Union Expansion on M&A-activity

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The influence of the 2004 European

Union Expansion on M&A-activity

By:

Gert-Jan Dobben

University of Groningen

MSC BA: Strategic Innovation Management

Supervisor: Dr. K.J. McCarthy

Co- assessor: Dr. P.M.M. de Faria

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Abstract

In 2004, ten mainly eastern European countries joined the European Union (EU). These countries all joined with the expectation to benefit from this accession in some way. This thesis aims to provide clearance in whether these expectations were legitimate. It does so by analyzing the effect of the 2004 EU Expansion on M&A-deals towards these countries from Germany by executing a poisson analysis which generates results on three different levels. Firstly, it finds that joining the European Union benefitted only one of these countries. Secondly, it finds that when countries shared a border with Germany, the negative effects were less extreme than for countries that do not share a border with Germany. Thirdly, it finds some evidence that the effects were less negative for core regions than they were for periphery regions throughout these countries. This implies that the positive effect that one would expect to experience when joining the EU are not that straightforward.

1. INTRODUCTION

In recent years, the European Union (EU) has undergone some major changes. One of these changes was the 2004 European Expansion. This meant that ten countries, mainly from Eastern Europe, were allowed to call themselves EU members from then on. This expansion brought the European Union to 450 million inhabitants, making it the world's largest trading bloc (“EU welcomes 10 new members,” 2004). Now, in 2016, the EU is still one of the world’s largest trading blocs, with approximately 508 million inhabitants (Living in the EU, 2016) and 28 member countries. This makes it one of the most important economies in the world. The group of countries that joined the EU in 2004 is referred to as the “EU2004-countries” from now on.

However, the effects of the 2004 EU expansion are unclear. A popular political opinion concerning the EU as a whole which was heard more and more during recent years, would state that the EU only costs its members money, without generating any returns. However, these arguments, pro-EU as well as anti-EU, are often not based on scientific research, as there is not a significant amount of research available. This thesis aims to provide some scientific guidance in this discussion. Therefore, the research question of this thesis is:

“What has been the economic influence of the expansion of the EU in 2004 on those countries that became EU-members in 2004?”

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From a more scientific point of view, there has been a rise in cross-border M&A worldwide during recent years, from 23% of total merger volume in 1998 to 45 % in 2007 (Erel et al., 2012). Next to that, acquisition is among the largest and most readily observable forms of corporate investment (Francis & Martin, 2010). However, there has not been very much extensive and elaborate research concerning this matter. Rather, the field is scattered and diverse (Meglio & Riesberg, 2010; Shimizu et al., 2004).

When exploring the literature, it can be expected that although some of the risks of investing in the EU2004-Countries disappeared when these countries joined the EU. However other risk, which are not so easily removable due to for example language and cultural differences, will still be experienced as too high by other countries in the EU. Therefore, no significant positive effect of the EU 2004 expansion should be found. However, this assessed risk should be lower when countries have higher degree of communalities. This thesis researches whether a shared border could act as a proxy for these communalities. Next to that, as the assessed risk should be lower when knowledge concerning a certain region is higher, it could be expected that companies would rather invest in core regions of countries than in their periphery regions.

The executed research finds that most of the expectations are true. Companies are not willing to invest more in countries after they joined the EU. We even find a negative effect of the 2004 EU expansion on M&A-activity towards the EU2004-Countries. However, this effect is lower for countries that have a shared border with companies that are already in the EU. Also, this effect seems to be lower for core regions than for periphery regions. However, due to a low amount of observations, this cannot be stated irrefutably. These results contribute to the existing literature in three ways. Firstly, it provides guidance in the pro/anti EU discussion by finding that it might not be so positive for countries to join the EU. Secondly, it finds that a shared border effect does exist. Thirdly, although not irrefutably, it finds that companies rather execute cross border investments in core regions of countries than they do in periphery regions.

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research, followed by an elaboration concerning the relevant literature that has already been executed in the field of cross-border M&A. After that, this research is applied to the specific topic of this thesis and its context, which eventually leads to the hypotheses which are tested in this thesis. Secondly, the methodology that is used to test the before mentioned hypotheses are elaborated on. Thirdly, the results of these tests are discussed. Fourthly, this is followed by a discussion and a conclusion concerning these results.

