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Master programme in Economic History
Determinants of Location in Outward U.S. Foreign
Direct Investment
Frank Ix
[email protected]
Abstract: The flow of capital across borders through foreign direct investment has
become a major driver in the world economy and continues to gain prominence due to the
rise of Asia and other emerging markets. The United States, as the largest economy in the
world for over half a century, has played a crucial role in this capital flow. Everyday,
millions of private U.S. dollars pour across borders towards direct investments in foreign
companies and development of international subsidiaries and joint ventures. This thesis
uses panel regression data to assess what factors determine which markets receive U.S.
investment and why. Out of a sample of fifteen initial independent variables, six are
proven to be significant, most notably exports, exchange rates, population size, CPI, and
CO2 emissions. Because of the possible limitations of a panel regression, further research
is also recommended for many variables.
Keywords: foreign direct investment, FDI, American investment, cross-border M&A,
international economics
EKHR21
Master thesis (15 credits ECTS)
June 2011
Supervisor: Håkan Lobell
Examiner: Lars-Olof Olander
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Table of Contents
Page
1.
Introduction
03
1.1
Research Problem
04
1.2
Aim and Scope
04
1.3
Outline of the Thesis
05
1.4
Defining FDI
05
1.5
Types of FDI
06
1.6
The American Case
07
2.0
Literature Review/Theory Background
09
2.1
Introduction of Variables
19
2.2
Hypotheses
30
3.
Data and Methodology
32
3.1
Data
32
3.2
Model
32
4.
Empirical Analysis
35
4.1
Results
35
4.2
Discussion
40
5.
Concluding Remarks
44
6.
References
47
7.
Appendices
49
Acknowledgements
Special thanks to my supervisor Dr. Håkan Lobell and Dr. Jonas Helgertz from the
Department of Economic History at Lund University, as well as Chris Culvern and
Michelle Snow, whose suggestions and advice helped significantly towards the
completion of this thesis.
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1. Introduction
With the integration of world markets over the last three decades, the role of foreign
direct investment (FDI) has deservedly garnered increased academic attention. The
movement of capital across borders in private transactions unlocks economic growth
potential not only in the FDI recipient country, but in the investing country, as well.
While individual cases may result in differing conclusions on the impact of FDI on both
firm and economic growth, the growing role of cross-border investment in global finance
is readily apparent. For the United States, outward FDI reached an all-time high in 2008,
with $3.1 trillion invested on foreign soil and investment income totaling $346B (U.S.
Bureau of Economic Analysis, or BEA). Although most countries in the world receive
FDI from the United States in one form or another, the recipients of most of this
investment are those that hold the most promise of delivering returns, as deemed by
individual investors. However, the political and economic environments that promote
these returns differ greatly between countries. Also, the dynamics within individual firms
are different from industry to industry. Therefore, the most attractive combination of
environments for a firm and recipient country vary greatly over time and space.
Over the last decade, countries that once received a large portion of American
investment have decreased-while other emerging markets have increased- their share.
Obvious explanations for this would point to the diverse landscape of political, economic,
and financial stabilities between countries, while other explanations could include wage
levels, natural resource wealth, and exchange rate volatility. At the same time, countries
that once received investment due to cheap labor resources now receive investment for
access to unique technologies. In other words, the makeup of outward FDI determinants
appears to have changing importance over time and space. In many cases, neighboring
countries will promote certain domestic characteristics to attempt to attract FDI, so that a
small country (like Singapore) might offer tax breaks, while a larger country (like China)
with more natural resources might not need to focus on tax incentives because FDI will
be attracted regardless.
Whether participating in cross-border mergers and acquisitions (M&A), joint equity
ventures, or overseas expansion, American investors must weigh many factors when
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deciding among future investment locations. While delivering on shareholder returns is
usually the goal, these investments and returns come in many forms, with each one
having unique impacts on an investor’s portfolio. Some companies may be going abroad
to avoid domestic tax rates or restrictive trade barriers, to attract unique technologies and
talent, or to keep up with the competition. More often, perhaps, companies invest in other
countries to diversify risk, take advantage of growth opportunities in high growth
markets, or to tap into a new group of previously unreachable consumers. In order to
understand why some countries have developed a certain talent for attracting FDI, and
why some companies have become adept at deciding which markets to invest in, it is
crucial first to understand the possible answers to these questions as determinants.
Furthermore, which of these determinants carries the most weight in investment
decisions, and do these decisions change over time? This paper will analyze the factors
that may affect the share of U.S. outward FDI a country receives over the period of 19822008.
1.1 Research Problem
The topic of research is how national political and economic environments affect
the amount of foreign investment that that country receives and how this may change
over time. Although the United States provides a great example because of the enormity
of its outward FDI and diversity of recipient countries, the hope with this study is that the
factors that attract FDI should be transferable across cases. In this sense, the results from
this study are aimed at contributing to the field of FDI determinants rather than the
specific case of the United States and its relationship with FDI. Because there have been
few recent studies using the United States in this type of experiment, this study should
contribute to the depth of research into FDI determinants, with particular benefit of
timing given that the dynamics of the global economy have changed greatly since 2008.
1.2 Aim and Scope
Toward the pursuit of determining the right economic and political atmosphere
for attracting FDI, this paper will use a panel regression to study 51 recipient countries of
US FDI over a 26-year period (1982-2008) to gain insight into why American firms
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invest in some foreign countries more than others. Therefore the aim of this paper is to
tackle this topic and provide evidence for multiple determinants. As stated, this paper will
hopefully contribute to the FDI determinants literature, which to date has yet to find a
general consensus. This paper does not aim to provide grounds for a general consensus,
but instead to simply add one large, important case study to the growing quantity of
research done previously in the hope that it will respond to some areas of debate.
1.3 Outline of thesis
The paper will proceed as such: in order to be able to speak fluently about the
determinants of FDI, it will be first necessary to fully define FDI and describe the
historical reasons that have driven FDI in the most basic sense. This description will be
followed by a review of similar research, focusing mostly on case studies on the
determinants of FDI around the world, as well as specific studies on the dynamics of U.S.
FDI. These two sections will lay the foundation for the rest of the paper: introducing the
variables to be used, a description of the data, methodology and framework of this
sample, followed by regression analysis, the results, and further concluding remarks.
1.4 Definition of FDI
Foreign direct investment is a term economists have used for decades to describe
investments in physical assets in international markets. Initially, this definition was
limited to a company purchasing or building a physical facility in another country, most
typically used as a production factory. As the nature of international markets has become
more complex and more entangled over the last few decades, this definition has expanded
to incorporate three other types of direct investment: 1. direct acquisition of a foreign
company or facility (further referred to as cross-border M&A); 2. joint venture with a
foreign company in their local market; or 3. strategic alliance with a foreign company in
their local market. This is opposed to an indirect (or portfolio) investment: an investment
in real estate or a corporate entity through stocks, bonds, or an investment fund. The
important distinction between the two is that the company making a direct investment has
a direct influence on the returns on their investments, whereas an indirect investor simply
provides capital for another entity to provide these returns. This study will focus solely
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on direct investment transactions. The dependent variable will be the U.S. FDI position in
each country, which measures “the value of U.S. direct investors' equity in, and net
outstanding loans to, their foreign affiliates. The position may be viewed as the U.S.
direct investors' net financial claims on their foreign affiliates, whether in the form of
equity (including reinvested earnings) or debt (BEA).”
1.5 Types of FDI
Another important component of defining FDI is the historical reasons that
companies have been compelled to invest in other markets. The most straightforward
breakdown of these reasons came from Chryssochoidis, Millar & Clegg’s 1997 book, in
which they provide the following five types of incentives for a company to commit
resources to FDI:
1. To gain access to currently unavailable factors of production. These
could be natural resources, technical skill, or patents owned by a company
in the recipient country
2. To gain access to low-cost factors of production, such as labor or
natural resources
3. International competitors creating a partnership across borders to gain
access to the other's product lines and client lists
4. To gain access to new consumers in the recipient country’s market
5. To avoid tariffs or other trade barriers
(Chryssochoidis, Millar, Clegg, 1997)
Which of these incentives drives FDI often varies depending on the home market
from which the investment derives, such that a company based in a low labor cost
economy would not invest abroad for low-cost labor, and a company based in a large
market would not prioritize investing in small foreign markets to gain more market
access. These priorities can also shift within a group of investors over time. Because this
thesis focuses on the U.S. as its home market, it will be important to analyze which of
these five factors has influenced FDI determination for Americans and which will be
important moving forward, as these factors ultimately determine the location of
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investment. This study will use multiple variables representing each of these five
categories to determine which have the most influence on U.S. FDI.
1.6 American Case
For United States outward FDI, investment position and direct investment income
have grown steadily over the last three decades and reached an all time high in 2008
(BEA). As stated, this investment is spread to all corners of the world, albeit unevenly (as
shown in Graph 1.). The European Union, for example, receives roughly half of U.S.
investment, coming in at $1.6 trillion, while China and India combined only received $62
billion. Despite this large gap, China and India have both had a huge boost in investment
over the last decade. In 2000, they received only a combined $13 billion, thus doubling
their US FDI income twice in the last eight years (BEA). The countries that receive the
most FDI are from Western Europe, such as the Netherlands ($426 billion) and the UK
($449 billion), who top the list each taking about a 15% share of American FDI. Canada
and Mexico benefit from their proximity to the U.S., with the former receiving $239
billion while the latter receives $89 billion. In contrast, lesser recipients include South
American countries such as Brazil ($44.5 billion) and Peru ($4.7 billion), Africa
countries like Nigeria ($3.2 billion) and Egypt ($8.3 billion), and the Middle Eastern
countries Saudi Arabia ($5.1 billion) and United Arab Emirates ($3.4 billion).
Within those countries that receive large amounts of U.S. FDI, the types of
transactions can vary across many industries for many different purposes. For example,
about half of investments in the Netherlands are directed at holding companies, with the
remaining investment spread around to industries such as IT, finance, and manufacturing.
In the UK, the lion’s share goes towards the banking industry, with the service and
manufacturing industries receiving most of the remainder. Between Canada and Europe,
the U.S. invests more heavily in the Canadian mining and manufacturing industries than
the same industries in Europe. But when it comes to finance and holding companies, the
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8
Graph 1. Distribution of American FDI, Year 2009, as % of selected BEA dataset, not total
U.S. invests far more in Europe than in Canada. Perhaps to an even greater degree,
Mexico shares the manufacturing pull with Canada. African and Middle Eastern
investment tends to be directed at the natural resources and the service industries. China
receives over half of its U.S. FDI for manufacturing, while India’s investment has
focused on information and service industries (BEA).
From these breakdowns, we’re able to see that certain industries tend to receive
more investment than others, and that certain countries have competitive advantages in
certain industries. For example, because of advanced financial markets, Europe is more
prone and prepared to give returns on U.S. FDI directed at the banking industry. The
Middle East has advantages in natural resources, and therefore receives investments
towards natural resource extraction (oil, mining, etc.). India has a well-developed IT
sector, and therefore receives funding for information services. However, despite the fact
that it is relatively easy to see which industries within countries receive the most U.S.
FDI, it does not answer why countries with similar industries receive different amounts.
Also, it is conceivable that some countries have developed these industries with the
express purpose of attracting U.S. FDI—so what makes some countries more successful
at this than others?
2. Literature Review
Previous research does not lead us to any simple answers to this question. Foreign
direct investment is one of the most studied topics in international economics, so it is
important to recognize the impact previous research will have on this study. Because of
this subject’s prominence in international economics, researchers from many fields have
attempted to determine which factors play a part in the decision process of investment
locations. Many of these theories have been posited either by micro-level, firm-focused
academics or by macro-level researchers, and consensus has yet to be reached within
these fields, let alone between them. For the purpose of this study, it will be important to
consider both fields in order to build a comprehensive model. Generally speaking,
microeconomic scholars argue that the firm is the focal point for all decision-making
processes in FDI—therefore results from these firms can then be extrapolated to explain
trends in national FDI data. With the macroeconomic perspective, it is assumed that
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individual firms’ decisions are based on larger economic trends in the hopes that they can
ride on a wave of economic growth. With each of these viewpoints there are different sets
of variables typically used. For example, firm-level research suggests that previous return
on investments, labor costs, tax incentives, and diversifying risk are usually key
determinants for firms in deciding where to invest. Macroeconomists tend to focus more
on the market size of the recipient country, political stability, exchange rates, and
distance between countries as the most significant possible variables.
