Spatial patterns and influencing mechanisms of international student mobility network among China and OECD countries
Establishing and facilitating high-quality talent flows is a crucial way to China's international talent cultivation and intellectual-attraction country construction.Drawing on international student mobility data among 39 countries including OECD countries and China in 2019,this research constructed a weighted and dir-ected international talent mobility network,and employed social network analysis to explore its spatial patterns and influencing mechanisms.1)International student exchanges were quite tight among the 39 countries,form-ing 4 visible regional communities,i.e.,the North American and Asia-Pacific community,the South America and European community,the Nordic and Baltic community,and the Czechoslovakian community.China and the United States were centers of international talent exchange.Moreover,China,the United Kingdom,Ger-many,the United States and France were also the gateways and hubs of international talent flows.2)The pat-terns of international student outflows and inflows of 39 countries were asymmetric.The outflows of Chinese students accounted for nearly half of the total,while the outflows of OECD countries were relatively balanced.The United States,the United Kingdom and Australia ranked the top 3 in international student attraction,fol-lowed by China with 7.17%international student inflows.3)The impact of openness degree,education quality,economic level on international student mobility all suggested sender and receiver effects.The proximity in geography,language and culture,and commodity trade could promote the bi-directional talent flows.The struc-ture dependence effect was an important driver to the evolution of international student mobility network,with the reciprocity,preferential attachment and transitive closure effects being most pronounced,which could somewhat substitute the effects of exogenous force.There is still much room for the improvement of the talent mobility among China and OECD countries.It can be optimized from the perspectives of the country's endow-ments,multi-dimensional proximity,and network structures,to promote a more flat and diversified patterns of talent mobility among these countries.
talent mobilityinternational student networksocial network analysisexponential random graph model
万方数据
维普
