上海上市公司对外投资网络演变及其影响因素研究

doi:10.18306/dlkxjz.2020.04.003

Abstract

Abstract:

Global cities are the management and control centers of the world's investment networks. Thus, building a powerful outward investment network will help Shanghai to implement its developmental strategy for becoming an outstanding global city by 2035. We created the A-share listed companies branch network database of Shanghai based on their annual reports in 2005, 2010, and 2017, then used rank-size analysis, social network analysis, and negative binomial regression analysis methods to explore the spatial network structure and its determinants. The results of this empirical research are as follow: 1) Shanghai listed companies' outward investment network agglomerates in Western Europe, North America, South East Asia and East Asia. It is worth noting that cities along the Belt and Road Initiative attracted 155 subsidiaries of Shanghai listed companies from 2005 to 2017. 2) Shanghai listed companies' outward investment agglomerates in overseas central cities initially, then diffuses to nearby cities surrounding the associated central cities. 3) Manufacturing, finance, transportation, warehousing and postal services are the main sectors of the outward investment activities. The outward investment network of manufacturing industry covers the largest number of oversea cities. From 2005 to 2017, the number of oversea cities involved in the outward investment network of Shanghai manufacturing listed companies increased by 102. 4) Social proximity, position in the hierarchy of world city network, lower tax of offshore financial centers, convenient flag countries and sea ports have a positive impact on the evolution of Shanghai listed companies' outward investment network.

Key words: outward investment network, listed companies, network structure, Shanghai Municipality

XU Ning, LI Xiande. Evolution of Shanghai listed companies’ outward investment network and its determinants[J].PROGRESS IN GEOGRAPHY, 2020, 39(4): 553-566.

Figures/Tables 8

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子公司名称	注册地	业务性质
上海汽车英国控股有限公司	英国伯明翰	汽车技术研发
……	……	……

区域	2005年		2010年		2017年
区域	子公司数量/家	比例/%	子公司数量/家	比例/%	子公司数量/家	比例/%
西欧	9	24.32	42	26.25	286	38.29
北美	9	24.32	17	1.63	121	16.20
东南亚^*	7	18.92	20	12.50	98	13.12
东亚	6	16.22	12	7.50	53	7.10
南美	1	2.70	9	5.63	40	5.35
大洋洲	3	8.11	7	4.38	36	4.82
西非	0	0	50	31.25	35	4.69
东欧^*	0	0	1	0.63	32	4.28
西亚^*	1	2.70	1	0.63	17	2.28
南亚^*	0	0	1	0.63	16	2.14
其他	1	2.70	0	0	13	1.74
合计	37	100.00	160	100.00	747	100.00

城市行业数排名	2005年		2010年		2017年
城市行业数排名	城市	城市的行业数/种	城市	城市的行业数/种	城市	城市的行业数/种
1	英属维尔京群岛	3	英属维尔京群岛	6	英属维尔京群岛	10
2	洛杉矶	3	新加坡	6	新加坡	9
3	纽约	3	开曼群岛	3	开曼群岛	8
4	新加坡	3	纽约	3	纽约	7
5	东京	3	东京	3	东京	7
6	悉尼	2	曼谷	2	悉尼	6
7	罗勇	1	汉堡	2	曼谷	5
8	休斯顿	1	洛杉矶	2	伦敦	5
9	汉堡	1	悉尼	2	汉堡	4
10	迪拜	1	圣何塞	2	孟买	4

行业	2005年		2010年		2017年
行业	数量/家	比例/%	数量/家	比例/%	数量/家	比例/%
制造业	9	50.00	22	44.90	68	49.28
信息传输、软件和信息技术服务业	2	11.11	5	10.20	19	13.77
金融业	1	5.56	2	4.08	9	6.52
交通运输、仓储和邮政业	0	0	3	6.12	9	6.52
批发和零售业	1	5.56	7	14.29	8	5.80
房地产业	2	11.11	2	4.08	4	2.90
建筑业	1	5.56	3	6.12	4	2.90
综合	1	5.56	2	4.08	3	2.17
其他	1	5.56	3	6.12	14	10.14
合计	18	100.00	49	100.00	138	100.00

维度	变量	说明	预期回归系数	数据来源
地理邻近性	地理距离(DIST)	上海到子公司所在城市的距离(取自然对数)	-	百度地图
政治邻近性	友好城市(SIST)	是否友好城市	+	上海市政府官网
社会邻近性	汉字文化圈(CULT)	是否属于汉字文化圈	+	根据胡兆量^[43]
控制变量：城市属性	首都(CAPI)	是否首都	+	百度地图
	海港(SEAP)	是否海港城市	+	搜航网
	机场(AIRP)	是否有机场	+	天巡网
	避税城市(TAX)	是否避税港型离岸金融中心或方便旗国城市	+	OECD/国际航运公会(ICS)
	Alpha级城市(ALP)	是否Alpha级城市	+	GaWC网站

Evolution of Shanghai listed companies’ outward investment network and its determinants

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自变量	全行业			制造业			交通业	金融业
自变量	2005年	2010年	2017年	2005年	2010年	2017年	2017年	2017年
DIST	-0.268	0.243	0.211	-0.330	0.053	0.372^**	0.478	1.390^***
SIST	-0.148	-0.042	0.150	-0.143	-0.251	-0.049	-0.280	-0.093
CULT	0.406	1.317^**	0.921^***	0.618	0.645	1.256^***	1.732^***	2.055^**
CAPI	-0.035	0.063	-0.173	-0.003	0.010	-0.258	0.189	-0.047
SEAP	0.060	0.309	0.443^***	0.034	-0.084	0.438^**	0.495	0.191
AIRP		0.233	0.160		0.226	-0.040	-0.466
TAX	1.599^**	2.123^***	2.491^***	0.026	-0.196	1.220^***	2.093^***	2.037^**
ALP	0.857	0.555	1.472^***	-0.064	0.478	0.748^***	0.624	0.931^***
常数	2.515	-2.395	-1.768	3.096	-0.452	-3.195	-4.269	-12.746
Pseudo R²	0.288	0.224	0.197	0.107	0.077	0.111	0.209	0.174
N	18	48	197	17	35	119	38	25

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