Johannes Kepler Planetary Motion Essay - 1058 Words

Johannes Kepler: Planetary Motion When one first thinks to astronomy, the first thing to come to mind might be the stars of the planets. It is always a fascinating thing to learn about the stars, but one should always start from somewhere when learning. One person’s research that is always going to be remembered is that of Johannes Kepler. He is not only the founder of contemporary astronomy but also an amazing mathematician. He was the first person to enlighten us on the theory of planetary motion. His three laws on planetary motion were a basis on Isaac Newton’s theory of universal gravitation. One of his books was the foundation of integral calculus and he advanced geometry. His research has been a huge influence on all kinds†¦show more content†¦He enjoyed his time at university to its fullest, using his educational opportunity to the highest of its ability. He enjoyed acting in theatrical plays and had an excellent reputation among his fellow stude nts and teachers. For a long time at university, Kepler was blinded of his love for math and astronomy. That is, until Magister Michael Maestlin was able to awaken the sleeping dragon within him. Maestlin was one of the best astronomers of his time. Though Maestlin, Kepler learned of the Coprenican system of planetary motion. Even though he excelled in school, his dream was still to be in the church (Caspar 41-50). Moreover, while struggling with is decisions with the church, Kepler’s life changed. With the death of the mathematician at the Protestant seminary in Graz, Kepler got nominated as the successor of the professor. Should he accept, he would become the new teacher of both mathematics and astronomy. This decision took a long time for Kepler to make. He has already had all of his life planed out in the Church. Living with the belief that if he were to ever receive a calling, not matter what it may be, he would follow it. He accepted the job, but still reserv ed the right to return to his clerical profession (Caspar 50-60). Additionally, while teaching in Graz, Kepler started to wander why there were only six planets in theShow MoreRelatedEssay on Johannes Kepler1478 Words   |  6 PagesJohannes Kepler was a German astronomer and mathematician who lived between 1671-1630. Kepler was a Copernican and initially believed that planets should follow perfectly circular orbits (â€Å"Johan Kepler† 1). During this time period, Ptolemy’s geocentric theory of the solar system was accepted. Ptolemy’s theory stated that Earth is at the center of the universe and stationary; closest to Earth is the Moon, and beyond it, expanding towards the outside, are Mercury, Venus, and the Sun in a straight lineRead MoreKepler s Laws Of Planetary Motion1017 Words   |  5 PagesKepler’s Laws Johannes Kepler formulated the Three Laws of Planetary Motion. The first is the Law of Orbits, stating that all planets move in elliptical orbits with the sun at one focus. The second law is the Law of Areas. This is the idea that a line that connects the planet to the sun sweeps out equal areas in the plane of the planet’s orbit in equal time intervals. Last is the Law of Periods which states that the square of the period of any planet is proportional to the cube of the semi majorRead MoreJohannes Kepler Essay991 Words   |  4 PagesJohannes Kepler Johannes Kepler is now remembered for discovering the three laws of planetary motion, and writing about them in books that were published in 1609 and 1619. He also did important work in optics, discovered two new regular polyhedra, gave the first mathematical treatment of close packing of equal spheres, gave the first proof of how logarithms worked, and devised a method of finding the volumes of solids of revolution. This can be seen as contributingRead MoreKeplers Laws Essay examples1479 Words   |  6 Pagesname was Johannes Kepler. Johannes Kepler was born on December 27, 1571 in the village of Leonberg outside the small town of Weil der Stadt, in Swabia. His father was a mercenary soldier and his mother the daughter of an innkeeper. Johannes was their first child out of seven children. His father left home for the last time when Johannes was five, and is believed to have died in the war of the Netherlands. As a child, Johannes lived with his mother in his grandfathers inn. When Kepler was a childRead MoreThe Life of Johannes Kepler Essay1952 Words   |  8 PagesThe Life of Johannes Kepler HIS LIFE Johannes Kepler was a German astronomer and mathematician ho discovered that planetary motion is elliptical. Early in his life, Kepler wanted to prove that the universe obeyed Platonistic mathematical relationships, such as the planetary orbits were circular and at distances from the sun proportional to the Platonic solids (see paragraph below). However, when his friend the astronomer Tycho Brahe died, he gave Kepler his immense collection of astronomicalRead MoreEssay on The Scientific Revolution1344 Words   |  6 Pagesuniverse according to discernible principles. Scientists who embraced the concept of divine design were Robert Boyle (1627 -1691), Johannes Kepler (1571-1630) and Sir Issac Newton (1642- 1727). Boyle’s Law demonstrated how the inverse relationship between pressure and volumes of gas is representative of the concept of cause and effect. Kepler’s First Law of Planetary Motion demonstrates how God designed the universe like a mechanism. Newton’s L aw of Universal Gravitation shows how God designed the universeRead MoreThe Genius Of Sir Isaac Newton1687 Words   |  7 Pageshave done the public any service, it is due to my patient thought†. Isaac Newton transformed the way people saw the universe in the 1600’s because of his law of universal gravitation, his laws of motion, and other discoveries and inventions. Isaac Newton s law of universal gravitation and planetary motion shed light on the clockwork of the universe. Newton’s discoveries about gravity all began while he was sitting under an apple tree. one of the apples fell from the tree making him begin to thinkRead More Keplers Laws and Planetary Movement Essay1399 Words   |  6 Pagessystem. Further advancements in astronomy came about through the research of Tycho Brahe and his assistant Johannes Kepler. The three planetary laws developed by Kepler with the data gathered by Brahe shaped the way in which science viewed the structure and motion of the planets of the solar system in profound ways, lasting to this day. A Brief History of Johannes Kepler Johannes Kepler began his studies in astronomy as an assistant to the astronomer Tycho Brahe, whom, by his own right, wasRead MoreEssay on The Scientific Revolution1263 Words   |  6 Pagesunknown realm of science and experimentation. Four of the many brilliant founders of the Scientific Revolution; Copernicus, Galileo, Kepler and Brahe, used previous scientific principles and their own genius to make advances in science that are still being used today. Scientific pamphlets, the telescope, observations of the universe and the creation of laws for planetary motion are some of the major advances that came out of the revolution and that were found by the scientists of its time. Nicholas CoperincusRead MoreJohannes Kepler, The Father Of Modern Astronomy1575 Words   |  7 PagesJohannes Kepler, the â€Å"Father of Modern Astronomy†, had an enormous impact on different aspects of science and mathematics such as geometry, physics, optics, crystallography and philosophy, eventually paving the way for more like-minded thinkers. His mathematical proofs supporting the heliocentric model of the universe was essential to progressing the scientific revolution. He reflected the Renaissance ideals of education, secularism, and observation while bridging medieval astronomy with modern science

Collective Bargaining And Bargaining Agreements - 1704 Words

