canfei he -foreign manufacturing investment in china- the role of industrial agglomeration and...

8/14/2019 Canfei He -Foreign Manufacturing Investment in China- The Role of Industrial Agglomeration and Industrial Linkages

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82 China & World Economy / 8299, Vol. 16, No. 1, 2008

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Journal compilation 2008 Institute of World Economics and Politics, Chinese Academy of Social Sciences

Foreign Manufacturing Investment in

China: The Role of Industrial

Agglomeration and

Industrial Linkages

Canfei He*

Abstract

This paper investigates the forces that determine the industrial distribution of foreign

manufacturing investment. It highlights the importance of industrial agglomeration and

industrial linkage in attracting foreign investment to manufacturing industries.Using panel

data for two-digit manufacturing industries in Beijing during the period of 19992004, this

study finds that geographically agglomerated industries with strong intra-industrial linkages

are indeed attractive to foreign investment. Previous foreign investment has led to the

current industrial concentration of foreign investment. Investors also favor capital-intensive

and technology-intensive industries, and they tend to be attracted to the most profitable and

exporting industries, but avoid industries with high real labor costs and high entry barriers.

Competitive local industries that possess comparative advantages are critical for attracting

foreign investment. The existence of ind ustrial clusters certain ly enhan ces a city !s

attractiveness to foreign investment.

Key words:Beijing, foreign direct investment, industrial agglomeration, industrial linkage

JEL codes:F21, L60, R10

I. Introduction

With the liberalization of foreign direct investment (FDI) restrictions and the acceleration of

economic reform, there has been a remarkable surge of FDI inflows into China since the late

1970s. As the largest developing host economy of FDI, China approved 552 942 foreign

* Canfei He, Associate Professor, Department of Urban and Regional Planning, Peking University,

Beijing, China. Email: [email protected]. This research was supported by a grant from the

Natu ral Science Foundation of China (No. 40401015).
mailto:[email protected]:[email protected]


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83The Role of Industrial Agglomeration and Industrial Linkages in China

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projects, with an accumulatively realized FDI value of US$622.43bn during the period of

1979!2005. In 2005, China attracted 18 percent of the US$334bn total FDI flowing into the

developing economies (UNCTAD, 2006).

Over the past two decades, there have been significant changes in the sectoral distribution

of FDI in China. In the early 1980s, a large part of FDI in China was directed to geological

prospecting, real estate and tourism. Foreign projects in manufacturing sectors were only

concentrated in labor-intensive sectors, such as food, electronics, construction materials, textiles

and toys. In the late 1980s, foreign manufacturing investment accounted for more than 70

percent of the total FDI flowing into China; investment continued to increase rapidly, reaching

more than 80 percent around 1990 (NBS, 1991). With increasing experience in the Chinesemarket, and accumulated knowledge about China"s industrial structure, foreign firms extended

their business scope into physical infrastructure facilities, including construction, energy,

transportation and capital-intensive and technology-intensive machinery and equipment. Such

investments involved more technological inputs, higher start-up costs, and larger financial

commitments and, therefore, foreign firms faced greater risks. With China"s accession to the

WTO, other sectors, especially services, have become popular to foreign investors.

In China, FDI is highly agglomerated. It favors cities where targeted industries are

fairly developed (Belderbos and Carree, 2002). Marshall (1898) proposes that the pool of

specialized skilled labor, trade of intermediate inputs, and spillovers were driving forces of

industrial agglomeration. Porter (2000) argues that industrial clusters increase the productivityof constituent firms, upgrade the capacity of cluster participants for innovation and

productivity growth, and stimulate new business formation. Because of the lack of local

knowledge, foreign investors encountered so-called #disadvantage of an alien status$in

China. Industrial clusters have helped foreign investors to attenuate these disadvantages

(He, 2002, pp.1030). Therefore, foreign investors like to select geographically agglomerated

industries with strong localized business linkages.

Traditional FDI theories (Hymer, 1976; Kojima, 1978; Dunning, 1980) suggest that

industrial distribution of foreign investment depends on comparative advantages in host

economies and the ownership-specific advantages that multinational corporations (MNCs)

hold. Dunning (2000) argues that factors influencing MNC industrial choices have gonebeyond the natural endowments in the era of globalization, and that benefits from industrial

agglomeration are playing an increasingly important role. Therefore, industrial distribution

of FDI in host economies might be influenced by industry-specific external economies,

which arise from geographical proximity of related firms and localized business linkages.

