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Charles University
Faculty of Social Sciences Institute of Economic Studies
MASTER'S THESIS
Effect of crisis on FDI flows: Winners and
Losers
Case study of Europe
Author: Bc. Vojtěch Korbelius
Supervisor: PhDr. Jaromír Baxa, Ph.D.
Academic Year: 2016/2017
ii
Declaration of Authorship
The author hereby declares that he compiled this thesis independently; using only the
listed resources and literature, and the thesis has not been used to obtain a different or
the same degree.
The author grants to Charles University permission to reproduce and to distribute
copies of this thesis document in whole or in part.
Prague, June 14, 2017
Signature
iii
Acknowledgments
Protože tato práce bude s největší pravděpodobností mým posledním akademickým
činem, chtěl bych tuto příležitost využít a poděkovat všem, kdo mě na mé 19 let dlouhé
pouti až k dnešnímu dni doprovázeli a podporovali. Prvně to jsou rodiče, kteří nejen
všemi materiálními ale i duševními prostředky dláždili mi cestu za poznáním. Sestra,
která mě svou dokonalostí vždy nutila být ještě lepší, abych se jí alespoň přiblížil a
hnala mě tak dál. Lucie, která mi byla v těch posledních letech oporou a zvládala mé
výlevy, že už nemůžu dál.
Ale jsou to i další, širší rodina, která mi dala pocit sounáležitosti. Učitelky, učitelé a
profesorky, profesoři, kteří s námi sdíleli své vědomosti a hlavně pak morální zásady
a principy. V neposlední řadě pak kamarádí. Díky Michale, Waldo, Martine, Honzo i
Jirko - díky vám bylo těch 19 let o něco snesitelnější.
iv
Abstract
JEL Classification B22, C11, C23, D92, E22, O52
Keywords FDI, Financial crisis, EU, integration
Author’s e-mail [email protected]
Supervisor’s e-mail [email protected]
Our work analyses the determinants of FDI in Europe, at the end of the 20th and
beginning of the 21st century. It finds out that the FDI is positively and significantly
influenced by the size of the economy (GDP, growth of GDP), total size of the labor
force, openness of the economy and institutional framework. The findings show the
EU accession does not have an immediate effect. However, long term membership
might positively affect the FDI inflow. According to our analysis the recent financial
crisis has changed the main determinants of the FDI inflows. It has warned the
investors it is important not to consider only immediate profits but also future
prospects. Generally the investment nowadays is below its potential level and the
governments should take action to change it, if the FDI is their priority.
Abstrakt
Klasifikace B22, C11, C23, D92, E22, O52
Klíčová slova FDI, finanční krize, EU, integrace
E-mail autora [email protected]
E-mail vedoucího práce [email protected]
Cílem této práce bylo definovat faktory ovlivňující přímé zahraniční investice (PZI)
v Evropě na konci 20. a na počátku 21. století. Z naší analýzy vyplynulo, že PZI jsou
pozitivně signifikantně ovlivňovány především velikostí ekonomiky (HDP, růst HDP),
velikostí pracovní síly, otevřeností ekonomiky a institucionálním rámcem. Přes
očekávání se neprokázal okamžitý pozitivní efekt vstupu do EU. Přesto jsme však
nebyli schopni vyvrátit možný pozitivní efekt dlouhodobého členství. Signifikantní
vliv na faktory měla též finanční krize. Ta změnila proměnné, které PZI ovlivňují.
Zatímco před krizí se investoři soustředili na velikost trhu, po krizi se poučili a začali
zvažovat budoucí výhled trhu. V dnešní situaci jsou investice obecně pod svým
potenciálem a vlády mají tak možnost upravit svou politiku, aby více investic opět
přilákaly.
Contents
List of Tables vii List of Figures viii Acronyms ix Master’s Thesis Proposal x 1 Introduction 1 2 Literature review 4 2.1 The context 4 2.2 Theoretical perspective of determinants of FDI 6 2.2.1 OLI eclectic paradigm 6 2.2.2 Knowledge-capital model 8 2.3 Empirical approaches of determinants of FDI 8 2.4 Effect of changes in the environment 12 3 Methodology & Data 15 3.1 Methodology 15 3.1.1 Finding the best model with Bayesian model averaging 15 3.1.2 Checking the results through Fixed effects 16 3.2 Data 18 3.2.1 Factors affecting the FDI 19 3.2.2 Approaches to FDI 22 4 Results 25 4.1 Finding the correct determinants 25 4.1.1 Across all times and all countries 25 4.1.2 Effect of the Financial crisis 28 4.1.2.1 Prosperity and growth before the crisis 28 4.1.2.2 Instability and doubts after the crisis 29 4.1.2.3 Differences before and after the crisis 29 4.1.3 West vs East 30 4.1.3.1 The more experienced West 30 4.1.3.2 Poorer East 30 4.1.3.3 Differences between West and East 31 4.1.4 Lesson learned 31 4.2 Robustness check – fixed effects 31 5 Discussion 35 5.1 Effect of gravity and institutional variables 35 5.2 Role of EU for FDI 36 5.3 The effect of crisis on FDI 37 5.4 Potential vs Real FDI 38
vi
6 Conclusion 42 Bibliography 44 Appendix A: List of countries and their classification 48 Appendix B: Comparison of existing studies 49
vii
List of Tables
Table.1 Variables description 24
Table.2 Results of Bayesian model averaging 26
Table.3 Results of Fixed effect analysis based on BMA results 32
Table.4 Comparison of selected coefficients from BMA and FE 34
Table.5 Potential vs real FDI inflow in 2007 40
Table.6 Potential vs real FDI inflow in 2014 41
viii
List of Figures
Figure.1 FDI inflow in Europe 1
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Acronyms
CEEC Central and Eastern European countries
CIS Commonwealth of Independent States
EMU European Monetary Union
EU European Union
FDI Foreign Direct Investment
GATT General Agreement on Tariffs and Trade
GDP Gross Domestic Product
MNE Multinational Enterprise
MNC Multinational Company
OECD Organization for Economic Cooperation and Development
OFDI Outward Foreign Direct Investment
OLI Ownership, Location, Internalization
x
Master's Thesis Proposal
Author: Bc. Vojtěch Korbelius
Supervisor: PhDr. Jaromír Baxa, Ph.D.
Defense Planned: February 2017
Proposed Topic:
Effect of crisis on FDI flows: Winners and Losers. Case study of Europe.
Motivtion:
During the 80s and 90s Foreign Direct Investment (FDI) increased on average more
then 20%. It means it grew faster then the world’s GDP or the international trade.
(Gast & Herrmann, 2008). Europe (especially European Union) became one of the
most attractive parts of the world for FDI. Just during the 90s the overall FDI to EU
has increased more then four times (Barry, 2002).
This sudden increase of capital flow wasn’t left without a reaction. Many
governments tried to become more attractive for the investors due to the fact that
FDI doesn’t bring only money but through increased efficiency and innovation it
causes economical growth (Iamsiraroj, 2015). And because each state took a little
different approach to attract the investors FDI became very appealing topic among
scholars. Who are trying to find out what are the main determinants influencing those
investments.
The main disadvantage of most of the studies lies in the very narrow point of view.
They usually focus on either one country or only a specific region (CEEC, South
East Asia). We would like to look at it from a bigger perspective of the whole Europe
where we have a mixture of well developed countries, transition countries within the
EU and transition countries outside of the EU and see how big role played European
integration in FDI flows.
The second problem lies in the selection of the determinants. All of them agree on
the importance of the gravity model related factors (size of the market, openness of
the market etc.) but they do not have the same point of view on the role of institution.
We would like to look whether even in Europe, which has a common historical
background does the institution play a significant role as a determinant for investors.
Nevertheless the main motivation for our study is the recent crisis. Most of the
existing studies found the recent crisis as an important determinant for FDI flows.
Even researches like Hunady & Orviska (2014) focusing on a very specific factor of
corporate taxes found the recent crisis being a significant factor. Unfortunately to
our knowledge nobody has tried to find out whether the recent crisis has changed the
effect of other factor on FDI. And thus whether it has changed the approach the
governments should take to increase the attractiveness of their country to the
investors.
Hypotheses:
1. Does the membership in the EU increases the FDI inflows?
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2. Does the membership in the EMU increases the FDI inflows? 3. Can we see a significant effect of the institution on the FDI? 4. Has the recent crisis had bigger effect on EU-12+3 countries? 5. Has the recent crisis changed the effect of some variables on the FDI?
6. Has the recent crisis decreased the potential FDI in Europe?
Methodology:
As mentioned above our study is going to focus on the whole Europe. We are going
to include EU28, but also countries in the east and south east of Europe that are not
part of the EU and at the same time members of European Free Trade Organization.
We are going to collect yearly country data on cumulative FDI inflow for all of our
countries and construct a model that will allow us to understand, which factors play
the most important role in Europe.
The factors are going to be based on analysis of 20 existing studies and looking for
most often used variables. The variables will include both gravity factors (that are
applied in almost every study) and institutional factors that vary greatly among
different studies.
To be able answer all of our research questions we will also include dummy variable
for EU membership, crisis and cross products for EU-12+3 members for all of the
factors.
Expected Contribution:
We believe that our study is going to be, if not the first one, one of a very few studies
focusing on FDI in not a fully homogenous region as CEEC, South East Asia or
among well-developed countries of OECD. We are going to look at the whole
Europe, which has at least two categories of countries, not all of them are
participating in the European integration, and even the participants has been
participating gradually.
Our study should enlighten the effect of the recent crisis on the FDI flows. We will
go beyond the obligatory dummy variable for years 2009-2014 stating that the crisis
played a role. We would like to know what has the recent crisis changed and how
can the governments actually take the maximum out of it looking at the potential and
actual FDI.
Outline:
1. Introduction
2. Historical development of FDI
3. Literature review
4. Data
5. Methodology
6. Results
7. Discussion
8. Conclussion
Core Bibliography:
Gast & Herrmann; 2008; Determinants of foreign direct investment of OECD countries 1991-2001; International Economic Journal; 22/4; 509-524
xii
Iamsiraroj; 2015, The foreign direct investment-economic growth nexus; International Review of Economics & Finance; 42; 116-133
Hunady & Orviska; 2014; Determinants of Foreign Direct Investment in EU Countries – Do Corporate Taxes Really Matter?; Procedia Economics and Finance; 12/14; 243-250
Demekas et. al.; 2007; Foreign direct investment in European transition economies – The role of policies; Journal of Comparative Economics; 35; 369-386
Dauti; 2015; Determinants of Foreign Direct Investment in South East European Countries and New Member States of European Union Countries; Economic and Business Review; 17; 93-115
Villaverde & Maza; 2015; The determinants of inward foreing direct investment: Evidence from the European regions; Internationa Business Review; 24/2; 209-223
Author Supervisor
1
1 Introduction
In 2007, the total amount of inward flow of foreign direct investment (FDI) in Europe
reached the record in history with a value of almost 1.9 trillion USD (Figure.1). That
is about 70 times more than 40 years ago. FDI in Europe grew by 7 400% during this
period. This shows the increasing importance of FDI in recent years. Even though the
recent financial crisis has decreased the speed of the development, the FDI is still
attracting an increasing interest from the side of the national governments. This has
forced researchers to pay attention to this new phenomenon.
Figure.1 FDI inflow in Europe
The existing academic studies investigating the FDI, its effects and its determinants
run into hundreds. On the one hand, the literature has found clear positive outcomes of
higher FDI on the host economy. On the other hand, it has not been able to define the
“unique” recipe how to attract the FDI. Thus, local governments are aware of the
positive effect of the FDI. However, despite their wish, there is only a little consensus
behind the drivers of FDI inflow. Both academics and governments seek to construct
a reliable model of this phenomenon, although one model which would be consistently
‘correct’ across all regions is rather a wishful thinking.
Our goal was to fill in those blank spaces in the literature and find the common
determinants of the FDI. The initial choices are based on the list of possible
determinants from existing studies. On which bases we are trying to find determinants
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$0.40
$0.60
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1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013
Bili
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of the FDI in Europe at the end of the 20th and the beginning of the 21st century, using
the Bayesian model averaging (BMA) method, which allows us to consider more
variables.
To be able to attract the FDI from the side of local governments, it is necessary to know
who the competitors are. Existing literature usually provides insight in very
homogenous regions such as the OECD countries (Western Europe) or CEEC (Eastern
Europe). However, due to the integration of the continent under the European Union
(EU), this distinction turns out to be obsolete. Therefore we believe it is worth
investigating Europe as a whole.
From all the considered factors, we found 7 variables to have an effect on the FDI in
Europe at the end of the 20th and the beginning of the 21st century. Four of them are
the so called gravity factors (GDP, growth of GDP, size of labor force and trade
openness). The other two fall in the category of institutional variables, proving the
importance of this category on the FDI. The last variable is a dummy variable for the
original 15 EU members that has a significant and positive effect. Those results signal
the importance of the second goal of our study which is to determine the effect of the
two most important structural changes in Europe in the last century.
Europe at the end of the 20th and the beginning of the 21st century is interesting for two
reasons. Firstly because of the deepening integration of the EU. Which has decreased
the barriers between states and thus increased the movement of goods and services,
labor force and capital. This had a profound effect on the FDI flow across the continent.
The second change was the recent financial crisis, which affected the Western world
in general, decreasing the strength of the economy.
Many of the existing studies have found those changes to have a significant effect.
