ERASMUS UNIVERSITY ROTTERDAM

Erasmus School of Economics

Master Thesis

Location Decisions of Chinese MNEs in European Regions:

The Role of Overseas Chinese Communities

J.L. (Jan) van den Hombergh

Student Number: 280481

November 2010

Supervisor: Martijn J. Burger MSc

TABLE OF CONTENTS

Table of Contents 1

Chapter 1: Introduction 3

Paragraph 1.1: Introduction 3

Paragraph 1.2: Outward Foreign Direct Investment (FDI) 4

Paragraph 1.3: Location Decisions of Chinese Multinational Enterprises (MNEs) 5

Paragraph 1.4: Research Question 5

Paragraph 1.5: Proceedings 6

Chapter 2: Theoretical Framework 7

Paragraph 2.1: Introduction 7

Paragraph 2.2: The General FDI Theory 7

Paragraph 2.2.1: The Eclectic (OLI) Paradigm 7

Paragraph 2.2.2: Four Motivations for FDI 8

Paragraph 2.3: A Special Theory for Chinese FDI? 9

Paragraph 2.4: Motivations Underlying Chinese FDI in Europe 9

Paragraph 2.5: The Role of Overseas Chinese Communities in the Location Decisions of

Chinese MNEs in European Regions

10

Paragraph 2.5.1: Introduction 10

Paragraph 2.5.2: The History of Chinese Immigration into Europe 11

Paragraph 2.5.3: Networks of Overseas Chinese 13

Paragraph 2.5.4: The Role of Overseas Chinese Communities in the Location

Decisions of Chinese MNEs in European Regions

14

Chapter 3: Data & Methodology 17

Paragraph 3.1: Introduction 17

Paragraph 3.2: Dataset 17

Paragraph 3.3: The Distribution of Chinese Greenfield Investments in Europe 18

Paragraph 3.4: Variables 19

Paragraph 3.4.1: Dependent Variable 19

Paragraph 3.4.2: Independent Variables 19

Paragraph 3.4.3: Control Variables 20

Paragraph 3.5: Methodology 23

Chapter 4: Empirical Results 25

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Paragraph 4.1: Introduction 25

Paragraph 4.2: Base Model 25

Paragraph 4.3: Testing for the Relationship between the Size of the Chinese Population in

a European Region and the Number of Chinese Greenfield Investments Made

26

Paragraph 4.4: Testing for Differences Between Firms Based in Mainland China and Firms

Based in Hong Kong

31

Paragraph 4.5: Testing for Differences Between Firms with Different Economic Functions

within the Value Chain of a Firm

36

Paragraph 4.6: Hausman-McFadden Test 37

Paragraph 4.7: Conclusion 38

Chapter 5: Conclusion 40

References 42

Appendix 48

Appendix A.1: Greenfield Investments in Europe in the Years 1997-2008 (China vs. Total) 48

Appendix A.2: A List of All NUTS-1 (Regions) in Europe (including the number of greenfield

investments made in each region in the period 1997-2008)

48

Appendix A.3: Chinese Greenfield Investments in Europe in the Period 1997-2008

(subdivided by economic function)

50

Appendix A.4: Descriptive Statistics 51

Appendix A.5: Correlation Matrix of All Independent Variables (including all control

variables)

52

Appendix A.6: Expected Signs (control variables) 53

Appendix A.7: Wald Test (to test for the equality of the coefficients) 53

Appendix A.8: Description of the Economic Functions (within the value chain of a firm) 54

Appendix A.9: Hausman-McFadden Test (to test the assumption of the independence of

irrelevant alternatives)

55

2

CHAPTER 1: INTRODUCTION

Paragraph 1.1: Introduction

Barely 30 years ago, most would consider China to be a poor agricultural economy. Today, China is

seen by many as an (emerging) major economic power (Morck et al., 2008). This phenomenal

development started in the late 1970s with the ‘Open Door’ policies, and accelerated in the late

1990s when China introduced its ‘Go Global’ (zou chu qu) initiative, which resulted in China’s

accession into the World Trade Organization (WTO) in 2001 (Buckley et al., 2007). Between 1978 and

2008, China’s gross domestic product (GDP) grew by an average of 9.9% per annum (World Bank,

2010).1 Furthermore, when GDP in current US$ is considered, China is ranked the third economy in

the world, superseded only by the United States and Japan (World Bank, 2010).2

In recent years, China has become increasingly active on the global economic stage (Prime,

2009). In 2002, exports of goods and services totaled to 25.1% of GDP (World Bank, 2010). In 2008,

this number had already risen to 36.6% (World Bank, 2010). Furthermore, in 2008, imports of goods

and service added up to 28.5% of GDP, while in 2002, this number had only been 22.6% (World Bank,

2010). What is more, in 2008, China was already responsible for 8.1% of the world’s exports and 6.4%

of the world’s imports (World Bank, 2010).3 More important, however, is that the projected growth

rate in the value of exports (7.8%) and imports (6.6%) for China for the period 2005-2020 is far above

the projected growth rates for Western economies (Winters & Yusuf, 2007).4 Furthermore, in 2008,

total foreign direct investment (FDI) inflows into China amounted to $US 173 billion, 5 representing

10.2% of the world’s total inward FDI flows (UNCTAD, 2009).6 In 2006, total FDI inflows into China

accumulated to only $US 119 billion,7 representing just 8.2% of the world’s total inward FDI flows

(UNCTAD, 2009).8 These calculations indicate that FDI inflows into China are increasing at a rapid

pace.

1 Calculations were made by the author.2 Year of measurement: 2008.3 Exports: China ($US 1,582 billion), World ($US 19,557). Imports: China ($US 1,233 billion), World ($US 19,165 billion). Calculations were made by the author.4 Exports: US: 3.4%, Japan: 4.2%, Germany: 1.8%. Imports: US: 3.4%, Japan: 3.5%, Germany: 2.0%.5 China: $US 108 billion, Hong Kong: $US 63 billion, Macau: $US 2 billion. Total: $US 173 billion. Calculations were made by the author.6 China: $US 173 billion. World: $US 1,697 billion. Calculations were made by the author.7 China: $US 73 billion, Hong Kong: $US 45 billion, Macau: $US 2 billion. Total: $US 119 billion. Calculations were made by the author.8 China: $US 119 billion. World: $US 1,461 billion. Calculations were made by the author.

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Paragraph 1.2: Outward Foreign Direct Investment (FDI)

The increasing importance and integration of China in the world economy is also signified by the

globalization of Chinese firms, as measured, for example, by increasing outward FDI (Brienen et al.,

2010). A substantial body of literature has grown over the years that examines the prominence of

China in terms of its position in global trade flows (e.g., Lall & Albalajedo, 2004), and as a recipient of

foreign direct investment (inward FDI) (e.g., Buckley et al., 2002). By contrast, less attention has been

paid to China’s position as an FDI source. Only recently, this increase in outward FDI from Chinese

multinational enterprises (MNEs) has received considerable attention in the (academic and public

policy) literature.

In 2002, outward FDI flows from China, including Hong Kong and Macau, amounted to $US

21 billion,9 representing only 3.2% of the world’s total outward FDI flows (UNCTAD, 2003).10 When

Hong Kong and Macau are not included in these calculations, outward FDI flows from China

amounted to only $US 3 billion in 2002, representing just 0.4% of the world’s total outward FDI flows

(UNCTAD, 2003). In 2008, outward FDI flows from China, including Hong Kong and Macau,

accumulated to $US 113 billion,11 representing 6.1% of the world’s total outward FDI flows (UNCTAD,

2009).12 When Hong Kong and Macau are not included in these figures, outward FDI flows from China

amounted to $US 52 billion in 2002, representing 2.8% of the world’s total outward FDI flows

(UNCTAD, 2009).13 Compared to outward FDI flows from other (developed) countries (UNCTAD,

2009),14 China’s share is still comparably small, but these calculations indicate that China is catching

up rapidly. With their continued economic growth, expanding purchasing power, market

liberalization and extensive state support, it may be expected that China will continue to boost its FDI

outflows in the near future (Brienen et al., 2010).

Not only the scale, but also the geographical distribution of Chinese outward FDI flows have

changed over the years. Until the 1990s, FDI from developing countries, such as China, was mainly

directed at other developing countries in the same region (Brienen et al., 2010). Although Chinese

MNEs still invest most heavily in their neighboring countries, particularly the countries of the

Association of Southeast Asian Nations (ASEAN), investments into developed countries outside their

9 China: $US 3 billion, Hong Kong: $US 18 billion, Macau: $US 0 billion. Total: $US 21 billion. Calculations were made by the author.10 China: $US 21 billion. World: $US 647 billion. Calculations were made by the author.11 China: $US 52 billion, Hong Kong: $US 60 billion, Macau: $US 1 billion. Total: $US 113 billion. Calculations were made by the author.12 China: $US 113 billion. World: $US 1,858 billion. Calculations were made by the author.13 It should be noted, however, that these official figures are probably inaccurate, since a substantial share of Chinese foreign direct investments in tax havens and Hong Kong are reinvested in other countries (Morck et al., 2008). In the literature, such investments are characterized as ‘round-trip’ investments.14 US: $US 312 billion, France: $US 220 billion, Germany: $US 156 billion, Japan: $US 128 billion.

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own region (i.e., Europe, North America) are found to be of increasing importance as well (Dunning,

2003; Gugler & Boie, 2008).

Paragraph 1.3: Location Decisions of Chinese Multinational Enterprises (MNEs)

Within the contemporary globalization literature, it is acknowledged that multinational enterprises

(MNEs) are the basic units of global production and integration (Barba Navaretti & Venables, 2004;

Brakman & Garretsen, 2008). Accordingly, with increasing levels of outward FDI flows, it may be

expected that Chinese MNEs will become an important aspect of the globalizing world economy. In

this context, the question arises where Chinese firms locate and what (location) factors determine

their location decisions. For host countries and regions, the location choice of Chinese MNEs is

important, as their investments can boost the host location’s prospects for (national and/or regional)

economic development through, for example, employment creation, capital growth and export

promotion (Romer, 1993; Young et al., 1994).15

With increasing numbers of Chinese firms choosing to invest in Europe, the question arises

where these firms locate and what (location) factors determine these location decisions. In recent

years, a number of empirical studies have been published that examine the location decisions of

Chinese firms abroad (e.g., Buckley, 2007). However, few of these studies focus specifically on the

location decisions of Chinese MNEs in Europe. Instead, most of these studies investigate the general

pattern of Chinese outward FDI. As a consequence of that, the empirical findings in these studies may

be biased, since location decisions of Chinese MNEs in Europe may be determined by different

(location) factors, compared to the (location) factors that determine the location decisions of Chinese

firms in other parts of the world. In their study, Brienen et al. (2010) specifically test which location

factors are important for Chinese MNEs seeking to establish their subsidiaries in Europe. One of the

findings is that a significant and positive relationship is found between the presence of an overseas

Chinese community in a European region and the location decision of a Chinese firm. Unfortunately,

Brienen et al. (2010) do not further investigate this relationship.

Paragraph 1.4: Research Question

The importance of business and social networks in facilitating international trade has been the focus

of many recent studies, both theoretical (Greif, 1993; Rauch & Casella, 1998) and empirical (Gould,

1998; Rauch & Trindade, 2002). Few papers, however, have focused on the role of networks in

promoting FDI. In her study, Tong (2005) investigates the role of overseas Chinese networks in

15 Following Barba Navaretti & Venables (2004), Brienen et al. (2010) note that inward FDI can also have adverse effects on the host location’s economy. The academic literature is still inconclusive regarding the overall positive effects of FDI on the host economy (Lipsey & Sjöholm, 2005).

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promoting cross border investments.16 By using a standard gravity model, it is found that overseas

Chinese networks play a crucial role in facilitating FDI. Tong (2005), however, does not focus

specifically on the role of these networks in the location decisions of Chinese firms in Europe.

Instead, their role in the general pattern of Chinese outward FDI is investigated. With increasing

numbers of Chinese firms seeking to establish their subsidiaries in Europe, it becomes of special

interest to investigate the impact of overseas the Chinese communities on location decisions of

Chinese MNEs in Europe. Accordingly, in this study, the following research question will be answered:

Research Question:

What is the role of overseas Chinese communities in the location decisions of Chinese MNEs in

European regions?

By answering this research question, this thesis contributes to the existing literature on the role of

overseas Chinese networks in promoting cross border investments in two ways. First, a novel

perspective will be taken, since only investments in Europe will be considered. Second, instead of

investigating the location decisions of Chinese MNEs in Europe at a national level, in this thesis, the

location decisions of Chinese MNEs in Europe will be studied at a regional level. In this context, 351

location decisions of Chinese firms in 89 European regions during the period 2002-2008 will be

studied. In this study, only greenfield investments will be considered. As the location choice in

greenfield investments is not directly influenced by past capital installments of the investor or

investee, unlike brownfield investments (i.e., expansion) or mergers and acquisitions, these types of

investments are very useful for studying the locational determinants of FDI (Brienen et al., 2010).

Paragraph 1.5: Proceedings

The remainder of this thesis is organized as follows. In the next chapter, the theoretical framework of

this study will be drafted. In this context, it will be investigated by which mechanisms overseas

Chinese communities in Europe may influence the location decisions of Chinese MNEs in European

regions. In chapter 3, the dataset that is used in this study will be presented. Furthermore, the

conditional logit model, which is used to test the hypotheses, will be explained. In chapter 4, the

empirical results will be discussed. In chapter 5, this thesis will be concluded with an answer to the

research question that was posed in paragraph 1.4.

