foreign direct investment in china: interrelationship

Foreign Direct Investment in China:

Interrelationship between Regional Economic

Development and Location Determinants of Foreign

Direct Investment

Yiyang Liu

Submitted in Fulfilment of the Requirements of the Degree of Doctor of

Philosophy

University of Western Sydney

i

Abstract

China has become an attractive destination for foreign direct investment (FDI). By the

end of 2010, the accumulated FDI inflows in China reached USD (United States

Dollars) 1,048.38 billion. FDI encourages economic growth by increasing capital

formation, providing employment opportunities and introducing advanced

technologies. However, due to its more favourable geographic location and the

industrial development in the coastal region, more than 70 per cent of FDI inflows

have gone to the coastal region, leaving the three inland regions, namely the northeast,

central and western regions, to share the remainder. The issue of continued uneven

regional distribution of FDI inflows in China has given rise to concerns regarding its

effect on increasing regional economic development disparities.

Two issues represent a substantial challenge to current policy-makers attempting to

reduce regional disparity by guiding FDI inflows into less developed inland regions.

The first relates to the identification of the location determinants of FDI inflows for

individual regions, the second issue requires the identification of the most appropriate

FDI for each region based onthe region’s comparative advantages.

In order to address these two issues, multiple regression models based on panel data

sets are used to examine the location determinants of FDI inflows in each region, as

well as the combinations of location determinants of FDI inflows and different

technology categories.

The results confirm that FDI is attracted by different factors and these factors are

specific to each region and FDI in technologies at difference levels of intensity are

attracted by different location factors.

Based on the empirical results, recommendations for policy-makers are provided,

which will help them to better utilise FDI inflows based on regional comparative

advantages and thus to reduce regional disparities.

ii

Declaration

This thesis comprises only my original work towards to the PhD. To the best of my

knowledge and belief, this thesis contains no material previously published or written

by another person, except when due references is made in the text of the thesis.

-------------------------------------------------

Yiyang Liu

December 2012

iii

Acknowledgements

Foremost, I would like to express my sincere gratitude to my principle supervisor,

Associate Professor Kevin Daly, for his continuous support of my PhD study and

research. His guidance helped me throughout the research for, and writing of, my

thesis.

I also wish to thank Dr. Maria Estela Varua and Dr.Selim Akhter, my thesis co-

supervisors, for their patience, motivation, enthusiasm, and immense knowledge and

interest and their continual encouragement over the past three years.

Finally, I wish to thank Jason, John and Xiaoxi for their support and encouragement

throughout the period of my PhD enrolment.

iv

Contents

Abstract............................................................................................................................. i

Declaration....................................................................................................................... ii

Acknowledgements ........................................................................................................ iii

List of Tables ................................................................................................................. vii

List of Figures.................................................................................................................. x

List of Abbreviations .................................................................................................... xii

PREFACE.....................................................................................................................xiii

Chapter 1: Introduction ............................................................................................... 14

1.1 Background............................................................................................................... 14

1.2 Objectives and Contributions.................................................................................... 21

1.2.1 Objectives .............................................................................................................. 21

1.2.2 Contributions.......................................................................................................... 23

1.3 Thesis Structure and Chapter Outlines ..................................................................... 24

Chapter 2: Overview of Foreign Direct Investment in China .................................. 26

2.1 Aggregate FDI Inflows in China............................................................................... 26

2.1.1 The Experimental Phase, 1979–1991 .................................................................... 27

2.1.2 The Boom Phase, 1992–2001 ................................................................................ 28

2.1.3 The Post-World Trade Organization Phase, 2002–2010 ....................................... 28

2.2 Regional Distribution of FDI Inflow in China.......................................................... 29

2.3 Sector and Industrial Distribution of FDI Inflow to China....................................... 34

2.4 Source Countries of FDI Inflow in China................................................................. 38

2.5 FDI from Hong Kong................................................................................................ 42

2.5.1 Industry Distribution of Hong Kong Direct Investment in China ......................... 43

2.5.2 Geographical Distribution of Hong Kong Direct Investment in China ................. 45

v

2.6 FDI from the United States ....................................................................................... 46

2.6.1 Industrial Distribution of US Direct Investment in China ..................................... 47

2.6.2 Geographical Distribution of US Direct Investment in China............................... 49

Chapter 3: Theoretical Models of Determinants of Foreign Direct Investment ..... 51

3.1 Ownership Advantage Theory .................................................................................. 51

3.2 Internalisation Theory............................................................................................... 53

3.3 Location Theory........................................................................................................ 54

3.4 The Eclectic Paradigm (Ownership, Location and Internalisation Framework) ......55

3.5 Spatial Interdependence Effects................................................................................ 57

Chapter 4: Regional Determinants of Foreign Direct Investment in China ........... 60

4.1 Regional Distribution of FDI Inflows across Four Regions in China ...................... 61

4.2 Literature Review on Location Determinants of FDI ............................................... 62

4.3 Determinant Factors of Regional Distribution of FDI Inflow .................................. 64

4.4 Data and Analytical Framework ............................................................................... 66

4.5 Empirical Results ...................................................................................................... 69

4.6 Conclusions............................................................................................................... 74

Chapter 5: Regional Analysis of Determinants of Foreign Direct Investment

in China’s Manufacturing Industry ............................................................................ 76

5.1 Transformation and Regional Distribution of FDI Inflows in China’s

Manufacturing Industry............................................................................................. 77

5.2 Literature Review on Location Determinants of FDI Inflows in the

Manufacturing Industry in China .............................................................................. 83

5.3 Determinant Factors of FDI in Manufacturing Industry........................................... 85

5.4 Data and Analytical Framework ............................................................................... 89

5.5 Empirical Results ...................................................................................................... 90

5.5.1 Empirical Results and Discussion of Location Determinants for Low-tech

Manufacturing FDI across the Four Regions ........................................................ 93

5.5.2 Empirical Results and Discussion of Location Determinants of High-tech

Manufacturing FDI across Four Regions .............................................................. 97

5.6. Conclusion ............................................................................................................... 99

Chapter 6: Summary of Findings, Policy Implications and Conclusions .............. 101

vi

6.1Summary of the Study ............................................................................................. 101

6.2Policy Review and Recommendations..................................................................... 104

6.3Future Research ....................................................................................................... 108

Appendix...................................................................................................................... 110

References .................................................................................................................... 120

vii

List of Tables

Table 1.1: Regional Distribution of Employees in FFEs, 1992–2010 (1,000

persons, per cent). ......................................................................................................... 19

Table 2.2: Amount and Shares of Utilised FDI Inflow across China’s Four

Regions, 1990–2009 (USD billion, percentage)........................................................... 31

Table 2.3: Amount and Percentage of Exports from Foreign Funded

Enterprises(FFEs) Per Total Regional Exports across China’s Four Regions,

1992–2010 (USD billion, percentage). ......................................................................... 34

Table 2.4: Sector Distribution of Utilised FDI Inflow to China, 1997–2010

(USD million, percentage). ........................................................................................... 35

Table 2.5: Amount and Share of Annual Utilised FDI Inflow by Source

Countries, 1985–2010 (USD billion, percentage). ...................................................... 41

Table 2.6: Characteristics of FDI from Hong Kong, 2001–2010 (USD million,

percentage)..................................................................................................................... 43

Table 2.7: Characteristics of FDI from US, 2001–2010 (USD million,

percentage)..................................................................................................................... 47

Table 3.1: Ownership Advantages for FDI................................................................. 52

Table 3.2: Mode of Entry of Foreign Investment Based on Duning’s OLI

Framework. ................................................................................................................... 56

Table 3.3: Spatial Effects of Different Types of Motivations for FDI...................... 59

Table 4.1: Determinants of FDI Inflow....................................................................... 66

viii

Table 4.2a: Descriptive Statistics—Coastal Region................................................... 70

Table 4.2b: Descriptive Statistics—Northeast Region............................................... 70

Table 4.2c: Descriptive Statistics—Central Region................................................... 71

Table 4.2d: Descriptive Statistics—Western Region. ................................................ 71

Table 4.3: Location Determinants of Regional Distribution of FDI Inflows

across Four Regions, 2001–2009.................................................................................. 72

Table 5.1: Classifications of Manufacturing Industries by High- and Low-

Technology Categories.................................................................................................. 78

Table 5.2: Amount and Shares of FDI Utilised in Manufacturing by Industry,

(USD billion, percentage). ............................................................................................ 80

Table 5.3: Determinants of FDI Inflow....................................................................... 88

Table5.4a:Descriptive Statistics-Coastal Region........................................................ 91

Table 5.4b: Descriptive Statistics-Northeast Region ................................................. 92

Table 5.4c: Descriptive Statistics-Central Region...................................................... 92

Table 5.4d: Descriptive Statistics-Western Region.................................................... 92

Table 5.5a. Determinants of Foreign Capital in Low-tech Manufacturing

Industries among Four Regions................................................................................... 94

Table 5.5b. Determinants of Foreign Capital in High-tech Manufacturing

Industries among Four Regions................................................................................... 98

Table A-1: Pattern of China’s Opening Up to Foreign Investors. ......................... 110

Table A-2: Industry Distribution of Utilised FDI Inflow, 2000–2010, (USD

billion, percentage)...................................................................................................... 111

ix

Table A-2 (Cont): Industry Distribution of Utilised FDI Inflow, 2000–2010,

(USD billion, percentage). .......................................................................................... 112

Table A-3: Utilised FDI from Hong Kong Investors, by industry 2001–2010,

(USD million, percentage). ......................................................................................... 113

Table A-4: Utilised FDI in Manufacturing Industry from Hong Kong

Investors by High and Low-technology Categories, 2001-2010(USD million,

Percentage) .................................................................................................................. 114

Table A-5: Provincial Distribution of Utilised FDI from Hong Kong, 2001–

2010 (USD million, percentage) ................................................................................. 115

Table A-6: Utilised FDI from USInvestors, by industry 2001–2010, (USD

million, percentage)..................................................................................................... 116

Table A-7: Utilised FDI in Manufacturing Industry from Hong Kong

Investors by High and Low-technology Categories, 2001-2010 (USD million,

percentage)................................................................................................................... 117

Table A-8: Provincial Distribution of Utilised FDI from US, 2001–2010 (USD

million, percentage)..................................................................................................... 118

Table A-9: Classification of Manufacturing Industry by Technology Intensity.

....................................................................................................................................... 119

x

List of Figures

Figure 1.1: Regional Distribution of FDI Inflows, 1987–2009 (USD billion). ......... 15

Figure 1.2: GDP Growth Rate across China’s Four Regions, 1980–2010 (per

cent). ............................................................................................................................... 17

Figure 1.3: GDP Per Capita across China’s Four Regions, 1978–2010 (RMB

Yuan).............................................................................................................................. 18

Figure 1.4: Average Wages across China’s Four Regions, 1978–2010 (RMB

Yuan).............................................................................................................................. 21

Figure 2.1: FDI Inflows into China, 1979–2010 (USD billion).................................. 26

Figure 2.2: Geographical Location of China’s Four Regions. .................................. 30

Figure 2.3: Share of Cumulative FDI Inflows across China’s Four Regions,

1987–2009....................................................................................................................... 33

Figure 2.4: Sector Distribution of Utilised FDI Inflows to China, 1997–2010

(USD million). ................................................................................................................ 36

Figure 2.5: Industrial Distribution of Accumulated FDI Inflows to China,

2000–2010 (USD billion). .............................................................................................. 37

Figure 2.6: Source Continents of FDI Inflows to China............................................ 38

Figure 2.7: Utilised FDI from Hong Kong Investors, by industry 2001–2010,

USD million.................................................................................................................... 44

Figure 2.8: Regional Distribution of Annual FDI from Hong Kong, 2001–

2010 (USD million). ....................................................................................................... 46

xi

Figure 2.9: Utilised FDI from U.S Investors, by industry 2001–2010, USD

million............................................................................................................................. 48

Figure 2.10: Regional Distribution of Utilised FDI from US, 2001–2010 (USD

million). .......................................................................................................................... 50

Figure 4.1: Regional Distribution of FDI Inflows, 1978–2009 (USD billion). ......... 62

Figure 4.2: Comparison of Realised FDI Inflow into China between the

National Statistical Yearbook and the 30 Provincial Yearbooks, 1987–2009

(USD billion). ................................................................................................................. 68

Figure 5.1: Utilised FDI Inflow inthe High and Low-technology

Manufacturing Industriesin China in2001, 2005 and 2008 (USD billion). .............. 79

Figure 5.2A: Regional Distribution of Utilised FDI in High-technology

Manufacturing Industries, 2001–2008 (percentage). ................................................. 82

Figure 5.2B: Regional Distribution of Utilised FDI in Low-technology

Manufacturing Industry, 2001–2008 (percentage). ................................................... 82

xii

List of Abbreviations

AFC Asian Financial Crisis

ASEAN Association of Southeast Asian Nations

EJV Equity Joint Ventures

EU European Union

FDI Foreign Direct Investment

FFE Foreign Funded Enterprises

GB/T Industrial Classification & Code of National Economy

GDP Gross Domestic Product

GFC Global Financial Crisis

IPR Intellectual Property Right

ISIC International Standard Industrial Classification of All Economic

Activities

NIE Newly Industrialised Economy

M&A Merge and Acquisition

MNE Multinational Enterprise

NDRC National Development and Reform Commission

NETDZ National Economic and Technology Development Zones

NNHIDZ National New and High-Technology Industrial Development Zones

OECD Organisation for Economic Co-operation and Development

OLI Ownership,Location, Internalisation

R&D Research and Development

RMB RenMinBi

SEZ Special Economic Zone

SOE State Owned Enterprise

US United States

USD United States Dollars

WFOE Wholly Foreign-Owned Enterprises

WTO World Trade Organization

xiii

PREFACE

Some of the research in this thesis has been previously published or presented at

international conferences. I duly acknowledge refereesand conference participants’

comments and suggestions which have greatly helped this thesis.

Chapter four (4) has been published at:

Liu, K, Daly, K & Varua, ME 2012, 'Determinants of regional distributionof FDIinflows across China's four regions',International Business Research, vol. 5, no. 12,pp. 119-26.

Chapter five (5) has been published at:

Liu, K, Daly, K & Varua, ME 2012, 'Regional determinants of foreign directinvestment in manufacturing industry',International Journal of Economics andFinance, vol. 4, no. 12, pp. 178-92.

This paper has also been presented as:

Liu, K & Daly, K. "Foreign Direct Investment in China Manufacturing Industry -

Transformation from a Low Tech to High Tech Manufacturing", Bangkok, Thailand.

14

Chapter 1: Introduction

1.1 Background

Foreign Direct Investment (FDI) was prohibited in China until the adoption of the

‘open-door’ policy in 1979. And after joining the World Trade Organisation (WTO)

in 2001, China gradually became one of the world’s most attractive destinations for

FDI inflows. By the end of 2010, the accumulated FDI inflows to China had reached

USD 1,048.38 billion. Over the past threedecades, FDI has promoted China’s

economic development by increasing capital formation, creating job opportunities,

improving labour quality and transferring new advanced technology (Cheung & Pin

2004; Mody & Wang 1997; Kuo& Yang 2008; Lin & Kwan 2011; Yao 2006;

Blomstrom &Kokko 1998; Lin et al 2009). However, the FDI inflows have been

concentrated in the most developed coastal region, leaving the three inland regions,

namely the central, northeast and western regions1, far behind. Over the period 1987–

2009, 79.1 per cent of the total utilised FDI inflows were located in the coastal region,

while the northeast, central and western regions received only 7.2 per cent, 7.9 per

cent and 5.8 per cent, respectively.

The uneven regional distribution of FDI inflows has been a crucial factor leading to

regional economic disparity between the coastal region and the other three inland

regions (Chen & Wu 2005; Golley 2002; Yao & Zhang 2001; Fu 2007; Wan, Lu &

Chen 2007; Whalley &Xin 2010; Wen 2007; Wei, Yao & Liu 2009; Yu et al. 2008;

Yu et al. 2011; Yang 2002). In order to promote economic development in the less

developed inland regions, China has gradually adopted a‘western development’

strategy, the so called‘revitalising the old industrial base in northeast region’ strategy

and the ‘rise of the central China’ strategy, as well as implementing preferential

1The coastal region includes ten provinces: Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang,

Fujian, Shandong, Guangdong, Hainan. The northeast region has three provinces: Liaoning, Jilin and

Heilongjiang.Six provincesareincluded in central region: Shanxi, Anhui, Jiangxi, Heinan, Hunan and

Hubei; and 11 provincesare included inthe western region: Chongqing, Sichuan, Guizhoyu, Yunan,

Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Guangxi and Inner Mongolia. Tibet is not included in thisstudy.

15

policies for FDI to invest in inland regions. Figure 1.1 shows, there is a trend towards

regional distribution of FDI inflows to China which is becoming more diversified

since 2000. Over the period 2000–2009, FDI inflows to the northeast, central and

western regions increased from USD 2.68 billion - USD 18.95 billion, USD 2.66

billion - USD 21.45 billion and USD 1.96 billion - USD 14.81 billion respectively.

Further, over this period, the share of FDI received in the northeast region increased

from 6.7 per cent to 12.3 per cent, for the central region, FDI increased from 6.6 per

cent to 13.9 per cent while for the western region FDI share increased from 4.9 per

cent to 9.6 per cent. Nevertheless, although these three regions have developed

strategies and implemented preferential policies to attract more FDI inflows, the

coastal region holds the dominate position in China, in both dollar terms and the

overall percentage, which exacerbate regional economic disparities.

Figure 1.1: Regional Distribution of FDI Inflows, 1987–2009 (USD billion).

Source: 30 Provincial Statistical Yearbooks, calculated by the author.

In term of gross domestic product (GDP) growth rates, the coastal region grew at a

faster rate than the inland regions in the early 1980s, as foreign investment was only

allowed in the coastal region. Demurger et al (2002) argued that the geographical

0

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1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

USDbillion

Coastal Norttheast Central Western

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location of the coastal region and preferential policies implemented in coastal region

in earlier stage of open-up explain the faster growth rate of the coastal region. Chen

and Fleisher (1996) and Fleisher and Chen (1997) also argue if non-coastal regions

could obtain the same level of foreign direct investment as the coastal region, the gap

between coastal and non-coastal regions would fall. Over the period 1980–1988, the

coastal region grew at 11.3 per cent on average, while the GDP growth rates in the

northeast, central and western regions were 9.6 per cent, 9.8 per cent and 10.0 per cent,

respectively. Figure 1.2 shows that there was a dramatic drop in the GDP growth rate

in the period 1989–1990; this was due to the Tiananmen Square incident. In order to

reverse the investment climate, the Chinese government adjusted foreign investment

laws and regulations and opened more areas along the coastal region to foreign

investors. As a result, the coastal region began to grow even faster, and the economic

development gap between the coastal and inland regions was increased.

Over the period1992–1999, the national GDP growth rate was 10.9 per cent, while the

growth rate in coastal region was 13.9 per cent, 3 per cent higher than national

average, while the northeast and western regions grew at 9.6 per cent and 10.2 per

cent, respectively. As Figure 1.2 shows, since 2000, there has been a convergence of

GDP growth rates between all regions. On one hand, this was a result of

implementing three regional development strategies and attracting foreign investment,

while on the other; the recent Global Financial Crisis (GFC) in late 2008 has had the

greatest negative impact on the coastal region which has experienced a reduction in

the rate of economic growth. In the coastal region, most FDI is export-oriented, while

the recession in the United States (US) and the European Union (EU) reduced demand

for imports, which in turn reduced the economic growth rate in the coastal region.

Between 2008 and 2010, the growth rate of the coastal region was the slowest

compared to the other three regions.

17

Figure 1.2: GDP Growth Rate across China’s Four Regions, 1980–2010 (percent).

Source: China’s Statistical Yearbooks, 1981–2011.

In terms of GDP per capita, the differences between the coastal region and the

northeast region were insignificant in the early 1980s. In 1978, when the national

GDP per capita was RenMinBi(RMB) 381 Yuan, the GDP per capita in coastal and

northeast regions was RMB 735 Yuan and RMB 542 Yuan, while in the central and

western regions, the GDP per capita was only RMB 289Yuan and RMB 295 Yuan,

respectively. Figure 1.3 shows that the difference in GDP per capita between the

regions has widened since the central government enforced the ‘uneven development’

strategy in the early 1990s.This strategy allowed the coastal region to develop first by

attracting large amounts of foreign capital, accepted regional disparities as inevitable

and adoptinga ‘trickle-down’ approach of growth from the coastal region to the

inland regions. Although the central government has taken steps to bridge the

spiralling gap between the coastal and inland regions by implementing the ‘western

development’, ‘rise of the central China’ and ‘revitalising the old industrial base in

northeast area’ development strategies, since 2000, the regional disparities in terms of

GDP per capita did not reduce as expected, but rather, were increased (Figure 1.3). In

2010, the GDP per capita in the coastal region was RMB 50,793 Yuan, 150 per cent

0

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1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010

%

Coastal Northeast Central Western

18

higher than northeast region, and more than double those of the central and western

regions.

Figure 1.3: GDP Per Capita across China’s Four Regions, 1978–2010 (RMBYuan).


FDI inflows provide great employment opportunities for host countries. In 2010, there

were 18.23 million people employed by Foreign Funded Enterprises (FFEs);

compared to 2.21 million in 1992, this has increased by 8.25-fold. Table 1.1 shows

the number of persons who were employed by FFEs within the four regions over the

period 1992–2010. In the coastal region, the number increased from 1.8 million in

1992 to 15.3 million in 2010, in the northeast region, the number of persons employed

by FFEs increased from 0.2 million to 0.7 million, while in the central and western

regions it increased from 0.095million and 0.1 million to 1.4 million and 0.8million,

respectively. Although the absolute number of people who were employed in FFEs

increased in all regions, the relative share shows a different picture. In 1993, nearly 80

per cent of people who employed by FFEs located in the coastal region; in 2010 this

figure increased to 84.2 per cent. This indicates that FFEs are providing more

employment opportunities in the coastal region rather than the inland regions.

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Table 1.1: Regional Distribution of Employees in FFEs, 1992–2010 (1,000

persons, per cent).

Year National Total Coastal Northeast Central WesternPersons Persons % Persons % Persons % Persons %

1992 2,210 1,802 81.5 204 9.2 95 4.3 106 4.81993 2,880 2,292 79.6 186 6.5 226 7.8 176 6.11994 4,060 3,153 77.7 339 8.3 337 8.3 232 5.71995 5,130 3,930 76.6 443 8.6 448 8.7 311 6.11996 5,400 4,165 77.1 433 8.0 454 8.4 343 6.41997 5,810 4,509 77.6 463 8.0 470 8.1 361 6.21998 5,870 4,628 78.8 451 7.7 452 7.7 340 5.81999 6,120 4,910 80.2 449 7.3 419 6.8 340 5.62000 6,420 5,215 81.2 463 7.2 418 6.5 328 5.12001 6,709 5,496 81.9 459 6.8 424 6.3 336 5.02002 7,580 6,255 82.5 494 6.5 458 6.0 372 4.92003 8,630 7,189 83.3 545 6.3 494 5.7 405 4.72004 10,330 8,666 83.9 620 6.0 618 6.0 418 4.02005 12,450 10,558 84.8 663 5.3 768 6.2 448 3.62006 14,070 11,964 85.0 696 4.9 927 6.6 471 3.32007 15,830 13,482 85.2 735 4.6 1,046 6.6 570 3.62008 16,220 13,790 85.0 728 4.5 1,080 6.7 614 3.82009 16,988 14,334 84.4 722 4.3 1,199 7.1 732 4.32010 18,232 15,347 84.2 732 4.0 1,347 7.4 805 4.4Source: China’s Statistical Yearbooks, 1993–2011.

FDI not only improves human capital accumulation, it also encourages individuals to

participate in higher education (Narula& Marin 2003; Hartmut et al 2010; Xu 2000).

The uneven regional distribution of FDI inflows and employment also contributes to

the vast regional human capital development disparity and wage disparity in China

(Zhuang 2011; Chen, Ge& Lai 2011; Xu 2000; Fleisher & Wang 2004) and have

negative impacts on regional growth (Cai et al 2002; Fleisher et al 2010). Almeida

(2010) argues, especially for middle-income countries, international integration and

technology transferred from FDI tend to be more skilled based; therefore, the demand

for skilled labour is increasing. Zhuang (2011) also argues that foreign investors are

more skill intensive than domestic firms and this investment may enhance the host

location’s human capital accumulation by technology diffusion. Due to the lack of

skilled labour force, foreign investors always offer higher wages compared to

domestic enterprises due to government restrictions and asymmetric information of

20

the labour market, preventing wage disparities between parent and foreign affiliate

firms and reducing labour turnover. Lipsey & Sjoholm (2004) confirm this argument

by examining foreign investors in Indonesia’s manufacturing industry and find out

foreign investors are paying premium for their employee than domestic firms of a

given education level.

