LOCATION DETERMINANTS OF JAPANESE DIRECTINVESTMENT BY INDUSTRY IN CHINA

Shuli LIU

Research Fellow
Global Institute for Asian Regional Integration
Graduate School of Asia-Pacific Studies
Waseda University

CONTENTSI

INTRODUCTION..................................................................................................... 3

II

RECENT TRENDS AND CHARACTERISTICS OF JAPANESEMANUFACTURING INDUSTRIES IN CHINA ................................................... 5

III A BRIEF SURVEY OF PREVIOUS STUDIES ...................................................... 7
IV THE MODEL ............................................................................................................ 9V

THE HYPOTHESES AND THE EXPLANATION OF VARIABLES ................. 10

VI THE RESULTS ...................................................................................................... 14
VII CONCLUDING REMARKS .................................................................................. 18

Table List
Table 1: Establishment of New JFDI in China in the manufacruing sector (2002-2005), by region
and industry .......................................................................................................................................... 20
Table 2：Variable list ............................................................................................................................ 21
Table 3：Basic statistics and Correlations ............................................................................................ 22
Table 4：Estimated result―Japanese manufurctry industry as Whole ................................................ 23
Table 5: Estimated result―Electric and Electronics ........................................................................... 24
Table 6: Estimated result―Transportation Equipment/Machinery..................................................... 25
Table 7: Estimated result―Metal products ......................................................................................... 26
Table 8: Estimated result―Chemicals................................................................................................. 27
Table 9: Estimated result―Textiles ..................................................................................................... 28
Table10: Estimated result―Food and Beverage ................................................................................. 292

I

Introduction

Research on foreign direct investment（FDI）and trade is indispensable to examineinternational economic relations in Asia.

Developing countries, such as China andThailand, receive a large amount of FDI, and Japan is the most important source of FDI
for Asian countries. Moreover, not only do Japanese multinational corporations in
China and Thailand play a major role in intra-regional trade, but they also are very
active in inter-regional trade, vis-à-vis US and EU markets.
As of the end of 2007, there were 12,710 1 overseas subsidiaries of Japanesemultinational corporations in Asia.

This number far exceeded the number of Japanesesubsidiaries in North America (3,547), a region that receives regular attention fromJapanese multinational corporations. It also accounts for more than half of the total
number of Japanese global subsidiaries (21,264).
Economic analysis of the overseas advance of Japanese enterprises in Asia is
necessary to understand the intra- and inter-regional economic relations in the Asian
region. China is home to 4,878 Japanese subsidiaries, exceeding any other country
with Japanese FDI. China is followed by Thailand (1,577), Singapore (991), Taiwan(896), and Malaysia (759).

In comparison, there are only a small number of Japaneseenterprises that have an FDI presence in other countries in Asia, such as Nepal. Similar
patterns are observable in China, with Japanese multinational corporations investing
significantly in Shanghai (1,709), Jiangsu Province (740), and Guangdong Province(721), among others.

1

aggregation by author from Survey of Overseas Japanese Companies Published by Toyo

Keizai Shinposha. 2008
3

What factors influence Japanese multinational corporations in their location choice
when investing China? There are several factors that attract FDI (foreign direct
investment) in the receiving country (or region). These are not limited to traditional
factors, such as market size, infrastructure, and labor cost, but also include theagglomeration effect throughout the international production network, which has
become more and more significant. Do these factors vary based on their industries as
well?
In this paper, I attempt to answer the above questions. The analysis use micro-datafrom the TOYO KEIZAI database by the Conditional Logit Model. Through a
comparison of location determinants of Japanese direct investment in 31 Chinese
regions from 2002 to 2006 by industry, we see that determinant factors related totheagglomeration effect are more important than the other “traditional factors” inattracting Japanese investment in China.
This paper has important implications in understanding Japanese economic
relationships and integration in the East Asia region. It also could serve as a reference to
Asian countries’ government policymaking regarding foreign direct investment in orderto reduce area disparity.
The structure of the paper is as follows: Section II briefly discusses recent trends andcharacteristics of Japanese manufacturing industries in China. Section III is a brief
overview of Survey of Previous Studies. Section IV presents the model and Section V
presents the hypotheses and the explanation of variables for the analysis, while Section
VI discusses the results of empirical analysis and Section VII concludes the paper.

4

II

Recent Trends and Characteristics of Japanese Manufacturing
Industries in China

As the Chinese government does not disclose firm-level data of FDI, we used
Japanese data on the firms investing in China, such as the Survey of Overseas JapaneseCompanies Published by Toyo Keizai Shinposha. This survey data covers the locationand outlines the business activities of Japanese affiliates in China. It includes the 4938
Japanese affiliates established in China before 2005. Of these, 70% were in the
manufacturing sector and 30% in non-manufacturing sectors. Among the industries in
the manufacturing sector, the transportation equipment/machinery industry accounted
for 32%, the electric and electronics industry for 23%, the chemical industry for 19%,
the metal products industry for 10%, the textile industry for 8%, the food and beverageindustry for 4% of all the locations. Let us briefly discuss some notable location
characteristics of these six industries.

Table 1 shows the patterns of JFDI (Japanese Foreign Direct Investment) inflows in
China during 2002-2005 in each location (provinces) by industry. These figures reveal
an inequality in the distribution of location by industry.

