Correction to: How socioeconomic and environmental factors impact the migration destination choices of different population groups in China: an eigenfunction-based spatial filtering analysis

Yu, Danlin; Zhang, Yaojun; Wu, Xiwei

doi:10.1007/s11111-020-00344-8

Cited by 6 publications

(3 citation statements)

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“…The spatial lag Durbin model applied in this study is inherently related to the diffusion theory of fertility decline. Recently, some studies have already recognised the usefulness of spatial statistical methods for demographic research (Evans & Gray, 2018;Goldstein & Klüsener, 2014;Sabater & Graham, 2018;Vitali & Billari, 2017;Wang & Chi, 2017;Yu et al, 2020). The exploration, however, merits further investigation to better understand the nature and dynamics of demographic variations in China, where extremely rapid changes are underway in economic, social, and demographic realms.…”

Section: Evidence From Comparison Longitudinal Analysismentioning

confidence: 99%

Low fertility spread in China: A blended adaptation and diffusion explanation

Zhang

et al. 2022

Population Space and Place

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Based on blended diffusion theory which reconciles the two mechanisms of fertility change (due to adaptation to social structure change or due to diffusion), this study examines the role of adaptation and diffusion of China's low fertility in the first decade of the 21st century. A two-period panel data set on fertility for 358 prefecture-level administrative units assembled in 2000 and 361 in 2010 population census is used. Exploratory and confirmatory analysis techniques from spatial econometrics are applied to analyze this data. Results from exploratory spatial data analysis reveal a general spatial autocorrelation for fertility and its change. Inspection with local indicators of spatial autocorrelation identifies local spatial clusters of fertility: high-value clusters in the western region and low-value clusters in northeastern and east coast areas. Total fertility rates and its change are modelled with socioeconomic and demographic factors using a spatial lag Durbin model. The results suggest that the spread of low fertility in China tends to be consistent with blended diffusion theory. Low fertility in China not only adapts to economic growth, social development and even institution structure, but also influenced by diffusion of new attitudes and ideas related to childbearing and family formation. More importantly, adaptation diffuses too. Socioeconomic circumstances in neighbouring cities often are strongly interdependent. This study highlights the importance of 'bringing space back' to demographic research.

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Section: Evidence From Comparison Longitudinal Analysismentioning

confidence: 99%

Low fertility spread in China: A blended adaptation and diffusion explanation

Zhang

et al. 2022

Population Space and Place

Self Cite

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“…In addition, many secondary industries were state-owned enterprises during in 2005 which often had relatively rigid rules when recruiting new employees. Higher proportion of secondary industries might be regarded as cities with less job opportunities (C. Chen & Zhao, 2017;Hao & Tang, 2018;Yu et al, 2020).…”

Section: Findings and Discussionmentioning

confidence: 99%

China's place attractivity, population mobility and its mechanisms: Perspectives from a full spectrum of spatial analyses

Zhang

2022

Population Space and Place

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Understanding the potential mechanisms of population mobility is important in population studies. The current study calculates a place attractivity score and collects 12 socioeconomic and natural condition factors at the prefecture-level in China. A full spectrum of spatial autoregression and eigenfunction-based spatial filtering models are employed to investigate the relationship between place attractivity and the factors at the global level, which assumes the relationship stays the same everywhere. A random-effects eigenfunction-based spatial filtering spatially varying coefficient model is used to check the relationships at local level, which admits that relationships might be different in different locations. Results suggest that the eigenvector spatial filtering analysis models perform better than other models. China's population mobility is primarily driven by economic push-pull factors. Accessibility, job opportunity, temperature and terrain also play roles in determining a prefecture's attractivity.Local analysis suggests that the relationships are likely only true in large migration destinations.

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“…The driving factors behind the formation and evolution of the spatial distribution of migrant populations also vary over time. As the two key driving forces, the government and the market promote each other, maintain checks and balances with each other, and exert a continuous influence on migrant populations [31][32][33]. Thus, the flow and spatial distribution of the school-age migrant population will inevitably be affected by factors, such as the economic and social development level and policy environment of the inflow area, and will be both promoted and restricted by government and market forces.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Differentiation of the School-Age Migrant Population in Liaoning Province, China, and Its Driving Factors

Song

Sun

et al. 2021

Land

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This study analyzed the spatiotemporal distribution and driving factors of the floating school-age population in Liaoning Province, China from 2008 to 2020 using county-level statistical education data combined with spatial autocorrelation and the multiscale geographically weighted regression model. The major findings are as follows. From 2008 to 2020, the distribution of the school-age migrant population exhibited obvious spatial imbalance characteristics both in terms of the number and proportion of school-age migrants. Specifically, the school-age migrant population was concentrated in the municipal districts of large and medium-sized cities and continued to increase over time in the suburbs of large and medium-sized cities. Over the past 12 years, the distribution of the school-age migrant population in Liaoning Province exhibited significant spatial autocorrelation. From the number of school-age migrants, the cold and hot spot area expanded. Conversely, from the proportion of school-age migrants, the cold and hot spot area decreased gradually, whereas the cold spot area became more diffuse. Regarding the driving factors, the quantity and quality of teaching staff, the quality of teaching equipment and conditions, and the quality of the education environment played a role in promoting or restraining the differentiation of the school-age migrant population in Liaoning Province. Moreover, the degree of influence of the driving factors exhibited substantial spatial differences.

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Correction to: How socioeconomic and environmental factors impact the migration destination choices of different population groups in China: an eigenfunction-based spatial filtering analysis

Cited by 6 publications

References 0 publications

Low fertility spread in China: A blended adaptation and diffusion explanation

Low fertility spread in China: A blended adaptation and diffusion explanation

China's place attractivity, population mobility and its mechanisms: Perspectives from a full spectrum of spatial analyses

Spatiotemporal Differentiation of the School-Age Migrant Population in Liaoning Province, China, and Its Driving Factors

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