Figure 1.4 shows that the average wage per employee in the coastal region is much

higher than in the other three regions, and also that the average wage grew more

rapidly in this region compared to the inland regions. Over the period 1984–1991, the

average wage per employee grew 15.3 per cent per year in the coastal region, while

the average growth rates in the northeast, central and western regions were 12.7 per

cent, 13.1 per cent and 12.6 per cent, respectively. With the further opening of the

coastal region in 1992, the average wage per employee grew even faster. The higher

wages paid in the coastal region attracts migrants in the form of both low-skilled

labours and high-skilled workers from the less developed interior regions to the

coastal region. On one hand, this may cause overpopulation in the coastal region and

widening of the income gap; on the other hand, this may also pull regional

development toward the inland regions. Although China has given priority to

developing the inland regions and implementing preferential policies to guide foreign

investors to invest in these less developed inland regions since 1999, large amounts of

FDI are still located in the coastal region, which in turn enlarges the income

disparities between the coastal and non-coastal regions.

21

Figure 1.4: Average Wages across China’s Four Regions, 1978–2010 (RMBYuan).


To summarise, during the past three decades of reform and opening up, FDI has

accelerated China’s economic development. However, the uneven regional

distribution of FDI, in a sense, is one of the main reasons which lead to regional

economic disparities. In the long run, regional economic disparities may leadoutflow

of human capital and material resources from the poorer inland regions to richer

coastal region, regional conflicts, local protectionism, social and political conflicts

(Fan 1997). Mitigating regional economic disparities has now become the top priority

for China’s national development strategy. Due to the interrelationship between

regional distribution of FDI inflows and economic development, attracting FDI

inflows to less developed regions is important for policy-makers to promote economic

development and reduce regional disparities.

1.2 Objectives and Contributions

1.2.1 Objectives

The interrelationship between location distribution of FDI inflows and disparities in

regional economic development, leads many researchers to believe that attracting FDI

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inflows into less developed regions may have important implications for regional

economic development, with a particular effect toward reducing regional disparity

(Sun & Chai 1998; Wen 2007; Chen & Wu 2005; Chen, Ge& Lai 2011; Wei, Yao &

Liu 2009). The regional distribution of FDI inflows reflects variations inthe regional

investment environment, such as government incentives, economic development,

social development, natural resource endowments and the quality and cost of the local

labour force. Since Chinaadopted the ‘western development strategy’, ‘revitalising

the old industrial base in the northeast area’ and ‘rise of Central China’ development

strategies, the investment environments have been improved in all regions. Thus, the

first objective of this thesis is to identify, test and compare the regional determinants

of FDI inflows across allof China’s four regions, based on empirical test results

providing recommendations to policy-makers on identified parameters with which to

attract FDI inflows across the regions.

Over the past three decades, more than 60 per cent of FDI inflows to China have

origins in the secondary sector, especially in manufacturing industry. China has not

only increased the attractiveness of its low-technology, labour-intensive

manufacturing production, but is also increasing the attractiveness of its high-

technology, capital-intensive manufacturing production. Temouri et al (2010) argue

that low production costs and better technological development in host country are

two main drivers to attract high-tech FDI. However, different regions of China are in

different stages of economic development and have different regional characteristics.

For example, the coastal region is more industrially developed and has a better skilled

labour force and more research institutions and facilities, while the interior regions are

less developed, but are rich in unskilled labour and natural resources. Thus, from the

perspective of comparative advantages, the question arises as to whether China should

continue to attract FDI in high-technology manufacturing industries in the more

developed coastal region while at the same time, increasing the attractiveness of FDI

in traditional low-technology, highly labour-intensive manufacturing industries in the

less developed interior regions? What are the location determinants of high-

technology FDI and what are the location determinants of low-technology FDI?

Hence, the second objective is to investigate the location determinants of FDI inflows

in each region in both the high- and low-technology categories, and based on the

findings, to help policy-makers to attract the best FDI inflows to each region.

23

In this thesis, multiple regression models based on panel data sets are used to examine

the location determinants of FDI inflows in each region, as well as the combinations

of location determinants of FDI inflows and different technology categories. The data

have been collected from the China National Statistical Yearbooks, 30 Provincial

Statistical Yearbook, China’s Labour Yearbooks and the China Industrial Statistical

Yearbooks.

1.2.2 Contributions

By investigating the location determinants of FDI inflows and the combination of

location determinants for different technology categories, this study contributes to

current research in various ways. Firstly, until the Eleventh Five-Year Plan (2006–

2010), all 31 provinces of China were classified into three regions, relatively poorly

based on geographic considerations, namely: the coastal region, the central region and

the western region2 . However, after China was decentralised, opened up and

experienced an explosion of trade and FDI, the provinces in each region were at

different levels of economic development. For example, in 2000 the GDP per capita

in the richest province in the coastal region (Shanghai) was USD 4,173, eight times

higher than the poorest province (Guangxi) in the same region, which had a GDP per

capita of only USD 522 (China Statistical Yearbook 2001).As a result, the dataset

used and empirical results from previous research may be inaccurate. In the Eleventh

Five-Year plan (2006–2010), the National Development and Reform Commission

(NDRC) re-classified all provinces into four regions based on their regional

geographic characteristics, industrial development, and national development

planning. A new region, named the northeast regionwas formed from one province

originally from the coastal region and two provinces originally belonging to the

central region. Most research on regional determinants of FDI inflows in China are

based on the old classification. However, this thesis uses adjusted economic data

according to this new regional classification, and thus the results will provide more

accurate and reliable information for policy-makers.

2 The original regional classification was identified in the Seventh Five-Year Plan

(1986-1990)

24

Second, there has been considerable research on regional determinants of FDI inflows

in the manufacturing industries; however, this is either based on the previous regional

classification or ignores the level of technology involved in different manufacturing

activities. FDI inflows within the different technology levels of manufacturing

activities are attracted by different factors. For instance, the supply of skilled labour is

more important for FDI in high-technology manufacturing activities, while the supply

of cheap inputs is more important for FDI in low-technology manufacturingactivities.

By classifying all manufacturing activities into two different categories based on their

technology level and investigating the regional determinants of FDI inflows in both

categories, this thesis will allow a more comprehensive analysis and formulation of

appropriate recommendations for policy-makers, which will help them to better utilise

FDI inflows based on regional comparative advantages and thus to reduce regional

disparities.

1.3 Thesis Structure and Chapter Outlines

This thesis consists of six chapters. Chapter two provides an overview of FDI inflows

to China. The content includes an overview of aggregate FDI inflows over the past

three decades, followed by an overview of the regional distribution of FDI inflows,

sector and industrial distribution of FDI inflows across China in addition to itemising

the source countries of China’s inward FDI. In addition, this research compares the

differences between FDI inflows from developed countries relative todeveloping

countries. Chapter three presents a review of the different theoretical models used to

explain FDI activities. Overall, five theories will be discussed: Ownership advantage

theory, internalisation theory, location theory, the eclectic paradigm (OLI framework)

and spatial interdependence effects on FDI.

In Chapter four, the focus shifts to the analysis of location determinants of FDI inflow

at the regional level. Four regions are analysed individually by running a multiple

regression model four times, with a particular focus on the effects of market size,

labour quality, labour costs, physical infrastructure development, agglomeration

effects, policy implementation and the degree of openness on regional FDI inflows in

the period between 2001 and 2009. There are four sets of panel data: ten (10)

25

provinces for the coastal region, three (3) provinces for the northeast region, six (6)

provinces for the central region and eleven (11) provinces for the western region.

In Chapter five, the location determinants of FDI in both high-technology and low-

technology manufacturing activities are analysed. The chapter begins with a

classification of the high-technology and low-technology categories, followed by an

examination of the regional distribution of FDI inflows in both categories, and reveals

a situation of significantly uneven regional distribution in both categories. In this

chapter, two questions will be answered: what are the regional location determinants

for high-technology FDI? And what are the location determinants of low-technology

FDI? To answer these questions, a set of hypotheses relating to regional location

factors affecting FDI in the different technology categories are developed and

discussed. An econometric test of the hypotheses is conducted using a multiple

regression technique with panel data for 30 provinces over the nine (9) years from

2000 to 2008. Based on the regression results, the uneven regional distribution of FDI

inflows in the high- and low-technology categories are explained.

Chapter six serves as the conclusion of this study. It summarises the main findings

and conclusions of the thesis and provides policy recommendations and implications

for further research.

26

Chapter 2: Overview of Foreign Direct Investment in China

This section provides an overview of FDI inflows to China, including aggregate FDI

inflows over the period 1979–2010 (Section 2.1), regional distribution of FDI inflows

(Section 2.2), industrial and sectoral distribution of FDI inflows (Section 2.3) and

source countries of FDI to China (Section 2.4). The final sections (Sections 2.5 and

2.6) will provide a comparative analysis of Hong Kong and the US as source

countries for China’s FDI inflow.

2.1 Aggregate FDI Inflows in China

Over the past three decades, the path of FDI inflows to China can be classified into

three phases (Chen 2011): the experimental phase: 1979–1991; the boom phase:

1992–2001; and the post-WTO phase: 2002–2010 (Figure 2.1).

Figure 2.1: FDI Inflows into China, 1979–2010 (USD billion).

Sources: China Statistical Yearbook, at current USD value.

0

10

20

30

40

50

60

70

80

90

100

1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009

USDbillion

27

2.1.1 The Experimental Phase, 1979–1991

The first phase began in 1979, when theLaw of the People’s Republic of China on

China-Foreign Equity Joint Ventures(EJV Law) was enacted, and four Special

Economic Zones (SEZs) were established to attract FDI. Three of these SEZs were

created in Guangdong Province and another one in Fujian Province; both provinces

are located inChina’s coastal region3. Foreign investors investing in SEZs were given

preferential treatment, including income tax exemptions and import tariff refunds. In

1984, another fourteen(14) coastal cities4 were opened to foreign investment after

Comrade Deng Xiaoping inspected one of the SEZs and pointed out, ‘Besides the

existing SEZs, we could further open up several areas, including some port cities,

which won’t be called special zones, but will enjoy the same favourable policies

granted to the SEZs.’ Thus, a total of fourteen (14) coastal cities were approved by the

State Council to establish National Economic and Technology Development Zones

(NETDZs). The aims of these NETDZs were to attract foreign investment in areas of

high-grade manufacturing products to increase exports and absorb new technologies

and management know-how. In 1985, another three (3) delta zones were identified,

these were the Yangtze River Delta, Pearl River Delta and South Fujian Triangle

regions.

Brickley (1988) argues that the EJV law was enthusiastically greeted by foreign

investors, however, due to its vagueness and lack of a clearly defined legal framework

to govern investment, the policy failed to attract a sufficient quantity of foreign

investment. Thus, in 1986, the Chinese government amended initial EJV law by

relaxing restrictions on expatriation of profits and dividends and allowing foreign

nationals to be the chairperson of a company’s board of directors. It also promulgated

the Law of People’s Republic of China on Wholly Foreign-Owned Enterprises

(WFOE Law),this change permitted greater freedom of independent operations for

foreign enterprises and granted more tax incentives for foreign investment.Over this

time frame, China also extended more areas further north and south: in the northern

coastal area, the Liaodong and Shandong Peninsulas and the Bohai Sea Rim were

3Detailed pattern of China’s open-up is in Table A-1, Appendix

4 The 14 coastal cities are Dalian, Qinghuangdao, Tianjin, Yantai, Qingdao, Lianyungang, Nantong,Shanghai, Ningbo, Wenzhou, Fuzhou, Guangzhou, Zhanjiang and Beihai.

28

opened for foreign investment; in the south, Hainan Island was isolated from

Guangdong Province in 1988 and became the fifth SEZ to attract FDI. Shanghai

Pudong was also listed in the development and opening up agenda, with the objective

of building Shanghai into an international financial trade and economic centre. As

shown in Figure 2.1, although China continually implemented new laws and opened

more areas for foreign investment, the response from foreign investors was not as

enthusiastic as anticipated by Central Government as the accumulated FDI inflows

were only USD 36.1 billion in the period between 1979 and 1991.

2.1.2 The Boom Phase, 1992–2001

The second phase of FDI inflow to Chinabegan following Comrade Deng Xiaoping’s

southern tour in 1992, when he pointed out that China should open more cities to

foreign investment. In the second phase, the Chinese government permitting number

of inland and border cities from the northeast to the southwest to open for foreign

investment. At the same time, several specific laws and regulations were introduced to

protect the legal status of foreign investors. Figure 2.1 shows the results were

remarkable in attracting FDI inflows to China with FDI increasing by 150 per cent or

USD 27.52 billion over the period 1991-1993, this growth rate continued until 1996.

With the onset of the 1997Asian Financial Crisis (AFC), FDI inflows to China

declined severly, with annual growth rates of only 0.46 per cent over 1997–1998, and

-11.0 per cent over 1999–2000 (Figure 2.1). This significant in fall in FDI inflow to

China was due to the major source countries (Hong Kong, Macau, Taiwan, South

Korea, Japan and Singapore) have been at the centre of the AFC crisis. However, FDI

inflows recovered quickly and reached a growth rate of 15.1 per cent in 2001, the year

that China became a member of the WTO.

2.1.3 The Post-World Trade Organization Phase, 2002–2010

The final phase of FDI inflow began after China became the 143th member of the

WTO in late 2001. With its accession to the WTO, China strengthened its legal

system, liberalised its markets, and also made certain reforms in terms of tariff

reduction, foreign exchange regulation, export requirements and also opened nearly

29

all industries to foreign investors. In addition, China continually set up different

special zones in order to attract FDI, such as the NETDZs, National New and High-

Technology Industrial Development Zones (NNHIDZ) and Export Processing Zones.

By the end of 2006, the sevarious types of zones covered all provinces in China. As a

result, the FDI inflows increased from USD 46.88 billion in 2001 to USD92.4 billion

in 2008. The current GFC which began in 2008 has had significant negative effects on

global FDI flows with China been no exception. Globally, FDI dropped from USD

1.77 trillion in 2008 to USD 1.11 trillion in 2009, which is equivalent to 37.3 per cent

decline, while the FDI inflows into China have declined by 2.67 per cent from USD

92.4 billion in 2008 to USD 90.0 billion in 2009. Compared with the decline in global

FDI, the fall in FDI inflows to China has been moderate. Chen (2011) explains this

resilience is due toChina’s relatively good infrastructure, abundant and well-educated

labour force, low labour costs, macroeconomic policies, large domestic market and

more decentralised FDI policy making China’s overall investment environment

remain attractive. In addition, the RMB 4 trillion Yuan economic stimulus package

has increased investors’ confidence in China. FDI inflows increased sharply in 2010,

reaching USD105.7 billion, which is equivalent to a 17.5 per cent increase.

2.2 Regional Distribution of FDI Inflow in China

Although FDI inflows in China have grown steadily over the past three decades, the

geographical distribution of inward FDI acrossChina’s regions has been very uneven.

In order to capture the whole picture of the uneven distribution of FDI inflows in

China, all provinces of China can be divided into four distinct regions by their

different geographical locations and economic development. These are the coastal

region, the northeast region, the central region and the western region (Figure2.2).

Figure 2.2: Geogr

Geographically, the southe

Taiwan. Along the coastal

Sea and the Yellow Sea,

western region has high pl

developing and underdeve

Nepal and Afghanistan. T

construction in the 1950s

industrial development and

planned economy. Howeve

and has a relatively lower

the other regions.

Table 2.2 shows the amount

1987 to 2009. Between 1987

cent of total utilised FDI

central and western regions

respectively. The uneven ge

Coastal N

30

Geographical Location of China’s Four Region

southeast part of the coastal region is next to H

stal region, there is the Southern China Sea, the

a, and sea transportation is well developed. I

gh plateaus, mountains and desert, and is borde

eveloped countries, such as Mongolia, Kaza

n. The northeast region was the priority area

1950s and 1960s and made a historical contr

and national security of China during the ope

ever, this area is currently on a restricted deve

er level of openness, and its economic growth

ount and shares of FDI inflows across the four

n 1987 and 1991, the coastal region received mor

I inflows, while the share of FDI inflows in

gions were 6.4 per cent, 2.9 per cent and

n geographical distribution of FDI inflows across

Northeast Central

gions.

o Hong Kong and

the Eastern China

d. In contrast, the

bordered by many

azakhstan, India,

rea for industrial

ontribution to the

operation of the

evelopment path

th is dwarfed by

four regions from

more than 84 per

s in the northeast,

nd 6.5 per cent,

ross China’s four

Western

31

regions was due to planned industrial development path taken before the Communist

Party takeover of China in 1949 (Lu & Wang 2002) which created an extremely

uneven distribution of infrastructure between the coastal and the non-coastal regions.

The coastal region was much more industrially developed than the central region; and

the central region was in turn far superior to the western region (Yang 1990). In order

to utilise the comparative advantages of the coastal region in terms ofgeographical

location, level of liberalisation and economic development, the Chinese government

decided to develop the coastal region first and implemented preferential policies in

this region to attract FDI.

Table 2.2: Amount and Shares of Utilised FDI Inflow across China’s Four

Regions, 1990–2009 (USD billion, percentage).

YearNational Coastal Northeast Central Western

USDbillion

USDbillion

%USD

billion%

USDbillion

%USD

billion%

1987 1.40 1.12 80.2 0.08 5.5 0.03 2.0 0.17 12.41988 2.71 2.24 82.7 0.14 5.0 0.11 4.1 0.22 8.21989 3.13 2.72 87.0 0.14 4.3 0.09 2.9 0.18 5.81990 3.23 2.77 85.6 0.29 9.0 0.07 2.2 0.11 3.31991 4.34 3.74 86.1 0.35 8.1 0.14 3.2 0.11 2.61992 12.75 10.11 79.3 0.61 4.8 1.51 11.9 0.52 4.11993 26.80 21.17 79.0 1.69 6.3 1.85 6.9 2.09 7.81994 33.65 27.02 80.3 2.08 6.2 2.02 6.0 2.53 7.51995 37.76 30.87 81.8 2.25 6.0 2.42 6.4 2.21 5.91996 42.62 35.19 82.6 2.67 6.3 2.86 6.7 1.90 4.41997 44.90 35.47 79.0 3.34 7.4 3.58 8.0 2.51 5.61998 45.29 36.41 80.4 3.13 6.9 3.39 7.5 2.36 5.21999 39.93 33.35 83.5 1.68 4.2 3.06 7.7 1.84 4.62000 40.14 32.84 81.8 2.68 6.7 2.66 6.6 1.96 4.92001 46.37 37.83 81.6 3.19 6.9 3.42 7.4 1.92 4.12002 52.47 42.05 80.1 4.01 7.6 4.41 8.4 2.01 3.82003 52.94 42.56 80.4 3.34 6.3 5.32 10.0 1.72 3.32004 74.46 56.64 76.1 7.10 9.5 7.58 10.2 3.14 4.22005 80.07 61.14 76.4 5.70 7.1 8.87 11.1 4.35 5.42006 98.48 72.61 73.7 8.45 8.6 11.56 11.7 5.86 5.92007 122.60 86.26 70.4 12.07 9.8 16.54 13.5 7.73 6.32008 143.82 96.52 67.1 15.56 10.8 19.40 13.5 12.34 8.62009 153.97 98.76 64.1 18.95 12.3 21.45 13.9 14.81 9.6Source: 30 Provincial Statistical Yearbooks (1988–2010).

Since areas located in the central, northeast and western regions were opened for

foreign investments in 1992, the share of FDI inflows across the coastal region

continued to fall and the three non-coastal regions became more important for FDI

32

inflows, particularly the central and western regions (Table 2.2). In 1992, the central

region received USD 1.51 billion in FDI, an increase of over 10-fold compared to the

USD 0.14 billion received in 1991, equivalent to an increased share from 3.2 per cent

in 1991 to 11.9 per cent in 1992. For the western region, the FDI inflow increased

from USD 0.52 billion in 1992, equivalent to a national FDI inflow increase from 2.6

per cent in 1991 to 4.1 per cent in 1992. Nevertheless, due to its light regulation,

better industrial development, remoteness from central government, better

infrastructure and proximity to sea ports, the coastal region received USD 10.1 billion

FDI, equivalent to 79.3 per cent of the national total.

In order to encourage more FDI inflows into the underdeveloped inland regions,

active preferential policies have been initiated. These include encouraging foreign

investors to participate in State Owned Enterprises (SOEs) restructuring, exemption

of debt legacies owned by SOEs after approval if foreign investors’ participating

SOEs are restructured through Merger and Acquisition (M&A) and equity joint

venture; loosening of foreign equity ratios if foreign investors invest in infrastructure

and advantageous projects, and opening up the service sector by encouraging foreign

investment in building urban public facilities. In addition, the physical infrastructure

has also been greatly improved in the western and central regions. The improved

investment environment, development of physical infrastructure and relatively low

labour costs are making the central and western regions more attractive to FDI

inflows. As a result, over the period 2000-2009, the regional distribution of FDI

inflows across China became more diversified. The national share of FDI inflows to

the coastal region decreased from 81.8 per cent in 2000 to 64.1 per cent in 2009,

while over this time frame, the share of FDI flows to the northeast, central and

western regions increased to 12.3 per cent, 13.9 per cent and 9.6 per cent, respectively.

Nevertheless, over the period 1987-2009, FDI inflow to China was overwhelmingly

concentrated in the coastal region, accounting for 64 per cent of total accumulative

FDI inflows, followed by the central region (14 per cent), the northeast region (12 per

cent) and the western region (10 per cent) (Figure 2.3).

Figure 2.3: Share of C

Sourc

The nature of FDI inflow

region is more export-ori

oriented. Table 2.3 shows

total FFE exports in the pe

value of total FFE exports

billion in 2008 (Table 2.3,

value of exports from FFE

exports from FFEs was U

from the coastal region, equi

Columns 2and 3). Compar

exports are low in the cent

per cent of FFE export wer

and 9). This provides a st

domestic market-oriented.

than that of the central and

until earlier 2000s (Table

major investors in the nor

economies are newly indust

resources. Due to the geogr

Northeast12%

Central14%

33

of Cumulative FDI Inflows across China’s Fou1987–2009.

Source: 30 Provincial Statistical Yearbooks.

ows among the regions is also different. FDI

oriented, while that in the inland regions is

s the amount and the proportion of regional F

he period from 1992 to 2010. In the coastal regi

xports increased from USD 16.1 billion in 1992

2.3, Column 2). Due to the effects of the GFC in

FEs decreased to USD 633.3 billion in 2009.

s USD 862.23 billion, with USD 808.83 billi

on, equivalent to 93.8 per cent of total FFEs expor

pared to the coastal region, the amount and propo

entral and western regions. In 2010, only 2.2 pe

ere originating from these two regions (Table 2.3,

a strong indication that FDI in these two regi

d. For the northeast region, the value of FFE expo

and western regions, but lower than that of the

ble 2.3, column 5). This can be explained by th

northeast region are from Japan and South

ndustrialised, rich in capital but lack cheap la

geographical proximity between the northeast re

Central14%

Western10%

our Regions,

DI in the coastal

is more market-

l FFEs exports to

region, the dollar

1992 to USD 743.9

in 2008, the total

2009. In 2010, total

billion originating

xports (Table 2.3,

proportion of FFE

2.2 per cent and 1.4

ble 2.3, Columns 7

regions are more

exports is higher

the coastal region

the fact that the

outh Korea, these

p labour and land

region and these

Coastal64%

34

two countries, manufacturers established operations in the northeast region from

which they export their final products back to their home countries.

Table 2.3: Amount and Percentage of Exports from Foreign Funded

Enterprises(FFEs) Per Total Regional Exports across China’s Four Regions,

1992–2010 (USD billion, percentage).

Year

Total Coastal Northeast Central WesternUSD

billionUSD

billion %USD

billion %USD

billion %USD

billion %

(1) (2) (3) (4) (5) (6) (7) (8) (9)

1992 17.4 16.1 92.8 0.9 2.6 0.2 1.0 0.2 1.2

1993 25.2 23.2 91.9 1.3 2.7 0.4 1.6 0.3 1.3

1994 34.7 31.9 91.9 1.9 2.8 0.5 1.5 0.4 1.2

1995 46.9 43.0 91.7 2.7 3.0 0.6 1.3 0.5 1.1

1996 61.5 56.1 91.3 3.7 3.1 1.0 1.5 0.7 1.2

1997 74.9 68.8 91.9 4.1 2.8 1.1 1.4 1.0 1.3

1998 81.0 74.6 92.2 4.3 2.8 1.1 1.3 0.9 1.1

1999 88.6 81.7 92.2 4.9 2.9 1.1 1.3 0.8 0.9

2000 119.4 109.7 91.8 6.9 3.0 1.6 1.4 1.2 1.0

2001 133.2 123.4 92.6 7.0 2.7 1.7 1.3 1.1 0.9

2002 169.9 158.6 93.3 7.9 2.4 2.1 1.2 1.4 0.8

2003 240.3 226.2 94.1 9.4 2.0 2.8 1.2 1.8 0.8

2004 338.6 320.2 94.6 11.8 1.8 4.2 1.2 2.4 0.7

2005 444.2 421.0 94.8 14.5 1.7 5.4 1.2 3.3 0.7

2006 563.8 535.2 94.9 16.5 1.5 7.8 1.4 4.3 0.8

2007 695.4 659.0 94.8 19.4 1.4 10.5 1.5 6.5 0.9

2008 790.5 743.9 94.1 22.4 1.5 14.8 1.9 9.4 1.2

2009 672.1 633.3 94.2 17.9 1.4 12.2 1.8 8.6 1.3

2010 862.2 808.8 93.8 22.6 1.4 18.7 2.2 12.1 1.4

Source: China’s Statistical Yearbook, 1993–2011.

