The Discrepancies Of Determinants Between Cross-Border M&A And Greenfield Fdi.doc

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UNIVERSITY OF ECONOMICS ERASMUS UNVERSITY ROTTERDAM
HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIES
VIETNAM THE NETHERLANDS
VIETNAM – THE NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
THE DISCREPANCIES OF DETERMINANTS
BETWEEN CROSS-BORDER M&A AND
GREENFIELD FDI: EVIDENCE IN EMERGING
COUNTRIES
BY
TA THI THANH TRA
MASTER OF ARTS IN DEVELOPMENT ECONOMICS

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES
HO CHI MINH CITY THE HAGUE
VIETNAM THE NETHERLANDS
VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
THE DISCREPANCIES OF DETERMINANTS
BETWEEN CROSS-BORDER M&A AND
GREENFIELD FDI: EVIDENCE IN EMERGING
COUNTRIES
A thesis submitted in partial fulfilment of the requirements for the degree of MASTER
OF ARTS IN DEVELOPMENT ECONOMICS By
TA THI THANH TRA
Academic Supervisor:
DR. VU VIET QUANG

DECLARATION
I declare that the thesis entitled “The discrepancies of determinants between
cross-border M&A and Greenfield FDI: evidence in emerging countries” has been
absolutely conducted by myself under the supervision and guidance of Dr. Vu Viet
Quang from The University of Economics Ho Chi Minh City. I commit that the whole
interpretations in the research are based on my academic knowledge and the
understanding of literature review presented following the references and this thesis
has not been submitted to any graduate program. I take full responsibility for the
concepts, contentment, and results of this study.
TA THI THANH TRA

ACKNOWLEDGEMENT
This thesis reflects not only my work in current times, this is also a milestone
that marks one of my most amazing journey. When I were freshman, I have been
introduced this master program. Since then, I have made plan to attend this course
because of its unique opportunities and advantages. Throughout two years I have
learned many different economic fields such as finance, development economics, and
international trade. This thesis presents the lessons learned in researching one of those
aspect: two entry modes of foreign direct investment namely Greenfield investment
and Cross-border M&A. This thesis is also sweet fruit of dozens of lucky encounters I
have experienced from many remarkable individuals who I wish to acknowledge.
I would first like to express my deep gratitude to my supervisor, Dr. Vu Viet
Quang for his wholehearted guidance, enthusiastic support and unceasing encouragement.
He has mentored me since I have just got an idea and no direction. He is the one who
supported me all the time without hesitation. His insightful knowledge, valuable
suggestions help me to brainstorm concept, expand the literature review, collect data and
finish this research. Additionally, his optimism and cheerfulness is what I will remember
most, the time spent working with him is truly precious and memorable.
Besides, I am always grateful to all the lecturers and the staff of Vietnam –
Netherlands Program. This is my biggest fortune to have the most conscientiously
supporters, they always take me in touch and support when I have any difficulty. I owe
my deepest gratitude to Dr. Nam, Dr. Thuy, who suppose me with all their resources.
Even this New Year holiday, they spend precious time to help me unconditionally.
I am indebted to my precious teammates for their sincerity and willingness to help.
Although we are very different in personality, interests, and even purpose while attending
this course, fate let us meet together and give us precious friendship in real life. It is an
honor for me to encounter and be friend with all of you. Additionally, I would like to give
my personal thanks to Mr. Khang, Mr. Tam, who help me a lot while I am confusing and
struggling with this research. I wholeheartedly wish that we all can

graduate on time and the friendship is forever. I wish you all the best and hope your
dream come true.
Last but not least, I would like to spend my inexpressible gratefulness to my
beloved family who always believe me and give me freedom to do whatever I have
decided. They have cherished with me every great moment and supported me all the
time. This thesis would not have been possible without their encouragement.

ABBREVIATION
Cross-border M&A: Cross-border Mergers and Acquisitions
FDI: Foreign direct investment
Greenfield FDI: Greenfield Foreign Direct Investment
IIAs: International Investment Agreements
ODA: Official development assistance
OLI – theory: Ownership, Location, and Internalization theory
RBV: Resource-based view
S&P500: The Standard & Poor's 500
TCE: Transaction Cost Economics

ABSTRACT
Cross-border M&A and Greenfield FDI have been a common strategy when a
firm intends to participate in new markets. Those two strategies are FDI mode choice
and represent a notable alternative for the expansion plan. Both Greenfield FDI and
Cross-border M&A play an important role in economic growth in the host country.
Therefore, this empirical research works on the determinants of home firm decision
based on two main respects. This study estimates the determinants affective those two
decisions. Then, this research also provides the empirical evidence of the
discrepancies of those determinants affecting Cross-border M&A and Greenfield FDI.
The observations are collected from emerging countries as host firms and use United
States firms as home firms. This research presents four results. Firstly, host countries’
macroeconomic determinants between Cross-border M&A and Greenfield FDI have
the similar effect. Secondly, an enhancement of IPR protection law in the host
countries will increase inward Greenfield FDI, while SHR protection law supports
cross-border M&A decision. Thirdly, firm level determinants have a strong impact on
Cross-border M&A decisions. Finally, macroeconomic determinants have a positive
effect on Greenfield FDI decisions.
Key words: Cross-border M&A, Greenfield FDI, emerging countries.
JEL Classification: F20, F21, F23
1

TABLE OF CONTENTS
LIST OF TABLES ..................................................................................................................... 3
CHAPTER 1: OVERVIEW OF RESEARCH ........................................................................... 4
1.1. Introduction: ................................................................................................................ 4
1.1.1. World Investment context: .................................................................................. 4
1.1.2. Problem statements: ............................................................................................. 5
1.2. Research objectives: .................................................................................................... 7
1.3. Research questions: ..................................................................................................... 7
1.4. The importance of the study: ....................................................................................... 7
1.5. Structure of research: .................................................................................................. 8
CHAPTER 2: LITERATURE REVIEWS ................................................................................. 9
2.1. Theoretical Review: .................................................................................................... 9
2.1.1. Cross-border M&A and Greenfield FDI as choice to entry in a foreign market . 9
2.1.2. Firm level and industry level factors affecting the decision of a mode of entry 11
2.1.3. Country level factors affecting the decisions of mode of entry ......................... 12
2.1.4. Research needs on cross-border M&As as a mode of entry .............................. 13
2.2. Empirical Review: ..................................................................................................... 14
2.2.1. Macroeconomic and firm level determinants: ................................................... 14
2.2.2. Legal environment in host-country: ................................................................... 17
2.3. Research hypotheses: ................................................................................................ 18
CHAPTER 3: DATA AND METHODOLOGY ..................................................................... 20
3.1. Data collection........................................................................................................... 20
3.2. Empirical model ........................................................................................................ 21
CHAPTER 4: RESULTS AND CONCLUSIONS .................................................................. 25
4.1. Summary statistics: ................................................................................................... 25
4.1.1. Data description: ................................................................................................ 25
4.1.2. The correlation matrix ....................................................................................... 28
4.1.3. Empirical results: ............................................................................................... 29
CHAPTER 5: CONCLUSION ................................................................................................ 36
5.1 Main findings: ........................................................................................................... 36
5.2 Policy implications: ................................................................................................... 38
5.3 Limitations and further research recommendation: .................................................. 39
REFERENCES ........................................................................................................................ 40
2

LIST OF TABLES
Table 4.1: Summary statistics..................................................................................................25
Table 4.2: Summary statistics of entry mode choice via regions ............................................27
Table 4.3: The correlation matrix ............................................................................................28
Table 4.4: The determinants of Cross-border M&A and Greenfield FDI ...............................29
Table 4.5: The estimated results for the value of Cross-border M&A and Greenfield FDI
using Seemingly Unrelated Regression (SUR)........................................................................33
3

