产业集聚空间格局演变及其对经济高质量发展的影响——基于中国278个城市数据的实证分析

doi:10.11821/dlyj020210226

摘要/Abstract

摘要：

产业集聚有利于发挥集聚经济外部性,是加快城市经济高质量发展的重要途径。本文基于2005—2018年中国278个城市面板数据,采用SBM-DEA模型对各城市经济高质量发展水平进行测度,并通过运用GIS空间分析和空间杜宾模型,深入分析产业集聚空间格局演变及其与城市经济高质量发展之间的关系。研究发现：① 在考虑不同空间权重矩阵情况下,产业集聚对经济高质量发展提升具有显著正向直接效应和空间溢出效应,且高铁开通引致的时空压缩使得两种效应的促进作用更大。② 制造业和生产性服务业集聚对经济高质量发展的作用路径不同,前者对经济高质量发展的影响更多地体现在直接效应;而后者更多地表现为空间溢出效应。生产性服务业经营范围更大,不仅有利于本地区制造业转型升级,也可通过产业关联和空间溢出带动临近城市经济质量提升。③ 东部城市更多依赖于生产性服务业集聚;而中西部城市更依赖制造业,且受产业发展层次影响,其空间溢出效应较低。④ 大城市生产性服务业集聚带来的空间溢出效应明显高于直接效应;中等城市生产性服务业集聚在调节制造业集聚的促进作用时起到的作用有限;小城市产业集聚对经济高质量发展的影响整体较弱,而对政府财政科技支出依赖性更大。

关键词: 产业集聚, 经济高质量发展, 空间杜宾模型, 异质性

Abstract:

Industrial agglomeration is conducive to the formation of specialized production processes and labor market, sharing infrastructure, which can promote the high-quality development of urban economy by giving full play to the externality of agglomeration economy and improving the degree of market competition. Based on the panel data of 278 cities in China from 2005 to 2018, this study adopts SBM-DEA model to measure the high-quality economic development and analyses the spatial distribution of industrial agglomeration by GIS. It then establishes a spatial Durbin model to deeply analyze the relationship between industrial agglomeration and the quality of urban economic development. The main findings are as follows: (1) Industrial agglomeration has significant positive direct effect and spatial spillover effect on the improvement of economic development quality, and the opening of high-speed railway amplifies the promotion effects. (2) Manufacturing industry and producer services agglomeration have different effects on the quality of economic development. The former has more direct effects on the quality of economic development and the latter has more spatial spillover effect. The scope of producer services may be larger, which is not only conducive to the transformation and upgrading of the local manufacturing industry, but also may promote the economic quality of nearby cities through industrial linkage and spatial spillover. (3) The cities in eastern China are more dependent on the agglomeration of producer services, while the cities in the central and western regions depends more on manufacturing industry, and the level of industrial development, so their spatial spillover effect is low. (4) The spatial spillover effect of producer services agglomeration in big cities is significantly higher than the direct effect. Producer services agglomeration in medium-sized cities plays a limited role in regulating the promotion of manufacturing agglomeration. The impact of industrial agglomeration in small cities on the quality of economic development is weak as a whole, which is largely dependent on the governments' financial expenditure on science and technology.

Key words: industrial agglomeration, high-quality economic development, spatial Durbin model, heterogeneity

李涛, 薛领, 李国平. 产业集聚空间格局演变及其对经济高质量发展的影响——基于中国278个城市数据的实证分析[J]. 地理研究, 2022, 41(4): 1092-1106.

LI Tao, XUE Ling, LI Guoping. The evolution of spatial pattern of industrial agglomeration and its impact on the high-quality economic development: Empirical analysis based on the data of 278 cities in China[J]. GEOGRAPHICAL RESEARCH, 2022, 41(4): 1092-1106.

图/表 9

表1

表2

变量说明

变量类型	变量	符号	定义
因变量	经济高质量发展	EDQ	依据表1中的指标体系,运用熵值法对其进行综合评价
核心自变量	制造业集聚	MA	$M A i = E ij ∑ i = 1 n E ij E i ∑ i = 1 n E i$ , $E ij$ 表示i城市制造业j的就业人数; $E i$ 表示i城市的就业人数
	生产性服务业集聚	PS	$P S i = P is ∑ i = 1 n P is E i ∑ i = 1 n E i$ , $P is$ 表示i城市生产性服务业s的就业人数; $E i$ 表示i城市的就业人数
控制变量	财政科技支出	GOV	各城市政府财政在科学技术支出
	外商直接投资	FDI	实际利用外商直接投资
	人力资本水平	NUS	每十万人口中在校大学生数量