2.3 Sector and Industrial Distribution of FDI Inflow to China

Over the past three decades, the sector distribution of FDI inflows to China has been

characterised by its concentration in the secondary sector, and with a relatively small

amount to the primary sector. Table 2.4 and Figure 2.4 provide the amount and shares

of the sector distribution of utilised FDI inflows to China over the period 1997-2010.

In this period, the share of utilised FDI toChina’s primary sector was low and stable

at 1.5 per cent (Table 2.4, Column 2). Chen (2006) explains the low level of FDI

35

inflows into China’s primary sector as being due to the land tenure system and the

traditional small-scale family-based agricultural production pattern. Foreign investors

favour large-scale production and the use of advanced technology, thus China is not

an attractive destination for FDI in the primary sector. In contrast, the secondary

sector is dominated by manufacturing industries which are very competitive in

attracting FDI, due to China having significant and abundant semi-educated and well-

educated human resources with relatively low cost. Column 4 of Table 2.4 shows that

in 2002, nearly 75 per cent of utilised FDI inflows to China were in the secondary

sector, while the average was 65 per cent over the period 1997-2010. Although the

secondary sector continued to receive large amounts of FDI inflows, Table 2.4 shows

that the share of the national total which began to decline in 2006. In 2010, only 50.9

per cent of national utilised FDI was in the secondary sector.

Table 2.4: Sector Distribution of Utilised FDI Inflow to China, 1997–2010 (USD

million, percentage).

Year

Primary Sector Secondary Sector Tertiary Sector

USDmillion

%USD

million%

USDmillion

%

(1) (2) (3) (4) (5) (6)

1997 627.6 1.4 32,269.9 71.8 12,059.5 26.8

1998 623.8 1.4 31,327.5 68.9 13,511.5 29.7

1999 710.2 1.8 27,779.8 68.9 11,824.2 29.3

2000 675.9 1.7 29,575.0 72.6 10,459.1 25.7

2001 898.7 1.9 34,798.0 74.2 11,170.4 23.8

2002 1,027.6 1.9 39,464.9 74.8 12,243.4 23.2

2003 1,000.8 1.9 39,179.2 73.3 13,306.9 24.9

2004 1,114.3 1.8 45,463.1 75.0 14,052.6 23.2

2005 718.3 1.2 44,692.4 74.1 14,914.0 24.7

2006 599.5 1.0 42,506.6 67.4 19,914.6 31.6

2007 924.1 1.2 42,861.1 57.3 30,982.8 41.4

2008 1,191.0 1.3 53,256.2 57.6 37,947.9 41.1

2009 1,428.7 1.6 50,075.8 55.6 38,528.2 42.8

2010 1,912.0 1.8 53,860.4 50.9 49,962.9 47.3

Total 13,452.5 1.5 56,7109.8 65.1 290,877.9 33.4

Source: China Statistical Yearbook.

36

Figure 2.4: Sector Distribution of Utilised FDI Inflows to China, 1997–2010(USD million).


The development of the tertiary sector was constrained until China joined the WTO in

2001.Before that, the country’s development strategy was focussed on manufactured

exports with substantial barriers placed on trade and investment in the tertiary sector

(Yin 2011). After China joined the WTO, it gradually opened its tertiary sector to

foreign participation (Figure 2.4). Columns 5 and 6, Table 2.4 shows FDI in tertiary

sector increased from USD 11,170 million in 2001 to USD 49,963 million in 2010,

and the share of the tertiary sector in total FDI inflows increased from 23.8 per cent in

2001 to 47.3 per cent in 2010.

Figure 2.5 shows the industrial distribution of FDI inflows to China between 2000 and

20105 . Over this period, the FDI inflows were mostly in manufacturing which

received USD 443.2 billion, equivalent to 61.9 per cent of total accumulatedFDI

inflow. The mining industry is relatively restricted for foreign investors; except for

coal, petroleum, natural gas, iron ore and manganese all other mining activities are

5The detailed industrial distribution of FDI in China is in Table A-2, Appendix

0

10000

20000

30000

40000

50000

60000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

USDmillion

Primary Secondary Tertiary

37

restricted or prohibited for foreign investment. In the period of 2000-2010, it only

received USD 5.9 billion. The distribution of FDI across the tertiary sector has also

been skewed. Over the period 2000-2010, most tertiary FDI has been concentrated in

real estate which has maintained a double-digit share. In 2010, FDI in real estate was

USD 24 billion, accounting for 22.7 per cent of total FDI. FDI in the wholesale and

retail trade and leasing and the business service industries also increased dramatically,

accounting for 6.2 per cent and 6.7 per cent of total FDI inflows respectively in 2010.

In contrast, FDI in the transport, storage and postage industries, information

transmission, computer services and software industries and scientific research,

technical services and geologic prospecting industry was significantly smaller (Figure

2.5).

Figure 2.5: Industrial Distribution of Accumulated FDI Inflows to China, 2000–

2010 (USD billion).Source: China Statistical Yearbook.

0 100 200 300 400 500

Agriculture, Forestry, Animal Husbandry…

Mining

Manufacturing

Production and Supply of Electricity, Gas…

Construction

Transport, Storage and Post

Information Transmission, Computer…

Wholesale and Retail Trades

Hotel and Catering Services

Financial Intemediation

Real Estate

Leasing and Business Services

Scientific Research, Technical Service and…

Mangement of Water Conservancy…

Sercies to Households and Other Services

USD billion

2.4 Source Countries

With the continuous inc

countries/economies that i

there were more than 180 c

Foreign Investment Repor

overwhelmingly dominated

of the accumulated utilised

per cent from Latin Amer

Oceania and the Pacific. Int

average, it has become the

The Latin American share

cent in the 1990s, growing

argues that the recent trend

haven countries such as the

America might involve ‘round

tax haven economies and

preferential tax treatment of

Figure 2.6: S

Sourc

Africa1%

Europe7%

Latin America9%

Nor

38

es of FDI Inflow in China

increase of FDI inflows to China, the

hat invest in China are also increasing. By the

0 countries and economies who had invested in

port 2011). Figure 2.6 shows that FDI inflow

nated by Asian countries, on average, representi

sed FDI. North America contributed 9 per cent,

erica, 7 per cent from Europe and less than 1

. Interestingly, even though Latin America has

the second most important investor in China i

re of FDI in China increased from zero in the 19

ing at a dramatic ratereaching25 per cent in 2007.

end towards FDI inflow from off-shore financia

s the Virgin Islands and the Cayman Islands loc

‘round-tripping’, in which Chinese investors

and then reinvest back in China to take adva

nt offered to foreign investors.

2.6: Source Continents of FDI Inflows to China.


Asia72%

rica

North America9%

Oceanic andPacific Islands

2%

the numbers of

the end of 2010,

d in China (China

nflow to China is

nting 72 per cent

nt, followed by 8

n 1 per cent from

has a low share on

na in recent years.

he 1980s to 2.5 per

2007. Chen (2007)

cial centres or tax

nds located in Latin

ors invest in these

advantage of the

ina.

39

For individual countries/economies, Hong Kong, Taiwan, Japan, Singapore, South

Korea, the Virgin Islands and the US are the major investors in China. Table 2.5

shows the relative importance of the top eight (8) sources countries/economies for

China’s FDI inflow between 1986 and 2010. Hong Kong is China’s earliest and

largest investor, accounting for a far greater share than any other country/economy;

equivalent to 60 per cent of the accumulated FDI inflows to China. Zhang (2005)

argues that this feature was initially associated with the evolution of China’s FDI

regime: early FDI policies favoured export-oriented FDI, coincidently, during this

time, raising labour costs and currency appreciation in Hong Kong caused the latter to

lose competitiveness in labour-intensive manufacturing industries. In response, Hong

Kong shifted part of its low-technology labour-intensive production such as textiles,

garments, footwear, toys, sporting goods and home electronics to China, thereby

taking advantage of cheap labour and resources. In addition, Hou (2002) argues that

in the early stages, it was difficult to convert and repatriate earnings generated from

China to their parent company and investors, as the Chinese currency (RMB) was not

a convertible currency. By contrast, Hong Kong investors were almost 100 percent

exported-oriented, and were hence able to directly earn foreign exchange from re-

exporting the China manufactured goods to the rest of the world. Table 2.5 shows,

following the AFC in 1997, FDI inflows from Hong Kong investors decreased from

USD 21.6 billion in 1997 to only USD 15.5 billion in 2000.There was a recovery from

2000 and on, in 2010, Hong Kong investors invested USD 60.6 billion, equivalent to

57.3 per cent of China’s total FDI inflows.

Taiwan is also a primary source forChina’s FDI inflow with an accumulated value of

USD 49.4 billion by the end of 2010. Long (2005) argues the actual amount of

Taiwan-originated FDI to mainland China maybe two to three timesgreater than the

data showed on Chinese official data, due to the fact that Taiwan investors invested in

mainland China via Hong Kong, the Virgin Island, and the Cayman Island in order to

avoid the multiple restrictions exerted by the Taiwanese authority. Japan was China’s

second largest investor by the late 1980s, contributing nearly 13 per cent of China’s

accumulated FDI inflows (Table 2.5, Column 6). However, after the AFC, this source

declined from USD 4.3 billion in 1997 to USD 2.9 billion in 2000 (Table 2.5, Column

5). Although there was a surge in Japan’s share, this continually decreased, and by

2010, only 3.9 per cent of total utilised FDI in China was from Japan. South Korea

40

did not begin to invest in China until 1992.FDI from this country increased from USD

0.1 billion in 1992 to USD 6.3 billion in 2004, but fell to USD 2.69 billion in 2010

(Table 2.5, Column 9). The US was another important investor in China in the earlier

stages and contributed more than 10 per cent of total national utilised FDI in the

1980s (Table 2.5, Column 12).However, due to unsatisfactory results, it decreased its

investment, and eventually the US became a minor investor in China relative to other

developing countries. In the late 2000s, investment from the US only represents 3 per

cent of China’s FDI inflows.

Zhang (2000) argues that large amount of FDI inflows to China were from developing

countries/economies rather than from western developed countries due to several

reasons: firstly, western investors are more domestic market oriented, cheap labour

and resources not being of interest to them, and thus they do not view China as an

attractive FDI destination relative to other countries. Secondly, the economic and

technological gap between the developed economies’ investors and China is relatively

large and thus the transfer of technology is hampered. Moreover, Intellectual Property

Rights (IPR) protection is weak in China, and for firms from developed countries with

advanced technology and production techniques, the risk of leaking their core

techniques and these being copied by domestic firm is high. Thirdly, with the strong

cultural ties and connections between Hong Kong and Taiwanese investors and

mainland China, they are able to bypass many of the official restrictions, while for

western investors, especially European investors with their lack of proximity and few

culture ties with China, the costs and risk for their firms to invest in China are

relatively high. Lastly, the developed countries have advanced in service sectors, but

most service sectors in China were strictly controlled by the government and closed to

FDI before China joined the WTO.

41

Table 2.5: Amount and Share of Annual Utilised FDI Inflow by Source Countries, 1985–2010 (USD billion, percentage).

Year

Hong Kong Taiwan Japan Singapore South Korea U.S Virgin IslandsUSD

billion%

USDbillion

%USD

billion%

USDbillion

%USD

billion%

USDbillion

%USD

billion%

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)1986 0.26 11.7 0.01 0.6 0.33 14.5 0.00 0.01987 0.22 9.5 0.02 0.9 0.26 11.4 0.00 0.01988 0.51 16.1 0.03 0.9 0.24 7.4 0.00 0.01989 0.36 10.5 0.08 2.5 0.28 8.4 0.00 0.01990 0.50 14.4 0.05 1.4 0.46 13.1 0.00 0.01991 0.53 12.2 0.06 1.3 0.32 7.4 0.00 0.01992 7.71 68.2 1.05 9.3 0.75 6.6 0.13 1.1 0.12 1.1 0.52 4.6 0.00 0.01993 17.44 62.8 3.14 11.3 1.36 4.9 0.49 1.8 0.38 1.4 2.07 7.4 0.01 0.11994 19.82 58.4 3.39 10.0 2.09 6.1 1.18 3.5 0.73 2.1 2.49 7.3 0.13 0.41995 20.19 53.4 3.17 8.4 3.21 8.5 1.86 4.9 1.05 2.8 3.08 8.2 0.30 0.81996 20.85 49.5 3.48 8.3 3.69 8.8 2.25 5.3 1.50 3.6 3.44 8.2 0.54 1.31997 21.55 41.1 3.34 6.4 4.33 8.3 2.61 5.0 2.23 4.3 3.46 6.6 1.72 3.31998 18.51 40.7 2.92 6.4 3.40 7.5 3.40 7.5 1.80 4.0 3.90 8.6 4.03 8.91999 16.36 40.6 2.60 6.4 2.97 7.4 2.64 6.6 1.27 3.2 4.22 10.5 2.66 6.62000 15.50 38.1 2.30 5.6 2.92 7.2 2.17 5.3 1.49 3.7 4.38 10.8 3.83 9.42001 16.72 35.7 2.98 6.4 4.35 9.3 2.14 4.6 2.15 4.6 4.43 9.5 5.04 10.82002 17.86 33.9 3.97 7.5 4.19 7.9 2.34 4.4 2.72 5.2 5.42 10.3 6.12 11.62003 17.70 33.1 3.38 6.3 5.05 9.4 2.06 3.8 4.49 8.4 4.20 7.8 5.78 10.82004 19.00 31.3 3.12 5.1 5.45 9.0 2.01 3.3 6.25 10.3 3.94 6.5 6.73 11.12005 17.95 29.8 2.15 3.6 6.53 10.8 2.20 3.7 5.17 8.6 3.06 5.1 9.02 15.02006 21.31 33.8 2.23 3.5 4.76 7.6 2.46 3.9 3.99 6.3 3.00 4.8 12.15 19.32007 27.70 37.0 1.77 2.4 3.59 4.8 3.19 4.3 3.68 4.9 2.62 3.5 16.55 22.12008 41.04 44.4 1.90 2.1 3.65 4.0 4.44 4.8 3.14 3.4 2.94 3.2 15.95 17.32009 46.08 51.2 1.88 2.1 4.11 4.6 3.61 4.0 2.70 3.0 2.56 2.8 11.30 12.52010 60.57 57.3 2.48 2.3 4.08 3.9 5.43 5.1 2.69 2.5 3.02 2.9 - -Source: China Statistical Yearbook and China Foreign Investment Report, 2011.

42

Recently, with China’s rapid economic expansion the demand for imported capital

and technology has also increased. In order to attract FDI with new and advanced

technology from developed countries/economies, China implemented certain

preferential policies towards FDI involving high-technology. This has created greater

opportunities and a better investment environment for investors from the developed

countries (Zhang 2005). The amount of FDI inflows from Japan and US have

gradually increased from USD1.3 billion in 1992 to USD 7 billion in 2010. Together

with the reduction in the technology gap between developed countries and China, a

more liberalised market, a better investment environment and a growing domestic

market, China has become an ideal destination for FDI from both the developing and

developed economies.

2.5 FDI from Hong Kong

Recently, Hong Kong has become one of the most important source economies for

world FDI. Table 2.6 shows, in 2010, Hong Kong invested USD 76,077 million

abroad and ranked as the fourth largest investor worldwide (China Foreign Investment

Report 2011).Hong Kong is the largest and most important investor in China. Over

the period 2001-2010, Hong Kong invested over130thousand unit projects in China,

worth USD 286 million (Table 2.6, Columns 3 and 5). In 2010, 13,070 unit projects

were set up, worth USD 60,567 million, it contributed 57.3 per cent of total utilised

FDI in China (Table 2.6, Columns 3, 5 and 6). However, some scholars argue certain

amount of FDI from Hong Kong is originally from mainland China due to‘round-

tripping’ FDI (Xiao 2004, Lai 2002). Round-tripping FDI refers to Chinese residents

establishing businesses in Hong Kong and reinvesting back into mainland China. This

behaviour is driven by the different tax regimes for foreign and domestic investors,

which motivates investors to channel funds out of mainland China and subsequently

back into China in the form of FDI in order to take advantage of favourable tax and

tariff treatments given to foreign investors.

43

Table 2.6: Characteristics of FDI from Hong Kong, 2001–2010 (USD million,

percentage)

Year

TotalOutflow

Percentage

Of theWorld

Utilised FDI in ChinaNumber

ofProjects

% ofChinaTotal

UtilisedAmount

% ofChinaTotal

USDmillion

% Unit %USD

million%

(1) (2) (3) (4) (5) (6)2001 11,345 1.5 8008 30.6 16,717 35.72002 17,463 3.3 10845 31.7 17,861 33.92003 5,514 1.0 13633 33.2 17,700 33.12004 45,726 4.9 14719 33.7 18,998 31.32005 27,196 3.1 14831 33.7 17,949 29.82006 44,979 3.2 15496 37.4 21,307 32.42007 61,081 2.8 16208 42.8 27,703 37.12008 50,581 2.6 12857 46.7 41,036 44.42009 63,991 5.4 10701 45.7 46,075 51.22010 95,396 6.6 13070 47.7 60,567 57.32001-2010

403,953 3.6 130,368 37.6 285,913 40.8

Source: China Foreign Investment Report 2011 and UNCTAD STAT Database

2.5.1 Industry Distribution of Hong Kong Direct Investment in China

Figure 2.7 shows the Hong Kong direct investment in China by major industries over

the period 2001-20106. Based on the data from China Foreign Investment Report

(2011), Hong Kong invested USD 143,340million in manufacturing activities,

equivalent to more than 50 per cent of FDI from Hong Kong investors. This high

share of FDI in manufacturing activities reflects the fact that one of the motivations of

Hong Kong multinationals who invest in China relates to the employment of

relatively cheap Chinese labour and resources (Zhang, 2005). However, with the

appreciation of RMB and the speeding up of industrial upgrading in China, the share

of FDI in the manufacturing activities from Hong Kong investors has continued to

decline. In contrast, unlike the FDI in manufacturing activities, Hong Kong investors

have gradually increased their investment in the tertiary sector, especially in real

6 The detailed industrial distribution of Hong Kong FDI in China is in Table A-3, Appendix

44

estate, leasing and business service industry, wholesale and retail trade industry. As

Figure 2.7 shows, over 2001–2010 period, real estate industry was the second most

attractive industry for Hong Kong investors in China, accounting for USD 68,155

million. Leasing and business service industry and wholesale and retail trade industry

were also ranked as the third and fourth most attractive industries for Hong Kong

investors. They received USD 14,800 million and USD 10,500 million, respectively.

Figure 2.7: Utilised FDI from Hong Kong Investors, by industry 2001–2010, USDmillion.

Source: China Foreign Investment Report, 2011.

Within the manufacturing industry, the distribution of Hong Kong FDI inflows also

witnessed systematic changes in the past 10 years7. Until earlier 2000s, FDI was

mainly concentrated on traditional labour-intensive, low-technology manufacturing

industries, especially in textile and garments. After 2005, the FDI from Hong Kong

investors gradually shifted to capital and high-technology manufacturing industries,

especially electrical machinery, transport equipment and telecommunication

equipment industries. For example, in the period of 2005-2009, the utilised FDI in

textile industry decreased from USD 1,050 million to USD 790 million, while the

7The detailed distribution of Hong Kong investment in manufacturing industrial in China is in Table

A-4, Appendix

0 50000 100000 150000 200000Agriculture

MiningManufacturing

Supply of Electricity, Gas and WaterConstruction

Transport, Storage and PostInformation, Computer Service and▁

Wholesale and Retail TradesFinance

Real EstateLeasing and Business service

Scientific and Technology ReserchService to Households

USD million

45

utilised FDI in telecommunication equipment industry increased from USD 1,470

million to USD 2,640 million.

2.5.2 Geographical Distribution of Hong Kong Direct Investment in China

Since China began to attract FDI, the regional distribution of FDI inflows has been

very uneven. FDI inflows into China are overwhelmingly concentrated in the coastal

region of the country. Although there have been some small fluctuations, the coastal

region still takes the dominant position of FDI inflows compared to the other regions.

Hong Kong investors also overwhelmingly invest in the coastal region, especially its

southeast section. The high concentration of Hong Kong FDI in southeast China was

due to its geographically adjacent to Guangdong province where the first and most

important SEZ is located.

In China, 17 out of the 30 provinces account for more than 95 per cent of the

accumulated FDI from Hong Kong, and most of them are located in coastal region8.

Figure 2.8 shows the regional distribution of the FDI inflows from Hong Kong

investors to these 17 provinces. In 2000, more than 85 per cent of utilised FDI went to

the coastal region, while the central and northeast regions received4 per cent and 8 per

cent, respectively, with the western region receiving the remaining 2 per cent. Since

2000, with more preferential policies being implemented by the central government in

order to attract FDI in the northeast, central and western regions and their relatively

cheap labour compared to coastal region, Hong Kong has increased its investment in

the three non-coastal regions, especially in northeast region (Figure 2.8).For instance,

in 2010, northeast region received USD 5,590 million from Hong Kong investors,

which equivalent to 10 per cent of total Hong Kong investment in China, while

central region and western region both received USD 3,510 million and USD 4,430

million, respectively, which equivalent to a 6 per cent and 7 per cent of total Hong

Kong investment.

8 The detailed provincial distribution of Hong Kong FDI in China is in Table A-5, Appendix

Figure 2.8: Regional Di

Source: C

To summarise, Hong Kong

China’s opening to foreign

inward FDI both by numbe

the actual FDI from Hong

years, Hong Kong investm

However, due to increase

China, Hong Kong has cont

activities. In contrast, the t

Hong Kong investors. Geogr

especially its southeast sec

investors to invest in the c

that have been implemente

2.6 FDI from the United

Table 2.7 shows a detailed

2010.The US is the most i

0

10000

20000

30000

40000

50000

60000

2001 2002

USDmillion

c

46

Distribution of Annual FDI from Hong Kong, 2001(USD million).


ong has been the most important investor i

ign investment, contributing more than 50 per c

ber of projects and total utilised amount. Even

ong Kong maintained a steady growth trend. Ov

stment was largely concentrated in manufactur

ased labour costs and economic and industria

continually decreased its investment in China’s

he trade, service and real estate have become mor

eographically, Hong Kong FDI concentrated in

section. However, there was an upward trend f

he central and western regions due to the prefe

nted in those two regions.

nited States

led description of US direct investment over the

ost important country of FDI outflow in the wor

2002 2003 2004 2005 2006 2007 2008 2009

coastal northeast Central Western

ong, 2001–2010

or in China since

per cent of China’s

ven after the GFC,

Over the past ten

cturing activities.

rial upgrading in

na’s manufacturing

more attractive to

in coastal region,

nd for Hong Kong

eferential policies

the period 2001-

orld. From 2001

2009 2010

47

to 2010, the total FDI outflow from the US was USD 2,237.07 billion (Table 2.7,

Column 1), ranking the US as the top source for global FDI. In 2010, US FDI

outflows represented 25 per cent of total global FDI outflows (Table 2.7, Column 2).

The US was an important investor in China in the early 1990s, ranking as the third

most important source country forChina’s FDI inflow (China Foreign Investment

Report 2011). However, more recently, the number of projects and the utilised

amount of FDI from the US has been decreasing. In 2002, the number of projects

from US were 3,363 units, worth USD 5,424 million (Table 2.7, Columns3 and 5),

which represented 9.8 per cent and 10.3 per cent of the national total (Table 2.7,

Columns 4 and 6). In 2010, US investment to China decreased to 1,502 units, and

worth only USD 3,017 million, representing5.5 per cent and 2.8 per cent of the

national total.

Table 2.7: Characteristics of FDI from US, 2001–2010 (USD million, percentage).

Year

TotalOutflow

PercentageOf theWorld

Utilised FDI in ChinaNumber

ofProjects

% of ChinaTotal

UtilisedAmount

% ofChinaTotal

USDmillion

% Unit %USD

million%

(1) (2) (3) (4) (5) (6)2001 124,873 16.7 2,606 10.0 4,433 9.52002 134,946 25.5 3,363 9.8 5,424 10.32003 129,352 22.7 4,060 9.9 4,199 7.92004 294,905 31.9 3,925 9.0 3,941 6.52005 15,369 1.7 3,741 8.5 3,061 5.12006 224,220 15.8 3,205 7.7 3,000 4.62007 393,518 17.9 2,627 6.9 2,616 3.52008 308,286 15.7 1,772 6.4 2,944 3.22009 266,955 22.7 1,530 6.5 2,555 2.82010 304,399 21.0 1,502 5.5 3,017 2.82001-2010

2,237,060 18.5 28,331 8.2 35,190 5.0

Source: China Foreign Investment Report, 2011 and UNCTAD STAT Database

2.6.1 Industrial Distribution of US Direct Investment in China

US direct investments abroad are mostly in the services sector. However, their

investments in China are primarily in the manufacturing sector (Figure 2.9).

