The Location Decisions of Foreign Logistics Firms in China: Does Transport Network Capacity Matter?

views updated

Chapter 8
The Location Decisions of Foreign Logistics Firms in China: Does Transport Network Capacity Matter?

Anthony Chin and Hong Junjie



This chapter looks at the location decisions of foreign logistics firms and identifies, with the aid of a multinomial logit model, factors that are crucial in attracting them to China. These firms have an important role to play in filling the gap left by traditional Chinese firms, which now largely concentrate on warehousing and distribution. The results of the study suggest that the location of logistics firms depends on the transport infrastructure, market size, labor quality and cost, agglomeration economies, communication costs, the extent of privatization, and government incentives. The importance of each of these factors depends on the origin of logistics firms. European and North American firms tend to favor higher population densities, lower labor costs, convenient air transport, and large cities, while logistics firms from Hong Kong, Macao, and Taiwan put more emphasis on communication infrastructure.

The tremendous change and growth of the Chinese economy has led to a proliferation of foreign multinationals (MNCs) eager for a piece of the pie. MNCs have doggedly pursued manufacturing and marketing opportunities in China during the past fifteen years. Often, the experience has left them feeling like Sisyphus, the mythological figure who was doomed for eternity to push a heavy boulder up a mountain; each time he neared the top, the boulder would roll down the mountain!

The thrust of MNCs in the 1980s and early 1990s centered on “strategic” considerations. This revolved around learning how to construct and consummate business deals, bringing new manufacturing capacity on line, and developing sufficient knowledge of how to market to commercial/industrial customers and to Chinese consumers. The result has been the development of a world-class manufacturing capacity across a broad range of industries, from consumer electronics to chemicals, food products, and fibers.

Marketing and promotion has become increasingly sophisticated. Urban consumers are being blanketed by brand names from around the world. However, MNCs have neglected an issue that threatens fundamentally to block the success of most large-scale investments in China. This challenge is a logistical one: as transport and warehousing capacity has not kept up with the growth in consumer demand, it has become increasingly difficult for manufacturers and marketers in China to get their products quickly, safely, and reliably, to customers. In this environment, those who can develop the means to deliver the goods stand to gain a substantial competitive advantage.

One definition of “logistics” is the management of business operations, such as the acquisition, storage, transportation, and delivery of goods along the supply chain. The Council of Logistics Management of the United States defines it as, “Logistics is that part of the supply chain process that plans, implements, and controls the efficient, effective flow and storage of goods, services, and related information from the point of origin to the point of consumption in order to meet customers’ requirements.” Perhaps, in addition to the above is the utilization of technology in processing the information that will lead to the efficient procurement and distribution of goods and services, and the establishment of a total support system that will enhance and reduce the cost of movement.

In this process, the role played by government policy in maintaining and enhancing economic competitiveness is crucial. While governments are also committed to reducing negative externalities arising from transport on the environment, policy measures need not only be aimed at vehicles or infrastructure, but can also be designed to influence the structure and behavior of the supply chain and individual companies’ logistics strategies. This requires an understanding of where and how these measures will impact on the relationship between economic activity and freight transport demand. This understanding must be derived from insight into the drivers, barriers, and enablers which affect individual logistics firms’ decision making. These can be identified by examining the current trends in logistics and supply chain management. This chapter focuses on one aspect in the supply chain, namely, strategic location. An understanding of the factors which affect the location of foreign logistics firms in China will assist in guiding strategic economic decisions.


The growth of the Chinese economy can be seen as either a threat, by creating a “hollowing” out effect on regional economies, or a complement, by pushing regional economies up the supply chain and providing a growing consumer market. Its sustained economic growth can act as a hedge for the region's economies. The Asia-Pacific Economic Cooperation Forum (APEC) and the World Economic Forum (WEF) view China as the cornerstone of economic stability in Asia.

Most analysts agree that China has a historic role to play. During the 1997 financial crisis, as Asian currencies fell in value like dominoes, China kept its currency closely fixed to the U.S. dollar, providing a measure of stability for the region but at the expense

of its own external competitiveness. Foreign direct investment (FDI) has increased. The Economists Intelligence Unit (EIU) estimated an annual capital inflow of almost US$50 billion in 2002. The EIU pointed out that investment seeks cheap, educated labor, and political stability. Asian economies and companies must seek to complement Chinese investments. Connectivity and investment in transport infrastructure, such as road and railway links to connect China with North and Southeast Asian regions, is crucial. Strategic investments in hub ports and airports will consolidate China's position as a logistics hub and enhance connectivity to regional hubs.

However, the Chinese economy is vulnerable, given the magnitude of bad debts. Non-performing loans account for 26.6 percent of total lending by China's four state-owned commercial banks: China Construction Bank, Industrial and Commercial Bank of China, Bank of China, and Agricultural Bank of China. These four banks issue 70 percent of all loans and hold 80 percent of all deposits in China. If this is not managed well it could cripple the fragile financial system and bring grave consequences to the country and the region.

