北京企业对河北直接投资的时空动态特征及影响因素

doi:10.18306/dlkxjz.2020.03.004

Abstract

Abstract:

With the initiation of the coordinated development strategy of the Beijing-Tianjin-Hebei region, moving out non-capital functions and industrial transfer in Beijing has been paid increasingly more attention. Based on the data of investment projects of over 100 million yuan in Hebei Province by Beijing's enterprises from 2010 to 2015 and the calculation results of geographic concentration, this study analyzed the investment scale, industry characteristics, and location characteristics over the years, and further used a conditional Logit regression model to explore the influencing factors of the investment location of enterprises from all industries, manufacturing industries, and service industries. The purpose of this study was to summarize the spatial change pattern and influencing mechanisms of investments in Hebei by Beijing's enterprises, and provide suggestions for industrial transfer policies in the context of moving out non-capital functions. The study found that: 1) The investment scale in Hebei by Beijing's enterprises increased dramatically. The proportion of manufacturing industry investments decreased and the proportion of service industry investments increased. The types of invested industries were diverse, showing an upgrading trend, and the moving out of industries had achieved some results. 2) The distribution of investments in the prefectural-level cities of Hebei Province was clearly stratified, and they were distributed across urban districts and counties, showing the characteristics of wide-area distribution. Tangshan City was in a dominant position, followed by Baoding, Shijiazhuang, Zhangjiakou, and Langfang cities. 3) From 2010 to 2015, the location of investments in Hebei by Beijing's enterprises was concentrated, but had become slightly dispersed. The investment was moving from Tangshan, Chengde, and Langfang to Baoding and Handan, and basically formed an investment pattern with Tangshan, Shijiazhuang, Baoding, and Zhangjiakou as the core agglomeration area, which was in line with the spatial planning of the coordinated development strategy of the Beijing-Tianjin-Hebei region. The investment location of the manufacturing industries had gradually dispersed, the investment was moving from Tangshan and Langfang to Baoding, Handan, and Shijiazhuang. The investment location of the service industries was dispersed firstly, then concentrated, and the investment was moving from Shijiazhuang and Chengde to Zhangjiakou, Baoding, Langfang, and Qinhuangdao. 4) Industrial foundation, time distance, policy environment, and innovation capacity were important factors that influenced the choice of investment location. Manufacturing industries were affected by time distance, labor cost, and policy environment, so it was easy to form "location lock-in" and “industry lock-in.” The higher economic benefits of service enterprises had overcome the increase of labor costs, and high-quality talents could attract investments from service enterprises.

Key words: enterprise investment, location choice, Beijing, Beijing-Tianjin-Hebei urban agglomeration

LU Minghua, ZHOU Yueyan, LIU Hanchu, XU Xin. Spatiotemporal dynamics and influencing factors of direct investment in Hebei Province by Beijing’s enterprises[J].PROGRESS IN GEOGRAPHY, 2020, 39(3): 389-401.

