The impact of spatial spillovers on interprovincial migration in China, 2005–10

Pu, Yingxia; Han, Xiao; Chi, Guangqing; Wang, Yaping; Ge, Ying; Kong, Fanhua

doi:10.1080/00343404.2018.1562173

Cited by 15 publications

(16 citation statements)

References 45 publications

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“…According to previous literature (Gu, Liu, et al, 2019; Gu, Shen, et al, 2019; Liu & Shen, 2014a, 2017; Niedomysl & Hansen, 2010; Pu et al, 2019; Shen, 2012), we model the migration of skilled and less‐skilled internal migrations in 2010–2015 using 21 variables of origins and destinations, including three gravity variables (population size at the origin, population size at the destination, and the distance between the origin and destination), and variables of economic opportunities, natural comforts, public services, environmental quality, service amenities, and several control variables. A region's economic opportunities can be represented by its average wage level, employment stability, and industrial structure (Gu, Meng, et al, 2019; Shen, 2012, 2015).…”

Section: Modelling China's Skilled and Less‐skilled Interprovincial Migrations 2010–2015mentioning

confidence: 92%

“…By doing so, network autocorrelation in model residuals is expected to reduce (Lesage & Pace, 2008). Besides, the results from spatial interaction models can provide the interpretation of the network spillover effect (Lesage & Pace, 2010; Pu et al, 2019). On the other side stands the spatial filtering models, which aim to decompose directly of the spatially structured components from spatial variables (Getis & Griffith, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, it is found significant network autocorrelation in China's internal migration, and different types of gravity models have been employed on modelling the internal migration in China (Gu, Liu, et al, 2019; Gu, Shen, et al, 2019; Liu & Shen, 2017; Pu et al, 2016; Pu et al, 2019). Nevertheless, few studies have been conducted on the model of two separate parts of the migration process using hurdle data analysis, although the data for skilled internal migration are found to comprise an excess of zeros (Liu & Shen, 2014a).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

2021

Population Space and Place

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In consideration of the issue of network autocorrelation and zero‐inflated migration data, the study constructs an eigenvector spatial filtering (ESF) hurdle gravity model (ESF HGM) to examine the determinants of China's skilled and less‐skilled internal migrations between 2010 and 2015. In our case, the ESF technique effectively reduces network autocorrelation bias, while the hurdle approach enhances the model prediction on the probability of zeros. Results from the ESF HGM have illustrated significant differences in the determinants between skilled and less‐skilled migrations. It is found that the gravity factors (population sizes at origins and destinations; migration distance), regional industrial structure, unemployment rate, and education level are more related to the migration of less‐skilled people. However, the migration of skilled people is more affected by the wage disparity, natural comforts, and medical services of a region. Our results have also highlighted the differences in some factors (employment rate; education level at origins) between the probability of having migration and the migration volume.

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Section: Modelling China's Skilled and Less‐skilled Interprovincial Migrations 2010–2015mentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

2021

Population Space and Place

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“…To assess the most important factors that influence regional attractiveness for highly qualified and highly educated personnel, an extended version of the widely used gravity model can be used (Andrienko & Guriev, 2004; Dotti et al, 2013; Lee, 1966; Pu et al, 2019; Vakulenko, 2016), which shows potential social and economic interactions and helps to assess the benefits of a region’s geographical position. The basic form of the model is the following:

V_{ij} = \sum \frac{{P^{α}}_{i} \times {P^{β}}_{j}}{{R_{ij}}^{a}}

…”

Section: Literature Overviewmentioning

confidence: 99%

Attracting highly skilled migrants to the Russian regions

Баринова

Rochhia

Земцов

2022

Regional Science Policy & Practice

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The study aims at identifying the role of traditional and new factors that contribute to attracting highly educated workers. We summarized the key literature facts and performed econometric analyses on previously unused data on both internal and external migration with higher education in the Russian regions from 2008 to 2019. Our methodology differs from traditional models based on migration flows between destinations and focuses on characteristics of receiving regions. We showed that densely populated metropolitan areas with broader labour markets opportunities stimulate highly skilled mobility; higher income, new vacancies and housing availability are among significant traditional factors. However, migrants with higher education

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Spatial–temporal dynamics and determinants of creative class concentration—A study on urban agglomerations in China

Cui

Hei

Nijkamp

et al. 2022

Population Space and Place

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The knowledge spillover effects and the uneven spatial distribution of the creative class have attracted much attention from academia and politicians. Our study provides a comparative quantitative analysis of the spatial–temporal evolution and determinants of creative class concentration in three Chinese agglomerations: Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Pearl River Delta, based on the panel data of 2011, 2012, and 2016 at the city level. It is found that the concentration of the creative class in the three above‐mentioned urban agglomerations in China has clear place‐specific differences. Results from the 2SLS regressions show that social tolerance is significantly and positively correlated with the creative class, but has no effect on the distribution of human capital in the three urban agglomerations. The level of economic development and also the degree of living convenience appear to be the main factors influencing the relative density of creative class (RDCC), while industry structure, university and Hukou all have positive effects on the RDCC in the three urban agglomerations. Moreover, the relative importance of the determinants varies according to the creative occupations. For major creative occupations, they are more inclined to concentrate in cities with high social tolerance, optimal industrial structure, high Hukou availability and qualified universities. For finance and health care occupations, cities with a comfortable climate are more attractive to them; while for management groups, cities with optimal industrial structure, high Hukou availability and adequate educational services are more attractive to them.

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The impact of spatial spillovers on interprovincial migration in China, 2005–10

Cited by 15 publications

References 45 publications

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

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

Attracting highly skilled migrants to the Russian regions

Spatial–temporal dynamics and determinants of creative class concentration—A study on urban agglomerations in China

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