However, none of them actually observed how they had affected the possible
determinants. Dornean & Vasile (2012) recommended for the future research to
investigate whether the financial crisis of 2008 has affected different states variously
as far as the FDI. For that reason, we have divided our datasets into pre-crisis and post-
crisis period and ran the analysis on the separate subsamples. We have also done the
same for the West and East part of Europe.
The analysis shows that while the FDI in the West is affected mainly by the institutions,
in the East the most important are the gravity factors. The time analysis shows that
before the crisis the investors focused on the GDP, representing the size of the economy
and thus possible demand. After the crisis, they started paying attention to the GDP
growth, which signals that they became interested in the future.
3
In the last part of our study we have found, quite interestingly, that after the crisis the
FDI in general dropped below its potential level. The largest drop was recorded in the
eastern non-EU members, and the smallest by the westers non-EU members.
The main limitation of our analysis is the investigation of the compound FDI inflow,
better results could have been achieved by using the bilateral FDI. This would have
allowed to investigate the factors also from the side of the investing countries.
However, this approach was beyond our capabilities.
The rest of the work is organized as follows: Chapter 2 provides a literature review of
the existing research on the topic of the FDI. Firstly, we give a theoretical insight into
the FDI and its history. Subsequently, we explain the two theories that stand behind
the FDI and look at the variables, which are being considered as determinants of the
FDI. In Chapter 3 we present our Methodology and Data. The chapter outlines the
methodological details of BMA and Fixed effects model. We continue with showing
which variables have been considered and which have been used. It includes an
overview of all variables, with the description, units and sources for our variables. The
next part looks at the regression results and compares them with the expected effects
from Chapter 3. After that, we discuss the results, comment on the outcome and
compare the potential FDI with the real FDI. In Conclusion chapter, we highlight the
main results of our study and give suggestions for the future research.
4
2 Literature review
2.1 The Context
The first capital flows that can be characterized as something similar to the FDI, as is
understood nowadays, have been recorded after the Second World War. The first
impulse for capital flows from one country to another country were the differences in
the interest rates. (Jadhav, 2012). Over the time new reasons have emerged that helped
to increase the importance of the FDI to today’s level.
The FDI does not concern only one country. The FDI is always a transaction between
an investing and a receiving country; or between one investing country and several
receiving countries (Villaverde & Maza, 2015; Faeth, 2009; Lochard & Sousa 2011).
Politicians and economists soon realized that the FDI represents many opportunities
for the host country. In neoclassical models, the FDI is expected to result in a short
term increase of the GDP through technological growth or labor force growth. On the
other hand, in the endogenous model, the FDI is seen as a source of know-how, R&D
or human capital (Iamsiraroj, 2015). Kornecki & Rhoades (2007) highlight the role of
the FDI as an instrument of globalization that helps the country to fully realize their
comparative advantage.
In the past, there were production factors (mineral resources) that could have been
found only in certain locations. Thus, it was necessary to install the production near
the source (Boateng et. al., 2015). The development in transportation and
communication at the beginning of the 20th century allowed companies not only to
optimize the location based on the production factors, but also to take into the account
other variables. This advancement allowed for example to transport mineral resources
on long distances (Bitzenis, 2003).
This development resulted in a new form of competition where the same resources are
accessible from other countries. Thus, the potential host countries are trying to attract
the investors through other means. For example by implementing lucrative policies
decreasing the cost of production or simplifying the export procedures of the final
product. (Boateng et. al., 2015). For the national governments, this new development
brought the need to understand what the Multi National Enterprises (MNEs) were
looking for. Many governments have recently been adopting different incentives; for
example: decreasing corporate taxes, increasing the minimum wage to pursue its
5
interests. The ultimate goal of any of those strategies is to attract the FDI inflows,
resulting in positive economic effects.
However, the positive effect of the FDI does not have to be that straight forward. Jude
& Silaghi (2016) found out, that the FDI can cause creative destruction. If the
investment is conducted as a merger or an acquisition (M&A), the company at first
tries to become more efficient and thus lets employees go. Once the transaction is
done, the company starts employing again. On the other hand, another possibility for
the FDI are the green field projects. In those cases, the investor installs a new
production and thus from the beginning it needs new employees and thus it is
decreasing the country unemployment.
Another positive effect is in the form of technological spillovers and increased number
of innovations. This effect might be realized either through imported knowledge and
R&D from the investing countries, or by funding the existing research facilities in the
host country, lacking the financial resources. (Ucap et. all., 2010; Vintila, 2010;
Radulescu, 2010; Sawalha, 2013). Furthermore, the FDI can also have a psychological
effect on the other investors. Albulescu et. all. (2010) points out, that the FDI into a
host country can improve the image of the country in the eyes of the other investors
and can bring more investments in the future.
In the long run, all of those effects have one common consequence. The FDI usually
brings about increase in the GDP of the host country. It is also the main reason why
the national governments are trying to find the most effective way to attract the
investors. According to Chan et. all. (2014), the FDI increases the GDP in both short
term and long term. Furthermore, Světličič & Kunčič (2013) found out that for the
OECD countries, 1% increase in the FDI results in 0.01% increase in the GDP. Based
on those findings, it is understandable why local governments are trying to attract as
much of the FDI as possible. That motivates an effort to seek determinants of the FDI
in order to find out which policies are effective in increasing the FDI.
Hence we can understand why the host country tries to attract the FDI. However as
stated above, the FDI is a game of at least two sides. Therefore, it is important to also
understand the investors’ side. The FDI is not the only instrument of how market
players can interact with each other. For a long time, the main way was the international
trade. Companies traded goods that were not possible to gain otherwise. Or when a
player had the comparative advantage in producing certain goods relatively cheaper
compared to the other market players due to competitive inputs. The FDI became an
extension to the international trade to MNE. When the international trade becomes less
profitable due to connected costs the MNE can try the FDI.
6
When a corporation is newly established, it is operating on a new market and needs to
find both its position on the market and the customers. When it has gained a solid
market share, established standardized processes and gained enough knowledge about
the market, it moves into the phase of a mature corporation. At this point, the company
has a stable cash flow with a certain demand, under the usual development. Afterwards,
a prospective MNEstarts to invest in increasing the production that is going to sell on
foreign markets as an established firm in the domestic market. It starts considering the
politics of export (Gavril, 2014)
Once again, it has to find the best sales channels and stabilize its position on the new
market (Gavril, 2014). Export is usually connected with high variable costs that are
increasing with the amount sold outside of the home country. At this point, the
management has to decide whether it is possible to substitute the export by the FDI.
Undergoing the international expansion (FDI) can be justified either by reducing the
export costs (lowering the variable costs of transportation, customs etc.) or by
decreasing the production cost (producing somewhere else and importing back to the
investment country) (Villaverde & Maza, 2015; Thanh & Duong, 2011). In 2010, more
than 50% of the FDI went into the emerging economies (Jadhav, 2012). We can witness
the same trend even within the EU, where more than half of the FDI has been realized
in emerging Central and Eastern European countries (CEEC) (Vechiu & Makhlouf,
2014). This can mean both the emerging markets are becoming the new sales area or
that those countries are being used for their relatively cheaper inputs.
2.2 Theoretical perspective of determinants of FDI
The greatest challenge in the literature focusing on the FDI is the specification of the
model and the investigation of possible determinants academics derive from theoretical
framework. There are two theoretical starting-points explaining why any corporation
chooses to invest abroad via the FDI.
2.2.1 OLI eclectic paradigm
The first possible explanation is summarized in the OLI eclectic paradigm (Ownership,
Location and Internalization advantage). According to this theory, an investor chooses
the FDI only if it yields some benefits, or if they have some advantage compared to
their competitors. This advantage can be either a tangible or an intangible asset they
own (Ownership), a unique approach to serve or to use new market or region (Location)
or an uncommon way how to gain from increasing production through economies of
scope or scale (Internalization), as presented in (Villaverde & Maza, 2015; Boateng
el.all., 2015; Dauti, 2015; Gast & Herrmann, 2008). In the existing literature, we cannot
7
find a unified opinion stating which of the factors are the most important and which
are the least important.
Boateng et. all. (2015) state that mainly in the past, the most significant reason for the
FDI was the location. Most of the FDI was accumulated in countries with large
amounts of mineral resources. This reason lost its relevance with the improvement in
the infrastructure of mobility of resources and globalization at the beginning of the 20th
century.
Nevertheless, location is still an important factor even now. Entering new markets
gives companies the opportunity to acquire new customers. Hence possibly a boost to
demand, with the opportunityof decreasing average costs. (Villaverde & Maza, 2015).
However, as Blonigen (2005) states, the FDI is accompanied with high fixed cost at
the start, comparing with only the cost of transportation and customs connected with
export that usually precede (Gavril, 2014). Thus, it is very seldom that the location
would fully justify the FDI, unless there has been a sudden increase in tariffs that would
make trade immediately unprofitable and change the situation.
On the other hand, Dauti (2015) or Jadhav (2012) stress the importance of the
ownership. They see the potential in building on deeper integration of processes within
the large MNEs. Those usually already have highly efficient internal processes and
large base of both human and financial capital. This allows them to minimize
transaction costs by becoming somehow independent on the market through sharing
financial, human and knowledge capital within the MNE. Firms without such a
background have to provide the financial and human resources through open market,
which is often more expensive.
There is a third option to the FDI and the export; and it is licensing (Cheng & Chung,
2012). In this way, an investor can access a new market without spending the fixed
costs. Nevertheless, in this case the investor risks the classical principal & agent
problem. That is the reason why the FDI might be a better option. With the FDI the
company can use up the potential of internalization advantage by controlling its
operation by itself (Gast & Herrmann, 2008).
Even though none of the theories covering the FDI is contradicting the others, each of
them is based on a different point of view. Ownership is focused on the characteristics
of the company, how many patents the company has, whether it already has some
international branches etc. Location is focused on the size of the market, which can be
measured by the GDP, population or purchasing power. And Internalization might be
measured by the inefficiencies on the local market that can be solved by the integration
8
of the MNE. This complexity of possible factors influencing the FDI forces researchers
to choose a point of view to approach the problem and find as many possible
determinants defining this perspective and it makes it almost impossible to create one
complex study.
2.2.2 Knowledge-capital model
The other possibility to explain the FDI is that the company is looking either for
lowering the production cost (vertical integration) or for a new market (horizontal
integration). Markusen has summed up those drivers in the late 90s in the knowledge-
capital model that has been used as a building stone for almost any study regarding the
FDI since then.
While vertical integration is trying to exploit underdevelopment of a country, the
horizontal is offering new products or services to a new untapped audience, or to
markets where it is expensive to import. While the vertical can increase trade by
exporting intermediate products back to investing country, the horizontal decreases
trade that can be too expensive due to the tariffs or transaction costs. These
contradictions create a problem in the process of defining the effect of some factors on
the FDI flows. In the case of vertical integration, the increase of labor cost would offset
the volume of the FDI. On the other hand, if the investors were looking to compete for
a new market, they can benefit from higher purchasing power and thus it might increase
the horizontal FDI. (Demekas et. all, 2007).
Because the vertical integration is more frequent in developing countries, and
horizontal in the developed ones, (Sánchéz, 2014) the researchers could be able to
control for this contradiction by including cross product of some country categories or
dividing the data sample, which we will try to do.
2.3 Empirical approaches of determinants of FDI
This complexity of possible factors and the dissension in the effect and inconsistency
in methodology makes this research very disunited and inconsistent (Jadhav, 2012).
The existing studies on the topic of FDI determinants can be clustered in several
categories based on three characteristics. The main feature is whether the study
investigates a bilateral FDI flows, or an aggregated FDI flows. The bilateral FDI allows
the study to be built on a gravity model inspecting not only the host country
characteristics, but also the investing country. As an example, we can take Bevan et.
all. (2004), Janicky & Wunnava (2004) or Dauti (2015). The issue with those studies
lies in the selection of the investing countries. Each of the studies focuses on a region,
9
where it is trying to understand how to improve the FDI flows of the recipient’s
countries. As for the investing countries, they usually consider EU-12, EU-15, OECD
or countries that represent the biggest investors in the region. However, this approach
can disregard an investor based on an irrelevant factor such as geographic
characteristics. The second possibility is to investigate the cumulative FDI flows,
without considering their origin. This approach is represented by Pantelidis et. all.
(2014), Dornean & Vasile (2012) or Hunady & Orviska (2014). The disadvantage of
this approach is the futility to fully use the potential of the gravity model that shows
what portion of the FDI is explained by factors that cannot be changed in the short run
or at all; - for example geographical distance.
Another possible distinction is whether to use cross-sectional or panel data. Most of
the studies use panel data. But either way the researchers have to take into account the
disadvantages. Cross-sectional analysis ignores the long-run effect. On the other hand,
panel data expect the long-run effect to be constant (Svetličič & Kunčič, 2013).
Therefore it is necessary to take this into consideration when drawing a conclusion.
The last distinction is according to the choice of the host country. Studies are either
focusing on one country, or on a region. The first category of studies is usually trying
to determine how some specific policy has changed the FDI flows, and whether some
cultural or historical ties play a significant role. The goal of those studies is to evaluate
institutional changes and define whether the course of the government is beneficial or
not. For example, Polat (2014) is trying to define the effect of the tax system reform in
Turkey that took place in 2006. The second category focuses on a certain group of
countries with some common characteristics allowing the researchers to judge what
position of which government attracted more FDI. Very popular has been the
investigation of the CEEC, Southeast Asia, EU-10 or OECD as host countries.
