Evidence of Fire-Sale m & a in European Transition Countries Josipa Visic The aim of this paper is to determine differences in variables that create investment climate and therefore affect the values of incoming cross-border m&a in selected European transition countries. Cluster analysis for 1999 and 2007 shows that countries with unfavourable investment climate received high levels of incoming cross-border m&a. Sole country in the cluster characterised by healthy investment area (Slovenia) had the highest gdp per capita accompanied with the lowest values of incoming cross-border m&a relative to its gdp. These results could be interpreted as an evidence of fire-sale m&a in most European transition countries. In 2010, situation changed and although recession left trace, data on cross-border m&a indicate that foreign investors focused more on companies which operated in countries with healthier economy. These results indicate that a healthy investment climate is necessary to provide a sustainable economic development of a country. Key Words: cross-border m&a, fire-sale m&a, sustainable economic development, investment climate jel Classification: F21, G34 Introduction Doing business in 21st century is marked by strong pressures of domestic and foreign competition, numerous market possibilities, and volatile environment. Therefore, the size of a company, regarded as a shield, becomes more important. With its size, companies try to respond to business challenges by diminishing their sensitivity to negative changes in business environment. Hence, companies often use different forms of external rather than internal growth, due to faster changes that external growth can provide them. Combining resources and abilities of two or more companies can be organised in several forms, but this research is directed toward mergers and acquisitions (m&a). Companies are forced to keep up with domestic and foreign competition and when a company decides to place its products on a foreign market it has a choice between exporting and local production in form of fdi. If it decides to produce Dr Josipa Visic is a Senior Research and Teaching Assistant at the Faculty of Economics, University of Split, Croatia. Managing Global Transitions 10 (4): 325-340 locally a company can built its own facilities (greenfield investment) or it can buy a share or an entire company on a foreign market in form of incoming cross-border m&a (Nocke and Yeaple 2007,337). There are numerous classifications of company's motives for undertaking m&a activities and the most commonly mentioned are: 1) using synergy effect of growth, 2) manager's interest, 3) dispersion of risk, 4) increasing market power and 5) reaction to changes in business environment (Tichy 2001, 368-372). However, the focus will be on determinants of m&a on the country level, or even more precise, on determinants of incoming investments in the form of cross-border m&a. Cross-border m&a act as a medium of geographical diversification and also can be used as a tool for overcoming risks specific for company's home country. Local companies can benefit from incoming cross-border m & a as they help them to prevail limitations in form of: 1) difficult and unfavourable financing, 2) outdated technology and business organization, 3) saturated and/or too small domestic market, 4) slow adjustment to market conditions etc. Yet, foreign acquirers do not necessarily have long-term goals in mind (i. e., short-term speculative motives are also a reason to undertake cross-border m&a). Negative influence of cross-border m&a can be manifested as: 1) asset stripping, 2) job cuts and lower wages, 3) poor operating results caused by insufficiently prepared integration process of involved companies etc. (Gugler and Burcin Yur-toglu 2004,481-502). However, it should be noted that some of the above mentioned positive and negative consequences are not solely the feature of cross-border m&a, but can also be a characteristic of domestic mergers and acquisitions. As expected, cross-border m&a conceal numerous risks, but sometimes, as in cases of financial and economic crisis, they may play a role that greenfield investments are not able to accomplish. Due to their nature, cross-border m&a are able to faster ensure financial and non-financial resources and by doing so, they are able to reconstruct existing capacities and increase the competitiveness of companies involved and indirectly the economy of the involved countries (Nocke and Yeaple 2007, 357). A research done by Aguiar and