创新型人才流动的空间结构与影响因素——基于高被引华人科学家履历分析

doi:10.11821/dlxb202208018

Abstract

Abstract:

As an important innovation resource, top world talents play a vital role in promoting regional innovation and competitiveness. Among all kinds of top world talents, highly cited scientists, whose achievements are highly original and subversive, form a crucial scientific and technological force determining the direction of future scientific development. The geographical structure and influencing factors of their immigration are important and are being investigated within the field of geography. According to previous studies, different population types have different location choices. Despite existing research on the mobility of the general labor force, the question of which model is suitable for the movement of highly cited scientists has not yet been settled. This paper integrates the world city theory into population mobility studies and investigates the mobility of highly cited Chinese scientists. We compiled the list of highly cited Chinese scientists based on the 2014-2015 global list of highly cited scientists on the Web of Science as well as a background and resume information search. We proceeded with the research in the following ways: We firstly constructed a composite talent migration theory on the basis of the population migration theory in order to explore which factors affect scientists' location choice in their working periods; we secondly described their mobility characteristics during their education and working periods, including a detailed analysis of domestic and transnational migration during the working period; we thirdly applied the Heckman two-stage selection model to explore the applicability of compound population migration theory in high-end talent flow; and we finally conducted a classification study from the perspective of domestic/transnational mobility of talents and used the Probit regression model to explore the influencing factors of scientists' willingness to return to China or go abroad along with the factors that influence scientists' domestic mobility. The main conclusions can be drawn as follows: (1) The talent flow trajectory of highly cited Chinese scientists showed that the target places of scientists are mainly core cities (measured by degree centrality) at home and abroad. (2) The transnational flow direction of highly cited Chinese scientists in the working stage is mainly from American cities to Chinese cities. (3) The composite population migration model has an obvious role in explaining the location selection of highly cited Chinese scientists in their working stage. Scientists mainly choose to move to core cities and their willingness to move is largely affected by their individual characteristics. (4) The factors influencing the choice of movement of highly cited Chinese scientists at home and abroad are different. The domestic mobility of scientists is limited by geographical distance, and highly cited Chinese scientists' transnational movement is mainly affected by the pull of the destination cities. We demonstrated that the composite population migration model was more suitable for the study of talents' movement inside the region (globally or within a country), while the push-pull model was more suitable for transnational movement.

Key words: highly cited Chinese scientists, resume analysis, talent mobility, Heckman model

SUN Kang, SI Yuefang. The geographical structure and influencing factors of talent mobility:The resume analysis of highly cited Chinese scientists[J].Acta Geographica Sinica, 2022, 77(8): 2113-2130.

Figures/Tables 12

Fig. 1

Tab. 1

Tab. 2

Descriptions and expected effects of factors

变量	指标	变量描述	预期效应	均值	标准差
因变量	核心城市(lntcdegree)	人才流动迁入城市的城市度中心性的自然对数		3.54	1.43
自变量	个体特征
	被引用量(lncitations)	科学家总被引用量的自然对数	未知	3.90	2.79
	论文数量(lnpapers)	科学家发表论文的数量的自然对数	未知	1.78	1.07
	制度邻近(samecountry)	虚拟变量,科学家的迁出城市与迁入城市处于同一个国家为1,反之为0	+	0.63	0.48
	阻碍因素
	地理距离(lndistance)	科学家移动迁出城市与迁入城市距离的自然对数	-	14.81	1.30
	迁出城市因素：
	迁出城市度中心性(lnocdegree)	人才流动迁出城市的城市度中心性的自然对数	-	3.39	1.32
	迁出城市科技活跃度(lnocsize)	人才流动迁出城市的城市论文数量的自然对数	-	8.03	2.06
	迁出城市为中国城市(oc_c)	虚拟变量,迁出城市为中国城市为1,反之为0	-	0.30	0.46
	迁出城市大学数量(ocuniversitynum)	人才流动迁出城市所包含进入世界大学学术排名前500强的大学数量	-	1.70	1.82
	迁出城市研究集群(ocresearchcluster)	表示相同的研究集群,人才流动迁出城市是否包含科学家所在学科排名前100的高校,有则为1,反之为0	-	0.70	0.46
	迁入城市因素
	迁入城市科技活跃度(lntcsize)	人才流动迁入城市的城市论文数量的自然对数	+	8.26	2.25
	迁入城市为中国城市(tc_c)	虚拟变量,迁入城市为中国城市为1,反之为0	+	0.36	0.48
	迁入城市大学数量(tcuniversitynum)	人才流动迁入城市所包含进入世界大学学术排名前500强的大学数量	+	2.14	2.24
	迁入城市研究集群(tcresearchcluster)	表示相同的研究集群,人才流动迁入城市是否包含科学家所在学科排名前100的高校,有则为1,反之为0	+	0.72	0.45