2. BACKGROUND

In this chapter of the thesis, a background research aims to provide the context in which the research is executed. Furthermore, the relevant research that has already been executed in the field is introduced, which is followed by the development of the hypotheses concerning this research. For both the hypotheses sections of this chapter, there are specified paragraphs concerning the main paradoxes that are researched in this paper. Namely, the Home vs. Foreign paradox, the Shared border vs. No shared border paradox and the Core vs. Periphery paradox.

2.1 The European Union

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2.2 M&A & Risk

For this thesis, M&A is defined as a transaction where the acquirer obtains a majority interest in the target by either acquiring an interest of 50% or over in the target raising its interest from below to above 50%, or acquiring the remaining interest it does not already own. (Hijzen et al., 2008). This is in line with the definition of M&A-deals utilized in the database that is used for the analysis. Only, in this research, the focus is on M&A-deals from one country to another; cross-border M&A deals.

There has been a significant rise of cross-border M&A activity in the last twenty years (Di Giovanni, 2005; Erel et al., 2012). This meant that firms had to reorganize their economic structures (Bertrand & Zuniga, 2006). To exemplify the magnitude of M&A activity, one can concern the following example. In 2004 only, 30 thousand acquisitions were recorded, meaning that there was one M&A deal agreed upon every 18 minutes (Cartwright & Schoenberg, 2006). However, while M&A seems to be a very popular form of business expansion, the main focus of current research concerning the field is on the US and the UK. (Cartwright, 2005). Next to this, as stated before, the field is scattered and diverse (Meglio & Riesberg, 2010; Shimizu et al., 2004). This thesis aims to contribute to this field by observing the EU, which has not been analyzed concerning cross-border M&A activity yet.

As North (1990) states: “institutions are the rules of the game that shape society or, more formally, are the humanly devised constraints that shape human interaction”. These rules, as North calls them, have the objective to create certainty in the game, and with that, lower transaction costs (De Groot, et al., 2004). In other words, institutions constrain human interaction to lower transaction costs. Therefore, institutional differences between for example countries within the EU and countries outside of the EU generate differences in transaction costs for companies that aim to participate in cross-border M&A-activity. On the other hand, it is a popular opinion that country borders are disappearing more and more and are becoming less important due to the rise of globalization. The growth of large trading blocs such as the EU aim to reduce significant components of these institutional differences and with that transaction costs, which should lead to reduction of the assessed risks of cross-border investments for companies, which then should to economic prosperity.

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acquisitions. They find that the euro has significantly increased the number of deals announced and the spread of deals that are made. Also, they find that this monetary union has improved performance throughout its member states. It would be interesting to see whether the same effects can be observed when not involving a monetary union. This would be in line with Leblang (2010), who finds that there is a positive effect of being a member of a trade union on portfolio investments. However, exploring the same type of effects, McCallum (1995) finds that even within trading blocs, national borders continue to matter to a high degree. In this case, the case of the North American Free Trade Agreement (NAFTA), it was found that the positive effects of the NAFTA could be named modest or gradual. Anderson and Van Wincoop (2003) find similar effects, when they state that in times of globalization borders still matter and inhibit large amounts of trade, although they find more moderate effects.

In an older paper, Anderson and Van Wincoop (2001) make a distinction between two types of trade costs. On the one hand, they identify non-border costs, which are mostly due to geographic distance and irregularity between two traders. On the other hand, they identify border-costs, which can be divided into two sub-categories, namely rent-bearing border costs (tariffs, taxes etc.) and non-rent bearing border costs (differences in languages, cultures, customs, etc.) Applying this to the situation that is researched for this thesis, it would be interesting to see what the effect on M&A-activity would be when a significant amount of the rent-bearing border costs, e.g. tariffs and taxes, are taken away. In other words, when the EU2004-Countries joined the EU, the degree of risk that companies experience to invest in these countries, should be lower.

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inhibiting trade between states of the US. This should imply that looking at these border costs and distance factors, it all comes down to the amount of risks assessed by a company concerning an investment. A company considers whether the decision to execute a cross-border acquisition is worth taking the risks that the trade and cross-border costs inhibit. In this case, it should be concluded from theory that the amount of risk that is experienced by these companies still could be significantly high.

