Factors influencing outward foreign
direct investments from Central and
Eastern European countries
Blazej Piotr Kobus 11086580
Submission date: June 23, 2016 Final version
MSc in Business Administration - International Management Faculty of Economics and Business, UvA
First supervisor: C. Gelhard Second supervisor: R. Kleinknecht
Statement of originality
This document is written by Student Blazej Piotr Kobus who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract
So far the main trend in analysing FDIs is based on the assumption that developed economies invest in developing countries. Now this tendency is abruptly changing and around 35% of all the FDIs have its source in the emerging markets. Scholars spotted that the change in perspective of analysis is needed and devoted their attention mostly to the BRICS countries, but other regions require a proper analysis as well. Therefore, this study offers a change in standpoint and focuses on Central and Eastern European countries, where modifications in the investments patterns are backed by progressing regional integration.
This paper on the basis of the panel data analysis, tries to identify influential factors that pull investments from particular countries. This study focuses mainly on Czech Republic, and suggests that countries which want to attract investments from this country must strongly underline factors that belong to six groups namely: geographical proximity, culture, workforce, economy, institutions and other. The same model used with data from Hungary gives a slightly different results, therefore proposed model can be used also for the other source countries, but after a proper modification.
Key words: outward FDI (OFDI), influential factor, regional integration, strategy to attract foreign investors
Acknowledgments
I appreciate help and support from my supervisor, Carsten Gelhard. He gave me a lot of independence during writing this thesis, but also provided valuable suggestions in the critical moments. Additionally, I would like to thank my fiancée and family for the received support during studies far from home.
Table of Contents
1. Introduction ... 7
2. Literature Review ... 10
2.1 Foreign direct investments ... 10
2.2 Regional integration ... 15
2.3 Location decisions according to the International Business theories ... 17
2.3.1 Geographical proximity ... 17
2.3.2 Culture ... 19
2.3.3 Workforce ... 20
2.3.4 Economical Factors ... 21
2.3.5 Institutions ... 22
3. Influence of regional integration on investments ... 23
3.1 Data and methods ... 23
3.2 OFDI Analysis ... 26
3.3 FDI Analysis ... 28
4. Factors influencing the outward foreign direct investments from the Czech Republic and Hungary ... 31
4.1 Data collection ... 31
4.2 Measurement ... 32
4.3 Model ... 34
4.4 Results for Czech Republic ... 35
4.5 Results for Hungary ... 39
5. Discussion ... 41
5.2 Managerial Implications ... 45 6. Conclusion ... 46 References ... 48
1. Introduction
The world is constantly changing. The economical rules that were treated as constant and unchangeable are now altering faster than even before. Perfect example of such anomaly are negative interest rates (Ilgmann & Menner, 2011). A couple of years ago a student that would assume negative interest rates in calculations would fail the exam. If such a groundbreaking change could happen to a basic instrument in the world of finance, the other parts of the global economy probably are changing as well.
Financial crisis, progressive regional integration, fluctuating oil prices, start-up madness, development of the eastern part of the world. All this trends only fuel the changes in the business relations.
So far scholars concentrated mainly on the western part of the world, namely North America and western Europe (Rugman, Verbeke & Nguyen, 2011). Developed economies were perceived as the engines of the World, therefore many studies focus on processes and behavior of the companies located in those regions of the world. Many theories that describe reality are based on data from United States, Germany, France or United Kingdom. But recently it’s feasible to observe a gradual change of perspective. Scholars started to descry the eastern part of the World and define those countries as emerging economies or transition economies (Hoskisson, Eden, Lau & Wright, 2000). They can’t deny the great influence of developing countries because of the fact that within the last 15 years share of the emerging markets in the global Outward Foreign Direct Investments (OFDI) increased from 7% to 35% (UNCTAD ,2015). What is more, in a not that distant future this relation may be reversed and developing countries will be responsible for the bigger part of the Foreign Direct Investments (FDI) than developed countries.
This change in capital flows is interesting because of a couple of reasons. Firstly, people living in the twenty-first century may witness change in the global order. Secondly, scholars
will be able to check whether theories created in the past for the developed economies are working in the changing world. Therefore, an increase of interest in this area among scholars can be seen (Hassan, 2015; Jain, Hausknecht & Mukherjee, 2013).
Great attention was put mainly to the BRICS countries (Sanfilippo, 2015; Andreff, 2014), which started to develop in the amazing pace. Brazil, Russia, India, China and South Africa, all these countries are very big taking area into the consideration. Additionally, citizens of China and India constitute 1/3 of the global population. Thus, it’s understandable that the devotion of the scholars went there in the first moment. But focus of the researchers should also be pulled to the Central and Eastern European Countries (CEE)
Eastern Europe has a very tumultuous history and not all, but many countries can fully enjoy the free market since 2004 when the EU was enlarged and ten of the CEE countries became members of the European association. Whether was it a turning point for the development of those countries, will be checked in this thesis.
This changing reality of the Eastern European region was a reason for formulation of two research questions that will be answered in this thesis.
1. Does the regional integration boost outward and inward foreign direct investments from and to newly integrated countries?
2. What factors influence on propensity to invest in different countries. What are the specific, influential factors of target nations that attract investments from foreign countries?
So far in the field of study concerning FDIs dominated the idea that assets from the developed countries go to other developed or developing countries (Rugraff, 2010). This view is backed by many theories presented in the following part of this thesis. It was clear, that wealthy countries have more assets, which can be transferred, to the other parts of the world in order to make profit. However, this direction of capital flows is changing and such enormous amount of money must be analyzed.
In the literature, when scholars took into consideration emerging markets, were they analyzed only as a host country for the capital. What is reasonable, because most of the time this direction was prevailing. Contribution of this study will be an analysis of the opposite direction, where EM are source countries and other countries are targets of investments.
Initially, OFDIs from the emerging countries and FDIs to the EM will be analyzed in relation to the EU accession. The idea is to check whether accession to the EU had any influence on outward and inward foreign direct investments for both, new and old members. Paper will focus on analysis based on a country level data.
Later on, OFDIs from two CEE countries will be examined in a more specific way. In order to refine a couple of variables that have a significant influence on propensity to invest in particular target countries was used a panel data analysis.
This thesis has a following structure. Firstly, will be presented a literature review which will focus on the notions of the FDIs in general, regional integration and location decisions in the international business theories. Theoretical background will be interspersed with the hypotheses that will be tested in the further parts of this paper. Later on, the interest will move to the first research question where all the analysis and results concerning this issue are presented. After that, will be presented in an extensive way a main study which focuses on refining factors which influence the level of OFDIs from the Czech Republic and Hungary. In the last part of the study are presented theoretical and managerial implications of this study.
2. Literature Review
Literature review discuss main areas of the research, and will be used as a base for the further analysis in this paper. Starting from the basic information about capital flows between countries, outflows and inflows from both perspectives- developed and developing countries. The second part will talk about regional integration in general, accompanied by a model of five levels of regional integration. Special focus will be given to the European Union, which is currently the most developed entity of a regional community. The third part will present main International Business (IB) theories concerning location decisions and motives of the Multinational Enterprises (MNEs), which encourage them to foreign investments.
