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The current issue and full text archive of this journal is available at www.emeraldinsight.com/1746-8779.htm JTMC 1,3 Determinants of foreign direct investment at the regional level in China 262 Lv Na University of International Business and Economics, Beijing, China, and W.S. Lightfoot International University of Monaco, Monaco Abstract Purpose – This paper sets out to analyze the determinants of foreign direct investment (FDI) on both the country and regional level through the extensive review of past research studies, as well as through the development of a multiple regression model for identifying key determinants of FDI at the regional level in China during the critical year of 2002. Design/methodology/approach – The development of a multiple regression model to identify statistically significant determinants of FDI by region in China. Findings – As reforms continue to take place, FDI in China has been unevenly distributed. This paper examines five potential determinants of FDI in 30 regions (including provinces, centrally controlled municipalities, and semi-autonomous regions) of China using a regression model. The specific focus is on 2002, as it is the first full year after China’s accession to the World Trade Organization, and the first year in which China exceeded the USA in attracting FDI. From this initial study, one can conclude that the government should consider encouraging capital-intensive FDI through the further development of a skilled workforce. This means increasing funding for higher education, and infrastructure, while also encouraging more openness in state-owned enterprises. This paper sets up further research that may help expose regions with greater potential for FDI, as well as identifying regions which need to improve certain conditions in order to receive more FDI. Originality/value – This paper analyzes the determinants of FDI by region in China in 2002. This year is particularly interesting as it is both the first full year after China’s accession to the World Trade Organization, as well as the first year in which FDI was greater in China than in the USA While this research study is only a snap shot of a topic that is of increasing importance to China, it has direct relevance to the FDI development efforts of the individual regions. This study provides evidence that GDP that proxies for the market size and potential is shown to be a big attraction for FDI. Labor quality and the progress of reform or the degree of openness are also important determinants of the distribution of FDI. There is some mild evidence that high labor cost deters the inflow of FDI and the level of infrastructure has positive relation to FDI. These results have important implications for both the central and regional governments as they can be useful in helping the authorities to allocate funds and resources which will help attract FDI. Keywords China, Regional development, Direct investment, Determinants Paper type Research paper Journal of Technology Management in China Vol. 1 No. 3, 2006 pp. 262-278 q Emerald Group Publishing Limited 1746-8779 DOI 10.1108/17468770610704930 Introduction The study of foreign direct investment (FDI) in China has increased a great deal since the reforms under Chairman Deng Xiaoping in the late 1970s and early 1980s, with further acceleration of interest and investment due to their accession to the World Trade Organization in 2001. As the rate of globalization increases, and the economic linkage between countries strengthens, FDI is playing an increasingly important role in the world economy. FDI is widely regarded as an amalgamation of capital, technology, marketing, and management (Cheng and Kwan, 2000). Many countries consider the attraction of FDI as a crucial element in their strategy for economic development. In this paper, we examine if certain variables are significant determinants of FDI distribution in 30 different regions in China. This is particularly notable as in 2002, China exceeded the USA in becoming the largest host of FDI (Lu, 2003a, b, c). Though there are many studies on the effects of FDI on the country level, relatively few have focused on the regional level. Some authors (Fujita and Hu, 2001; Cheng and Kwan, 2000; Sun et al., 2002) have focused on the disparity between coastal and inland provinces, citing historic relationships between Pacific territories and the coastal regions, export activity, and the amount of privatization of state owned enterprises (SOE’s). This study will focus on determining if there is a significant relationship between foreign direct investment at the regional level and market demand, infrastructure development and agglomeration, labor quality, labor cost, and the degree of industrialization and openness[1]. Literature review As the worlds most populated country, China has attracted