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
10
Chinese MNEs in European Regions
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
14
Decisions of Chinese MNEs in European Regions
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
1
Chapter 4: Empirical Results
25
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
26
a European Region and the Number of Chinese Greenfield Investments Made
Paragraph 4.4: Testing for Differences Between Firms Based in Mainland China and Firms
31
Based in Hong Kong
Paragraph 4.5: Testing for Differences Between Firms with Different Economic Functions
36
within the Value Chain of a Firm
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
48
investments made in each region in the period 1997-2008)
Appendix A.3: Chinese Greenfield Investments in Europe in the Period 1997-2008
50
(subdivided by economic function)
Appendix A.4: Descriptive Statistics
51
Appendix A.5: Correlation Matrix of All Independent Variables (including all control
52
variables)
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
55
irrelevant alternatives)
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 $US 119 billion,7 representing just 8.2% of the world’s total inward FDI flows
(UNCTAD, 2009).8 These calculations indicate that FDI inflows into China are increasing at a rapid
pace.
1
Calculations were made by the author.
Year of measurement: 2008.
3
Exports: China ($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.
2
3
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.
4
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).
5
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.
16
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.
6
CHAPTER 2: THEORETICAL FRAMEWORK
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
7
asserts that, at any given moment of time, the extent and pattern of international production, i.e.
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
8
characteristics than, for example, efficiency-seeking, resource-seeking or strategic asset-seeking
MNEs.
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
17
It is referred to paragraph 1.1.
9
(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
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
10
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
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
11
Belgium, and later on Germany and France). The second wave occurred in the late 1970s, with the
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 shortterm 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
Austria
Belgium
Bulgaria
Switzerland
Cyprus
Czech Republic
Germany
Denmark
Estonia
Spain
Finland
France
Greece
Hungary
Ireland
Italy
Lithuania
Luxembourg
Latvia
Malta
Netherlands
Norway
Poland
Portugal
1955
30
118
1
11
1
11
800
900
1
43
1
3,300
4
1
1
260
1
1
1
1
2,017
3
1
73
1980
4,500
4,000
25
3,200
1
16
20,165
2,000
1
3,500
9
210,000
186
24
1,000
3,500
1
1
1
15
60,000
600
77
2,500
1990
6,000
13,000
30
5,000
1
20
39,500
6,000
1
15,000
10
200,000
100
23
1,000
20,662
1
1
1
1
45,500
950
80
4,700
2000
41,000
23,000
10
13,000
10
12,000
100,000
7,257
20
35,000
1,500
225,000
600
10,000
10,000
70,000
40
1,300
100
10
127,500
5,000
15,000
2,700
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).
12
Romania
Sweden
Slovenia
Slovakia
United Kingdom
Total
1
24
1
1
2,549
10,157
33
5,000
1
1
230,000
550,357
1
12,000
1
1
250,000
619,584
3,000
12,800
10
10
250,000
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
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
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
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
14
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
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
20
In the literature, this effect is termed the liability of foreignness (Johanson & Vahlne, 2009).
15
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
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 marketseeking 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.
16
CHAPTER 3: DATA & METHODOLOGY
Paragraph 3.1: Introduction
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.
21
Detailed information on this database can be found at <http://www.eyeim.com>.
17
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
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.
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
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.
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.
24
It is referred to Appendix A.4 for the descriptive statistics of all variables.
It is referred to Appendix A.5 for a correlation matrix of all variables.
26
Sources: Parsons et al. (2005), Özden & Schiff (2007).
25
19
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) 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
27
Sources: Parsons et al. (2005), Özden & Schiff (2007). The year of measurement of these variables is 2000.
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.
28
20
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:
𝑀𝑃𝑗𝑡 = ∑
𝑘
𝐾𝜀
(
𝑌𝑘𝑖
)
𝐷𝑗𝑘𝑡
(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 20022008, 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,
30
Source: Brienen et al. (2010).
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.
31
21
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
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 (logtransformed) 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 (logtransformed) 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
35
Source: Brienen et al. (2010).
The social charges rate is measured as a percentage of the total labor costs. Source: Ernst & Young
International Human Capital Database.
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.
36
22
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
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 = 𝛽𝑋𝑗 + 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:
42
Source: Cambridge Econometrics Database.
This assumption fits right into the neoclassical economic theory of utility-maximizing behavior (McFadden,
2001).
43
23
𝑃𝑖𝑗 =
𝑒𝑥𝑝(𝑋𝑖𝑗 𝛽𝑗 )
𝑆
∑𝑠=1 𝑒𝑥𝑝(𝑋𝑖𝑠 𝛽𝑗 )
(3)
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 subsample 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 𝑙𝑛 𝑃𝑗
= 𝛽𝑘 (1 − 𝑃)
𝑑 𝑙𝑛 𝑋𝑘
(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):
𝐿(𝑚𝑎𝑥)
𝜌2 = 1 − [
]
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.
24
CHAPTER 4: EMPIRICAL RESULTS
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
44
45
It is referred to paragraph 3.4.2.
It is referred to appendix A.5.