2.1 Foreign direct investments
Free flow of capital is crucial for the development of countries with deficit of funds. Countries, which perform better, have possibility to invest in other countries in order to gain profit. Indirect effect of these investments is boosting of the development in the poorer countries. German company investing in an automobile factory in Poland is an example of a Foreign Direct Investment (FDI). According to the (OECD) “Foreign direct investment is a
category of cross-border investment in which an investor resident in one economy establishes a lasting interest in and a significant degree of influence over an enterprise resident in another economy.” To the benefits of the FDIs we can also include creation of stable links between
countries and markets. This connection is important because as (Bhaumik, Driffield & Pal, 2010) claim strategic cooperation with foreign investors facilitate outward foreign direct investments. What is more, FDIs are not only a transfer of capital, but also of knowledge, what can be later on used independently by other companies located in a beneficiary country (Altomonte &Pennings, 2009; Khan, Shenkar, & Lew, 2015).
Foreign investments can be divided into 2 groups: inflows and outflows. Inflow is a capital that is invested in a particular country. Outflow is a capital, which is invested by a
particular country in a different one. Another expression that can be used to describe outflow of capital is the Outward Foreign Direct Investment (OFDI).
Before describing the evolution of the capital flows, it is important to define two main characters of the upcoming concept, namely: developed and developing countries.
What does it actually mean that one country is developing, and one is developed? According to the (Hoskisson, Eden, Lau, & Wright, 2000) a developing economy or an emerging market (EM) is a country, where a very high economic growth is noted, as a result of liberalization of the economy. On the other hand, there is no consistent definition of a developed economy. To such we can include countries with high GDPs and high standards of living, as an example can be given western European countries such as Germany, France and United Kingdom.
FDIs exist in three forms, as greenfield investment, mergers & acquisition (M&A) and joint venture (JV). Greenfield can be described as founding of a new company in a foreign country. M&A means that a company merge or acquire an existing company in a foreign country (Hill, 2010, p.232). JV also means founding of a new company, not independently, but with a partner. Companies decide on different types of FDIs dependently on their level of knowledge of doing business abroad, law restrictions in a host location and willingness to taking risk (Malhotra, Agarwal & Ulgado, 2003). The level of development of the host location also plays a major role. According to the statistics, most of the FDIs in the developing countries are greenfields, because of the fact that in these regions the number of companies to acquire or merge is lower than in the developed markets (Hill, 2010, p.237).
In the last couple of decades, the direction of the capital flows constantly evolved and can be examined on two different levels: country level and firm level. Country level is described by the investment development path (IDP) theory, which assumes that countries in the process of development pass through five levels (Rugraff, 2010). Starting with being a target country for the FDIs in the sector of natural resources because of a lack of other comparative
advantages. In that point companies from a particular country are not capable of investing on the foreign markets. In the second stage FDIs start to rise but indigenous companies are still too feeble to invest outside the country borders. In the third stage internal companies develop and start to be able to compete with the foreign firms. The increase of FDIs weakens and occur first OFDIs from the national companies. In the last two stages domestic companies become able to compete with the foreign competitors and at the end the amounts of OFDIs and FDIs should offset. Graphic presentation of the theory is given below.
To some extent a similar theory was presented by (Vernon, 1966) as his product life cycle theory, but now the evolution went even beyond Vernon’s theory. According to the (Vernon, 1966), new products were invented in developed countries, in Vernon’s case in the USA. The main reason for that was, that in these countries firms had access to: capital, research and development (R&D) and other resources. What is more, it may sound like truism but in the wealthy countries live more wealthy people than in the other parts of the world. That was a reason, which enabled the companies successfully market new products in higher prices in order to cover not only cost of the production process, but also cost of the development. At this stage,
Stage 1 Stage 2 Stage 3 Stage 4 Stage 5
Investment Development Path (IDP)
there is no FDI, because companies prefer to invest in their home locations. Geographical proximity of the factories allows for implementation of changes in the production quickly. It is crucial at the beginning of the product life cycle, when the production is not standardized. The approach change, when the product is maturing and some elements of standardization occur. Demand for the product is rising also in the other parts of the world. Of course, it starts in western European countries, which are also considered as developed countries. At the beginning producers may try to satisfy consumers needs with export, but it is not sufficient in a long term. Rising demand for products from USA triggers the FDI from North America to developed parts of Europe (Vernon, 1966). For example, companies build factories in France and Germany. Can be said that in this moment IDP theory and Vernon’s cycle converge. Nevertheless, capital flows are still within developed countries and western European needs are satisfied with local production. It may happen, that if the production cost is low enough, the difference can cover transportation cost. Then, the products manufactured in Europe may be sold in the USA. The last stage in the cycle is, when the product is well known. Then production is standardized and self-sufficient. At this step it is not a problem to move production to the less developed places, where labour costs are lower what allows for cutting the production costs (Vernon, 1966). At this point occurs FDI from the developed to the less-developed or the developing countries. It was the last stage within (Vernon, 1966) product life cycle.
In short, it can be said that the flow of FDIs start from the developed countries to the other developed countries, and later on are transferred to the developing countries. Emerging markets are at the end of this chain, because they cannot offer sufficient resources to satisfy needs of investors. On the other hand, companies from developing countries are able to successfully compete on the global market only after foreign investments. They give resources but also knowledge.
Currently, some companies in developed countries launch their products in a standardized form from its beginning. The reason for that is that now not many companies truly innovates. When a company has a unique product that still needs to be improved, then the production is kept in a home country. A perfect example of such behaviour is Tesla Motors (Tesla, 2016), a company which produces electric cars in the United States. Although other car makers like Opel or Volkswagen try to lower their cost by moving their production to the cheaper, developing countries. Because of lack of ground-breaking innovations, there is no need to wait for maturing stage in order to transfer production to the less developed countries. Two important factors that had a greatest influence on that can be pointed out: globalization and development of information technology (IT) (Javalgi et al., 2009; Dunning, 1998). Though not all the scholars agree with this statement (Ghemawat, 2001) it can be said that, those two elements enabled managers to control business entities all over the world. Worldwide spread of the Internet connection and communicators combined with “shift toward a more integrated and
interdependent world economy” (Hill, 2010, p.6) diminished the impact of distance. Taking
advantage of globalization many multinational enterprises decide to locate their factories but also innovation centres (Sartor & Beamish, 2014) in the developing countries or at least offshore their production processes, because factories or subcontractors in remote locations can do it more economically. That is why, it can be said that the evolution of the chain is not stopped and now the capital flow directly from the developed to the developing countries, which offer ability of lowering the production costs. So far 55% of FDIs follow this path (UNCTAD, 2015). It’s also worth mentioning that Asia is now the most desired location of FDIs, with constant gradual increase of inflows in a period from 2012 till 2014, while other regions such as: Europe, Latin America and the Caribbean, North America and transition economies were fluctuating and eventually in 2014 the level of FDIs inflows was lower than in 2012. Although the level of investments in Africa remained stable during that time, it isn’t a good sign for this continent.