48

According to China Foreign Investment Report (2011), over the period 2001-2010,

16,924 projects were established and USD 25,128 million wasinvested in China’s

manufacturing industry, representing 59.7 per cent of the total number of projects and

70.7 per cent of total utilised FDI from the US, respectively9 .Recently, FDI in

manufacturing industry became less attractive for US investors, with utilised US FDI

in this industry decreasing from USD 3,881 million in 2001 to USD 1,756 in 2010. In

contrast, US FDI is increasing in the leasing and business services sector, growing

from USD 25 million (0.6 per cent of the total utilised direct investment from US) in

2001 to USD 405 million (13.4 per cent of the total utilised FDI from US) in 2010.

Compared with US direct investment in other industries, the agriculture, forestry,

animal husbandry and fisheries industry received a relatively small amount of total

FDI from the US, in the period 2001-2010, only USD 516 million (1.7 per cent of

total utilised investment from US) US investments were in this industry.

Figure 2.9: Utilised FDI from U.S Investors, by industry 2001–2010, USD million.Source: China Foreign Investment Report, 2011.

Within the manufacturing industry, unlike the pattern of investment from Hong Kong

investors, FDI from the US is mainly concentrated in high-technology manufacturing

activities, especially in electrical machinery, telecommunication equipment

9The detailed industrial distribution of US FDI in China is in Table A-6, Appendix

0 10000 20000 30000

Agriculture, Forestry, Animal Husbandry andFishery

Manufacturing

Wholesale and Retail Trades

Real Estate

Leasing and Business service

USD million

49

industries10. Over the period of 2001-2010, FDI inflows into high-tech manufacturing

industries represented more than 60 per cent of total US FDI in China’s

manufacturing industry. In 2010, the US invested USD 201 million in electrical

machinery and USD 303 million in telecommunication equipment industries,

accounting for 11.4 per cent and 17.3 per cent of US investment in both these

industries respectively. For low-tech manufacturing industry, the US mainly invest in

food processing, textiles and clothing industries along with other fibre products,

plastic products and metal products industries. However, with the exception of the

metal product industry, none of these low-tech manufacturing industries received

more than 5 per cent of total US FDI in manufacturing.

2.6.2 Geographical Distribution of US Direct Investment in China

Similar to Hong Kong investment, FDI inflows from the US are overwhelmingly

concentrated in the coastal region of China. China Foreign Investment Report (2011)

reports 13 out of 30 provinces in China represent more than 90 per cent of the total

utilised FDI from the US11. Figure 2.10 shows the regional distribution of the total

utilised FDI from the US hosted by these 13 provinces. The figure illustrates the

persistence of location preferences of US firms in direct investment in China. Over

the period 2001-2010, the coastal region received USD 27,096 million, which

represents 86 per cent of total utilised FDI from US, followed by USD 2,862 million

and USD 1,472 million received in northeast region and central region, which

represents 9 per cent and 4 per cent of total US FDI inflows in China, respectively.

The western region has never been an attractive destination for US investors,

receiving only USD 426 million, and equivalent to only one (1) per cent of total US

direct investment in China (Figure 2.10).

10The detailed distribution of US investment in manufacturing industrial in China is in Table A-7,

Appendix11The detailed provincial distribution of US FDI in China is in TableA-8, Appendix

Figure 2.10: Regional D

Source:

Comparing FDI from Hong

US direct investment in Chi

from Hong Kong as they bot

manufacturing industry rec

investing 70.7 per cent (U

FDI into manufacturing indust

US is the most technologic

more in high-tech manufac

0

1000

2000

3000

4000

5000

6000

2001 200

USDmillion

50

al Distribution of Utilised FDI from US, 2001million).

Source: China Foreign Investment Report, 2011

ong Kong and US investors, the geographical

n China is consistent with the geographical distr

y both invest enormous amount in coastal region.

received most FDI from both US and Hong K

(USD 25.1 billion) and 60.3 per cent (USD 143.3

ng industry over the period 2001-2010, however,

ogically advanced country in the world, the US

nufacturing compared to Hong Kong investors.

002 2003 2004 2005 2006 2007 2008


, 2001–2010 (USD

cal distribution of

distribution of FDI

gion. In addition,

ong Kong investors,

143.3 billion) of

er, given that the

S invest relative

8 2009 2010

51

Chapter 3: Theoretical Models of Determinants of Foreign

Direct Investment

Faeth (2009) argues that there is not one single theory can fully explain FDI activities,

but a variety of theoretical models attempting to explain FDI and their location

decisions, therefore, analysis of determinants of FDI activities should be explained by

a combination of different theoretical models instead a single model. This chapter

presents a review of the different theoretical backgrounds of the determinants of FDI.

Five theories will be discussed. They areHymer’s (1976) and Kindleberger’s (1969)

ownership advantage theory; Buckley and Casson’s (1976) internalisation theory;

location theory and Dunning (1980, 1981, 1988) eclectic paradigm or Ownership,

Location and Internalisation (OLI) framework and spatial interdependenceeffects as

a determinant of FDI inflow.

3.1 Ownership Advantage Theory

Ownership advantage theory explains how a firm can compete successfully in a

foreign market. Hymer (1976) identifies three barriers for a firm wanting to setup

production plant abroad: uncertainty, host country nationalism and risks. Uncertainty

regarding unfamiliar local customers, different legal systems, language, economy and

government will place foreign firms at a disadvantage. The second barrier is

nationalistic discrimination by host countries governments’ protectionism as well as

consumers who prefer to purchase goods from local firms. The final barrier concerns

exchange rate risk when firms paying dividends to the shareholders in the home

country must convert their earnings to their home currency. In spite of these barriers,

firms who engage in FDI must have certain advantages to compete with local firms.

According to Kindleberger (1969), these advantages must be firm-specific,

transferable to foreign subsidiaries and large enough to overcome the disadvantages

outlined above. Blonigen (2005) argues that these firm-specific advantages are public

good within a firm to the extent that using such assets in one plant does not diminish

use of the asset in other plants. Such advantages include well-known brand names,

patent-protected technologies, managerial expertise, marketing facilities and other

52

firm-specific factors. Lall and Streeten (1977) present a list some of these advantages

(Table 3.1). In contrast to Hymer’s ownership advantage, Kojima (1978) and Ozawa

(1979) argue for Japanese investors that they choose to invest abroad because they are

at positive of comparative disadvantage and invest overseas simply to take location-

specific advantages of the host country in order to compensate their disadvantages.

Table 3.1: Ownership Advantages for FDI.

Advantages Description

CapitalLarger or cheaper cost of capital than local or smaller foreigncompetitors.

ManagementSuperior management in the form of greater efficiency ofoperations or greater entrepreneurial ability to take risks or toidentify profitable ventures.

Technology

Superior technology in the form of ability to translate scientificknowledge into commercial uses. This involves the functions ofdiscovering new processes and products, product differentiationand various support activities.

MarketingThe functions of market research, advertising and promotion, anddistribution.

Access to rawmaterials

Privileged access to raw materials arising from the control of finalmarkets, transportation of the product, processing, or theproduction of the material itself.

Economies ofscale

The finance and expertise to set up and operate facilities that enjoythese economies.

Bargaining andpolitical power

The ability to extract concessions and favourable terms from thehost government.

Source: Lall and Streeten, (1977)

Mossa (2002) argues there are two drawbacks of the Ownership advantage theory to

explain FDI activities. First, it does not explain why the firm has not utilised its

advantages by producing in its home country and exporting abroad, which is an

alternative to FDI. Second, it fails to explain why firms invest in a given foreign

country rather than another. The location theory of determinants of FDI, which will be

discussed in section 3.3 will provide an answer to this question.

53

3.2 Internalisation Theory

The internalisation theory of FDI explains why firms use FDI in preference to

exporting or licensing. This theory emerges with Buckley and Casson (1976) who

have explained FDI by focussing on Multinational Enterprises (MNEs) making FDI

decisions. They argue that firm-specific advantage or ownership advantage is a

necessary but not sufficient condition for FDI to take place, where the firm aspires to

develop their own internal markets whenever transactions can be made at lower cost

within the firm is primary motivation for firm.

Coase (1937) and Hymer (1976) argue that in the case of imperfections or failures in

the intermediate goods market, firms replace market transactions with internal

transactions that are more efficient both domestically and internationally. The

imperfections and failures of the intermediate goods market include human capital,

knowledge, marketing and management expertise. Buckley and Casson (1976) argue

that in modern business, firms do not simply carry out the traditional activities of

production; they also undertake intermediate production activities such as training

labour, R&D and marketing and the intermediate product market are primarily based

on knowledge and expertise. However, due to the externality of market imperfection,

intermediate markets are difficult to organise and control. Therefore, in order to

bypass these imperfections, businesses create their own market, which involves

internalisation. In addition, Rugman (1980) argues internalisation gives MNEs the

ability to utilise information to produce and distribute goods and services through

overseas subsidiaries that are similar to those they produce in their home countries

without dissipation of their knowledge or other firm-specific advantages by

maintaining control over the production and sales distribution process.

Rugman (1980) and Buckley (1988) argue that there are two problems with

internalisation theory: first, this theory is so general that it has no empirical content

and cannot be tested directly due to its complex assumptions. Second, this theory

concentrates on industries with a relatively high proportion of R&D expenditure.

54

3.3 Location Theory

The location theory of FDI refers to the geographic location of FDI activity, which

explains MNEs’ location choices for their foreign operations. Foreign investors act in

their own self-interest, and firms choose locations that can maximise their utility.

Location theory uses policy variables, economic variables and agglomeration

economies to explain why different locations are more or less attractive for FDI.

The FDI policies implemented by a country have an important effect on the

attractiveness of FDI inflow. Bond and Samuelson (1986) argue that FDI can be seen

as a game with two players: MNEs and host governments. Governments may use

different preferential policies to influence MNEs’ investment decisions, such as tax

holidays and subsidised loans. In contrast, if a country places conditions on FDI, such

as requiring technology transfer, import and export requirements, limits on the

repatriation of profits and currency exchange transaction controls, with other factors

being equal, these conditions will deter FDI inflow into that country. Other features of

a country’s policy variables will also affect MNEs’ decisions, including factors such

as labour market unionisation, protection of property rights and intellectual property,

capital market governance, corruption, and environmental regulations. For instance,

Pan (2003) finds country risk in China as Tiananmen Square incident in 1989 has a

strong negative effect on FDI inflows over the period 1984-1998. Wei (2000) find

increase tax rate and corruption level in a host country will reduceFDI and

Smarzynska (2004) and Maskus and Yang (2003) find the in adequate intellectual

property right in a host country may not only deter FDI inflows as it may increase the

probability of imitation, but also encourage foreign investors choose exporting

products rather than setting up foreign affiliates, especially for high-technology FDI.

The economic environment also has significant effects on the profit and cost

competitiveness of overseas investment. It involves the production factors, namely,

the physical capital, human capital, natural resource endowments, domestic market

size, and population of the host location. The economic environment also has different

effects on different types of FDI. For instance, market-seeking FDI is more focussed

on the purchasing power, economic growth rate, and population of the host location.

55

In general, locations with a larger market size, higher population density and faster

economic growth would be more attractive than those with a small market size, low

population density and slower economic growth. Export-oriented FDI is more

focussed on the resource endowments in a location, such as the availability of cheap

labour and land.

Besides the economic and political environments in the host location, when an MNE

intends to invest in a particular location, two competing forces also shape the location

decision: agglomeration force and dispersion force (Henderson, Shalizi&Venables

2000). Agglomeration force occurs when increased returns to scale exist when MNEs

locate close to each other (Krugman 1995). These increased returns to firms can be

explained either externally and/or internally. Externally, the newly entered firm can

potentially gain experience of business activities from the existing firms, acquiring

knowledge of their proprietary technology and enjoying the spill-over effect from the

labour market. For example, a newly entered firm may hire workers who have already

been trained during their employment by other firms, which can reduce training costs

(Krugman 1991). Henderson, Shalizi&Venables (2000) argue that internally, firms

located as a cluster that shares the demand for the same intermediate goods will attract

intermediate producers. This in turn makes the location attractive for firms that use

these same intermediate goods, as they can reduce the transport costs of their input.

They also argue that the opposite direction is dispersion and this is caused by

congestion and supply of immobile factors. Congestion refers to a large number of

firms locating within a limited area, and causes inefficiency as well as environmental

issues, while the supply of immobile factors causes the costs in the centre to be bid up

(land), which may force firms to locate in lower-cost locations.

3.4 The Eclectic Paradigm (Ownership, Location and Internalisation

Framework)

Dunning (1988) integrated a variety of theories and developed a single theory to

explain FDI; the eclectic paradigm (OLI framework). The basic premise of the

eclectic paradigm is that it links together Hymer’s (1976) ownership advantage theory

with internalisation theory, while simultaneously adding location theory. The eclectic

56

paradigm of FDI uses three different advantages to explain different modes of entry

by foreign firms: ownership advantage, location advantage and internalisation

advantage (Table 3.2). Dunning (1988)argues that a firm will directly investment in a

foreign country only if all three advantages are fulfilled. Ownership advantages refer

to firm-specific advantages, such as patents, technical knowledge, management skills

and reputation. These types of advantages can be transferred internationally within a

firm but are difficult to transfer between firms in the same region. Location

advantages refer to the motivations that drive an MNE choice to produce in a

particular location, such as the potential market, favourable tax treatments, lower

production costs, lower risk, and favourable structural competition. Internalisation

advantages arise when an MNE is better off to use its ownership advantages internally,

rather than sell or lease them, these are due to lower transaction costs, minimising

technology imitation, maintaining reputation and protection of intellectual property.

According to this theory, three advantages must be satisfied for a firm choose to

expand its scale of operation by FDI instead of by other channels. In addition, Kogut

and Singh (1988) also argue that the national cultures between home and host

countries also relate to entry mode.

Table 3.2: Mode of Entry of Foreign Investment Based on Duning’s OLI

Framework.

Mode of EntryAdvantages

Ownership Location Internalisation

Licensing Yes No No

Exporting Yes Yes No

FDI Yes Yes Yes

Source: Dunning 1981.

The conditions for the OLI framework vary depending on the motivation behind the

firm’s overseas investments. In general, MNEs are export-oriented, involve

standardised technology and will locate at least one stage of production in which

unskilled labour is used intensively abroad. Thus, export-oriented FDI is attracted by

host countries in which wages are low relative to source countries, and countries that

offer favourable incentives such as tax holidays. For domestic market-oriented FDI,

57

MNEs will establish similar production facilities abroad to benefit from economies of

scale and to gain access to foreign markets. To compete with local producers, MNEs

must possess ownership advantages such as superior technologies and brand names

that are not owned by firms in the host location. The host market size also plays a

significant role in attracting FDI from MNEs because the larger the market size, the

greater the opportunities to realise economies of scale (Markusen 1984). Since

domestic market-oriented FDI requires the host country to absorb the superior

technologies brought by the MNEs, it generally has more stringent requirements for

human capital and infrastructure in the host country.

3.5 Spatial Interdependence Effects

All the models discussed above do not allow spatial effects as a determinant of FDI.

The spatial interdependence between the host and surrounding regions/countries has

been largely ignored by previous empirical literature. Blonigen et al (2007) used a

‘spatial lag’ coefficient to explain the contemporaneous correlation between one

region’s FDI and other geographically proximate regions’ FDI by the motivations

behind MNEs. They started with pure horizontal FDI. Horizontal FDI is motivated by

market access and substitutes for exporting. Based on the ‘proximity-concentration

hypothesis’, an MNE’s decision on taking horizontal FDI will governed by the trade-

off between the fixed cost of building a new production facility in the host market and

the trade costs, such as transport costs, tariff and import protection (Markusen 1984;

Horstmann & Markusen 1987). If the trade costs are significantly high in the host

market, exports from the home country are not an attractive option, thus, MNEs will

make investments in the host country. In this case, the FDI in a particular market will

have no spatial effect on neighbouring markets since MNEs make independent

decisions about how to serve the host market by exporting affiliate sales. Under pure

vertical FDI, MNEs evaluate all potential destination markets and choose the lowest-

cost provider to increase the return to scale (Helpman 1984). Under this form of FDI,

the FDI in one market is at the expense of surrounding markets, and thus there must

be a negative spatial relationship between the host and neighbouring markets.

However, the surrounding markets potential has an insignificant effect on FDI in the

58

host market, as the output of subsidiaries in the host market are simply shipped back

to the home country.

The more complicated forms of FDI are ‘export-platform FDI’ and ‘complex vertical

FDI’. Unlike pure horizontal and vertical FDI, which rely on a two-country basis,

these two forms of FDI involve a third market. Under export-platform FDI, MNEs

invest in a particular host market with the intention to use this affiliate to serve the

‘third’ market by exporting the final goods to this country (Ekholm, Forslid &

Markusen 2007; Yeaple 2003). The condition for this form of FDI is that the trade

protection regimes between the destination markets must be low relative to the trade

frictions between the home and host markets. Under this form of FDI, the spatial

relationship is also negative, as FDI in the destination market chosen by the MNEs

substitutes the FDI to other destination markets. However, the proximal markets will

have a positive effect on the export-platform FDI destination as they can affect the

demand for final products. The natural augmentation of the export-platform FDI can

be regarded as regional trade platform FDI (Hong, Sun & Li 2008). In addition to

exports, the output of subsidiaries in the host market will largely be sold in the host

market and surrounding markets. It is clear that the FDI in the host country is at the

expense of the surrounding markets, but these potential markets are positively

affected by MNEs’ host decisions as the destination market is used as a platform to

serve the market demands of surrounding markets. Baltagi, Egger & Pfaffermayr

(2007) developed a ‘complex vertical FDI’ model, in which MNEs may set up in

multiple geographic regions, each of them carrying out different production activities

to exploit the comparative advantages of the various locations. Unlike pure vertical

FDI, complex vertical FDI requires intermediate inputs to be exported from affiliates

of the host market to the third market for further or final processing before they are

shipped to their final destination. As a consequence, suppliers and resources in

neighbouring regions can increase the attractiveness of FDI in a particular market.

This shows a clear positive spatial relationship between the host and neighbouring

markets. However, the market potential in surrounding regions has no effect on MNEs’

FDI destination decisions. Hong, Sun & Li (2008) argue that when complex vertical

and regional trade platform FDIs combine another form of FDI, ‘agglomeration with

regional trade platform FDI’, exists. The condition underpinning this form of FDI is

that inter-regional trade barriers must be high but still lower than international ones.

59

As a result, the agglomeration effect will create a positive spatial lag coefficient

between the host and surrounding markets’ FDI but a negative relationship between

the host’s FDI and surrounding market potential. This is because MNEs focus on the

host region’s market potential in order to avoid border costs. Coughlin and Segev

(2000) find the evidence of agglomeration externalities in China when they find FDI

into neighbouring provinces increases FDI into a province. Table 3.4 below shows the

sign of the spatial relationships and the effect of the surrounding markets, potential of

various forms of MNE behaviours.

Table 3.3: Spatial Effects of Different Types of Motivations for FDI.

FDI Motivations Sign of Spatial LagSign of Surrounding Market

Potential Variable

Pure horizontal 0 0

Pure vertical - 0

Export-platform - +

Regional trade platform - +

Complex-vertical + 0

Agglomeration with regionaltrade platform FDI

+ -

Source: Blonigen et al. (2007), Hong, Sun & Li. (2008).

60

Chapter 4: Regional Determinants of Foreign Direct

Investment in China

Since China opened its economy to foreign investors in 1979, the FDI inflows into

China have increased dramatically, especially after China joined the WTO in 2001.

By the end of 2010, the cumulative FDI inflows to China had reached USD 1,047

billion (China Statistical Yearbook 2011). However, the geographical distribution of

FDI inflows in China indicates a great disparity across China’s four geographic

regions. FDI inflows to China were overwhelmingly concentrated in the coastal

region, which accounted for more than 70 per cent of accumulated utilised FDI.

Several studies indicate that FDI inflows play an increasingly important role in

regional economic development (Kang & Lee 2007;Yao & Zhang 2001; Fu 2007;

Wan, Lu & Chen 2007; Whalley & Xin 2010). In order to accelerate economic

development in the underdeveloped inland regions, China’s central government has

favoured a policy of encouraging foreign investment in those regions. In an attempt to

prioritise the importance of factors driving FDI inflows, this chapter performs an

empirical assessment of China’s inward FDI by analysing the relative importance of

the potential determinants of FDI inflows across the four regions of China over the

period 2001-2009. The most recent regional classification adopted by the National

Development and Reform Commission (NDRC), which is based on regional

geographical characteristics, industrial development and national planning12is used in

this chapter. Accordingly, all provinces in China are grouped into four regions,

namely, the coastal, northeast, central and western regions. Recall figure 2.2 in

Chapter 2, the coastal region is located in the southeast of China and represents 9.5

per cent of the national land area; the northeast region represents 8.2 per cent of the

national land area, while the western region has the largest share of the national land

area, accounting for appropriately 71.5 per cent of China’s land mass. Finally, the

central region accounts for 10.7 per cent of the total national land area.

The structure of this chapter is as follow: Section 4.1 examines the regional

distribution of FDI inflows over the period 1987- 2009. Section 4.2 provides literature

12This classification was adopted in the Eleventh Five-year Plan (2006–2010).

61

review on location determinants of FDI inflows to China. Section 4.3 presents

determinant variables of FDI inflows. Data and methodology and empirical results

will be presented in section 4.4 and 4.5, respectively. The conclusion and policy

recommendations are presented in section 4.6.

4.1 Regional Distribution of FDI Inflows across Four Regions in

China

Figure 4.1 shows the FDI inflows to China’s four regions over the period 1987-2009

and it can be divided into three (3) sub-periods: 1987-1991, 1992-2001 and 2001-

200913. Over the first sub-period (1987-1991), total FDI inflows to China wereat a

low level, and overwhelmingly concentrated in the coastal region, with the coastal

region attracting 85 per cent of cumulative FDI inflows to China, while northeast,

central and western regions received 6.7 per cent, 3.0 per cent and 5.3 per cent,

respectively. The Chinese government gradually opened more areas to foreign

investment and continually implemented new policies and regulations to encourage

FDI inflows especially after former leader Deng Xiaoping calls for deeper, faster and

wider economic reform and liberalisation in 1992. However, FDI inflows were still

concentrated in the coastal region in the second sub-period (1992-2001) due to its

industrial development and better infrastructure development. Figure 4.1 indicates a

decreasing trend of FDI inflows to coastal region over the period 1998-2000, this

trend was due to the negative impact of the Asian financial crisis.

In the third sub-period (2001-2009), in order to reduce regional disparity, maintain

balanced economic growth and social stability, China has adopted a series of policies

to enhance economic growth in its inland regions, either directly, through appropriate

economic policies including encouraging FDI in the western and central regions, or

indirectly, by facilitating growth spill-over from the rapidly developing coastal region

(Demurger 2001).Since then, the FDI inflow has begun to spread from the coastal

region to the inland regions. Figure 4.1 shows an increasing trend of FDI inflows in

three inland regions over the period 2002-2009. For the western region, FDI inflows

improved from USD 2 billion in 2002 to USD 14 billion in 2009, the northeast and

13 The sub-period was followed by Chen (2011)

62

central regions also experienced increased inflows from USD 4.0 billion to USD 19

billion and USD 4.4 billion to USD 21 billion, respectively. Although there was a

diversified trend of regional distribution, the coastal region still held the dominant

position in the 2002-2009 period by attracting USD 98.9 billion, equivalent to some

71.5 per cent of cumulative FDI inflows. In contrast, the northeast region, central and

western regions only received 9.7 per cent, 12.2 per cent and 6.7 per cent, respectively.

Figure 4.1: Regional Distribution of FDI Inflows, 1978–2009 (USD billion).

Source: Calculated from China Statistical Yearbook (various issues).

4.2 Literature Review on Location Determinants of FDI

There are many theories to explain FDI activities, such as ownership advantage theory

and theDunning’s OLI framework. In this chapter, the most relevant theory is the

location theory, which explains why multinationals would choose to invest in a

particular host country, or a specific location within a particular host country. The

location theory draws on policy, economic variables and the cost of production to

explain why different locations are more or less attractive for FDI.

Based on location theory, the main location determinants of FDI are market size,

labour cost, infrastructure development, government incentives to attract FDI inflows,

geographical location and the degree of openness. Sun, Tong & Yu (2002) identify

GDP, annual average wage per employee, total trade volume, transportation (railway

density) and degree of industrialisation as the most important determinants of FDI

0

10

20

30

40

50

60

70

80

90

100

1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

USDbillion

Coastal Norttheast Central Western

63

inflows across 30 provinces in China. Better labour quality and infrastructure

development also attracted more foreign investment. Furthermore, wages were found

to positively affect FDI inflows. Similarly, Fung et al. (2005) examine the location

determinants of FDI inflows into China from the US, Japan, Hong Kong, Taiwan and

South Korea. They find that GDP, highway length and the number of special zones in

a region had a highly significant effect on FDI location decisions, while the average

wage was negatively significant for Taiwanese investors. For Taiwan investors only,

Chen (1992) finds the degree of export of a location has positive effect on FDI

inflows. Zhang (2001) argues the FDI has concentrated in coastal region was a result

of implementation of preferential policies, superior economic developments and the

historical-cultural links between Hong Kong, Taiwan and Macao investors.