The total number of newly

established Japanese manufacturing industry affiliates amounted to 610 in China.
Among 31 provinces and special cities, the dominant number of newly established JFDI
was established in the eastern region of China. The Yangtze delta (Shanghai:116,
Jiangsu:163, Zhejiang:56), and the Zhujiang delta (Guangdo:104) had 72% of the total
number of establishments, indicating a very high geographical concentration. New
establishments in the transportation equipment/machinery industry in particular wereconcentrated in the Yangtze delta (Shanghai:16.84%, Jiangsu:27.04%, Zhejiang:6.63%),5

the Zhujiang delta (Guongdo: 22.45%) and the Beijing Tianjin Area (10.20%). The
electric and electronics industry were concentrated in these three areas: The Yangtze
delta (Shanghai: 17.81%, Jiangsu: 32.88%, Zhejiang:7.53%), the Zhujiang delta(Guongdo: 15.07%) and the Beijing Tianjin Area (8.22%). JFDI in the chemicalindustry and metal products industry were preferred in Shangdo (5.79%, 4.55%) than in
the Beijing Tianjin Area. These industries were also were concentrated in the Yangtze
delta and the Zhujiang delta. Japanese affiliates in the textile industry were concentrated
in the Yangtze delta (Shanghai:25%, Jiangsu: 23.08%, Zhejiang: 23.08%), with a low
concentration in the Zhujiang delta, while the food and beverage industry was not as
concentrated as the other five industries. The difference in the number of establishedlocations by region and by industry suggests that market conditions have different
effects on location choice.

6

III A Brief Survey of Previous Studies

Most studies on FDI location choice in China use overall FDI data without
sectoral disaggregation and firm level data. Some earlier studies (He and Chen
1999, Wei et al. 1999, He and Liang 1999) highlight the significance of traditional
location factors such as market size (GDP), potential market size (rate of GDP
growth), wages, education level, and infrastructure (highways, railways, domestic
shipping industry, electric power, etc.). These studies mostly show positive impactsof the market size, potential market size, infrastructure on FDI inflows, as well as
the negative effects of higher wages. Chen and Xu (2007), Liu and Wang (2005),
and Wang and Xu (2004) found that higher wages, and lower education levels
might attract more FDI.
The vast majority of recent publications argue that other factors such as
agglomeration economies are more important, and that the traditional approach
does not take this into account sufficiently (He 2002, Xiao and Lin 2008, He andLiu 2006).
He (2002) focuses on information costs, agglomeration economies and the
local foreign direct investment in China. He found that the coefficient of theeffective wage rate is not significant. Instead, the statistical results show that
MNE’s prefer coastal cities with favorable policies and form regional clusters withother MNE’s to reduce market uncertainties and minimize information costs. Xiao
and Lin (2008) tried to analyze the reasons for why the Yangtze Delta could attractsuch a great deal of FDI from industrial clusters. They used several different
variables (like rate of own industry, rate of overall industry) to test the7

agglomeration effects of different industries.
In order to deepen the understanding of the pattern of FDI location, several
studies have attempted to use firm-level data for the investigation. As it is rather
difficult to gain access to firm-level information from Chinese sources, several
researchers used data obtained from the sources outside China.
Wakasugi (2005) analyzed the geographical allocation of Japanese firms in
China by using a conditional logit model and found that industry agglomeration
proxied by the special economic zones and the abundance of well-educated humanresources were largely responsible for a rise in the probability of a region to bechosen as a FDI host, whereas a rise in the real wage cost lowered this probability.
Jin and Tokunaga used data from Chugoku Shinshutsu Kigyou Ichiran2 toanalyze location choices of Japanese FDI in China’s food industry from 1992 to
2003. Using a negative binominal model, Jin and Tokunaga found that high wages
and distance from Japan discourage FDI by Japanese food manufactures, while theavailability of raw materials and seaport facilities positively attracted FDI. Unlike
other studies, they found that provincial purchasing power, measured by per capita
GDP, but not by provincial market size, appeals to Japanese investors in China.
Kang and Lee (2004) investigated the recent FDI trends and the determinantsof location choice by using firm level data of Korean and Japanese MNEs in China.
Their conditional logit estimation results differ between Korean and Japanesecompanies. The agglomeration variable is shown to be positive and significant for
FDI from the two countries, and regional income is shown to be positive for Japanbut negative for Korea.
2

A View of Japanese Enterprise Investments in China, 2003-2004, In Japanese.
8IV The model
The location choice of a company (belonging to a certain industry) at t (year) is
considered. There are M (＝31) provinces in China which are the targets that of
selection. If the logarithm value of the profit function is expressed as lnП s,t, whenchoosing s (province) and t (year), the company should choose m (province) which fills
the following equation:

ln 

m, t

 Max ln s, t

: S  1,  , M (1.1.)

Here, logarithm value of a profit function shall be expressed as follows:
ln 

s, t

  '  s ,t   s ,t

(1.2.)

However, χs,t expresses the vector of an attribute of the province in t (year). （β is acoefficient vector presumed about this industry.） εs,t，expresses an attribute of the area
and the characteristics particular to the company which cannot be observed.
As McFadden (1973) showed, when a residual εs,t，independently follows extreme-value
distribution of the same type I the probability of this company which chooses m(province) in t (year) is given by the following formulae:exp(  ' xm ,t )Pm ,t  M
 exp(  ' xs ,t )(1.3.)s 1

Therefore, if expressing the number of times from which s (province) is chosen in theindustry concerned at t as Ｗｓ,t，（s =1,…, M, t =1, …,Ｔ) and a residual becomesmutually independent in all the location choices, the probability that a location pattern{Wｓ,t ：s =1,…, M, t =1, …,T}will be observed is obtained in the following formulae:TML   Ps ,tWs ,t(1.4.)