CHAPTER 2: THEORETICAL FRAMEWORK16 In Tong (2005), the presence of an overseas Chinese network in a country is proxied by the size of the overseas Chinese community in a country.

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Paragraph 2.1: Introduction

The theory of MNEs indicates that the goal of firms in a global market economy is to increase or

protect their profitability and/or capital value (UNCTAD, 2006). As noted by UNCTAD (2006), one of

the ways in which firms are achieving this goal is by engaging in FDI, either to better exploit their

existing competitive advantages or to safeguard, increase or add to these advantages. Following a

definition from the OECD (1996, p. 7), “foreign direct investment reflects the objective of obtaining a

lasting interest by a resident entity in one economy (‘direct investor’) in an entity resident in an

economy other than that of the investor (‘direct investment enterprise’)”. In this context, three types

of foreign direct investment can be distinguished (Van Marrewijk, 2002; Brienen et al., 2010). First,

greenfield investments, which is a form of FDI where a parent company is setting up a new

production location in a foreign country. Second, brownfield investments, which is a form of FDI

where a parent company increases the production facility of an existing production location in a

foreign country. Third, mergers & acquisitions, which is a form of FDI where a parent company

acquires the existing assets of a local firm in a foreign country. As it is explained in chapter 1

(paragraph 1.4), in this thesis, only greenfield investments will be considered.

The remainder of this chapter is organized as follows. In paragraph 2.2, the general theory on

(outward) foreign direct investment will be introduced. In this context, special attention will be paid

to how mainstream theory explains location decisions of MNEs. In paragraph 2.3, it will be argued

that location decisions of Chinese firms in Europe cannot fully be understood by applying the general

FDI theory, and that some special theories, nested within the general theory, are needed as well. In

paragraph 2.4, it will be studied which location-specific factors are identified by the mainstream

theory to be important to Chinese firms in their location decisions abroad. Finally, in paragraph 2.5, it

will be investigated by which mechanisms overseas Chinese communities in Europe may influence

the location decisions of Chinese MNEs in European regions.

Paragraph 2.2: The General FDI Theory

Paragraph 2.2.1: The Eclectic (OLI) Paradigm

In the field of economic geography, international business and international economics, many

theories have been proposed over the years to understand the extent and pattern of (outward) FDI

by MNEs. Among those theories, the eclectic (OLI) paradigm is one of the most powerful tools, as it

integrates many of the existing concepts (Dunning, 1981b; Gugler & Boie, 2008). The OLI paradigm

asserts that, at any given moment of time, the extent and pattern of international production, i.e.

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production financed by FDI and undertaken by MNEs, will be determined by the configuration of

three sets of forces (Dunning, 2001).

First, ownership-specific (O) advantages: the (net) competitive advantages that firms of one

nationality possess over those of another nationality in supplying any particular market or set of

markets, given by the ownership of products or production processes. Second, location (L)

advantages: the extent to which firms choose to locate these activities in a foreign country rather

than in their home country. Third, internalization (I) advantages: the extent to which firms perceive

it to be in their best interests to internalize their foreign activities in wholly owned subsidiaries,

rather than carrying them out through market transactions (trade) or hybrid relationships with other

firms (e.g., franchising and licensing). Given the scope of this thesis, location advantages will be the

prime subject of this study.

Paragraph 2.2.2: Four Motivations for FDI

As the FDI literature suggests, investments by MNEs are attracted by favorable economic location

factors in the host countries (Brienen et al., 2010). In this context, Dunning (1993, 1998) has

distinguished four motivations of firms to internationalize their activities, each of which stress the

locational aspects of FDI. First, foreign market-seeking FDI, which may be undertaken by firms to

supply their goods and services to foreign markets. Accordingly, existing markets can be sustained or

protected and new markets can be exploited or promoted (Dunning & Lundan, 2008). A new

subsidiary may not only be used to serve the region in which it is located, but also the surrounding

regions, which is especially interesting if a new location provides access to a large integrated market

(Brienen et al., 2010). Second, efficiency-seeking FDI, which may undertaken by firms to reduce their

costs of production related to labor, machinery and materials. Accordingly, differences in costs of

production across regions, countries or continents may induce a firm to decide to split up its activities

geographically (Brienen et al., 2010). Third, resource-seeking FDI, which may be undertaken by firms

to acquire specific resources of a higher quality and/or at lower costs than could be obtained in their

home country, if available at all (Dunning & Lundan, 2008). Fourth, strategic asset-seeking FDI, which

may be undertaken by firms to acquire the assets of foreign corporations, in order to promote their

long-term strategic objectives, i.e. sustaining and advancing the firm’s global competitiveness

(Dunning & Lundan, 2008). In this sense, Dunning’s (1993, 1998) outline represents a general

framework, which links a firm’s motives to invest abroad to location-specific factors of host countries

that provide a competitive advantage for the firm (Brienen et al., 2010). Accordingly, foreign market-

seeking MNEs are considered to be attracted to locations with different characteristics than, for

example, efficiency-seeking, resource-seeking or strategic asset-seeking MNEs.

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Paragraph 2.3: A Special Theory for Chinese MNEs?

The general FDI theory, as it is outlined in paragraph 2.2, has been developed to understand the

extent and pattern of foreign direct investment by firms from developed countries (i.e., Europe,

North America). Accordingly, the question then arises as to whether FDI from emerging economies

and, more specifically, from China can also be explained by using conventional theory. In the context

of this study, the question arises if location decisions of Chinese MNEs in Europe can be understood

by applying the general FDI theory. Some scholars argue that an alternative framework is needed to

explain the extent and pattern of FDI by MNEs from developing countries, such as China (Matthews,

2002; Moon & Roehl, 2001). The majority view, however, is that mainstream theory does work, but

that some special theories, nested within the general theory, are needed as well to account for the

special character of late-comer MNEs (Lecraw, 1977; Wells, 1983; Lau, 2003; Child & Rodrigues,

2005; Erdener & Shapiro, 2005; UNCTAD, 2006; Buckley et al., 2007; Morck et al., 2008).

In this thesis, the majority view is adopted. Accordingly, two questions arise. First, which of

the four aforementioned motivations may be expected to drive Chinese MNEs to invest in Europe?

By answering this question, it may be identified which location-specific factors of host countries,

derived from the mainstream theory, are expected to be important to Chinese firms in their location

decisions in Europe. Second, how can the presence of an overseas Chinese community in a European

region affect location decisions of Chinese firms in Europe? Accordingly, by answering the second

question, a special theory will be introduced that can be nested within the general FDI theory, to

account for the special character of Chinese FDI in Europe. The first question will be addressed in

paragraph 2.4, while the second question will addressed in paragraph 2.5.

Paragraph 2.4: Motivations Underlying Chinese FDI in Europe

Brienen et al. (2010) argue that Chinese MNEs are primarily motivated to invest in Europe for

market-seeking reasons. Following Child & Rodrigues (2005), it is argued that, although the Chinese

economy is growing at an incredible rate,17 its home market is limited in scale and opportunities to

expand. Moreover, Cheng & Stough (2007) posit that increasingly severe competition and

overcapacity are one of the most important pushing factors to expand abroad. Combined with the

existence of trade barriers and a lack of international linkages with customers in target markets,

Chinese MNEs are increasingly induced to set up subsidiaries abroad to serve these markets

(Mathieu, 2006). This argument is supported by several recent studies (Cai, 1999; Taylor, 2002;

Zhang, 2003; Deng; 2004), which signal the rise of offensive market-seeking motives driving Chinese

17 It is referred to paragraph 1.1.

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MNEs. It is posited by Buckley et al. (2007) that these investments may be increasingly directed at

large markets. Moreover, it is noted by UNCTAD (2006) that market-seeking FDI is by far the most

common type of strategy for MNEs from developing countries in their process of internationalization.

Strategic-asset seeking FDI is considered to be the second motivation for Chinese MNEs to

invest in Europe (Deng, 2007; Gugler & Boie, 2008; Milelli & Hay, 2008). However, given the need to

develop some technical and cognitive abilities to absorb these assets, for the upcoming years, it may

be expected that such FDI will more likely occur through mergers and acquisitions than through the

greenfield investments that are analyzed empirically in this study (Milelli & Hay, 2008). Given the low

production costs in China, efficiency-seeking will unlikely be an important motive for Chinese MNEs

to invest in Europe (or in any other place in the world) (UNCTAD, 2006; Buckley et al., 2007).

Furthermore, due to the high extraction costs, resource-seeking FDI by Chinese MNEs will unlikely be

directed towards any European region (Brienen et al., 2010). Instead, such FDI will more likely be

directed towards other developing countries, for example in Africa and Central Asia (Dunning, 2003;

Gugler & Boie, 2008). With Chinese MNEs expected to be primarily motivated to invest in Europe for

market-seeking reasons, it may be hypothesized that they will be drawn to locations with good

market access. Market access may be good, because the location has a large (high-income)

population, or because it is well located to access to such markets (Barba Navaretti & Venables,

2004).

Paragraph 2.5: The Role of Overseas Chinese Communities in the Location Decisions of

Chinese MNEs in European Regions

Paragraph 2.5.1: Introduction

As it is outlined in paragraph 1.4, the importance of business and social networks in facilitating

international trade has been the focus of many recent studies, both theoretical (Greif, 1993; Rauch &

Casella, 1998) and empirical (Gould, 1998; Rauch & Trindade, 2002). Among the various types of

business and social networks, co-ethnic networks have attracted the most empirical research (Rauch

& Trindade, 2002). This may not be surprising, since the members of these networks are fairly easy to

identify, compared to the members of other business and social networks. In the literature, two

mechanisms have been identified, whereby co-ethnic networks may promote international trade.

The first mechanism is identified by Greif (1993). In his paper, he argues that co-ethnic networks may

govern agency relations. In this sense, a co-ethnic network may provide community enforcement of

sanctions, in order to deter violations of contracts, mainly in a weak international legal environment.

A second mechanism is identified by Gould (1994). In his study, he posits that co-ethnic networks

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may influence bilateral trade flows through a decrease in transaction costs associated with obtaining

foreign market information and establishing trade relationships.

To this date, few papers have focused on the role of networks in promoting cross border

investments (FDI). As it is argued by Tong (2005), foreign direct investment requires large start-up

costs and intensive information. In this context, compared to international trade, FDI calls for

cooperation and commitment at a much deeper level between the parties concerned. Accordingly,

co-ethnic networks may be promote foreign direct investment by providing foreign investors with

both (foreign market) information and support with the establishment of business relationships, as

well as providing community enforcement of sanctions, in order to deter violations of contracts. In

this sense, it may be reasonable to expect that co-ethnic networks will play an important role in

facilitating FDI, maybe even more important than in encouraging international trade (Tong, 2005). In

this thesis, it is examined how both mechanisms, whereby co-ethnic networks may facilitate

international trade, may also promote foreign direct investment of Chinese MNEs in European

regions. Accordingly, it will be investigated how location decisions of Chinese firms in Europe can be

influenced by the presence of an overseas Chinese community in a European region. Since it is

hypothesized that Chinese MNEs in Europe will be drawn to locations with good market access, it is

of special interest to study how overseas Chinese communities in Europe may help Chinese firms

with obtaining such market access. Before these issues will be addressed, however, in paragraph

2.5.2, the history of Chinese immigration into Europe will be examined. Furthermore, in paragraph

2.5.3, it will be studied how networks among overseas Chinese are formed.

Paragraph 2.5.2: The History of Chinese Immigration into Europe

Compared to Chinese immigration into Southeast Asia, which started hundreds of years ago (Poston

& Yu, 1990), Chinese immigration into Europe is only a relatively recent phenomenon. The earliest

Chinese immigrants in Europe arrived at the beginning of the twentieth century (Li, 2005). With the

end of World War II, Europe entered a new stage of economic development. Not surprisingly, this

has attracted many people from around the world (Ember et al., 2005). In the past 65 years, the

migration of Chinese people into Europe has seen three major waves (Li, 2005).

The first wave of Chinese immigration into Europe occurred in the early 1960s, with the

migrants mainly coming from the rural areas located in the New Territories of Hong Kong (Li, 2005).

During that time, the so-called ‘green revolution’ took place (Karim, 1986). As a consequence, many

peasants had to look for alternative means of livelihood. Accordingly, a steady stream of people

decided to leave their homeland for countries in Europe (first Britain, then the Netherlands and

Belgium, and later on Germany and France). The second wave occurred in the late 1970s, with the

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migrants mainly coming from Indochina (Live, 1998; Li, 2005). During that time, political conflicts

made it impossible for many (ethnic) Chinese to stay in their (new) home country. The third wave of

Chinese immigration into Europe started in the early 1980s and continues on until today (Li, 2005).

Contrary to the first two waves, this wave mainly consists of Chinese immigrants from mainland

China. Not surprisingly, it coincides with China’s ‘Open Door’ policies, which were initiated in the late

1970s (Thunø, 2001). Following Li (2005), the regular entry channels include work-permit or short-

term work contracts, the setting up of businesses or investments, education and family

reunifications. In Table 1, for 29 European countries in four different years of measurement (1955,

1980, 1990, 2000), the number of (ethnic) Chinese immigrants are listed.