The reality is that FDI determination most likely incorporates both of these two
viewpoints. To build an optimal model will require a review of the theory behind these
variables so that it can be determined whether they can be meshed successfully. Three
especially useful pieces of literature are theoretical overviews of previous research done
on FDI determinants. Each gives broad overviews before getting into specifics of which
theories have proven most successful: the first being Lizondo’s 1990 article which
focuses on FDI from a micro viewpoint, then Blonigen’s 2005 review focusing on firmlevel to economic-level variables, and lastly Faeth’s study in 2009, which focuses heavily
on macroeconomic research. Each successfully lays out the state of FDI research at
publication time in addition to constructing their various theoretical justifications. In
addition to these three works, John Dunning also presents a model based on combining
many of the frameworks described by these three authors.
Lizondo
While Lizondo’s was written over 20 years ago, the current state of the field still
resembles the structure that he describes. This structure is compromised of determinants
that are broken down into three different types: 1) Perfect Market Theories 2) Imperfect
Markets/Industrial Organization Theories 3) Liquidity/Currency Theories (Lizondo, Saul
1990). Each of these theoretical backgrounds considers multiple variables—even multiple
types of variables—that affect the determination of FDI levels, though each is based on
different initial assumptions.
1) Perfect Markets Framework
The first of this group—those that are predicated on perfect market conditions—is
based on the initial assumption that money will flow across borders freely, from
economies with low returns on investment towards economies with high returns on
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investment. If a firm assumes a cost of capital (or, cost of investment) that is fair when
weighted with exchange rates, then the investments will be made where returns are
predicted to be higher. There are, however, issues with this theory. Considering this
assumption was first popularized in the 1950’s when world markets were heavily
burdened with tariffs and trade barriers, it would be misguided to consider it a perfect
market landscape. Even now, despite an increasing globalization that accounts for the
freeing of markets over time, very real differences in culture, as well as political, legal
and tax systems remain entrenched, making it difficult to assume progress toward perfect
markets.
Tests of this theory have attempted to measure whether companies simply make
locational investment decisions based on year-over-year returns, and almost always prove
to be inconclusive. One reason for this is that it is very difficult to capture return on
investment data clearly. For example, many of these tests measure a company’s domestic
investment versus their foreign investment in multiple countries. The studies hope to
show that the company invested abroad for specific reasons in each case, namely that
each foreign environment provided a unique environment, thus enabling raised
expectations of rates of returns for each project. While the company may have decided to
embark on these investments because of forecasting high rates of returns, it is very
difficult for even the investing company to determine what these returns are over time.
This is due to numerous reasons, such as a lack of standard procedure in terms of time
lagging the return schedule, how financial markets and political situations can change
over the course of the investment, etc. Therefore, for academics studying FDI versus
domestic investment decisions, it has proven difficult to parse out the exact nature of
market perfection and rates of returns. Another problem that must be faced in this type of
study is the fact that rates of return must be assumed stable within each country, when
this is clearly not the case. For example, if a company determines that a project carried
out in China has a higher rate of return (15%) than a similar investment in their home
country of the U.S. (12%), it would be assumed in such a study that the rate of return for
China is 15% while the U.S. is 12%, regardless of the dynamics of the individual project.
Another consideration for firms’ investment decisions is risk. Under the
assumption of perfect markets, a firm may be investing abroad to diversify their
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investment portfolio to mitigate risk. For example, a company with 10 manufacturing
plants in 10 different countries faces less catastrophic risk than the company with 10
plants in a single country. Therefore, it follows that investment decisions could consider
this risk in their future rates of return and invest abroad not to earn a higher profit margin,
but to prevent steeper losses in the case of a catastrophic event. While risk is a productive
consideration to add to this model (because it inherently accounts for each nation’s
inflow and outflow of capital (which rates of returns does not)), some argue that risk
cannot be logically considered while also assuming perfect markets, because there would
be no reason for firms to diversify risk, only individual investors. Also, previous studies
have had trouble proving that diversification of risk is a significant FDI determinant.
The last consideration under the perfect market assumption is market size. Market
size can sway investors from their home countries towards larger markets because of the
hopes of capturing a larger consumer base. This consideration, unlike the previous two,
has actually proven to have empirical merit, but is perhaps less useful than it seems. For
example, a common explanation given for why China has received so much FDI over the
last two decades is the size of the local market. Though the market is the largest on earth,
the companies that were investing heavily in China were often building manufacturing
plants that would then export products back to their home markets in the U.S. or Europe.
In this situation, market size may appear to be significant but ultimately may be
insignificant. In order to capture the effects of market size, it would also be necessary to
measure wealth per capita to see which how many individuals could be considered viable
consumers.
2) Imperfect markets/Industrial Organization Framework
In 1976, Hymer’s paper introduced doubt in the perfect market assumption and
argued for the individual dynamics within companies and countries that have significant
influence in determining FDI distribution. Two sources of this doubt were transactioncost imperfections and structural imperfections; Lizondo defined each as such:
Structural imperfections, which help the multinational firm to increase its
market power, arise as a result of scale economies, knowledge
advantages, distribution networks, product diversification, and credit
advantages. Transaction-costs, on the other hand, make it profitable for
the multinational firm to substitute an internal "market" for external
transactions. (Lizondo 1990, pg. 6)
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Each of these market imperfections has lead to a different branch of FDI determinant
studies—those incorporating structural imperfections have used Industrial Organization
(IO) theory, while those accounting for transaction-cost imperfections have used the
Internalization Theory of FDI. The IO viewpoint stresses the advantages and
disadvantages a firm faces when doing business abroad. Most importantly, the
disadvantages should and do weigh heavy in terms of investment decisions. These
disadvantages include language and distance barriers, as well as a potential lack of
knowledge about local consumer preferences. Therefore, a company will be more likely
to invest if these disadvantages are minimized (investing in countries with similar
language/cultures, neighboring states, etc.), or if the advantages clearly outweigh them.
Typical advantages of foreign companies, especially in the case of those from the U.S.,
could be dominant brand recognition, economies of scale, or management technique. As
Graham and Krugman wrote in their 1989 paper, U.S. companies historically had a
significant advantage in all of these areas post-World War II leading up to the 1990’s.
Since then, American companies have faced increased competition with global
technological and management technique on the rise. Supporting this theory, there has
been a clear increase in foreign investment towards the United States over the last two
decades, causing domestic fear in the U.S. over ownership and dominance compared with
China and Europe.
The Internalization Theory of FDI is based on the idea that because international
markets are imperfect, it is less risky and less costly to have cross-border transactions
within a company than across borders between two companies. In other words, it is safer
and cheaper to open a foreign division or subsidiary of your company rather than
counting on a foreign company to do the same work. Therefore, if a foreign country has
an advantage in production (cheap labor, higher technological capability, etc.), an
American company should invest directly into that country rather than indirectly. This
theory leans heavily on the theory of the firm from the financial disciplines, which has a
main belief that the firm is able to make better decisions for itself than the market can. Of
all the theories used to describe FDI determinants, this is perhaps the most broad and all
encompassing, as considerations for a firm’s motivation lies at the heart of FDI
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determinants. An offshoot of the IO and Internalization theories is the Oligopolistic
approach, which states that once some companies have been internally motivated to
invest abroad, this will kick off an FDI reaction within their industry. Competitors will
then race to these foreign markets so as to not lose market share, regardless of whether
there are internal advantages of FDI for those companies.
3) Liquidity/Currency Frameworks
Apart from these two main theories regarding determinants of FDI, several other,
less comprehensive, theories have arisen to suggest the incorporation of other variables
that have proven to be significant. Liquidity factors, exchange rates and currency issues,
as well as government profiles all can have impacts on where companies invest abroad.
There has been a large amount of documented accounts that suggest that when investing
abroad, domestic firms do not make large leaps into these markets at one time. In other
words, in most cases firms will make a small initial investment and then build their
foreign operations over time from the profit that this division makes, therefore only
putting a small amount of initial money at risk. When extrapolated to national level
examination rather than firm level, this produces investment based on previous returns
rather than other environmental factors. While these environmental factors no doubt have
an effect on previous returns, they play less into the decision process of firms about
whether to invest now than how well their investments paid off last year. Much like the
theory of rates of returns in perfect markets, this liquidity theory does rely on the difficult
to measure rate of returns numbers, but the incorporation of internal investment history
makes more of a theoretical impact on determinations of future investments.
Another factor that every company must take into account when investing abroad
is the currency effects that foreign ownership will have on their balance sheet. If a
country has a strong currency, it is more likely that companies in that country will invest
abroad, and less likely that foreign companies will invest in that country. Because
exchange rates fluctuate daily, this presents potential risk for long-term investments as
investors have little influence over the currency advantages/disadvantages that their
investment may inherit. And while companies must consider this upon foreign
investments, studies such as Moore’s 1993 study on German FDI have proven that
exchange rates often have little to no influence over FDI determination. He states that
14
because of exchange rate volatility, companies rarely have the capacity to base long-term
investment decisions on short-term fluctuation possibilities.
Lastly, government policy in foreign countries can pose significant challenges or
incentives for investment through tax rates, tariffs, and further regulatory measures. Also,
overall political stability can be a source of major risk, especially in emerging markets.
When these last three considerations are added to the three major frameworks Lizondo
first discusses, the article thoroughly summarizes the possible determinants of FDI that
academics still face today. Blonigen proves that this is the case is his article, written
fifteen years later, while formulating his own take on which frameworks may work best.
Blonigen
His paper, entitled “A Review of the Empirical Literature on FDI Determinants”,
reviews numerous variables in depth while relating them back to theory, but theories
classified in different ways from those described by Lizondo. Instead of describing the
theories as firm-level versus macro-level, Blonigen uses “internal” versus “external”
factors to describe the choices firms face when choosing investment locations. Internal
factors are potential advantages that the firm themselves have control over, much like the
micro-level factors described earlier, while external are clearly macro factors. He only
briefly touches on these internal variables, and only differs from Lizondo in that he
introduces “R&D intensity” and “advertising intensity”, meaning the amount spent by the
firm on R&D and advertising. Blonigen rationalizes these additions through results of
previous research: “R&D [and advertising] intensity is almost invariably positively
correlated with multinationality”.
The rest of his research reveals the importance of macroeconomic variables,
which he narrows down to four: Exchange rates, Interest Rates, Institutional Policy, and
Trade Policy. Each of these determinants has been proven significant in some way or
another, and according to Blonigen, should not be neglected in future studies. However,
in concluding his paper, he states that he has gathered that “most determinants of crosscountry FDI are fairly fragile statistically” and that “The more insightful and innovative
papers in the literature have developed hypotheses about when a factor should matter and
when it should not matter, and then find creative ways to test these hypotheses in the
data”. Faeth’s 2009 article reveals many similar conclusions.
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Faeth
Faeth covers much of the same theoretical structure in 2009 that Lizondo and
Blonigen covered years prior, but she focuses more on empirical results from both
financial and economic research. Her survey of literature concludes that when trying to
reveal determinants for outward FDI from a finance perspective, the following variables
have been proven to be significant at times: exchange rates, tax rates, trade protection,
and interest rates. The exchange rate factor is derived from two decades of research in the
field, most notably Froot and Stein (1991) whose work established the significance of
exchange rates and exchange rate volatility on FDI. Tax affects on FDI were brought to
light with Hartman (1985), and have been honed by Blonigen and Davies (2004), with the
former claiming that adverse tax regulations drive FDI and the latter finding that tax rates
have little to no effect on FDI. Other financial studies that model outward determinants,
such as Duanmu and Guney (2009), find currency levels, institutional support, and
interest rates to be significant explanatory causes for FDI levels.
Faeth also describes how economists and economic historians have contributed
equally, if not more so, to the debate surrounding FDI determinants. Such models often
use variables that differ from those previously stated. These variables include: GDP, GDP
growth, education level of workforce, population size, political stability, distance from
host to recipient country, cultural difference, and number of patents issued annually.
From these variables, many different models have been employed. Faeth (2009) has
helped summarize these works in her article, which discusses the nine most prominent
models used in studying FDI determinants. After analyzing each model’s strengths and
weaknesses, she comes to the conclusion:
…Any analysis of determinants of FDI should not be based on a single
theoretical model. Instead, FDI should be explained more broadly by a
combination of factors from a variety of theoretical models such as
ownership advantages or agglomeration economics, market size and
characteristics, cost factors, transport costs, protection, risk factors and
policy variables. (Faeth 2009)
Dunning
John Dunning wrote a seminal work on FDI determinants as well, though his
study is a unique analysis rather than theoretical summary like the last three. Dunning
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built a model based on three different historical frameworks in 1988, and though perhaps
not as comprehensive as the patchwork framework Faeth suggests, it is certainly one of
the most comprehensive single frameworks to emerge. His OLI model, also know as the
“Eclectic Paradigm”, considers three main categories of advantages that lead to FDI:
Ownership advantages, Locational advantages, and Internalization advantages (Dunning
1988). Companies investing abroad seeking ownership advantages consider the benefits
of ownership higher than the cost of setting up businesses across borders. These
advantages include higher returns to scale, minimized transaction costs, and the ability to
ensure high quality management skills and production technique while ensuring patents
and trademarks stay in house. Locational advantages are broken down into economic
advantages, political advantages, and social advantages, but could be broadly measured
by natural resource wealth, low-cost labor, and attractive tax or trade policy. Other
common measures are size of market (measure by population, GDP, etc.), exports, and
distance between countries. Internalization advantages are similar to ownership
advantages, but focus more on the retaining earnings that would inevitably be lost in joint
ventures or partnership agreements that can be kept by internalizing all production
processes.
