Influence of urban network characteristics on attracting domestic investment: A case study of Urban Agglomeration in the Middle Reaches of Yangtze River

doi:10.3969/j.issn.1004-9479.2023.11.2021903

Abstract

Abstract:

Investment is one of the important forces shaping the regional economic pattern. In the network society, the status and network relations of cities have a significant impact on attracting investment. Based on enterprise investment data, this study builds an internal urban network of Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR) and investment links of other domestic cities to UAMRYR, and describes its network characteristics and investment spatial pattern respectively. Then, using the negative binomial regression method to answer the question: how do the urban network characteristics of UAMRYR affect domestic investment? The results reveal: First, the network of UAMRYR presents a "hub-and-spoke" network structure centered on the provincial capital city, and there are barriers between provinces. There is obvious heterogeneity in the spatial distribution of multiplex urban networks. Second, the sources of domestic investment to UAMRYR are mainly from the eastern regions and a few from the western ones. The ability to attract investment from capital cities in UAMRYR is far better than that of other cities. Third, In-degree Centrality and Weighted In-degree Centrality symbolizing urban prestige and Out-degree Centrality symbolizing urban power have a positive impact on attracting domestic investment significantly. Network structure indicators have no significant impact on absorbing investment, which may be caused by the immature network of UAMRYR.

Key words: urban network, domestic investment, urban agglomeration in the middle reaches of Yangtze River, investment flow

摘要：

投资是塑造区域经济格局的重要力量之一，城市在区域网络中的地位和组织关系对投资吸引力存在重要影响。基于企业投资联系数据，本文分别构建了长江中游城市群内部城市网络和吸引区外的国内投资联系，并刻画了其网络特征和投资空间格局；采用负二项回归方法回答了“长江中游城市群城市网络特征如何影响国内其他城市对该区域投资”的这一研究问题。研究发现：（1）长江中游城市群城市网络呈现以省会城市为中心的“轴-辐”式网络空间结构，省际存在着明显的壁垒，城市网络特征各项指标的时空分布存在明显的异质性；（2）长江中游城市群吸引国内投资的来源地呈现出“东密西疏”的空间格局，投资吸纳地集中在武汉、长沙、南昌三大省会城市；（3）长江中游城市群城市网络特征和区位因素共同对城市投资吸引力存在影响，网络特征中象征城市威望权的入度和权重入度，以及象征城市控制权的出度对国内投资的吸引力起到显著正向影响，然而由于网络发育尚不成熟，网络结构指标对投资吸引力的影响尚不显著。

关键词: 城市网络, 国内投资, 长江中游城市群, 投资流

Wenwen CHENG, Yayou DU, Manxin ZHENG, Peng GAO, Dan HE. Influence of urban network characteristics on attracting domestic investment: A case study of Urban Agglomeration in the Middle Reaches of Yangtze River[J]. World Regional Studies, 2023, 32(11): 119-129.

程雯雯, 杜雅优, 郑曼欣, 高鹏, 何丹. 城市网络特征对吸引国内投资的影响[J]. 世界地理研究, 2023, 32(11): 119-129.

Figures/Tables 10

Fig.1 Study area

Tab.1 Calculation formula of network analysis index

网络特征	指标	计算公式	公式解释
网络密度	网络密度	$D = ∑ i = 1 k ∑ j = 1 k d (n i, n j) k (k - 1)$	D网络密度反映网络的整体紧密程度；k为节点数，d( n_i，n_j)为节点n_i、n_j之间的联系，若有联系赋值为1，否则为0；n为城市数量
内向威望权	入度	$C (D i) i n = ∑ j = 1 N x i j (i ≠ j)$	$C (D i) i n$ 为入度，x_ij 表示以城市i为终点和城市j是否有联系，若有联系则为1，否则为0
内向威望权	权重入度	$S (D i) i n = ∑ j = 1 N a i j (i ≠ j)$	$S (D i) i n$ 为权重入度，a_ij 表示以城市i为终点和城市j的所有联系的数量和
外向控制权	出度	$C (D i) o u t = ∑ j = 1 N x i j (i ≠ j)$	$C (D i) o u t$ 为出度中心度，x_ij 表示以城市i为起点和城市j是否有联系，若有联系则为1，否则为0
外向控制权	权重出度	$S (D i) o u t = ∑ j = 1 N a i j (i ≠ j)$	$S (D i) o u t$ 为权重出度，a_ij 表示以城市i为起点和城市j的所有联系的数量和
网络结构特征	中介中心度	$C B i = ∑ j = 1 N ∑ k = 1 N g j k i g j k$ (i≠j, i≠k, j<k)	$C (B i)$ 中介中心度衡量网络中城市节点i控制其他城市交往的中介能力； $g j k$ 为城市j与 k之间的捷径数量； $g j k i$ 为城市j与k之间经过城市i的捷径数量
	邻近中心度	$C C i = n - 1 ∑ j = 1 n d i j (i ≠ j)$	$C C i$ 邻近中心度衡量网络中城市节点与其他城市的接近程度；d_ij 为节点城市i与j之间的距离
	集聚系数	$C i = 2 E i k i k i - 1$	$C i$ 集聚系数衡量网络中某一节点城市与其他城市集聚的意愿；k_i(k_i-1)为节点城市i的所有邻点对数目； $E i$ 为节点城市i实际直接相连的邻点对数目