a great deal of attention from a wide range companies seeking to leverage the low relative cost of employing a Chinese work force, or to gain access to the growing Chinese middle and upper classes. From the investor’s point of view, picking the right location that provides a competitive advantage is critical to developing a sustainable business model. The location he or she would choose as the destination of FDI must ultimately be more profitable to invest in than in others (Coughlin et al., 1991). Choosing the right location often involves many different factors. Hymer (1960) found that American FDI was mainly concentrated in a few industries and monopolized by several companies. Multinational companies (MNC’s) were the product of imperfect markets and monopoly advantages where the companies had the advantage with regards to choosing where to invest. A number of conclusions can be drawn from Hymer’s analysis that helps frame up this study: . First, FDI tends to flow into differentiated markets where a MNC believes they will have an advantage competitively. . Second, companies that are able to make investments overseas all have certain advantages, such as economies of scale, differentiated products, special skills, and low-cost production. These companies will make investments in regions that do not have these advantages. . Third, there are many ways in which MNCs can invest overseas in such as exporting, and licensing, in addition to direct investment. MNCs without local partners always prefer to choose foreign direct investment. . Last but not the least Hymer found that about half of the overseas operating capital of American firms came from host countries; thus FDI tends to flow into the countries or regions that have developed financial systems and capital markets. The case for China as a host for FDI has increased substantially since Hymer first published his thoughts in 1960. China as a source of low cost labor, and with Determinants of foreign direct investment 263 JTMC 1,3 264 Figure 1. Flow of FDI from source to host countries an attractive, rapidly growing domestic market represents growth for many companies. The fact that the coastal regions – especially those in close proximity to Hong Kong, and with historic ties to the West and Taiwan also have sophisticated financial systems and capital markets supports Hymers conclusions (Figure 1). Other factors may also contribute to the emergence of China as a leading host for FDI. In the 1970s and early 1980s, Buckley and Casson (1976) and Rugman (1981) put forward the concept of internalization. Internalization[2] means the process of establishing a market inside the company and substituting the internal market for an external market. The transfer price inside the company enables the internal market to operate as effectively as the external market. The theory holds the opinion that the imperfection of external markets compels the company to exchange certain products inside the company. When this happens across national boundaries, it acts as FDI of MNCs. Countries that bring about the imperfections in the external markets (such as high tariff of non-tariff barriers) are where FDI tends to flow. The trend toward collaboration through joint ventures, partnerships, and other forms of collaborative relationships may suggest that companies are attempting to create “internal markets” with a select group of companies. The central and regional governments – through the degree of openness (i.e. foreign, private ownership of state owned enterprises) may be significantly responsible for the creation of these “internal markets.” While low cost, access to the domestic markets, and the creation of internal markets that simplify the transfer of FDI are often compelling from a country point of view from a regional perspective, selecting the right location can be a significant challenge. Dunning (1977) assumed that to make a FDI, companies should have three advantages: an ownership advantage; a location advantage; and an internalization advantage. Influenced by industry location theory, Dunning determined the definition of location advantage as the advantage one location had over another based upon: . natural and human resources; . the price, quality and productivity of inputs; . international transportation and communication cost; . investment favor or discrimination; . man-made barriers to trade; . fundamental facilities; . cross-national values, language, culture, commercial practice and politics; . research and development; . the concentration of production and sales; . economic system and political strategy; . resource allocation system. (pp. 395-418) Source Country: Leading Edge Technique Intellectual Capital MNC’s Host Country: Low Labor Cost Domestic Market The location advantage (or edge) theory is relevant to this study as it holds the opinion that the reason why an international