Development of the Western Region (xibu da kaifa)

The Great Western Development Strategy (xibu da kaifa) was launched in January 2000 to attract and allocate money and other resources for the development of China's poorer, and historically more neglected, central and western regions. The provinces that lie inland from China's coast cover 56 percent of the country's land area where 23 percent of its population live, including a substantial portion of the country's ethnic minorities. The per capita gross domestic product is only 60 percent of the national average. A secondary reason for the strategy is, therefore, to develop the minority areas and integrate them with the rest of China. The area covered by the plan includes six provinces: Gansu, Guizhou, Qinghai, Shaanxi, Sichuan, and Yunnan; three autonomous regions: Ningxia, Tibet, and Xinjiang; one province-level municipality, Chongqing; and Inner Mongolia and Guangxi Zhuang Autonomous Region.

Much of China's fiscal budget is thus expected to shift from the coastal provinces to the west. The state has allocated 70 percent, or RMB4.78 trillion, of fixed-asset investments and foreign loans to the western provinces. The initial investment of RMB31 billion has already been allocated for infrastructure development. While many remain skeptical about the immediate impact of this strategy, it is expected to take ten to fifteen years for the western regions to reach the current level of economic development of the eastern regions.

Growth of the Eastern Region: Impediments and Challenges

There are eleven “regional cities” in China, comprising metropolitan cities with populations of five to seven million each. These are Shenzhen, Shanghai, Dalian, Beijing, Tianjin, Shenyang, Xiamen, Qingdao, Suzhou, Fuzhou, and Dongguan. These cities are growing at 15 to 20 percent a year and are the nucleus of six mega-regions with populations of 100 million, each sharing common dialects and histories yet competing among themselves

domestically for labor and investment. If these Chinese mega-regions were nations, the gross domestic product (GDP) of five of them would put them among Asia's top ten countries, surpassing Singapore, Malaysia, and the Philippines. Short of a global war and domestic socio-political instability, these cities will continue to drive the national economy for at least another fifteen to twenty years, fuelled by an inexhaustible supply of cheap labor and the successful implementation of economic reforms begun by former Premier Zhu Rongji. More than half of the revenues earned in Shanghai are returned to Beijing. The other Asian economies have a ten to twenty-year reprieve to make their economies complementary to that of China or be left behind in the global race for competitiveness. As it takes some ten to twenty years to build up a global brand, China will be receptive to foreign investors during this period. In 2000, about US$45 billion entered China as FDI, compared with only US$10 billion to Japan, and even less to other Asian economies.

Many Chinese cities possess high quality labor and managers and supervisors whose mantra is “performance.” Many have been educated abroad. The “regional states” are also competing to attract the tens of thousands of Chinese studying at overseas universities back home by offering them low-cost, fully-equipped office space in prime areas, as well as introductions to venture capitalists. Even the much-maligned bureaucracy in China has improved. In the mega-regions, rules and regulations are fewer than those found in places such as Japan and many European economies!

However, corruption is still rampant and despite its entry into the World Trade Organization (WTO), China will take a long time to ensure copyright protection. There are also the hidden problems of China's bad debts and its inefficient state-run enterprises. The widening gap between the small group of noveau riche and the rest of the population, and unemployment resulting from failed state-owned enterprises (SOEs) may be time-bombs slowly ticking away. However, in the next five to ten years China will continue to see growth at an increasing rate.


The Main National Trunk Highway System

The major national trunk and highway network in China is shown in Figure 8.1. The Ministry of Communications (MOC) is responsible for the development of the long-term plan for highway construction and water transport network under the Seventh National Five-year Development Plan. The underlying strategies of the transportation plan are best summarized as “The three majors, one support.” The three major strategies are the construction of main trunk highways; the building of high-grade roads; and the development of major transportation hubs, supported by efficient administration and operations. These strategies guide the integration with other transportation sectors at every level of government. For example, the auto-only high-grade highway is an important part of the highway network, but it must complement the objectives above. The strategic

draft plan consisting of twelve main national trunk highways (Table 8.1) has been called the “Five Longitudinal, Seven Latitudinal Highway Strategy” (FSHS).

The construction of “Two longitudinal Two” highways comprising the Tongjiang–Sanya (F1), Beijing–Zhuhai (F2), Lianyungang–Hu’erkuosi (S4), Shanghai–Chengdu (S5) highways, together with the Beijing–Shenyang (650 km), Beijing–Shanghai (1,330 km), and Chongqing–Beihai (1,270 km) highways completed before 2000, were given priority by the MOC to promote economic liberalization and the opening up of areas on the coast, the Yangtze region, and the bordering areas. The development of these regions is designed to act as a catalyst for the development of the larger economic regions in the hinterland.

