Determinants of foreign direct investment across China

Journal of International Money and Finance 21 (2002) 79–113 www.elsevier.com/locate/econbase

Determinants of foreign direct investment across China Qian Sun a, Wilson Tong a

b,*

, Qiao Yu

c, d

Division of Banking and Finance, Nanyang Business School, Nanyang Technological University, Singapore, Singapore 639798 b Department of Accountancy, Faculty of Business and Information Systems, The Hong Kong Polytechnic University, Hong Hum, Kowloon, Hong Kong, People’s Republic of China c Department of Finance, Fudan University, Shanghai, People’s Republic of China d Department of Economics, National University of Singapore, Singapore, Singapore 119074

Abstract We analyze the spatial and temporal variation in foreign direct investment (FDI) among China’s 30 provinces from 1986 to 1998. Motivated by Naughton (Brooklings Pap Econo Activ 2 (1996) 273), we distinguish our study from similar studies by examining changes in importance of FDI determinants through time. We do find supporting evidence. This is due to the shift in the nature of FDI in China. We also find that the cumulative FDI relative to cumulative domestic investment has a negative impact on the new FDI. Provincial officials have to improve the investment environment. Otherwise, multinational corporations may choose to invest in provinces with fewer FDI competitors. Our analysis is robust across different specifications. However, it explains the FDI distribution in the coastal provinces better than it does for Central and Western provinces.  2002 Elsevier Science Ltd. All rights reserved. JEL classification: F21; F30 Keywords: Foreign direct investment; Foreign portfolio investment; Location theory; Agglomeration; China

* Corresponding author. Tel.: +852-2766-4399; fax: +852-2356-9550. E-mail addresses: [email protected] (Q. Sun); [email protected] (W. Tong). 0261-5606/02/$ - see front matter  2002 Elsevier Science Ltd. All rights reserved. PII: S 0 2 6 1 - 5 6 0 6 ( 0 1 ) 0 0 0 3 2 - 8

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1. Introduction Interest in the study of foreign direct investment (FDI) has grown rapidly. Existing studies of FDI in the US help us to understand factors that are important to attracting foreign investments across different states.1 In this paper, we examine if these factors are also important for FDI distribution across different provinces in a developing country, China. For sure, there are many studies of FDI on developing countries but most of them are cross-country analyses.2 As such, the interwoven relationships between social, cultural, economic, and political factors are difficult to delineate. By focusing on only one country, we can make a cleaner study on the economic determining factors that attract FDI. Analysis of FDI in China is also timely. After China’s successful talks with the US and the European Union on WTO and US granting China PNTR, China entering into the WTO is almost a sure thing. China’s entering of the WTO would likely sparkle another round of FDI projects. MNCs in Shanghai and Tianjin have already planned to expand their investment scale in China. Erickson plans to double its current investment amount of US$3 billion by 2001. On the other hand, China has promised to open more of its servicing industries to foreign investments, especially on areas like financial sector, insurance, telecommunication, trade, and tourism. In fact, China has already been the second largest host of FDI in the world since 1994, next only to the US. An American credit-rating agency projects that by 2005, annual FDI in China will reach U$100 billion.3 However, there are only a few empirical studies on the overall FDI situation in China. Wang and Swain (1995) examine the host country determinants of FDI in China. They find that the FDI in manufacturing sector is positively related to China’s GDP, GDP growth, wages, and trade barriers, but negatively related to interest rate and exchange rate for the period of 1978–1992. Chen et al. (1995) study the effect of FDI on China’s output and find that the FDI has a positive impact on the output growth between 1978 and 1990. Using cross-country data, Wei (1995a,b) find that despite the large amount of FDI China has received in recent years, the country still appears to host too little FDI compared to an ‘average’ host country. The present analysis focuses upon the spatial and temporal variation in FDI among China’s all 30 provinces from 1986 to 1998. Such studies are minimal except a few that use data at the city level with a relatively short time span. Heid and Ries (1996) study 931 joint ventures in 54 cities from 1984 to 1991. They intentionally exclude investments by overseas Chinese (Hong Kong, Macau, Singapore) which probably have a different set of location determinants due to familial, linguistic, and cultural ties. Their conditional logit regression shows that cities with good infrastructure, established industrial base and foreign investment presence are more attractive to 1

See Coughlin et al. (1991), Graham and Krugman (1991), Lipsey (1993), Klein and Rosengren (1994), Heid et al. (1995) and Hines (1996), among others. 2 See Kravis and Lipsey (1982), Edwards (1990) and Lipsey (1999) and the survey paper by De Mello (1997). 3 Shenzhen Commercial, November 17, 1999.

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investors. Wei (1995a) also looks at individual cities. He finds clear evidence that in the late 1980s, FDIs contribute to higher growth of the cities. Kinoshita (1997) examines the data from a special survey conducted by the World Bank in 1992 on 468 firms in eight cities in China (six located in coastal provinces and two in inland cities). She investigates the possible effects of FDI on improving a firm’s total factor productivity during the 1990–1992 period. She finds no evidence that foreign investment helps increase the productivity growth of local firms via foreign joint ventures, foreign linkages, and the mere presence of foreign firms in the industry. Hence, she concludes that opening up to foreign investments is not sufficient for a country to benefit from foreign technology spillovers. Branstetter and Feenstra (1999) have an interesting working paper, which does look at FDI in China at the provincial level. However, their focus is different. They use 29 provincial data over the years 1984–1995 to estimate the structural parameters of government’s welfare function. They want to examine the tradeoff between the benefits of increased trade and FDI against the losses incurred by state-owned enterprises as a result of such liberalization. Indeed, they find that the government places much heavier weights on the output of the state-owned enterprises than on consumer welfare, although such preference has declined somewhat over time. They hence post skepticism on China’s entering into the WTO. Similar to our study is a paper by Cheng and Kwan (1999). They look at data from 29 provinces from 1986 to 1995 and observe agglomeration effects of foreign capital stock. However, there is an important fact that none of these studies has taken account into, and that is the big differences in the scale and nature of FDIs in the 1980s and the 1990s. In his important paper, Naughton (1996) points out the critical role that Hong Kong and Taiwan has played on the FDI in Guangdong and Fujian provinces, especially in the 1980s and early 1990s. The total FDI never exceeded 1 percent of GDP before 1991 with over 40 percent of all FDI in Guangdong and 10 percent in Fujian. This is probably due to the linguistic and cultural ties of Guangdong with Hong Kong and Fujian with Taiwan. In fact, between 1984 and 1990, Hong Kong accounted over 50 percent of China’s total FDI all along (Wei, 1995a,b). Naughton (1996) also points out that until 1991 virtually all of the industrial output of FDI was exported. Such lop-sided phenomenon began to fade away when China began to offer significant domestic market access to foreign investors in 1992. The output of these foreign-invested enterprises (FIEs) has increasingly been directed toward the domestic market. Given this, it is conceivable that the factors drawing FDI into China in the early period may not be the same, or at least may not be as important as those in the later period. Pooling everything together under one regression model hence ignores the possible dynamic complexity of the issue. As an attempt to address Naughton’s point, our paper makes contributions on this line of research by examining FDI determinants over different periods of time. Furthermore, we contrast results based on the full sample of 30 provinces against those based on 28 provinces, excluding Guangdong and Fujian. By doing so, we are able to see if FDI determinants change through time and if Guangdong and Fujian, two of the earliest provinces opening to and drawing in most of the FDI, have different attributes attracting FDI.

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Our results do show that the importance of the FDI determinants changes through time. However, there is no evidence that Guangdong and Fujian are very different from other provinces in terms of attracting FDI. Surprisingly, we find that the cumulative foreign investment relative to the cumulative domestic investment has a negative impact on the new FDI. It provides implications to both policy-makers in China and foreign investors interested in the China market. Specifically, provincial officials have to improve the investment environment. Otherwise, multinational corporations (MNCs) may choose to invest in provinces with fewer FDI competitors. The analysis is organized as follows. Section 2 gives an overview of FDI development in China. Section 3 discusses the conceptual framework. Data and empirical methodology are described in Section 4. Section 5 presents the empirical results, and Section 6 concludes the paper.

2. FDI in China FDI is conventionally defined as a form of international inter-firm cooperation that involves a significant equity stake in or effective management control of host country enterprises. However, in China, FDI is considered to encompass also other, non-equity co-operations such as contractual joint ventures, compensation trade, and joint exploration. 2.1. Three stages of FDI development China has attracted a spectacular amount of FDI since its opening to the outside world in 1979. FDI jumps from virtually zero in 1979 to an amount of US$45.46 billion in 1998. Its development can be viewed as going through three stages. First stage starts from 1979 when the “law of the People’s Republic of China on Joint Ventures Using Chinese and Foreign Investment”, the first of its kind, was enacted. A state foreign investment commission was established to direct and oversee the investment process. Four special economic zones (SEZs) were quickly set up at Shenzhen, Zhuhai, Xiamen, and Shantou in the early 1980s. In 1984, 14 more coastal cities and Hainan Island were opened to foreign investment. Furthermore, three zones were opened to FDI in early 1985: the Yangtze River delta, the Pearl River delta, and the Zhangzhou–Quanzhou–Xiamen region. FDI hence spread out from the SEZs but the boom ended in late 1985 due to high inflation. During this stage, foreign investments were concentrated in small-sized assembling and processing for exports. In response to a decline in FDI, the government promulgated the PRC Law on Foreign Enterprises in April 1986, formally granting legal rights to wholly owned foreign enterprises in China. This marks the beginning of the second stage of FDI development. In October 1986, the State Council further issued the “Provisions for the Encouragement of Foreign Investment” to encourage foreign investment, permitting more freedom of independent operations for FIEs and granting more tax incentives for foreign investment. Local governments were also given more authority in reviewing the applications of foreign investment. In 1988, Hainan was incorporated

