The role of transport infrastructure in attracting FDI in china a regional analysis

THE ROLE OF TRANSPORT INFRASTRUCTURE IN ATTRACTING FDI IN CHINA: A Regional Analysis ZHU LICHAO (MASTER OF SOCIAL SCIENCES), NUS A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF ECONOMICS DEPARTMENT OF ECONOMICS NATIONAL UNIVERSITY OF SINGAPORE 2011 Acknowledgement I would like to express my deepest appreciation to those who have helped me with this thesis. I owe sincere gratitude to my most respected supervisor, A/P Anthony Chin, for his patience, encouragement and illuminating guidance. Through the writing of this thesis and the presentation, he has spent much time in correcting my draft and offered me many valuable suggestions. Without his help, this thesis could not have been completed. I would also like to thank A/P Liu Haoming and A/P Tomoo KIKUCHI, for their insightful inputs and advice during presentation of this thesis. I also want to thank Liu Yuhai, Cliff, Lu Yunfeng and Zeng Ting, for the discussions and help on this research. i Content Acknowledgement ......................................................................................................... i Abstract ........................................................................................................................iii List of Tables................................................................................................................ iv List of Figures .............................................................................................................. iv 1. Introduction ........................................................................................................... 1 2. Literature Review .................................................................................................. 7 2.1 Comparative Advantage Theory ...................................................................... 8 2.1.1 Size of Local Market ............................................................................ 8 2.1.2 Labor Market Conditions ...................................................................... 9 2.1.3 Degree of Openness ............................................................................ 10 2.2 New Economic Geographic Theory .............................................................. 11 2.2.1 Agglomeration Effect .......................................................................... 11 2.3 New Institutional Economics Theory and Policy Incentive .......................... 12 2.3.1 Policy .................................................................................................. 12 2.3.2 Infrastructure....................................................................................... 13 2.4 Summary ....................................................................................................... 15 3. Empirical Framework and Data .......................................................................... 18 3.1 Empirical Model Set-up ................................................................................ 18 3.2 Data Availability ............................................................................................ 23 4. Results and Interpretations .................................................................................. 25 4.1 Results without transport infrastructure spillover effect ............................... 25 4.2 Results taking into account transport infrastructure spillover effect ............. 31 5. Conclusion and Policy Implication: .................................................................... 35 References ................................................................................................................... 37 Appendix ..................................................................................................................... 46 ii Abstract This study investigates the relative importance of transport infrastructure in attracting FDI to three different regions in China based on 28 Chinese provincial economies from 1995 to 2008. Using fixed effect panel data approach, transport infrastructure is seen to have contributed to the relative attractiveness of the provinces, especially in west and middle regions, while the effect is not significant in the east region. The impact of other forms of infrastructure, such as telecommunication infrastructure, seems to exhibit similar positive impact, though to a lesser extent. Consistent with previous studies, other variables such as agglomeration effect, market size and policy incentive yielded the expected signs and results and seem as main drivers of FDI. Regional differences exist. Very interestingly, the extent and presence of good transport infrastructure in neighboring provinces have a significantly positive impact on local FDI decision. Key words: transport infrastructure, FDI, telecommunication infrastructure, regional differences, spillover effect iii List of Tables Table 1: Descriptive Statistics ............................................................................. 24 Table 2: Correlation Matrix ................................................................................. 25 Table 3: Impact of FDI determinants without spillover effect of transport infrastructure ................................................................................................ 26 Table 4: Impact of FDI determinants with spillover effect of transport infrastructure ................................................................................................ 32 List of Figures Figure 1: China‟s map consisting of the three regions .......................................... 5 Figure 2: Real FDI Inflow (1995-2008) East China ............................................ 46 Figure 3: Real FDI Inflow (1995-2008) Middle China ....................................... 47 Figure 4: Real FDI Inflow (1995-2008) West China .......................................... 48 Figure 5: Real FDI Inflow Growth Rate and Transport Infrastructure Growth Rate (1996-2008) East China ............................................................................... 49 Figure 6: Real FDI Inflow Growth Rate and Transport Infrastructure Growth Rate (1996-2008) Middle China .......................................................................... 50 Figure 7: Real FDI Inflow Growth Rate and Transport Infrastructure Growth Rate (1996-2008) West China .............................................................................. 51 iv 1. Introduction The past decade has witnessed foreign direct investment as an important engine for China‟s economic growth, with FDI increasing from 1.96 billion USD in 1985 to 92.4 billion USD in 2008 (Chinese Statistical Yearbook). China moved from restrictive to reformed policies in the early 1980s. From the mid 1980s the focus was on encouraging FDI in general. The focus shifted to encouraging more high-tech and more capital-intensive FDI projects in the mid-1990s (Fung et al., 2004). During the reformed period, the Chinese government established four Special Economic Zones (SEZs) in Guangdong and Fujian provinces and offered special-incentive policies to FDI in these SEZs. While FDI inflows were highly concentrated within these provinces, the amount remained rather limited (Cheung and Lin, 2004). After 1984, Hainan Island and 14 coastal cities across ten provinces were „opened‟, and FDI levels really started to take off. The realized value of inward FDI to China reached 3.49 billion USD in 1990. The preferential regimes policy resulted in an overwhelming concentration of FDI in the east. The expected spillover effects from coastal to inland provinces failed to materialize. In reaction to the widening regional gap, more broadly-based economic reforms and open door policies were advocated in the 1990s. By the second quarter of 1992, Deng Xiaoping adopted a new approach which turned