There is some published literature concerning industrial patterns of FDI in host

economies. Caves (1974), for instance, considers foreign firms"shares in Canadian and UK

manufacturing industries in the 1960s and emphasizes the importance of intangible capital,


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advantages accruing from the operation of multiplant enterprises and the strength of

entrepreneurial resources. Ratnayake (1993) concludes that foreign ownership of industry

tends to be higher in skill-intensive and technology-intensive industries and those in

industries enjoying high-level protection in Australia. Aswicahyono and Hill (1994) examine

determinants of foreign investment shares in the Indonesian manufacturing sector and find

product differentiation, technological capacity, skill intensity, absolute capital requirements,

economies of scale and domestic policy regime to be significant in driving FDI. Driffield

and Munday (2000) investigate the relationship between comparative advantages in UK

industries and new inward investment into these industries. They find revealed industrial

comparative advantage, R&D intensity, advertising intensity, presence of economies ofscale, past profits, absolute sales and industrial growth to be important in attracting FDI.

Furthermore, strong indigenous capital investment and industrial concentration have

discouraged inward FDI.

Because of data limitations, few studies have examined the industrial distribution of

foreign investment in China. Huang (1999) determines that foreign enterprises tend to

populate industries with a low ratio of#knowledge workers$, low value added per employee,

high sales expenses, high product differentiation, and industries characterized by a large

absolute capital requirement and significant economies of scale. Controlling for industrial

characteristics, foreign enterprises populate those industries in which state-owned

enterprises incur low profit margins, carry high debt on their books, and have a greatdegree of local control. Overall, the existing studies on industrial distribution of FDI concern

mostly traditional industrial attributes, and have not paid sufficient attention to the

importance of industrial agglomeration and industrial linkages.

Taking Beijing as an example, the present study investigates the determinants of

industrial distribution of foreign manufacturing investment and stresses the role of industrial

agglomeration and industrial linkages in attracting FDI. This paper is structured as follows.

Section II presents a theoretical perspective on the industrial distribution of FDI. Section

III discusses the geographical agglomeration of manufacturing industries in Beijing. Section

IV investigates determinants of industrial distribution of foreign manufacturing industries,

highlighting the role of industrial agglomeration and linkages. Finally, our major findingsand conclusions are provided in Section V.

II. Industrial Distribution of Foreign Direct Investment:

A Theoretical Perspective

Theoretically, industrial distribution of FDI depends on the monopolistic advantages of


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MNCs or comparative advantages in host economies (Hymer, 1976; Kojima, 1978; Dunning,

1980, 2000). In the era of globalization, the importance of resource endowments has been

declining, whereas localized industrial competitive advantages have become increasingly

important for industrial development. According to Porter (1990), competitive advantages

of local industries depend on four factors, which can be effectively represented by a

diamond-shaped diagram (see Figure 1). These four factors are factor conditions, demand

conditions, context for firm strategy and rivalry, and presence of competitive related and

supporting industries. In addition, external factors and government also play an important

role in influencing industrial competitive advantages. Dunning (1993) criticizes Porter for

ignoring the significant role of MNCs in improving industrial competitive advantages andinternationalize Porter"s diamond by adding MNC influence. The original interrelated

influences in Porter"s diamond in the host economies all affect the investment decisions of

foreign investors, including decisions regarding location, entry mode and industrial selection

(see Figure 1). The present study emphasizes the importance of competitive-related and

supporting industries in attracting foreign investment.

Clustered firms benefit significantly from a pool of skilled workers, trade of intermediate

inputs, and easy flow of information and ideas among firms (Marshall, 1898). More

specifically, Henderson (1986) points out that localized external economies relate to four

elements: (i) economies of intra-industry specialization where increased industry size permits

greater specialization among firms in addition to a greater availability of specializedintermediate inputs suppliers, business services and financial markets; (ii) labor market

Figure 1. Local Industrial Competitiveness

and Multinational Corporations (MNCs)

Sources: Porter (1990) and Dunning (1993).

