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Factors Affecting Foreign Direct Investment Region Selection

Posted on:2015-08-28Degree:MasterType:Thesis
Country:ChinaCandidate:H ZhengFull Text:PDF
GTID:2309330431964410Subject:Western economics
Abstract/Summary:PDF Full Text Request
Since joined the WTO, foreign investors optimistic about the prospects for thedevelopment of China’s foreign direct investment poured into China, China’s onceagain usher in a significant opportunity for openness and development. However,affected by political, economic, cultural traditions and history of various factors,foreign direct investment in China ’s central and western regions severely unevendistribution of FDI mostly gathered in the eastern coastal areas, while the central andwestern regions to attract foreign direct investment few. Specific to the eastern coastalareas, all the provinces to attract foreign direct investment flows actually there aresome gaps, of which only two provinces of Jiangsu and Liaoning, the cumulativeamount of foreign direct investment in2012has reached50percent of the nationaltotal of the total. This uneven distribution of FDI in the state will seriously hamper thecoordinated development of regional economy. In the economic restructuring andgrowth is slowing down the current background, research factors Shandong, Liaoning,Jiangsu and Zhejiang provinces in four selected areas of foreign direct investment,for the provinces how to better attract foreign direct investment, promote industrialupgrading and regional economic development is important.The first part of this article on the study of the theory of foreign directinvestment to sort out the theoretical basis for the empirical analysis; followed by thecase of foreign direct investment in the provinces of comparative studies,comprehensive multi-angle analysis of the provinces to attract foreign investmentadvantages and disadvantages. The second part of the factors affecting the provincesto attract foreign direct investment are described and analyzed in accordance withestablished empirical analysis variables selected in this article. In the third part of the empirical analysis, this paper four provinces in1984-2012in various economicindicators as research subjects, according to the data characteristics of select models.According to the provincial economic indicators combined with the availability ofresearch scholars at home and abroad, the paper select the level of economicdevelopment, market size, the proportion of tertiary industry, market opennessvariables such as the impact of foreign direct investment factors and four provinces inthe selected area. Finally, principal component analysis, a comprehensive study ofthe various factors affecting the degree of the four provinces in attracting foreigndirect investment size. The empirical results show that: In addition to the market andthe level of urbanization in Jiangsu Province, Liaoning Province and the secondaryindustry accounted for the provinces to attract foreign direct investment has a negativeeffect on most of the other factors affecting the provinces to attract foreign directinvestment has positive effect.Finally, the four provinces of how to optimize the investment environment toattract foreign direct investment to further enhance the ability to makerecommendations on improving the main task of economic construction andinfrastructure level, to attract foreign direct investment through the RegionalEconomic radiation effects and agglomeration factors; increase regional investmentin education, providing quality human resources for foreign direct investment,effectively reducing the cost of foreign investment; same time to adjust and improveirrational industrial structure, continue to increase efforts to attract foreign investment,and thus to change that because the foreign direct investment uneven regionaldistribution of regional economic development leads to imbalance and promote thecoordinated development of the economy and even the various regions of the fourprovinces of the country.
Keywords/Search Tags:Foreign direct investment, Market factors, Cost factors, Agglomeration, Principal ComponentAnalysis
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