Gopinath (2005,451), using samples from East Asian countries, indicates that during financial crisis and high insolvency of domestic companies an increase in economic activity in the form of cross-border mergers and acquisitions can be noticed. However, such processes are often encouraged by the opportunity to buy a company at much more favourable conditions due to its bad business 1100000_ figure 1 Value of worldwide cross-border m&a in period between 1990 and 2010 (millions usd) situation and which is called a fire-sale m&a. Globalisation makes cross-border m&a more important, more valuable and more numerous which can be seen in figure 1. Yet, contrary to theoretical assumptions that capital should flow from capitally rich to capitally poor countries, most of the global capital flows still occur among developed countries and thus confirm a Lucas paradox. Theoretical explanations of Lucas paradox can be grouped in two categories: 1) explanations based on differences in preconditions of economic development among countries such as technological differences, availability of production factors, government policies and institutional structures among countries, 2) explanations oriented towards imperfections of international capital market and especially towards asymmetric information. Only certain number of large growing economies such as China, India, Brazil and Russia attract large investments in form of incoming cross-border m&a (Hyun and Kim 2007, 7). Hence, it is interesting to analyse which macroeconomic factors of target countries are drivers of this form of investments. The aim of this paper is to determine how selected European transition countries are grouped considering differences in variables that influence the value of incoming cross-border m&a. In former research studies (Visic and Skrabic Peric 2011, 180-181; Visic, Tomas and Skrabic 2009, 274-277) dynamic panel models were used to analyse different determinants of incoming cross-border m&a value for a group of selected countries. Based on former results on relevant determinants, this research aims to determine if differences among these countries exist. Also, it aims to answer if less developed countries have a reasonable fear of foreign ownership i. e., could one regard inward investments in form of cross-border m&a as a proof of fire-sale m&a in European transition countries. Namely, it has been noticed that European transition countries with stronger domestic economy tend to be more careful when encouraging foreign investments. It is important to note that foreign ownership is not regarded as a negative consequence of globalisation, yet it is often characterised by opportunities that foreign owners have used due to problems domestic companies were facing. However, the purpose of the research is to help to further investigate sources that drive capital in order to find an answer how to accomplish sustainable development of transition countries without disturbing free capital flows. The paper has a four-chapter structure. The second chapter provides literature review, while the third describes the data and research analysis. Conclusion is given in the last chapter. Theoretical Review During 20th century, m&a activity has expanded and today it is even more interesting due to growing interest of foreign investors towards transition countries. Most studies on determinants of m&a use mi-croeconomic perspective. Scientific researches on macroeconomic determinants of m&a directed towards transition countries are rare, especially when it comes to providing empirical evidence on determinants of cross-border m&a directed towards European transition countries. Hereinafter, a concise review of the most significant studies on the respective theme is given, while some of these studies will be more extensively presented while presenting results of this research. Aguiar and Gopinath (2005) analysed connection between country's financial system and its m&a activity on the sample of East Asian countries. They concluded that liquidity crush, which domestic companies faced as a result of East Asian crises, increased m&a activity. Kamaly (2007), on the other hand, analysed cross-border m&a in sixty different developing countries to determine their flows and macroeconomic determinants. Di Giovanni (2005) used panel data on cross-border m&a in order to estimate the importance of certain macroeconomic, financial and institutional variables and to explain flows of international m&a. Unlike di Giovanni, Rossi and Volpin (2004, 