25
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
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
26
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
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
27
a European region (in all measured years) and the number of Chinese greenfield investments made
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)
Demand Factors
Location Factors
Market Potential (log)
Airport Dummy
Seaport Dummy
Supply Factors
Wage per Hour (log)
Unemployment Rate
University Education
Social Charges Rate
Corporate Tax Rate
Agglomeration
Effects
Localization Advantages (log)
Urbanization Advantages (log)
Total Greenfield Investments (log)
Chinese Greenfield Investments
(log)
(1)
0.72a
(0.23)
0.35b
(0.15)
-0.06
(0.17)
0.31b
(0.12)
0.02
(0.02)
0.01
(0.01)
-0.01
(0.01)
-0.02
(0.02)
0.31a
(0.10)
-0.03
(0.17)
0.27a
(0.10)
0.68a
(0.10)
(2)
0.50b
(0.24)
0.45a
(0.15)
0.14
(0.16)
0.24c
(0.13)
0.04b
(0.02)
-0.01
(0.01)
-0.01
(0.02)
-0.05a
(0.02)
0.35a
(0.10)
-0.19
(0.17)
0.39a
(0.11)
0.56a
(0.10)
(3)
0.52b
(0.25)
0.35b
(0.15)
0.09
(0.16)
0.24c
(0.13)
0.03c
(0.02)
0.01
(0.01)
-0.01
(0.01)
-0.05a
(0.02)
0.32a
(0.10)
-0.16
(0.17)
0.40a
(0.11)
0.60a
(0.09)
(4)
0.33
(0.24)
0.50a
(0.15)
0.11
(0.17)
0.26b
(0.12)
0.03c
(0.02)
-0.01
(0.01)
-0.01
(0.01)
-0.02
(0.02)
0.34a
(0.10)
-0.12
(0.17)
0.35a
(0.11)
0.57a
(0.09)
(5)
0.51b
(0.24)
0.45a
(0.15)
0.13
(0.16)
0.24c
(0.13)
0.04b
(0.02)
-0.01
(0.01)
-0.01
(0.01)
-0.05a
(0.02)
0.35a
(0.10)
-0.19
(0.17)
0.38a
(0.11)
0.56a
(0.09)
(6)
0.26
(0.25)
0.44a
(0.15)
0.15
(0.17)
0.22c
(0.13)
0.02
(0.02)
0.01
(0.01)
-0.01
(0.01)
-0.03c
(0.02)
0.31a
(0.10)
-0.03
(0.18)
0.34a
(0.11)
0.58a
(0.09)
(7)
0.57b
(0.25)
0.48a
(0.16)
-0.02
(0.16)
0.25c
(0.13)
0.02
(0.02)
-0.01
(0.01)
-0.01
(0.01)
-0.04b
(0.02)
0.33a
(0.10)
-0.10
(0.18)
0.28a
(0.10)
0.63a
(0.09)
(8)
0.61b
(0.24)
0.51a
(0.17)
-0.01
(0.16)
0.23c
(0.14)
0.02
(0.02)
-0.01
(0.01)
-0.01
(0.01)
-0.03c
(0.02)
0.33a
(0.10)
-0.12
(0.18)
0.27b
(0.11)
0.64a
(0.09)
(9)
0.62a
(0.24)
0.51a
(0.17)
-0.01
(0.16)
0.20
(0.14)
0.02
(0.02)
-0.01
(0.01)
-0.01
(0.01)
-0.04b
(0.02)
0.33a
(0.10)
-0.15
(0.18)
0.29a
(0.11)
0.62a
(0.09)
(10)
0.51b
(0.26)
0.51a
(0.17)
-0.01
(0.16)
0.26b
(0.13)
0.02
(0.02)
-0.01
(0.01)
-0.01
(0.01)
-0.03c
(0.02)
0.34a
(0.10)
-0.14
(0.18)
0.27a
(0.11)
0.63a
(0.09)
29
Table 2: Model Specifications (1)-(10) (continued)
Chinese
Population
Test Statistics
Location Factors
Migrant Stock
(Total) (log)
Migrant Stock
(Mainland China) (log)
Migrant Stock
(Hong Kong + Macau) (log)
Migrant Stock (Mainland China +
Hong Kong + Macau) (log)
Migrant Stock
(Taiwan) (log)
Size Ethnic Chinese Community in
1955 (log)
Size Ethnic Chinese Community in
1980 (log)
Size Ethnic Chinese Community in
1990 (log)
Size Ethnic Chinese Community in
2000 (log)
(1)
Number of Observations
Number of Cases
Likelihood Ratio Index (Pseudo R2)
AIC
BIC
28666
338
0.1672
2523.9
2623.0
(2)
0.27a
(0.07)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
0.27a
(0.07)
0.09a
(0.03)
0.27a
(0.06)
0.15a
(0.05)
0.09b
(0.04)
0.08b
(0.03)
0.11a
(0.04)
0.16a
(0.06)
28666
338
0.1743
2504.4
2611.9
28666
338
0.1724
2510.1
2617.6
28666
338
0.1723
2510.6
2618.0
28666
338
0.1742
2504.7
2612.2
28666
338
0.1711
2514.2
2621.7
28666
338
0.1687
2521.4
2628.9
28666
338
0.1689
2520.6
2628.0
28666
338
0.1698
2518.1
2625.5
28666
338
0.1696
2518.5
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
Origin of the Firm
Function of the Firm
Demand Factors
Market Potential (log)
Airport Dummy
Seaport Dummy
Supply Factors
Wage per Hour (log)
Unemployment Rate
University Education
Social Charges Rate
Corporate Tax Rate
Agglomeration
Effects
Localization Advantages
(log)
Urbanization Advantages
(log)
Total Greenfield
Investments (log)
Chinese Greenfield
Investments (log)
(11)
China*
x
0.63b
(0.28)
