Migration flows are affected by both origin and destination attributes as well as spatial interaction between them.However,most studies build their insights on one-step gravity spatial interaction models,which fail to...Migration flows are affected by both origin and destination attributes as well as spatial interaction between them.However,most studies build their insights on one-step gravity spatial interaction models,which fail to distinguish the spatial interaction effect from the attributes as the dispersal capacity and the attraction capacity.In particular,the effect of spatial interaction,often approximated solely by distance and viewed as the most intractable component in the models,leads to model biases and hinders the theoretical interpretation of migration mechanisms and spatial patterns.Drawing on the method of migration flow decomposition and structural gravity model theory,this paper replaces the one-step“flow–variable”approach with a two-step“flow–effect–variable”framework for modelling migration flows.Based on interprovincial migration data from China's Sixth and Seventh National Population Censuses,the paper decomposes migration flows to measure the effects of dispersal capacity,attraction capacity,and spatial interaction,identifying temporal trends in their effects.Building on measured effects of spatial interaction,the paper uses multiple regression analyses to identify key influencing factors of spatial interaction,and examines how bilateral explanatory variables shape the spatial structures.展开更多
This paper uses the 2010 and 2020 Population Census micro-data to analyze relevant characteristics and trends over the decade.The analysis includes the following aspects:population size of rural children left-behind,t...This paper uses the 2010 and 2020 Population Census micro-data to analyze relevant characteristics and trends over the decade.The analysis includes the following aspects:population size of rural children left-behind,their age structure and sex composition,regional distribution,living arrangements,and educational development.The findings show that the population size of rural children left-behind remained large,and the likelihood of children being left-behind was closely related to age,with no significant difference by sex.An increasing proportion of rural children left-behind were cared for by grandparents as more mothers migrated between 2010 and 2020.Rural children left-behind no longer had an advantage in educational opportunities compared with average rural children.Over-age attendance was more prevalent among rural children left-behind than all rural children.Continuous attention should be paid to the issue of rural children left-behind and efforts should be made to address its root causes.展开更多
文摘Migration flows are affected by both origin and destination attributes as well as spatial interaction between them.However,most studies build their insights on one-step gravity spatial interaction models,which fail to distinguish the spatial interaction effect from the attributes as the dispersal capacity and the attraction capacity.In particular,the effect of spatial interaction,often approximated solely by distance and viewed as the most intractable component in the models,leads to model biases and hinders the theoretical interpretation of migration mechanisms and spatial patterns.Drawing on the method of migration flow decomposition and structural gravity model theory,this paper replaces the one-step“flow–variable”approach with a two-step“flow–effect–variable”framework for modelling migration flows.Based on interprovincial migration data from China's Sixth and Seventh National Population Censuses,the paper decomposes migration flows to measure the effects of dispersal capacity,attraction capacity,and spatial interaction,identifying temporal trends in their effects.Building on measured effects of spatial interaction,the paper uses multiple regression analyses to identify key influencing factors of spatial interaction,and examines how bilateral explanatory variables shape the spatial structures.
基金derivative products of the Seventh National Population Census research project called“Development Status of Children and Adolescents in China”and has received technical and financial support from the NBS/UNICEF/UNFPA Joint Data Project.
文摘This paper uses the 2010 and 2020 Population Census micro-data to analyze relevant characteristics and trends over the decade.The analysis includes the following aspects:population size of rural children left-behind,their age structure and sex composition,regional distribution,living arrangements,and educational development.The findings show that the population size of rural children left-behind remained large,and the likelihood of children being left-behind was closely related to age,with no significant difference by sex.An increasing proportion of rural children left-behind were cared for by grandparents as more mothers migrated between 2010 and 2020.Rural children left-behind no longer had an advantage in educational opportunities compared with average rural children.Over-age attendance was more prevalent among rural children left-behind than all rural children.Continuous attention should be paid to the issue of rural children left-behind and efforts should be made to address its root causes.