Conflicts and diseases effectively discourage investors what effects in a significantly lower level of FDIs in comparison to the other regions (UNCTAD, 2015).
Earlier mentioned definitions and directions of capital flow can be acknowledged as a classic version. Although, within the last 15 years the share of the emerging economies in the world FDI outflows is rising (UNCTAD, 2015), what supports predictions given in a theories. In year 2000 emerging economies invested around $50bn what stated around 7% of global FDI outflows, while in year 2014 this amount rose to $500bn and accounted for almost 35%. As can be seen the growth is considerable. It is highly possible that in the future the classical version will be reversed or equalled and the emerging economies will invest more or at least as much as developed countries. This shift in capital flows is the reason why this field of studies is that interesting and receives such a generous amount of attention among scholars (Hassan, 2015; Jain, Hausknecht, & Mukherjee, 2013) .
It is worth to emphasize that foreign direct investments can be considered on a multiple levels. Scholars started to examine this area of study on the country level and later on they moved to the firm level, which presents a lower level of generality. The reason of such order is very simple. Every theory is a product of its times and back in 60s or 70s (Johanson& Vahlne, 1977) or (Vernon, 1966) had access merely to data which was collected only on the country level (Rugman, Verbeke & Nguyen, 2011). Over time the specificity of databases was rising, same as precision of the theories.
2.2 Regional integration
By regional integration are meant “agreements among countries in a geographic region
to reduce, and ultimately remove, tariff and nontariff barriers to the free flow of goods, services, and factors of production between each other” (Hill, 2010). All over the world are initiatives,
which bond countries together in order to boost trade and investments. Just to name the most well known: European Union (EU), North American Free Trade Agreement (NAFTA),
Association of Southeast Asian Nations (ASEAN), MERCOSUR in South America and Asia-Pacific Economic Cooperation (APEC). As can be seen every region of the world has its organization, which helps to facilitate trade between countries in order to rise development of its members (Hill, 2010).
Economic integration can be disaggregated into 5 levels. From least to the most integrated regions: Free Trade Area, Customs Union, Common Market, Economic Union and Political Union (Hill, 2010). European Union is somewhere in between economic union and political union. The process is still ongoing because on one hand there exist a common currency but still isn’t used in all the member countries. On the other hand, exist The European Parliament, where representatives of countries are elected within particular countries. That is why can be said that the goal of creating political union is still not finished because demands of previous layer of integration are still not met (Hill, 2010).
EU integrates gradually. Started from the European Coal and Steel Community in 1951 to which belonged 6 countries (Belgium, France, West Germany, Italy, Luxembourg and the Netherlands). Successor of that institution was the European Community consisting of 12 countries (additionally: Great Britain, Ireland, Denmark, Greece, Spain and Portugal), and then in 1994 the name changed into the European Union. Since 1996 till 2004 the community consisted of 15 (additionally: Austria, Finland and Sweden) countries (Hurt, 2010). On 1 May 2004 enlargement of EU took place. New countries joined association: Poland, Slovakia, Czech Republic, Lithuania, Latvia, Estonia, Slovenia, Hungary, Cyprus and Malta. Later on in 2007 joined Romania and Bulgaria (Hill, 2010). Since 1 June 2013 when Croatia joined community, EU consists of 28 countries.
According to studies by (Frankel & Rose, 2002) presented in (Ghemawat, 2001) can be claimed that regional integration directly influence on economic activities between associated countries, although it varies by region (UNCTAD, 2015). Therefore, study of this paper
considers the level of FDIs and OFDIs from and to the countries which entered EU in 2004. The main goal is to check whether entering an association by particular countries increases the capital flows between this new member and old members.
The presented information leads to formulation of the hypothesis:
H1: Entering to the association of countries boost capital flows from and to newly admitted members
2.3 Location decisions according to the International Business theories
Admitting that the further parts of this paper will focus on the country level of analysis, presentation of the theories considering firm level of analysis is also required. Understanding of behaviour on the firm level may help to draw conclusions concerning the capital flows on the country level.
2.3.1 Geographical proximity
One of the most known International Business theories is the Uppsala model (Johanson & Vahlne, 1977) . It is a theory, which describes the incremental expansion of the companies abroad. According to (Johanson & Vahlne, 1977) companies gradually increase the level of commitment in the foreign countries. The reason behind such practice is that the companies progressively learn how to overcome liability of foreignness (LoF), defined as additional cost of doing business abroad in comparison to the local competitors (Zaheer, 1995). LoF has four major sources. Firstly, are spatial costs, in general everything what connects with transportation and coordination. Secondly, discrepancies in the level of possessed information between foreign and local companies and being unaware of local conditions. Thirdly, support for the local companies and lack of brand awareness among foreign consumers. And the last source of additional costs, although not that obvious are charges coming from the home-country, which are an effect of different restrictions, taxes and embargos (Bell, Filatotchev & Rasheed, 2012). While the level of LoF is lowering the companies become more competitive and increase their
engagement in the remote locations. They start from exporting of their products, then they establish a sales office in a particular country, and at the end they invest in a wholly owned subsidiary (WOS). What is also worth mentioning, according to the model the distance between the home and host market is gradually increasing during consecutive investments (Johanson & Vahlne, 1977) . Companies start to invest in neighbour countries and increase the distance while the time is passing what results in gaining experience in doing business abroad. (Benito & Gripsrud, 1992) claim that decision about a foreign location is a discrete rational choice (static approach) rather than a cultural learning process (process-oriented approach) which is advocated by the Uppsala model. According to the study of (Benito & Grisprud, 1992) there is a weak propensity of firms to invest firstly in the culturally close countries and increase the distance gradually. (Johanson & Vahlne, 1977) based their theory on small Scandinavian economies that needed additional markets to develop themselves, what could bias their study. Hence, it will be interesting to check which patterns can be observed in Central and Eastern European countries (CEE).
Translating this theory onto the country level can be claimed that the countries as a whole may want to invest in countries that are located in the same region because of lower LoF and the uncertainty avoidance. What is supported by (Rugman & Verbeke, 2004), according to their research based on the analysis of the top 380 MNEs, 84,2% (320) of them were home region oriented, while 6,6% (25) of them were bi-regional and only 2,4% (9) of them were truly global. Combining these two studies can be said, that model regarding the company level is reflected in data on the country level. From this observation can be drawn a conclusion that the decisions made by the entities on the firm level can be observed on the country level of analysis as well.
H2: EU10 countries as a target of investments more often take into consideration countries which belong to the EU and are geographically close than geographically distant.