0.48a
(0.17)
-0.03
(0.19)
0.23
(0.14)
0.04c
(0.02)
-0.01
(0.01)
0.01
(0.01)
-0.06a
(0.02)
0.41a
(0.11)
-0.36c
(0.19)
0.42a
(0.12)
0.57a
(0.10)
(12)
China*
x
0.64b
(0.29)
0.36b
(0.16)
-0.10
(0.19)
0.23
(0.14)
0.03
(0.02)
-0.01
(0.01)
-0.01
(0.02)
-0.06a
(0.02)
0.39a
(0.11)
-0.33c
(0.19)
0.43a
(0.12)
0.61a
(0.10)
(13)
China*
x
0.48c
(0.27)
0.55a
(0.17)
-0.08
(0.19)
0.26c
(0.14)
0.03
(0.02)
-0.01
(0.01)
-0.01
(0.02)
-0.03c
(0.02)
0.41a
(0.10)
-0.29
(0.19)
0.37a
(0.12)
0.60a
(0.10)
(14)
HK**
x
0.11
(0.48)
0.16
(0.37)
0.67c
(0.36)
0.41
(0.30)
0.06
(0.05)
0.05c
(0.03)
-0.01
(0.04)
-0.01
(0.04)
-0.06
(0.27)
0.65
(0.43)
0.39
(0.27)
0.48b
(0.22)
(15)
HK**
x
0.13
(0.48)
0.15
(0.37)
0.65b
(0.36)
0.42
(0.31)
0.06
(0.05)
0.05b
(0.03)
-0.02
(0.04)
-0.01
(0.05)
-0.07
(0.27)
0.67
(0.44)
0.38
(0.27)
0.48b
(0.21)
(16)
HK**
x
-0.09
(0.57)
0.22
(0.39)
0.74b
(0.38)
0.40
(0.30)
0.06
(0.05)
0.05c
(0.03)
-0.01
(0.04)
-0.01
(0.04
-0.05
(0.27)
0.64
(0.42)
0.41
(0.28)
0.44c
(0.23)
(17)
x
H**
0.61
(0.74)
1.22a
(0.45)
0.55
(0.76)
-0.54
(0.45)
-0.01
(0.08)
0.09a
(0.03)
0.19b
(0.08)
-0.22a
(0.07)
0.72a
(0.25)
-0.76c
(0.42)
0.23
(0.35)
0.46
(0.29)
(18)
x
L**
0.71
(0.92)
-1.00
(0.65)
0.64
(0.72)
0.29
(0.57)
0.06
(0.06)
0.04
(0.04)
0.01
(0.05)
-0.04
(0.06)
-0.05
(0.27)
-0.03
(0.62)
0.71c
(0.41)
0.12
(0.34)
(19)
x
M**
-0.87
(0.60)
-0.63
(0.48)
-0.75
(0.80)
0.24
(0.47)
0.09b
(0.04)
-0.05
(0.04)
-0.01
(0.04)
-0.02
(0.04)
0.22
(0.52)
-0.05
(0.60)
0.91a
(0.34)
0.44c
(0.26)
(20)
x
RD**
-0.94
(0.75)
-0.16
(0.49)
-15.10a
(0.51)
1.35a
(0.36)
-0.33a
(0.10)
-0.10b
(0.04)
0.01
(0.05)
0.09c
(0.05)
0.21
(0.23)
-0.33
(0.48)
0.77a
(0.26)
0.44
(0.27)
(21)
x
SM**
0.74b
(0.36)
0.73a
(0.21)
0.44b
(0.18)
0.37b
(0.17)
0.04
(0.03)
0.01
(0.01)
0.01
(0.02)
-0.05c
(0.02)
0.34a
(0.12)
-0.04
(0.23)
0.19
(0.14)
0.61a
(0.14)
34
Table 3: Model Specifications (11)-(21) (continued)
Location Factors
Origin of the Firm
Function of the Firm
Chinese Population
Test Statistics
Migrant Stock
(Total) (log)
Migrant Stock
(Mainland China) (log)
Migrant Stock
(Hong Kong + Macau) (log)
Number of Observations
Number of Cases
Likelihood Ratio Index
(Pseudo R2)
AIC
BIC
(11)
China*
x
0.32a
(0.07)
(12)
China*
x
(13)
China*
x
(14)
HK*
x
0.02
(0.13)
0.33a
(0.08)
(15)
HK*
x
(16)
HK*
x
(17)
x
H**
0.50a
(0.41)
(18)
x
L**
0.01
(0.17)
(19)
x
M**
0.21
(0.23)
(20)
x
RD**
-0.15
(0.17)
(21)
x
SM**
0.37a
(0.10)
-0.01
(0.15)
0.11a
(0.03)
0.04
(0.06)
23660
279
0.1773
23660
279
0.1749
23660
279
0.1746
4921
58
0.2100
4921
58
0.2099
4921
58
0.2110
4165
49
0.3846
1947
23
0.0990
2797
33
0.1035
2205
26
0.2410
16449
194
0.2208
2064.5
2169.4
2070.4
2175.3
2071.2
2176.1
433.0
517.5
433.0
517.5
432.4
517.0
293.9
376.3
210.0
282.4
288.7
365.9
201.2
275.3
1368.4
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 HausmanMcFadden 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
might generate improper estimations and forecasts (Cheng, 2007). Following Cheng & Stough (2006),
37
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
found to vary, though, depending on the variable that was chosen to measure the size of the
(overseas) Chinese population in an area.
38
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
Chinese community in a European region may also be considered to be an indicator of good market
access for Chinese MNEs.
40
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
REFERENCES
Barba Navaretti, G. & Venables, A.J. (2004). Multinational Firms in the World Economy. Princeton, NJ:
Princeton University Press.
Basile, R., Castellani, D. & Zanfei, A. (2008). ‘Location Choices of Multinational Firms in Europe: The
Role of EU Cohesion Policy’. Journal of International Economics, Vol. 74(2): 328-340.
Ben-Akiva, M. & Lerman, S.R. (1985). Discrete Choice Analysis: Theory and Application to Travel
Demand. Cambridge, MA: MIT Press.