278) analysed determinants of international and domestic m&a focusing mainly on the role of the law and regulation in different countries. Globerman and Shapiro (2004) estimated which variables have statistically significant impact on incoming and outgoing flows of m&a and fdi. Further, what they investigated are the determinants of international m&a activities similar to those of other forms of fdi, such as greenfield investments. Neto, Brandao, and Cerqueira (2008) used Globerman and Shapiro's (2004) study as a guidance for their research; however, they expanded their panel data analysis to include location specific investment determinants. Hyun and Kim (2007) explored factors important for determining size and direction of m&a flows for acquiring and target countries. Manchin (2004) observed determinants of the number of incoming m&a in eu countries while investor countries were eu members, usa, Canada, Norway and Switzerland. Aminian, Campart and Pfister (2005), on the other hand, analysed macroeconomic determinants of cross-border m&a flows between European and Asian countries. Cluster Analysis data description Data on the value of cross-border m&a are taken from the unctad data base The Cross-Border Mergers and Acquisitions, while all other data are taken from World Bank database named World Development Indicators & Global Development Finance (March 2012) and The Worldwide Governance Indicators (2011 Update). Following countries are included in this research: Bulgaria, Czech Republic, Estonia, Croatia, Latvia, Lithuania, Hungary, Macedonia, Poland, Rumania, Slovakia, and Slovenia. Undoubtedly, all selected countries are not at the same level of economic development, but they all could be regarded as European transition countries and they all are a potential area of interest for foreign investors. Exact differences in their economic development and business surrounding are interesting when analysing value of cross-border m & a. Cluster analysis (K-means approach) using spss Statistics 17.0 is used to sort selected countries into three1 groups according to similarity of values of following indicators: • Ratio of value of incoming cross-border m&a to country's GDP ('m&a/gdp') - annual percentage. Cross-border m&a sales are calculated on a net basis as follows: sales of companies in the host economy to foreign tncs (-) sales of foreign affiliates in the host economy. The data cover only those deals that involved an acquisi- tion of an equity stake of more than 10%. Data refer to the net sales by the region/economy of the immediate acquired company. m&a to gdp ratio is used instead of using just the value of incoming m&a in order to alleviate differences among sizes of selected economies. Decision to use value of m&a to gdp ratio also has a theoretical anchorage in Kamaly's paper (2007,22). • gdp per capita ('gdp pc') - converted us dollars at constant 2000; annual%; • Interest rate spread ('ir spread') - lending rate minus deposit rate; annual%; • Inflation rate ('Inflation') - consumer prices; annual%; • Rule of Law ('Law') - a measure capturing perception of the extent to which agents have the confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence; • Control of Corruption ('Corruption') - a measure that captures perception of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as 'capture' of the state by elites and private interests. The Word Bank has formed six indicators of governance: Voice and Accountability, Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law, Control of Corruption. All indicators are constructed using an unobserved component methodology and measured in units ranging from about -2.5 to 2.5, with higher values corresponding to better governance outcomes (for more details, cf. Kaufmann, Kraay, and Mastruzzi 2009). Based on results from a former study (Visic, Tomas, and Skrabic 2009,275) on impact of governance indicators on the value of incoming cross-border m&a in European transition countries the last two indicators have been chosen for cluster analysis. Cluster analysis for the chosen countries will be preformed on data from 1999, 2007, and 2010. Year 1999 has been selected due to peak of value of worldwide cross-border m&a in this year, as it can be seen in figure 1. This growing trend and a wave shaped movement with the peak around year 2000 are characteristic for chosen European transition countries as well, as shown in figure 2. Shen (2008,1) explains this wave shaped m&a movement with the following arguments: government in- 1300 