away from special regimes 1 toward a more nationwide implementation of open policies for FDI inflows. New policies and regulations encouraging FDI inflows were implemented and produced remarkable results. Since 1992 inward FDI in China has accelerated and reached a peak level of 45.5 billion USD in 1998. After a drop due to the Asian crisis, FDI inflows into China surged again, so that by 2003 China received more than US$50 billion in FDI, surpassing the United States to become the world‟s largest single recipient of FDI (Forbes, 2005). China‟s entry to the WTO in 2001 enhanced its integration in the international economy and reinforced the FDI attractiveness of China. Now the crucial issue for China‟s policy makers is to guide FDI to disadvantaged areas such as west and middle provinces given severe regional economic development disparities between coastal and interior regions. Transport infrastructure development gap represents one of the regional inequality problems among Chinese provinces. Transport infrastructure improvements help to attract FDI through reduction in monetary and time costs of procurement of primary and intermediate inputs and distribution of finished products. Most studies on the determinants of FDI look at the impact of general infrastructure without differentiating the effect of transport infrastructure from that of other forms of infrastructure. In addition, the existing literature 2 generally paid little attention to regional variations of FDI location determination mechanisms. As east region is not in the same level in terms of market size, transport infrastructure and early year‟s policy support when comparing with the other two regions, the important FDI location determination factors in east region may not work the same way in middle and east regions. Furthermore, with regard to research on the spillover effect of transport infrastructure in determining FDI decision, there is an even greater dearth of literature, among the very few being Yang et al. (2010), while some studies demonstrate the existence of spillover effect of transport infrastructure in economic growth and productivity (Hulten, 2004). Given these considerations, evidence on how the transport infrastructure in different Chinese regions influences foreign investors‟ decision making remains incomplete. This study aims to provide a detailed analysis of the role played by transport infrastructure in attracting FDI in three different regions. FDI data at the provincial level for the period 1995-2008 was employed in this study. This is the period in which FDI spread from highly concentrated Pearl River Delta (PRD), and hence Guangdong province, towards other eastern regions as well as recently the western and middle provinces (Chan et al., 2008). Before the econometric model is made, I first explore the economic theories such as regional comparative advantage theory, new economic geography theory, new 3 institutional economics theory and the related empirical literature, and comprehensively identify the potential determinants which can explain the spatial distribution of FDI in China and format two hypotheses on transport infrastructure. Panel data regressions employing fixed effect approach to control for province specific effects and time fixed effects were then carried out. The results are summarized as follows: First, transportation infrastructure has been a significant determinant in making the province attractive to foreign direct investors and this impact is much more prominent in west and middle regions than that in east region. A similar pattern is also observed for non-transport infrastructures. Second, the result confirms the existence of transport infrastructure spillover effect in attracting FDI, suggesting that a comprehensive network in neighboring province has had a significant positive impact on local FDI inflows. Third, market size is a very important factor in attracting FDI in the east region while policy incentive is more prominent in middle and west regions. A China‟s map consisting of the three regions is shown in Figure 1. 4 Figure 1: China’s map consisting of the three regions Middle Region West Region East Region 5 By providing an assessment of the relative importance of transport infrastructure to FDI location decision in different regions, this study provides some guidance on the kinds of policy instruments that would be most successful in attracting FDI to disadvantaged regions. This study also sheds some light on the spillover effect of transport infrastructure in attracting FDI, suggesting the transport network should be considered in entirety to maximize benefit when major transport development projects are planned by the government. The rest of the paper is structured as follows: Section 2 provides a comprehensive literature review which summarizes the determinants of the regional distribution of FDI and formats two hypotheses on transport infrastructure. Section 3 presents the empirical framework and the choice of proxies and data. The results are presented in Section 4, and Section 5 concludes. 6 2. Literature Review The location determinants of FDI have been widely studied by international and regional economist. Traditional theories assuming imperfect markets such as Vernon‟s product life cycle hypothesis (Vernon, 1966), Hymer‟s Industrial Organization Hypothesis (1976) and the Internalization Hypothesis developed by Buckley and Casson (1976) have investigated the motivation and location determinants of FDI from different perspectives. Dunning (1977, 1979, 1988) synthesized the existing theories of foreign direct investment and developed a new theoretical framework. The theoretical framework has been referred to as the eclectic theory of international production which analyzes the pattern and determinants of FDI in terms of ownership-specific, internalization and location advantages (OIL). Location advantages are those advantages specific to a country due to labor costs, market factors, resource endowments, infrastructures and government policies etc., which determine the choice of production site. FDI inflows into China have been high and regionally dispersed. Figures 2, 3, 4 illustrate FDI inflow trends for 28 Chinese provinces in three different regions. Most of them exhibit increasing trends. China has explicit policies to encourage FDI in some specific regions, and has set up different Economic and Technical Development Zones for foreign investor. In addition, FDI inflows have created a lot of policy debate within China because of its close 7 links to the diversion in economic growth rate (Huang et al., 2003; Chan et al., 2008). Common determinants of FDI which have been identified and tested in various empirical studies include market size, degree of openness, labor cost, labor quality, policy incentives, infrastructure and FDI stock. These determinants have been the focus of theories such as regional comparative advantage, new economic geography and new institutional economics. 2.1 Comparative Advantage Theory The location choice of FDI involves corporate decision-making behavior which seeks to maximize the rate of return on investment. Foreign investors in choosing FDI sites, compare the relative attractiveness of alternative locations of investment. The comparative advantage of a region is based on cost comparison of market size, degree of openness, labor cost, labor quality and other intermediate inputs. 2.1.1 Size of Local Market When FDI is oriented toward serving domestic market, a large local market increases the likelihood of the location being chosen. This is because larger market size represents better economic conditions and larger potential 8 consumer demand for goods and services. There is evidence that market size factor explains variation of FDI across provinces. Broadman and Sun‟s cross sectional study (1997) showed that a province‟s FDI stock increases with its market size. Buckley and Meng (2005) examined the horizontal and vertical FDI motives in Chinese manufacturing sector, and they found that for the period 1992-2002 the market-oriented FDI dominated. The significant and positive impact of market size on regional distribution of FDI is also confirmed by Wei et al. (1999), Sun et al. (2002) and Lee et al. (2004). 