CHAPTER 1: OVERVIEW OF RESEARCH
1.1. Introduction:
1.1.1. World Investment context:
In 2017, global investment is slightly getting better with cautiously optimism
forecast from UNCTAD report. FDI will be increased by higher economic growth
projections around the world, the report expects the value of FDI will reach nearly
$1.8 trillion in 2017. However, this report show the ineffective legal policy and
geopolitical risks are significant factors that practically damage the recovery. In
contrast, tax policy represented by corporate tax mostly affect Cross-border M&A
decision. In 2006, FDI prospects to emerging countries are generally positive
excluding those countries in Latin America and the Caribbean. Emerging countries as
a whole are predict to get about 10 percent of FDI total value in 2017. This
assumption bases on considerable development in emerging countries in Asia and
Africa, where investors are more confidence to invest due to a better view in major
economies through oil prices and regional integration. In contrast, Latin America and
the Caribbean still face uncertain macroeconomic and policy outlook, especially
terrorism and wars which lead to investor’s hesitance. Therefore, FDI flows to
emerging economies would be continue their recover with a steadily speed, while
those flows to developed economies would remain unchanged in 2017.
FDI flows come to growth momentum period in 2016 about 2 percent after a
strong boost in 2015. Noteworthy is the fall of 14 percent from emerging economies.
However, they still remain the largest and the most important external source of financial
capital for emerging countries – compared with investment portfolio, remittances and
ODA. In Asia emerging markets, FDI flows fell the first time in five years by nearly 15
percent to $443 billion in 2016. In those market in Africa, FDI flows decrease
continuously 3 percent from 2015 to $59 billion. In Latin America and the Caribbean,
trend in FDI flows speed up to 14 percent to $142 billion, based on weak commodity
prices and pressures on exports. In conclusion, FDI in lower middle income economies
remained delicate with a decline of 13 percent in 2016, while middle income countries
fall 6 percent. In contrast, upper middle income countries stay stable at $24
4

billion. In developed countries, however, FDI flows increase at least 5 percent to $1
trillion in 2016.
Investment policymaking becomes more and more complex over time.
Governments appear to move towards sustainable development and globalization so
the policies are more challenging and multifaceted, so this reduces the predictability of
policies for speculators. Therefore, a criterion system gets international support and
focuses on sustainability and inclusiveness that may lower uncertainty and improve
investment stability. In recently years, investments deals mostly focus on investment
promotion, facilitation and liberalization. Foreign investors receive more and more fair
treatment from governments while entering markets. Compared to 1900s, policy
makers set new restrictions and regulations more than 20 percent, which mainly aim at
manifest the context of mergers and acquisitions controls foreign investors. At least
108 countries launch specific investment laws the same as IIAs. These laws share
basic components in prologue, definitions, entrance and treatment of takeovers,
investment incentives and debate agreement, so they should go hand in hand. New
challenge for government in target countries is digital development because of its
important impact on global patterns of investment.
1.1.2. Problem statements:
In most countries, foreign investment is considered as a key indicator to
accelerating the rate of economic growth, notably into emerging countries. FDI flows
to these countries rise rapidly from the 1990s and have become by far the single
largest component of their net capital inflows, especially the services sector. In the
past decades, FDI flows to emerging markets have been increased dramatically, owing
in large part to a sharp increase in flows to Asia. In FDI flows over the years, United
States outflows investments take a key role, expressed through the proportion of over
20% in the 2008-2016 period. Even though this proportion is reducing, United States
remains a major participant in foreign investment (Untacd, 2017).
United States firms invest their capital to foreign countries via two primary modes,
Greenfield FDI and Cross-border M&A. According to World investment report 2017
from United Nations Conference on Trade and Development, Cross-border M&A volume
from United States capital in 2016 stands at $77,948.5 mil, sudden decrease of
5

39.04% over 2015, Greenfield FDI, by the other hand, reaches to $155,152.7 mil, up
30% compared to 2015. In the 2006-2016 period, Cross-border M&A changes
irregularly, while investments through Greenfield FDI have been more balanced and
changed little over the years. Overall, total investment via Greenfield FDI is
practically higher in both quantity and quality compared to Cross-border M&A.
Emerging market is a country that contains several characteristics of a
developed market, however, it does not meet standards to be a developed market
(MCSI, 2014). This means that those countries would turn to developed markets in the
near future or were in the past lately. The economies of China and India are
considered to be the largest emerging markets (IMF, 2016). By emphasizing the
variables of World Bank economic category, Bremmer (2014) defines an emerging
market or emerging country as “a country where politics matters at least as much as
economics to the markets”. In the end, emerging country or emerging market contains
following characteristics. Firstly, the purchasing power parity (PPP) per capita income
of this country is consisted of the range from 10% to 75% of the average EU per
capita income. Secondly, this country has experienced a brisk economic growth that
has narrowed the income gap with developed countries in the latest decade. Finally,
during this decade, this country has conducted serious institutional transformations
which donated to merge its market more deeply into the world economy. Therefore,
emerging countries appears to be a result of the current globalization. In this case, the
list of emerging market are taken from IMF in 2016.
Emerging countries remain attractive investment destinations for a variety of
reasons, including their large consumer base, a productive workforce, a business
environment that encourages innovation, and their legal protections are getting better and
better. In the 2008-2016 period, both of volume and number of foreign direct investment
to Asia emerging countries increase steadily over the years, especially after the economic
crisis in 2008. In parallel with global trends from 2008 to 2016, the volume of capital
from U.S firms to Asia emerging countries grow gradually. However, their Greenfield
FDI has a higher investment value than Cross-border M&A over the years. Because of
this phenomenon, various research has been done to investigate factors affecting
investment decisions of foreign firms to emerging countries through
6

different periods. However, these trends raise questions about Cross-border M&A and
Greenfield FDI characteristics, which determinants led to the difference between
Greenfield FDI and Cross-border M&A investment decision, even if they are all
modes from FDI? Is there any similarity between these two flows?
1.2. Research objectives:
Although research about both cross-border M&A and Greenfield FDI
determinants have increased in recently, those comparing between these factors are
still generally absent. This research aims at discovering not only the factors affecting
Cross-border M&A and Greenfield FDI from U.S firms to emerging countries but the
comparison between them. By identifying this blank space in the previous literature,
this paper therefore contributes to the current research by estimating these
determinants by two modes of FDI and, consequently, by indicating the heterogeneity
of cross-border M&A and greenfield FDI determinants, which have received little
academic attention until now. This paper investigates whether common determinants
lead to both cross-border M&A and Greenfield FDI decisions and evaluates whether
such factors have difference impact between them. Especially, this research focus on
developing countries from six regions around the world as host countries and United
States firms as home-country investors in the 2008-2016 period. Therefore, this study
aims at supporting a panoramic view of this issue.
1.3. Research questions:
Based on the research objectives, the study focuses on the questions as follows:
 What are the determinants of Cross-border M&A and Greenfield FDI?

 How do these determinants affect the value of Cross-border M&A and
Greenfield FDI?
1.4. The importance of the study:
Various studies have been estimated the Cross-border M&A and Greenfield FDI
determinants, the discrepancies among them are simultaneously analyzed. Moreover, this
is the one of the few studies that take into account the interaction of macro and firm-
specific variables of host countries in order to identify the differences and similarities
among cross-border M&A and Greenfield FDI determinants. The research also
contributes to a lot of studies that have been published in discovering the effect of those
7

FDI flows in developing countries. The empirical results will support the policy
makers in emerging countries while making efforts to attract foreign investments from
developed countries. Not only does the study give governments suggestions on how to
adjust their policies to attract investments, but it also provides host firm with some
recommendations on whether enclitic foreign investments is a right way to follow.
1.5. Structure of research:
The remainder of this paper is organized as follows. In Section 2, the brief
literature review of both theory and empirical research will be presented. In Section 3, the
data and methodology are described. In Section 4, empirical results and explanation will
be gave out. Section 5 discusses new empirical evidence and concludes.
8

CHAPTER 2: LITERATURE REVIEWS
2.1. Theoretical Review:
2.1.1. Cross-border M&A and Greenfield FDI as choice to entry in a
foreign market
There is an extensive literature on Cross-border M&A and Greenfield FDI as a
potential mode of entry into a foreign market such as Andersen (1997), Brouthers and
Brouthers (2000), Kogut and Singh (1988). Greenfield FDI includes the establishment
of owned subsidiaries in new countries so they offer the highest level of internal
resources and technology control at a highest costs as well (Hennart & Park, 1993).
Costs come from not only set up physical facilities but also build the relationships and
networks with distributors, customers, and even government to run effectively and
smoothly in the new markets (Andersson, Johanson, & Vahlne, 1997). Cross-border
M&A allows the home firm to obtain host firm resources such as facilities,
technology, and human resources, so home firm gain access to markets and local
government. Cross-border M&A also give a high control level over capital and
resources, which is less than Greenfield FDI is but more than international alliances
are (Newburry & Zeira, 1997). Cross-border M&A may be initiated to internalize an
activity to reduce or avoid transaction costs.
Those theoretical research also prove that the decision of a Cross-border M&A
or Greenfield FDI as a mode of entry into a foreign market is frequently affected by
three level factors. Firstly, factors at firms level such as international trade experience,
local business, production diversification, internal similarity, and worldwide
investment strategy. Secondly, factors at industry level such as technological power,
advertising power, and sales ability. Finally, factors at country level such as host
country’s market growth, culture differences to avoid risk propensity.
Many researchers have compared Cross-border M&A with Greenfield FDI to
identify factors that affect the FDI entry mode choice. However, the experimental
consequences considering the determinants of them are mixed. Based on equity or non-
equity approaches, theory and research are probably robust, the theoretical foundations of
the determinants of these two entry mode stay weak. The theoretical review prove that
most of perspectives have applied Transaction cost economics theory (TCE) and
9