His three-pronged framework leans heavily on transaction cost theory, and he
applies this model to FDI after distinguishing three types of investment. Those
investments can be efficiency seeking, strategy seeking, or support seeking. Efficiencyseeking investments usually fall into the ownership and internalization advantages
columns, while strategy-seeking investments are usually made with locational advantages
in mind. Support-seeking investments are those that usually have location in mind also,
because these are usually foreign operations aimed at supplementing domestic operations
rather than offering a separate income stream. These classifications of investment type
and advantage type will be helpful in analyzing the American FDI data. Although the
framework is not all encompassing, it may be the best able to describe most cases.
Other References
Taking all of the frameworks and variable sets that Lizondo, Blonigen, Faeth, and
Dunning lay out in their studies, this research will lean heavily on pieces of each.
17
Lizondo’s theories on imperfect markets will provide the base set of assumptions, while
also incorporating Industrial Organization theory. Blonigen’s key contribution will be his
concluding thoughts on the appropriate use of variables depending on circumstance and
interpretation, though his four variables will also make an appearance. Finally, Faeth’s
description of “Aggregate variables as determinants of FDI” will be crucial because of its
simplicity and openness to both micro and macroeconomic concerns. Adding to these
three will be the wide scope with which Dunning brought in his OLI framework. And
while these four works provide the foundation for this study, there are a handful of others
that will be referenced.
Also influencing this thesis are Kalotay and Sulstarolva’s 2010 case study of
Russian outward FDI, Buckley, et al.’s 2007 study on Chinese outward FDI determinants,
as well as Bevin and Estrin’s 2000 paper on FDI in transition economies, as each use
similar subsets of variables with differing effectiveness. Kalotay and Sulstarolva’s
research, done for the U.N., looked at the determinants of outward FDI for Russia with
special focus given to financial considerations- especially from a firm perspective.
Because of the amount of transnational corporations that grew out of the uncertainty in
domestic markets during the 1990’s in Russia, the flow of outward FDI is relatively high
for Russian companies and investors. The authors model this FDI using standard
variables such as recipient country GDP, market size, exports, natural resources,
technological assets, and geographic and cultural distance from Russia, and find GDP,
natural resources, and cultural differences to be significant. However, they then also
employ a model to map how Russian companies decide between countries for crossborder M&A transactions. Included in this model are new variables, such as patent levels
in recipient country and exchange rates with the ruble. While this model did not find
much significance with any variables in the Russian case, the authors explained tweaks
that could have helped, all of which will be taken into account in this study.
Other studies have focused on U.S. FDI, but often using specific subsets of
variables. One such article, written by Yeaple (2003), takes a look at how skill
endowments play a role in determining U.S. FDI flow by measuring market access and
the availability of skilled workers recipient countries have. Another, done by Filippaios,
Papanastassiou, and Pearce (2003), also studied outward FDI from the U.S., but only
18
directed at countries on the Pacific Rim. In many ways, this study is an extension of
theirs to areas outside of the Pacific Rim, though some variables are of course different.
This study will use these previous works as a theoretical background and
foundation for the use of new data. By using these models, the study will not only add to
the growing literature on determinants of outward FDI, but it will also provide valuable
insight into for the American case. While studies measure outward FDI in the U.S. have
been done before, the lack of recent studies as well as a lack of studies using such large
samples will allow this research to add significant findings to the field. With most the
theoretical background now considered, the focus shifts to the variables and data that will
be specifically used in this study. The patchwork model employed here is perhaps
nontraditional, but has been used repeatedly in this type of study. Davidson (1980) was a
user of this framework when he wrote on U.S. FDI in North America and Europe by
using a concoction of economic variables. Also, using this type of framework allows for
this study to introduce and lean on many frameworks throughout, as most have something
to contribute.
2.1 Introduction to Variables
Variable
Abbrev.
FDI Position
FDIP
FDI Income
FDII
Exports
EXP
Exchange Rate
EXCH
Corp Tax Rate
TAX
Population
POP
GDP per cap
GDPPC
GDP Growth
GDPG
Geo Distance
DIST
Political Freedom
FREE
Common Lang.
LANG
CPI
Openness to FDI
CPI
OPEN
Description
Foreign Investment Position, total
amount received to date
FDI Income to date in year t-1
Exports of goods and services (% of
GDP)
Real effective exchange rate index
(2005 = 100)
Corporate tax rate (% of profit)
Population, total
GDP per capita, PPP (constant 2005
international $)
Percent annual growth in GDP
Distance in miles between countries'
main economic centers
Political freedom rankings (Free,
Partly Free, Not Free)
Common Language Dummy,
whether at least 9% share language
Consumer price index (2005 = 100)
Foreign direct investment, net
inflows (% of GDP)/GDP
Approximation for…
Source
Dependent variable
BEA
Amount of returns, t-1
BEA
Trade policy (% of GDP)
World Bank
Currency issues
World Bank
Tax issues
OECD
Market size
World Bank
Market size, market demand
World Bank
Market size, market demand
World Bank
Locational Advantage
CEPII
Political stability
Free. House
Cultural distance
CEPII
Inflation, prices
World Bank
Incoming FDI /GDP
World Bank
19
CO2 Emissions
Empl. Protection
CO2
EP
CO2 emissions (metric tons per
capita)
Ranking (1-6) on strictness of
Employee Protection
Industrial stage, Env. Regul.
World Bank
Strictness of Empl. Prot.
OECD
Table 1. Variable Descriptions, including abbreviations to be used moving forward,
what each variable is an approximation for, and source.
Based on the foundation of previous literature on this topic, the empirical model
to be employed in this paper will include variables for both financial and economic
structural data (See Table 1). The list of variables has been chosen based on significance
in previous research or particular theoretical importance in the U.S. case. As will be
shown, not all variables will make it to the final model because of overlap issues or
insignificance, but all will be tested in one form or another. The sample size of the data is
rather large for this type of study, as it includes 51 countries over 28 years. Because of
the bonuses- and limitations (lack of time observations for a time-series regression)- of
such a sample size, a panel regression will be utilized. A description of this type of
regression will be covered more in depth in Section 3. First, it is important to discuss the
theoretical background for each of these variables before hypothesizing which will be
significant and in which direction.
The dependent variable in this study is the FDI position of a country in a given
year in terms of FDI received from the United States. This data was sourced from the
Bureau of Economic Analysis within the U.S. government. They defined the FDI position
variable as such: “U.S. direct investment (position) abroad is defined as the ownership or
control, directly or indirectly, by one U.S. resident of 10 percent or more of the voting
securities of an incorporated foreign business enterprise or the equivalent interest in an
unincorporated foreign business enterprise (BEA).”An important distinction that must be
made with this statistic is that it does not necessarily represent new investment made that
in a given year, but instead total investment regardless of year that remains in that foreign
country. This number will rise when new investments are made, and lowered when
investments are pulled out of that country, whether it is in losses or gains.
Below is a statistical overview of the values of FDI positions across countries and
time in this dataset (in millions USD):
20
FDIP
Percentiles
Smallest
1%
-70
-194
10%
87
-157 Observations
1356
25%
280
-143 Sum of weight
1356
50%
1649.5
75%
7040
426762 Std. Dev.
47344.07
95%
82965
471384 Variance
2.24e+09
Largest
Mean
15868.87
Table 2. Dependent Variable (FDIP) Overview Statistics, in millions USD
Table 2 shows the total number of observations of the dependent variable (1356), the
mean FDI received for all countries over the period 1982-2009 ($15.87 billion), the
median ($16.5 billion) and the standard deviation ($47.34 billion). These numbers
suggest that the FDI positions vary greatly over time and space, with recent positions in
the UK and the Netherlands, for example, skewing the standard deviation to nearly three
times the mean and median. This variation is neither good nor bad, as it is an accurate
picture of U.S. outward FDI. If anything, the wide distribution should allow the
independent variables more room for explanatory power. For the purpose of the model,
however, this variable will be tested after being logged. The main reason for this is that
the data given by the BEA is FDI Position rather than FDI received in year x, meaning
that this statistic can accumulate year over year. Therefore, investments made in 1984
would still count in this statistic for 1985, 1986, etc. as long as the investment has not
been cashed out and profits brought back to American soil. The dynamics of this statistic
make it very difficult to differentiate investments made in any given year, so logging the
variable will give us the ability to interpret it a percent change in FDI position, or the net
change in FDI received, which will allow for the analysis of new investments in a given
21
year. The other benefit of logging this variable, as well as some of our independent
variables, is dealing with potential stationarity problems. This will be discussed more in
the Data and Methodology section. The first of these logged independent variables is
perhaps the most closely related to FDI position, FDI Income.
Relating back to Lizondo’s first framework that assumed perfect markets, the
biggest factor for determining investment locations could be returns from that country in
previous investments. In other words, if a subsidiary or joint venture in a foreign country
has proven profitable for its parent company in the United States, that company may be
more confident and willing to invest greater amounts towards that country in the future.
Not only do returns speak to previous success, but it may also act as an assurance against
political and economic risk. Proven profits and returns may give investors a sense,
whether deserved or not, that if it was safe the last time then it is a more trustworthy
environment for investment going forward. This may mean political instability, exchange
rate volatility, or trade policy in the foreign country had no adverse effect on returns.
Although there are obvious dangers with that (the lack of problems in previous
investment periods does not determine future conditions), it is also logical for investors to
consider previous investment successes or failures. The variable is noted as “FDI
income”, a measure calculated by the BEA and defined as “is the return on the U.S. direct
investment position abroad. It consists of earnings (that is, the U.S. parents' shares in the
net income of their foreign affiliates) and the net interest received by the U.S. parents on
outstanding loans and trade accounts between the U.S. parents and their foreign affiliates
(BEA).”
The power of this variable is counterbalanced by its potential pitfalls. There are a
couple of potential dangers with this variable, the first being time lags. In other words, it
is very difficult to determine how long it takes for investment to pay off. An investment
made in 1982, and therefore could be contributing to returns every year leading up to
2009, or it could provide returns only in 1983. It is impossible to parse out the differences
between the two in the data, meaning that investments in year t might not only be
effected by income from year t-1, but also years t-2, t-3, etc. For this study, the FDI
income variable will be for year t-1, meaning that income from 1983 might have an effect
on 1984 FDI decisions. This construction is not perfect but may makes the most sense
22
due to the linear sequence of time and the up-to-date availability of in-house data for
investing companies. Just with FDI position, this variable will also be logged to see the
percent change in income year over year, but it still remains difficult to tell when the
original investment was made. There is no easy way to fix this, so the issue will have to
be considered moving forward.
Another potential danger of including this variable in the model is that it is closely
related to many others, including the dependent variable itself. It follows that FDI income
is very closely tied to FDI position, in that if one million USD were invested, it would
normally yield a return within 20% of that investment, negatively or positively.
Therefore, those numbers could be highly correlated. If this is the case, the FDI Income
variable could be dropped from the model without much theoretical damage, as the
assumption of perfect markets is not seen as crucial in this study.
Variable
Observations
Mean
Stand. Dev.
Minimum
Maximum
ln(FDIP)
1321
7.483423
2.225199
.6931472
13.06382
ln(FDII)
1315
6.5612
1.616537
.6931472
10.8715
EXP
1380
40.15725
31.12923
5.3
233.5
EXCH
1044
106.9318
63.85078
36.3
995.9
CORPTAX
684
36.01509
9.962063
12.5
61.8
POP
1428
7.85e+07
2.07e+08
365500
1.33e+09
ln(GDPPC)
1093
9866114
.8742362
-2.302585
2.785011
GDPG
1402
3.342483
3.97981
-18.78335
18.28661
DIST
1428
8916.125
3325.801
2079
15536
FREE
1400
2.560714
.6650895
1
3
LANG
1428
1.470588
.4993091
1
2
CPI
1339
69.85519
34.71952
0
228
OPEN
1334
5.04e-06
.000082
-.0002083
.0022725
ln(CO2)
1317
1.639541
.9538837
-1.203973
3.666122
EP
568
2.253011
.949557
.6
4.19
Table 3. Variable statistical overview. The potentially non-stationary variables
(FDIP, FDII, GDPPC, and CO2) have been logged, so their statistics represent a
percent change interpretation rather than raw data.