Especially the CEEC region is often studied. During the communist era, this region
had zero experience with FDI, as we understand it today. Thus, after the fall of
communism, it has been considered as a perfect testing environment for determination
of both gravity and institutional factors (Bevan & Estrin, 2004).
Even though the FDI has been attracting the interest of the academics for at least 40
years, there is no unanimous agreement among the significant determinants affecting
the FDI in positive or negative way. The factors defined by the literature can be
clustered in several categories.
The most common category comprises of so-called gravity factors. Those factors
explain the flows of the FDI based on the size of the economies and their distance - the
same principle as gravitation. The bigger the economy and the smaller the distance the
10
more flows we can expect from investing countries. The size of the economy has been
used in overwhelming majority of studies and always proved to be significant. Another
commonly used gravity factor is the distance between the countries. However, not all
of the studies employ the bilateral FDI flows. Thus in such cases, it is not possible to
use the geographical distance.
The second most often investigated phenomenon is the effect of trade barriers on the
FDI. One of the reasons for a company to consider the FDI is to avoid tariffs imposed
on export; the so called tariff jumping (Sánchez-Martín, 2014). To investigate this,
openness of the country is usually used as the metric trade and is measured by the
portion of export, export and import or net export, to GDP. This ratio is usually higher
for countries with a high trade liberalization that are members of some free trade area
or a customs union (Bevan & Estrin, 2004; Gast & Herrmann, 2004; Barry, 2002).
Interestingly enough, this factor can have either positive or negative effect on the FDI.
Depending on the motivation, whether it is horizontally or vertically driven FDI.
|Considering the horizontal FDI, great trade openness means that it is easy to export to
this country and thus starting a local factory is not necessary. Unless the transportation
costs in total exceed the fixed costs of starting a new unit (Blonigen, 2005). So the
effect of trade openness on the horizontal integration is negative. On the contrary,
models considering vertical FDI suggest that a high degree of trade openness can attract
investors to capitalize on, for example cheap labor, and then export the products
(Demekal et. all., 2007; Blonigen & Piger, 2014; Cheng & Chung, 2012). Villaverde
& Maza (2015) define this phenomenon as market seeking in case of horizontal
integrationand as resource/assets seeking in case of vertical integration.
When an MNE wants to expand into a new market it faces the risk of lack of
information about the market and thus the managers need to evaluate the new territory
based on empirical metrics. One of the possible points of view is the financial
perspective. Research usually takes into account both stability of the host country as
well as differences between the two countries that could be used to the advantage of
the MNE. Most often considered are the exchange rate, inflation rate and interest rate.
Boateng et. all. (2015) find a positive effect of the exchange rate on the FDI through
wealth effect and relative production prices channel. If the host country’s currency
depreciates, it means that the production factors are cheaper compared to the investing
country - relative production price channel. Thus, it is cheaper to start production in
this country. Furthermore, home country currency depreciation also means an increase
in the value of companies measured in foreign currency, because all the production
factors can be acquired cheaper - wealth effect. On the other hand, depreciating of the
11
host currency results in lower repatriated earnings back to the investor. This drawback
can be avoided by investing the profits in the host country, instead of transferring them
back and thus increasing the value in form of fixed assets (Blonigen, 2005).
The inflation rate is considered to indicate the future real value of the investment. High
inflation rate can decrease the value of future earnings by decreasing the stability.
Distinguishing the stability is a very complex task and thus the Governing Council on
ECB has stated: “Price stability shall be defined as a year-on-year increase in the
Harmonised Index of Consumer Prices (HICP) for the euro area of below 2%.”
(European Central Bank, 1998). Thus we can excpect a negative effect on the FDI.
Financial instability of the economy discourage the investor.
The same negative effect applies in case of the interest rate (Blonigen, 2005; Sánchez-
Martín; 2014) where the negative sign is interpreted as the investors preferring to
finance foreign branches through host country’s capital. As an alternative measure,
some of the researchers use the credit rating of the country to evaluate the financial
stability of the market. In general, this approach eliminates the need to collect three
different variables.
The crucial factor of the production process is human capital. As human capital is one
of the inputs for the production, it is logical that the researchers include the labor force
in the group of important factors. The studies have taken different approaches to
evaluate how expensive and productive the workforce in the host country is. Some of
the studies looked only at the average wage (Chan et. all., 2014) expecting that the
wage reflects the productivity compared to other countries. Other looked at the average
number of working hours approximating the productivity; Gavril (2014) assumed that
more hours meant less efficiency and thus less attractive region for FDI. The most
complex point of view is measuring the unit labor cost that combines both productivity
and average wage. Some of the academics also believe that it is necessary to look closer
both at the wage and on the productivity by investigating unemployment rate and
schooling. The idea is that high unemployment rate indicates easy availability of the
workforce willing to work for lower salary. The positive effect is also assumed for
highly educated people. Better schooled workers can achieve higher productivity.
In the production, the human capital is deeply integrated with a technological
advancement. Pantelis et. all. (2014) believe that the market cannot have only people
that are well educated and trained, but it has to be able to transform technological
improvements into a reality through production and distribution. This can be measured
by a number of patents or by a portion of technological exports. However, it is not only
12
about the technology and human capital, also certain role is played by the institutions
concerning intellectual property.
The intellectual property laws are just a small part of the complex institutional
framework influencing the investor’s decision regarding the host country. Institutions
are reflecting the stability of the government, political situation within the country and
they are signaling how safe it is to invest in the particular economy. (Blonigen, 2005).
The most important characteristic of the institutions is that they are endogenous to the
system. Thus, as Demekas et. all. (2007) point out, they can be changed by the
government to become more attractive to foreign direct investors in short or medium
run, a special case of which being the area of CEEC.
This area has begun its transformation process after the fall of the Soviet Union and
was a blank page as far as the FDI until the early 1990s (Bevan & Estrin, 2004). That
is the reason why a lot of academics have been focusing on this area to determine which
institutions do play a role and with what effect and, subsequently applying those
finding to other areas. The most influential determinants are various measures of
corruption, governmental effectiveness, rules of law, hiring and firing practices and tax
burden. It is worth to point out that to lower the number of factors, some of the
researchers have decided to apply a compound index of economic freedom that
includes measurement of rule of law, limited government, regulatory efficiency and
open markets (The Heritage Foundation, 2016), giving a general image of the country.
All of those indices serve not only the investors, but also the government and help to
understand what their position is in comparison to other countries. Especially the taxes
and the hiring and firing practices can be easily adapted and governments can use them
to create more favorable environment for the investors.
2.4 Effect of changes in the environment
In the past 30 years, Europe has been affected by two major events, as was realized by
some of the researches. First of all, it has been the economic integration in form of the
European Community (later European Union), second of all it has been the recent
financial crisis. Both of those developments has affected Europe deeply and thus they
have been analyzed in relation to the FDI.
Since the end of the World War II, Europe has been trying to integrate to prevent any
future conflicts and to help rebuild itself. This process has led to a free movement of
goods & services, capital and people (Kalotay, 2007). As far as the FDI is concerned,
it means less reasons for horizontal FDI (no need for tariff jumping) and easier vertical
FDI (cheaper to produce in a country with lower inputs cost and then export) (Pantelis
13
et. all., 2014). Of course, this is not just the case of the EU, preferential trade
agreements and custom unions are generally seen as a way to encourage the FDI
worldwide (Blonigen & Piger, 2014; Cheng & Chung, 2012, Demekas et. all., 2007;
Dauti, 2015). Since 1947 when the GATT was signed, almost 500 of such agreements
have been ratified and more than 350 since 1990 (Treibilcock; 2013).
However, some of the authors state that, in the EU, we can observe an interesting
phenomenon. The market already takes an announcement about a future accession to
the EU as a reason to start investing into the country, instead of waiting for the
accession itself, followed by the disappearance of the barriers (Özkan-Günay, 2011;
Lucyna & Rhoades, 2007). The EU is also unique due to its size, cohesion and
continuous growth. Allowing investors to reach new markets continuously and grow
steadily. This leads to a great economic integration across the whole continent. In
comparison, joining the OECD usually brings only local economic integration reflected
in an increased trade and investment with only neighboring countries (Gast &
Herrmann; 2008). This continuous growth, at the same time representing new
opportunities, highlights the great inequality among the old-rich and new-poor states.
The main division line is usually drawn between the original EU-12+3 in the West and
the new EU-10+3 (Romania, Bulgaria and Croatia) in the East. To put this into a
perspective; when Romania and Bulgaria joined the EU in 2007, they have added 7%
to the EU population but they have added less than one percent of the EU GDP.
(Kalotay, 2007; Mirela et. all., 2015).
The second major event has been the recent financial crisis. In 2008, the FDI flows
dropped to 1.49 trillion USD from the historical maximum of 1.87 trillion USD in
2007, reaching the lowest value in 2009 of 1.19 trillion USD (Unctadstat.unctad.org,
2016). This is the reason why many researchers became interested in the effect of the
recent crisis on the FDI inflows.
However, it is necessary to put this into perspective as well. Looking back at Figure.1
we can see that the value in 2009 was actually already higher than the FDI in 2005. In
reality the recent crisis has just erased the effect of the latest growth. The question is
how the recent crisis has affected the FDI. As Vintila (2010) states, the crisis can be
characterized by three main outcomes: “a drastic reduction in asset prices, an important
reduction in production and the rising of unemployment, and the increase of the public
debt to alarming figures”.
The drastic reduction of asset prices usually leads to the so-called fire sale. The process
in which a foreign country, not affected by the crisis, has the opportunity to purchase
relatively cheap assets in the affected country. This phenomenon was largely
14
documented during the 1997-1998 Asian crisis, during which the amount of cross-
border M&A largely increased (Athukorala; 2003). However, we could not witness
this process in the recent crisis in Europe (Weitzel et. all., 2014; Cavoli, 2014). The
reason is that the mother companies of the MNEs have to be in a good financial
condition to be able to subsidize unprofitable branches during the crisis and to undergo
more M&As. Unfortunately, the last crisis has affected not only the whole Europe but
also the USA, which is the biggest investor in Europe. (Unctad.org, 2016). This meant
that the MNEs needed all their financial resources to stabilize their already existing
businesses. Hence they were not able to act on the opportunity represented by the crisis.
The second effect is the decrease in production and increase in unemployment. The
drop in the production is caused by lower demand. This is an unfavorable factor for
market seeking FDI (horizontal). (Vintila, 2010) On the contrary, higher
unemployment rate means the possibility for the vertical integration. The last
characteristic of the crisis is the increasing public debt that will be considered by the
MNE as a part of the financial stability of the state.
Researchers have also documented another interesting element of the recent crisis in
Europe. As mentioned above, after the fall of the Iron curtain, the CEEC and CIS
countries became aware of the opportunity that FDI represented for their economy in
form of employment, technology and overall growth. This led the countries to heavily
relay on FDI doing anything to attract more. When the crisis hit in 2008, this resulted
in almost zero effect on FDI to CEEC and CIS in 2008, but instead in sudden and
continuous drop ever since. (Světličič & Kunčič, 2013) On the other hand, Western
Europe has witnessed a significant fall in 2008 and then smaller effect later on. (Hunya,
2009). This can be explained by the fact that MNE tried to stay present in the
developing market with great perspectives (CEEC and CIS) until they realized that the
crisis would have bigger impact than previously expected. Thus, they decided to invest
more in the stable and more reliable environment of the Western Europe. This is a
similar case to the Asian crisis at the end of 90s when the MNEs have switched from
prosperous emerging markets to stable, financially sound territories. (Park et. all,
2006).
15
3 Methodology and data
In our study, we are applying two different methodologies. First of all, the Bayesian
Model Averaging is used. This probability-based procedure determines which
independent variables are the most likely to influence the dependent variable.
Furthermore, we are going to use a classical panel data methodology of fixed effects,
which is so widely applied in the existing stream of literature.
3.1 Methodology
3.1.1 Finding the best model with Bayesian model averaging
One of the goals of our study is to find from a broad variety of possible determinants
the ones that are the most influential for the FDI. Having analyzed the existing studies,
we have come to 58 possible determinants (Appendix B). After selecting the ones with
the highest frequency of consideration and dropping those with the insufficient number
of observation, we have arrived to possible 14 variables. The usual approach is to
choose the ones we believe to be the key determinants and then trying to add (subtract)
those of them which are strongly (in)significant. Either we can start from the simplest
model and keep adding up the variables or start with the complex model with all of the
variables and then taking the insignificant out. However, the prefect execution of this
methodology is almost impossible. Analyzing all of the possible models and deciding
which one is better than the other is not an exact approach.
For that reason, we have decided to use the BMA. This methodology estimates all
possible combinations of independent variables and constructs a weighted average over
all of them.