1200 1100 1000 900 800 700 600 500 400 300 200 100 99 19 9 19 9 9 9 9 9 figure 2 Average value of incoming cross-border m&a in observed countries (millions usd) centives and deregulation, business cycle effects, reactions characteristic for oligopoly, stock market boom etc. His observations correctly explain cross-border m&a movement in these countries, which all are at different stage of adjustment to capitalistic globalisation. Year 2007 has been selected to check for changes in assigning countries to cluster groups, also it is the last year, which can be surely regarded as a 'recession free' year. World economic crises cannot be considered while making conclusion about similarities/differences among the chosen countries in order to detect factors significant for their sustainable development and free capital flows in this period. However, the year 2010 was affected by global recession and it will be interesting to see how countries have changed in twelve years. results of cluster analysis Twelve chosen European transition countries are new eu members or about to be very soon. It is interesting to explore their macroeconomic and financial indicators observed from the focus of value of incoming cross-border m&a. Investments are undoubtedly important for a country; however, it was interesting to analyse if low levels of incoming cross-border m&a necessarily indicate poor investment climate. Considering cluster analysis was made for three years, results for year 0 table 1 Descriptive statistics for year 1999 Items N Minimum Maximum Mean Std. dev. m&a/gdp 12 0.00 10.46 2.5764 2.96565 GDP pC 12 1486.93 9662.97 4014.2854 2231.30613 iR spread 12 4.20 19.78 8.3430 4.21629 Inflation 12 -1.28 45.80 7.8075 12.48843 Corruption 12 -.091 0.93 0.1783 0.53752 Law 12 -.056 1.05 0.3145 0.48922 notes Valid N (listwise) = 12. 1999 will be presented and then compared to those from 2007 and 2010. While observing indicators of descriptive statistics presented in tables 1 and 2, one could notice that the value of gdp per capita have not significantly changed and for the richest country it is approximately six times larger than the minimal value. Although in 2010 (table 3) the maximum value of gdp per capita was lower than in 2007, the difference between the richest and the poorest country slightly declined. Similar conclusion is applicable for indicators of rule of law and control of corruption. The difference between minimum and maximum values has declined over the years thanks to improvement for countries with unfavourable values of governance indicators. Inflation rates stabilised over the years and their values are not as drastic as they were in 1999. Interest rate spread is, in its absolute value, lower in 2007 than in 1999, yet relative difference between its minimal and maximal values in some countries was still high and amounted 500% i. e., 700%. In 2010, values of interest rate spread grew and were higher than in 2007. Ratio of value of incoming cross-border m&a to gdp has drastically decreased in 2007 and continued to decline in 2010. However, the relative difference between its minimal and maximal values has decreased as well. Cluster analysis results (shown in tables 4, 5, and 6) detect significant differences between clusters. It can be observed that in 1999 and 2007 cluster with the lowest gdp per capita, the highest interest rate spread, highest inflation, and the worst value of governance indicators is at the same time a cluster with the highest level of investments in form of incoming cross-border m&a. Moreover, the cluster with the highest gdp per capita is the cluster with the lowest interest rate spread, lowest inflation, and the best value of governance indicators but with the lowest level of incoming cross-border m&a as well. This relation among values of table 2 Descriptive statistics for year 2007 Items N Minimum Maximum Mean Std. dev. m&a/gdp 12 -.057 5.08 1.1644 1.65928 gdp pc 12 2110.69 13377.92 6178.7459 3046.95047 iR spread 11 1.46 7.00 4.2763 1.98131 Inflation 12 2.39 10.11 5.1494 2.58768 Corruption 12 -.091 0.98 0.1995 0.51708 Law 12 -•043 1.00 0.3667 0.45730 notes Valid N (listwise) = 11. table 3 Descriptive statistics for year 2010 Items N Minimum Maximum Mean Std. dev. m&a/gdp 11 -.059 2.64 0.6274 0.83209 gdp pc 12 2220.58 12729.45 5918.1827 2904.02205 iR spread 8 2.42 8.62 5.8358 2.29958 Inflation 12 -1.09 6.09 2.2253 1.87031 Corruption 12 -.018 0.91 0.2733 0.34732 Law 12 -.029 1.15 0.5504 0.46922 notes Valid N (listwise) = 8. TABLE 4 Average indicator values