Brakman, S. & Garretsen, H. (2008). ‘Foreign Direct Investment and the Multinational Enterprise: An
Introduction’, in: Brakman, S. & Garretsen, H. (eds.), Foreign Direct Investment and the
Multinational Enterprise. Cambridge, MA: MIT Press, pp. 1-10.
Braütigam, D. (2003). ‘Close Encounters: Chinese Business Networks as Industrial Catalysts in SubSaharan Africa’. African Affairs, Vol. 102(408): 447-467.
Breschi, S. & Malerba, F. (eds.) (2005). Clusters, Networks & Innovation. Oxford, UK: Oxford
University Press.
Brienen, M.J., Burger, M.J. & Van Oort, F.G. (2010). ‘The Geography of Chinese and Indian Greenfield
Investments in Europe’. Eurasian Geography and Economics, Vol. 51(2): 254-273.
Buckley, P.J., Clegg, L.J., Wang, C. (2002). ‘The Impact of Inward FDI on the Performance of Chinese
Manufacturing Firms’. Journal of International Business Studies, Vol. 33(4): 637-655.
Buckley, P.J., Clegg, L.J., Cross, A.R., Liu, X., Voss, H. & Zheng, P. (2007). ‘The Determinants of Chinese
Outward Foreign Direct Investment’. Journal of International Business Studies, Vol. 38(4): 499-518.
Cai, K.G. (1999). ‘Outward Foreign Direct Investment: A Novel Dimension of China’s Integration into
the Regional and Global Economy’. The China Quarterly, No. 160: 856-880.
Cameron, A.C. & Trivedi, P.K. (2009). Microeconomics using Stata. College Station, TX: Stata Press.
Cheng, S. & Stough, R.R. (2006). ‘Location Decisions of Japanese New Manufacturing Plants in China:
A Discrete-Choice Analysis’. Annals of Regional Science, Vol. 40(2): 369-387.
Cheng, S. (2007). ‘Structure of Firm Location Choices: An Examination of Japanese Greenfield
Investment in China’. Asian Economic Journal, Vol. 21(1): 47-73.
Cheng, S. & Stough, R.R. (2007). ‘The Pattern and Magnitude of China’s Outward FDI in Asia’. Paper
presented at the conference: ICRIER Project on Intra-Asian FDI Flows. New Delhi, India.
Child, J. & Rodrigues, S.B. (2005). ‘The Internationalization of Chinese firms: A Case for Theoretical
Extension?’ Management and Organization Review, Vol. 1(3): 381-410.
Defever, F. (2006). ‘Functional Fragmentation and the Location of Multinational Firms in the Enlarged
Europe’. Regional Science and Urban Economics, Vol. 36(5): 658-677.
42
Deng, P. (2004). ‘Outward Investment by Chinese MNCs: Motivations and Implications’. Business
Horizons, Vol. 47(3): 8-16.
Deng, P. (2007). ‘Investing for Strategic Resources and Its Rationale: The Case of Outward FDI from
Chinese Companies’. Business Horizons, Vol. 50(1): 71-81.
Devereux, M.P. & Griffith, R. (1998). ‘Taxes and the Location of Production: Evidence from a Panel of
US Multinationals’. Journal of Public Economics, Vol. 68(3): 335-367.
Dunning, J.H. (1981b). International Production and the Multinational Enterprise. London, UK: Allen
and Unwin.
Dunning, J.H. (1993). Multinational Enterprises and the Global Economy. Wokingham, UK: AddisonWesley.
Dunning, J.H. (1998). ‘Location and the Multinational Enterprise: A Neglected Factor?’ Journal of
International Business Studies, Vol. 29(1): 45-66.
Dunning, J.H. (2001). ‘The Eclectic (OLI) paradigm of International Production: Past, Present and
Future’. International Journal of the Economics of Business, Vol. 8(2): 173-190.
Dunning, J.H. (2003). ‘Determinants of Foreign Direct Investment: Globalization-Induced Changes and
the Role of Policies’. Proceedings of Annual World Bank Conference on Development Economics.
Washington, DC: World Bank, pp. 279-290.
Dunning, J.H. & Lundan, S.M. (2008). Multinational Enterprises and the Global Economy. Wokingham,
UK: Addison-Wesley.
Ember, M., Ember, C.R. & Skoggard, I. (eds.) (2005). Encyclopedia of Diasporas, Immigrant and
Refugee Cultures Around the World. New York, NY: Springer.
Erdener, C. & Shapiro, D.M. (2005). ‘The Internationalization of Chinese Family Enterprises and
Dunning’s Eclectic MNE Paradigm’. Management and Organization Review, Vol. 1(3): 411-436.
Field, A. (2005). Discovering Statistics Using SPSS. London, UK: SAGE.
Gould, D.M. (1994). ‘Immigrant Links to the Home Country: Empirical Implications for US Bilateral
Trade Flows’. The Review of Economics and Statistics, Vol. 76(2): 302-316.
Greene, W.H. (2008). Econometric Analysis. Upper Saddle River, NJ: Pearson/Prentice Hall.
Greif, A. (1993). ‘Contract Enforceability and Economic Institutions in Early Trade: the Maghribi
Traders’. American Economic Review, Vol. 83(3): 525-548.
Gugler, P. & Boie, B. (2008). ‘The Emergence of Chinese FDI: Determinants and Strategies of Chinese
MNEs’. Paper presented at the conference: Emerging Multinationals: Outward Foreign Direct
Investment from Emerging and Developing Economies. Copenhagen, Denmark: Copenhagen
Business School.
Harris, C.D. (1954). ‘The Market as a Factor in the Localization of Industry in the United States’.