Ownership – Location – Internalization (OLI) (Shimizu, Hitt, Vaidyanath, & Pisano,
2004). Besides, firm characteristics and their heterogeneous resources and capabilities
have not get enough attention (Madhok, 1997). Therefore, complementary resources of
target firms and international impacts demands more examination in future research on
Cross-border M&A and Greenfield FDI. Home firms should take a careful holistic
sentiment about firm level, industry level and country level factors while entering foreign
market to eliminate information asymmetry (Kogut & Singh, 1988) or liability of
foreignness (Zaheer, 1995). Researchers have employed plenty of theoretical perspectives
to explain the entry mode decision (Andersen, 1997), but the vast majority have approved
the TCE and OLI framework by Dunning (1993) and (Williamson (1975)). However,
some of theoretical researchers have estimated the effects from each of the factors
differently. For example, Brouthers and Brouthers (2000) and (Hennart
& Park, 1993) prove the effect of industry level and country level factors such as high or
low market growth, while Kogut and Singh (1988) found the impact of low cultural
distance between home and host countries. Additionally, other researchers demonstrate
the influences of firm level factors such as the product diversity level by Wilson (1980),
international trade experiment (Harzing, 2002) and local customs knowledge (Barkema
& Vermeulen, 1998).
Besides, the impact of IPR and SHR to Cross-border M&A and Greenfield FDI
have been researched by various theoretical studies. Those studies explore the link
between the legal environment concerning those legal enviroments and FDI entry
mode in emerging countries with mix results. For example, the all of theoretical
studies by Grossman and Helpman (1993), Maskus and Penubarti (1995), Glass and
Saggi (2002), and Lai and Qiu (2003) affirmed that an enhancement in IPR legal
protection laws and SHR legal protection laws of emerging countries leads to the
decrease in FDI inflows, whereas those by Braga, Grepioni, and Desiraju (1998),
Javorcik (2004), and (Branstetter & Saggi, 2011) prove the opposite results.
Therefore, this study will take a careful look at IPR and SHR impact on Cross-border
M&A and Greenfield FDI in the empirical model. The empirical literature review of
these factors will also be considered more in the following section.
10

2.1.2. Firm level and industry level factors affecting the decision of a mode
of entry
Based on investment strategy of home firm, the diversification action impacts
decision of a mode of entry. If home firm strategy is to access complementary utility,
it will focus on the qualities of resources, host firm current resource portfolio, and
even the embeddedness level in the targeted firm. Based on a theoretical framework
from TCE, Hennart and Park (1993) concluded that home firm primarily chooses
Cross-border M&A if its interest in a part of the target assets, because it can focus on
assets which suitable with firm strategy. In contrast, home firm commonly chooses
Greenfield FDI if host firm assets are strictly embedded and distributed, so it probably
take more control of host firm.
Madhok (1997) researches choice of mode of entry based on TCE and OLI
framework. These theories bring up some valuable concerns that the organizational
power framework becomes more suitable with nowadays investment context, which
means future research should take a careful look in industry factors due to the
importance of firm resources. Moreover, (Anand & Delios, 2002) discovered the
fungibility technological power across countries, and the power of brand by using host
firm and industry characteristics also influence choice of a mode of entry. Therefore,
those papers recommend that home firms should have clearly distinguish between
capability-seeking and capability-exploiting acquisitions based on the availability and
importance of the different kinds of potential host firms resources.
The most value resources by investing firms are commonly intangible and
knowledge-based resources. However, it is extremely difficult for home firms while
identifying and managing those assets. In this case, they prefer equity-based modes to
manage intangible assets and R&D and advertising intensity (Delios & Beamish,
1999). Vermeulen and Barkema (2001) use organizational learning perspective to
prove that Cross-border M&A will broaden the firm knowledge-base. While
Greenfield FDI creates a path dependence that eventually produces inertial corporate
governance in new firm, Cross-border M&A expand the firm’s knowledge base and
reduce organizational inertia.
11

In another way, Hennart and Park (1993) employ a multiple theoretical
framework composed of TCE and RBV to research about the kind of advantage
possessed by home firm. They discovered that Japanese firms, those have a strong
technological advantage, prefer Greenfield FDI over Cross-border M&A. This entry
mode support them better ways to transfer the advantage to the foreign facility. The
result can be explained that firm with high and complex technology base get more and
more difficult to transfer that technology to foreign country. Moreover, their training
this to acquired firms will face the same difficulties in human resources, capital and
may be unsuccessful. That is why high-tech firm would rather choose Greenfield FDI,
this is more simple and affordable to train their own staff transferred to the foreign
facility.
Brouthers and Brouthers (2000) investigate the impact of investment size on
the entry choice. They verify that home firms prefer Greenfield FDI with relatively
small investments, whereas Hennart and Reddy (1997) found exactly the opposite
results, they concluded that large firms have more difficult in integrity so they prefer
Greenfield FDI. In contracts, Padmanabhan and Cho (1995) found no relationship
between investment size and entry mode choice. The mode of entry choice can also be
influenced by other firm-level factors, such as time pressure. However, they suggest
that the investor can only choice Cross-border M&A when they has a short amount of
time. In fact, Greenfield FDI process demand more time to moderate.
2.1.3. Country level factors affecting the decisions of mode of entry
First of all, a cultural issue that has significant impact on entry choice is the ability
to combine resources, especially human resources. The phenomenon of combining
different cultures has been examined in the literature by Brouthers and Brouthers (2000),
Hennart and Reddy (1997), and Kogut and Singh (1988). By those researches, cultural
distance is a convenient proxy for calculating firm’s country risk. In fact, cultural distance
declares the discrepancies between home firm and host firm. Therefore, it also shows the
level of the potential investor’s strategic advantages to impose to other countries. For
example, both Child and McGrath (2001) and Kogut and Singh (1988) use a multiple
theoretical framework namely TCE, institutional theory to verify that cultural distance
among home firm and host countries practically affect home
12

firm decision. Moreover, the bigger the gap, the greater the investor's preference for
Greenfield FDI. Home firm specifically emphasized that the primary difficulty of
cultural distance would be reflected by high management costs during the integrating.
In contrast, Brouthers and Brouthers (2000) verify an opposite results with the same
theoretical framework. They confirmed that small cultural distance leads home firm to
choose Greenfield FDI to avoid specific aid of host firm, while large cultural distance
makes home firm decide Cross-border M&A to receive a higher level of legitimacy
from host country.
Moreover, various theoretical research reported mixed results for product
diversification by Barkema and Vermeulen (1998) and multinational experience by
Barkema and Vermeulen (1998); Brouthers (2002); Wilson (1980).
2.1.4. Research needs on cross-border M&As as a mode of entry
Many previous researches accept the benefits of international investment as
given. However, there are only several researches compare Cross-border M&A and
Greenfield FDI determinants. As above discussion, while factors at level country and
industry findings are relatively robust, factors at host firm level findings are more
mixed. For example, the level of global experience calculated by the export ratio has
been found to be associated with both Cross-border M&A and Greenfield FDI from
Brouthers (2002); Barkema and Vermeulen (1998); Kogut and Singh (1988) results.
Host firm has more advantages when firm already has trade experience, so trade
should be take as an influent on the entry mode selection, the similar results has been
proved by Beckman and Haunschild (2002); Agarwal and Ramaswami (1992); and
Anderson and Gatignon (1986). Additionally, this field needs more attention to the
comparison between Cross-border M&A and Greenfield FDI determinants.
Furthermore, although value of the investment is frequently reported as determinants
of Cross-border M&A and Greenfield FDI, it still lack empirical evidence to prove.
Therefore, researchers should pay attention to the value of Cross-border M&A and
Greenfield FDI. Finally, it is not clear whether research findings related to large
multinational enterprises can be extended to small- or medium-sized firms Erramilli
and D'Souza (1993). So firm size should be taken to estimate as elements that affect
Cross-border M&A and Greenfield FDI.
13