for each cluster in 1999 Items Clusters 1 2 3 m&a/gdp 3.30 0.08 2.39 gdp pc 2203.81 9662.97 4581.57 iR spread 11.29 5.14 6.43 Inflation 10.04 6.15 6.22 Corruption -0.023 0.93 0.39 Law -0.007 1.05 0.51 indicators among clusters alters in 2010. The cluster with highest level of gdp per capita still has the lowest value of incoming cross-border m&a but it also has the highest interest rate spread. The main difference is that in 2010 the cluster with the highest value of incoming cross-border m&a to gdp is not a cluster with the lowest values of gdp per capita, highest interest rate spread, highest inflation, and the worst value of governance indicators. table 5 Average indicator values for each cluster in 2007 Items Clusters 1 2 3 m&a/gdp 3.25 0.53 0.21 gdp pc 2400.01 6804.93 13377.92 ir spread 6.11 3.77 2.32 Inflation 5.62 5.56 3.61 Corruption -0.046 0.38 0.95 Law -0.025 0.57 0.83 table 6 Items Average indicator values for each cluster _ in 2010 m&a/gdp GDP pc ir spread Inflation Corruption Law Clusters 1 2 3 0.46 0.51 0.06 2467.76 5318.24 6649.84 5.41 5.18 6.69 3.55 1.90 1.81 -0.013 0.27 0.42 -0.011 0.80 0.76 table 7 Distance between the central points for each cluster in 1999 Cluster 3 7459.168 2377.775 7459.168 5081.398 2377.775 5081.398 Differences among clusters become even more noticeable when one analyses data in tables 7, 8, and 9, which represent distances between central points of clusters. Additionally, these differences among clusters became greater in 2007 but decreased in 2010. Presented data may at first seem contradictory. According to the results of cluster analysis countries with unfavourable investment climate (characterised by high interest rate spread, high inflation rate, low level Cluster 123 1 4404.929 10977.909 2 4404.929 6572.982 3 10977.909 6572.982 table 8 Distance between the central points for each cluster in 2007 table 9 Distance between the central points for each cluster in 2010 Cluster 2 2850.478 4182.080 2850.478 4182.080 1331.603 1331.603 2 3 3 of economic development measured with gdp per capita, and low value of governance indicators) received relatively high levels of incoming cross-border m&a in 1999 and 2007. Possible explanation hides in tables 10, 11, and 12, which provide detailed list of all observed countries that belong to each cluster. The country with persuasively the highest gdp per capita accompanied with low level of incoming cross-border m&a is Slovenia, which based its economic growth on different approaches compared to those chosen by other European transition countries. Namely, Slovenia protected its companies and banks during the transition and on time perceived potential danger of foreign capital. Investments (m&a) are usually considered to have a positive impact on county's economy and they are often encouraged by different politic and economic measures. Namely, international expansion in form of m&a provides companies an opportunity to acquire strategic asset and enhance its competitive edge (Deng 2009, 76). However, it is important to notice that their impact on involved companies and countries can be both positive and negative. If m&a are performed according to long-term business goals for the target markets, they should be welcomed. Yet, acquiring companies often have short-term goals, which result in exploitation of target companies. In that manner, m&a can negatively affect involved companies and indirectly their domestic economies if they are characterised by buying (a part of) companies in crises i. e., when their value reaches fire-price level or by asset stripping. That is exactly what seems to be happening in the period from 1999 to 2007. Countries with stimulating investment climate had lower levels of incoming cross-border m&a, while foreign investors exploited unfavourable business environment and increased their investment in countries with low level of gdp per capita. In 2010, the situation changed and foreign investors obviously shifted their focus from companies whose values reached fire-price level to companies from healthier investment climate. These results are in accordance to Coeurdacier, De Santis, and Aviat (2009,70). They used database on European cross-border m&a and found that the quality of institutions in the host country is an important determinant of cross- table 10 The observed countries according to the cluster they belong to in 1999 Case Country Cluster Distance N. of countries in each cluster 1 Bulgaria 1 0.000 Cluster 1 5 2 Croatia 3 767.205 2 1 3 Czech Republic 3 0.000 3 6 4 