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

doi:10.11821/dlyj201502015

摘要/Abstract

摘要：

企业对外直接投资海外集群和区位选择均是当前理论和政策的热门话题,着眼于研究两者之间的关系。由于东道国国别经济存在较大的差异,因此中国企业在对外直接投资区位选择过程中较为注重特定的国别状况,东道国经济规模、人均收入、贸易开放、资源条件等因素均构成了企业对外直接投资区位选择的影响因素,此外本国企业在东道国的海外集群也是企业对外直接投资区位选择决策的不可忽视因素。利用浙江省微观企业数据构造二值选择模型,并使用浙江省在某东道国的对外直接投资项目数度量在该国的海外集群状况,研究发现：企业海外集群是东道国吸引新晋企业对外直接投资的重要区位优势,并且这种区位优势在资源越丰富和市场规模越大的国家发挥的作用也越大。为了消除回归的内生性问题,使用地理距离作为企业海外集群的工具变量,通过二阶段二值选择模型得出了类似的结论;并且通过分样本实证研究发现无论在发达国家还是发展中国家东道国,企业海外集群吸引新晋企业对外直接投资的正向影响均保持不变。结果说明了企业对外直接投资海外集群具有自我延续性,也反映了信息在企业对外直接投资区位选择中的重要性。

关键词: 企业对外直接投资, 海外集群, 区位选择, 二值选择模型, 工具变量回归

Abstract:

In this paper, we focused on the relationship of the overseas clusters and location choice for Chinese enterprises' foreign direct investment, both of which have recently become hot topics of theory research and policy concern. For there exists a big difference of economy condition in different host countries, China's enterprises pay attention to specific economic factors of host countries in location choice of foreign direct investment, and host countries' economic scale, per capita income, trade openness, resource conditions and other elements constitute the factors influencing location choice of enterprise' foreign direct investment. In addition, the overseas cluster of home countries' enterprises in host countries is another significant factor that cannot be ignored for enterprise' decision of foreign direct investment location choice. This paper structured the binary choice model by using the micro data of enterprises in Zhejiang province, and used the number of foreign direct investment projects in certain host country of Zhejiang's enterprises to measure the overseas cluster in that country. Based on these data, an empirical study was conducted in this paper and found that overseas enterprise cluster is an important location advantage for the host country to attract foreign direct investment from new enterprises. Furthermore, this advantage plays a greater role in countries that have richer resources and larger market scales. What is more, in order to eliminate the endogenous regression problem, the geographic distance was taken as the instrumental variable of enterprises' overseas cluster and a similar conclusion through two stage binary choice model was reached. We also found that the positive influence of enterprise overseas clusters on the attraction of new enterprises' foreign direct investment exist in both developed and developing host countries. The results of this paper indicated that there is self-continuity in the direct investment of foreign enterprises overseas cluster and reflected the importance of information in location choices for enterprises' foreign direct investment as well.

Key words: foreign direct investment, overseas cluster, location choice, binary choice model, instrumental variable regression

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.

Guansheng YU, Li LIN. Enterprises overseas cluster and foreign direct investment location choice of new entrants：An empirical research based on micro enterprises data in Zhejiang province[J]. GEOGRAPHICAL RESEARCH, 2015, 34(2): 364-372.

图/表 6

表1

表2

二值选择模型回归结果"

变量	Probit模型			Logit模型
变量	(1)	(2)	(3)	(1')	(2')	(3')
C	-4.085^*** (0.000)	-3.935^*** (0.000)	-2.965^*** (0.000)	-8.974^*** (0.000)	-8.947^*** (0.000)	-6.300^*** (0.000)
lnNum_i	0.354^*** (0.000)	0.330^*** (0.000)	-0.011 (0.885)	0.821^*** (0.000)	0.770^*** (0.000)	0.094 (0.566)
lnGDP_i	0.136^*** (0.000)	0.129^*** (0.000)	-	0.349^*** (0.000)	0.330^*** (0.000)	-
lnPGDP_i	-0.054^* (0.070)	-0.056^* (0.062)	-0.064^** (0.016)	-0.124^* (0.071)	-0.123^* (0.073)	-0.114^* (0.075)
Trade_i	0.268^*** (0.000)	0.262^*** (0.000)	0.303^*** (0.000)	0.552^*** (0.000)	0.531^*** (0.000)	0.589^*** (0.000)
Eng_i	1.125^*** (0.000)	-	0.827^*** (0.000)	2.706^*** (0.000)	-	1.844^*** (0.008)
lnNum_i×Eng_i	-	0.477^*** (0.000)	-	-	1.042^*** (0.000)	-
lnNum_i×lnGDP_i	-	-	0.045^*** (0.000)	-	-	0.090^*** (0.000)
$R 2$	0.210	0.209	0.217	0.219	0.219	0.224
样本数	12220	12220	12220	12220	12220	12220

表2

表3

表4

工具变量二阶段回归结果"