company makes direct investments in a particular region is that the company would like to attain certain location advantages which do not exist in other host countries. Various studies (Table I) have highlighted the factors that investors care about including: labor cost, market size and market potential, trade barriers and country risks. Those host countries with lower labor cost, lower transport cost, greater market potential, trade protectionism, smaller country risk, better infrastructure and better educated and skilled populations are typically the focus of FDI. According to traditional location theory, investment incentives can be mainly divided into two types: First, those targeted at cost savings, which pursue a production cost edge in host countries where FDI is mainly focused on producing locally and then exporting; and second, for those companies who aim to expand their market presence through increasing their penetration in “local” markets. FDI mainly focuses on local production and local sale (as opposed to exporting), so this kind of FDI places a high emphasis on market size, market growth, and consumption ability. Determinants of foreign direct investment 265 Empirical works Local markets are often considered by country or by region. There is a vast amount of existing literature focused on the geographical distribution of FDI (Table II). Numerous studies have been done that identify the determinants of FDI for regions, countries, regions within countries, as well as FDI flows between countries. While there is not necessarily a single set of factors that consistently determine FDI, infrastructure, skilled labor, labor cost/wage, agglomeration, and government support in terms of openness, and/or attempts at attracting FDI are often cited as having a significant relationship between FDI and the location choice. And while regions that receive substantial FDI may benefit, it is ultimately the linkages between education, government, and industry that perpetuate the development of new activities in the region, leading to innovation, and further investment (Porter and Stern, 2001). With the fast development of its economy, China has been of crucial importance to FDI, thus the study of investments in China has been growing (Table III). Wang and Swain (1995) examine the determinants of FDI in China, finding that FDI in the manufacturing sector is positively related to China’s GDP, GDP growth, wages, and trade barriers, but negatively related to interest rate and exchange rate. Bhagwati and Srinivasan (1983) conducted a survey to rank provinces of China with the best investment environment. They identified a number of variables: market size; wage, Year 2000 2001 2002 Number of agreements Increase Number (percent) 22,347 26,140 34,171 30.68 16.97 30.72 Capital in signed agreement Amount Increase ($ billion) (percent) 623.80 691.95 827.68 50.17 10.93 16.92 Source: Commercial Ministry Foreign Investment Statistics (2003) Capital actually utilized Amount Increase ($ billion) (percent) 407.15 468.78 527.43 078 15.14 12.51 Table I. General situation of attracting FDI in China (2000-2002) JTMC 1,3 266 Table II. Selected articles on determinants of FDI outside of China Table III. Selected articles on determinants of FDI in China Authors and dates Countries Scaperlanda and Mauer (1969) Korbin (1976) Kravis and Lipesey (1982) Schollhammer and Nigh (1984) Bagchi-Sen and Wheeler (1989) Europe Europe USA USA USA Significant determinants Market capacity Market capacity Market demand, market size GDP, international conflict and cooperation Per capita retail sales, population growth, population growth Coughlin et al. (1991) USA Agglomeration, funds to attract FDI, labor market conditions, per capita income, state land area, taxes, transportation network, wages Wheeler and Mody (1992) USA Agglomeration, infrastructure, labor cost, market capacity Friedman et al. (1996) USA Construction cost, funds to attract FDI, market potential, major port, skilled labor, wage Braunerhjelm and Svensson (1996) Sweden Agglomeration, exports, R&D expenditures Mody and Srinivasan (1998) USA/Japan Educated work force, good infrastructure, low country risk, low wage inflation Fan and Dickie (2000) Asia Infrastructure, skilled labor, stable macroeconomic conditions Akinlo (2004) Nigeria Education Authors and Dates Countries Significant determinants Bhagwati and Srinivasan (1983) China Wang and Swain (1995) China Chen (1996) Wei (1999) LuMinghong (2000) Tong and Yueting (2000) Zhao and Zhu (2000) China China China China China Fujita and Hu (2001) Sun et al. (2002) China China Education, extent of industrialization, infrastructure, level of scientific research, living environment, market size, age Exchange rate, GDP, GDP growth, interest rate, trade barriers, wages Market size Bribery Degree of openness, infrastructure, labor quality Hometown connections