Having the function:
𝑦 =∝𝛾+ 𝑋𝛾𝛽𝛾 + 휀 휀~𝑁(0, 𝜎2𝐼) (1)
Where 𝑦 is the dependent variable, ∝𝛾 the constant, 𝛽𝛾 the coefficient and 휀 a normal
IID error term with variance 𝜎2. The BMA answers the question, which of the all
possible X should be included in the model. If X contains n possible variables the BMA
estimates all possible 2𝑛 variable combination, which means 2𝑛 models. Where all the
possible models can be denoted as 𝑀𝛾. Based on the marginal distribution:
𝑝(𝑦|𝑀𝛾) = ∬ 𝑝(𝑦|𝛽𝛾, 𝑀𝑦)𝑝(𝛽𝛾|𝑀𝛾) 𝑑𝛽𝛾𝑑𝑀𝛾 (2)
16
We arrive to a posterior probability of the model:
𝑝(𝑀𝛾|𝑦, 𝑋) =𝑝(𝑦|𝑀𝛾,𝑋)𝑝(𝑀𝛾|𝑋)
∑ 𝑝2𝑛
𝛾′=1(𝑦|𝑀𝛾′ , 𝑋)𝑝(𝑀𝛾′|𝑋)
(3)
The equation (3) summarizes the uncertainty of each of the possible models. Based on
that we can easily derive for example the value of 𝛽𝛾 by averaging across all of the n
models 𝑀𝛾. As a result, we get:
𝐸(𝛽𝛾|𝑦) = ∑ 𝑝(𝑀𝛾|𝑋, 𝑦)𝐸(𝛽𝛾|𝑀𝛾,𝑦, 𝑥)2𝑛
𝛾=0 (4)
In the equation (4) the term 𝐸(𝛽𝛾|𝑦) represents the weighted expected value of 𝛽𝛾
across every possible model.
Based on this methodology, we hope to find the most important determinant for the
FDI in the Europe at the end of 20th and the beginning of 21st century. We want to run
this procedure on both the full sample and all four of our subsamples to see whether
there are any differences in the results.
3.1.2 Checking the results through fixed effects
The next step in our analysis is to take the results from the BMA and to use the standard
approach of researchers in the field of FDI with panel data in form of fixed effects.
This method allows each cross-section unit, in our case state, to have its own intercept,
which can be correlated with any of the independent variables. (Wooldridge, 2008,
p.493).
In general, any panel data are characterized with the following equation:
𝑦𝑖𝑡 = ∑ 𝑥𝑖𝑡𝑘𝛽𝑘
𝐾
𝑘=1
+ ∑ 𝑧𝑖𝑙𝛿𝑙
𝐿
𝑙=1
+ 𝑎𝑖 + 𝑢𝑖𝑡 𝑖 = 1 … 𝑁, 𝑡 = 1 … 𝑇 (5)
In our case index i represents the country, t stands for the year and k are the independent
variables.
In the equation (5) 𝑎𝑖 is so-called unobserved effect, which in our case is the intercept
that will stay constant for each state. This is the reason why 𝑎𝑖 has only index i for the
country but is missing index t for the time. The term 𝑢𝑖𝑡 is often called idiosyncratic or
time-varying error, representing unobserved factor changing over time. The sum:
17
𝑣𝑖𝑡 = 𝑎𝑖 + 𝑢𝑖𝑡 (6)
is called composite error.
This shows why it is impossible to consistently estimate the equation (5) with a simple
OLS. The OLS expects the 𝑣𝑖𝑡 to be uncorrelated with 𝑥𝑖𝑡, which in our case is not true.
This is the reason why we are going to implement the fixed effects. As said this
methodology allows each of the countries to have its own intercept 𝑎𝑖 for which:
𝐶𝑜𝑣(𝑥𝑖𝑡𝑘, 𝑎𝑖) ≠ 0 (7)
To be able to use the fixed effects we have to calculate the average equation for each
cross-sectional unit
𝑦�̅� = ∑ 𝑥𝑖𝑘̅̅ ̅̅ 𝛽𝑘
𝐾
𝑘=1
+ ∑ 𝑧𝑖𝑙𝛿𝑙
𝐿
𝑙=1
+ 𝑎𝑖 + 𝑢�̅� 𝑖 = 1 … 𝑁 (8)
Now when we subtract the equation (8) from (5) we get:
𝑦�̃� = ∑ 𝑥𝑖�̃�𝛽𝑘
𝐾
𝑘=1
+ 𝑢�̃� 𝑖 = 1 … 𝑁 (9)
The equation (9) is called within transformation and allows us to use the pooled OLS.
This is thanks to the fact that the unobserved effect 𝑎𝑖 has disappeared. At the same
time, we have also loose the time constant variables in our case labeled as 𝑧𝑖𝑙
(Wooldridge, 2008).
18
With this methodology, we run the same specification which showed to be the best in
the BMA. Once again we use both the full sample and all the subsamples. We will
study the results closely and if necessary, we will adjust the independent variables
accordingly. At the end, we should be able to see whether the results from the BMA
and fixed effects are the same and what the differences between the model on the full
sample and the models on the subsamples are.
3.2 Data
To identify the potential determinants, we have analyzed 19 existing studies on the
topic of the FDI. Appendix B presents what variables each of the studies considered
and what was the total frequencies with which each variable was considered as
influential. There has been a great disparity in ways of measuring the same
phenomenon. For example, the trade openness can be measured as a ratio between
export, net export or export and import to GDP. For the simplicity, we have decided to
count it always as a single variable even though the measures were different. Grouping
the independent variables in this way gave us 58 potential determinants. When
considering only the variables which have appeared at least 4 times we are left with 13
variables. As far as the GDP is concerned, we have decided to include three different
measures: GDP, GDP per capita and GDP growth. All of them are being used in the
existing literature mostly without any comments and thus we would like to see whether
there is any difference among them. For the human capital characteristics, we have
used the unemployment rate and a portion of people with tertiary education. It means
in total 16 variables. 13 according to Appendix B plus two additional measures of GDP
and one additional measure of human capital characteristics. We have also considered
5 dummy variables, connected to the either European integration project or to the recent
financial crisis (EU, EU-15, EU-13. CEEC, crisis). In total, we have considered 21
variables. After dropping 3 (distance, corruption index and tax burden) of the variables
due to the insufficient number of observations, there are 18 independent variables left.
Most of our data were obtained from the World Bank database which covers all of the
countries of our interest during the period. Some of the variables such as interest rate
had to be obtained elsewhere, in this case Bloomberg terminal but that is an exception.
The independent variables cumulative FDI is from the UNCTAD database, the only
database covering the countries of our interest during the specified period.
The data in our analysis are used as provided in the database without any linearization,
with only one exception regarding the inflation, which we have taken in the absolute
value to reflect the negativity of instability of both increasing inflation and deflation.
19
However, there was an important decision to take. The process of FDI is a long-term
process, where the decision is made a certain amount of time before the investment.
Therefore we can expect it to be influenced by the conditions from the past rather than
current situation. Thus, many of the existing studies stress the importance of lagging
the independent variables (Özkan-Günay, 2011; Dauti, 2015; Thanh & Duong, 2011).
Thanh & Duong (2011) suggest to use one or two years’ lag. In our case, we have found
out that one year lag is sufficient. Inclusion of two years’ lag would force us to shorten
our dataset by one year because for many of our variables the data were not available
before the year 1996 without any additional value.
3.2.1 Possible determinants of FDI
In this section, let us present all of the independent variables that are summarized in
Table.1 with shortcuts, descriptions, units and source of the variable. The following
variables are all possibly influential factors that we have considered.
The most important factor for the FDI is the market size. In the majority of the studies
this is approximated by some measure of GDP. This factor is more important when the
company is considering the horizontal integration. In that case the venture is looking
for a market where cutting the transportation costs, by supplying from a local factory,
is worth the initial investment. This means that the investor is looking for markets with
a high demand. This is usually dependent on the strength of the economy. For this
reason, we have decided to follow the literature and include GDP in current USD.
Furthermore, the FDIs are usually conducted as a part of a long-term plan and the
company has to consider its future prospects in the new market as well. It is also
important to take into account how the size of the market has been changing in the past
years. For this purpose, we are using the GDP growth measured as the annual
percentage change in GDP. The last way of measuring GDP in the literature is to use
GDP per capita, which once again reflects mainly the horizontal integration. Where
high purchasing power of the population is incorporated more into the FDI. For all
three of the measures relating to the GDP, we expect a positive effect. Higher GDP
means higher purchasing power and thus increase in the FDI.
The second most commonly considered factor is the trade openness. For evaluating
this effect, we have decided to use a total sum of exports and imports of goods and
services as a percentage of the GDP. We can expect both a positive and a negative sign
depending on the motivation of the FDI. In case of the vertical integration, the investors
are interested in producing the goods in one place and then exporting them to another.
In this case, higher customs means lower profitability from exploiting cheaper inputs
in the host country and exporting the intermediate products into the final destination.
20
So for countries that are interesting for their cheaper inputs we can expect a positive
sign at the beta coefficient. Higher trade openness means more FDI.
On the other hand, for the horizontal integration high customs mean additional costs
on trade so it might be profitable to build a new factory even with lower sales because
the customs cannot be fully reflected in the end price but have to be absorbed by the
producer to some extent. In this case, lower customs and higher trade openness result
in lower FDI, so the coefficient sign is expected to be negative.
Third group of variables we have included in our study are financial factors. We have
decided to follow the previous researchers and consider four variables: exchange rate,
inflation rate, interest rate and credit rating. Nevertheless, based on the time interval of
this study, we were able to find data only for the exchange rate and inflation rate.
Exchange rate is measured as an average value of a local currency unit to USD over
the specified year. The expected effect of this variable is positive, the higher the
exchange rate, the relatively cheaper the host for the foreign investors and thus the FDI
increases.
Inflation rate is considered to be a factor reflecting the macro-economic stability of the
economy, expressing the pace at which money is losing their value. The theory does
not state one best value. However, in the European Union the ECB has stated the target
value to be between 0% and 2% (European Central Bank, 1998). In our case, it is
measured as a GDP deflator, which is more general then the other option in form of
consumer price index. The general macro-economic theory states that the best situation
for the economy is low positive stable and predictable inflation (Oner, 2012). This
results in the negative relationship between inflation and the FDI that can be found in
all of the considered studies. However, the negative relationship ignores the possibility
of deflation (negative inflation), situation when money is gaining their value. Without
any care, the expected negative effect would mean a deflation would be a positive
effect. To control this, we have decided to use the absolute value of the inflation rate.
Taking the absolute values together with the expected negative effect means that any
movement of the inflation is undesirable. However, it is necessary to interpret this
potential effect with caution. The target inflation is in most of the Europe in the range
from 0-2% and thus the zero value is not the only possibility for the stability. However,
we have decided to take it as the ground point.
The next category consists of characteristics in relation to the labor force. We have
decided to look at four factors: total size of the workforce, unemployment rate,
productivity and education. The workforce is expected have a positive correlation with
FDI, based on the two reasons. First of all, the larger the workforce the higher the
21
chance to find qualified employees and secondly larger workforce means larger
potential customer base, which can be a positive signal for the horizontal FDI.
Unemployment, considered as a number of unemployed out of total workforce, on the
other hand can have effect either way. High unemployment signals cheap workforce
immediately employable and thus positive relationship with vertical FDI. At the same
time, it means low purchasing power of the population and thus negative relationship
with horizontal FDI. Thus, the overall effect of unemployment depends on the type of
the FDI. We can predict from the existing literature that, for the Eastern poorer
countries the effect will be positive and for the Western richer ones negative. This is
due to cheaper inputs – such as labor in the east, and higher R&D in the west (Cheng
& Chung, 2012; Sánchéz, 2014).
Another important variable is productivity. In our case, it is measured as a productivity
of any employed person. This variable reflects not only the average cost of work but
also the average skills in a particular state. We can simply expect that higher
productivity will lead to higher FDI. Nevertheless, the productivity does not account
for the wage of the employees. It captures the value created but not the cost of it. The
last factor relating to human capital is tertiary enrolment as a percentage of the
population. We believe that higher education usually means more skilled labor and thus
higher productivity. Which means it should have a positive effect.
To fully utilize skilled labor, it is important to be able to use the newest technology in
the production. Skilled labor and successful use of technology usually leads to high
end products. To approximate the capability of the country to achieve such an outcome
some of the researchers have been using the ratio of high tech export to all export.
Higher ratio means more high-tech export which is connected with more efficient
industry and thus higher FDI (Barry, 2002). Based on that we expect a positive effect
on the FDI.
The most often omitted category of factors are institutions. The original gravity models
did not account for institutional factors at all. Nowadays, most of the authors believe
they do significantly influence the FDI flows. According to Demekas et. all. (2007), in
all of the studies gravity factors explain only 60% of the FDI flows, which means there
is a considerable possibility for institution to play an important role and thus both
gravity factors and institutional should be considered (Faeth, 2009)
The most often used institutional factors can be broken down into two sub-groups.
Firstly, equality among the market competitors (perception of corruption, rule of law)
and secondly, the difference between the home and the host market (government
22
effectiveness and tax burden). Unfortunately, we were able to collect enough data only
for one factor from each category. As far as the rule of law is concerned, we have used
the WTO Governance Indicators database that defines the Rule of law as follows: “…
extent to which agents have confidence in and abide by the rules of society, and in
particular the quality of contract enforcement, property rights, the police, and the
courts, as well as the likelihood of crime and violence…” (Data.worldbank.org; 2016)
It is measured in units of standard normal distribution i.e. ranging from approximately
-2,5 to 2,5. Governmental effectiveness looks at the quality of public services, its
independence from the political pressure and on the quality and implementation of the
new policies and, at the same time, to which extent they are in line with the political
program of the government. It is also measured in the units of standard normal
distribution. For both of the factors we believe there is a positive relationship with the
FDI. The more efficient and trustful the government is, the better the investment
environment is.
The last category of explanatory variables are the dummy variables. Firs of all it is the
dummy variable acquiring the value one for the years since the beginning of the
financial crisis in 2008. We excpect this variable to have a negative effect on the FDI,
due to the general negative impact of the crisis on the economy.
