M 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 M as a foreign direct investment (FDI) entry modality into developing regions has motivated this study. As a first step in exploring the M paradigm in developing markets this paper will classify and rank the M attractiveness of 117 developing economies.
Further, the distinction between FDI attractiveness and M 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 M 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 M which are distinctive from those locations attracting greater greenfield activity.
In order to understand these elements, M attractive and unattractive locations must first be identified and classified. M and greenfield are two distinct modes of entry with differing motivations and dissimilar host country effects. M 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 M as a process distinct from the FDI umbrella in the literature; these same authors have begun to explore in greater depth the M concept (Kogut & Singh, 1988; Raff et al, Ryan & Stahler, 2005; Nocke & Yeaple, 2007 & Haller, 2008).
The M literature is concentrated on the developed economies of the world as the greatest volume of M activity has historically occurred in developed regions. Much of the literature on M 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 M 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 M but also to its particular developing economy paradigm. The methodology of this study allows for the identification and ranking of FDI attractive economies, M attractive economies and for the distinction to be drawn between M attractive economies at the country level and M attractiveness at a regional level. At the country level M attractive economies are economies which attracted more M than greenfield deals internally i. e. economies attracting a greater ratio of M activity to greenfield investments.
Regional M attractive economies were defined as economies which whilst attracting large volumes of M activity within a region were not attracting a greater number of 2 M deals internally. Greenfield deals continue to dominate these markets. In other words these countries were M 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 “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. exchange 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 M 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 M 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 M attraction.
Therefore it may be expected that the larger a countries GDP the greater the M 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 M 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 R 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 M. 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 M, 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 “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 M 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 account 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 M 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. M 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 M 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 M 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 M 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 M to be. The second strategic choice of greenfield over M is related to the firm’s 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 attractiveness 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 the 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. An understanding of location factors and macro-economic markers of development in developing countries may also be beneficial to MNC’s searching for optimal M locations in new global neighbourhoods. 24
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An analysis of firms’ growth options, Journal of International Business Studies, Vol 39, No 3, p 387-405 UNCTAD (2008) World Investment Report 2008, Transnational Corporations and the Infrastructure Challenge, Chapter 1 Global Trends, p 7-9 27 TABLE 1: REGIONAL DIVISIONS OF 117 ECONOMIES No. 1. 2. 3. 4. 5. 6. Regional Divisions North Africa West Africa Central Africa East Africa Southern Africa South America No. 7. 8. 9. 10. 11. 12. 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 of 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 M sales relative to the value of all FDI inflows into the region showing the country’s 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 $’s B – Regional level attractiveness variables 1 – M deals per country as a % of total regional M’s 2004-2006 2 – no of per country MA deals as a % of all regional deals 2004-2006 3 – M sales per country as a % of total regional FDI inflow ( US$ 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 M sales per country as a % of FDI inward stock per country (US $millions) 2004 -2006 MA sales as % of GDP average 2004-2006 M deals per country as a % of total regional M’s 2004-2006 no of per country MA deals as a % of all regional deals 2004-2006 M sales per country as a % of total regional FDI inflow ( US$ 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 Argentina 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.
M sales per country as a % of FDI inward stock per country (US $millions) 2004 -2006 MA sales as % of GDP average 2004-2006 M deals per country as a % of total regional M’s 20042006 no of per country MA deals as a % of all regional deals 20042006 M sales per country as a % of total regional FDI inflow ( US$ 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 Kyrgyzstan 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 M attractiveness- most attractive countries Country Level M 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 M 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
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