Estonia 3 1555.465 Valid 12 5 Hungary 3 974.182 Missing 0 6 Latvia 1 1578.594 7 Lithuania 1 1649.371 8 Macedonia 1 227.688 9 Poland 3 1072.138 10 Rumania 1 136.696 11 Slovakia 3 70.709 12 Slovenia 2 0.000 border m&a. Although this research indicates the existence of fire-sale m&a in 1999 and 2007, it is necessary to stress that researches on fire-sale m&a are rare and studies using microeconomic data can result in different conclusions about existence of fire-sale m&a. In that manner, Ang and Mauck (2011, 542) provided significantly different conclusion about fire-sale acquisitions during economic crises depending on used reference points while analysing conventional stock price. Analyses of previously presented data bring to attention that in 1999 and 2007 Slovenia was the only member of the most successful cluster when all indicators are observed except the ratio of value of incoming cross-border m&a to gdp. Slovenian macroeconomic and financial indicators direct us to conclusion that Slovenia had a healthy investment climate, while low levels of incoming cross-border m&a indicate high level of protection of domestic market. During observed period Bulgaria, Macedonia, and Rumania remained members of a cluster with the worst results considering economic development and investment climate, but with a high ratio of value of cross-border m & a to gdp. Other countries joined the cluster of countries with moderately good investment climate and relatively high ratio of value of cross-border m&a to gdp. Even though global recession obviously had a negative influence on cross-border m&a, gdp per capita and interest rate spread of selected countries, differences among clusters decreased in 2010. table 11 The observed countries according to the cluster they belong to in 2007 Case Country Cluster Distance N. of countries in each cluster 1 Bulgaria 1 383.105 Cluster 1 3 2 Croatia 2 812.623 2 7 3 Czech Republic 2 1636.260 3 1 4 Estonia 2 1553.864 Valid 11 5 Hungary 2 45.070 Missing 1 6 Latvia 2 457.115 7 Lithuania 2 0.000 8 Macedonia 1 0.000 9 Poland 0. 0. 10 Rumania 1 484.919 11 Slovakia 2 2255.733 12 Slovenia 3 0.000 table 12 The observed countries according to the cluster they belong to i in 2010 Case Country Cluster Distance N. of countries in each cluster 1 Bulgaria 1 325.863 Cluster 1 3 2 Croatia 3 1051.654 2 2 3 Czech Republic 3 0.000 3 3 4 Estonia 3 1142.778 Valid 8 5 Hungary 2 631.582 Missing 4 6 Latvia 2 0.000 7 Lithuania - - 8 Macedonia 1 0.000 9 Poland - - 10 Rumania 1 415.758 11 Slovakia - - 12 Slovenia - - Conclusion The purpose of this paper was to determine how European transition countries were grouped according to differences in the value of incoming cross-border m&a in 1999, 2007, and 2010. Statistical program spss Statistics 17.0 was used to perform cluster analysis on data for Bulgaria, Czech Republic, Estonia, Croatia, Latvia, Lithuania, Hungary, Macedo- nia, Poland, Rumania, Slovakia, and Slovenia. Selected countries were grouped in three clusters according to values of the following indicators: ratio of value of incoming cross-border m&a to gdp, gdp per capita, interest rate spread, inflation rate, rule of law, and control of corruption. The research results indicate that in 1999 and 2007 the countries with unfavourable investment climate (characterised by high interest rate spread, high inflation rate, low level of economic development measured by gdp per capita, and low levels of governance indicators) received high levels of inward investments in form of cross-border m&a. The only country characterised by healthy investment climate (Slovenia) had the highest level of gdp per capita accompanied with the lowest value of incoming cross-border m&a to gdp ratio. Presented results confirm the presence of fire-sale m&a in the chosen countries i. e., confirm that foreign investors took advantage of companies in countries with unenviable values of selected indicators. In 2010, the situation changed and although recession left trace on countries, data on cross-border m&a indicate that foreign investors changed their focus from companies whose price was low due to unfavourable investment climate to companies, which operated in countries with healthier economy. It is necessary to develop an investment climate that provides sustainable development of domestic economies of transition countries. Positive impacts of foreign investments are undeniable; however, domestic economy should be a carrier of the economic growth of a country. Economic development should balance