2.2. Empirical Review:
2.2.1. Macroeconomic and firm level determinants:
In previous research, many empirical results prove that common macroeconomic
factors of host country have an impact on Cross-border M&A and Greenfield FDI. Even
though those previous studies focus on the factors that affect Greenfield FDI and Cross-
border M&A separately, they both suggest that macroeconomic determinants of host
countries have a strong impact on Cross-border M&A and Greenfield FDI decisions.
Because these are two modes of FDI, the previous results suppose the statement that the
most of host-country macroeconomic factors are similar between the two types of FDI.
Ayers, Lefanowicz, and Robinson (2003) base on a tax policy perspective of 565
taxable cash-for-stock and 370 tax free stock-for-stock acquisitions, their results suggest
that capital gains tax policy plays an important role in corporate acquisitions. One more
suggestion of this paper is that changing capital gains tax ratio adjusts the cost of
corporate acquisitions and thereby may influence the movement of capital via corporate
acquisitions. Huizinga and Voget (2009) also suppose for Ayers et al. (2003) research,
they found that the international tax system influence the organizational results of Cross-
border M&A. For example, emerging countries that oblige low levels of international
taxation are more attractive the parent companies of recently created multinational firms.
Besides, GDP per capita, stock market index between home country and host country
have a positive correlation to number of Cross-border M&A.
Rossi and Volpin (2004) apply a sample containing all Cross-border M&A deals
announced in 49 major countries, announced in the 1990s and completed by the end of
2002, and reported by SDC Platinum - a database from Thomson Financial. This paper
suppose evidence that the volume of Cross-border M&A activity is significantly of
considerable size in countries with better accounting levels and powerful shareholder
protection. In Cross-border M&A deals, target firms typically come from countries with
poorer investor protection, namely emerging countries, suggesting that cross-border
transactions play a governance role by improving the degree of government policy to
protect firms. Erel, Liao, and Weisbach (2012) investigate majority of cross-border
mergers from private firms outside of the United States. By analyzing a sample of 56,978
Cross-border M&A in the 1990-2007 period, their results show that geography,
14

the quality of accounting revelation, and bilateral trade raise the likelihood of mergers and
acquisitions between two countries. Furthermore, this paper contribute evidence that firms
possibly be purchasers if they allocated from countries whose value of stock market is
increasing, whose currency has appreciated lately, and high market-to-book value of firm,
in contrast, firms tend to be targets. In addition, Di Giovanni (2005)make an effort to
answer the question that what roles do macroeconomic and financial variables play in the
Cross-border M&A decision of home-country firms. He uses a large panel data set of
Cross-border M&A deals from 1990 to 1999. Specifically, the size of financial markets
measured by the stock market capitalization to GDP ratio, has a significantly affirmative
association with home-country firms investing overseas.
Javorcik (2004) studies on macroeconomic determinants of host country on
Greenfield FDI via 1405 firms that answered questions regarding their undertaken and
planned investments in Eastern Europe and the former Soviet Union through
Worldscope from 1989 to 1994. The result also shows that countries with a larger
population size attract more Greenfield FDI, which is clarified by the economies of
scale enjoyed in large markets. GDP per capita appears to be negatively affect to
Greenfield FDI attraction because the higher GDP per capita, higher labor cost is.
Corporate taxation and openness to trade appear to have a statistically significant
influence to home country investment decision. Moreover, this paper supposes
evidence that Greenfield FDI are more likely to be tackled by firms without previous
regional experience. Cheng (2006) proves that market transaction situation, industrial
concentration have strong impacts on Greenfield FDI through estimating data from a
2005 mail survey of Taiwanese manufacturing firms engaged in international
operations.
Globerman and Shapiro (2005) study about the determinants of both inward and
outward M&A in a cross-section data of 154 countries in the period 1995-2001. They
suppose that the majority variables which effect on inward and outward M&A are
generally the same that really influence the overall FDI and even FDI modes. However, it
obviously has some differences in the structure of Cross-border M&A and Greenfield
FDI. In particular, the economic growth play an important role of FDI and Greenfield
FDI, but not of the Cross-border M&A flows. In other words, a country that shows a
15

fast economic growth tends to be host, primarily, of foreign investment via the
establishment of new firms; simultaneously, this country encourages its companies to
invest abroad, through Cross-border M&A. Neto, Brandão, and Cerqueira (2010)
studies about macroeconomic determinants of Cross-border M&A and Greenfield
FDI. After collecting and analyzing a panel data of 53 countries from 1996 to 2006,
the results show that size of the economy, degree of openness and the governance
level are generally positively correspond with both inward and outward investment.
Furthermore, the coefficient values are very similar in each group of equations.
However, some differences between the structure of Cross-border M&A, Greenfield
FDI and aggregate FDI.
Head and Ries (2008) develop a model explaining foreign direct investment based on an international
market for corporate control. After controlling for geographic and cultural distance effects between bilateral partners,
FDI depends on origin-country effects (outward effects) and destination country effects (inward effects). The latter
includes a “bid competition” term that reflects characteristics of competing countries. Our model highlights the
importance of taking into account the influence of third-country effects in bilateral estimation, thus echoing the insight
of Anderson and Gatignon (1986) for the application of the gravity equation to bilateral trade.
The empirical results from Nagano (2013) on home firm determinants confirm
that there is no significant correlation between the home firm's stock price and its FDI
decision. Especially, the author finds the empirical evidence to prove this
phenomenon. Although two variables have a positive relationship, the reason for the
opposite statement is that purpose of various Cross-border M&A deals by United
States firms acquiring new additional market and responding to demand in foreign
countries. Therefore, the stock market expects an enhancement in the financial
performance of the host firm through annual revenue or assets.
Those variables are examined by much more authors such as Kogut and Singh
(1988), Hennart and Park (1993), Andersson et al. (1997), Brouthers (2002), Larimo
(2003), Dyck and Zingales (2004), Porta, Lopez‐de‐Silanes, and Shleifer (2006), Comment and Schwert (1995), and
Nagano (2013), who investigated the choice between Greenfield FDI and Cross-border M&A via host-country
macroeconomic
16

determinants. Based on summary the previous studies, this paper will estimate the
discrepancies of macroeconomic factors of host-country between Cross-border M&A
and Greenfield FDI. The variables represents the macroeconomic determinants in this
research conclude per capita income, population size, corporate tax rate, and bilateral
trade, following Nagano (2013). For the results, this paper expected that only
corporate tax rate negatively impacts on both investments, while the remaining
variables have a positive influence.
2.2.2. Legal environment in host-country:
The biggest debate of determinants impacts on cross-border M&A and
Greenfield FDI, however, comes from host-country legal environment. For example,
the theoretical research conclude that a decrease in FDI inflows could be explained by
an enhancement in IPR legal protection laws by Madhok (1997), Glass and Saggi
(2002), Mathew and Mukherjee (2014), whereas Braga et al. (1998), Javorcik (2004),
and Branstetter and Saggi (2011) cites evidence about the opposite result. In additions,
both positively and negatively impacts regarding the relationship between IPR
protection laws and FDI inflows in host countries are studied by Klein (2015),
Branstetter (2017), and Nair‐Reichert and Duncan (2008).
While many existing studies such as Grossman and Helpman (1993), Javorcik
(2004), and Branstetter (2017) confirmed the relationship among IPR protection laws
of host country and its inward FDI entry mode, whereas this relationship between IPR
protection laws and Cross-border M&A does not have. However, SHR protection laws
of host firm have a negligible influence the Greenfield FDI choice of home firms on
the results from the comprehensive literature review by Neary (2009). Meanwhile,
Ahern and Dittmar (2012) and Erel et al. (2012) argue that SHR protection laws affect
Cross-border M&A choice. Therefore, this research assumes that IPR protection law
of host country has positively impact to Greenfield FDI, not Cross-border M&A,
while SHR protection law in host country leads to opposite results. So home firm
likely to choose Cross-border M&A rather than Greenfield FDI when the host country
efficiently commits shareholder rights laws and the home firm prefer to choose
Greenfield FDI rather than Cross-border M&A if the host country sufficiently imposes
intellectual property rights laws.
17

2.3. Research hypotheses:
Based on the research questions, the study has four hypotheses, two for the
determinants of Cross-border M&A and Greenfield FDI and the others for the value of
these two entry modes.
Firstly, this paper hypothesizes that some of the macroeconomic factors applied
in previous research are common, such as by Erel et al. (2012) for Cross-border M&A
decision and Javorcik (2004) for Greenfield FDI. In this present research, we use host
country per capita GDP, population size, corporate tax rate, and bilateral trade as
independent variables which are proven by Javorcik (2004), Huizinga and Voget
(2009), and Ivus (2010). Therefore, this paper hypothesizes that per capita GDP,
population size, and bilateral trade have positive significant impacts on both Cross-
border M&A and Greenfield FDI, except for corporate tax because the relationships of
these independent variables with FDI are all supported in Cross-border M&A and
Greenfield FDI studies, respectively.
Hypothesis 1. Host countries’ macroeconomic determinants between Cross-
border M&A and Greenfield FDI are similar to each other.
Secondly, while many existing studies, which have been reviewed, confirm the
relationship between host-country IPR protection laws and inward FDI, such a
relationship between IPR protection laws and cross-border M&A does not hold. In
contrast, previous studies also verify the relationship between host country SHR
protection laws and Cross-border M&A, while it hardly impacts Greenfield FDI
choice. Therefore, we hypothesize that host country IPR protection law enhances
inward Greenfield FDI but not cross-border M&A, while host country SHR protection
law shows the opposite results.
Hypothesis 2. An enhancement of IPR protection law in the host country will
increase inward Greenfield FDI, while SHR protection law supports cross-border
M&A decision.
Thirdly, the above two hypotheses have not mention host firm factors which
various papers support the positive relationship between host firm's financial situation
and the FDI mode choice. Javorcik (2004) and Branstetter and Saggi (2011) illustrate
that host firm’s total assets and total sales positively impact the decision of home firm
18

while integrating, which is mostly accounted for by Cross-border M&A. However,
Greenfield FDI has less affected by firm level variables.
Hypothesis 3. Firm level determinants have a strong impact on Cross-border
M&A decisions
Finally, Greenfield FDI choice is almost affected by macroeconomic
determinants. The reason has been estimated by Nagano (2013) based on the strategy
of home firm while choosing Greenfield FDI. In this case, home firms prefer to select
a country with suitable conditions and construct new facilities. Cross-border M&A
deals, however, have less interest in host country. Cross-border M&A deals focus on
the host firm's performance, the size of host firm and the benefit of shareholders.
Hypothesis 4. Macroeconomic determinants have a positive effect on
Greenfield FDI decisions.
19

