A Simple Measure of the Similarity of the Sectoral Composition of Inward Investment and Its Possible Uses

Antonios-Nikolaos Kalyvas and Allan Webster October 2011

Research papers

ABSTRACT This paper proposes an investment similarity index to capture similarities and dissimilarities between countries in the sectoral composition of inward foreign direct investment (FDI) in their economies. The index is based upon existing indices of export similarity. One proposed use of the index is for policy and, in particular, investment promotion purposes. Since the index helps to identify those countries most likely to be directly competitive, it allows investment promotion efforts to be focused accordingly. The paper produces measures of investment similarity for a sample of countries, using US outward FDI data and finds that there are quite distinctive patterns of similarity and dissimilarity between countries. The index is also proposed as being a tool for analysis and testing of the theory of FDI. In particular, the paper presents cross-sectional econometric analysis linking investment similarity with a series of variables capturing export platform, efficiency and resource seeking, market seeking and tariff jumping effects. Existing research has only produced a limited number of papers seeking to test the OLI (ownership, location, internalisation) paradigm so this model provides a useful addition to testing of this theory. Our results find a statistically significant, positive relationship between investment and export similarity indices. They also find a statistically significant relationship with both market seeking, trade barriers and efficiency seeking variables. In this respect they provide empirical support for the economic (OLI) theory of foreign direct investment. JEL classifications: F21, F23 Keywords: foreign direct investment, location, investment similarity, investment motives. Antonios-Nikolaos Kalyvas and Allan Webster October 2011 The Business School Bournemouth University Executive Business Centre Holdenhurst Road Bournemouth BH8 8EB akalyvas@bournemouth.ac.uk awebster@bournemouth.ac.uk

1. INTRODUCTION

This paper proposes a simple measure of similarity between countries in terms of the sectoral composition of their inward foreign direct investment. One motivation for this is to provide a simple summary index that can be use by policy makers to identify other countries which are effective competitors in attracting inward investment. That is, theories of inward investment do not suggest that countries are effective substitutes for each other as a suitable location for all investment projects and that whether or not they are a potentially attractive location depends on the sector. In the simplest case, one oil rich country is a valid alternative for another as a location for inward investment in oil extraction but a country without oil deposits is not. In a more complex world the proposed investment index is a simple way for policy makers to identify which other countries are their closest competitors in attracting inward investment. The underlying framework which we use to justify the index is the Ownership Location Internalisation (OLI) approach associated with Dunning (1977,1988). We review the underlying arguments of the OLI paradigm to demonstrate the predictions of the theory imply that countries will to some extent specialise in attracting inward investment in certain sectors but not in others. To further examine the link between the proposed index (specialisation by sector) and the underlying theory we use the index to provide an econometric test of the OLI paradigm. We further extend this analysis to consider a wider range of determinants of inward foreign direct investment (FDI). In section 3 we provide a basic exposition of the proposed investment similarity index and present some results of these indices using data from US outward investment surveys. The results are interpreted from the perspective of policy makers as well as from that of economic research. In section 4 we examine more closely the relationship between the proposed index and the OLI theory. In this section we provide some econometric tests of a number of key predictions implied by the OLI theory and extend the model further to consider other possible determinants of the sectoral composition of inward FDI. Finally section 5 draws conclusions as to the possible role of this proposed index both in academic research into FDI and for policy makers.

2. BACKGROUND

In economic theory, the choice of the location of international production depends on several set of factors. Some of them are external to the firm and others internal. One of the most widely used frameworks for the explanation of the internalisation of production is the eclectic (OLI) paradigm developed by Dunning (1977, 1998). The core preposition of the OLI framework is that the level and the structure of a firm’s foreign value added activities will depend on three types of conditions:

• A firm must have some kind of ownership advantages (O) vis-à-vis firms of other nationalities, in the servicing of particular markets or groups of markets. Building on Hymer (1976/1960).

• Assuming that a firm posses ownership (O) advantages then the firm needs to enjoy internalization (I) advantages. This means that the firm would prefer to internalize its O advantages instead of licensing them to firms in a foreign country. This incorporates the work of Buckley and Casson (1976)

• Assuming the existence of both O and I advantages, then some location (L) advantages should be present in a foreign country for direct investment to occur. That is, there need to be locational (L) advantages such that it will be profitable for the firm to exploit its O advantages in this foreign country instead of the domestic market. If not it would be preferable to service the foreign market in another way such as by exports.

The focus of this paper is on the third component of the eclectic paradigm that refers to the location (L) advantages; the where of production. Location (L) advantages are characteristics of a potential host country that could motivate an MNE to establish operations there instead of another foreign country. Location (L) advantages can be of several types and are highly linked to the strategic motives for FDI derived by the eclectic framework. These are grouped in a number of categories that correspond to different types of location (L) advantages. These are not mutually exclusive and it is common for several motives to overlap in individual investment projects and in direct investment more generally. The motives include:

• Export platform motives. Export platform investment is normally considered to be investment intended to supply markets which are predominantly outside the host country. These may be either regional or global export markets. The locational decision in this respect is one which involves choosing the location which is most suitable as a base for exports, often on cost and efficiency grounds. As such there are obvious connections to trade theory and, in particular, that of comparative advantage. Export platform motives often encompass or overlap with, for example, natural resource seeking and efficiency seeking motives, in so far that both enhance the prospects of exporting from the location concerned. There are a number of theoretical studies which provide the basis for export platform investment motives - see, for example, Ekholm et al (2007). There are also a small number of empirical studies of export platform investment, such as that of Kumar (1998).

• Natural resources seeking motives. In this case FDI has as its target gaining access to a particular resource that can is either not present in the home country of the MNE or relatively expensive and/or of lower quality. Location (L) advantages for the natural resource seeking motive of FDI are usually found in studies that examine the FDI determinants in developing economies where the natural resource seeking motive is prevalent. Such studies, for example that by Asiedu (2011), typically use a country’s natural resource export intensity as proxy for the natural resource seeking motive. Some common measures include the share of fuel in total merchandise exports, the share of minerals in total merchandise exports, or the combined share of fuel and minerals in total merchandise exports. Note also that international trade theory, particularly relating to comparative advantage, would also link natural resource endowments to exports.

• Efficiency seeking motives. This type of FDI represents an internationalisation strategy targeting the rationalisation of the structure of established resource and market seeking FDI investments in order for the MNEs to reap the economies of scale and scope benefits as a result of the common governance of geographically dispersed activities (Dunning & Lundan 2008: 72). Low labour costs are considered to be a significant location (L) advantage for attracting FDI according to this motive for FDI. Labour cost is usually measured by real unit labour cost corrected for productivity. Occasionally, the unit labour cost differential between host and home country (Culem, 1988) or the ratio of real host country wages to real home country wages is employed (Liu et al, 1997). In this way, labour cost conditions in the home country are also being considered and this measure is probably more accurate in indicating the attractiveness of a potential host economy in terms of labour costs.

• Market seeking motives. This type of FDI represents an internationalisation strategy of supplying a national market (in some cases adjacent national markets) via the creation of a local self-contained production subsidiary. This market (or markets) was usually being served by exports but its market size (or rate of growth) justifies FDI as the optimal way of supplying it (Dunning & Lundan, 2008). Previous research on FDI determinants usually uses two variables to proxy for the determinants of market seeking FDI; the quantity of demand as measured by the host country Gross Domestic Product (GDP) in real terms, and the quality of demand, as measured by its GDP per capita. Both of these variables have been found to significant positive impact on FDI attraction (Pistoresi 2000; Billington 1999; Wang & Swain 1995). Some studies also employ GDP growth as proxy for the future market potential of the host economy (Asiedu 2002, Billington 1999; Culem 1988). One particular form of investment that is often separately identified but is a form of market seeking investment is tariff jumping investment. Tariff jumping motives apply when a market would otherwise be supplied by exports but is protected by tariffs or trade barriers. Tariff jumping motives are where FDI replaces exports in the supply of the domestic market in order to overcome these barriers. See, for example, Hwang and Mai (2002).

• Knowledge seeking motives. This type of FDI represents an internationalisation

strategy targeting augmenting an MNE’s ownership (O) advantages via the acquisition of assets of firms located in foreign countries and integrating them in their global subsidiary network (Dunning & Lundan 2008). In order for a country to attract this type of FDI it needs to have a strong science base and local skilled labour. In most empirical studies, R&D intensity and patents per million of population is thought to attract this kind of FDI (Narula & Wakelin 1997; Neven & Siotis 1996).