Tab.1 Calculation formula of network analysis index

网络特征	指标	计算公式	公式解释
网络密度	网络密度	$D = ∑ i = 1 k ∑ j = 1 k d (n i, n j) k (k - 1)$	D网络密度反映网络的整体紧密程度；k为节点数，d( n_i，n_j)为节点n_i、n_j之间的联系，若有联系赋值为1，否则为0；n为城市数量
内向威望权	入度	$C (D i) i n = ∑ j = 1 N x i j (i ≠ j)$	$C (D i) i n$ 为入度，x_ij 表示以城市i为终点和城市j是否有联系，若有联系则为1，否则为0
内向威望权	权重入度	$S (D i) i n = ∑ j = 1 N a i j (i ≠ j)$	$S (D i) i n$ 为权重入度，a_ij 表示以城市i为终点和城市j的所有联系的数量和
外向控制权	出度	$C (D i) o u t = ∑ j = 1 N x i j (i ≠ j)$	$C (D i) o u t$ 为出度中心度，x_ij 表示以城市i为起点和城市j是否有联系，若有联系则为1，否则为0
外向控制权	权重出度	$S (D i) o u t = ∑ j = 1 N a i j (i ≠ j)$	$S (D i) o u t$ 为权重出度，a_ij 表示以城市i为起点和城市j的所有联系的数量和
网络结构特征	中介中心度	$C B i = ∑ j = 1 N ∑ k = 1 N g j k i g j k$ (i≠j, i≠k, j<k)	$C (B i)$ 中介中心度衡量网络中城市节点i控制其他城市交往的中介能力； $g j k$ 为城市j与 k之间的捷径数量； $g j k i$ 为城市j与k之间经过城市i的捷径数量
	邻近中心度	$C C i = n - 1 ∑ j = 1 n d i j (i ≠ j)$	$C C i$ 邻近中心度衡量网络中城市节点与其他城市的接近程度；d_ij 为节点城市i与j之间的距离
	集聚系数	$C i = 2 E i k i k i - 1$	$C i$ 集聚系数衡量网络中某一节点城市与其他城市集聚的意愿；k_i(k_i-1)为节点城市i的所有邻点对数目； $E i$ 为节点城市i实际直接相连的邻点对数目

Fig.2 Evolution of urban network in UAMRYR

Fig.3 Spatial pattern of urban prestige in UAMRYR

Fig.4 Spatial pattern of urban power in UAMRYR

Fig.5 Spatial pattern of urban betweenness， closeness and clustering in UAMRYR

Fig.6 Spatial pattern of attracting domestic investment in UAMRYR

Tab.2 Top 10 cities of investment attraction in UAMRYR

排序		1	2	3	4	5	6	7	8	9	10
2003年	城市	武汉市	长沙市	南昌市	宜昌市	襄阳市	株洲市	抚州市	九江市	岳阳市	上饶市
2003年	吸引投资占比	35.00%	21.41%	15.80%	3.96%	2.80%	1.83%	1.71%	1.57%	1.41%	1.31%
2008年	城市	武汉市	长沙市	南昌市	九江市	宜昌市	襄阳市	抚州市	荆州市	黄冈市	上饶市
2008年	吸引投资占比	42.82%	19.35%	11.31%	2.4%	2.3%	2.19%	1.38%	1.29%	1.24%	1.22%
2013年	城市	武汉市	长沙市	南昌市	九江市	宜昌市	襄阳市	宜春市	岳阳市	吉安市	衡阳市
2013年	吸引投资占比	37.13%	22.75%	11.58%	2.47%	2.26%	2.14%	1.50%	1.44%	1.39%	1.38%

Tab.3 Selection and explanation of variables

变量	名称	指标（缩写）	单位
解释变量	城市内向威望权	入度（ID）	—
	城市内向威望权	权重入度（WID）	—
	城市外向控制权	出度（OD）	—
	城市外向控制权	权重出度（WOD）	—
	城市网络结构	中介中心度（BT）	—
		邻近中心度（CL）	—
		集聚系数（CLU）	—
控制变量	经济水平	城市人均实际GDP（RGDP）	元/人
	人力资源	城市从业人员数占常住人口比重（HUM）	%
	地理邻近性	城市间空间直线距离（DIS）	公里
	创新能力	城市万人专利授权数（PAT）	项
	政府支持	地方公共财政支出占GDP比重（GOV）	%

Tab.4 Estimation results of the influence mechanism of investment pattern

变量	Model 1	Model 2	Model 3
ID	0.213^**(2.415)
WID	0.236^***(3.780)
OD		0.228^**(2.382)
WOD		－0.068(－0.735)
BT			0.030(0.269)
CL			0.161(1.270)
CLU			0.051(0.651)
DIS	－0.295(－1.263)	－0.141(－0.577)	－0.130(－0.538)
RGDP	0.105(1.555)	0.187^**(2.122)	0.190^**(2.037)
HUM	0.050(0.814)	0.168^**(2.176)	0.163^**(2.123)
PAT	－0.059(－1.360)	－0.014(－0.234)	－0.025(－0.426)
GOV	－0.040(－0.730)	0.006(0.111)	0.009(0.167)
Constant	2.811^***(11.560)	2.377^***(11.31)	2.338^***(11.100)

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