2.1.2 Labor Market Conditions Much production facilities for assembly in China have been driven by the comparative advantage in labor intensive production (Liu et al., 1997). From this observation alone, however, it is not clear whether differences in labor costs can also explain the regional distribution of FDI across Chinese provinces. Some studies conclude that lower labor costs were an important factor for China to attract FDI inflows (Ying Chen, 1997; Coughlin and Segev, 2000; Fung et al., 2002). Others show that there is no significant relationship between the labor cost and the locational differences in distribution of FDI (Head and Ries, 1996; Broadman and Sun, 1997; Xu and Wang, 2002). A few even reported a positive relationship between them (Fu, 2000; Wei, 2000; Zhao, 2009), suggesting the higher the wage rate, the more the FDI comes in. 9 Recent literature stress the growing importance of labor quality, since high technology level productions require highly skilled and quality labor. Sun et al. (2002) argue that there is a nonlinear relation between wage rates and FDI for the period 1986-1998, positive before 1991 and negative thereafter, while labor quality is a positive attractor of FDI throughout the sample period. As the relationship between labor cost, labor quality and spatial distribution of FDI remains ambiguous, labor market condition factor is decomposed into two indices in this study, i.e. labor cost and labor quality, to test the impacts on regional FDI inflows respectively. 2.1.3 Degree of Openness The degree of openness represents the link between the local market and international market. It is a standard hypothesis that degree of openness promotes FDI (Hufbauer et al., 1994). Foreign investors originate mainly from open market economy background, and are more likely to choose potential FDI locations with a high level of openness. It is especially true when the foreign direct investors are motivated by rather the prospects of export market than the domestic market. Empirically, many previous studies show that degree of openness is a quite important factor in influencing choice of FDI locations by foreign investors (Guo et al., 2009; Zhao, 2009; Yang et al., 10 2010). 2.2 New Economic Geographic Theory 2.2.1 Agglomeration Effect The importance of agglomeration effects to explain FDI is related to the emergence of the new economic geography literature (Paul Krugman, 1991). The central thinking is that a firm‟s location choice involves a trade-off between making use of positive externalities that come from agglomeration and the negative effects such as congestion problem that agglomeration has on factor costs. Since China just recently opened up to foreign capital, it provided for an ideal research ground to observe the dynamics of FDI location choice. The seminal paper in this approach is Head and Ries (1996) who, controlling for other factors, found strong agglomeration effects in FDI decisions, concentrated in the coastal areas‟ export processing zones. Following them, there are many empirical study investigating this effect. For example, Cheng and Kwan (2000) found a strong self-reinforcing effect of FDI on itself using a partial stock adjustment model. Coughlin and Segev (2000) apply a spatial error model to control for special autocorrelation among Chinese provinces. Their results indicate that foreign investors tend to choose provinces with FDI intensive neighbors. And recently Amiti and Javorcik (2008) use firm level data to show agglomeration effects does influence FDI decision. 11 2.3 New Institutional Economics Theory and Policy Incentive The new institutional economics literature stresses the role of „rules of game‟ in economic development. It has been noticed that there are large differences in institutional quality across Chinese provinces, for example in controlling corruption (Li and Park, 2006; Cole et al., 2006) and property rights protection (Cheung & Lin, 2004). Previous studies show that institutional variables such as control of corruption and legal development have had a positive impact on attracting FDI. Moreover, institutions also refer to central government‟s policy support, local government‟s effectiveness and provision of public goods (La Porta et al., 1999). For example, Zhao (2009) claims that foreign direct investment flows are higher to regions with smaller government size and lower state-own economy ratio. Li and Park (2006) showed that MNCs favor provinces with better infrastructure in the form of communication facilities, roads and electricity provision. 2.3.1 Policy Establishing regional variations by offering preferential economic policies is crucial in order to attract foreign investment into China. It is well known that the reform and opening-up of China began with the preferential policies granted to Guangdong and Fujian provinces to establish and enhance their 12 economic and foreign trade activities. In 1980, four special economic zones(SEZ), typical of which was the Shenzhen SEZ, were set up as a pilot scheme. In 1984, 14 more eastern coastal cities were opened to FDI and this gathered the pace of the coastal regions‟ opening up by 1985. In 1988 the whole island of Hainan was established as a SEZ. In 1990, Pudong in Shanghai was opened up for development, followed by the opening-up of the inland cities along the Yangtze River and land frontier cities after 1992. Empirically, many studies have concluded that preferential policy has significantly positive influence on the location decision of foreign investors in China (Cheng and Kwan, 2000; Wu, 2004; Deng, 2010). 2.3.2 Infrastructure Infrastructure can have a positive impact on FDI through increased accessibility and reduced costs in transportation and information collection. Many previous studies demonstrate that the development of various kinds of infrastructure has a positive effect on the location of FDI (Coughlin et al.,1991; Wheeler and Mody, 1992; Cheng and Kwan, 2000; Globerman and Shapiro, 2003; Lee, 2004). To better investigate the specific impact of transport infrastructure, the infrastructure element is decomposed into two parts, namely transport infrastructure and telecommunication infrastructure. 2.3.2.1 Transport Infrastructure 13 The variable central to this study is transport infrastructure. Superior transport infrastructure development may have positive influence on the location choice of FDI through reducing cost in transportation and promoting flow of production factors, taking advantage of scale economies. Figures 5, 6 and 7 illustrate transport infrastructure growth rates and real FDI inflow growth rates through the years for three Chinese regions. Most of them exhibit positive correlation. In addition, roads and highways, for example, are lumpy joint networks with many different segments. The benefits associated with any one segment of the network depend on the size and configuration of the entire network, and not just with that segment. Spillover externalities between network segments are therefore potentially important. Interesting study from Hulten (2004) demonstrated the existence of spillover effect of transport infrastructure in productivity and economic growth. Based on this thinking, the hypothesis of the existence of spillover effect of transport infrastructure in attracting FDI is also brought out in this study. Of particular interest is the transport infrastructure in neighboring provinces. These “external” infrastructures may be relevant because they can directly affect the local FDI firms, especially those targeting at markets in neighboring provinces. Compared with local transport infrastructure, the external ones 14 could also be less affected by endogeneity bias due to omitted local factors (e.g. local market development level). 