The seven highways total 17,000 kilometers in length, of which 47 percent are express-ways. The completion of the whole FSHS by 2010 will lead to increased accessibility across China, linking 203 out of 467 major cities and serving a population of 0.7 billion (55 percent of the total urban population). Thus, all cities with a population of about one million and 93 percent of the cities with a population of half a million people will be linked. The automobile will be able to travel at least 1,000 kilometers per day. The enhanced mobility is expected to have a positive impact on economic growth and development.

Table 8.1 The national Five Longitudinal, Seven Latitudinal Highway Strategy
Main highwaysName of highwaysMileage (km)
Five Latitudinal HighwaysFl. Tongjiang-Sanya (including Changchun -Yunchun branch)5,200
F2. Beijing-Fuzhou (including Tianjing-Tanggu branch and Tai'an-Huaiying link road)2,500
F3. Beijing-Zhuhai3,600
F4. Erenhot-Hekou1,400
F5. Chongqing-Zhanjiang 
Seven Longitudinal HighwaysSI. Suifenhe-Manzhouli1,300
S2. Dandong-Lhasa (including Tianjing-Tangshan branch)4,600
S3. Qingdao-Yinchuan1,600
S4. Lianyungang-Hu'erkuosi4,400
S5. Shanghai-Chengdu (including Wangxian-Nanchong-Chengdu branch)2,500
S6. Shanghai-Ruili (including Lingbo-Hangzhou-Nanjing branch)4,000
S7. Hengyang-Kunming (including Nanning-Youyiguang branch)2,000


Railways are very important for both freight and passengers in China. In the midst of rapid economic growth and reform, Chinese railways have been slow to respond to capacity pressures. Rail has traditionally enjoyed a high modal share for both passengers and freight, and thus plays a vital economic role, but it is inefficient.

Rail transport accounts for almost half of the total ton-kilometers moved and more than one-third of passenger kilometers travelled. This situation arises because of the relatively poor conditions of roads and highways in the past. Secondly, it is affordable, given the modest income levels of most of the population. As rail capacity reaches network limitations it would be difficult to accommodate the increasing volume of traffic, given the high economic growth.

The traffic density of Chinese railway networks is about three times that of American railways (class one) and seven times that of European railways, such as those of France and Germany. Table 8.2 shows that the productivity of the rolling stock is comparatively higher than the productivity of the employees. This is perhaps an outcome of the past. The weak train-kilometers per employee allows much room for improvement. Major raw material production centers are located far from the fast developing coastal regions. For example, 40 percent of coal is transported from areas outside the coastal region. Furthermore, the “floating” population of low-income earners generates much travel,

Table 8.2 Partial productivity measures of employees and rolling stock, 2000
 Train distance per employee traffic (km)Traffic units per employee (million)*Pax Km per unit of passenger rolling stockTon Km per wagon
U.S. class 14.812.713.82
U.S. Amtrak2.20.354.74
United Kingdomn.a.n.a.3.94n.a.
Sweden (SJ + BV)n.a.2.196.00n.a.

which tends to be seasonal. The Chinese railway, which has fourteen geographically-based bureaus, carries more freight than passengers, and is about the same size as the European national railways, but passenger trips tend to be longer (more than 400 km), comparable to that in the United States (390 km). Trips tend to be shorter in the European Union, being about 40 km for the United Kingdom, and 80 km for France.

The Ministry of Railway (MOR) operates and regulates the railways. The regulatory system does not provide any incentives for improvement. All the revenue from the sub-bureaus goes to the MOR which, in turn, distributes the profit back to the bureaus, sub-bureaus, stations, and depots. While revenue is accounted for at the bureau and sub-bureau levels and by type of activity, the division of costs is not readily identified. There is no direct link to the profit generated and, as such, no incentive to be efficient. There are thus suboptimal cross-subsidies between some provinces and between freight and passengers. The railway network is a relic of a controlled economy where it was the main transportation mode. As freight and passenger traffic increase it faces competition from air and road transport. The management and organization of the railways are still centralized and directly controlled by the state. This seems to be out of sync with market reforms and decentralization in other sectors.


The massive inflow of FDI into China has spawned a wealth of studies and received much attention from researchers in such areas as economics, business, and geography. However, few deal with the issue of location choice as these firms are assumed to respond largely to investment and fiscal incentives rather than local factors. Some location studies are descriptive and

anecdotal (Hayter and Han, 1997; and Luo, 1997) while those based on statistical methodologies focus largely on the FDI location at the provincial level (Wei et al., 1999; Coughlin and Segev, 2000; Chen, 1996; Cheng and Kwan, 2000; and Sun and Parikh, 2001). Even fewer focus on location behavior at the city level (Head and Ries, 1996; He, 2002; and He, 2003).

