Spatio-temporal differentiation and influencing factors of entrepreneurship in the Yangtze River Economic Belt

doi:10.3969/j.issn.1004-9479.2024.02.20220162

Abstract

Abstract:

Under the strategic background of new urbanization and "mass entrepreneurship and innovation", it is of great significance to analyze the internal laws of entrepreneurship at the city level for scientifically constructing an entrepreneurial support system and promoting the highquality urban development. Based on the theory of entrepreneurial ecosystem, this paper proposes an analytical framework of entrepreneurship, and empirically explores the impact of en‐trepreneurial ecosystem on entrepreneurship in the cities of Yangtze River Economic Belt, an important strategic region, by using urban panel data and Spatial Panel Dubin Model. The results show that: 1) The spatial distribution and spatial growth trend of entrepreneurship in the cities of Yangtze River Economic Belt show obvious spatial and temporal differentiation characteristics, and the overall trend of entrepreneurship concentration continues to increase. 2) The entrepreneurial ecosystem of the city has a differentiated impact on entrepreneurship in the Yangtze River Economic Belt. The influence of market environment, government size, innovation support, physical infrastructure is confirmed, but the impact of financial capital and human capital is not significant. 3) The spatial spillover effect of entrepreneurial ecosystem in neighboring cities can not be ignored. The influence of market environment, government size and physical infrastruc ture is dominated by negative effects, while innovation capability is dominated by positive effects. 4) The study further proposes countermeasures and suggestions for improving the entrepreneurial support system from the aspects of the adjustment of the regional spatial structure of the Yangtze River Economic Belt, and the construction of urban entrepreneurial ecosystem.

Key words: entrepreneurship, city, entrepreneurial ecosystem, Spatial Panel Dubin Model, angtze River Economic Belt

摘要：

在新型城镇化与“大众创业、万众创新”的战略背景下，剖析城市层面企业家创业精神的内在规律对于科学构建创业支持体系，推动城市高质量发展具有重要意义。在创业生态系统理论的基础上，提出企业家创业精神的分析框架，并针对长江经济带这一重要的战略区域，运用城市面板数据和空间计量模型方法，实证探究了创业生态系统对企业家创业精神的影响。研究发现：（1）长江经济带城市中，企业家创业精神的空间分布与空间增长趋势均存在明显的空间分异特征，总体上空间集聚态势不断增强；（2）本城市创业生态系统对企业家创业精神产生了差异化影响。市场环境、政府规模、研发支持、基础设施等因素的影响得到证实，但金融资本、人力资本的影响不显著；（3）邻近城市创业生态系统对本城市的空间溢出效应不容忽视。其中市场环境、政府规模、基础设施等以负向效应为主，研发能力则以正向效应为主；（4）研究进一步从经济带区域空间结构调整、城市创业生态系统建设等方面，提出完善创业支持体系的对策建议。

关键词: 企业家创业精神, 城市, 创业生态系统, 空间面板杜宾模型, 长江经济带

Min ZHAO, Jiangbin YIN, Xiaoyan HUANG, Wulin WANG. Spatio-temporal differentiation and influencing factors of entrepreneurship in the Yangtze River Economic Belt[J]. World Regional Studies, 2024, 33(2): 164-175.

赵敏, 殷江滨, 黄晓燕, 王武林. 长江经济带企业家创业精神的时空分异与影响因素[J]. 世界地理研究, 2024, 33(2): 164-175.

Figures/Tables 7

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类型	变量	均值	标准差
市场环境	国内生产总值/亿元	1 585.3	3 181.8
	第二产业比重/%	44.7	8.2
	公路货运量/万吨	11 391.2	17 092.7
金融资本	人均贷款余额/元	96 056.7	95 319.1
劳动力资源	人口密度/（人/平方千米）	892.2	605.0
	每万人在校大学生数/人	506.2	422.0
	在岗职工平均工资/元	54 801.8	24 673.5
政府规模	政府财政支出占GDP比重/%	17.2	9.4
研发支持	科技企业孵化器数/个	1.50	3.98
研发支持	专利申请量/件	12 007.9	21 783.6
基础设施	人均道路面积/（平方米/人）	12.7	8.6
	建成区绿化覆盖率/%	40.3	6.0
	中小学校师生比	0.06	0.01

类型	变量	均值	标准差
市场环境	国内生产总值/亿元	1 585.3	3 181.8
	第二产业比重/%	44.7	8.2
	公路货运量/万吨	11 391.2	17 092.7
金融资本	人均贷款余额/元	96 056.7	95 319.1
劳动力资源	人口密度/（人/平方千米）	892.2	605.0
	每万人在校大学生数/人	506.2	422.0
	在岗职工平均工资/元	54 801.8	24 673.5
政府规模	政府财政支出占GDP比重/%	17.2	9.4
研发支持	科技企业孵化器数/个	1.50	3.98
研发支持	专利申请量/件	12 007.9	21 783.6
基础设施	人均道路面积/（平方米/人）	12.7	8.6
	建成区绿化覆盖率/%	40.3	6.0
	中小学校师生比	0.06	0.01

年份	Moran’s I值	z值	p值
2010	0.018	1.750	0.080
2011	0.024	2.108	0.035
2012	0.021	1.910	0.056
2013	0.044	3.274	0.001
2014	0.049	3.706	0.000
2015	0.068	4.983	0.000
2016	0.027	2.299	0.022
2017	0.061	4.462	0.000
2018	0.027	2.319	0.020

年份	Moran’s I值	z值	p值
2010	0.018	1.750	0.080
2011	0.024	2.108	0.035
2012	0.021	1.910	0.056
2013	0.044	3.274	0.001
2014	0.049	3.706	0.000
2015	0.068	4.983	0.000
2016	0.027	2.299	0.022
2017	0.061	4.462	0.000
2018	0.027	2.319	0.020

检验方法	统计量	检验方法	统计量	P值
LM-spatial lag	25.344	Wald-spatial lag	39.48	0.000
Robust LM-spatial lag	30.635	LR-spatial lag	95.52	0.000
LM-spatial error	84.720	Wald-spatial error	36.01	0.001
Robust LM-spatial error	90.012	LR-spatial error	89.40	0.000