of expatriates Cost factors, infrastructure adequacy, market potential Degree of openness (reducing share of SOE’s) Agglomeration, country risk, degree of openness, labor cost, labor quality, level of scientific research, market demand, market size education, extent of industrialization, infrastructure (transport facilities, communication facilities, living environment), and the level of scientific research. Chen (1996) divided China into East, Central and West regions, and found that wage has no relation with spatial distribution of FDI but, that market size has the most influence on the Central Area. Wei (1999) found that bribery and other dishonest behaviors of governors influenced the inflow of FDI. LuMinghong (2000) identified some additional determinants besides general economic factors such as an infrastructure factor with energy consumption as proxy; labor quality with the literacy percentage as proxy; and a system factor with honest government and degree of openness as proxy. Bhagwati and Srinivasan (1983), and Sun et al. (2002) found a correlation between investment in scientific research and FDI flows. Tong and Yueting (2000) considered the hometown connections of overseas Chinese investors as a determinant of FDI flow into China, noting that Taiwanese investors have often invested through Hong Kong to limit delays due to government and bureaucracy. Sun et al. (2002, p. 88) identified possible determinants of FDI flow within China including market demand and size, agglomeration, labor quality, labor cost, level of scientific research, degree of openness, country risk. Determinants of foreign direct investment 267 Empirical framework The location of FDI in China has striking spatial characteristics. The provinces in China are classified into three regions: Eastern, Central, and Western. FDI is unevenly distributed across the three. Table IV shows that in 2002, the Eastern region received more than 86 percent of the total FDI amount while the Central and Western regions together received less than 14 percent. This is consistent with past trends for FDI in China and has been cited as one of the reasons that have led to the fast development of the coastal provinces and the widening gap in terms of economic development between the coastal and inland provinces. (Cheng and Zhang, 1998) Moreover, the FDI flow into Eastern China has continued to increase. From 2001, the amount of FDI in the eastern regions has increased by 1.19 percent while in the central regions, FDI has only risen by 0.2 percent. In the western regions, FDI actually decreased by 1.39 percent (Lu, 2003d). From these figures it is clear that the location of FDI in China is characterized by enormous spatial diversity. There are other two main characteristics of China’s FDI pattern, which may have some certain links with the spatial pattern. Table IV shows that investment activity tends to concentrate on secondary industries like utilities, manufacturing, and property development, while the primary sector attracts only about one fourth of the total FDI. Table IV also shows that foreign capital flows mainly from Asian countries, with more than 60 percent of the total foreign capital comes from Hong Kong, Taiwan, Japan, Korean and other Southeast Asian countries. In terms of the total accumulative amount of foreign capital that the country or area actually used ($100 million), the top four sources of FDI were: Hong Kong (2,048.75 mn), the USA (398.89 mn), Japan (363.40 mn), and Taiwan Province (331.10 mn). (Lu, 2003d) These four sources of FDI contribute more than two thirds of total FDI; however, Hong Kong’s share of FDI far exceeds those of other countries and regions (Figure 2). Part of this may be explained to the pass through investments made by investors from Taiwan and other countries to ease the flow of capital. Region Eastern Central Western New foreign company Capital in signed agreement Capital actually utilized Number (units) Percent Amount ($100 million) Percent Amount ($100 million) Percent 30,001 2,730 1,440 Source: Lu (2003d) 87.80 7.99 4.21 731.78 59.53 35.97 88.41 7.24 4.35 47.29 50.09 20.05 86.70 9.50 3.80 Table IV. FDI regional distribution in China in 2002 JTMC 1,3 Other 30% Hong King 46% 268 Taiwan 7% Japan 8% Figure 2. Foreign capital used United States 9% At the regional level, the data shows that distribution of FDI across China’s regions is very uneven. In 2002, the top four areas are Guangdong, Jiangsu, Shandong, and Shanghai, which accounted for 59.28 percent of the total FDI (Figure 3) (Lu, 2003a, b, c). The reasons for FDI by region are – as stated previously – varied. In the Eastern regions, it has been argued that proximity, history, culture, and language all play a significant role in determining FDI. Others (Table III) have found a wide range of other significant determinants of FDI. From these past analyses of