015). However, most of the prior literature has taken the migrants in China as a homogeneous group, failing to capture the various characteristics of different and specific groups of migrants.…”