金融网络中心性对中国城市经济增长的影响机理

doi:10.12082/dqxxkx.2023.220614

Abstract

Abstract:

The development of financial network has profoundly changed the form of knowledge spillover between cities and further affected the level of urban economic growth. The research on the externality of financial network has gradually become a research hotspot in financial geography. With the assistance of the data of headquarters and branches of enterprises in China's financial industry from 2005 to 2020, this paper combines the methods of social network analysis and recursion thought to calculate the centrality of financial network to analyze its spatial and temporal distribution pattern. Meanwhile, we use the OLS model to analyze the influence mechanism of financial network centrality on urban economic growth in China. The study finds that: (1) From 2005 to 2020, the centrality of financial network showed a "core-edge" distribution pattern, but the spatial agglomeration degree of financial network was decreasing. In this paper, with the help of the Core/Periphery algorithm in UCINET software, cities are divided into core and peripheral cities according to the 2020 financial network，and finally 52 cities with core status, such as Beijing, Guangzhou, and Hangzhou, and 239 cities with peripheral status, such as Anshan, Binzhou, and Foshan, are identified. Cities in the eastern region play an important role in the allocation of financial network resources, forming a "core-periphery" pattern with Beijing, Shanghai, and Shenzhen as the core cities and the other cities as the periphery cities； (2) The development of financial network can not only alleviate financing constraints, but also affect urban economic growth by promoting knowledge spillovers under the influence of network externalities. Cities with larger network links and better accessibility have higher levels of economic growth, and the knowledge spillover effect is more dependent on network proximity than location advantage in "place space"; (3) The degree of knowledge spillover promoted by financial network centrality shows spatial heterogeneity, and the core city which plays the role of "knowledge gatekeeper" can obtain greater benefits from network links. Due to the lack of "knowledge gatekeepers", peripheral cities are unable to make full use of external resources, which exacerbates the risk of being at the low end of the value chain and finally leads to the economic growth level of core cities much higher than that of peripheral cities. In the future, we should attach great importance to the construction of financial network and give full play to the driving role of financial network in urban economic growth in China.

Key words: financial network centrality, recursive thought, PageRank index, spatial-temporal pattern, spatial heterogeneity, knowledge spillover, influence mechanism, economic growth

YANG Yu, SONG Futie, ZHANG Jie. Research on the Influence Mechanism of Financial Network Centrality on Urban Economic Growth in China[J].Journal of Geo-information Science, 2023, 25(5): 982-998.DOI:10.12082/dqxxkx.2023.220614

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References 39

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变量	名称	含义	数据来源	时段
ln(realGDP)	城市经济增长	实际GDP（亿元）取对数	《中国统计年鉴》^[34]、《中国城市统计年鉴》^[35]	2005—2020
PageRank	金融网络中心性	金融网络中心性指数	启信宝网站（http://www.qixin.com/）	2005—2020
ln(degree)	金融网络中心性	金融链接强度取对数	启信宝网站（http://www.qixin.com/）	2005—2020
ln(populdensity)	人口集聚变	人口密度（万/平方千米）取对数	《中国城市建设年鉴》^[36]	2005—2020
ln(percapital)	人均固定资本存量	人均固定资本（亿元）取对数	《中国城市统计年鉴》^[35]	2005—2020
ln(teacher)	人力资本	普通高校专任教师数（人）取对数	《中国城市统计年鉴》^[35]	2005—2020
ln(knwlspillover)	知识溢出变量	每万人发明专利申请数量（件）取对数	佰腾网(https://www.baiten.cn/)	2005—2020
ln(technology)	技术进步	全市科学技术支出（万元）取对数	《中国城市统计年鉴》^[35]	2005—2020
ln(slagrealGDP)	空间滞后变量	实际GDP（亿元）取对数*地理距离权重矩阵	《中国统计年鉴》^[34]、《中国城市统计年鉴》^[35]	2005—2020
findeepth	金融深化率	金融机构年末存款余额占 GDP 的比重	《中国城市统计年鉴》^[35]	2005—2020

2005年				2020年				2005年				2020年
城市名	排名	总部/个数	占比	城市名	排名	总部/个数	占比	城市名	排名	出度	占比	城市名	排名	出度	占比
上海	1	105	19.92	上海	1	199	23.86	北京	1	68 877	74.04	北京	1	119 875	56.41
北京	2	87	16.51	北京	2	156	18.71	上海	2	10 342	11.12	上海	2	26 605	12.52
深圳	3	53	10.06	深圳	3	100	11.99	深圳	3	4853	5.22	深圳	3	19 395	9.13
广州	4	15	2.85	广州	4	23	2.76	广州	4	841	0.90	福州	4	3069	1.01
杭州	5	14	2.66	杭州	5	21	2.52	西安	5	641	0.69	天津	5	2741	1.06
天津	6	12	2.28	南京	6	16	1.92	南京	6	561	0.60	南京	6	2474	1.29
南京	7	10	1.90	天津	7	16	1.92	福州	7	475	0.51	杭州	7	2254	1.16
西安	8	9	1.71	成都	8	10	1.20	长沙	8	267	0.29	武汉	8	2162	0.92
成都	9	9	1.71	重庆	9	13	1.56	天津	9	258	0.28	广州	9	2148	0.62
长沙	10	8	1.52	福州	10	9	1.08	成都	10	248	0.27	西安	10	1965	0.63
总和	-	322	61.12	总和	-	563	67.51	总和	-	87 363	93.91	总和	-	182 688	84.75