2.3.2.2 Telecommunication Infrastructure To control for other types of infrastructure, telecommunication infrastructure has been incorporated. The existence of good telecommunication infrastructure enhances a region's position in attracting FDI as it reduces costs in transaction, operation and information collection as well as saves time. The significant and positive impact of telecommunication infrastructure on regional distribution of FDI is shown by Khadaroo and Seetanah (2009). 2.4 Summary While FDI determinants have been analyzed extensively, these studies focused on the general level of infrastructure and paid little attention to the variation of FDI location determination mechanisms in three different parts of China. Notable exception is the study by Guo and Han (2009) which showed that the relaxed environmental regulation in the east of China has a positive impact on FDI entry, while the relation between environmental regulation and FDI isn‟t notable in the middle and west regions. It was also found that good infrastructure is more important in middle and west regions than that in east region. However a composite infrastructure index is used as proxy variable 15 without differentiate transport infrastructure‟s impact from those of other infrastructures. This study focuses on transport infrastructure using telecommunication infrastructure as a control variable. In addition, very few empirical studies paid attention to the spillover effect of transport infrastructure in determining FDI decision, while some literature demonstrate the existence of spillover effect of transport infrastructure in productivity and economic growth. Thus, the current study attempts to supplement the literature in the following aspects: First, to investigate importance of transport infrastructure on FDI while controlling for other types of infrastructure in recipient provinces, another proxy for the general level of other infrastructure - the ratio of total business volume of post and telecommunication to GDP was constructed, in addition to the traditional measure of infrastructure, the total length of highway and railway per unit of land mass. Second, studying the significance of the FDI determinants and comparing different FDI location determination mechanisms in three different regions of China provide an assessment of the relative importance of transport 16 infrastructure as well as other factors in different regions and provide better understanding of regional differences in attracting FDI. Third, by testing the impact of neighboring provinces‟ transport infrastructure on local province‟s FDI inflow, this study identifies a significantly positive relationship between them, suggesting the existence of “spillover effect” of transport infrastructure in attracting FDI. 17 3. Empirical Framework and Data 3.1 Empirical Model Set-up Further to discussion of literature in Section 2, two main hypotheses about transport infrastructure are brought out and the following economic relationship is postulated. Hypothesis 1: Transport infrastructure plays an important role in attracting FDI. The area with better improvement in transport infrastructure, has the tendency to achieve larger FDI inflow growth. Hypothesis 2: Not only the local transport infrastructure matters, but also the neighboring transport infrastructure plays a crucial role in attracting FDI. (1) Where, index the provincial economies and index time. The LOCALTRAN and NEIGHTRAN are the central variables in this study and the others act as control variables. Below are the detailed definitions of the variables. FDI inflow (FDI): The annual inflow of real foreign direct investment in 18 China in terms of RMB, measured in 1990 constant price. FDI Stock (CFDI): The real cumulative FDI stock, measured in terms of RMB in 1990 constant price, to act as a proxy to capture the agglomeration effect of FDI. Local Transport Infrastructure (LOCALTRAN): The total length of highway and railway per unit mass of land in local province, to act as the proxy to assess the level and quality of local transport infrastructure. Neighboring Transport Infrastructure (NEIGHTRAN): The total length of highway and railway per unit mass of land in neighboring provinces is the proxy used to assess neighboring province‟s transport infrastructure condition. Telecommunication Infrastructure (TELE): The ratio of total business volume of post and telecommunication to GDP as a proxy for telecommunication infrastructure. This ratio is a more comprehensive measure for telecommunication infrastructure than the number of telephones available per 1000 people. Degree of Openness (OPEN): The ratio of trade (export plus import) to GDP as a measure of openness. 19 Labor cost (WAGE): The real wage calculated in 1990 constant price as a proxy for labor cost. Labor quality (EDU): The Barro & Lee Educational Attainment Dataset (Average Educational Year) as the proxy for labor quality. Market Size (PERGDP): Real GDP per capita using 1990 constant price as an indicator of the market potential for the products of foreign investors. Policy incentive (ETDZ): Policy incentive is represented by a dummy variable ETDZ. Chinese regional governments have been aggressive in competing for FDI through concessionary policies such as discounted land price and tax breaks. One popular instrument at the disposal of local Chinese governments is the Economic and Technical Development Zones that have been established across China to attract FDI. To capture this factor, I collected all the years when different national Economic and Technical Development Zones (ETDZ) were set up in various provinces. The ETDZ variable measures the number of ETDZ in a specific province from 1995 to 2008. The ETDZ variable is calculated as the real number of National Economic and Technical Development Zone plus 1 where „1‟ is added to allow for zero Economic and Technical Development Zone in some provinces in early years. Furthermore, 20 compared with other commonly used policy proxy such as Special Economic Zone and Open Coastal Cities, ETDZ is a more reliable and comprehensive proxy because it is much more widely distributed across China. Instead of assuming a simple linear relationship between the dependent and independent variables, the basic model is of the semi log-linear form following the literature (Adeisu, 2002; Chen and Xu, 2007; Guo et al., 2009): (2) where, and captures province specific effects, captures time fixed effects, is an error term. All independent variables in lagged one period form are used to avoid the simultaneity problem, because this year‟s FDI inflow can not affect last year‟s transport infrastructure, wage level, etc. Unfortunately the correlation matrix of all explanatory variables in equation (2) shows the existence of relatively high correlation relationship among some of the explanatory variables which may lead to multi-collinearity problem. In addition, there are obvious trends in the dependent and independent variables which may affect stationarity. The first step is to take first difference to solve 21 these problems, and the model becomes: (3) where, captures province specific effects (because of taking the first difference, the here has a different meaning with the one in equation (2)), captures time fixed effects, and is an error term. Note that the policy proxy variable ETDZ does not take a first difference form due to its relatively time invariant in some provinces. Since our main purpose here is only to identify whether policy incentive is significantly important in attracting FDI and not to measure the magnitude, this treatment does not matter a lot. Of particular interest is to further test the impact of