地理学报 ›› 2019, Vol. 74 ›› Issue (6): 1112-1130.doi: 10.11821/dlxb201906004

• 城市与区域发展 • 上一篇    下一篇

上海市风险投资企业的空间分布与“技术—资本”地理邻近性

林晓1,2,徐伟2,3,杜德斌1,2(),杨凡1,2,4   

  1. 1. 华东师范大学全球创新与发展研究院,上海 200062
    2. 华东师范大学城市与区域科学学院,上海 200062
    3. 莱斯布里奇大学地理系,加拿大 阿尔伯塔 T1K3M4
    4. 上海社会科学院信息研究所,上海 200235
  • 收稿日期:2018-01-02 修回日期:2019-05-08 出版日期:2019-06-25 发布日期:2019-06-20
  • 通讯作者: 杜德斌 E-mail:dbdu@re.ecnu.edu.cn
  • 作者简介:林晓(1988-), 女, 辽宁丹东人, 博士生, 主要从事金融地理和区域发展研究。E-mail: linxiao1001@126.com
  • 基金资助:
    国家自然科学基金项目(414711080)

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

LIN Xiao1,2,XU Wei2,3,DU Debin1,2(),YANG Fan1,2,4   

  1. 1. Institute for Global Innovation and Development, East China Normal University, Shanghai 200062, China
    2. School of Urban and Regional Science, East China Normal University, Shanghai 200062, China
    3. Department of Geography, University of Lethbridge, Lethbridge T1K3M4, Alberta, Canada
    4. The Institute of Information, Shanghai Academy of Social Sciences, Shanghai 200235, China
  • Received:2018-01-02 Revised:2019-05-08 Online:2019-06-25 Published:2019-06-20
  • Contact: DU Debin E-mail:dbdu@re.ecnu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(414711080)

摘要:

风险投资企业与“标的”产业、金融行业存在紧密关联,表现为“技术—资本”的耦合性联系。该特征如何映射在空间上,尤其是经济密度较高的城市内部空间,对于理解区位的重要性及其空间尺度一致性具有启示。以上海市街区为基本研究单元,选取2005年和2015年两个时间截面,运用核密度估计、时序稳定性“热点”和空间Tobit模型分析方法,从中外资比较视角探讨风险投资企业的空间分布与“技术—资本”地理邻近性。研究表明:① 中外资企业分别呈现单中心和多中心集聚格局,以及“面域扩散”和“点域扩散”趋势,并均表现出邻近“技术”与“资本”的分布特征;② 风险投资企业的“资本”地理邻近性超越“技术”地理邻近性,并在“技术”地理邻近性中,风险投资企业与高科技产业的地理邻近关系超越新创企业。同时,中外资企业的“技术—资本”地理邻近性差异主要体现在,中资企业的“资本”地理邻近性强于外资企业,外资企业的“技术”地理邻近性偏重于“技术”中的创业层面,而中资企业则偏重于“技术”中的高科技产业层面。③ 随时间的变化,中外资企业具有不同的区位模式偏好及时间演变特征,但“资本”地理邻近型、“技术”地理邻近型、“技术—资本”地理邻近型和自身集聚型这四类典型区位模式始终稳定存在。研究认为促进区位内部企业间的多维邻近性和互动联系,对基于“知识和风险资本”的创新集群和创新空间发展具有重要的实践意义。

关键词: 风险投资企业, 空间分布, 区位选择, 地理邻近性, 上海市

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