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as another SEZ and the Chinese government further amended the joint venture laws, which included a legal ban on expropriation, relaxed restrictions regarding expatriation of profits and dividends, and allowed foreign nationals to be the chairman of the board of directors in FIEs. Starting from a very low base, China experienced double- or triple-digit annual growth of FDI from 1979 to 1988 and received a total of US$12.05 billion actual FDI during this period. In contrast to the first stage, over 70 percent of FDI projects were involved in manufacturing industries in this stage. The Tianman Square Incident slowed down the FDI growth rate to a single digit in 1989 and 1990, which ends this second stage of FDI development. To reverse the worsening investment climate, the Chinese government issued the Amendments to the Joint Venture Law in April 1990. In 1991, the Income Tax Law for Enterprises with Foreign Capital and Foreign Enterprises was passed. FDI situation was improved and resumed double-digit growth in 1991. The third stage of FDI development begins. Following Deng Xiaoping’s South China tour, actual FDI surged by 150 percent to US$11 billion in 1992. It surged another 150 percent in 1993 and maintained the double-digit growth thereafter. In the 1980s, FDI mainly takes the form of contractual or equity joint ventures. However, the wholly owned foreign firm is the fastest growing form of FDI in the 1990s. It accounts for 40% of the FDI value in 1996. The average capital size of FDI increases, with the main focus shifting to large infrastructure and manufacturing projects. The first few columns of Table 1 show the FDI and GDP figures of China. Specifically, the ‘FDI/GDP’ column shows that FDI never exceeds 1 percent of China’s total GDP during the early stages of FDI development. Deng’s South China Tour in 1992 indeed stirs up the momentum of foreign capital inflow and the proportion of FDI begins to climb up. 2.2. Characteristics of FDI in China There are several characteristics of FDI in China. First, investments concentrate on the secondary industry like manufacturing, utilities, and property development. Between 1979 and 1998, number of foreign enterprises in secondary industry takes up 75.76 percent with capital taking up 62.18 percent of the total.4 Second, foreign capital flows mainly from Asian countries. The ‘By Source’ column of the ‘Distribution of Foreign Capital Actually Utilized: FDI’ in Table 1 shows that over 80 percent of the total foreign capital comes from Hong Kong, Taiwan, Japan, Korean and other Southeast Asian countries. Third, FDI is unevenly distributed across provinces within China. The provinces in China are officially classified into three regions: the Eastern or Coastal, the Central, and the Western. The ‘By Region’ column of Table 1 shows that from 1985 to 1998, the Eastern region received a lion’s share of the total FDI amount, more than 85 percent, while the Central and the Western regions together only received less than 15 percent. Cheng and Zhang (1998) consider this as one of the reasons that

4

Economic Times, December 2, 1999.

3rd Stage

1992 1993 1994 1995 1996 1997 1998

11007 27515 33767 37521 41726 45257 45463

2.35 4.60 6.23 5.36 5.08 5.01 4.71

0.64 0.73 0.81 0.78 0.91 1.09

0.21 0.41 0.55

0.11

90 87 84 82 80 68 69

74 84 82 77 84 82

67

8 11 13 15 15 16 18

23 14 12 14 16 13

27

3 3 3 4 5 16 13

3 3 6 9 0 5

6

91.30 87.38 87.83 87.71 88.04 86.90 88.04

87.95 88.58 87.00 92.16 93.87 92.46

90.18

6.82 8.88 7.85 9.21 9.52 10.56 9.86

7.28 6.02 5.94 3.84 3.87 4.48

5.56

Central

1.89 3.74 4.31 3.08 2.45 2.54 2.10

4.76 5.40 7.06 3.99 2.26 3.06

4.26

34 28 28 28 28 27 30

50 36 43 38 46 44

46

13 11 11 11 10% 9% 10%

6 4 5 11 9 11

11

Western Guangdong Fujian

By province (%)

53 62 60 62 62 64 61

44 59 52 51 45 45

44

Other province

The data are computed based on DRI CEIC Database, which groups FDI and other investments together. Hence, the base of the percentage given in this column is FDI and other investments, not the ‘pure’ FDI figures shown in the table.

a

293466 316614 395046 437239 382949 399805

2nd Stage

1986 1987 1988 1989 1990 1991

468977 598771 541737 700666 821852 903451 964528

636 1258 1661

307552 307881 299387

1874 2314 3194 3392 3487 4366

1166

Eastern

Asia

Western Others

By region

By sourcea (%)

FDI FDI/GDP Distribution of foreign capital actually utilized: FDI (US$ m) (%)

1083337

1st Stage

GDP (US$ m)

1979– 1982 1983 1984 1985

Stage of FDI Dev.

Year

Table 1 Basic features of FDI in China (source: DRI CEIC Database and various issues of the Statistical Yearbook of China)

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have led to the fast development of the coastal provinces in the east and the widening of the gap in economic development between coastal and inland provinces since 1979. The increasing regional differences have created social and political problems. In order to narrow or slow down the widening of the gap, China’s central government has adopted a series of measures which includes encouraging FDI in the Central and Western regions. The provinces in these regions also try to jump onto the bandwagon to attract FDI. As a result, the share of FDI in the Central and Western regions have been slowly increasing since 1989. The concentration of FDI in the coastal region can be explained by many factors (Cheng and Zhang, 1998). However, the fact that the coastal region has high population density but poor natural resources, while inland provinces have low population density but rich natural resources seems to suggest that the purpose of FDI in China is mainly for the potential market and labor abundance but not natural resources. 2.3. Macroeconomic impacts of FDI FDI is expected to help the development of the host countries in various aspects. In China, FDI has significant impact on its trade flows. Sun (1999) notices that China’s total trade volume (i.e. export plus import) relative to GDP rose from 15.4 percent in 1981 to 26.6 percent in 1995. During the same period, exports by FIEs grew at an annual rate of 63.3 percent. In the coastal region, the contribution of FIEs is even more significant. He concludes that FDI is trade-creating in China. Chen (1999) runs cross-sectional regressions on 29 provinces in different years and confirms that FDI has a positive impact both on promoting China’s host province total trade flows with the rest of the world and on increasing the bilateral trade flows between China and its trade partners. Yet, about the FDI impact on technological development and economic growth, empirical studies are not conclusive.5 In the case of China, Naughton (1996) casts doubt that FDI engines China’s economic growth. He highlights a few facts that limited the impact of foreign investment on the domestic economy. As stated above, FDI was geographically concentrated, especially in the Guangdong province. Since Guangdong is relatively small and remote, its benefits from Hong Kong investments can hardly spillover to other provinces. Second, until 1991 virtually all of the industrial output of FIEs was exported; from which it follows that the domestic market presence of foreign firms was insignificant. Furthermore, foreign investment never exceeded 1 percent of GDP before the 1990s. Hence, the external reforms are only

5 Early studies like those of Caves (1974) on Australia, Globerman (1979) on Canada, and Blomstro¨ m and Persson (1983) on Mexico have found a positive impact of FDI on the productivity of local firms. However, later studies like those of Cantwell (1989) on European countries, Haddad and Harrison (1991) on Morocco, Aitken and Harrison (1991) on Venezuela do not find that technological spillovers are significant. More recent studies tend to find supporting evidence. See the studies of De Gregorio (1991) on 12 Latin American countries, Blomstro¨ m et al. (1992) on 78 developing countries and 23 developed countries, Kokko (1994) on 216 Mexican manufacturing industries, Borensztein et al. (1998) on 69 developing countries, Hejazi and Safarian (1999) on the OECD countries.

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secondary to the main progress of domestic economic reforms in the early stage of China’s development. Foreign trade and investment only become important to China’s economic growth in the 1990s. Kinoshita (1998) focuses on FDI impact on factor productivity of Chinese firms. Although she finds both ‘catch-up’ and training being important sources of productivity growth, the effects are more important for local-owned firms than foreign-owned firms. Local firms give more training to workers whereas foreign firms import intermediate inputs. Yet, some other studies do find FDI impacts being important to China’s technological development and economic growth. Sit (1985) points out that FDI has provided a substantial impetus in modernizing China’s existing industries, including the transfer of technological know-how, managerial expertise, and international marketing skill. Wei (1995b) also finds that in the late 1980s, the contribution to growth comes mainly from foreign investment. Furthermore, the contribution of foreign investment comes in the form of technological or managerial spillovers across firms as opposed to an infusion of new capital. Based on time series data from 1960 to 1991, Zhao (1995) finds that imported technology has significantly enhanced China’s technological build-up. Chen et al. (1995) study the role of FDI in China’s post1978 economic development. They find that FDI has positive association with economic growth. The pooled regression results of Sun (1998) on ten coastal provinces from 1983 to 1995 confirm that FDI significantly promoted the economic growth of China. Shan et al. (1999) apply a six-variable VAR model on China’s quarterly data from 1985 to 1996 to examine the causality between FDI and economic growth. They conclude that there is a two-way causality running between the two. As seen, the impacts of FDI are quite controversial. However, our focus lies on the provincial characteristics that draw in foreign capital.

3. Determining factors on FDI As pointed out by Braunerhjelm and Svensson (1996), the theoretical foundation of FDI is rather fragmented, comprising bits and pieces from different fields of economics to elucidate the locational pattern of firms. Several theories have been put forward to explain the FDI. Hymer (1960) views the MNC as an oligopolist. FDI is considered to be the outcome of broad corporate strategies and investment decisions of profit-maximizing firms facing worldwide competition. Dunning (1977) and Rugman (1981) invoke transaction costs to explain firms’ internationalization, putting emphasis on the intangible assets firms have acquired. Bhagwati and Srinavasan (1983) and Grossman and Helpman (1991) use the international trade theory to explain the allocative aspects of FDI. However, more relevant to our study is the location theory, which is often used to explain why a MNC would choose to invest in a particular host country. It can also be used to explain why foreign investors would choose to invest in a specific location within a particular host country. Previous researchers have identified quite a few determinants for the location of FDI. In their study on state characteristics and the location of FDI within the US, Coughlin et al. (1991) assume that a foreign firm will choose to invest in a particular

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state if and only if doing so will maximize profit. The FDI in a particular state depends on the levels of its characteristics that affect profits relative to the levels of these characteristics in the other states. They identify state land area, per capita income, agglomeration, labor market conditions (wage rates, the degree of unionization, the unemployment rate), transportation network, taxes, and the state expenditures to attract FDI as the determinants of FDI across the states within the US. Per capita income and densities of manufacturing activities affect market demand that, in turn, affects the revenue. State land area, labor market conditions, transportation network, taxes and expenditures to attract FDI affect the cost. Their results indicate that states with higher per capita incomes and higher densities of manufacturing activities attract relatively more FDI. In addition, higher wages deter FDI, while higher unemployment rates attract it. Overall, higher taxes deter FDI; more extensive transportation infrastructures and larger promotional expenditures are associated with higher FDI. Similarly, Bagchi-Sen and Wheeler (1989) find that population size, population growth, and per capita retail sales are important determinants of the spatial distribution of FDI among metropolitan areas in the US. Friedman et al. (1996) find that market potential, wage, skilled labor measured by per capita number of scientists and engineers, construction cost, major port, and funds spent on attracting FDI have significant impact on the location of foreign branch plants in the US. Braunerhjelm and Svensson (1996) further show that agglomeration, exports, and R&D are important factors affecting Swedish MNCs’ FDI location. Mody and Srinivasan (1998) find that during the 1980s, US and Japanese multinationals were attracted by some similar country characteristics like low wage inflation, low country risk, good infrastructure, and an educated work force. Both groups of investors were also strongly attracted to locations with significant past investment. Xin and Ni (1995) conducted a survey to rank provinces of China with the best investment environment. They identified eight variables with following weightings: market scale 30%, wage level 20%, education level 10%, extent of industrialization 10%, transport facilities 10%, communication facilities 10%, living environment 5%, and the level of scientific research 5%. Built on the above findings, we identify eight potentially important determinants of FDI distribution across provinces within China, as summarized in Table 2. First of all, the market demand and market size has positive impact on the FDI because it directly affects the expected revenue of the investment. In fact, one major motivation for FDI is to look for new markets.6 The larger the market size of a particular province is, other things being constant, the more FDI the province should attract. Kravis and Lipsey (1982) and many other empirical studies find such positive relationship. Blomstro¨ m and Lipsey (1991) show a significant size threshold effect for firms’ decision to invest abroad. We use GDP, GDP per capita, retail sales, and retail sales per capita to capture demand and size effect. By doing so, we implicitly assume away the possibility of the demand on a province’s FDI output coming from

6

See Shapiro (1998) for a detailed discussion on FDI motivations.