neighboring provinces‟ transport infrastructure on local province‟s FDI inflow by including neighboring provinces‟ transport infrastructure proxy into equation (3) to get equation (4): (4) 22 where, captures province specific effects, such as national wide shock, and captures time fixed effects is an error term. Here the small size of the sample fails the asymtotic assumptions required to perform the Hausman test, so the random effect estimation method can not be technically rejected. A fixed effect estimation approach is thus taken. The reason is that the random effects analysis assumes that the measurements are some kind of random sample drawn from a larger population, while my sample is basically already all the provinces in China, not randomly selected. In addition, modeling an effect as random effect goes with the assumption that the random effects are uncorrelated with the explanatory variables. Very likely this is not in accordance with reality, which then can lead to biased results. By using the fixed effect, several assumptions are made. First, we assume the effect of all other possible factors for FDI inflow as fixed effect for each province. It rules out the possible impact of culture, distance, etc. Second, all explanatory variables are exogenous, due to data limitations. 3.2 Data Availability The provincial data sets in this study are mainly collected from various issues of the China Statistical Yearbook (1995-2008) and China Labor Statistical 23 Yearbook (1995-2008), published by the National Bureau of Statistics of China. Some missing data are obtained from local government statistical yearbooks. The sample in this study includes all inland provinces except for Tibet and Qinghai, due to lack of some data sets. Table 1 presents summary statistics of the sample. Table 1: Descriptive Statistics Variable Obs Mean Std. Dev. Min Max FDI 392 1087543 1554887 7712 8254299 CFDI 392 8474553 14237308 90512 97599103 LOCALTRAN 392 4551.956 3283.599 190.6084 18631.18 NEIGHTRAN 392 4194.854 2664.638 500.1354 13749.84 TELE 392 0.091695 0.054060 0.016007 0.272509 OPEN 392 0.324776 0.415369 0.019073 2.051279 WAGE 392 5633.194 2997.882 2093.043 18566.89 EDU 392 7.760448 1.008159 5.350000 11.08531 PERGDP 392 6419.346 4898.958 1150.647 32857.49 24 4. Results and Interpretations Table 2 reports the correlation matrix of all independent variables in equation (3) and (4). No correlation coefficient is especially large. No severe multi-collinearity problem exists in this panel regression. Table 2: Correlation Matrix ln ln ln ln ln 1.000 ln -0.087 1.000 ln -0.103 0.655 1.000 -0.159 -0.055 -0.031 1.000 -0.075 0.009 0.015 0.043 1.000 -0.292 0.108 0.130 -0.039 0.058 1.000 -0.123 0.132 0.194 0.056 0.038 0.115 0.055 0.116 0.118 -0.030 0.081 -0.076 -0.102 1.000 -0.026 0.030 0.052 -0.118 0.175 0.155 0.010 1.000 ln 1.000 0.045 4.1 Results without transport infrastructure spillover effect To test the hypothesis 1 to confirm that transport infrastructure plays an important role in attracting FDI in China, which means the area with better improvement in transport infrastructure, has the tendency to achieve larger FDI inflow growth, regressions based on equation (3) were carried out. Transport infrastructure is the central variable and the other factors act as control variables. The results without the inclusion of transport infrastructure spillover effect are given in Table 3. 25 Table 3: Impact of FDI determinants without spillover effect of transport infrastructure Estimation 1 Estimation 2 Estimation 3 Estimation 4 Estimation Method FE FE FE FE C -0.998560*** -0.347236*** -0.905701*** -1.106352*** (-10.01395) (-4.036080) (-7.109256) (-6.263256) 2.025331*** 0.958639*** 1.950476*** 3.575045*** (5.659773) (4.711828) (5.691040) (4.911688) 0.202517*** 0.052651 0.170675** 0.234033*** (3.865419) (1.040319) (1.955445) (3.515802) 2.050560** 1.179412 1.108273 6.232695** (1.981446) (0.959543) (0.678380) (2.019977) 0.322860** 0.190177** 1.285570 3.454173** (2.153946) (1.998938) (0.502204) (2.142456) 1.109320** 0.800640* 0.688728* 1.123632* (2.261867) (1.939739) (1.819141) (1.685083) 1.857543** 2.716804*** 2.217672** 0.475951 (2.262933) (3.834081) (2.464429) (0.279822) 0.046230 0.085267 0.147838 -0.123093 (0.908952) (1.445914) (0.998170) (-0.978913) 0.157338*** 0.049114 0.187677*** 0.222373** (3.382161) (0.515398) (2.916511) (2.210786) 364 143 104 117 0.256 0.228 0.341 0.310 F statistic 3.229 2.031 3.816 2.911 D-W stat 1.983 1.803 2.081 2.067 ∆Log(CFDI) ∆Log(Localtran) ∆(Tele) ∆(Open) ∆Log(Wage) ∆Log(PerGDP) ∆(Edu) ETDZ Obs R 2 Notes: Robust t statistics in parentheses. Significance at * 10%, **5%, ***1%. The estimations 1, 2, 3, 4 are for the whole country, the east region, the middle region and the west region respectively. East region: Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, Liaoning Middle region: Jilin, Heilongjiang, Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan West region: Inner Mongolia, Guangxi, Sichuan( including Chongqing), Guizhou, Yunnan, Shaanxi, Gansu, Ningxia, Xinjiang The criteria of this regional partition is based on the official explanation from National Bureau of Statistics of China, adjusted according to “Go West” Development Strategy 26 Table 3 summarizes the results. For the whole country estimation shown as estimation 1, the signs of estimated coefficients are generally as expected and consistent with findings from literature. Some interesting findings appear at a regional level. The positive sign and statistical significance of coefficient for transport infrastructure in estimation 1 suggests that from a national wide perspective, transport infrastructure improvement is a very important factor in location choice of FDI. This is consistent with hypothesis 1. From results of regional regressions, it can be observed that the coefficients for transport infrastructure are both larger and more significant in west and middle regions than that in east region. In east region, the coefficient for transport infrastructure is even insignificant. This suggests that FDI inflows into west and middle regions are more sensitive to transport infrastructure improvement than that into east region. First, Wheeler and Mody (1992) found that infrastructure quality is an important variable for developing countries seeking to attract FDI, but is less important for developed countries that already have high quality infrastructure. A similar pattern is observed here, suggesting that for countries with poor infrastructure, investment in improvements in transport infrastructure is 27 especially important in attracting FDI. Second, there seems to be some diminishing returns in transport infrastructure. For example, the first bridge has a greater impact on a region than the second or third. So given the transport infrastructure in east region has been already well established, further improvement has not been that important when foreign direct investors are looking for more crucial factors such as large market size and policy preference. Third, market size effect may also be a reason why transport infrastructure is important in west and middle regions. As is shown in Table 3, market sizes (PerGDP) are positively correlated with the provinces‟ FDI inflows at 5% statistical significance, except for west region. This result verifies the local market effect of the location distribution of FDI, which means FDI prefers areas with large market size and high level of consumption capacity, especially within the east region. A possible interpretation for this is that eastern China‟s FDI is more local market oriented than those in western China. Since west and middle regions‟ FDI is generally not local market oriented, the target is very likely east region‟s markets. So it is likely that a convenient transport network linked to the east region will be a crucial factor for FDI decision to locate in the west and middle China. 