1. Define the term â€Å"collective bargaining† and list and describe four issues that are mandatory components of a collective bargaining agreement. The term collective bargaining is defined as the system of bargaining when representatives of the employer and the employees negotiate the terms and conditions of employment that will apply to the employees. In the United States collective bargaining agreements are legally binding and typically last one to five years. (Budd, 2013,) PAGE 235 Collective bargaining is one of the main responsibilities and reasons unions were formed. In the late 1700’s the first permanent union was created by Philadelphia shoemakers. These craftsman established working standards and a minimum wage rate. The†¦show more content†¦The Philadelphia shoemakers union was created to create working standards for craftsman as well as establish a minimum wage for workers. Employer rights and responsibilities include management rights, just cause discipline and discharge, subcontracting, and safety standards. (Budd, 2013) 2. List and discuss three U.S. laws that support collective bargaining, and three examples of employer unfair labor practices. Three laws that support collective bargaining include the National Labor Relations Act (NLRA) of 1935, the Labor Management Relations Act of 1947, and the Labor Management Reporting and Disclosure act of 1959. The National Labor Relations Act of 1935, also known as the Wagner Act, gave workers the right to form unions and bargain collectively. The Wagner Act was enforced by President Roosevelt shortly after the National Industrial Recovery Act (NIRA) was ruled to be unconstitutional. In an attempt to correct the misbalanced power the Wagner Bill proposed to create a new independent agency, the National Labor Relations Board that was made up of three members appointed by the president. The National Labor Relations Board was created to not mediate disputes but enforce employee rights. (The 1935 Passage) The Wagner Act was created to correct the imbalance power in collective bargaining between employees and employers. The Wagner Act was intended to move the power from being in favor of employers but overcorrected and moved the power intoShow MoreRelatedBargaining Agreements : A Collective Bargaining Agreement906 Words   |  4 Pagesculmination of a collective bargaining process is a collect ive bargaining agreement (CBA) between employers and members of a labor union. In some instances, where firms hire contractors who are under a different supervision regime from the bonafide employees of an organization, it brings about complications in the formulation of collective bargaining agreements for the contractor and organizational staff under an umbrella labor union covering both staff. Since a collective bargaining agreement is a contractualRead MoreCollective Bargaining Agreement Analysis1449 Words   |  6 PagesThe most recent Collective Bargaining Agreement between San Luis Coastal Unified School District and the San Luis Coastal Teachers Association contains clauses that are of substantial benefit to the district, though some of the clauses included are not. A close analysis of the agreement reveals a wide variety of stakeholders with varying needs, from the school board and administrative personnel to all regular permanent and probationary certificated employees ( full-time and part-time), including,Read MoreLabor Relations: Collective Bargaining Agreements1588 Words   |  7 PagesCollective Bargaining Collective bargaining is the process which involves negotiation on the employment’s terms between the employer and employees. The employment terms possibly include the items like working conditions, employment conditions and workplace rules, overtime pay, base pay, work hours, work holidays, shift length, vacation time, sick leave, health care benefits and retirement benefits. In US, the collective bargaining is done among the leaders of labor union and the company’s managementRead MoreSeniority Clause Of Collective Bargaining Agreements828 Words   |  4 PagesSeniority Clause in Collective Bargaining Agreements One of the major concerns for union representatives in negotiating collective bargaining agreements is job security for members the union represents. In order to ensure that this is captured in the collective bargaining agreements, the seniority clause is one of the provisions that have been factored-in in collective bargaining agreements. As per this provision, an employer is to take into consideration the length of service of an employee in makingRead MoreStudent Collective Bargaining Agreement Simulation Essay1223 Words   |  5 PagesFor this Student Collective Bargaining Agreement simulation, I was in the Union that was negotiating the terms of the group project. Our group felt that the mandatory items that needed to be negotiated in good faith were the items that all pertained to the details of how the group project was going to be constructed and the guidelines of the group project that our union proposed. Since the group project was seen as a form of compensation it was mand atory we negotiated it with management. The permissibleRead MoreUnion Unions And Collective Bargaining Agreements772 Words   |  4 Pagesup with collective bargaining agreements, thus a negotiation process to articulate the interests of employees as represented by their union and employers. The union representatives and employers’ representatives need to have the full mandate of the parties they represent and be willing to participate in the collective bargaining negotiation process in order to ensure that the interests of the parties they represent are well captured and enumerated in the final collective bargaining agreement (CarrellRead MoreHow Collective Bargaining Agreements Effect The Players And The Owners1416 Words   |  6 Pagesand most other companies around the United States have in common? If you guessed Collective Bargaining Agreements (CBA), Unions, arbitration and strikes/work stoppages you are absolutely correct. In the coming paragraphs, labor relations wi thin the world of sports will be discussed from their unions to how collective bargaining agreements effect both the players and the owners. What is a Collective Bargaining Agreement? CBA is a written and signed document between a company and a labor organizationRead MoreEssay The NHL Lockout1706 Words   |  7 Pagesthe market value for players throughout the league. Teams did not use their leverage with restricted free agents. At contract time, most under-31 players have two options: sign and play or stay home and earn nothing. Despite holding this obvious bargaining advantage, teams often gave in to salary demands, offering players huge raises and long-term contracts. For example, in 1997 Joe Sakic of the Colorado Avalanche signed a three-year, $21-million US offer sheet with the New York Rangers. But ColoradoRead MoreStages and Strategies of Collective Bargaining1499 Words   |  6 PagesThe ILO Right to Organize and Collective Bargaining Convention (No. 98), 1949 describes collective bargaining as: Voluntary negotiation between employers or employers organizations and workers organizations, with a view to the regulation of terms and conditions of employment by collective agreements. Collective bargaining could also be defined as negotiations relating to terms of employment and conditions of work between an employer, a group of employers or an employers organization onRead MoreArbitration And Sports Association And Dispute Resolution Under The American Arbitration Association1099 Words   |  5 Pagesto receive a final incumbent decision in the form of an award. Arbitration is sensitive, classified, and modeled to be a swift, and inexpensive solution to dispute. Participating parties may include additional terms in the agreement identifying arrangements to their agreements’ arbitration clauses to meet the requirements of their discrete dispute. In summation, arbitration is process that is private, speedy, cost efficient, and customized to the liking of the parties involved. Alternative dispute

Mcdonalds Five Forces free essay sample