t 1 s 1

A model of this type is called Conditional Logit Model

9VThe hypotheses and the explanation of variables

According to previous studies, foreign firms consider market size, infrastructure,
wage level, education level, and the agglomeration effect as the important factors inlocation decisions.
In my estimation, I adopt log of real GDP (Gross Domestic Product) as therepresentative variable of market size. There are more investment opportunities as themarket size is bigger, as usual. The estimated coefficient of real GDP (ln_gdp) is
expected to be positive. One of the important factors in the location decision is thewell-organized infrastructure, especially the infrastructure in the transportation
department. In this paper I adopted transportation networks such as roads, railway, andinland sea routes, and defined the infrastructure by the total length of road, railway andinland sea routes per square kilometer. The estimated coefficient is also expected to bepositive.
Three different types of labor variables are considered: one is the labor cost, which
captures a level of produce cost. Lower wages mean that firms could bring theirproduction costs down, particularly in labor-intensive industries. So the expected sign oflabor cost (ln_wage) is negative. Labor productivity is also an important factor that
influences Japanese multinational corporations when chosing a location. Labor
productivity is an index of efficiency of labor, defined by output-real GDP per worker.
The estimated coefficient is also expected to be positive. The third type is human capital,
which captures the quality of labor. The ration of high-school-educated workers is
expected to have positive sign.
In addition, regional dummies are used to capture the policy differences in different

10regions. For example, the Chinese government launched the China Western
Development Project in 2000 to foster the economies in western areas, which isrelatively underdeveloped in China. There are three regional dummies: the western
dummy (dwest), the central (or middle region) dummy (dmid) and the eastern dummy(reference group). These dummies control policy differences.
Finally, there is the agglomeration effect. Under certain conditions, theagglomeration of industries and firms in a region will prove beneficial to firms seeking
to employ qualified labor forces and intermediate materials in an efficient manner. This
Marshallian externality serves as a favorable factor in attracting further entrants to theregion. Some previous studies on the location choice of foreign firms have noted theagglomeration effect. Head et al. (1995) pointed out that the agglomeration of Japaneseaffiliates in the United States indicated a positive effect of such agglomeration. In such aperiod of transition in China from a planned economy to a market-oriented one, it wasdifficult for foreign firms to collect accurate information regarding regulations andincentives provided by both the central government in Beijing and the local government
in each province. Firms sometimes encountered inconsistencies or contradictions in thepolicies of the central and local governments. In addition to efficiency in employing
labor and procuring arts and materials, a high degree of agglomeration is an indicatorthat a region is providing foreign affiliates with favorable economic conditions,especially in the regions with a concentration of local firms or foreign affiliates.
The agglomeration of foreign affiliates attracts other plants from their homecountries; this fact has been confirmed by statistical data on the location of Japaneseaffiliates. Wakasugi (2005) pointed out that a major reason for this is that Japaneseaffiliates in China constitute a type of vertical fragmentation of the manufacturing
11

process. A number of Japanese affiliates were established in China for the purpose of
expanding the development of the parts-assembly network from the home market toplants abroad. Once a firm from the network shifts its plant to China, it triggers otherfirms to shift their plants as well. Moreover, it is easier for Japanese firms to collectinformation regarding the Chinese market from Japanese affiliates than from Chinese
firms themselves. This suggests that new Japanese affiliates are apt to locate in regions
characterized by an agglomeration of Japanese firms.
To test the agglomeration effect, I use five variations of the “number of firms” forthe estimation: (1) the number of industry firms in China, (2) the number of othercountries' affiliates in China, (3) the number of Japanese affiliates in China, (4) the
number of Japanese manufacture affiliates, (5) the number of Japanese affiliates in thesame industry. With regard to the estimation of the estimation of the agglomeration
effect, I estimated five cases: (1) the agglomeration effect with Chinese firms, (2) the
agglomeration effect with other foreign affiliates, (3) the agglomeration effect withJapanese affiliates, (4) the agglomeration effect with Japanese manufacturing affiliates,(5) the agglomeration effect with Japanese affiliates in same industry.
I estimated the parameters for six industries as a whole and by industry. These
industries are: (1) the electric and electronics industry, (2) the transportationequipment/machinery industry, (3) the chemical industry, (4) the metal products
industry, (5) the textile industry, and (6) the food and beverage industry. These six
industries account for approximately 80% of the total number of plant locations in the
manufacturing sector.
Basic information on the variables used in the analysis are shown in Table 3. The
explaining variables are taken from the China Statistical Yearbook (2002-2005), while12the explained variables are taken from the Survey of Overseas Japanese Companies
published by Toyo Keizai Shinposha.
The explaining variables and the explained variables comprise of the panel data of
31 provinces and special cities spanning 4 years. Since I assumed that Japaneseaffiliates make their location choice on the basis of market conditions in the previous
year, I gave a one-year lag to all the explaining variables in the estimation. In astatistical sense, this method is effective for avoiding the simultaneous equations bias.

