The evolution and influencing factors of specialized agglomeration of leading industries in the Yangtze River Delta region

doi:10.3969/j.issn.1004-9479.2024.06.20220196

Abstract

Abstract:

Taking 312 county units in the Yangtze River Delta region as the research objects, this paper expounds the spatial cluster characteristics and change rules of the three leading manufacturing industries with the comprehensive application of spatial cluster index, and analyzes the influencing factors by using the spatial panel data model. The main conclusions are as follows: First of all, the spatial cluster characteristics of the leading manufacturing showing a significant spatial imbalance in the Yangtze River Delta region. The cluster center is mainly located in the region composed of southern region of Jiangsu Province, Shanghai and northwestern region of Zhejiang Province, and this distribution pattern is relatively stable. The north region of Jiangsu Province and southwest region of Zhejiang Province are mainly in the sub-marginal position, and the leading industries cluster are less. Anhui Province is mainly in the marginal position, and most of the county units in Anhui Province do not have cluster advantages. The electronic information manufacturing shows the most obvious characteristics of core-periphery distribution. Although there are differences between textile industry and automobile manufacturing, it can be proved this characteristic. Secondly, the advantages of land cost and labor cost will attract leading industries to agglomeration. The expansion of local market scale has a negative effect on the spatial cluster of electronic information manufacturing. The construction of development zones above the provincial level will have different effects on the spatial cluster of textile industry and electronic information manufacturing. Thirdly, the development of urbanization and the growth of city size will promote the spatial cluster of automobile manufacturing and electronic information manufacturing, which is not conducive to the spatial cluster of textile industry. Fourthly, the globalization factors such as foreign capital entry and export increase will promote the improvement of spatial cluster of leading industries in the Yangtze River Delta region. Finally, we put forward specific countermeasures and suggestions from the aspects of promoting specialized industrial agglomeration and intra-regional industrial division of labor.

Key words: textile industry, automobile manufacturing, electronic information manufacturing, specialized agglomeration, Yangtze River Delta region

摘要：

以长三角地区三省一市312个县域单元为研究对象，利用专业化集聚指数分析了纺织业、汽车制造业以及电子信息制造业三大主导产业空间集聚特征与变化趋势，并利用空间面板数据模型分析其影响因素，最后提出区域产业协同发展的对策建议。结论如下：首先，长三角地区主导产业集聚中心主要位于苏南-上海-浙东北地区且变化较小，苏北和浙西南地区主要处于次边缘，安徽则处于边缘位置。其中，以电子信息制造业表现的“核心-边缘”空间集聚特征最为明显，纺织业和汽车制造业虽有所不同，但均可佐证这种空间非均衡分布特征。其次，土地和劳动力成本优势会吸引主导产业空间集聚。市场规模的扩大会对电子信息制造业的集聚产生不利影响。省级以上开发区的建设会对纺织业和电子信息制造业的集聚产生相反的作用。再次，城市化水平提升和城市规模增长会对汽车和电子信息制造业集聚产生促进作用，而不利于纺织业集聚水平的提升。第四，外资进入和出口增加均会提升长三角地区主导产业专业化集聚水平。最后，从推动专业化产业集聚、促进区域内部产业有序分工等方面提出了推动产业协同发展的对策建议。

关键词: 纺织业, 汽车制造业, 电子信息制造业, 专业化集聚, 长三角地区

Jiangjiang KANG, Yuemin NING. The evolution and influencing factors of specialized agglomeration of leading industries in the Yangtze River Delta region[J]. World Regional Studies, 2024, 33(6): 116-127.

康江江, 宁越敏. 长三角地区主导产业专业化集聚演变与影响因素[J]. 世界地理研究, 2024, 33(6): 116-127.

Figures/Tables 5

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变量类型	变量名称		定义/ 单位	最大值	最小值	均值
因变量		Ln(SCI)	专业化集聚指数	6.17	－20.72	－7.06
自变量	地方化变量	Ln(Market size)	消费品零售总额/10亿元	7.32	0.14	3.57
		Ln(Wage)	在岗职工平均工资/元	11.28	8.26	9.95
		Ln(Land price)	招标、拍卖或挂牌出售土地的平均价格/ (万元/公顷)	9.07	－0.13	6.4
		SEDZ	省级以上开发区数量/个	2	0	0.12
	城市化变量	Ln(Urban size)	户籍城镇人口规模/万人	5.530	－0.003	2.740
	城市化变量	Urbanization rate	非农人口总数/人口总数/%	100	4.31	40.20
	全球化变量	Forcapital	外资企业资产/总资产/%	100	0	6.39
	全球化变量	Exports proportion	出口交货值/销售总产值/%	100	0	18.81
控制变量		Ln(GDP)	国内生产总值/亿元	8.77	0.23	4.51
		Ln(Investment)	固定资产投资/亿元	7.43	－20.72	3.69
		Ln(MED)	制造业从业人口数量/县域行政区划面积/ (人/km²)	8.75	－1.22	3.85
		Ln(MS)	县域制造业从业人数/企业数量	7.49	3.79	5.40