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Table 2 The possible determinants of FDI distribution Category

Proxy

1. Market demand and market size

GDP GDP per capita Retail sales Retail sales per capita

2. Agglomeration Infrastructure

Degree of industrialization Level of foreign investment

3. Labor quality 4. Labor Cost 5. The level of scientific research

6. Degree of Openness 7. Country risk 8. FDI substitutes

GDP per km2 Highway per km2 (Highway) Railway per km2 (Railway) Domestic investment (INV) Domestic investment per worker (PERWI) Cumulative domestic investment (CINV) Cumulative FDI (CFDI) CFDI/CINV RSET — number of research engineers, scientists and technicians as a percent of the total employees Average wage (Wage) R&D expenditures Number of patents Number of universities Total trade amount Import/GDP Risk ranking by political risk services Foreign portfolio investment

other provinces.7 According to Young (1997), the provinces in China have only limited trade with each other and hence multinationals can only serve the market where they locate. In fact, based on the firm interviews done by Branstetter and Feenstra (1999), even local Chinese firms can hardly develop into truly ‘multi-provincial’ enterprises. Agglomeration refers to the concentration and co-location of economic activities that give rise to the economies of scale and positive externalities. The level of agglomeration of a particular province should be positively related to the FDI. Following Wheeler and Mody (1992), we use infrastructure quality, the degree of industrialization, and cumulative foreign investment to capture the agglomeration benefits. The GDP per square kilometer is proxied for the quality of infrastructure. Related to infrastructure is the transportation network. More highway and railway mileage per square kilometer are expected to relate positively with FDI. Domestic investment per worker reflects the degree of industrialization. The cumulative FDI amount captures the possible ‘herding effect’ among foreign investors. Other than the absolute measure, we construct also a relative measure, the ratio of the cumulative FDI relative to the cumulative domestic investment, CFDI/CINV. Since both CFDI and CINV 7

We thank the referee for pointing out the assumption we have made.

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increase over time, the larger the ratio, the faster the accumulation of FDI relative to that of domestic investment. Labor quality should be an important factor for FDI consideration. It is proxied by the number of research scientists, engineers and technicians per 1000 of the employees (RSET) which has been used by Braunerhjelm and Svensson (1996). This variable measures the relative endowment of skilled labor in each province and should have a positive impact on FDI. Labor cost, as measured by WAGE, is a negative factor to FDI. However, such a measure is not without problems. Workers in the SOEs are typically provided with housing benefits and health care whereas workers in the private sector get ‘pure’ salaries with cash bonuses (which may not be reported for tax purpose). That weakens the ability of the variable to capture the true labor cost.8 On the other hand, in recent years of fast economic development, China attracts foreign investment not purely through cheap labor. As reflected in the model of Branstetter and Feenstra (1999), multinational firms in China tend to pay a wage premium to their workers. This may be because multinational firms want to hire quality workers. Higher wage may well reflect higher labor quality. Hence, it is conceivable that wages in those provinces that can attract relatively more FDIs can be higher, too. Furthermore, as pointed out by Lipsey (1999), most studies show no evidence that low wages, associated with low per capital real income, were the main attraction for FDI. We will go back to this variable later. The level of scientific research indicates the level of human capital and the level of general development. Measured by R&D expenditures and the number of patents, the higher level of scientific research should promote FDI in a province. Education is another variable measuring human capital. It is commonly proxied by the percentage of population (or employee) who have received the secondary or above education. Since such data are not available, we use the number of universities as a rough proxy for the level of education. Of course, the level of education is expected to have positive impact on the inflow of FDI. The degree of openness has mixed blessings on FDI. On the one hand, a more open economy attracts FDI because it welcomes foreign capital and foreign investors are more familiar with the host economy. Edwards (1990) finds supporting evidence on that. But on the other hand, openness can have a negative impact on FDI due to keen competition. Wheeler and Mody (1992) find that Brazil and Mexico attracted major US investment in their sample period despite these two countries have very low ratings in openness. Hence, the exact relationship between the two is an empirical question. We use the ratio of total import over GDP of a province to measure its degree of openness.9 Political risk is an important factor to consider, especially in developing countries. However, as we are dealing with a single country, the difference in political risk 8

We thank the referee for pointing out this. Using total trade value (summation of export and import value) divided by GDP may overstate the openness of China. See footnote 4 of Naughton (1996) for some of the reasons. We use only the import figure to hopefully reduce the noise of the proxy. 9

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among different provinces should not be much. We use it only as a macro variable and see how it affects the FDI through time. The variable is constructed using the risk ranking provided by Political Risk Services. The last factor to consider is the FDI substitutes. FDI brings in a lot of benefits and one of those is the inflow of foreign capital. A province may not need much FDI if it can tap the foreign capital market and use the money to invest in the local industry. In this sense, other means of foreign capital inflow can partly substitute the need for FDI. One major source we consider is foreign equity capital. We look at the number of firms in each province that have B-share, H-share, and/or N-share issues. These are means to raise foreign equity capital. The total number becomes our variable of foreign portfolio investment, FPI. We expect the variable to be negatively related to the amount of FDI within the province. Certainly, there are other commonly used variables like number of tourists, number of telephone sets, promotion expenditures for attracting FDI, tax structure, and the special treatment offered to foreign investors that may have impacts, too. However, such data are either not available or hard to measure. We use fixed effect panel data analysis to control for that.

4. Data and methodology Panel data analysis is adopted because we examine the determinants of FDI distribution across provinces and over time. Most of the data used in this study are obtained from various issues of China’s Statistical Yearbook. The Yearbooks provide two figures of FDI, ‘Signed Agreement’ and ‘Actually Utilized’. We use the later one, which is the actual amount of FDI invested in the province. The data on the number of firms that issue foreign equity shares are from Datastream. Political risk data are from Political Risk Yearbook published by Political Risk Services. They give an 18-month forecast on the risk level of a country on several aspects. We look at their forecasts on the risk level of Financial Transfer, Direct Investment, and the Currency Market in China. During our sample period, their forecasts range from ‘A’ (lower risk) to ‘B-’ (higher risk). Our risk variable is constructed by assigning ‘1’ to ‘A-’, ‘2’ to ‘B+’, 3 to ‘B’, so on and so forth. As such, when the risk level goes up, the variable becomes larger in value. Due to the lacking of complete set of provincial variables in the early period, our sample begins in 1986 and covers up to 1998. Since GDP, retail sales, domestic investment, R&D expenditures, and wage are denominated in RMB (Chinese currency) and the FDI in US dollars, we convert the FDI into RMB using yearly average dollar/RMB exchange rate. The swap market rate is used for the period from 1986 to 1993 while China was still under the dual exchange rate system. Then all monetary data are converted to 1990 constant RMB using the relevant deflater10. The Cumulative FDI (CFDI) is the total sum of the FDIs in the previous years. For

10

Retail sales and wage are adjusted by CPI and the others are deflated by the GDP deflator.

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instance, the 1990 CFDI is the sum of FDIs up to 1989. Since we do not have the complete data history of China’s FDI figure, we take the FDI figure of 1985 as the CFDI figure of 1986. We do not think this would constitute a serious problem as the amount of FDI relative to the GDP level is very small in the early period anyway, as mentioned before.11 A problem with this data set is the possible high correlation between the various proxies. It is quite obvious that the proxies listed in Table 2 may overlap with one another. This may lead to serious multicollinearity. In order to ascertain the degree of multicollinearity, we calculate the correlation matrix between all the potential determinants. We will exclude those highly correlated pairs. Specifically, we transform all variables into the natural logarithm form and stack these transformed variables up across the 30 provinces, then calculate the correlation coefficients between them.12 The results are presented in Panel A of Table 3. As expected, high degree of correlation (correlation coefficient of 0.7 or above, as highlighted) exists in many pairs of proxies. To avoid multicollinearity in the subsequent analysis, we select only seven proxies in our panel regression model. For Market Demand factor, we use only the GDP series. For Agglomeration factor, we use railway length per squared kilometer (RLWAY) to capture the infrastructure level. The annual domestic investment per worker (PERWI) is used to capture the degree of industrialization. As CFDI is highly correlated with GDP, we use the relative measure CFDI/CINV, which is the relative accumulation of foreign investment to domestic investment. For Labor Quality, we only use RSET. Panel B of Table 3 gives the new correlation matrix for the seven selected proxies and confirms that none of the variables are highly correlated now. Since we will add two more variables, the foreign portfolio investment FPI and the degree of openness OPEN that have a shorter data span from 1992 to 1998 into our second set of tests, we also want to make sure they are not highly correlated with the seven chosen variables. The correlation matrix of the nine variables shown in Panel C of Table 3 confirms that it is the case. A general pooled regression model is used on these variables and is specified as ln(FDIit) ⫽ ai ⫹ b1 ln(GDPit⫺1) ⫹ b2 ln(PERWIit⫺1) ⫹ b3 ln(WAGEit⫺1) ⫹ b4 ln(RSETit⫺1) ⫹ b5 ln(RLWAYit⫺1) ⫹ b6RISKt ⫹ b7 ln(CFDI / CINV)it ⫹ ⑀it, (i ⫽ 1,2,...,30 and t ⫽ 1,2,...,12)

(1)

where subscript i refers to individual provinces, t refers to years from 1987 to 1998, and ai is the intercept. Notice that all explanatory variables except RISK and

11

We have tried another approach. We have aggregate FDI figures beginning 1979. Using the provincial proportion of FDI in 1985, we pro-rate the aggregate figures in these early years into different provinces. The sum of the pro-rated FDI figures of a province from 1980 to 1984 with the 1985 figure becomes the CFDI of that province in 1986. The results using this approach are qualitatively the same. 12 Since log 0 is undefined, 10⫺4 is used to replace the zero whenever it occurs in our data set.