Other three dummy variables are connected to the European integration project. EU
acquires the value one in the year when the state becomes a member of the EU and has
the value ever since. The EU-15 and EU-13 is one for all the original 15 states of the
EU, respectively the newest 13 states of the EU. The sign in front of this variable is
excpected to be positive thanks to the economy enhancing role which EU plays.
The last dummy variable has the value one for the so-called CEEC. By including this
dummy variable we are testing whether the porrer countries in the East attracts more
FDI.
3.2.2 Measuring FDI
In our analysis, we are applying the aggregated FDI inflows in current USD. Generally,
there are three main possible ways of measuring the FDI – aggregated FDI flows,
bilateral FDI flows and FDI stock. Aggregated FDI flows look at the complex situation
in the host country. The host country is not making distinction between the investments
from different countries.
On the other hand, bilateral FDI allows deeper analysis of the differences between the
investing and the host country. This approach does not consider only the value of the
host country but also analyses the relationship between them. In this case one
23
observation of the FDI corresponds to the value invested from country A to country B
allowing to include in the analysis the characteristics of not only country B but also the
county A.
The last option is looking at the FDI stock. The advantage of this approach is the
limitation of fluctuations and the possible trends (Dauti; 2015). On the other hand, FDI
stock is affected by the economical position of the host country and can change even
when there were no FDI flows in that year due to the financial factors. Thus, this
analysis is able to model the position of the country compared to the other countries
accounting also the economic situation. However, it does not show what to do in order
to attract more FDI.
With our goal in mind, in order to analyze the effect of European integration and the
crisis in the whole Europe we have decided to use the aggregated FDI flows data.
Comparing to the bilateral FDI flows this approach does not force us to choose only a
limited sample of investing countries. That could cause biased results, but rather allows
us to evaluate the attractiveness of the country to all investors. The FDI stock would
be in our case strongly influenced by the financial crisi and thus it would be harder to
determine the factors influencing the FDI by giving too much explanatory power to the
financial crisis.
There are several possible sources for such data. The main ones are OECD, Eurostat,
European Central Bank, IMF and UNCTAD (OECD Benchmark Definition of Foreign
Direct Investment; 2008). All of those sources are considered reliable even though they
show some differences in the framework of adjusting the national statistics. The main
fact is that they all share the same definition of FDI that have been defined by OECD
in 1983 in Benchmark Definition of Foreign Direct Investment, and already three times
revised (OECD Benchmark Definition of Foreign Direct Investment; 2008).
When possible, most of the studies are using the OECD database. Unfortunately, this
database is limited to the OECD members and only a few non-members and thus it is
not applicable in our case. We have found the same problem with both Eurostat and
ECB. The only applicable sources are IMF and UNCTAD but because IMF is to some
extent based on the UNCTAD data (Data.worldbank.org; 2016) we have decided to
use the original UNCTAD database.
24
Table.1 Variables description
Na
me
Abre
v.
Expla
na
tio
nM
ea
ssru
eS
ourc
e
Pro
ductivity
prd
La
bo
r pro
ductivity p
er
pe
rso
n e
mplo
ye
d in 2
01
4 U
S$
(co
nve
rte
d t
o 2
01
4 p
rice
le
ve
l w
ith u
pda
ted 2
01
1 P
PP
s)
$C
onfe
rence
Bo
ard
Se
co
nda
ry e
nro
lnm
ent
se
ce
n
To
tal e
nro
llme
nt
in s
eco
nda
ry e
duca
tio
n,
rega
rdle
ss o
f a
ge
, e
xpre
sse
d a
s a
pe
rce
nta
ge
of
the
po
pula
tio
n o
f o
ffic
ial
se
co
nda
ry e
duca
tio
n a
ge
. G
ER
ca
n e
xce
ed 1
00
% d
ue
to
the
inclu
sio
n o
f o
ve
r-a
ge
d a
nd u
nde
r-a
ge
d s
tude
nts
be
ca
use
of
ea
rly o
r la
te s
cho
ol e
ntr
ance
and g
rade
re
pe
titio
n.
% o
f po
pula
tio
nW
orldba
nk
Te
rtia
ry e
nro
lnm
ent
tere
n
To
tal e
nro
llme
nt
in t
ert
iary
educa
tio
n (
ISC
ED
5 t
o 8
), r
ega
rdle
ss o
f a
ge
, e
xpre
sse
d a
s a
pe
rce
nta
ge
of
the
to
tal po
pula
tio
n
of
the
fiv
e-y
ea
r a
ge
gro
up f
ollo
win
g o
n f
rom
se
co
nda
ry s
cho
ol le
avin
g.
% o
f po
pula
tio
nW
orldba
nk
Hig
h-t
echno
logy e
xpo
rts
teche
x
Hig
h-t
echno
logy e
xpo
rts a
re p
roducts
with h
igh R
&D
inte
nsity,
such a
s in a
ero
spa
ce
, co
mpute
rs,
pha
rma
ce
utica
ls,
scie
ntific instr
um
ents
, a
nd e
lectr
ica
l m
achin
ery
. W
e m
ea
sure
the
hig
h-t
echchno
logy e
xpo
rts a
s a
s a
po
rtio
n o
f to
tal e
xpo
rts.
% o
f m
anufa
ctu
red
expo
rts
Wo
rldba
nk
Co
rruptio
n inde
xco
r
A c
ountr
y o
r te
rrito
ry’s
sco
re indic
ate
s t
he
pe
rce
ive
d le
ve
l o
f public
se
cto
r co
rruptio
n o
n a
sca
le o
f 0
(hig
hly
co
rrupt)
to
10
0
(ve
ry c
lea
n).
A c
ountr
y's
ra
nk indic
ate
s its
po
sitio
n r
ela
tive
to
the
oth
er
co
untr
ies in t
he
inde
x.
Va
lue
be
twe
en
1 a
nd 1
0
Tra
nspa
rency
Inte
rna
tio
na
l
Rule
of
law
rlw
Rule
of
La
w c
aptu
res p
erc
eptio
ns o
f th
e e
xte
nt
to w
hic
h a
ge
nts
ha
ve
co
nfide
nce
in a
nd a
bid
e b
y t
he
rule
s o
f so
cie
ty,
and in
pa
rtic
ula
r th
e q
ua
lity o
f co
ntr
act
enfo
rce
me
nt,
pro
pe
rty r
ights
, th
e p
olic
e,
and t
he
co
urt
s,
as w
ell
as t
he
lik
elih
oo
d o
f cri
me
and v
iole
nce
. E
stim
ate
giv
es t
he
co
untr
y's
sco
re o
n t
he
aggre
ga
te indic
ato
r, in u
nits o
f a
sta
nda
rd n
orm
al dis
trib
utio
n,
i.e
.
rangin
g f
rom
appro
xim
ate
ly -
2.5
to
2.5
.
Va
lue
be
twe
en
appro
x.
-2.5
and 2
.5W
orldba
nk
Go
ve
rnm
ent
eff
ectivne
ss
go
ve
f
Go
ve
rnm
ent
Eff
ective
ne
ss c
aptu
res p
erc
eptio
ns o
f th
e q
ua
lity o
f public
se
rvic
es,
the
qua
lity o
f th
e c
ivil
se
rvic
e a
nd t
he
de
gre
e o
f its inde
pe
nde
nce
fro
m p
olit
ica
l pre
ssure
s,
the
qua
lity o
f po
licy f
orm
ula
tio
n a
nd im
ple
me
nta
tio
n,
and t
he
cre
dib
ility
of
the
go
ve
rnm
ent's c
om
mitm
ent
to s
uch p
olic
ies.
Estim
ate
giv
es t
he
co
untr
y's
sco
re o
n t
he
aggre
ga
te indic
ato
r, in u
nits o
f
a s
tanda
rd n
orm
al dis
trib
utio
n,
i.e
. ra
ngin
g f
rom
appro
xim
ate
ly -
2.5
to
2.5
.
Va
lue
be
twe
en
appro
x.
-2.5
and 2
.5W
orldba
nk
Co
rpo
rate
ta
xco
rpt
Co
rpo
rate
inco
me
ta
x r
eve
nue
as a
pe
rce
nta
ge
of
GD
P%
Wo
rldba
nk
Fin
ancia
l cri
sis
cri
sis
Dum
my v
ari
able
s a
cquir
ing t
he
va
lue
1 in t
he
ye
ars
20
08
-20
12
, 0
oth
erw
ise
EU
me
mbe
rship
EU
Dum
my v
ari
able
fo
r th
e m
em
be
rship
of
the
co
untr
y in t
he
Euro
pe
an U
nio
n
Fir
st
15
EU
me
mbe
rsE
UD
um
my v
ari
able
fo
r be
ing o
ne
of
the
Ne
we
st
13
EU
me
mbe
rsE
UD
um
my v
ari
able
fo
r th
e m
em
be
rship
of
the
co
untr
y in t
he
Euro
pe
an U
nio
n
CE
EC
EU
Dum
my v
ari
able
fo
r th
e m
em
be
rship
of
the
co
untr
y in t
he
Euro
pe
an U
nio
n
25
4 Results
In this chapter, we will comment on the results that can be found in the Table 2. The
first part will be focused on the results from the BMA method on the full sample and
all of the subsamples. Then, a sensitivity test will be run in the form of fixed effects
with the variables indicated by the BMA, eventually adjusted if necessary, in order to
see whether there are any differences.
4.1 Finding the correct determinants
For finding the correct model we have chosen a BMA. In the STATA statistical
software we are using when running the BMA procedure, we can take some of our
dependent variables as the main factors and the rest as auxiliary. We have decided to
set as the main variables those which have been in the previous research considered
the most. Thus we have chosen three lagged variables GDP growth, size of the labor
force and productivity, as the main variables. The forth possibility of a main variable
was the trade openness. However, due to the European integration we believe it should
not play such an important role as in the other studies, never the less we included it as
an auxiliary variable. All of the remaining 14 variables will be included in the model
as auxiliary variables. Thus evaluated whether it is worth to include them in the model
or not based on the “pip” value. Which represents posterior inclusion probabilities. The
rule of thumb states that whenever the “pip” value is larger than 0.5 we should consider
the variable as significant.
4.1.1 Full sample
Except the three main variables there are another four variables, which should be,
according to the “pip” value, included in our final model GDP, trade openness, rule of
law and governmental effectiveness. Except that the model shows a strong effect of the
dummy variable for the EU15 members. This is an interesting signal supporting our
theory for different factors being significant for West and for East. The positive sign
of the variable suggests that just being one of the first members of the EU increases the
FDI by almost 3.3 billion USD.
Let us start with describing the three main variables. First of all, we have found a
significant effect of the GDP growth with expected positive sign. This indicates that
investors are considering the future prospects of the country and invest more in
26
Table.2 Results of Bayesian model averaging
Model # 1 2 3 4 5
Variables ° Total 1997-2007 2008-2014 EU15 CEEC
GD{ growth °° 232.7763 179.3411 142.0303 722.6623 127.1655
°°° (1.72) (1.10) (0.67) (01.06) (2.69)**
labor force 0.0003 0.00003 0.001 0.001 0.0003
(3.39)*** (0.37) (8.04)*** (6.23)*** (3.08)***
productivity -0.0328 -0.1105 0.1907 -0.2456 0.0356
(-0.57) (-1.28) (3.32)*** (-1.02) (1.69)
cons -5394.177 -6201.98 -8876.141 -5449.678 -1494.482
(-1.29) (-1.15) (-1.37) (-0.36) (-1.25)
GDP °°°° 0.013 0.027 0.0005 0.0001 0.0121
°°°°° (7.14)*** (10.07)*** (0.38) 0.06 (2.36)**
°°°°°° 1 1 0.18 0.07 0.93
GDP per capica -0.0032 0.0731 0.0002 0.0085 -0.0023
(-0.13) (0.50) (0.01) (0.11) (-0.13)
0.05 0.25 0.06 0.07 0.07
unemployment 3.0652 -0.6562 5.9157 -10.1676 1.6627
(0.10) (-0.01) (0.13) (-0.08) (0.11)
0.04 0.05 0.07 0.07 0.07
terciary educated -2.9187 1.021 -43.1215 7.1483 -0.5836
(-0.19) (0.6) (-0.66) (0.14) (-0.14)
0.07 0.05 0.37 0.07 0.08
trade openness 52.083 92.7501 13.8134 239.2993 2.4031
(1.46) (2.01)* (0.51) (2.31)* (0.39)
0.76 0.88 0.27 0.92 0.19
high-tech export 4.91 2.2486 0.5792 10.8247 2.3338
(0.16) (0.06) (0.02) (0.13) (0.26)
0.06 0.07 0.06 0.07 0.12
exchange rate -0.0006 -1.1101 0.0358 -1.5154 0.2616
(-0.00) (-0.40) (0.11) (-0.28) (0.22)
0.04 0.18 0.07 0.12 0.09
inflation rate 0.017 0.0723 0.864 7.7367 -0.0552
(0.01) (0.02) (0.02) (0.02) (-0.07)
0.04 0.05 0.06 0.06 0.06
rule of law -5515.997 -8329.892 249.6227 -3850.862 -126.4438
(-1.09) (-1.26) (0.22) (-0.40) (-0.31)
0.62 0.7 0.1 0.25 0.14
government effectivness 10151.95 12621.36 16.073 9042.09 13.7628
(2.19)** (1.82)* (0.02) (0.89) (0.05)
0.98 0.9 0.07 0.62 0.07
crisis -50.3339 -259.3131 46.4536
(-0.12) (-0.17) (0.22)
0.05 0.08 0.1
EU -14.7387 101.8386 83.4327
(-0.02) (0.11) (0.09)
0.06 0.07 0.08
EU-15 3296.52 1271.999 1751.134
(0.98) (0.45) (0.61)
0.56 0.22 0.34
EU-13 -1069.79 -617.1158 -264.6993
(-0.49) (-0.34) (-0.23)
0.25 0.15 0.11
CEEC 209.3305 119.0776 22.43681
(0.19) (0.10) (0.03)
0.07 0.07 0.06
# of observations 684 418 266 252 234
We indicate pip>0.25% pip>0.50% pip>0.75%
Notes:° all of the variables are lagged°° coefficient°°° t-value°°°° coefficient°°°°° t-value°°°°°° pip-value - values larger than 0.5 are written in bold and italicFor interpreting the pip value we can use the following distinction:Value between 0.5 and 0.75 is considered weak, between 0.75 and 0.95 substantial,
between 0.95 and 0.99 strong and decisive if it exceeds 0.99. (Havranek & Irsova, 2016)
*p<0.1 **p<0.05 ***p<0.01
27
countries with a bigger potential. This indicates that the FDI would be horizontally
driven, focusing on big markets where it is worth to be present. Considering the average
yearly change of -0.15% and the coefficient 232.78, the average effect is almost -35
mil USD. Even though the effect is quite small on average, we have to understand the
growth can be much higher or lower in particular cases.