CHAPTER 3: DATA AND METHODOLOGY
3.1. Data collection
The data of this research are collected mainly from World Bank and Orbis. The
macroeconomic factors are taken from World Bank’s data source, while other
variables are collected from Orbis. To avoid any bias, this research eliminate any
observation concluded missing variables. The period is from 1st
January, 2008 to 31st
December, 2016. The data gathers all Cross-border M&A and Greenfield FDI deals
from United firms as home firm to estimate the similarities and differences between
the two entry mode of U.S firms’ foreign investment.
Firstly, this research take out 72 firms which have highest value of stock
capitalization from S&P 500 on 31st
December, 2016 to represent for U.S firms (host
countries) in this research. From those countries, this research find all their foreign
investments through two entry modes: Cross-border M&A and Greenfield FDI. These
investments conclude over 500 deals in both two entry mode choice. However, this
study focus on deals between U.S firms and emerging countries. Therefore, unsuitable
deals are excluded from the sample. In the end, the dataset contain of 120 observation
with 60 Cross-border M&A deals and 60 Greenfield FDI deals. The total emerging
countries is 15 in 5 regions concluded: East Asia Pacific (China, Indonesia, Malaysia,
Thailand, Viet Nam), Eastern Europe and Central Asia (Romania, Russia), Latin
America and the Caribbean (Argentina, Brazil, Colombia, Mexico, Peru), Middle East
and North Africa (Egypt, India), and Sub-Saharan Africa (South Africa). Those
investment deals are collected from Thomson Reuters with the details such as time,
home firms, host firms, countries of host firms (host countries), deal synopsis, deal
value.
Secondly, at country level variables, this research estimate: per capita GDP,
Population, Corporate Tax and Bilateral Trade. All of these variables come from World
Bank. Per capita GDP calculated by taking natural logarithm of ratio between the gross
domestic product (GDP) and total population in the country. This variable represents for
purchasing power of host countries or standard of living in host country. Home firms may
use this variable to symmetrical the deal value and future strategy. Population is denoted
by natural logarithm of population of host firms. This variable represents for
20

the labor and domestic market if U.S firm make a deal. Bilateral Trade is calculated
by ratio between U.S export to home country and total trade between U.S and
emerging countries year by year. Corporate Tax denote the tax barriers for U.S firms
to entry emerging markets.
Thirdly, the legal environment has been received special attention by various
researchers. So, this study finds the index denoted the power of legal investments in
host countries. Moreover, the determinants by Gompers, Ishii, and Metrick (2003) do
not necessarily because they estimate the corporate governance structure of the
existing law. Therefore, this study applies IMD's annual quantitative data conclude
shareholder rights protection (SHR score), intellectual rights protection (IPR score) to
represent for legal environment. Based on literature review, home firms consider SHR
score while applying Cross-border M&A strategy and IPR score while applying
Greenfield FDI strategy.
Finally, home firms also consider firm level variables and industry level
variables. At host firms’ level, this paper consider firm size of host firm due to
previous literature review. The firm size is calculated by total sales and total assets of
host firms. These variables are commonly denoted for the large of firm. Home firms
research about firm size as an important factor to decide the strategy. At industry
level, the type of host firms are considered. Based on literature review, home firm
prefers to invest similar its firm type. Moreover, home firm consider firm type to
choose entry mode. Firm with strictly and clearly shareholder protection attract more
and more Cross-border M&A deals, while firm have intellectual protection rights
policy got more attention in Greenfield FDI deals. Therefore, this research denote IPR
sensitive for firm had intellectual protection policy and SHR sensitive for firm had
shareholder protection policy.
3.2. Empirical model
This research focus to three main sides that may affect macroeconomic and
social factors host country, their legal environment, and host firm-specifics. To test the
hypotheses described in literature review, we estimate two empirical equations below.
In these two equations, the two dependent variables equal one when the target firm
employ in cross-border M&A and Greenfield FDI and zero otherwise:
21

=+++
(1)
1 2 3
=+++ (2)
1 2 3
The first equation denotes for Cross-border M&A determinants and the second
denotes for Greenfield FDI determinants. In both equations, this research pretend that
firm i (home firm) has 15 potential emerging countries from 5 regions around the
world. If firm i chooses to invest in country A, the selected dependent variable of this
choice would be equal one and those for remaining countries automatically equal zero.
The independent variables of both equation are similar with all target countries. Xc
M
represents to the variables that denote the macroeconomic determinants of host
country that has been prove the influent to investment decision from U.S firms, while
Xc
L
represents to the variables that denote the level of the development of the legal
environment through intellectual property rights protection and shareholder rights
protection in host country.
For Xc
M
, this paper employ per capita GDP (per capita GDP), population size
(Population), corporate tax rate (Corporate Tax), the bilateral trade between home and
host countries (Bilateral Trade). Per capita GDP and population denote the level of
household income in the host country and host country size, respectively. These
variable represent the potential consumption and labor market of home firm.
Corporate tax presents the cost of taxation to local operations in the host country.
Bilateral trade represents the export values from United States to host country divided
by the total international trade values between United States and the 15 sample
countries. This variable proxies for the level of international trade development
between United States and host country c. All variables at level country excluded
Cooperate Tax are converted into their natural logarithm values.
For the Xc
L
variables, we employ IPR protection (IP Rights Score) and SHR
protection (SH Rights Score) to calculate the role of legal environment. To examine the
above hypothesis, the signs of the coefficients of IP Rights Score would be positive in Eq.
(2), which mean positive with Greenfield FDI and negative or no effect on Cross-border
M&A. In contrast, those of SH Rights Score would be negative in Eq. (1) – negative with
Cross-border M&A and positive in Eq. (2) – positive with Greenfield FDI. This paper
also apply IPR Sensitive and SHR Sensitive dummy variables. By

22

following Nagano (2013), IPR Sensitive equals one when the firm belongs to any of the
following sectors and zero otherwise: pharmaceuticals, chemicals, machinery, electronic
machinery, or precision machinery. We follow argument that the above sectors are IPR-
sensitive. SHR Sensitive equals one when host firm belongs to any of those sectors and
zero otherwise: food, paper and pulp, transportation equipment, and precision machinery.
IPR sensitive would follow Lee and Mansfield (1996) for voting.
represents the firm-specific variables of potential host firm operating after
getting investment. This paper apply total assets (Total Assets), and total sales (Total
Sales) as control variables. Both variables are converted into their natural logarithm
values.
This study simultaneously analyze the above two empirical equations using
Probit model. After running regression, the results show the effect of dependent
variables on Cross-border M&A and Greenfield FDI decision. Then, this research
estimates the impacts of those determinants to Cross-border M&A value and
Greenfield FDI value. All dependent variables are similar to equation (1) and (2). The
form of equation (3) and (4) are represented below:
=+ + + (3)
1 2 3
=++ + (4)
1 2 3
The empirical results of the third and fourth equations are statistically
comparable because both of them employ a common sample dataset. In other words, the
coefficient of the equation (3) and (4) results can objectively compare together.
Therefore, this study apply a seemingly unrelated regression (SUR) to estimate the
discrepancies between Cross-border M&A value and Greenfield FDI value. This model is
a basic type of a linear regression model that consists of some regression equations, each
equation has its own dependent variables and potentially different sets of exogenous
explanatory variables. In this case, the independent variables are CBMAvalue and
GFFDIvalue. The explanatory variables consist of three main factors: macroeconomic
factors, host firms’ specific factors and host firms’ industry factors. Neither equation (3)
nor equation (4) is a valid linear regression on its own and can be estimated separately,
that is why the system is called seemingly unrelated. Although previous literature review
recommend that the term seemingly related would be more
23

appropriate because the error terms are assumed to be correlated across those
equations. The SUR model can be calculated separately using standard ordinary least
squares (OLS) or probit model. Those estimation results are consistent, however SUR
are more efficient than them in comparing the results. Because it show the amounts to
practical generalized least squares with a particular form of the variance-covariance
matrix. In fact, SUR is equivalent to OLS base on two important cases. Firstly, the
error terms in SUR model are truly uncorrelated between the equations (that is why it
is named “unrelated”). Secondly, and when each equation in SUR model includes
exactly the similar set of variables on the right-hand-side.
The SUR model are possible used to simplify the general linear model where
the value of certain coefficients are limited to be equal to zero. Moreover, it can be
used as the generalization of the general linear model where the dependent variables
are allowed to be different in each equation. The SUR model can be further developed
to simultaneous equations model, where the dependent variables are allowed to be the
endogenous variables as well.
In the end, the similarities and differences between the coefficients results in
SUR model find out which dependent factors have a stronger effect on either
investment value based on SUR. Therefore, the empirical results of four equations in
two models will answer the research question in this study. The results of all
equations are displayed and explained below.
24