Related and supporting industries

Firm strategy and rivalry

Factor conditions Demand conditions

Chance MNCs

Government

Related and supporting industries

Firm strategy and rivalry

Factor conditions Demand conditions

Chance MNCs

Government


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economies resulting from a larger pool of trained, specialized workers reduce searching

costs for skilled labors; (iii) economies of scale for networking among firms; and (iv)

economies of scale in providing public goods and services that have been tailored to the

needs of a specific industry. Venables (1996) identifies vertical linkages between upstream

and downstream firms as forces for agglomeration of industries in one location. Upstream

firms are close to downstream firms as these are the main sources of demand for their

goods. Downstream firms want to be close to a large number of upstream firms because this

allows them to find cheaper intermediate inputs. For example, car components firms are

located close to car assembly firms as a result of demand linkages and car assembly firms

are close to car components firms because of cost linkages. Demand and cost linkagesreinforce each other, leading to the agglomeration of car components and car assembly

firms in one location (Amiti, 1998).

Geographical proximity of raw material suppliers, intermediate goods and professional

services can significantly lower production and transaction costs, and improve

productivity and industrial competitiveness (Porter, 2000). Benefits from the geographical

proximity and business linkages of related firms are particular ly important for foreign

investors, whose decision-making processes are considerably affected by internal and

external uncertainties. Business risks usually arise from inadequacy of information or

from an unpredictable business environment. Unlike domestic investors, foreign investors

encounter significant disadvantages, such as lack of local knowledge of social, politicaland economic conditions and lack of stable intermediate suppliers. Foreign investors

face additional business risks in transitional economies because of unpredictable policy

and institutional changes. Information asymmetry and business uncertainties, however,

can be attenuated by entering geographically agglomerated industries with strong localized

business linkages (He, 2002).

Studies have showed that foreign investors could significantly benefit from industrial

clusters (Head and Ries, 1996; Kinoshita and Mody, 2001; Belferbos and Carree, 2002;

Yeung et al., 2006). First, foreign investors could gain market information from related firms

and other foreign firms, especially information from the earlier entrants concerning the

performance of labor markets, the foreign investment regulatory policies, partnershipselection strategies, and access channels to the local labor market, distribution channels of

commodities, infrastructure and raw materials required for the business operation (Kinoshita

and Mody, 2001; He, 2003). Second, foreign firms can realize economies of scale by taking

advantage of business networks as suppliers or buyers, thus lowering costs through

localized trade linkages, and avoiding uncertainties of input supply and market demand

(Belderbos and Carree, 2002; Yeunget al., 2006). Third, foreign firms can share transportation

and telecommunication infrastructure and professional services, such as training, logistics


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and governmental services, with related firms (Head and Ries, 1996). Finally, geographically

agglomerated industries tend to develop strong local business linkages. Therefore, foreign

investors could easily join localized business networks by entering these industries, reducing

transaction costs and business risks (Yeung et al., 2006)

The above discussion shows that industrial agglomeration and linkage could reduce

production and transact ion costs and business risks through market and nonmarket

mechanisms. Foreign investors favor geographically agglomerated industries and those

with strong localized business linkages to attenuate their disadvantages of being foreigners

or late-comers. The following section will test this theoretical proposition using foreign

manufacturing investment data for Beijing during the period of 1999!

2004.

III. Geographical Agglomeration of Manufacturing

Industries in Beijing

1. Measurement of Industrial Agglomeration

This section applies the index proposed by Maurel and Sedilot (1999) to quantify the

degree of industrial agglomeration. This index has a more natural specification than other

similar indices, and it is derived directly from the probability model (Maurel and Sedilot,

1999). The empirical index proposed by Maurel and Sedilot (1999) is as follows:

2 2 2

^( ) /(1 )

1 1

i i i

i i i MS MS

s x x HG H

H H

= =

, (1)

where ix and is are the fractions of aggregate employment and industry employment in

question located in region i.His the industry Herfindahl index, defined as: =

=N

j

jzH1

2 , where

jz is the share of employment in plantj. MSG measures the raw geographical concentration

of industries. A positive value of means that clustering forces dominate dispersing

forces and related plants are agglomerated. A negative value of indicates that plants are

scattered. The index can be interpreted as the excess of raw geographic concentration on

productive concentra tion and, therefore, can be regarded as an index of industr ial

geographical agglomeration, controlling for the distribution of employment size in

enterprises (Maurel and Sedilot, 1999). With this index, an industry will not be considered

as localized only because its employment is concentrated in a small number of plants.