28 The role of telecommunication infrastructure in attracting FDI is similar to that of transport infrastructure. Table 3 shows that the signs of coefficients for telecommunication infrastructure (TELE) are positive and the coefficients are statistically significant for the whole country and west region, but insignificant in east and middle regions, suggesting again while telecommunication infrastructure remains important in FDI decision in the whole country, west region FDI is much more sensitive to infrastructure (both transport and telecommunication) than east region FDI. The impact on FDI of policy incentive seems have exhibited the same pattern as that of infrastructure. It is observed that the coefficients for ETDZ for all samples are statistically significant with the east region as an exception, and the signs are generally positive. In addition, the magnitude of the ETDZ coefficient in the east region sample regression is smaller than that in other samples. This result not only confirms the policy incentive effect in attracting FDI in Chinese provinces, but also suggests that this effect is more significant in the west and middle regions than that in the east region. This finding seems to be counterintuitive, as some may argue that policy favoritism has benefited the east a lot, and the impact should be therefore greater in this region than the rest of China. From the multinational enterprises‟ point of view, however, the east region has enjoyed policy support for a long time, and there are already many ETDZs in this region, so the new establishment of the ETDZ can be 29 seen as a sign of over-crowded FDI in certain „hot‟ eastern provinces, thus has no significant impact on attracting new FDI. But in west and middle region, this process has just begun and the policy support remains crucial because it brings in preferential land price and tax breaks which help to reduce the cost. In other words, this finding can be seen as the evidence for the effectiveness of the “Go West” Development Strategy. The signs of the coefficients for cumulative FDI stock are significantly positive in all four regressions, indicating that there exist strong agglomeration effects in the location choices of FDI, i.e. the cumulative FDI stock in an area can be an important demonstration for potential FDI inflows. Different from some of the previous studies, wage level has a positive impact on FDI attraction in the whole country especially in the east region after controlling for labor quality. It basically says that high wage can result in more FDI which is counter intuitive. However, other studies indicate the same phenomenon and the explanation might be the following: within a developing country, where its wage is far too low than that in developed countries, the relative lower labor cost in certain regions is not the deterministic consideration to foreign investors, as the higher wage level area enjoys larger market size, better infrastructure and greater investment environment. This result can also been seen as an evidence that low labor cost is no longer a 30 prominent advantage for Chinese provinces to attract FDI. The coefficients for labor quality are statistically insignificant for all the four estimations and the signs of coefficients are mainly positive except for west region. It shows that education level of the labor force has not been an important determinant for FDI location decision in this study. The negative sign for the west region sample also indicates that the FDI expansion fell behind the education progress in the west provinces. 4.2 Results taking into account transport infrastructure spillover effect To further test hypothesis 2 to show the existence of transport infrastructure spillover effect and to investigate the determinants of the FDI inflows when this spillover effect is considered, equation (4) is estimated by including a proxy for neighboring provinces‟ transport infrastructure. Regression results are reported in Table 4. 31 Table 4: Impact of FDI determinants with spillover effect of transport infrastructure Estimation 1a Estimation 2a Estimation 3a Estimation 4a Estimation Method FE FE FE FE C -0.978406*** -0.595755* -0.909503*** -1.081677*** (-9.128341) (-1.759404) (-7.256157) (-5.767503) 2.021288*** 1.095639*** 1.966121*** 3.517189*** (5.735218) (4.703323) (5.668434) (4.645720) 0.051532** 0.034199 0.114862* 0.137738** (1.998783) (0.866696) (1.756912) (1.852122) 0.212099** 0.133917* 0.189378 0.137923** (2.490108) (1.751136) (0.438472) (1.913385) 2.067260** 1.741312 1.112984 6.260558** (1.999709) (1.306596) (0.671032) (2.000201) 0.325557** 0.263746* 1.358343 3.330765* (2.106813) (1.844364) (0.529722) (1.972947) 1.076479** 1.069562*** 0.731802* 1.075279 (2.177004) (2.649471) (1.764324) (1.583829) 1.679292** 2.730332*** 2.283042** 0.363560 (1.988461) (4.077683) (2.571552) (0.205575) ∆Log(CFDI) ∆Log(Localtran) ∆Log(Neightran) ∆(Tele) ∆(Open) ∆Log(Wage) ∆Log(PerGDP) ∆(Edu) 0.051930 0.040831 0.140651 -0.126899 (1.007212) (0.756728) (0.660242) (-0.837901) 0.155911*** 0.027275 0.183629*** 0.219725** (3.299190) (0.313390) (2.881499) (2.180122) 364 143 104 117 0.261 0.229 0.339 0.319 F statistic 3.208 2.910 3.567 2.731 D-W stat 1.971 1.851 2.108 2.058 ETDZ Obs R 2 Notes: Robust t statistics in parentheses. Significance at * 10%, **5%, ***1%. The estimations 1a, 2a, 3a, 4a are for the whole country, the east region, the middle region and the west region respectively. East region: Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, Liaoning Middle region: Jilin, Heilongjiang, Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan West region: Inner Mongolia, Guangxi, Sichuan( including Chongqing), Guizhou, Yunnan, Shaanxi, Gansu, Ningxia, Xinjiang 32 From the results in Table 4, neighboring transport infrastructures are positively correlated with the local province‟s FDI inflow at 5% statistical significance in whole country and west region sample and at 10% statistical significance in east region sample. This result is consistent with Hypothesis 2, and confirms the existence of “spillover effect” of transport infrastructure in attracting FDI. A comprehensive transport network is thus very important in attracting FDI, and the lack of development in either local or neighboring transport infrastructure will hamper the FDI growth. By adding neighboring transport infrastructure variable to account for spillover effect of transport infrastructure, coefficients of local transport infrastructures in Table 4 are still significant, but smaller when comparing with those in Table 3. This means that excluding neighboring transport infrastructure variable, a common practice in existing studies, overstates the impact of local transport infrastructure on FDI inflow. The insignificant coefficient for neighboring transport infrastructure in the estimations for the middle region is possibly due to the fact that FDI expansion in the middle region did not catch up with its neighboring east region‟s fast transport infrastructure development. The results for other variables in Table 4 are generally in accordance with 33 those we obtained in Table 3, validating the robustness of our findings in previous. 