Figures/Tables 12

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References 40

[1]	陆大道 . 京津冀城市群功能定位及协同发展[J]. 地理科学进展, 2015,34(3):265-270.
	[ Lu Dadao . Function orientation and coordinating development of subregions within the Jing-Jin-Ji urban agglomeration. Progress in Geography, 2015,34(3):265-270. ]
[2]	祝尔娟, 何皛彦 . 基于大数据分析京津冀产业协同进展与动向[J]. 产业创新研究, 2017,1(2):30-34.
	[ Zhu Erjuan, He Xiaoyan . Analysis of progress and trend of industrial cooperation in Beijing-Tianjin-Hebei region based on big data. Industrial Innovation, 2017,1(2):30-34. ]
[3]	魏后凯 . 现代区域经济学 [M]. 北京: 经济管理出版社, 2006.
	[ Wei Houkai. Modern regional economics. Beijing, China: Economic Management Press, 2006. ]
[4]	Procher V . Agglomeration effects and the location of FDI: Evidence from French first-time movers[J]. The Annals of Regional Science, 2011,46(2):295-312.
[5]	Kolstad I, Wiig A . What determines Chinese outward FDI[J]. Journal of World Business, 2012,47(1):26-34.
[6]	余官胜, 林俐 . 企业海外集群与新晋企业对外直接投资区位选择: 基于浙江省微观企业数据[J]. 地理研究, 2015,34(2):364-372.
	[ Yu Guansheng, Lin Li . Enterprise overseas cluster and foreign direct investment location choice of new entrants: An empirical research based on the micro enterprises data in Zhejiang Province. Geographical Research, 2015,34(2):364-372. ]
[7]	郑蕾, 刘志高 . 中国对“一带一路”沿线直接投资空间格局[J]. 地理科学进展, 2015,34(5):563-570.
	[ Zheng Lei, Liu Zhigao . Spatial pattern of Chinese outward direct investment in the Belt and Road Initiative area. Progress in Geography, 2015,34(5):563-570. ]
[8]	王茂军, 徐永平 . 中国在美洲OFDI的基本特征与决定因素[J]. 地理学报, 2017,72(8):1373-1391.
	[ Wang Maojun, Xu Yongping . China's OFDI in America: Basic features and determinants. Acta Geographica Sinica, 2017,72(8):1373-1391. ]
[9]	宋渊洋, 黄礼伟 . 为什么中国企业难以国内跨地区经营[J]. 管理世界, 2014,30(12):115-133.
	[ Song Yuanyang, Huang Liwei . Why is it so difficult for Chinese companies to operate across borders. Management World, 2014,30(12):115-133. ]
[10]	谢专, 张佳梁, 张晓波 . 京津冀的产业结构现状、变迁与空间资本流动: 来自工商注册数据的证据[J]. 人口与发展, 2015,21(5):24-33.
	[ Xie Zhuan, Zhang Jialiang, Zhang Xiaobo . The structure of production and capital flow in Beijing-Tianjin-Hebei: Evidence from firm registry database. Population and Development, 2015,21(5):24-33. ]
[11]	步丹璐, 屠长文, 罗宏 . 产业政策能否缓解市场分割: 基于企业异地股权投资视角的实证研究[J]. 产业经济研究, 2017,16(6):75-88.
	[ Bu Danlu, Tu Changwen, Luo Hong . Can industrial policy mitigate market segmentation? An empirical study from the perspective of cross-regional equity investment. Industrial Economics Research, 2017,16(6):75-88. ]
[12]	方嘉雯, 刘海猛 . 京津冀城市群创业风险投资的时空分布特征及影响机制[J]. 地理科学进展, 2017,36(1):68-77.
	[ Fang Jiawen, Liu Haimeng . How and why venture capital flows in the Beijing-Tianjin-Hebei Urban Agglomeration. Progress in Geography, 2017,36(1):68-77. ]
[13]	桑瑞聪, 刘志彪 . 中国产业转移趋势特征和影响因素研究: 基于上市公司微观数据的分析[J]. 财贸研究, 2014,35(6):53-60.
	[ Sang Ruicong, Liu Zhibiao . The study of the trend and the influential factors of industry transference in China: Analysis on listed companies. Finance and Trade Research, 2014,35(6):53-60. ]
[14]	段小薇, 苗长虹, 赵建吉 . 河南承接制造业转移的时空格局研究[J]. 地理科学, 2017,37(1):72-82.
	[ Duan Xiaowei, Miao Changhong, Zhao Jianji . Spatio-temporal pattern of undertaking manufacturing's transfer in Henan Province. Scientia Geographica Sinica, 2017,37(1):72-82. ]
[15]	张建伟, 苗长虹, 肖文杰 . 河南省承接产业转移区域差异及影响因素[J]. 经济地理, 2018,38(3):106-112.
	[ Zhang Jianwei, Miao Changhong, Xiao Wenjie . Regional differences and influence factor of Henan's undertaking industrial transfer. Economic Geography, 2018,38(3):106-112. ]
[16]	许欣 . 北京企业对河北直接投资区位选择及影响因素研究[D]. 北京: 首都师范大学, 2017.
	[ Xu Xin . Location selection and influencing factors of direct investment in Hebei by Beijing enterprises. Beijing, China: Capital Normal University, 2017. ]
[17]	王帅, 席强敏, 李国平 . 北京制造业企业对河北投资的空间特征与影响因素[J]. 经济地理, 2015,35(4):90-98.
	[ Wang Shuai, Xi Qiangmin, Li Guoping . The geographical characteristics & influencing factors of Beijing manufacture enterprises investment on Hebei. Economic Geography, 2015,35(4):90-98. ]
[18]	李仙德 . 基于上市公司网络的长三角城市网络空间结构研究[J]. 地理科学进展, 2014,33(12):1587-1600.
	[ Li Xiande . Spatial structure of the Yangtze River Delta urban network based on the pattern of listed companies network. Progress in Geography, 2014,33(12):1587-1600. ]
[19]	蔡高明, 李志斌, 王东宇 , 等. 中原城市群产业投资网络结构特征分析[J]. 城市发展研究, 2017,24(12):16-21, 91.
	[ Cai Gaoming, Li Zhibin, Wang Dongyu , et al. A study on characteristics of the investment network of central plains city group. Urban Studies, 2017,24(12):16-21, 91. ]
[20]	胡国建, 陈传明, 陈丽娟 , 等. 企业跨区域投资格局及其影响因素: 以福建上市企业为例[J]. 经济地理, 2018,38(9):138-146.