The above list of the different motives for the choice of location of FDI is intended to provide a summary overview, not to be exhaustive. In addition to these potential determinants of FDI according to the strategic motives derived from the eclectic framework approach, there are several other kinds of host country characteristics that are thought as potential determinants of FDI but are not particularly tied to a specific strategic motive. Institutional quality measures such as low governance quality, corruption or country risk has been found to have a negative impact on FDI flows (Globerman and Shapiro 2003; Lehmann 1999; Wei 2000). Other country characteristic that have been employed in the FDI determinants literature include the level of taxation (Billington 1999; Hines 1996), investment incentives (Ihrig 2000), trade barriers (Culem 1988), the inflation rate (Schneider & Frey 1985) and several others. Blonigen (2005) provides a detailed review of this literature. There exists an extensive empirical literature on the determinants of FDI. Unsurprisingly, only part of this literature focuses on the locational aspects. Where empirical studies do exist they tend to focus on levels of FDI and, hence, tend to focus on aggregate inward FDI or, at best, inward FDI in highly aggregated sectors, such as the primary, secondary (manufacturing) and tertiary (service) sectors. For example, Walsh & Yu (2010) find that FDI in the tertiary sector is positively correlated with several macroeconomic variables and the level of total FDI stock (as a proxy for agglomeration). On the other hand they find FDI in the secondary sector is correlated only with the agglomeration proxy while FDI in the primary sector with none of the aforementioned variables. In another recent study Ramasamy & Yeung (2010) compare the FDI determinants between services and manufacturing in a sample of developed (OECD) economies and find that although both market-seeking and efficiency-seeking motives are important for both these sectors, their importance is greater for FDI in manufacturing. For services FDI they find the single most important determinant is the presence of manufacturing FDI. Other studies tend to focus on one of the three broad sectors and/or examine the variation in the FDI determinants of FDI within in the different sub-sectors of this broad sector. Kolstad & Villanger (2007) analyse FDI determinants within the services sector and its different sub-sectors in a sample of 57 both developed and emerging economies over the period 1989-2000. The authors find that services FDI is predominantly market-seeking and not affected significantly by a country’s trade openness; a result that reflects the high proportion of non-tradeable activities within the service sector. They find that variation in FDI determinants for producer services (such as finance and transport) is positively and significantly correlated with FDI in the manufacturing sector but that other types of services are not significantly influenced by manufacturing FDI. Resmini (2000) focuses on the determinants of FDI in different manufacturing sub-sectors in a sample of Central and Eastern European countries over the period 1990-1995. The author uses the Pavitt (1984) taxonomy to classify different manufacturing sub-sectors according to their technological intensity. The results of this study indicate that market seeking considerations appear to be more important for traditional sectors while structural reforms are

more important for FDI flows in capital-intensive and science-intensive sectors. Wage differentials (between the source and host country) appeared to be positively correlated with FDI in scale-intensive and in the science-intensive sectors. Assuming that lower wages are correlated with lower labour skills, the results of Resmini (2000) contrast with those of Globerman & Shapiro (2003), who examine the determinants of US-originated investment in a broad sample of developed and emerging economies over the 1995-1997 period. The authors provide evidence that FDI determinants in the high-tech sectors (defining the chemicals and electrical equipment sectors as ‘high tech’) respond differently from those for other sectors. In particular they find that the both the probability of a country receiving FDI in the high-tech sectors and the level of FDI in such sectors is more closely correlated with the quality of human capital than it is the case for all the industries. Narula & Wakelin (1997) examine the determinants of US originated manufacturing FDI in seven industrialised economies over the 1972-1991 period. The authors conclude that FDI determinants vary according to the specific host country. For some countries for example such as the UK and Japan the technological advantages of the US, and the lower relative wages of the host country are the most important determinants. On the other hand for Germany it is the combination of the country’s technological assets relative to the US, lower unit labour costs and past exports appear to be the determining factors. A common pattern across the countries of this study though is that market size has a generally positive and significant impact on manufacturing FDI while unit labour costs a generally negative effect. The comparative advantage of a host economy at the industry level has not been linked often to FDI determinants at the industry level even though, as argued earlier, it has much intrinsic common ground with export platform and other motives for FDI. Yeaple (2003) in a study related to the FDI determinants of US outward FDI at the sector level finds that FDI at the industry level reflects an interaction between country skilled-labour abundance and industry skilled-labour intensities that is consistent with comparative advantage. He concludes that research in FDI determinants at the industry level should consider both market-access and comparative advantage motives. In another study, Driffield and Munday (2000) have provided robust evidence that industry comparative advantage itself is an important determinant of new inward FDI in the UK manufacturing sectors. Furthermore, Maskus & Webster (1995) and later Palangkaraya & Waldkirch (2008) use factor content analysis for both trade and inward FDI for a set of developed economies and confirm that in most cases the factor content of net inward FDI at the industry level is significantly correlated with the factor content of the comparatively advantaged industries of the host economy. In consequence this paper makes several contributions to the existing empirical literature. It adds to the limited amount of evidence on the determinants of the composition of inward investment by sector. It also contributes to the sparse evidence on links between comparative advantage and the location of FDI. By developing a measure of investment similarity and analysing its relationship with other key variables it provides a new way of looking at these under-researched issues.

3. AN INDEX OF INVESTMENT SIMILARITY

3.1 The Proposed Index Finger and Kreinin (1979) proposed an index of export similarity between countries. This measures the degree of similarity in exporting between any two countries with respect to their pattern of specialisation in different goods. The export similarity index between country j and country k (XSjk) is defined as:

XSjk = ∑ (min xij, xik) (1)

where xij is the share of good i in country j’s total exports and xik the share of good i in country k’s total exports. The minima of these shares are then summed across all goods. The export similarity index takes on a minimum value of 0 (the two countries are completely dissimilar) and a maximum of 1 (the two countries have an identical commodity composition of exports). Although the index only directly measures similarities in the composition of each country’s exports it is often interpreted as providing a measure of the extent to which the two countries have similar patterns of underlying comparative advantage. Dunning (1997, 1988) identifies a number of different motives for undertaking investment projects. For the purpose of exposition we simplify these into three generic categories: export platform, resource seeking and market seeking. An export platform investment is one that seeks the most economically advantageous location from which to export to regional or global markets. A resource seeking investment is one that attempts to exploit locally available resources. Finally, a market seeking investment is one that is made with a view to supplying a large market where there are costs or barriers to otherwise supplying it through exports. The theory of comparative advantage is also, in essence, a theory in the location of economic activity. That is, it sees a pattern of international specialisation arising from the underlying comparative advantage of each location. In this respect it has common ground with both export platform and resource seeking investment. Both exports and inward investment generated by these motives should be determined by essentially the same economic process. This suggests that the pattern of inward investment by sector may, like the resulting exports, reflect a pattern of specialisation determined by the underlying advantages of each location. For this reason we define an investment similarity index on essentially the same lines as the export similarity index. Thus, we define the index ISkj to be:

ISkj = ∑ (min fsj, fsk) (2) where fsj is the share of sector s in country j’s total inward FDI and fsk is the share of sector s in country k’s total inward investment. For the investment similarity index we use the term sector rather than good, as with the export similarity index. This simply reflects the nature of the data available for both – investment data is typically only available with a comparatively higher level of aggregation. As with the export similarity index it has a minimum value of 0 (the two countries have a completely different sectoral composition of inward investment) and a maximum value of 1 (the two countries have an identical composition). The following section presents values of this investment similarity index for a sample of countries. 3.2 Observed Values of the Investment Similarity Index To calculate investment similarities we use sector-level data on the US outward FDI stock at a historical cost basis according to the North American Industrial Classification System (NAICS) averaged over the 2002-209 period. Following Harding & Javorcik (2011) we treat data points that are suppressed by the BEA for confidentially reasons as missing and data points reported as values between $ -500,000 and $ 500,000 are treated as equal to $ 500,000. The data are available online and are provided by the Bureau of Economic Analysis (BEA), US Department of Commerce. Details of the sectors covered are presented in Appendix 1. Table 1 below presents values of the investment similarity index for a sample of 35 countries From the perspective of the host countries they, therefore, measure the degree of similarity of one country with another in the sectoral composition of inward investment from the US. Thus, those countries with a high value of the index are those with the most similar pattern of US FDI by sector and those with a low value the least similar. The indices, therefore, provide a summary measure of the extent to which any two countries share a common pattern of US investment by sector.

The investment similarity index, like that for export similarity, has a minimum value of 0 (complete dissimilarity) and a maximum value of 1 (an identical composition). In between these two limits there is no clear cut-off value which makes one country similar and another dissimilar. To provide such a benchmark we calculate the degree of similarity between each country and the sample total. Values of the index for country j with country k that are higher than country j’s index with the sample total are, therefore deemed to be similar. Values which fall below country j’s index with the sample total are deemed to be dissimilar. For example, France exhibits a degree of (inward) investment similarity with the full sample of 68.5%. Countries with which it exhibits a higher degree of similarity include Canada (78%), Denmark (70%), Germany (73%), Italy (74%), Spain (69%), Brazil (70%), Mexico (76%) and Australia (69%). In contrast, the majority of the sample countries indicate a lower degree of similarity – Norway (27%), Russia (27%) and Honduras (34%) to provide a few examples. That there is considerable variation in the similarity indices between countries suggests that the sectoral composition of inward investment from the US is of relevance to understanding FDI; that different countries receive inward FDI in different sectors. From the discussion of FDI theory in the preceding section such variations have a theoretical basis.