The second main variable by hard is the total size of employed and unemployed people.
Once again the sign is positive as expected, which does not support only horizontal or
vertical integration. In both cases, bigger labor force should bring positive effect. Either
on the demand side of the product or in form of the possibility to choose from a larger
pool of employees. As we can see, the coefficient in this case is very small - only
0.0003. On the other hand, the average yearly change is 51,244 which together gives
15.4 million USD.
The last main variable is the productivity of the labor force. Surprisingly, the effect of
the productivity is negative, which would suggest the investor to prefer countries where
the workers do not work as efficiently as in the other markets. This is illogical and we
believe should not be part of our final model, at least not on the full sample. In line
with this argumentation is also the t statistic of the variable, which is very low.
Therefore, in the next models, we have decided to ignore the fixed variables which are
insignificant according to the t-statistic
The most significant variable from the remaining four is the measure of GDP in the
absolute value. This shows that the investor is not interested only in the future prospects
of the economy but also in the size of the economy as it is. This is typical once again
for the horizontal integration, where larger economy means higher purchasing power.
In this case the positive coefficient 0.013 signals that the average yearly change of 19
billion USD attracts additional 247 million USD in FDI.
The second variable that should be taken into consideration according to the BMA is
the proportion of import and export to the GDP with also a positive sign. This means
that open economies attract more FDI, compared to the closed ones. The importance
of the trade openness is more typical for the vertical integration, which is based on the
opportunity to produce cheaply and then export to other countries. In our case with the
EU, it is also possible for the investor to prefer investing to the EU as a whole
comparing to the other possible locations in Europe. Multiplying the coefficient 52.1
by the average change in the trade openness of 1.56% gives us additional 81.3 million
USD on FDI.
28
The last two variables with the “pip” value higher than 0.5 are the two considered
institutional variables: rule of law and government effectiveness. Interestingly the
coefficient for rule of law is is negative and for the governmental effectiveness positive,
which is unexpected. On one hand the investors prefer unequal conditions for the
competitors on the market (rule of law) on the other hand they prefer markets with
better infrastructure and clear policies, which is contradicting each other. Interestingly
enough, dropping one of those variables gives us the coefficient in front of the
remaining one equal to approximately the sum of those two coefficients. Based on that
it seems that due to a high correlation those two factors are influencing the FDI
together. Thus we have decided to interpret their compound effect together as positive.
The average yearly change for both of the variables is 0.01 which together with the
compound effect of 10152-5516 means additional more than 46 mil USD on FDI due
to the institutions.
Overall it seems that the investors were in the end of the 20th and beginning of the 21st
century investing in big open economies with future potential where it was possible to
find both consumers and workers. And where it was possible to work in a transparent
environment with sturdy enforceable policies.
4.1.2 Before and after the crisis
One of the possible ways of dividing our sample is by time. Looking back in time and
reading thoroughly the existing stream of literature, it seems that the most influential
milestone was the recent financial crisis. It was found to be a significant explanatory
variable for the FDI by many of the researchers (Ucap et. al., 2010; Hunady & Orviska;
2014 & Dornean et. all., 2012).
4.1.2.1 Prosperity and growth before the crisis
Looking at the results of our BMA analysis on the dataset before the financial crisis
(Model 2 in Table.2) we can see the results are identical as far as the significance and
signs of the auxiliary variables are concerned, as in on the whole sample. On the other
hand, it seems that all of the fixed variables became insignificant. In the following part
those differences will be discussed.
First of all, the insignificance of the GDP growth and significance of the GDP in
absolute value indicates before the financial crisis the investors focused mainly on the
size of the economy and not on the prospects of the economy. Furthermore, the effect
of the absolute value of the economy has more than doubled. The average yearly
change of 26.8 billion USD before the crisis combined with the coefficient 0.027 means
additional 723.6 mil USD in FDI. That is almost three times bigger than the effect of
GDP and GDP growth on the full sample.
29
Another significant variable is the trade openness. Once again, the sign is positive and
thus signaling the interest of the investors to invest in open economies. Also, this
variable has the coefficient more than twice the size as on the full sample. And the
resulting effect of almost 167 million USD on the FDI is almost three time bigger
thanks to the average yearly change of 1.8% and coefficient 0.027.
The last two variables that should be considered, according to the “pip” value, are the
variables associated with the labor force. In this case, both coefficients increased in
absolute size. However, their sum of 4291 is almost the same as before, the same as
the average yearly change, which is still 0.01 for both of the variables. This means that
average yearly change in institutions brings additional 43 million USD to the FDI.
4.1.2.2 Instability and doubts after the crisis
The FDI in the second part of our dataset has been influenced by the recent financial
crisis. As was said, the FDI has dropped greatly at the beginning of the crisis. Based
on our analysis not only the economic situation but also the determinants for the FDI
has changed due to the financial crisis.
First of all, we do not find any significant measures of GDP to play any role in the FDI
flows which suggests that the investors do not consider the size or the prospects of the
economy as a crucial determinant. This seems logical due to great instability and
changes. Looking back in time, the depression in many European countries shifted the
investment environment, erasing almost all of the certainty.
Once again, the labor force becomes significant with a positive coefficient more than
three times bigger than on the full sample 0.001. It shows the investors assigned higher
priority to look for both consumers and employees. Multiplying the coefficient with
the average yearly change of 35,773 we get the effect of 35.8 million USD.
The productivity becomes significant in this model as well. The positive coefficient of
0.191 means investors are interested in markets where the employees are able to
produce more. Which can be later on either sold or exported. This is typical rather for
vertical integration where the market players want to build a factory and then serve the
surrounding territory through trade. The average yearly effect on the FDI is almost 70
mil USD (367 USD multiplied by the coefficient 0.191).
4.1.2.3 Differences before and after the crisis
Evaluation of the significant variables in the two periods divided by the financial crisis
has showed some clear differences. Before the crisis, the investors focused more on the
market - its size and prospects. It has also considered the institutions and how the
30
markets worked. On the other hand, after the crisis investors focused only on the labor
force its size and productivity thus preferring the Western countries. Hence we
conclude that the crisis has brought about a structural change.
4.1.3 West vs East
The second possibility how to divide our sample is into the Western and Eastern part
of Europe. The West is an approximation for developed countries with more experience
in market economy. We have approximated those countries by the old EU members -
EU15. On the other hand, the East is represented by the countries which mostly had to
transform from the centrally planned economies to market driven economies at the
beginning of the 90s and joined the EU in the near past. This group is represented in
our analysis by the EU13 group. Once again we perform the BMA on both of our
subsamples and comment and compare the results.
4.1.3.1 The more experienced West
Surprisingly, there are only three variables significant for the initial EU 15. The results
show once again a strong positive effect of total size of labor force with coefficient
0.001. This means that investors wanted to invest mainly in more populated markets.
Combining the coefficient with the average yearly change of 98,124 we get an effect
of labor force more than 98 million USD.
The second variable that seems to play a role is the openness of the economy with
almost five times the coefficient as on the whole sample. The coefficient 239.3
multiplied by the average yearly change 1.5% means additional increase of 359 million
USD.
The last variable that seems to play a role is the governmental effectiveness. In this
model, it is the only significant institutional variable. Comparing the effect of only this
institutional variable with the previous compound effect of both institutional variables,
we reach more than twice higher value than before. Multiplying the average change of
-0.02 with the coefficient 9042.09 gives on average 180.8 million USD less on FDI
every year due to the institutions.
4.1.3.2 Poorer East
The second part of our dataset contains the countries new to the FDI game and thus we
are interested whether there are some differences in comparison to the West. Once
again both of the measures of GDP are significant. On one hand, it is the prospect in
the form of the GDP growth with the coefficient of 127. Multiplying it with the average
yearly change of -0.14 we get every year on average -18 mil USD less. Which is quite
a small effect. On the other hand, the GDP in absolute value with a coefficient of 0.012
31
and the average yearly change of 5.2 billion USD brings every year additional 62
million USD. This means investors prefer big and growing markets. Which is logical
because it means more revenue both now and in the future.
Once again the labor force becomes significant. Coefficient is again only 0.0003 which
in combination with the average yearly change of -5957 signals the FDI to decrease
yearly by 1.8 million USD. Once again a very small effect.
4.1.3.3 Difference among the West and East
Analyzing the differences of the two previous models is to some extent difficult. The
model about the EU15 does not provide us with a lot of information. However, it seems
that the investors in the West do not take into account the size or prospects of the
economy. It looks like they consider all of the countries the same and distinguish them
only based on the size of the labor force. On the other hand, in the EU13 the investors
evaluate both the size and the future prospects very much. The second difference is
caused by the institutions. While in the East we can observe lack of their importance,
in the West the effect is significant. Overall, it seems that while in the East investors
need the stability in form of economical measure, in the West the situation is
comparable and thus they are focusing mainly on the institutions.
4.1.4 What have we learned
Looking back at all of the five models resulting from the BMA we do not find anything
particularty surprising. We feel confident to say that the model on the full sample of
38 countries across 18 years, shows the most promising results. It indicates role of the
size of the economy, labor force, trade openness and institutional variables.
Furthermore, it provided us with the basis for dividing our sample into the West and
East part by the significance of the EU15 dummy variable. It might seem that the
remaining models did not work so well. All of them showed significance of lower
number of variables. However, it is the opposite, the models on the subsample provided
us with the information how the significant variables on the subsamples differed.
Allowing us to analyze the differences in investor’s behavior in before and after the
crisis or in the West and East. Based on this information we will model the situation
using the fixed effects, as a sensitivity analysis.
4.2 Sensitivity analysis
In this chapter, we will construct the models on the full sample and all four subsamples
according to the significance levels based on the BMA using the standard panel data
32
Table.3 Results of Fixed effects based on BMA results
Model # 6 7 8 9 10 11 12
variable ° Total Total 1997-2007 2008-2014 2008-2014 EU12 CEEC
GDP growth °° 180.718 121.325 190.0342 127.9913
°°° (2.05)** (1.28) (2.56)** (2.27)**
GDP °° 0.006 0.005 0.052 0.014
(0.76) (0.57) (4.34)*** (2.33)**
labor force 0.002 0.002 -0.005 -0.005 0.0021 -0.001
(1.84)* (2.07)** (5.18)*** (5.35)*** (0.82) (-1.79)*
productivity -0.313 -0.4384735
(1.25) (-2.08)**
trade openness -24.473 -24.026 67.989 -81.4617
(0.56) (0.52) (0.73) (-0.31)
rule of law -7183.745 -9415.393 -3025.681
(1.89)* (1.85)* (0.41)
government effectivness 10272.609 11662.786 4449.046 5445.708
(2.41)** (1.87)* (0.51) (0.63)1998 4038.168
(21.35)***1999 10094.59
(32.22)***2000 14518.721
(60.45)***2001 2304.567
(7.42)***2002 2912.849
(10.12)***2003 1701.894
(6.01)***2004 -64.111
(0.13)2005 6343.085
(6.42)***2006 10532.309
(7.70)***2007 19284.429
(10.9)***2008 5373.716
(2.05)**2009 6079.863
(1.85)*2010 5990.325
(2.25)**2011 8926.737
(3.32)***2012 4295.389
(1.26)2013 4342.292
(1.3)2014 1858.7
(0.56)
cons -13177.526 -18864.48 -12791.122 82830.009 96765.39 -5420.868 6059.046
(1.40) (2.96)*** (1.17) (3.57)*** (4.51)*** (-0.10) (2.42)**
N 684 684 418 266 266 252 234
Notes:° all of the variables are lagged°° coefficient°°° t-value
p<0.1 *; p<0.05 ** ; p<0.01 ***
33
analysis fixed effects. We will discuss the results. If necessary, we will create a second
benchmark model and use once again the fixed effect to see if it is possible to find a
better fitting model. All of the results can be found in Table 3. To be able to compare
the coefficients we have also created a Table 4 where we can find only the abbreviation
of the variables found important in the BMA analysis and their equivalents from the
FE models.
We will start with the full sample. Comparing the results of the fixed effects model and
BMA we can see that they are quite similar. The size of the economy and compound
effect of the institutions have bigger effect on FDI under BMA on the other hand the
FE assigns more power to the labor force. The main difference is in the trade openness.