2.3.2 Culture
As stated above, in order to diminish the LoF and increase their competitiveness companies can pursue a couple of strategies, such as mirroring of the local firms (Zaheer, 1995) or can choose countries, which are culturally close to their home country. One of the tools, which help to compare different cultures are cultural dimensions proposed by (Hofstede, 1984). In his initial version of the study are indicated 4 aspects, which can describe culture. The power distance is a dimension, which describe to what extent members of the group accept the fact, that the power within a society is distributed not equally. Second dimension is the individualism versus collectivism. This one assess whether people in a particular country care only about themselves or maybe also about a group. Third, the masculinity versus feminity describes the disposal of roles between genders. Fourth, the uncertainty avoidance describes how societies can deal with the uncertainty. Later on has been added the fifth dimension. Long-term versus short-term orientation which describes what is more important, determination or respect for heritage respectively (Hofstede, 1994). Although Hofstede’s study has its drawbacks such as biased results because of the place of employment of respondents, all were IBM employees (Baskerville, 2003), is widely considered as a good tool because gives simple numerical result.
On the basis of data from (Hofstede, 1984) has been created a Kogut-Singh Index (Kogut & Singh, 1988), which is based on a deviation from the 4 initial Hofstede’s dimensions. Calculation allows to compare cultural distance between 2 countries in an aggregated way, because index contains all the proposed by Hofstede values and later on is corrected by differences in the variance of every dimension and finally averaged (Benito & Gripsrud, 1992). Taking into considerations the ways of measuring culture, and why similarities in culture increase the competitiveness of the companies is presented a following hypothesis:
H3: Culture of the target country matters for investors from EU10 countries, therefore they tend to invest in culturally close countries which belong to the EU.
2.3.3 Workforce
Second widely known theory on the company level of analysis is the Dunning’s eclectic paradigm, called also an OLI model- Ownership, Location and Internalization (Dunning, 1998). Because of the fact that this part focuses just on the location decisions of the investments, only the ‘L’ part of this theory will be considered.
(Dunning, 1998) focused on the location decisions on the macro level. Till the 1950s, the main idea, which prevailed in the analysis, was Ricardian theory of comparative advantages. Countries should concentrate on such economic activities, which were consistent with their natural resources and capabilities. Products from this area should be exported to the other countries. Products, which could not be produced efficiently in a particular country, should be imported. Of course, this theory has many assumptions such as zero transportation costs or similar quality of the products all over the world. But, starting from the 1950s the approach started to change significantly. According to (Dunning, 1998) can be pointed out three main areas of change.
The first one is implementation of such variables as economies of scale or disaggregation of the value chain. What means that companies may locate their factories in different countries and specialize them in particular activities. It may sound like something new, but actually it has strong linkages to the Ricardian theory. Firms choose such locations for their factories, which will work best for a particular part of the value chain. It means that host locations must have country specific advantages (CSAs), which will draw investments. To such CSAs we can include profile of the labour force, for example low costs but also level of education. Natural resources or infrastructure can also be considered as CSAs. Every factory does something in what is the most effective. One may produce parts, while the other may
assemble them. Such division of production can lower the production costs of the company because of economies of scale or tax incentives given by a host location.
H4: Investors from EU 10 countries consider workforce as an influential factor while choosing location of the investments within EU.
2.3.4 Economical Factors
According to (Dunning, 2000) MNEs have four main FDI motives: market seeking, resource seeking, efficiency seeking and strategic asset seeking. By market seeking is meant satisfying needs of a new market, what effects for example in an increased sale. Resource seeking, by this is meant looking for resources, but defined widely, because it can be minerals, but unskilled labour force as well. Efficiency seeking means that company wants for example to benefit from the economies of scale. Strategic asset seeking means obtaining resources, which are crucial for the company. Possessing them, simultaneously prevent use of them by the competitors.
(Jain et al., 2013) concentrated on the location determinants of the MNEs from emerging countries, and proposed a couple of measures in order to explain behaviour of the companies. Because of the fact, that not all proposed variables can be measured on the country level, in the following part will be discussed only country level measurements. To the market-seeking motive they matched such values as market size and growth (Jain et al., 2013) in the analysis as proxy was used the growth of the GDP. To efficiency-seeking motive they matched such variables as infrastructure, tax regulations, exchange rates and cheap labour (Jain et al., 2013).
CAGE model by (Ghemawat, 2001) , claims that companies tend to invest in countries, which have comparable profile of the economy. Because of the fact, that only then enterprises can exploit their FSAs through replication of their business models. Taking that into consideration can be drawn a conclusion that EM countries should not invest in more developed countries as they are. However, in case of EM MNEs this assumption is pointless, because
companies from developing markets tend to invest in more developed countries in order to gain knowledge. Investments in less developed countries would be risky, because such economies don’t have sufficiently developed institutions that may encourage for investing in these locations and may actually don’t have knowledge which is worth acquiring.
The presented information leads to formulation of the hypothesis:
H5: Economical factors characterizing target country which belong to the EU matters as influential issue during location choice for the investments from EU10 countries.
2.3.5 Institutions
Institutions are often colloquially called as “the rules of the game”. More specific definition was presented by (North, 2005) who conceptualize them as a group of constraints on political, economic and social level which maintains a structure within a particular group. Institutions can be analysed as two separate groups: formal and informal. On the one hand, informal institutions are taboos, customs, etc. They can be described as laws within a society that are not written. People and companies are not forced to respect them, but they usually do. On the other hand, formal institutions are constitutions, property rights, etc. All the policies that are codified and people are obligated by law to respect them. Every uncertainty increases the cost of doing business, therefore the main task of the institutions is to reduce uncertainty. Entities agree on particular constraints in order to have organized processes which lowers costs. Companies which invest in foreign countries want to be sure that their assets will be legally protected. As suggested by (Dikova & Van Witteloostuijn, 2007), environment of formal institutions conceptualized by them as institutional advancement defined as “the degree to
which a host country’s institutional environment matches the standards well established in developed market economies” is positively associated with establishing of the new companies
Taking all into consideration can be said that formal institutions play a major role while considering a target location for FDIs what leads to formulation of a following hypothesis:
H6: Formal institutions are important factors for EU10 countries while choosing target location for the FDIs.
3. Influence of regional integration on investments 3.1 Data and methods
In order to asses the credibility of the first hypothesis of this paper which claims that “H1: Entering to the association of countries boost capital flows from and to newly admitted
members.”, an extensive analysis of the FDIs and OFDIs stocks of the countries which belong
to the EU is conducted. Because of the fact that the European association expands gradually the countries which belonged to the EU before enlargement in 2004 can be considered as developed countries, and countries entering EU in 2004 as developing countries.
Data for this part of the analysis is collected from the Eurostat database. This source of information is the most reliable when it comes to the European statistics. Amounts of FDIs and OFDIs within the databases are divided into groups. For sake of this paper, analysis focuses on two of them. Firstly, on capital flows between particular EU countries and EU27. Secondly on capital flows between particular EU country and the all world. Although, the datasets are very rich some of the countries didn’t provide all the necessary data concerning OFDIs and FDIs between them and the EU27. In order to get rid of this issue a data clearing is conducted in pursuance of preparing possibly the best refined database for further analysis.