Inspecting the theory as it is elaborated on, it can be drawn that those countries that have a larger amount of communalities with the countries already in the EU, for example in language, culture, customs and regulations, should expect higher growth effects than countries that do not. This is due to the fact that the risks to invest abroad are lower when there are more communalities between the countries. This section is focused on a proxy of these communalities, mainly whether or not these countries have a shared border. The claim of a positive effect of a shared border between countries is confirmed by the study of Huizinga and Voget (2009) and Petrakos (2001), where they state that a shared border between two countries is positive for trade between those countries. Frankel and Rose (2000) find similar effects, as they state that when a pair of countries shares a land border, trade is 50% then when they do not share a border. On the other hand, Leblang (2010) finds that a common border is statistically insignificant for portfolio investments. From this theory, it could be stated that a shared border should generate a positive effects on trade between two countries.

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countries.” Borgatti & Everet (1999) make this distinction by referring to a dense, cohesive core and a sparse, unconnected periphery. Concluding, the difference between core and periphery concerns which region is the densest, most dominant and most thriving. The application of this distinction will be elaborated on in the methodological section of this thesis. Now that the difference between core and periphery regions is explained, it should be attempted to find theory concerning the differences in company decision making when choosing between core and peripheral regions. Although large policy expenditures, there has not been a lot of convergence between core and periphery inequalities in Europe (Puga, 2002). Amin & Malmberg (1992) state that the Economic and Monetary Union (EMU) will only strengthen core areas and that it will support peripheral regions to a lower degree. Petrakos (2001) states something similar, namely that the implementation of the Single European Market and the EMU will be positive for core areas while it will generate a disproportionate mix of threats and opportunities for the less developed, peripheral regions, which mainly summarizes the paragraph as it is formulated above. When looking at the research introduced above, the main concern of doing business abroad is the risk involved with crossing borders. Following this line of reasoning, it does not matter whether the home company is in a core or peripheral region. The home company’s main concern will be what risks are involved when crossing borders.

Although these authors find interesting results concerning border effects, never has there been an analysis done concerning this specific event, namely the expansion of the EU in 2004. Neither has there been enough research concerning these effects within the EU, as most of the research has been focused on the UK and the US (Cartwright, 2005). Next to that, this case is unique because there has never been such a unique and sudden growth of such a tightly bonded trading bloc. In other words, to our knowledge, it has never been that the scores for the countries that are researched were this “even” for the joining countries.

2.3 Hypotheses

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2.3.1 Home vs. Foreign. On this level of analysis, there is a research of the effects of lowering border costs. Specifically, in the situation before 2004, a company would face non-border costs, rent-bearing and non-rent bearing non-border costs when investing in companies in the EU2004-Countries. After 2004, one of these costs, namely the rent-bearing border costs, is taken away. At first sight, this should mean that the experienced risk of the acquiring company should be lower after 2004 than before. However, according to literature, although the rent-bearing border costs were taken away, there will still be a high risk experienced by acquiring companies due to other costs. This should mean that companies would not be that eager to invest in companies in the EU2004-Countries, mainly because of the fact that the risk experienced due to non-rent bearing costs is too much of a burden for companies to decide to invest abroad. Therefore, it should be hypothesized that:

H1: Although rent-bearing borders costs have disappeared when the EU2004-Countries joined the EU, this event will not have a significant positive effect on the number of M&A-deals towards the EU2004-Countries.

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EU2004-countries that have a shared border with countries that are already a member of the EU than for countries than those that do not share a border with these countries.

H2: Companies will rather invest in companies in EU2004-Countries with which they have a shared border than in companies within EU2004-Countries that they do not share a border with.

2.3.3 Core vs. Periphery. As there is not that many (recent) research concerning the differences between cross-border investments into core and periphery regions, the following hypothesis are reasoned combining common sense and some older research, which has already been introduced in the theory section of this thesis. As explained before, companies decide about crossing borders by assessing the risk that this border-crossing will inhibit. Of course, risk is always closely related to uncertainty. It is logical that the higher the uncertainty of an investment opportunity, the higher the risk that is assigned to such an opportunity. These investment opportunities can be in completely different areas throughout countries which all bring different risks with them. For example, it seems only logical that there is less uncertainty involved with doing business within a core region of a country, of which obviously more knowledge is available and which often has a better infrastructure. This comes from the fact that there is simply more business activity within a core region than in a periphery region. On the other side there would be more risk involved when doing business in a periphery region of a country, of which the opposite of the arguments posed above is true. These arguments, combined with the research introduced before (Puga, 2002; Amin, 1992; Petrakos, 2001), which all state that core regions will benefit more from European integration than peripheral regions, make that it should be hypothesized that:

H3: Companies will rather invest in companies within the core of the Core/Periphery-Countries than in companies within the periphery of those countries.