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2. Testing Geographical Agglomeration of Manufacturing

Industries in Beijing

MS , MSG and H were computed for each of two-digit, three-digit and four-digit industries

at the county level using data from the 1996 and 2001 census of basic units in Beijing (NSB,

1998, 2003). Strong positive correlations between MSG andHare confirmed, indicating that

geographic concentration of industries can be largely accounted for by industrial

concentration. The correlation coefficients between MSG andHin 1996 were 0.85 and 0.86

in 2001 for three-digit classified industries and 0.90 in both 1996 and 2001 for four-digit

classified industries. The correlation coefficient between MS andHwas much weaker and

insignificant.

Table 1 presents the overall agglomeration of Beijing manufacturing industries. The

more disaggregated industries are more geographically agglomerated. In 1996, the mean

values of MS were 0.07, 0.08 and 0.09 for two-digit, three-digit and four-digit classified

industries, respectively. The mean values of MS in 2001 were 0.04, 0.05 and 0.07. They are

greater than 0.02, indicating that manufacturing industries in Beijing are fairly agglomerated.

On average, industries were more dispersed in 2001. Based on classification standards

proposed by Ellison and Glaeser (1997), approximately 12.36 percent of four-digit industries

belonged to the category of #somewhat agglomerated$(0.02 < < 0.05) and 35.74 percent

of them were #very agglomerated$( > 0.05) in 1996, whereas the corresponding values

were 11.41 and 36.88 percent in 2001.

Industrial distribution of foreign manufacturing investment and industrial agglomeration

at the two-digit level was the main focus of the present paper and the corresponding MS

Table 1. Result of Agglomeration Indices at Various

Disaggregated Levels

?MS Number of industries

4-digit Mean Skewness Kurtosis MS< 0 0 < MS< 0.02 0.02 < MS< 0.05 MS> 0.05

1996 0.09 2.46 6.24 192 80 65 188

2001 0.07 2.79 9.59 209 62 60 194

3-digit

1996 0.08 2.62 8.36 45 30 28 62

2001 0.05 3.33 14.26 62 32 21 50

2-digit

1996 0.07 3.82 16.10 4 10 5 10

2001 0.04 4.32 21.00 6 12 7 4

MS

Source:Calculated by the author.


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are listed in Table 2. At the county level, there were 25 industries with positive MS in 1996.

The most spatially agglomerated industries include petroleum refining and coking, ferrous

metal processing and smelting, electronics and telecommunication equipment, timber

processing, textile, medical and pharmaceutical products, special purpose machinery,

transportation equipment, furniture making, and the chemical industry, all with MS greater

than 0.05. Some industries are characterized by strong internal economies of scale, stimulating

the formation of industrial clusters around the core enterprises. For instance, many

enterprises located around the Shoudu Iron and Steel Company conduct activities associated

with ferrous metal processing and smelting industry in the Shijingshan district. Othershave strong industrial linkages, and benefit from localized linkages and external economies

of scale, stimulating related firms to agglomerate in a given area. Electronics and

telecommunication equipment manufacturing is one such industry. Tobacco processing,

beverages, chemical fiber, papermaking and paper products and miscellaneous products

are the most spatially dispersed. These industries are either close to their localized markets

or they require local resource inputs or support by local governments.

During the period between 1996 and 2001, most manufacturing industries in Beijing

became more spatially dispersed. In 2001, only ferrous metal processing and smelting,

electronics and telecommunication equipment, petroleum refining and coking and special

purpose machinery were #very agglomerated$, with MS greater than 0.05. The number of

#very agglomerated$industries was 10 in 1996. The MS for petroleum refining and coking

fell from 0.78 in 1996 to 0.13 in 2001. Chemical materials and products, medical and

pharmaceutical products, timber processing and furniture making, textile, and transportation

equipment moved from the category of #very agglomerated$to #somewhat agglomerated$.

On the one hand, the government in Beijing worked hard to relocate manufacturing plants

from the inner city to the suburbs during this period, leading to the dispersion of industries.

On the other hand, every county or district was eager to develop high-technology industries

and modern manufacturing industries and they made significant efforts to attract domestic

and foreign investments to their industrial parks. For example, transportation equipment

industries in Beijing are located in Shunyi, Huairou, Changping, Chaoyang and Daxing.