	[ Hu Guojian, Chen Chuanming, Chen Lijuan , et al. Enterprise's cross-region investment pattern and its determinants: A case study of Fujian listed firms. Economic Geography, 2018,38(9):138-146. ]
[21]	桑瑞聪, 彭飞, 康丽丽 . 地方政府行为与产业转移: 基于企业微观数据的实证研究[J]. 产业经济研究, 2016,15(4):7-17.
	[ Sang Ruicong, Peng Fei, Kang Lili . Behavior of local government and industrial transfer: An empirical analysis based on firm micro-data. Industrial Economics Research, 2016,15(4):7-17. ]
[22]	贾兴梅, 刘俊杰 . 中西部地区承接产业转移的影响因素研究[J]. 地域研究与开发, 2015,34(1):14-18.
	[ Jia Xingmei, Liu Junjie . Study on influencing factors of undertaking industrial transfer in the Midwest of China. Areal Research and Development, 2015,34(1):14-18. ]
[23]	Lee Y . Geographic redistribution of US manufacturing and the role of state development policy[J]. Journal of Urban Economics, 2008,64(2):436-450.
[24]	Hong S H . Agglomeration and relocation: Manufacturing plant relocation in Korea[J]. Papers in Regional Science, 2013,93(4):803-818.
[25]	Deller S C, Conroy T, Tsvetkova A . Factors affecting the interstate migration of manufacturing firms: Much to do about nothing [R]. Madison, USA: University of Wisconsin-Madison, 2015.
[26]	Conroy T, Deller S, Tsvetkova A . Regional business climate and interstate manufacturing relocation decisions[J]. Regional Science and Urban Economics, 2016,60(12):155-168.
[27]	Jiang Y, Timmermans H J P, Yu B . Relocation of manufacturing industry from the perspective of transport accessibility: An application of percolation theory[J]. Transport Policy, 2018,63(1):10-29.
[28]	潘峰华, 夏亚博, 刘作丽 . 区域视角下中国上市企业总部的迁址研究[J]. 地理学报, 2013,68(4):19-33.
	[ Pan Fenghua, Xia Yabo, Liu Zuoli . The relocation of headquarters of public listed firms in China: A regional perspective study. Acta Geographica Sinica, 2013,68(4):19-33. ]
[29]	李俊峰, 陶世杰, 高凌宇 . 跨江发展下杭州市企业迁移空间模式及影响机制[J]. 地理科学, 2018,38(1):87-96.
	[ Li Junfeng, Tao Shijie, Gao Lingyu . The space model and the influence mechanism of enterprise migration under river-crossing development in Hangzhou. Scientia Geographica Sinica, 2018,38(1):87-96. ]
[30]	田山川, 张文忠 . 中国钢铁工业空间格局的演化及影响机制[J]. 地理科学进展, 2009,28(4):537-545.
	[ Tian Shanchuan, Zhang Wenzhong . Evolution of spatial pattern of Chinese iron and steel industry and the influencing factors. Progress in Geography, 2009,28(4):537-545. ]
[31]	赵浚竹, 孙铁山, 李国平 . 中国汽车制造业集聚与企业区位选择[J]. 地理学报, 2014,69(6):850-862.
	[ Zhao Junzhu, Sun Tieshan, Li Guoping . Agglomeration and firm location choice of China's automobile manufacturing industry. Acta Geographica Sinica, 2014,69(6):850-862. ]
[32]	李佳洺, 孙威, 张文忠 . 北京典型行业微区位选址比较研究: 以北京企业管理服务业和汽车制造业为例[J]. 地理研究, 2018,37(12):2541-2553.
	[ Li Jiaming, Sun Wei, Zhang Wenzhong . Comparative study on micro-scale location choice of typical industries: The case study of management service and automobile manufacturing in Beijing. Geographical Research, 2018,37(12):2541-2553. ]
[33]	McFadden D . Frontiers of econometrics [M]. New York, USA: Academic Press, 1974: 54-86.
[34]	陈强 . 高级计量经济学及STATA的应用 [M]. 北京: 高等教育出版社, 2014: 192-205.
	[ Chen Qiang. Application of advanced econometrics and STATA. Beijing, China: Higher Education Press, 2014: 192-205. ]
[35]	Vernon R . International investment and international trade in the product cycle[J]. Quarterly Journal of Economics, 1966,8(4):190-207.
[36]	Kojima K . Macroeconomic versus international business approach to foreign direct investment[J]. Hitosubashi Journal of Economics, 1982,23(1):1-19.
[37]	王志锋, 王优容, 王云亭 , 等. 城市行政等级与经济增长: 基于开发区的视角[J]. 宏观经济研究, 2017,39(11):115-127.
	[ Wang Zhifeng, Wang Yourong, Wang Yunting , et al. Urban administrative hierarchy and economic growth: From the perspective of development zones. Macroeconomics, 2017,39(11):115-127. ]
[38]	贺灿飞, 朱彦刚, 朱晟君 . 产业特性、区域特征与中国制造业省区集聚[J]. 地理学报, 2010,65(10):1218-1228.
	[ He Canfei, Zhu Yangang, Zhu Shengjun . Industrial attributes, provincial characteristics and industrial agglomeration in China. Acta Geographica Sinica, 2010,65(10):1218-1228. ]
[39]	曾春水, 申玉铭, 李哲 , 等. 京津冀城市职能演变特征与优化对策[J]. 经济地理, 2018,38(9):67-77.
	[ Zeng Chunshui, Shen Yuming, Li Zhe , et al. 2018. Evolutional characteristics and optimization countermeasures of urban functions in Beijing-Tianjin-Hebei urban agglomeration. Economic Geography, 2018,38(9):67-77. ]
[40]	赵映慧, 初楠臣, 郭晶鹏 , 等. 中国三大城市群高速铁路网络结构与特征[J]. 经济地理, 2017,37(10):68-73.
	[ Zhao Yinghui, Chu Nanchen, Guo Jingpeng , et al. High-speed railway network structure and characteristics in three urban agglomerations in China. Economic Geography, 2017,37(10):68-73. ]

