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中国对“一带一路”沿线国家OFDI的区位选择:要素环境竞争力视角 被引量:6

China’s Location Choice for OFDI in the Countries Along the Belt and Road:From the Perspective of Factor Environment Competitiveness
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摘要 为检验中国是否因“素”制宜进行对外投资,文章从要素环境竞争力视角切入,在理论分析的基础上,采用商务部《境外投资企业(机构)名录》等相关数据以及零膨胀负二项回归模型展开实证检验。结果表明,中国对“一带一路”沿线国家的对外直接投资(OFDI)更倾向于选择劳动力成本更低以及与中国的制度距离更小的区位,同时,也会选择资源禀赋、资本存量及技术水平表现更好的国家或地区。进一步讨论发现,中国OFDI区位选择存在区域异质性和国别差异性,而且加入其他要素发现,资本成本与创新距离均会显著影响中国的OFDI。此外,劳动资本等有形要素的成本优势与制度距离之间存在“增强”效应,与创新距离之间存在“削弱”效应。 In order to test whether China chooses foreign investment projects based on the“factor”conditions,this paper uses the relevant data from“List of Overseas Investment Enterprises(Institutions)”issued by Ministry of Commerce and zero-inflated negative binomial regression model to carry out empirical tests from the perspective of factor environment competitiveness based on theoretical analysis.The results indicate that China’s outward foreign direct investment(OFDI)in the countries along the Belt and Road(B&R)is more inclined to choose the locations with lower labor costs and smaller institutional distance from China as well as the countries or regions with better resource endowment,capital stock and technological level.Further discussion reveals that there are regional heterogeneity and country differences in the location choice of China’s OFDI and it is found that both capital cost and innovation distance will affect China’s OFDI significantly after adding other factors.Moreover,there is an“enhancing”effect existing between the cost advantage of tangible factors(such as labor capital)and institutional distance,and a“weakening”effect existing between the cost advantage of tangible factors(such as labor capital)and innovation distance.
作者 史瑞祯 桑百川 SHI Rui-zhen;SANG Bai-chuan
出处 《国际经贸探索》 CSSCI 北大核心 2022年第8期85-100,共16页 International Economics and Trade Research
基金 国家社会科学基金重大项目(19ZDA064)。
关键词 一带一路 对外直接投资 要素环境竞争力 区位选择 零膨胀负二项回归模型 the Belt and Road(B&R) outward foreign direct investment(OFDI) factor environment competitiveness location choice zero-inflated negative binomial regression model
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