POP

RTL

RTL/P PAT NT

UNIV WAGE RD

RDSTF HI WAY

RL WAY

RISK CFDI CFDI/ CINV

GDP/P 0.45 INV 0.95 0.56 CINV 0.88 0.65 0.89 PERWI 0.24 0.84 0.47 0.48 POP 0.82 ⫺0.14 0.69 0.55 ⫺0.28 RTL 0.97 0.40 0.92 0.80 0.20 0.82 RTL/P 0.37 0.90 0.48 0.50 0.78 ⫺0.17 0.43 PATNT 0.86 0.53 0.84 0.84 0.36 0.61 0.83 0.46 UNIV 0.86 0.24 0.77 0.60 0.03 0.80 0.89 0.26 0.76 WAGE 0.21 0.73 0.33 0.58 0.66 ⫺0.24 0.13 0.59 0.31 ⫺0.12 RD 0.82 0.60 0.81 0.77 0.41 0.52 0.80 0.55 0.82 0.80 0.32 RDSTF 0.73 0.36 0.68 0.52 0.18 0.57 0.76 0.40 0.71 0.87 ⫺0.06 0.90 HIWAY 0.70 0.36 0.65 0.53 0.17 0.54 0.72 0.38 0.68 0.73 0.06 0.71 0.72 RLWAY 0.55 0.36 0.51 0.41 0.22 0.38 0.56 0.37 0.62 0.63 ⫺0.11 0.66 0.75 0.81 RISK ⫺0.28 ⫺0.43 ⫺0.36 ⫺0.52 ⫺0.42 ⫺0.03 ⫺0.15 ⫺0.20 ⫺0.30 0.00 ⫺0.58 ⫺0.32 ⫺0.01 ⫺0.06 ⫺0.01 CFDI 0.83 0.63 0.84 0.85 0.47 0.52 0.80 0.55 0.79 0.62 0.38 0.76 0.63 0.65 0.61 ⫺0.36 CFDI/CINV 0.64 0.49 0.65 0.58 0.38 0.40 0.65 0.48 0.62 0.52 0.17 0.62 0.59 0.62 0.64 ⫺0.18 0.93 RSET ⫺0.08 0.47 0.00 ⫺0.04 0.50 ⫺0.39 ⫺0.03 0.55 0.14 0.12 0.12 0.43 0.50 0.21 0.45 0.06 0.13 0.23 (continued on next page)

Panel A: All potential independent variables GDP GDP/P INV CINV PER WI

Table 3 Correlation matrix for potential determinants

92 Q. Sun et al. / Journal of International Money and Finance 21 (2002) 79–113

PERWI WAGE RSET RLWAY RISK FPI OPEN CFDI/CINV

0.32 0.32 ⫺0.03 0.52 ⫺0.03 0.29 ⫺0.10 0.57

0.68 0.53 0.30 ⫺0.12 0.52 0.64 0.42 0.13 ⫺0.08 ⫺0.04 0.59 0.54 0.21 0.52 ⫺0.02 0.25 0.48 0.29

Panel C: Independent variables in the second sub-sample regressions GDP PERWI WAGE RSET

⫺0.11 ⫺0.58 0.17 0.12

0.00 0.12 0.06 0.64

RLWAY

⫺0.01 0.65 0.45

0.09 ⫺0.24 ⫺0.01

RISK

0.42 0.30

FPI

⫺0.18 0.06

0.65 0.22 ⫺0.42 0.38 0.50

PERWI WAGE RLWAY RISK CFDI/CINV RSET

0.24 0.21 0.55 ⫺0.28 0.64 ⫺0.08

RISK

Panel B: Independent variables included in the whole sample and first sub-sample regressions GDP PERWI WAGE RLWAY

Table 3 (Continued)

0.33

OPEN

0.23

CFDI/CINV

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Q. Sun et al. / Journal of International Money and Finance 21 (2002) 79–113

CFDI/CINV are in one-period lag.13 The amount of foreign investment and the explanatory variables may very likely affect each other. For instance, larger market demand (captured by GDP) may attract FDI which, in turn, bring up the GDP of a province. To avoid such endogeneity problem, we follow Blomstro¨ m et al. (1992) and use lag variables to ensure that we are looking at the impact of variables a year earlier on the current FDI situation. The log linear specification allows us to interpret the coefficient estimates as elasticities. A major advantage of using the panel data method, as pointed out by Hsiao (1989), is to resolve or reduce the magnitude of a key econometric problem that often arises in empirical studies, namely, the omitted (mis-measured, not observed) variables that are correlated with explanatory variables. In the application here, Eq. (1) allows for fixed effects in the cross-section so that intercepts need not be identical across different provinces. As such, unique but missing or unobserved factors driving the FDI amount of individual provinces would be captured in the respective intercepts in the equation. Two major types of omitted variables are the individual time-invariant variables and the period individual-invariant variables. The individual time-invariant variables are variables that are the same for a given cross-sectional unit through time but vary across cross-sectional units. Examples of omitted provincial specific variables in our study are the geographical proximity and/or historical connection to the source countries or regions, special economic policies granted by the central government, the degree of openness to the outside world, good sea and air linkages. The period individual-invariant variables are variables that are the same for all cross-sectional units at a given point in time but vary through time. Examples of these are changes of political and macroeconomic policy, widespread optimism or pessimism. All these omitted variables may correlate with the independent variables in Eq. (1). RISK in the model is used to control for such omitted period individual-invariant variables. However, the provincial specific omitted variables are more of our concern because our main focus is FDI distribution across provinces. The provincial specific characteristics may also give rise to cross-sectional heteroskedasticity. To cater for this, we follow Bekaert and Harvey (1997). The initial estimation is done using OLS with the standard White (1980) correction for heteroskedasticity, then followed by GLS estimation which allows for heteroskedasticity across provinces (‘group-wise heteroskedasticity’). However, we do not adjust for autocorrelation. In view of the short time series and the long time interval, it does not make much sense to use Prais–Winsten correction (Greene, 1993). We also do regressions on the first difference data. Since our data are transformed into natural logarithm, the first difference gives growth rate for the respective variables. This allows us to test whether FDI growth is determined by the growth rate of GDP and/or other factors. Note that the first difference also ‘sweeps out’ the provincial specific intercept, ai.

13 Recall that we compute the accumulation of domestic and foreign investments of year t by summing the past investment amount up to year t⫺1.

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95

Other than estimating the full sample, we also split the sample into two periods, 1986–1991 and 1992–1998. As discussed at the beginning, it is important to cater for the fact that the composition of investors and the nature of FDIs change significantly before and after 1992, the year of the famous South China Tour of Deng Xiaoping. Splitting the sample enables us to examine the possibility that the factors drawing FDI into China before and after 1992 are different. Notice that for the latter sample, we add two more variables into Eq. (1), FPI and OPEN, which we do not have earlier data. Along this line, there is also a concern that Guangdong and Fujian, having close cultural and historical ties with Hong Kong and Taiwan, respectively, may have distinct features in attracting foreign capital. In fact, from the last major column of Table 1, it is seen that the two provinces draw in half of the total FDI of China, especially during the early stages. To cater for this possibility, we run a separate set of tests excluding them to see if the results with and without the two provinces would be different.

5. Results 5.1. Full sample period: 1986–1998 Table 4 reports the estimation results of Eq. (1) for the entire sample period. Panel A gives the results for the full sample of 30 provinces and Panel B gives the results of 28 provinces excluding Guangdong and Fujian. Within the panel, Model (1) is the fixed effect regression with a different intercept for each province. Model (2) is the regression with common intercept and Model (3) is the pooled regression on the differencing data. Within each model, both OLS and GLS results are reported. In Panel A, the OLS estimates for Model (1) are not quite significant in general except for GDP and RISK. A 1% increase in GDP leads to a 2.89% increase in FDI. This supports the hypothesis that the market size and general development level of a province have a positive impact on attracting FDI. This is also consistent with previous findings in the US and other countries. RISK is a very important factor. One-level jump in the political risk ranking in China leads to 56% drop in FDI. RSET and RLWAY enter only marginally significant into the regression at the 10 percent level. However, as a proxy of the level of industrialization, PERWI shows no significance in the regression. Since this variable is used to capture the agglomeration effect, the result is not supportive to the agglomeration argument. The second variable to capture the effect is the level of the relative cumulative FDI amount, CFDI/CINV. The result is surprising. A 1% increase in CFDI/CINV leads to a 0.30% decrease in FDI. Although a t-value of ⫺1.23 indicates no statistical significance, this seems to suggest that FDI cannot create a ‘herding effect’. The more FDI accumulated relative to the domestic investment accumulated, the less FDI to come. We will come back to this point later. OLS regression with White adjustment cannot adjust for group-wise heteroskedas-

2.89421 (4.7445)*** Per Worker Inv. ⫺0.052999 (⫺0.3303) Wage ⫺0.634059 (⫺0.9195) RSET 0.452918 (1.6450)* Railway 1.69318 (1.6385)*

GDP

Constant

⫺1.156236 (⫺0.5018) 3.194055 1.163835 (12.9016)*** (9.3742)*** ⫺0.113644 0.368463 (⫺1.4742) (2.7115)*** ⫺0.751419 ⫺0.385717 (⫺2.9879)*** (⫺1.4106) 0.777509 ⫺0.19788 (5.8935)*** (⫺2.3806)** 1.824953 0.3632 (3.8274)*** (2.9199)***

⫺0.053953 (⫺0.0497) 1.203408 (11.7483)*** 0.335969 (4.6775)*** ⫺0.450698 (⫺3.4343)*** ⫺0.090281 (⫺1.9430)* 0.161664 (2.4185)**

GLS

2.837098 (2.4957)** 0.111353 (0.4028) 0.44454 (0.3889) ⫺0.38834 (⫺1.2884) 0.329052 (0.3763)

OLS

2.363072 (7.4470)*** 0.106794 (1.0348) 0.144117 (0.3256) ⫺0.11819 (⫺0.8364) 0.87072 (1.7223)*

GLS

2.948 (4.660)** ⫺0.0064 (⫺0.027) ⫺0.5996 (⫺0.685) 0.3563 (0.81) 1.4744 (1.28)