3. METHODOLOGY

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section of this chapter explains the process in which the original dataset is transformed towards a dataset which makes it possible for our analysis to be executed. Fifthly, validity and reliability are concerned.

3.1 Research Setting

The first issue that should be concerned here is the choice for Germany as the country of interest. Due to reasons of complexity, not all the member-countries of the EU could be included in the analysis of M&A-activity. Therefore, it would be wise to assign a specific country to focus on. In this case, Germany has been chosen to perform as the country of interest. Germany is the best fit to perform as the main country of analysis for several reasons. As Germany is one of the largest and most stable countries within the EU, it will provide a relevant and interesting view for this analysis. Also, Germany is one of the founding member countries of the EU. Next to this, as we want to research the existence of a shared border effect influencing M&A activity within the EU, Germany is also the perfect pick. This is not only because of its size, but also due to the fact that while it shares a border with Czech Republic and Poland, it does not share a border with Hungary, Slovenia and Slovak Republic. All in all, these arguments are making Germany an excellent candidate for this analysis. 3.2 Data

In the following section of this thesis, an elaboration concerning the method of analysis is provided, followed by an elaboration on the dataset itself and on the modifications made to this database to make it suitable for analysis.

3.2.1 Data Analysis. The research for this thesis is executed in a quantitative manner and for the analysis of the data, the statistical software program Stata 14 SE is used. This program is chosen for its wide applicability and reliability. To evaluate the relationships as they are hypothesized in the Theoretical Development section of this thesis, a poisson regression is executed. This type of analysis is usually seen as appropriate when a certain subject is followed for a variable length of time (Zou, 2004) analyzing discrete events (Osgood, 2000). This is exactly the case here, analyzing the amount of M&A deals over a period of time.

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a European target company, a European acquiring company, or both. The original dataset contained the following variables. There were five variables concerned with the date that the deal was announced (“DA” (Date Announced), “Day”, “Month”, “Quarter”, “Year”). Next to that, the “SDC” variable represents a unique code for every deal made. Also, some locational variables are included. Namely, a target and acquiring city, and a target and acquiring country (T_Nation and A_Nation). The same goes for the industry of the target and the acquiring company. (T_Macro_Industry, A_Macro_Industry, T_Mid_Industry and A_Mid_Industry). Further, some dummy variables are included. The first dummy variables indicated whether the target company was within Europe (European_Target), the acquiring company was within Europe (European_Acquirer) and whether both the target and the acquiring company were in Europe (European_Both). From this database, the deals that had a target in the EU2004-Countries were manually matched to a NUTS2 region. From 1995 on, there was a total amount of 982 deals in which the target was within the EU2004-Countries and the acquirer was German. In some cases, errors in the data made it impossible to match cities to regions; for example, when the city is listed as Hamburg, and the country as Austria, it is impossible to attribute the deal to any particular NUTS 2 region, as both the city and the country could be right.

3.3 Methodological Choices

In the following section, the methodological choices as they have been made by the author of this paper are elaborated on, explaining some of the most important choices as they were concerned above.

3.3.1 Timeslot & Geography. For this research, a comparison is performed between two timeslots, namely the 1995-2003 timeslot and the 2004-2015 timeslot. This decision has been made for several reasons. Firstly, from 1995, when Austria, Sweden and Finland joined the EU, until 2003, the year before the EU2004-Countries joined the EU, the composition of the researched group, the EU2004-Countries, has not changed. After the expansion in 2004 Bulgaria, Croatia and Romania also joined the EU. For clarity and reliability reasons, these are not included in the EU2004-Countries, mainly to keep the scores as “even” as possible, as mentioned before. Secondly, a significant amount of control data is only available from 1995 on.