TABLE 1: INVESTMENT SIMILARITY (ALL SECTORS), 2002-2008

Country: Comparison Countries :

ALL Canada Belgium Czech Rep. Denmark Finland France Germany Greece Ireland Italy Norway Poland Portugal Russia Spain Sweden Switz Turkey UK Argentina Brazil Venezuela Honduras Mexico Panama Sth Africa Australia China Hong Kong India Japan Korea Malaysia Singapore

ALL COUNTRIES 100.00%Canada 72.15% 100.00%Belgium 52.97% 58.27% 100.00%Czech Republic 40.44% 44.11% 35.44% 100.00%Denmark 52.30% 64.06% 44.52% 31.24% 100.00%Finland 36.55% 44.72% 36.54% 40.15% 57.54% 100.00%France 68.50% 78.53% 56.07% 44.00% 70.32% 48.18% 100.00%Germany 82.03% 69.97% 51.76% 45.00% 61.14% 51.13% 72.63% 100.00%Greece 58.53% 66.18% 59.76% 29.30% 61.21% 54.25% 61.06% 58.57% 100.00%Ireland 72.19% 64.08% 45.86% 34.84% 55.39% 38.04% 66.27% 72.54% 52.59% 100.00%Italy 54.21% 69.98% 59.05% 46.97% 71.66% 62.40% 74.20% 66.74% 57.14% 63.52% 99.50%Norway 31.14% 36.60% 20.91% 25.03% 29.75% 33.96% 27.12% 29.05% 20.50% 25.69% 33.55% 100.00%Poland 43.45% 51.91% 48.57% 72.97% 43.51% 49.84% 58.80% 51.75% 46.20% 39.34% 60.44% 24.25% 100.00%Portugal 46.52% 54.01% 39.94% 44.02% 52.67% 58.84% 51.85% 54.09% 57.47% 46.68% 54.51% 24.62% 45.91% 100.00%Russia 27.80% 32.67% 16.95% 21.68% 19.58% 18.18% 27.30% 21.68% 20.72% 16.61% 22.86% 73.49% 26.31% 18.74% 100.00%Spain 79.42% 61.99% 48.86% 37.82% 48.60% 35.95% 69.04% 75.00% 51.88% 65.31% 55.45% 22.15% 44.56% 36.66% 23.59% 100.00%Sweden 74.83% 51.24% 54.29% 26.07% 40.47% 22.55% 49.95% 65.34% 52.79% 61.58% 37.03% 18.91% 26.83% 33.40% 15.06% 75.15% 100.00%Switzerland 68.22% 53.28% 35.72% 30.02% 52.16% 31.75% 61.37% 70.60% 47.85% 57.77% 45.88% 21.74% 38.19% 40.66% 25.11% 80.93% 67.93% 100.00%Turkey 30.00% 42.30% 42.79% 63.79% 36.08% 53.24% 42.85% 36.68% 50.30% 23.32% 45.75% 16.17% 71.38% 46.20% 25.24% 36.20% 16.09% 33.44% 100.00%UK 79.99% 67.96% 60.13% 42.00% 54.88% 34.56% 68.35% 70.87% 58.11% 73.90% 57.00% 29.77% 42.46% 47.54% 24.89% 63.97% 72.74% 58.77% 27.02% 100.00%Argentina 74.85% 64.75% 39.64% 37.45% 47.60% 32.67% 61.67% 68.07% 45.67% 66.67% 52.87% 36.53% 40.02% 33.14% 34.13% 70.98% 56.50% 62.14% 31.83% 67.39% 100.00%Brazil 66.91% 74.63% 60.52% 51.47% 54.04% 41.60% 70.30% 61.21% 59.91% 55.87% 66.29% 31.16% 57.53% 41.86% 28.42% 57.36% 46.87% 43.00% 47.70% 61.70% 64.24% 100.00%Venezuela 70.14% 72.06% 42.40% 32.66% 50.92% 35.38% 62.71% 67.72% 51.33% 58.06% 52.56% 35.42% 39.21% 28.89% 32.61% 67.25% 54.85% 57.33% 37.29% 56.91% 75.92% 59.36% 100.00%Honduras 25.00% 32.55% 32.53% 32.43% 39.05% 25.98% 34.49% 28.83% 37.92% 18.50% 33.81% 13.84% 45.98% 24.01% 20.76% 26.57% 16.94% 30.26% 44.36% 22.74% 21.15% 34.10% 28.73% 100.00%Mexico 71.79% 80.09% 51.19% 53.50% 54.57% 36.77% 76.39% 68.24% 61.02% 63.36% 62.63% 26.07% 59.20% 45.43% 31.56% 63.17% 52.75% 55.97% 49.22% 70.60% 63.89% 74.65% 67.39% 40.08% 100.00%Panama 67.40% 51.83% 33.47% 24.05% 50.11% 20.33% 59.13% 65.16% 43.90% 55.42% 40.97% 18.21% 28.64% 49.03% 18.79% 73.35% 67.73% 78.54% 21.81% 58.00% 59.15% 39.43% 52.10% 21.99% 51.66% 100.00%South Africa 47.70% 56.13% 46.36% 61.90% 54.77% 61.18% 59.36% 60.54% 47.16% 47.42% 65.19% 31.49% 64.62% 49.53% 27.55% 47.79% 29.70% 46.15% 60.42% 48.87% 45.82% 57.37% 47.83% 40.35% 61.36% 31.72% 100.00%Australia 74.47% 80.33% 50.34% 43.86% 56.10% 39.27% 69.47% 67.05% 55.84% 71.84% 62.99% 44.17% 47.42% 47.05% 38.68% 60.64% 50.78% 52.01% 31.65% 74.99% 75.80% 67.91% 67.30% 24.85% 72.39% 52.24% 54.39% 100.00%China 51.63% 65.33% 54.05% 49.05% 59.96% 57.77% 62.26% 55.60% 44.26% 40.94% 69.28% 34.35% 58.59% 39.14% 35.15% 50.28% 29.96% 42.44% 54.80% 44.57% 53.98% 71.56% 58.01% 36.60% 61.69% 32.78% 64.34% 56.10% 100.00%Hong Kong 76.22% 56.87% 56.64% 32.78% 55.47% 38.52% 61.22% 78.14% 63.33% 66.51% 49.51% 23.10% 42.34% 49.32% 22.91% 63.19% 74.19% 66.04% 32.55% 78.02% 56.91% 54.60% 53.14% 30.07% 58.97% 60.63% 49.63% 58.22% 43.19% 100.00%India 49.05% 47.60% 41.57% 56.36% 40.13% 41.83% 51.37% 50.45% 34.04% 58.83% 59.45% 33.87% 54.09% 39.96% 27.61% 44.22% 30.69% 37.37% 39.63% 51.04% 48.31% 58.46% 37.34% 30.73% 54.31% 32.44% 60.36% 57.38% 52.06% 44.01% 100.00%Japan 60.01% 58.40% 82.92% 38.14% 52.28% 46.56% 56.17% 61.45% 59.71% 57.65% 65.04% 27.38% 47.29% 51.56% 18.59% 48.40% 60.46% 39.95% 32.38% 67.13% 41.48% 55.38% 41.24% 27.92% 51.98% 36.38% 51.25% 56.98% 49.74% 66.21% 49.51% 100.00%Korea 51.02% 59.86% 52.30% 65.88% 53.12% 53.86% 61.26% 54.73% 48.34% 45.69% 64.33% 23.79% 76.61% 46.39% 29.98% 47.69% 35.22% 37.12% 63.41% 47.78% 38.03% 60.00% 44.72% 41.32% 66.12% 29.54% 56.62% 51.55% 67.35% 47.20% 56.29% 53.75% 100.00%Malaysia 52.57% 53.58% 32.62% 36.33% 49.46% 36.48% 51.93% 49.48% 34.93% 47.51% 40.33% 31.36% 37.58% 23.48% 33.95% 49.01% 39.58% 46.94% 33.59% 48.71% 58.81% 51.96% 56.40% 27.62% 57.51% 35.74% 46.61% 55.86% 63.37% 46.24% 42.85% 34.00% 50.72% 100.00%Singapore 59.04% 38.70% 26.23% 21.79% 41.94% 25.81% 43.53% 57.41% 29.50% 46.85% 34.85% 17.93% 22.42% 23.19% 12.68% 69.55% 63.02% 70.47% 14.58% 45.96% 49.65% 36.18% 50.11% 15.82% 38.72% 60.13% 27.61% 38.86% 37.19% 51.74% 27.03% 30.93% 31.32% 45.02% 100.00%Thailand 46.09% 59.85% 53.88% 49.82% 57.97% 52.22% 57.03% 49.22% 45.00% 36.77% 63.96% 28.43% 60.42% 37.66% 28.97% 45.87% 26.21% 35.79% 56.07% 39.15% 42.31% 66.12% 49.48% 50.76% 58.62% 27.96% 56.48% 46.86% 79.12% 38.70% 52.74% 44.79% 70.52% 57.46% 31.82%

ALL Canada Belgium Czech Rep. Denmark Finland France Germany Greece Ireland Italy Norway Poland Portugal Russia Spain Sweden Switz Turkey UK Argentina Brazil Venezuela Honduras Mexico Panama Sth Africa Australia China Hong Kong India Japan Korea Malaysia Singapore

Source: Bureau of Economic Analysis, US Department of Commerce

TABLE 2: INVESTMENT SIMILARITY INDICES FOR MANUFACTURING SECTORS ONLY, 2002-2008

Country: Comparison Countries :

ALL Canada Belgium Czech Rep. Denmark Finland France Germany Greece Ireland Italy Netherlnd. Norway Poland Portugal Russia Spain Sweden Switz. Turkey UK Argentina Brazil Colombia Peru Venezuela Honduras Mexico Panama Sth Africa Israel Saudi A. Australia China Hong Kong India Japan Korea Malaysia Singapore