While BMA found the coefficient positive the FE shows a negative sign signaling
closed economies to be preferred comparing to open ones. This is a crucial difference.
Positive sign means FDI to be driven by the vertical integration, on the other hand a
negative sign shows in the direction of the horizontal one.
Continuing with the time division model and starting with the period before the
financial crisis, the results from the BMA and fixed effects are almost identical. Thus
we can consider our model based on the BMA to be robust. This time the results do not
show any differences in the sign. There are still some differences in the size of the
coefficient however nothing that would affect our interpretation from the previous
section where we have used the BMA methodology.
However, the subsample covering the period after the financial crisis tells a different
story. While in the previous two models, the FE results matched almost perfectly the
BMA results, in this case the FE shows the exactly opposite sign on both of our
significant variables. According to the FE, the investors were after the crisis looking
for a smaller markets as far as the labor force with a lower productivity. This is a very
unaccountable result, which forced us to try to adjust the model to find a better and
more intuitive result. We have tried to add a third variable with the highest t-statistic
from the BMA, which is GDP growth. FE shows the coefficient to be positive and
significant as we expected, however it has not changed the coefficient in front of the
other two variables. Because the results of the BMA are more intuitive, we believe they
should be taken into account.
The last two models are for the West and East Europe. In case of the West we find the
coefficient under FE almost identical to those from the BMA. The sizes differ a little
bit but not significantly. It is almost the same within the East part of the Europe. The
only difference is in the coefficient for the labor force. While under BMA higher labor
force attracted more FDI, according to FE more labor force means less investment.
34
And that is hard to explicate. Generally it seems to be hard to explicate the size of the
labor force under the FE.
Overall, the BMA shows either the same results as the FE or in two of our models more
intuitive results. Based on our belief that the BMA methodology should be always
considered when trying to choose the real determinants of the FDI mainly on a bigger
set of data. In our case, it yields intuitive results.
Table.4 Comparison of selected coefficients from BMA and FE
BMA FE
gdpg1 232.7763 180.718
gdp1 0.0003 0.006
labf1 0.013 0.002
trdop1 52.083 -24.473
rullw1 -5516 -7183.75
govef1 10151.95 10272.61
BMA FE BMA FE FE
gdp1 0.027 0.052 gdpg1 190.0342
trdop1 92.7501 67.989 labf1 0.001 -0.005 -0.005
rullw1 -8329.89 -3025.68 prodc1 0.1907 -0.313 -0.43847
govef1 12621.36 4449.046
BMA FE BMA FE
labf1 0.001 0.0021 gdpg1 127.1655 127.9913
trdop1 239.2993 -81.4617 gdp1 0.0121 0.014
govef1 9042.09 5445.708 labf1 0.0003 -0.001
EU12 CEEC
Total
1997-2007 2008-2014
35
5 Discussion
In this chapter we would like to look closer at the results presented in the previous
section. Our goal is to highlight the findings which are in our opinion the most
important or surprising. Apart from the findings on the full sample we will also discuss
all four models on the subsamples to see if there are some clear differences before and
after the financial crisis or for the West and East. At the end the potential level of the
FDI for all of the countries in our subsample will be calculated and compared with the
real level to see whether the countries under- or over-perform.
5.1 Effect of gravity and institutional variables
Most of the existing studies usually focused on a specific region or countries with a
common characteristic. Most often it was CEEC, South America, North America,
South East Asia or OECD countries for a very simple reason. As Özkan-Günay (2011)
explains the FDI analysis is very easily influenced by the geopolitical or economical
characteristics of the region and thus it is safer to look at to some extent similar
countries. However, we believe that the EU in the past more than 20 years can be
considered a region with a homogenous development. Furthermore, with the
continuously spreading EU we should be able to evaluate the factors playing the role
in Europe in general. From the investors’ point of view it might be considered as a one
region.
Our first task was to determine the main factors influencing the FDI in the whole
Europe at the end of the 20th and beginning of the 21st century. Both of our
methodologies (BMA and FE) showed four main categories of determinant to play a
role. First of all it was the size of the economy (GDP and GDP growth), characteristics
of the labor force (size of the labor force) and openness of the economy (trade
openness). All of those three categories represent the so called gravity factors. The
existing literature assigns to those factors the majority explanatory power over the FDI.
For example Demekas et. all (2007) believes that at least a 60% of the FDI can be
explained by them. In our case when summing up the average effects of all of the
significant variables (except the dummy variables) the gravity factors were responsible
for more than 80% of the average yearly change. Showing the significant influence of
the gravity factors on the FDI.
The last category of significant factors were the institutional variables (rule of law and
36
governmental effectiveness). In our case explaining around 20% of the FDI. Even
though in the earlier researches those factors were not considered in the recent years
more and more academics are highlighting their importance (Faeth, 2009; Dauti, 2015).
Based on our analysis we can only confirm the importance of institutional variables.
The last significant variable on our full sample is the dummy variable for the original
fifteen states of the EU. At the same time we have not find the dummy variable for
being the EU member to be significant. We can thus conclude that our analysis does
not support the theory that not only the membership but even the mere announcement
of the accession increases the FDI. In our case we have not find any support for it. On
the other hand the significance of the EU-15 shows that there is some disparity among
the European states in the role of determinants for FDI. This can be explained as
follows: either the West is preferred by the investor due to its longer experience with
both market economy and FDI, or the EU membership in fact increases the FDI, though
in a long run, not immediately as believed. We can find a support for this hypothesis
in the literature. As Kalotay (2008) states the countries will be granted a full access to
the EU funds after the first ten years. This condition has not yet been fulfilled by all
the EU13 countries. Villaverde & Maza (2015) found out that the access to the
cohesion funds is a great motivation for the investors. Thus it is possible that we will
not be able to see the effect of the EU accession on FDI sooner than after 10 years after
joining. However, this should be further investigated.
5.2 Role of the EU on FDI
Our analysis has been constructed to be able to find the differences in the perception
of the East and West part of the EU. Even though the EU is viewed as a great way of
integration: reducing the barriers and unitizing some of the administration (Demekas
et. al., 2004; Dauti, 2015; Cheng & Chung, 2012), there are still differences among the
countries. The problem is a great economic inequality among the member states within
the EU. When Romania and Bulgaria joined the EU in 2007 the population of the EU
increased by 7%. However, the GDP of the EU didn’t increase even by a 1%. This
characterizes the relationship between the old member states and the new perspective
markets (Iloiu et. all., 2015).
Based on this disparity we have decided to test whether the investors consider the
Europe as one region with all countries competing against each other. The other
possibility is that there are different factors involved while considering the investment
in the EU-15 (original, long term members of the EU) and EU-13 (new EU member
states). From our analysis, we can see that while the West is influenced by the
37
institution and the East by the size of the economy the whole EU is influenced by its
combinations.
The investment in the West are driven mainly by the quality of institution with a higher
coefficient than for the whole Europe. The data for the institutions in the EU-15 suggest
those countries have the highest institutional level in our sample. Making it extremely
difficult to improve their position. Thus, any small improvement has to be rewarded
with a considerably large increase in the FDI.
On the other hand, the investment in the East is solely driven by the size and prospects
of the economy explained by the GDP and growth of GDP. It is important for the
investors to be present on markets which are big enough and offer future prospects.
From this finding we can draw an interesting conclusion. According to the OLI
paradigm, the size of the economy is important mainly for the horizontally motivated
investments (Sánchez-Martin; 2014). Where bigger the GDP and larger the GDP
growth are pushing for higher salaries, increasing the demand and eventually pushing
for better living standards. Combining this finding with the fact that the developed
countries are usually considered to be the target of horizontally driven FDI we can
confidently state that within the EU the majority of the FDI are motivated by the
horizontal integration. This finding contradicts the findings of Vechiu & Mistru (2014)
who found that approximately 70% of the FDI within the EU are vertically motivated.
On the other hand, it supports the finding of Gast & Hermann (2008) on the OECD
countries denoting that the horizontal FDI are more common than the vertical. It is
important to say that Vechiu & Mistru (2014) have based their finding on the dataset
between the years 1996-2005. In our analysis, we had the possibility to test the longer
period, which might have influenced our results. For that reason, we would like to find
out whether the trend in FDI in Europe has not changed in the past few years.
5.3 The effect of the 2008 financial crisis on FDI
To analyze the recent trend in the FDI in Europe we can use our two models on time
subsamples before and after the financial crisis. Based on those two models we can
identify whether the recent events have shifted the direction of the FDI.
Before the crisis the FDI has been strongly influenced by the GDP, trade openness and
institutions. The effect of absolute value of GDP suggest a focus on big markets. The
biggest markets before the financial crisis were Germany, the Great Britain, France,
Italy and Spain. All of those countries represent well developed Western markets where
the investors focused due to their significant purchasing power. Furthermore, all of
those countries are part of the European free trade area, which allows the producer to
38
serve the other markets from the existing production. This corresponds with the
positive effect of the trade openness. It seems that before the crisis the investors were
not interested to set up their businesses based on the size of the host market. They
preferred the possibility to serve the rest of the Europe. Thus, they took into account
the openness of the country to trade. Even though many of the aspects of the trading
procedure are unified within the EU some of the countries can still have more proactive
stand, which is awarded with higher FDI. The positive effect of the institutions just
completes the picture of the investor before the crisis focusing on stable open markets
from where it is possible to serve the rest of the EU and optimally the rest of the Europe.
The period after the financial crisis is harder to understand. While the BMA analysis
suggests the main influence of the FDI was carried out by the labor force characteristic,
the fixed effect analysis points out also the effect of the future prospect of the economy
in form of the growth of GDP. The positive effect of productivity is logical. The
positive effect of the size of labor force shows the interest of the investors in countries
with a high purchasing power which suggest horizontally motivated FDI. The most
interesting variable is the significance of the growth of GDP. Considering our data, the
highest growth of GDP since the outbreak of the financial crisis was on our dataset
recorder by mainly CEEC countries (except Ireland and Luxemburg). This is a shift
from the pre-crisis period when the investors focused on the absolute size of the
economy. This actually signals the investors to look more into the future. It seems the
financial crisis has showed the short-sightness of targeting the immediate situation and
they started to consider also the aspect of the future prosperity.
The comparison of those two models suggests that the main trend has shifted the
investment from the largest economies to the markets with the highest potential. This
shift in the interest from the side of the investors would suggest also a major
geographical drift in the investment. However, we have to remember that even after
the crisis just being among the original fifteen members of the EU brings additional
3.3 billion USD. Overall, even though the investors recently started to prefer
developing European countries, the EU-15 is still a reliable target to invest.
5.4 Potential vs real FDI
Our previous findings suggest that the West has lost a little of its attractiveness for the
investors while the East gained some. On the basis of this theory, we would like to find
out which of the countries perform to their potential, which over-perform and which
under-perform. In this section the potential FDI level for each of the country from our
sample will be calculated and compared with the real value of FDI in 2007 and 2014.
39
In order to achieve our goal, we have decided to use the FE model based on the BMA
results on the full sample. First of all we had to recalculate the constant, which is given
in STATA as the average of all the constants. This is not useful in case of recalculating
the potential FDI for each of our countries. Second of all, we had to divide our factors
into two categories. The first category are pure gravity factors that cannot be influenced
by the government. This category includes the GDP and labor, each of them strongly
linked to the market. The second category contains variables that can be to some extent
affected by the government and thus can be changed in a short to medium term. Those
variables are GDP growth, trade openness, rule of law and governmental effectiveness.
The last step was to cluster the countries in our dataset to gain the possibility to
compare them and to find the potential value of the influencing variable as the
maximum in the group. We have decided to divide Europe into West and East and then
each of the parts to divide into EU and non-EU members resulting in four categories.
The division of the countries is indicated in Appendix A. The only change in
comparison to the previous division are Malta and Cyprus. Even though those countries
joined the EU in 2004 with the CEEC, we believe those countries based on their
economic and political background belongs to the West. In each of the groups we have
determined the maximum and average value in the year 2007 and 2014 respectively.
After that we have plugged both maximum and average values to recalculate the
potential and average level of the FDI for each of the country for both years. Those
values have been recorded along the real FDI values in the Table.5 & Table.6 and we
have observed how the position of the countries has changed.
The best performing countries are the Western non-members of the EU. On the other
hand the worst are the Eastern non-members of the EU, with almost all of them
dropping even below the average group potential. This corresponds to the fact that
Norway, Iceland and Switzerland opted out of the EU based on their own decision and
generally they are considered to be rich countries. On the other hand, most of the
Eastern states are in the process of accession however still not fulfilling the conditions.
Which indicates indicating their poor socio-economical position.
Considering the EU, we can observe that both in the East and in the West, the level of
the FDI dropped in most of the countries below its potential. However, in the East there
is a higher percentage of countries which have maintained at least the position above
the group average. This analysis confirms our previous findings regarding the shift in
the trend after the crisis from the Eastern to the Western Europe. Furthermore, it
suggests that the FDI has really dropped below its potential after the financial crisis.