Tab. 2

Tab. 3

Fig. 2

Fig. 3

Tab. 4

Tab. 5

Tab. 6

Fig. 4

Tab. 7

Tab. 8

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研究主题	对应理论	一级指标	二级指标
人才分布	新经济迁移理论	教育水平	万人高校学生数、985/211高校数量、中学生师比、人均教育事业费
	新经济迁移理论	经济发展与家庭因素	平均工资、家庭支出占收入比、失业率和住房均价、人均GDP、二产与三产产值比重
	社会网络理论	人才政策	人均政府财政收入、引进海外高端人才资金、人均财政支出收入比
	世界体系理论	城市因素	城市等级、是否为省会/直辖市、市辖区人口密度
	消费城市理论	公共服务	每万人医院卫生院床位数、万人医生数、万人网民数、城市公路交通通达性、平均铁路客运量
	消费城市理论	环境质量	二氧化硫排放量、绿地率、污水处理达标率、自然舒适度指数
		科研环境	R&D经费占GDP比重、人均科研事业费、政府研发资金
流动网络	推拉理论/人口迁移理论	教育水平	世界大学学术排名前500强的大学数量、目的国高等教育生师比、目的国高等教育入学率、目的国政府对高等教育的支出占GDP的比率、平均受教育年限、高校在校生数量
	推拉理论/人口迁移理论	经济发展	目的地国人均GDP、两国人均GDP的差异、平均GDP增长率、平均收入、失业率、工资房价比
	人口迁移理论	迁移距离	迁出地和迁入地省会城市之间空间距离、目的地国与来源国的地理距离、两城市之间的地理距离
	社会网络理论	人才政策	人才落户、人才安居、人才就业指数
	世界体系理论	全球化水平	国家或地区的商品进出口总额与GDP的比值、各省外商直接投资占GDP比重
	消费城市理论	公共服务	常住人口数量、电影院数量、人均公共汽车运营数量
	消费城市理论	环境质量	平均温差
		科技创新	人均专利授权量

流动方向	学士—硕士迁移人数(人)	硕士—博士迁移人数(人)	总人数 (人)
北京—北京	29	15	44
南京—南京	20	4	24
长春—长春	10	12	22
合肥—合肥	12	3	15
上海—上海	9	6	15
杭州—杭州	10	3	13
西安—西安	9	4	13
哈尔滨—哈尔滨	7	4	11
剑桥[美]—剑桥[美]	4	4	8
新竹—新竹	5	1	6
大连—大连	4	2	6
广州—广州	4	2	6
武汉—武汉	3	3	6
新加坡—新加坡	3	2	5
南京—北京	3	2	5
台北—台北	3	1	4
兰州—兰州	3	1	4
天津—天津	3	1	4
南京—上海	2	2	4
北京—纽约	2	2	4
合肥—剑桥[美]	0	4	4

城市/国家	入度	出度	入度出度差值	度中心性	接近中心度	中间中心度
北京	26	10	16	36	0.363	0.11
香港	14	6	8	20	0.232	0.041
新加坡	11	4	7	15	1.000	0.007
圣路易斯	7	1	6	8	0.400	0.014
剑桥[美]	6	12	-6	18	0.367	0.028
帕罗奥多	6	11	-5	17	0.388	0.076
伯克利	6	8	-2	14	0.321	0.025
上海	5	6	-1	11	0.276	0.01
洛杉矶	5	5	0	10	0.311	0.019
西雅图	4	9	-5	13	0.274	0.016

国内/跨国流动	上一次工作所在国家	当前工作所在国家	人数(人)	国内/跨国流动	上一次工作所在国家	当前工作所在国家	人数(人)
国内流动	美国	美国	111	跨国流动	美国	新加坡	5
	中国	中国	67		日本	中国	5
	加拿大	加拿大	5		加拿大	中国	4
	英国	英国	5		美国	加拿大	4
	澳大利亚	澳大利亚	4		中国	日本	4
	新加坡	新加坡	2		澳大利亚	中国	3
	德国	德国	1		英国	中国	3
跨国流动	美国	中国	26		中国	沙特阿拉伯	3
	中国	美国	11		加拿大	美国	2
	德国	中国	5		加拿大	新加坡	2
	加拿大	新加坡	2