OLS

3.5973 (9.202)** ⫺0.0578 (⫺0.398) ⫺1.0042 (⫺2.070)** 0.6438 (2.582)** 1.4718 (1.813)*

GLS

OLS

GLS

⫺1.4295 (⫺0.562) 1.1688 (11.02)** 0.3679 (2.320)** ⫺0.3847 (⫺1.167) ⫺0.1743 (⫺1.492) 0.4327 (4.033)**

OLS

⫺1.9017 (⫺1.045) 1.263 (13.26)** 0.2815 (2.508)** ⫺0.3402 (⫺1.531) ⫺0.0406 (⫺0.524) 0.2744 (2.933)**

GLS

3.1852 (3.446)** 0.0819 (0.31) 0.4342 (0.33) ⫺0.375 (⫺0.803) ⫺0.0241 (⫺0.012) (continued

OLS

2.8972 (5.314)** 0.0468 (0.29) 0.1875 (0.24) ⫺0.0961 (⫺0.346) 0.2299 (0.20) on next page)

GLS

Diff. data (Model 3)

OLS

Common Int. (Model 2)

Fixed effects (Model 1)

Common Int. (Model 2)

Fixed effects (Model 1)

Diff. data (Model 3)

Panel B: 28 provinces (excluding Guangdong and Fujian)

Panel A: 30 provinces (full sample)

where FDI and GDP are in millions RMB, PERWI is defined as domestic investment divided by the employment and is in thousands of RMB per worker, RSET is the total number of research engineers, scientists and technicians divided by the employment, W is the wage rate in thousands of RMB, CFDI/CINV is the cumulative foreign direct investment in millions of RMB divided by the cumulative domestic investment in millions of RMB, and Risk takes the value of 1–4 according to the ranking in the Political Risk Yearbook and International Country Risk Guide for the period of 1987–1998. All RMB are in 1990 constant price. i refers to individual provinces and t refers to each year in the sample period. White Heteroskedasticity-consistent t-statistics in parentheses

ln(FDIit) ⫽ ai ⫹ b1 ln(GDPit⫺1) ⫹ b2 ln(PERWIit⫺1) ⫹ b3 ln(RSETit⫺1) ⫹ b4 ln(WAGEit⫺1) ⫹ b5 ln(RLWAYit⫺1) ⫹ b6 ln(CFDIit / CINVit) ⫹ b7RISKt ⫹ ⑀it, (i ⫽ 1,2,...,30 and t ⫽ 1,2,...,12)

Table 4 Pooled regression results (1987–1998). The pooled regression model is

96 Q. Sun et al. / Journal of International Money and Finance 21 (2002) 79–113

Risk

0.882476 1.843917

0.973492 1.854335

0.808963 1.855999

0.892836 1.873495

0.166164 2.36993

⫺0.376431 ⫺0.23606 (⫺13.7451)*** (⫺2.6964)*** 0.885817 ⫺1.72747 (17.6827)*** (⫺5.0729)***

0.142161 2.08022

⫺0.2585 (⫺9.4681)*** ⫺1.01131 (⫺5.6097)***

0.8732 1.8474

⫺0.5717 (⫺8.645)** ⫺0.3499 (⫺2.210)**

OLS

0.9729 1.8873

⫺0.5166 (⫺13.03)** ⫺0.5275 (⫺4.106)**

GLS

⫺0.50491 ⫺0.488982 (⫺21.1925)*** (⫺7.1433)*** ⫺0.367945 0.859729 (⫺3.4146)*** (14.4494)***

GLS

⫺0.562067 (⫺8.7383)*** ⫺0.303411 (⫺1.2335)

OLS

Note: *,**, and *** denote significance at the 10, 5, and 1 percent level, respectively.

Adjusted R2 DW

CFDI/CINV

GLS

OLS

GLS

0.8485 1.8519

⫺0.5187 (⫺7.629)** 0.7938 (12.11)**

OLS

0.9497 1.8612

⫺0.4125 (⫺9.443)** 0.7655 (14.05)**

GLS

0.1712 2.3749

⫺0.2339 (⫺2.611)** ⫺1.7691 (⫺7.967)**

OLS

0.159 2.0752

⫺0.2603 (⫺4.978)** ⫺1.1029 (⫺6.224)**

GLS

Diff. data (Model 3)

OLS

Common Int. (Model 2)

Fixed effects (Model 1)

Common Int. (Model 2)

Fixed effects (Model 1)

Diff. data (Model 3)

Panel B: 28 provinces (excluding Guangdong and Fujian)

Panel A: 30 provinces (full sample)

Table 4 (Continued)

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Q. Sun et al. / Journal of International Money and Finance 21 (2002) 79–113

ticity. In fact, a standard Lagrange multiplier test reveals that homoskedasticity across provinces can be easily rejected at the 5 percent level. Using GLS approach is necessary and gives much stronger results. All estimates on the full sample become highly significant except for PERWI. For instance, a 1% increase in RSET leads to a 0.77% increase in FDI, which is statistically significant at any conventional level. Recall that this variable is the number of engineers, scientists, and technicians relative to the total number of employees within a province. Its significance suggests that labor quality is indeed important to FDI consideration. Wage variable enters significantly negative into the regression. A 1% increase in WAGE leads to a 0.75% decrease in FDI. That is to say, high labor cost deters the inflow of FDI. This is consistent with Coughlin et al. (1991) and Friedman et al. (1996). RAILWAY that proxies for the infrastructure level shows up to be a big attraction for FDI. A 1% increase in infrastructure buildup almost doubles the inflow of FDI. The most interesting result is that CFDI/CINV is significantly negative with a tvalue of ⫺3.41. A 1% increase in CFDI/CINV leads to a 0.36% decrease in FDI. Such a result seems to suggest that the more the accumulated FDI, the less the amount of FDI that will come. It is surprising given the fact that studies typically find agglomeration effect of FDI.14 However, the interpretation has to be careful. Recall that we use relative cumulative foreign investment and not absolute cumulative foreign investment, CFDI because CFDI is highly correlated with GDP. That is to say, the major impact of CFDI is already captured in the positive impact of GDP on FDI found in the regression results. Another important point to look into is the linkage between CFDI and CINV. From Table 3, their correlation coefficient is as high as 0.93. In fact, Borensztein et al. (1998) have found a strong ‘crowding-in’ effect of foreign investment on domestic investment. In the paper, they investigate whether the inflow of foreign capital ‘crowds out’ domestic investment. But their results show the opposite, a one-dollar increase in the net inflow of FDI is associated with 1.5- to 2.3-dollar increase in total investment in the host economy. Now, if such an effect in China is stronger than the agglomeration effect, i.e. FDI leads to more accumulation of domestic capital than more inflow of foreign capital, a negative relationship between FDI and CFDI/CINV will be observed. Hence, the result needs not be inconsistent with the agglomeration argument. Yet, it does indicate that existing FDI does not attract further inflow of FDI fast enough. It may be that agglomeration has a limit. Beyond certain level, positive externalities of investing in the same location turn into negative externalities. This has several important implications. First, the FDI growth in China may not be sustainable without creating more special incentives for FDI. Second, foreign investors in general are not satisfied with the results of their investment in China. This is consistent with many anecdotal stories and the survey results of Chen (1993) in which 22 FDI firms in Tianjin and Shenzhen are asked to evaluate the investment environment in China. The average score given for all evaluated items by these firms

14 For instance, Heid et al. (1995), Heid and Ries (1996) and Cheng and Kwan (1999) find support evidence for the agglomeration effect of FDI in China.

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99

are at the low end of the scale. Through firm interviews, Branstetter and Feenstra (1999) find that the foreign invested enterprises compete with state-owned firms and the Chinese government tries to impede the ability of foreign firms to compete in the Chinese market. No wonder Wei (1995b) find that China has received too little FDI compared to ‘an average host country’ although it has attracted large amount in absolute terms.15 Finally, from the point of view of multinational enterprises, there may be diminishing return for FDI in certain ‘hot’ provinces and it may be better to invest in provinces that are not flooded with FDIs. Although the fixed effect model seems to be a sensible model to use here and gives significant results, one may query if it is due to the missing factors captured by the intercepts of individual provincial groups. To see how much independent variation remains in the explanatory variables, we take away the provincial fixed effects by running a common-intercept regression. The results presented in Model (2) show that the variables still give highly significant estimates and the model gives an adjusted R-squared of 80 percent for the OLS regression and 89 percent for the GLS regression. This clearly shows that the major explanatory power comes from the independent variables, not the fixed effect. Yet, fixed effects do provide additional explanatory power, as reflected in higher adjusted R-squared values. Anyway, we should not pay too much attention on the common-intercept results as they are misspecified with omitted variable problems. In fact, some coefficients have signs different from that of fixed effect regressions. Model (3) examines whether FDI growth rate is affected by the growth rate of the various determinants. For the OLS results, GDP, RISK, and CFDI/CINV are the three variables enter significantly into the regression. Specifically, the FDI growth of a province is positively related to its GDP growth, negatively related to the increase inflow of FDI to the province and to the increase in political risk of China. The GLS results are qualitatively the same except that the estimate for RAILWAY is also marginally significant at the 10-percent level. This means that the FDI growth of a province is positively related to the improvement in the province’s infrastructure. Notice that the Durbin–Watson statistics do not show serious autocorrelation across various models. Since Guangdong and Fujian provinces draw in most of the FDI, they may be the main driving forces for the results found so far. However, the results shown in Panel B, which come from regressions excluding the two provinces, do not support such view. The results are qualitatively the same as before. Specifically, results in Model (1) show that large market (GDP), high labor quality (RSET), and good infrastructure (Railway) are still found to be the significant attractions for FDI. On the other hand, high labor cost (Wage), high risk (Risk) and too much FDI presence (CFDI/CINV) are still big negatives to attract new FDI. Results in Model (2) indicate that the explanatory power of the chosen variables remains strong even without Guangdong and Fujian provinces. All in all, results in

15 A new working paper by Wei (2000) suggests that corruption is an important deterring factor for FDI in China.

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Q. Sun et al. / Journal of International Money and Finance 21 (2002) 79–113