23
Blonigen’s four main FDI determinants were taxes, exchange rates, trade policy,
and institutions, and they will all be addressed in one form or another in this model.
Exchange rates and taxes are simple enough to add to the model, but perhaps a bit trickier
to interpret. Exchange rates are always a concern for a multinational company as
significant profits can be lost when exchanged across borders. Therefore, exchange rates
are increasingly monitored, forecasted, and considered in most international transactions.
FDI is presumably no different. If the currency is strong in the United States relative to
world markets, it is more likely that American companies will invest abroad as the dollar
goes a longer way in such investments, and should make it easier to recoup their
investments. The same is true of the opposite: if the Yuan is strong relative to the dollar
(a present concern), it may be less likely for American investors to consider investments
in the near term. In this dataset, exchange rates were found from the World Bank and are
explicitly defined as the “Real effective exchange rate index (2005 = 100)”. While this
measure does not measure the exchange rates of the home currencies in each of the 51
countries against the U.S. specifically, it is a good measure of relative strength of
currency against all others internationally and should therefore be sufficient. As seen in
Table 3, some observations are missing for this variable and the effects of this will have
to be analyzed in the Empirical section.
Tax rates have long been linked as a possible FDI determinant because of
corporations’ interest in avoiding high tax rates. But is it worth it for companies to move
operations across borders just to shave half a percent off their tax rate? That is more
difficult to answer. As Blonigen states: “An obvious hypothesis is that higher taxes
discourage FDI with the more important question one of magnitude (Blonigen 2005).”
While some studies have found that corporate tax rates do have a significant effect on
FDI location (most notably De Mooij and Ederveen in 2003), most have a hard time
determining the effect. Results are muddied by the complicated tax systems within and
between countries, with double taxation, tax relief, and tax rates that vary by FDI source
and industry all necessary to consider. The variable that will be used in this study is a
weighted corporate tax rate taken from the OECD that will potentially face many of these
issues head on. The measure is defined as “the basic combined central and sub-central
(statutory) corporate income tax rate given by the adjusted central government rate plus
24
the sub-central rate (OECD Tax Database).” While this measure is great for giving
international tax rates a uniform data parameter, government tax structures still vary
greatly between countries. Therefore, though impossible to sift though 51 countries’ tax
laws, it will be necessary to interpret the results with these possible flaws in mind. Also,
just like exchange rates, the corporate tax rates were less available than most other
variables. The data was only presented for roughly 35 of our countries, and even less so
for the pre-2000 time period. Being the best and biggest data that could be found on
corporate tax rates, and because it is a crucial variable to consider in a theoretical sense, it
will still be included in initial regressions to test for significance. One interesting thing to
note is that the mean tax rate for foreign countries over this time period was roughly
36%, while the U.S. tax rate over the same period averaged 41% (OECD Tax Database).
A five percent decrease in taxes would certainly provide some U.S. companies sufficient
reason to investment abroad, which provides further support for including this statistic in
the regression, at least initially.
Trade policy and institutions are the final two areas that Blonigen stresses, but
application to specific variables relies on approximation. For this study, the trade policy
variable will be approximated by a measure of exports. Taken from the World Bank data,
it specifically measures the exports of goods and services as a percent of GDP, which
allows us to grasp how open and easy it is to participate in the global market from each
country. Factors that could affect this could be government policy that encourages (or
discourages) trade, taxes or tariffs, or free trade zones. In many cases, U.S. FDI is
directed towards the manufacturing industries, where the products will then be shipped
back to the United States at a lower cost. In such a case, the statistic for exports will be
very important to consider.
The institutional role on FDI will be approximated by the countries’ Freedom
House classification, for which each is broken down into political freedom rankings and
civil freedom rankings. These rankings are then translated to give whether a country is
Free, Partly Free, or Not Free, which is represented in the data set as 1, 2, or 3,
respectively. The use of this variable as an approximation for institutional policy is not
ideal but should suffice. As Blonigen wrote, institutional policy can influence FDI
potentially if political and economic institutions draft policy that openly encourage or
25
discourage international trade, foreign ownership, or copyright/patent law. A perfect
measure system would measure each of these dynamics separately. While freedom
rankings cannot capture this completely, countries that are classified as free do tend to
have more open policies for trade, more encouraging policies for foreign ownership, and
better copyright protection. Though there are some exceptions, looking towards the
membership requirements for the WTO provides ample support of this assumption.
Therefore it should be an adequate approximation.
Other issues that need to be considered can be traced back to the five types of
FDI presented in Section 1.5. Two main considerations still unaddressed are market size
and locational advantages, both of which can important drivers of FDI. Therefore, market
size will be considered with three variables: population, GDP per capita, and GDP
growth for the recipient countries, all of which were source from the World Bank. The
population variable could be significant because of its representation of new potential
consumers for American products. China, for example, has a population well over 1
billion people and represents a potentially huge market of consumers for American made
(or owned) products. This may drive American companies to open production facilities,
operational subsidiaries, etc. in China to get easier access to their consumers. In this
dataset, populations vary from 365 thousand people, to 1.33 billion, with the average
settling at 78.5 million.
The importance of GDP per capita goes hand in hand with population, because in
order for a population to be considered potential consumers, their GDP per capita must be
at a level that allows for discretionary spending. GDP per capita, like FDI position and
FDI income, will be a logged variable because of its upward trend for most countries.
Regardless, this variable will allow us to see whether American investors prefer investing
in wealthier countries rather than poorer ones, or the opposite; there are theoretical
justifications for both. It may be that U.S. investors prefer to deal with wealthier,
economically stable countries because there are cultural similarities, because they want to
form relationships with consumers and other companies in advanced economies, or
because wealthier countries have access to greater technology and labor skill. The
opposite may be equally true: because America itself is such a large, relatively wealthy
economy itself, FDI is then directed at less wealthy countries to take advantage of lower
26
wages, more relaxed environmental regulation, or tax law, or to simply get a foothold in a
potentially emerging market. The results from this variable will be interesting to interpret.
GDP growth is a straightforward variable that also has a potentially
straightforward link to FDI. As stated earlier, macroeconomist would suggest a main
determinant for FDI is the fact that companies want to take advantage of growth rates in
foreign countries that are more optimistic than those in their home country. If they invest
in that foreign market, they could automatically receive the benefits from the wave of
high growth. Recently, China has been the largest recipient of this attention with growth
rates close to 10% over the last decade (World Bank). It makes sense, then, that investors
worldwide have shifted their attention towards China because it may be seemingly easier
to get returns on investment in such a high growth environment. Many opportunities are
to be had in such an economy, and corporate investors are well aware of this. In this
dataset, there is a large variation in GDP growth, with the maximum being roughly 18%,
the minimum being roughly -18%, and the average settling at 3.34% (Table 3).
Now that market size has been covered, locational advantages variables are
important to consider. The first of these variables included is labor dynamics, as
approximated by the Employee Protection statistic at the OECD. Much like GDP per
capita, it is possible that labor dynamics could attract or dissuade FDI, depending on what
investors are looking to achieve. In the case of the United States, of course, it may be rare
that companies invest overseas to capture unique labor talent, as there is an abundance in
the domestic market. For other countries, however, it may make sense to invest abroad to
attract talent that may be lacking at home. Following this logic, it has long been assumed
that if labor cost and skill affect United States outward FDI, it must be towards low cost,
low skilled labor that American labor laws prevent. This may be difficult to prove
statistically because many investment decisions may not consider labor factors
whatsoever. Employee Protection measures “the procedures and costs involved in
dismissing individuals or groups of workers and the procedures involved in hiring
workers on fixed-term or temporary work agency contracts (OECD EP Database)” on a
scale from 0 (least stringent protection) to 6 (most stringent).
Two other variables that are implemented in the model are geographic distance
from the U.S. and common language, which is used as an approximation for cultural
27
distance. Both of these variables were devised and calculated by CEPII. The geographic
distance variable used is a weighted average of capital cities and economic centers within
countries, and their distance to the American economic center average. While far from
perfect, this is a very good approximation for a simple distance data set, as it takes into
account major access points, harbors, and border cities that would make distances shorter
than just measuring from geographic centers. The thought with a distance variable is that
FDI in neighboring countries is easier than those further away, as shipping costs,
logistics, and travel expenses would be significantly less. There may also be a cultural
difference tied to distance, but this is tough to determine in this statistic.
Common language is better approximation for this cultural distance. It is a
dummy variable based on whether the at least 9% of a foreign population shares a
common language with at least 9% of the domestic country. In this case, a foreign
country must have 9% of its population speak either English or Spanish, the languages
that at least 9% of the American population speaks. The presumed effect on FDI this may
have is that a foreign country that speaks English or Spanish would share more of a
cultural identity through colonial history or modern media, and therefore would be easier
to deal with and understand in a business situation. One great example of this issue is
Hong Kong. As a British colony with a more diffused use of English, Hong Kong has a
long tradition of strong trade and investment from the U.S. and the UK. Mainland China
differs in language and history, and therefore took much longer to open up to, and take
advantage of, the benefits of globalization. The apparent problem with both of these
variables, however, is the fact that common language and distance between countries
does not change over time, and therefore cannot be modeled in a panel, fixed-effects
model. In order to overcome this, they will be tested as dynamic variables multiplied with
their political freedom classifications, which do change over time. The reason why
political freedom rankings work in this case is because there may be a correlation
between distance to the U.S. and political freedom, as most countries in the Americas are
considered politically free, while those in the Middle East, Africa, and Asia or less likely
to be free. With language, the bond is even stronger, as the Americas speak English and
Spanish, while countries that do not speak either are, on average, farther from the U.S.
28
Whether this proves significant as a determinant of FDI or not will be determined in the
results.
The World Bank CO2 data represents CO2 emissions in metric tons emitted per
capita. Including CO2 emissions data in the model will hopefully tell us one of two
things: either 1) U.S. investors tend to invest in well developed economies with high tech
industry that gives off a lot of pollutants, and in economies with high per capita energy
usage, or 2) U.S. investors tend to invest in developing economies with heavy industry
(perhaps in raw materials) where environmental regulation is less strict than the U.S.
Because of this dichotomy, CO2 emissions is a complicated variable to include in the data
as developed, strict regulation economies and developing, lax regulation economies could
have similar statistics in a per capita sense. For the case of this dataset, it is likely that the
CO2 statistic will be positive because of those countries with large emissions per capita
that also have strict regulations (the UK and Germany, for example). Therefore it will be
a difficult variable to pinpoint an exact interpretation on, but necessary to include
nonetheless. This variable will also be logged due to its typical upward trend in order to
remove any potential stationarity issues.
Openness to FDI is a less complicated variable, as it is simply the incoming FDI
for a country divided by that country’s GDP. The formation of this variable stems from
the 2007 Buckley, et al. study on Chinese outward FDI determinants, in which case this
variable was proven significant in some samples and with a positive coefficient. It is a
ratio that is an approximation for the host country’s policies on openness to FDI, as a
large ratio represents a country that is open to, and receives, a large amount of foreign
investment relative to their economy size, and a small ratio represents the opposite. Both
of these input statistics (incoming FDI and GDP) were sourced from the World Bank.
Lastly, CPI is a statistic also found from the World Bank and represents the
consumer price index for each country annually. This index is meant to show the price of
a typical basket of goods at a point in time such that it standardizes price comparisons
between currencies and between countries with common currencies. It is also used to give
reference to the level of inflation and the real level of wages, which makes it one of the
benchmark measures of overall economic strength and health. In this study, it will be
used as an approximation for inflation and wage levels, as it may be that U.S. investors
29
are looking to take advantage of opportunities in countries with undervalued currency and
labor costs.
2.2 Hypotheses
Now that these variables have been introduced, they can be discussed in terms of
probable effect on the change in FDI position for each country. While there is
justification for thinking that each and every variable may be significant, this will most
likely not be the case in this model. Some will inevitably be more statistically relevant
than others. Based on the theoretical background, results in previous studies, and
discussion on each variable in the previous section, the hypothesis for each variable is
listed below:
Hypotheses
Independent Variables
Abbrev.
Significant?