Table 1: Ethnic Chinese Immigrants in Europe (1955-2000)18

Country 1955 1980 1990 2000Austria 30 4,500 6,000 41,000Belgium 118 4,000 13,000 23,000Bulgaria 1 25 30 10Switzerland 11 3,200 5,000 13,000Cyprus 1 1 1 10Czech Republic 11 16 20 12,000Germany 800 20,165 39,500 100,000Denmark 900 2,000 6,000 7,257Estonia 1 1 1 20Spain 43 3,500 15,000 35,000Finland 1 9 10 1,500France 3,300 210,000 200,000 225,000Greece 4 186 100 600Hungary 1 24 23 10,000Ireland 1 1,000 1,000 10,000Italy 260 3,500 20,662 70,000Lithuania 1 1 1 40Luxembourg 1 1 1 1,300Latvia 1 1 1 100Malta 1 15 1 10Netherlands 2,017 60,000 45,500 127,500Norway 3 600 950 5,000Poland 1 77 80 15,000Portugal 73 2,500 4,700 2,700Romania 1 33 1 3,000Sweden 24 5,000 12,000 12,800

18 Sources: 1955:Poston & Yu (1990), 1980: Poston et al. (1994), 1990: Poston et al. (1994), 2000: Li (2005). For further information on these figures, it is referred to chapter 3 (more specifically, paragraph 3.4.2).

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Slovenia 1 1 1 10Slovakia 1 1 1 10United Kingdom 2,549 230,000 250,000 250,000Total 10,157 550,357 619,584 965,867

Paragraph 2.5.3: Networks of Overseas Chinese

Overseas Chinese, like many other co-ethnic groups living outside their country of origin, establish

various formal or informal associations to which co-ethnic business people from both the host

country and the home country have access to (Rauch & Trindade, 2002). In this context, it is argued

by Tong (2005) that these associations have traditionally been based on kinship, dialect and place

(region) of origin in China. In the early days of Chinese immigration (into Europe), such associations

were mainly created to help those in need in the community, especially new immigrants. As an

overseas Chinese community becomes more commercially developed, these associations start to

serve as nodes for information exchange between co-ethnic business people working both locally

and internationally (Rauch & Trindade, 2002; Tong, 2005). In this sense, the overseas Chinese can be

considered to form a set of inter-connected networks at various levels, both regionally and

nationally, but not as a unified international network. It should be noted, however, that the

international links have become more formalized through the biennial meetings of the World

Chinese Entrepreneurship Convention, which are held since 1991 (Rauch & Trindade, 2002).

Furthermore, Thunø (2001) emphasizes that the Chinese government, contrary to PRC policies on

overseas Chinese before 1978, is now actively seeking to retain transnational ties to the millions of

Chinese citizens and ethnic Chinese spread across the world by connecting to local networks of

overseas Chinese. In some countries, where Chinese migrants have not yet organized (along regional

lines), PRC embassy personnel have encouraged local Chinese to form locality associations to be

better able to meet official PRC delegations.

Compared to other co-ethnic groups, the overseas Chinese have been exceptionally

successful in their networking activities (Redding, 1995; Weidenbaum & Hughes, 1996). 19

Accordingly, the question arises why the (overseas) Chinese have always been and continue to be

such excellent networkers. In this thesis, it is suggested that the answer to this question may be

found in a special characteristic of the Chinese society and culture, namely guanxi. Following Luo

(1999) and Park & Luo (2001), guanxi refers to the concept of drawing on a web of connections in

order to secure favors in personal and organizational relations. In this context, it should be

emphasized that in China transactions follow successful guanxi, while in the Western world a

19 In Southeast Asia, the overseas Chinese are particularly well known for their commercial success. What is more, it is believed that overseas Chinese networks have played a crucial role in the region’s fast economic growth in recent years (Tong, 2005).

13

relationship follows successful transactions. Accordingly, following Kao (1993), the guanxi network

may be considered the lifeblood of the Chinese business society. When a situation arises that is

beyond an individual’s or firm’s capacity, the guanxi network may be mobilized to accomplish the

desired results (Redding & Ng, 1982). In this sense, guanxi is valuable entrepreneurial tool to bridge

gaps in information (and resource) flows between unlinked firms and important outside stakeholders

(Park & Luo, 2001). Likewise, it represents an informal social obligation to another party as a result of

invoking a guanxi relationship (Standifird & Marshall, 2000). In the next paragraph, it will be

examined how the guanxi network may help Chinese MNEs in their quest for European success.

Paragraph 2.5.4: The Role of Overseas Chinese Communities in the Location Decisions of

Chinese MNEs in European Regions

In paragraph 2.5.1, two mechanisms have been identified, whereby co-ethnic networks may facilitate

international business transactions, such as international trade and foreign direct investment (Tong,

2005). In this paragraph, it will be investigated how these mechanisms may promote FDI by Chinese

MNEs in Europe, and more specifically how location decisions of Chinese firms in Europe can be

influenced by the presence of an overseas Chinese network in a European region. Since it was

hypothesized in paragraph 2.4 that Chinese MNEs in Europe are expected to be drawn to locations

with good market access, special attention will be paid in this regard to how overseas Chinese

networks in Europe may help Chinese firms with obtaining such market access.

In his paper on the Maghribi traders’ coalition, Greif (1993) argues that a co-ethnic network

may provide community enforcement of sanctions, in order to deter violations of contracts, mainly in

a weak international legal environment, which is primarily found in developing countries (Tong,

2005). In the Chinese society and culture, this mechanism is embedded in guanxi. When a party

violates the terms of a (unwritten) contract, he or she loses face (Yang, 1994). The Chinese have

traditionally compared ‘losing face’ to the physical mutilation of an eye, the nose or the mouth

(Hwang, 1987). What is more, losing face jeopardizes the guanxi network (Park & Luo, 2001). As

guanxi may be considered the lifeblood of the Chinese business society (Kao, 1993), violating a

contract may seriously harm future business opportunities (with third parties) (Weidenbaum &

Hughes, 1996). Accordingly, guanxi provides an incentive not to violate the terms of a contract

(Standifird & Marshall, 2000). All European regions and countries (included in the sample) have a

solid legal system, which provides Europe (as a whole) with a sound international legal environment.

When a contract is violated or other legal problems occur, a party may appeal to the legal system

that is imposed by the government. Accordingly, for Chinese MNEs in Europe, doing only business

with overseas Chinese does not seem necessary from this point of view. As a consequence, when

14

only the first mechanism is considered, locating in close geographical proximity to overseas Chinese

communities will unlikely be a prerequisite for successful FDI of Chinese firms in Europe.

As it is outlined in paragraph 2.5.1, a second mechanism, whereby co-ethnic networks may

facilitate international business transactions, is identified by Gould (1994). In his study, he posits that

co-ethnic networks may influence bilateral trade flows through a decrease in transaction costs

associated with obtaining foreign market information and establishing trade relationships. In the

context of this thesis, the question then arises how this mechanism may influence location decisions

of Chinese firms in Europe. For Chinese MNEs, successful foreign direct investment (in Europe)

requires intensive information (Tong, 2005), such as information on the local/regional/country

market in the host region/country, information on potential business partners, information on the

most suitable and profitable investment opportunities, information on local business practices

and/or information on local government regulations (Buckley et al., 2007; Gould, 1994; Rauch &

Casella, 1998). For a Chinese firm, which is not already embedded in the local (host country) market,

this information may be difficult and costly to obtain (Zhan, 1995). Furthermore, establishing fruitful

commercial relationships with local partners may be difficult and costly to realize. Accordingly, both

transaction costs and business risks associated with investing in Europe will be high (Sung, 1996;

Braütigam, 2003; Erdener & Shapiro, 2005; Tong, 2005). Therefore, a linkage to a local network,

through which the firm can more easily gain access to (foreign market) information and establish

business relationships, may be a crucial determinant for successful foreign direct investment by

Chinese MNEs in Europe.

In the 1970s, the concept of ‘psychic distance’ was introduced, which is defined as the

distance between a firm’s country of origin (e.g., China) and another country (e.g., countries/regions

in Europe), resulting from the perception and understanding of cultural, linguistic, institutional,

(economic) developmental and business differences between two countries (Johanson &

Wiedersheim-Paul, 1975; Johanson & Vahlne, 1977). The larger the psychic distance, other things

being equal, the more difficult it will be to build new relationships (Johanson & Vahlne, 2009). 20 Due

to the large psychic distance between China and Europe, it may be expected that establishing a

linkage to a local network is difficult for Chinese firms in Europe. With this mind, the extant theory

asserts that early foreign direct investments of firms frequently occur in countries with a similar

cultural background to the home country (Johanson & Vahlne, 1977), or where relational assets, in

the form of ethnic or familial ties with a specific minority population in the host country, can be

exploited (Lecraw, 1977; Wells, 1983; Lau, 2003).

For Chinese MNEs, the presence of an overseas Chinese community in a European region

may offer the best possibility to establish a linkage to a local network in Europe. As these overseas

20 In the literature, this effect is termed the liability of foreignness (Johanson & Vahlne, 2009).

15

Chinese networks function as nodes for information exchange (Gould, 1994), Chinese firms may

obtain the information they need for successful FDI in Europe (at lower costs) by connecting to the

members of these networks. Following paragraph 2.5.3, contacts between Chinese MNEs and local

Chinese networks are probably made through guanxi (Luo, 1997; Standifird & Marshall, 2000; Tong,

2005). In this context, the development of trust through guanxi may decrease the transaction costs

associated with communicating, negotiating and coordinating transactions (e.g., business

relationships), as well as maladaptation and/or failure to adapt (Gould, 1994; Standifird & Marshall,

2000). In this sense, the flexible and socially-based nature of guanxi permits the members of a guanxi

network to deal with unforeseen contingencies that arise after agreement is reached (Standifird &

Marshall, 2000). Accordingly, fruitful commercial relationships can be established both more easily

and at lower costs within overseas Chinese networks, which may facilitate market entry and

development of Chinese firms in Europe (Buckley et al., 2007). Furthermore, overseas Chinese

communities may be sources of specific human capital, such as local highly educated Chinese

workers. For Chinese MNEs, this offers advantages, because these workers are proficient in the

Chinese language, and have knowledge of both the Chinese culture and Chinese entrepreneurship.

With Chinese MNEs expected to be primarily motivated to invest in Europe for market-

seeking reasons, it is hypothesized in paragraph 2.4 that Chinese firms will be drawn to locations with

good market access. This may not only be indicated by the market potential of a region or how

quickly a location can be reached, but also by the presence of an overseas Chinese community. In this

context, overseas Chinese networks may be particularly useful to Chinese MNEs by providing them

with linkages to customers in international (European) target markets. Based on the arguments

provided in this paragraph, it is expected that Chinese MNEs, who consider to invest in Europe, are

attracted to locations with a sizeable (overseas) Chinese community. Accordingly, it is hypothesized

that:

Hypothesis:

The probability of Chinese greenfield investments in a European region is positively associated with

the size of the overseas Chinese population in a region.

CHAPTER 3: DATA & METHODOLOGY

Paragraph 3.1: Introduction

16

With the theoretical framework of this thesis having been drafted in the previous chapter, this

chapter will discuss the dataset that is being used in this study and the empirical method (conditional

logit model) that will be applied to test the hypothesis that was posed in paragraph 2.4. In paragraph

3.2, the Ernst & Young European Investment Monitor (EIM) 2009 database will be discussed,

followed by an analysis of the distribution of Chinese greenfield investments across 89 European

regions in paragraph 3.3. In paragraph 3.4, the variables that will be entered into the empirical model

will be described. This chapter will be concluded with a discussion of the conditional logit model in

paragraph 3.5.

Paragraph 3.2: Dataset

In this study, the Ernst & Young European Investment Monitor (EIM) 2009 database is used to

analyze the spatial pattern of Chinese FDI in European regions.21 This database monitors foreign

direct investments in Europe, which may be new projects, expansions to existing ventures or

relocation investments. The main sources of information that are used by Ernst & Young are formal

announcements by the media (e.g., newspapers), financial information providers (e.g., Reuters) and

national investment agencies (e.g., Invest in France Agency). To be considered as cross border

investments, projects have to comply with several criteria. In this sense, the EIM database excludes

acquisitions, license agreements and joint ventures, except in cases where these operations lead to

an extension or a new establishment. Furthermore, investments in retail establishments, hotels and

leisure activities, fixed infrastructures, extraction facilities and portfolio investments are also

excluded from the database. There are no minimum investment size criteria, but it turns out that

small investment projects, i.e. involving a total investment of less than US$ 1 million or where less

than 10 jobs are created, are relatively uncommon.

Although the Ernst & Young European Investment Monitor is recognized as one of the most

comprehensive (international) investment databases across Europe, not all greenfield investment

projects are covered. Nevertheless, estimates indicate that the majority of the total number of

investments are included, especially the larger ones (Brienen et al., 2010). The investment project

data are at the individual firm level, and include the name of the firm, the parent company name, the

parent firm’s country of origin, the industry sector served, the firm’s function within the value chain,

and the region of destination of each investment.

Overall, the Ernst & Young EIM 2009 database consists of 32,535 investment projects in the

EU-27, Norway and Switzerland, during the period 1997-2008, of which 23,615 are new

establishments or relocations, and 8,920 represent firm expansions. In this study, brownfield

investments (i.e., firm expansions) are excluded from the analysis, because no new location choice is 21 Detailed information on this database can be found at <http://www.eyeim.com>.

17

made. Henceforth, the term “greenfield investments” will refer to both new investments and

relocations. Between 1997 and 2008, Chinese MNEs made 407 greenfield investments in Europe,

which is 1.7% of the total number of greenfield investments made in that period. In this regard, it

should be noted, however, as it is outlined in Appendix A.1, that the number of Chinese greenfield

investments in Europe has grown exponentially over the years. In the period 1997-2001, only 40 of

such investments were made, representing just 0.52% of all greenfield investments in Europe in that

period. In 2008, China’s share had already risen to 2.9%. Furthermore, compared to the preceding

year (i.e., 2007), the number of greenfield investments from all source countries (in total) stabilized

in 2008, which is probably due to the worldwide financial crisis, while China’s investments in Europe

increased by more than 50% in 2008. Hence, it seems that the global financial crisis has not affected

the number of Chinese greenfield investments in Europe in any major way (Brienen et al., 2010).