Panel (B) suggest that factors determining FDI in China are similar across provinces. Guangdong and Fujian are not that special, especially after controlling for the fixed effect, as reflected in the high adjusted R-squared values. 5.2. Sub-sample period: 1986–1991 As discussed earlier, FDI development in China takes several stages and the nature and source of FDI are different in different stages. 1991 marks the end of the second stage of FDI development. We hence split the sample into pre- and post-1991 periods and examine individually to see if the relationship between FDI and the factors behaves differently. The results of the earlier period from 1987 to 1991 are shown in Table 5. Both OLS and GLS results in Model (1) of Panel A again indicate that political risk and the relative cumulative FDI have adverse effect in attracting FDI. However, there are two notable differences from the previous results, GDP and WAGE. GDP is no longer significant in this period whereas the labor cost factor, WAGE, now enters significantly positive in the regressions. For the OLS regression, the WAGE estimate is 6.06 with a t-value of 2.74. For the GLS regression, the estimate is 4.66 with a t-value of 6.72. Such a result is opposite from what is found in the full sample. Recall that during this early period, most of the FDI originates from Hong Kong. Other than the ethnic and historical linkage between the Mainland and the then British colony, one important reason for the influx of Hong Kong capital into China is the exceedingly high costs of production in Hong Kong like land cost and labor cost. During the 1980s, most of the Hong Kong manufacturers moved their factories into the Guangdong province of China and maintained only the head offices in Hong Kong. Since the Hong Kong manufacturing industry was export-oriented, goods produced by these Chinese factories were export out eventually. Quality control for these export products was essential. In this period, factory managers with skilled workers were sent to China to train up the local workers. Needless to say, it was quite costly and hence Chinese skilled workers were in big demand. This situation may explain why WAGE was positively related with the amount of FDI. The variable captures more on the skill level of the workers than the labor cost, as the labor cost in China in the 1980s was quite low anyway. The Hong Kong manufacturers were too willing to give higher wages to quality workers than to station expatriates in China to train the local workers. Such interpretation is supported by other variables. RSET, the proxy for the labor quality, also enters significantly positive into the GLS regression. A 1% increase in RSET leads to 0.85% increase in FDI. On the other hand, the market demand proxy, GDP, does not enter significantly in either the OLS regression or the GLS regression. Since FDI from Hong Kong was essentially export-oriented, the size of the local market would not be of serious concern. In Model (2) where we force the provincial intercepts to be common, the results again indicate that the variables have explanatory power, especially for the GLS results. In Model (3), we look at the impact of changes in the variables on the changes in FDI. Both OLS and GLS results show only RISK and CFDI/CINV being

GDP

0.63982 (1.5995) ⫺0.055782 (⫺0.4897) 4.667018 (6.7241)*** 0.854237 (5.4296)*** 3.946782 (5.9206)***

⫺1.9438 (⫺1.1010) ⫺0.35418 (⫺1.9498)* ⫺1.134719 (⫺1.2204) ⫺0.100498 (⫺0.5008) 1.352906 (1.6159)

⫺1.97092 (⫺0.9169) 0.238882 (0.3381) ⫺1.444421 (⫺0.5059) ⫺0.696517 (⫺0.9192) ⫺0.72472 (⫺0.1690)

⫺0.3955 (⫺0.225) 0.0985 (0.14) 6.5904 (2.566)** 0.4043 (0.50) ⫺0.7883 (⫺0.150)

OLS

⫺0.3978 (⫺0.437) ⫺0.115 (⫺0.372) 5.7109 (4.136)** 0.8077 (2.167)** 2.3016 (1.08)

GLS

⫺8.205007 (⫺3.1843)*** 1.224527 (8.5179)*** 0.189101 (1.9588)* 0.762889 (2.3193)** ⫺0.111335 (⫺1.9746)** 0.201277 (2.6272)***

GLS

⫺8.682827 (⫺1.1482) 1.302713 (5.0327)*** 0.228532 (0.7771) 0.671328 (0.6619) ⫺0.159888 (⫺0.8965) 0.295265 (1.5951)

⫺0.080974 (⫺0.0558) Per Worker Inv. 0.036787 (0.0789) Wage 6.062576 (2.7479)*** RSET 0.517995 (0.9475) Railway 1.784075 (0.7867)

Constant

OLS

GLS

OLS

GLS

⫺4.7661 (⫺0.561) 1.3327 (6.480)** 0.1266 (0.36) 0.1319 (0.11) 0.0229 (0.08) 0.3184 (1.59)

OLS

⫺5.6771 (⫺1.313) 1.2618 (8.871)** 0.03 (0.17) 0.3725 (0.70) 0.0428 (0.35) 0.2824 (2.342)**

GLS

⫺2.0997 (⫺0.975) 0.3282 (0.38) ⫺1.5919 (⫺0.481) ⫺0.8033 (⫺0.866) ⫺2.0077 (⫺0.271) (continued

OLS

⫺2.1392 (⫺1.902)* ⫺0.2109 (⫺0.468) ⫺2.1152 (⫺1.133) ⫺0.3529 (⫺0.752) 1.0502 (0.39) on next page)

GLS

Diff. data (Model 3)

OLS

Common Int. (Model 2)

Fixed effects (Model 1)

Common Int. (Model 2)

Fixed effects (Model 1)

Diff. data (Model 3)

Panel B: 28 provinces (excluding Guangdong and Fujian)

Panel A: 30 provinces (full sample)

where FDI and GDP are in millions RMB, PERWI is defined as domestic investment divided by the employment and is in thousands of RMB per worker, RSET is the total number of research engineers, scientists and technicians divided by the employment, W is the wage rate in thousands of RMB, CFDI/CINV is the cumulative foreign direct investment in millions of RMB divided by the cumulative domestic investment in millions of RMB, and Risk takes the value of 1–4 according to the ranking in the Political Risk Yearbook and International Country Risk Guide for the period of 1987–1991. All RMB are in 1990 constant price. i refers to individual provinces and t refers to each year in the sample period. White Heteroskedasticity-consistent t-statistics in parentheses

ln(FDIit) ⫽ ai ⫹ b1 ln(GDPit⫺1) ⫹ b2 ln(PERWIit⫺1) ⫹ b3 ln(RSETit⫺1) ⫹ b4 ln(WAGEit⫺1) ⫹ b5 ln(RLWAYit⫺1) ⫹ b6 ln(CFDIit / CINVit) ⫹ b7RISKt ⫹ ⑀it, (i ⫽ 1,2,…,30 and t ⫽ 1,2,…,5)

Table 5 Pooled regression results (1987–1991). The pooled regression model is

Q. Sun et al. / Journal of International Money and Finance 21 (2002) 79–113 101

Risk

0.870325 2.254817

0.984443 2.07453

0.779206 2.026641

0.903881 1.864959

0.365118 2.222257

⫺0.94974 ⫺1.296421 (⫺14.4696)*** (⫺3.4435)*** 0.844502 ⫺2.164398 (16.2874)*** (⫺9.0889)***

0.310792 2.297133

⫺1.235211 (2.7803)*** ⫺2.422911 (⫺5.5331)***

0.8551 2.5601

⫺1.1737 (⫺3.460)** ⫺0.9979 (⫺4.138)**

OLS

0.9806 2.0831

⫺1.0188 (⫺6.650)** ⫺0.8595 (⫺4.165)**

GLS

⫺1.070434 ⫺1.037411 (⫺18.8367)*** (⫺4.3260)*** ⫺0.886592 0.8603 (⫺4.6030)*** (8.6832)***

GLS

⫺1.167061 (⫺4.6668)*** ⫺0.983432 (⫺2.5216)**

OLS

Note: *,**, and *** denote significance at the 10, 5, and 1 percent level, respectively.

Adjusted R2 DW

CFDI/CINV

GLS

OLS

GLS

0.7568 2.0119

⫺0.9167 (⫺3.504)** 0.7545 (6.315)**

OLS

0.9732 1.9111

⫺0.8902 (⫺7.664)** 0.7286 (11.82)**

GLS

0.3082 3.0097

1.2713 (2.766)** ⫺2.424 (⫺7.161)**

OLS

0.3568 2.2822

⫺1.4204 (⫺6.132)** ⫺2.0968 (⫺6.843)**

GLS

Diff. data (Model 3)

OLS

Common Int. (Model 2)

Fixed effects (Model 1)

Common Int. (Model 2)

Fixed effects (Model 1)

Diff. data (Model 3)

Panel B: 28 provinces (excluding Guangdong and Fujian)

Panel A: 30 provinces (full sample)

Table 5 (Continued)

102 Q. Sun et al. / Journal of International Money and Finance 21 (2002) 79–113

Q. Sun et al. / Journal of International Money and Finance 21 (2002) 79–113

103

statistically significant and are negatively related to changes in FDI. The proxy for degree of industrialization, PERWI, enters marginally significant at the 5 percent level, which we do not think as of importance. Panel B reports results on excluding Guangdong and Fujian provinces. Again, the results are essentially the same. For instance, labor quality rather than potential market demand is an important consideration. Higher quality, as reflected in higher labor wages, attracts more FDI. Market size, as proxied by GDP, does not enter significantly into the fixed-effect regressions. On the other hand, risk and relative cumulative FDI are strong deterring factors for new inflows of foreign capital. 5.3. Sub-sample period: 1992–1998 The current period from 1992 to 1998 allows us to consider two more interesting variables, foreign portfolio investment in each province and the degree of openness of China. As discussed before, we believe foreign portfolio investment would be a substitute to FDI and the degree of openness will attract more FDI. The OLS and GLS regressions of Eq. (1) are run again under three different settings, the fixed effects (Model 1), the common intercept (Model 2), and the differencing data (Model 3). The results are given in Table 6. In Panel A, Model (1) gives very interesting results. First, consistent with our expectation, the degree of openness of China to the outside world enters significantly positive into the regression. For the OLS estimate, the coefficient is 0.50 and the tvalue is 1.97. For the GLS estimate, the coefficient is 0.37 and the t-value is 7.68. That is, openness does attract foreign investment. On the other hand, foreign portfolio investment, FPI, has only marginal impact on FDI. The variable is significant in the GLS estimation but at a statistical level of 10 percent only. The economic significance is also minimal. Another interesting result is that the market demand variable, GDP, now becomes highly significant in both OLS and GLS regressions. In both estimates, a 1% increase in provincial GDP will give rise to around 4.7% increase in FDI. This is quite different from what is found in the early sample period. As discussed before, the continuous opening of China to the outside world attracts foreign capital other than Hong Kong and Taiwan. Direct investments from Japan, Europe and US play increasingly important part. Furthermore, the nature of the investments is different. Instead of export-oriented, the investments target mainly at local market demand within China. Conceivably, provincial GDP becomes an important consideration for foreign investing capital. More interestingly, labor cost WAGE enters significantly negative into the regressions. For the OLS result, 1% increase in labor cost reduces 3.23 percent of FDI. For the GLS result, it sends FDI down by 2.75 percent. Recall that in the previous sample period, the variable gives a significantly positive estimate. In recent years, China has impressive, continuous economic growth. Since 1983, China has maintained a two-digit per capita GDP grow rate with exceptions only in 1990, 1997, and 1998. For the latter two years, the Chinese government intentionally slowed down the growth to curb inflation. It is conceivable that the Chinese labor cost picks

Railway

RSET

Wage

Per Worker Inv.