ALL COUNTRIES 100.00%Canada 80.80% 100.00%Belgium 71.46% 68.83% 100.00%Czech Republic 55.31% 61.62% 41.22% 100.00%Denmark 56.89% 57.65% 46.18% 27.02% 100.00%Finland 65.54% 60.55% 41.45% 57.53% 56.34% 100.00%France 84.46% 79.57% 70.03% 49.04% 52.67% 60.29% 100.00%Germany 78.53% 73.38% 53.74% 65.67% 48.82% 79.42% 69.80% 100.00%Greece 61.12% 64.38% 61.43% 28.24% 48.19% 30.27% 65.82% 40.77% 100.00%Ireland 72.24% 60.92% 80.12% 33.63% 62.17% 55.05% 66.49% 57.35% 58.11% 100.00%Italy 90.45% 75.48% 64.68% 54.38% 62.14% 72.57% 80.99% 81.48% 56.33% 70.43% 100.00%Netherlands 72.53% 61.78% 72.83% 44.81% 40.49% 51.35% 75.19% 63.35% 55.77% 71.73% 69.06% 100.00%Norway 30.86% 29.86% 23.22% 33.36% 20.34% 44.91% 33.07% 38.77% 19.89% 22.27% 34.75% 33.29% 100.00%Poland 73.99% 72.43% 60.56% 51.56% 36.14% 58.56% 80.41% 67.13% 59.12% 52.85% 71.73% 71.13% 29.45% 100.00%Portugal 53.28% 53.33% 36.49% 83.93% 30.28% 56.09% 44.13% 62.98% 24.75% 42.58% 54.82% 36.65% 22.50% 44.22% 100.00%Russia 59.42% 57.31% 50.77% 38.50% 34.42% 42.78% 63.67% 48.15% 50.91% 45.63% 59.87% 58.93% 19.72% 73.00% 36.77% 100.00%Spain 82.88% 74.07% 86.85% 46.28% 46.00% 50.25% 75.30% 63.64% 60.02% 81.47% 74.40% 77.94% 22.22% 68.40% 45.56% 57.59% 100.00%Sweden 69.70% 70.45% 56.36% 40.73% 80.26% 65.24% 65.47% 62.60% 61.75% 71.76% 75.44% 52.94% 32.42% 48.42% 41.58% 44.25% 56.52% 100.00%Switzerland 62.70% 52.38% 78.47% 44.83% 37.62% 49.07% 56.31% 60.94% 42.40% 68.39% 60.58% 75.04% 29.35% 48.27% 36.52% 42.46% 73.72% 48.70% 100.00%Turkey 70.34% 77.25% 77.76% 53.67% 41.31% 45.21% 65.77% 59.44% 62.64% 69.11% 62.58% 60.85% 20.02% 66.74% 50.18% 51.45% 80.78% 50.92% 56.24% 100.00%UK 90.67% 77.77% 68.46% 56.36% 53.47% 65.94% 84.00% 77.00% 61.66% 64.87% 90.52% 74.84% 35.13% 77.03% 49.40% 62.73% 79.18% 66.74% 63.88% 68.31% 100.00%Argentina 68.96% 60.40% 86.01% 40.82% 37.14% 42.85% 70.39% 53.35% 59.44% 69.21% 66.30% 81.21% 24.86% 68.11% 34.35% 59.94% 79.07% 46.76% 78.21% 69.73% 72.28% 100.00%Brazil 89.88% 78.68% 74.52% 52.92% 55.54% 62.08% 85.89% 72.58% 62.79% 71.83% 85.86% 76.36% 35.02% 73.64% 44.82% 59.06% 83.06% 67.81% 65.84% 73.98% 89.13% 73.58% 100.00%Colombia 75.56% 72.61% 81.56% 46.47% 39.31% 47.72% 72.93% 58.99% 64.08% 73.75% 68.25% 75.04% 22.45% 74.73% 42.95% 58.31% 87.71% 48.95% 63.64% 84.94% 76.85% 82.42% 78.16% 100.00%Peru 62.20% 64.33% 69.11% 28.20% 48.15% 30.23% 66.57% 40.73% 87.85% 65.77% 56.28% 66.50% 21.25% 62.12% 24.71% 52.56% 67.70% 61.71% 50.08% 69.76% 62.74% 67.89% 68.15% 73.14% 100.00%Venezuela 71.37% 86.66% 65.82% 50.67% 50.85% 48.66% 73.61% 60.36% 76.46% 55.87% 67.17% 55.64% 22.67% 70.96% 46.39% 58.76% 68.39% 66.52% 44.44% 75.49% 72.65% 61.73% 69.09% 78.29% 77.31% 100.00%Honduras 34.10% 44.55% 38.16% 14.30% 45.28% 17.52% 36.70% 20.86% 68.30% 37.09% 30.85% 24.59% 9.47% 29.29% 9.63% 26.97% 34.83% 50.95% 21.47% 38.60% 31.33% 27.84% 35.44% 34.36% 57.88% 51.90% 100.00%Mexico 76.21% 91.38% 65.87% 57.66% 51.99% 52.93% 76.69% 67.44% 67.27% 54.84% 70.88% 59.02% 27.46% 72.03% 48.67% 57.78% 69.87% 64.27% 47.65% 76.02% 76.54% 62.02% 72.64% 77.21% 68.66% 90.81% 44.17% 100.00%Panama 52.98% 44.68% 72.09% 29.61% 25.96% 31.64% 54.76% 42.14% 53.25% 60.50% 50.32% 77.68% 22.54% 66.67% 24.83% 63.70% 65.86% 35.64% 68.11% 57.65% 56.30% 80.73% 57.61% 69.63% 64.56% 48.22% 23.21% 48.98% 100.00%South Africa 67.38% 72.74% 53.10% 82.29% 35.92% 63.93% 62.99% 79.47% 40.12% 45.62% 66.16% 58.74% 35.63% 65.52% 66.54% 47.35% 58.74% 49.63% 55.76% 65.55% 68.14% 52.70% 65.61% 58.34% 40.08% 61.79% 23.65% 69.74% 41.01% 100.00%Israel 30.43% 22.32% 19.69% 18.27% 58.26% 42.33% 25.27% 32.28% 16.47% 38.07% 34.97% 23.89% 10.46% 18.81% 27.39% 19.84% 22.84% 39.41% 22.32% 15.83% 25.82% 19.72% 25.55% 16.90% 15.73% 17.61% 14.02% 17.10% 15.05% 18.50% 100.00%Saudi Arabia 69.70% 62.51% 86.54% 45.79% 41.88% 50.46% 66.97% 59.42% 51.23% 72.27% 67.01% 75.91% 35.39% 60.11% 31.37% 44.59% 77.05% 55.59% 81.77% 67.35% 70.27% 83.52% 76.77% 75.39% 60.26% 55.18% 31.65% 58.96% 71.08% 59.93% 15.66% 100.00% 73.36%Australia 87.20% 81.01% 74.94% 53.28% 48.60% 57.90% 89.94% 69.03% 67.63% 67.03% 79.53% 77.86% 33.38% 81.83% 42.92% 62.71% 80.46% 61.41% 59.28% 74.67% 86.75% 76.13% 89.60% 82.09% 71.70% 76.70% 36.44% 79.77% 60.15% 67.23% 20.58% 73.36% 100.00%China 83.92% 66.80% 58.39% 53.20% 58.93% 77.53% 69.90% 77.94% 48.37% 68.32% 83.55% 66.67% 28.25% 64.77% 56.75% 53.37% 69.45% 64.36% 60.86% 57.80% 79.26% 61.71% 74.17% 62.99% 49.09% 59.14% 21.39% 63.32% 50.50% 62.36% 45.98% 55.87% 72.27% 100.00%Hong Kong 64.24% 54.29% 48.39% 34.82% 83.58% 61.37% 56.81% 57.31% 40.16% 65.31% 65.49% 46.85% 23.67% 40.70% 35.76% 37.84% 52.76% 73.01% 45.31% 40.52% 56.99% 40.35% 58.12% 42.54% 40.12% 45.17% 33.87% 51.31% 29.65% 47.23% 58.49% 48.34% 52.70% 65.90% 100.00%India 71.44% 58.19% 58.57% 57.72% 37.16% 70.97% 57.39% 75.84% 34.82% 59.54% 70.37% 64.63% 45.38% 54.07% 53.60% 39.82% 68.47% 51.36% 66.46% 58.76% 73.16% 58.18% 69.77% 61.27% 42.50% 47.41% 13.21% 53.51% 46.97% 61.44% 26.38% 62.04% 62.44% 69.42% 44.20% 100.00%Japan 87.23% 77.89% 71.60% 49.83% 64.50% 66.42% 77.34% 75.71% 59.54% 79.80% 84.58% 60.23% 25.76% 64.13% 54.51% 53.44% 77.83% 75.55% 60.14% 70.22% 78.91% 61.39% 78.58% 68.35% 59.49% 70.08% 38.94% 74.04% 43.69% 63.96% 35.54% 63.05% 77.68% 80.26% 70.31% 66.12% 100.00%Korea 79.25% 69.15% 57.37% 48.37% 67.86% 73.79% 71.61% 68.40% 50.48% 68.05% 82.84% 59.83% 24.91% 66.65% 56.29% 61.01% 68.09% 68.59% 50.11% 57.14% 76.36% 60.73% 70.93% 63.94% 50.44% 65.92% 28.97% 67.64% 45.33% 57.47% 49.46% 52.26% 70.27% 87.32% 73.00% 56.74% 78.53% 100.00%Malaysia 42.10% 33.98% 31.12% 31.15% 55.65% 53.32% 36.93% 43.95% 26.29% 49.25% 46.64% 35.55% 22.78% 30.25% 42.35% 30.20% 34.27% 46.83% 34.54% 26.86% 37.49% 31.01% 37.22% 28.33% 26.04% 29.17% 11.64% 28.77% 25.36% 30.72% 84.17% 27.88% 32.24% 57.65% 62.81% 40.61% 47.76% 61.00% 100.00%Singapore 46.59% 36.90% 30.21% 38.16% 60.37% 62.00% 39.76% 50.42% 19.10% 44.03% 53.11% 33.73% 24.87% 29.72% 43.19% 30.76% 34.97% 51.56% 37.72% 25.80% 43.96% 31.68% 42.28% 28.13% 18.82% 29.87% 10.42% 31.37% 18.14% 38.06% 75.83% 32.77% 35.96% 61.34% 69.29% 47.09% 51.35% 62.69% 86.31% 100.00%Thailand 82.42% 75.19% 69.53% 45.55% 70.41% 70.30% 77.03% 69.40% 59.96% 81.68% 82.47% 58.76% 26.20% 63.60% 51.71% 53.42% 72.92% 78.92% 55.51% 68.03% 75.04% 60.43% 78.56% 65.85% 59.92% 66.82% 39.31% 69.11% 45.19% 57.28% 42.16% 62.29% 74.90% 82.07% 72.22% 60.05% 91.56% 82.03% 54.38% 55.49%