The electronic equipment industry has been favored by all industrial parks in Beijing.

Some other industries have been more agglomerated since 2001, including chemical

fiber, rubber products, ferrous metal processing and smelting, metal mineral products,

general-purpose machinery, special purpose machinery, and telecommunication and

electronic equipment industries. These industries are characterized by strong internal and


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external economies of scale. Geographical clustering improves productivity and lowers

production and transaction costs.

In short, some manufacturing industries in Beijing are more spatially agglomerated

than others. Because of the cost-savings from geographical proximity to related firms,

agglomerated industries might be more competitive in attracting foreign investment. The

following section examines the industrial pattern of foreign manufacturing investment and

tests the significance of industrial agglomeration for industries to attract foreign investment.

IV. Industrial Distribution of Foreign

Manufacturing Investment in Beijing

In 2004, Beijing actually utilized US$1.127bn of FDI in its manufacturing industries,

accounting for 36 percent of total FDI (Beijing Statistical Bureau, 2005). Up to 2004, the

major FDI recipients in Beijing included electronics and telecommunication equipment,

Table 2. Industrial Agglomeration of Two-digit

Manufacturing Industries in Beijing

Source:Calculated by the author based on Equation (1).

Industry Code "MS(1996) Rank "MS(2001) Rank

Food processing 13 0.0066 22 0.0040 21

Food manufacturing 14 !0.0010 27 !0.0177 27

Beverage 15 !0.0175 28 !0.0083 26

Tobacco processing 16 !0.0523 29 !0.0957 29

Textiles 17 0.0676 9 0.0465 5

Clothing and other fibers 18 0.0141 18 !0.0037 24

Leather and fur 19 0.0476 11 0.0131 15

Timber processing 20 0.1254 4 0.0253 9

Furniture making 21 0.0707 8 0.0390 7

Paper making and products 22 0.0029 24 0.0015 22

Publishing and copying 23 0.0139 19 0.0073 18

Cultural, education and sports goods 24 0.0292 14 0.0075 17Petroleum refining and coking 25 0.7828 1 0.1316 3

Chemical materials and products 26 0.0566 10 0.0228 10

Medical and pharmaceutical products 27 0.0917 5 0.0137 13

Chemical fiber 28 !0.0005 26 0.0220 11

Rubber products 29 0.0067 21 0.0136 14

Plastic products 30 0.0188 16 0.0055 20

Non metal mineral products 31 0.0031 23 0.0013 23

Ferrous metal smelting and pressing 32 0.3759 2 0.6505 1

Non ferrous meta l smelting and pressing 33 0.0346 13 !0.0202 28

Metal mineral products 34 0.0078 20 0.0083 16

General purpose machinery 35 0.0155 17 0.0176 12

Special purpose machinery 36 0.0742 6 0.0948 4

Transportation equipment 37 0.0728 7 0.0440 6

Electrical machinery and meters 40 0.0214 15 0.0060 19

Electronics and telecommunication

equipment41 0.1503 3 0.1599 2

Instruments and meters 42 0.0350 12 0.0261 8

Miscellaneous products 43 0.0000 25 !0.0042 25


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electrical machinery and equipment, transportation equipment, special-purpose equipment,medical and pharmaceutical products, chemical materials and products, non-metal mineral

products and food manufacturing.

Figure 2 shows the industrial distribution of cumulative contracted foreign projects

and contracted foreign investment up to 2004. Clearly, foreign investments in Beijing strongly

favor capital-intensive and technology-intensive industries, which are geographically

agglomerated with strong localized business linkages. During the period between 1999 and

2004, foreign manufacturing investment also flowed mainly to electronics and

telecommunication equipment, medical and pharmaceutical products, transportation

equipment and machinery for special purposes, which is consistent with Beijing"s

comparative advantages in market size, technology, information, services and highly

educated and productive labor. The question is, why are some industries more attractive to

foreign investment than others. This deserves further investigation.

1. Model Specification and Variables

The purpose of this subsection is to explain the industrial variation of foreign manufacturing

investment by examining variables associated with industrial agglomeration and industrial

linkages and other industrial attributes. Based on discussion in Section II, we assume that

Figure 2. Industrial Distribution of Cumulated Foreign Direct

Investment (FDI) in Manufacturing Industries in Beijing

(Up to 2004)

0.00

5.00

10.00

15.00

20.00

25.00

30.00

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 40 41 42 43

Percent

Industrial Share of Contracted Foreign Projects

Industrial Share of Contracted FDI

Sources:Beijing Municipal Burea of Commerce (2004) and Beijing Municipal Bureau of Industrial

Development (2005).