However, there seems to have been no research into the location behavior of service-oriented firms. Building on previous theoretical and empirical findings from studies on manufacturing firms, an attempt will be made to determine the location behavior of foreign firms in the logistics sector. Dunning (1989) reviewed the conceptual and theoretical issues of applying the eclectic theory of international production to explain the behavior of service MNCs. Some researchers have argued that the theories of MNCs and FDI can be applied to service multinationals, bearing in mind some distinctive characteristics of international services (Boddewyn et al., 1986). Empirically, efforts have also been made to apply FDI theory to manufacturing and service industries (Dunning and McQueen, 1982; Terpstra and Yu, 1998; and Li and Guisinger, 1992). The results have been encouraging in that many of the theories about the locational determinants of manufacturing MNCs are also applicable to the locational decisions of service-oriented MNCs.

The logistics sector is the focus of this study for several reasons. First, between 1993 and 2001, the contribution of the transportation, storage, post and telecommunications sector to GDP grew from RMB17 billion to RMB522 billion. Hong et al. (2004) observed that, although the logistics market in China is still in its infancy, the speed and potential for development have made it attractive to investors. Secondly, the government has recognized the sector as being important for the national economy and business. Cities such as Beijing, Tianjin, Shanghai, Shenzhen, and Guangzhou aspire to be regional or international logistics hubs and have strategically adopted preferential investment and fiscal policies to attract logistics FDI. Finally, foreign investment will continue to play an important role in the logistics sector. From 1996 to 2001, the foreign capital invested in the area of transportation, storage, post and telecommunications increased from US$6.96 billion to US$15.16 billion.


The multinomial logit model is employed here. Each firm is assumed to locate where expected profits are highest from a range consisting of N cities. Thus, city j is chosen by the firm if, and only if,

where, Пij denotes the profit of the firm i at location city j. Following Bartik (1985) and Chen (1996), it is assumed that profit is a function of the characteristics of the location and a disturbance term:

where c is a constant, Xj is a vector of observable characteristics of city j, and εij is an error term. If ij is independently and identically distributed as a Weibull density function, and the

alternatives are assumed to satisfy the “independence of irrelevant alternatives (IIA)” property, the probability of firm i choosing city j can be described by the following equation:

Based on the principle of utility maximization, the advantages of a simple mathematical structure and ease in estimation means that multinomial logit models have been widely used in the study of industrial location (Coughlin et al., 1991; Woodward, 1992; Chen, 1996; Head and Ries, 1996; Head et al., 1999; and Cheng and Kwan, 2000).

Data Sources and Model Rationalization

Data is drawn from the Second Census on All Basic Units in the People's Republic of China (PRC). Logistics firms are defined as those conducting logistics services, including all modes of freight transport, warehousing, and arrangement of freight (or cargo) transportation.

To treat all cities as alternatives is impractical, given that there are more than 200 Chinese cities at prefecture level or higher. Fortunately, McFadden (1978) suggests a methodology to limit the number of alternatives considered while still obtaining consistent estimates of the parameters. One way is to choose a fixed sample from the full choice set, independent of the observed choice. In this chapter, to make data collection and estimation manageable and following Head and Ries (1996), only those cities that received at least five foreign logistics investments during the period 1992–2001 are chosen. Based on these criteria, 1,175 foreign-funded logistics firms were selected and distributed over forty cities.

The explanatory variables for location attributes are obtained primarily from the Urban StatisticalYearbook of China and the StatisticalYearbook of China. The definitions and descriptions of these variables are given in the Appendix. The influence of transport, market size, the labor market, government policy, privatization and the structure of the urban economy, information cost, agglomeration, and urbanization economies will be analyzed.

The impact of transport has been widely studied (Bartik, 1985; Coughlin et al., 1991; Woodward, 1992; Smith and Florida, 1994; Chen, 1996; Head and Ries, 1996; Mcquaid et al., 1996; Cheng and Kwan, 2000; and Coughlin and Segev, 2000). It has been suggested that transportation access to raw materials and markets is the central element in location choice. Although most studies conclude that transportation is important for industrial location, some found it a very important element in choice (Head and Ries, 1996; Cheng and Kwan, 2000; and Bartik, 1985). Recent conceptual and empirical research suggests that the importance of transportation may diminish due to the rise of new transportation and communication technologies and globalization (Glickman and Woodward, 1989). Previous studies have also revealed that the impact of transport on the location decision varies with firm-specific characteristics, such as firm size, type of industry, and ownership (Hayashi et al., 1986; Button et al., 1995; and Leitham et al., 2000). To understand the influence of transport network capacity, this chapter will investigate the impact of each mode of transport, including waterways, airways, and road and railway transport.

Given revenue considerations, one primary factor affecting firms’ location decisions is the size of the market. Local authorities in China concerned with local economic growth often impose barriers to keep out goods and services from competing provinces, such as the imposition of employment and tax revenue quotas and tariff and non-tariff barriers (Jiang and Prater, 2002). Two variables are used in this study as proxy for the demand for logistics services at the provincial level: provincial foreign industrial output (LFORALL1), and provincial overall industrial output (LINDOUT1). The market size is expected to have a positive effect on firms’ location.