potential determinants of FDI in China, five were chosen to determine if there was any significant relationship between them and FDI across the regions within China (Table V). Table VI reveals the distributions of FDI according to these determinants (Table VII). Guangdong 22% Other Provinces 41% Jiangsu 20% Figure 3. Distribution of FDI across China Table V. Definitions and expected impacts of independent variables Shanghai 8% Shandong 9% Market demand and market size GDP per capita (GDPi) Agglomeration Labor quality Highway and railway per km2 (ROADi) The total number of primary, secondary schools and universities (SCHNi) Wage level of retail price index (WAGEi) Share of state-owned enterprises in industry output (SHAREi) Labor cost The degree of openness Average Max Min Median VARP/deviation Observation Regions 1420800.454 9380925.12 15717.6432 345357.7568 6.58094E þ 12 30 FDIRMB 0.995862 3.328468 0.308845 0.752341 0.533238 30 GDP 12307.43 23,959 9,174 11,147 16,571,050 30 WAGE 1.01924699 9.78649652 0.11565894 0.45693793 5.19080721 30 ROAD (/Km2) 46.43333 93 9 47 506.5479 30 SCHN (school number) 0.574754064 0.857266728 0.136123791 0.604105024 0.040940919 30 SHARE (of SOE’s) Determinants of foreign direct investment 269 Table VI. The distributions of FDI according to the determinants 387.56 129.59 Secondary Tertiary 73.48 24.57 1.95 Percent Asia Europe North America Free port Region 2,538,571 447,946 466,367 472,583 2000 2,948,833 418,270 487,452 611,309 2001 3,241,119 370,892 601,190 817,640 2002 25.25 0.01 2.94 45.94 2000 16.16 26.62 4.52 39.93 2001 9.91 211.31 23.33 23.64 2002 62.35 11 11.45 11.61 2000 62.9 8.92 10.4 14.1 2001 Percent 61.45 7.03 11.4 15.5 2002 Notes: Primary industries include mining, agriculture – any industries involved in the conversion of raw materials to sellable items; Secondary industries include manufacturing; Tertiary refer to the service sector, including distribution and professional services Source: Commercial Ministry Foreign Investment Statistics (2003) 10.28 Primary Table VII. FDI distribution in China in 2002 by sectora and by source Sector By source Capital actually utilized ($100 mn) Increasing extent (percent) 270 Capital actually utilized ($100 mn) By sector JTMC 1,3 . . . . . The first determinant is the market size (GDP). It directly affects the expected revenue of the investment. In fact, one major motivation for FDI is to look for new markets (Shapiro, 1998). The larger the market size of a particular region, the more FDI the region should attract given other things remain constant. Kravis and Lipesey (1982), Blomstrom and Lipsey (1991) and others have identified market size as having a positive impact on FDI. We use GDP per capita for demand and size effect. The second determinant is agglomeration (ROAD) which refers to the concentration of economic activities that leads to positive externalities and the economies of scale. Coughlin et al. (1991), Wheeler and Mody (1992) and Braunerhjelm and Svensson (1996) amongst others found that the level of agglomeration was positively related to the FDI in a particular country. This study uses infrastructure quality to capture the agglomeration benefits. The highway and railway mileage per square kilometer is proxy for the quality of infrastructure. The third determinant is labor quality (SCHN). This study used the total number of the primary, and secondary schools, as well as universities as a proxy for education and further for labor quality. Mody and Srinivasan (1998), LuMinghong (2000) and Akinlo (2004), all found that labor quality has a positive impact on FDI. The fourth determinant is labor cost (WAGE), as measured by wage. Bhagwati and Srinivasan (1983), Coughlin et al. (1991), Wang and Swain (1995) all found a relationship between wage or labor cost, and FDI. However, labor cost may have a negative correlation. Multinational firms in China tend to hire quality workers who earn higher wages as a possible reflection of this higher labor quality. Hence, wages in those provinces that attract more FDI may be higher. The fifth determinant is the degree of openness and progress of reform (SHARE). Sun et al. (2002) and Fujita and Hu (2001) found that a significant relationship between the degree of openness – as defined by the percentage of states owned enterprises (SOE’s) and FDI. On the one hand, a more open economy means that foreign investors are more familiar with the host economy and may therefore be more willing to invest in the country. On the other hand, openness can have a negative impact on FDI as it may attract more competition, lessening any competitive advantage a firm may have hoped to realize. We use the share of state-owned enterprises in all enterprises of a region to measure its degree of openness and progress of reform. As noted previously, there are many other commonly used determinants that may have a correlation with