ALL Canada Belgium Czech Rep. Denmark Finland France Germany Greece Ireland Italy Netherlnd. Norway Poland Portugal Russia Spain Sweden Switz. Turkey UK Argentina Brazil Colombia Peru Venezuela Honduras Mexico Panama Sth Africa Israel Saudi A. Australia China Hong Kong India Japan Korea Malaysia Singapore

Source: Bureau of Economic Analysis, US Department of Commerce

Restricting our sample to manufacturing only allows for a larger sample of countries since data are reported only for manufacturing sectors for some. Table 2 presents the investment similarity analysis repeated for manufacturing sectors only, with a sample of 40 countries. Table 2, like Table 1, shows that most countries are found to be similar (in their sectoral composition of inward FDI) to a small group of countries but dissimilar to the clear majority of countries in the sample. For example, Mexico is found to have a 76% degree of similarity with the full sample and a substantially higher degree of similarity with Canada (91%), Argentina (80%), Australia (79%) and Japan (74%). For most of the remaining countries in the sample Mexico has a much lower degree of inward investment similarity – such as Israel (17%), Norway (27%) and Malaysia (29%). Both Table 1 and Table 2 suggest that there is a pattern of specialisation with respect to inward investment from the US. That is, small groups of countries tend to be specialised in some sectors of the economy and different groups in other sectors. 3.3 Policy Implications There are two main sets of implications of the proposed investment similarity indices – for policy and for the testing of the theory of FDI. In terms of policy the investment similarity indices show that countries are typically only in direct competition with a small group of countries. The majority of other countries are dissimilar in their investment composition – they attract investment in a different type of sector. For the purposes of policies such as investment promotion activities it, therefore, makes sense to adopt policies which emphasise the strengths of, say, Malaysia relative to its direct competitors. Accordingly we envisage the investment similarity index being a potentially useful tool for policy makers and, in particular, investment promotion agencies (IPAs). For example, it would enable investment promotion authorities in Singapore to easily identify that it has a high degree of investment similarity only with Spain, Sweden and Switzerland. In terms of promotional efforts its key task, therefore, is to persuade investors of its strengths relative to these countries and not the majority, with which it is not competing for inward investment in the same sectors. Such implications are in accordance with the view of investment promotion professionals that set sector targeting at the top of the agenda of IPAs (Loewendahl 2001, Proksch 2004). 3.4 Export Similarity Investment similarity indices also offer possibilities for testing of theories of FDI and, in particular, locational aspects of the OLI paradigm. As discussed previously JH Dunning () identified several types of motive for the choice of location for FDI. These include, for example, export platform, resource seeking and efficiency seeking motives. The export platform motive directly proposes that firm will tend to locate FDI where the conditions for potentially successful production for export exist. Reference to well established trade theory also suggests that other motives may overlap with this. For example, Ricardian comparative advantage suggests that countries will specialise in production of goods with a relative productivity (efficiency) advantage. Likewise, the Heckscher-Ohlin theory emphasises the role of endowments of factors of production, including natural resources.

TABLE 3: EXPORT SIMILARITY INDICES (ALL GOODS), 2007-2009

Country: Comparison Countries :

Canada Belgium Czech Rep. Denmark Finland France Germany Greece Ireland Italy Norway Poland Portugal Russia Spain Sweden Switz Turkey UK Argentina Brazil Venezuela Honduras Mexico Panama Sth Africa Australia China Hong Kong India Japan Malaysia Singapore Thailand

Belgium 46.58%Czech Republic 42.36% 44.57%Denmark 38.98% 47.29% 41.86%Finland 37.60% 40.10% 37.09% 37.93%France 48.40% 58.80% 52.09% 50.20% 40.99%Germany 49.53% 59.24% 61.73% 50.61% 44.34% 69.73%Greece 30.10% 41.75% 31.11% 40.77% 30.15% 42.37% 38.44%Ireland 16.64% 34.40% 24.40% 30.40% 15.83% 29.41% 28.10% 22.18%Italy 40.20% 49.38% 49.56% 51.63% 42.46% 59.54% 63.33% 42.67% 20.42%Norway 41.37% 20.68% 15.96% 26.03% 22.10% 19.89% 19.12% 20.30% 8.95% 20.25%Poland 43.38% 47.56% 62.23% 45.21% 37.63% 53.88% 56.88% 35.74% 19.27% 52.31% 17.44%Portugal 40.81% 44.78% 49.95% 40.45% 34.90% 49.48% 47.85% 41.60% 18.66% 53.14% 18.75% 52.01%Russia 43.26% 22.41% 13.90% 18.14% 19.33% 17.38% 15.37% 21.89% 6.40% 15.10% 65.40% 15.83% 15.50%Spain 47.45% 57.69% 53.20% 46.59% 40.16% 61.21% 64.07% 46.54% 21.11% 60.65% 20.42% 56.52% 56.11% 17.59%Sweden 47.85% 53.38% 49.37% 47.98% 56.27% 58.88% 60.88% 37.50% 24.63% 53.30% 22.62% 49.90% 45.90% 19.43% 54.39%Switzerland 25.83% 44.09% 32.32% 39.71% 26.84% 43.35% 47.52% 28.51% 40.90% 41.61% 14.29% 28.53% 25.90% 9.09% 33.91% 37.38%Turkey 37.40% 40.73% 42.97% 35.22% 31.91% 40.83% 42.29% 42.94% 12.19% 49.61% 17.45% 49.89% 52.46% 15.83% 51.77% 39.93% 23.41%UK 50.70% 62.10% 48.13% 53.01% 39.74% 63.20% 61.91% 38.77% 31.77% 50.84% 27.84% 46.25% 42.82% 24.83% 53.49% 56.24% 44.49% 39.37%Argentina 39.22% 34.80% 24.42% 26.95% 22.70% 34.64% 29.48% 25.56% 14.20% 28.99% 20.02% 28.78% 32.28% 20.58% 36.59% 28.87% 15.05% 32.18% 32.91%Brazil 41.84% 34.25% 29.73% 31.12% 30.28% 37.45% 35.42% 25.68% 12.81% 34.24% 21.72% 32.54% 31.64% 24.21% 35.68% 33.12% 18.81% 29.80% 36.91% 43.19%Venezuela 20.45% 8.75% 3.15% 10.33% 8.35% 5.25% 4.18% 13.15% 1.92% 6.05% 46.64% 3.99% 7.16% 56.81% 7.24% 8.55% 1.80% 6.95% 13.61% 10.62% 12.90%Honduras 18.61% 15.71% 14.36% 14.86% 9.54% 14.62% 14.05% 15.60% 7.54% 13.32% 9.60% 17.35% 17.00% 10.18% 15.56% 13.59% 10.57% 16.68% 13.02% 14.23% 13.99% 1.49%Mexico 49.79% 39.44% 54.52% 39.38% 35.08% 44.61% 49.85% 28.45% 20.52% 40.87% 28.32% 50.49% 41.78% 24.89% 47.17% 43.11% 25.39% 42.08% 50.38% 28.84% 34.75% 17.44% 14.99%Panama 9.54% 10.07% 7.19% 13.74% 6.14% 10.96% 9.52% 12.77% 8.61% 9.10% 7.38% 10.28% 9.71% 3.61% 10.78% 9.89% 7.36% 6.98% 10.15% 11.56% 10.10% 1.24% 27.61% 8.08%South Africa 36.34% 37.77% 29.61% 24.62% 26.63% 34.47% 33.43% 24.45% 11.31% 30.51% 18.59% 31.81% 32.89% 20.68% 37.13% 32.09% 19.64% 30.31% 35.90% 30.13% 34.80% 7.27% 11.66% 29.34% 7.20%Australia 38.41% 24.20% 18.25% 25.57% 18.41% 26.04% 22.32% 21.40% 16.24% 21.53% 22.92% 21.42% 21.13% 24.47% 22.93% 22.03% 15.33% 18.40% 27.15% 30.33% 33.51% 9.45% 15.86% 22.71% 9.77% 33.87%China 25.38% 35.02% 49.48% 38.38% 34.45% 38.84% 42.26% 29.69% 27.31% 45.89% 15.43% 41.32% 40.44% 12.65% 39.26% 37.67% 27.02% 39.01% 37.19% 16.41% 24.77% 4.10% 10.97% 42.29% 5.17% 22.57% 14.31%Hong Kong 13.67% 14.39% 16.48% 16.51% 12.82% 17.22% 17.32% 17.85% 12.23% 18.00% 6.48% 13.83% 18.54% 4.43% 14.73% 18.42% 17.65% 19.88% 19.18% 7.90% 7.05% 1.12% 11.43% 14.97% 9.20% 9.25% 15.67% 21.74%India 27.89% 40.30% 26.61% 31.73% 28.62% 34.67% 32.57% 38.80% 14.61% 39.09% 17.33% 28.95% 34.51% 28.21% 38.87% 33.21% 27.10% 36.95% 36.52% 25.51% 33.49% 19.46% 10.58% 24.62% 8.28% 30.23% 22.40% 33.77% 17.03%Japan 37.75% 42.97% 49.88% 29.93% 37.34% 48.88% 61.63% 22.76% 21.07% 44.66% 16.99% 43.51% 38.95% 14.24% 48.96% 43.52% 29.92% 34.18% 47.07% 23.06% 29.65% 4.17% 8.72% 44.49% 4.43% 28.48% 16.70% 40.39% 15.09% 27.95%Malaysia 38.41% 31.29% 36.36% 33.05% 26.73% 31.55% 32.83% 25.16% 26.98% 30.31% 27.78% 28.34% 31.82% 26.47% 29.53% 30.54% 21.25% 25.30% 39.43% 22.24% 26.09% 12.41% 19.64% 36.96% 5.62% 19.34% 22.61% 43.79% 15.28% 24.06% 34.42%Singapore 26.31% 32.80% 32.79% 28.45% 31.42% 36.16% 37.13% 31.01% 28.46% 31.99% 17.53% 25.65% 28.64% 25.53% 31.06% 35.91% 26.59% 22.15% 39.03% 18.79% 21.13% 18.72% 8.15% 31.59% 6.16% 18.45% 16.41% 36.88% 16.57% 38.29% 42.57% 48.42%Thailand 36.11% 40.64% 47.01% 36.05% 31.09% 43.89% 43.45% 32.22% 28.23% 44.07% 16.71% 40.46% 44.75% 14.53% 45.50% 39.31% 26.14% 42.65% 43.48% 30.60% 31.90% 7.16% 16.21% 43.24% 9.76% 28.01% 18.75% 49.03% 19.49% 35.14% 42.72% 50.22% 41.47%All WTO 57.17% 55.30% 51.65% 48.13% 42.90% 56.40% 57.46% 39.14% 27.92% 53.18% 33.28% 48.50% 49.59% 32.13% 54.42% 52.09% 34.43% 44.03% 63.32% 38.99% 45.51% 17.73% 16.56% 56.35% 9.74% 36.88% 32.46% 54.77% 21.13% 42.81% 52.34% 52.24% 48.42% 56.81%