Firstly, according to the given statistics from countries which dutifully fulfilled the obligation of providing information in any year between 1998 and 2012 (which are all the EU25 countries at least once), is assessed whether exists correlation between FDIs to the whole world and FDIs within the EU. As can be seen from the Graph 1 (x-axis values for FDIW, y-axis values for FDI-EU27) the correlation is very strong. Most of the observations are located on
the trend line with just a couple of exceptions. The value of coefficient of determination (R2) is very high what suggests that model truly explains the relation. The same analysis on the same set of data is conducted for the OFDIs. As can be seen from the Graph 2 (x-axis values for OFDIW, y-axis values for OFDI-EU27) the dependence is similar and the value of R2 is very high as well. What is more in the analysed dataset the share of OFDIs to EU 27 within OFDIs to the all world is on average around 65% for every country and the share of FDIs to EU 27 within FDIs to the all world is on average around 75% for every country. Taking all into consideration was decided to analyse only the data considering the flows between particular country and the whole world, because the provided data is much more extensive and reliable.
Graph 1- Correlation between FDI EU27 and FDIW
y = 0,7481x + 1958 R² = 0,98251 0 100000 200000 300000 400000 500000 600000 0 100000 200000 300000 400000 500000 600000 700000 800000
Graph 2- Correlation between OFDI EU27 and OFDIW
In the statistics the amounts of the foreign direct investments both inward and outward are presented in two forms- flows and stocks. According to the Eurostat metadata guide “FDI
flows denote the new investment made during the period” and “FDI stocks denote the value of the investment at the end of the period” (Eurostat). Therefore, in this paper for the analysis are
used stocks values only.
Next step in database adjusting is changing of the absolute values which are millions of Euros into fixed base indexes in order to make values comparable between each other. Without this change it would be impossible to compare the values meaningfully, because of the fact that the economies differ in size and absolute values are not a proper gauge. Initially, the dataset consisted of 25 countries, but two of them: Austria and Belgium didn’t provide values for year 2004, therefore were they removed from the dataset. Year 2004 is crucial for the further analysis because of the fact that then group of ten countries entered EU. That is why 2004 is established as a fixed base year.
y = 0,5549x + 8474,3 R² = 0,97022 0 100000 200000 300000 400000 500000 600000 700000 800000 0 200000 400000 600000 800000 1000000 1200000 1400000
Following countries were divided into 2 groups: EU15 countries which belonged to the EU before 2004, EU10 countries which entered the EU in 2004. EU15 group is treated as a control group in order to asses whether changes that might be observed are an effect of a regional integration or are an effect of a global increase or decrease in the capital flows.
Some of the groups consist of a lower number of countries than suggests the name because of the fact of data clearing.
Name of a group Countries
EU15 Germany, Denmark, Greece, Spain, Finland, France, Ireland, Italy, Luxembourg, Netherlands, Portugal, Sweden, United Kingdom EU10 Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta,
Poland, Slovenia, Slovakia Table 1- Sample Countries
3.2 OFDI Analysis
Firstly, the analysis focuses on the Outward Foreign Direct Investments from EU10 countries to the whole world (OFDIW).
Conducting of the 2-sample Wilcoxon test for the time range from 1998 to 2003 and from 2004 to 2012 gave following results, which can be seen in the table on the left. P-value for all the analysed countries is significant (p-value<0,05). Results for the control group of EU 15 countries, are presented in a table below. P-value for all the EU15 countries is also significant (p-value<0,05). The reason of this test was to check whether increase of the OFDIs values was significant in all the analysed countries, but it doesn’t give an unambiguous answer whether the increase was higher in EU10 than in EU15.
No. ISO p-value 1. CY 0.0363636364 2. CZ 0.0003996004 3. EE 0.0003996004 4. HU 0.0090909091 5. LT 0.0003996004 6. LV 0.0015984016 7. MT 0.0003996004 8. PL 0.0003996004 9. SL 0.0090909091 10. SK 0.0047952048 Table 2- Wilcoxon test p-value for EU10
No. ISO p-value No. ISO p-value No. ISO p-value 1. DE 0.0015984016 6. FR 0.0003996004 11. PT 0.0003996004 2. DK 0.0003996004 7. IE 0.0003996004 12. SE 0.0003996004 3. EL 0.0009990010 8. IT 0.0007992008 13. UK 0.0047952048 4. ES 0.0003996004 9. LU 0.0003996004 5. FI 0.0003996004 10. NL 0.0003996004 Table 3- Wilcoxon test p-value for EU15
In order to be able to asses in which countries the boost of the OFDIs stocks was higher, an analysis of the comparative values for the EU10 and EU15 countries with a year 2004 as a fixed base is conducted. In the tables below (Table 4 and Table 5) are presented values for the two years before regional integration and for six, seven and eight years after integration.
As expected value of the investment in both groups EU10 and EU15 increased in the time period from 1998 till 2012. Table 4 clearly shows that the increase of OFDIs from newly admitted countries to the rest of the world abruptly increased after entering EU and was significantly higher than in EU15 in relative values.
As can be seen from the analysis the progressive regional integration boost OFDIs within the region with the most significant increase in a group of newly admitted countries which are considered as developing countries. Capital flows from other countries which belonged to the EU earlier increased as well but the effect of enlargement is weaker and growth can be explained by the development of particular economies. Conducted analysis confirms first part of the hypothesis which claims that entering to the association of countries boost
3.3 FDI Analysis
In this part, analysis focuses on the Foreign Direct Investments to EU10 countries from the whole world (FDIW).
Conducting of the 2-sample Wilcoxon test for the same time range as in the previous case, which is from 1998 to 2003 and from 2004 to 2012 gave following results, which can be seen in the table below. P-value for all the analysed EU10 countries is significant (p-value<0,05). Results for the control group of EU 15 countries, are presented in a table below as well. P-value for most of the EU15 countries is also significant (p-value<0,05). The reason of
GEO/TIME 2002 2003 2004 2010 2011 2012 Cyprus 52% 70% 100% 384% 415% 239% Czech Republic 50% 64% 100% 405% 370% 477% Estonia 62% 78% 100% 416% 353% 437% Hungary 47% 58% 100% 348% 457% 638% Latvia 32% 52% 100% 377% 382% 483% Lithuania 18% 31% 100% 509% 518% 630% Malta 32% 89% 100% 156% 132% 4005% Poland 56% 70% 100% 1349% 1649% 1777% Slovakia 84% 107% 100% 418% 502% 584% Slovenia 64% 83% 100% 275% 272% 255% Table 4- Relative OFDI from EU10 GEO/TIME 2002 2003 2004 2010 2011 2012 Denmark 80% 78% 100% 175% 189% 196% Finland 98% 96% 100% 165% 166% 184% France 85% 92% 100% 173% 146% 150% Germany 103% 100% 100% 148% 161% 169% Greece 85% 97% 100% 319% 369% 338% Ireland 72% 74% 100% 325% 326% 399% Italy 90% 92% 100% 178% 195% 194% Luxembourg 75% 83% 100% 457% 405% 341% Netherlands 88% 96% 100% 166% 176% 174% Portugal 63% 85% 100% 155% 173% 179% Spain 82% 85% 100% 180% 186% 177% Sweden 90% 94% 100% 178% 186% 191% United Kingdom 104% 103% 100% 133% 143% 146% Table 5- Relative OFDI from EU15
this test was exactly the same as in the previous section, to check whether increase of the FDIs values was significant in all the analysed countries, but it doesn’t give a clear answer whether the increase was higher in EU10 than in EU15.
