In recent years, China's government investment funds（GIF）have shown a booming development trend. The total amount and the size of GIF have become decisive forces in China's venture capital market. However, GIF is encountered with the problem of fund-raising and funds are increasingly concentrated in core cities and regions. Related research has found that venture capital has the characteristics of spatial agglomeration. At present, there are few studies on the geographic proximity characteristics of government investment funds due to the difficulty of obtaining data on government investment funds. It will help to further solve the practical problems in fundraising and investment if the source of the funds of government investment is sorted out. Therefore, it is necessary to study the characteristics of government investment funds from the perspectives of spatial agglomeration, geographic proximity, and investment networks.#br#In order to study whether the establishment of GIF in various cities is evenly distributed, whether the relationship between different cities is close, and whether some cities form close communities, this paper uses crawler technology to obtain large sample data of GIF, and then uses a complex network model to conduct empirical analysis. The main research tools of this paper include web crawler, Python networkx package and social network analysis tool Gephi.#br#The current mainstream databases of government investment funds mainly include Zero2IPO Group Private Equity, China Investment Group Investment Data, CCID Consulting Data, Venture Capital Yearbook of China Venture Capital Research Institute, China Venture Capital Development Report of China Science and Technology Development Strategy Research Institute. However, the data statistics of the various databases are not consistent. At the same time, the information disclosure of government investment funds is not timely and comprehensive, statistical monitoring is not in place, and relevant information registration has not been publicized. In order to obtain more comprehensive fund information, this article integrates multiple databases to obtain a more complete list of GIF and applies web crawler technology to obtain relevant information of 1,489 government investment funds, including their establishment time, place of registration, registered capital, name of each shareholder and place of registration, share ratio of each shareholder and fund investment information, etc. On this basis, the complex network method is used to investigate the capital agglomeration of government investment funds from the city level.#br#The following conclusions are found as follows. The degree distribution of the GIF financing network nodes conforms to the power rate distribution, indicating that the network has obvious scale-free characteristics and serious heterogeneity, and the connection status of each city has a serious uneven distribution.GIF plays obvious "extreme effects" at the level of financing networks. Cities such as Beijing, Shenzhen, Shanghai and Suzhou are the main agglomeration areas, and the western and northeastern regions failed to form an agglomeration effect. GIF has significant geographic proximity, local institutions have a strong willingness to participate in the establishment of local funds, and cities in the province have formed a clear ethnic structure. In addition, GIF in Shenzhen and Suzhou are more market-oriented than other cities such as Beijing and Shanghai. #br#The main enlightenment from the research results of this paper on policy are as follows. Chinese GIF should pay more attention to market orientedness and strengthen the guidance of social capital. It is recommended to appropriately reduce the leverage ratio of GIF in the Midwest and Northeast regions, and use GIF to attract advantageous industries in the east to transfer to the Midwest, so as to promote local capital accumulation and promote regional industrial upgrading and innovative development.#br#
Jin Zhiwei
,
Zhou Daishu
. The Characteristics of Government Investment Fund Financing Network: City Distribution, Network Relationship and Community Discovery[J]. Science & Technology Progress and Policy, 2022
, 39(5)
: 40
-49
.
DOI: 10.6049/kjjbydc.2021020183
[1] FLORIDA R L, KENNEY M.Venture capital, high technology and regional development[J].Regional Studies, 1988, 22(1):33-48.
[2] CHEN K, MARCHIONI M, et al.Spatial clustering of venture capital-financed biotechnology firms in the US[J].Industrial Geographer, 2008, 5(2):19-38.
[3] MARTIN R, BERNDT C, KTAGGE B, et a1.Spatial proximity effects and regional equity gaps in the venture capital market: evidence from Germany and the United Kingdom[J].Environment and Planning A,2005,37(7):1207-1231.
[4] CHEN H, GOMPERS P, KOVNER A, et al.Buy local? the geography of venture capital[J].Journal of Urban Economics,2010, 67(1):90-102.
[5] 段了了, 孙伟增, 郑思齐.中国风险投资活动的城市与行业扩散机制[J].清华大学学报(自然科学版), 2019，95(8):655-662.
[6] 王谦，王迎春.风险投资的区域集聚与投资的地理亲近性研究——基于英、德两国风险投资的分析[J].国际商务(对外经济贸易大学学报)，2005，19(4)：67-71.
[7] 汪明峰，魏也华，邱娟.中国风险投资活动的空间集聚与城市网络[J].财经研究，2014，40(4)：117-131.
[8] 徐宜青，潘峰华，江小雨，等.北京市风险投资的空间分布与合作网络研究[J].地理科学进展，2016，35(3)：358-367.
[9] 李婷婷.风险投资集聚与城市创新能力实证研究[J].金融理论探索,2017，33(1):10-16.
[10] 王亮,李子联,靳振忠.风险投资空间选择：重产业还是重资本[J].当代财经,2020，41(12):50-62.