13

VI The Results

I applied the conditional logit model to investigate the locational determinantsof JFDI inflows in the manufacturing industry to Chinese provinces. The results of
the analysis are shown in Table 4-10. Table 6 presents the estimated results basedon the panel data of Japanese Manufacture industry as a whole, while Table 5-10
presents the estimated results by industry.
For the Japanese manufacturing industry as a whole (Table 4), the results showthat market size (ln_gdp) and transportation infrastructure (infra) have positive
impacts on JFDI in manufacture industry inflows. The relationships, which are
shown to be statistically significant, are consistent with that expectation. Labor cost(ln_wage ) is estimated to be negative and labor productivity (ln_labpro) to bepositive with strong statistical significance as expected. The provinces with lower
labor costs and higher labor productivity can attract more Japanese firms in the
manufacturing industry invest in those provinces. But the estimated coefficient on
human capital (edu), which is found to be negative and statistically significantly, is
not as expected. High human capital may reflect high wages and high labor costs.
Compared to the low costs or lower educated workers, the highly educated worker
may not be necessary in the manufacturing industry. This is why the coefficient oflabor costs may be negative and labor productivity is positive. The three variables
about labor have a correlative relationship but different means. The estimatedresults on the dummy of middle region and western region are negative as expected,
although the coefficients are not statistically significant.
Turning to the results on the agglomeration effect variables,

we see positive14

impacts that are statistically significant. The coefficient of the agglomeration effectwith Chinese firms (ln_agglo1)(1.09) is higher than those with Japanese affiliates(ln_agglo3)(0.94) and with other countries’ affiliates (ln_agglo2)(0.29). This may
reflect the fact that Japanese affiliates, while facing on the pressure on cost-cutting,depend on Chinese local firms for the supply of parts and components, instead of
depending only on Japanese affiliates, as they had before. Japanese manufacturing
affiliates have a complementary relationship with Chinese industrial firms on thevertical specialization based on the production network, while others havecompetitive problems with other countries’ affiliates in China market.

The examination of the results by different industries brings out someinteresting trends in the loactional determinants.
In the electric and electronics industry (Table5), and the transportationequipment/machinery industry (Table6), the traditional variables, such as market
size, infrastructure, labor cost, labor productivity, and human capital show similar
trends as whole. On the other hand, the agglomeration effect variables havedifferent results. For the agglomeration effect variables, unlike the electric andelectronics industry, there are negative impacts with Japanese affiliates in the same
industry (ln_agglo5) in the transportation equipment/machinery industry, whileboth have positive effect with Japanese affiliates (ln_agglo3), especially withJapanese manufacturing affiliates ( ln_agglo4). This appears to reflect the fact thatthe keiretsu product network, which is a grouping product network in the Japanesetransportation equipment/machinery industry, is not only in Japan, but also in
China.
15

In the metal products industry (Table 7), the regional dummies (dmid, dwest)
turn to negative in the transportation equipment/machinery industry, although thecoefficient is not statistically significant. For the agglomeration effect variables,
there are positive impacts with Chinese firms (ln_agglo1), Japanese affiliates(ln_agglo3), and Japanese manufacturing affiliates ( ln_agglo4). These results are
similar to those from the electric and electronics industry. The results of these fourkinds of agglomeration effects are shown to be statistically significant as well.
From among these effects, the coefficient of agglomeration effect with Chinese
firms (1.88) is higher than in the electric and electronics industry (0.89) and thetransportation equipment/machinery industry (0.53). This may reflect the fact thatJapanese metal products affiliates are likely to get together with Chinese firms than
the two industries are, although they are likely to get together with others Japaneseand Japanese origin manufacturers (ln_agglo3, ln_agglo4) in order to gain an
agglomeration economy.
In the chemical sector (Table 8), the results of labor cost (ln_wage) turn
positive, and labor productivity turns negativity with statistical significance, whichis unexpected. Japanese chemical affiliates are centered on capital-intensive
industries such as petroleum chemistry, drugs and medicines. The quantity ofcapital input is more important than the labor input. Lower labor costs and higher
labor productivity are not as favorable in other labor-intensive industries. For the
agglomeration effect variables, there are positive impacts with Chinese firms(ln_agglo1), Japanese affiliates (ln_agglo3), Japanese manufacturing affiliates( ln_agglo4) and with Japanese affiliates in chemicals industry (ln_agglo4), withstatistical significance. These results are similar with the electric and electronics
16

industry and the metal products industry.
In the textiles industry (Table 9) and the food and beverage industry (Table 10),
there are some differences in the agglomeration effect variables, although theresults are not strong as other industries. In the textile industry, the agglomeration
effect with Chinese firms (ln_agglo1) is higher than others. This may reflect thefact that Japanese textiles affiliates are likely to get together with Chinese firms
than get together with others Japanese and Japanese origin manufacturers(ln_agglo3, ln_agglo4) even in the same (textiles) industry (ln_agglo5). In contrast,in the food and beverage industry, the agglomeration effects are weak with Chinese
firms (ln_agglo1) and three kinds of Japanese affiliates because the results show nostatistical significance or only significance at 0.1 levels. Unlike the textiles industry,completed products, which were made in China but by Japanese affiliates, areexported to Japan or another market like USA and European countries. Meanwhilemost Japanese food and beverage affiliates make their products in China and sellthem in China too. Those affiliates choose the location near the consumer market toestablish business for reduce distribution costs.

17

VIIConcluding Remarks

This paper analyzed the locational determinants of JFDI in manufacturing in China
based on the firm-level database by conditional model. The use of firm-level date alsoenabled me to focus on the differences in the determinate by industry. This paper
obtained results similar to earlier studies and generally consistent with the priorexpectations. The consistent finding was that provinces with a big market size, a goodtransportation infrastructure, low labor costs, high efficient labor productivity, and theagglomeration effect are shown to attract JFDI in manufacture industry. As for five-tieragglomeration effect tests, we found the agglomeration effect in Chinese firms(ln_agglo1), Japanese affiliates (ln_agglo3), Japanese manufacturing affiliates(ln_agglo4), and Japanese affiliates in same industry (ln_agglo5), but not for otherforeign affiliates (ln_agglo2). These findings provide some useful policy implications.
Provinces with a keen interest in attracting JFDI could make efforts to develop local
firms, local manufacturing industry and local product networks.
This analysis would also be useful for the governments of other East Asian
countries that are interested in attracting FDI. If the results of the analysis would be
used effectively by these governments to attract FDI, I would expect that regional
integration in East Asia could be promoted through the expansion of FDI and foreign
trade.