表2

图1

表3

表4

表5

表6

表7

表8

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一级指标	二级指标	单位
投入	固定资产投资	万元
投入	从业人员期末人数	人
产出	人均GDP	元
	地方财政收入	元
	每万人拥有医院、卫生院床位数	张
	人均城市道路面积	m²
	工业废水排放强度	t/万元
	工业二氧化硫排放强度	t/万元
	工业烟(粉)尘排放强度	t/万元

年份	制造业	生产性服务业	年份	制造业	生产性服务业
2005	0.157^***	0.032^*	2012	0.290^***	0.069^***
2006	0.198^***	0.051^*	2013	0.302^***	0.074^**
2007	0.217^***	0.053^**	2014	0.303^***	0.075^***
2008	0.243^***	0.058^**	2015	0.305^***	0.076^***
2009	0.250^***	0.059^**	2016	0.303^***	0.078^***
2010	0.255^***	0.063^**	2017	0.312^***	0.081^***
2011	0.233^***	0.067^**	2018	0.314^***	0.089^***

变量	系数		t统计量
MA	0.149^***		2.828
PS	0.105^**		1.981
MP	0.063^**		2.136
GOV	0.078^***		13.891
FDI	0.076^***		5.937
NUS	0.015^**		2.281
Cons	0.297^*		1.848
R²	0.729	Durbin-Watson	1.456
sigma^²	0.075	loglikols	1141.143
LM test no spatial lag	417.913^***	LM test no spatial error	341.371^***
robust LM test no spatial lag	288.534^***	robust LM test no spatial error	112.991^***
Wald_Spatial lag	85.201^***	Wald_Spatial error	115.713^***
LR_Spatial lag	67.095^***	LR_Spatial error	101.393^***
Hausman test	78.058^***

变量	SDM			变量	SDM
变量	空间固定	时间固定	时空固定	变量	空间固定	时间固定	时空固定
MA	0.107^*** (4.334)	0.123^*** (3.484)	0.135^*** (5.771)	W×MA	0.071^*** (3.213)	0.068^*** (4.645)	0.101^*** (4.166)
PS	0.072^*** (2.678)	0.073^*** (2.801)	0.092^*** (3.491)	W×PS	0.034^* (1.667)	0.044 (1.501)	0.068^** (2.151)
MP	0.034^* (1.865)	0.042^** (2.046)	0.047^*** (2.952)	W×MP	0.022^** (2.230)	0.018^* (1.921)	0.062^** (2.341)
GOV	0.063^*** (4.763)	0.055^*** (3.261)	0.035^** (2.351)	W×GOV	0.016^* (1.775)	0.022^* (1.865)	0.024^* (1.932)
FDI	0.039^*** (5.551)	0.019^*** (5.331)	0.028^*** (6.001)	W×FDI	0.024^** (2.276)	0.031 (0.136)	0.045^** (2.091)
NUS	-0.019^*** (-3.048)	-0.018^*** (-3.407)	-0.016^*** (-5.716)	W×NUS	-0.063^*** (-3.795)	-0.064^** (-2.274)	-0.084^*** (-2.815)
Adj R²	0.716	0.778	0.871	κ	0.468^*** (5.439)	0.818^*** (3.431)	0.763^*** (6.391)
log-L	767.759	719.611	869.211	κ	0.468^*** (5.439)	0.818^*** (3.431)	0.763^*** (6.391)

变量	地理距离		经济距离		时间距离
变量	直接效应	间接效应	直接效应	间接效应	直接效应	间接效应
MA	0.101^*** (5.721)	0.061^*** (2.653)	0.108^*** (4.365)	0.073^** (2.455)	0.131^*** (4.443)	0.087^*** (3.678)
PS	0.072^*** (4.344)	0.066^** (2.208)	0.075^* (1.888)	0.064^** (2.393)	0.078^** (2.219)	0.087^* (1.825)
MP	0.052^*** (2.714)	0.017^* (1.878)	0.037^** (1.985)	0.027 (1.173)	0.057^* (1.821)	0.035^* (1.950)
GOV	0.045^*** (3.776)	0.032^*** (4.304)	0.032^** (2.386)	0.024^*** (4.319)	0.031^*** (3.263)	0.034^** (2.005)
FDI	0.011^*** (4.115)	0.009 (0.842)	0.014^*** (3.234)	0.005 (0.936)	0.012^*** (3.477)	0.007^*** (2.806)
NUS	-0.013^*** (-3.413)	-0.036^*** (-3.115)	-0.015^** （-2.182）	-0.033^** (-2.217)	-0.009^*** （-3.663）	-0.019^** (-2.638)