变量类型	变量名称		定义/ 单位	最大值	最小值	均值
因变量		Ln(SCI)	专业化集聚指数	6.17	－20.72	－7.06
自变量	地方化变量	Ln(Market size)	消费品零售总额/10亿元	7.32	0.14	3.57
		Ln(Wage)	在岗职工平均工资/元	11.28	8.26	9.95
		Ln(Land price)	招标、拍卖或挂牌出售土地的平均价格/ (万元/公顷)	9.07	－0.13	6.4
		SEDZ	省级以上开发区数量/个	2	0	0.12
	城市化变量	Ln(Urban size)	户籍城镇人口规模/万人	5.530	－0.003	2.740
	城市化变量	Urbanization rate	非农人口总数/人口总数/%	100	4.31	40.20
	全球化变量	Forcapital	外资企业资产/总资产/%	100	0	6.39
	全球化变量	Exports proportion	出口交货值/销售总产值/%	100	0	18.81
控制变量		Ln(GDP)	国内生产总值/亿元	8.77	0.23	4.51
		Ln(Investment)	固定资产投资/亿元	7.43	－20.72	3.69
		Ln(MED)	制造业从业人口数量/县域行政区划面积/ (人/km²)	8.75	－1.22	3.85
		Ln(MS)	县域制造业从业人数/企业数量	7.49	3.79	5.40

模型/自变量	SAR model			SEM model
模型/自变量	SIC-17	SIC-36	SIC-39	SIC-17	SIC-36	SIC-39
Ln(Market size)	－0.240 (－0.56)	0.445	－1.224^**	0.383	0.437	－1.216^**
Ln(Market size)	－0.240 (－0.56)	(0.82)	(－2.27)	(0.85)	(0.81)	(－2.24)
Ln(Wage)	－0.875^*	－0.323	0.206	－0.414	－0.341	0.107
Ln(Wage)	(－1.61)	(－0.47)	(0.30)	(－0.67)	(－0.49)	(0.15)
Ln(Land price)	－0.100	－0.435^**	－0.479^**	－0.161	－0.444^**	－0.456^**
Ln(Land price)	(－0.58)	(－1.99)	(－2.17)	(－0.68)	(－2.00)	(－1.98)
SEDZ	1.336^**	－0.460	－1.137^**	1.290^**	－0.451	－1.101^**
SEDZ	(2.29)	(－0.74)	(－2.36)	(2.22)	(－0.72)	(－2.27)
Urban rate	－0.039^*	－0.022	0.088^***	－0.0344	－0.022	0.087^***
Urban rate	(－1.85)	(－0.81)	(3.25)	(－1.59)	(－0.80)	(3.19)
Ln(Urbanization size)	－0.551	2.501^**	－0.670	－0.615	2.461^**	－0.686
Ln(Urbanization size)	(－0.64)	(2.28)	(－0.61)	(－0.72)	(2.24)	(－0.63)
Forcapital	0.016^*	0.022^**	0.010	0.024^***	0.022^**	0.009
Forcapital	(1.96)	(2.03)	(1.24)	(2.98)	(2.01)	(1.15)
Exports proportion	0.029^***	0.052^***	0.035^***	0.028^***	0.052^***	0.035^***
Exports proportion	(3.76)	(3.83)	(3.41)	(3.71)	(3.83)	(3.40)
Ln(GDP)	0.051	－1.222^***	－0.163	－0.377	－1.204^***	－0.175
Ln(GDP)	(0.14)	(－2.75)	(－0.36)	(－1.01)	(－2.70)	(－0.39)
Ln(Investment)	－0.031	－0.099	－0.379^**	0.003	－0.091	－0.363^**
Ln(Investment)	(－0.26)	(－0.65)	(－2.46)	(0.02)	(－0.59)	(－2.35)
Ln(MED)	3.543^***	3.191^***	3.771^***	3.621^***	3.227^***	3.896^***
Ln(MED)	(12.19)	(9.01)	(10.64)	(11.68)	(9.03)	(10.57)
Ln(MS)	－0.802^***	－1.267^***	0.099	－1.770^***	－1.298^***	0.020
Ln(MS)	(－2.71)	(－3.36)	(0.26)	(－3.99)	(－3.38)	(0.05)
rho	0.398^***	0.018	0.031
rho	(0.03)	(0.043)	(0.73)
lambda				0.455	0.03	0.070
lambda				(0.037)	(0.045)	(1.54)
Log-likelihood	－3 435.35	－3 709.25	－3 712.62	－3 447.44	－3 709.1	－3 711.72
AIC	6 898.70	7 446.5	7 553.25	6 922.88	7 446.2	7 451.43
BIC	6 970.51	7 518.3	7 525.06	6 994.69	7 518.0	7 523.24
r²	0.29	0.13	0.16	0.23	0.13	0.16