年份	全行业			制造业			服务业
年份	投资总额/ 亿元	项目个数/个	投资额增长率/%	投资总额/ 亿元	比重/%	投资额增长率/%	投资总额/ 亿元	比重/%	投资额增长率/%
2010	607.97	199	—	319.18	52.50	—	144.72	23.80	—
2011	1175.40	393	93.33	453.62	38.59	42.12	439.51	37.39	203.69
2012	1685.85	536	43.43	619.37	36.74	36.54	590.09	35.00	34.26
2013	2461.25	699	45.99	878.52	35.69	41.84	986.17	40.07	67.12
2014	2815.17	790	14.38	981.83	34.88	11.76	1188.79	42.23	20.55
2015	2540.50	743	-9.76	1022.83	40.26	4.18	1041.70	41.00	-12.37

层级	全行业	制造业	服务业
第一层级	唐山	唐山	唐山
第二层级	保定、石家庄、张家口、廊坊	保定、廊坊、沧州	石家庄、保定、廊坊
第三层级	承德、沧州、秦皇岛、邯郸	邯郸、石家庄	张家口、承德、秦皇岛
第四层级	邢台、衡水	邢台、承德、张家口、秦皇岛、衡水	沧州、邯郸、邢台、衡水

年份	全行业	制造业	服务业
2010	0.54	0.71	0.77
2011	0.46	0.58	0.60
2012	0.52	0.55	0.57
2013	0.46	0.49	0.54
2014	0.50	0.57	0.58
2015	0.52	0.53	0.62