Note:See the industrial code listed in the second column in Table 2.

Industrial code

Percentage

Industrial share of contracted foreign projects

Industrial share of contracted FDI


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the amount of FDI flow to industryiin a particular year tis a function of a number of

industrial attributes that are likely to influence foreign investors"industrial choice. The

model takes the following form:

1 1 1 1 1 1 1 1 1( , , , , , , , , )it it it it it it it it it it FDI f MS INTRA INTER EXPT KL LCOST CFDI PRFT HERT =; (2)

that is, industrial distribution of FDI depends on industrial agglomeration (MS), intra-

industrial linkage (INTRA), inter-industrial linkage (INTER), export intensity (EXPT), fixed

asset per employee (KL), effective wage rate (LCOST), share of industrial output by foreign

enterprises (CFDI), sales profit margin (PRFT) and industrial concentration (HERF) ( see

Table 3). To avoid the endogeneity issue, all explanatory variables are lagged by 1 year.This analysis focuses on the industrial FDI flows from 1999 to 2004.

As discussed above, foreign investors might favor agglomerated and linked industries.

To test this hypothesis, we introduce three variables into the empirical model.MSquantifies

the degree of industrial agglomeration.INTRAandINTERmeasure the intra-industrial and

inter-industrial linkages, respectively. All three variables are expected to have positive

regression coefficients. The census of basic units was conducted only in 1996 and 2001

and, therefore,MScannot be computed for the years in between. Hence, we take the 1996

MSvalues as the variableMSduring the period of 1999!2001 and the 2001MSvalues for

the period of 2002!2004. The variables ofINTRAandINTERduring the period 1999!2002

were computed based on the Beijing 1997 input!output table and those in 2003 and 2004were calculated based on the Beijing 2002 input!output table.INTRAis defined as the ratio

of intermediate inputs from an industry to total inputs.INTERis the average of two shares;

that is, the share of intermediate inputs from other manufacturing industries in total inputs

and the share of intermediate sales to other manufacturing industries in total outputs.

Table 3. Definitions of Explanatory Variables

and Their Expected Signs

Variables Definitions Expected sign

MS MS index +

INTRA Intermediate inputs from own industry/total inputs +

INTER([Intermediate inputs from other manufacturing industries/total inputs] +[intermediate sales to other manufacturing industries/ total output])/2

+

EXPT Exports/gross industrial output +

KL Total fixed assets/total employee +

LCOST Average wage/value added per employee !

CFDI Industrial output by foreign enterprises/gross industrial output +

PRFT Sales profits/sale revenues +

HERF Herfindal index of plant employees !


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It is believed that FDI might be concentrated in industries with matched comparative

advantages. The present paper introduces and examines another three variables to study

industrial comparative advantages in Beijing:EXPT,KLandLCOST.EXPTis the ratio of

exports to gross industrial output,KLrepresents the fixed assets per employee, andLCOST

the ratio of average wage to value added per employee. An exporting industry has significant

revealed comparative advantage in international markets. Industries with larger fixed asset

per employee are usually capital-intensive and technology-intensive.LCOSTquantifies

real labor cost: productive but low-cost workers are one of the key sources for comparative

advantages. Positive coefficients onEXPTandKLand a negative coefficient onLCOST

indicates that the industries have comparative advantages and are attractive to foreigninvestment.

Past FDI performance also influences an industry"s attractiveness to new FDI. There

are many possible reasons why FDI might be concentrated in some industries. There might

be sequential investments from foreign investors and investments from followers who

have established strong business linkages with the early entrants. The previous foreign

investment also has demonstration effects, information spillover effects and experience

effects, which can drive new foreign investments to particular industries (Liu, 1990). To test

the significance of past FDI performance, the present study quantifies FDI presence in an

industry as the ratio of industrial output by foreign enterprises to gross industrial output

(CFDI), which is expected to have a positive coefficient.In addition, FDI is profit-driven. Foreign investors prefer profitable industries in host

economies. Industrial profitability has been included in the model and is defined as the

ratio of sale profits to sale revenues (PRFT). This variable is expected to have a positive

effect.