Sign
ln(FDI Income, t-1)
FDII
Y
+
Exports
EXP
Y
+
Exchange Rate
EXCH
N
Corp Tax Rate
TAX
N
Population
POP
Y
+
ln(GDP per cap)
GDPPC
Y
+
GDP Growth
GDPG
Y
+
Geographic Distance x Freedom Rankings
DIST_FREE
Y
-
Common Language x Freedom Rankings
LANG_FREE
Y
+
CPI
Y
+
OPEN
N
CO2
Y
EP
N
CPI
Openness to FDI
ln(CO2 Emissions)
Employment Protection
+
H1 FDI Income (t-1) should prove to be significant and positive, meaning that a
positive change in FDI income in year t-1 should lead to a positive change in FDI
position in year t.
H2 Corporate tax rates will prove to be insignificant because of the complications
in accurately predicting the overall tax effects for investment decisions.
30
H3 Exchange rates will also prove to be insignificant because direct investment
decisions are rarely short-term, and long-term exchange rate fluctuations are difficult to
project.
H4 Institutional and trade policy, approximated by exports, will have a positive
significance on FDI received, as higher levels of exports allows for export oriented
investors.
H5 Market size variables (population, GDP per cap and growth) will be positively
related to FDI received because American investors tend to expand overseas towards
areas where they can reach as many consumers who can consume their products that they
can. Growth will be interesting to analyze because it will show whether investors are
attracted to simple growth statistics.
H6 Employee Protection as a measure for locational advantages will be
insignificant in this model, despite the amount of theoretical justification supporting this
framework. While some investors may seek cheaper wages and greater labor skill,
American investors should rarely seek labor skill abroad, and because most of investment
goes towards Europe, clearly cheap, unregulated labor shouldn’t be a crucial attractor of
FDI.
H7 Cultural barriers, as measured by distance and common language will be
negatively related to FDI received. This is hypothesized because although some
investment decisions are almost certainly made to connect American businesses with
truly foreign parts of the world, most American investors have long histories of setting up
businesses, joint ventures, or subsidiaries in countries such as Mexico, Canada, and the
UK. The amount of investment directed at these neighbors and culturally similar
countries carries too much weight to be insignificant.
H8 CO2 emissions will prove to be significant and positive, as U.S. investment is
generally directed at countries with large production and manufacturing industries, as
well as countries with high-energy consumption (electric, coal, etc.). Another
contributing factor could be U.S. investors’ desire to invest abroad to avoid
environmental regulations, which may be this case in raw materials production and other
heavy industries.
31
H9 Openness to FDI (Incoming FDI /GDP) will have an insignificant effect on
the model. Pressure from investors usually trumps FDI policy in host countries as foreign
investment, especially from the U.S., is rarely turned away. Therefore, investment won’t
seek out countries open to investment, only countries that can provide good return.
Openness will prove to be a secondary consideration.
H10 CPI will prove to be a positive and significant determinant of FDI position,
as it is a good estimator for economic health and strength, inflation, and labor costs. It
may not be something that weighs heavy on the mind of investors, but simply looking at
the data, U.S. FDI definitely appears to trend towards countries with strong CPI data.
3. Data and Methodology
3.1 Data
The data for these variables where chosen across 51 recipient countries to
incorporate a wide geographical and characteristic range. The list of these countries can
be seen in Appendix VII. Most of the FDI data was sourced from the U.S. Bureau of
Economic Analysis (BEA), while most of the indexes and independent variables were
sourced from databases such as the World Bank and OECD (refer to Table 1 for specific
source locations). Each of the countries used has a significant U.S. FDI position and
published data to prove it. Multiple countries represent each continent (excluding
Antarctica, of course), which provides the sample with greater explanatory power and
variation. Included are some of the U.S.’s largest trading partners (Germany, Japan) as
well as some of the slower developing countries (Honduras, Philippines). It includes
some of their greatest political allies (the U.K., Canada), and some with strained
relationships (Venezuela, Russia). While this sample is not random (chosen based on data
availability and size of U.S. FDI position), it does represent a good mix within those
parameters. The inclusion of all of them will allow us to see what kind of political and
economic factors are significant in determining outward FDI.
3.2 The Model
As stated, these variables will be analyzed using panel analysis for the years
1982-2009. The hope is that using the last year of data available will also allow for the
32
model to capture the outlook for FDI post the 2008 global downturn, though the
availability for this year is lower than the rest of the time series, which will need to be
taken into account. The use of a two-dimensional panel setup allows for the examination
of all of these variables across a panel set of 51 countries over the course of 28 years.
This is a relatively long and wide dataset, as most comparable studies use far less
countries over a period of five to ten years. The time unit is what distinguishes a panel
regression from a cross-section study and what gives us the ability to interpret FDI over
time within a set of nation specific characteristics. The fact that it is 28 years makes it too
short of a time span to utilize a time-series model. A panel model accounts for both of
these issues and allows for us to consider a long time series and large set of dependant
variables.
In order to decide which panel regression should be used, it is important to
consider each option. The three most commonly used panel techniques are the Seemingly
Unrelated Regression (SUR), the fixed-effects model, and the random-effects model
(Hill, Griffith, and Lim 2008). The SUR model is optimally used when a dataset is long
and narrow— or when there are few cross-section measurements. Because our dataset is
wide, with 51 countries and 15 variables, this model will not provide the robustness and
accuracy that the other two may.
The fixed-effects model and the random-effects model both have the ability to
capture wider datasets, but are based on different assumptions; namely that the randomeffects model assumes that the dataset is randomly gathered, while fixed-effects does not
(Hill, Griffith, Lim 2008, pg. 398). In other words, the random-effects model in would
work best if the 51 countries were selected at random. This is not the case. The countries
were selected mostly based on availability of data, which is undoubtedly dependent on
the size, wealth, and transparency of each country. Therefore, it was likely easier to find
data on developed economies with democratic governments than developing countries
with autocratic rule. This makes justifying a random-effects model in this case very
difficult. The fixed-effects model, on the other hand, does not depend on randomness
because it holds non-observed traits constant in its regression. In this case, any country
specific constants that are unmeasured, such as historical values or innovativeness, will
be held as a constant, “fixed” effect separate from the regression results. This “allows us
33
to focus on the marginal effects of the included explanatory variables (Hill, Griffith, Lim
2008, pg. 398).” For this test, unmeasured differences between countries should be held
constant throughout the time period as it allows us to isolate and test only the chosen
variables. While fixed-effects appears to be the model that would work best, the
Hausman test can also reveal explicitly whether the sample is random (Appendix I). The
test “compares the coefficient estimates from the random-effects model to those from the
fixed-effects model (Adkins, Hill, 2008).” As seen, the null hypothesis for this test is that
the difference in random-effects estimate coefficients is not symmetric, and the chi2
statistic is significant, meaning that the sample is not random. This makes the fixedeffects model the clear choice. The model will look as such:
ln(FDI positioncountry i, time t) = αi + βln(fdiii,t) + βexpi,t + βexchi,t + βcorptaxi,t +
βpopi,t + β ln(gdppci,t )+ βgdpgi,t + βcpii,t + βopeni,t + β ln(co2i,t) + βepi,t +
βdist_freei,t + βlang_freei,t + ui,t
Where:
– αi (i=1....n) is the unknown intercept for each country (represents the country
specific constant factors that do not change over time).
– FDIP i,t is the dependent variable (DV) where i = country and t = year.
– βx i,t represents one independent variable (IV),
– β is the coefficient for that IV,
– ui,t is the error term
With a fixed-effects model comes a lot of important considerations not unlike other
econometric models. However, because of the robustness of the model, many of these
considerations present less of a danger than these other models. For example, the nature
of a fixed-effects model prevents us from worrying about both heteroskedasticity and
autocorrelation. This is because the individual-level robust estimators remain consistent
regardless of the presence of heteroskedasticity and autocorrelation (Hill, Griffith, and
Lim 2008). The danger of autocorrelation is minimized because of the fixed-effects
estimator: “Any variable that does not change over time will be exactly collinear with this
dummy variable (Hill, Griffith, Lim 2008, pg. 398)” and therefore would be dropped
from the model, so no remaining variables would be collinear. In other words, because
unseen variation in the model is accounted for, autocorrelation and heterskedasticity
34
concerns are also accounted for in ways that most econometric models are not. And even
though the robustness of this model minimizes some of these dangers, it does not mitigate
them all.
The first of these considerations is the balance of the model, which represents
whether there is an equal amount of time-dimension observations for each country in the
dataset. From the outset, data was sought for the years 1982-2009 for all countries, but
there are missing observations for many countries and many variables. This makes it
important to distinguish between unbalanced and balanced, as the interpretations of the
data will depend on this distinction. A simple test on the statistics software Stata (xtset,
Appendix I) reveals that this dataset is “strongly balanced”, which should be sufficient to
consider the panel balanced despite the missing observations. Stationarity is taken into
consideration through the use of natural logging in the variable sequence, and the use of
year dummy variables. Stationarity issues arise when variables are nonstationary, as
trending variables are difficult to interpret in a regression. To counteract nonstationarity,
variables with trends will be logged to give a natural log of the original variable. These
variables must then be interpreted as a percent change of the original variable rather than
raw change. Examples of these variables are FDI position, FDI income, CO2, and GDP
per capita (all of which usually trend upward). Lastly, it is important to recognize the use
of year dummy variables. Year dummy variables have been implemented in this model to
minimize time fixed-effects that could be present- trends within years and within
countries that could go unseen by the model.
4. Empirical Analysis
4.1 Results
Four regressions were run in order to eventually gain results that are optimal for
interpretation. Regression 1 included every variable, though the language-freedom and
distance-freedom rankings variables were dropped due not having enough variation over
time. Unfortunately, this study is unable to comment on the importance of these
determinants and would need to be followed up upon in future research that crafted a
regression without a time dimension. The rest of the variables were tested, however, in a
first regression that only included 393 observations- a rather low number considering
35
nearly 1400 observations for our dependent variable. This is caused by the low number of
observations for corporate tax rates (CORPTAX) and employment protection standards
(EP). Because of this, Regression 2 was run without these variables, and the number of
observations nearly doubled to 694. In Regression 3, the variables that proved significant
in Regression 2 were run in isolation, with the insignificant variables dropped. This gave
us 860 observations and strong significance among all the tested variables. Regression 4
was run with the variables of Regression 2, but dropping FDI income because of
correlation concerns with the dependent variable. Each of these regression results can be
seen in more detail in Appendix II-V. For the purpose of this analysis, it is important to
interpret each variable’s significance and coefficient. If a variable is insignificant, it is not
worthwhile to interpret its coefficient, though it will be worthwhile to consider their
insignificance in the discussion. All results are shown in Table 4, with the R-squared
(overall) and F-value at the bottom. While both of these statistics are provided, they are
not considered important or as driving factors in regression analysis, as the goal with
most panel regressions is to determine significant variables and interpret the results,
rather than to maximize the R-squared and minimize the F-statistic.
Variable
Regression 1
Regression 2
Regression 3
Regression 4
(Appendix II)
(Appendix III)
(Appendix IV)
(Appendix V)
ln(FDIP)
DV
DV
DV
DV
ln(FDII)
.4764231***
.6725031***
.7654562***
-
EXP
.0072796*
.0029467
-
.0230847***
EXCH
.004702*
.044067***
.0038739***
.009865***
CORPTAX
-.0012791
-
-
-
POP
9.66e-08***
1.64e-08***
1.34e-08***
.0000000156***
ln(GDPPC)
.0092207
.0204904
-
.060502
GDPG
.0440804*
-.0155357
-
-.0195196
DIST_FREE
(dropped)
-
-
-
LANG_FREE
(dropped)
-
-
-
CPI
.0025988
-.0039873*
-.0037509***
-.0036556*
36
OPEN
-3345.844
181.1324
-
683.3816***
ln(CO2)
-.760288***
-.3649243***
-.1659632***
-.4759179***
EP
-.1427961*
-
-
-
Observations
393
694
860
696
R2 (overall)
.52
.2112
.3473
.1313
P>F
0.00
0.00
0.00
0.00
Table 4. Regression results from three regressions. Regression 1 includes all
variables, Regression 2 drops CORPTAX and EP due to observation size,
Regression 3 tests only those significant from Regression 2, and Regression 4 uses
Regression 2 variables while dropping ln(FDII).
***= statistically significant at the 1% level
**= statistically significant at the 5% level
*= statistically significant at the 10% level
Regression 1
In Regression 1, three variables are significant at the three-star level (meaning
their p-value <.01): FDI income (t-1), Population, and CO2. Exchange rates, Exports, and
Employment Protection are significant at the one-star level (p-value <.10). Corporate tax
rates, GDP per capita, GDP growth, CPI, and Openness to FDI were all proven
insignificant to varying degrees (see Appendix II for t-stat and p-values for all variables).