Annals of the Association of American Geographers, Vol. 44(4): 315-348.
43
Hausman, J. & McFadden, D. (1984). ‘Specification Tests for the Multinomial Logit Model’,
Econometrica, Vol. 52(5): 1219-1240.
Hensher, D.A. & Johnson, L.W. (1981). Applied Discrete-Choice Modeling. New York, NY: Wiley.
Hwang, E.R. (1987). ‘Face and Favor: The Chinese Power Game’. American Journal of Sociology, Vol.
92(4): 944-974.
Johanson, J. & Vahlne, J.E. (1977). ‘The Internationalization Process of the Firm: A Model of
Knowledge Development and Increasing Foreign Market Commitments’. Journal of International
Business Studies, Vol. 8(1): 23-32.
Johanson, J. & Vahlne, J.E. (2009). ‘The Uppsala Internationalization Process Model Revisited : From
Liability of Foreignness to Liability of Outsidership’. Journal of International Business Studies, Vol.
40(9): 1411-1431.
Johanson, J. & Wiedersheim-Paul, F. (1975). ‘The Internationalization of the Firm: Four Swedish
Cases’. Journal of Management Studies, Vol. 12(3): 305-322.
Kao, J. (1993). ‘The World-Wide Web of Chinese Business’. Harvard Business Review, Vol. 71(2): 2436.
Karim, M.B. (ed.) (1986). The Green Revolution: An International Bibliography. New York, NY:
Greenwood Press.
Knapp, T., White, N. & Clark, D. (2001). ‘A Nested Logit Approach to Household Mobility’. Journal of
Regional Science, Vol. 41(1): 1-22.
Lall, S. & Albalajedo, M. (2004). ‘China’s Competitive Performance: A Threat to East Asian
Manufactured Exports?’ World Development, Vol. 32(9): 1441-1466.
Lau, H.-F. (2003). ‘Industry Evolution and Internationalization Processes of Firms from a Newly
Industrialized Economy’. Journal of Business Research, Vol. 56(10): 847-852.
Lecraw, D.J. (1977). ‘Direct Investment by Firms from Less Developed Countries’. Oxford Economic
Papers, Vol. 29(3): 442-457.
Li, M. (2005). ‘Chinese in Europe’, in Ember, M., Ember, C.R. & Skoggard, I. (eds.), Encyclopedia of
Diasporas, Immigrant and Refugee Cultures Around the World. New York, NY: Springer, pp. 656663.
Lipsey, R.E. & Sjöholm, F. (2005). ‘The Impact of Inward FDI on Host Countries: Why Such Different
Answers?’, in: Moran, T.H., Graham, E.M. & Blomström, M. (eds.), Does Foreign Direct Investment
Promote Development? Washington, DC: Institute for International Economics and Centre for
Global Development, pp. 23-44.
Live, Y.S. (1998). ‘The Chinese Community in France: Immigration, Economic Activity, Cultural
Organization and Representations’, in Benton, G. & Pieke, F.N. (eds.), The Chinese in Europe.
Houndmills, UK: Macmillan, pp. 96-124.
44
Luo, Y. (1997). ‘Guanxi: Principles, Philosophies, and Implications’. Human Systems Management,
Vol. 16(1): 43-51.
Matthews, J.A. (2002). Dragon Multinational: A New Model for Global Growth. Oxford, UK: Oxford
University Press.
Mathieu, E. (2006). ‘Investments from Large Developing Economies in France and Europe’. IFA
Research Papers, 2006/11.
McFadden, D. (1974). ‘Conditional Logit Analysis of Quantitative Choice Behavior’, in: Zarembka, P.
(ed.), Frontier in Econometrics. New York, NY: Academic Press, pp. 105-142.
McFadden, D. & Train, K. (2000). ‘Mixed MNL Models of Discrete Response’. Journal of Applied
Economics, Vol. 15(5): 447-470.
McFadden, D. (2001). ‘Economic Choices’. American Economic Review, Vol. 91(3): 351-378.
Milelli, C. & Hay, F. (2008). ‘Characteristics and Impacts of the Arrival of Chinese and Indian Firms in
Europe: First Evidence’. Paper presented at the conference: Emerging Multinationals: Outward
Foreign Direct Investment from Emerging and Developing Economies. Copenhagen, Denmark:
Copenhagen Business School.
Moon, H.-C. & Roehl, T.W. (2001). ‘Unconventional Foreign Direct Investment and the Imbalance
Theory’. International Business Review, Vol. 10(2): 197-215.
Morck, R., Yeung, B. & Zhao, M. (2008), ‘Perspectives on China’s Outward Foreign Direct Investment’.
Journal of International Business Studies, Vol. 39(3): 337-350.
OECD (Organization for Economic Co-Operation and Development) (1996), OECD Benchmark
Definition of Foreign Direct Investment. Paris, France: OECD.
Özden, C. & Schiff, M. (eds.) (2007). International Migration Policy and Economic Development:
Studies Across the Globe. Washington, DC: World Bank (Chapter 1: 17-58).
Park, S.H. & Luo, Y. (2001). ‘Guanxi and Organizational Dynamics: Organizational Networking in
Chinese Firms’. Strategic Management Journal, Vol. 22(5): 455-477.
Parsons, C., Skeldon, R., Walmsley, T. & Winters, A. (2005). ‘Quantifying the International Bilateral
Movements of Migrants’. Sussex University, DRC Working Paper, No. WP-T13.
Poston, D.L. & Yu, M.Y. (1990). ‘The Distribution of the Overseas Chinese in the Contemporary
World’. International Migration Review, Vol. 24(3): 480-508.