FDI in China – determinants like the number of telephone sets, number of tourists, level of scientific research, degree of industrialization, agglomeration of FDI, promotion expenditures for attracting FDI, tax structure, and the special treatment offered to foreign investors that may have impacts, too. However, such data is more difficult to obtain on a regional or provincial level in China, which led to the focus on the five aforementioned determinants. The typical method of estimating the effect of potential determinants of FDI is to regress the chosen dependent variable, such as the amount of FDI in a location, on a set Determinants of foreign direct investment 271 JTMC 1,3 272 of independent variables which would possibly affect the profitability of investment. These variables typically reflect local market potential, and the cost of production and transport, as well as the general environment faced by the multinational company. On the basis of the existing statistical analyses of the location of FDI in China, we postulate that for the amount of FDI in region i: Y ¼ f ðGDP; ROAD; SCHN; WAGE; SHAREÞ where GDP, ROAD, SCHN, WAGE, and SHARE represent the variables for market size and demand, agglomeration/infrastructure, labor quality, wage, degree of openness and progress of reform, respectively. Since, our dependent variable is the per capita amount of FDI, we use per capita GDP to capture the regional market potential or market size. In order to experiment with an appropriate choice of the infrastructure variable (ROAD), three alternative proxies were tested: (1) the total lengths of roads per unit of land mass; (2) the total lengths of high grade paved roads per unit of land mass; and (3) the total lengths of railway per unit of land mass. A region’s labor cost (WAGE) is given by its average labor cost divided by its retail price index. Our education variable (SCHN) is the total number of primary, secondary schools, and universities for a given region. All real variables are measured in current prices. In our sample, a region is either a province, centrally administered municipality, or an autonomous region. As the determinants of FDI distribution were examined across regions regardless of time trends and with known data limitations, this assignment only requires ordinary least-squares (OLS) estimation. The data used in this study are obtained from China’s Statistical Yearbook 2003, the China Statistical Yearbook for Regional Economy 2003, the China Foreign Economic Statistical Yearbook 2003, and the White Books of China’s Trade and Economics (2003). For this study, two elements of FDI were used: “Signed Agreement” and “Actually Utilized”. The later one is the actual amount of FDI invested in the region. Since, GDP and wage are denominated in RMB and the FDI in US dollars, we converted the FDI into RMB using the average yearly exchange rate in 2002. It is possible that a high correlation between the various proxies may come out and that the proxies may overlap with one another, which may lead to serious multicollinearity. In order to cope with multicollinearity, those highly correlated pairs were excluded. Specifically, all variables were transformed into the natural logarithm form and were then stacked up across the 30 regions. In summary, foreign direct investment at the regional level in China was expected to be affected by the region’s market demand and market size (GDP), infrastructure (ROAD), labor quality (SCHN), labor cost (WAGE), and the degree of openness and progress of reform (SHARE). A general pooled regression model is used on these variables and is specified as: InðFDIi Þ ¼ ai þ b1 InðGDPi Þ þ b2 InðROADi Þb3 þ InðSCHNi Þ þ b4 InðWAGEi Þ þ b5 InðSHAREi Þ þ 1i ði ¼ 1; 2; . . . ; 30Þ ð1Þ Where subscript i refers to individual provinces; ai is the intercept; 1 is an error term; bi (i ¼ 1, 2, 3, 4, 5) are vectors of unknown coefficients to be estimated. Analysis of results This section determines how location factors determine flows of FDI. Table VIII reports the OLS estimation results of equation (1) on the regional level. The model is regression with a common intercept and the OLS estimates are significant except for ROAD and SCHN. The Adjusted R 2 value of 0.793 further suggests that the model fits the data well. Determinants of foreign direct investment 273 Summary of results This study has found that three of the variables have a statistically significant relationship with FDI in the 30 different regions studied in China. The relationship between a regions market demand and market size (GDP) was a significant and positive factor in attracting FDI in 2002. The Coefficient estimate 2.843 indicates that foreign direct investment is very responsive to differences in per capita GDP across provinces. A 1 percent increase in GDP is estimated to lead to a 2.843 percent increase in FDI. This supports the