MA ATTRACTIVENESS IN THE DEVELOPING WORLD Mergers and acquisitions form the majority of FDI deals in the developed world, but remain relatively scarce as a mode of entry in the developing world. The infrequent use of MA as a foreign direct investment (FDI) entry modality into developing regions has motivated this study. As a first step in exploring the MA paradigm in developing markets this paper will classify and rank the MA attractiveness of 117 developing economies. Further, the distinction between FDI attractiveness and MA attractiveness at a country and regional level will be illustrated. Mergers and acquisitions, as a mode of FDI are rare in developing countries. Only 26, 9 percent of the 11059 FDI developing economy deals documented in this study and concluded between 2004 and 2006 were cross border merger and acquisition deals, the remaining 73% of deals were all greenfield. Within the period 2002 to 2004, mergers and acquisitions made up a mere 19% of the total number foreign direct investment (FDI) deals concluded in developing economies. In contrast, cross- country mergers and acquisitions held far greater appeal in the developed world where MA’s outnumbered greenfield FDI deals by making up 51% of the total FDI deals concluded over the same period 2002 to 2004 (UNCTAD, 2007). The clear preference for greenfield deals in the developing world indicates that there exist elements within locations attractive to MA’s which are distinctive from those locations attracting greater greenfield activity. resume writing service free In order to understand these elements, MA attractive and unattractive locations must first be identified and classified. MA and greenfield are two distinct modes of entry with differing motivations and dissimilar host country effects. MA involves the purchase of a controlling share of stock in an existing host country firm with production capacity (Raff et al, H. , Ryan, M. and Stahler, 2008) whereas 1 greenfield investments see the foreign firm building its own independent business, and sourcing all resources directly from the market (Nocke and Yeaple, 2007). The FDI attractiveness of economies has been well explored in the literature. However, research on the role of FDI in economic development is dominated by a generalised view of FDI where the separation of entry mode strategies was not central. Several authors have commented on the underreporting of MA as a process distinct from the FDI umbrella in the literature; these same authors have begun to explore in greater depth the MA concept (Kogut Singh, 1988; Raff et al, Ryan Stahler, 2005; Nocke Yeaple, 2007 Haller, 2008). The MA literature is concentrated on the developed economies of the world as the greatest volume of MA activity has historically occurred in developed regions. Much of the literature on MA’s describes the increasing number of these deals and its importance in global FDI, often by referring to the global total (Haller, 2008; Bjorvatn, 2004; Horn Persson, 2001, Shimizu, Hitt, Vaidyanath, Pisano, 2004). None of these studies have referred to the relative scarcity in utilisation of MA‘s in the developing world relative to the developed regions of the globe. This paper aims to make a contribution not just to the emerging literature on MA’s but also to its particular developing economy paradigm. The methodology of this study allows for the identification and ranking of FDI attractive economies, MA attractive economies and for the distinction to be drawn between MA attractive economies at the country level and MA attractiveness at a regional level. At the country level MA attractive economies are economies which attracted more MA than greenfield deals internally i. e. economies attracting a greater ratio of MA activity to greenfield investments. Regional MA attractive economies were defined as economies which whilst attracting large volumes of MA activity within a region were not attracting a greater number of 2 MA deals internally. Greenfield deals continue to dominate these markets. In other words these countries were MA attractive by virtue of being FDI attractive. FOREIGN DIRECT INVESTMENT IN DEVELOPED AND DEVELOPING ECONOMIES Understanding the distinction between developed and developing economies and foreign direct investment in these markets is fundamental to this study. Per capita income, an indicator of the wealth and potential of a market, is an important manifestation of the differences between developing and developed economies. Unfortunately however, developing economies are subject to frequent policy regime switches and growth rate volatility when compared against the group of developed economies (Aguiar and Gopinath, 2007). Productivity in emerging markets is unstable, here the cycle of political and economic shocks have become trends (Aguiar and Gopinath, 2007). The income inequality, higher poverty levels, governance, institutional contexts (North, 1994; Peng and Heath, 1996) and the level of economic and human development of developing economies is offset by the fact that since the early 1990’s these countries have also been the fastest growing market in the world for products and services (Khanna and Palepu, 2005). The strategic choices made by multinationals engaging in developing markets must necessarily be considered with respect to the above mentioned host country factors. Many developing economies which are characterised by an accelerated pace of economic development and a liberalisation or opening of their economies by the application of free market principles are termed emerging economies (Hoskisson, Eden, Lau, Wright, 2000). Other rapid growth countries included in this group are the transition economies of Eastern Europe which were historically planned economies but have now adopted free market principles (Hoskisson et al, 2000). 3 The literature is dominated by developed economy FDI. However, FDI patterns observed in developed countries cannot be generalized to transitional or developing economies (Pan, 2003). Blonigen and Wang (2005) have established that the factors determining the location of FDI â€Å"vary systematically† between developing and developed countries (Blonigen and Wang, 2005). In their paper, Phylatakis and Xia (2006) investigate the dynamics of global, country and industry effects in firm level returns between developed and emerging, markets. Their findings show that especially for emerging markets, country effects are more important than ndustry effects in explaining return variation for firms (Phylatakis and Xia, 2006). Sethi, Guisinger, Phelan and Berg (2003) believe that FDI flow should not only be studied at a firm level but additionally at a country level as country level factors affect the decisions of all firms over time (Sethi et al, 2003). In addition, not all of the hypothesized relationships in the literature on FDI (e. g. ex change rates and source country size) were supported in a study on the transitional economy of China (Pan, 2003). This suggests that the developed and developing region FDI paradigms should be studied as distinct entities. LOCATION FACTORS Encouraged by superior technology, faster and cheaper communications and motivated by intensifying competition, businesses are able to scour the globe in search of locations offering advantages which increase the competitiveness of the firm. Location advantages refer to the institutional and productive factors which are present in the particular geographic area chosen for FDI (Galan and Gonzalez-Benito, 2006). Dunning’s OLI theory explains a firm’s choice for a particular FDI destination. First the home based firm must possess an ability which it is able to 4 exploit abroad and which is portable. This is termed the ownership advantage (the O advantage) of the firm. The ‘L’, which is the focus of our research, refers to the location which must have desirable qualities and offer advantages to the firm. Examples of this would include large markets, production factors including cheap or skilled labour or natural resources. A locational advantage would enhance the profits of a firm. The ‘I’ refers to internalisation, which implies the firm has more to gain from the total control of the asset than by allowing control to rest with export agents or licensees (Dunning, 2001). Tong, Alessandri, Reur and Chintakananda (2008) find that country and industry effects and their interaction substantially influence firm performance. The authors advocate that industries with growth opportunities learn how to exploit country specific factors by locating operations there. Even though low labour costs are used by many developing economies to attract FDI (e. g. China and Vietnam) studies show that it is of far less consequence to FDI attraction than host market size and distance. Total costs of production taken together are however largely influential in the direction of FDI flows. High labour costs may be mitigated by the infrastructural spend on health and education which would result in a healthy, skilled and more efficient workforce which in turn acts to lower costs (Bellak, Leibrecht and Riedl, 2008). In understanding MA attraction it is important to first mention the literature on FDI attraction, that is why firms go to foreign locations. According to Fontagne and Mayer (2005), firms will go to foreign locations if there exists sufficient demand in the country or region, total production costs incurred at the location are low, intense competition is not a threat, public policies are advantageous and institutions create productive and efficient economies in which to operate. Foreign locations may also be desirable in order to leverage economies of scale, take advantage of arbitrage opportunities involving factor costs, to diversify and reduce risk, exploit distinctive 5 dvantages