In a period of transition, the level of privatization may reflect a city's degree of marketization and openness. The percentage of employment in private enterprises is used as a measure of the level of privatization (LPRIVATE1). It is expected to be positively correlated with FDI in logistics. The variable LSEC, the employment percentage of the secondary industry, has been used as an index of the level of development of the secondary industry in an urban economy. The sign is uncertain.

Government tax and land use policy do impact on the location of FDI. Since 1978, the Chinese government has adopted a series of preferential policies to attract FDI. Initially, foreign investment was limited to four Special Economic Zones (SEZs). FDI promotion was broadened in 1984 to include fourteeen coastal cities, or Open Coastal Cities (OCCs). The government has also set up free trade zones (FTA), where some preferential policies are used to attract foreign logistics firms. Finally, provincial capital cities offer a number of incentives for foreign investment (CAPITAL). A variety of investment incentives, such as tax reduction, easy market entry, and SEZs and OCCs have been included as a group, where OCC is a dummy variable.

While China is often perceived as a low-wage economy, disparities exist across cities and provinces. Higher wage rates are expected to deter foreign investment. Previous studies on FDI location in China have confirmed this proposition (He, 2003; Coughlin and Segev, 2000; Wei et al., 1999; and Cheng and Kwan, 2000). However, it must be noted that higher wage rates and greater productivity are correlated. Furthermore, empirical studies on wage returns to labor are sometimes complicated by the lack of data. Labor cost often does not have a significant impact (Carlton, 1983; and Chen, 1996). Here, two variables, LWAGE and LTECHPOP, are used to measure labor cost and quality, respectively.

The availability and processing of information is becoming increasingly important in location choice and this is shown by the response of MNCs in the 1990s (Dunning, 1998). Foreign investors in China may encounter external uncertainties and business risks caused by an economy undergoing transition. Asymmetric information leads to high costs (He, 2002). The number of telephones per 100 people (TELEPH) was used to measure the quality of information infrastructure.

Agglomeration economies are also important in drawing foreign investment. Foreign investors may be attracted to areas with existing concentrations of foreign-owned firms in order to reduce uncertainty as well as to share spillovers from agglomeration economies (Guimaraes et al., 2000). The number of foreign direct investments in the corresponding year is used as an index of foreign-related agglomeration economies.

Urbanization too has an effect on firms’ location in the following ways. Firstly, urbanization yields positive external benefits through economies of scale, better infrastructure, and so forth. However, this may lead to higher factor costs, which deter foreign investment. Earlier studies have shown that service firms tend to cluster in large metropolitan areas (Harrington and Warf, 1995), which implies that urbanization has a positive effect on logistics firms’ location. Urban population density (LPOPDEN) is taken as a measure of the degree of urbanization.


The key empirical results of the study using NLOGIT 3.0 are reported in this chapter. The estimation proceeds by maximizing the likelihood of the alternative choices made by the 1,175 firms in the sample. Overall, these models performed well. Since the underlying profit function is log-linear, the coefficients of the explanatory variables can be interpreted as elasticities. Recognizing the large number of potential independent variables and the problem of multicolinearity, the results of four models were finally chosen. The variables included in the model are defined in the Appendix.

Results from Generic Models

In Table 8.3, road and railway density are positive and statistically significant. In addition, BERTHDUM and LROADEN, which are measures of urban sea and road capacity are positively correlated and significant. The presence of an airport (AIRDUM) was not an important consideration. These findings support to some extent the proposition that transportation network capacity does matter in attracting FDI in logistics. Foreign and industrial outputs, the proxies for market demand, are positive and important. The coefficient estimate of 0.40 for LINDOUT1 and 0.10 to 0.31 for LFORALL1 suggest that logistics FDI is very responsive to market demand.

The results also show that higher wage rates deter FDI, with an elasticity of–0.52. This wage elasticity is lower than the–0.9 estimate by Bartik (1985) and substantially lower than–4.4 by Coughlin et al. (1991). As expected, higher labor quality attracts foreign investment. The proportion of technical staff (LTECHPOP) was used as a measure of labor quality in the city and the impact was found to be positive and significant.