投资区域	全行业	制造业	服务业
市区投资占主导	唐山(77.3%)、石家庄(74.9%)、沧州(69.8%)、廊坊(59.6%)	秦皇岛(75.6%)、沧州(72.1%)、唐山(71.6%)、石家庄(66.1%)、保定(65.7%)	衡水(87.0%)、石家庄(87.0%)、唐山(84.3%)、廊坊(76.4%)、沧州(70.4%)、邢台(68.8%)、邯郸(63.9%)
县域投资占主导	衡水(29.2%)、张家口(20.9%)、承德(17.4%)	廊坊(36.4%)、衡水(28.0%)、承德(25.3%)	张家口(29.0%)、承德(28.5%)
市区、县域投资相当	保定(56.5%)、秦皇岛(54.9%)、邯郸(52.9%)、邢台(50.3%)	邯郸(50.6%)、张家口(48.0%)、邢台(42.5%)	秦皇岛(50.7%)、保定(47.5%)

城市类型	全行业	制造业	服务业
增强型	保定、邯郸	保定、邯郸、石家庄	张家口、保定、廊坊、秦皇岛
减弱型	唐山、承德、廊坊	唐山、廊坊	承德、石家庄
抵消型	石家庄、张家口、沧州、秦皇岛	沧州	唐山、沧州
补缺型	邢台、衡水	邢台、张家口、承德、秦皇岛、衡水	邢台、邯郸、衡水

Spatiotemporal dynamics and influencing factors of direct investment in Hebei Province by Beijing’s enterprises

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属性	指标	定义	预处理	预期影响
因变量	是否进行投资	北京某企业对某地级市进行投资赋值为1,否则赋值为0
经济水平	人均GDP(PGDP)	各地级市地区生产总值与人口总数的比值(元/人)	取对数	+
创新能力	专利数量(PAT)	各地级市当年授权专利总量(件)	取对数	+
产业基础	就业人数(EMP)	各地级市全行业就业人数(万人)	取对数	+
产业基础	产业结构(IND/SER)	工业、服务业增加值占地区总产值的比重(%)	取对数	+
投资成本	劳动力成本(WAGE)	各地级市职工平均工资(元)	取对数	-
交通通达性	时间距离(TRAN)	距北京最短的公路行驶时间(min)	取对数	-
政策环境	国家级开发区/高新区 (ZONE)	国家级经济技术开发区赋值1;国家级高新区赋值1.23;国家级保税区赋值0.36;国家级出口加工区赋值0.14;省级开发区赋值0.125	无	+

变量	2011年			2015年
变量	全行业	制造业	服务业	全行业	制造业	服务业
lnPGDP	0.167(0.30)	-0.458(-0.60)	0.763(0.67)	0.279(0.33)	0.724(0.57)	-0.488(-0.30)
lnPAT	-0.066(-0.43)	0.144(0.46)	0.327(1.16)	-0.655^**(-1.20)	-0.317(-0.51)	-0.228^*(-0.13)
lnEMP	1.457^***(6.60)	0.314(0.84)	1.853^***(4.29)	3.587^***(5.17)	1.741(1.86)	4.825^**(3.27)
lnIND	1.980(0.82)	0.180^*(0.12)	3.641(0.80)	-0.145(-0.17)	2.634^***(1.70)	-2.904^***(-1.84)
lnSER	3.468^*(2.07)	-0.481(-0.13)	3.563^*(3.21)	0.810(0.61)	2.710^**(1.90)	0.302^*(0.13)
lnTRAN	-1.891^***(-5.23)	-1.485^***(-2.67)	-2.402^**(-3.21)	0.336(1.37)	-1.036^*(-2.17)	1.192(2.01)
lnWAGE	-1.318(-0.73)	-0.566^**(-0.82)	-2.433(-0.68)	4.699(0.87)	-1.099^**(-0.91)	6.550^***(2.82)
ZONE	-0.686^**(-2.77)	0.218^*(0.99)	-1.182^*(-2.31)	-0.537^*(-4.12)	-0.515(-1.32)	-0.150(-0.69)
Log-likehood	-1252.64	-434.331	-453.94	-2369.49	-960.636	-838.123
LR chi2	128	56	84	240	129	142
R²	0.148	0.198	0.251	0.133	0.175	0.237
N	4323	1518	1628	8173	3366	2992

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