18

REFERENCES:
Cheng L. and Kwan Y. (2000) “What are the Determinants of the Location of Foreign
Direct Investment? The Chinese Experience”, Journal of International
Economic 51.2, 2000.
HE C. and CHEN Y. (1997) ”Locational distribution and spatial diffusion of direct
investment from Hong Kong and Macao in mainland China”, Chinese George.
Sci.7, 328–38. (in Chinese).
HE C. and LIANG J. (1999) ”Regional differences of foreign direct investment in
China”, Acta Geographica Sinica 54, 1999 97–105 (in Chinese).
He C. (2002) “Information costs, agglomeration economies and the location of foreign
direct investment in China”. Regional Studies 36, 2002, 1029-1036
He C. and Liu Y. (2006) "Industrial agglomeration and sectoral distribution offoreign direct investment: A case Study of Beijing. Acta Geographica Sinica 61,
2006 , 1259-1270 (in Chinese).
Jin S. and Tokunaga S.(?) “Location of Japanese investment in China: the case of foodindustry”
Kang S.J. and Lee H. (2004) “location choice of Multinational Companies in China:
Korean and Japanese Companies”, Korea Institute for International Economic
Policy, KIEP working paper 04-13 2004.
Liu B.C and Wang Z.M (2005) "Factors analysis about FDI and entry model in China”,
Journal of China University of Geosciences 5, 2005 10-16 (in Chinese).
McFadden D. (1973) “Conditional Logit Analysis of Qualitative Choice Behavior”, InPaul Zarembka, ed., Frontiers in Econometrics, New York: Academic.
WEI Y., LIU X., PARKER D. and VAIDYA K. (1999) “The regional distribution offoreign direct investment in China”, Reg. Studies 33, 1999, 857–67.
Wang J. and Xu.K.N. (2004) “Regional agglomeration and spatial of FDI”, China
Industrial Economy 12, 2004, 61-67 (in Chinese).
Waskasugi R. (2005) “The effects of Chinese regional conditions on the location choiceof Japanese affiliates”, The Japanese Economic Review 12.4、390-407
Xiao W. and Lin G.B. (2008) “Industrial clusters and FDI location selection: Evidencesfrom the Yangtze Delta”, International trade issues 7, 2008, 82-86 (in Chinese).
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19

Table 1: Establishment of New JFDI in China in the manufacruing sector (2002-2005), by region and industry
Numbers of New Establishment

Province
Industry

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

Beijing
Tianjin
Hebei
Shanxi
Inner Mongolia
Liaoning
Jilin
Heilongjiang
Shanghai
Jiangsu
Zhejiang
Anhui
FujianJiangxi
Shandong
Henan
Hubei
Hunan
Guangdong
Guangxi
HainanChongqing
Sichuan
Guizhou
YunnanTibet
Shaanxi
Gansu
Qinghai
Ningxia
Xinjiang

Tatal:

Electric andElectronics

23.93%
4
8
1
0
2
6
0
0
26
48
11
0
2
1
3
1
1
1
22
0
0
0
0
0
0
0
0
0
0
0
0

146

Transportatio
MetalnEquipment/M products
achinery

Chemicals

Share of New Establishment(%)
Food andBeverage

Textiles32.13%
2
18
2
0
0
6
1
0
33
53
13
0
1
0
11
3
6
0
44
0
0
1
2
0
0
0
0
0
0
0
0

10.82%
0
1
1
0
0
1
0
0
19
14
7
0
0
0
3
0
0
0
11
0
0
0
0
0
0
0
0
0
0
0
0

19.84%
3
3
2
0
0
4
0
0
21
32
11
1
0
0
7
0
1
0
23
0
0
0
3
0
1
0
0
0
0
0
0

8.52%
1
1
1
0
0
3
1
0
13
12
12
0
1
0
5
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0

4.75%
1
3
0
0
0
2
1
0
4
4
2
0
2
0
5
0
1
0
2
0
0
0
0
0
0
0
2
0
0
0
0

196

66

121

52

29

Electric andElectronics

2.74
5.48
0.68
0.00
1.37
4.11
0.00
0.00
17.81
32.88
7.53
0.00
1.37
0.68
2.05
0.68
0.68
0.68
15.07
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

Transportatio
MetalnEquipment/M products
achinery

1.02
9.18
1.02
0.00
0.00
3.06
0.51
0.00
16.84
27.04
6.63
0.00
0.51
0.00
5.61
1.53
3.06
0.00
22.45
0.00
0.00
0.51
1.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

0.00
1.52
1.52
0.00
0.00
1.52
0.00
0.00
28.79
21.21
10.61
0.00
0.00
0.00
4.55
0.00
0.00
0.00
16.67
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

Chemicals

2.48
2.48
1.65
0.00
0.00
3.31
0.00
0.00
17.36
26.45
9.09
0.83
0.00
0.00
5.79
0.00
0.83
0.00
19.01
0.00
0.00
0.00
2.48
0.00
0.83
0.00
0.00
0.00
0.00
0.00
0.00