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regions stand out highly in their countries These regions are all the regions that contain the capital of the given countries, which is another argument to name them Core-regions. Figure 1 is a visualization of this, where the green areas mark the core NUTS2-regions and the red areas mark the periphery regions. The yellow areas are the other EU2004-Countries which are not in the NUTS2-level analysis due to the fact that they only have one NUTS2-region assigned to them.

Figure 1

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European Union, Nomenclature of Territorial Units for Statistics NUTS 2013/EU28) there has been a border shift between the two regions within Slovenia. For this thesis, the most recent grouping of NUTS2-regions is used and therefore, SI04 is be used for this analysis.

3.3.3 Controls. To reduce the possibility of bias as much as possible, there are some control variables added to the model. Firstly and obviously, there will be controlled for overall growth of the economy. The growth of the total amount of deals in the original database acts as a proxy for this. Next to that, many authors control for population differences (Chen & Wall, 2005; Head et al., (2010) and economic size (Bussière et al., 2005; Head et al.,

2010) of the units. However, after running a Variance Inflation Factor (VIF) test checking for multicollinearity, it was found that GDP and population had extremely high factors when using a baseline-factor of 10 for some of the countries and regions. Therefor the population variable is not used in the test. These variables also have been added in this case, per country and per NUTS2-level. The data to generate these control variables is derived from the European bureau for statistics, Eurostat. Next to that, there are two controls added of to control for major economic events during the period of interest. Firstly, a variable DOTCOM is added to control for the burst of the Dot-Com bubble in 2001 (O’reilly, 2007). Secondly, the economic crisis of 2007 (Reinhart & Rogoff, 2008) is controlled for by the variable CRISIS. As there are already controls for yearly specific effects, a control for year also generates extremely high VIF-values. Therefore, such a control will not be included.

3.4 Data Preparation

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variables were added concerning the EU2004-Countries, to make it possible to group them. After this was executed, the data was ready to be analyzed.

4. ANALYSIS & RESULTS

In this section of this thesis, an elaboration is made concerning the analysis and results of this paper. This section is structured “from large to small”. First, there is a discussion provided of the general effects of the EU 2004 expansion on the EU as a whole, followed by the same discussion, however then specified for the EU2004-Countries. Malta and Cyprus will not be included in the country-level analysis, as there is a problem with collinearity which makes it impossible to analyze (Cyprus) or there are not enough observations found to analyze (Malta). After this, the hypotheses as they were provided in the theoretical development chapter of this thesis are tested and discussed. A note has to be made concerning the terms used for this. When the term “an effect” is used in combination with a number, this means that this number is the amount of deals that deviates from what should be expected. For example, when it is stated that an effect of 1.5 is found, there were 1.5 more M&A-deals per year than would be expected for the country or region of interest. All the general effects of groups of countries are embedded in the text, and a further conclusion concerning the bigger picture of this thesis can be found in the concluding chapter and all country- and region-specific results can be found in the appendices of this thesis.

4.1 General Effects

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(1) (2) VARIABLES EU EU2004-Countries EU 0.012*** -0.173*** (0.003) (0.019) log_Total_Deals_Year 0.118*** 0.357*** (0.003) (0.020) CRISIS 0.002 -0.024 (0.004) (0.018) DOTCOM 0.005*** 0.028** (0.002) (0.013) log_germany_gdp 0.145*** -0.279** (0.016) (0.132) log_poland_gdp -0.106*** -0.018 (0.006) (0.055) Constant 0.072 2.278 (0.186) (1.460) Observations 252 252 PseudoR2 0.00121 0.0149

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 1

Estonia Czech Rep Latvia Lithuania Hungary Slovenia Slovak Rep

Poland GDP GDP GDP GDP GDP GDP GDP GDP Poland GDP 1 Estonia GDP 0,984 1 Czech Rep GDP 0,9812 0,9771 1 Latvia GDP 0,9701 0,9872 0,9741 1 Lithuania GDP 0,9888 0,9977 0,9835 0,9839 1 Hungary GDP 0,9293 0,9548 0,9631 0,9565 0,593 1 Slovenia GDP 0,9672 0,973 0,9912 0,9766 0,9803 0,9811 1 Slovak Rep GDP 0,9773 0,9845 0,9789 0,9751 0,9888 0,9733 0,9866 1 Table 2 4.2 Home vs. Foreign.

