TY  - JOUR
A1  - Pu, Yingxia
A1  - Chi, Guangqing
A1  - Zhao, Xinyi
A1  - Zhao, Jin
A1  - Kong, Fanhua
T1  - A spatial dynamic panel approach to modelling the space-time dynamics of interprovincial migration flows in China
Y1  - 2019/10/09
JF  - Demographic Research
JO  - Demographic Research
SN  - 1435-9871
SP  - 913
EP  - 948
DO  - 10.4054/DemRes.2019.41.31
VL  - 41
IS  - 31
UR  - https://www.demographic-research.org/volumes/vol41/31/
L1  - https://www.demographic-research.org/volumes/vol41/31/41-31.pdf
L2  - https://www.demographic-research.org/volumes/vol41/31/41-31.pdf
L3  - https://www.demographic-research.org/volumes/vol41/31/files/readme.41-31.txt
L3  - https://www.demographic-research.org/volumes/vol41/31/files/demographic-research.41-31.zip
N2  - Background: Migration plays an increasingly crucial role in regional growth and development. However, most empirical studies fail to simultaneously capture the spatial and temporal aspects of migration flows, and thus fail to reveal how space-time dynamics shape path-dependent migration processes 

Objective: This study attempts to incorporate space-time dimensions into a traditional gravity model and to measure the impact of regional socioeconomic changes on migration flows. Doing so allows us to better understand the space-time dynamics of complex migration processes. 

Methods: We construct a spatial dynamic panel data model for dyadic migration flows and decompose origin, destination, and spillover effects into their contemporaneous, short-, and long-term components. We then apply this approach to panel data of interprovincial migration flows in China from 1985 to 2015. 

Results: The empirical results indicate that space-time interactions are crucial to the formation and evolution of migration flows. Population size and age structure play an important role in the Chinese interprovincial migration process, particularly dominated by significant and positive spillover effects in the contemporaneous period. The relationship between development and migration tends to take an inverted U-shaped curve, a fact not revealed in nonspatial models. 

Conclusions: This fine-grained decomposition of origin, destination, and spillover effects over contemporaneous, short-, and long-term periods can help understand the spatial and temporal mechanisms of migration and its driving factors. 

Contribution: We propose a spatial dynamic panel data model for migration flows and estimate space-time effects of regional explanatory variables. This method could be applied to model other flow data such as trade and transportation flows. 

ER  -