to gain market and to escape from increasing home market competition (Rugman Li, 2007 and Rugman and Verbeke, 2001). Therefore we may expect that economies offering locational factors conducive specifically to MA’s will display greater attractiveness values. In light of the statements above, host country demand amongst other factors is responsible for the decisions of firms to choose foreign locations it leads us to believe that market size or the GDP of a country has an important role to play in MA attraction. Therefore it may be expected that the larger a countries GDP the greater the MA activity it will attract. First documented by Knickerbocker (1973) is an idiosyncrasy in the movement of firms. Firms follow into locations where other firms from their industry have already entered despite the increase in competitive intensity this generates. Therefore MA attractiveness may also be related to the number of firms already functioning within the host market. This agglomeration tendency may be linked to supply chain and input-output linkages. Further by locating affiliates close to other multinational affiliates they may be able to benefit from absorbing technological spillovers. The effect of this would be the lowering of RD costs and raising the firm’s competitiveness by enabling it to stay abreast of competitor strategy (Fontagne and Mayer, 2005). REGIONAL COUNTRY LEADER EFFECT Part of the focus of this paper is to explore a regional dimension of FDI and MA’s. Much of the literature on regional leadership effects concerns Japanese FDI into the Asia-Pacific region. The ‘flying geese’ model by Ozawa describes the trend where mature products and industries are shifted from one country to another more peripheral lower cost destination within the region 6 (Ozawa, 2003 and Kojima, 2000). As the host country costs rise so it too moves toward higher value add products and the production of the good moves to the next low cost destination (Edgington and Hayter, 2000; Hart-Landsberg and Burkett, 1998). In this way advantages such as technology, employment, real incomes and innovation may cascade through a region (Clark, 1993). Several studies have shown that when MNC’s first plan to internationalise they choose geographically and culturally proximate regions, this is known as the ‘market familiarity principle’. In this way home based skills, advantages, management and resources may be leveraged to minimize transaction costs (Gomes and Ramaswamy, 1999). In ‘Regionalism and the Regionalisation of International Trade’, Gaulier, Sebastien and UnalKesenci (2004) explain the idea that regionalisation is a natural pattern and that the volume of inter-neighbour trade between countries is high due to the economic sense of trading over shorter distances. Various studies find that countries have the bulk of their foreign trade concentrated within a particular triad region (Gaulier, Sebastien and Unal-Kesenci, 2004; Rugman and Verbeke, 2004). In their study on 64 Japanese multinationals Collinson and Rugman (2008) found that only three operated globally with the remainder concentrating 80 % of their operations (sales assets) intra-regionally. More importantly, with implications for this study and the attraction of MA’s, was the finding that region-specific regionalisation trends are linked to changes in infrastructure, information or cultural ties. Large regional trade agreements, especially when a custom union exists, were also shown to have positive effects on trade volume and created lucrative opportunities for foreign producers. The trade agreements allowed access to a large market from a single country, even if it was a smaller market than its neighbours (Gaulier, Sebastien and Unal-Kesenci, 2004). This paper 7 reinforces the importance of institutions in developing regional trade and mentions specifically that a positive â€Å"gravity† factor of regionalisation could be the swift acceleration of GDP growth of other countries within a region. Policy makers should take note that contractual relationships present significant risks to foreign MNE’s in host countries which have linguistic, legal and economic institutions systems vastly different from the home country (Clark, 1993). Promoting and facilitating corporate governance would have a positive impact on inter-company linkages with the resultant promotion of regional development. The ability to access risk finance and instruments make it critical for a firm to operate in an advantageous national location within a region (Clark, 1993). Pajunen (2008) reinforces the above idea of a MNE firm searching for the most advantageous location within a region. In order to access the rapidly expanding emerging economy market a firm may make a strategic decision to enter South America or South–East Asia and will then search for the most attractive location within that region to trade from (Pajunen, 2008). As we have seen in an earlier paragraph, the growing number of regional trade agreements allows the MNE to transact with minimal trade costs within a region. The regional leader attracts the most FDI in a region. This research asks the question who attracts the most MA’s and why? This question may be answered by the findings of Qian, Li, Li and Qian (2008). Qian, Li, Li and Qian (2008) confirm that firms are regionally focused and also offer an explanation for the regional internationalisation of firms rather than a fully global expansion. They find that firms’ costs are lower intra-regionally and hence performance is enhanced. They add however that a threshold to performance is reached intra-regionally and that a developed country MNE may maximise performance by entering into a moderate number of developed country regions and a strictly limited number of developing regions as costs here are substantially 8 ifferent. They advocate the careful selection and allocation of resources in developing regions as over-diversification here will result in costs outweighing benefits (Qian et al, 2008). This reinforces the idea of a regional FDI leader in the developing country context that is a ‘safer’ haven for MNE resource allocation. Taking into ac count this evidence, it is possible to assume that as regional cooperation is enhanced so inter-regional trade is encouraged which results in greater amounts of FDI and MA’s which will flow into a regional leader country with the safest reputation. MERGERS AND ACQUISITIONS An imperative of a foreign investment entry strategy is to minimise the cost of entry in order to render the venture more profitable. Cultural barriers and socio-political differences between the entrant and host raise the cost of transacting and thus the entry mode chosen will attempt to reduce this. MA’S AND CAPABILITY SEEKING MULTINATIONALS Firms have capabilities in their own markets which are not necessarily internationally mobile, may not be useful in a foreign market or the firm may require a set of additional competencies to operate successfully in the foreign market (Anand and Delios, 2002). Anand and Delios (2002) offer a description of upstream capabilities which are described as fungible and portable; an example of this may be intangible technological know-how. By engaging in a cross-border MA the firm is able to access the local knowledge and downstream capabilities of a local firm and use this to supplement its portable advantages in serving the new host market (Nocke and Yeaple, 2007). Examples of capabilities or advantages which the local firm may possess include brand, marketing and sales force knowledge, privileged access to 9 istribution channels, a capability to manoeuvre through local ‘institutional voids’ and challenges (Khanna and Palepu, 2005), emission rights for environmental pollution, landing slots at airports, scarce land or oil/mineral extraction rights amongst others (Horn and Persson, 2001). Fungible upstream capabilities are a stronger driver for acquisitions than downstream capabilities which are less fungible (Anand and Delios, 2002 ). Developing countries are less likely to have superior technological capabilities than the potential developed country acquiring firm. The lower sophistication of the developing market would therefore limit the number of acquisition targets available for a developed country MNE. Acquisition targets for downstream capabilities (marketing, brand etc. ) would hold greater appeal in countries with large target markets. The number of MA deals can therefore be expected to relate to market size (GDP) and market sophistication (represented by aspects like the level of human development