模型/自变量	SAR model			SEM model
模型/自变量	SIC-17	SIC-36	SIC-39	SIC-17	SIC-36	SIC-39
Ln(Market size)	－0.240 (－0.56)	0.445	－1.224^**	0.383	0.437	－1.216^**
Ln(Market size)	－0.240 (－0.56)	(0.82)	(－2.27)	(0.85)	(0.81)	(－2.24)
Ln(Wage)	－0.875^*	－0.323	0.206	－0.414	－0.341	0.107
Ln(Wage)	(－1.61)	(－0.47)	(0.30)	(－0.67)	(－0.49)	(0.15)
Ln(Land price)	－0.100	－0.435^**	－0.479^**	－0.161	－0.444^**	－0.456^**
Ln(Land price)	(－0.58)	(－1.99)	(－2.17)	(－0.68)	(－2.00)	(－1.98)
SEDZ	1.336^**	－0.460	－1.137^**	1.290^**	－0.451	－1.101^**
SEDZ	(2.29)	(－0.74)	(－2.36)	(2.22)	(－0.72)	(－2.27)
Urban rate	－0.039^*	－0.022	0.088^***	－0.0344	－0.022	0.087^***
Urban rate	(－1.85)	(－0.81)	(3.25)	(－1.59)	(－0.80)	(3.19)
Ln(Urbanization size)	－0.551	2.501^**	－0.670	－0.615	2.461^**	－0.686
Ln(Urbanization size)	(－0.64)	(2.28)	(－0.61)	(－0.72)	(2.24)	(－0.63)
Forcapital	0.016^*	0.022^**	0.010	0.024^***	0.022^**	0.009
Forcapital	(1.96)	(2.03)	(1.24)	(2.98)	(2.01)	(1.15)
Exports proportion	0.029^***	0.052^***	0.035^***	0.028^***	0.052^***	0.035^***
Exports proportion	(3.76)	(3.83)	(3.41)	(3.71)	(3.83)	(3.40)
Ln(GDP)	0.051	－1.222^***	－0.163	－0.377	－1.204^***	－0.175
Ln(GDP)	(0.14)	(－2.75)	(－0.36)	(－1.01)	(－2.70)	(－0.39)
Ln(Investment)	－0.031	－0.099	－0.379^**	0.003	－0.091	－0.363^**
Ln(Investment)	(－0.26)	(－0.65)	(－2.46)	(0.02)	(－0.59)	(－2.35)
Ln(MED)	3.543^***	3.191^***	3.771^***	3.621^***	3.227^***	3.896^***
Ln(MED)	(12.19)	(9.01)	(10.64)	(11.68)	(9.03)	(10.57)
Ln(MS)	－0.802^***	－1.267^***	0.099	－1.770^***	－1.298^***	0.020
Ln(MS)	(－2.71)	(－3.36)	(0.26)	(－3.99)	(－3.38)	(0.05)
rho	0.398^***	0.018	0.031
rho	(0.03)	(0.043)	(0.73)
lambda				0.455	0.03	0.070
lambda				(0.037)	(0.045)	(1.54)
Log-likelihood	－3 435.35	－3 709.25	－3 712.62	－3 447.44	－3 709.1	－3 711.72
AIC	6 898.70	7 446.5	7 553.25	6 922.88	7 446.2	7 451.43
BIC	6 970.51	7 518.3	7 525.06	6 994.69	7 518.0	7 523.24
r²	0.29	0.13	0.16	0.23	0.13	0.16

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