40
Table.5 Potential vs real FDI inflow in 2007
CG* FDI FDI max FDI group avg PI**
ALB 4 658.5067 -66.5123 -785.6224348 3
AUT 1 25484.27 9558.771 6520.472976 3
BLR 4 1807.3 2919.42 2200.309716 1
BEL 1 93429.3 42294.64 39256.33742 3
BIH 4 1819.244 4682.76 3963.649629 1
BGR 3 12388.86 3572.24 4591.511434 3
HRV 3 4589.58 -666.112 353.1593227 3
CYP 3 2226.059 4766.508 1728.209946 2
CZE 3 10443.82 5592.262 6611.534096 3
DNK 1 7268.035 6097.086 3058.787414 3
EST 3 2311.212 655.0762 1674.347776 3
FIN 1 12451.04 5635.972 2597.673308 3
FRA 1 63499.57 33284.98 30246.68548 3
DEU 1 80212.09 50370.64 47332.34099 3
GRC 1 2111.305 7746.947 4708.648852 1
HUN 3 3950.835 4425.066 5444.338072 1
ISL 2 6824.401 -156.584 -1410.231236 3
IRL 1 24707.17 18426.01 15387.7137 3
ITA 1 43849.35 26068.04 23029.73954 3
LVA 3 2323.662 8.392706 1027.664314 3
LTU 3 2015.013 684.8931 1704.164666 3
MKD 4 692.5098 2658.35 1939.239659 1
MLT 3 762.4081 12089.86 9051.566627 1
MDA 4 541.26 5148.464 4429.353733 1
NLD 1 119635.9 35311.43 32273.12947 3
NOR 2 7988.338 9197.092 7943.445074 2
POL 3 21642.56 10213.99 11233.26084 3
PRT 1 2875.035 10313.91 7275.610168 1
ROU 3 9732.81 6731.346 7750.617571 3
RUS 4 55873.68 36758.77 36039.65672 3
SVK 3 4017.245 676.1469 1695.418484 3
SVN 3 757.2919 986.5017 2005.773273 1
ESP 1 64264.41 39741.61 36703.30922 3
SWE 1 28845.6 18784.05 15745.75539 3
CHE 2 32435.17 16039.15 14785.5015 3
TUR 4 22047 11315.2 10596.09218 3
UKR 4 9891 5149.65 4430.540201 3
GBR 1 181660.8 87118.99 84080.69216 3
Notes:* CG - Country group:
1 West Europe - EU members2 West Europe - non-EU members3 East Europe - EU members4 East Europe - non-EU members
** PF - Performance index1 country performs worse then shouldo on average2 country is performs above average but below its potential3 country performs above its potential
41
Table.6 Potential vs real FDI inflow in 2014
CG FDI FDI max FDI group avg PI
ALB 4 1093.48 2574.015 -1170.554939 2
AUT 1 4674.942 8584.705 6825.691541 1
BLR 4 1798.2 6898.41 3153.839526 1
BEL 1 -4956.68 41977.19 40218.17464 1
BIH 4 564.0034 7700.921 3956.350964 1
BGR 3 1732.979 3940.707 3214.924717 1
HRV 3 3451.241 -637.588 -1363.37029 3
CYP 3 679.0044 1828.785 69.77114472 2
CZE 3 5908.501 6573.208 5847.426412 2
DNK 1 3651.868 3842.203 2083.188994 2
EST 3 982.9196 1533.754 807.972394 2
FIN 1 18624.68 3339.042 1580.027943 3
FRA 1 15191.12 32954.92 31195.90516 1
DEU 1 1830.892 51763.56 50004.54471 1
GRC 1 2171.597 5493.204 3734.190374 1
HUN 3 4039.38 6026.798 5301.016055 1
ISL 2 436.0821 -1091.64 -948.8707066 3
IRL 1 7697.706 17592.3 15833.2895 1
ITA 1 11450.82 24869.31 23110.29882 1
LVA 3 473.5333 921.9824 196.2004729 2
LTU 3 217.1373 1441.211 715.4293255 1
MKD 4 348.0488 4425.608 681.0374868 1
MLT 3 9278.885 9101.368 7342.353781 3
MDA 4 207.39 7522.715 3778.145106 1
NLD 1 30253.29 34601.83 32842.81755 1
NOR 2 8682.46 7963.29 8106.054458 3
POL 3 13882.85 12678.99 11953.20618 3
PRT 1 8807.147 7186.364 5427.350411 3
ROU 3 3234.007 7454.849 6729.066964 1
RUS 4 20957.66 41694.4 37949.82785 1
SVK 3 478.7477 1497.773 771.991499 1
SVN 3 1564.291 991.5188 265.7368987 3
ESP 1 22904.12 38222.4 36463.38178 1
SWE 1 10036.18 17772.12 16013.1011 1
CHE 2 21914.31 15244.2 15386.96559 3
TUR 4 12146 20220.18 16475.61154 1
UKR 4 410 8580.907 4836.336797 1
GBR 1 72240.96 86351.27 84592.25332 1
Notes:* CG - Country group:
1 West Europe - EU members2 West Europe - non-EU members3 East Europe - EU members4 East Europe - non-EU members
** PF - Performance index1 country performs worse then shouldo on average2 country is performs above average but below its potential3 country performs above its potential
42
6 Conclusion
Our study defined the most influential factors affecting the FDI inflow in Europe at the
end of the 20th and the beginning of 21st century. We have built our analysis on a sample
of 38 European countries, covering almost all European states (except city-states,
Luxemburg, Montenegro and Serbia due to lacking data) since the end of Cold War
between the years 1997 and 2014. Our study focused particularly on the role of the
European integration and the recent Financial Crisis and their effect on the FDI.
We have used a Bayesian model averaging methodology allowing to work with a wide
variety of possible factors and showing how probable those variables were to have an
effect on the FDI. From the 18 considered possible factors we have found 6 to play a
significant role on the full sample Those 6 variables can be easily grouped into 4
categories. First of all, the positive current and future size of the economy are signaling
the interest of the investor to horizontally invest in big markets. Secondly, it is the
characteristic of the labor force, however in our case we found only the size of the labor
force to play a role, not the individual characteristics such as unemployment rate,
tertiary education or productivity. This suggests that the investors are not interested or
not able to analyze those specifics. The third category characterizes the openness of the
economy. The positive coefficient means the investors appreciate the possibility to
serve other countries from the host state. All of those variables are the so-called gravity
factors that the literature denotes as the major determinants of FDI (Demekas et. all;
2007). In our case they explain 80% of average yearly change of FDI. The remaining
20% can be explained by the institutional variables which seem to play a role in Europe.
Apart from the determinants above, our analysis also uncovered the positive and
significant effect of being among the first 15 states of the EU on the FDI inflow. Just
the membership attracts yearly 3.3 billion USD. On the other hand, we were not able
to find evidence for the membership in the EU to play any role in general. This suggest
that either the EU membership does not bring any additional FDI inflow and the initial
15 states receive more on FDI due to their classification as Western states, or that the
effect is not immediate and thus it was not possible to observe it. The hypothesis of
Özkan-Günay, (2011) and Lucyna & Rhoades; (2007) about an immediate increase of
FDI after a state joints the EU thus had to be rejected.
Dividing the sample into the West (EU-15) and East (EU-13) and running the same
models on the subsample showed us the difference in the FDI inflow determinants.
43
While in the West the investors focus on the labor force, openness of the economy and
institution in the East the prime plays the size of the economy. It seems that in the East
the MNE are looking for markets big enough with a stable growth assuring the future
demand. This is interesting because the positive effect of GDP and GDP growth is
usually connected with horizontal integration. However, the developing part of the
world, where the CEEC used to fall in, have been usually connected with vertical
integration (Sánchez-Martín; 2014), which means using the cheap production inputs to
create the products that will then be exported. On the contrary, our analysis shows the
investors see those countries as final destinations. This means a horizontal integration
plays nowadays an important role across the whole Europe.
Also the analysis on the time subsamples before and after the financial crisis shows the
importance of horizontal integration. The main outcome of this analysis suggests that
while before the crisis the FDI inflows were affected by the GDP, an absolute value of
the economy, after the crisis it changed into being affected by GDP growth, prospects
of the future state of the economy. Investors became aware of the uncertainty
uncovered by the financial crisis and decided to look not only at the immediate state of
the host country but also into the future. Because, as we have seen, the FDI inflows in
Europe are mainly horizontally integrated, it is logical that the MNE, looking for the
new markets (Demekas et. all, 2007), pays attention to the future economic prospects
of the countries. Analyzing the potential FDI after the crisis we can generally see the
drop in the FDI level after the crisis. Overall, most of the countries are underperforming
what they could potentially attract. The national governments should thus use this study
as a suggestion what policies can be changed in order to attract more FDI.
We hope that this analysis will attract some attention and will become one of the pieces
of the puzzle providing the complex picture of the FDI. For the future research, we
would suggest to try to undergo the same analysis but with bilateral FDI inflows, which
was beyond our capabilities. Another extension of our work could be done by analyzing
the effect of European monetary union. Nowadays we can see that the EU will in future
inevitably face some turbulent times (Brexit, migration crisis) and we can only try to
guess what the impact on the FDI inflows will be. If the government believes the FDI
is a positive phenomenon, they can use our and similar analyses to improve it.
However, we believe that a research such as this one should be firstly extended to show
how the adjustment of the policies would affect the economy in other fields. The
governments should not miss the forest for the trees.
44
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Appendix A: List of countries and their classification
Country name Shortcut EU15 EU13 EU-West EU-East nonEU-West nonEU-East
Albania ALB X
Austria AUS X X
Belarus BLR X
Belgium BEL X X
Bosna and Herzegovina BIH X
Bulgaria BGR X X
Croatia CRO X X
Cyprus CYP X X
Czech Republic CZE X X
Denmark DEN X X
Estonia EST X X
Finland FIN X X
France FRA X X
Germany GER X X
Greece GRE X X
Hungary HUN X X
Iceland ISL X
Ireland IRL X X
Italy ITA X X
Latvia LVA X X
Lithuania LTU X X
Macedonia MKD X
Malta MLT X X
Moldova MDA X
Netherlands NLD X X
Norway NOR X
Poland POL X X
Portugal PRT X X
Romania ROU X X
Russia RUS X
Slovakia SVK X X
Slovenia SVN X X
Spain ESP X X
Sweeden SWE X X
Switzerland CHE X
Turkey TUR X
Ukraine UKR X
United Kingdom GBR X X
49
Appendix B: Comparison of existing studies
Bevan&
Estrim
Pantelidis
et.all.
Dornean
et.all.
Janicki&
Wunnava
Gast&
Herman
Blonigen
&Piger
Sawalha
et.all.
Dauti Demekal
et.all.
Hunady&
Orviska
Jadhav
bilateral inflow inflow bilateral bilateral bilateral bilateral bilateral inflow inflow
distance X X X X X
market size (GDP per capita) X X X X X X X X X X X
trade opennes X X X X X X X X
bilaterall export X X
trade & foreign exchange liberalization X
trade barriers X
market potential
cultural and historical ties X X
market potential
risk rating X X X
interest rate X X
leading stock market indicator X X
exchange rate X
consumer price index X
inflation rate X X X
current account balance
public debt X X
gross capital formation X
money supply
activity rate
investments
market potential
population characteristics X X
unit labor cost/labor productivity X X X X X
human capital X X X X
announcement about joining EU X
dummy for EMU X
bilateral investment treaties X X
plus controling for SEE countries X
DR CAFTA dummy
crisis X X
year before crisis
developement of the labor force X
technological cappabilities X X X
manufacturing share
index of economic freedom X
economic freedom indicator X
corruption X X X X
regulatory quality X X
government effectivnes X X X
government stability
political risk X X X
voice and accountability X X
rule of law X X X
transition progress X
hiring and firing prcatices index X X
tax burden X X X
cost of construction of a warehouse X
compensation per employee
time to start up business X
time to resolve insolvency X
inst
itu
tio
ngr
av-
ity
fin
anci
altr
ade
hu
man
cap
ital
cris
iste
chn
o-
logy
EU
50
Sanchez
et.all.
Polat Ucap
et.all.
Iamsiraroj Villaverde
&Maza
Boateng Gavril Özkan-
Günay
inflow inflow inflow FDI/GDP inflow inflow inflow
distance 5
market size (GDP per capita) X X X X X X 17
trade opennes X X X X X 12
bilaterall export X 3
trade & foreign exchange liberalization 1
trade barriers 1
market potential X 1
cultural and historical ties 2
market potential X 1
risk rating 3
interest rate X 3
leading stock market indicator 2
exchange rate X X X 4
consumer price index 1
inflation rate X X 5
current account balance X 1
public debt X 3
gross capital formation 1
money supply X 1
activity rate X 1
investments X 1
market potential X 1
population characteristics X X X 5
unit labor cost/labor productivity X X X 8
human capital X X X 7
announcement about joining EU 1
dummy for EMU 1
bilateral investment treaties 2
plus controling for SEE countries 1
DR CAFTA dummy for investment profile X 1
crisis X 3
year before crisis X 1
developement of the labor force X 2
technological cappabilities X X 5
manufacturing share X 1
index of economic freedom X X 3
leadership - economic freedom indicator 1
corruption - perception index, control for corruption X 5
regulatory quality 2
government effectivnes X 4
government stability X 1
political risk 3
voice and accountability 2
rule of law X 4
transition progress 1
hiring and firing prcatices index X 3
tax burden X X 5
cost of construction of a warehouse 1
compensation per employee X 1
time to start up business 1
time to resolve insolvency 1
natural resources (energy) 1
transportation and communication 1
internet/telephone statistics X X X 4
energy statistics X 2
infrustructure reforms X X 2
dummy for indtroduction of new tax system in Turkey X 1
scientific papers X 1oth
erin
fras
tru
ctu
rein
stit
uti
on
grav
-
ity
fin
anci
altr
ade
hu
man
cap
ital
cris
iste
chn
o-
logy
EU
