上海市风险投资企业的空间分布与“技术—资本”地理邻近性

doi:10.11821/dlxb201906004

Abstract

Abstract:

Venture capital firms (VCFs), target enterprises, and financial firms have close business relations, which are often manifested as the technology-capital coupling. How this coupling relation is mapped onto space, especially the urban internal space with high economic density, is imperative to an understanding of location significance and scalar consistency in geographic space. Using street block as the spatial unit of analysis, this study investigates the spatial pattern and "technology-capital" geographic proximity of Chinese and foreign-owned VCFs in Shanghai based on the cross-section data in 2005 and 2015. The analytical methods include kernel density estimation, temporal hot spots analysis method and spatial Tobit model. The empirical results are as follows. First, Chinese-funded VCFs are characterized by single centered agglomeration and "surface domain diffusion", while foreign-funded VCFs are characterized by polycentric agglomeration and "point domain diffusion". However, both types of VCFs tend to be in close proximity to the place where technology and capital are located. Second, VCFs tend to emphasize location proximity more to capital than to technology. In terms of location proximity to technology, VCFs have a closer relation with high-tech industries than with newly established enterprises. The differences between Chinese- and foreign-owned VCFs in geographic proximity to technology and capital include: (1) Chinese VCFs tend to have a higher propensity in location proximity to capital than foreign-owned VCFs (do), (2) with respect to location proximity to technology, foreign-owned VCFs focus more on business entrepreneurship, while Chinese-funded VCFs prioritize the technology side of high-tech industries. Third, over time, Chinese and foreign-owned VCFs are associated with different location models and temporal evolutionary characteristics, while location proximity to capital, location proximity to technology, capital-technology location proximity, and VCFs agglomeration are four typical and stable location models observed in the study. The findings of this research imply that promoting multi-dimensional location proximity and interactions among firms is significant, especially for knowledge and venture capital based innovation clusters and innovation space development.

Key words: venture capital firms, spatial distribution, location choices, geographic proximity, Shanghai

LIN Xiao,XU Wei,DU Debin,YANG Fan. Spatial pattern and technology-capital geographic proximity of venture capital firms in Shanghai[J].Acta Geographica Sinica, 2019, 74(6): 1112-1130.

Figures/Tables 9

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	中心城区(个)		全市(个)		占比(中心城区/全市,%)
	2005年	2015年	2005年	2015年	2005年	2015年
总体风险投资企业	171	1472	171	1624	100.00	90.64
中资风险投资企业	137	1360	137	1509	100.00	90.13
外资风险投资企业	34	112	34	115	100.00	97.39
金融机构	211	526	219	534	96.35	98.51
高科技企业	592	391	1431	1029	41.37	38.00
新创企业	78	750	82	892	95.12	84.08

	2005年		2015年
	中心城区	全市	中心城区	全市
总体风险投资企业	0.277^***	0.238^***	0.423^***	0.373^***
中资风险投资企业	0.283^***	0.244^***	0.429^***	0.379^***
外资风险投资企业	0.185^***	0.165^***	0.266^***	0.228^***
金融机构	0.258^***	0.089^***	0.304^***	0.149^***
高科技企业	0.270^***	0.416^***	0.241^***	0.304^***
新创企业	0.198^***	0.220^***	0.093^**	0.209^***

	2005年			2015年
	总体	中资	外资	总体	中资	外资
金融机构	0.133^***	0.119^***	0.024^**	1.516^***	1.248^***	0.237^***
高科技企业	0.019	0.033^**	-0.024	1.230^***	1.361^***	0.063
新创企业	0.874^***	0.613^***	0.396^***	0.140^***	0.056^***	0.100^***
国家级经济开发区	0.320	1.701^***	-0.266	48.294^***	53.709^***	0.061
市级经济开发区	-0.203	0.167	-0.163	0.262	0.085	0.659
现代服务业集聚区				3.269^***	5.154^***	-0.759^*
大学校区	1.392^***	1.371^***	-0.377	8.709^***	9.625^***	-0.602^*
交通道路	-0.518^**	-0.329	0.184	-4.499^***	-3.531^***	-0.451
地铁站点	-0.555^***	-0.953^***	0.771^***	2.202^***	2.643^***	0.146
地价	0.712^***	0.970^***	0.099	5.197^***	3.400^***	0.801^***
常数项	-0.775^**	-1.177^***	-0.239	-2.692^***	-1.872^***	-1.277^***
ρ	0.116^***	0.038^*	0.124^***	0.038^***	0.043^***	0.184^***
样本数	134	134	134	134	134	134
Log likelihood	-260.726	-223.680	-179.922	-1965.413	-1754.465	-387.057
LR	57.809^***	3.389^*	17.115^***	440.862^***	906.845^***	52.113^***
经验P值		0.000^***			0.000^***
经验p值		0.586			0.019^**

	2005年			2015年
	总体	中资	外资	总体	中资	外资
金融机构	0.162^***	0.131^***	0.025^**	0.987^***	0.771^***	0.279^***
高科技企业	0.017^*	0.026^**	-0.012	0.394^***	0.466^***	0.008
新创企业	0.829^***	0.603^***	0.407^***	0.327^***	0.338^***	0.116^***
国家级经济开发区	0.209	1.744^***	-0.324	42.749^***	39.283^***	-0.497
市级经济开发区	0.098	0.048	0.046	-1.087^***	-0.665^**	-0.090
现代服务业集聚区				0.233	3.627^***	-0.217
大学校区	1.242^***	1.109^***	-0.329^*	-1.420^***	-1.178^***	-0.334
交通道路	-0.201	-0.078	0.025	-1.022^***	-0.892^***	-0.214
地铁站点	-0.420^**	-0.628^***	0.610^***	0.510	0.154	-0.098
地价	0.562^***	0.897^***	0.038	9.748^***	7.375^***	0.347^**
常数项	-0.620^***	-1.084^***	-0.010	-7.769^***	-5.977^***	-0.473^*
ρ	0.126^***	0.043^***	0.139^***	0.069^***	0.111^***	0.198^***
样本数	232	232	232	232	232	232
Log likelihood	-380.884	-347.224	-281.906	-2817.657	-2761.001	-484.267
LR	61.728^***	4.232^**	17.538^***	300.321^***	373.419^***	74.766^***

Spatial pattern and technology-capital geographic proximity of venture capital firms in Shanghai

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