CHAPTER 4: RESULTS AND CONCLUSIONS
4.1. Summary statistics:
4.1.1. Data description:
Firstly, this paper summarize the data set to represents an over view about
data collection and observations in the summary statistics table below:
Table 4.1: Summary statistics
Variable Obs Mean
Standard
Min Max
Deviation
Ln(Population) 120 19.631 1.473 16.829 21.044
Ln(GDP) 120 8.334 0.854 6.899 9.651
Bilateral Trade 120 0.03 0.036 0.001 0.142
Corporate Tax 120 0.286 0.048 0.16 0.35
IPRightsScore 120 3.519 0.572 2.335 5.223
SHRightsScore 120 3.448 0.731 1.2 5.22
IPRsensitive 120 0.483 0.502 0 1
SHRsensitive 120 0.317 0.467 0 1
Ln(Total sales) 120 11.864 2.789 2.825 17.591
Ln(Total assets) 120 12.315 2.63 2.737 17.398
Source: Author’s estimation
Table 4.1 presents the summary statistics of the research variables for 120 deals
concluded both Cross-border M&A and Greenfield FDI entry mode choice. The
observation of both variables are equal 120, which mean there is no missing variables
in this dataset. The dependent variables are divided into three main aspects. Firstly, the
variables represent at firm level included by Ln(Total sales) and Ln(Total assets). These
variables also denote for the main country level factors included by Ln(Population),
Ln(GDP), Bilateral Trade, Corporate Tax. Especially, there is IPRightsScore and
SHRighscore to present for legal environment which are important determinants for
home firm investment decisions. IPRsensitive and SHRsensitive denote for industry
level which define as the industry of host firms. IPRsensitive represents for firm having
intellectual protection policy, while SHRsensitive denote for firm having strong
shareholder protection policy.
25

As what has been displayed in above summary statistics table, Ln(Population)
and Ln(GDP) are representative variables for macroeconomic determinants of host
firms (Population and GDP per capita). Ln(Population) represent for the total
population of host countries in the time of investment year. Ln(Population) is
calculation by the natural logarithm of population from host firm, which ranges from
16.829 to 21.044, its mean is 19.631 and the standard deviation is 1.472. Ln(GDP)
denotes for the total revenue of host firm in investment year’s announcement. The
largest value of Ln(GDP) is 9.651, while the smallest one is 6.899. Its mean stays at
8.334 and the standard deviation is 0.854. Bilateral Trade measured by the ratio of
export proportion from United States to host country divided by the total international
trade values between United States and the 15 sample countries each year. This
variable proxies for the level of international trade development between United State
and host country. This variable also represents for the trade level of United Stated and
the 15 sample emerging countries. The mean value shows that the average export
value of U.S and emerging countries in the sample is 0.030, while the discrepancies
between them is 0.036. The maximum ratio of it is 0.001 and its minimum is 0.142.
For legal environment, IPR score and SHR score range the powerful of
Intellectual Property Rights and Shareholder Rights protection of host countries, the
value of them is measure from 1 to 6 from World Economic Forum. Those score
present for the powerful of laws in emerging countries to protect IPR protection laws
and SHR protection laws, separately. Leverage ranges of IPR score from 2.335 to
5.223 with the mean value at 3.519, while those of SHR score from 1.220 to 5.220
with the mean value at 3,448. IPR sensitive and SHR sensitive have the mean value at
0.483 and 0.317, respectively. The value of them is just 0 or 1. These variables denote
the proxies for industry in emerging countries that the home country invested.
Finally, Ln(Total sales) and Ln(Total asset) both represent for targeting firm size.
Ln(Total sales) reaches minimum value at 2.825 and maximum value at 17.591, while
their ratio of Ln(Total assets) values are 2.737 and 17.398, respectively. The mean value
of Ln(Total sales) and Ln(Total assets) are 11.864 and 12.315, respectively. Firm size of
host firms have a strong impact on Cross-border M&A and Greenfield FDI investment due to
literature review.
26

Table 4.2: Summary statistics of entry mode choice via regions
REGION N Mean Standard deviation Min Max
East Asia & Pacific 44 0.5 0.506 0 1
Europe & Central Asia 12 0.583 0.515 0 1
Latin America & Caribbean 25 0.48 0.51 0 1
Middle East & North Africa 37 0.46 0.505 0 1
Sub-Saharan Africa 2 1 0 1 1
Total 120 0.5 0.502 0 1
In the data set, this paper collect deals from 7 region: East Asia & Pacific, Europe
& Central Asia, Latin America & Caribbean, Middle East & North Africa, North
America, South Asia, Sub-Saharan Africa. However, there is missing deals from North
America and South Asia, deals from other regions are not suitable, and so the table show
the distribution of 5 regions around the world. If any region has Cross-border M&A or
Greenfield FDI deals, the value of its region will be denoted as 1, otherwise, the value
will be equal to 0. Therefore, the smallest value of them is equal to 0 and the largest one
is equal to 1. This table show how the ratio of choosing Cross-border M&A differs from
region to region. Moreover, the ratio of choosing Cross-border M&A also represents the
ratio of choosing Greenfield FDI. Higher ratio of choosing Cross-border M&A leads to
lower ratio of choosing Greenfield FDI. In later regression, region become dummy
variable to test whether the decision is effected by region. In this table, the region which
has most deals are East Asia & Pacific. This region accounts for 37 percent of total deals
in this research. The following ranks are Middle East & North Africa, Latin America &
Caribbean, Europe & Central Asia, Sub-Saharan Africa, respectively. In Sub-Saharan
Africa, this region has just two deals which concluded both Cross-border M&A and
Greenfield FDI. Therefore, the min and max value of them are equal one.
27

4.1.2. The correlation matrix
The correlation matrix show the between independence variables. Based on this results, this study test the multicollinearity.
Table 4.3: The correlation matrix
Ln(Population) Ln(GDP) BilateralTrade Corporate Tax IPRightsScore SHRightsScore IPRsensitive SHRsensitive Ln(Total sales) Ln( Total assets)
Ln(Population)
Ln(GDP)
BilateralTrade
Corporate Tax
IPRightsScore
SHRightsScore
IPRsensitive
SHRsensitive
Ln(Total sales)
Ln( Total assets)
1.0000
-0.5241*** 1.0000
0.0000
0.2329** 0.3315*** 1.0000
0.0105 0.0002
0.1743** -0.5135*** -0.1189 1.0000
0.0569 0.0000 0.1957
0.2769*** -0.1208 0.1927** 0.1673* 1.0000
0.0022 0.1889 0.0349 0.0678
-0.3550*** 0.0604 -0.2018** 0.3246*** 0.3838*** 1.0000
0.0001 0.5123 0.0271 0.0003 0.0000
0.0162 0.0478 -0.0716 -0.2288** -0.2763*** -0.2690*** 1.0000
0.8605 0.6038 0.4372 0.0120 0.0023 0.0030
0.0451 0.0135 0.2613*** 0.1401 0.1509 0.0545 -0.3358 *** 1.0000
0.6251 0.8839 0.0039 0.1270 0.1000 0.5542 0.0002
-0.0401 0.0261 0.2250** 0.1410 -0.0862 -0.1243 -0.0515 0.1196 1.0000
0.6634 0.7770 0.0135 0.1244 0.3489 0.1763 0.5766 0.1931
-0.0536 0.0455 0.2669*** 0.1363 -0.0102 -0.1266 -0.0903 0.1046 0.8791*** 1.0000
0.5608 0.6220 0.0032 0.1377 0.9118 0.1684 0.3265 0.2555 0.0000
*, **, and *** are respectively the level of significance at 10%, 5%, and 1%
28

The correlation between variables ranges from -0.524 for the correlation between
Ln(Population) and Ln(GDP) to 0.8791 for the correlation between Ln(Total sales) and
Ln(Total assets). Most of the correlations are not higher than the absolute value of 0.8,
proving that there is no multicollinearity among those variables in the estimation model.
However, the correlation between Ln(Total sales) and Ln(Total assets) to 0.8791, there is
multicollinearity between these two variables. In literature review, the previous authors
agree that there is a strong relationship between the total assets and the total sales of a
company. They argue that the more total assets a firm get, the more total sales it gains
from operating. This research, however, plan to test the impacts of both determinants on
Cross-border M&A and Greenfield FDI. Therefore, the next regression would separately
run these two variables to eliminate the multicollinearity. That is means that this study
estimate the influence of firm size to Cross-border M&A and Greenfield FDI by choosing
Ln(Total sales) and Ln(Total assets) separately.
28