Finally, foreign investors also encounter a variety of entry barriers, including restricted

industrial policies, economies of scale and industrial concentration. Industries with

significant economies of scale require foreign investors to make more resource commitments

in the host economies, which increases investment risk. Foreign investors might face more

difficulties in entering a domestic market dominated by several large companies. Industrial

policies are rather difficult to quantify. The present study introduces the Herfindahl index(HERF), which measures the distribution of plant employee size in an industry to represent

entry barriers. The larger the Herfindahl index value, the greater the entry barrier. Foreign

investors tend to avoid industries with high entry barriers. Like the variableMS,HERFcan

only be computed for the years 1996 and 2001. Following the treatment for the variable of

MS, the 1996 Herfindahl index values of are used forHERTfrom 1999 to 2001 and the 2001

Herfindahl index forHERTfrom 2002 to 2004.

The present study took the yearly contracted FDI inflows and contracted foreign


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projects in 28 two-digit manufacturing industries during the period of 1999!2004 as the

dependent variable. FDI data were collected from various issues of the Survey of Beijing

Foreign Economic Relations and Trade(Beijing Municipal Bureau of Commerce, 2004)

and theBeijing Industrial Yearbook(Beijing Municipal Bureau of Industrial Development,

2005). The present study considers the following panel data regression model:

1 1 2 1 3 1 4 1 5 1

6 1 7 1 8 1 9 1

it it it it it it

it it it it i t it

FDI MS INTRA INTER EXPT KL

LCOST CFDI PRFT HERT

= + + + + +

+ + + + + +

, (3)

where idenotes industry, tdenotes time, i and t the unobservable industrial and time

effect, and it the residual stochastic disturbance term. There are three statistical models

for a pooled time-series and cross-sectional dataset: the OLS model, the random effects

model (REM/GLS) and the fixed effects model (FEM/LSGV). The choice between OLS and

LSGV/GLS is made based on the traditional Lagrange multiplier (LM) test, whereas selection

between LSGV and GLS is based on the Hausman"s test. The following subsection presents

the regression results based on the estimations of OLS, LSGV and REM.

2. Empirical Results

Table 4 presents the Spearman correlation coefficients between explanatory variables. The

correlation coefficient betweenKLandLCOSTis !0.74, indicating that capital-intensiveand technology-intensive industries bear lower real labor costs, and other correlations are

fairly small. To avoid the collinearity problem,KLandLCOSTare included separately and

the model has been computed twice. Because the two separate model runs produced similar

results, only the model results with theLCOSTvariable are reported here. The estimations

were performed based on OLS, GLS and LSGV methods, but LM tests suggest that either

GLS or LSGV estimation was necessary. Our analysis is based on the OLS results. Breusch!

Pagan tests indicate the existence of heteroskedasticity and results are corrected using the

White"s heteroskedasticity method. TheF-tests are significant at the 0.01 level, indicating

that the explanatory variables explain the industrial variation of foreign investment in Beijing.

In particular, the model could account for some 27 and 37 percent of industrial variation incontracted FDI and contracted foreign projects, respectively. Table 5 summarizes the

estimated results.

Statistical results suggest that industrial agglomeration improves an industry"s

attractiveness to foreign investment. This is shown by the highly significant and positive

coefficients onMS. The more geographically agglomerated industries can attract more FDI,

suggesting that foreign investors take advantage of geographical proximity of related firms

in their decisions.INTRAalso has a very significant positive coefficient in all model


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specifications, indicating that foreign investors strongly favor industries with significant

localized intra-industry linkages. For instance, the electronics and telecommunication

equipment industry in Beijing has many linked sub-industries through the value chain.

Industrial clusters of electronics provide a large number of suppliers and also attract the

largest share of inward FDI flows in Beijing .