Source: UN COMTRADE database

Dunning’s (1977, 1988) view of the different motives for FDI suggest that the very underlying economic characteristics that create specialisation in some sectors of the economy in international trade would also be likely to generate FDI in the same sectors. That is, several of the motives for FDI suggest that both specialisation (by sector) in exports and specialisation in inward FDI are driven by essentially the same forces. However, the OLI paradigm is somewhat broader in that it also includes motives for inward FDI which have little to do with export specialisation and comparative advantage. One important example of this would be market seeking investment. The OLI framework therefore predicts, in line with its eclectic tradition that FDI will be shaped by a ranges of forces, only some of which are in common with those that shape exports. This means that extent to which one set of forces or another are important is essentially one for empirical testing. The following section addresses the use of investment and export similarity indices, amongst other variables, for econometric testing. In this section we take a simpler, less formal approach. Table 3 presents export similarity indices for the same sample of countries as in Table 1 for the period 2007-2009. Data were taken from the UN’s COMTRADE database at the 4 digit (HS) level and are for goods only (that is, do not include services). The indices were calculated on the period average of the export data to minimise the year to year random fluctuations that are common with disaggregated trade data. If the combination of export platform, efficiency and resource seeking motives are dominant in US FDI in other countries then we should expect to observe a similar pattern of both export and investment similarity. That is, if specialisation in both exports and inward FDI are driven by essentially the same forces then the two sets of indices should yield similar results. The evidence from simple observation offers some support for this hypothesis. For example, according to the commodity composition of exports the Czech Republic is found to be similar to Mexico, Germany and France. Cross-checking with investment similarity (Table 1) shows that it is also similar to all three of these countries with respect to the sectoral composition of its inward investment. Likewise, Russia is found to be similar to both Canada and Norway in the composition of both its exports and inward investment. For other countries there is also an observable common pattern between investment and export similarity but with more variation. Thus, China is similar in the commodity composition of its exports to Belgium, Denmark, Germany, Italy, Poland, the Czech Republic and Portugal. With respect to investment similarity (Table 1) it is found to have a similar sectoral composition to the first four of these countries but not the last two. The comparison between export similarity and investment similarity indices does indeed suggest that there exists a common pattern to both. However, the relationship between the two is imperfect, also suggesting that other forces are also likely to be relevant. For these reasons we further explore in the following section the relationship between investment similarity on the one hand and export similarity and other possible determinants of the location of inward FDI on the other.

4. CROSS-SECTIONAL ANALYSIS OF INVESTMENT SIMILARITY

4.1 Objectives

In this section we turn to the role of investment similarity in testing theoretical predictions with respect to the location of inward FDI. In so doing we start by again emphasising the OLI paradigm associated with Dunning (1977, 1988). If all inward investment were of an export platform type then we would expect investment similarity indices to be very closely related to export similarity. That is, if investment is intended to exploit whatever advantages a particular location possesses in export markets then the pattern of both exports and inward investment by sector should be closely related. For the purposes of linking to theory this is most easily done through the concept of comparative advantage but other forms of specialisation are just as applicable. The presence of possible export platform investment means that, in theory, both investment and export similarity should be positively related. However, OLI theory does not just specify export platform investment. In particular, it allows a role for market seeking investment. From the theory we should also expect market seeking motives to affect the composition of inward investment by sector. Thus, if market seeking reasons were the sole determinant for the locational choice for inward investment we should expect investment to be higher in:

• sectors where transport costs and similar barriers to exporting are higher,

• sectors and countries where import tariffs and other institutional barriers to trade are high, and • countries where the overall market size is large.

Combining export platform and market seeking motives suggests that we should expect the pattern of inward investment by sector to be linked to both sets of determinants. In addition to these, we also incorporate the quality of human capital and the level of technological capabilities of each country since different industries have different technological and skills intensity that require from the host country the possession of human capital and technology related location advantages (L). Although education and technology could be partly reflected in the export structure of a country, and so in the export similarity indices, given the importance of this created assets (Narula 1996) type of variables in terms of public policy, especially for developing economies, it becomes worthwhile investigating if they have an independent from the export structure effect on investment similarity.

4.2 Data Sources and Methodology

For the purposes of our econometric analysis we use the values of the investment similarity index as our dependent variable. Investment similarity indices were calculated for all sectors of the economy (see Table 1) and for manufacturing sectors only (see Table 2), with the latter resulting in a larger sample of countries. These were calculated, as described earlier, using data on US outward FDI stocks, averaged over the period 2002-2009. Averaging stocks over a period of time was necessary to obtain a decent sample of countries since observations are often missing or suppressed for confidentiality reasons for any one year. A number of explanatory variables were included specifically to provide a basis for testing the OLI paradigm. The most important of these was the export similarity index (see Table 3), Export Similarity. As discussed previously the OLI paradigm specifies export platform as one motive for undertaking FDI. To the extent that this motive is important in practice, we should expect to observe a positive relationship between the pattern of inward investment (investment similarity) and the pattern of exports (export similarity). That is, we would expect countries which are similar with respect to their export specialization to also exhibit a common pattern of specialization by sector in inward investment. As discussed earlier export similarity can also be seen as also encompassing both efficiency seeking and resource seeking motives, in so far that both resources and local efficiencies are sought to service export markets. The OLI paradigm also specifies other motives for undertaking FDI. Market seeking motives are of theoretical importance and, by definition, distinct from export platform motives. Our econometric specifications, therefore, include a number of variables intended to capture market seeking influences. The first of these variables is the difference in market size (ln GDP). This is the natural logarithm of the average absolute difference in GDP (in constant 2000 $) between each pair of countries over the 2002-2009 period. Market seeking motives can also encompass investment that seeks to supply high or low income consumers so we also include differences in per capita GDP as an explanatory variable. In particular, the variable ln GDP per capita is the natural logarithm of the absolute difference of the average GDP per capita (in constant 2000 $) between each pair of countries over the 2002-2009 period. Data for both of these variables were taken from the World Development Indicators provide by the World Bank. In terms of expected results it is not clear from the underlying theory (OLI) whether the relationship between investment similarity on the one hand and differences in GDP or in per capita GDP on the other should be positive or negative. The OLI paradigm would predict a significant and positive relationship between market size and levels of FDI. Certainly this means that if market seeking motives are of importance we should observe an effect on the sectoral composition of FDI and, hence, similarities between countries in this composition. This suggests that we should observe a statistically significant effect. However, whether similarities in market size make the sectoral composition more or less similar between countries is a more open question. Since arguments can be made for both, the approach of this paper is that it is an empirical question; that theory predicts a relationship between investment similarity and market size variables, not the nature of this relationship. The influence of market seeking motives for FDI presumably also depends on barriers to trade, natural and policy induced. That is, if large markets can be supplied just as well by exports then the need to supply them through FDI would not exist. In this respect market seeking motives overlap with tariff jumping explanations of FDI. To capture these effects on investment and, hence, investment similarity we employed two measures from the Fraser Index of Economic Freedom (Gwartney et. al, 2010). This index has been widely used – see, for example, Carlsson and Lundstrom (2002). From this index we took two of the

variables used to calculate the ‘freedom to trade internationally’ component of the index (in which both the overall index and its components vary from a score of 0 indicating the lowest and a score of 10 indicating the highest degree of economic freedom). These were: (a) taxes on international trade (Trade Taxes) and (b) regulatory trade barriers (Business Regulation). These two variables are represented for each country pair by the the absolute difference of each country’s average over the period 2002-2008 (no data were available for years after 2008). As discussed, theory (market seeking and tariff jumping motives) predicts that trade barriers should be positively related with levels of FDI. This also implies that we should expect to find a statistically significant relationship between trade barriers and investment similarity. However, whether this relationship should be positive or negative is again less clear from theory – trade barriers can be expected to influence the sectoral composition of FDI but, depending on the nature of the barriers, not necessarily in a way that makes countries more or less similar in this respect. The appropriate test of theory in this respect is whether a relationship exists. The final set of explanatory variables are intended to develop further tests of the role of efficiency seeking motives, with respect to differences between countries in (a) human capital and (b) technological progress. Human capital differences (Ln School Years) are measured with the natural logarithm of the absolute difference of the average of the means years of schooling of the total population over 25 years old as provided by the Barro and Lee (2010) dataset for educational attainment in the world over the 2002-2009 period. As the Barro & Lee (2010) dataset is reported every five years between 1960 and 2010 we use linear interpolation for the years 2002-2009. A similar procedure was also performed by Blonigen et al. (2007). To account for technological level differences (Ln Patent Appls) we use the natural logarithm of the absolute difference of the average of utility patent applications filed in the United States Patent and Trademark Office (USPTO), between each pair of countries over the 2002-2009 period. Data for the patents variable were provided by the USPTO. Given the need to average both investment and export data over a period of years our sample was, of necessity, a cross-section of pairs of countries. The regression that was used to estimate this cross-sectional model was of the general form:

= α+ + (3)

where is the vector of investment similarity values between country k and country m, is the vector of export similarity values between country k and country m, a vector of variables reflecting the level of similarity in factors that could attract market seeking FDI between country k and country m, is the difference in the level of education between k and m, the difference in the level of technological development between k and m, vector of variables from the Fraser Index described above that reflect trade policy differences and is a vector of random errors. Correlations between these explanatory variables are provided in Appendix 2. 4.3 Cross-Sectional Results for the Whole Sample

The sample includes 595 country pair observations for the investment similarity values for all sectors and 780 country pair observations for the investment similarity values in the manufacturing sectors (see Tables 1 and 2 for the countries included in each). The sample represents the most important markets in terms of US originated inward FDI and each major region of the world is represented. Table 4 presents the regression results for investment similarity across all sectors and all countries.

Table 4: Determinants of Investment Similarity (all sectors/all countries) for 2002-2009 (1) (2) (3) VARIABLES Model 1: Export and

Market Seeking Model 2: Model 1 with

Education and Technology

Model 3: Model 2 with Trade Policy

Export Similarity 0.308*** 0.295*** 0.280*** (0.0425) (0.0430) (0.0451) Ln GDP 0.0154*** 0.0146*** 0.0141*** (0.00366) (0.00367) (0.00368)

Ln GDP per capita -0.0168*** -0.0234*** -0.0199*** (0.00497) (0.00662) (0.00679) Ln School Years -0.00565 -0.00411 (0.00486) (0.00477) Ln Patent Appls. 0.00706** 0.00549* (0.00335) (0.00332) Trade Taxes -0.0293*** (0.00671) Business Regulation 0.00767 (0.00716) Constant 0.131 0.181* 0.198* (0.101) (0.106) (0.106) Observations 595 593 593 R-squared F-Statistic

0.150 34.80***

0.156 22.08***

0.183 18.99***

Note: We observe that there is not a high level of correlation between the variables used in the models. ***, ** and * indicate 1%, 5% and 10% significance levels respectively. Robust (White) standard errors are in parentheses. Variables, other than the export similarity index, are paired differences between countries. All three models produce consistent results. The export similarity variable is significant at the 1% level and positive in all specifications. This result, also taking into account also the magnitude of the coefficient, provides strong support for the export platform hypothesis by providing evidence that the allocation of inward FDI at the sector level follows closely the export patterns of a country. The coefficient of the absolute difference in GDP per capita is negative and statistically significant at the 1% level. Although it is quite low at around 0.02, it lends some support to the adapted to FDI hypothesis of Linder (1962) - that is, the more similar the demand structures of countries, the more similar will be the industrial structure of their inward FDI stock. The coefficient of the absolute difference in market size (ln GDP) is statistically significant at the 1% level but with a positive coefficient. As noted earlier we would have expected to find a statistically significant relationship between investment similarity and GDP or per capita GDP but theory is less clear on whether this should be a positive or negative relationship (that is, whether market seeking motives will make countries more or less similar). The results of model 3 suggest that trade taxes have a (negative) statistically significant (at 1%) effect on investment similarity but that trade regulation does not. Overall, the model provides empirical support for the conclusions of the OLI paradign and, in particular, with respect to its predicted motives for the location of FDI. Table 5 presents essentially the same regression analysis but for investment similarity indices based on manufacturing sectors only. As discussed earlier this results in a larger sample of countries.

Table 5: Determinants of Investment Similarity (manufacturing sectors/all countries) over the 2002-2009 Period

(1) (2) (3) VARIABLES Model 1: Export and

Market Seeking Model 2: Model 1

with Education and Technology

Model 3: Model 2 with Trade Policy

Export Similarity 0.335*** 0.347*** 0.472*** (0.0419) (0.0431) (0.0495) Ln GDP 0.0219*** 0.0229*** 0.0246*** (0.00360) (0.00363) (0.00374) Ln GDP per capita -0.0105** -0.00180 -0.0112 (0.00515) (0.00659) (0.00704) Ln School Years -0.00251 -0.00210 (0.00526) (0.00534) Ln Patent Appls. -0.00742** -0.00849** (0.00348) (0.00371) Trade Taxes -0.0206*** (0.00747) Business Regulation 0.0410*** (0.00742) Constant -0.0229 -0.0893 -0.115 (0.0986) (0.104) (0.107) Observations 780 778 739 R-squared F-Statistic

0.137 46.33***

0.142 28.28***

0.201 27.02***

Note: We observe that there is not a high level of correlation between the variables used in the models. ***, ** and * indicate 1%, 5% and 10% significance levels respectively. Robust (White) standard errors are in parentheses. Variables, other than the export similarity index, are paired differences between countries. The results for manufacturing are essentially similar to those for all sectors. Export similarity and differences in market size retain their signs and are significant at the 1% level in all three specifications. One notable feature though is the increase in the magnitude of the coefficient for export similarity, presumably reflecting the inclusion of a number of non-tradeables in the services sector. Per capita GDP is significant at the 5% level only the in the first model and loses its significance once the technological development variable (ln Patent Appls) enters the regression. When we limit the investment similarity indices to the manufacturing sectors, differences in technological development have a significant (at the 5% level) and negative (albeit small in magnitude) impact on investment similarity reflecting the different local technological capacity requirements for some hi-tech manufacturing sectors. As before, the taxes on international trade variable has a significant and negative impact on investment. Another difference is that this time regulatory trade barriers are significant at the 1% level. 4.4 Cross-Sectional Results for Developed Countries (DCs)

One limitation of the analysis presented so far is that it treats all countries as behaviourally similar. For a number of reasons it may be reasonable to suppose that developed and developing countries are behaviourally different, particularly when it comes to attracting inward FDI (Blonigen & Wang, 2005). For this reason the next stage in our analysis is to consider heterogeneity between countries with respect to development by dividing our sample broadly into developed (DCs) and less developed countries (LDCs). Dividing the sample in this way enables us to identify such potential heterogeneity in the determinants of investment similarity. To divide countries into DCs and LDCs we used the 2002 version of the world income classification of the World Bank. Countries in the high-income category were classified as DCs while countries in the middle and low-income categories were defined as LDCs.

The results for the analysis for DCs and for all sectors of the economy are presented in Table 6. This subsample includes 190 country pair observations.

Table 6: Determinants of Investment Similarity (all sectors/ developed countries) for 2002-2009

(1) (2) (3) VARIABLES Model 1: Export and

Market Seeking Model 2: Model 1 with

Education and Technology

Model 3: Model 2 with Trade Policy

Export Similarity 0.207*** 0.205*** 0.113 (0.0755) (0.0764) (0.0836) Ln GDP 0.0223*** 0.0219*** 0.0236*** (0.00679) (0.00688) (0.00680) Ln GDP per capita -0.0198** -0.0205* -0.0184* (0.00993) (0.0107) (0.0109) Ln School Years -0.00206 0.00280 (0.00790) (0.00814) Ln Patent Appls. 0.00388 0.00558 (0.00968) (0.00944) Trade Taxes -0.0407*** (0.0146) Business Regulation 0.00752 (0.0252) Constant 0.0307 0.0239 0.00973 (0.183) (0.185) (0.184) Observations 190 190 190 R-squared F-Statistic

0.151 10.48***

0.152 6.25***

0.187 5.38***

Note: We observe that there is not a high level of correlation between the variables used in the models. ***, ** and * indicate 1%, 5% and 10% significance levels respectively. Robust (White) standard errors are in parentheses. Variables, other than the export similarity index, are paired differences between countries. The cross-section results for the DCs sub-sample reveal that the impact of export similarity remains positive, though not significant in Model 3, when the Trade Taxes and Business Regulation variables are included. It is of interest that the impact of taxes on international trade remains a negative and significant determinant of investment similarity even when we limit our analysis to high-income economies, where theory predicts that the market-seeking motive for FDI is more likely to be dominant. With respect to the other variables differences in market size (ln GDP) remains, as in the whole sample, a significant and positive determinant of investment similarity in all the specifications, while differences in per capita GDP retain their negative impact on investment similarity although with a weaker statistical significance in the second and third model. Finally, the education and technological development variables are generally statistically insignificant in all the three models. Table 7 presents the comparable analysis for manufacturing sectors only in DCs. This subsample includes 231 country pair observations.