No. ISO p-value No. ISO p-value No. ISO p-value
1. DE 0.0015984016 6. FR 0.0003996004 11. PT 0.0003996004 2. DK 0.0003996004 7. IE 0.2721278721 12. SE 0.0003996004 3. EL 0.0009990010 8. IT 0.0003996004 13. UK 0.0007992008 4. ES 0.0003996004 9. LU 0.0003996004 5. FI 0.0003996004 10. NL 0.0003996004 Table 7- Wilcoxon test p-value for EU15
Exactly as in the analysis of the outward investments, in order to be able to asses in which countries the boost of the FDIs stocks was higher, an analysis of the comparative values for the EU10 and EU15 countries with a year 2004 as a fixed base is conducted. In the tables below are presented values for the two years before regional integration and for six, seven and eight years after the integration.
As expected, value of the foreign direct investments in both groups EU10 and EU15 increased in the time period from 1998 till 2012. Table 8 clearly shows that the increase of FDIs to the newly admitted countries from the rest of the world increased after entering EU.
No ISO p-value No ISO p-value
1. CY 0.0363636364 6. LV 0.0003996004 2. CZ 0.0003996004 7. MT 0.0003996004 3. EE 0.0003996004 8. PL 0.0003996004 4. HU 0.0090909091 9. SI 0.0090909091 5. LT 0.0003996004 10. SK 0.0003996004 Table 6- Wilcoxon test p-value for EU10
Although, in this case the difference between EU10 and EU15 countries wasn’t that extraordinary as in the case of OFDIs.
As can be seen from the analysis the progressive regional integration boost FDIs to the region with reasonably higher increase in a group of the newly admitted countries. Capital flows to the other countries which belonged to the EU earlier increased as well but still by a lower percentage than in the new countries. Conducted analysis confirms second part of the hypothesis which claims that entering to the association of countries boost capital flows to
newly admitted members.
GEO/TIME 2002 2003 2004 2010 2011 2012 Czech Republic 86% 84% 100% 229% 222% 246% Estonia 55% 75% 100% 169% 178% 200% Cyprus 74% 85% 100% 208% 254% 253% Latvia 79% 79% 100% 242% 281% 309% Lithuania 81% 85% 100% 214% 235% 258% Hungary 75% 72% 100% 149% 142% 170% Malta 78% 87% 100% 406% 400% 4225% Poland 72% 73% 100% 253% 245% 282% Slovenia 68% 89% 100% 196% 210% 210% Slovakia 51% 78% 100% 234% 250% 260% Table 8- Relative FDI to EU10 GEO/TIME 2002 2003 2004 2010 2011 2012 Denmark 82% 82% 100% 127% 131% 132% Germany 96% 102% 100% 128% 138% 142% Ireland 114% 116% 100% 140% 147% 181% Greece 71% 85% 100% 144% 107% 92% Spain 84% 93% 100% 162% 164% 164% France 76% 88% 100% 153% 109% 111% Italy 74% 88% 100% 152% 169% 175% Luxembourg 91% 91% 100% 201% 222% 243% Netherlands 95% 96% 100% 125% 134% 132% Portugal 87% 98% 100% 170% 176% 185% Finland 77% 94% 100% 153% 164% 174% Sweden 79% 87% 100% 179% 185% 191% United Kingdom 97% 93% 100% 164% 184% 223% Table 9- Relative FDI to EU15
3.4 Results
Taking above into the consideration can be said that: “H1: Entering to the association
of countries boost capital flows from and to newly admitted members.” is statistically
significant and supported by the results. While the increase in the amount of investments from and to EU15 countries can be explained by overall economical growth, case of EU10 countries is different, because in the analysed period occurred serious change in relation with the foreign investors. Entering such association as the European Union has many advantages for the new members, but also for investors. Becoming part of the association allows to freely invest in all the countries of the EU. What is more, can be claimed that entering into the association leads to the augmentation of the EU credibility on the new countries. Every country before becoming part of EU must fulfil particular requirements specified in the contracts (Ram, 2012). That is why investors are not afraid to invest in regions, where their rights are protected and law is greatly unified.
4. Factors influencing the outward foreign direct investments from the Czech Republic and Hungary
Aim of this chapter is to answer the following question: “What are the specific, influential factors of target nations that attract investments from foreign countries?”. In order to check whether proposed model is universal, data for the OFDIs from Czech Republic was used as base and the same dataset with values for Hungary was used as a control country. Aforementioned countries were chosen because of couple of reasons. First of all, both of them entered EU in 2004. What is more those countries have similar area, number of citizens and geographical location.
4.1 Data collection
Purpose of the second part of this thesis is to examine the relation between the level of the OFDIs (from Czech Republic and Hungary) and the influential factors of the target countries
that may attract investments. Data has been gathered from three different sources. The first source is Eurostat, the second source is the Hofstede Centre and the third source is the World Bank and metadata for Doing Business Ranking.
Data for both source countries was collected for the time range from 2008 till 2013, because statistics only for such period is available. As target nations are considered all the countries which belong to the EU. Table 10 presents target countries of the OFDIs from the Czech Republic, and Table 11 presents target countries of the OFDIs from the Hungary.
Austria Belgium Bulgaria
Croatia Cyprus Denmark
Estonia Finland France
Germany Greece Hungary
Ireland Italy Latvia
Lithuania Luxembourg Malta
Netherlands Poland Portugal
Romania Slovakia Slovenia
Spain Sweden United Kingdom
Table 10- Target countries for Czech Republic
Austria Belgium Bulgaria
Croatia Czech Republic Cyprus
Denmark Estonia Finland
France Germany Greece
Ireland Italy Latvia
Lithuania Luxembourg Malta
Netherlands Poland Portugal
Romania Slovakia Slovenia
Spain Sweden United Kingdom
Initial database consisted of 162 records for every source country, but because of the fact that data concerning a couple of values of OFDIs were missing the number of observations was cut for both countries to 137. Any method of replacing missing values such as serious mean wasn’t used in order to achieve as reliable results as possible, because value of the OFDIs is a component of dependent variable. The rest of the data concerning explaining variables was very good and didn’t have missing values.