and infrastructure). The number of MA deals will also be related to the number of local acquisition targets available which in turn is dependent on the level of development of the country. ACQUISITION DRIVERS The initial choice to engage in FDI over export is dependent on how profitable the firm expects the greenfield or MA to be. The second strategic choice of greenfield over MA is related to the firms ownership of productive assets and varies both across and within industries (Raff, Ryan and Stahler, 2005). A cross border-merger provides access to a foreign market whilst a national merger relieves domestic competitive pressure. When trade costs are low however national mergers do not reduce competitive pressure and firms will seek access to foreign markets through a cross-border merger. Economic integration results in lowered trade costs and therefore increased competition which is likely to increase the profitability of acquisitions (Bjorvatn, 2004). The lowering of trade costs 10 which is dependent on host country regulations will therefore increase the level of cross-border M activity. The literature describes one of the main advantages of cross-border M to be the access which it provides to a foreign market (Horn and Persson, 2001) whilst within border mergers are generally attributed to relieving domestic competitive pressure (Bjorvatn, 2004). Raff et al (2008) explains that firms entering a foreign market will approach local firms with a merger and acquisition or joint venture proposal in order to enjoy the synergies of such a relationship. Raff et al (2008) maintain that a merger acquisition offer will be accepted by the local firm if the profitability and success of a greenfield investment by the multinational is likely and credible. Further, the greater the anticipated profitability of the greenfield investment the lower the merger acquisition price offered to the local firm. Hence M A would be preferred over greenfield as the entry costs would be lowered. The choice of greenfield over M will depend on the number of competitors in the market and the market potential as this affects the anticipated profitability of the greenfield venture or the cost of the M (Raff et al, 2007). This leads us to hypothesize that countries with greater market potential (GDP, GDP per capita and HDI) and fewer local competitors will result in a lowering of the cost of an M which in turn results in increased volumes of M. CULTURAL CHALLENGES AND THE ‘LIABILITY OF FOREIGNNESS’ Mergers and acquisitions and partially owned ventures offer the opportunity for a foreign MNE to access local assets such as brand, distribution networks and a client-base which is difficult to mobilise from home by working with local established companies (Petrou 2007). In instances where large cultural distances exist between home and host countries, Brouthers and Brouthers 11 (2000) advocate the use of acquisitions in order to confer legitimacy and cceptance on the foreign MNE. However, M involve greater costs when the cultural distance is high and therefore Chang and Rosenzweig, (2001) assert that firms would be more likely to choose greenfield entry to avoid the costs of integrating diverse company cultures. Greenfield investments offer total affiliate control and avoid post merger cultural difficulties but take a far longer time period to establish market presence and require substantial experience and know-how of local conditions (Chang and Rosenzweig, 2001). Most recently Slangen and Hennart (2008) have found that MNE’s will prefer acquisitions in culturally distant locations if they have little international experience or if they plan to grant the subsidiary autonomy in marketing. If they are internationally experienced or have no market related concerns then a greenfield is preferred in culturally distant locations. The entry choice is also industry-specific depending on the resource requirements of the firm. Manufacturing operations tend to favour greenfield deals whereas in advertising where brand and product are tailored to local tastes acquisitions are preferred as FDI entry strategies (Kogut and Singh, 1988). The above information alludes to the idea that M will tend to occur in the services industry as it confers on the MNE an understanding of, acceptance within and access to a foreign market. The information examined above dealt with the cultural challenges of M. The next section will broach the subject of institutional challenges in M deals especially in developing economies. M FAILURE 12 Approximately 70%-80% of all mergers fail (Bretherton, 2003) and KPMG reports only 17 % of cross border M s create value while 53% destroy value (Shimizu, Hitt, Vaidyanath, Pisano, 2004). These statistics may be part of the explanation for the lower volumes of M deals in developing economies where investor firms may be wary of entering into deals already known to have high failure rates and then compounding this in an environment fraught with challenges i. . developing regions. Therefore many organisations choose to enter into strategic alliances and joint ventures which allow them the benefits of searching for new market opportunities, sharing in innovation and technology, overcoming host regulatory requirements and developing new capabilities. Importantly however these alliances are easier and less costly for companies to enter and exit should the need arise. IMPORTANCE OF LEGAL AND FINANCIAL FRAMEWORKS TO SUPPORT MNE’S Market inefficiencies related to the resource profile and institutional profile of a host economy may be overcome by the entry strategy of the MNE. Chang and Rosenzweig (2001) assert that an acquisition is the quickest way for a firm to build a sizable presence in a foreign market. The challenges of this mode however involve the post acquisition cultural merge, the risk of overpaying and an inability to fully assess the value of the acquired assets (Chang and Rosenzweig, 2001). In a developing market context additional challenges to M include the scarcity or absence of legal, financial and institutional organisations and structures through which the deal could be investigated, formalised and protected and is further complicated by the existence of burdensome host country regulations relating to ownership (Khanna and Palepu, 2005). HYPOTHESIS 13 It is expected that M attractive economies in the developing world may be identified as a group distinct from FDI attractive economies depending on the context of the location factors of the host economies. It can therefore be hypothesised that M attractiveness does not equal FDI attractiveness and that varying levels of M attractiveness occur. RESEARCH DESIGN SAMPLE AND DATA SOURCES The World Bank and UNCTAD, through the annual World Investment Report and World Investment directory, publish data on over 210 economies which are divided into developed and developing economies. In this study data were assembled for 117 developing and transition economies. Blonigen and Wang (2004) in their examination of the FDI experiences of developed and developing economies conclude that the variation of data across these groups makes it inappropriate to pool data on them in empirical analyses. A further rationalisation for the isolation of developing economies from developed economies in this paper can be found in North (1994), he writes that the experiences of actors in highly developed modern economies may not be compared to that of individuals operating under conditions of uncertainty, political or economic. In order to identify regional FDI leaders, for the purpose of this study, the country data was divided into regional groupings (see table below) according to the United Nations Statistical Office as published in the UNCTAD World Investment Report classification for 2007. [Table 1 about here] VARIABLES AND MEASURES The analysis aims to separate FDI attractiveness from M attractiveness and to rank the attractiveness of developing countries to mergers and acquisitions. The data for value and volume 14 of M in the sample of developing economies was taken from the latest available M and greenfield data published by UNCTAD (based on data from Thomson Financial) over the period 2004 to 2006. Six variables were created. The table below describes, explains and shows the grouping of the variables. Group A in table 2 below represents country M attractiveness. Two measures numbers 1 and 2 were used to measure attractiveness at the country level. One is volume based; that is the number of deals in one country as a percentage of the country’s total deals, whilst two is value