Our combined variables DIST_FREE and LANG_FREE were dropped from the
regression because of a lack of variation over time. Even though the Freedom House
rankings did vary over time, they only changed once or twice on average per country over
the 28 year time period. These variables would need to be tested in another model in a
future study to gather the information needed to determine their importance.
One interesting thing to note about Regression 1 is the one-star significance of
Exports. The interpretation of this statistic is that every percent increase in exports as a
percent of GDP causes a .0073 percent increase in FDI position (at the one-star level).
However, this significance changes in Regression 2. Previous research (covered in
Section 2) shows that exports (as a percent of total GDP) have historically been found as
both significant and insignificant, depending on the study and the dataset used. This
occurs in these regressions as well, with significance not only changing, but also the
coefficient, which changes from .0073 to .0029 between Regression 1 and 2, a relatively
37
large shift. Therefore, the results from these two Regressions would be considered
inconclusive, but more analysis can be done for Regression 4, where Exports proves to be
significant at the three-star level.
Two variables proved to be significant in only Regression 1: GDP growth and
Employment Protection. It was hypothesized that GDP growth would be significant and
positive, which is the result in this regression. A one percent increase in GDP growth
causes a .044 percent increase in FDI position, but with a p-value of .061. The opposite is
true in Regression 2, with a negative coefficient (-.0155) and insignificant p-value (.316).
This casts doubt on the significant determination of GDP growth. Employment protection
is a bit different because it was dropped from Regression 2 because of a lack of
observations, so Regression 1 results are all that we can go by. It’s coefficient (-.1428) is
significant at a one-star level, meaning that a decrease in Employment Protection status
by one causes an increase of .1428 in percent change of FDI position. While its one-star
significance and lack of observations must be considered, it is at least an interesting result
worth analyzing in the discussion.
Regression 2
Regression 2 was run like Regression 1, except dropping CORPTAX,
DIST_FREE, LANG-FREE, and EP from the equation. This allowed for a greater
number of observations, which in turn gives us more confidence in the results. It also
gives us the ability to deem GDP per capita, GDP Growth, and Openness to FDI as
insignificant. While these were also significant in Regression 1, we can be more certain
now of the results because they are consistent even with more observations added (from
393 up to 694). The added observations also strengthened the significance for Exchange
Rates and CPI, with Exchange Rates moving from one-star to three-star significance, and
CPI moving from insignificant to one-star significant.
Regression 3
In Regression 3, all variables that have proven insignificant have been dropped,
which has resulted in all remaining variables being significant at the three-star level. FDI
income is the important variable to consider in this regression, as it is dropped in
38
Regression 4. The results in Regression 3 helped support the fact that an additional
regression was needed without FDI Income, as the t-statistic was very large (31.74). This
result, coupled with a correlation test run on all of our variables (Appendix VI), revealed
that FDI Income had a strong correlation (.79) with FDI position, and could be effecting
the regression. Nonetheless, it is important to analyze FDI income in Regression 3, as it
clearly is significant. The variable has a coefficient of .7655, meaning that an increase in
one in percent change of FDI Income (t-1) gives an increase of .7655 percent in FDI
position change. Therefore, if FDI income changes 10% in one year, the FDI position
should change 7.655% in the next year.
CPI also reaches the height of its significance in Regression 3, achieving a three
star significance level with a coefficient of (-.0038). This can be interpreted as an
increase of one in the Consumer Price Index decreases FDI Position by .0038 percent.
This makes sense, as investors are usually looking to take advantage of low price
positions and undervalued assets.
Regression 4
As stated, Regression 4 is a run of Regression 2 without FDI Income, which is
dropped because of correlation issues. It is also considered the final regression in this
study, in which five variables were proven significant at the three-star level: Exports
(coefficient of .0231), Exchange Rates (.009865), Population (.0000000156), Openness
to FDI (683.3816), and the natural log of CO2 emissions per capita (-.4759). CPI was
proven significant at the one-star level, a step back from Regression 3. The natural log of
GDP per capita and GDP growth were proven insignificant again, leaving little doubt in
this model. It can be seen that dropping FDI Income had a large effect on the significance
of Exports and Openness to FDI, as well as their coefficients. This must be attributable to
the decrease in correlation problems within the regression from the previous run, where
FDI Income’s presence in the model skewed significance away from other variables
towards itself.
These significant variables in Regression 4 can be interpreted as such: 1. An
increase in one percent of exports as a percent of total GDP increases FDI position in that
country by .0231 percent. 2. An increase in one in the real exchange rates of a country
39
increases FDI position in that country by .009865 percent. 3. An increase in one person in
a country’s population increases FDI position in that country by .0000000156 percent. 4.
An increase in one in the fraction of FDI incoming divided by total GDP increases FDI
position in that country by 683.3816 percent (which seems huge, but the Openness to
FDI variables are very, very small). 5. An increase in the percent change of CO2
emissions per capita by decreases the FDI position in that country by .4759 percent.
4.2 Discussion
The results from these four regressions showed a lot of interesting dynamics when
variables were dropped from respective runs. It also gave many significant variables with
telling coefficients in terms of the American case of outward FDI. Table 5 shows the
results of these regressions against the hypotheses listed in Section 2.2.
Hypotheses
Independent
Results
Abbrev.
Significant?
Sign
Significant?
Sign
ln(FDI Income)
FDII
Y
+
Y*
+
Exports
EXP
Y
+
Y
+
Exchange Rate
EXCH
N
Y
+
Corp Tax Rate
TAX
N
N
Population
POP
Y
+
Y
ln(GDP per cap)
GDPPC
Y
+
N
GDP Growth
GDPG
Y
+
N
Geo Distance
DIST
Y
-
N
Political Freedom
FREE
Y
+
N
Common Lang.
LANG
Y
+
N
CPI
Y
+
Y
-
OPEN
N
Y
+
CO2
Y
Y
-
EP
N
Variables
CPI
Openness to FDI
ln(CO2 Emissions)
EP
+
+
N
Table 5. Hypotheses vs. Results (with differences in italics)
*significant in Regressions 1,2,3, but dropped in 4 due to correlation
concerns
40
As shown, there were many incorrect hypotheses, most noticeably the
insignificance of GDP per capita, GDP Growth, and Geographic Distance, Political
Freedom, Common Language, measured by the variables (DIST_FREE) and
(LANG_FREE). None of them were proven to be determinants of outward FDI in the
American case, though, as stated, the last three of these were dropped from the regression
from the beginning because of a lack of variation over time. It would be necessary to
study these more in isolation before firmly concluding that they have no significant
determination on outward FDI from the U.S. The other two variables, GDP per capita and
GDP growth, appears to have no significance either. This could be one of two
explanations: 1. These variables are insignificant in reality, because American FDI is
spread throughout high GDP per capita/low GDP growth countries (such as Canada,
those in Western Europe, etc.) and throughout countries bearing the opposite traits of low
GDP per capita/high growth (China, India, etc.). In other words, American investors can
look for markets with wealthy consumers, or they can look for markets with cheap labor
(though these are not necessarily mutually exclusive). Likewise, they can be attracted to
high growth economies in order to ride this growth wave, or they can be attracted to slow
growth economies that are implementing policies that try to attract FDI to encourage
future growth. These two variables attract FDI on both sides of their spectrums and, in
this case, prove to be insignificant because of it.
The other two variables that prove insignificant (corporate tax rates and
employment protection) were hypothesized to prove this. In both cases, like the GDP
measures, the data proves that American FDI is attracted to countries with high and low
corporate tax rates, as well as high and low employment protection standards. For
example, Japan and Belgium receive large amounts of American FDI each year, but in
2008 their corporate tax rates were among the highest (39.5% and 34%, respectively).
Poland and Turkey received much less American FDI despite much lower tax rates (19%
and 20%, respectively). Therefore, it makes sense that tax rates cannot be proven
significant as a FDI determinant. Some investments are presumably made to take
advantage of low corporate tax rates abroad, but most investments are most likely made
because of other determinants, after which tax rates are the second, third, or fourth
considerations in that investment. The same can be said for employment protection, as it
41
appears that its insignificance points to it being a secondary consideration for American
investors rather than a totally non-determining factor.
Four of the variables that were proven significant were either surprises, or their
coefficient sign was a surprise. Exchange rates were hypothesized to be insignificant, as
previous research (Faeth) pointed to the belief that investors rarely made or canceled a
long-term investment because of short-term currency concerns. This proved to be
incorrect, as exchange rates were significant in Regression 4 with a positive coefficient.
This suggests that the stronger the real exchange rate a country has, the more FDI that
country will receive from the United States. This is not a logical result in a sense because
American investors should not be trying to invest abroad to get poor currency
conversions from the dollar, but the reality of the international investment landscape
makes this result understandable. This is because the majority of U.S. FDI still goes to
the European Union and the U.K., both of which have had strong currencies in relation to
the U.S. dollar over the last decade. This undoubtedly had an effect of exchange rate
significance in this model. It would be interesting to isolate this variable in a follow-up
study to see whether the coefficient changes in the future, especially with Europe
undergoing currency struggles at the present time.
Another surprise was Openness to FDI, which was insignificant in Regression 1
and 2, but jumped to three-star significance in Regression 4 when FDI income was
dropped. This variable was an approximation for a country’s openness and
encouragement of FDI, and in this case, American FDI. While it may make sense that this
has a relationship with the amount of American FDI position, it was hypothesized to be
insignificant in this model because it was presumed that investors do not consider a
country’s “openness” when deciding between locations. Also, there was a worry about
the correlation between this variable and the dependent variable, though this proved not
to be an issue (Appendix VI). The significance proven in Regression 4 can be explained
by discussing the fact that American FDI tends to go towards countries that receive a lot
of FDI from all countries (not just the U.S), especially relative to their GDP. This makes
sense, as countries that attract foreign investment in general should also be better at
attracting U.S. investment. Consumer Price Index was also significant and positively
related to FDI position. This suggests that countries with high CPI indexes receive more
42
American FDI, which could result because CPI is an approximation for prices, which are
inevitably higher in countries with high GDP per capita. In this sample, the largest
recipients of U.S. FDI are Western Europeans, who also were among the largest CPI’s in
the sample.
The last surprise was the logged variable of CO2 emissions per capita, which was
hypothesized to be significant, but with a positive coefficient. The results proved that it
was significant but with a negative coefficient. In Section 2.6, it was stated that CO2
emissions per capita could be positively or negatively related to FDI position, but that it
was more likely positive because the countries that receive the most American FDI tend
to be industrialized with large manufacturing industries, as well as high energy use per
capita. This model showed that a one percent increase in CO2 emissions caused a .476
percent decrease in FDI position. This suggests that American FDI tends towards low
CO2 emissions per capita economies. This is possibly caused by very high per capita
emissions countries like the United Arab Emirates, a country that receives relatively little
American FDI, but emits the most CO2 by far in this dataset. Countries like this
(receiving little FDI but creating large emissions, such as Russia) have most likely
skewed the significance despite the variable being logged. Despite a significant result,
further research with this variable would help build confidence in this interpretation.
The rest of the significant variables followed the logic of the hypotheses. FDI
income was positively related to FDI position in Regressions 1, 2, and 3, but concerns
over correlation between these two variables makes interpreting this relationship
complicated. As discussed in Section 2.1, the FDI income variable faces lag issues as
income is measured from investments made over long periods and short periods alike, so
it is difficult to parse out whether high income in year t-1 equals a strong FDI
environment in year t-1, or if it means that years t-10 through t-2 were great
environments, but t-1 was worse and caused investors to pull out of foreign investments.
Also, a correlation with FDI position of .79 (Appendix VI) shows that regression results
could be ruined by correlation effects. Despite these warning flags, it is clear that FDI
income has a strong, positive relationship with the dependent variable, meaning that an
increase in FDI income in year t-1 equals an increase in change in FDI position in year t.
This makes sense because positive returns on previous investments should encourage
43
future investments, especially if companies are reinvesting profits from a foreign
subsidiary back into that subsidiary, as Lizindo suggested in his discussion on liquidity
factors (refer to Section 2.). Although future research would be needed to clear up the
correlation issues in this model, it is reasonable to conclude that FDI income is an
important consideration for firm-level FDI determination.
Another important consideration is Exports (as a percent of total GDP). This was
hypothesized to be significant and positive, and this was the case in Regressions 1 and 4.
This variable was an approximation for the importance of the ease of exportation of
manufactured goods by American companies in foreign countries. In other words, for
Americans investing abroad to build manufacturing facilities or to acquire manufacturing
companies, it is crucial that the economic, political, and geographic characteristics allow
for easy exportation of those goods back to the United States or other markets. This
model proved this to be the case, as high export economies tended to receive more
American FDI than those that do not export as much.