Textiles

1.92
1.92
1.92
0.00
0.00
5.77
1.92
0.00
25.00
23.08
23.08
0.00
1.92
0.00
9.62
0.00
0.00
0.00
3.85
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

Food andBeverage

3.45
10.34
0.00
0.00
0.00
6.90
3.45
0.00
13.79
13.79
6.90
0.00
6.90
0.00
17.24
0.00
3.45
0.00
6.90
0.00
0.00
0.00
0.00
0.00
0.00
0.00
6.90
0.00
0.00
0.00
0.00

20

Table 2：Variable list

Variable:

Contents

Date Source

Real GDP(Railway+Inland Sea Route+Road):km/Square:Km2
Average Wage

China Statistical Yearbook 2002~2005 year

Real GDP/The Working Population

as above

Human Capital

The Ration of high-school-educatedas above

Dummy of Middle
RegionDummy of Western
RegionAgglomeration Effectwith Chinese Firms
Agglomeration Effectwith Other foreign
Agglomeration Effectwith Japanese Affiliates
Agglomeration Effectwith Japanese
Manufacture Affiliates
Agglomeration Effectwith Japanese Affiliates
in same Industry

the 8 provinces in the middle of China

Administrative Division by China Government

the 10 provinces in the western of China

as above

the Number of industry Firms in China

China Statistical Yearbook 2002~2005 year

1

ln_gdpMarket Size
Transportation
Infrastructure
Labor cost

2

infra

3

ln_wage4ln_labpro Labor Productivity

5

edu

6

dmid

7

dwest

8

ln_agglo19ln_agglo210ln_agglo311ln_agglo412ln_agglo5

the Number of other Countries' Affiliates in China
the Number of Japanese Affiliates in China

as above
as above

as above
the Survey of Overseas Japanese Companies 2008 year

the Number of Japanese Manufacture Affiliates inChina

as above

the Number of Japanese Affiliates in same
Industry in China

as above21

Table 3：Basic statistics and Correlations

Basic Statistics
VariableMean

ln_gdpinfrarrl
edu3
ln_wage2
ln_labpro
dmid
dwest
ln_agglo1
ln_agglo2
ln_agglo3
ln_agglo4
ln_agglo5

Std.Dev

8.068
0.452
0.195
9.598
0.366
0.226
0.387
8.377
5.970
2.900
2.612
1.136

1.013
0.325
0.086
0.303
0.525
0.418
0.487
1.246
2.003
1.972
1.924
1.440

Min

5.110
0.030
0.030
9.120
-0.710
0.000
0.000
5.230
0.000
0.000
0.000
0.000

Max

9.910
1.710
0.490
10.440
1.760
1.000
1.000
10.630
9.660
7.280
6.390
4.950

Correlations
ln_gdpln_gdpinfrarrl
edu3
ln_wage2
ln_labpro
dmid
dwest
ln_agglo1
ln_agglo2
ln_agglo3
ln_agglo4
ln_agglo5

1.000
0.513
0.309
0.061
0.487
0.127
-0.601
0.956
0.902
0.774
0.771
0.624

infrarrl

1.000
0.676
0.478
0.734
-0.089-0.595
0.579
0.682
0.771
0.748
0.697

edu3

1.000
0.428
0.757
-0.061-0.412
0.311
0.456
0.579
0.518
0.489

ln_wage2

ln_labpro

1.000
0.727
-0.374-0.085
0.125
0.167
0.440
0.404
0.483

1.000
-0.212-0.545
0.523
0.644
0.805
0.770
0.761

dmid

1.000
-0.4290.064-0.057-0.241-0.194-0.288

dwest

1.000
-0.619-0.656-0.565-0.610-0.514ln_agglo1

ln_agglo2

ln_agglo3

ln_agglo4

ln_agglo5

1.000
0.935
0.816
0.835
0.708

1.000
0.876
0.877
0.764

1.000
0.979
0.878

1.000
0.879

1.000

22

Table 4：Estimated result―Japanese manufurctry industry as Whole

Japanese manufurctry industry as Wholemodel1ln_gdpinfra
ln_wageln_labpro
edumodel2model3model4model5

1.03

1.02

0.12

0.75

0.06

( 7.88 )( 7.69 )( 0.59 )( 3.96 )( 0.35 )

***
1.54

***
1.57

1.13

***
1.47

0.28

( 7.45 )( 7.58 )( 5.02 )( 6.93 )( 1.10 )

***
-2.44

***
-2.16

***
-3.02

***
-2.16

-0.19

( -4.37 )( -3.30 )( -5.27 )( -3.83 )( -0.32 )

***
3.48

***
3.08

***
3.06

***
2.62

0.71

( 7.62 )( 5.27 )( 6.43 )( 4.21 )( 1.27 )

***
-6.70

***
-6.22

***
-2.10

***
-4.64

-5.65

( -5.77 )( -5.26 )( -1.46 )( -2.98 )( -4.76 )

***

***
-0.49

***

***

dmid

( -1.59 )
dwest

-0.10

( -0.25 )ln_agglo1

1.09( 5.64 )***ln_agglo2fdi

0.29( 1.98 )**ln_agglo3

0.94( 7.59 )***ln_agglo4
ln_agglo5

Log likelihood
Pseudo R2-1354.47
0.35-1353.00
0.35-1338.30
0.36-1352.49
0.35-1325.18
0.37

choices
31
31
31
31
31
Number of Firms
18910
18910
18910
18910
18910
＊、＊＊and＊＊＊ give the statistical significanece at 10%, 5% and 1%, respectivily.
The figures in parentheses expres t - statistics.