In the following section, the results concerning hypothesis 1 are discussed. More specific, the effects of the EU 2004 expansion on M&A deals from Germany towards the different EU2004-Countries are discussed, followed by a short conclusion concerning hypothesis 1. When nothing else mentioned, all analyses are significant, as well as they are controlled for by the economic size of the specific country.

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collinearity. Analyzing Hungary, a negative effect of -0.0910991 is found of the 2004 EU expansion. Concerning the country of Latvia, a negative effect of -1.564862 has been found. For Lithuania, a positive effect of 3.851323 is found, making this the only country which seems to have benefitted from the EU 2004 expansion. When concerning the effect of the EU 2004 expansion on German M&A-deals towards Poland, it is found that there was a negative significant effect of -0.4084304. Applying the analysis towards Slovak Republic, a negative effect of -2.435989 is found. For Slovenia, we find a negative effect of the 2004 EU expansion on M&A-deals of -0.4349295. However, this effect has p-value of 0.488, which makes it insignificant.

4.2.2 Conclusion. A summary of these results can be found in table 3 and table 4. A further specification concerning these results can be found in Appendix A. In the background section of this thesis, it was hypothesized that although rent-bearing costs were removed for the EU2004-Countries, there would still not be a significant positive effect on M&A-deals towards these countries. When looking at the results, we find that only for the country Lithuania, there is a positive effect found. For the seven other countries which were analyzed, the effect is negative. Therefore, H1 is confirmed

Effect of EU 2004 Expansion on M&A Towards

EU2004-Countries

Latvia Lithuania Estonia Hungary

Slovenia (not significant)

_Effect

Slovak Republic

Czech Republic Poland

-15,00 -10,00 -5,00 0,00 5,00

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Country Effect Pseudo R2

Poland -0,408430 0,0259

Czech Republic -0,250708 0,0325

Slovak Republic -2,435989 0,1569

Slovenia (not significant) -0,434930 0,2806

Hungary -0,910991 0,0898

Estonia -12,028770 0,5449

Lithuania 3,851323 0,0728

Latvia -1,564862 0,1497

Table 4

4.3 Shared border vs. No shared border

Of all of the EU2004-Countries, there are two with which Germany shares a border, namely Poland and Czech Republic. Concerning this topic, it was hypothesized that there would be a more positive effect of the EU 2004 expansion on M&A towards those EU2004-Countries that share a border with Germany than towards those that do not share a border with Germany. First, when combining the countries that share a border with Germany and those that do not share a border with Germany, it can be found that there is a negative effect found of -0.2350652 for countries that share a border with Germany (Table 5, model 2), where there is a negative effect found of -0.0961842 for countries that do not share a border with Germany (Table 5, model 1). This is opposite from what should be expected from our hypothesis.

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VARIABLES NO SHARED BORDER SHARED BORDER

EU -0.096** -0.235*** (0.043) (0.026) log_Total_Deals_Year 0.502*** 0.414*** (0.044) (0.025) CRISIS 0.006 -0.031 (0.035) (0.021) DOTCOM 0.146*** 0.005 (0.035) (0.015) log_germany_gdp -0.524 -0.646*** (0.340) (0.186) log_poland_gdp -0.296** 0.140** (0.129) (0.064) Constant 7.480* 5.062** (3.865) (2.163) Observations 252 252 PseudoR2 0.0318 0.0191

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table 5

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Germany, and a negative effect found for all the other EU2004-Countries. However, it is also found that these negative effects are the smallest for those countries that share a border with Germany, with Lithuania as an exception. Therefore, it is reasonable to conclude that although the grouped effects are not completely in line with hypothesis 2, the country specific effects are, with an exception for Lithuania. Therefore, H2 is confirmed.

4.4. Core vs. Periphery

In the next section, the third hypothesis is discussed, concerning the preference of German companies to invest in companies in core regions of the EU2004-Countries rather than in companies in periphery regions. The results concerning this will be discussed per region and the specified result tables can be found in Appendix B. All different regions are controlled for their economic size. However, firstly, there is a discussion provided concerning the EU2004-Countries cores and peripheries combined.

