中国上市公司总部空间格局演变及其驱动因素研究

doi:10.13249/j.cnki.sgs.2018.04.001

摘要/Abstract

摘要：

借助空间计量模型,考察2005~2015年中国上市公司空间格局演化特征及其驱动因素,研究发现：① 中国上市公司主要集中分布在长三角、珠三角及京津冀地区,向高等级城市集聚明显。②空间重心迁移表明中国上市公司呈现“迅速-平稳”发展的阶段性特征;空间关联格局由沿海向内陆呈现出热点区-过渡区-冷点区的梯度推移态势。③三大密集型上市公司专业化空间演变形态各异,劳动密集型呈现“单核”向“双核”模式演化;资本密集型呈“破碎化点状”极化分布态势;技术密集型呈“多核心串珠状”扩散态势。④OLS回归模型显示城市区位及规模、全球化水平、投资状况、科教水平、信息化水平等因素共同驱动中国上市公司空间格局演变。

关键词: 上市公司, 格局特征, GIS空间分析, 中国

Abstract:

In this study, we use a variety of spatial measurement model to study the spatial evolution characteristics and its driving factors of China’s listed companies’ from 2005 to 2015. It is found that: 1) China’s listed companies are mainly concentrated in the Yangtze River Delta, the Pearl River Delta and Beijing-Tianjin-Hebei Region, and are concentrated in high administrative grade cities obviously. 2) The shift of space center indicates that the listed companies in China have the stage characteristics of “fast and stable” development. Spatial association pattern shows a gradient trend which is “Hot spot to transition zone to cold spot area” from the coastal to inland. 3) The spatial evolution pattern of the three major listed companies of specialization is different, the mode of the labor intensive area evaluated from “single core” to “dual core”. Capital intensive is the polarization distribution situation of “breaking point” significantly. Technology intensive area emerges a situation of “multi-core beaded”, and it has a significant characteristics like the creep spread. 4) The regression model shows that the spatial pattern of listed companies is driven by factors such as city location grade and scale, global level, investment situation, science and education level and information level and so on.

Key words: listed companies, pattern characteristics, GIS spatial analysis, China

中图分类号:

K902

钟业喜, 傅钰, 郭卫东, 邱静. 中国上市公司总部空间格局演变及其驱动因素研究[J]. 地理科学, 2018, 38(4): 485-494.

Yexi Zhong, Yu Fu, Weidong Guo, Jing Qiu. Spatial Pattern Evolution and Driving Factors of China’s Listed Companies[J]. SCIENTIA GEOGRAPHICA SINICA, 2018, 38(4): 485-494.

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城市级别	2005年		2010年		2015年
城市级别	城市	比重（%）	城市	比重（%）	城市	比重（%）
第一级（101~300）	上海	9.88	北京、上海、深圳	23.60	北京、上海、深圳	24.43
第二级（51~100）	北京、深圳	12.40	杭州	3.12	杭州、苏州、广州、成都、南京、宁波	14.10
第三级（21~50）	武汉、杭州、南京、成都、重庆、广州、天津、无锡	15.85	广州、苏州、成都、武汉、南京、天津、宁波、长沙、重庆、无锡、绍兴、福州、西安、厦门、合肥、大连、乌鲁木齐、台州、海口、哈尔滨	29.39	长沙、武汉、无锡、绍兴、重庆、天津、合肥、台州、厦门、西安、烟台、福州、乌鲁木齐、嘉兴、佛山、大连、汕头、哈尔滨、沈阳、海口、郑州、常州、珠海、昆明、金华、济南	28.10
第四级（6~20）	长沙等39个	34.15	昆明等48个	25.58	淄博等51个	19.05
第五级（0~5）	岳阳等	27.72	盐城等	18.31	许昌等	14.43

变量	均值	标准差	最小值	最大值	观测值
N	1.619	0.950	0.693	5.468	2154
rank	0.754	0.157	0.693	1.386	2154
gdp	16.192	0.957	13.310	19.278	2154
fdi	9.990	1.755	3.091	14.450	2154
finpop	9.403	0.808	7.003	12.975	2154
invest	15.658	1.014	12.595	18.691	2154
edu	1.511	1.114	0.6931	4.533	2154
tele	12.378	1.003	8.686	16.183	2154

固定效应模型	模型1	模型2	模型3	模型4	模型5	模型6	模型7	混合模型8
rank	3.941***	2.283***	2.277***	2.181***	2.235***	1.668***	1.611***	1.601***
	(0.0557)	(0.0974)	(0.0960)	(0.137)	(0.117)	(0.0835)	(0.0715)	(0.0720)
gdp		0.614***	0.533***	0.470***	0.555***	0.520***	0.432***	0.435***
		(0.0492)	(0.0614)	(0.0385)	(0.0752)	(0.0794)	(0.0775)	(0.0782)
fdi			0.0531***	0.0555***	0.0607***	0.0513***	0.0467***	0.0527***
			(0.00799)	(0.00744)	(0.00601)	(0.00537)	(0.00493)	(0.00513)
finpop				0.0843**	0.0777**	0.0469	0.0136	0.0390
				(0.0356)	(0.0336)	(0.0344)	(0.0363)	(0.0330)
invest					-0.104*	-0.158**	-0.126**	-0.194***
					(0.0494)	(0.0539)	(0.0536)	(0.0561)
edu						0.182***	0.180***	0.195***
						(0.0132)	(0.0147)	(0.0132)
tele							0.106***	0.106***
							(0.0157)	(0.0167)
常量	-1.352***	-10.04***	-9.255***	-8.979***	-8.757***	-6.816***	-6.786***	-6.109***
	(0.0420)	(0.725)	(0.850)	(0.743)	(0.628)	(0.671)	(0.666)	(0.649)
R²	0.430	0.673	0.677	0.678	0.679	0.691	0.694	0.7305