Earlier studies have shown that foreign investors are inclined to favor the locations that offer a variety of agglomeration economies (Head et al., 1999; Guimaraes et al., 2000; and He, 2002). The number of foreign direct investments in a city was used in this study to capture foreign-specific agglomeration. The impact of this variable is significant, with elasticity measuring between 0.47 and 0.64. Since FDI location decisions are made under conditions of uncertainty, investors would tend to locate in a city with a large number of existing foreign direct investors to take advantage of

Table 8.3 Results for cities with at least five logistics FDIs
 Model 1Model 2Model 3Model 4
βT stat.βT stat.βT stat.βT stat.
LINDOUTI      0.40*”7.43
LPOPDEN  0.11 **3.470.09***2.760.051.57
LTELEPH  0.12*1.680.111.570.30*”4.10
LRAIDEN1  0.46**11.080.46***10.950.58*”10.89
CAPITAL    0.38***
Res. Log-L–6548–6548–6548–6548
R2 Adj.0.2940.3010.3010.299

information exchange and gain access to a common pool of skilled labor. As with the earlier studies, population density is used as an index of urbanization. The results suggest a positive influence. Uncertainties and information asymmetry faced by foreign investors push firms to locate in a city where information costs are minimized (He, 2002). The number of telephones per 100 people in a city was used as a proxy for information infrastructure, and this was found to exert a positive influence on the location decision.

Three dummy variables were used in an attempt to capture the influence of government policy: OCC, FTA, and CAPITAL. It was found that logistics FDI was more likely to locate in a city with a free trade zone, and in provincial capitals or major cities. However, the preferential policies provided through OCC have no significant impact.

LSEC and LPRIVATE1 measure, respectively, the proportion of secondary industry in the whole economy and the degree of urban economic privatization. The first variable was found to significantly deter FDI, while the latter attracted it. In big cities, LSEC could be taken as a counter of the proportion of tertiary industry in an economy. The concentration of business services is another type of agglomeration that may attract FDI, as suggested by Guimaraes et al. (2000). The level of economic privatization is also important for foreign investors in transitional economies, such as China. Obviously, foreign investors prefer a location with a higher degree of privatization.

Table 8.3 indicates that models 2 and 3 give the best estimation in terms of the adjusted R-square. For simplicity, a study is made of the competitiveness of a city in attracting logistics FDI based on model 2. Competitiveness in this chapter is defined as the probability that a city was chosen by representative foreign logistics firms. The ranking of forty Chinese cities in terms of their competitiveness in 2001 are reported in Table 8.4. The top six cities—Shanghai, Tianjin, Shenzhen, Guangzhou, Beijing, and

Table 8.4 Competitiveness of Chinese cities in attracting logistics FDI

Qingdao—receive almost 70 percent of all logistics FDI. This figure is quite consistent with observations based on the census data (73 percent). The variation in the degree of competitiveness of these six cities between 1995 and 2001 is plotted in Figure 8.2. It reveals that the competitiveness of Shanghai is highest and still increasing while that of Tianjin and Guangzhou is decreasing. The competitiveness of Shenzhen decreased after 1998, while that of Beijing and Qingdao was almost unchanged during this period.

The robustness of the specifications was tested by applying the same specifications to subsamples of the data set. The results for the regressions computed on the subsamples are reported in Tables 8.5 and 8.6. Despite the smaller size of the samples, the magnitudes of the coefficient estimates are remarkably consistent across sample sets. This, combined with the near unanimity of the signs and the statistical significance of the coefficients estimated, leads to the conclusion that the results are robust, and not confined to the initial sample chosen. One interesting finding is that when the number of location choice alternatives decreases, open coastal cities (OCC) become more attractive while capital cities (CAPITAL) lose their advantage in attracting logistics FDI.

Differences Between Foreign and Oversees Chinese Firms

In order to study country of origin effects, the observations were classified into two groups: foreign-owned firms and those from Hong Kong, Macao, and Taiwan (HMT). Earlier studies have shown that location behavior may be different for foreign-funded and HMT firms. There is a widespread use of Hong Kong companies by mainland firms in order to take advantage of preferential policies provided to foreign investors by the

Table 8.5 Results for cities with at least seven logistics FDIs
 Model 1Model 2Model 3Model 4
βT stat.βT stat.βT stat.βT stat.
LINDOUTI      0.36**5.85
LPOPDEN  0.09**2.180.08*1.870.071.62
LTECHPOP0.39**5.690.44*”6.800.41 *”6.340.45**6.99
LTELEPH  20.0220.2620.0120.120.15*1.85
LSEC20.60**2 3.3220.49*”2.9420.47*”2.7920.46**2 2.68
LRAIDEN1  0.41 *”8.770.41 *”8.830.48**8.12
FTA0.28**2.620.58*”5.540.51 *”4.570.84**7.25
CAPITAL    0.070.5120.2021.58
Res. Log-L5698569856985698
R Adj.0.2780.2830.2830.280

Chinese government. Moreover, HMT firms are more likely to locate in southern China, especially Guangdong and Fujian province, since they share a similar language and culture and also value the proximity to their parent company.

The locational determinants of foreign-owned and HMT firms are given in Tables 8.7 and 8.8, respectively. The results indicate that location behavior is different in some aspects. Foreign firms favor the location with a higher population density, lower labor cost, convenient air transport, and capital cities. It is surprising that better communication infrastructure (LTELEPH) deters foreign firms. One possible interpretation is that the LTELEPH variable captures some effects of wage rates. Better communication infrastructure is normally accompanied by higher wage rates. The results also show that firms from HMT put more emphasis on communication infrastructure.