Table 7: Determinants of Investment Similarity (manufacturing sectors/ developed countries) for 2002-2009 (1) (2) (3) VARIABLES Model 1: Export and

Market Seeking Model 2: Model 1

with Education and Technology

Model 3: Model 2 with Trade Policy

Export Similarity 0.407*** 0.419*** 0.416*** (0.0797) (0.0807) (0.0944) Ln GDP 0.0243*** 0.0232*** 0.0234*** (0.00689) (0.00695) (0.00721) Ln GDP per capita -0.0270** -0.0294** -0.0311** (0.0107) (0.0115) (0.0121)

Ln School Years 0.00636 0.00682 (0.00837) (0.00871) Ln Patent Appls. 0.0101 0.00986 (0.00972) (0.00979) Trade Taxes -0.00354 (0.0174) Business Regulation 0.0147 (0.0251) Constant 0.00645 -0.0113 -0.00753 (0.191) (0.190) (0.195) Observations 231 231 231 R-squared F-Statistic

0.219 23.36***

0.224 14.14***

0.225 9.95***

Note: We observe that there is not a high level of correlation between the variables used in the models. ***, ** and * indicate 1%, 5% and 10% significance levels respectively. Robust (White) standard errors are in parentheses. Variables, other than the export similarity index, are paired differences between countries. Export similarity is the dominant determinant of investment similarity in the manufacturing sectors as its coefficient turns out significant at the 1% level in all the specifications and with a high magnitude (0.41) confirming the results of the regression for the whole sample. It is evident that export similarity is more influential when investment similarity excludes the services sectors. The variables reflecting differences in the market size (ln GDP) and per capita GDP retain their signs with the difference that the latter appears more robust as determinant of investment similarity in the manufacturing sectors as it is statistically significant at the 5% in all the three specifications. Finally it is interesting to note that the taxes on international trade coefficient (Trade Taxes) loses its statistical significance but retains its negative sign.

4.5 Cross-Sectional Results for Less Developed Countries (LDCs)

The next two tables; Table 8 and Table 9 present the results for the LDCS sub-sample that include 105 and 153 country pair observations respectively. The results for all the industries, presented in Table 8, reveal the heterogeneity of the determinants of investment similarity when we divide the sample according to the stage of economic development. For the first time, the variable reflecting differences in the education level becomes statistically significant and has the expected, negative, sign; LDCs with higher quality of human capital attract different sectors of inward FDI. The export similarity index is a more robust determinant of investment similarity in LDCs sub-sample as it is statistically significant at the 1% level in all the specification and with a high magnitude. This indicates that comparative advantage considerations are relatively more important when US-based MNEs invest in LDCs as opposed when they invest in DCs. It is interesting to note also the change in the sign of the coefficient of the technological development variable from positive in the DCs sub-sample to negative, but not statistically different from zero, in the LDCs sub-sample. Another observation is that differences in per capita GDP become insignificant when the sample is restricted to LDCs. Nevertheless some common patterns in the determinants of investment similarity between DCs and LDCs exist; the Trade Taxes and the ln GDP variables retain their respective positive and negative impact on investment similarity as in the case of the DCs although ln GDP appears less robust in this case as its statistical significance is weaker.

Table 8: Determinants of Investment Similarity (all sectors/ less developed countries) for 2002-2009

(1) (2) (3) VARIABLES Model 1: Export and

Market Seeking Model 2: Model 1 with

Education and Technology

Model 3: Model 2 with Trade Policy

Export Similarity 0.518*** 0.576*** 0.572*** (0.0967) (0.0982) (0.102) Ln GDP 0.0177* 0.0142 0.0186* (0.0103) (0.0104) (0.0110) Ln GDP per capita 0.000319 0.00205 0.00490 (0.0128) (0.0121) (0.0119) Ln School Years -0.0262*** -0.0204** (0.00889) (0.00932) Ln Patent Appls. -0.0113 -0.0103 (0.00844) (0.00798) Trade Taxes -0.0470*** (0.0170) Business Reg. 0.0149 (0.0177) Constant -0.130 -0.0314 -0.137 (0.288) (0.298) (0.311) Observations 105 105 105 R-squared F-Statistic

0.287 12.20***

0.342 8.85***

0.403 10.35***

Note: We observe that there is not a high level of correlation between the variables used in the models. ***, ** and * indicate 1%, 5% and 10% significance levels respectively. Robust (White) standard errors are in parentheses. Variables, other than the export similarity index, are paired differences between countries. Table 9 presents the comparable results for manufacturing sectors only in LDCs. The results of the investment similarity determinants in the manufacturing sectors for the LDCs sub-sample are fairly heterogeneous in comparison with the ones for the DCs. The positive influence the export similarity exerts on investment similarity is still significant at the 1% level in all the specifications but its magnitude weakens once the trade policy variables are controlled for. Positive as expected and statistically significant at the 1% is also the influence of the regulatory barriers variable on investment similarity, although such an effect is partly offset by the significant and negative coefficient of the taxes on international trade variable (Trade Taxes). The patent variable is also statistically significant at the 1% level and has a negative sign. It becomes evident that differences in technological development can partly explain the concentration of relatively high-tech manufacturing sectors such as the computers and electronics sector in some specific LDCs as Malaysia. Finally the, variables related to the classic market seeking motive, ln GDP and ln GDP per capita, are generally insignificant suggesting that the sectoral distribution of US originated FDI in the manufacturing sectors in LDCs is influenced mainly by export platform considerations.

Table 9: Determinants of Investment Similarity (manufacturing sectors/ less developed countries) for 2002-2009

(1) (2) (3) VARIABLES Model 1: Export and

Market Seeking Model 2: Model 1

with Education and Technology

Model 3: Model 2 with Trade Policy

Export Similarity 0.221*** 0.281*** 0.496*** (0.0815) (0.0896) (0.0923) Ln GDP 0.0172** 0.00538 0.0118 (0.00845) (0.00807) (0.00789) Ln GDP per capita 0.00881 0.0110 0.00558 (0.0133) (0.0107) (0.0118) Ln School Years -0.0178 -0.0160 (0.0130) (0.0138) Ln Patent Appls. -0.0579*** -0.0591*** (0.00982) (0.00909)

Trade Taxes -0.0413** (0.0193) Business Regulation 0.0513*** (0.0159) Constant -0.0118 0.374* 0.188 (0.230) (0.210) (0.215) Observations 153 153 136 R-squared F-Statistic

0.067 4.80***

0.265 10.63***

0.390 15.76***

Note: we observe that there is not a high level of correlation between the variables used in the models. ***, ** and * indicate 1%, 5% and 10% significance levels respectively. Robust (White) standard errors are in parentheses. Variables, other than the export similarity index, are paired differences between countries.

5. CONCLUSIONS

This paper has proposed an index of investment similarity to provide a ready way of identifying both similar (competitor) and dissimilar (non-competing) locations to any particular country, according to the sectors in which they attract inward FDI. In part this is intended to be a tool of use to policy makers, particularly those concerned with investment promotion. In this respect, this paper is timely as recent research (Harding & Javocik, 2011) has showed that, at least for developing countries, investment promotion at the sector level is a viable policy to attract inward FDI. In part it is also intended as an analytical tool for researchers. To this end we used investment similarity indices to produce cross-sectional econometric tests of the well-known OLI paradigm. Our findings were strongly supportive of the locational aspects of the OLI theory. In particular, we found a statistically significant positive relationship between investment and export similarity. That is, we find the sectoral composition of inward FDI to be linked to that of exports, providing support for the existence of export platform, resource seeking and efficiency seeking motives for investment. We also find evidence of statistical significant relationships between investment similarity and a series of other variables based on the OLI paradigm. These include market seeking variables, trade barriers, human capital and technological indicators.

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APPENDIX 1: Sectors Included

Table A1: Sectors Included in the Calculation of the Investment Similarity Indices

Sectors used for Total Investment Similarity Sectors used for Manufacturing Investment

Similarity

Mining Food (M)

Utilities Chemicals (M)

Food (M) Primary and fabricated metals (M)

Chemicals (M) Machinery (M)

Primary and fabricated metals (M) Computers and electronic products (M)

Machinery (M) Electrical equipment; appliances and components (M)

Computers and electronic products (M) Transportation Equipment (M)

Electrical equipment; appliances and components (M) Other Manufacturing (M)

Transportation Equipment (M)

Other Manufacturing (M)

Wholesale Trade

Information

Depository Institutions

Finance (except depository institutions) and insurance

Professional; scientific; and technical services

Holding Companies (nonbank)

Other Industries Note: (M) denotes manufacturing. From 1999, the BEA-data are classified under the 1997 North American Industry Classification System (NAICS). Previously, data were classified under the Standard Industrial Classification System (SIC).

APPENDIX 2: Correlation Matrices for Explanatory Variables

Table A2: Correlation Matrix for Explanatory Variables (Whole Sample)

ExSim lnGDP lnGDPcap lnPT lnSCHL TradeTax RegB

ExSim 1

lnGDP 0.20 1

lnGDPcap -0.03 0.05 1

lnPT 0.11 0.15 0.62 1

lnSCHL -0.01 0.02 0.19 0.14 1

TradeTax -0.15 -0.06 0.19 0.03 0.11 1

RegB -0.32 -0.14 0.40 0.22 0.09 0.37 1

Table A3: Correlation Matrix for Explanatory Variables (DCs Sub-sample)

ExSim lnGDP lnGDPcap lnPT lnSCHL TradeTax RegB

ExSim 1

lnGDP 0.29 1

lnGDPcap -0.09 0.0005 1

lnPT -0.11 -0.04 0.07 1

lnSCHL -0.05 0.13 0.19 -0.11 1

TradeTax -0.4 0.0028 0.17 0.22 0.08 1

RegB -0.13 -0.02 0.32 0.02 0.11 0.47 1

Table A4: Correlation Matrix for Explanatory Variables (LDCs Sub-sample)

ExSim lnGDP lnGDPcap lnPT lnSCHL TradeTax RegB

ExSim 1

lnGDP 0.11 1

lnGDPcap 0.07 -0.0003 1

lnPT 0.18 -0.08 0.05 1

lnSCHL 0.04 -0.18 0.06 0.15 1

TradeTax -0.02 0.14 0.03 0.25 0.1 1

RegB -0.3 -0.18 0.03 0.06 0.13 -0.036 1