2005年				2020年				2005年				2020年
城市名	排名	分支/个	占比	城市名	排名	分支/个	占比	城市名	排名	入度	占比	城市名	排名	入度	占比
上海	1	2542	3.38	上海	1	4854	3.06	上海	1	3119	3.35	上海	1	6376	3.00
北京	2	2208	2.94	北京	2	4715	2.97	北京	2	2679	2.88	北京	2	6046	2.85
广州	3	1668	2.22	重庆	3	2859	1.80	重庆	3	2016	2.17	重庆	3	4357	1.69
重庆	4	1666	2.22	广州	4	3384	2.13	广州	4	1919	2.06	深圳	4	3762	2.05
天津	5	1470	1.95	深圳	5	2739	1.72	天津	5	1690	1.82	成都	5	3697	1.56
成都	6	1198	1.59	成都	6	2827	1.78	深圳	6	1509	1.62	广州	6	3590	1.74
深圳	7	1179	1.57	天津	7	2828	1.78	成都	7	1423	1.53	天津	7	3315	1.77
苏州	8	1106	1.47	杭州	8	2074	1. 31	苏州	8	1273	1.37	杭州	8	2901	1.19
杭州	9	1061	1.41	武汉	9	2257	1.42	杭州	9	1252	1.35	武汉	9	2676	1.37
武汉	10	1032	1.37	苏州	10	2127	1.34	武汉	10	1210	1.30	西安	10	2578	1.26
总和	-	15 130	20.12	总和	-	30 664	19.31	总和	-	18 090	19.44	总和	-	39 298	18.48

年份	Moran's I	Z	P值
2005	0.35	7.29	0.001
2006	0.35	8.28	0.009
2007	0.34	8.12	0.032
2008	0.34	9.99	0.034
2009	0.32	8.32	0.044
2010	0.30	10.44	0.002
2011	0.32	10.44	0.002
2012	0.27	10.32	0.023
2013	0.27	11.86	0.015
2014	0.27	11.10	0.003
2015	0.21	11.99	0.004
2016	0.24	12.79	0.006
2017	0.19	12.60	0.000
2018	0.19	13.19	0.004
2019	0.18	13.19	0.000
2020	0.18	13.79	0.002

变量	模型1	模型2		模型3		模型4	模型5
变量	基准回归	滞后1期	滞后2期		工具变量分析		更换核心解释变量
PageRank	0.039^***					0.310^***
	(0.007)					(0.032)
lag1PageRank		0.033^*
		(0.019)
lag2PageRank				0.032^**
				(0.019)
ln(degree)							0.333^***
							(0.022)
ln(populdensity)	0.051^***	0.045^***		0.046^***		0.056^***	0.047^**
	(0.014)	(0.014)		(0.014)		(0.013)	(0.013)
ln(percapital)	0.037^***	0.025^***		0.043^***		0.126^***	0.034^***
	(0.004)	(0.004)		(0.004)		(0.007)	(0.004)
ln(teacher)	0.034^***	0.031^***		0.033^***		0.006	0.027^***
	(0.007)	(0.007)		(0.007)		(0.007)	(0.007)
ln(slagrealGDP)	0.899^***	0.776^***		0.903^***		0.397^***	0.721^***
	(0.008)	(0.008)		(0.008)		(0.007)	(0.006)
N	4176	4176		4176		4176	4176
R²	0.923	0.934		0.925		0.955	0.912
Durbin						0.000
Wu-Hausman						0.000
固定效应	控制	控制		控制		控制	控制

Research on the Influence Mechanism of Financial Network Centrality on Urban Economic Growth in China

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变量	PageRank指数作为核心解释变量		金融链接强度作为核心解释变量
变量	模型1：核心	模型2：外围	模型3：核心	模型4：外围
PageRank	0.067^**	0.014^**
	(0.025)	(0.007)
ln(degree)			0.562^***	0.277^***
			(0.045)	(0.027)
ln(populdensity)	0.089^***	0.043^***	0.069^**	0.034^**
	(0.031)	(0.015)	(0.027)	(0.015)
ln(percapital)	0.069^***	0.038^***	0.037^***	0.029^***
	(0.009)	(0.005)	(0.008)	(0.005)
ln(teacher)	0.046^**	0.035^***	0.048^***	0.020^***
	(0.019)	(0.008)	(0.017)	(0.007)
ln(slagrealGDP)	0.900^***	0.899^***	0.493^***	0.744^***
	(0.016)	(0.010)	(0.036)	(0.018)
N	832	3344	832	3344
R²	0.931	0.922	0.944	0.925
固定效应	控制	控制	控制	控制

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