4.1.3. Empirical results:
In the empirical results, this research will display the empirical results via two
Probit model method and Seemingly Unrelated Regression method. The results from
Probit regression answer for the first research question, while SUR provide empirical
results for second research question. First of all, this paper estimates the impact of
region to investment decision.
Table 4.4: The determinants of Cross-border M&A and Greenfield FDI
Probit Cross-border M&A Greenfield FDI Cross-border M&A Greenfield FDI
Ln(Population)
-0.101 0.121 -0.115 0.127
(-0.45) (0.55) (-0.48) (0.54)
Ln(GDP)
-0.199 0.461 -0.206 0.442
(-0.56) (1.44) (-0.60) (1.44)
Bilateral Trade
3.166 0.131 3.55 -0.152
(0.87) (0.03) (0.83) (-0.03)
Coporate Tax
-0.871 2.313 0.437 1.258
(-0.09) (0.27) (0.04) (0.13)
SHRightsScore
0.244* 0.229**
(1.87) (2.04)
IPRsensitive
0.675*** -0.775*** 0.686*** -0.774***
(2.89) (-3.40) (2.8) (-3.22)
SHRsensitive
-0.0744 0.117 -0.0531 0.0979
(-0.17) (0.27) (-0.13) (0.23)
Ln(Total assets)
-0.0804** 0.0692*
(-2.31) (1.91)
EAP
-0.0482 -0.127 0.0483 -0.185
(-0.06) (-0.20) (0.07) (-0.30)
ECA
0.0599 -0.902 0.203 -0.942
(0.04) (-0.72) (0.16) (-0.81)
LAC
0.0264 -0.937 -0.00742 -0.849
(0.03) (-1.61) (-0.01) (-1.32)
IPRightsScore
-0.774*** -0.715***
(-3.62) (-4.09)
Ln(Total sales)
-0.0908* 0.0721
(-1.91) (1.42)
Constant
3.629 -4.332 3.676 -4.184
(1.33) (-1.16) (1.33) (-1.11)
N 120 120 120 120
t statistics in parentheses: * p<0.1, ** p<0.05, *** p<0.01
29

This table is the empirical results from 4 Probit models. As the
multicollinearity in table 4.2 shows that both Ln(Total sales) and Ln(Total assets) are
typical representatives for firm size. In previous study, the correlation between total
assets and total sales has been proven by both theoretical and empirical research. So
this research will run Ln(Total sales) and Ln(Total assets) separately. In this way, the
multicollinearity is eliminated and the results become more reliable.
Moreover, shareholder right policy affect Cross-border M&A strategy and
intellectual right policy influence Greenfield FDI. Because Cross-border M&A deals
are done by funding into existing firms in emerging countries, the investment decision
by Cross-border M&A deals practically depend on Shareholder Right law protection.
In contrast, Greenfield FDI deals are done by funding to create new firms with the
highest control of this future firm. So Greenfield FDI deals virtually depend on
Intellectual Right law protection. Therefore, the first regression to find out which
determinants affect Cross-border M&A decision will eliminate Intellectual Property
Rights score (IPRightscore). In contrast, Intellectual Property Rights (SHRightscore)
affect Greenfield FDI, so the regression to find which impacts influence Greenfield
FDI decision excludes SHRightscore variable. In the end, the results are an
assemblage of four columns displayed above.
The results of determinants affecting Cross-border M&A and Greenfield FDI
decision using different kinds of firm size are consistent together. That means the total
sales and total assets are both commonly represented for hos firm size. Firstly, all macro
determinants of host firms including regions in this research have no significant effect on
the entry mode choice of U.S firms. So the decision of domestic firm chooses either
Cross-border M&A or Greenfield FDI typically does not depend on population and
income of host country. This results suppose for the hypothesis 1 in this research. This
results can be explained by the Electic theory. The Electic theory concludes that all of
foreign investments possible have the location appealing through location advantages,
ownership advantages, and internalization advantages. If a firm has strategy to invest in
one country, neither Cross-border M&A nor Greenfield FDI will receive the same
incentives from host countries. This means they receive the same advantages such as
30

customers, purchasers power, labor force, which are denote by population and GDP
per capita. Moreover, Cross-border M&A and Greenfield FDI are both entry mode
choice from FDI. In this case, the impacts of macroeconomic determinants are the
same in both strategy. In this study, both literature review and empirical model show
the same resuts. So the hypothesis 1 is undeniable.
SHRights score is denoted for legal environment impact on Cross-border M&A
strategy. From Cross-border M&A, even though it is insignificant, it is positive with
the Cross-border M&A decision. The host firm size has a significant and negative
influence on Cross-border M&A. Domestic firms will have to contribute a large
amount of capital to host firms if they tend to implement Cross-border M&A deals.
Moreover, Ln(Total sales) has a positive significant impact on Cross-border M&A
decision. That means both Ln(Total sales) and Ln(Total assets) generally influent the
impact Cross-border M&A deals.
The determinants of Greenfield FDI and Cross-border M&A decision are
similar to each other in term of the impact of these factors. However, Intellectual
Property Rights score influences the advantages of U.S firms, while as for Greenfield
FDI, this research estimate the IPRightscore instead of SHRightscore in the model of
Greenfield FDI choice. If U.S firms already have sustainable infrastructure in the U.S
or other countries, U.S firms generally hesitate to choose Greenfield FDI. The host
firm size, however, has a significant and positive impact. Local firms prefer total
assets than total sales of host firms due to its stability over time.
The separately regressions of Cross-border M&A and Greenfield FDI determinant
factor suppose for the hypothesis 2. An enhancement of IPR protection law in the host
country will increase inward Greenfield FDI, while SHR protection law supports cross-
border M&A decision. In literature review show that both on theoretical and empirical
studies have the same answer. The reason for this result come from the essence of Cross-
border M&A and Greenfield FDI. Firstly, Cross-border M&A are the strategy that merge
with an existing firm, so the role of shareholder must be considered to protect the host
firm advantages. Therefore, home firm with Cross-border M&A deals will access a
deeply research about shareholder protection of host firm and host country.
31

In contrast, Greenfield FDI is investing a new firm to a new market. In this case, home
firm will consider about intellectual protection to protect its intangible treasure such
as technology, patents, and licenses. Moreover, Greenfield FDI gives home firm the
highest control of firm, so it has less interest in shareholder right protection.
In conclusion, macroeconomic determinants have no effect on both Cross-
border M&A and Greenfield FDI. Firm size via both total assets and total sales has
influence on two entry modes. However, U.S firms hesitate to attend Cross-border
M&A deals with large host firms, while they will invest with large capital using
Greenfield FDI mode. In the end, the hypotheses 1 and 2 are both undeniable.
32

Table 4.5: The estimated results for the value of Cross-border M&A and
Greenfield FDI using Seemingly Unrelated Regression (SUR)
SUR
Cross-border Greenfield Cross-border Greenfield
M&A FDI M&A FDI
Ln(Population)
43.80 154.7*** 72.20 152.3***
(0.66) (2.68) (1.11) (2.65)
Ln(GDP)
-85.31 230.6* -128.4 235.0*
(-0.64) (1.66) (-0.98) (1.69)
Bilateral Trade
-537.6 -3991.2* -1340.5 -3895.7*
(-0.23) (-1.69) (-0.59) (-1.66)
Corporate Tax
-7607.5*** -1948.6 -7839.7*** -1669.7
(-3.12) (-0.84) (-3.36) (-0.74)
SHRightsScore
280.1*** 329.5***
(2.88) (3.43)
IPRightsScore
-44.80 -62.34
(-0.32) (-0.46)
IPRsensitive
-8.542 -102.4 5.470 -103.8
(-0.08) (-0.96) (0.05) (-0.97)
SHRsensitive
-60.12 -107.0 -46.60 -101.2
(-0.53) (-0.98) (-0.42) (-0.93)
Ln(Total sales)
44.00** 18.12
(2.26) (0.95)
Ln(Firm size)
71.20*** 17.45
(3.46) (0.88)
EAP
-176.5 11.84 -83.15 26.27
(-0.77) (0.05) (-0.37) (0.12)
ECA
-478.8 -441.4 -285.0 -431.8
(-1.12) (-0.98) (-0.68) (-0.96)
LAC
287.1 370.6 420.9 345.4
(0.95) (1.06) (1.42) (1.00)
Contanst
764.5 -4139.1** 37.53 -4152.2**
(0.44) (-2.54) (0.02) (-2.53)
N 120 120
R-squared 0.163 0.207
Log likelihood -1837.1 -1833.9
Chi-squared 22.75 30.61
t statistics in parentheses
* p<0.1, ** p<0.05, *** p<0.01
33