变量	Probit模型			Logit模型
变量	(1)	(2)	(3)	(1')	(2')	(3')
C	-4.222^*** (0.000)	-3.906^*** (0.000)	-3.073^*** (0.000)	-9.235^*** (0.000)	-8.608^*** (0.000)	-6.143^*** (0.000)
lnNum_i	0.358^*** (0.000)	0.317^*** (0.000)	-0.986^*** (0.000)	0.836^*** (0.000)	0.723^*** (0.000)	-2.591^*** (0.000)
lnGDP_i	0.332^*** (0.000)	0.298^*** (0.000)	-	0.803^*** (0.000)	0.747^*** (0.000)	-
lnPGDP_i	-0.130^*** (0.000)	-0.119^*** (0.000)	0.065^*** (0.002)	-0.307^*** (0.000)	-0.292^*** (0.000)	0.122^** (0.032)
Trade_i	0.489^*** (0.000)	0.453^*** (0.000)	0.317^*** (0.000)	1.080^*** (0.000)	1.012^*** (0.000)	0.733^*** (0.000)
Eng_i	1.331^*** (0.000)	-	0.225 (0.381)	3.199^*** (0.000)	-	0.628 (0.347)
lnNum_i×Eng_i	-	0.943^*** (0.063)	-	-	2.975^** (0.030)	-
lnNum_i×lnGDP_i	-	-	0.177^*** (0.000)	-	-	0.448^*** (0.000)
$R 2$	0.208	0.203	0.188	0.218	0.214	0.197

表4

表5

分样本二值选择模型回归结果"

变量	发达国家(地区)样本		发展中国家(地区)样本
变量	Probit模型	Logit模型	Probit模型	Logit模型
C	-5.849^*** (0.000)	-13.094^*** (0.000)	-2.981^*** (0.000)	-6.436^*** (0.000)
$lnNu m i$	0.414^*** (0.000)	0.889^*** (0.000)	0.195^*** (0.002)	0.525^*** (0.001)
$lnGD P i$	0.226^*** (0.000)	0.460^*** (0.000)	0.073 (0.226)	0.182 (0.250)
$lnPGD P i$	0.003 (0.982)	0.135 (0.689)	-0.069 (0.314)	-0.161 (0.366)
$Trad e i$	0.313^*** (0.000)	0.598^*** (0.000)	0.211 (0.117)	0.529 (0.127)
$En g i$	2.074^*** (0.001)	4.391^*** (0.002)	0.301 (0.387)	0.829 (0.394)
R²	0.241	0.247	0.041	0.041
样本数	4680	4680	7540	7540

表5

表6

分样本工具变量二阶段回归结果"

变量	发达国家(地区)样本		发展中国家(地区)样本
变量	Probit模型	Logit模型	Probit模型	Logit模型
C	-7.743^*** (0.000)	-16.656^*** (0.000)	-2.796^*** (0.000)	-5.946^*** (0.000)
$lnNu m i$	0.411^*** (0.000)	0.884^*** (0.000)	0.206^*** (0.001)	0.549^*** (0.001)
$lnGD P i$	0.499^*** (0.000)	1.038^*** (0.000)	0.186^*** (0.000)	0.490^*** (0.000)
$lnPGD P i$	0.024 (0.850)	0.193 (0.566)	-0.163^*** (0.005)	-0.415^*** (0.006)
$Trad e i$	0.565^*** (0.000)	1.149^*** (0.000)	0.410^*** (0.001)	1.067^*** (0.000)
$En g i$	3.134^*** (0.000)	6.802^*** (0.002)	0.402 (0.242)	1.116 (0.244)
$R 2$	0.238	0.245	0.042	0.042
样本数	4680	4680	7540	7540

表6

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洲别	国家(地区)
亚洲	阿联酋、巴基斯坦、菲律宾、哈萨克斯坦、韩国、柬埔寨、老挝、马来西亚、蒙古国、孟加拉国、日本、泰国、土耳其、新加坡、伊朗、印度、印度尼西亚、越南、中国香港(地区)
欧洲	比利时、波兰、德国、俄罗斯、荷兰、瑞典、瑞士、乌克兰、西班牙、意大利、英国
非洲	埃及、埃塞俄比亚、安哥拉、刚果(金)、加纳、马里、摩洛哥、南非、尼日利亚、坦桑尼亚
北美洲	加拿大、美国、墨西哥
拉丁美洲	巴拿马、巴西、秘鲁
大洋洲	澳大利亚

变量	Probit模型			Logit模型
变量	(1)	(2)	(3)	(1')	(2')	(3')
C	-4.203^*** (0.000)	-3.933^*** (0.000)	-2.860^*** (0.000)	-8.783^*** (0.000)	-8.417^*** (0.000)	-5.977^*** (0.000)
lnNum_i	0.390^*** (0.000)	0.362^*** (0.000)	-0.021 (0.789)	0.861^*** (0.000)	0.805^*** (0.000)	0.090 (0.585)
lnGDP_i	0.149^*** (0.000)	0.140^*** (0.000)	-	0.368^*** (0.000)	0.346^*** (0.000)	-
lnPGDP_i	-0.061^* (0.058)	-0.064^** (0.049)	-0.074^*** (0.009)	-0.135^* (0.051)	-0.133^* (0.053)	-0.126^* (0.051)
Trade_i	0.308^*** (0.000)	0.303^*** (0.000)	0.350^*** (0.000)	0.612^*** (0.000)	0.590^*** (0.000)	0.648^*** (0.000)
Eng_i	1.264^*** (0.000)	-	0.943^*** (0.000)	2.967^*** (0.000)	-	2.045^*** (0.003)
lnNum_i×Eng_i	-	0.540^*** (0.000)	-	-	1.142^*** (0.000)	-
lnNum_i×lnGDP_i	-	-	0.050^*** (0.000)	-	-	0.096^*** (0.000)
R₂	0.237	0.237	0.246	0.249	0.249	0.255
样本数	7912	7912	7912	7912	7912	7912