Boermans, Roelfsema and Zhang (2011) further show that foreign investors are

attracted by low labour costs, large market sizes, and good transportation and

communication facilities. Sethi, Judge and Sun (2011) have developed a

comprehensive intra-country framework of FDI inflows, and when analysing FDI

trends within China’s 31 provinces, they found that infrastructure and preferential

policies in other regions, rather than the coastal region, may help decongest the FDI

inflow distribution. Wei et al (1999) find the high level of international trade, lower

wage rate, higher research manpower, better infrastructure and more preferential FDI

policies have strong positive effect on FDI inflows. Chen (2011) finds that regional

economic development, measured by regional GDP, GDP per capita, total trade per

regional GDP, and infrastructure, measured by density of railways, highways and

waterways, as well as telecommunications all have positive effects on FDI inflows to

China over the period 1986-2005. In contrast labour costs, measured by the average

wage adjusted by industrial productivity, had a significantly negative effect on FDI

inflows. Liu et al (1997) select 12 source countries/economics who invest in China,

and find out relative wage had a significant negative effect on FDI inflows while total

trade has a significant positive effect on FDI inflows over the period of 1983-1994.

Na and Lightfoot (2006) test the regional determinants of FDI inflows across 30

provinces in China in 2002, as that year was the first year after China’s accession to

WTO and the first year which China exceed US as the most attractive nation for FDI

inflow. They find domestic market size, the degree of openness and infrastructure

development is encouraging FDI inflows while labour cost has no effect.

64

Luo et al (2008) argue significant portion of FDI inflows in China come from

subsequent investment instead of initial investment, and the determinants of

subsequent investment and initial investment may different. Thus, the empirically

testing the determinants of subsequent investment and find human capital

development, greater amount of previous investment and degree of openness of a

region have significant effect on the decision of subsequent investment. Head and

Swenson (1995) find for Japanese investors, the location choice of new FDI is

positively correlated with the existing FDI in a particular region, especially for

business with vertical linkage. Blonigen, Ellis &Fausten (2005) further investthe

location choice of Japanese investors, they find existing FDI also has positive effect

on new FDI even firms are in often-unrelated industries. In addition, He (2006) tests

the location determinants of FDI inflows in China over the period 1995-2002 and

finds that region with greater market decentralisation and less government

interference are more attractive to foreign investors. Grosse and Trevion (1996) test

the determinants of FDI in the US and find the cultural differences and geographic

distances have significant negative influences on FDI in US. Head and Ries (1996)

find there is a self-reinforcing phenomenon of FDI inflows in 54 Chinese cities over

the period of 1984-1991, in other words, previous FDI in a particular region has

positive impact of location choice for foreign investments. Li and Park (2006) also

confirmed that higher foreign firms’ concentration may attract more foreign firms

while higher domestic firms clustering have a negative effect on foreign firms.

4.3 Determinant Factors of Regional Distribution of FDI Inflow

Based on location theory and the empirical studies discussed previously, seven

potentially important determinants of FDI inflows across four regions in China were

identified. These are market size(i), labour cost(ii), labour quality(iii), infrastructure

development(iv), telecommunications(v), and degree of openness(vi) and government

incentives to attract FDI inflows(vii).

First of all, market sizewas hypothesised has a positive impact on FDI inflows,

because it directly affects the expected revenue of the investment. AsChina has

65

gradually opened its domestic market, recent FDI inflows have become more

domestic market-oriented, thus, for foreign investors, the larger the market size of a

particular region, the more FDI flows to that region. In this chapter, GDP per capita is

used to measure market size(GDPPC in USD dollar).Since GDP per capita and the

annual wage are denominated in Chinese Yuan (RMB), RMB were converted to USD

using yearly average USD/RMB exchange rate.

Labour cost, measured by the annual average wage paid to employee (WAGE in USD

dollar), is expected to have a negative effect on FDI. Higher wages, other things been

equal, lower profit, which will in turn reduce the attractiveness for foreign investment,

especially for labour-intensive FDI. On the other hand, as China attracts more foreign

investment in technology-intense industries, MNEs are willing to pay a premium to

hire qualified workers. Hence, labour cost also reflects the endowment of skilled

labour in a region, which should have a positive impact on FDI.

The Labour quality is indicated by the number of graduates from university as a

proportion of the total regional population (GRAD in %). Better labour quality

reflects a greater capability to process and understand information and to cope with

new task and procedures (Zhao & Zhang 2010). Thus, this variable should have a

positive impact on FDI.

Physical infrastructure and telecommunicationsdevelopment are other major

determinants of FDI inflows. The adequate infrastructure allows foreign investors not

only to decrease transportation costs but also improves the effectiveness and

efficiency of operation. Hence, MNEs should choose a location with better

infrastructure. The level of physical infrastructure development of a particular region

is measured by total length of its highways, railways and inland waterways

(HRWLENGTH in kilometres), and telecommunications infrastructure development

is measured by the total length of cable (TELECOM in kilometres).

Thedegree of opennessis measured by the total foreign trade per total regional trade

(FORETRADE in %). The more open an economy is, the more linkage and activities

it has with the rest of the world. Thus, the greater the degree of openness in a region,

the more attractive it should be to FDI inflows, especially for export-oriented FDI. On

66

the other hand, income situation, the FDI inflows were caused by the high trade

barriers of a location, thus, MNEs may choose FDI instead of trade to avoid high

trading costs. In this case, the variable degree of openness will have a negative effect

on FDI inflow.

The last factor to consider is thegovernment incentivesto attract FDI, which is

proxied by the number of special zones in each region. The central government

exclusively established different types of special zones in the coastal region until the

1990s. In these special zones, foreign investors can enjoy preferential policies, such as

exemption or reduction of profit taxes, land fees, import duties and priority in

obtaining infrastructure services. In turn, it gradually creates an unfair competition

between coastal region and the inland regions in terms of attracting foreign

investment. Thus, the number of zones in each region (SZONES in unit) is expected

to have a positive relationship with FDI inflow. Table 4.1 summarises the various

determinants identified in the literature that will be used in this research

Table 4.1: Determinants of FDI Inflow.

IndependentVariable Proxy Variable (Variable Name) Expected Sign

Market size GDP per capita (GDPPC) +

Labour costs Annual wage per employee (WAGE) +/-

Labour qualityNumber of graduates from universitiesand colleges per total region regionalpopulation (GRAD)

+

Physical infrastructureLength of highway and railway andinland waterway (HRWLENGTH)

+

Telecommunications Length of cable (TELECOM) +

Degree of opennessTotal trade from foreign enterprisesper total regional trade(FORETRADE)

+/-

Government incentivesTotal number of different types ofzones(SZONE)

+

4.4 Data and Analytical Framework

67

Multiple regression analysis was carried out to examine the regional determinants of

FDI inflows. Unlike other FDI studies in China, the data used in this chapter were

collected from various issues of 30 individual provincial yearbooks14. However, due

to the unavailability of provincial variables in the early periods, thesample begins in

2001 and ends in 2009.

A problem associated with the data selection is the inconsistency between the

cumulative regional FDI inflows collected from the 30 provinces and the total FDI

reported in the Chinese Statistical Yearbooks. Figure 4.2 shows the differences

between the national statistics and the aggregated value as derived from the provincial

statistics on FDI inflows for the period 1987 to 2009. The red line represents the

aggregated value from the 30 provincial yearbooks, and the blue line is the total FDI

value from ‘China Statistical Yearbook’. The graphical further shows that the

discrepancy has become higher in recent years. For instance, in 2009, the aggregated

value was USD 153.97 billion, whereas the figure reported by theChina Statistical

Yearbookwas USD 90.03 billion only. This may be partly explained by regional

governors exaggerating figures of FDI inflows in an attempt to attract foreign capital

and promote economic growth. As this chapter is focused on the regional distribution

of realised FDI inflows, the data collected from the provincial yearbooks is the more

appropriate to use.

14According to the National Statistical Yearbook, the provincial distribution of FDI inflows were onlyprovided until 2004, thereafter, no data is available from the National Statistical Yearbook.

68

Figure 4.2: Comparison of Realised FDI Inflow into China between the NationalStatistical Yearbook and the 30 Provincial Yearbooks, 1987–2009 (USD billion).

Source: China Statistical Yearbooks and 30 Provincial Statistical Yearbooks.

The empirical strategy was to establish a benchmark model for determinants of FDI

inflows to the coastal region and then apply the same model to the other three regions.

All location determinants, except the number of special zones were lagged by one (1)

period. This was due to two reasons: firstly, decisions to undertake FDI in the current

year will not be realised in the sense that actual FDI flows do not eventuate until the

year later, in other words, MNEs’ FDI activities in a given year are based on

information gathered from the previous year. Secondly, as mentioned by Sunet al

(2002), the amount of FDI inflows and the independent variables may affect each

other. For instance, better economic conditions in a province attracts FDI inflows, in

turn, FDI promotes economic development in the region. In order to avoid the

endogeneity problem, lagged variables were used. GDPPC, WAGE, HRWLENGTH

and TELECOM were also transformed into natural logarithms to allow the testing of

whether FDI growth is determined by the growth rate of GDP and other factors.

0

20

40

60

80

100

120

140

160

1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

USDbillion

National Yearbook Provincial Yearbook

69

The regression model specified for this research is written as:

= + , + , + ,+ , + ,+ , + , + ▁▁▁▁▁▁(1)

Where subscript refers to individual provinces and refers to the year, from 2001 to

2009.

4.5 Empirical Results

The descriptive statistics for all variables across the four regions are presented in

tables 4.2a to 4.2d. There are ten (10) provinces in the coastal region, three (3)

provinces in the northeast region and six (6) provinces and eleven (11) provinces in

the central region and western region, respectively. Based on the mean value of FDI

inflow to each region, the coastal region has the highest average inflow, followed by

the western, northeast and central regions, which are USD 6,604.1 million, USD

2,902.5 million, USD 1,825.2 million and USD 544.21 million, respectively. However,

the coastal region also has the highest standard deviation. This indicates that the FDI

inflows into the 10 provinces in the coastal region are more varied compared to those

in the other regions. The coastal region also has the highest average GDP per capita,

indicating that economic development is much faster in this region than the other

three regions.

GRAD represents the number of graduates from university per 10,000 people in each

regional and was used here to indicate labour quality. The results reveal that there are

three university graduates per 10,000 people in the coastal region, while the northeast,

central and western regions have 1.2, 1.4 and 1 graduates per 10,000 people,

respectively. This also indicates that the coastal region has a better supply of highly

educated labour compared to the other three regions. The physical infrastructure and

telecommunications are denoted by HRWLENGTH and TELECOM respectively. As

expected, the total length of highways, railways and inland waterways and the cable

length in the western and central regions are much longer than those in the coastal

region, as they represent a much larger land area. In terms of the percentage of foreign

70

trade per total regional trade, the coastal region accounts for 54 per cent of total trade

from foreign investors, while only 39 per cent, 26 per cent and 17 per cent are in the

other three regions. Moreover, in the western region, the minimum percentage of

foreign trade is recorded at only 1 per cent; this is much lower than that of the coastal

region (16 per cent). Furthermore, in terms of special zones, the coastal region also

surpasses the other three regions as it is the most open to foreign investment of all

regions in China. The number of special zones in coastal region ranges from 3 to 31,

in contrast, the number of special zones in western region ranges from 1 to 7.

Table 4.2a: Descriptive Statistics—Coastal Region.

Observations Mean Std. Dev. Min MaxFDI 90 660,406.6 552,133.2 42,125 2,532,298GDPPC 90 3,285.7 2,136.2 832.8 10,814.7WAGE 90 2,611.6 1,511.7 844.3 8,144.5GRAD 90 0.0030364 0.00224 0.000485 0.0089781HRWLENGTH 90 68,487.1 57,771.8 6,681.5 225,333TELECOM 90 13,557.7 12,209.7 333 45,807TORETRADE 90 0.54 0.19 0.16 0.82SZONES 90 12.1 7.5 3 31

Table 4.2b: Descriptive Statistics—Northeast Region.

Observations Mean Std. Dev. Min MaxFDI 27 290,245.6 377,224.8 19,059 1,544,390GDPPC 27 1,907.3 910.8 827.1 4,569.6WAGE 27 1,909.8 886.7 842.4 3,992.6GRAD 27 0.0025973 0.0012079 0.000924 0.0046886HRWLENGTH 27 77,666.0 33,713.5 40,477.4 161,731TELECOM 27 21,239.7 9,361.8 6,162 40,483TORETRADE 27 0.39 0.21 0.06 0.65SZONES 27 7.7 3.9 5 14

71

Table 4.2c: Descriptive Statistics—Central Region.

Observations Mean Std. Dev. Min MaxFDI 54 182,524.1 1,133,895.2 21,164 479,858GDPPC 54 1,293.9 651.6 586.0 3,096.6WAGE 54 2,068.2 952.3 837.1 4,916.6GRAD 54 0.0020994 0.001473 0.000475 0.006161HRWLENGTH 54 110,098.4 53,325.9 44,827.6 245,954TELECOM 54 21,083.4 6,798.3 7,019 35,719TORETRADE 54 0.26 0.11 0.10 0.65SZONES 54 3.8 1.1 2 6

Table 4.2d: Descriptive Statistics—Western Region.

Observations Mean Std. Dev. Min MaxFDI 99 54,420.9 80,491.6 1,534 401,643GDPPC 99 1,227.5 7,74.5 321.6 5,077.4WAGE 99 2,119.7 1,108.8 835.7 6,807.7GRAD 99 0.0015209 0.0010389 0.000366 0.005776HRWLENGTH 99 84,894.2 53,981.2 11,289.1 238,208TELECOM 99 21,478.4 11,456.5 2,638 58,611TORETRADE 99 0.17 0.10 0.01 0.41SZONES 99 3.9 2.0 1 7

To summarise, the results show that in terms of economic development, labour quality

and degree of openness, the coastal region is in a much superior position compared to

the other three regions. However, the coastal region also has the highest labour costs

(measured by average annual wage per employee) compared to the other regions. In

terms of physical and telecommunications infrastructure development, the inland

regions have greater development than the coastal region. This is partly due to the fact

that the inland regions have the largest land area and thus requires more infrastructure

development. Overall, the regression results show a mixed picture of the regional

determinants of FDI among the four (4) regions. The model has a poor explanatory

power for the western region, as the overall R-square is only 0.492, and none of the

location determinant variables is statistically significant. In contrast, the R-square of

northeast region and the coastal region are both greater than 0.90. Data shows that the

western region has never been an attractive destination for foreign investment since

China opened its doors to trade. This result is further supported by the fact that the

western region only receives 6 per cent of total utilised FDI.

72

Among the other three regions, there is no single common explanatory variable that

has a significant effect on location determinants of FDI inflows. The determinants that

were identified to be of significance are market size (GDPPC), labour cost (WAGE),

labour quality (GRAD), physical infrastructure development (HRWLENGTH) and

government incentives to attract FDI inflows, represented by the different types of

special zones (SZONES).

Table 4.3: Location Determinants of Regional Distribution of FDI Inflows across

Four Regions, 2001–2009.

Coastal(a)

Northeast(b)

Central(c)

Western(d)

CONS5.20(0.000)***

21.38(0.007)***

-6.28(0.138)

-2.19(0.433)

GDPPC0.56(0.000)***

1.65(0.032)**

0.71(0.204)

0.81(0.105)

WAGE-0.25(0.016)**

-2.70(0.002)***

-0.15(0.551)

0.21(0.685)

GRAD110.18(0.010)***

981.14(0.002)***

-140.72(0.399)

95.09(0.550)

HRWLENGTH0.05(0.608)

-0.30(0.597)

1.17(0.001)***

0.38(0.110)

TELECOM0.19(0.003)***

-0.16(0.672)

-0.03(0.942)

-0.00(0.991)

FORETRADE0.36(0.233)

-1.67(0.169)

2.54(0.054)*

0.19(0.847)

SZONES0.08(0.000)***

0.18(0.000)***

0.16(0.125)

0.15(0.196)

R2(overall) 0.9057 0.9469 0.7237 0.492Prob>Chi2 0.0000 0.0000 0.0000 0.0000Note: () represents p-value, ***, **, * denote statistical significance at the 1, 5, and10 per cent levels, respectively.

Table 4.3 reveals that there are different drivers of FDI inflows for each region. For

the coastal region, all variables were significant, apart from physical infrastructure

and the degree of openness as measured by HRWLEGTHB and FORETRADE,

respectively. In addition, the results showed that an one(1) per cent increase in GDP

per capita leads to a 0.56 per cent and 1.65 per cent increase in FDI inflows into the

coastal and the northeast regions, respectively. This indicates that the northeast

region’s domestic market is more attractive to foreign investors than that of the

73

coastal region. This finding can partly be explained by the fact that a higher degree of

competition among foreign investors exist in the coastal region as it has been opened

to FFDI for much longer compared to the newly opened and higher market potential

northeast region.

Although the labour cost (WAGE) was found to have a negative effect on FDI inflows

in the coastal, northeast and central regions, it is only statistically significant for

coastal and northeast regions. The results show that a one (1) per cent increase in the

average annual wage paid to employee reduces FDI by 0.25 per cent flowing into the

coastal region and 2.7 per cent in the northeast region. In recent years, labour costs in

China increased significantly due to changes in minimum wage legislation15 and

increased competition among firms in the regions. Higher labour costs result in higher

production cost which has direct adverse effect on profits. Thus, labour cost is found

to be negatively correlated with FDI inflows in the sample period.

Due to the high speed of economic growth and spill-over effect from previous FDI

inflows, the productivity and labour quality in the coastal and northeast regions has

increased dramatically. The nature of FDI inflows into the coastal and northeast

regions are described as more high-technology and capital-intensive. The traditional

low-technology, labour-intensive FDI is shifting to the inland regions. It was therefore

hypothesised that labour quality is a crucial determining factor for the FDI into the

northeast and coastal regions. The regression results support this as the labour quality

represented by the variable GRAD, had the expected sign and are significant in these

regions. In contrast, the bulk of FDI inflows into central and western regions have

been from Asian newly-industrialised economies and the focus is on the production of

labour-intensive products, thus GRAD was found not to be significant in these regions.

For the same reason, however, require more physical infrastructure rather than

telecommunications to conduct their FDI activities. Thus, the physical infrastructure

(HRWLENGTH) is found to be an important variable to explain FDI inflow into the

central region.

15The minimum wage level across China was introduced by China’s Ministry of Human Resources and

Social Security.

74

The coefficients of the variable representing government incentives to attract FDI

inflow (SZONES) are positive and statistically significant at theone (1) per cent level

for the coastal and northeast regions (Table 4.3, Columns a, b). This indicates that the

FDI inflows into these two regions are influenced by the preference policies

implemented in their special zones. However, the same variable is found to be

statistically insignificant but with the right expected sign for both the central and

western regions. The results suggest that setting up special zones and implementing

preferential treatments for foreign investors cannot compensate the underdeveloped

economic condition and human capital.

4.6 Conclusions

Regional disparity of inward FDI inflows has important policy implications as there is

a link between FDI inflows and China’s economic growth. Inland regions consider

FDI inflows as one of the most important source of economic development. Thus, in

order to provide useful information to regional policy makers on how to attract FDI to

their areas, location theory was used in this chapter to empirically identify the factors

that significantly determined FDI inflows across four regions in China over the period

2001-2009. A panel data analysis was used to ascertain which of the determinants are

the drivers for each region.

The empirical results clearly show that the uneven regional distribution of FDI

inflows into China is caused by the differences in provincial characteristics and the

location factors of each individual region. The location determinants in the coastal

region and the northeast region were found to be quite similar. The results further

reveal that market size, labour quality, and government incentives to attract FDI are

significantly positively affecting the FDI inflows, while high labour cost reduces the

attractiveness of a region. The physical infrastructure in the central region is a crucial

factor to attracting FDI, as huge amounts of low-technology, labour-intensive FDI

from newly-industrialised economies are concentrated in the central region.

More importantly, the results here have significant implications for policy-makers.

First, in order to assist the less developed inland regions to attract FDI, there is a need

to upgrade their human capital and further develop their infrastructure. Second,

75

preferential policies should be industry-based, rather than location-based. In other

words, government policies to encourage high-technology FDI inflow into the well-

developed coastal region, while attracting low-technology and labour-intensive FDI

inflows into the less developed inland regions need to be put into action. Third, in

order for China to attract FDI, the government needs to encourage the transfer of

skilled labour and technology to the inland regions, for these regions to obtain the

maximum benefit from FDI.

76

Chapter 5: Regional Analysis of Determinants of Foreign

Direct Investment in China’s Manufacturing Industry

In Chapter 4, the determinants of regional distribution of FDI inflows in China were

described and examined. This chapter will focus on determinants of FDI inflow in the

manufacturing industry, in both the high and low-technology categories.

Separating the manufacturing industry into high- and low-technology categories was

required for two reasons. First, China is still in the process of industrialisation, and

FDI in manufacturing industry plays an important role in the national economy. It has

an enormous effect on industrial upgrading, technological advancement and export

competitiveness (Buckley, Clegg & Wang 2002; Buckley, Clegg & Wang 2005;

Buckley, Clegg & Wang 2006; Yang 2010). Over the past threedecades, China’s

manufacturing industryhas made outstanding progress and has become the world’s

largest producer of more than 100 products, including textiles, apparel, sheet glasses,

fertiliser, refrigerators and televisions. However, after the GFC of 2008, the demand

for low-technology products decreased, while the international demand for high-

technology products increased, as the latter have positive effects on productivity and

competitiveness when used throughout an economy (Organisation for Economic and

Co-operation and Development (OECD) 2009). Consequently, at the national level,

China should attract FDI in the high-technology manufacturing industries rather than

the traditional low-technology manufacturing industries.

Secondly, FDI provides access to new technology, capital, R&D facilities and

management know-how in the host location, which in turn increases economic

development. Some scholars argue that the uneven distribution of FDI is responsible

for the widening gap of regional development across China (Wei, Yao & Liu 2009).

Thus, if FDI is the major contributor to this regional disparity, increasing FDI in the

underdeveloped inner and western regions will reduce that disparity. However, after

consideration of the comparative advantages among the regions, the coastal region

with its highly trained labour supply and better industrial development should focus

on attracting FDI in the high-technology manufacturing industries, while the western

77

and central regions, which are rich in natural resources but possess large unskilled

labour forces, should focus on low-technology manufacturing industries, thus

achieving high-speed economic development by specialisation. As a result, it is

argued that dividing China’s manufacturing industries into different categories may

provide better quality information for policy-makers.

This chapter examines specific determinant of FDI for both the high- and low-

technology manufacturing industries across the four regions of China. Specifically the

size of regional economy, labour costs, labour quality, physical and technology

infrastructure as determinants for locating high/low technologies in manufacturing

activities across all of China’s four regions. The remainder of the chapter is structured

as follow: Section 5.1 describes the transformation and regional distribution of FDI in

manufacturing industry among four regions. Section 5.2 reviews previous studies on

location determinants of FDI while section 5.3 presents determinants of FDI inflows

in both high-tech and low-tech categories. Data and analytical framework and

empirical results will be presented in section 5.4 and 5.5, respectively. The

conclusions and policy implications are discussed in section 5.6.

5.1 Transformation and Regional Distribution of FDI Inflows in

China’s Manufacturing Industry

The OECD classifies all manufacturing industries16 into four (4) categories based on

technology intensity namely: high technology category, medium-high technology

category, medium-low technology category and low technology category. Technology

intensity is measured by two indicators: ratio of Research and Development (R&D)

expenditure and the value added of products and the ratio of R&D expenditure and

production cost. Industries ranked in higher technology category have higher research

intensity indicators compared to industries in lower category. Based on Zhao’s (2004)

comparison between China’s Industrial Classification & Code of National Economy

(GB/T4754) and the International Standard Industrial Classification (ISIC Rev.3), this

chapter classifies manufacturing industries in China into only two categories, namely:

16All industries are classified based on International Standard Industrial Classification of All EconomicActivities (ISIC) Rev.3. More detailed classification is in Table A-9, Appendix.

78

low-technology (low and medium-low technology) and high-technology (medium-

high and high technology) categories. Table 5.1shows that the high-technology

manufacturing category includes the following industries: pharmaceutical, medicinal

chemicals and botanical products, telecommunication, office machinery, chemical and

chemical products, machinery and equipment, electrical machinery and transport

equipment. The low-technology manufacturing industries include food and beverages

production, tobacco production, textiles, wearing apparel, paper and paper products,

coke and petroleum production, non-metallic mineral production, basic metal

production and fabricated metal production.

Table 5.1: Classifications of Manufacturing Industries by High- and Low-

Technology Categories17.

Technology Industries

High-technologyCategory

Manufacture of pharmaceuticals, medicinal chemicals andbotanical productsManufacture of radio, television and communication equipmentManufacture of office, accounting and computing machineryManufacture of chemicals, chemical products and fibresManufacture of machinery and equipment n.e.c.Manufacture of electrical machinery and apparatus n.e.c.Manufacture of transport equipment

Low-technologyCategory

Manufacture of food products and beveragesManufacture of tobacco productsManufacture of textilesManufacture of apparel and footwearManufacture of paper and paper productsManufacture of coke, refined petroleum products and nuclearfuelManufacture of other non-metallic mineral productsManufacture of basic metals

Manufacture of fabricated metal products, except machineryand equipment

Source: OECD and National Bureau of Statistics of China. Note: n.e.c. indicates notelsewhere classified.