GDP

Constant

8.647761 (2.0385)** 4.691472 4.737627 1.055985 (2.6911)*** (18.1825)*** (5.6724)*** 0.243811 0.028064 0.246181 (1.0440) (0.4979) (1.1269) ⫺3.2331 ⫺2.753889 ⫺1.619164 (⫺2.8886)*** (⫺13.2881)*** (⫺2.3830)*** 1.154472 1.332576 ⫺0.355467 (1.7131)* (7.7773)*** (⫺2.5504)** 1.056926 1.545166 0.557504 (2.1033)** (4.2248)*** (2.1932)**

8.444845 (6.3035)*** 1.093751 (12.3831)*** 0.223671 (2.7820)*** ⫺1.60372 (⫺10.1573)*** ⫺0.319258 (⫺7.4665)*** 0.437804 (3.6348)***

GLS

9.674571 (3.1522)*** ⫺0.238025 (⫺0.9221) ⫺0.914114 (⫺0.7347) 1.215654 (2.2357)** 0.307708 (0.4100)

OLS

9.247752 (22.8026)*** ⫺0.269296 (⫺1.5238) ⫺0.813439 (⫺2.3090)** 1.008052 (4.9836)*** 0.377866 (1.4026)

GLS

3.248 (3.995)** 0.0877 (0.39) ⫺1.0811 (⫺1.032) 0.8085 (0.85) 1.2724 (1.14)

OLS

2.844 (7.144)** ⫺0.0757 (⫺0.711) ⫺1.0126 (⫺1.826)* 1.112 (2.670)** 1.8925 (3.494)**

GLS

OLS

GLS

OLS

5.7009 (1.465) 1.0259 (10.14)** 0.2419 (1.56) ⫺1.1911 (⫺2.592)** ⫺0.3202 (⫺2.733)** 0.5557 (5.085)**

OLS

6.2206 (2.809)** 1.0394 (13.94)** 0.134 (1.31) ⫺1.2281 (⫺4.709)** ⫺0.3166 (⫺4.675)** 0.4524 (5.413)**

GLS

Common Int. (Model 2)

Fixed effects (Model 1)

Common Int. (Model 2)

Fixed effects (Model 1)

Diff. data (Model 3)

Panel B: 28 provinces (excluding Guangdong and Fujian)

Panel A: 30 provinces (full sample)

3.4136 (5.935)** ⫺0.1014 (⫺0.870) 0.9739 (1.21) 0.3388 (0.79) 1.0998 (1.42)

GLS

(continued on next page)

4.7892 (5.384)** ⫺0.1389 (⫺0.634) 0.9829 (0.76) 0.4444 (0.47) 0.6888 (0.45)

OLS

Diff. data (Model 3)

where FDI and GDP are in millions RMB, PERWI is defined as domestic investment divided by the employment and is in thousands of RMB per worker, RSET is the total number of research engineers, scientists and technicians divided by the employment, W is the wage rate in thousands of RMB, CFDI/CINV is the cumulative foreign direct investment in millions of RMB divided by the cumulative domestic investment in millions of RMB, and Risk takes the value of 1–4 according to the ranking in the Political Risk Yearbook for the period of 1992–1998, FPI is the number of total B-, H- and N-shares available for foreign investors, and Openness is defined as total import as a percentage of GDP. All RMB are in 1990 constant price. All RMB are in 1990 constant price. i refers to individual provinces and t refers to each year in the sample period. White Heteroskedasticity-consistent t-statistics in parentheses

ln(FDIit) ⫽ ai ⫹ b1 ln(GDPit⫺1) ⫹ b2 ln(PERWIit⫺1) ⫹ b3 ln(RSETit⫺1) ⫹ b4 ln(WAGEit⫺1) ⫹ b5 ln(RLWAYit⫺1) ⫹ b6 ln(CFDIit / CINVit) ⫹ b7RISKt ⫹ b8 ln(FPIit⫺1) ⫹ b9 ln(OPENit⫺1) ⫹ ⑀it, (i ⫽ 1,2,...,30 and t ⫽ 1,2,...,7)

Table 6 Pooled regression results (1992–1998). The pooled regression model is

104 Q. Sun et al. / Journal of International Money and Finance 21 (2002) 79–113

Risk

0.926192 1.794873

0.99578 1.760374

0.856518 1.672308

⫺0.259773 (⫺1.8356)* 0.780759 (7.4586)*** 0.007642 (1.452841) 0.391054 (2.459535)**

0.907837 1.662592

⫺0.137516 (⫺4.3092)*** 0.814422 (14.2446)*** 0.008847 (3.812687)*** 0.395718 (8.445541)***

0.419859 1.866542

⫺0.160854 (⫺1.3849) ⫺2.15665 (⫺3.0199)*** ⫺0.021673 (⫺0.873141) 0.652732 (2.103793)**

0.741055 1.867958

⫺0.078645 (⫺3.0766)*** ⫺1.996246 (⫺19.0892)*** ⫺0.017717 (⫺2.1931)** 0.693281 (6.598367)***

GLS

0.9249 1.7697

⫺0.3282 (⫺4.396)** ⫺0.4055 (⫺1.676)* ⫺0.0119 (⫺0.477) 0.4595 (2.337)**

OLS

0.9912 1.8042

⫺0.2735 (⫺7.648)** ⫺0.212 (⫺1.652)* 0.0035 (0.32) 0.3269 (3.200)**

GLS

⫺0.259744 (⫺16.0145)*** ⫺0.53086 (⫺8.4666)*** ⫺0.007182 (⫺1.772829)* 0.37378 (7.681227)***

OLS

⫺0.36683 (⫺4.2044)*** ⫺0.66417 (⫺1.5608) ⫺0.00705 (⫺0.44376) 0.508833 (1.969719)**

Note: *,**, and *** denote significance at the 10, 5, and 1 percent level, respectively.

Adjusted R2 DW

Openness

FPI

CFDI/CINV

GLS

OLS

GLS

OLS

0.9027 1.5373

⫺0.2318 (⫺3.342)** 0.7948 (11.00)** ⫺0.0003 (⫺0.030) 0.3206 (3.425)**

OLS

0.9715 1.4269

⫺0.1452 (⫺3.646)** 0.8296 (18.45)** 0.0058 (0.98) 0.39953 (6.482)**

GLS

Common Int. (Model 2)

Fixed effects (Model 1)

Common Int. (Model 2)

Fixed effects (Model 1)

Diff. data (Model 3)

Panel B: 28 provinces (excluding Guangdong and Fujian)

Panel A: 30 provinces (full sample)

Table 6 (Continued)

0.1715 1.7077

⫺0.1991 (⫺1.789)* ⫺1.4018 (⫺4.088)** 0.0163 (0.36) 0.3758 (2.022)**

OLS

0.2288 1.7624

⫺0.093 (⫺1.525) ⫺0.8028 (⫺3.565)** 0.022 (0.86) 0.4574 (3.473)**

GLS

Diff. data (Model 3)

Q. Sun et al. / Journal of International Money and Finance 21 (2002) 79–113 105

106

Q. Sun et al. / Journal of International Money and Finance 21 (2002) 79–113

up significantly in recent years, especially in comparison to that in the Southeast Asian countries. More importantly, the products of joint-venture companies and foreign enterprises are for the local China market. As a result, high labor cost means high product cost which has direct adverse effect on the market demand. Labor cost hence becomes negatively correlated with FDI in this sample period. Notice that labor quality, as captured by RSET, is still a significantly determining factor for FDI. So are other factors like infrastructure, political risk, and openness. It is worth mentioning that cumulative FDI relative to cumulative domestic investment is always negative to new FDI whereas PERWI is almost always insignificant to FDI. This is inconsistent with the findings of Edwards (1999) that domestic investment and FDI are complements, and does not support the agglomeration argument of FDI. Model (3) on change in FDI posts the same picture. Both the OLS and GLS results show that increase in market demand (GDP), labor quality (RSET), and country openness (OPEN) significantly attract more FDI whereas increase in relative cumulative foreign investment (CFDI/CINV) deters further inflow of FDI. Increase in labor cost (WAGE) and political risk (RISK) are also found to be significant deterring factors for FDI in the GLS results. Increase in foreign portfolio investment (FPI) seems to substitute away some of real FDI. However, improvement in infrastructure (RAILWAY) does not bear high enough t-values to indicate significant impact on drawing in FDI. Again, results excluding Guangdong and Fujian provinces, as shown in Panel B, do not change the overall picture except for the labor cost variable, Wage. During this period, labor cost does not seem to be an important consideration for foreign investments although it tends to be an adverse factor. Such a result hence posts a similar picture that in the earlier period (1987–1991), foreign investors concerned more on labor quality than labor cost. Later, labor cost becomes more important. Yet, it is mostly a concern for FDI in Guangdong and Fujian, the two earliest provinces opening their doors to foreign investors. It is conceivable that intensive competitions for quality workers drive up the labor cost. This is less serious a problem for other provinces that open up later. 5.4. Model fitness Before concluding this section, we would like to examine how well the predicted FDI that is based on our model fits the actual FDI of each province. Specifically, we look at the residuals generated by the GLS fixed-effect model applied to the full sample period of 1987–1998. A standardized residual greater than 1.65 constitutes a significant outlier. A positive outlier indicates that, after accounting for the impact of the model, a province attracted more than its share of FDI in a particular year. A negative outlier indicates that a province attracted less than its predicted amount of FDI. Since the sum of time series residuals are forced to be zero by model construction, we cannot identify, ceteris paribus, which provinces received higher or lower than the average amount of FDI for the whole period. However, we calculate the standard deviation of time series residuals for each province to identify the FDI of which provinces can be better explained by our fixed effect regression model.

Q. Sun et al. / Journal of International Money and Finance 21 (2002) 79–113

107

Table 7 presents the standardized residuals of individual provinces across time. The time series standard deviation of residuals, denoted by s, for each province is shown in the last column. There is no discernible pattern of positive or negative residuals across the 30 provinces overtime. Among 360 standardized residuals, there are only 24 significant outliers. This is consistent with the high goodness of fit of our model. However, the outliers are almost exclusively concentrated in the Western region, while Eastern provinces only have one and Central provinces have two. The standard deviations shown in the last column also indicate that s is generally smaller for eastern provinces than for central and western provinces. This means the model explains the FDI distribution better for Eastern provinces than it does for the Central and Western provinces. This may suggest that the FDI in the Eastern provinces is more predictable because these provinces started earlier in receiving FDI and have a more matured FDI receiving mechanism and more stable environment. By contrast, Central and Western provinces did not start to get FDI until the mid-1980s. The less matured mechanism and environment to attract FDI may make the FDI there less predictable.