Poston, D.L., Mao, M.X. & Yu, M.Y. (1994). ‘The Global Distribution of the Overseas Chinese Abroad
Around 1990’. Population and Development Review, Vol. 20(3): 631-645.
Prime, P.B. (2009), ‘China and India Enter Global Markets: A Review of Comparative Economic
Development and Future Prospects’. Eurasian Geography and Economics, Vol. 50(6): 621-642.
Rauch, J.E. & Casella, A. (1998). ‘Overcoming International Barriers to International Resource
Allocation: Prices and Group Ties’. The Economic Journal, Vol. 113(484): 21-42.
45
Rauch, J.E. & Trindade, V. (2002). ‘Ethnic Networks in International Trade’. The Review of Economics
and Statistics, Vol. 84(1): 116-130.
Redding, G. (1995). ‘Overseas Chinese Networks: Understanding the Enigma’. Long Range Planning,
Vol. 28(1): 61-69.
Redding, G. & Ng, M. (1982). ‘The Role of Face in the Organizational Perceptions of Chinese
Managers’. Organizational Studies, Vol. 3(3): 201-219.
Romer, P.M. (1993). ‘Idea Gaps and Object Gaps in Economic Development’. Journal of Monetary
Economics, Vol. 32(3): 543-573.
Standifird, S.S. & Marshall, R.S. (2000). ‘The Transaction Cost Advantage of Guanxi-Based Business
Practices’. Journal of World Business, Vol. 35(1): 21-42.
Sung, Y.-W. (1996). ‘Chinese Outward Investment in Hong Kong: Trends, Prospects, and Policy
Implications’. OECD Development Centre Working Papers, 113.
Taylor, R. (2002). ‘Globalization Strategies of Chinese Companies: Current Developments and Future
Prospects’. Asian Business and Management, Vol. 1(2): 209-225.
Thunø, M. (2001). ‘Reaching Out and Incorporating Chinese Overseas: The Trans-Territorial Scope of
the PRC by the End of the 20th Century’. The China Quarterly, No. 168: 910-929.
Tong, S.Y. (2005). ‘Ethnic Networks in FDI and the Impact of Institutional Development’. Review of
Development Economics, Vol. 9(4): 563-580.
Train, K.E. (2003). Discrete Choice Models with Simulation. Cambridge, UK: Cambridge University
Press.
UNCTAD (United Nations Conference on Trade and Development) (2003), World Investment Report
2003 – FDI Policies for Development: National and International Perspectives. New York, NY:
United Nations.
UNCTAD (United Nations Conference on Trade and Development) (2006), World Investment Report
2006 – FDI from Developing and Transition Countries: Implications for Development. New York,
NY: United Nations.
UNCTAD (United Nations Conference on Trade and Development) (2009), World Investment Report
2009 – Transnational Corporations, Agricultural Production and Development. New York, NY:
United Nations.
Van Marrewijk, C. (2002). International Trade & The World Economy. Oxford, UK: Oxford University
Press.
Weidenbaum, M. & Hughes, S. (1996). The Bamboo Network: How Expatriate Chinese Entrepreneurs
Are Creating a New Economic Superpower in Asia. New York, NY: The Free Press.
Wells, L.T. (1983). Third World Multinationals: The Rise of Foreign Investments from Developing
Counties. Cambridge, MA: MIT Press.
46
Winters, L.A. & Yusuf, S. (2007), ‘Introduction: Dancing with Giants’, in Winters, L.A. & Yusuf, S. (eds.),
Dancing with Giants: China, India and the Global Economy. Washington, DC/Singapore: World
Bank and the International Institute for Policy Studies, pp. 1-34.
World Bank (2010). ‘World Development Indicators & Global Development Finance’ (database),
Washington, DC: World Bank, 2010, <http://databank.worldbank.org>.
Wu, X. & Strange, R. (2000). ‘The Location of Foreign Insurance Companies in China’. International
Business Review, Vol. 9(3): 383-398.
Yang, M.M. (1994). Gifts, Favors and Banquets: The Art of Social Relationships in China. New York,
NY: Cornell University Press.
Young, S., Hood, N. & Peters, E. (1994). ‘Multinational Enterprises and Regional Economic
Development’. Regional Studies, Vol. 28(7): 657-677.
Zhan, J.X. (1995). ‘Transnationalization and Outward Investment: The Case of Chinese Firms’.
Transnational Corporations, Vol. 4(3): 67-100.
Zhang, Y. (2003). China’s Emerging Global Businesses: Political Economy and Institutional
Investigations. Basingstoke, UK: Palgrave Macmillan.