4.2 Measurement
Dependent variables are the values of the OFDIs from the Czech Republic or Hungary to a particular country in a particular year divided by the GDP of a recipient at the market prices in a particular year. Currency used for all the calculations was Euro. By formulating dependent variable in that way was received a comparative value which allows to compare the values between different target countries.
Independent variables were divided into 6 groups: geographical proximity, culture,
workforce, economy, institutions and other.
Group proximity consists of only one independent variable called common border and relates to the Uppsala model of (Johanson & Vahlne, 1977) . This is a simple 0-1 variable based on analysis of the map. When countries have a common border then the value is 1, when they don’t then the value is 0.
Group culture consists of five variables such as 4 Hofstede’s dimensions (Hofstede, 1984) and calculated Kogut-Singh index.
Group workforce consists of four independent variables and relates to the notion of CSAs (Dunning, 1998), because people living in a particular place can be considered as a source of country specific advantages. Their level of education, number of people but also unemployment rate and level of wages. According to (Laamanen, Simula & Torstila, 2012) companies tend to relocate HQ there where unemployment rate is quite high because then they have more
opportunities to hire experienced people in a relatively low price. Therefore, can be assumed that countries may want to invest there where unemployment rate is quite high.
Group economy consist of four variables which measures export, import and GDP in different configurations.
Group institutions consist of six variables which measure the efficiency of the formal regulations within a particular country. Data used in this group is very interesting because of the fact that consisted of time in days for particular business activities. Therefore, it’s possible to asses whether particular regulations work and also compare the results between countries. Group other consist of two variables which measure the GDP of the source country and total environmental tax revenues in % of GDP.
Group Influential Factors Code Source
Proximity Common Border 1 Map
Culture Power Distance 2 The Hofstede
Centre
Uncertainty Avoidance 3 The Hofstede
Centre
Individualism/Collectivism 4 The Hofstede
Centre
Masculinity/Femininity 5 The Hofstede
Centre
KOGUT-SINGH INDEX 6 Own
calculations Workforce Wages and salaries- as % of GDP 7 Eurostat
Population aged 30-34 with tertiary education ( in
%) 8 Eurostat
Average population – total 9 Eurostat
Unemployment rates from 20 to 64 years (in%) 10 Eurostat Economy Exports of goods- current prices in million Euro 11 Eurostat GDP of recipient at market prices in million Euro 12 Eurostat
Import/population 13 Own
calculations
GDP/population 14 Own
Institutions Starting a business- time in days 15 World Bank Dealing with construction permits- time in days 16 World Bank Registering property- time in days 17 World Bank Paying Taxes -time hours per year 18 World Bank Enforcing Contracts- time in days 19 World Bank Recovery rate- in cents on the dollar 20 World Bank Other Total environmental tax revenues in % of GDP 21 Eurostat
GDP of a source country in million Euro 22 Eurostat Table 12-List of independent variables
4.3 Model
As a method of statistical analysis was used a panel data analysis with random effects (Clark & Linzer, 2015). Because of the fact that this is a tool which is specifically designed to analyse simultaneously longitudinal and cross-sectional data. Thanks to opportunity to analyse data from different years it’s possible to asses the influence of the independent variables more accurately. Model with fixed effects would strongly influence on degrees of freedom. Therefore, model with random effects in this case is more effective because of the fact of a very big number of target countries.
Models applied in the analysis looks as follows:
0
,
it it it it i ity
=
β
+
x β
+
υ υ
=
α
+
ε
where 0 β is an intercept, itx vector of independent variables for i-country in t- year,
β vector of regression coefficient,
it
υ random error for i-country in t-year, which is a sum of α ( random error of i-country which i
is constant through all period of time) and ε (random error). it It has been assumed that elements of random effects are:
) , 0 ( ~ ) , 0 ( ~ 2 2 α σ α N N i
Calculations associated with the aforementioned model were performed in R software environment (R Development Core Team, 2005) with usage of the PLM package (Croissant and Millo, 2008).
The model consists of 20 independent variables with p-value below 0,05. Additionally, 2 variables also included in the model have p-value from the range of 0,05 to 0,1 what suggest a weaker significance. So in total model consists of 22 independent variables presented in the (Table 12).
4.4 Results for Czech Republic
Analysis revealed that 22 independent variables are significant. Exact results for all the variables used in the model are in a table below (Table 13). They belong to 6 aforementioned groups: proximity, culture, workforce, economy, institutions and other.
Group proximity consisted of one variable common border. P-value for this variable was (p-value< 2.2e-16) therefore, is strongly significant. Hence, Hypothesis 2 which suggests that geographical proximity has an influence on the amount of FDIs invested in a particular country is supported.
Group culture consisted of five variables. P-value for all of them is significant but with a different strength. Variables with (Code: 2,3,5) are strongly significant with (p-value< 2.2e-16). Variable (Code: 4) is also significant but less strongly with p-value=0.0052459. Variable (Code: 6) points out weak dependence, because significance level (p-value=0.0610031) was below 0,1. Taking everything into consideration can be said that Hypothesis 3 which suggests that culture of the target country has an influence on the amount of FDIs invested in a particular country is supported.
Group workforce consisted of four variables. Three of them are strongly significant (Code: 8-10) with p-value=4.502e-08, p-value=1.897e-11 and p-value=7.831e-06 respectively and one is less significant (Code: 7) with p-value=0.0167744. Because of the fact, that all the
variables support its group, Hypothesis 4 which suggests that workforce is an influential factor while considering location of the investment location is supported.
Group economy consisted of four variables, and four of them are significant (Code: 11-14) with p-value= 7.650e-13, p-value < 2.2e-16, p-value= 2.928e-09 and p-value= 6.120e-05 respectively. Explaining variables are consistent with the Hypothesis 5 which claims that economical factors play an influential role while choosing a target location for the OFDIs therefore, the hypothesis is supported.
Group institutions consisted of six variables (Code: 15-20) and all of them are significant but with a different strength. Variables (Code: 15, 18, 19) are strongly significant with p-value=2.658e-05, p-value=3.181e-08 and p-value= 2.187e-05 respectively. Variables (Code: 16,17) are also significant but less strongly with p-value= 0.0065086 and p-value= 0.0059737 respectively. Variable (Code: 20) indicates weak dependence because the significance level (p-value=0.0522901) was from the range 0,05-0,1. All in all, can be stated that the Hypothesis 6 which claims that formal institutions are an important factor while choosing target location for the FDIs is supported.
Two variables (Code: 21,22) are also considered as significant but formally they belong to the set called other. P-values for this variables are p-value= 4.611e-05 and p-value= 0.0421104 respectively.
Independent Variables (Code)
Estimate Std. Error t-value p-value
(Intercept) 1.3584e-02 6.1338e-03 2.2147 0.0289640 *
1 6.2845e-03 6.3075e-04 9.9636 < 2.2e-16 ***
2 3.4903e-04 1.4319e-05 24.3759 < 2.2e-16 ***
3 -2.1055e-04 1.6261e-05 -12.9481 < 2.2e-16 ***
4 -5.4145e-05 1.8987e-05 -2.8517 0.0052459 **
5 3.1057e-05 9.1694e-06 3.3870 0.0009985 ***
6 -8.1370e-04 4.2962e-04 -1.8940 0.0610031 .