34 5. Conclusion and Policy Implication: This study investigated the role of transport infrastructure in enhancing the attractiveness of FDI recipient regions and is based on a sample of 28 Chinese provincial economies over the period 1995–2008. Using panel data framework, results from the analysis show that transportation infrastructure improvement has been an important factor in attracting foreign direct investors to provinces and regions. This impact is greater in west and middle regions than that in east region. The same pattern is also observed in the case of non-transport infrastructures. This may due to the diminishing returns in infrastructure. Given the transport infrastructure in east region has been already well established, further improvement has not been that important when foreign direct investors are looking for other crucial factors. In addition, we also test for the impact of neighboring transport infrastructure‟s development on local FDI attractiveness. The positive and significant sign of the neighboring transport infrastructure confirms the existence of transport infrastructure spillover effect in attracting FDI, suggesting that a comprehensive neighboring network of transport infrastructure has a significantly positive impact on local FDI decision. The other classical factors included in the study generally yield the expected overall signs and results, with agglomeration effect, market size and policy 35 incentive among the main drivers of FDI from the national wide perspective. On the regional level, market size is a very important factor in attracting FDI in the east region while policy incentive is more prominent in west and middle regions. These findings suggest that transport and other infrastructure development as well as policy support are among the necessary conditions in attracting FDI. This is particularly true for west and middle regions. Since the China‟s government now is attempting to divert FDI into disadvantaged west and middle regions and boost the economic development there, these results would be quite important in guiding policies on FDI. First the government should put more investment into infrastructure development (especially transport infrastructure) in west and middle provinces so as to achieve higher FDI growth in these areas. Second, the spillover effect of transport infrastructure should be fully aware and the transport networks should be considered in entirety to maximize benefit. Third, more preferential policies should be made for FDI in west and middle regions to boost the FDI growth. 36 References Amiti, M. & Javorcik, B. S. (2008): Trade Costs and Location of Foreign Firms in China. Journal of Development Economics, 85(1-2), pp. 129-149. Broadman, H. G. & Sun, X. (1997): The Distribution of Foreign Direct Investment in China. The World Economy, 20(3), pp. 339-361. Buckley, P.J. and Casson, M.C. (1976) The Future of the Multinational Enterprise. Homes & Meier: London. Buckley, P. J. & Meng, C. (2005): The Strategy of Foreign-invested Manufacturing Enterprises in China: Export-orientated and Market-orientated FDI Revisited. Journal of Chinese Economic and Business Studies, 3(2), pp. 111-131. Canfei He, Ying Chen (1997): Locational Distribution and Spatial Diffusion of Hong Kong-Macao firms FDI in China. Geographic Study, 17(3), pp.193-200 Chan, K. W. J., Henderson, V. & Tsui, K. Y. (2008): Spatial Dimensions of Chinese Economic Development, in China's Great Economic Transformation. Cambridge University Press 37 Charles R. Hulten (2004): Transportation Infrastructure, Productivity, and Externalities. Paper prepared for the 132nd Round Table of the European Conference of Ministers of Transport, at the Joint OECD/EMCT Transport Research Center, Paris, France, December 2nd and 3rd, 2004. Chen, C. H. (1996): Regional Determinants of Foreign Direct Investment in Mainland China, Journal of Economic Studies, 23(2), pp. 18-30. Cheng, L. K. & Kwan, Y. K. (2000): What are the Determinants of the Location of Foreign Direct Investment? The Chinese Experience, Journal of International Economics, 51(2), pp. 379-400. Cheung, K. Y. & Lin, P. (2004): Spillover Effects of FDI on Innovation in China: Evidence from the Provincial Data, China Economic Review, 15(1), pp. 25-44. Chien-Hsun Chen (1996): Regional Determinants of Foreign Direct Investment in Mainland China. Journal of Economic Studies, 23, pp.18-30 Chyan Tuan, Linda F. Y. (2003): FDI Facilitated by Agglomeration Economies: Evidence from Manufacturing and Services Joint Ventures in 38 China. Journal of Asian Economics, Number 13, pp.749 – 765 Cole, M., Elliott, R. & Zhang, J. (2006): "Corruption, Governance and FDI Location in China: A Province-Level Analysis," Department of Economics, University of Birmingham. Coughlin, C. & Segev, E. (2000): Foreign Direct Investment in China: A Spatial Econometric Study. The World Economy, 23(1), pp. 1-23. Du, J. L., Lu, Y. & Tao, Z. G. (2008): FDI Location Choice: Agglomeration vs Institutions. International Journal of Finance & Economics, 13(1), pp. 92-107. Elizabeth Asiedu (2002): On the determinants of Foreign Direct Investment to Developing Countries: Is Africa Different? World Development, 30(1), pp. 107-119 Fu, X. L. (2008): Foreign Direct Investment, Absorptive Capacity and Regional Innovation Capabilities: Evidence from China. Oxford Development Studies, 36(1), pp. 89-110. Fung, K. C., Iizaka, H. & Parker, S. (2002): Determinants of U.S. and 39 Japanese Direct Investment in China. Journal of Comparative Economics, 30(3), pp. 567-578. Guo Hongyan and Han Liyan (2009): Environmental Regulation and Location of FDI in China. On Economic Problems, (11), pp.13-17 Head, K. & Ries, J. (1996): Inter-City Competition for Foreign Investment: Static and Dynamic Effects of China's Incentive Areas, Journal of Urban Economics, 40(1), pp. 38-60. Huang, Y. S. (2003): Selling China: Foreign Direct Investment during the Reform Era. Cambridge, UK: Cambridge University Press. Houkai Wei (2000): Locality Determinants of FDI from EU, U.S., Japan and Korea in Manufacturing Sectors. China Industrial Economics, Number 11, pp.65-73 Hymer S H (1976): The International Operations of National Firms: A Study of Foreign Direct Investment, MIT Press, Cambridge, MA Jameel Khadaroo and Boopen Seetanah (2009): The role of Transport Infrastructure in FDI, Evidence from Africa using GMM Estimates. Journal of 40 Transport Economics and Policy, 43(3), pp. 365-384 John H. Dunning (1980): Toward an Eclectic Theory of International Production: Some Empirical Tests. Journal of International Business Studies, 11(1), pp.9-31 John H. Dunning (1988): The Eclectic Paradigm of International Production: A Restatement and Some Possible Extensions. Journal of International Business Studies, 19(1), pp.1-31 Kangning Xu, Jian Wang (2002): An Analysis of Determinant Factors of American Direct Investment in China (1983 - 2000). Social Sciences in China, Number 5, pp.66-77 K.C. Fung, H Iizaka, S Parker (2002): Determinants of US and Japanese Foreign Direct Investment in China. Journal of Comparative Economics, 2002 La Porta, R., Lopez-de-Silanes, F., Shleifer, A. & Vishny, R. (1999): The Quality of Government, Journal of Law. Economics and Organization, 15(1), pp. 222-279. Lixin Li, Rungui Jin (2002): A Comparative Analysis on Location Factors of 41 Different Groups of FDI in China. China Soft Science, Number 7, pp.89-94• Li, S. & Park, S. H. (2006): Determinants of Locations of Foreign Direct Investment in China. Management and Organization Review, 2(1), pp. 95-119. Liu, X., Song, H., Wei, Y. & Romilly, P. (1997): Country Characteristics and Foreign Direct Investment in China: A Panel Data Analysis. Review of World Economics, 133(2), pp. 313-329. Luo, L. J., Brennan, L., Liu, C. & Luo, Y. Z. (2008): Factors Influencing FDI Location Choice in China's Inland Areas. China & World Economy, 16(2), pp. 93-108. Marlon G. Goarnet (1998): Spillovers and the Locational Effects of Public Infrastructure. Journal of Regional Science, 38(3), pp. 381-400 Minghong Lu (1999): An Empirical Study on Institutional Elements and FDI Locality Distribution. Economic Research Journal, Number 7, pp.57-66 Minsoo Lee, MoonJoong Tcha (2004): The Color of Money: The Effects of Foreign Direct Investment on Economic Growth in Transition Economies. Review of World Economics, 140(2), pp.211-229 42 P.J. Buckley, M Casson (1976): The Future of the Multinational Enterprise. Macmillan London Qian Sun, Wilson Tong, Qiao Yu (2002): Determinants of Foreign Direct Investment across China. Journal of International Money and Finance, 21(1), pp.79-113 Raymond Vernon (1966): International investment and international trade in the product cycle. Quarterly Journal of Economics, 80(2), pp. 190-207 Sethi, D., Guisinger, S. E., Phelan, S. E. & Berg, D. M. (2003): Trends in Foreign Direct Investment Flows: A Theoretical and Empirical Analysis. Journal of International Business Studies, 34(4), pp. 315-326. Shaomin Li, Seung Ho Park (2006): Determinants of Locations of Foreign Direct Investment in China. Management and Organization Review, 2(1), pp.95-119 Shapiro, D., Tang, Y. & Ma, X. J. (2007): Mode of Entry and the Regional Distribution of Foreign Direct Investment in China. Journal of Chinese Economic and Business Studies, 5(3), pp. 261 - 277. 