Table 8.6 Results for cities with at least ten logistics FDIs
 Model 1Model 2Model 3Model 4
βT stat.βT stat.βT stat.βT stat.
LINDOUTI      0.45**7.04
LPOPDEN  0.09**2.040.08*1.910.071.52
LTELEPH  20.07–0.8620.06–0.790.15*1.72
LSEC20.75*”24.1020.46*”2.6620.51***2 2.8320.58**2 3.21
LRAIDEN1  0.39*”7.950.40**8.030.56**8.52
FTA0.131.190.52*”5.000.54**5.050.91 **8.09
CAPITAL    20.2121.3720.48**2 3.30
Log-L2 37202 370036992 3701
Res. Log-L2 50422 50422 50422 5042
R2 Adj.0.2620.2660.2660.266
Table 8.7 Results for foreign-owned firms
Foreign firmsModel 1Model 2Model 3Model 4
βT stat.βT stat.βT stat.βT stat.
LFORALLI0.091.540.21 **4.120.24*”4.42  
LINDOUTI      0.35***4.02
LPOPDEN  0.32**5.670.32*”5.570.29***4.82
LTELEPH  20.21*21.8920.22**21.9820.04***4.58
LWAGE2 1.46*”5.57      
LSEC BERTHDUM20.53** 0.79*”2.39 5.0720.31 0.55**21.48 3.5520.32 0.59*”21.51 3.7620.40* 0.55***21.85 3.49
Log-L21642 21192 21192 2119
Res. Log-L2 31842 31842 31842 3184
R2 Adj.0.320.330.330.33
Table 8.8 Results for firms owned by Hong Kong, Taiwan, and Macao (HMT)
 Model 1Model 2Model 3Model 4
βT stat.βT stat.βT stat.βT stat.
LINDOUTI      0.43**6.02
LPOPDEN  0.0070.1720.0220.54–0.0621.40
LTELEPH  0.29***3.110.28***2.960.47**4.75
LSEC20.2521.3120.54***2.9920.51***2.7620.63**2 3.33
BERTHDUM0.61 **3.820.49***3.030.51***3.200.41 **2.49
LRAIDEN1  0.41 ***7.200.40***7.030.53**7.36
CAPITAL    0.221.1520.1220.68
Log-L2397239123912 2403
Res. Log-L3364336433643364
R2 Adj.0.2870.2890.2890.285


This chapter examined the location determinants of logistics FDI, as well as country (or region) of origin effects using a multinomial logit model. The results indicate that the location of logistics FDI depends on the transport infrastructure, market size, labor quality and cost, agglomeration economies, communication cost, the degree of economic privatization, and government policy incentives. Moreover, the importance of these factors varies with the source of country or region. Foreign firms favor a location with a higher population density, lower labor cost, convenient air transport, and capital cities while logistics firms from Hong Kong, Macao, and Taiwan put more emphasis on communication infrastructure.


Bartik, T. 1985. Business location decisions in the United States: Estimates of the effects of unionization, taxes, and other characteristics of states. Journal of Business and Economic Statistics 3(1): 14–22.

Boddewyn, J., M. Halbrich, and A. Perry. 1986. Service multinationals: Conceptualization, measurement and theory. Journal of International Business Studies Fall: 41–57.

Button, K., S. Leitham, R. McQuaid, and J. Nelson. 1995. Transport and industrial and commercial location. The Annals of Regional Science 29: 189–206.

Carlton, D. 1983. The location and employment choices of new rms: An econometric model with discrete and continuous endogenous variables. The Review of Economics and Statistics: 440–49.

Chen, C. 1996. Regional determinants of foreign direct investment in mainland China/Journal of Economic Studies 23 (2): 18–30.

Cheng, L., and Y. Kwan. 2000. What are the determinants of the location of foreign direct investment? The Chinese experience. Journal of International Economics 51: 379–400.

Coughlin, C., and E. Segev. 2000. Foreign direct investment in China: A spatial econometric study. World Economy 23: 1–23.

Coughlin C., J. Terza, and V. Arromdee. 1991. State characteristics and the location of foreign direct investment within the United States. The Review of Economics and Statistics LXXIII (4): 675–83.

Dunning, J. 1998. Location and the multinational enterprises: A neglect factor? Journal of International Business Studies 29 (1): 45–66.

Dunning, John H. 1989. Transnational corporations and growth of services: Some Conceptual and theoretical issues. New York: United Nations.

Dunning, J., and M. McQueen. 1982. The eclectic theory of the multinational enterprise and the international hotel industry. In New theories of multinational enterprise, ed. Allen M. Rugman.New York: St. Martin's.