Lastly, Population turned out to be a positive, significant determinant of
American FDI position as hypothesized. This can be interpreted by saying that countries
with bigger populations tend to receive more American FDI. This follows logic because
larger populations tend to be associated with larger economies, larger consumer
populations, larger segments of educated populations, etc. This may not be true
worldwide, but in this data sample, in which there are few African, Middle Eastern, South
American, and Asian countries, larger populations can be associated with the larger
economies (China, Germany, the UK, Japan etc.). Therefore, American investors seek
out countries with more possible consumers and larger labor pools.
5. Concluding Remarks
The aim of this thesis was to inquire more deeply into why American investors
invest abroad and—when they do—why they choose some recipient countries and not
others. Previous research and theoretical frameworks have suggested many different
significant determinants. Some believe firm-level considerations are the biggest
determinants, while others believe macro-level structures and incentives entice FDI. The
approach of this study was to consider both viewpoints while building a unique
44
composition of variables that have historically had the most empirical support. Firm-level
variables (such as FDI Income and Corporate Tax Rates) were implemented to gather
how foreign conditions may be beneficial to individual/corporate interests, and macrolevel variables (such as Exports, Population, and GDP Growth) were incorporated to
gauge large-scale structural benefits for American companies abroad. The results of this
were a fixed-effects panel regression on the determinants of American outward FDI,
which gave insight into multiple variables with differing results. In total, six variables
were found to be significant (though one (FDI Income) with a large asterisk beside it
because of correlation issues), and seven were found insignificant.
The strength of this analysis was the size of the panel data set, with 51 countries
included over a 28-year time period. This allowed the regression to capture large
variation between countries, and trends within countries, over time, more so than many
similar studies done in the field. Previous, smaller studies on FDI determinants have
suggested the significance of many of the variables used in this thesis, though some of the
variables found significant in this model have been proven insignificant in other studies
(like exchange rates), and some found insignificant in this model have been proven
significant in other studies (such as corporate tax rates). This study does not provide
conclusive answers on the significance of these variables in the study of outward FDI
determinants, but instead is another case to be added to the lexicon for consideration in
future research. The American case is a large and important one to consider, and the
results are, by and large, clear. However, future researchers are not the only ones to
benefit from this study.
In addition, American corporations looking to invest abroad could gain from
reviewing this study. While it does not answer which countries are most successful in
delivering returns, it could allow for businesses to examine the characteristics that
American investors look for among recipient countries, and how investment rates change
over time. A company with a mastery of this information could analyze potential
recipient countries by each variable, determining which was the most crucial in their
future investment success. It is also helpful in that it tracks investments changes over
time and space, giving indications on which countries FDI positions are growing faster
than others, not only allowing a potential company to join in with fellow investors, but
45
also to highlight those countries that receive too few investment considerations. The size
of the country panel and variable list would provide ample variation for any U.S.
company looking to make their mark abroad.
However, despite this model’s strength in size, it also had possible issues with
correlation between variables, misinterpretations, or simple data collection errors. Two of
the biggest failures of this model were the small observation numbers for corporate tax
rates and employment protection, and the ineffectiveness of the distance/common
language/political freedom variables. These deserve much more attention and
determination from a researcher than this study provided. Nevertheless, despite all of
these possible pitfalls, it is still important to recognize the possible contribution that this
study can make in the FDI determination field. This study’s findings proved Exports,
Exchange rates, Population, CPI, and CO2 emissions per capita to be significant FDI
determinants and should offer a meaningful impact on future FDI studies, especially in
the case of the American FDI. The ultimate hope of this study is that these findings will
help further catalog the complex composition of FDI determinants at play, providing
insight into a field of study that is becoming so vital to the integration of world
economies.
46
6. References
Adkins, Lee C., and Hill, R. Carter. Using Stata for Principles of Econometrics: Third
Edition. John Wiley & Sons Publishing. 2008. Hoboken, NJ, USA.
Bevin, Alan and Estrin, Saul. The Determinants of Foreign Direct Investment in
Transition Economies. The William Davidson Institute, Working Paper 342.
London Business School. October 2000.
Blonigen, Bruce A. A Review of the Empirical Literature on FDI Determinants. Atlantic
Economic Journal 33 (4), 383–403 (December 2005).
Blonigen, Bruce A.; Davies, Ronald B. The Effects of Bilateral Tax Treaties on US FDI
Activity, International Tax and Public Finance, 11, 5, 601-Y22 (2004).
Chryssochoidis, G., C. Millar, and Clegg, J. Internationalisation Strategies. New York,
MacMillan Press: 3-17 (1997).
Buckley, Peter J., Clegg, L. Jeremy, Cross, Adam R., Liu, Xin, Voss, Hinrich and Zheng,
Ping. The Determinants of Chinese Outward Foreign Direct Investment. Journal of
International Business Studies. Vol. 38, No. 4, International Expansion of
Emerging Market Businesses (Jul., 2007), pp. 499-518.
Davidson, W.H. The location of foreign direct investment activity: country
characteristics and experience effects. Journal of International Business Studies 11:
9–22 (1980).
Duanmu, J.L. and Guney, Y. A panel data analysis of locational determinants of Chinese
and Indian outward foreign direct investment. Journal of Asia Business Studies
(2009).
Dunning, John. The Eclectic Paradigm of International Production: A Restatement and
Some Possible Extensions. Journal of International Business Studies. Vol. 19, No.
1 1-31 (Spring, 1988).
Faeth I. Determinants of Foreign Direct Investment: A Tale of Nine Theoretical Models.
Journal of Economic Surveys, 23, 1, 165-96 (2009).
Filippaios, F., Papanastassiou, M. and Pearce, R. The evolution of US outward foreign
direct investment in the Pacific rim: a cross-time and country analysis. Applied
Economics 35, 1779-1787 (2003).
.
Froot, Kenneth A.; Stein, Jeremy C. “Exchange Rates and Foreign Direct Investment: An
Imperfect Capital Markets Approach”, Quarterly Journal of Economics, 106, 4,
1191-1217 (1991).
47
Graham, E. and P. Krugman. Foreign direct investment in the United States. Institute for
International Economics, Washington, DC. 1990.
Hartman, David G. “Policy and Foreign Direct Investment. Journal of Public Economics,
26, 1, 107-121 (1985).
Hill, R. Carter, Griffiths, William E., and Lim, Guay C. Principles of Econometrics:
Third Edition. John Wiley & Sons Publishing. Hoboken, NJ, USA. (2008).
Hymer, S. The international operations of national firms: A study of direct foreign
investment. Cambridge, MA: MIT Press. (1976).
Kalotay, Kalman, and Astrit Sulstarova. Modeling Russian Outward FDI, paper presented
at the conference UN Conference on Trade and Development, (2010)
Lizondo, J. Saul. Foreign direct investment: In Determinants and systemic consequences
of international capital flows. Washington, D.C.: IMF, Research Department.
(1991).
Mooij De R. and S. Ederveen. Taxation and Foreign Direct Investment: A Synthesis of
Empirical Research. International Tax and Public Finance, 10: 673–693 (2003).
Moore, Michael O. Determinants of German Manufacturing Direct Investment: 1980–
1988. Review of World Economics. Volume 129, Number 1, 120-138 (1993).
Yeaple, Stephen R. The Role of Skill Endowments in the Structure of U.S. Outward
Foreign Direct Investment. Review of Economics and Statistics, 85, 3, 726-34
(2003).
Data Sources
CEPII FDI Database. Sourced in March 2011 from:
<http://www.cepii.fr/anglaisgraph/bdd/fdi.htm>
Freedom House, Freedom in the World Reports, 1982-2009. Sourced in March 2011
from: < http://www.freedomhouse.org/template.cfm?page=15>
OECD Statistics. Sourced in March 2011 <from: http://stats.oecd.org>
UNCTAD Database: WIR2009 FDI outflows, 17/09/09, sourced from
<http://www.unctad.org/Templates/Page.asp?intItemID=3277&lang=1>
U.S. Bureau of Economic Analysis (BEA). Databases sourced in February 2011 from:
<http://www.bea.gov/international/index.htm>
World Bank Data Indicators Database. Sourced in February 2011 from:
<http://data.worldbank.org>
48
7. Appendices
APPENDIX I
Balance Test (xtset)
. xtset country_n year
panel variable:
time variable:
delta:
country_n (strongly balanced)
year, 1982 to 2009
1 unit
Hausman test
Coefficients
(b)
(B)
fe
re
lnfdiinc
exp
exch
corptax
pop
lngdppc
gdpg
cpi
open
lnco2
ep
.6015543
.0575847
-.018866
.0071929
3.10e-07
-.1526944
.0334058
.0447269
-61188.12
-1.346529
-.3417275
.8062056
.028767
-.0334709
.0136201
9.43e-09
-.200789
.045087
.063209
-80925.35
-1.417195
-.205793
(b-B)
Difference
sqrt(diag(V_b-V_B))
S.E.
-.2046513
.0288177
.0146049
-.0064272
3.01e-07
.0480946
-.0116812
-.0184821
19737.23
.0706664
-.1359345
.0537352
.0085157
.0025037
.0001231
5.22e-08
.
.
.0040188
.
.3212093
.0881547
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test:
Ho:
difference in coefficients not systematic
chi2(1) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=
-1.97
chi2<0 ==> model fitted on these
data fails to meet the asymptotic
assumptions of the Hausman test;
see suest for a generalized test
49
APPENDIX II
Regression 1
Fixed-effects (within) regression
Group variable: country_n
Number of obs
Number of groups
=
=
393
22
R-sq:
Obs per group: min =
avg =
max =
9
17.9
22
within = 0.9769
between = 0.4266
overall = 0.5200
corr(u_i, Xb)
F(33,338)
Prob > F
= -0.8092
lnfdip
Coef.
lnfdiinc
exp
exch
corptax
pop
lngdppc
gdpg
dist_free
lang_free
cpi
open
lnco2
ep
yd1
yd2
yd3
yd4
yd5
yd6
yd7
yd8
yd9
yd10
yd11
yd12
yd13
yd14
yd15
yd16
yd17
yd18
yd19
yd20
yd21
yd22
yd23
yd24
yd25
yd26
yd27
yd28
_cons
.4764231
.0072796
.004702
-.0012791
9.65e-08
.0092207
.0440804
(dropped)
(dropped)
.0025988
-3345.844
-.760288
-.1427951
(dropped)
(dropped)
(dropped)
(dropped)
.1014535
.2242514
.2241552
.2964154
.3607097
.4125648
.2749009
.1346004
.1968406
.3786482
.3948142
.3468027
.3557677
2.672094
2.652093
2.8944
2.997799
2.854679
2.874933
2.831187
2.769169
2.817752
(dropped)
(dropped)
1.737622
sigma_u
sigma_e
rho
2.6842895
.26088309
.99064271
F test that all u_i=0:
Std. Err.
t
P>|t|
=
=
432.65
0.0000
[95% Conf. Interval]
.0392651
.0056148
.0025607
.0033112
2.17e-08
.0438709
.0234468
12.13
1.30
1.84
-0.39
4.46
0.21
1.88
0.000
0.196
0.067
0.700
0.000
0.834
0.061
.3991883
-.0037648
-.0003349
-.0077923
5.39e-08
-.0770737
-.0020397
.5536579
.0183241
.0097389
.0052342
1.39e-07
.0955152
.0902005
.0033888
19367.03
.1675616
.0551452
0.77
-0.17
-4.54
-2.59
0.444
0.863
0.000
0.010
-.0040669
-41440.92
-1.089883
-.2512662
.0092646
34749.24
-.4306931
-.0343241
.0983792
.1013552
.1070437
.1043988
.115956
.121524
.1319559
.1547675
.1315989
.1406277
.1448218
.1499258
.1539758
.1554962
.1683403
.1784244
.1831479
.1912412
.1976175
.2103927
.2213194
.232423
1.03
2.21
2.09
2.84
3.11
3.39
2.08
0.87
1.50
2.69
2.73
2.31
2.31
17.18
15.75
16.22
16.37
14.93
14.55
13.46
12.51
12.12
0.303
0.028
0.037
0.005
0.002
0.001
0.038
0.385
0.136
0.007
0.007
0.021
0.021
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
-.0920591
.024885
.0135996
.0910622
.1326235
.1735263
.0153427
-.1698285
-.0620155
.1020325
.1099487
.0518975
.0528962
2.366232
2.320967
2.543438
2.637545
2.478506
2.486218
2.417343
2.333833
2.360574
.2949661
.4236179
.4347109
.5017686
.5887959
.6516034
.5344592
.4390292
.4556966
.6552638
.6796797
.6417078
.6586393
2.977956
2.98322
3.245362
3.358052
3.230852
3.263648
3.245031
3.204506
3.27493
.8027647
2.16
0.031
.158578
3.316666
(fraction of variance due to u_i)
F(21, 338) =
48.80
Prob > F = 0.0000
50
APPENDIX III
Regression 2
Fixed-effects (within) regression
Group variable: country_n
Number of obs
Number of groups
=
=
694
37
R-sq:
Obs per group: min =
avg =
max =
6
18.8
25
within = 0.9538
between = 0.0532
overall = 0.2112
corr(u_i, Xb)
F(34,623)
Prob > F
= -0.8334
lnfdip
Coef.