47
APPENDIX
Appendix A.1: Greenfield Investments in Europe in the Years 1997-2008 (China vs. Total)
Year
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
total
Chinese Greenfield Investments
5
7
9
11
8
16
21
51
54
60
65
100
407
Total Greenfield Investments
1708
1416
1435
1672
1406
1355
1249
1962
2144
2480
3373
3415
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
Austria
Belgium
Bulgaria
Switzerland
Cyprus
Czech Republic
Germany
Region Code
AT1
AT2
AT3
BE1
BE2
BE3
BG1
BG2
CH
CY
CZ
DE1
DE2
DE3
DE4
DE5
DE6
DE7
DE8
DE9
DEA
DEB
DEC
Region Name
Ostösterreich
Südösterreich
Westösterreich
Région de Bruxelles-Capitale/
Brussels Hoofdstedelijk Gewest
Vlaams Gewest
Région Wallonne
Severna Bulgaria
Yuzhna Bulgaria
Switzerland
Cyprus
Czech Republic
Baden-Württemberg
Bayern
Berlin
Brandenburg
Bremen
Hamburg
Hessen
Mecklenburg-Vorpommern
Niedersachsen
Nordrhein-Westfalen
Rheinland-Pfalz
Saarland
China
0
1
0
6
Total
304
50
120
268
14
7
1
3
4
2
2
3
9
4
0
32
9
12
1
2
32
0
0
600
240
181
187
688
11
844
179
443
184
70
32
132
428
35
49
360
31
17
48
Denmark
Estonia
Spain
Finland
France
Greece
Hungary
Ireland
Italy
Lithuania
Luxembourg
Latvia
Malta
Netherlands
Norway
Poland
DED
DEE
DEF
DEG
DK
EE
ES1
ES2
ES3
ES4
ES5
ES6
FI
FR1
FR2
FR3
FR4
FR5
FR6
FR7
FR8
GR1
GR2
GR3
GR4
HU1
HU2
HU3
IE
ITC
ITD
ITE
ITF
ITG
LT
LU
LV
MT
NL1
NL2
NL3
NL4
NO
PL1
PL2
PL3
PL4
PL5
PL6
Sachsen
Sachsen-Anhalt
Schleswig-Holstein
Thüringen
Denmark
Estonia
Noroeste
Noreste
Comunidad de Madrid
Centro (ES)
Este
Sur
Finland
Île de France
Bassin Parisien
Nord - Pas-de-Calais
Est
Ouest
Sud-Ouest
Centre-Est
Méditerranée
Voreia Ellada
Kentriki Ellada
Attiki
Nisia Aigaiou, Kriti
Közép-Magyarország
Dunántúl
Alföld és Észak
Ireland
Nord Ovest
Nord Est
Centro (IT)
Sud (IT)
Isole (IT)
Lithuania
Luxembourg
Latvia
Malta
Noord-Nederland
Oost-Nederland
West-Nederland
Zuid-Nederland
Norway
Centralny
Poludniowy
Wschodni
Pólnocno-Zachodni
Poludniowo-Zachodni
Pólnocny
0
0
1
2
14
0
0
0
3
1
6
2
1
17
3
0
3
3
3
7
3
1
0
0
0
9
4
1
1
10
2
0
0
0
0
0
0
0
0
0
12
3
0
1
0
0
0
1
4
70
76
26
46
454
160
51
92
441
71
628
86
179
1291
352
210
317
185
251
385
367
22
3
54
0
438
313
228
795
344
49
100
26
18
182
66
154
10
50
56
593
191
84
383
207
67
188
210
82
49
Portugal
Romania
Sweden
Slovenia
Slovakia
United Kingdom
PT
RO
SE
SI
SK
UKC
UKD
UKE
UKF
UKG
UKH
UKI
UKJ
UKK
UKL
UKM
UKN
Portugal
Romania
Sweden
Slovenia
Slovakia
North East
North West (including Merseyside)
Yorkshire and The Humber
East Midlands
West Midlands
Eastern
London
South East
South West
Wales
Scotland
Northern Ireland
1
3
18
0
1
29
10
8
3
9
5
34
39
3
7
2
2
260
696
748
72
376
231
303
207
171
366
194
2060
827
202
221
416
161
Appendix A.3: Chinese Greenfield Investments in Europe in the Period 1997-2008 (subdivided
by economic function)
Economic Function
Contact Centre
Education & Training
Headquarters
Logistics
Manufacturing
Research & Development
Sales & Marketing
Shared Services Centre
Testing & Servicing
total
Chinese Greenfield Investments
2
2
49
24
39
26
200
4
5
351
50
Appendix A.4: Descriptive Statistics
Variable
A Market Potential (log)
B Airport Dummy
C Seaport Dummy
D Wage per Hour (log)
E Unemployment Rate
F University Education
G Social Charges Rate
H Corporate Tax Rate
I
Localization Advantages (log)
J
Urbanization Advantages (log)
K Total Greenfield Investments (log)
L Chinese Greenfield Investments (log)
M Migrant Stock (Mainland China) (log)
N Migrant Stock (Hong Kong + Macau) (log)
O Migrant Stock (Mainland China + Hong Kong + Macau) (log)
P Migrant Stock (Taiwan) (log)
Q Migrant Stock (Total) (log)
R Size Ethnic Chinese Community in 1955 (log)
S Size Ethnic Chinese Community in 1980 (log)
T Size Ethnic Chinese Community in 1990 (log)
U Size Ethnic Chinese Community in 2000 (log)
Obs.
31239
31239
31239
29804
31239
31239
31239
31239
30373
31239
31239
31239
31239
31239
31239
31239
31239
31239
31239
31239
31239
Mean
9.47
0.20
0.11
2.64
7.99
26.10
22.69
29.07
4.01
7.56
3.97
0.59
9.26
6.41
9.50
5.84
9.56
4.46
7.96
8.27
9.90
Std. Dev.
0.45
0.40
0.32
0.77
5.42
8.10
4.83
7.32
1.31
0.77
1.29
0.79
2.21
3.96
2.35
3.09
2.35
3.13
3.75
4.00
2.86
Min.
8.13
0
0
0
0
8.85
6.92
10
0
4.99
0
0
3.14
0
3.18
0
3.22
0
0
0
2.30
Max.