In order to answer the research question that was posed in Chapter 1, 351 Chinese greenfield

investments in 89 European regions (NUTS-1) during the period 2002-2008 will be studied.2223 It is

referred to Appendix A.2 for a list of these regions, which also includes the number of investments

made in each region in the period 1997-2008, both from China and from all source countries in total.

Paragraph 3.3: The Distribution of Chinese Greenfield Investments in Europe

Appendix A.2 provides an overview of the spatial distribution of Chinese greenfield investments in

Europe across 89 (NUTS-1) regions for the period 1997-2008. It may be noticed that these

investments are not evenly spread across the continent, as such investments occur

disproportionately within the United Kingdom, Germany, France, the Benelux Countries and

Scandinavia, especially Denmark and Sweden. In particular, Chinese greenfield investments are

clustered in Southeast England (Greater London area), West Germany (Ruhr-Rhine area), Newcastle

and Paris. Furthermore, Appendix A.2 also provides an overview of the spatial distribution of all

greenfield investments (in total) made in Europe in the period 1997-2008. It may be noticed that

these investments, compared to only Chinese greenfield investments, are more evenly spread across

the continent. In this context, it is remarkable that both the Greater London area and the Ruhr-Rhine

area are less prominent, when all greenfield investments made in Europe are considered.

Furthermore, it may be noticed that there is a general lack of interest in investing in Southern and

Southeastern Europe. Central European regions, on the other hand, in particular the Czech Republic

and Romania, have received a considerably higher total share of greenfield investments, compared

to what they receive from Chinese MNEs.

22 In this context, for each of these firms, it is known whether their parent company originates from either mainland China or Hong Kong.23 NUTS: Nomenclature of Territorial Units for Statistics.

18

Appendix A.3 provides an overview of the distribution of Chinese greenfield investments in

Europe across nine economic functions, i.e., a stage or activity within the value chain of a firm. As

shown in this table, more than 55% of Chinese greenfield investments in Europe are in sales and

marketing offices, followed by headquarters (14%), production plants (11%), research and

development centers (7%), and logistics centers (7%). Only a very limited number of investments are

made in contact centers (<1%), education and training centers (<1%), shared services centers (1%),

and testing and servicing centers (1%). Accordingly, following Brienen et al. (2010), it may be

concluded that Chinese MNEs in Europe, compared to firms from other source countries, tend to

invest relatively more in sales and marketing offices and headquarters and less in production plants.

This would support the argument that Chinese firms that invest in Europe are primarily motivated by

market-seeking (Brienen et al., 2010).

Paragraph 3.4: Variables2425

Paragraph 3.4.1: Dependent Variable

Each of the 351 Chinese firms that is included in the dataset and has established a subsidiary in

Europe in the period 2002-2008 has faced 89 possible location alternatives (i.e., European regions) in

their location decision-making process. In this empirical study, the dependent variable is equal to 1

for region j if firm i is set in region j, and zero for all regions different from j.

Paragraph 3.4.2: Independent Variables

To test the hypothesis that the probability of Chinese greenfield investments in a European region is

positively associated with the size of the overseas Chinese population in a region, for each European

region, the size of the (total) Chinese migrant stock in the year 2000 is measured and entered into

the empirical model as an explanatory variable.26 To test for the robustness of the empirical results,

eight additional variables will be included in the empirical model, which each measure the size of an

overseas Chinese community in a European region in a different way.

In this context, four variables will be entered into the empirical model that measure the size

of the Chinese migrant stock in a region, subdivided by the immigrants’ region of origin in China. In

this study, the following regions of origin in China are distinguished: (a) mainland China, (b) Hong

Kong and Macau, (c) mainland China, Hong Kong and Macau, and (d) Taiwan.27 The data on the (total)

24 It is referred to Appendix A.4 for the descriptive statistics of all variables.25 It is referred to Appendix A.5 for a correlation matrix of all variables.26 Sources: Parsons et al. (2005), Özden & Schiff (2007).27 Sources: Parsons et al. (2005), Özden & Schiff (2007). The year of measurement of these variables is 2000.

19

Chinese migrant stock in a region is obtained by summing up the variables (c) and (d). In this study, a

migrant in a European region is defined to be Chinese, if he/she holds citizenship (i.e., nationality) of

either the People’s Republic of China (mainland China), Hong Kong or Macau, or Taiwan at the time

of his/her migration to Europe (Parsons et al., 2005; Özden & Schiff, 2007). Unfortunately, data on

these five variables is only available on a national level.

Furthermore, to test for the robustness of the empirical results, four variables will be

included in the empirical model that measure the size of the overseas Chinese population in a region

in four different years: (a) 1955, (b) 1980, (c) 1990, and (d) 2000.28 Although this data has been

collected from various sources, for all measured years, a uniform definition of the overseas Chinese

has been employed. In this sense, the overseas Chinese are broadly defined to refer to all Chinese

living outside mainland China and Taiwan, including Huaqiao (Chinese citizens residing abroad),

Huaren (naturalized citizens of Chinese descent), and Huayi (descendants of Chinese parents) (Poston

et al., 1994). Accordingly, this definition of the overseas Chinese includes all persons with any

Chinese ancestry. Unfortunately, this data is also only available on a national level. To conclude this

paragraph with, for all nine variables,29 a positive relationship is hypothesized with location choice.

Paragraph 3.4.3: Control Variables

Following paragraph 2.4, the presence of an overseas Chinese community in a European region is not

likely to be the only (location) factor that determines the location choice of Chinese MNEs in Europe.

Therefore, a number of control variables will be included in the empirical model.

Demand Factors – In chapter 2, it is argued that Chinese MNEs are expected to be primarily

motivated to invest in Europe for market-seeking reasons. Accordingly, it was hypothesized that

Chinese firms will be drawn to locations in Europe with good market access. Therefore, control

variables should be included in the empirical model that measure the attractiveness of a European

region in this respect. In this context, not only local demand should be considered, but also the

proximity of a region to other important sources of demand. Following Head & Mayer (2004), in this

thesis, the market potential function of Harris (1954), which gravitationally equates the potential

demand for goods and services produced in a location with that location’s proximity to areas of

consumer demand, will be included in the empirical model:

MP jt=∑Kε k

( Y kiD jkt) (1)

The market potential of a region j in year t is the sum of the GDP in the accessible regions k, weighted

by the distance between j and k. This data is available on a regional level (NUTS-1) for the years 2002-

28 Sources: 1955; Poston & Yu (1990), 1980: Poston et al. (1994), 1990: Poston et al. (1994), 2000: Li (2005).29 In order to reduce the positive skew (Field, 2005), all nine variables are log-transformed.

20

2008, and is log-transformed.30 Market potential is not the only indicator of market access, however.

Specifically, the location choice of headquarters and sales and marketing offices is also based on how

quickly a particular location (European region) can be reached (Brienen et al., 2010). Accordingly,

both an international airport dummy, which takes the value 1 if a region has an international airport

with over 10 million international flights per year, and an international seaport dummy, which takes

the value 1 if a region has an international seaport with a total cargo volume of at least 40 million

metric tons per year, are included in the empirical model.31 Both variables are available on a regional

level (NUTS-1), but are not year-specific.32

The presence of an overseas Chinese community in a European region may also be

considered to be an indicator of good market access for Chinese MNEs, because these communities

may provide Chinese firms in Europe with important (foreign market) information, and thereby with

market access. In this paragraph, it is referred to chapter 2 for further information on this argument,

and to paragraph 3.4.2 for the variables that have been selected in this study to measure the

presence and size of an overseas Chinese community in a European country/region.

Supply Factors – As it is only an assumption that Chinese MNEs are expected to be primarily

motivated to invest in Europe for market-seeking reasons, there should also be controlled for other

location factors, which may be particularly important to firms that are investing in Europe for

efficiency-seeking, resource-seeking or strategic asset-seeking reasons. In this context, following

Brienen et al. (2010), there will be controlled for costs of production, most notably labor and capital

costs. As a proxy for labor costs, sector-specific wage per hour is selected. This data is available on a

regional level (NUTS-1) for the years 2002-2008, and is log-transformed.33 Wages do not represent all

labor costs, however, since the functioning of the labor market (measured by the unemployment

rate),34 the efficiency of the labor force (measured by the percentage of the population that has a

university education (ISCED 5 and 6)), and non-wage labor costs (measured by the social charges

rate) may also contribute to the total costs of labor (Head & Mayer, 2004). For the years 2002-2008,

the data on the first two variables is available on a regional level (NUTS-1), 35 while the data on the

third variable is only available on a national level.36 Finally, the corporate tax rate in a country, which

30 Source: Brienen et al. (2010).31 Source: Brienen et al. (2010).32 The year of measurement of these variables is 2005.33 Source: Cambridge Econometrics Database.34 The unemployment rate is measured as a percentage of the total labor force.35 Source: Brienen et al. (2010).36 The social charges rate is measured as a percentage of the total labor costs. Source: Ernst & Young International Human Capital Database.

21

is measured as the statutory tax percentage rate at the national level, is included as a proxy for the

costs of capital.37 Data on this variable is year-specific (2002-2008).

Agglomeration Effects – The presence of an overseas Chinese community in a region can be

characterized as an agglomeration advantage for Chinese firms locating in that particular region.

More ‘traditional’ forms of agglomeration advantages may also play a role in the location choice of

Chinese firms in Europe, however. Therefore, four variables are added to the model to test for these

agglomeration effects. In their empirical study on location decisions of Japanese firms in Europe,

Head & Mayer (2004) find that related firms tend to cluster in the same regions. In this thesis, two

forms of relatedness will be considered. First, as a proxy for localization advantages, the (log-

transformed) sector-specific employment level in a European region for the years 2002-2008 will be

included in the empirical model.38 This variable captures the idea that firms demonstrate a statistical

tendency to locate in regions, where firms from the same (sub)sector have already located. Second,

for the years 2002-2008, a (log-transformed) variable will be entered into the empirical model that

measures the number of greenfield investments in a European region (NUTS-1), which were made by

Chinese firms in the five years previous to the year of measurement. 39 Accordingly, this variable

captures the idea that firms demonstrate a statistical tendency to locate in regions, where firms from

the same country of origin have already located.40 Likewise, for the years 2002-2008, a (log-

transformed) variable will be entered into the empirical model that measures the number of

greenfield investments in a European region (NUTS-1), which were made by firms from all source

countries in total in the five years previous to the year of measurement.41 Accordingly, this variable

may serve as a proxy to measure the foreign direct investment climate in a region. In this sense, firms

may derive agglomeration benefits by locating in regions where many foreign firms are already

established. Finally, as a proxy for urbanization advantages, the total employment level in a region

will be included in the empirical model.42 Urbanization advantages can be derived from locating in an

urban environment, close to firms in different (sub)sectors (Breschi & Malerba, 2005). This data is

37 Source: Ernst & Young International Tax Database.38 Source: Cambridge Econometrics Database.39 For a specific year (e.g., 2002), this number is measured by taking the sum of the Chinese greenfield investments in a region in the previous five years (e.g., for 2002: 1997-2001). As it is not possible to get a log value of zero or negative numbers, a constant (1) is added to this data. Calculations were made by the author. Source: Ernst & Young European Investment Monitor 2009 database.40 Following Brienen et al. (2010), this may result in a path-dependent process of FDI concentration in space.41 For a specific year (e.g., 2002), this number is measured by taking the sum of all greenfield investments in a region in the previous five years (e.g., for 2002: 1997-2001). As it is not possible to get a log value of zero or negative numbers, a constant (1) is added to this data. Calculations were made by the author. Source: Ernst & Young European Investment Monitor 2009 database.42 Source: Cambridge Econometrics Database.

22

available on a regional level (NUTS-1) for the years 2002-2008, and is log-transformed. Appendix A.6

contains a list of all control variables, with the expected sign for the coefficient.

Paragraph 3.5: Methodology

In order to answer the research question that was posed in Chapter 1, a conditional logit model will

be used (McFadden, 1974). Essentially, this model assumes that each Chinese firm is faced with a set

of alternative location options (i.e., 89 European regions) for the establishment of its European

subsidiary, with each firm comparing the relevant (location) attributes (Cameron & Trivedi, 2009).

Accordingly, each location decision is considered to be the outcome of a discrete choice among 89

alternatives (Wu & Strange, 2000).

In this context, it is assumed that a rational firm will choose to locate its subsidiary i in region

j, if and only if this decision maximizes the expected future profits from its investment (Cheng &

Stough, 2006).43 Unfortunately, the true profits yielded by each alternative location cannot be

observed. One does, however, observe the actual choice of each firm and the characteristics of each

alternative location (Cheng & Stough, 2006; Defever, 2006). Accordingly, it is assumed that the

expected profit for firm i derived from locating in region j is a function of the observable attributes of

region j (Xj), and a random disturbance term, εij (Cheng, 2007). This disturbance term reflects the

unique advantages of region j to firm i (Wu & Strange, 2006). It differs across regions for any one

firm, and across firms for any one region. Hence:

Rij=βX j+εij (2)

Rij is the expected profit earned by a Chinese firm if their subsidiary i is located in region j. Xj is a

vector of choice-specific attributes for region j, β is a vector of coefficients to be estimated by

maximum likelihood, and εij is the unobservable advantage of location j for firm i.