based that is the dollar value of deals which flowed into the respective country as a percentage of GDP. Thus the measure for country level M activity has two dimensions in this way the variable carries richer information and is less likely to be skewed by a single, large dollar value deal. As this measure is computed using per country total deals and per country GDP as the denominator, it is an intra-country measure. Group B in table 2 represents regional M attractiveness and contains 3 measures. Again both a volume and a dollar value were used to measure regional M activity for the same reasons listed above for country attractiveness. If for example a country attracted one very large dollar value deal, but no other deals, it may be read as an M attractive economy when in fact it only attracted a single deal. This regional group of variables is computed using the number of total regional M deals, the number of total regional FDI deals and the dollar value of the total regional FDI inflow as the denominators. Thus it measures the country’s M volume and value respective to the regional total. It is an intra- regional value. Group C in table 4 contains one measure for the FDI attractiveness of a country in a region. This measure includes all deals (greenfield and M) which a country attracts with respect to the total number of deals concluded in its geographic region. 15 [Table 2 about here] METHOD OF ANALYSIS The statistical challenge in this study was to find a method which would allow for the separation of FDI attractive economies from M attractive economies and of M attractive from M unattractive economies. Two statistical methods were utilised to test the variables. A cluster analysis allowed for countries with similarities based on the variables to be clustered together. A principal component analysis was performed in order to create an M attractiveness ranking of the sample countries. CLUSTER ANALYSIS INTRODUCTION TO CLUSTER THEORY A cluster analysis is a statistical tool which allows for the discovery of meaningful structures within data without explaining why they exist. This allows data to be sorted into groups or categories where the members of each group have a high degree of association with each other and a minimal association if they belong to another group. Thus this technique places the economies under study into clusters based on well defined similarity rules and finds the most significant groups of objects. (http://www. statsoft. com/textbook/stcluan. html) Clustering is the term used to describe the presence of separate and distinct groups in the data however if clustering is not recognized by failing to visually inspect the data (scatterplots or another graphing technique), the correlation coefficient may suggest that no relationship exists even though within each cluster a clear relationship may indeed exist (Siegel, 2000). As an initial exploratory step and in order to determine which of the variables listed in Table1 were most successful in dividing the economies a cluster analysis was performed. 16 The data for some variables such as GDP had a very different scale to the some of the smaller scale values e. g. Polcon 3 index. The data was thus standardized to allow each variable an equal opportunity to display significance in the cluster analysis and prevent any one variable dominating (Boudier-Bensebaa, 2008). A cluster analysis was run on the variables listed in table 2 above. A four cluster solution was accepted as all the clustering variables proved to be significant. PRINCIPAL COMPONENTS ANALYSIS A principal components analysis allows for the identification of underlying factors in the variables which account for the largest variance amongst the data set of 117 countries. Table 3 below shows the variables used in the principal component analysis grouped at the country and regional level. This analysis is undertaken in order to create an attractiveness value per country which allows the developing countries to be ranked based on their M attractiveness score. Understanding Principal Component Analysis The principal component analysis (PCA) is a data reduction technique that distils the essence of several variables into a smaller number of components which explain the variance in the data. The regional and country variables listed above showed correlations but rather than discard them they are rolled into a two factor composite M attractiveness value one factor for regional attractiveness and one factor for country attractiveness. The principle of parsimony (simplicity and reduction) is followed by creating an attractiveness value out of the variables, in this way more meaningful and richer measure is created and the dimensions of the data set become more manageable (Siegel, 2000 p586; Berenson Levine, 1986). 17 The Eigen analysis is the name of the mathematical technique used in PCA. Eigen values show the percentage of variance explained by each component, the largest Eigen value is the first principal component, the second largest Eigen value is the second principal component, and so on. (http://www. fon. hum. uva. nl/praat/manual/Principal_component_analysis. tml). The Eigen values for our study were determined; these values were then plotted on a scree plot to illustrate the importance of each of the components. A factor analysis was performed on the all the variables in table 3 above. The PC analysis will create factors by reducing the data into its underlying dimensions. These factors allow for an attra ctiveness score to be generated for each country. THE VARIABLE DENOMINATORS [Table 3 about here] The country level variables were expressed as percentages of per country GDP, per country FDI inward stock and total number of per country FDI deals. Therefore outcome values expressed are all calculated with respect to intra-country measures. The regional level variable denominators included the total FDI flows into a geographic region, the total number of M deals in a region and the total number of FDI deals in a region (e. g. Central America, North Africa etc) and are expressed as percentages. Therefore all values are calculated with respect to regional totals. By separating the variables a richer result is obtained, the analysis is able to pick out regional leaders and interesting countries which may not be FDI attractive but nevertheless are M attractive. If the analysis had not made the distinction between attractiveness at the country level 18 and regional level the interesting case of Libya where M deals predominate would have been lost as its total FDI is so small. RESULTS: THE FOUR CLUSTER SOLUTION, DESCRIPTIONS AND MEMBER COUNTRIES The results of the four cluster solution is summarised as a profile plot with the means percentages included in table 4 below. The premise that a country level and regional level group exist in the data was confirmed with the cluster analysis. All the countries in cluster 1 showed a high value for the intra-country number (or volume) of M deals respective to the other clusters. Cluster 1 countries are intra-country performers. They do not perform well at a regional level. Cluster 4 countries are country level performers like cluster 1 but perform better on M dollar sales value than on M volume. For the purpose of this study clusters 1 and 4 are both considered as country level performers, their distinction lies in a difference of measure that is volume of M deals versus value of M deals respectively. Cluster 2 displays a strong performance on the regional level M variables. Cluster 2 also displays the strongest regional FDI attraction. Cluster 2 countries are regional performers. [Table 4 about here] [Table 5 about here] [Figure 1 about here] Cluster 3 countries do not perform on any of the variables; they may be labelled poor M performers. Table 5 above lists the member countries of each cluster. In light of the descriptions defined above, each of the four clusters has displayed distinctive mean characteristics based on a regional and country distinction and on the strength of the M 19 ttraction. In order to illustrate each clusters level of attractiveness graphically, the clusters have been plotted onto the axes above (Figure 1), the y axis representing country attractiveness and the x axis representing regional attractiveness. PC ANALYSIS AND EIGEN VALUES: The PC analysis in table 6 below shows the reduction of the five variables into a two factor solution which explains 80, 3 % of the variance of the underlying variables. The Eigen value is the variance explained by each factor of the underlying variables. [Table 