10.17
1.00
1.00
6.06
24.90
51.52
31.12
40.22
7.81
9.14
6.90
3.37
11.55
11.50
11.92
9.06
11.96
8.10
12.35
12.43
12.43
51
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
U
*
**
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
1.00
0.24
0.22
0.52
-0.14
0.21
0.18
0.48
0.22
0.23
0.38
0.31
0.61
0.61
0.61
0.68
0.61
0.66
0.60
0.61
0.65
1.00
0.09
0.30
-0.22
0.28
0.10
0.08
0.23
0.39
0.46
0.26
0.20
0.05
0.17
0.10
0.17
0.11
0.15
0.19
0.14
1.00
0.21
-0.01
0.12
0.23
0.26
0.01
0.06
0.11
0.16
0.17
0.05
0.15
0.06
0.15
0.18
0.17
0.19
0.15
1.00
-0.43
0.43
0.15
0.49
-0.08
0.13
0.18
0.32
0.62
0.54
0.62
0.55
0.63
0.63
0.64
0.64
0.52
1.00
-0.30
0.01
0.04
-0.04
-0.04
-0.21
-0.29
-0.13
-0.19
-0.17
-0.13
-0.17
-0.19
-0.16
-0.15
-0.03
1.00
-0.05
0.06
-0.04
0.02
0.38
0.35
0.22
0.18
0.25
0.13
0.25
0.25
0.26
0.26
0.16
1.00
0.30
0.19
0.28
0.15
0.07
0.35
0.10
0.28
0.15
0.29
0.24
0.21
0.28
0.27
1.00
0.04
0.07
-0.13
0.05
0.66
0.51
0.64
0.52
0.64
0.67
0.62
0.64
0.52
1.00
0.62
0.40
0.15
0.22
0.08
0.20
0.09
0.20
0.15
0.17
0.21
0.25
1.00
0.57
0.27
0.34
0.12
0.33
0.11
0.32
0.27
0.32
0.38
0.42
1.00
0.50
0.13
0.09
0.15
0.11
0.15
0.17
0.21
0.22
0.28
1.00
0.26
0.30
0.29
0.31
0.29
0.35
0.34
0.34
0.29
1.00
0.73
0.99
0.73
0.99
0.85
0.85
0.90
0.88
1.00
0.79
0.93
0.79
0.80
0.76
0.73
0.65
1.00
0.75
0.99
0.87
0.88
0.92
0.82
1.00
0.76
0.80
0.76
0.74
0.69
1.00
0.87
0.88
0.92
0.88
1.00
0.93
0.93
0.80
1.00
0.98
0.89
1.00
0.91
1.00
It is referred to Appendix A.3 for a list of all variables.
Observations: 31,239.
52
Appendix A.6: Expected Signs (control variables)
Control Variables
Market 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)
Expected Sign
+
+
+
?
+
+
+
+
+
Appendix A.7: Wald Test (to test for the equality of the coefficients)
Coefficient 1
mchin (0.27)
mchin (0.27)
mchin (0.27)
mchin (0.27)
mhkmc (0.09)
mhkmc (0.09)
mhkmc (0.09)
mchhm (0.27)
mchhm (0.27)
mtwan (0.15)
Coefficient 2
mhkmc (0.09)
mchhm (0.27)
mtwan (0.15)
mchtot (0.27)
mchhm (0.27)
mtwan (0.15)
mchtot (0.27)
mtwan (0.15)
mchtot (0.27)
mchtot (0.27)
Appendix A.7B
eth55 (0.09)
eth55 (0.09)
eth55 (0.09)
eth80 (0.08)
eth80 (0.08)
eth90 (0.11)
eth80 (0.08)
eth90 (0.11)
eth00 (0.16)
eth90 (0.11)
eth00 (0.16)
eth00 (0.16)
Appendix A.7C
mchin (0.33)
mchin (0.33)
mhkmc (0.11)
mhkmc (0.11)
mchtot (0.32)
mchtot (0.32)
Appendix A.7A
chi2(1)
8.90
0.11
0.53
0.01
14.09
3.26
14.56
4.58
8.91
4.95
prob>chi2
0.00
0.74
0.06
0.92
0.00
0.07
0.00
0.03
0.00
0.03
0.37
0.35
1.50
6.95
3.15
1.00
0.54
0.55
0.22
0.00
0.08
0.32
9.33
0.07
15.27
0.00
0.78
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.
53
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.
47
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.
54
Appendix A.9: Hausman-McFadden Test
(to test the assumption of the independence of irrelevant alternatives)
Omitted
Variable
AT1
AT2
AT3
BE1
BE2
BE3
BG1
BG2
CH
CY
CZ
DE1
DE2
DE3
DE4
DE5
DE6
DE7
DE8
DE9
DEA
DEB
DEC
DED
DEE
DEF
DEG
DK
EE
ES1
P-value
1.00
1.00
0.89
1.00
1.00
0.01
1.00
1.00
1.00
0.99
0.99
0.99
1.00
0.99
1.00
0.99
0.00
1.00
1.00
1.00
1.00
0.00
1.00
0.99
1.00
0.56
0.85
1.00
1.00
1.00
Omitted
Variable
ES2
ES3
ES4
ES5
ES6
FI
FR1
FR2
FR3
FR4
FR5
FR6
FR7
FR8
GR1
GR2
GR3
GR4
HU1
HU2
HU3
IE
ITC
ITD
ITE
ITF
ITG
LT
LU
LV
P-value
1.00
1.00
1.00
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.20
1.00
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.85
1.00
0.82
1.00
Omitted
Variable
MT
NL1
NL2
NL3
NL4
NO
PL1
PL2
PL3
PL4
PL5
PL6
PT
RO
SE
SI
SK
UKC
UKD
UKE
UKF
UKG
UKH
UKI
UKJ
UKK
UKL
UKM
UKN
P-value
0.04
0.99
1.00
0.01
0.05
0.99
1.00
1.00
1.00
0.06
1.00
1.00
1.00
1.00
0.50
0.24
1.00
0.72
1.00
0.99
1.00
0.71
0.00
0.95
1.00
0.99
1.00
1.00
1.00
* Hausman-McFadden test for model specification (2) – Table 2 (Chapter 4)
55