[11] 王晓红,张少鹏,张奔.风险投资集聚与高技术产业创新——产学知识流动的中介作用和关系导向的调节作用[J].西安交通大学学报(社会科学版),2020,40(6):22-32.
[12] 金永红,汪巍,奚玉芹.风险投资网络与区域创新能力研究[J].软科学,2020,34(11):33-39.
[13] MATTHEW A ZOOK.Taking risks in regions: the geographical anatomy of europe's emerging venture capital market[J].Journal of Economic Geography, 2002(2):151-177.
[14] KORTUM S，LERNER J.Assessing the contribution of venture capital to innovation[J].Rand Journal of Economics，2000(4):674-692.
[15] SHACHMUROVE Y.Geography and industry meets venture capital[J].PIER Working Paper Archive, 2007（4）：1-34.
[16] SMITH F D F, JR.Venture capital formation, investment, and regional industrialization[J].Annals of the Association of American Geographers, 2015, 83(3):434-451.
[17] BERNARD GUILHON, SANDRA MONTCHAUD.The dynamics of venture capital industry[J].International Journal of Technology Management, 2006, 34(1):146-160.
[18] LANGELAND O.Financing innovation: the role of Norwegian venture capitalists in financing knowledge-intensive enterprises[J].European Planning Studies,2007,15(9):1143-1161.
[19] SUBHASH K B.Geography of venture capital financing: a global perspective[J].Journal of Wealth Management, 2007，9（4）：13-28.
[20] F WRAY.Rethinking the venture capital industry: relational geographies and impacts of venture capitalists in two UK regions[J].Journal of Economic Geography, 2011, 12(1):297-319.
[21] GREEN M, MCNAUGHTON R.Interurban variation in venture capital investment characteristics[J].Urban Studies, 1989, 26(2):199-213.
[22] ZHANG JUN.The spatial dynamics of globalizing venture capital in China[J].Environment & Planning A, 2011, 43(7):1562-1580.
[23] GRIFFITH T L, YAM P J, SUBRAMANIAM S.Silicon valley's 'one-hour' distance rule and managing return on location[J].Venture Capital, 2007, 9(2):85-106.
[24] LAI K.Differentiated markets: Shanghai, Beijing and Hong Kong in China's financial centre network[J].Urban Studies, 2011, 49(6):1275-1296.
[25] JAEAESKELAEINEN M, MAULA M.Do networks of financial intermediaries help reduce local bias? evidence from cross-border venture capital exits[J].Journal of Business Venturing, 2014, 29(5):704-721.
[26] CUMMING D, DAI N.Local bias in venture capital investments[J].Journal of Empirical Finance, 2010, 17(3):362-380.
[27] 龙玉, 李曜.风险投资应该舍近求远吗——基于我国风险投资区域退出率的实证研究[J].财贸经济, 2016,35(37):145.
[28] 崔毅.风险投资区域集聚支持环境综合评价及差异分析[J].科技管理研究，2011,31（2）：70-73.
[29] 张玉华.中国风险投资地域集聚现象及其驱动因素分析———基于空间面板数据模型的实证研究[J].上海师范大学学报(哲学社会科学版) ，2014，57(6):51-59.
[30] 张晓晖，尹海英.中国创业投资的区域分布及其影响因素[J].社会科学战线，2012,35（8）：63-69.
[31] 张海燕，袁新敏，沈玉芳.风险投资空间行为研究动态与展望[J].科技进步与对策，2012，29（11）：151-155.
[32] 汪小帆，李翔，陈关荣.复杂网络理论及其应用[Ｍ].北京：清华大学出版社，2006.
[33] 乔少杰,郭俊,韩楠,等.大规模复杂网络社区并行发现算法[J].计算机学报,2017,40(3):687-700.
[34] 何大韧，刘宗华，汪秉宏.复杂系统与复杂网络[Ｍ].北京：高等教育出版社，2017.
[35] CLAUSET A, NEWMAN M E J, MOORE C.Finding community structure in very large networks[J].Physical Review E, 2004, 70(6): 066111.
[36] BLONDEL V D,GUILLAUME J L,LAMBIOTTE R,et al.Fast unfolding of communities in large networks[J].Journal of Statistical Mechanics Theory & Experiment, 2008, 2008(10): P10008.
[37] 邓君，马晓君，毕强.社会网络分析工具Ucinet和Gephi的比较研究[J].情报理论与实践，2014，37(8)：133-138.
[38] 约翰·斯科特.社会网络分析法[M].重庆：重庆大学出版社, 2007.
[39] 何杰 ,彭兴庭.政府引导基金运行中存在的三大矛盾五大风险[N].证券时报,2017-08-11(A10).
[40] 胡志坚, 张晓原, 贾敬敦,等.中国创业风险投资发展报告[M].北京：科学技术文献出版社, 2020.
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