aspirations of target countries to become stronger with aspirations of foreign capital to invest freely abroad. If these two streams were balanced it would be a win-win situation, although a share of incoming cross-border of m&a might, in that case, decrease in comparison to other forms of inward investments such as joint ventures etc. Cluster analysis does not use statistical tests; therefore, conclusions based solely on results from this analysis do not have the strength they might have had if they had been obtained using some other econometric method. Hence, this fact could be regarded as a weakness of this paper. However, the presented results are indicative and serve as guidance for forthcoming (post)recession studies of cross-border m&a in European transition countries. Notes 1 During the research a cluster analysis has been made for two and four groups of countries. However, due to limitation of relatively small number of selected countries, relevant results have been obtained only for three- group cluster analysis. References Aguiar, M., and G. Gopinath. 2005. 'Fire-Sale Foreign Direct Investment and Liquidity Crises.' The Review of Economics and Statistics 87 (3): 439-52. Aminian, N., S. Campart, and E. Pfister. 2005. 'Macroeconomic Determinants of Cross-Border Mergers and Acquisitions: European and Asian Evidence.' Paper presented at International Conference International Trade and Logistics, Corporate Strategies and the Global Economy, Le Havre, 28-9 September. Ang, J., and N. Mauck. 2011. 'Fire Sale Acquisitions: Myth vs. Reality.' Journal of Banking & Finance 35 (3): 532-43. Coeurdacier, N., R. A. De Santis, and A. Aviat. 2009. 'Cross-Border Mergers and Acquisitions and European Integration.' Economic Policy 57 (527): 5-106. Deng, P. 2009. 'Why Do Chinese Firms Tend to Acquire Strategic Assets in International Expansion?' Journal of World Business 44 (1): 74-84. di Giovanni, J. 2005. 'What Drives Capital Flows? The Case of Cross-border m&a Activity and Financial Deepening.' Journal of International Economics 65 (1): 127-49. Globerman, S., and D. Shapiro. 2004. 'Assessing International Mergers and Acquisitions as a Mode of Foreign Direct Investment.' Paper presented at the Conference on Multinationals, Growth and Governance, in Honor of A. Edward Safarian, Toronto, 24-25 April. Gugler, K., and B. Yurtoglu. 2004. 'The Effects of Mergers on Company Employment in the usa and Europe.' International Journal of Industrial Organization 22 (4): 481-502. Hyun, H.-J., and H. H. Kim, 2007. 'The Determinants of Cross-Border m&a: The Role of Institutions and Financial Development in Gravity Model.' Working Paper 07-03, Korea Institute for International Economic Policy, Seoul. Kamaly, A. 2007. 'Trends and Determinants of Mergers and Acquisitions in Developing Countries in the 1990s.' International Research Journal of Finance and Economics 8:16-30. Kaufmann, D., A. Kraay, and Mastruzzi, M. 2009. 'Governance Matters viii; Aggregate and Individual Governance Indicators 1996-2008.' Macroeconomics and Growth Team Policy Research Working Paper 4978, The World Bank, Washington, dc. Manchin, M. 2004. 'Determinants of European Cross-Border Mergers and Acquisitions.' Economic Papers 212, European Commission, Brussels. Neto, P., A. Brandao, and A. Cerqueira. 2008. 'The Macroeconomic Determinants of Cross Border Mergers and Acquisitions and Greenfield Investments.' fep Working Papers 281, University of Porto. Nocke, V., and S. Yeaple. 2007. 'Cross-border Mergers and Acquisitions vs. Greenfield Foreign Direct Investment: The Role of Firm Heterogeneity.' Journal of International Economics 72 (2): 336-65. Rossi, S., and P. F. Volpin. 2004. 'Cross-Country Determinants of Mergers and Acquisitions.' Journal of Financial Economics 74 (2): 277-304. Shen, C.-H., S.-W. Chen, and M.-R. Lin 2008. 'Common Wave Behavior for Mergers and Acquisitions in oecd Countries? A Unique Analysis Using New Markov Switching Panel Model Approach.' Economics Bulletin 7 (8): 1-12. Tichy, G. 2001. 'What Do We Know about Success and Failure of Mergers?' Journal of Industry, Competition and Trade 1 (4): 347-94. Visic, J., and B. Skrabic Peric. 2011. 'The Determinates of Value of Incoming Cross-border Mergers & Aquisitions in European Transition Countries.' Communist and Post-Communist Studies 44 (3): 173-82. Visic, J., I. Tomas, and B. Skrabic. 2009. 'Cross-Border m&a Activity in Developed and Developing Euopean Countries.' Paper presented at the conference Economic Policy and Global Recession, Belgrade, 2526 September.