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

Modelling interprovincial migration in China from 1995 to 2015 based on an eigenvector spatial... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Population, Space and Place Wiley

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

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References (62)

Publisher
Wiley
Copyright
© 2019 John Wiley & Sons, Ltd.
ISSN
1544-8444
eISSN
1544-8452
DOI
10.1002/psp.2253
Publisher site
See Article on Publisher Site

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.

Journal

Population, Space and Place – Wiley

Published: Nov 1, 2019

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