23

Table 5: Estimated result―Electric and Electronics
Electric and Electronics
model1ln_gdpinfra
ln_wageln_labpro
edu

0.80( 3.03 )***2.13( 4.57 )***-4.10( -3.15 )***4.94( 4.61 )***-9.58( -3.83 )
***

dmid
dwest

model20.85( 3.08 )***2.10( 4.48 )***-4.61( -2.95 )***5.40( 3.86 )***-9.74( -3.70 )***0.12( 0.17 )model30.02( 0.05 )1.87( 3.75 )***-4.48( -3.40 )***4.64( 4.17 )***-6.40( -2.17 )**model40.84( 2.11 )**2.14( 4.54 )***-4.14( -3.08 )***5.07( 3.46 )***-9.88( -2.87 )***model5model6-0.11( -0.28 )-0.16( -0.44 )0.18( 0.60 )0.92( 1.60 )0.51( 0.81 )0.74( 1.35 )-1.78( -1.28 )-0.94( -0.64 )2.13( 1.59 )1.27( 0.88 )-8.19( -3.15 )
***

-4.17
( -1.40 )-2.42( -1.91 )*2.37( 2.01 )**-3.71( -1.29 )0.71( 0.74 )ln_agglo1

0.89( 2.05 )**ln_agglo2fdi-0.04( -0.13 )ln_agglo3

0.87( 3.06 )***ln_agglo4

1.06( 3.52 )***ln_agglo5

Log likelihood
Pseudo R2

model70.80( 3.98 )***-305.800.39-305.540.39-303.670.39-305.790.39-300.990.40-298.790.40-297.08
0.41

choices
31
31
31
31
31
Number of Firms
4526
4526
4526
4526
4526
＊、＊＊and＊＊＊ give the statistical significanece at 10%, 5% and 1%, respectivily.
The figures in parentheses expres t - statistics.

31
4526

31
4526

24

Table 6: Estimated result―Transportation Equipment/MachineryTransportation Equipment/Machinerymodel1ln_gdpinfra
ln_wageln_labpro
edu

0.98( 4.27 )***1.90( 5.27 )***-2.59( -2.66 )***3.41( 4.33 )***-7.82( -3.77 )
***

dmid
dwest

model20.98( 4.10 )***1.88( 5.17 )***-2.83( -2.34 )**3.74( 3.47 )***-8.23( -3.77 )***0.33( 0.66 )model30.51( 1.32 )1.77( 4.73 )***-2.72( -2.80 )***3.11( 3.83 )***-5.93( -2.42 )**model40.58( 1.73 )*1.84( 4.96 )***-2.17( -2.20 )**2.13( 1.96 )**-4.98( -1.83 )*model5model60.04( 0.14 )-0.09( -0.30 )0.71( 1.62 )0.26( 0.55 )-0.75( -0.73 )0.25( 0.23 )0.95( 0.98 )-0.10( -0.10 )-6.83( -3.24 )
***

-2.83
( -1.21 )1.19( 4.91 )***2.32( 5.56 )***-3.33( -3.19 )***4.32( 4.90 )***-9.10( -4.22 )***0.09( 0.11 )ln_agglo1

0.53( 1.49 )ln_agglo2fdi

0.43( 1.61 )ln_agglo3

0.88( 4.01 )***ln_agglo4

1.14( 4.85 )***ln_agglo5

Log likelihood
Pseudo R2

model7-0.28( -2.28 )**-434.120.36-433.880.36-433.010.36-432.810.36-425.880.37-420.910.37-431.51
0.36

choices
31
31
31
31
31
Number of Firms
6076
6076
6076
6076
6076
＊、＊＊and＊＊＊ give the statistical significanece at 10%, 5% and 1%, respectivily.
The figures in parentheses expres t - statistics.

31
6076

31
6076

25

Table 7: Estimated result―Metal products
Metal products
model1ln_gdpinfra
ln_wageln_labpro
edumodel2model3model41.34( 3.16 )***0.38( 0.53 )1.21( 2.89 )***0.57( 0.80 )-0.11( -0.15 )-0.64( -0.71 )1.20( 1.97 )**0.32( 0.44 )-7.31( -2.93 )***7.82( 3.57 )***-5.38( -1.50 )-5.59( -2.09 )**6.04( 2.46 )**-4.57( -1.30 )-9.82( -3.27 )***9.14( 3.39 )***0.82( 0.19 )-7.13( -2.80 )***7.32( 2.77 )***-4.12( -0.79 )dmid-15.62( -0.01 )dwest-12.82( -0.01 )ln_agglo1

model5model6-0.76( -1.13 )-0.75( -1.09 )0.37( 0.71 )-2.30( -2.69 )***1.36( 0.49 )-3.34( -3.22 )***2.16( 0.67 )-1.05( -1.28 )-1.15( -0.43 )-2.03( -0.65 )-2.39( -0.53 )7.04( 1.38 )-7.14( -2.53 )**8.57( 3.20 )***-8.23( -2.04 )**1.88( 2.45 )**ln_agglo2fdi

0.16( 0.33 )ln_agglo3

2.34( 4.37 )***ln_agglo4

2.76( 4.25 )***ln_agglo5

Log likelihood
Pseudo R2

model70.85( 3.27 )***-134.530.41-132.95
0.41-131.150.42-134.480.41-121.600.46-120.420.47-128.08
0.43

choices
31
31
31
31
31
Number of Firms
2046
2046
2046
2046
2046
＊、＊＊and＊＊＊ give the statistical significanece at 10%, 5% and 1%, respectivily.
The figures in parentheses expres t - statistics.