17 With its continuing opening to foreign investment, in order to increase international comparison andinformation communication, China adopted the ‘Industrial Classification for National Economic

Activities’ (GB/T475-2011) in 2011. This classification mainly obeys the same principles, methodsand industrial classification system as ISIC Rev.3, but with adjustment of some classes’ context

compared to the ISIC Rev.3. This researcher established corresponding classes and transformed theChinese manufacturing industry classifications into ISIC Rev.3 and classified them into the relevantdifferent technology categories.

79

Figure 5.1 shows the amount of utilised FDI inflows in both high- and low-technology

manufacturing industries for the years 2001, 2005 and 2008, respectively. The graph

further reviews that the value of FDI increased dramatically for the high-tech category.

The total utilised FDI in high-tech manufacturing industry grew from USD40.2 billion

in 2001 to USD 208.3 billion in 2008, equivalent to an increase of around 418 per

cent; On the other hand, the total utilised FDI in low-tech industries grew from USD

30.3 billion in 2001 to USD 106.1 billion in 2008, an increase of 250 per cent only.

Although China has a strong comparative advantage in low-tech, labour-intensive

activities which is not only due to its abundant supply of labour, its comparative

advantage in high-tech activities has been driven by high economic growth, huge

improvement in human capital development and technology spill-over from previous

FDI (Shaukat & Guo 2005; Chen 2011). As a result, the distribution of FDI among

high and low-tech categories has changed. As shown in table 5.2 columns 2 and 4, the

share of utilised FDI in high-tech category reached 66.3 per cent of total national

utilised manufacturing FDI in 2008, an increase of 9.2 per cent compared to that in

2001, while the share of FDI in low-tech category has continued to fall, it decreased

from 42.9 to 32.7 per cent in the same period.

Figure 5.1: Utilised FDI Inflow inthe High and Low-technology ManufacturingIndustriesin China in2001, 2005 and 2008 (USD billion).

Source: ChinaIndustrial Economic Statistical Yearbook and author’s own

calculation

0

50

100

150

200

250

2001 2005 2008

USDmillion

High-tech Low-tech

80

Table 5.2: Amount and Shares of FDI Utilised in Manufacturing by Industry,

(USD billion, percentage).

Technology Category2001 2008

Changein %USD

billion %USDbillion %

(1) (2) (3) (4) (5)High-technology categoryPharmaceuticals, medicinal chemicals andbotanical products

1.7 2.5 7.2 2.3 316

Radio, television and communicationequipment

12.2 17.3 72.2 23.0 493

Office, accounting and computingmachinery

1.4 2.0 6.4 2.0 351

Chemicals, chemical products and fibres7.2 10.2 33.9 10.8 372Machinery and equipment n.e.c. 5.7 8.1 33.1 10.5 480Electrical machinery and apparatus n.e.c.6.3 8.9 26.0 8.3 315Transport equipment 5.7 8.2 29.5 9.4 414Sub-total 40.2 57.1 208.3 66.3 417.7Low-technology categoryFood products and beverages 9.3 13.2 16.7 5.3 80Tobacco products 0.0 0.1 0.0 0.0 -57Textiles 5.1 7.3 19.1 6.1 274Apparel and footwear - 10.2 3.2 -Paper and paper products 3.1 4.5 12.3 3.9 290Coke, refined petroleum products andnuclear fuel

0.9 1.3 3.3 1.1 270

Non-metallic mineral products 5.0 7.2 15.9 5.0 214Basic metals 2.6 3.6 15.9 5.0 517Fabricated metal products, exceptmachinery and equipment

4.2 5.9 12.6 4.0 204

Sub-total 30.3 42.9 106.1 33.7 250.2Source: China Industrial Statistical Yearbook and the researcher’s calculation.

Table 5.2 also reveals that the expansion of FDI across high-tech manufacturing

industries over the period 2001- 2008 was concentrated in radio, television and

communication equipment industry (493 per cent), as well as the machinery

equipment(480 per cent) and transport equipment industries (414 per cent), (Column

5). In 2008, the radio, television and communication equipment industries attracted

the largest amount of FDI totalling USD 72.2 billion, which accounting for 23 per

cent of the total utilised FDI in the manufacturing industry, followed by chemicals

and the chemical product industry (USD 33.9 billion & 10.8 per cent), machinery and

equipment industry (USD 33.1 billion & 10.5 per cent), and transport equipment

81

industry (USD 29.5 billion & 9.4 per cent). Among all low-tech manufacturing

industries, the manufacturing of basic metal has increased the most, compared to 2001

increasing from USD 2.6 billion to USD 15.9 billion an increase of 517 per cent. The

paper and paper product industry recorded an increase of 290 per cent increase, textile

industry at 270 per cent while the coke, refined petroleum products and nuclear fuel

industry experienced an increase of 214 per cent. The tobacco industry is the only

industry that experienced a fall in FDI, a decrease of 57 per cent from 2001 to 2008,

although the dollar amount is less than USD 0.1 billion

The FDI inflows in both high-technology and low-technology manufacturing

industries have shown a strong location preference, and have been highly

concentrated in the coastal region, with little going to the northeast, central and

western regions. Figures 5.2A and 5.2B show the regional distribution of FDI utilised

in the high- and low-technology manufacturing industries between 2001 and 2008. In

the high-technology category, the coastal region received 88percent of total FDI in

high-technology industries, with the northeast, central and western regions accounting

for 5, 4 and 3percent, respectively (Figure 5.2A). Compared with high-technology

category, the share of utilised FDI in the low-technology category in the coastal

region is slightly smaller, accounting for 85 per cent, while the central region became

more important for low-technology FDI, which took 6 per cent of national FDI in the

manufacturing industries (Figure 5.2B).

This location preference is the result of a variety of factors, including FDI policies

and regional economic development. The coastal region has historically received

privileged status, which in turn resulted in comparative advantages in infrastructure,

capital, technology and management skills, and is leading China in high-technology

and high value-added manufacturing activities. By comparison, the inland regions are

rich sources of semi-skilled labour, but have inadequate capital, infrastructure and

technology. This is also a clear sign of the uneven distribution of foreign capital

between the coastal region and other three regions between 2001 and 2008.In addition,

the uneven regional distributions of foreign direct investment have also contributed to

the increased wage disparity across regions (Yu, et al, 2011). The average wage level

across the coastal region is higher than all three non-coastal regions. This feature also

partly explains why low-t

from the coastal region to i

Figure 5.2A: RegionManufactu

Source: China I

Figure 5.2B: RegionManufact

Source: China I

Northea5%

Northe5%

82

-tech, labour-intensive manufacturing produc

on to inland regions.

gional Distribution of Utilised FDI in High-tecacturing Industries, 2001–2008 (percentage).hina Industrial Statistical Yearbook, 2002–2009.

gional Distribution of Utilised FDI in Low-tecacturing Industry, 2001–2008 (percentage).hina Industrial Statistical Yearbook, 2002–2009.

Coastal88%

theast%

Central4%

Western3%

Coa85

theast%

Central6%

Western4%

oduction is shifting

-technology).

2009.

technology).

2009.

tal

oastal85%

83

5.2 Literature Review on Location Determinants of FDI Inflows in

the Manufacturing Industry in China

There is a growing amount of literature on the determinants of foreign capital inflow

to China, including those of Broadman and Sun (1997), Dee (1998), Sun et al (2002),

Hou (2002), Cheng and Kwan (2000b), Chen and Wu (2005), Wang (2003) and Gao

(2005). However, little research has been done on identifying the regional

determinants of FDI on an industry basis especially those that differentiates the degree

of technological intensity. Such a study is necessary to inform policy on FDI location

given that FDI inflow into China is transforming from traditional labour-intensive,

low-technology manufacturing industries to high-tech, and capital-intensive

manufacturing industries in recent years

Previous researchers have identified determinants of foreign investment, and most

assume that foreign investors will choose to invest in a particular location based on

per capita income, agglomeration, labour quality, labour cost, transportation network

and expenditures to attract foreign investment. Wang and Swain (1997) find that the

FDI in manufacturing sectors is positively related to China’s GDP, GDP growth rate,

wages and trade barriers, but negatively related to interest rate and exchange rate for

the period of 1978-1992. Cheng and Kwan (2000a) report that good infrastructure

positively influences the location decision of foreign investors in China. Similarly,

Sun, Tong &Yu (2002) report that good infrastructure has a positive effect on FDI

inflow to China for the period 1986-1998. However, Mudambi&Mudambi (2005)

show that good infrastructure supports do not always attract significant FDI,

especially into the high-technology category. Zhang and Yuk (1998) examine the

determinants of FDI in manufacturing industry coming from Hong Kong’s investors

into Guangdong province of China and also compare the difference between capital-

intensive and labour-intensive FDI. They find that labour-intensive industries attract

more export-oriented FDI, while capital-intensive industries attract FDI that are more

domestic market-oriented. They also found the most important determinants are cheap

labour and land, stable political environment, government incentive policies, good

infrastructure, absence of language barrier and the geographical proximity of

Guangdong province to Hong Kong. When NG and Tuan (2006) identify the

84

determinants of FDI in manufacturing in Guangdong province by using firm-level

data, they find that institutional force and agglomeration are both have positively

related to the level of FDI inflows. Buckley, Wang & Jeremy (2007) find overseas

Chinese investors (Hong Kong, Taiwan and Macau) generate spillover effect in

labour-intensive industries, while in contrast, western investors generate spillover

effect in technology-intensive industries. Crespo and Fontoura (2007) argue thatFDI

provides potential for knowledge transfer through spillover effects, however, the

extent of the spillovereffect are depends on the host locations’ absorptive capacities.

Fung, Lizaka& Parker (2002) when comparing the determinants of FDI from

technology advanced countries (Japan and US) and export-oriented investors (Hong

Kong and Taiwan) find that the major differences between US and Japaneseinvestors

and Hong Kong and Taiwan investors are based on the importance of labour quality

and infrastructure development in their choice process. The US and Japanese

investors are influenced significantly by regional labour quality, while infrastructure

development is more important for Hong Kong and Taiwanese investors. The rational

given by them is that US and Japanese investors are more technology advanced, thus,

require higher level of education of domestic labour force in order to acquire

integrative skills involved in FDI. Furthermore, when Fung et al (2003) examine the

determinants of FDI among US and Hong Kong investors, they find regional

economic development is import for both investors, however, labour quality is

particularly important for US investors, as their investments in China are more

capital-intensive in nature, while tax preferences related to the special zones and

lower labour cost are important for Hong Kong investors, as their investments in

China are more labour-intensive. Li (2005) finds that FDI inflows from the newly

industrialised economies (NIEs) and Association of Southeast Asian Nations

(ASEAN) are more export-oriented. In contrast, FDI inflows from developed western

countries are more market-oriented.

Based on comparative advantage, Qiu (2003) constructs a FDI model and finds that

host country’s comparative advantage sectors is more attractive to FDI than its

comparative disadvantage sectors. He then explains as to why prominent FDI invest

in China’s labour-intensive sector, arguing that this is expected as the case as China

has a comparative advantage due to its large supply of labour and relative low cost of

input materials. Milner and Pentecost (2006) come to the same conclusions when they

85

test the determinants of US foreign direct investment in the UK’s manufacturing

industry. They also find that the comparative advantage in UK in terms of unskilled

labour is an important factor attracting U.S. FDI. Furthermore, Dunning (2009)

argues that as foreign affiliates became more embedded in the host countries leading

firms to engage in innovation activities. Lin and Kwan (2011) extend this argument

and test the determinants of FDI across 29 manufacturing industries in China for the

period 2000-2007.Their study find that FDI location is also influenced by high

productivity, level of the SOE, the latter allowing the foreign firms to sustain

monopolistic power in the market. In addition, they find that MNEs rely on R&D

activities, and thus, the degree of FDI penetration is higher in R&D intensive industry.

On the other hand, Blonigen and Slaughter (2001) find Japanese Greenfield

investment in US manufacturing industries did not increase the demand for skilled

labour, but rather lower the demand for skilled labour.

5.3 Determinant Factors of FDI in Manufacturing Industry

The theoretical foundation used in this chapter is Dunning’s eclectic paradigm (the

OLI framework). According to this paradigm, to offset the disadvantages of

establishing foreign production, MNEs must have three advantages, namely:

ownership advantage, location advantage and internalisation advantage. An ownership

advantage can take the form of innovatory capacity, trademarks, reputation, or other

assets. The location advantages of a host country arise from the better factor quality,

transport development, endowments, government policies and lower costs. The

internalisation advantage refers to whether a firm is better off when producing

internally rather than outsourcing. In this chapter, the ownership and internalisation

advantages are taken as given; thus, in line with the OLI framework, the level of

inward FDI to a particular destination may be explained in terms of different location

characteristics.

Based on OLI framework and empirical studies previously discussed, eight (8)

potentially important determinants of FDI inflows across four regions in China are

identified in this study. As summarised in Table 5.3, these are market size (i),

absorptive capacity (ii), supply of unskilled labour (iii), labour cost (iv), physical

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infrastructure (v), telecommunication (vi), government incentives (vii) and

agglomeration (viii).

Market size,is hypothesised to have a positive impact on regional attraction of foreign

capital on both high and low-tech manufacturing industries, as it directly affects the

expected revenue from domestic market. In fact, one of the motivations of setting up

foreign affiliates in the host country is to supply goods and services to the domestic

market. This kind of investment is undertaken in order to exploit new markets, and

thus, the market size, market growth rate, and degree of development of host country

are very important for FDI. This implies that the larger the host market, the faster the

rate of economic growth, and the area will attract more FDI. In this chapter, GDP per

capita (GDPPC in USD dollar) is used to represent market size.

Absorptive ability refers to the effectiveness of technology transfer from home

country to host location. Previous research shows that the host country must have a

moderate technological gap with MNEs in order to attract MNEs to establish foreign

subsidiaries and to transfer advanced technology. If the technological gap is too wide,

the host may not have the ability to adopt the technology associated with MNEs and

thus will not experience an increase in total productivity (Lin, Lee & Wang 2011;

Kinoshita 2001). Research also reveals that higher expenditure on R&D increases the

regional capacity to absorb more advanced foreign technology, which in turn, attracts

more high-tech manufacturing FDI (Griffith, Redding & Van 2003). Further,Feenstra

and Hanson (1997) argue that FDI, especially for those that are skilled-labour-

intensive FDI, require a higher level of skilled labour is also high. In this chapter, the

government spending on R&D (RESEARCH in USD million) is used to indicate

absorptive capacity.

Supply of unskilled labouris another crucial factor in attracting low-technology

manufacturing FDI. Initially, FDI into China was based on comparative advantage in

labour-intensive production due to the large amount of unskilled labour and lower

production cost. Since previous literatures show that a large amount of unskilled

labour in a region is an essential factor for low-tech manufacturing FDI, thus, it is

hypothesised that supply of unskilled labour has a positive relationship with low-tech

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FDI. This variable is measured by the percentage of total labour force with primary

school education or below denoted as UNSKILL in %.

Labour costcalculated as the average wage paid in the manufacturing industry

(WAGE in USD Dollar) is expected to have either a positive or a negative effect on

FDI. Here it is argued that the higher the wage, the lower the revenue, which in turn

reduces the attractiveness for FDI. On the other hand, in recent years, China attracts

foreign investment in more technology-intensive industries and MNCs are paying a

premium to attract better quality workers. Labour cost may therefore reflect labour

quality, thus the sign of wage variable can be either positive or negative.

Physical infrastructure and telecommunicationdevelopment is another major

determinant of FDI. Adequate and effective transportation can influence a firm's cost

and revenue and hence their location decision. The level of infrastructure

development of a particular region is found in many studies to be positively correlated

to FDI. In this study, direct measure of physical infrastructure is measured by the total

length of highway, railway and inland waterway in a region (HRWLENGTH in

kilometres). Graf and Mudambi (2005) argue that telecommunication infrastructure is

especially important for IT-enabled business, and the availability of

telecommunication infrastructure is a significant condition for the attractive location

for FDI. In this research, the telecommunication infrastructure development is

measured by total length of cable (TELECOM in kilometres).

The government incentive to attract FDI is proxied by the number of special zones in

each region. In these special zones, foreign investors can enjoy preferential policies,

such as exemption from or reduction in the payment of profit taxes, land fees, import

duties, as well as receive priority to obtain infrastructure services. In turn, the regions

with more zones become more attractive to foreign investment. Thus, it is

hypothesised that the number of zones in each region (SZONES in unit) has a positive

relationship with FDI inflow for both high and low-tech manufacturing industries.

Agglomerationrefers to the concentration of economic activities that can lead to

economies of scale and positive externalities (Sun et al, 2002). The level of

agglomeration of a particular region is found in many studies to be positively related

to higher FDI inflows. Similarly, the presence of low-tech FDI inflows may provide a

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signal for further FDI flows from other MNCs. On the other hand, high concentration

of one type of industry may cause competition and therefore reduce the attractiveness

of a location. Given these arguments, the agglomeration effect on FDI can be either

positive or negative. In this research, the value of the foreign capital received in

previous year in low and high-tech manufacturing industries is used as a measure of

the agglomeration effects on both high and low-tech FDI, denoted as (AGGLOL and

AGGLOH in USD million).Table 5.3 summarises the various determinants identified

in the literature that will be used in this research

Table 5.3: Determinants of FDI Inflow.

IndependentVariable Proxy Variable (Variable Name) Expected Sign

Market size GDP per capita (GDPPC) +

Absorptive capacityGovernment spending on R&D(RESEARCH)

+ (high-technologymanufacturingonly)

Labour costs Annual average wage (WAGE) +/-

Supply of unskilledlabour

Percentage of total labour force withprimary school education or below(UNSKILL)

+ (Low-technologymanufacturingonly)

Physicalinfrastructure

Total length of highways, railways andinland waterways (HRWLENGTH)

+

TelecommunicationsLength of cable (TELECOM) +

Governmentincentives

Total number of different types of specialzones(SZONES)

+

Agglomeration

Foreign capital in low-technology industryin the previous year (AGGLOL); Foreigncapital in high-technology industry in theprevious year (AGGLOH)

+/-

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5.4 Data and Analytical Framework

This chapter employs a panel data set from the manufacturing industries reported by

the China Industrial Economic Statistical Yearbook2000-2008. This is the only

official source that provides detailed and consistent data under the separate categories

of different industries in different provinces. It should be noted that there isa

mismatch between the sectorclassifications in China’s Industrial Classification

&Codes of National Economy (GB/T4754-2011) and the International Standard

Industrial Classification (ISIC Rev.3) of all economic activities. By carefully

comparing the definitions for these two systems and combining some industries under

the GB/T 4754-2011 classification, it was possible to construct a balanced panel data

set containing of sixteen (16) manufacturing industries. Of this, seven (7)

manufacturing industries are in the low-technology category and nine (9) in the low-

technology category. TheChina Industrial Statistical Yearbook2005, which reports

data from 2004 is not available, hence the foreign investment in the different

industries was calculated from the averages of the 2003 and 2005 data.

The empirical strategy used was to establish two models to test the determinants of

FDI in both the high-technology and low-technology manufacturing industries

separately, and then apply the same model to the different regions respectively. Using

the same determinants for both the high and low-technology manufacturing using in

the model may provide misleading results, as different categories of FDI may be

attracted by different determinants and different locations. For instance, FDI in the

high-technology manufacturing industries may be attracted by the higher observing

capacity in the coastal region, while the low-technology manufacturing industries may

be attracted by the large labour supply in the inland regions. Thus, specifying a

different model for each type of manufacturing is essential.

When specifying the model, all determinants, except the number of special zones are

lagged by one (1) period. This is partly due to the fact that the investment decisions to

undertake FDI in a current year will not be realised as actual FDI flows do not

eventuate until a year later, in other words, multinational FDI activities in a given year

are based on information from the previous year. The variables GDPPC, WAGE,

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HRWLENGTH, TELECOM, RESEARCH and UNSKILL are transformed into

natural logarithm.

Given the above arguments, the low-tech manufacturing industry locational model is

specified as:

= + , + , + ,+ , + , + ,+ , + ▁▁▁▁▁▁▁▁▁▁▁▁ . . ▁▁(1)

Similarly, the high-tech manufacturing industry locational model is expressed as

follows:

= + , + , + ,+ , + , + , +

, + ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁.(2)

Where subscripti refers to individual provinces, t refers to years from 2002 to 2010.

5.5 Empirical Results

The descriptive statistics for all variables across the four regions are presented in

Tables 5.4a to 5.4d. There are ten (10) provinces in the coastal region, three (3)

provinces in northeast region and six (6) and eleven (11) provinces in central and

western regions, respectively. Based on the mean value of FDI inflows in each region,

the coastal region recorded the highest average inflow in both high-tech and low-tech

manufacturing industries (USD 12163 million and USD 6520 million), followed by

the northeast region (USD 2159 million and USD 1391 million), western region (USD

1334 million and USD 810 million) and central region (USD 3328 million and USD

372 million). However, the coastal region also has the highest standard deviation in

both categories. This indicates that the FDI inflows into the 10 provinces in the

coastal region are more varied compared to other regions.

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The GDP per capita also exhibit a similar trend, with the coastal region recording the

highest GDP per capita, and followed by northeast region, central region and then the

western region. Further, the wage which is measured by average annual salary paid to

employee in manufacturing industry indicate that the western region has a higher

annual wage compare to central region. This situation can be explained by the

subsidies given to workers for working in the plateau area in the western region.

Regarding to the level of unskilled labour which is measured as the percentage of total

employees with primary degree or lower, the central and western region prove to have

the worst compared to the coastal and northeast regions. For example, the percentage

of 47.1 for the western region indicates that for every 100 employees in

manufacturing industry, only 47 persons have received the level of education of

primary degree or below.

Government spending indicates the region’s capacity for new and high technology.

The coastal region government invests significant amounts of money on R&D (USD

2660 million) compared to northeast region (USD 1020 million), central region (845

million) and western region (USD 450 million). As to the physical infrastructure and

telecommunications denoted by HRWLENGTH and TELECOM, respectively, the

total length of highway, railway and inland waterway and cable in central and western

regions are much longer than the coastal region as the former represent a much larger

land area compare to coastal region. In terms of special zones the coastal region has

the highest number of zones at 40, where the northeast, central and western regions

only have16, 11 and 11, respectively.

Table5.4a:Descriptive Statistics-Coastal Region

Variable Observations Mean Std. Dev. Min Max

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FDIH 90 12163.1 14831.1 32.9 70474.0FDIL 90 6519.9 6646.9 207.4 28141.4GDPPC 90 3827.8 2455.4 862.0 11563.3WAGE 90 2552.6 1274.2 935.7 6832.4UNSKILL 90 25.8 10.9 6.6 44.0RESEARCH 90 2659.8 2823.4 9.7 12558.9HRWLENGTH 90 74892.7 62952.9 8366.5 231391.0TELECOM 90 14919.0 12952.2 333.0 45807.0SZONES 90 12.8 8.2 3.0 40.0AGGLOH 90 10111.3 12747.1 32.9 63104.2AGGLOL 90 5567.3 5794.2 188.6 26105.8

Table 5.4b: Descriptive Statistics-Northeast Region

Variable Observations Mean Std. Dev. Min MaxFDIH 27 2158.9 2464.3 156.6 9092.0FDIL 27 1390.6 1196.8 224.2 4636.2GDPPC 27 2245.1 1121.0 923.0 5158.7WAGE 27 2103.4 969.4 997.3 4130.9UNSKILL 27 25.9 3.1 20.7 30.6RESEARCH 27 1020.3 898.3 199.3 4208.8HRWLENGTH 27 85487.1 35985.8 45095.6 162357.0TELECOM 27 23133.3 9158.6 10108.0 41304.0SZONES 27 8.0 4.1 5.0 16.0AGGLOH 27 1818.9 2071.0 156.6 7940.7AGGLOL 27 1112.2 1032.7 209.0 4636.2

Table 5.4c: Descriptive Statistics-Central Region

Variable Observations Mean Std. Dev. Min MaxFDIH 54 1033.6 965.0 80.8 3883.9FDIL 54 810.2 609.6 130.2 2450.0GDPPC 54 1542.3 788.1 630.8 3319.7WAGE 54 1866.4 872.7 805.1 3686.6UNSKILL 54 33.8 7.8 20.7 48.6RESEARCH 54 845.0 783.1 94.2 3866.2HRWLENGTH 54 123686.5 56171.8 60348.2 247530.0TELECOM 54 23060.5 6287.7 11352.0 36431.0SZONES 54 3.8 1.1 2.0 11..0AGGLOH 54 849.3 835.8 80.8 3883.9AGGLOL 54 665.2 509.1 121.3 2338.9

Table 5.4d: Descriptive Statistics-Western Region

Variable Observations Mean Std. Dev. Min Max

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FDIH 99 327.8 439.0 1.8 2316.4FDIL 99 371.9 421.2 8.8 1805.9GDPPC 99 1482.2 964.3 349.8 5896.9WAGE 99 2076.0 899.8 813.5 4065.3UNSKILL 99 47.1 20.6 30.3 225.8RESEARCH 99 449.5 648.2 14.5 3869.1HRWLENGTH 99 94377.5 57878.6 12087.6 263146.0TELECOM 99 23992.3 12471.2 3895.0 77292.0SZONES 99 3.9 2.0 1.0 11.0AGGLOH 99 274.2 362.9 1.8 1881.6AGGLOL 99 294.5 336.4 8.2 1590.5

The estimation results of the model are presented separately in two tables. Table 5.5a

shows the determinants of foreign capital in low-technology manufacturing industries,

while table 5.5b shows these for the high-tech manufacturing industries. The high

value of the overall R-square in both models, except for the northeast region, indicates

that the explanatory variables in the model explain most of the variation in the

dependent variables, i.e., the models are capturing the major determinants of FDI in

both low and high-tech manufacturing FDI in the coastal region, central and western

regions of China. However, both models have a poor explanatory power for the

northeast region, with an overall R-square values are only 0.2742 and 0.0016 for the

low-technology and high-technology categories respectively. This result may be

partly due to the relatively small number of observations for this region in this study.