This table presents the empirical results for the value of Cross-border M&A
and Greenfield FDI using Seemingly Unrelated Regression. Based on the literature
review, the regression for Cross-border M&A value eliminates the IPRightscore, while
SHRightscore is excluded in Greenfield FDI value regression. The correlation table
shows the multicollinearity between Ln(Total assets) and Ln(Total Sales) because
both variables represent the firm size. In this regressions, these dependent variables
are still estimated separately. Therefore, this table continue show the four columns as
the previous estimation. Those results come out are consistent with hypothesis 3 and
4. This research will explained more about this results.
In case of Cross-border M&A, all macroeconomic determinants displayed in
this study have no effect on home firms’ choice. IPRsensitive has a significant
positive impact. This variable represents the industry of host firms. The higher
IPRsensitive score of the host country gains, the more facilities host firms already
have. Home firms prefer to invest in those firms to save the budget and time after
making investment. The existing firms provide not only facilities but also distribution
network in the host country. When Corporate Tax in host countries is kept at the high
level, companies in the United States hesitate to invest much more money in Cross-
border M&A, reducing its value. This results can be explained by the transaction cost
theory, the bigger firm size of host firm, the more costs and risks the home firm get
when it invest by Cross-border M&A strategy.
The Greenfield FDI value regression proves the hypothesis 4, host firm size has
no impact on Greenfield FDI. This is an undeniable result because the firm size of host
firm is what home firm decide by its own capital. However, macroeconomic
determinants have a positive significant to this entry mode value. Population has a
positive effect on the value of Greenfield FDI, meaning that the increase in population
will result in higher value of Greenfield FDI. Based on my analysis, in emerging
countries with the endowment of human resources within their economies, home firms
will take advantages of abundant labor source to increase the value of Greenfield FDI.
GDP per capita is an index representing a country’s potential economic growth.
Therefore, in this situation, higher GDP per capita means that the investment through
Greenfield FDI will contribute to its value in host countries. Bilateral Trade is
34

negatively related to Greenfield FDI value. The increase in Bilateral Trade proves that
the US is exporting more goods and products to the host countries. This action makes
the domestic currency depreciate compared to the foreign currency, causing home
firms to pay more money to carry out Greenfield FDI in host countries. In this case,
home firm pay more costs if it invest to country which has high Bilateral Trade
In short, although the determinants on whether to conduct Cross-border M&A
and Greenfield FDI are similar to each other, their values are affected by discrepant
factors. While the value of Cross-border M&A is relevant to legal regulations
(Corporate Tax and SHRightscore) together with firm size (total sales or total assets),
the value of Greenfield FDI is controlled by microeconomic elements including
population, GDP per capita and Bilateral Trade. The results suppose for hypotheses 3
and 4 in literature review and answer for the second research question.
35

CHAPTER 5: CONCLUSION
5.1 Main findings:
Based on the empirical evidence presented above, this paper comes to the
following conclusions. Firstly, host countries’ macroeconomic determinants between
Cross-border M&A and Greenfield FDI are similar to each other. Secondly, an
enhancement of IPR protection law in the host country will increase inward Greenfield
FDI, while SHR protection law supports cross-border M&A decision. Thirdly, firm
level determinants have a strong impact on Cross-border M&A decisions. Finally,
macroeconomic determinants have a positive effect on Greenfield FDI decisions.
These are also the four hypotheses in literature review of both theoretical and
empirical researches.
Firstly, this research shows that all the variables at hypothesis 1 are entirely
consistent with the findings of previous studies or the impact of country level factors
has been estimated once again. The first hypothesis is proven by this research to
suppose for electic theory. The empirical research show that GDP per capita and
population are significantly and negatively coefficient to Greenfield FDI. Although
the initial originally hypothesis assume that there is a positive relationship between
GDP per capita and those two entry modes choice, a significant correlation between
emerging countries and both of unit labor cost and household income. That is why
these variables significantly and negatively affect home firms’ investment decisions.
This paper’s empirical results support valuable insights for the literature review in
researching country level variables and entry mode choice of home firm. As the results
above, this paper supports empirical proof that GDP per capita, Population, and
Corporate Tax impact both two entry modes. Because all of above country level
factors generally raise or reduce Cross-border M&A and Greenfield FDI, they do not
determine the home firm's investment choice. In other words, this research uses the
country level variables as required conditions to encourage FDI flows to emerging
countries. Those variables generally do not affect the type of entry mode choice. Both
of strategies will receive the similar incentives of host countries through
macroeconomic determinants while investing to emerging countries.
36

Secondly, the empirical estimation shows that the fluctuation of legal
environment affects home firm choice based on type of investment and type of laws.
For example, Cross-border M&A is not affected by the fluctuation of IPR protection
laws in host countries. Greenfield FDI, on the other hands, commonly increases or
decreases by such fluctuation; these results are consistent with previous literature
review from by Lai and Qiu (2003), Branstetter and Saggi (2011), and Javorcik
(2004). Moreover, in the world’s investment context section, Cross-border M&A
becomes a new trend with dramatic increases recently. This paper suggests that
emerging countries’ governments, who want to attract this FDI flows, should focus on
enhancing SHR protection laws as prerequisites. This results also support Di Giovanni
(2005) and Nagano (2013) with the similar conclusion of a positive correlation of the
legal environment concerning IPR protection and Greenfield FDI, SHR protection and
Cross-border M&A. However, Vietnam is the country with the lowest score of the 15
countries through 2008-2016 period. So the government of Vietnam should take a
careful look and needs to improve this phenomenon in the soonest time to catch the
FDI flows. Vietnam can focus on neighbor countries such as China, Malaysia,
Thailand to learn how paralleling the current Cross-border M&A deals grow in these
countries. The Cross-border M&A trend reflects the influence of Cross-border M&A
to home firm choice through its characteristics such as SHR protection laws. The more
improve the SHR protection laws in emerging countries, the more benefit home firm
gain. Home firm mostly choose country with powerful SHR protection to control host
firm followed its strategy, even ready-make-strategy.
In summary, this research shows some more empirical evidence to contribute to
this topic in three specific ways. Firstly, macroeconomic determinants contained host
country's population size, GDP per capita, corporate tax rate and bilateral trade generally
consistent with FDI entry mode choice. Furthermore, this paper takes a careful look at
host country's legal environment through IPR laws and SHR laws. We show that the host
country's IPR protection law impacts the Greenfield FDI but not the Cross-border M&A
decision of the home firms. By contrast, the host country's SHR protection law influences
the incoming cross-border M&A but not the Greenfield FDI decision of the home-country
firm. Thirdly, this paper tests the financial operating of host firms
37

after receiving investment from home firm as an aspect of potential operating as home
firms’ desire.
5.2 Policy implications:
Based on the results, this study supposes for governments in emerging
countries to several empirical evidence to enhance the opportunities of attracting FDI
via Cross-border M&A and Greenfield FDI. In case of macro determinants, the faster
GDP per capita and population increase in emerging countries, the more opportunities
these countries will receive from U.S firms via both Cross-border M&A and
Greenfield FDI deals. Emerging countries should develop their economy to access the
interest of foreign countries, especially United States.
The most important results for policy makers are the impact of SHR laws and
IPR laws, the state governments should continue to achieve the legal system,
especially SHR laws and IPR laws, guaranteeing the clarity and equality for all foreign
countries. However, emerging countries’ government have choices to decide which
kinds of investment they want to receive. If their selection is Cross-border M&A, this
study recommend they should focus on SHR laws. United States firms will consider to
invest by Cross-border M&A strategy if home countries’ shareholder right laws is
strong enough to cover risks and protect home firms’ advantages. In contrast, if
emerging countries’ government choices are to attract Greenfield FDI deals, they
should focus on intellectual protection laws. Even though home firms gain a highest
control while investing by Greenfield FDI, they have higher risks at their invisible
treasure. So the country which has strong protection in intellectual will get the higher
attention of home firms while considering the new investment location.
Besides, this research also show some impact of host firm specific factors. By
these results, this study recommend that host firms need to continue to achieve the
regulations and principles in managing both human and capital. Host firms will
receive more chances to gain foreign investment to their countries if they already have
a solid institution. This will not only attract more attention from foreign firms but also
protect their firms if Cross-border M&A appears. Within the companies, the more
clarified and equal host firms’ access is, the more opportunities they receive and the
more protection barriers they get while attending a foreign investment.
38

5.3 Limitations and further research recommendation:
The following limitations must also be recognized in this study. Even though
this research tries to approach more U.S home firms to suppose for the results, the
process is difficult to collect private information of home firms. U.S firms’ deals
information is limited not only by name but also by the value of these deals. So this
study only take 72 companies with the highest capitalization in the S&P500 from
Thomson Reuters. Therefore, the data are limited to 120 observations in 15 emerging
countries. Future researches should take care of this limitation.
Emerging countries are potential but complex markets. For example, this paper
employs two of the most important laws in investment decisions: shareholder right
laws and intellectual rights laws, but there are still many legal environment factors that
may also impact Cross-border M&A and Greenfield FDI. Moreover, the correlation
between IPR and SHR should be more investigate because some host firm not only
has IPR policy but also SHR policy. Even though this study tries to find the
consistency of some more variables in legal factors, but the missing is undeniable.
Which host country legal environment determinants except IPR and SHR laws
impacts home firms choice should be investigated by future researchers.
39

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