6. Conclusion and further study China’s economic reform has attracted worldwide attention. From the early stage of pulling in exported-oriented industries from Hong Kong and Taiwan that lured by cheap production cost in China to the later stage of drawing in real investments from the Western world that eager to tap the huge domestic market, China has gradually opened up to the rest of the world. The most recent giant step is a series of successful talks with the US and the European Union on getting into the WTO. Our paper provides a timely study in this area. Motivated by Naughton (1996), we differentiate our study from other similar studies on China’s FDI by looking at possible changes in importance of determining factors through time. As such, the study advances our understanding in the factors affecting the level of FDI across provinces in China. Our study does provide evidence that the importance of the FDI determinants moves through time. Wage has positive relationship with FDI before 1991 but has a negative relationship after then. Similarly, provincial GDP bears no significant relationship with GDP before 1991 but becomes highly positive after 1991. This reflects the fact that the nature of FDI before and after 1991 is quite different. Labor quality and infrastructure are also important determinants of the distribution of FDI. High labor quality and good infrastructure attract foreign investors. For the country as a whole, it political stability and its openness to the foreign world add another important dimensions to drawing in foreign capital. We have some mild evidence that foreign portfolio investments are substitutes of FDI. A surprising but important finding is that cumulative FDI relative to cumulative domestic investment has a negative impact on new FDI. We argue that it needs not imply non-existence of the agglomeration effect. However, it does carry an important policy implication that provincial officials have a lot more to do to improve the

⫺0.14 ⫺0.38 ⫺0.71 ⫺1.05 ⫺0.96 ⫺0.48 ⫺0.95

⫺0.69 0.49 1.08 ⫺0.29 0.39 ⫺0.03 ⫺0.68

0.35 ⫺0.21 0.52 ⫺0.15 ⫺0.51 0.18 ⫺1.60

⫺2.04** ⫺0.39 ⫺1.40 ⫺0.83 0.21 ⫺0.62 ⫺1.52

0.17 ⫺0.89 ⫺2.59** 0.86

0.12 1.08

⫺0.69 0.75

0.43 ⫺0.61 ⫺0.20 0.31 ⫺0.20 ⫺0.53 ⫺0.43 ⫺0.33 ⫺0.15 0.26 ⫺0.59 0.08

1990

Central Shanxi Inner Mongolia JiLin Heilongjiang Anhui Jiangxi Henan Hubei Hunan

1989 0.69 ⫺0.93 ⫺0.42 ⫺0.53 ⫺0.18 ⫺1.05 0.08 ⫺0.55 0.07 0.29 ⫺0.02 0.01

1988

0.02 1.14 ⫺0.52 1.07 ⫺1.04 ⫺0.34 ⫺0.35 0.20 ⫺0.31 ⫺0.24 ⫺0.64 ⫺0.49 0.21 0.17 0.13 0.31 ⫺0.18 0.23 0.50 0.82 0.14 0.32 ⫺1.83* 0.23

1987

Eastern Beijing Tianjin HeBei Liao Ning Shandong Jiangsu Shanghai Zhejiang Fujian Guangdong Guangxi Hainan

Province

⫺0.02 ⫺0.68 ⫺0.55 ⫺0.19 0.21 ⫺0.07 ⫺0.31

⫺0.88 ⫺1.11

0.31 0.32 0.18 0.64 ⫺0.10 ⫺0.18 ⫺0.66 0.01 0.06 0.23 ⫺0.81 0.32

1991

0.60 0.26 0.69 0.86 0.07 0.82 0.86

1.27 ⫺0.32

0.18 ⫺0.21 0.39 0.63 0.96 1.11 0.05 0.26 0.82 0.50 0.46 1.05

1992

0.66 0.24 0.42 0.63 0.44 0.64 1.04

0.35 0.40 0.46 0.66 0.42 0.36 0.71

⫺0.45 0.34

⫺0.02 0.24 0.45 0.09 0.54 0.74 0.35 0.42 0.41 ⫺0.01 0.70 0.57

⫺0.35 0.14 0.50 0.32 0.55 0.51 0.73 0.50 0.44 0.09 0.85 0.36

0.65 1.10

1994

1993

0.02 ⫺0.10 ⫺0.09 0.16 ⫺0.13 ⫺0.20 0.32

⫺0.61 ⫺0.26

⫺1.01 ⫺0.22 ⫺0.22 ⫺0.66 ⫺0.01 0.25 ⫺0.30 ⫺0.20 ⫺0.39 ⫺0.62 ⫺0.21 ⫺0.09

1995

0.06 ⫺0.18 ⫺0.06 ⫺0.20 ⫺0.28 ⫺0.35 0.45

⫺0.29 ⫺0.27

⫺0.93 ⫺0.12 ⫺0.05 ⫺0.74 ⫺0.24 ⫺0.11 ⫺0.28 ⫺0.16 ⫺0.26 ⫺0.66 ⫺0.51 ⫺0.38

1996

0.72 0.24 ⫺0.38 0.31 ⫺0.01 ⫺0.05 0.84

1.02 0.55

0.18 0.55 0.43 0.24 ⫺0.65 0.10 0.02 ⫺0.57 ⫺1.06 ⫺0.94 ⫺0.39 ⫺0.25

1998

0.75 0.37 0.69 0.58 0.42 0.43 0.97

0.75 1.06

0.64 0.55 0.46 0.48 0.45 0.62 0.37 0.36 0.48 0.55 0.52 0.69

s

(continued on next page)

0.15 0.31 0.02 0.09 0.14 ⫺0.20 0.84

0.58 ⫺0.12

⫺0.63 0.28 0.32 ⫺0.17 ⫺0.11 0.29 0.07 0.19 0.02 ⫺0.47 0.06 ⫺0.06

1997

Table 7 Standardized difference between actual and predicted FDI (based on GLS Fixed-Effect Model for 1987–1998) (*(**) denotes significant at 10(5) percent)

108 Q. Sun et al. / Journal of International Money and Finance 21 (2002) 79–113

3.84** ⫺3.41** ⫺1.86*

⫺1.48

⫺0.91 ⫺0.04 ⫺1.24 1.21 0.20 ⫺0.85 ⫺2.83** ⫺0.22 1.16

⫺1.51 0.31 0.21 1.21 0.86 ⫺0.39 ⫺3.04** ⫺4.62** ⫺0.17

Total

1990

1989

1.24 0.79 0.46 3.16** 1.32 1.43 2.94** 2.26** 2.56**

1988

0.30 ⫺2.50** 0.43 0.13 0.82 ⫺2.46** ⫺3.66** 1.33 2.76**

1987

Western Sichuan Guizhou Yunan Tibet Shaanxi Gansu Qinghai Ningxia Xingjiang

Province

Table 7 (Continued)

⫺1.25

0.72 0.94 ⫺1.14 1.01 0.19 0.63 ⫺3.12** ⫺0.66 ⫺2.10**

1991

1993

1994

1.20

3.12**

⫺0.31 ⫺0.23 ⫺0.20 ⫺1.66* ⫺0.79 0.64 0.63 ⫺0.24 0.44

1995

2.52** ⫺1.15

0.34 0.67 0.85 0.83 0.60 0.62 0.54 0.75 0.36 0.97 ⫺0.54 ⫺0.96 ⫺0.36 0.09 ⫺0.33 ⫺2.04** 0.01 1.42 2.00** 2.34** 1.79* ⫺0.24 1.81 1.08 ⫺6.78** 0.88 1.23

1992

⫺1.05 ⫺0.41 0.32 ⫺1.64 ⫺0.39 0.15 0.98 ⫺0.57 ⫺0.12

1997

⫺2.03** ⫺0.20

⫺0.74 ⫺1.00 ⫺0.59 ⫺2.07** ⫺1.18 0.65 ⫺0.07 ⫺0.93 0.38

1996

0.70

0.40 0.09 0.10 ⫺0.81 ⫺0.44 0.81 2.03** 1.01 ⫺0.22

1998

0.88 0.98 0.66 1.55 0.73 1.24 2.47 1.79 2.50

s

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investment environment. On the other hand, MNCs may want to consider investing in provinces not yet flooded with FDI competitors. In fact, accompanied with the so-called ‘tilted policy’ towards developing the Western Region, the Chinese government begins to encourage foreign capital to move investments into the inland and the Western region by providing various incentive schemes like investment benefits, tax benefits, and financing benefits.16 Our model is robust whether Guangdong and Fujian, the two early and major provinces opening up for FDI, are excluded or not. Our model is also robust across different fixed effect specifications and has a high goodness of fit. However, it explains the FDI distribution in the coastal provinces better than it does for Central and Western provinces. Our study has several limitations that deserve further investigations. First, we have not broken down the nature of the FDI. For instance, Wheeler and Mody (1992) find that factors important to FDI in the electronics industry may not be important to the manufacturing industry as a whole. There may be a clientele effect in the sense that different provinces may attract different types of FDI industries. Lumping them together may well conceal some important factors. Second, due to data limitation, we are not able to consider the tax effect of FDI on China. Hines (1996) demonstrates that in the US, higher state tax rates have a significantly negative effect on investment. Third, Branstetter and Feenstra (1999) suggest that FDI in China are competing with state-owned enterprises. Hence, opening up the domestic market to foreign importers and investors means sacrificing the benefits gained by state-owned enterprises. It would be quite interesting to examine empirically whether and to what extent such a trade-off relationship exists. Fourth, Kinoshita and Mody (2000) suggest a new and interesting perspective that private information of investing firms about the host country is an important factor in making FDI decisions. On the other hand, Wei (2000) suggests that the corruption and red tape problems are important deterring factors that make China under-achiever as a host of FDI. These are very interesting aspects that worth further investigations. Acknowledgements We would like to thank the anonymous referee for detailed comments and suggestions. The paper was presented at the 2000 meeting of the FMA European Conference in Barcelona and the Seventh Annual Asia Pacific Finance Association Conference in Shanghai. References Aitken, B., Harrison, A., 1991. Are there spillovers from foreign direct investment: evidence from panel data for Venezuela, Mimeo. MIT, Cambridge, MA and World Bank, Washington, DC.

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Beijing Economic Post, December 15, 1996.

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