31
2046

31
2046

26

Table 8: Estimated result―Chemicals
Chemicals
model1ln_gdpln_labpro

0.34( 0.35 )1.63( 4.67 )***1.61( 3.64 )***2.51( 1.91 )*-1.01( -0.87 )

edu

-4.33
( -1.62 )-2.64( -1.00 )infra
ln_wage

1.64( 4.75 )***1.53( 3.35 )***1.63( 1.38 )model2dmid

model30.64( 1.40 )model40.76( 0.60 )1.15( 2.23 )**1.44( 3.07 )***1.80( 1.55 )-0.35( -0.34 )-0.67( -0.53 )3.06( 0.86 )-1.92( -0.57 )0.79( 1.55 )model5model6

0.66( 1.66 )*0.10( 0.16 )0.61( 1.59 )-0.14( -0.22 )3.58( 2.83 )***-2.13( -1.76 )*-3.47( -1.35 )4.10( 3.10 )***-2.70( -2.09 )**-0.14( -0.05 )1.02( 2.89 )***0.40( 0.74 )2.11( 1.87 )*-0.71( -0.72 )-1.72( -0.64 )-2.45( -2.28 )**-0.81( -1.04 )dwest
ln_agglo1

1.37( 3.48 )***ln_agglo2fdi

0.44( 1.22 )ln_agglo3

1.00( 3.38 )***ln_agglo4

1.12( 3.56 )***ln_agglo5

Log likelihood
Pseudo R2

model70.65( 3.30 )***-262.710.37-258.30
0.38-256.270.38-261.960.37-257.00
0.38-255.860.38-257.070.38

choices
31
31
31
31
31
Number of Firms
3751
3751
3751
3751
3751
＊、＊＊and＊＊＊ give the statistical significanece at 10%, 5% and 1%, respectivily.
The figures in parentheses expres t - statistics.

31
3751

31
3751

27

Table 9: Estimated result―Textiles
Textilesmodel1ln_gdpmodel21.14( 2.62 )***0.71( 1.05 )0.96( 2.25 )**0.96( 1.44 )ln_wage-2.18( -1.12 )-0.16( -0.07 )ln_labpro

3.59( 2.21 )**-3.82( -1.03 )1.42( 0.77 )

infra

edu

-2.87
( -0.79 )

dmid

-1.53
( -1.30 )dwest-15.35( -0.01 )ln_agglo1

model3-1.69( -2.44 )**-2.01( -2.04 )**-8.12( -2.82 )***3.94( 1.75 )*21.30( 3.23 )***model4model5model60.89( 1.37 )0.80( 1.25 )0.39( 0.68 )0.70( 1.43 )0.62( 0.88 )0.30( 0.35 )-0.31( -0.35 )-0.11( -0.15 )-1.93( -0.98 )-1.34( -0.61 )-0.01( -0.01 )-0.53( -0.27 )2.83( 1.31 )2.64( 1.30 )1.21( 0.59 )1.80( 1.02 )-1.93( -0.37 )-3.71( -0.99 )-1.45( -0.36 )-1.77( -0.46 )4.47( 4.75 )***ln_agglo2fdi

0.26( 0.52 )ln_agglo3

0.31( 0.72 )ln_agglo4

0.74( 1.74 )*ln_agglo5

Log likelihood
Pseudo R2

model70.60( 2.33 )**-116.75
0.35-114.770.36-102.38
0.43-116.620.35-116.50
0.35-115.220.35-113.84
0.36

choices
31
31
31
31
31
Number of Firms
1612
1612
1612
1612
1612
＊、＊＊and＊＊＊ give the statistical significanece at 10%, 5% and 1%, respectivily.
The figures in parentheses expres t - statistics.

31
1612

31
1612

28

Table10: Estimated result―Food and Beverage

Food and Beveragemodel1model2model3model4model5model6model7ln_gdp0.60( 1.37 )0.69( 1.51 )-0.50( -0.61 )-0.06( -0.10 )-0.05( -0.08 )-0.02( -0.04 )0.62( 1.20 )

infra

0.65
( 0.74 )0.79( 0.88 )-0.25( -0.22 )0.25( 0.26 )-0.01( -0.01 )-0.13( -0.13 )0.69( 0.68 )ln_wage-5.44( -2.35 )**4.41( 2.68 )***-1.90( -0.41 )-5.94( -2.28 )**4.46( 2.15 )**-1.21( -0.25 )-6.27( -2.63 )***3.97( 2.45 )**4.43( 0.68 )-4.67( -2.07 )**1.99( 0.91 )-3.88( -1.68 )*2.29( 1.20 )-3.57( -1.49 )4.95( 0.76 )-1.01( -0.21 )0.59( 0.12 )-5.48( -2.28 )**4.46( 2.51 )**-1.95( -0.42 )ln_labpro
edu
dmid

-0.45
( -0.46 )

dwest

0.62
( 0.54 )ln_agglo1

2.11( 1.04 )1.46( 1.48 )ln_agglo2fdi

0.77( 1.48 )ln_agglo3

0.64( 1.71 )*ln_agglo4

0.65( 1.65 )*ln_agglo5

Log likelihood
Pseudo R2-0.02( -0.07 )-78.440.21-77.950.22-77.290.22-77.290.22-77.010.23-77.080.23-78.440.21

choices
31
31
31
31
31
Number of Firms
899
899
899
899
899
＊、＊＊and＊＊＊ give the statistical significanece at 10%, 5% and 1%, respectivily.
The figures in parentheses expres t - statistics.

31
899

31
899

29