hypothesis that the market demand and size as well as the general development level of a region have a positive impact on attracting FDI. This would seem to suggest that foreign firms may be motivated to invest in China under an assumption that to do so will allow them to gain access to China’s growing middle class. The higher the quality of labor the more attractive a region was to FDI. The results suggest a positive relation between FDI and the quality of labor, as proxied by the number of primary, and secondary schools, and universities. A 1 percent increase in SCHN is estimated to lead to a 0.847 percent increase in FDI. The level of significance suggests that labor quality is important to FDI consideration. Although China is making major investments in education, the still relatively low quality of labor in China may discourage some Western FDI in capital-intensive projects where a skilled work force is a prerequisite for success. The final variable that exhibited a significant relationship with FDI was the degree of openness and level of reform in each region. A 1 percent increase in the share of state enterprise in industrial output is estimated to lead to a 1.147 percent drop in FDI. The more ownership governments have in business enterprises, the lower the FDI. The positive and significant coefficient of SHARE indicates that foreign investors respond Variable Coefficient T-start GDP ROAD SCHN WAGE SHARE 2.843 0.064 0.847 20.779 21.147 5.342 * * * 0.356 33.008 * * * 2 0.700 2 2.175 * * Notes: Adjusted R 2 value ¼ 0.793; constant coefficient ¼ 5.000; number of observations ¼ 30; * * ¼ 5 percent significance level; * * * ¼ 1 percent significance level Table VIII. Determinants of foreign direct investment JTMC 1,3 274 positively to the economic reform – as determined by a reduction in the level of state ownership for enterprises in a particular region. The result also gives evidence that an open economic policy and further economic reforms are crucial in attracting FDI to China. The other two variables did not meet the test of statistical significance. As a proxy of the level of infrastructure, ROAD shows no significance in the regression. Since, this variable is used to capture the agglomeration effect, the result is not supportive of the agglomeration argument, i.e. that the level of similar and related businesses in a region may lead to an increase in FDI. There is some evidence, however, to suggest that the positive result gives mild evidence that there is a positive relationship between infrastructure and FDI. With a t-value of 2 0.700, the impact of labor cost (WAGE) on FDI was also not statistically significant. The data does suggest, however, that a 1 percent increase in WAGE is estimated to lead to a 0. 779 percent decrease in FDI. That is to say, high labor cost deters the inflow of FDI, which is consistent with Coughlin et al. (1991) and Friedman et al. (1996). This wage elasticity is roughly comparable to the 2 0.9 estimate by Bartik (1985) who evaluated the wage elasticity of new branch plants. Many foreign direct investments are encouraged by the enticement of low-cost labor in China, but the lack of statistical significance indicates that China attracts foreign investment based on other determinants. Labor cost may not be a major concern for foreign investors who are motivated by accessing to China’s domestic market. A major disadvantage of using the OLS regression is that it cannot resolve or reduce the magnitude of key econometric problems that often arise in empirical studies, namely, the unobserved variables that are correlated with explanatory variables. In the application here, equation (1) does not allow for fixed effects in the cross-section so that intercepts need to be identical across different provinces. As such, unique but unobserved factors driving the FDI amount of individual provinces would not be captured in the respective intercepts in the equation. Conclusion and recommendations for further research China’s economic reform has attracted worldwide attention. From the early stage of pulling in export-oriented industries with cheaper production costs to more recent investments aiming to tap the huge domestic market, China has gradually opened up to the rest of the world. While this research study is only a snap shot of a topic that is of increasing importance to China, it has direct relevance to the FDI development efforts of the individual regions. This study provides evidence that GDP that proxies for the market size and potential show up to be a big attraction for FDI. Labor quality and the progress of reform or the degree of openness are also important determinants of the distribution of FDI. We have some mild evidence that high labor cost deters the inflow of FDI and the level of infrastructure has positive relation to FDI. These results are generally consistent with Cheng and Kwan (2000), which suggests that they may be applicable over a longer time period. The twenty-first century is a century of intellectual competition; thus the human capital of knowledge, intelligence, and talents will surely be crucial factors relative to FDI. Past studies (Bhagwati and Srinivasan, 1983; Sun et al., 2002) found a correlation between the level of scientific research, and FDI. It is clear that there is some movement in selected regions of China to develop the human capital. The development of human capital is a key point to both export-oriented FDI and market-seeking FDI. But on the Chinese mainland, for every 100,000 people, there are only 3,611 who have received a higher education and 11,146 ones who have received a secondary school education. There are 85 million illiterate people representing approximately 6.72 percent of the Chinese population. The relative scarcity of highly qualified laborers in China has seriously discouraged investment in capital-intensive projects. This is especially critical when put in the context of the future competitiveness of Chinese based enterprises. As Porter and Stern (2001, p. 36) emphasized: . . . long-term competitive advantage relies on being able to avoid imitation by competitors. Ironically, then, location-based advantages in innovation may prove more sustainable than simply implementing corporate best practices. For China to develop a sustainable, nationally competitive advantage, it will have to move from being a country which attracts FDI based on low costs and growing middle class, to one which has strategically located agglomerations – this may mean increasing the support for openness, and FDI in selected regions rather than in all regions. Limitations Our study has several limitations that deserve further investigation. First, the importance of determining factors may change over time. Similarly, the location determinants of foreign direct investment may differ by industry. Furthermore, due to data limitation, we were not able to consider the effect of FDI policies, tax incentives, cumulative FDI, and foreign portfolio investment to FDI in China. Finally, we believe corruption and the effects of bureaucratic red tape are also important deterring factors of FDI. A future study would seek to determine if the same relationships exist between the variables for a longer period of time. Although there are numerous other topics which are worth researching further, the current results do carry an important policy implication in that they highlight that provincial officials have more to do to encourage domestic-market oriented and capital-intensive FDI in the future. FDI that seeks markets are more likely to occur if economic reforms go even further and if the government opens more markets to foreign investors. Furthermore, the government should provide more financial support for education to enhance the labor quality and to improve the skill level of laborers. Notes 1. Openness refers to the amount of state owned enterprises (“SOE”) in a particular region. 2. Internalization refers to a situation where a company creates other companies under one corporate group umbrella that produces many different components – most of which are consumed within the group. References Akinlo, A. (2004), “Foreign direct investment and growth in Nigeria: an empirical investigation”, Journal of Policy Modeling, Vol. 26 No. 5, pp. 627-39, (accessed June 08, 2006). Determinants of foreign direct investment 275 JTMC 1,3 276 Bagchi-Sen, S. and Wheeler, J.O. 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About the authors Lv Na is a master’s degree student pursuing a degree in Economics at the University of International Business and Economics. She has translated and edited the book Export Guide for Automotive Components for UNCTAD in 2005 and has also had a paper titled “Theoretical Determinants of foreign direct investment 277 JTMC 1,3 278 analysis to nation-owned company’s regulating in rivalrousness areas” published on International Business in 2004. W.S. Lightfoot, PhD, is the Associate Dean of Institutional Advancement and Special Projects, and Director of the Centre for Entrepreneurship at the International University of Monaco. He has delivered lectures in the USA, Canada, Greece, China, France, and Monaco. Prior to becoming a Professor, he spent over 15 years in technical marketing and management for companies including Schneider Electric (Square D), and Eaton Corporation (Cutler Hammer). He has also been involved in the early stages of several ventures, including SemiPower Systems, and Motioninfo.com. W. S. Lightfoot is the corresponding author and can be contacted at: [email protected] To purchase reprints of this article please e-mail: [email protected] Or visit our web site for further details: www.emeraldinsight.com/reprints Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.