Surprisingly, stronger inter-industrial linkages occur to discourage inward FDI, as

shown by the significant negative coefficients onINTER. In Beijing, industries with strong

inter-industrial linkages are resource-based industries, such as ferrous metal processing

and smelting, nonmetal mineral products, rubber and plastic products, petroleum processing

and coking. These industries currently hold certain scale advantages, but are not suitable

Table 4. Spearman Correlation Coefficients between

Independent Variables

M S IN TRA IN TER CFDI EXPT KL LCOST PRFT HERT

MS 1.00 0.15 0.11 !0.13 0.10 0.08 0.17 !0.25 0.23

INTRA 1.00 !0.17 0.08 !0.09 0.45 !0.26 !0.19 0.13

INTER 1.00 !0.28 0.06 !0.12 0.19 0.15 !0.02

CFDI 1.00 0.40 !0.23 0.06 !0.07 !0.46

EXPT 1.00 !0.38 0.33 !0.18 !0.15

KL 1.00 !0.74 0.12 0.38

LCOST 1.00 !0.19 !0.13

PRFT 1.00 0.01

HERT 1.00

Sources:Calculated by the author.

Table 5. OLS, GLS and LSGV Regression Results

Contracted FDI inflows Contracted foreign projectsVariable

OLSREM(GLS)

FEM(LSGV)

OLSREM(GLS)

FEM(LSGV)

Constant !1698.92 !2087.25 11.07 11.06

MS 13926.28*** 13890.67*** 14006.11** 23.45** 23.47** 23.86***

INTRA 15320.37*** 15365.45*** 15372.18** 18.63** 18.63** 18.49**

INTER !2745.18* !2880.44* !2929.50** !11.46*** !11.47*** !11.64***

EXPT 10129.38 9206.12 8871.18 32.57** 32.49** 31.05**

LCOST !16858.23** !15829.44** !15452.74** !47.28*** !47.17*** !45.20***

CFDI 14722.75*** 14786.98*** 14811.90** 24.87*** 24.88*** 24.95**

PRFT 26666.12 28330.41 28803.06***

102.35***

99.18***

100.46***

HERF !2459.47 !2225.36 !2145.27 !31.45**

!31.44**

!31.22***

Number of

observations 168 168 168 168 168 168R

2 0.27 0.27 0.29 0.37 0.37 0.37

F

7.31***

7.31***

4.81***

11.74***

11.74***

7.04***

D-W 2.00 1.79

B-P 559.63***

42.42***

Test LM = 0.20; Hausman = !0.05 LM = 2.74*; Hausman = 0.98

Notes:Results corrected for heteroskedasticity. *, **, ***, represent significance at the 0.10, 0.05 and

0.01 levels, respectively. D-W, Durbin!Watson; B-P, Breusch!Pagan; REM, random effects model;

FEM, fixed effects model.


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in Beijing. However, foreign investors do encounter significant entry barriers. Industrial

concentration significantly discourages foreign investors"entry in Beijing, as shown by

the negative coefficients onHERT. Monopolistic industries such as tobacco processing,

ferrous metal processing and smelting, petroleum processing and coking are difficult for

foreign investors to enter because of institutional and policy restrictions imposed by the

Chinese Government, and market barriers.

V. Conclusions

Foreign direct investment in China has been a hot research topic since the early 1990s.

However, insufficient attention has been paid to the issue of industrial distribution of FDI.

The present study illustrates the importance of industrial agglomeration and localized

business linkage in attracting foreign investment. This study finds that up to 2004 the

major FDI recipients in Beijing were electronics and telecommunication equipment, electric

machinery and equipment, transportation equipment, special-purpose equipment, medical

and pharmaceutical products, chemical materials and products, nonmetal mineral products

and food manufacturing. Using a panel dataset of two-digit manufacturing industries in

Beijing during the period of 1999!2004, the present study finds that geographically

agglomerated industries with strong intra-industrial linkages have attracted most of the

foreign investment. Previous foreign investment has had demonstration effects, information

spillover effects and linkage effects, leading to industrial concentration of foreign investment.

The marriage between comparative advantages in the host economies and MNCs"

monopolistic advantages has driven FDI into capital-intensive and technology-intensive

industries and to those with significant revealed comparative advantages. Foreign investors

are drawn to the most profitable and to exporting industries, but avoid industries with high

effective wage rates and high entry barriers. The present study supports the argument that

competitive and comparative advantages of local industries are critical for attracting foreign

investment and the presence of industrial clusters enhances a city"s competitiveness to

attract investments from abroad. The empirical results have important policy implications.

To further attract foreign investments, especially those from MNCs, the local governments

should cultivate business networks, promote local business linkages, and encourage

geographical agglomeration of related firms.

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