43 Sun, Q., Tong, W. & Yu, Q. (2002): Determinants of Foreign Direct Investment across China. Journal of International Money and Finance, 21(1), pp. 79-113. Wei, Y., Liu, X., Parker, D. & Vaidya, K. (1999): The Regional Distribution of Foreign Direct Investment in China. Regional Studies, 33(9), pp. 857-867. Yang Haisheng, Nie Haifeng, Xu Xianxiang (2010): The Third - Province Effects in the Selection of FDI Region. Quantitative & technical economics, Number 4, pp. 122 - 136 Yingqi Wei, Xiaming Liu, David Parker, Kirit Vaidya (1999): The Regional Distribution of Foreign Direct Investment in China. Regional Studies, 33(9), pp.857-867 Zhang, K. H. (2005): Why Does so Much FDI from Hong Kong and Taiwan Go to Mainland China? China Economic Review, Number 16, pp. 293-307. Zhang, X. & Yuk, H. P. (1998): Determinants of Hong Kong Manufacturing Investment in China: A Survey, Marketing Intelligence & Planning, 16(4), pp. 260-267. 44 Zhao, H. X. & Zhu, G. T. (2000): Location Factors and Country of Origin Differences: An Empirical Analysis of FDI in China, Multinationals Business Review, 8(1), pp. 60-73. 45 Appendix Figure 2: Real FDI Inflow (1995-2008) East China 9000000 8000000 7000000 6000000 5000000 4000000 3000000 2000000 1000000 0 Beijing Jiangsu Guangdong Tianjin Zhejiang Hainan Hebei Fujian Liaoning Shanghai Shandong 46 Figure 3: Real FDI Inflow (1995-2008) Middle China 1400000 1200000 1000000 800000 600000 400000 200000 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Jilin Heilongjiang Shanxi Anhui Jiangxi Henan Hubei Hunan 47 Figure 4: Real FDI Inflow (1995-2008) West China 2000000 1800000 1600000 1400000 1200000 1000000 800000 600000 400000 200000 0 19951996199719981999200020012002200320042005200620072008 Mongolia Guangxi Sichuan Guizhou Shaanxi Gansu Ningxia Xinjiang Yunnan 48 Figure 5: Real FDI Inflow Growth Rate and Transport Infrastructure Growth Rate (1996-2008) East China 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 -10.0% -20.0% transport infrastructure growth FDI inflow growth 49 Figure 6: Real FDI Inflow Growth Rate and Transport Infrastructure Growth Rate (1996-2008) Middle China 120.0% 100.0% 80.0% 60.0% 40.0% 20.0% 0.0% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 -20.0% -40.0% transport infrastructure growth FDI inflow growth 50 Figure 7: Real FDI Inflow Growth Rate and Transport Infrastructure Growth Rate (1996-2008) West China 80.0% 60.0% 40.0% 20.0% 0.0% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 -20.0% -40.0% transport infrastructure growth FDI inflow growth 51 [...]... infrastructure matters, but also the neighboring transport infrastructure plays a crucial role in attracting FDI (1) Where, index the provincial economies and index time The LOCALTRAN and NEIGHTRAN are the central variables in this study and the others act as control variables Below are the detailed definitions of the variables FDI inflow (FDI) : The annual inflow of real foreign direct investment in 18 China in. .. Data Availability The provincial data sets in this study are mainly collected from various issues of the China Statistical Yearbook (1995-2008) and China Labor Statistical 23 Yearbook (1995-2008), published by the National Bureau of Statistics of China Some missing data are obtained from local government statistical yearbooks The sample in this study includes all inland provinces except for Tibet and... terms of RMB, measured in 1990 constant price FDI Stock (CFDI): The real cumulative FDI stock, measured in terms of RMB in 1990 constant price, to act as a proxy to capture the agglomeration effect of FDI Local Transport Infrastructure (LOCALTRAN): The total length of highway and railway per unit mass of land in local province, to act as the proxy to assess the level and quality of local transport infrastructure. .. without transport infrastructure spillover effect To test the hypothesis 1 to confirm that transport infrastructure plays an important role in attracting FDI in China, which means the area with better improvement in transport infrastructure, has the tendency to achieve larger FDI inflow growth, regressions based on equation (3) were carried out Transport infrastructure is the central variable and the other... discussion of literature in Section 2, two main hypotheses about transport infrastructure are brought out and the following economic relationship is postulated Hypothesis 1: Transport infrastructure plays an important role in attracting FDI The area with better improvement in transport infrastructure, has the tendency to achieve larger FDI inflow growth Hypothesis 2: Not only the local transport infrastructure. .. traditional measure of infrastructure, the total length of highway and railway per unit of land mass Second, studying the significance of the FDI determinants and comparing different FDI location determination mechanisms in three different regions of China provide an assessment of the relative importance of transport 16 infrastructure as well as other factors in different regions and provide better understanding... Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, Liaoning Middle region: Jilin, Heilongjiang, Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan West region: Inner Mongolia, Guangxi, Sichuan( including Chongqing), Guizhou, Yunnan, Shaanxi, Gansu, Ningxia, Xinjiang The criteria of this regional partition is based on the official explanation from National Bureau of Statistics of China, adjusted according... decomposed into two parts, namely transport infrastructure and telecommunication infrastructure 2.3.2.1 Transport Infrastructure 13 The variable central to this study is transport infrastructure Superior transport infrastructure development may have positive influence on the location choice of FDI through reducing cost in transportation and promoting flow of production factors, taking advantage of scale economies... rejected A fixed effect estimation approach is thus taken The reason is that the random effects analysis assumes that the measurements are some kind of random sample drawn from a larger population, while my sample is basically already all the provinces in China, not randomly selected In addition, modeling an effect as random effect goes with the assumption that the random effects are uncorrelated with the. .. Telecommunication Infrastructure To control for other types of infrastructure, telecommunication infrastructure has been incorporated The existence of good telecommunication infrastructure enhances a region's position in attracting FDI as it reduces costs in transaction, operation and information collection as well as saves time The significant and positive impact of telecommunication infrastructure on regional ... making remains incomplete This study aims to provide a detailed analysis of the role played by transport infrastructure in attracting FDI in three different regions FDI data at the provincial... data limitations 3.2 Data Availability The provincial data sets in this study are mainly collected from various issues of the China Statistical Yearbook (1995-2008) and China Labor Statistical... infrastructure matters, but also the neighboring transport infrastructure plays a crucial role in attracting FDI (1) Where, index the provincial economies and index time The LOCALTRAN and NEIGHTRAN are the