4.4.1 Combined core and periphery regions. When combining the core and periphery regions of the EU2004-Countries, a significant negative effect is found of -0.0860621 for the core regions (Table 6, model 1) and a significant negative effect of -0.1730675 for the periphery regions (Table 6, model 2). This means that although the EU as a whole had a negative effect on M&A-deals from Germany towards the EU2004-Countries as, this effect is less negative for the core regions than for the periphery regions.

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VARIABLES CORE PERPIHERY

EU -0.086* -0.173*** (0.051) (0.038) log_Total_Deals_Year 0.453*** 0.517*** (0.033) (0.038) CRISIS 0.014 0.014 (0.029) (0.018) DOTCOM 0.125*** 0.069** (0.025) (0.027) log_germany_gdp -0.525* 0.343 (0.298) (0.337) log_poland_gdp -0.085 -0.200 (0.093) (0.138) Constant 5.213 -6.427* (3.677) (3.623) Observations 252 252 PseudoR2 0.0141 0.0160

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4.4.2 Disaggregate core and periphery regions. Due to the fact that there are not enough deals recorded from Germany towards the core of Czech Republic, no results can be provided for this. When concerning the periphery of Czech Republic, we find a negative effect of -0.2605284. When analyzing the number of M&A-deals towards the core region in Poland, a significant negative effect of the EU 2004 expansion of -0.6408431 is found. However, when concerning the periphery regions of Poland, the effect is -0.8363241. This means that although there is a negative effect for both core and periphery regions, the effect is more negative for periphery regions. In Hungary, there are interesting effects. While there is a slight positive effect of the 2004 EU expansion on the core regions (0.3541312), this effect is very negative for the periphery regions (-11.8857). The effects on the periphery regions are that high, that this is in support of hypothesis 3. When generating results for the core region of Slovak Republic, a negative effect of -7.821295 is found. Due to the fact that there are not enough observations from Germany into the periphery of Slovak Republic, no effects can be found. Due to the fact that for the core as well as the periphery region in Slovenia not enough observations are found, no results can be presented concerning Slovenia either.

4.4.3 Conclusion. Although there is evidence that points in the right direction to support hypothesis 3, there are too many problems concerning the trustworthiness of this analysis to support hypothesis 3, as the results are incomplete due to the fact that there were not enough deals towards some of the regions, making an analysis impossible. Concluding, although the results for Poland, Hungary and the Core/Periphery Countries as a whole point towards confirming hypothesis 3, the evidence is not strong enough to support this hypothesis.

5. DISCUSSION AND CONCLUSION

In the following section, the concluding remarks that follow from the results are discussed. Also, the theoretical and managerial implications of this research are discussed, as well as the limitations and the recommendations for further research.

5.1 Key Findings

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concerning the Core/Periphery paradox are not irrefutable. It was stated in the beginning of this thesis that institutions constrain human interaction (North, 1990), in order to reduce transaction costs (De Groot, et al., 2004). Lower transaction costs should lower the risk companies experience when investing abroad. However, the results of this thesis imply that the institution of interest here, the EU, does not lower the transaction costs. These results imply the opposite, as there is a negative effect found regarding the EU on investment activity.

5.2 Limitations

Throughout this section, an explanation is provided concerning what the limitations of this thesis are and what the recommendations could be to execute further research. Firstly, this research is limited due to the fact that it focusses on deals towards the EU2004-Countries where there was a German Acquirer. This limits the research due to a lower amount of deals that is researched then would be if more countries would be included in the analysis. Also there could be different effects when researching other countries. For example, it could be that different effects would be found when looking at a country that is less in the heart of the EU, for example Spain. Secondly, this research is limited due to the fact that the NUTS2-regions are assigned to M&A-deals manually by the author of this thesis. This provides room for mistakes, although the author has done his best to overcome these. Thirdly, when analyzing the results of this thesis concerning hypothesis 3, it can be found that the amount of deals towards some of the countries were not significant, making a trustworthy analysis impossible. This limits this research in such a way that although there are indications in support of the hypothesis, this hypothesis cannot be confirmed indisputable.

5.3 Future Research

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This is not included in this research. However, it would be interesting to see whether the EU would help as some kind of marketing tool towards countries form outside Europe. Fourthly, as this thesis finds a negative effect of the EU 2004 expansion on M&A deals towards the EU2004-Countries, it would be interesting to see if there is a positive effect on other comparable countries which are not in the EU. If this effect would be found, this means for example that a German company which was doing business in Poland before it was a member of the EU because there were less regulations (environmental, financial etc.) would shift its business towards a country comparable with Poland which is not in the EU to overcome these regulatory problems.