6 about here] The PC analysis onfirmed the premise held of there being both a regional and a country effect in the data by loading all the regional variables on factor 1 and the country variables on factor 2. Factor 1 is a regional M attractiveness factor and factor 2 is an intra- country M attractiveness factor. The 117 countries on the data table are run against these attractiveness values in order to obtain a regional and a country level attractiveness value for each. This is accomplished by multiplying each country’s variable score by the factors in the table. The regional PC factor value allows for the generation of a regional attractiveness value for each country whilst the intracountry PC value allows for the generation of an intra-country attractiveness value for each country. Two lists are thus created, a list of the 117 developing countries with regional attractiveness values and another containing the same 117 developing countries with intra-country attractiveness values. PER COUNTRY ATTRACTIVENESS VALUES AND RANKING: 20 In order to make sense of the country and regional attractiveness values each list was ranked and ordered so that the countries appear in order of attractiveness. The top quartile or quartile 1 (Q1) is the least attractive to M activity, the bottom quartile or quartile 4 (Q4) is the most attractive. Therefore the higher the ranking the more M attractive the country is. The following countries were not ranked as they had no M activity: Azerbaijan, Brunei Darussalam, Cameroon, Equatorial Guinea, Eritrea, Ethiopia, Guyana, Honduras, Myanmar, Nepal, Paraguay, Qatar, Senegal and Suriname. At the regional level the most M attractive economies were India, RSA and Brazil, Russia, Turkey and Mexico, Table 7 below lists and ranks the most regionally M attractive economies. Table 8 ranks the least attractive regional economies with Burkina Faso, Yemen and Albania being the most unattractive M economies regionally. The countries most attractive to M at the country level that is those countries attracting a greater number of intra-country M than greenfield deals are listed in Table 9, the top ranked countries are Mauritius, Burkina Faso, Bulgaria, Panama, and Ghana. The most unattractive country level economies for M activity are listed in Table 10, with the UAE as the most unattractive followed by Tanzania and Saudi Arabia. Table 7 about here] [Table 8 about here] [Table 9 about here] [Table 10 about here] [Figure 2 about here] 21 Figure 2 above is a scatter plot of the country level economies list on the ‘y’ axis and the regional level economies list on the ‘x’ axis. The most attractive country level economies (attract more M than greenfield internally) can be seen on the upper left section. The most attractive M economies on t he regional list can be seen on the lower right section of the plotted area. These economies attract the most M deals in their geographic regions. The line drawn through the origin recreates the M attractiveness axes shown in Figure 1 which can be superimposed over this plot. DISCUSSION For both sets of analyses the regional FDI leaders correlated. This list included the Cluster 2 countries and top ranked regional M attractive countries (India, RSA and Brazil, Russia, Turkey and Mexico). The large market sizes of these regional leader countries have several implications in terms of M attraction. First, large markets attract market seeking MNE’s, the literature shows that these firms are likely to utilise M as a mode of entry (Buch and De Long, 2001). The fact that they are economic hubs and attract greater volumes of FDI than other developing countries also results in an increased presence of foreign affiliates operating in their markets (Qian and Delios 2008; and Kolstad and Villanger, 2008). These affiliates are likely to be followed by service industry firms (following their domestic clients) into these foreign markets (Qian and Delios 2008) thereby creating a virtuous circle for increased FDI and M activity. These countries are FDI poster boys in their respective regions and are M attractive by virtue of being FDI attractive. A distinct group of countries emerged as country level M leaders in the PC analysis and as the members of clusters 1 and 4. These comprise an interesting and eclectic mix of countries which include amongst others Mauritius, Burkina Faso, Bulgaria, Panama, Ghana, Kyrgyzstan, Armenia, Croatia, Ukraine, Colombia, Yemen and Azerbaijan. They are not regional FDI leaders but 22 attracted a greater amount of M activity than greenfield activity. In these countries, M attractiveness is not distorted by the regional leader effect and associated FDI attractiveness; hence M host location attractiveness can be studied in a purer form. Differences exist between the regional leader group and the country level leader groups which make these groups unique. The Cluster 4 and top ranked country level M attractive economies must possess some interesting locational features considering that these are smaller economies which do not comprise the largest markets in the sample. Given that M are more frequently used as a mode of entry in developed countries, location features may exist in the country level attractive group which mimic certain developed market conditions. M attractiveness at the country level may be a marker for development. The cluster 2 and regional leader groups whilst attracting large volumes of M activity within a region were not attracting a greater number of M deals internally. Greenfield deals continue to dominate these markets. In other words, it is partly true that these countries were M attractive by virtue of being FDI attractive. Examining however the PC analysis at the country level of M attraction and the cluster 4 countries in the cluster analysis, we are able to identify true M attractive economies i. e. economies attracting a greater ratio of M activity to greenfield investments. It can now be stated that FDI attractiveness does not automatically mean M attractiveness as the analysis has isolated clear groups of countries which are FDI attractive and which attract more greenfield activity and those which are M attractive. Lipsey comments on the absence in the literature of the effects which FDI may have on a country’s consumers. Mergers and acquisitions may result in the consolidation of industries increasing the monopoly power of firms with resulting higher prices (Haller, 2008; Nocke and 23 Yeaple, 2007). Greenfield operations would have the opposite effect by reducing the power of local producer monopoly positions and increasing local competition. At the same time superior technology and innovation brought in by the acquiring firms may improve local production efficiencies thereby lowering the local cost of goods (Lipsey, 2002). The dissimilar spillover effects of greenfield versus M is a clear motivation for the two modes of entry to be analysed and understood as distinct entities, even though much of the literature on the developmental role of FDI treats FDI as a single entity (Dunning Narula, 1996; Dunning 2001; Rugman Li, 2007). The effects of M investment into developing regions, local linkages and their impact on growth and development in the host may also be areas of great interest especially to policy makers. Future research directions would be to identify exactly what the macro-economic markers of development are which attract M to certain developing economies. 