5.5.1 Empirical Results and Discussion of Location Determinants for Low-tech

Manufacturing FDI across the Four Regions

The regression results of location determinants of low-tech manufacturing FDI shows

a mixed picture amongst the four regions of China (Table 5.5a). For the coastal region,

market size (GDPPC) has a significant positive effect on low-tech FDI (Table 5.5a,

Column 1) which implied that rapid growing domestic market attracts low-tech FDI to

the coastal region. This result indicates that low-tech FDI in the coastal region is not

only export-oriented but also domestic market-oriented given that the coastal region

has the highest population density and highest household income compared to other

regions. The labour cost (WAGE) and supply of unskilled labour (UNSKILL) both

have a negative effect on low-tech FDI inflows, however, only the WAGE variable

has significant effect. Generally speaking, low-tech FDI is motivated by cheap and

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unskilled labour in the host location, however, the overall labour quality in coastal

region is much higher compared to the other three regions, thus, even for low-tech

FDI, and foreign investors need to pay more for the same task. In terms of employee

educational attainment, by the end of 2008, 14 per cent of total employees in the

coastal region has college degree or above. In big progressive municipalities like

Beijing and Shanghai, the proportion were 35.9 per cent and 31.3 per cent,

respectively, On the other hand, the proportion of total employee with college degree

or higher were 12.2 per cent, 9.3 per cent and 6.3 per cent in the western region, the

northeast region and central region, respectively.

Table 5.5a. Determinants of Foreign Capital in Low-tech Manufacturing

Industries among Four Regions

Coastal Northeast Central Western(1) (2) (3) (4)

CONS-0.1060 -6.5670 -1.3539 -5.8572(0.910) (0.027)** (0.584) (0.034)**

GDPPC0.7600 -0.0031 0.1546 1.5108(0.006)*** (0.995) (0.755) (0.052)*

WAGE-0.4791 0.3616 0.2599 -0.9733(0.075)* (0.632) (0.602) (0.330)

UNSKILL-0.0113 -0.0037 -0.0052 0.0001(0.152) (0.813) (0.603) (0.982)

HRWLENGTH0.1689 0.3276 0.0838 0.4393(0.124) (0.165) (0.614) (0.091)*

TELECOM0.0579 0.3783 -0.0160 0.2841(0.213) (0.290) (0.954) (0.246)

SZONES0.0014 -0.1320 -0.0424 -0.0130(0.880) (0.007)** (0.119) (0.858)

AGGLOL0.4777 0.6668 0.6844 0.0006(0.000)*** (0.001)*** (0.000)*** (0.996)

R-sq(overall) 0.9133 0.2742 0.8825 0.4439Prob> chi2 0.0000 0.0000 0.0000 0.0000Note: () represents p-value, ***, **, * denote statistical significance at 1%. 5% and10% level, respectively

Physical infrastructure (HRWLENGTH) and telecommunication infrastructure

(TELECOM) both have positive but insignificant effects on low-tech FDI in coastal

region. The positive sign implies that physical and telecommunication developments

are important for foreign investors as most of products produced by FFEs are

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exported to FFEs’ home countries or other destinations. For instance, in 2010, the

total export from FFEs in coastal region was USD 808.3 billion, which equal 93.8 per

cent of total export from FFEs. However, inland highways, railways and waterways

and telecommunication development were not as important as seaports located in the

coastal region. The government incentive to attract FDI was measured by the number

of special zones (SZONES).

Until 2010, there are 302 different zones established in China, out of which 165 were

located in coastal region. Special zones offer preferential policies to foreign investors

in terms of tax deduction, cheap land and energy fees, however, a high concentration

of special zones located in the same region may actually reduce their attractiveness

and it may be seen as an increase in the level of competition between firms who are

located in the special zones as they may face competitions in terms of labours, land

uses and infrastructure. Thus, even though setting special zones has a positive effect

on the attractiveness of the coastal region, the effect is not significant. The regression

results show a highly significant positive agglomeration effect (AGGLO) on low-tech

FDI in the coastal region. Agglomeration not only measures the positive economic

externality but also indicates the risk involved for new investors. Large amount of

FDI inflows located in the coastal region may indicate that the region is more market-

oriented with a foreign business environment.

The empirical results of the regional determinants of low-tech manufacturing FDI in

the northeast and central regions are represented in Table 5.5a, columns 2 and 3. The

wage variable (WAGE) has a positive sign for both regions, while the supply of

unskilled labours (UNSKILL) has a negative sign in both regions. Combining these

two variables, the result indicates that even for low-tech manufacturing activities,

there was a mismatch between the basic skills of labour force and FFEs requirements,

this indicates that FFEs are willing to pay a higher wage to get better skilled labour.

Meanwhile, the inland physical infrastructure development appears insignificant in

attracting low-tech manufacturing FDI inflow in both regions. These results indicate

that low-tech FDI inflows in these two regions are more domestic market-oriented

rather than export-oriented, and thus as a result, inland infrastructure development is

not an important location factor for low-tech FDI. The government incentive to attract

low-tech FDI by setting up special zones (SZONES) also failed in these two regions.

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For northeast region, there were ten (10) different types of special zones were set up

during the period of 2000-2010.Of the ten (10) special zones, 4 are export-processing

zones, one (1) was high-tech development zone and five (5) were national economic

and technological development zones. In the same period, the central region

established 32 special zones, with 23 zones established as economic and high-tech

development zones. Based on the nature of special zones, regional governments are

trying to attract more high-tech FDI instead of low-tech FDI. However, this will not

match with the regional characteristics, such as labour quality. Thus, establishing

special zones may reduce the attractiveness of low-tech FDI in northeast region. The

agglomeration effects (AGGLOL) have a statistical significant positive effect on low-

tech FDI in both two regions; at one (1) per cent level of significance.

In the western region, market size measured by GDPPC and physical infrastructure

measured by HRWLENGTH are the only two variables that have a significant effect

on low-tech FDI. Geographically the western region comprises of mountains, plateaus

and basins, with a difficult environment to construct basic infrastructure such as roads

and utilities supply which in turn makes if costly for the region to export products

overseas. As a consequence, domestic market and intraregional infrastructure

development are two important variables for low-tech FDI. Since thewestern region’s

labour supply predominantly live in rural areas, and do not have the basic skills for

manufacturing production, the supply of unskilled labour (UNSKILL) has a positive

but not significant effect on low-tech FDI.

The effect of establishing special zones to attract low-tech FDI was also not

significant. Since China’s adoption of the ‘western development’ strategy in 1999,

there are now 35 different types of special zones, 24 economic and technology

development zones, three (3) high-tech zones, eight (8) were export-processing zones.

However, due to the economic development, labour quality and physical

infrastructure development, setting up zones seems not sufficient enough to attract

foreign investors. Specifically, three (3) high-technology zones and eight (8) export-

processing zones were not suitable for western region. Firstly, the research and

research development facilities and availability of human resources were not

superior.Secondly, the major importers of China’s products are U.S. and European

Union, for them, the transportation cost is high due to the distance and infrastructure

97

development. Thus, the setting up of more export-processing zones does not

necessarily increase attractiveness of the western region.

5.5.2 Empirical Results and Discussion of Location Determinants of High-tech

Manufacturing FDI across Four Regions

The regional determinants of high-tech FDI inflows across the regions provide a

mixed picture (Table 5.5b). Due to the large amount of high-tech located in coastal

region, the model fits the coastal region the most, with overall R-square of 0.9758. In

the period 2001-2008, the coastal region attracted 88 per cent of total high-tech FDI to

China. The empirical results shows FFEs establish high-tech manufacturing

production in the coastal regiondue to the region’s better telecommunication

development (TELECOM) and the economic externality (AGGLOH). Both variables

are highly significant at one (1) per cent level. Compared to other developed countries,

the average wage in the coastal region is low, thus, the wage variable (WAGE) has a

negative sign but has an insignificant effect on high-tech FDI. Again, variable

RESEARCH which measures the government spending on R&D shows an

insignificant effect on high-tech FDI. This indicates that the training provided by

coastal region was not valued by foreign investors, as they are willing to increase

labour skills by on-job training.

In contrast, in the northeast region, government spending on research and

development (RESEARCH) has a positive significant effect on high-tech FDI. A one

(1) per cent increased in government spending on R&D would increase high-tech FDI

by 0.82 per cent. Moreover, the physical infrastructure (HRWLENGTH) and

telecommunication (TELECOM) development appear with expected signs in

northeast region (Table 5.5b, column 2).The results show that the better the physical

and telecommunication developments, the more FDI are attracted into northeast

region. However, establishing special zones was not a significant aspect in attracting

high-tech FDI over the period 2001-2008. The same argument applies as in thecase

of low-tech FDI, i.e. the effect of setting up special zones depends on the nature of

zones and the number of zones in an area. If there are too many zones in an area, this

may limit the advantages business coming from outside the zones may benefit due to

increased level of competitions.

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In the central region, the domestic market size (GDPPC) has no significant effects on

high-tech FDI (Table 5.5b, column 3). This can be explained by the fact that major

investors in the northeast region are from Japan and South Korea, whose final

products are exported back to their home countries instead of serving domestic market.

Thus, the size of domestic market has no effect on high-tech FDI. The labour cost

(WAGE) has the expected sign but is insignificant. Although the overall labour cost in

China is low, the wage level has limited implication for foreign investors. Both

telecommunications (TELECOM) and physical infrastructure (HRWLENGTH) have

positive signs, but again insignificant.

Table 5.5b. Determinants of Foreign Capital in High-tech Manufacturing

Industries among Four Regions

Coastal Northeast Central Western(1) (2) (3) (4)

CONS-2.2356 -10.1900 -3.4573 0.4994(0.034) (0.004)** (0.271) (0.794)

GDPPC0.4004 0.6206 -0.2520 0.5805(0.167) (0.384) (0.705) (0.318)

WAGE-0.1805 -1.3179 -0.3818 -0.7477(0.453) (0.264) (0.561) (0.291)

RESEARCH0.0035 0.8207 0.3976 0.1847(0.975) (0.015)** (0.282) (0.344)

HRWLENGTH0.1194 0.7014 0.0041 -0.0219(0.181) (0.010)*** (0.983) (0.910)

TELECOM0.1832 0.9367 0.6561 0.1860(0.000)*** (0.019)** (0.060)* (0.314)

SZONES-0.0066 -0.1298 -0.0588 -0.0142(0.430) (0.023)** (0.180) (0.793)

AGGLOH0.7269 0.1212 0.9071 0.7045(0.000)*** (0.594) (0.000)*** (0.000)***

R-sq(overall) 0.9758 0.0016 0.8877 0.9501Prob> chi2 0.0000 0.0000 0.0000 0.0000Note: () represents p-value, ***, **, * denote statistical significance at 1%. 5% and10% level, respectively

Comparing high-tech FDI to low-tech FDI, the former involves significant costs such

as the large amount of initial investment on machinery, R&D and labour training;

however experiences of existing firms play an important role for new investors. The

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agglomeration (AGGLOH) effect has a positive effect on attracting high-tech FDI,

and is significant at the one (1) per cent level. Specifically, an one (1) percent in

increase in AGGLOH will increase high-tech FDI inflows by 0.91 per cent for the

next year. Similarly, government incentive to attract high-tech FDI by setting up

special zones (SZONES) has also failed in the northeast region; with the variable

appearing to be insignificant with a negative sign. This result further implies that the

preferential policies implied in zones are not matched with foreign investors’

decisions to set up in the region.

For the western region, none of the location determinant variables are statistically

significant, except for the agglomeration effect (Table 5.5b, Column 4). Results show

that the western region has never been an attractive destination for foreign investment

since China opened its door to trade and investment. This result is further supported

by the fact that the western region only receives 3 per cent of high-tech FDI in China.

Market size (GDPPC), labour quality (RESEARCH), physical infrastructure

(HRWLENGTH) and telecommunication (TELECOM) all have insignificant effects

on high-tech FDI in the western region. This implies although government has tried to

establish special zones and implemented preferential policies to attract foreign

investors, it cannot attract investors due to the nature of the underdeveloped economic

environment and unskilled labour force this is further confirmed by the negative and

insignificant signs appearing for SZONES variable.

5.6. Conclusion

The purpose of this chapter is to investigate the local determinants of FDI. In order to

provide accurate information for policy makers to attract the appropriate FDI

technology into a location, the manufacturing industries are separated into low and

high tech categories based on research intensity. Overall, the findings show that

foreign capital investment in China has migrated from low to high tech manufacturing

industry. However, from a geographical perspective each region has distinct physical

characteristics, where one region attracts high-tech while another region is attracted to

low-tech manufacturing.

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The empirical testing suggests that the important determinants of low-tech FDI in the

coastal region are the domestic market, labour cost, and agglomeration effect, while

the important determinants for high-tech are the development of telecommunication.

To attract greater FDI into this area, policy makers should continue to invest in

telecommunication development and open more domestic markets to foreign investors.

For the three inland regions, physical infrastructure is essential to increase the

attractiveness for both high-tech and low-tech FDI, while the telecommunication

development is especially important for northeast and central regions.

Based on the empirical results, the policy implications are with regards tothe coastal

region, the government should continue to invest in research and development in

order to attract more high-tech FDI. In order to reduce regional disparities, the

Chinese government should improve the regional investment environment, and

implement more preferential policies. However, the preferential policies should be

more location specific and industry based. For the inland regions, especially the

western region, which is endowed with rich natural resources, the government should

encourage resource-seeking, low-tech FDI, such as mining and raw material industries.

In addition the coastal region should continue attracting export-oriented FDI with

government promoting export-oriented FDI, and exporting to their neighbouring

countries, e.g. Russia and India. Infrastructure development is essential to encourage

FDI into the western region. This region should invest more in physical infrastructure,

to increase its attractiveness to low-tech FDI.

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Chapter 6: Summary of Findings, Policy Implications and

Conclusions

The uneven regional distribution of FDI inflows into China and the consequences of

this provide a valuable experience for scholars and policy-makers in other nations,

particularly those in the developing countries, to study the general issues surrounding

FDI inflow. This thesis presents a detailed analysis of the location determinants of

FDI inflows into four regions of China, including the investments from all source

countries and covering all industries in these four regions, this is followed by an in-

depth analysis of the regional determinants of FDI in the high-technology and low-

technology manufacturing sectors in these regions.

The results obtained from this thesis provide considerable insight into the general

issues of regional distribution of FDI inflow in China. In particular, by using the most

recent and previously unexplored datasets, this thesis has extended the existing

knowledge in three ways: (1) by providing a better understanding of the regional

determinants of FDI inflows; (2) allowing more in-depth knowledge of the differences

between the requirements of high-technology and low-technology manufacturing

activities; and (3) establishing how to attract greater FDI inflows in terms of

technology involvement in different regions. This chapter is structured as follows:

Section 6.1summarises the main finding of this study, while Section 6.2 provides

policy implications based on the main findings. Suggestions of further research are

provided in Section 6.3.

6.1Summary of the Study

China has shifted from the total prohibition of foreign investment to one of the most

attractive destinations for FDI inflows worldwide over the last three decades. At the

national level, China has successfully attracted significant FDI inflows. However,

there are major differences in FDI inflows at the regional level. Until 2000, nearly 80

per cent of FDI inflows were located in the coastal region, leaving the three inland

regions to share the remaining 20 per cent. This unbalanced regional distribution of

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FDI inflows has been one of the major causes of regional disparities. Reducing

regional disparity by redirecting FDI inflows into the less developed inland regions

has received considerable attention from both the central and regional governments

since 2000. To provide a policy background for this thesis, it begins with an overview

of China’s regional development and an examination of the evolution of changes to

China’s FDI policies in during different development phases. In general, China has

taken a gradual reform approach, shifting from adopting an ‘uneven development’

strategy to a ‘regional coordinated development’ strategy, initially establishing four

SEZs in the coastal region, followed by the nationwide implementation of special

zones. These gradual changes to China’s FDI policies clearly indicate that China has

continued to reduce economic disparities through promoting a more open investment

environment.

Since 1999, China has gradually adopted the ‘western development strategy’, the

‘revitalising the old industrial base in the northeast area strategy’ and the ‘rise of

central China strategy’ to boost economic development and attract FDI inflows into

the underdeveloped western, northeast and central regions. However, the regional

distribution still shows a skewed distribution towards the coastal region. According to

Dunning’s eclectic paradigm, if the ownership and internalisation advantages of

foreign investors are taken as given, the host location’s overall attractiveness to FDI

inflows is determined by the location advantages it possesses. This provides the most

comprehensive explanation for the effect of location determinants on FDI inflows.

Because resource endowments are not evenly distributed between regions, and

economic factors as well as government policies also differ between regions, the

attractiveness of a host region to FDI inflows varies. In order to help regional and

central governments to redirect FDI inflows, this thesis has addressed two broad set of

questions. First, what are the factors that determine the location of FDI in China’s

four regions? Do these factors have the same effects on FDI location decisions in each

region?

Based on location theory, Chapter 4 investigated the location determinants of FDI

inflows across China’s four regions using an economic regression analysis. The

results provide strong support for the hypothesis that the location determinants of FDI

differ between regions. The main findings of this analysis are: first, the location

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determinants of FDI in both the coastal and northeast regions show strong similarities.

In these two regions, the larger domestic market size, higher quality labour force and

establishment of more special zones attract more FDI inflows, while their higher

labour costs decrease their attractiveness for FDI inflows. Second,

telecommunications infrastructure has a significant effect on FDI inflows in the

coastal region, but is insignificant for the northeast, central and western regions. In the

coastal region the FDI inflows are more capital-intensive and involve high-technology

industries, while FDI in the other three regions is directed to the more labour-

intensive, low-technology industry. Third, in the central region, due to its less

developed economic environment and relatively low wages, the labour costs and the

development of the domestic market have no significant effect on FDI inflows.

However, its more extensive inland transport infrastructure and greater degree of

openness, represented by the proportion of foreign trade per total regional trade,

attracts more FDI inflows. Fourth, the regression model has a poor explanatory power

for the western region, with none of the location determinant variables being

statistically significant, confirming that the western region has not been an attractive

destination for FDI since China opened for foreign investment.

FDI inflows in China have been overwhelmingly concentrated in the manufacturing

industry. By the end of 2010, this sector received more than 60 per cent of total

utilised FDI inflows. With the rapid increase in FDI inflows into the manufacturing

industry, the distribution of FDI within this sector has also undergone a

transformation from low-technology to high-technology manufacturing. In particular,

after China joined the WTO in 2002, the growth rate of FDI in the high-technology

manufacturing industries has been significantly greater than that of FDI inflows into

low-technology manufacturing industries. The implications of this are that as a nation

China should continue to develop the attractiveness of its high-technology

manufacturing industry to FDI; however, due to the differences in regional

characteristics, each region needs to exploit its comparative advantages to attract the

most appropriate FDI.

How best to achieve this goal? The coastal region has the best labour quality and

R&D facilities, hence should it continue to improve its attractiveness to high-

technology FDI? The inland regions have a rich supply of unskilled labour; should

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they consider attracting more FDI to their low-technology manufacturing industries?

To address these important questions, Chapter 5 analysed the differences between

high-technology and low-technology manufacturing activities in all four regions and

tested the location determinants of high-technology and low-technology

manufacturing FDI separately. The empirical results of this analysis show the

similarities and differences between the location determinants of FDI inflows into

high-technology and low-technology manufacturing. On one hand, the agglomeration

effect, measured by previous FDI inflows, shows a significant positive effect on both

high-technology and low-technology manufacturing FDI inflows in most of the four

regions of China. This indicates that previously received FDI will attract more FDI

inflows in the future. However, the government incentives to attract FDI inflows, as

measured by the number of special zones established, have failed to have a significant

effect on either the high- or low-technology categories in most regions. For low-

technology manufacturing FDI, domestic market size and labour costs are the most

important determinants in the coastal region, while larger domestic market size and

better development of inland transport only has a positive effect in the western region.

For high-technology manufacturing FDI, telecommunications development is

important in all regions except the western region, while better labour quality and

inland transportation only has a significant positive effect in the northeast region.

6.2Policy Review and Recommendations

China has achieved unprecedented economic growth since the economic reform;

however, due to the historical development of these reforms, policy issues in the

earlier stages of the reform process, and China’s geography, inequalities between the

regions have risen sharply. The coastal region was the first area opened to the outside

world, and is much more industrially developed than the central region, while the

central region is in turn far superior compared to the western region (Yang 1990). In

order to mitigate these disparities in economic development, the central government

has advocated regional cooperation between the coastal and inland regions, using

strategies such as domestic joint ventures, compensation, trade and technology

exchanges, and has also launched three development strategies since 1999. The first

of these was the ‘western development strategy’, which was instituted in September

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1999 and covered 12 provinces and autonomous provinces, namely, Chongqing,

Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang,

Central Mongolia and Guangxi18. The main components of this strategy included the

development of infrastructure, enticement of foreign investment, increased efforts on

ecological protection, the promotion of education, and the retention of talent that was

flowing to the richer provinces. Among these policies, attracting increasing inflows of

foreign capital is crucial in contributing to the rapid regional economic and

technological growth.

The second strategy, ‘revitalising the old industrial base in the northeast area’, was

implemented in2003 and covers three northeast provinces, Liaoning, Jilin and

Heilongjiang. During the planned economy in the period of 1950s and 1960s, these

three provinces were the priority areas in the heavy industrial construction sector, and

they have made a historical contribution to the industrial development and national

security of China. However, the northeast region was on a restricted development path

and had a relatively lower level of openness to foreign investment, and as a

consequence, its economic growth is dwarfed by the other regions (Invest-China 2006,

p. 99).

The third strategy was the ‘rise of central China’ which began in 2006. It involves six

provinces in the central part of China, namely, Shanxi, Anhui, Jiangxi, Henan, Hubei

and Hunan. This strategy aimed to strengthen independent innovation, improve

industrial structure, transform the growth mode, protect the ecology and environment,

promote social harmony, improve transportation and promote high-technology

manufacturing industries in this region. Although FDI inflows in the northeast, central

and western regions have increased since 2000, inequality still exists between the

coastal region and the other three inland regions, and the overall amounts of FDI

received in the three inland regions is still low compared to the coastal region. As a

result, the continuing acquisition of foreign capital, advanced technology, modern

management skills and opportunities to access the international market continue to

enlarge the economic disparities between the coastal region and the inland regions.

18Geographically, Guangxi is classified as a coastal region and Inner Mongolia is classified as an innerregion. However, these regions are far less developed than the other provinces in their regions.

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Based on the results from Chapters 4 and 5, the policy recommendations can be

provided for each region.

The coastal region is still the most attractive destination for FDI inflows; however,

previous FDI inflows have been more export-oriented, which has meant that

economic growth in this region has fluctuated with the world economy. In 1992, the

proportion of export from FFEs to total regional exports was 26 per cent; in 2008, this

figure increased to 60 per cent. The 2008 GFC meant that the US and the EU went

into recession. This reduced the FFEs’ exports in the coastal region, which in turn

reduced the region’s rate of economic growth. Since2008, the rate of economic

growth in the coastal region has been lower than that of the inland regions. In order to

reduce the dependence of economic growth on FFE exports, the empirical results of

this study suggest that the central and regional governments in the coastal region

should encourage more domestic market-oriented FDI inflows, since the average

income in the coastal region is much higher compared to that in the other regions. The

regional government should also continue to open more markets to foreign investors.

In addition, the special zones located in the coastal region have attracted foreign

investors throughout the past three decades; however, they have also increased the

competition between foreign investors. Wen (2007) argues many manufacturing

industries are experiencing excess production capacity, cut-throat price competition

and reduced profit margins. The overall labour cost in the coastal region has increased

dramatically and this has had a significant negative effect on FDI inflows, especially

for low-technology FDI. The coastal region has lost its advantage of low labour costs

and there has been a growing trend for many foreign investors, especially in low-

technology manufacturing, to prepare to enter the central and western regions in order

to take advantage of their large supplies of relatively cheap labour. Policy-makers

should adjust the nature of the special zones to make them more attractive for high-

technology FDI, as they are inelastic with regard to labour costs. The government

should also enforce a legal framework on intellectual property rights, which will not

only encourage high-technology FDI, but also have a positive impact on foreign

investors’ decisions to bring technology into the coastal region.

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For central and western regions, the regional governments should consider the

industrial and technology upgrading in the coastal region as a great opportunity for

them to attract labour-intensive and low-technology FDI and increase their economic

development through specialisation, as the labour costs remain low in these two

regions. Their unfavourable geographical location and poor transportation

infrastructure may deter FDI inflows because of their higher transportation costs.