lnfdiinc
exp
exch
pop
lngdppc
gdpg
cpi
open
lnco2
yd1
yd2
yd3
yd4
yd5
yd6
yd7
yd8
yd9
yd10
yd11
yd12
yd13
yd14
yd15
yd16
yd17
yd18
yd19
yd20
yd21
yd22
yd23
yd24
yd25
yd26
yd27
yd28
_cons
.6725031
.0029467
.0044067
1.64e-08
.0204904
-.0155357
-.0039873
181.1324
-.3649243
(dropped)
.2174441
.3002443
.2808464
.3557171
.4118741
.5367986
.5643156
.6313968
.6909794
.5410582
.6710701
.6199449
.7760032
.8061102
.8428582
.7876777
3.106257
3.13379
3.291647
3.401238
3.125371
3.226569
3.173884
3.180208
3.319875
(dropped)
(dropped)
.9688947
sigma_u
sigma_e
rho
3.7889548
.39189047
.98941553
F test that all u_i=0:
Std. Err.
t
P>|t|
=
=
378.50
0.0000
[95% Conf. Interval]
.0321109
.002705
.0008456
2.05e-09
.0335265
.0154862
.0015004
157.5484
.1185072
20.94
1.09
5.21
8.03
0.61
-1.00
-2.66
1.15
-3.08
0.000
0.276
0.000
0.000
0.541
0.316
0.008
0.251
0.002
.6094443
-.0023654
.002746
1.24e-08
-.0453482
-.0459473
-.0069337
-128.2579
-.5976463
.7355618
.0082588
.0060674
2.05e-08
.086329
.0148758
-.0010409
490.5227
-.1322024
.1408246
.133607
.1346705
.1337382
.1331191
.1350099
.1369728
.1388058
.1435999
.145564
.1628429
.1429881
.1493768
.1495159
.1508223
.1583071
.1581271
.1570884
.1615665
.16556
.1703263
.1736072
.1793091
.1846384
.1884174
1.54
2.25
2.09
2.66
3.09
3.98
4.12
4.55
4.81
3.72
4.12
4.34
5.19
5.39
5.59
4.98
19.64
19.95
20.37
20.54
18.35
18.59
17.70
17.22
17.62
0.123
0.025
0.037
0.008
0.002
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
-.0591043
.0378697
.0163834
.0930847
.1504575
.2716689
.2953312
.358813
.4089809
.2552026
.3512826
.3391478
.4826601
.512494
.5466766
.4767974
2.79573
2.825303
2.974366
3.076115
2.790887
2.885642
2.82176
2.817619
2.949865
.4939925
.5626189
.5453095
.6183494
.6732906
.8019283
.8332999
.9039806
.9729779
.8269137
.9908576
.9007421
1.069346
1.099726
1.13904
1.098558
3.416784
3.442277
3.608928
3.726361
3.459854
3.567495
3.526007
3.542797
3.689885
.2767044
3.50
0.000
.4255083
1.512281
(fraction of variance due to u_i)
F(36, 623) =
25.82
Prob > F = 0.0000
51
APPENDIX IV
Regression 3
Fixed-effects (within) regression
Group variable: country_n
Number of obs
Number of groups
=
=
860
37
R-sq:
Obs per group: min =
avg =
max =
8
23.2
26
within = 0.9528
between = 0.1033
overall = 0.3473
corr(u_i, Xb)
F(30,793)
Prob > F
= -0.7437
lnfdip
Coef.
lnfdiinc
exch
pop
cpi
lnco2
yd1
yd2
yd3
yd4
yd5
yd6
yd7
yd8
yd9
yd10
yd11
yd12
yd13
yd14
yd15
yd16
yd17
yd18
yd19
yd20
yd21
yd22
yd23
yd24
yd25
yd26
yd27
yd28
_cons
.7654562
.0038739
1.34e-08
-.0037509
-.1659632
(dropped)
.0507193
.0860099
.0771928
.198381
.2595178
.2811882
.2991356
.3446632
.3187163
.2456882
.3779526
.3812081
.4826837
.512881
.5335212
.4832027
2.770232
2.824022
3.075363
3.140411
2.849738
2.857661
2.811093
2.806915
2.914971
(dropped)
(dropped)
.5903316
sigma_u
sigma_e
rho
3.1781846
.39483386
.98480086
F test that all u_i=0:
Std. Err.
t
P>|t|
=
=
533.74
0.0000
[95% Conf. Interval]
.0241128
.0005009
1.96e-09
.0012451
.0978039
31.74
7.73
6.83
-3.01
-1.70
0.000
0.000
0.000
0.003
0.090
.7181237
.0028907
9.56e-09
-.006195
-.3579484
.8127886
.0048571
1.73e-08
-.0013069
.026022
.1047984
.1042922
.1044875
.1057751
.1060062
.1070108
.107792
.109399
.1094356
.1110402
.1117812
.1140526
.1180394
.1192324
.1209777
.121175
.1226847
.1244266
.1241487
.1268828
.133285
.1387627
.1415799
.145577
.1489602
0.48
0.82
0.74
1.88
2.45
2.63
2.78
3.15
2.91
2.21
3.38
3.34
4.09
4.30
4.41
3.99
22.58
22.70
24.77
24.75
21.38
20.59
19.86
19.28
19.57
0.629
0.410
0.460
0.061
0.015
0.009
0.006
0.002
0.004
0.027
0.001
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
-.1549958
-.1187116
-.127912
-.0092513
.0514318
.0711303
.0875443
.1299173
.1038986
.0277208
.1585307
.1573275
.2509771
.2788327
.2960468
.2453411
2.529406
2.579778
2.831664
2.891345
2.588105
2.585276
2.533177
2.521153
2.622568
.2564343
.2907313
.2822977
.4060134
.4676038
.491246
.5107269
.5594091
.5335341
.4636557
.5973746
.6050888
.7143902
.7469294
.7709956
.7210644
3.011057
3.068267
3.319062
3.389477
3.111371
3.130047
3.089009
3.092677
3.207374
.227818
2.59
0.010
.1431339
1.037529
(fraction of variance due to u_i)
F(36, 793) =
32.58
Prob > F = 0.0000
52
APPENDIX V
Regression 4
Fixed-effects (within) regression
Group variable: country_n
Number of obs
Number of groups
=
=
696
37
R-sq:
Obs per group: min =
avg =
max =
6
18.8
25
within = 0.9214
between = 0.0083
overall = 0.1313
corr(u_i, Xb)
F(33,626)
Prob > F
= -0.8080
lnfdip
Coef.
exp
exch
pop
lngdppc
gdpg
cpi
open
lnco2
yd1
yd2
yd3
yd4
yd5
yd6
yd7
yd8
yd9
yd10
yd11
yd12
yd13
yd14
yd15
yd16
yd17
yd18
yd19
yd20
yd21
yd22
yd23
yd24
yd25
yd26
yd27
yd28
_cons
.0230847
.009865
1.56e-08
.060502
-.0195196
-.0036556
683.3816
-.4759179
(dropped)
.4422376
.5908647
.4882761
.8308885
.96871
1.239042
1.282796
1.419921
1.51968
1.408294
1.535927
1.541526
1.856683
1.869136
1.958445
1.858248
4.208698
4.173237
4.251679
4.43751
4.468902
4.69114
4.69213
4.733571
4.902619
(dropped)
(dropped)
3.285476
sigma_u
sigma_e
rho
3.6237858
.51075132
.98052169
F test that all u_i=0:
Std. Err.
t
P>|t|
=
=
222.26
0.0000
[95% Conf. Interval]
.0032886
.0010482
2.67e-09
.0436168
.0201626
.0019484
202.9418
.1542911
7.02
9.41
5.83
1.39
-0.97
-1.88
3.37
-3.08
0.000
0.000
0.000
0.166
0.333
0.061
0.001
0.002
.0166266
.0078065
1.03e-08
-.0251509
-.0591141
-.0074819
284.8525
-.7789087
.0295428
.0119235
2.08e-08
.1461549
.0200749
.0001707
1081.911
-.1729272
.1829983
.1731477
.1750107
.171737
.1699538
.1703765
.1727411
.17403
.1798044
.1817487
.2051325
.1771181
.1824719
.1830684
.1836415
.1950319
.191911
.1940727
.2017835
.2057296
.2053959
.2067599
.2133966
.2199815
.2245069
2.42
3.41
2.79
4.84
5.70
7.27
7.43
8.16
8.45
7.75
7.49
8.70
10.18
10.21
10.66
9.53
21.93
21.50
21.07
21.57
21.76
22.69
21.99
21.52
21.84
0.016
0.001
0.005
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
.0828726
.2508441
.1445969
.4936381
.6349614
.9044634
.9435741
1.078168
1.166587
1.051383
1.133096
1.193708
1.498352
1.509634
1.597817
1.475252
3.83183
3.792125
3.855425
4.033506
4.065554
4.285113
4.27307
4.30158
4.461741
.8016025
.9308853
.8319552
1.168139
1.302458
1.573621
1.622019
1.761675
1.872773
1.765205
1.938758
1.889343
2.215014
2.228639
2.319074
2.241244
4.585565
4.55435
4.647934
4.841514
4.872251
5.097167
5.11119
5.165562
5.343496
.3298384
9.96
0.000
2.637752
3.9332
(fraction of variance due to u_i)
F(36, 626) =
115.58
Prob > F = 0.0000
53
54
open
lnco2
ep
lnfdip
lnfdiinc
exp
exch
corptax
pop
lngdppc
gdpg
dist
free
lang
cpi
open
lnco2
ep
1.0000
-0.5004
lnco2
open
1.0000
-0.0149
-0.1118
1.0000
0.0247
0.0938
-0.0085
0.5318
-0.0918
-0.1066
-0.2073
.
0.3938
0.3050
-0.0720
0.3988
-0.3363
1.0000
0.7880
0.1712
0.0415
-0.3190
0.3776
-0.1290
-0.1206
-0.1280
.
0.2728
0.6209
0.0110
0.2489
-0.4406
lnfdip lnfdiinc
1.0000
ep
1.0000
-0.0511
-0.3495
-0.4801
0.0531
0.0154
-0.2839
.
-0.3540
0.2786
0.2268
0.0882
-0.0661
exp
1.0000
0.1112
0.1956
-0.1676
-0.2459
-0.1061
.
-0.3027
0.3282
-0.0840
0.0197
0.1358
exch
1.0000
0.3139
-0.0884
-0.1473
-0.0684
.
-0.0379
-0.5324
-0.1078
0.1167
0.3315
corptax
1.0000
-0.0835
-0.1861
-0.0014
.
0.1080
0.1248
-0.1697
0.0485
-0.0570
pop
1.0000
0.8508
0.0149
.
0.0365
-0.1610
-0.4133
-0.0369
0.0389
lngdppc
Correlation Table
APPENDIX VI
1.0000
0.0912
.
0.1803
-0.1673
-0.2334
0.0137
-0.0445
gdpg
1.0000
.
0.1286
0.0207
0.0793
0.0688
-0.0948
dist
.
.
.
.
.
.
free
1.0000
-0.1066
-0.0056
0.3509
-0.4664
lang
1.0000
0.1090
0.1693
-0.3293
cpi
APPENDIX VII
List of countries used
Argentina
Australia
Austria
Belgium
Brazil
Canada
Chile
China
Colombia
Costa Rica
Czech Republic
Denmark
Ecuador
Egypt, Arab Rep.
Finland
France
Germany
Greece
Hong Kong SAR, China
Hungary
India
Indonesia
Ireland
Israel
Italy
Japan
Korea, Rep.
Luxembourg
Malaysia
Mexico
Netherlands
New Zealand
Nigeria
Norway
Panama
Peru
Philippines
Poland
Portugal
Russian Federation
Saudi Arabia
Singapore
South Africa
Spain
Sweden
Switzerland
Thailand
Turkey
United Arab Emirates
United Kingdom
Venezuela, RB
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