As a result of the existence of the random component (εij) in the profit function, the exact

choice made by a firm cannot be estimated or predicted through function (2). Instead, only the

probability can be identified that a Chinese firm chooses region j, out of S potential regions, to locate

its subsidiary i (Cheng & Stough, 2006). Following McFadden (1974), by assuming that the random

terms are independently and identically distributed (IID) in the conditional logit model, the

probability that subsidiary i will be located in region j may be mathematically expressed as follows:

Pij=exp (X ij β j )

∑s=1

S

exp (X is β j )(3)

43 This assumption fits right into the neoclassical economic theory of utility-maximizing behavior (McFadden, 2001).

23

Following Cheng (2007), the IID assumption implies an important property of the conditional

logit model: independence from irrelevant alternatives (IIA). The IIA property specifies that for any

firm the probability ratio of any two alternatives depends only on the (location) attributes of those

two alternatives and is independent of other available alternatives. In other words, working on a sub-

sample of the dataset (e.g., only the EU-15), instead of on the whole dataset (i.e., the EU-27, Norway

and Switzerland), should produce the same empirical results, except of course for the loss of

information in the omitted location decisions. The IIA property is violated when the unobserved

disturbance terms (εij) are correlated (Train, 2003; Cheng & Stough, 2006). In such a case, estimations

of conditional logit models may inconsistent. Therefore, in order to detect potential violations of the

IIA property, a Hausman-McFadden test should be conducted (Hausman & McFadden, 1984).

In a conditional logit model, the interpretation of estimated coefficients is not

straightforward, because they are not directly related to marginal effects (Cheng & Stough, 2006).

Following Head & Mayer (2004), in this study, average probability elasticity is used to measure the

marginal magnitudes of the estimated parameters. When the explanatory variables have been

entered into the model as natural logarithms, the elasticity’s with respect to these variables may be

calculated directly from the estimated coefficients, by using the following formula (Greene, 2008):

Ek=d lnP jd lnX k

=βk (1−P ) (4)

Pj is the probability of locating in region j, P is the average probability elasticity, Xk is the explanatory

variable under consideration, and βk is the estimated coefficient associated with variable X (Wu &

Strange, 2000). Given that 89 European regions may be considered by Chinese firms, the average

probability elasticity is 1/89 and thus (1 – P) equals 0.989 (Head & Mayer, 2004).

A commonly used goodness-of-fit measure in conditional logit regressions is McFadden’s

likelihood ratio index (Wu & Strange, 2006):

ρ2=1−[ L (max )L (0 ) ] (5)

The McFadden ρ2 index ranges from 0 to 1, just as the conventional R2 does. Following Hensher &

Johnson (1981) in their comprehensive review of discrete choice models, values of ρ2 between 0.2

and 0.4 are considered to represent very good fits.

CHAPTER 4: EMPIRICAL RESULTS

24

Paragraph 4.1: Introduction

In the previous chapter, the dataset that is being used in this study has been discussed. Furthermore,

the conditional logit model has been explained. In this chapter, the empirical results will be

presented. In paragraph 4.2, the base model will be discussed, followed by paragraph 4.3, in which

nine model specifications will be introduced that will enable to test for the role of overseas Chinese

communities in the location decisions of Chinese MNEs in Europe. In paragraph 4.4, six additional

model specifications will be presented that will enable to test for differences between firms that are

based in mainland China and Hong Kong. Furthermore, in paragraph 4.5, five model specifications

will be introduced that will enable to test for differences between firms with different economic

functions within the value chain of a firm. In paragraph 4.6, a Hausman-McFadden test will be

conducted, in order to test for potential violations of the IIA assumption. In paragraph 4.7, this

chapter will be ended with a conclusion.

Paragraph 4.2: Base Model

As it is outlined in paragraph 3.4.3, the selected control variables can be categorized into three

groups: (a) variables related to market access (i.e., demand factors), (b) variables related to costs of

production (i.e., supply factors), and (c) variables related to agglomeration advantages (i.e.,

agglomeration effects). In model specification (1), all control variables are included. Accordingly, this

model may be characterized as the base model. Furthermore, in this thesis, nine variables have been

selected to measure the presence and size of an overseas Chinese community in a European region. 44

Due to the high correlation between these nine variables,45 they will be entered into the model

separately. Accordingly, model specifications (2) to (10) add these variables (separately) to the first

model specification. Hence, nine new model specifications are constructed, which can be found in

Table 2.

In chapter 2, it was hypothesized that Chinese greenfield investments in Europe are primarily

motivated by market-seeking. In this study, evidence is found that the probability of Chinese

greenfield investments in a European region is indeed positively associated with variables that

indicate good market access. Following Table 2, a positive relationship is found between market

potential and the number of yearly investments made. Only in model specifications (4) and (6), this

relationship is not found to be significant. The presence of an international airport in a region is also

found to be positively associated with the number of yearly investments made. This relationship is

found to be significant in all model specifications. The presence of an international seaport in a

44 It is referred to paragraph 3.4.2.45 It is referred to appendix A.5.

25

region, on the other hand, is not found to have a significant relationship with the number of yearly

investments made in any of the model specifications.

Following Table 2, when supply factors are considered, a positive relationship is found

between wage per hour and the number of yearly investments made. Only in model specification (9),

this relationship is not found to be significant. The sign of the coefficient is contrary to what was

expected, however, as a negative relationship was hypothesized. The unemployment rate in a region

is found to be positively associated with the number of yearly investments made. However, only in

model specifications (2) to (5), this relationship is found to be significant. The corporate tax rate in a

country is found to be negatively associated with the number of yearly investments made, as it was

hypothesized. Only in model specifications (1) and (4), this relationship is not found to be significant.

The remaining control variables, i.e. the percentage of university educated people and the social

charges rate, are not found to have a significant relationship with the number of yearly investments

made in any of the model specifications. Accordingly, it may be concluded that the hypothesis that

Chinese greenfield investments in Europe are primarily motivated by market-seeking is not

invalidated by these empirical results.

Following Table 2, when agglomeration effects are considered, for all model specifications, a

significant and positive relationship is found between localization advantages and the number of

yearly investments made. Furthermore, for all model specifications, a significant and positive

relationship is found between total previous (Chinese) greenfield investments in a region and the

number of yearly investments made, potentially indicating a path-dependent process of FDI

concentration in space (Brienen et al., 2010). Accordingly, following Appendix A.6, the signs of these

coefficients are as expected. Urbanization advantages, on the other hand, are not found to have a

significant relationship with the number of yearly investments made in any of the model

specifications.

Paragraph 4.3: Testing for the Relationship between the Size of the Chinese Population in a

European Region and the Number of Chinese Greenfield Investments Made

In this thesis, nine variables have been selected to measure the presence and size of an overseas

Chinese community in a European region. Following column (2), and in accordance with the main

hypothesis of this thesis, it is found that there is a significant and positive relationship between the

size of the (total) Chinese migrant stock in a European region and the number of Chinese greenfield

investments made. The question then arises what the magnitude of this relationship is. As it is

outlined in paragraph 3.5, in a conditional logit model, the interpretation of the estimated

coefficients is not straightforward, because they are not directly related to marginal effects. In this

26

thesis, for all log-transformed variables, the average probability elasticity of a location attribute (Xk),

i.e. its estimated marginal effect, is equal to 0.989βk. Accordingly, with the coefficient of the variable

at stake being 0.2730728, the estimated marginal effect is 0.27%. Hence, a 10 percent increase in the

size of the (total) Chinese migrant stock in a European region increases the probability of attracting

Chinese greenfield investments into that region by 2.7%. To test for the robustness of these empirical

results, in the columns (3) to (10), eight alternative measures of the size of the overseas Chinese

population in a European region are (separately) added to the base model.

In this context, model specifications (3) to (6) add four variables to the base model, which

measure the size of the Chinese migrant stock in a region, subdivided by the immigrants’ region of

origin in China: (3) mainland China; (4) Hong Kong and Macau; (5) mainland China, Hong Kong and

Macau; (6) Taiwan. Following Table 2, for each of these four variables, a significant and positive

relationship is found with the number of yearly investments made. Following column (3), a 10

percent increase in the size of the Chinese migrant stock in a region originating from mainland China

increases the probability of attracting Chinese greenfield investments into that region by 2.7%.

Following column (4), a 10 percent increase in the size of the Chinese migrant stock in a region

originating from Hong Kong increases the probability of attracting Chinese greenfield investments

into that region by 0.9%. Following column (5), a 10 percent increase in the size of the Chinese

migrant stock in a region originating from mainland China, Hong Kong and Macau increases the

probability of attracting Chinese greenfield investments into that region by 2.7%. Finally, following

column (6), a 10 percent increase in the size of the Chinese migrant stock in a region originating from

Taiwan increases the probability of attracting Chinese greenfield investments into that region by

1.5%. To test for the equality of these coefficients, ten Wald tests have been performed, which can

be found in Appendix A.7A.

In model specifications (7) to (10), to test for the robustness of the empirical results, four

variables are added to the base model that measure the size of the ethnic Chinese community in a

European region in four different years: (7) 1955; (8) 1980; (9) 1990; (10) 2000. As it is outlined in

chapter 2, Chinese firms are hypothesized to locate in regions with a sizeable overseas Chinese

community, through which they may obtain both important (foreign market) information and

support in the establishment of business relationships. In this respect, the size of such a community

in the past seems of less relevance to Chinese MNEs than the size of the current (ethnic) Chinese

population in a European region. Nevertheless, the presence of these communities in the past may

indicate the longtime presence of an ethnic Chinese community in a European region. Therefore, a

significant and positive relationship is expected between the size of the ethnic Chinese community in

a European region (in all measured years) and the number of Chinese greenfield investments made

27

between 2002 and 2008. In this respect, the more recent the year of measurement, the stronger the

relationship is hypothesized to be, though.

For each of these four variables, a significant and positive relationship is found with the

number of yearly investments made. For the year 1955, the estimated marginal effect is 0.09%, as

can be calculated from column (7). Following column (8), for the year 1980, the estimated marginal

effect is 0.08%. When a variable is added to the base model that measures the size of the ethnic

Chinese community in a European region in 1990, it is found that its estimated marginal effect is

0.11%, as can be calculated from column (9). Following column (10), for the year 2000, the estimated

marginal effect is 0.16%. To test for the equality of these coefficients, six Wald tests have been

performed, which can be found in Appendix A.7B. Following these empirical results, it may not be

concluded that the relationship between the size of the ethnic Chinese community in a European

region and the number of Chinese greenfield investments made in a region between 2002 and 2008

is stronger for more recent years of measurement of the size of the ethnic Chinese community in a

European region.

To conclude this paragraph with, in all nine model specifications, a significant and positive

relationship is found between the size of the overseas Chinese community in a European region and

the number of Chinese greenfield investments made. In this regard, it should be noted, however,

that the magnitude of this relationship varies, depending on the variable that is chosen to measure

the size of the Chinese population in an area. Based on the empirical results that were presented in

this paragraph, it may be concluded that the probability of Chinese greenfield investments in a

European region is indeed positively associated (at a significant level) with the size of the overseas

Chinese community in a European region, as it was hypothesized in chapter 2 of this thesis. The

question then remains whether these model specifications represent a good fit. Following Hensher &

Johnson (1981) in their comprehensive review of discrete choice models, values of ρ2 between 0.2

and 0.4 are considered to represent very good fits. Following Table 2, model specifications (2) to (10)

are found to have values of ρ2 between 0.169 and 0.174.

28

Table 2: Model Specifications (1)-(10)

Location Factors (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Demand Factors Market Potential (log) 0.72a

(0.23)0.50b

(0.24)0.52b

(0.25)0.33

(0.24)0.51b

(0.24)0.26

(0.25)0.57b

(0.25)0.61b

(0.24)0.62a

(0.24)0.51b

(0.26)Airport Dummy 0.35b

(0.15)0.45a

(0.15)0.35b

(0.15)0.50a

(0.15)0.45a

(0.15)0.44a

(0.15)0.48a

(0.16)0.51a

(0.17)0.51a

(0.17)0.51a

(0.17)Seaport Dummy -0.06

(0.17)0.14

(0.16)0.09

(0.16)0.11

(0.17)0.13

(0.16)0.15

(0.17)-0.02(0.16)

-0.01(0.16)

-0.01(0.16)

-0.01(0.16)

Supply Factors Wage per Hour (log) 0.31b

(0.12)0.24c

(0.13)0.24c

(0.13)0.26b

(0.12)0.24c

(0.13)0.22c

(0.13)0.25c

(0.13)0.23c

(0.14)0.20

(0.14)0.26b

(0.13)Unemployment Rate 0.02

(0.02)0.04b

(0.02)0.03c

(0.02)0.03c

(0.02)0.04b

(0.02)0.02

(0.02)0.02

(0.02)0.02

(0.02)0.02

(0.02)0.02

(0.02)University Education 0.01

(0.01)-0.01(0.01)

0.01(0.01)

-0.01(0.01)

-0.01(0.01)

0.01(0.01)

-0.01(0.01)

-0.01(0.01)

-0.01(0.01)

-0.01(0.01)

Social Charges Rate -0.01(0.01)

-0.01(0.02)

-0.01(0.01)

-0.01(0.01)

-0.01(0.01)

-0.01(0.01)

-0.01(0.01)

-0.01(0.01)

-0.01(0.01)

-0.01(0.01)

Corporate Tax Rate -0.02(0.02)

-0.05a

(0.02)-0.05a

(0.02)-0.02(0.02)