Thus, the regional governments should overcome these location disadvantages by

improving their investment environments. The central government should shift its

preferential policies from the coastal region to the central and western regions, and at

the same time, the regional governments should learn from the experience of the

coastal region. However, as the results of this study show that establishing different

special zones is not enough to attract FDI, particularly in the low-technology sector,

these two regions also need to improve their overall investment environments, by

improving their physical infrastructure, energy supply, telecommunications and

labour quality. Heckman (2005) argues China’s investment favour physical capital

investment over human capital investment and it below average compare to other

developing countries. Recent government spending on human capital development is

favourable, but its still low compare to world standard. Improved human capital not

only improve basic skills, it also improves adaptability and efficiency, If human

capital are still undeveloped in those regions, low-technology FDI will be shifted to

other low labour cost Asian countries, such as India and Thailand. The regional

governments should invest more in human capital by building more universities and

R&D institutions. On one hand, this will improve labour quality, attract FDI inflows,

while on the other, it will reduce the outflow of skilled workers, technical personnel

and capital from the inland regions to the coastal region. Some of the provinces

located in the central and western regions are strong in R&D, and possess more

workers with higher degree educations. These institutions should form partnerships

with MNEs and become R&D centres. The western region in particular should create

an attractive environment for skilled labour, which will increase its ability to attract

high-technology FDI. It should also imply WTO accession into inland regions. It

should also relax restrictions on foreign investment in inland regions.

As to the northeast region, it is close to Japan and South Korea. These two countries

are rich in foreign capital and advanced technology, but have a limited labour supply

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and land. Due to this geographical proximity and the relative low labour cost,

Japanese and South Korean investors are establishing manufacturing plants in China’s

northeast region and exporting their final products back to their home countries.

Empirical results suggest human capital is important for the northeast region to attract

more FDI inflows, especially FDI in high-technology industries; thus, increasing

government spending on education, providing training and re-education may help

workers to gain skills which improve labour productivity so as to absorb more high-

technology FDI inflows. The ‘revitalising the old industrial base in the northeast area’

strategy should pay more attention to the promotion of education and the retention of

talent that is currently flowing into the relatively better developed coastal region,

because the quality of human capital is the key factor in promoting technological

progress. The empirical results of this study also suggest that the regional government

in the northeast region should establish more special zones to attract FDI inflows. Due

to its particular geographical location and economic development, export-processing

zones are more relevant in this region. Over the long term, FDI in high-technology

manufacturing industries will be the major driving force of sustainable economic

growth in China. In order to promote FDI in high-technology industries in the inland

regions, the government should encourage the coastal area to transfer managerial and

technologically skilled personnel to the inland regions, and at the same time, invest

more in R&D institutions, and establish more higher education institutions.

6.3Future Research

In this section, suggestions are offered for future research on the regional distribution

of FDI inflows into China. While this study makes a major contribution in terms of

understanding the regional determinants of overall FDI inflows and FDI inflows in the

secondary sector, it did not cover determinants of FDI inflows in the primary and

tertiary sectors.

Firstly, China has attracted large amounts of FDI in the secondary sector, with a share

amounting to 60 per cent, while its share in the tertiary sector was only 20 per cent in

the 1990s. China’s service sector was underdeveloped due to its development strategy,

which focused on the manufacturing industry. Since China joined the WTO, it has

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gradually opened its service sector to foreign investment and FDI in the tertiary sector

become increasingly important in China. In 2010, nearly half of the total utilised FDI

inflows in China went to the tertiary sector. Compared to the global trend of FDI in

the services sector, FDI in the service sector in China is small. Thus, how to attract

FDI to the service sector is becoming an important question for current policy-makers.

Secondly, most of China’s pastures are located in the western and central regions. In

order to encourage FDI inflows into these two regions, China should encourage FDI

inflows into the agriculture sector. In addition, these two regions have large rural

workforces. These workers are considerably less suitable for the manufacturing and

tertiary sectors. Based on the comparative advantages of each region, the western and

central regions are more suitable to attract FDI in the agricultural sector. Thus, the

regional determinants of FDI inflows in the agricultural sector should be researched in

future studies.

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Appendix

Table A-1: Pattern of China’s Opening Up to Foreign Investors.

Stage Pattern of Opening

1

In July 1979, the Chinese government decided to implement preferential policies andflexible measures in Guangdong and Fujian Provinces in terms of their foreign economicactivities.

In May 1980, Shenzhen, Zhuhai, Shantou and Xiamen were selected to become SEZs.In1983, Hainan was also granted preferential policies to enable the building of SEZs.

In May 1984, the Chinese government decided to open all 14 coastal cities in China fromthe north to the south: Dalian, Qinhuangdao, Tianjin, Yantai, Qingdao, Lianyunggang,Nantong, Shanghai, Ningbo, Wenzhou, Fuzhou, Guangzhou, Zhanjiang, and Beihai.

In February 1985, the Yangtze River Delta, Pearl River Delta and South Fujian Triangleregion were identified as coastal economic open areas.

At the beginning of 1988, Liaodong and Shandong Peninsulas, Dalian, Qinghuangdao,Tianjin, Yantai and Qingdao were connected and formed the Bohai Rim open area.

In April 1990, the Pudong area in Shanghai was also listed in the development andopening up agenda, with the objective of transformingShanghai into an internationalfinancial, trade and economic centre.

2

In June 1992, five cities, including Wuhu, JiuJiang, Yueyang, Wuhan and Chongqingalong the Yangtze River were opened, followed by 17 inland capital cities: Hefei,Nanchang, Changsha, Chengdu, Zhenzhou, Taiyuan, Xian, Lanzhou, Yinchuan, Urumqi,Guiyang, Kunming, Naning, Harbin, Changchun, and Huhhot.

Meanwhile, border cities in the hinterland area were also opened gradually, from thenortheast and northwest to the southwest, covering Heihe, Suifenhe, Huichun, Manchura,Erenhot, Yining, Bole, Tacheng, Pulan, Zhangmu, Ruili, Wanting, Hekou, Pingxiang, andDongxing.

3

After China joined the WTO, foreign capital was able enter into an all-dimensionalopening up era. With the progress that has been made opening up the regional areas,market liberalisation and legal system-building, China has become a highly openeconomy. In total, 49 NETDZ, 53 NNHIDZs and five industrial parks were set upcovering all provinces, municipalities and autonomous regions and enjoying the status ofnational economic technology development zones.

Source: Invest-China (2006), Special column 3-1, p. 102.

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Table A-2: Industry Distribution of Utilised FDI Inflow, 2000–2010, (USD billion, percentage).

Sector/Industry 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010Primary Sector

Agriculture, forestry, animal husbandry and fisheries 68 90 103 100 111 72 60 92 119 143 191

(0.2) (0.2) (0.2) (0.2) (0.2) (0.1) (0.1) (0.1) (0.1) (0.2) (0.2)Secondary SectorMining 58 81 58 34 54 35 46 49 57 50 68

(1.4) (1.7) (1.1) (0.6) (0.9) (0.6) (0.7) (0.7) (0.6) (0.6) (0.6)

Manufacturing 2,584 3,091 3,680 3,694 4,302 4,245 4,008 4,086 4,989 4,677 4,959(63.5) (65.9) (69.8) (69.0) (71.0) (70.4) (63.6) (54.7) (54.0) (51.9) (46.9)

Production and supply of electricity, gas and water 224 227 138 130 114 139 128 107 170 211 212(5.5) (4.8) (2.6) (2.4) (1.9) (2.3) (2.0) (1.4) (1.8) (2.3) (2.0)

Construction 91 81 71 61 77 49 69 43 109 69 146(2.2) (1.7) (1.3) (1.1) (1.3) (0.8) (1.1) (0.6) (1.2) (0.8) (1.4)

Tertiary SectorTransport, storage and post 101 91 91 87 127 181 198 201 285 253 224

(2.5) (1.9) (1.7) (1.6) (2.1) (3.0) (3.1) (2.7) (3.1) (2.8) (2.1)Information transmission, computer services andsoftware

92 101 107 149 277 225 249

(1.5) (1.7) (1.7) (2.0) (3.0) (2.5) (2.4)

Wholesale and retail trade 86 117 93 112 74 104 179 268 443 539 660

(2.1) (2.5) (1.8) (2.1) (1.2) (1.7) (2.8) (3.6) (4.8) (6.0) (6.2)

Hotel and catering services 84 56 83 104 94 84 93

(1.4) (0.9) (1.3) (1.4) (1.0) (0.9) (0.9)

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Table A-2 (Cont): Industry Distribution of Utilised FDI Inflow, 2000–2010, (USD billion, percentage).

Sector/Industry 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010Financial intermediaries 8 4 11 23 25 22 29 26 57 46 112

(0.2) (0.1) (0.2) (0.4) (0.4) (0.4) (0.5) (0.3) (0.6) (0.5) (1.1)

Real estate 466 514 566 524 595 542 823 1709 1859 1680 2399

(11.4) (11.0) (10.7) (9.8) (9.8) (9.0) (13.1) (22.9) (20.1) (18.7) (22.7)

Leasing and business services 282 375 422 402 506 608 713

(4.7) (6.2) (6.7) (5.4) (5.5) (6.8) (6.7)Scientific research, technical services and geologic prospecting 6 12 20 26 29 34 50 92 151 167 197

(0.1) (0.3) (0.4) (0.5) (0.5) (0.6) (0.8) (1.2) (1.6) (1.9) (1.9)Management of water conservation, environment and public facilities 23 14 20 27 34 56 91

(0.4) (0.2) (0.3) (0.4) (0.4) (0.6) (0.9)Service to households and other services 219 259 294 316 16 26 50 72 57 159 205

(5.4) (5.5) (5.6) (5.9) (0.3) (0.4) (0.8) (1.0) (0.6) (1.8) (1.9)Education 5 4 4 6 4 2 3 3 4 1 1

(0.1) (0.1) (0.1) (0.1) (0.1) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0)Culture, sports and entertainment 45 31 24 45 26 32 44

(0.7) (0.5) (0.4) (0.6) (0.3) (0.4) (0.4)Health, social security and social welfare 11 12 13 13 9 4 2 1 2 4 9

(0.3) (0.3) (0.2) (0.2) (0.1) (0.1) (0.0) (0.0) (0.0) (0.0) (0.1)Source: China Statistical Yearbook, 2001–2011.

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Table A-3: Utilised FDI from Hong Kong Investors, by industry 2001–2010, (USD million, percentage).

Industry 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010Agriculture, forestry, animal husbandry and fisheries 350 450 440 350 240 190 320 500 800 1,090

(2.1) (2.5) (2.5) (1.8) (1.3) (0.9) (1.2) (1.2) (1.7) (1.8)Mining 260 130 130 110 70 180 240 270 220 450

(1.6) (0.7) (0.7) (0.6) (0.4) (0.8) (0.9) (0.7) (0.5) (0.7)Manufacturing 11,460 10,620 9,910 11,800 11,520 12,980 13,150 18,320 19,680 23,900

(68.6) (59.5) (56.0) (62.1) (64.2) (60.9) (47.5) (44.6) (42.7) (39.5)Production and supply of electricity, gas and water 230 410 510 570 750 450 550 970 1290 990

(1.4) (2.3) (2.9) (3.0) (4.2) (2.1) (2.0) (2.4) (2.8) (1.6)Construction 320 380 240 350 270 450 240 530 400 1,010

(1.9) (2.1) (1.4) (1.8) (1.5) (2.1) (0.9) (1.3) (0.9) (1.7)Transport, storage and post 220 390 300 530 550 910 850 1,620 1,510 1,540

(1.3) (2.2) (1.7) (2.8) (3.1) (4.3) (3.1) (3.9) (3.3) (2.5)Information transmission, computer services and software 420 260 180 270 360 280 300 1,220 1,100 1,340

(2.5) (1.5) (1.0) (1.4) (2.0) (1.3) (1.1) (3.0) (2.4) (2.2)Wholesale and retail trades 100 220 340 170 330 430 900 2,150 2,590 3,270

(0.6) (1.2) (1.9) (0.9) (1.8) (2.0) (3.2) (5.2) (5.6) (5.4)Finance 0 40 50 190 20 0 50 120 140 620

(0.0) (0.2) (0.3) (1.0) (0.1) (0.0) (0.2) (0.3) (0.3) (1.0)Real estate 2,260 3,280 3,020 3,100 2,680 3,840 7,940 11,560 12,120 18,350

(13.5) (18.4) (17.1) (16.3) (14.9) (18.0) (28.7) (28.2) (26.3) (30.3)Leasing and business services 380 770 1230 650 450 720 1,840 1,800 3,120 3,840

(2.3) (4.3) (6.9) (3.4) (2.5) (3.4) (6.6) (4.4) (6.8) (6.3)Scientific research, technical services and geological prospecting 50 90 140 110 140 150 350 850 1020 1,070

(0.3) (0.5) (0.8) (0.6) (0.8) (0.7) (1.3) (2.1) (2.2) (1.8)Sub-total 16,080 17,095 16,610 18,270 17,500 20,690 26,870 40,150 44,970 59,020Total 16,717 17,861 17,700 18,998 17,949 21,307 27,703 41,036 46,075 60,567Share of total (96.2) (95.7) (93.8) (96.2) (97.5) (97.1) (97.0) (97.8) (97.6) (97.4)


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Table A-4: Utilised FDI in Manufacturing Industry from Hong Kong Investors by High and Low-technology Categories, 2001-

2010(USD million, Percentage)

Technology Industry 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

High-tech

Chemical Materials and Products 410 830 600 630 670 610 770 1200 1350 1100(3.6) (7.8) (6.1) (5.3) (5.8) (4.7) (5.9) (6.6) (6.9) (4.6)

Medical and Pharmaceutical Products 230 300 270 210 200 140 180 260 330 500(2.0) (2.8) (2.7) (1.8) (1.7) (1.1) (1.4) (1.4) (1.7) (2.1)

General Machinery 190 200 340 430 420 470 560 1670 1230 1620(1.7) (1.9) (3.4) (3.6) (3.6) (3.6) (4.3) (9.1) (6.3) (6.8)

Special Purpose Machinery 150 340 310 450 490 470 670 760 1150 1460(1.3) (3.2) (3.1) (3.8) (4.3) (3.6) (5.1) (4.1) (5.8) (6.1)

Transport Equipment 250 330 380 450 420 550 550 900 960 1000(2.2) (3.1) (3.8) (3.8) (3.6) (4.2) (4.2) (4.9) (4.9) (4.2)

Electrical Machinery and Equipment 370 610 540 740 900 960 1020 1860 2020 2650(3.2) (5.7) (5.4) (6.3) (7.8) (7.4) (7.8) (10.2) (10.3) (11.1)

Electronics and Telecommunications Equipment 1010 1340 1240 1220 1470 1670 1820 2530 2640 3730(8.8) (12.6) (12.5) (10.3) (12.8) (12.9) (13.8) (13.8) (13.4) (15.6)

Low-tech

Food Processing 340 300 290 490 350 220 200 330 320 780(3.0) (2.8) (2.9) (4.2) (3.0) (1.7) (1.5) (1.8) (1.6) (3.3)

Food Manufacturing 170 240 200 130 160 240 150 340 500 470(1.5) (2.3) (2.0) (1.1) (1.4) (1.8) (1.1) (1.9) (2.5) (2.0)

Beverage Manufacturing 250 120 140 100 120 90 120 200 160 330(2.2) (1.1) (1.4) (0.8) (1.0) (0.7) (0.9) (1.1) (0.8) (1.4)

Textiles 1020 1230 1150 1220 1050 970 900 880 790 1040(8.9) (11.6) (11.6) (10.3) (9.1) (7.5) (6.8) (4.8) (4.0) (4.4)

Clothing and other Fibre Products 770 1040 1110 1380 1340 1220 1340 1450 1460 1600(6.7) (9.8) (11.2) (11.7) (11.6) (9.4) (10.2) (7.9) (7.4) (6.7)

Paper and Paper Products 360 290 290 270 200 290 440 460 660 990(3.1) (2.7) (2.9) (2.3) (1.7) (2.2) (3.3) (2.5) (3.4) (4.1)

Plastic Products 430 650 530 480 500 540 510 680 600 770(3.8) (6.1) (5.3) (4.1) (4.3) (4.2) (3.9) (3.7) (3.0) (3.2)

Non-metal Mineral Products 430 460 450 730 400 490 750 930 1110 1440(3.8) (4.3) (4.5) (6.2) (3.5) (3.8) (5.7) (5.1) (5.6) (6.0)

Metal Products 320 360 490 560 630 770 710 820 1400 1030(2.8) (3.4) (4.9) (4.7) (5.5) (5.9) (5.4) (4.5) (7.1) (4.3)

Total 11460 10620 9910 11800 11520 12980 13150 18320 19680 23900

115

Table A-5: Provincial Distribution of Utilised FDI from Hong Kong, 2001–2010

(USD million, percentage)

Province 2003 2004 2005 2006 2007 2008 2009 2010Jiangsu 2,430 1,980 2,040 4,240 5,300 8,030 8,670 13,070Guangdong 4,320 5,010 5,820 6,600 7,200 9,670 10,140 10,740Zhejiang 1,870 2,270 2,250 2,670 2,500 3,210 4,120 5,890Liaoning 940 1470 720 590 1,610 2,440 3,980 5,590Shanghai 1,180 1,250 810 1,180 2,030 2,970 2,970 4,150Shandong 1,090 1,740 1,610 1,270 1,360 2,390 2,070 3,060Fujian 1,200 970 960 1,040 1,130 1,950 2,200 2,720Beijing 540 440 500 940 1,510 1,280 2,390 2,640Chongqing 110 80 100 120 510 1,480 1,860 2,250Sichuan 160 100 290 310 380 1,350 1,300 2,180Tianjin 340 240 470 370 540 1,210 1,520 1,350Hunan 590 750 670 380 370 540 470 950Jiangxi 890 1,100 410 330 510 740 670 950Anhui 150 170 260 280 320 440 440 590Hebei 360 240 130 130 210 430 480 550Henan 250 190 190 140 300 520 330 520Hubei 510 410 160 160 220 510 430 500Sub-total 16,930 18,410 17,390 20,750 26,000 39,160 44,040 57,700Total from HK 17,700 18,998 17,949 21,307 27,703 41,036 46,075 60,567Share of total (95.6) (96.9) (96.9) (97.4) (93.9) (95.4) (95.6) (95.3)Source: China Foreign Investment Report, 2011.

116

Table A-6: Utilised FDI from USInvestors, by industry 2001–2010, (USD million, percentage).

Industry 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010Agriculture, Forestry, Animal Husbandry and Fishery47 76 82 111 40 27 37 49 20 27

(1.1) (1.4) (2.0) (2.8) (1.3) (0.9) (1.4) (1.7) (0.8) (0.9)Manufacturing 3881 3822 2905 2754 2262 2082 1854 2228 1584 1756

(87.5) (70.5) (69.2) (69.9) (73.9) (69.4) (70.9) (75.7) (62.0) (58.2)Wholesale and Retail Trades 61 42 64 32 61 99 81 144 260 222

(1.4) (0.8) (1.5) (0.8) (2.0) (3.3) (3.1) (4.9) (10.2) (7.4)Real Estate 181 370 289 262 120 204 158 67 59 96

(4.1) (6.8) (6.9) (6.6) (3.9) (6.8) (6.0) (2.3) (2.3) (3.2)Leasing and Business service 25 314 313 316 155 278 174 142 283 405

(0.6) (5.8) (7.5) (8.0) (5.1) (9.3) (6.7) (4.8) (11.1) (13.4)Sub-total 4195 4624 3653 3475 2638 2690 2304 2630 2206 2506Total 4433 5424 4199 3941 3061 3000 2616 2944 2555 3017Share of total (94.6) (85.3) (87.0) (88.2) (86.2) (89.7) (88.1) (89.3) (86.3) (83.1)Source: China Foreign Investment Report, 2011.

117

Table A-7: Utilised FDI in Manufacturing Industry from Hong Kong Investors by High and Low-technology Categories, 2001-

2010 (USD million, percentage)

Technology Industry 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

High-tech

Chemical Materials and Products 247 246 228 232 134 144 109 170 239 122(6.4) (6.4) (7.8) (8.4) (5.9) (6.9) (5.9) (7.6) (15.1) (6.9)

Medical and Pharmaceutical Products 90 72 205 111 54 38 41 79 79 33(2.3) (1.9) (7.1) (4.0) (2.4) (1.8) (2.2) (3.5) (5.0) (1.9)

General Machinery 169 157 165 242 164 188 163 172 140 185(4.4) (4.1) (5.7) (8.8) (7.3) (9.0) (8.8) (7.7) (8.8) (10.5)

Special Purpose Machinery 74 141 117 138 89 111 123 156 95 90(1.9) (3.7) (4.0) (5.0) (3.9) (5.3) (6.6) (7.0) (6.0) (5.1)

Transport Equipment 235 224 198 217 399 162 176 409 146 141(6.1) (5.9) (6.8) (7.9) (17.6) (7.8) (9.5) (18.4) (9.2) (8.0)

Electrical Machinery and Equipment 96 143 142 179 129 125 130 177 186 201(2.5) (3.7) (4.9) (6.5) (5.7) (6.0) (7.0) (7.9) (11.7) (11.4)

Electronics and Telecommunications Equipment 370 1491 666 401 342 186 258 236 160 303(9.5) (39.0) (22.9) (14.6) (15.1) (8.9) (13.9) (10.6) (10.1) (17.3)

Information transmission, computer and software service 25 182 118 104 59 64 79 91 122 105(0.6) (4.8) (4.1) (3.8) (2.6) (3.1) (4.3) (4.1) (7.7) (6.0)

Low-tech

Food Processing 107 84 114 135 52 84 41 45 19 27(2.8) (2.2) (3.9) (4.9) (2.3) (4.0) (2.2) (2.0) (1.2) (1.5)

Textiles 67 95 131 98 95 84 90 82 54 43(1.7) (2.5) (4.5) (3.6) (4.2) (4.0) (4.9) (3.7) (3.4) (2.4)

Clothing and other Fibre Products 41 99 125 131 104 102 92 89 78 52(1.1) (2.6) (4.3) (4.8) (4.6) (4.9) (5.0) (4.0) (4.9) (3.0)

Plastic Products 74 106 77 110 56 79 68 89 54 24(1.9) (2.8) (2.7) (4.0) (2.5) (3.8) (3.7) (4.0) (3.4) (1.4)

Metal Products 84 168 106 149 114 99 85 93 42 93(2.2) (4.4) (3.6) (5.4) (5.0) (4.8) (4.6) (4.2) (2.7) (5.3)

Total 3881 3822 2905 2754 2262 2082 1854 2228 1584 1756

118

Table A-8: Provincial Distribution of Utilised FDI from US, 2001–2010 (USD

million, percentage)

Province 2003 2004 2005 2006 2007 2008 2009 2010

(1) (2) (3) (4) (5) (7) (8) (9)Beijing 146 116 157 201 183 161 184 203Tianjin 120 114 126 84 40 51 48 56Liaoning 291 641 198 165 113 233 154 216Shanghai 506 448 394 378 401 342 391 432Jiangsu 854 620 559 850 858 824 791 908Zhejiang 406 445 313 301 224 243 216 272Fujian 216 49 165 88 68 67 60 40Jiangxi 115 110 42 17 13 27 28 54Shandong 505 606 549 369 272 167 92 348Hubei 114 49 47 8 16 23 14 9Hunan 58 129 78 18 13 14 40 6Guangdong 448 310 256 319 193 277 191 207Sichuan 56 32 20 19 6 13 39 44Sub-total 3835 3669 2904 2817 2400 2442 2248 2795Total from US 4199 3941 3061 3000 2616 2944 2555 3017

Share of total (91.3) (93.1) (94.9) (93.9) (91.7) (82.9) (88.0) (92.6)

Source: China Foreign Investment Report, 2011

119

Table A-9: Classification of Manufacturing Industry by Technology Intensity.

Industries ISIC3 Code

High-tech

High-technology industriesAircraft and spacecraftPharmaceuticalsOffice, accounting and computing machineryRadio, TV and communications equipmentMedical, precision and optical instruments

Medium-high-technology industriesElectrical machinery and apparatus n.e.c.Motor vehicles, trailers and semi-trailersChemicals excluding pharmaceuticalsRailroad equipment and transport equipment, n.e.c.Machinery and equipment, n.e.c

3532423303233

313424

excl.2423352+359

29

Low-tech

Medium-low-technology industriesBuilding and repairing of ships and boatsRubber and plastics productsCoke, refined petroleum products and nuclear fuelOther non-metallic mineral productsBasic metals and fabricated metal products

Low-technology industriesManufacturing, n.e.c.; recyclingWood, pulp, paper, paper products, printing and publishingFood products, beverage and tobaccoTextiles, textile products, leather and footwear

351252326

27–28

36–3720–2215–1617–19

Source: Annex 1.1. Classification of manufacturing industries based on technology,Directorate for Science, Technology and Industry Economic Analysis and StatisticsDivision. Note: n.e.c. indicates not elsewhere classified.

120

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foreign direct investment in china: interrelationship

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