5.4 Conclusion

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APPENDIX I

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VARIABLES (1) Poland Republic (3) Slovenia (4) Latvia (5) Lithuania Hungary (7) Slovak Rep (8) Estonia EU -0.408*** -0.251*** -0.435 -1.565*** 3.851*** -0.091** -2.436*** -12.029*** (0.037) (0.039) (0.627) (0.413) (0.522) (0.037) (0.626) (0.445) log_Total_Deals_Year 0.479*** 0.592*** 2.388*** 1.635*** -1.245*** 0.678*** 1.413*** 4.749*** (0.029) (0.034) (0.329) (0.249) (0.138) (0.041) (0.178) (0.294) CRISIS 0.042*** -0.195*** -16.288*** -15.981*** -1.082*** -0.118* 0.851** (0.010) (0.069) (0.396) (0.388) (0.052) (0.064) (0.333) DOTCOM -0.022 0.072*** 1.520*** -17.004*** 0.765*** -0.003 0.803*** (0.022) (0.014) (0.176) (0.308) (0.127) (0.014) (0.091) log_germany_gdp -0.245 -1.975*** 15.361*** 7.330*** -15.403*** -5.026*** -9.088*** -61.995*** (0.192) (0.320) (2.463) (1.489) (2.296) (0.439) (2.345) (4.697) log_poland_gdp 0.129* (0.069) log_czech_republic_gdp 0.440*** (0.105) log_slovenia_gdp -5.929*** (1.013) log_latvia_gdp -1.035** (0.477) log_lithuania_gdp -0.254 (0.615) log_hungary_gdp 0.666*** (0.124) log_slovak_rep_gdp 3.179*** (0.531) CRISIS = o, -DOTCOM = o, -log_estonia_gdp 5.745*** (0.672) Constant -1.441 19.223*** -188.986*** -113.789*** 238.766*** 60.381*** 86.769*** 805.623*** (2.255) (3.680) (32.737) (20.674) (29.605) (5.086) (30.779) (61.039) Observations 252 252 156 156 120 252 192 144 PseudoR2 0.0259 0.0325 0.281 0.150 0.0728 0.0898 0.157 0.545 Robust standard errors in parentheses

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APPENDIX II

(1) (2) (3) (4) (5) (6)

log_T_PL12_Count_Yea log_PERIF_Polan log_PERIF_Czech_Republi log_T_HU10_Count_Yea log_PERIF_Hungar log_T_SK01_Count_Yea

VARIABLES r d c r y r EU -0.641*** -0.836*** -0.261*** 0.354*** -11.886*** -7.821*** (0.143) (0.097) (0.039) (0.036) (1.243) (0.000) log_Total_Deals_Year 0.951*** 0.460*** 0.440*** 0.784*** 6.026*** 4.994*** (0.106) (0.042) (0.023) (0.093) (0.631) (0.000) CRISIS 0.151** 0.135*** -0.254*** -0.340*** 3.052*** -2.917*** (0.073) (0.028) (0.065) (0.072) (0.428) (0.000) DOTCOM 0.160*** -0.217*** -0.024*** 0.070*** -0.301*** -0.372*** (0.061) (0.035) (0.007) (0.024) (0.080) (0.000) log_germany_gdp 3.124** -6.282*** -3.000*** -6.457*** 22.914*** 80.102*** (1.227) (0.760) (0.401) (1.235) (3.104) (0.000) log_pl12_gdp -0.691 (0.425) log_PERIF_Poland_gdp 2.656*** (0.322) log_PERIF_Czech_Rep_gdp 0.874*** (0.121) log_hu10_gdp 0.793*** (0.298) log_PERIF_Hungary_gdp 7.933*** (0.721) log_sk01_gdp = o, -Constant -46.857*** 56.233*** 30.737*** 78.878*** -475.174*** -1,217.136*** (14.962) (8.107) (4.674) (14.193) (58.172) (0.000) Observations 180 180 180 156 144 72 PseudoR2 0.0492 0.0644 0.0281 0.0646 0.340 0.0403

Union Expansion on M&A-activity

The influence of the 2004 European