-0.05a

(0.02)-0.03c

(0.02)-0.04b

(0.02)-0.03c

(0.02)-0.04b

(0.02)-0.03c

(0.02)AgglomerationEffects

Localization Advantages (log) 0.31a

(0.10)0.35a

(0.10)0.32a

(0.10)0.34a

(0.10)0.35a

(0.10)0.31a

(0.10)0.33a

(0.10)0.33a

(0.10)0.33a

(0.10)0.34a

(0.10)Urbanization Advantages (log) -0.03

(0.17)-0.19(0.17)

-0.16(0.17)

-0.12(0.17)

-0.19(0.17)

-0.03(0.18)

-0.10(0.18)

-0.12(0.18)

-0.15(0.18)

-0.14(0.18)

Total Greenfield Investments (log) 0.27a

(0.10)0.39a

(0.11)0.40a

(0.11)0.35a

(0.11)0.38a

(0.11)0.34a

(0.11)0.28a

(0.10)0.27b

(0.11)0.29a

(0.11)0.27a

(0.11)Chinese Greenfield Investments(log)

0.68a

(0.10)0.56a

(0.10)0.60a

(0.09)0.57a

(0.09)0.56a

(0.09)0.58a

(0.09)0.63a

(0.09)0.64a

(0.09)0.62a

(0.09)0.63a

(0.09)

29

Table 2: Model Specifications (1)-(10) (continued)

Location Factors (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Chinese Population

Migrant Stock(Total) (log)

0.27a

(0.07)Migrant Stock(Mainland China) (log)

0.27a

(0.07)Migrant Stock(Hong Kong + Macau) (log)

0.09a

(0.03)Migrant Stock (Mainland China + Hong Kong + Macau) (log)

0.27a

(0.06)Migrant Stock(Taiwan) (log)

0.15a

(0.05)Size Ethnic Chinese Community in1955 (log)

0.09b

(0.04)Size Ethnic Chinese Community in1980 (log)

0.08b

(0.03)Size Ethnic Chinese Community in1990 (log)

0.11a

(0.04)Size Ethnic Chinese Community in2000 (log)

0.16a

(0.06)

Test Statistics Number of Observations 28666 28666 28666 28666 28666 28666 28666 28666 28666 28666Number of Cases 338 338 338 338 338 338 338 338 338 338Likelihood Ratio Index (Pseudo R2) 0.1672 0.1743 0.1724 0.1723 0.1742 0.1711 0.1687 0.1689 0.1698 0.1696AIC 2523.9 2504.4 2510.1 2510.6 2504.7 2514.2 2521.4 2520.6 2518.1 2518.5BIC 2623.0 2611.9 2617.6 2618.0 2612.2 2621.7 2628.9 2628.0 2625.5 2626.0

Note: Standard errors in parentheses with a, b and c respectively denoting significance at the 1%, 5% and 10% levels.

30

Paragraph 4.4: Testing for Differences Between Firms Based in Mainland China and Firms

Based in Hong Kong

In Table 3, six model specifications (11-16) are introduced that enable to test for differences between

firms that are based in mainland China and Hong Kong. Accordingly, model specifications (11) to (13)

contain exactly the same variables as respectively columns (2) to (4). The difference is that in model

specifications (2) to (4) the location choice of all firms in the dataset is analyzed, irrespective of their

region of origin in China (i.e., mainland China or Hong Kong), and that in columns (11) to (13) only

firms are included in the analysis that are based in mainland China. Likewise, model specifications

(14) to (16) only include firms in the analysis that are based in Hong Kong. Accordingly, by comparing

the two sets of model specifications, differences between firms that are based in mainland China and

Hong Kong – on the role of overseas Chinese communities in their location decisions in Europe – can

be analyzed.

Both for firms that are based in mainland China and for firms that are based in Hong Kong, it

is hypothesized, in accordance with the main hypothesis of this thesis, that their location choice is

positively associated with the size of the (total) Chinese migrant stock in a region, the size of the

Chinese migrant stock in a region originating from mainland China and the size of the Chinese

migrant stock in a region originating from Hong Kong and Macau. For firms based in mainland China,

the magnitude of these coefficients is hypothesized to be stronger for the size of the Chinese migrant

stock in a region originating from mainland China than for the size of the Chinese migrant stock in a

region originating from Hong Kong and Macau. Likewise, for firms based in Hong Kong, the

magnitude of these coefficients is hypothesized to be stronger for the size of the Chinese migrant

stock in a region originating from Hong Kong and Macau than for the size of the Chinese migrant

stock in a region originating from mainland China. Accordingly, it is hypothesized that Chinese firms

have a preference for European regions with a (sizeable) overseas Chinese community, with whom

they share a region of origin in China. Following Tong (2005), associations of overseas Chinese (in

Europe) have traditionally been based on kinship, dialect and place (region) of origin in China. Since

contacts between Chinese MNEs and local Chinese networks are probably made through guanxi, it is

hypothesized that relationships are more likely to exist or develop between Chinese firms and local

Chinese networks that share the same region of origin in China. As a consequence of that, it is

hypothesized that Chinese firms have a preference for European regions with a (sizeable) overseas

Chinese community, with whom they share a region of origin in China.

In this thesis, no hypothesis is proposed on whether the relationship between the size of the

(total) Chinese migrant stock in a region and the probability of Chinese greenfield investments in a

region is stronger for firms based in mainland China or for firms based in Hong Kong, because it could

31

be argued both ways. Since Hong Kong has been a colony/dependent territory of the United Kingdom

until 1997 (Thunø, 2001), Hong Kong has been integrated in the global economy for a much longer

time than mainland China, which has only recently decided to open up its borders. Accordingly, it

may be assumed that over the years firms based in Hong Kong have been able to develop and

maintain better contacts with overseas Chinese communities in Europe than their mainland Chinese

counterparts. Hence, it could be argued that firms based in Hong Kong will more easily be able to

connect to local Chinese networks in Europe. Accordingly, following this reasoning, when their

location decisions in Europe are considered, it could be hypothesized that the relationship between

the size of the of the (total) Chinese migrant stock in a region and the probability of Chinese

greenfield investments in a region is stronger for firms based in Hong Kong than for firms based in

mainland China. However, it could also be argued that, because firms based in Hong Kong have been

active on the global economic stage for a much longer time than their mainland Chinese

counterparts, they no longer need to connect to local Chinese networks to obtain important (foreign

market) information and support in the establishment of business relationships. Accordingly,

following this reasoning, it could also hypothesized that the relationship between the size of the of

the (total) Chinese migrant stock in a region and the probability of Chinese greenfield investments in

a region is stronger for firms based in mainland China than for firms based in Hong Kong.

Following model specification (12), for firms based in mainland China, a 10 percent increase

in the size of the Chinese migrant stock in a region originating from mainland China increases the

probability of locating in that region by 3.3%. Following model specification (13), for firms based in

mainland China, a 10 percent increase in the size of the Chinese migrant stock in a region originating

from Hong Kong and Macau increases the probability of locating in that region by 1.1%. Following

model specification (11), for firms based in mainland China, a 10 percent increase in the size of the

(total) Chinese migrant stock in a region increases the probability of locating in that region by 3.2%.

To test for the equality of these coefficients, three Wald tests have been performed, which can be

found in Appendix A.7C. The question then remains whether these model specifications represent a

good fit. Following Hensher & Johnson (1981), values of ρ2 between 0.2 and 0.4 are considered to

represent very good fits. Following Table 3, model specifications (11) to (13) are found to have values

of ρ2 between 0.165 and 0.177.

Following model specifications (14) to (16), for firms based in Hong Kong, it is found that

neither of the selected variables that measure the size of the Chinese migrant stock in a European

region has a significant coefficient. Furthermore, many of the control variables that were found to be

significant in most of the other model specifications are found to be insignificant, when only firms

based in Hong Kong are considered. Moreover, it is found that some control variables, such as the

presence of an international seaport in a region and the percentage of university educated people,

32

have a significant coefficient in these model specifications, while these variables are not found to be

significant in most of the other model specifications. Accordingly, based on these empirical results, it

can be questioned if the estimated coefficients for Hong Kong firms offer an accurate picture of

reality, as it may also be possible that these empirical results may be biased due to the (relatively)

low number of observations of firms in the dataset that are based in Hong Kong. Therefore, it seems

prematurely to draw any conclusions on location decisions of Hong Kong based firms in Europe,

grounded on only these empirical results. As a consequence of that, no comparison can be made

between firms based in mainland China and firms based in Hong Kong, regarding the role of overseas

Chinese communities in the location decisions of these firms in Europe.

Following Table 3 and Appendix A.7C, support is found for the hypothesis that for firms

based in mainland China, the relationship between the size of the Chinese migrant stock in a region

and the probability of Chinese greenfield investments in a region is stronger for the size of the

Chinese migrant stock in a region originating from mainland China than for the size of the Chinese

migrant stock in a region originating from Hong Kong and Macau. Accordingly, it would seem that

support is found for the hypothesis that Chinese firms have a preference for European regions with a

(sizeable) overseas Chinese community, with whom they share a region of origin in China. However,

one should be very precautious about drawing such a conclusion, when only these empirical results

are considered. Following paragraph 3.4.2, a migrant in a European region is defined to be Chinese, if

he/she holds citizenship of either the People’s Republic of China, Hong Kong or Macau, or Taiwan at

the time of his/her migration to Europe. However, a person’s nationality does not imply that a

person may not be rooted in another country/region in China. Likewise, regarding a firm’s region of

origin in China, such a registration does not imply that a firm may not be rooted in another

country/region in China. Accordingly, the data may give an indication of a person’s or firm’s region of

origin in China, but not a definitive answer.

33

Table 3: Model Specifications (11)-(21)

Location Factors (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21)Origin of the Firm China* China* China* HK** HK** HK** x x x x xFunction of the Firm x x x x x x H** L** M** RD** SM**Demand Factors Market Potential (log) 0.63b

(0.28)0.64b

(0.29)0.48c

(0.27)0.11

(0.48)0.13

(0.48)-0.09(0.57)

0.61(0.74)

0.71(0.92)

-0.87(0.60)

-0.94(0.75)

0.74b

(0.36)Airport Dummy 0.48a

(0.17)0.36b

(0.16)0.55a

(0.17)0.16

(0.37)0.15

(0.37)0.22

(0.39)1.22a

(0.45)-1.00(0.65)

-0.63(0.48)

-0.16(0.49)

0.73a

(0.21)Seaport Dummy -0.03

(0.19)-0.10(0.19)

-0.08(0.19)

0.67c

(0.36)0.65b

(0.36)0.74b

(0.38)0.55

(0.76)0.64

(0.72)-0.75(0.80)

-15.10a

(0.51)0.44b

(0.18)Supply Factors Wage per Hour (log) 0.23

(0.14)0.23

(0.14)0.26c

(0.14)0.41

(0.30)0.42

(0.31)0.40

(0.30)-0.54(0.45)

0.29(0.57)

0.24(0.47)

1.35a

(0.36)0.37b

(0.17)Unemployment Rate 0.04c

(0.02)0.03

(0.02)0.03

(0.02)0.06

(0.05)0.06

(0.05)0.06

(0.05)-0.01(0.08)

0.06(0.06)

0.09b

(0.04)-0.33a

(0.10)0.04

(0.03)University Education -0.01

(0.01)-0.01(0.01)

-0.01(0.01)

0.05c

(0.03)0.05b

(0.03)0.05c

(0.03)0.09a

(0.03)0.04

(0.04)-0.05(0.04)

-0.10b

(0.04)0.01

(0.01)Social Charges Rate 0.01

(0.01)-0.01(0.02)

-0.01(0.02)

-0.01(0.04)

-0.02(0.04)

-0.01(0.04)

0.19b

(0.08)0.01

(0.05)-0.01(0.04)

0.01(0.05)

0.01(0.02)

Corporate Tax Rate -0.06a

(0.02)-0.06a

(0.02)-0.03c

(0.02)-0.01(0.04)

-0.01(0.05)

-0.01(0.04

-0.22a

(0.07)-0.04(0.06)

-0.02(0.04)

0.09c

(0.05)-0.05c

(0.02)AgglomerationEffects

Localization Advantages(log)

0.41a(0.11)

0.39a

(0.11)0.41a

(0.10)-0.06(0.27)

-0.07(0.27)

-0.05(0.27)

0.72a

(0.25)-0.05(0.27)

0.22(0.52)

0.21(0.23)

0.34a

(0.12)Urbanization Advantages(log)

-0.36c

(0.19)-0.33c

(0.19)-0.29(0.19)

0.65(0.43)

0.67(0.44)

0.64(0.42)

-0.76c

(0.42)-0.03(0.62)

-0.05(0.60)

-0.33(0.48)

-0.04(0.23)

Total Greenfield Investments (log)

0.42a

(0.12)0.43a

(0.12)0.37a

(0.12)0.39

(0.27)0.38

(0.27)0.41

(0.28)0.23

(0.35)0.71c

(0.41)0.91a

(0.34)0.77a

(0.26)0.19

(0.14)Chinese Greenfield Investments (log)

0.57a

(0.10)0.61a

(0.10)0.60a

(0.10)0.48b

(0.22)0.48b

(0.21)0.44c

(0.23)0.46

(0.29)0.12

(0.34)0.44c

(0.26)0.44

(0.27)0.61a

(0.14)

34

Table 3: Model Specifications (11)-(21) (continued)

Location Factors (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21)Origin of the Firm China* China* China* HK* HK* HK* x x x x xFunction of the Firm x x x x x x H** L** M** RD** SM**Chinese Population Migrant Stock

(Total) (log)0.32a

(0.07)0.02

(0.13)0.50a

(0.41)0.01

(0.17)0.21

(0.23)-0.15(0.17)

0.37a

(0.10)Migrant Stock(Mainland China) (log)

0.33a

(0.08)-0.01(0.15)

Migrant Stock(Hong Kong + Macau) (log)

0.11a

(0.03)0.04

(0.06)

Test Statistics Number of Observations 23660 23660 23660 4921 4921 4921 4165 1947 2797 2205 16449Number of Cases 279 279 279 58 58 58 49 23 33 26 194Likelihood Ratio Index (Pseudo R2)

0.1773 0.1749 0.1746 0.2100 0.2099 0.2110 0.3846 0.0990 0.1035 0.2410 0.2208

AIC 2064.5 2070.4 2071.2 433.0 433.0 432.4 293.9 210.0 288.7 201.2 1368.4BIC 2169.4 2175.3 2176.1 517.5 517.5 517.0 376.3 282.4 365.9 275.3 1468.6

Note: Standard errors in parentheses with a, b and c respectively denoting significance at the 1%, 5% and 10% levels.