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Regional Divisions Central America Middle East (West Asia) South Asia South-East Asia Southeast Europe CIS (Transition economies) 8 TABLE 2: EXPLANATION OF VARIABLES Variables for the Cluster Analysis Value Based or Volume Explanation of Variable Distinction A Country level attractiveness variables 1 M deals per country as a % of total number of country deals 2 MA sales as % of GDP avg 2004-2006 volume based Examines the volume of per country M deals relative to the total number of FDI deals entering that country. The intra- country proportion o f M to FDI in terms of volume. Examines the value of per country M deals relative to the GDP of the same country. An intra-country measure of the proportion of M to GDP in terms of value. Examines the volume of per country M deals relative to the M deal volume of countries in the region. An inter-country but intra-regional measure. Examines the volume of per country M deals relative to the volume of total FDI deals (greenfield M) of countries in the region. An inter-country but intraregional measure. Examines the value in $s of per country MA sales relative to the value of all FDI inflows into the region showing the countrys share or proportion of M sales value in the region. Examines which country in a region attracts the most FDI deals in total (greenfield M) to show regional FDI leader. value based in US $s B Regional level attractiveness variables 1 MA deals per country as a % of total regional MAs 2004-2006 2 no of per country MA deals as a % of all regional deals 2004-2006 3 M sales per country as a % of total regional FDI inflow ( US$ millions) 20042006 no of deals per country as % of total regional deals 20042006 volume volume value in US $s C Overall FDI attractiveness variable volume 29 TABLE 3: PRINCIPAL COMPONENT VARIABLES Level attraction Country level of Combined Country Level And Regional Level Variables In Order To Create Component Attractiveness Values At The Country Level And At The Regional Level MA sales per country as a % of FDI inward stock per country (US $millions) 2004 -2006 MA sales as % of GDP average 2004-2006 MA deals per country as a % of total regional MAs 2004-2006 no of per country MA deals as a % of all regional deals 2004-2006 M sales per country as a % of total regional FDI inflow ( US$ millions) 20042006 Regional level 30 Table 1: profiles of cluster means for a 4 cluster solution 31 Table 5: CLUSTER COUNTRY MEMBERS Cluster 1 Belize Brunei Daruss Burkina Faso Congo Guatemala Kyrgyzstan Libya Macedonia, Mozambique Nicaragua Paraguay Qatar Rwanda Swaziland Zimbabwe Cluster 2 Brazil India Indonesia Malaysia Mexico Romania Russian Fed South Africa Thailand Turkey UAE Cluster 4 Armenia Bulgaria Colombia Croatia Ghana Mauritius Panama Ukraine Cluster 3 Albania Algeria Angola Argentin a Azerbaijan Bahrain Bangladesh Belarus Bolivia Bosnia Herz Botswana Cambodia Cameroon Chile Congo, DRC Costa Rica Cote d Ivoire Ecuador Egypt El Salvador Equatorial Guinea Eritrea Cluster 3 Ethiopia Gabon Georgia Guinea Guyana Honduras Iran Iraq Jordan Kazakhstan Kenya Kuwait Lao PDR Lebanon Madagascar Mali Mauritania Moldova Morocco Myanmar Namibia Nepal Cluster 3 Nigeria Oman Pakistan Peru Philippines Saudi Arabia Senegal Sierra Leone Sri Lanka Sudan Suriname Syria Tajikistan Tunisia Turkmenistan Uganda Tanzania Uruguay Uzbekistan Venezuela Viet Nam Yemen, Zambia 32 Table 6: Results of PC Analysis Level Of Attraction Country level Regional level Combined Country Level And Regional Level Variables In Order To Create Component Attractiveness Values At The Country Level And At The Regional Level. MA sales per country as a % of FDI inward stock per country (US $millions) 2004 -2006 MA sales as % of GDP average 2004-2006 MA deals per country as a % of total regional MAs 20042006 no of per country MA deals as a % of all regional deals 20042006 M sales per country as a % of total regional FDI inflow ( US$ millions) 2004-2006 Expl. Var Regional Attractiveness Factor 1 Intra-Country Attractiveness Factor 2 %Variance Explained Components by -0. 015066 0. 857492 0. 085347 0. 847898 0. 936657 0. 036875 0. 962411 0. 013174 0. 864350 2. 558174 0. 051764 1. 458437 80. 3 % 33 Table7: REGIONAL LEVEL ATTRACTIVENESS- most attractive ranking Regional Level M Attractiveness Quartile 4 -Most Attractive Rank Regional Attractiveness M Attractiveness Value Above Average India South Africa Brazil Russian Federation Turkey Mexico Indonesia Malaysia Thailand Romania Argentina UAE Egypt Bulgaria Ukraine Chile Colombia Peru Pakistan Philippines 87 86 85 84 83 82 81 80 79 78 77 76 75 74 73 72 71 70 69 68 4. 47456 3. 59947 3. 11423 2. 70295 2. 18032 2. 10503 1. 96844 1. 83932 1. 50218 1. 00295 0. 95504 0. 71507 0. 58127 0. 49219 0. 48130 0. 41931 0. 40345 0. 13893 0. 12567 0. 10631 34 Table 8: Regional level attractiveness- least attractive east attractive Regional Level M Attractiveness Quartile 1Least Attractive Rank Regional M Attractiveness Attractiveness Value Below Average Regional Level M Attractiveness Quartile 1Least Attractive2 Rank Regional M Attractiveness 2 Attractiveness Value Below Average 2 Burkina Faso Yemen Albania Tajikistan Belize Turkmenistan Lao PDR Gabon Sri Lanka Botswana Guinea Kuwait Cote d Ivoire Kyrgyzsta n Iran Swaziland Sierra Leone Mali Libyan Arab Jamahiriya Mauritania Armenia Algeria Bolivia Cambodia Moldova, Republic of Belarus Macedonia, TFYR Lebanon Nicaragua Congo, Republic of Angola Congo Democratic 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 -0. 81391 -0. 62301 -0. 59695 -0. 58134 -0. 56980 -0. 56586 -0. 55855 -0. 54206 -0. 53908 -0. 53824 -0. 53655 -0. 53403 -0. 53331 -0. 52797 -0. 52388 -0. 51088 -0. 51028 -0. 50993 -0. 50966 -0. 50856 -0. 50707 -0. 50669 -0. 50637 -0. 50389 -0. 50075 -0. 49762 -0. 49691 -0. 49085 -0. 48372 -0. 48345 -0. 48291 -0. 48068 Costa Rica El Salvador Rwanda Madagascar Syrian Republic Bangladesh Uzbekistan Georgia Iraq Viet Nam Bosnia Herzegovina Tanzania Kenya Mozambique Namibia Oman Bahrain Saudi Arabia Zimbabwe Zambia Ecuador Uganda Panama Sudan Venezuela Kazakhstan Mauritius Ghana Tunisia Nigeria Jordan Croatia and Arab 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 -0. 46264 -0. 46137 -0. 46100 -0. 45911 -0. 45391 -0. 45035 -0. 44220 -0. 42553 -0. 42284 -0. 41269 -0. 41006 -0. 40278 -0. 37712 -0. 37626 -0. 36841 -0. 35828 -0. 35541 -0. 35395 -0. 35140 -0. 34751 -0. 31359 -0. 31281 -0. 31113 -0. 30115 -0. 25848 -0. 22807 -0. 21374 -0. 21133 -0. 17359 -0. 13017 -0. 12656 -0. 09001 35 Uruguay Guatemala 33 34 -0. 46757 -0. 6471 Morocco 67 -0. 07754 Table 9: Country level MA attractiveness- most attractive countries Country Level MA Attractiveness Quartile 4 Most Attractive Rank Attractiveness Value Above Average Mauritius Burkina Faso Bulgaria Panama Ghana Kyrgyzstan Armenia Croatia Ukraine Colombia Yemen Romania Turkey Sudan Tunisia Uzbekistan Mauritania Peru Ecuador Indonesia Lao PDR South Africa Macedonia Pakistan Belize Kuwait 87 86 85 84 83 82 81 80 79 78 77 76 75 74 73 72 71 70 69 68 67 66 65 64 63 62 5. 44211 4. 67217 2. 45823 2. 04796 1. 89195 1. 06603 0. 90303 0. 87151 0. 82457 0. 81623 0. 78430 0. 77845 0. 71227 0. 65421 0. 2570 0. 36499 0. 32190 0. 26612 0. 24742 0. 23859 0. 20139 0. 10116 0. 04362 0. 04359 0. 03089 0. 01879 36 Table 10: Country level attractiveness- least attractive Country level MA attractive Q1- least attractive UA E Tanzania Saudi Arabia Angola Libya Belarus Sri Lanka Algeria Guinea Iraq Iran Sierra Leone Mali Zimbabwe Cote d Ivoire Viet Nam Mozambique Bahrain Madagascar Oman Tajikistan Cambodia Congo Turkmenistan Mexico Zambia Lebanon Venezuela Congo Swaziland Rank Attractiveness value below average -0. 69652 -0. 68043 -0. 68009 -0. 67564 -0. 67419 -0. 66567 -0. 66410 -0. 66351 -0. 66076 -0. 66060 -0. 64409 -0. 3906 -0. 62707 -0. 62270 -0. 62038 -0. 61471 -0. 61461 -0. 59631 -0. 58028 -0. 57740 -0. 57596 -0. 56811 -0. 56112 -0. 55555 -0 . 55058 -0. 54445 -0. 53035 -0. 51967 -0. 50304 -0. 48027 Country level M attractive Q1- least attractive2 Rwanda Russian Fed Guatemala Philippines Gabon Brazil Bangladesh Uruguay Costa Rica Botswana India Moldova Bolivia Egypt Nigeria Argentina Thailand Namibia Albania Bosnia Herzeg Malaysia Kazakhstan Kenya Georgia Morocco Chile Uganda Nicaragua Jordan Syria El Salvador Rank2 Attractiveness value below average2 -0. 46953 -0. 46579 -0. 46387 -0. 45862 -0. 43042 -0. 40607 -0. 39852 -0. 8454 -0. 38399 -0. 33595 -0. 31087 -0. 30362 -0. 28460 -0. 28442 -0. 28428 -0. 25341 -0. 23769 -0. 22207 -0. 22091 -0. 22082 -0. 21129 -0. 18592 -0. 18396 -0. 16633 -0. 14784 -0. 09800 -0. 06308 -0. 03914 -0. 03806 -0. 01932 -0. 00700 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 37 Figure 1: M attractiveness axes -regional/country 38 Figure 2: REGIONAL LEVEL ATTRACTIVENESS COUNTRIES PLOTTED ON Y AXIS; COUNTRY LEVEL M ATTRACTIVE COUNTRIES PLOTTED ON ‘X’ AXIS. 39 APPENDIX 1-EXCLUDED DATA In addition to the developed economy data, the following economies were also excluded from the study: Caribbean and Oceania economies (many of these island economies were very small, atypical and had missing data); China (over 48 % of the total number of deals for South and SouthEast Asian region were concluded in China in order to avoid skewing the findings for the rest of the region, Chinese data was excluded); Hong Kong, Singapore, Taiwan and Korea (these economies exhibit higher levels of development and sophistication than the rest of the sample and exhibit FDI levels higher than the typical developing countries of the sample group of this study); St Helena, Guinea Bissau, Mayotte, Reunion, Falkland Islands, French Guiana, Palestinian Territory, Afghanistan, Bhutan, Maldives and Timor Leste (these