7 -1.4064e-04 5.7856e-05 -2.4308 0.0167744 *
8 -1.9413e-04 3.2881e-05 -5.9039 4.502e-08 ***
9 -3.1515e-10 4.1836e-11 -7.5329 1.897e-11 ***
10 2.2315e-04 4.7423e-05 4.7055 7.831e-06 ***
11 -2.2017e-08 2.6934e-09 -8.1744 7.650e-13 ***
12 1.5933e-08 1.3607e-09 11.7095 < 2.2e-16 ***
13 7.5416e-01 1.1614e-01 6.4935 2.928e-09 ***
14 -1.5234e-01 3.6457e-02 -4.1787 6.120e-05 ***
15 8.3656e-05 1.9026e-05 4.3969 2.658e-05 ***
16 -1.2528e-05 4.5113e-06 -2.7770 0.0065086 **
17 -1.0787e-05 3.8429e-06 -2.8069 0.0059737 **
18 1.3704e-05 2.2916e-06 5.9802 3.181e-08 ***
19 -5.2156e-06 1.1728e-06 -4.4470 2.187e-05 ***
20 3.2733e-05 1.6673e-05 1.9632 0.0522901 .
21 2.2362e-03 5.2573e-04 4.2536 4.611e-05 ***
22 -6.5783e-08 3.1968e-08 -2.0578 0.0421104 *
Table 13- Influential Factors for Czech Republic
Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 R-Squared: 0.96851
Adj. R-Squared: 0.79311
4.5 Results for Hungary
In order to check whether aforementioned model is comprehensive for other countries, in the second stage of the analysis was it applied to data gathered for Hungary. Similarity in the results would suggest that the model is universal and explains investment paths for all the countries with similar traits such as geographical location, GDP, area, population etc. Unfortunately, it’s not the case because from 22 variables significant for Czech Republic only 12 are significant also for Hungary. Some of them show the same level of significance while the other lower and in one case higher (Table 14).
In order to apply criterion of accepting hypotheses, the hypothesis is recognized: as supported when all the variables support the group, as partially supported when less than 100% variables support the group, as not supported when 50% or more variables do not support the group.
Group proximity in case of Hungary is not significant. Therefore, Hypothesis 2 is not supported.
Group culture is mixed, because variables (Code: 2,3,5) are significant with
p-value=5.159e-12, p-value value=1.111e-05 and p-value value=0.0002346 respectively. Variables (Code: 4,6) are not significant. Taking into consideration aforementioned way of assessing the hypotheses, because of the fact that more than 50% variables supports its group, the Hypotheses 3 is partially supported.
Group workforce has only one significant variable (Code: 9) and four not significant (Code: 7,8,10). Hence, the Hypotheses 4 is not supported.
Group economy has four significant variables (Code:11-14) with p-value= 0.0120158, p-value= 5.513e-06, p-value= 7.093e-06, p-value= 0.0002370 respectively. All of them support its group, therefore Hypotheses 5 is supported.
Group institutions consisted of six variables. Four of them are significant (Code: 15,16,18,19) with value=0.0432529, value=0.0009768, value<2.2e-16 and p-value=0.0103380 respectively. Variables (Code: 17, 20) are not significant. Majority of the variables support its group therefore Hypotheses 6 is supported.
Variables from the group other (Code: 21,22) are statistically not significant.
Independent Variables (Code)
Estimate Std. Error t-value p-value
(Intercept) 4.8493e-03 1.0998e-02 0.4409 0.6601932 1 4.8886e-04 1.3019e-03 0.3755 0.7080625 2 2.3338e-04 2.9868e-05 7.8138 5.159e-12 *** 3 -1.5476e-04 3.3477e-05 -4.6230 1.111e-05 *** 4 -2.8952e-05 5.2073e-05 -0.5560 0.5794309 5 1.1829e-04 3.1016e-05 3.8139 0.0002346 *** 6 7.7744e-04 7.8860e-04 0.9858 0.3265403 7 -5.0832e-05 1.0058e-04 -0.5054 0.6143789 8 -5.7865e-05 5.9001e-05 -0.9808 0.3290360 9 -2.4340e-10 6.1647e-11 -3.9483 0.0001450 *** 10 1.1889e-04 7.3587e-05 1.6156 0.1092680 11 -1.1343e-08 4.4351e-09 -2.5574 0.0120158 * 12 1.1347e-08 2.3654e-09 4.7970 5.513e-06 ***
13 7.7257e-01 1.6317e-01 4.7348 7.093e-06 ***
14 -2.3747e-01 6.2313e-02 -3.8109 0.0002370 *** 15 5.9309e-05 2.8977e-05 2.0468 0.0432529 * 16 -2.5322e-05 7.4571e-06 -3.3956 0.0009768 *** 17 -8.9690e-06 5.8565e-06 -1.5315 0.1287488 18 4.4091e-05 4.1430e-06 10.6424 < 2.2e-16 *** 19 -5.0219e-06 1.9220e-06 -2.6128 0.0103380 * 20 -5.4071e-06 2.4734e-05 -0.2186 0.8273910 21 7.4646e-04 8.6522e-04 0.8627 0.3903042 22 -9.6796e-08 5.5553e-08 -1.7424 0.0844542.
Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 R-Squared: 0.8956
Adj. R-Squared: 0.73081
F-statistic: 39.7733 on 22 and 102 DF, p-value: < 2.22e-16 5. Discussion
First part of this thesis answered the question whether entering to the association of countries boost capital flows from and to the newly admitted countries. According to the presented theory (Frankel & Rose, 2002) regional integration straightforwardly influence on economic activities between countries. Therefore, such substantive increase in FDIs to EU10 countries was noted after entering EU. For many countries noticed surge exceeded the initial level by 2-4 times. The most prominent country recognized increase by 42 times.
Substantial increase in the outflows can be explained by three factors, namely regional integration (Hill, 2010), investment patterns (Rugraff, 2010) and global trends (UNCTAD, 2015).
Countries evaluated in this study entered the association of the countries, what as mentioned before strongly influence on economic activities between members of the association (Frankel & Rose, 2002). Results of the conducted analysis seem to confirm the theory that regional integration boost capital flows between countries, because OFDIs from EU10 significantly increased after entering EU.
As (Vernon, 1966; Rugraff, 2010) claims, exist a particular pattern which is followed by countries and companies. They start to invest in home countries, later in the other developed countries and at the end in developing countries. The final effect of such activities is that companies from emerging markets acquire knowledge from the foreign investors which allows them to become competitive on the global markets. At the end it leads to increase in the OFDIs from EM. It could happen that now the global economy is facing the stage 4 of the investment development path (IDP) and inflows and outflows from developed and developing countries