Glickman, N., and D. Woodward. 1989. The new competitors: How foreign investors are changing the U.S. economy. New York: Basic Books.

Guimaraes, P., O. Figueiredo, and D. Woodward. 2000. Agglomeration and the location of foreign direct investment in Portugal. Journal of Urban Economics 47: 115–35.

Harrington, J., and B. Warf. 1995. Industrial location: Principles, practice and policy. London and New York: Routledge.

Hayashi, Y., T. Isobe, and Y. Tomita. 1986. Modeling the long-term effects of transport and land use policies on industrial locational behavior. Regional Science and Urban Economics 16 (1): 123–43.

Hayter, R., and S. Han. 1997. Reactions on China's open policy towards foreign direct investment. Regional Studies 32 (1): 1–16.

He, C. 2002. Information costs, agglomeration economies, and the location of foreign direct investment in China. Regional Studies 36 (9): 1029–36.

He, C. 2003. Location of foreign manufacturers in China: Agglomeration economies and country of origin effects. Studies in Regional Science 82: 351–72.

Head, C., and J. Ries. 1996. Inter-city competition for foreign investment: Static and dynamic effects of China's incentive areas. Journal of Urban Economics 40: 38–60.

Head C., J. Ries, and D. Swenson. 1999. Attracting foreign manufacturing: Investment promotion and agglomeration. Regional Science and Urban Economics 29: 197–218.

Hong, J., A. Chin, and B. Liu. 2004. Logistics outsourcing by Chinese manufacturer: A survey of the industry. Transportation Journal.

Jiang, B., and E. Prater. 2002. Distribution and logistics development in China: The revolution has begun. International Journal of Physical Distribution and Logistics Management 32 (9): 783–98.

Leitham, S., R. Mcquaid, and J. Nelson. 2000. The inuence of transport on industrial location choice: A stated preference experiment. Transportation Research Part A 34: 515–35.

Li, J., and S. Guisinger. 1992. The globalization of service multinationals in the “triad” regions: Japan, Western Europe and North America.Journal of International Business Studies Fourth Quarter: 675–96.

Luo, Y. 1997. International investment strategies in the People's Republic of China. Dartmouth: Dartmouth Publishing Co.

McFadden, D. 1978. Modeling the choice of residential location. In Spatial interaction theory and planning models, ed. A. Karlquist.Amsterdam: North Holland.

Mcquaid, R., S. Leitham, and J. Nelson. 1996. Accessibility and location decisions in a peripheral region of Europe: A logit analysis. Regional Studies 30 (6): 579–88.

Smith, D., and R. Florida. 1994. Agglomeration and industrial location: An econometric analysis of Japanese-afflliated manufacturing establishments in automotive-related industries. Journal of Urban Economics 36: 23–41.

Sun, H., and A. Parikh. 2001. Exports, inward foreign direct investment (FDI) and regional economic growth in China. Regional Studies 35 (3): 187–96.

Terpstra, V., and C. Yu. 1998. Determinants of foreign investment of U.S. advertising agencies. Journal of International Business Studies Spring: 33–46.

Wei, Y., X. Liu, D. Parker, and K. Vaidya. 1999. The regional distribution of foreign direct investment in China. Regional Studies 33 (9): 857–67.

Woodward, D. 1992. Locational determinants of Japanese manufacturing start-ups in the United States. Southern Economic Journal 58 (3): 690–708.

Appendix: Definitions and descriptions of explanatory variables
AIRDUM1 for city with airport; 0 for others0.660.001.00
BERTHDUM1 for city with deep-water seaport; 0 for others0.500.001.00
CAPITAL1 for Beijing or the capital of a province0.530.001.00
FTA1 for city with free-trade zone; 0 for others0.230.001.00
LFDINatural logarithm of number of FDI5.751.798.66
LFORALL1Natural logarithm of provincial foreign and HMT output6.721.679.11
LINDOUT lNatural logarithm of provincial industrial output8.554.649.55
LPOPDENNatural logarithm of urban population density0.37–4.433.18
LPRIVATE1Natural logarithm of provincial percentage of private employment0.98–1.833.38
LRAIDEN1Natural logarithm of provincial railway density–0.23–2.521.92
LROADENNatural logarithm of urban roadway density1.00–7.215.57
LROADEN1Natural logarithm of provincial road density3.470.304.36
LSECNatural logarithm of employment percentage of the secondary industry3.642.074.23
LTECHPOPNatural logarithm of urban percentage of technical workers–3.80–6.500.00
LTELEPHNatural logarithm of number of telephones of 100 persons2.56–0.114.89
LWAGENatural logarithm of average wage rate8.957.7710.04
OCC1 for SEZ or OCC; 0 for others0.380.001.00

About this article

The Location Decisions of Foreign Logistics Firms in China: Does Transport Network Capacity Matter?

Updated About content Print Article