指标	OLS	Heckman
指标	lntcdegree	lntcdegree
lncitations	0.0546^* (0.0277)	0.0634^** (0.0285)
lnpapers	-0.0232 (0.0680)	-0.0315 (0.0619)
samecountry	0.195 (0.153)	0.191 (0.144)
lnocdegree×lncitations	-5.74e-05 (0.000122)	-6.26e-05 (0.000111)
lndistance	0.100^** (0.0495)	0.0992^* (0.0537)
lnocdegree	0.131 (0.0831)	0.135^* (0.0763)
lnocsize	-0.0457^* (0.0248)	-0.0454^* (0.0266)
oc_c	-0.131 (0.154)	-0.163 (0.165)
ocuniversitynum	0.00699 (0.0359)	0.00632 (0.0389)
ocresearchcluster	-0.315^** (0.155)	-0.313^** (0.156)
lntcsize	0.0769^*** (0.0217)	0.0783^*** (0.0232)
tc_c	1.342^*** (0.168)	1.342^*** (0.133)
tcuniversitynum	0.155^*** (0.0485)	0.155^*** (0.0312)
tcresearchcluster	1.290^*** (0.158)	1.294^*** (0.131)
Constant	-0.421 (0.786)	-0.492 (0.884)
move
lncitations		0.155^*** (0.0493)
lnpapers		-0.120 (0.120)
lnocdegree×lncitations		-4.09e-05 (0.000194)
circulation		0.911^*** (0.281)
lnocdegree		-0.275^*(0.159)
lnocsize		-0.0145 (0.0474)
oc_c		-0.270 (0.255)
ocuniversitynum		-0.0624 (0.0778)
ocresearchcluster		0.183 (0.298)
IMR		0.185 (0.319)
Constant		1.039^**(0.478)
Observations		309
R²	0.724

The geographical structure and influencing factors of talent mobility:The resume analysis of highly cited Chinese scientists

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Figures/Tables 12

References 40

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城市/国家	学士	硕士	博士	工作1	工作2	工作3
北京	41	46	31	17	26	7
南京	28	25	10	4	4	-
合肥	21	14	4	4	6	2
上海	19	21	11	6	7	1
杭州	14	13	3	4	2	1
西安	14	11	5	3	3	1
长春	11	14	14	2	3	1
剑桥[美]	4	8	16	8	8	1
新加坡	3	3	5	13	12	3
香港	2	2	7	17	9	5

指标	模型1	模型2	模型3
指标	国内—国内	国内—国外	国外—国内
lncitations	-0.0940 (0.0842)	-0.0567 (0.117)	0.0285 (0.0682)
lnpapers	0.301^* (0.159)	0.287 (0.246)	0.0505 (0.187)
lnocdegree×lncitations	-0.000870^* (0.000447)	7.52e-05 (0.000994)	-0.000633 (0.000457)
lndistance	-1.024^*** (0.161)	1.456^*** (0.293)	1.411^*** (0.183)
lnocdegree	1.389^*** (0.324)	1.812^*** (0.504)	-0.360^* (0.189)
lnocsize	-0.0349 (0.0755)	0.0916 (0.118)	0.133^* (0.0782)
ocuniversitynum	0.101 (0.102)	0.446^*** (0.114)	-0.0733 (0.116)
ocresearchcluster	-2.589^*** (0.641)	-4.487^*** (0.994)	0.470 (0.382)
lntcdegree	1.032^*** (0.285)	-1.149^*** (0.286)	1.246^*** (0.245)
lntcsize	-0.0643 (0.0662)	0.688^*** (0.260)	-0.119^** (0.0562)
tcuniversitynum	0.223^* (0.119)	-0.0167 (0.182)	0.209^*** (0.0613)
tcresearchcluster	-2.645^*** (0.642)	-0.283 (0.588)	-2.944^*** (0.729)
Constant	8.363^*** (2.045)	-32.08^*** (6.461)	-25.39^*** (3.116)
Observations	255	255	255

[1]	HOU Chunguang, DU Debin, LIU Chengliang, GUI Qinchang, LIU Shufeng, QIN Xionghe. Spatio-temporal evolution and factors influencing international student mobility networks in the world [J]. Acta Geographica Sinica, 2020, 75(4): 681-694.
[2]	WANG Yahui,LI Xiubin,XIN Liangjie,TAN Minghong,JIANG Min. Regional differences of land circulation in China and its drivers:Based on 2003-2013 rural fixed observation points data [J]. Acta Geographica Sinica, 2018, 73(3): 487-502.