* China: Mainland China; HK: Hong Kong** H: Headquarters; L: Logistics; M: Manufacturing: RD: Research & Development; SM: Sales & Marketing

35

Paragraph 4.5: Testing for Differences Between Firms with Different Economic Functions

within the Value Chain of a Firm

The Ernst & Young European Investment Monitor 2009 database contains information on 351

Chinese greenfield investments in Europe in the period 2002-2008. In this context, Appendix A.3

provides an overview of the distribution of these investments across nine economic functions: (1)

contact centre; (2) education & training; (3) headquarters; (4) logistics; (5) manufacturing; (6)

research & development; (7) sales & marketing; (8) shared services centre; (9) testing & servicing.46 In

this context, it may be expected that greenfield investments in different stages or activities within

the value chain of a firm will be drawn to locations with different (location) characteristics.

Accordingly, firms that invest in sales and marketing offices are hypothesized to make different

location decisions than firms that invest in manufacturing plants. As in model specifications (1) to

(16) no distinction is made between firms with different economic functions, the empirical results

that were obtained in these regression analyses may be biased, since these firms may prefer

different location characteristics because of those differences.

Accordingly, model specifications (17) to (21) each contain exactly the same variables as

model specification (2). However, where in model specification (2), the location choice of all firms in

the dataset is analyzed, irrespective of their economic function, in model specification (17), only

firms are included in the empirical analysis that invest in headquarters. Likewise, in model

specifications (18) to (21), only firms are included in the empirical analysis that invest in

(respectively) logistics centers, manufacturing plants, research & development centers, and sales &

marketing offices. No separate regression analyses are run for contact centers, education & training

facilities, shared service centers and testing & servicing facilities, because too little observations are

available for these economic functions to run a statistically sound regression analysis.

Following model specifications (17) to (21), only for firms that invest in headquarters and

sales & marketing offices, a significant and positive relationship is found between the size of the

(total) Chinese migrant stock in a European region and the number of Chinese greenfield investments

made. For firms that invest in either logistics centers, manufacturing plants or research &

development centers, no significant relationship is found, whether positive or negative, between the

size of the (total) Chinese migrant stock in a European region and the number of Chinese greenfield

investments made. Furthermore, when the coefficients of the control variables in the model

specifications (18) to (20) are considered, some very remarkable results, compared to the other

model specifications, are found as well. Therefore, following the same argument as in paragraph 4.4,

it can be questioned if the estimated coefficients in these model specifications offer an accurate

46 It is referred to Appendix A.8 for a definition of these economic functions.

36

picture of reality. More likely, these empirical results are biased due to the (relatively) low number of

observations of firms in the dataset that invest in either of these economic functions. Hence, drawing

any conclusions on location decisions of Chinese firms in Europe that invest in either logistics centers,

manufacturing plants or research & development centers seems prematurely, when only these

empirical results are considered.

Following model specification (17), for Chinese firms in Europe that invest in headquarters, it

is found that there is a significant and positive relationship between the size of the (total) Chinese

migrant stock in a European region and the number of such greenfield investments made, with the

coefficient being 0.50. Accordingly, when only Chinese greenfield investments in headquarters are

considered, a 10 percent increase in the size of the (total) Chinese migrant stock in a region increases

the probability of attracting such investments into that region by 5.0%. Likewise, following column

(21), for Chinese firms in Europe that invest in sales and marketing offices, it is found that there is a

significant and positive relationship between the size of the (total) Chinese migrant stock in a

European region and the number of such greenfield investments made, with the coefficient being

0.37. Accordingly, when only Chinese greenfield investments in sales and marketing offices are

considered, a 10 percent increase in the size of the (total) Chinese migrant stock in a region increases

the probability of attracting such investments into that region by 3.7%. In this context, it should be

emphasized, however, that the empirical results that were obtained in model specification (17) may

also be biased, due to a (relatively) low number of observations.

Accordingly, with almost 70% of all Chinese greenfield investments in Europe being made in

headquarters and sales and marketing offices, running these separate regression analyses enables to

obtain interesting insights on the role of overseas Chinese communities in the location decisions of

the majority of Chinese MNEs in Europe. The question then remains whether these model

specifications represent a good fit. Following Table 3, model specifications (17) and (21) are found to

have values of ρ2 of respectively 0.385 and 0.221, which are both considered to represent very good

fits (Hensher & Johnson, 1981).

Paragraph 4.6: Hausman-McFadden Test

As it is outlined in chapter 3, in order to detect potential violations of the IIA property, a Hausman-

McFadden test should be conducted (Hausman & McFadden, 1984). For model specification (2), such

is a test is carried out in Appendix A.8. It is found that the IIA property is violated for 8 alternatives. In

this context, it should be noted, however, that such a violation of the IIA property is not that

surprising, when it is taken into account that 89 different alternatives may be considered.

Nevertheless, this violation indicates that this conditional logit model might not be well specified and

37

might generate improper estimations and forecasts (Cheng, 2007). Following Cheng & Stough (2006),

however, the IIA assumption is often violated in the industrial location literature. This is primarily the

case because discrete choice models, such as the conditional logit model, are not developed in a

spatial context. Unlike non-spatial alternatives, spatial alternatives are less likely to be equally

substitutable as a result of their geographic locations (Cheng, 2007).

To remedy the potential violation of the IIA property, a number of alternative methodologies

have been suggested, that may be applied in the industrial location literature, such as a nested logit

model or a mixed logit model (Head & Mayer, 2004). The nested logit model is a viable alternative to

the conditional logit model, because it relaxes the restrictive IIA property by grouping closely

substitutable alternatives into one group (nest). Accordingly, the nested logit model is able to

preserve equal substitutability within each nest, in order to satisfy the IIA property, while

accommodating substitutability across different nests (Ben-Akiva & Lerman, 1985). Accordingly, an

investor faces a hierarchical and staged decision process, i.e. first the investor chooses a country

(group of countries), then he/she chooses a region within a country (a country within a group of

countries). The nested logit model also has a few drawbacks, however. First, it offers only a partial

solution, because it is still assumed that the IIA assumption holds among alternatives within the same

nest (Cheng & Stough, 2006). Second, the determination of the nests requires a priori information,

and this information, in some cases, might seem arbitrary (Knapp et al., 2001).

A mixed logit model, with the help of simulation techniques, does not rely on the IIA

assumption for coefficient estimation (McFadden & Train, 2000; Train, 2003). In addition, the mixed

logit model, unlike the conditional logit model or the nested logit model, allows heterogonous tastes

among decision-makers regarding a particular alternative attribute (Cheng & Stough, 2006).

Accordingly, this taste heterogeneity can reveal different reactions in response to a change of

alternative attributes. The mixed logit model also has its drawbacks, however. Particularly, the

computational burden of simulation techniques has discouraged many scholars from applying it to

empirical applications on large datasets (Basile et al., 2008).

Paragraph 4.7: Conclusion

In this chapter, the relationship between the size of the (overseas) Chinese population in a European

region and the number of Chinese greenfield investments made has been tested. In this context, nine

different variables have been selected to measure the presence and size of an overseas Chinese

community in a European region. In this study, for all nine variables, a significant and positive

relationship was found between the size of the (overseas) Chinese population in a European region

and the number of Chinese greenfield investments made. The magnitude of this relationship was

38

found to vary, though, depending on the variable that was chosen to measure the size of the

(overseas) Chinese population in an area.

Furthermore, the relationship between the size of the (overseas) Chinese population in a

European region and the number of Chinese greenfield investments made has been tested, separate

for firms based in mainland China and for firms based in Hong Kong. Unfortunately, too little

observations were available for firms based in Hong Kong, as a result of which no conclusions could

be drawn on the role of overseas Chinese communities in the location decisions of these firms in

Europe. As a consequence of that, it could not be tested for differences on this topic between firms

with different regions of origin in China. Furthermore, the relationship between the size of the

(overseas) Chinese population in a European region and the number of Chinese greenfield

investments made has been tested, separate for firms with different economic functions within the

value chain of a firm. For both headquarters and sales & marketing offices, it was found that there is

a significant and positive relationship between the size of the (total) Chinese migrant stock in a

European region and the number of such greenfield investments made.

Based on the empirical results that were presented in this chapter, it may be concluded that

the probability of Chinese greenfield investments in a European region is indeed positively associated

(at a significant level) with the size of the (overseas) Chinese population in a region, as it was

hypothesized in chapter 2 of this thesis.

39

CHAPTER 5: CONCLUSION

In this thesis, the role of overseas Chinese communities in the location decisions of Chinese MNEs in

European regions has been investigated. In this context, only greenfield investments have been

considered. In this study, it has been argued that successful foreign direct investment in Europe by

Chinese MNEs requires intensive information. For a Chinese firm, which is not already embedded in

the local (host country) market, this information may be difficult and costly to obtain, however.

Furthermore, for Chinese MNEs, establishing fruitful commercial relationships with local partners

may be difficult and costly to realize. Accordingly, following this reasoning, both transaction costs

and business risks associated with investing in Europe would be high for Chinese firms. Therefore, it

was argued that a linkage to a local network, through which a firm can more easily gain access to

(foreign market) information and establish business relationships, may be a crucial determinant for

successful foreign direct investment by Chinese firms in Europe. For Chinese MNEs, it was argued

that the presence of an overseas Chinese community in a European region may offer the best

possibility to establish a linkage to a local network in Europe. Accordingly, it was hypothesized that

the probability of Chinese greenfield investments in a European region is positively associated with

the size of the overseas Chinese population in a region.

In this thesis, 351 Chinese greenfield investments in 89 European regions (NUTS-1) during the

period 2002-2008 have been studied. To test the relationship between the size of the (overseas)

Chinese population in a European region and the number of Chinese greenfield investments made,

nine different variables have been selected to measure the presence and size of an overseas Chinese

community in a European region. In this study, by employing a conditional logit model, for all nine

variables, a significant and positive relationship was found between the size of the (overseas)

Chinese population in a European region and the number of Chinese greenfield investments made.

The magnitude of this relationship was found to vary, though, depending on the variable that was

chosen to measure the size of the (overseas) Chinese population in an area.

Furthermore, in this thesis, it was hypothesized that Chinese greenfield investments in

Europe were primarily motivated by market-seeking. In this study, evidence was found that the

probability of Chinese greenfield investments in a European region is indeed positively associated

with variables that indicate good market access, such as market potential and the presence of an

international airport in a region. In this context, it should be noted that the presence of an overseas

40

Chinese community in a European region may also be considered to be an indicator of good market

access for Chinese MNEs.

An important property of the conditional logit model is the assumption of the independence

of irrelevant alternatives (IIA). In this thesis, a Hausman-McFadden test has been conducted, in order

to detect potential violations of the IIA property. In this context, it was found that the IIA property is

violated for 8 alternatives. Although it is not quite uncommon in the industrial location literature for

the IIA assumption to be violated, this violation indicates that the conditional logit model employed

in this thesis might not be well specified and might generate improper estimations and forecasts. It

should be noted, however, that a violation of the IIA property is not that surprising, when it is taken

into account that 89 different alternatives may be considered. Nevertheless, it may be suggested for

future research to use a different model, such as a nested logit model or a mixed logit model.

However, each of these alternative methodologies also has its own drawbacks.

As China increasingly makes its appearance on the global economic stage and becomes an

important and assertive player in the world and European economies, the countries’ outward FDI is

expected to develop at an even more rapid rate in the near future. This analysis has shown that the

probability of Chinese greenfield investments in a European region is positively associated (at a

significant level) with the size of the overseas Chinese population in a region. For host countries and

regions, Chinese greenfield investments can boost the host location’s prospects for (national and/or

regional) economic development through, for example, employment creation, capital growth and

export promotion. Accordingly, for policy-makers in European regions, it may be in their own interest

to try to increase the size of the overseas Chinese population in their region, for example by relaxing

restraints on Chinese immigration into Europe. Furthermore, policy-makers in Europe may connect

to local overseas Chinese communities and offer them their help in the support they offer to Chinese

MNEs, who consider to invest in Europe. Accordingly, they may be able to increase the attractiveness

of their region to Chinese MNEs and, as a results of that, boost the region’s prospects of economic

growth.

41

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47

APPENDIX

Appendix A.1: Greenfield Investments in Europe in the Years 1997-2008 (China vs. Total)

Year Chinese Greenfield Investments Total Greenfield Investments1997 5 17081998 7 14161999 9 14352000 11 16722001 8 14062002 16 13552003 21 12492004 51 19622005 54 21442006 60 24802007 65 33732008 100 3415

total 407 23615

Appendix A.2: A List of All (NUTS-1) Regions in Europe (including the number of greenfield

investments made in each region in the period 1997-2008)

Country Region Code Region Name China TotalAustria AT1 Ostösterreich 0 304

AT2 Südösterreich 1 50AT3 Westösterreich 0 120

Belgium BE1 Région de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest

6 268

BE2 Vlaams Gewest 14 600BE3 Région Wallonne 7 240

Bulgaria BG1 Severna Bulgaria 1 181BG2 Yuzhna Bulgaria 3 187

Switzerland CH Switzerland 4 688Cyprus CY Cyprus 2 11Czech Republic CZ Czech Republic 2 844Germany DE1 Baden-Württemberg 3 179

DE2 Bayern 9 443DE3 Berlin 4 184DE4 Brandenburg 0 70DE5 Bremen 32 32DE6 Hamburg 9 132DE7 Hessen 12 428DE8 Mecklenburg-Vorpommern 1 35DE9 Niedersachsen 2 49DEA Nordrhein-Westfalen 32 360DEB Rheinland-Pfalz 0 31DEC Saarland 0 17

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DED Sachsen 0 70DEE Sachsen-Anhalt 0 76DEF Schleswig-Holstein 1 26DEG Thüringen 2 46

Denmark DK Denmark 14 454Estonia EE Estonia 0 160Spain ES1 Noroeste 0 51

ES2 Noreste 0 92ES3 Comunidad de Madrid 3 441ES4 Centro (ES) 1 71ES5 Este 6 628ES6 Sur 2 86

Finland FI Finland 1 179France FR1 Île de France 17 1291

FR2 Bassin Parisien 3 352FR3 Nord - Pas-de-Calais 0 210FR4 Est 3 317FR5 Ouest 3 185FR6 Sud-Ouest 3 251FR7 Centre-Est 7 385FR8 Méditerranée 3 367

Greece GR1 Voreia Ellada 1 22GR2 Kentriki Ellada 0 3GR3 Attiki 0 54GR4 Nisia Aigaiou, Kriti 0 0

Hungary HU1 Közép-Magyarország 9 438HU2 Dunántúl 4 313HU3 Alföld és Észak 1 228

Ireland IE Ireland 1 795Italy ITC Nord Ovest 10 344

ITD Nord Est 2 49ITE Centro (IT) 0 100ITF Sud (IT) 0 26ITG Isole (IT) 0 18

Lithuania LT Lithuania 0 182Luxembourg LU Luxembourg 0 66Latvia LV Latvia 0 154Malta MT Malta 0 10Netherlands NL1 Noord-Nederland 0 50

NL2 Oost-Nederland 0 56NL3 West-Nederland 12 593NL4 Zuid-Nederland 3 191

Norway NO Norway 0 84Poland PL1 Centralny 1 383

PL2 Poludniowy 0 207PL3 Wschodni 0 67PL4 Pólnocno-Zachodni 0 188PL5 Poludniowo-Zachodni 1 210PL6 Pólnocny 4 82

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Portugal PT Portugal 1 260Romania RO Romania 3 696Sweden SE Sweden 18 748Slovenia SI Slovenia 0 72Slovakia SK Slovakia 1 376United Kingdom UKC North East 29 231

UKD North West (including Merseyside) 10 303UKE Yorkshire and The Humber 8 207UKF East Midlands 3 171UKG West Midlands 9 366UKH Eastern 5 194UKI London 34 2060UKJ South East 39 827UKK South West 3 202UKL Wales 7 221UKM Scotland 2 416UKN Northern Ireland 2 161

Appendix A.3: Chinese Greenfield Investments in Europe in the Period 1997-2008 (subdivided

by economic function)

Economic Function Chinese Greenfield InvestmentsContact Centre 2Education & Training 2Headquarters 49Logistics 24Manufacturing 39Research & Development 26Sales & Marketing 200Shared Services Centre 4Testing & Servicing 5

total 351

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Appendix A.4: Descriptive Statistics

Variable Obs. Mean Std. Dev. Min. Max.A Market Potential (log) 31239 9.47 0.45 8.13 10.17B Airport Dummy 31239 0.20 0.40 0 1.00C Seaport Dummy 31239 0.11 0.32 0 1.00D Wage per Hour (log) 29804 2.64 0.77 0 6.06E Unemployment Rate 31239 7.99 5.42 0 24.90F University Education 31239 26.10 8.10 8.85 51.52G Social Charges Rate 31239 22.69 4.83 6.92 31.12H Corporate Tax Rate 31239 29.07 7.32 10 40.22I Localization Advantages (log) 30373 4.01 1.31 0 7.81J Urbanization Advantages (log) 31239 7.56 0.77 4.99 9.14K Total Greenfield Investments (log) 31239 3.97 1.29 0 6.90L Chinese Greenfield Investments (log) 31239 0.59 0.79 0 3.37M Migrant Stock (Mainland China) (log) 31239 9.26 2.21 3.14 11.55N Migrant Stock (Hong Kong + Macau) (log) 31239 6.41 3.96 0 11.50O Migrant Stock (Mainland China + Hong Kong + Macau) (log) 31239 9.50 2.35 3.18 11.92P Migrant Stock (Taiwan) (log) 31239 5.84 3.09 0 9.06Q Migrant Stock (Total) (log) 31239 9.56 2.35 3.22 11.96R Size Ethnic Chinese Community in 1955 (log) 31239 4.46 3.13 0 8.10S Size Ethnic Chinese Community in 1980 (log) 31239 7.96 3.75 0 12.35T Size Ethnic Chinese Community in 1990 (log) 31239 8.27 4.00 0 12.43U Size Ethnic Chinese Community in 2000 (log) 31239 9.90 2.86 2.30 12.43

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Appendix A.5: Correlation Matrix of All Independent Variables (including all control variables)

A B C D E F G H I J K L M N O P Q R S T UA 1.00B 0.24 1.00C 0.22 0.09 1.00D 0.52 0.30 0.21 1.00E -0.14 -0.22 -0.01 -0.43 1.00F 0.21 0.28 0.12 0.43 -0.30 1.00G 0.18 0.10 0.23 0.15 0.01 -0.05 1.00H 0.48 0.08 0.26 0.49 0.04 0.06 0.30 1.00I 0.22 0.23 0.01 -0.08 -0.04 -0.04 0.19 0.04 1.00J 0.23 0.39 0.06 0.13 -0.04 0.02 0.28 0.07 0.62 1.00K 0.38 0.46 0.11 0.18 -0.21 0.38 0.15 -0.13 0.40 0.57 1.00L 0.31 0.26 0.16 0.32 -0.29 0.35 0.07 0.05 0.15 0.27 0.50 1.00

M 0.61 0.20 0.17 0.62 -0.13 0.22 0.35 0.66 0.22 0.34 0.13 0.26 1.00N 0.61 0.05 0.05 0.54 -0.19 0.18 0.10 0.51 0.08 0.12 0.09 0.30 0.73 1.00O 0.61 0.17 0.15 0.62 -0.17 0.25 0.28 0.64 0.20 0.33 0.15 0.29 0.99 0.79 1.00P 0.68 0.10 0.06 0.55 -0.13 0.13 0.15 0.52 0.09 0.11 0.11 0.31 0.73 0.93 0.75 1.00Q 0.61 0.17 0.15 0.63 -0.17 0.25 0.29 0.64 0.20 0.32 0.15 0.29 0.99 0.79 0.99 0.76 1.00R 0.66 0.11 0.18 0.63 -0.19 0.25 0.24 0.67 0.15 0.27 0.17 0.35 0.85 0.80 0.87 0.80 0.87 1.00S 0.60 0.15 0.17 0.64 -0.16 0.26 0.21 0.62 0.17 0.32 0.21 0.34 0.85 0.76 0.88 0.76 0.88 0.93 1.00T 0.61 0.19 0.19 0.64 -0.15 0.26 0.28 0.64 0.21 0.38 0.22 0.34 0.90 0.73 0.92 0.74 0.92 0.93 0.98 1.00U 0.65 0.14 0.15 0.52 -0.03 0.16 0.27 0.52 0.25 0.42 0.28 0.29 0.88 0.65 0.82 0.69 0.88 0.80 0.89 0.91 1.00

* It is referred to Appendix A.3 for a list of all variables.** Observations: 31,239.

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Appendix A.6: Expected Signs (control variables)

Control Variables Expected SignMarket Potential (log) +Airport Dummy +Seaport Dummy +Wage per Hour (log) -Unemployment Rate ?University Education +Social Charges Rate -Corporate Tax Rate -Localization Advantages (log) +Urbanization Advantages (log) +Total Greenfield Investments (log) +Chinese Greenfield Investments (log) +

Appendix A.7: Wald Test (to test for the equality of the coefficients)

Coefficient 1 Coefficient 2 chi2(1) prob>chi2Appendix A.7A mchin (0.27) mhkmc (0.09) 8.90 0.00

mchin (0.27) mchhm (0.27) 0.11 0.74mchin (0.27) mtwan (0.15) 0.53 0.06mchin (0.27) mchtot (0.27) 0.01 0.92mhkmc (0.09) mchhm (0.27) 14.09 0.00mhkmc (0.09) mtwan (0.15) 3.26 0.07mhkmc (0.09) mchtot (0.27) 14.56 0.00mchhm (0.27) mtwan (0.15) 4.58 0.03mchhm (0.27) mchtot (0.27) 8.91 0.00mtwan (0.15) mchtot (0.27) 4.95 0.03

Appendix A.7B eth55 (0.09) eth80 (0.08) 0.37 0.54eth55 (0.09) eth90 (0.11) 0.35 0.55eth55 (0.09) eth00 (0.16) 1.50 0.22eth80 (0.08) eth90 (0.11) 6.95 0.00eth80 (0.08) eth00 (0.16) 3.15 0.08eth90 (0.11) eth00 (0.16) 1.00 0.32

Appendix A.7C mchin (0.33) mhkmc (0.11) 9.33 0.00mchin (0.33) mchtot (0.32) 0.07 0.78mhkmc (0.11) mchtot (0.32) 15.27 0.00

* mchin: Migrant Stock (Mainland China), mhkmc: Migrant Stock (Hong Kong + Macau), mchhm: Migrant Stock (Mainland China + Hong Kong + Macau), mtwan: Migrant Stock (Taiwan), mchtot: Migrant Stock (Total), eth55: Size Ethnic Chinese Community in 1955, eth80: Size Ethnic Chinese Community in 1980, eth90: Size Ethnic Chinese Community in 1990, eth00: Size Ethnic Chinese Community in 2000.

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Appendix A.8: Description of the Economic Functions (within the value chain of a firm)47

1. Headquarters:

Following Defever (2006), this economic function within the value chain of a firm

corresponds to all the administration, management and accounting activities localized

internationally. It includes decision centers, but unfortunately the dataset does not describe

their importance in the global decision-making process. Nevertheless, it is known that these

headquarters are not the principal decision center. In most cases, these centers correspond

to European or regional headquarters or are only intended for the network organization at

the national level.

2. Logistics Centers:

Following Defever (2006), logistics centers refer to all the entities linked to the transport of

goods, including warehousing (e.g., regional distribution of goods). These centers can be

internal to the firm or external, being involved in the distribution of goods to customers or

with suppliers. Furthermore, they may also be viewed as acting as an intermediary between

component production and assembly.

3. Manufacturing Plants:

This economic function within the value chain of a firm corresponds to the whole entity

related to the physical production of goods.

4. Research & Development Centers:

Following Defever (2006), R&D centers refer to all activities related to either fundamental

scientific research or to applied development, which is directly linked to the production

process.

5. Sales & Marketing Offices:

This economic function within the value chain of a firm includes both wholesale trade and

business representative offices.

Appendix A.9: Hausman-McFadden Test47 The activities of contact centers, education & training facilities, shared services centers, and testing & servicing facilities are not defined in this appendix, because too little observations are available for these economic functions for a separate regression analysis to be run. For further information on this topic, it is referred to paragraph 4.3.3.

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(to test the assumption of the independence of irrelevant alternatives)

Omitted Variable

P-value Omitted Variable

P-value Omitted Variable

P-value

AT1 1.00 ES2 1.00 MT 0.04AT2 1.00 ES3 1.00 NL1 0.99AT3 0.89 ES4 1.00 NL2 1.00BE1 1.00 ES5 0.99 NL3 0.01BE2 1.00 ES6 1.00 NL4 0.05BE3 0.01 FI 1.00 NO 0.99BG1 1.00 FR1 1.00 PL1 1.00BG2 1.00 FR2 1.00 PL2 1.00CH 1.00 FR3 1.00 PL3 1.00CY 0.99 FR4 1.00 PL4 0.06CZ 0.99 FR5 1.00 PL5 1.00DE1 0.99 FR6 1.00 PL6 1.00DE2 1.00 FR7 1.00 PT 1.00DE3 0.99 FR8 1.00 RO 1.00DE4 1.00 GR1 1.00 SE 0.50DE5 0.99 GR2 0.20 SI 0.24DE6 0.00 GR3 1.00 SK 1.00DE7 1.00 GR4 0.99 UKC 0.72DE8 1.00 HU1 0.99 UKD 1.00DE9 1.00 HU2 1.00 UKE 0.99DEA 1.00 HU3 1.00 UKF 1.00DEB 0.00 IE 1.00 UKG 0.71DEC 1.00 ITC 1.00 UKH 0.00DED 0.99 ITD 1.00 UKI 0.95DEE 1.00 ITE 1.00 UKJ 1.00DEF 0.56 ITF 1.00 UKK 0.99DEG 0.85 ITG 0.85 UKL 1.00DK 1.00 LT 1.00 UKM 1.00EE 1.00 LU 0.82 UKN 1.00ES1 1.00 LV 1.00

* Hausman-McFadden test for model specification (2) – Table 2 (Chapter 4)

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